-
- Call of Duty Mobile APK Version 1.0.19: Everything You Need to Know
-If you are a fan of first-person shooter games, you have probably heard of Call of Duty Mobile, one of the most popular and successful games in this genre. Call of Duty Mobile is a free-to-play game that brings the thrill and excitement of Call of Duty to your mobile device. You can play online multiplayer modes with your friends or solo missions against enemies. You can also customize your character, weapons, loadouts, and skills to suit your playstyle.
-What is an APK file?
-An APK file is a file format that stands for Android Package Kit. It is used to distribute and install applications on Android devices. An APK file contains all the necessary files and data for an app to run on your device. You can download APK files from various sources online, such as Uptodown or APKCombo.
-call of duty mobile apk version 1.0.19 DOWNLOAD » https://urllie.com/2uNzSK
Why download Call of Duty Mobile APK version 1.0.19?
-Call of Duty Mobile is constantly updated with new features, improvements, bug fixes, and content. The latest version of the game is 1.0.19, which was released on June 15, 2023. By downloading this version, you can enjoy the following benefits and advantages:
-New features and improvements
-Call of Duty Mobile APK version 1.0.19 introduces some new features and improvements that enhance the gameplay and performance of the game. Some of the highlights are:
-
-A new season called "Undead Siege" that features a zombie mode, new maps, new weapons, new characters, and new rewards.
-A new battle royale mode called "Warzone" that allows up to 150 players to compete in a large map with vehicles, loot, contracts, and more.
-A new weapon balance system that adjusts the stats and performance of different weapons based on their categories and tiers.
-A new optimization system that reduces the size of the game and improves the loading speed and stability.
-A new anti-cheat system that detects and bans hackers and cheaters from the game.
-
-Compatibility and requirements
-Call of Duty Mobile APK version 1.0.19 is compatible with most Android devices that have at least 2 GB of RAM and run on Android 4.3 or higher. However, some devices may not support all the features or modes of the game. Here is a table of the compatibility and requirements for this version:
-
-
-Device
-RAM
-OS
-Supported Features/Modes
-
-
-Samsung Galaxy S10
-8 GB
-Android 9.0
-All features/modes supported
-
-
-Huawei P30 Pro
-8 GB
-Android 9.0
-All features/modes supported
-
-
-Xiaomi Redmi Note 8 Pro
-6 GB
-Android 9.0
-All features/modes supported except Warzone
-
-
-Moto G7 Power
-4 GB
-Android 9.0
-All features/modes supported except Warzone and Undead Siege
-
-
-Nokia 6.1 Plus
-4 GB
-Android 8.1
-All features/modes supported except Warzone, Undead Siege, and high graphics settings
- How to download and install Call of Duty Mobile APK version 1.0.19?
-If you want to download and install Call of Duty Mobile APK version 1.0.19, you can follow these simple steps:
-Download from Uptodown
-
-Go to the Uptodown website and search for Call of Duty Mobile.
-Click on the green "Download" button and choose the version 1.0.19.
-Wait for the download to finish and locate the APK file in your device's storage.
-
-Download from APKCombo
-
-Go to the APKCombo website and search for Call of Duty Mobile.
-Click on the blue "Download APK" button and choose the version 1.0.19.
-Wait for the download to finish and locate the APK file in your device's storage.
-
-Install the APK file
-
-Before installing the APK file, make sure you have enabled the "Unknown sources" option in your device's settings. This will allow you to install apps from sources other than the Google Play Store.
-Tap on the APK file and follow the instructions on the screen to install the app.
-Once the installation is complete, you can launch the app and enjoy playing Call of Duty Mobile.
-
-Tips and tricks for playing Call of Duty Mobile
-Now that you have downloaded and installed Call of Duty Mobile APK version 1.0.19, you might want to know some tips and tricks for playing the game better. Here are some of them:
-Customize your controls
-One of the first things you should do when you start playing Call of Duty Mobile is to customize your controls. You can choose from different layouts, sensitivity settings, aim assist options, and more. You can also adjust the size and position of the buttons on your screen. You can find the control settings in the game's menu under "Settings". Experiment with different configurations until you find the one that suits you best.
-Choose your loadout wisely
-Your loadout is your set of weapons, equipment, perks, and skills that you use in each match. You can have up to five different loadouts that you can switch between depending on the mode, map, and situation. You can unlock new items and upgrade them as you level up and earn credits. You can find the loadout settings in the game's menu under "Loadout". Choose your loadout wisely based on your playstyle, strategy, and preference.
-call of duty mobile legends of war apk 1.0.19
-Use voice chat with your friends
-Call of Duty Mobile is more fun when you play with your friends. You can invite them to join your team or clan, or join a random team online. You can also use voice chat to communicate with your teammates during matches. Voice chat can help you coordinate your moves, share information, and have fun. You can find the voice chat settings in the game's menu under "Settings". Make sure you have a good microphone and headset for optimal sound quality.
Conclusion
-Call of Duty Mobile APK version 1.0.19 is the latest and most exciting version of the game. It offers new features, improvements, modes, and content that will keep you hooked for hours. You can download and install it easily from Uptodown or APKCombo, and enjoy playing it on your Android device. You can also use some tips and tricks to improve your skills and have more fun. Call of Duty Mobile is a game that you don't want to miss. Download it now and join the action!
-FAQs
-Here are some frequently asked questions and answers about Call of Duty Mobile APK version 1.0.19:
-
-Is Call of Duty Mobile APK version 1.0.19 safe to download and install? Yes, it is safe to download and install Call of Duty Mobile APK version 1.0.19 from trusted sources like Uptodown or APKCombo. These sources scan the APK files for viruses and malware before uploading them. However, you should always be careful when downloading and installing any APK file from unknown or unverified sources, as they may contain harmful or malicious code.
-Is Call of Duty Mobile APK version 1.0.19 free to play? Yes, Call of Duty Mobile APK version 1.0.19 is free to play, meaning you don't have to pay anything to download and install it, or to play the game online or offline. However, the game does have some optional in-app purchases that you can make to enhance your gameplay experience, such as buying credits, cod points, skins, crates, bundles, or passes. You can also watch ads or complete tasks to earn some free rewards.
-How can I update Call of Duty Mobile APK version 1.0.19? You can update Call of Duty Mobile APK version 1.0.19 by downloading and installing the latest version of the game from Uptodown or APKCombo, or by using the in-game update feature that will notify you when a new update is available. You should always update your game to the latest version to enjoy the new features, improvements, bug fixes, and content.
-How can I contact the developers of Call of Duty Mobile? You can contact the developers of Call of Duty Mobile by visiting their official website, Facebook page, Twitter account, Instagram account, YouTube channel, Reddit community, Discord server, or customer support page. You can also send them an email at codm.helpshift.com or leave a review on the Google Play Store.
-How can I share my feedback or suggestions for Call of Duty Mobile? You can share your feedback or suggestions for Call of Duty Mobile by using the in-game feedback feature that allows you to rate the game, report bugs, suggest ideas, or ask questions. You can also share your feedback or suggestions on the official social media platforms or forums of the game, where you can interact with other players and developers.
- 401be4b1e0'
- suf = '
'
- if text.startswith(pre) and text.endswith(suf):
- return text
-
- if '```' in text:
- # careful input
- return pre + text + suf
- else:
- # wtf input
- lines = text.split("\n")
- for i, line in enumerate(lines):
- lines[i] = lines[i].replace(" ", " ")
- text = "".join(lines)
- return pre + text + suf
-
-@lru_cache(maxsize=128) # 使用 lru缓存 加快转换速度
-def markdown_convertion(txt):
- """
- 将Markdown格式的文本转换为HTML格式。如果包含数学公式，则先将公式转换为HTML格式。
- """
- pre = ''
- suf = '
'
- if txt.startswith(pre) and txt.endswith(suf):
- # print('警告，输入了已经经过转化的字符串，二次转化可能出问题')
- return txt # 已经被转化过，不需要再次转化
-
- markdown_extension_configs = {
- 'mdx_math': {
- 'enable_dollar_delimiter': True,
- 'use_gitlab_delimiters': False,
- },
- }
- find_equation_pattern = r'\n', '')
- return content
-
- def no_code(txt):
- if '```' not in txt:
- return True
- else:
- if '```reference' in txt: return True # newbing
- else: return False
-
- if ('$' in txt) and no_code(txt): # 有$标识的公式符号，且没有代码段```的标识
- # convert everything to html format
- split = markdown.markdown(text='---')
- convert_stage_1 = markdown.markdown(text=txt, extensions=['mdx_math', 'fenced_code', 'tables', 'sane_lists'], extension_configs=markdown_extension_configs)
- convert_stage_1 = markdown_bug_hunt(convert_stage_1)
- # re.DOTALL: Make the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline. Corresponds to the inline flag (?s).
- # 1. convert to easy-to-copy tex (do not render math)
- convert_stage_2_1, n = re.subn(find_equation_pattern, replace_math_no_render, convert_stage_1, flags=re.DOTALL)
- # 2. convert to rendered equation
- convert_stage_2_2, n = re.subn(find_equation_pattern, replace_math_render, convert_stage_1, flags=re.DOTALL)
- # cat them together
- return pre + convert_stage_2_1 + f'{split}' + convert_stage_2_2 + suf
- else:
- return pre + markdown.markdown(txt, extensions=['fenced_code', 'codehilite', 'tables', 'sane_lists']) + suf
-
-
-def close_up_code_segment_during_stream(gpt_reply):
- """
- 在gpt输出代码的中途（输出了前面的```，但还没输出完后面的```），补上后面的```
-
- Args:
- gpt_reply (str): GPT模型返回的回复字符串。
-
- Returns:
- str: 返回一个新的字符串，将输出代码片段的“后面的```”补上。
-
- """
- if '```' not in gpt_reply:
- return gpt_reply
- if gpt_reply.endswith('```'):
- return gpt_reply
-
- # 排除了以上两个情况，我们
- segments = gpt_reply.split('```')
- n_mark = len(segments) - 1
- if n_mark % 2 == 1:
- # print('输出代码片段中！')
- return gpt_reply+'\n```'
- else:
- return gpt_reply
-
-
-def format_io(self, y):
- """
- 将输入和输出解析为HTML格式。将y中最后一项的输入部分段落化，并将输出部分的Markdown和数学公式转换为HTML格式。
- """
- if y is None or y == []:
- return []
- i_ask, gpt_reply = y[-1]
- # 输入部分太自由，预处理一波
- if i_ask is not None: i_ask = text_divide_paragraph(i_ask)
- # 当代码输出半截的时候，试着补上后个```
- if gpt_reply is not None: gpt_reply = close_up_code_segment_during_stream(gpt_reply)
- # process
- y[-1] = (
- None if i_ask is None else markdown.markdown(i_ask, extensions=['fenced_code', 'tables']),
- None if gpt_reply is None else markdown_convertion(gpt_reply)
- )
- return y
-
-
-def find_free_port():
- """
- 返回当前系统中可用的未使用端口。
- """
- import socket
- from contextlib import closing
- with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
- s.bind(('', 0))
- s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
- return s.getsockname()[1]
-
-
-def extract_archive(file_path, dest_dir):
- import zipfile
- import tarfile
- import os
- # Get the file extension of the input file
- file_extension = os.path.splitext(file_path)[1]
-
- # Extract the archive based on its extension
- if file_extension == '.zip':
- with zipfile.ZipFile(file_path, 'r') as zipobj:
- zipobj.extractall(path=dest_dir)
- print("Successfully extracted zip archive to {}".format(dest_dir))
-
- elif file_extension in ['.tar', '.gz', '.bz2']:
- with tarfile.open(file_path, 'r:*') as tarobj:
- tarobj.extractall(path=dest_dir)
- print("Successfully extracted tar archive to {}".format(dest_dir))
-
- # 第三方库，需要预先pip install rarfile
- # 此外，Windows上还需要安装winrar软件，配置其Path环境变量，如"C:\Program Files\WinRAR"才可以
- elif file_extension == '.rar':
- try:
- import rarfile
- with rarfile.RarFile(file_path) as rf:
- rf.extractall(path=dest_dir)
- print("Successfully extracted rar archive to {}".format(dest_dir))
- except:
- print("Rar format requires additional dependencies to install")
- return '\n\n解压失败! 需要安装pip install rarfile来解压rar文件'
-
- # 第三方库，需要预先pip install py7zr
- elif file_extension == '.7z':
- try:
- import py7zr
- with py7zr.SevenZipFile(file_path, mode='r') as f:
- f.extractall(path=dest_dir)
- print("Successfully extracted 7z archive to {}".format(dest_dir))
- except:
- print("7z format requires additional dependencies to install")
- return '\n\n解压失败! 需要安装pip install py7zr来解压7z文件'
- else:
- return ''
- return ''
-
-
-def find_recent_files(directory):
- """
- me: find files that is created with in one minutes under a directory with python, write a function
- gpt: here it is!
- """
- import os
- import time
- current_time = time.time()
- one_minute_ago = current_time - 60
- recent_files = []
-
- for filename in os.listdir(directory):
- file_path = os.path.join(directory, filename)
- if file_path.endswith('.log'):
- continue
- created_time = os.path.getmtime(file_path)
- if created_time >= one_minute_ago:
- if os.path.isdir(file_path):
- continue
- recent_files.append(file_path)
-
- return recent_files
-
-def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
- # 将文件复制一份到下载区
- import shutil
- if rename_file is None: rename_file = f'{gen_time_str()}-{os.path.basename(file)}'
- new_path = os.path.join(f'./gpt_log/', rename_file)
- if os.path.exists(new_path) and not os.path.samefile(new_path, file): os.remove(new_path)
- if not os.path.exists(new_path): shutil.copyfile(file, new_path)
- if chatbot:
- if 'file_to_promote' in chatbot._cookies: current = chatbot._cookies['file_to_promote']
- else: current = []
- chatbot._cookies.update({'file_to_promote': [new_path] + current})
-
-def on_file_uploaded(files, chatbot, txt, txt2, checkboxes):
- """
- 当文件被上传时的回调函数
- """
- if len(files) == 0:
- return chatbot, txt
- import shutil
- import os
- import time
- import glob
- from toolbox import extract_archive
- try:
- shutil.rmtree('./private_upload/')
- except:
- pass
- time_tag = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
- os.makedirs(f'private_upload/{time_tag}', exist_ok=True)
- err_msg = ''
- for file in files:
- file_origin_name = os.path.basename(file.orig_name)
- shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}')
- err_msg += extract_archive(f'private_upload/{time_tag}/{file_origin_name}',
- dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract')
- moved_files = [fp for fp in glob.glob('private_upload/**/*', recursive=True)]
- if "底部输入区" in checkboxes:
- txt = ""
- txt2 = f'private_upload/{time_tag}'
- else:
- txt = f'private_upload/{time_tag}'
- txt2 = ""
- moved_files_str = '\t\n\n'.join(moved_files)
- chatbot.append(['我上传了文件，请查收',
- f'[Local Message] 收到以下文件: \n\n{moved_files_str}' +
- f'\n\n调用路径参数已自动修正到: \n\n{txt}' +
- f'\n\n现在您点击任意“红颜色”标识的函数插件时，以上文件将被作为输入参数'+err_msg])
- return chatbot, txt, txt2
-
-
-def on_report_generated(cookies, files, chatbot):
- from toolbox import find_recent_files
- if 'file_to_promote' in cookies:
- report_files = cookies['file_to_promote']
- cookies.pop('file_to_promote')
- else:
- report_files = find_recent_files('gpt_log')
- if len(report_files) == 0:
- return cookies, None, chatbot
- # files.extend(report_files)
- file_links = ''
- for f in report_files: file_links += f'{f} '
- chatbot.append(['报告如何远程获取？', f'报告已经添加到右侧“文件上传区”（可能处于折叠状态），请查收。{file_links}'])
- return cookies, report_files, chatbot
-
-def is_openai_api_key(key):
- API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
- API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{32}$", key)
- return bool(API_MATCH_ORIGINAL) or bool(API_MATCH_AZURE)
-
-def is_api2d_key(key):
- if key.startswith('fk') and len(key) == 41:
- return True
- else:
- return False
-
-def is_any_api_key(key):
- if ',' in key:
- keys = key.split(',')
- for k in keys:
- if is_any_api_key(k): return True
- return False
- else:
- return is_openai_api_key(key) or is_api2d_key(key)
-
-def what_keys(keys):
- avail_key_list = {'OpenAI Key':0, "API2D Key":0}
- key_list = keys.split(',')
-
- for k in key_list:
- if is_openai_api_key(k):
- avail_key_list['OpenAI Key'] += 1
-
- for k in key_list:
- if is_api2d_key(k):
- avail_key_list['API2D Key'] += 1
-
- return f"检测到： OpenAI Key {avail_key_list['OpenAI Key']} 个，API2D Key {avail_key_list['API2D Key']} 个"
-
-def select_api_key(keys, llm_model):
- import random
- avail_key_list = []
- key_list = keys.split(',')
-
- if llm_model.startswith('gpt-'):
- for k in key_list:
- if is_openai_api_key(k): avail_key_list.append(k)
-
- if llm_model.startswith('api2d-'):
- for k in key_list:
- if is_api2d_key(k): avail_key_list.append(k)
-
- if len(avail_key_list) == 0:
- raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源。")
-
- api_key = random.choice(avail_key_list) # 随机负载均衡
- return api_key
-
-def read_env_variable(arg, default_value):
- """
- 环境变量可以是 `GPT_ACADEMIC_CONFIG`(优先)，也可以直接是`CONFIG`
- 例如在windows cmd中，既可以写：
- set USE_PROXY=True
- set API_KEY=sk-j7caBpkRoxxxxxxxxxxxxxxxxxxxxxxxxxxxx
- set proxies={"http":"http://127.0.0.1:10085", "https":"http://127.0.0.1:10085",}
- set AVAIL_LLM_MODELS=["gpt-3.5-turbo", "chatglm"]
- set AUTHENTICATION=[("username", "password"), ("username2", "password2")]
- 也可以写：
- set GPT_ACADEMIC_USE_PROXY=True
- set GPT_ACADEMIC_API_KEY=sk-j7caBpkRoxxxxxxxxxxxxxxxxxxxxxxxxxxxx
- set GPT_ACADEMIC_proxies={"http":"http://127.0.0.1:10085", "https":"http://127.0.0.1:10085",}
- set GPT_ACADEMIC_AVAIL_LLM_MODELS=["gpt-3.5-turbo", "chatglm"]
- set GPT_ACADEMIC_AUTHENTICATION=[("username", "password"), ("username2", "password2")]
- """
- from colorful import print亮红, print亮绿
- arg_with_prefix = "GPT_ACADEMIC_" + arg
- if arg_with_prefix in os.environ:
- env_arg = os.environ[arg_with_prefix]
- elif arg in os.environ:
- env_arg = os.environ[arg]
- else:
- raise KeyError
- print(f"[ENV_VAR] 尝试加载{arg}，默认值：{default_value} --> 修正值：{env_arg}")
- try:
- if isinstance(default_value, bool):
- env_arg = env_arg.strip()
- if env_arg == 'True': r = True
- elif env_arg == 'False': r = False
- else: print('enter True or False, but have:', env_arg); r = default_value
- elif isinstance(default_value, int):
- r = int(env_arg)
- elif isinstance(default_value, float):
- r = float(env_arg)
- elif isinstance(default_value, str):
- r = env_arg.strip()
- elif isinstance(default_value, dict):
- r = eval(env_arg)
- elif isinstance(default_value, list):
- r = eval(env_arg)
- elif default_value is None:
- assert arg == "proxies"
- r = eval(env_arg)
- else:
- print亮红(f"[ENV_VAR] 环境变量{arg}不支持通过环境变量设置! ")
- raise KeyError
- except:
- print亮红(f"[ENV_VAR] 环境变量{arg}加载失败! ")
- raise KeyError(f"[ENV_VAR] 环境变量{arg}加载失败! ")
-
- print亮绿(f"[ENV_VAR] 成功读取环境变量{arg}")
- return r
-
-@lru_cache(maxsize=128)
-def read_single_conf_with_lru_cache(arg):
- from colorful import print亮红, print亮绿, print亮蓝
- try:
- # 优先级1. 获取环境变量作为配置
- default_ref = getattr(importlib.import_module('config'), arg) # 读取默认值作为数据类型转换的参考
- r = read_env_variable(arg, default_ref)
- except:
- try:
- # 优先级2. 获取config_private中的配置
- r = getattr(importlib.import_module('config_private'), arg)
- except:
- # 优先级3. 获取config中的配置
- r = getattr(importlib.import_module('config'), arg)
-
- # 在读取API_KEY时，检查一下是不是忘了改config
- if arg == 'API_KEY':
- print亮蓝(f"[API_KEY] 本项目现已支持OpenAI和API2D的api-key。也支持同时填写多个api-key，如API_KEY=\"openai-key1,openai-key2,api2d-key3\"")
- print亮蓝(f"[API_KEY] 您既可以在config.py中修改api-key(s)，也可以在问题输入区输入临时的api-key(s)，然后回车键提交后即可生效。")
- if is_any_api_key(r):
- print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
- else:
- print亮红( "[API_KEY] 正确的 API_KEY 是'sk'开头的51位密钥（OpenAI），或者 'fk'开头的41位密钥，请在config文件中修改API密钥之后再运行。")
- if arg == 'proxies':
- if r is None:
- print亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问OpenAI家族的模型。建议：检查USE_PROXY选项是否修改。')
- else:
- print亮绿('[PROXY] 网络代理状态：已配置。配置信息如下：', r)
- assert isinstance(r, dict), 'proxies格式错误，请注意proxies选项的格式，不要遗漏括号。'
- return r
-
-
-def get_conf(*args):
- # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
- res = []
- for arg in args:
- r = read_single_conf_with_lru_cache(arg)
- res.append(r)
- return res
-
-
-def clear_line_break(txt):
- txt = txt.replace('\n', ' ')
- txt = txt.replace(' ', ' ')
- txt = txt.replace(' ', ' ')
- return txt
-
-
-class DummyWith():
- """
- 这段代码定义了一个名为DummyWith的空上下文管理器，
- 它的作用是……额……就是不起作用，即在代码结构不变得情况下取代其他的上下文管理器。
- 上下文管理器是一种Python对象，用于与with语句一起使用，
- 以确保一些资源在代码块执行期间得到正确的初始化和清理。
- 上下文管理器必须实现两个方法，分别为 __enter__()和 __exit__()。
- 在上下文执行开始的情况下，__enter__()方法会在代码块被执行前被调用，
- 而在上下文执行结束时，__exit__()方法则会被调用。
- """
- def __enter__(self):
- return self
-
- def __exit__(self, exc_type, exc_value, traceback):
- return
-
-def run_gradio_in_subpath(demo, auth, port, custom_path):
- """
- 把gradio的运行地址更改到指定的二次路径上
- """
- def is_path_legal(path: str)->bool:
- '''
- check path for sub url
- path: path to check
- return value: do sub url wrap
- '''
- if path == "/": return True
- if len(path) == 0:
- print("ilegal custom path: {}\npath must not be empty\ndeploy on root url".format(path))
- return False
- if path[0] == '/':
- if path[1] != '/':
- print("deploy on sub-path {}".format(path))
- return True
- return False
- print("ilegal custom path: {}\npath should begin with \'/\'\ndeploy on root url".format(path))
- return False
-
- if not is_path_legal(custom_path): raise RuntimeError('Ilegal custom path')
- import uvicorn
- import gradio as gr
- from fastapi import FastAPI
- app = FastAPI()
- if custom_path != "/":
- @app.get("/")
- def read_main():
- return {"message": f"Gradio is running at: {custom_path}"}
- app = gr.mount_gradio_app(app, demo, path=custom_path)
- uvicorn.run(app, host="0.0.0.0", port=port) # , auth=auth
-
-
-def clip_history(inputs, history, tokenizer, max_token_limit):
- """
- reduce the length of history by clipping.
- this function search for the longest entries to clip, little by little,
- until the number of token of history is reduced under threshold.
- 通过裁剪来缩短历史记录的长度。
- 此函数逐渐地搜索最长的条目进行剪辑，
- 直到历史记录的标记数量降低到阈值以下。
- """
- import numpy as np
- from request_llm.bridge_all import model_info
- def get_token_num(txt):
- return len(tokenizer.encode(txt, disallowed_special=()))
- input_token_num = get_token_num(inputs)
- if input_token_num < max_token_limit * 3 / 4:
- # 当输入部分的token占比小于限制的3/4时，裁剪时
- # 1. 把input的余量留出来
- max_token_limit = max_token_limit - input_token_num
- # 2. 把输出用的余量留出来
- max_token_limit = max_token_limit - 128
- # 3. 如果余量太小了，直接清除历史
- if max_token_limit < 128:
- history = []
- return history
- else:
- # 当输入部分的token占比 > 限制的3/4时，直接清除历史
- history = []
- return history
-
- everything = ['']
- everything.extend(history)
- n_token = get_token_num('\n'.join(everything))
- everything_token = [get_token_num(e) for e in everything]
-
- # 截断时的颗粒度
- delta = max(everything_token) // 16
-
- while n_token > max_token_limit:
- where = np.argmax(everything_token)
- encoded = tokenizer.encode(everything[where], disallowed_special=())
- clipped_encoded = encoded[:len(encoded)-delta]
- everything[where] = tokenizer.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char
- everything_token[where] = get_token_num(everything[where])
- n_token = get_token_num('\n'.join(everything))
-
- history = everything[1:]
- return history
-
-"""
-========================================================================
-第三部分
-其他小工具:
- - zip_folder: 把某个路径下所有文件压缩，然后转移到指定的另一个路径中（gpt写的）
- - gen_time_str: 生成时间戳
- - ProxyNetworkActivate: 临时地启动代理网络（如果有）
- - objdump/objload: 快捷的调试函数
-========================================================================
-"""
-
-def zip_folder(source_folder, dest_folder, zip_name):
- import zipfile
- import os
- # Make sure the source folder exists
- if not os.path.exists(source_folder):
- print(f"{source_folder} does not exist")
- return
-
- # Make sure the destination folder exists
- if not os.path.exists(dest_folder):
- print(f"{dest_folder} does not exist")
- return
-
- # Create the name for the zip file
- zip_file = os.path.join(dest_folder, zip_name)
-
- # Create a ZipFile object
- with zipfile.ZipFile(zip_file, 'w', zipfile.ZIP_DEFLATED) as zipf:
- # Walk through the source folder and add files to the zip file
- for foldername, subfolders, filenames in os.walk(source_folder):
- for filename in filenames:
- filepath = os.path.join(foldername, filename)
- zipf.write(filepath, arcname=os.path.relpath(filepath, source_folder))
-
- # Move the zip file to the destination folder (if it wasn't already there)
- if os.path.dirname(zip_file) != dest_folder:
- os.rename(zip_file, os.path.join(dest_folder, os.path.basename(zip_file)))
- zip_file = os.path.join(dest_folder, os.path.basename(zip_file))
-
- print(f"Zip file created at {zip_file}")
-
-def zip_result(folder):
- import time
- t = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
- zip_folder(folder, './gpt_log/', f'{t}-result.zip')
- return pj('./gpt_log/', f'{t}-result.zip')
-
-def gen_time_str():
- import time
- return time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
-
-class ProxyNetworkActivate():
- """
- 这段代码定义了一个名为TempProxy的空上下文管理器, 用于给一小段代码上代理
- """
- def __enter__(self):
- from toolbox import get_conf
- proxies, = get_conf('proxies')
- if 'no_proxy' in os.environ: os.environ.pop('no_proxy')
- if proxies is not None:
- if 'http' in proxies: os.environ['HTTP_PROXY'] = proxies['http']
- if 'https' in proxies: os.environ['HTTPS_PROXY'] = proxies['https']
- return self
-
- def __exit__(self, exc_type, exc_value, traceback):
- os.environ['no_proxy'] = '*'
- if 'HTTP_PROXY' in os.environ: os.environ.pop('HTTP_PROXY')
- if 'HTTPS_PROXY' in os.environ: os.environ.pop('HTTPS_PROXY')
- return
-
-def objdump(obj, file='objdump.tmp'):
- import pickle
- with open(file, 'wb+') as f:
- pickle.dump(obj, f)
- return
-
-def objload(file='objdump.tmp'):
- import pickle, os
- if not os.path.exists(file):
- return
- with open(file, 'rb') as f:
- return pickle.load(f)
-
diff --git a/spaces/fclong/summary/fengshen/data/sequence_tagging_dataloader/sequence_tagging_datasets.py b/spaces/fclong/summary/fengshen/data/sequence_tagging_dataloader/sequence_tagging_datasets.py
deleted file mode 100644
index f2e53cbf3d6bd3d2185e66dd0b7fdcfa1b8c44d0..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/data/sequence_tagging_dataloader/sequence_tagging_datasets.py
+++ /dev/null
@@ -1,116 +0,0 @@
-from torch.utils.data import Dataset
-from fengshen.metric.utils_ner import get_entities
-
-import os
-
-def get_datasets(args):
- processor = DataProcessor(args.data_dir, args.decode_type)
-
- train_data = TaskDataset(processor=processor, mode="train")
- valid_data = TaskDataset(processor=processor, mode="dev")
- test_data = TaskDataset(processor=processor, mode="dev")
-
- return {"train":train_data,"validation":valid_data,"test":test_data}
-
-# def get_labels(decode_type):
-# with open("/cognitive_comp/lujunyu/data_zh/NER_Aligned/weibo/labels.txt") as f:
-# label_list = ["[PAD]", "[START]", "[END]"]
-
-# if decode_type=="crf" or decode_type=="linear":
-# for line in f.readlines():
-# label_list.append(line.strip())
-# elif decode_type=="biaffine" or decode_type=="span":
-# for line in f.readlines():
-# tag = line.strip().split("-")
-# if len(tag) == 1 and tag[0] not in label_list:
-# label_list.append(tag[0])
-# elif tag[1] not in label_list:
-# label_list.append(tag[1])
-
-# label2id={label:id for id,label in enumerate(label_list)}
-# id2label={id:label for id,label in enumerate(label_list)}
-# return label2id, id2label
-
-class DataProcessor(object):
- def __init__(self, data_dir, decode_type) -> None:
- super().__init__()
- self.data_dir = data_dir
- self.decode_type = decode_type
-
- def get_examples(self, mode):
- return self._create_examples(self._read_text(os.path.join(self.data_dir, mode + ".all.bmes")), mode)
-
- @staticmethod
- def get_labels(args):
- with open(os.path.join(args.data_dir, "labels.txt")) as f:
- label_list = ["[PAD]", "[START]", "[END]"]
-
- if args.decode_type=="crf" or args.decode_type=="linear":
- for line in f.readlines():
- label_list.append(line.strip())
- elif args.decode_type=="biaffine" or args.decode_type=="span":
- for line in f.readlines():
- tag = line.strip().split("-")
- if len(tag) == 1 and tag[0] not in label_list:
- label_list.append(tag[0])
- elif tag[1] not in label_list:
- label_list.append(tag[1])
-
- label2id = {label: i for i, label in enumerate(label_list)}
- id2label={id:label for id,label in enumerate(label_list)}
- return label2id,id2label
-
- def _create_examples(self, lines, set_type):
- examples = []
- for (i, line) in enumerate(lines):
- guid = "%s-%s" % (set_type, i)
- text_a = line['words']
- labels = []
- for x in line['labels']:
- if 'M-' in x:
- labels.append(x.replace('M-', 'I-'))
- else:
- labels.append(x)
- subject = get_entities(labels, id2label=None, markup='bioes')
- examples.append({'guid':guid, 'text_a':text_a, 'labels':labels, 'subject':subject})
- return examples
-
- @classmethod
- def _read_text(self, input_file):
- lines = []
- with open(input_file, 'r') as f:
- words = []
- labels = []
- for line in f:
- if line.startswith("-DOCSTART-") or line == "" or line == "\n":
- if words:
- lines.append({"words": words, "labels": labels})
- words = []
- labels = []
- else:
- splits = line.split()
- words.append(splits[0])
- if len(splits) > 1:
- labels.append(splits[-1].replace("\n", ""))
- else:
- # Examples could have no label for mode = "test"
- labels.append("O")
- if words:
- lines.append({"words": words, "labels": labels})
- return lines
-
-
-class TaskDataset(Dataset):
- def __init__(self, processor, mode='train'):
- super().__init__()
- self.data = self.load_data(processor, mode)
-
- def __len__(self):
- return len(self.data)
-
- def __getitem__(self, index):
- return self.data[index]
-
- def load_data(self, processor, mode):
- examples = processor.get_examples(mode)
- return examples
\ No newline at end of file
diff --git a/spaces/fclong/summary/fengshen/pipelines/multiplechoice.py b/spaces/fclong/summary/fengshen/pipelines/multiplechoice.py
deleted file mode 100644
index 39293ee24d6262dbea6d75aca8f3a76d3a37a259..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/pipelines/multiplechoice.py
+++ /dev/null
@@ -1,195 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. All rights reserved.
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-# http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from logging import basicConfig
-import torch
-from torch import nn
-import json
-from tqdm import tqdm
-import os
-import numpy as np
-from transformers import BertTokenizer
-import pytorch_lightning as pl
-
-from pytorch_lightning import trainer, loggers
-from transformers import AlbertTokenizer
-from transformers import AutoConfig
-from transformers.pipelines.base import Pipeline
-import argparse
-import copy
-from fengshen.utils.universal_checkpoint import UniversalCheckpoint
-import warnings
-from fengshen.models.unimc.modeling_unimc import (
- UniMCDataModel,
- UniMCLitModel,
- UniMCPredict,
-)
-
-
-class UniMCPipelines(Pipeline):
- @staticmethod
- def piplines_args(parent_args):
- total_parser = parent_args.add_argument_group("piplines args")
- total_parser.add_argument(
- '--pretrained_model_path', default='', type=str)
- total_parser.add_argument('--load_checkpoints_path',
- default='', type=str)
- total_parser.add_argument('--train', action='store_true')
- total_parser.add_argument('--language',
- default='chinese', type=str)
-
- total_parser = UniMCDataModel.add_data_specific_args(total_parser)
- total_parser = UniversalCheckpoint.add_argparse_args(total_parser)
- total_parser = UniMCLitModel.add_model_specific_args(total_parser)
- total_parser = pl.Trainer.add_argparse_args(parent_args)
- return parent_args
-
- def __init__(self, args, model_path):
- self.args = args
- self.checkpoint_callback = UniversalCheckpoint(args)
- self.logger = loggers.TensorBoardLogger(save_dir=args.default_root_dir)
- self.trainer = pl.Trainer.from_argparse_args(args,
- logger=self.logger,
- callbacks=[self.checkpoint_callback])
- self.config = AutoConfig.from_pretrained(model_path)
- if self.config.model_type == 'albert':
- self.tokenizer = AlbertTokenizer.from_pretrained(
- model_path)
- else:
- self.tokenizer = BertTokenizer.from_pretrained(
- model_path)
-
- if args.language == 'chinese':
- self.yes_token = self.tokenizer.encode('是')[1]
- self.no_token = self.tokenizer.encode('非')[1]
- else:
- self.yes_token = self.tokenizer.encode('yes')[1]
- self.no_token = self.tokenizer.encode('no')[1]
-
- if args.load_checkpoints_path != '':
- self.model = UniMCLitModel.load_from_checkpoint(
- args.load_checkpoints_path, args=args, yes_token=self.yes_token, model_path=model_path)
- print('load model from: ', args.load_checkpoints_path)
- else:
- self.model = UniMCLitModel(
- args, yes_token=self.yes_token, model_path=model_path)
-
- def train(self, train_data, dev_data, process=True):
- if process:
- train_data = self.preprocess(train_data)
- dev_data = self.preprocess(dev_data)
- data_model = UniMCDataModel(
- train_data, dev_data, self.yes_token, self.no_token, self.tokenizer, self.args)
- self.model.num_data = len(train_data)
- self.trainer.fit(self.model, data_model)
-
- def predict(self, test_data, cuda=True, process=True):
- if process:
- test_data = self.preprocess(test_data)
-
- result = []
- start = 0
- if cuda:
- self.model = self.model.cuda()
- self.model.model.eval()
- predict_model = UniMCPredict(
- self.yes_token, self.no_token, self.model, self.tokenizer, self.args)
- while start < len(test_data):
- batch_data = test_data[start:start+self.args.batchsize]
- start += self.args.batchsize
- batch_result = predict_model.predict(batch_data)
- result.extend(batch_result)
- if process:
- result = self.postprocess(result)
- return result
-
- def preprocess(self, data):
-
- for i, line in enumerate(data):
- if 'task_type' in line.keys() and line['task_type'] == '语义匹配':
- data[i]['choice'] = ['不能理解为：'+data[i]
- ['textb'], '可以理解为：'+data[i]['textb']]
- # data[i]['question']='怎么理解这段话？'
- data[i]['textb'] = ''
-
- if 'task_type' in line.keys() and line['task_type'] == '自然语言推理':
- data[i]['choice'] = ['不能推断出：'+data[i]['textb'],
- '很难推断出：'+data[i]['textb'], '可以推断出：'+data[i]['textb']]
- # data[i]['question']='根据这段话'
- data[i]['textb'] = ''
-
- return data
-
- def postprocess(self, data):
- for i, line in enumerate(data):
- if 'task_type' in line.keys() and line['task_type'] == '语义匹配':
- data[i]['textb'] = data[i]['choice'][0].replace('不能理解为：', '')
- data[i]['choice'] = ['不相似', '相似']
- ns = {}
- for k, v in data[i]['score'].items():
- if '不能' in k:
- k = '不相似'
- if '可以' in k:
- k = '相似'
- ns[k] = v
- data[i]['score'] = ns
- data[i]['answer'] = data[i]['choice'][data[i]['label']]
-
- if 'task_type' in line.keys() and line['task_type'] == '自然语言推理':
- data[i]['textb'] = data[i]['choice'][0].replace('不能推断出：', '')
- data[i]['choice'] = ['矛盾', '自然', '蕴含']
- ns = {}
- for k, v in data[i]['score'].items():
- if '不能' in k:
- k = '矛盾'
- if '很难' in k:
- k = '自然'
- if '可以' in k:
- k = '蕴含'
- ns[k] = v
- data[i]['score'] = ns
- data[i]['answer'] = data[i]['choice'][data[i]['label']]
-
- return data
-
- def _forward(self, model_inputs):
- return self.model(**model_inputs)
-
- def _sanitize_parameters(self, return_all_scores=None, function_to_apply=None, top_k="", **tokenizer_kwargs):
- # Using "" as default argument because we're going to use `top_k=None` in user code to declare
- # "No top_k"
- preprocess_params = tokenizer_kwargs
-
- postprocess_params = {}
- if hasattr(self.model.config, "return_all_scores") and return_all_scores is None:
- return_all_scores = self.model.config.return_all_scores
-
- if isinstance(top_k, int) or top_k is None:
- postprocess_params["top_k"] = top_k
- postprocess_params["_legacy"] = False
- elif return_all_scores is not None:
- warnings.warn(
- "`return_all_scores` is now deprecated, if want a similar funcionality use `top_k=None` instead of"
- " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.",
- UserWarning,
- )
- if return_all_scores:
- postprocess_params["top_k"] = None
- else:
- postprocess_params["top_k"] = 1
-
- if function_to_apply is not None:
- postprocess_params["function_to_apply"] = function_to_apply
- return preprocess_params, {}, postprocess_params
diff --git a/spaces/feregVcuzo/sanity-test-midi/Chota Bheem Full Episodes In English Download REPACK.md b/spaces/feregVcuzo/sanity-test-midi/Chota Bheem Full Episodes In English Download REPACK.md
deleted file mode 100644
index d24e3368d1efe2f2c6779b0f8e6a92bd48dc081c..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/Chota Bheem Full Episodes In English Download REPACK.md
+++ /dev/null
@@ -1,82 +0,0 @@
-## Chota Bheem Full Episodes In English Download
-
-
-
-
-
- 
-
-
-
-
-
-**Click Here === [https://www.google.com/url?q=https%3A%2F%2Fblltly.com%2F2txvNE&sa=D&sntz=1&usg=AOvVaw1Uv\_mMNdtUCgyP\_RiUhhqV](https://www.google.com/url?q=https%3A%2F%2Fblltly.com%2F2txvNE&sa=D&sntz=1&usg=AOvVaw1Uv\_mMNdtUCgyP\_RiUhhqV)**
-
-
-
-
-
-
-
-
-
-
-
- Here is a possible title and article for your keyword:
-
-# How to Download Chhota Bheem Full Episodes in English
-
-
-
-If you are a fan of Chhota Bheem, the popular Indian animated comedy adventure television series, you might be wondering how to watch or download full episodes in English. Chhota Bheem is a show about a brave, strong and intelligent young boy who solves everyone's problems in the village of Dholakpur[^3^]. The show is available in English, Hindi, Telugu and Tamil[^3^], but finding the English version online can be tricky. Here are some tips on how to download Chhota Bheem full episodes in English.
-
-
-
-- One option is to use JioCinema, an online streaming platform that offers Chhota Bheem all seasons, latest episodes, popular clips and videos in HD quality[^1^]. You can watch online or download to watch later. However, you need to have a Jio SIM card or a Jio account to access JioCinema.
-
-- Another option is to use YouTube, where you can find many Chhota Bheem videos uploaded by the official channel of Green Gold TV, the creators of the show[^2^]. You can watch them online or use a YouTube downloader tool to save them offline. However, not all episodes are available in English on YouTube, and some may have low quality or subtitles.
-
-- A third option is to use a torrent site or a file-sharing site that hosts Chhota Bheem full episodes in English. You can search for them using keywords like "Chhota Bheem English" or "Chhota Bheem Season X Episode Y". However, this option may be illegal, unsafe or unreliable depending on the source and the quality of the files.
-
-
-
-These are some of the ways you can download Chhota Bheem full episodes in English. We hope you enjoy watching this fun and adventurous show with your family and friends.
-
-Here is a possible continuation of the article:
-
-Chhota Bheem is not only a popular TV show, but also a successful franchise that includes movies, games, comics, merchandise and more. The show has been running since 2008 and has won several awards and accolades for its animation, storytelling and entertainment value. Some of the famous characters of the show are Chutki, Raju, Jaggu, Kalia, Dholu-Bholu and King Indravarma. The show also features various villains and mythical creatures that challenge Bheem and his friends.
-
-
-
-If you want to know more about Chhota Bheem and its world, you can visit the official website of Green Gold TV, where you can find news, updates, videos, games, contests and more. You can also follow them on social media platforms like Facebook, Twitter and Instagram. You can also check out their YouTube channel, where they upload new videos every week.
-
-
-
-Chhota Bheem is a show that appeals to both children and adults alike. It is a show that celebrates friendship, courage, humor and adventure. It is a show that you don't want to miss. So download Chhota Bheem full episodes in English today and enjoy the fun.
-
-Here is a possible continuation of the article:
-
-Now that you know how to download Chhota Bheem full episodes in English, you might be wondering what to watch next. Well, you are in luck, because Chhota Bheem has a lot of spin-offs and movies that you can enjoy as well. Some of them are:
-
-
-
-- Chhota Bheem and the Throne of Bali: A movie where Bheem and his friends travel to Bali to help the king and princess from an evil witch.
-
-- Chhota Bheem and the Curse of Damyaan: A movie where Bheem and his friends are trapped in a book by an ancient demon and have to escape.
-
-- Chhota Bheem: Kung Fu Dhamaka: A movie where Bheem and his friends participate in a martial arts tournament in China and face a powerful enemy.
-
-- Mighty Raju: A spin-off series where Raju, Bheem's friend, is a superhero who fights evil with his gadgets and powers.
-
-- Super Bheem: A spin-off series where Bheem and his friends go on intergalactic adventures with the help of a magical portal.
-
-
-
-These are some of the amazing shows and movies that you can watch after downloading Chhota Bheem full episodes in English. You can find them on JioCinema, YouTube or other platforms. You can also buy DVDs or merchandise from the official store of Green Gold TV. You can also play Chhota Bheem games online or on your mobile devices.
-
- dfd1c89656
-
-
-
-
-
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Spider-Man Fan Made v1.15 by R-user Games and Swing Around New York City on Android.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Spider-Man Fan Made v1.15 by R-user Games and Swing Around New York City on Android.md
deleted file mode 100644
index 35ea7ba6e3647d378ed4158ebf4cb80363f6c755..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Spider-Man Fan Made v1.15 by R-user Games and Swing Around New York City on Android.md
+++ /dev/null
@@ -1,114 +0,0 @@
-
-Spider Man R User Apk: A Fan-Made Game for Android
-If you are a fan of Spider-Man and want to experience his web-slinging adventures on your Android device, you might be interested in spider man r user apk. This is a fan-made game developed by R-user Games, a team of indie developers who love Spider-Man and wanted to create their own version of the game. In this article, we will tell you everything you need to know about spider man r user apk, including its features, reviews, tips and tricks, and download link.
-spider man r user apk Download ——— https://gohhs.com/2uPqJg
Features
-Spider man r user apk is a 3D action-adventure game that lets you play as Spider-Man in an open-world New York City. You can explore the city, fight crime, swing from building to building, and face off against iconic villains like Electro and Venom. The game features:
-
-A realistic and detailed city environment with dynamic weather and day-night cycle.
-A variety of missions and challenges to complete, ranging from saving civilians to stopping robberies.
-A combat system that allows you to use Spider-Man's abilities, such as web-shooting, web-swinging, wall-crawling, stealth, and acrobatics.
-A skill tree that lets you upgrade Spider-Man's powers and gadgets.
-A collection of suits that you can unlock and customize, each with its own special abilities and bonuses.
-A photo mode that lets you capture your favorite moments and share them with your friends.
-
-Spider man r user apk is not an official game from Marvel or Sony, but a fan-made project that aims to recreate the experience of playing Spider-Man on a mobile device. The game is inspired by various Spider-Man media, such as comics, movies, cartoons, and games. The developers have tried to make the game as faithful as possible to the source material, while also adding their own original touches. The game is still in development and may have some bugs and glitches, but it is constantly updated with new content and improvements.
- Reviews
-Spider man r user apk has received positive feedback from both critics and players who have tried it out. The game has been praised for its graphics, gameplay, story, and fan service. Some of the reviews are:
-"Spider man r user apk is a stunning fan-made game that captures the essence of Spider-Man. The game looks amazing on Android devices, with realistic lighting, shadows, reflections, and textures. The gameplay is smooth and responsive, with intuitive controls and satisfying combat. The story is engaging and well-written, with familiar characters and plot twists. The game also pays homage to Spider-Man's history, with references to his comics, movies, cartoons, and games. This is a must-play for any Spider-Man fan."
-- IGN
-"Spider man r user apk is a remarkable achievement for a fan-made game. The game offers a rich and immersive Spider-Man experience that rivals some of the official games. The game has a lot of content to explore, with a large open-world city, diverse missions, multiple suits, and challenging enemies. The game also has a lot of personality, with witty dialogue, humorous moments, and easter eggs. The game is not perfect, as it has some bugs and performance issues, but it is still very enjoyable and impressive."
-- GamesRadar+
-spider man fan made by r user games download
-"Spider man r user apk is a fun and entertaining game that lets you live out your Spider-Man fantasies on your Android device. The game has a lot of features that make it stand out from other Spider-Man games. The game has a realistic city environment that changes according to the time of day and weather conditions. The game has a combat system that lets you use Spider-Man's abilities in creative ways The game has a skill tree that lets you upgrade Spider-Man's powers and gadgets. The game has a collection of suits that you can unlock and customize, each with its own special abilities and bonuses. The game also has a photo mode that lets you capture your favorite moments and share them with your friends."
-- Android Authority
-As you can see, spider man r user apk is a game that has received a lot of praise from both critics and players. The game is a great example of how fan-made games can be creative, innovative, and high-quality. If you are a fan of Spider-Man, you should definitely give this game a try.
- Tips and Tricks
-Spider man r user apk is a game that can be challenging and rewarding at the same time. To help you enjoy the game more, here are some tips and tricks that you can use:
-
-Use the web-swinging mode to travel faster and avoid traffic. You can also use the web-zip and web-rush abilities to move more precisely and quickly.
-Use the stealth mode to sneak up on enemies and take them out silently. You can also use the web-trap and web-strike abilities to immobilize and knock out enemies.
-Use the wall-crawling mode to climb walls and ceilings. You can also use the web-pull and web-throw abilities to grab and toss objects and enemies.
-Use the combat mode to fight enemies head-on. You can also use the web-shoot and web-bomb abilities to damage and stun enemies.
-Use the suit menu to change your suit and customize its appearance and abilities. You can also use the suit power button to activate your suit's special ability, such as bulletproof, electric, or symbiote.
-Use the skill menu to upgrade your skills and gadgets. You can also use the skill points button to earn more skill points by completing missions and challenges.
-Use the photo mode to take pictures of your gameplay and share them with your friends. You can also use the photo filters, stickers, and frames to enhance your photos.
-
-These are some of the tips and tricks that you can use to improve your gameplay and enjoy spider man r user apk more. Of course, you can also experiment with different combinations of modes, abilities, suits, skills, and gadgets to find your own style of playing Spider-Man.
- Download Link
-If you are interested in downloading spider man r user apk, you can do so by following these steps:
-
-Go to the official website of R-user Games at [rusergames.com].
-Click on the download button for spider man r user apk.
-Wait for the download to finish.
-Open the downloaded file and install it on your device.
-Launch the game and enjoy!
-
-Note that spider man r user apk is not available on Google Play Store or any other app store. You can only download it from the official website of R-user Games. Also note that spider man r user apk is compatible with Android devices running Android 4.4 or higher. You may need to enable unknown sources in your device settings to install the game.
- Conclusion
-Spider man r user apk is a fan-made game that lets you play as Spider-Man on your Android device. The game features a realistic and detailed city environment, a variety of missions and challenges, a combat system that allows you to use Spider-Man's abilities, a skill tree that lets you upgrade Spider-Man's powers and gadgets, a collection of suits that you can unlock and customize, and a photo mode that lets you capture your favorite moments. The game has received positive feedback from both critics and players who have praised its graphics, gameplay, story, and fan service. The game is not an official game from Marvel or Sony, but a fan-made project that aims to recreate the experience of playing Spider-Man on a mobile device. The game is still in development and may have some bugs and glitches, but it is constantly updated with new content and improvements. If you are a fan of Spider-Man, you should definitely download spider man r user apk and give it a try.
- FAQs
-Here are some frequently asked questions and answers about spider man r user apk:
-Q: Is spider man r user apk free?
-A: Yes, spider man r user apk is free to download and play. However, the game may contain some optional in-app purchases that can enhance your gameplay.
- Q: Is spider man r user apk safe?
-A: Yes, spider man A: Yes, spider man r user apk is safe to download and install. The game does not contain any viruses, malware, or spyware. However, you should always download the game from the official website of R-user Games and not from any other sources that may be unreliable or malicious.
- Q: How can I contact the developers of spider man r user apk?
-A: You can contact the developers of spider man r user apk by visiting their official website at [rusergames.com] and filling out the contact form. You can also follow them on their social media accounts, such as Facebook, Twitter, Instagram, and YouTube. You can send them your feedback, suggestions, bug reports, or fan art.
- Q: Can I play spider man r user apk offline?
-A: Yes, you can play spider man r user apk offline without an internet connection. However, some features of the game may require an internet connection, such as updating the game, accessing the photo mode, or making in-app purchases.
- Q: Can I play spider man r user apk on other devices?
-A: No, spider man r user apk is only compatible with Android devices running Android 4.4 or higher. The game is not available for iOS, Windows, or any other platforms.
- Q: Can I play spider man r user apk with a controller?
-A: Yes, you can play spider man r user apk with a controller if your device supports it. The game has a controller mode that lets you adjust the sensitivity and layout of the buttons. You can also use the touch screen mode if you prefer.
- I hope this article has helped you learn more about spider man r user apk and how to download and play it on your Android device. If you have any questions or comments, feel free to leave them below. Thank you for reading and have fun playing Spider-Man!
401be4b1e0Zeroscope Image-to-Video
-
- A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output.
-
- [](https://huggingface.co/spaces/fffiloni/zeroscope-img-to-video?duplicate=true)
-
- """
- )
-
- image_init = gr.Image(label="Image Init",type="filepath", source="upload", elem_id="image-init")
- #inference_steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=40, interactive=False)
- submit_btn = gr.Button("Submit")
- coca_cap = gr.Textbox(label="Caption", placeholder="CoCa Caption will be displayed here", elem_id="coca-cap-in")
- video_result = gr.Video(label="Video Output", elem_id="video-output")
-
- with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
- community_icon = gr.HTML(community_icon_html)
- loading_icon = gr.HTML(loading_icon_html)
- share_button = gr.Button("Share to community", elem_id="share-btn")
-
- submit_btn.click(fn=infer,
- inputs=[image_init],
- outputs=[coca_cap, video_result, share_group])
-
- share_button.click(None, [], [], _js=share_js)
-
-demo.queue(max_size=12).launch()
-
\ No newline at end of file
diff --git a/spaces/fgbwyude/ChuanhuChatGPT/run_Linux.sh b/spaces/fgbwyude/ChuanhuChatGPT/run_Linux.sh
deleted file mode 100644
index 62af07283093d8e580763d7acfe493c3d88e7b08..0000000000000000000000000000000000000000
--- a/spaces/fgbwyude/ChuanhuChatGPT/run_Linux.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$0")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir"
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
- # 如果有更新，关闭当前运行的服务器
- pkill -f ChuanhuChatbot.py
-
- # 拉取最新更改
- git pull
-
- # 安装依赖
- pip3 install -r requirements.txt
-
- # 重新启动服务器
- nohup python3 ChuanhuChatbot.py &
-fi
diff --git a/spaces/fl399/matcha_chartqa/app.py b/spaces/fl399/matcha_chartqa/app.py
deleted file mode 100644
index 5dde2f48daba4f2da8ec504baf8af5dfa601aabd..0000000000000000000000000000000000000000
--- a/spaces/fl399/matcha_chartqa/app.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import gradio as gr
-from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
-import requests
-from PIL import Image
-import torch
-
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/20294671002019.png', 'chart_example.png')
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/multi_col_1081.png', 'chart_example_2.png')
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/18143564004789.png', 'chart_example_3.png')
-torch.hub.download_url_to_file('https://sharkcoder.com/files/article/matplotlib-bar-plot.png', 'chart_example_4.png')
-
-
-model_name = "google/matcha-chartqa"
-model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
-processor = Pix2StructProcessor.from_pretrained(model_name)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-def filter_output(output):
- return output.replace("<0x0A>", "")
-
-def chart_qa(image, question):
- inputs = processor(images=image, text=question, return_tensors="pt").to(device)
- predictions = model.generate(**inputs, max_new_tokens=512)
- return filter_output(processor.decode(predictions[0], skip_special_tokens=True))
-
-
-image = gr.inputs.Image(type="pil", label="Chart")
-question = gr.inputs.Textbox(label="Question")
-answer = gr.outputs.Textbox(label="Model Output")
-examples = [["chart_example.png", "Which country has the second highest death rate?"],
- ["chart_example_2.png", "What is the B2B sales in 2017?"],
- ["chart_example_3.png", "Which country has the lowest CPA received across all times?"],
- ["chart_example_4.png", "How much revenue did Furious 7 make?"]]
-
-title = "Interactive demo: Chart QA with MatCha🍵"
-description = "Gradio Demo for the [MatCha](https://arxiv.org/abs/2212.09662) model, fine-tuned on the [ChartQA](https://paperswithcode.com/dataset/chartqa) dataset. To use it, simply upload your image and click 'submit', or click one of the examples to load them. \n Quick links: [[paper]](https://arxiv.org/abs/2212.09662) [[google-ai blog]](https://ai.googleblog.com/2023/05/foundation-models-for-reasoning-on.html) [[code]](https://github.com/google-research/google-research/tree/master/deplot)"
-
-interface = gr.Interface(fn=chart_qa,
- inputs=[image, question],
- outputs=answer,
- examples=examples,
- title=title,
- description=description,
- theme='gradio/soft',
- enable_queue=True)
-
-interface.launch()
\ No newline at end of file
diff --git a/spaces/flava/semantic-image-text-search/app.py b/spaces/flava/semantic-image-text-search/app.py
deleted file mode 100644
index b7e63e72ccd021947fe2451e938008e8b5d81ee2..0000000000000000000000000000000000000000
--- a/spaces/flava/semantic-image-text-search/app.py
+++ /dev/null
@@ -1,259 +0,0 @@
-from html import escape
-import re
-import torch
-import streamlit as st
-import pandas as pd, numpy as np
-from transformers import CLIPProcessor, CLIPModel, FlavaModel, FlavaProcessor
-from st_clickable_images import clickable_images
-
-MODEL_NAMES = ["flava-full", "vit-base-patch32", "vit-base-patch16", "vit-large-patch14", "vit-large-patch14-336"]
-
-
-@st.cache(allow_output_mutation=True)
-def load():
- df = {0: pd.read_csv("data.csv"), 1: pd.read_csv("data2.csv")}
- models = {}
- processors = {}
- embeddings = {}
- for name in MODEL_NAMES:
- if "flava" not in name:
- model = CLIPModel
- processor = CLIPProcessor
- prefix = "openai/clip-"
- else:
- model = FlavaModel
- processor = FlavaProcessor
- prefix = "facebook/"
- models[name] = model.from_pretrained(f"{prefix}{name}")
- models[name].eval()
- processors[name] = processor.from_pretrained(f"{prefix}{name}")
- embeddings[name] = {
- 0: np.load(f"embeddings-{name}.npy"),
- 1: np.load(f"embeddings2-{name}.npy"),
- }
- for k in [0, 1]:
- embeddings[name][k] = embeddings[name][k] / np.linalg.norm(
- embeddings[name][k], axis=1, keepdims=True
- )
- return models, processors, df, embeddings
-
-
-models, processors, df, embeddings = load()
-source = {0: "\nSource: Unsplash", 1: "\nSource: The Movie Database (TMDB)"}
-
-
-def compute_text_embeddings(list_of_strings, name):
- inputs = processors[name](text=list_of_strings, return_tensors="pt", padding=True)
- with torch.no_grad():
- result = models[name].get_text_features(**inputs)
- if "flava" in name:
- result = result[:, 0, :]
- result = result.detach().numpy()
- return result / np.linalg.norm(result, axis=1, keepdims=True)
-
-
-def image_search(query, corpus, name, n_results=24):
- positive_embeddings = None
-
- def concatenate_embeddings(e1, e2):
- if e1 is None:
- return e2
- else:
- return np.concatenate((e1, e2), axis=0)
-
- splitted_query = query.split("EXCLUDING ")
- dot_product = 0
- k = 0 if corpus == "Unsplash" else 1
- if len(splitted_query[0]) > 0:
- positive_queries = splitted_query[0].split(";")
- for positive_query in positive_queries:
- match = re.match(r"\[(Movies|Unsplash):(\d{1,5})\](.*)", positive_query)
- if match:
- corpus2, idx, remainder = match.groups()
- idx, remainder = int(idx), remainder.strip()
- k2 = 0 if corpus2 == "Unsplash" else 1
- positive_embeddings = concatenate_embeddings(
- positive_embeddings, embeddings[name][k2][idx : idx + 1, :]
- )
- if len(remainder) > 0:
- positive_embeddings = concatenate_embeddings(
- positive_embeddings, compute_text_embeddings([remainder], name)
- )
- else:
- positive_embeddings = concatenate_embeddings(
- positive_embeddings, compute_text_embeddings([positive_query], name)
- )
- dot_product = embeddings[name][k] @ positive_embeddings.T
- dot_product = dot_product - np.median(dot_product, axis=0)
- dot_product = dot_product / np.max(dot_product, axis=0, keepdims=True)
- dot_product = np.min(dot_product, axis=1)
-
- if len(splitted_query) > 1:
- negative_queries = (" ".join(splitted_query[1:])).split(";")
- negative_embeddings = compute_text_embeddings(negative_queries, name)
- dot_product2 = embeddings[name][k] @ negative_embeddings.T
- dot_product2 = dot_product2 - np.median(dot_product2, axis=0)
- dot_product2 = dot_product2 / np.max(dot_product2, axis=0, keepdims=True)
- dot_product -= np.max(np.maximum(dot_product2, 0), axis=1)
-
- results = np.argsort(dot_product)[-1 : -n_results - 1 : -1]
- return [
- (
- df[k].iloc[i]["path"],
- df[k].iloc[i]["tooltip"] + source[k],
- i,
- )
- for i in results
- ]
-
-
-description = """
-# FLAVA Semantic Image-Text Search
-"""
-instruction= """
-### **Enter your query and hit enter**
-
-**Things to try:** compare with other models or search for "a field in country side EXCLUDING green"
-"""
-
-credit = """
-*Built with FAIR's [FLAVA](https://arxiv.org/abs/2112.04482) models, 🤗 Hugging Face's [transformers library](https://huggingface.co/transformers/), [Streamlit](https://streamlit.io/), 25k images from [Unsplash](https://unsplash.com/) and 8k images from [The Movie Database (TMDB)](https://www.themoviedb.org/)*
-
-*Forked and inspired from a similar app available [here](https://huggingface.co/spaces/vivien/clip/)*
-"""
-
-options = """
-## Compare
-Check results for a single model or compare two models by using the dropdown below:
-"""
-
-howto = """
-## Advanced Use
-- Click on an image to use it as a query and find similar images
-- Several queries, including one based on an image, can be combined (use "**;**" as a separator).
- - Try "a person walking on a grass field; red flowers".
-- If the input includes "**EXCLUDING**", text following it will be used as a negative query.
- - Try "a field in country side which is green" and "a field in countryside EXCLUDING green".
-"""
-
-div_style = {
- "display": "flex",
- "justify-content": "center",
- "flex-wrap": "wrap",
-}
-
-
-def main():
- st.markdown(
- """
- """,
- unsafe_allow_html=True,
- )
-
- st.sidebar.markdown(description)
- st.sidebar.markdown(options)
- mode = st.sidebar.selectbox(
- "", ["Results for FLAVA full", "Comparison of 2 models"], index=0
- )
- st.sidebar.markdown(howto)
- st.sidebar.markdown(credit)
- _, c, _ = st.columns((1, 3, 1))
- c.markdown(instruction)
- if "query" in st.session_state:
- query = c.text_input("", value=st.session_state["query"])
- else:
- query = c.text_input("", value="a field in the countryside which is green")
- corpus = st.radio("", ["Unsplash", "Movies"])
-
- models_dict = {
- "FLAVA": "flava-full",
- "ViT-B/32 (quickest)": "vit-base-patch32",
- "ViT-B/16 (quick)": "vit-base-patch16",
- "ViT-L/14 (slow)": "vit-large-patch14",
- "ViT-L/14@336px (slowest)": "vit-large-patch14-336",
- }
-
- if "Comparison" in mode:
- c1, c2 = st.columns((1, 1))
- selection1 = c1.selectbox("", models_dict.keys(), index=0)
- selection2 = c2.selectbox("", models_dict.keys(), index=3)
- name1 = models_dict[selection1]
- name2 = models_dict[selection2]
- else:
- name1 = MODEL_NAMES[0]
-
- if len(query) > 0:
- results1 = image_search(query, corpus, name1)
- if "Comparison" in mode:
- with c1:
- clicked1 = clickable_images(
- [result[0] for result in results1],
- titles=[result[1] for result in results1],
- div_style=div_style,
- img_style={"margin": "2px", "height": "150px"},
- key=query + corpus + name1 + "1",
- )
- results2 = image_search(query, corpus, name2)
- with c2:
- clicked2 = clickable_images(
- [result[0] for result in results2],
- titles=[result[1] for result in results2],
- div_style=div_style,
- img_style={"margin": "2px", "height": "150px"},
- key=query + corpus + name2 + "2",
- )
- else:
- clicked1 = clickable_images(
- [result[0] for result in results1],
- titles=[result[1] for result in results1],
- div_style=div_style,
- img_style={"margin": "2px", "height": "200px"},
- key=query + corpus + name1 + "1",
- )
- clicked2 = -1
-
- if clicked2 >= 0 or clicked1 >= 0:
- change_query = False
- if "last_clicked" not in st.session_state:
- change_query = True
- else:
- if max(clicked2, clicked1) != st.session_state["last_clicked"]:
- change_query = True
- if change_query:
- if clicked1 >= 0:
- st.session_state["query"] = f"[{corpus}:{results1[clicked1][2]}]"
- elif clicked2 >= 0:
- st.session_state["query"] = f"[{corpus}:{results2[clicked2][2]}]"
- st.experimental_rerun()
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/flowers-team/Interactive_DeepRL_Demo/js/bodies/walkers/spider_body.js b/spaces/flowers-team/Interactive_DeepRL_Demo/js/bodies/walkers/spider_body.js
deleted file mode 100644
index 83121b88c8956b392bb59922ee2c9a94e38db44f..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/Interactive_DeepRL_Demo/js/bodies/walkers/spider_body.js
+++ /dev/null
@@ -1,189 +0,0 @@
-const MAIN_BODY_POLYGONS = [
- [[-10, +10], [+10, +10], [+10, -10], [-10, -10]]
-]
-const MAIN_BODY_BOTTOM_WIDTH = 20
-const SPIDER_SPEED_HIP = 4
-const SPIDER_SPEED_KNEE = 6
-
-/**
- * @classdesc Spider morphology.
- */
-class SpiderBody extends WalkerAbstractBody {
-
- constructor(scale, motors_torque=100, nb_pairs_of_legs=2,
- nb_steps_under_water=600, reset_on_hull_critical_contact=false){
- super(scale, motors_torque, nb_steps_under_water);
-
- this.LEG_DOWN = 4 / this.SCALE;
- this.LEG_W = 6 / this.SCALE;
- this.LEG_H = 20 / this.SCALE;
- this.reset_on_critical_contact = reset_on_hull_critical_contact;
-
- this.nb_pairs_of_legs = nb_pairs_of_legs;
-
- this.TORQUE_PENALTY = 0.00035 / this.nb_pairs_of_legs;
-
- // not exacts but works
- this.AGENT_WIDTH = MAIN_BODY_BOTTOM_WIDTH / this.SCALE + this.LEG_H * 4;
- this.AGENT_HEIGHT = 20 / this.SCALE + this.LEG_H * 2;
- this.AGENT_CENTER_HEIGHT = this.LEG_H + this.LEG_DOWN;
- }
-
- draw(world, init_x, init_y, force_to_center){
-
- let fd_polygon;
- let vertices;
-
- /* Creates the different fixtures */
-
- let MAIN_BODY_FIXTURES = [];
- for(let polygon of MAIN_BODY_POLYGONS){
- fd_polygon = new b2.FixtureDef();
- fd_polygon.shape = new b2.PolygonShape();
- vertices = [];
- for(let vertex of polygon){
- vertices.push(new b2.Vec2(vertex[0] / this.SCALE, vertex[1] / this.SCALE));
- }
- fd_polygon.shape.Set(vertices, polygon.length);
- fd_polygon.density = 5.0;
- fd_polygon.friction = 0.1;
- fd_polygon.filter.categoryBits = 0x20;
- fd_polygon.filter.maskBits = 0x000F;
- MAIN_BODY_FIXTURES.push(fd_polygon);
- }
-
- let LEG_FD = new b2.FixtureDef();
- LEG_FD.shape = new b2.PolygonShape();
- LEG_FD.shape.SetAsBox(this.LEG_W / 2, this.LEG_H / 2);
- LEG_FD.density = 1.0;
- LEG_FD.restitution = 0.0;
- LEG_FD.filter.categoryBits = 0x20;
- LEG_FD.filter.maskBits = 0x000F;
-
- let LOWER_FD = new b2.FixtureDef();
- LOWER_FD.shape = new b2.PolygonShape();
- LOWER_FD.shape.SetAsBox(0.8 * this.LEG_W / 2, this.LEG_H / 2);
- LOWER_FD.density = 1.0;
- LOWER_FD.restitution = 0.0;
- LOWER_FD.filter.categoryBits = 0x20;
- LOWER_FD.filter.maskBits = 0x000F;
-
- /* Creates the different bodies */
-
- // Main body
- let main_body_bd = new b2.BodyDef();
- main_body_bd.type = b2.Body.b2_dynamicBody;
- main_body_bd.position.Set(init_x, init_y);
- let main_body = world.CreateBody(main_body_bd);
- for(let fd of MAIN_BODY_FIXTURES){
- main_body.CreateFixture(fd);
- }
- main_body.color1 = "#803366"; // [0.5, 0.2, 0.4]
- main_body.color2 = "#4D1A33"; // [0.3, 0.1, 0.2]
- main_body.ApplyForceToCenter(new b2.Vec2(force_to_center, 0), true);
- main_body.SetUserData(new CustomBodyUserData(true, this.reset_on_hull_critical_contact, "main_body"));
- this.body_parts.push(main_body);
- this.reference_head_object = main_body;
-
- // Legs bodies and joints
- let legs_coef = [];
- for(let i = 0; i < this.nb_pairs_of_legs; i++){
- legs_coef.push(+1, -1);
- }
- for(let i of legs_coef){
-
- // First part of the leg
- let upper_leg_angle = 0.25 * Math.PI * i;
- let upper_leg_x_distance = Math.sin(upper_leg_angle) * this.LEG_H / 2;
- let upper_leg_y_distance = Math.cos(upper_leg_angle) * this.LEG_H / 2;
- let upper_leg_x = init_x - i * MAIN_BODY_BOTTOM_WIDTH / this.SCALE / 2 - upper_leg_x_distance;
- let upper_leg_y = init_y + upper_leg_y_distance - this.LEG_DOWN;
-
- let upper_leg_bd = new b2.BodyDef();
- upper_leg_bd.type = b2.Body.b2_dynamicBody;
- upper_leg_bd.position.Set(upper_leg_x, upper_leg_y);
- upper_leg_bd.angle = upper_leg_angle;
- let upper_leg = world.CreateBody(upper_leg_bd);
- upper_leg.CreateFixture(LEG_FD);
- upper_leg.color1 = "#B36699"; // [0.7, 0.4, 0.6]
- upper_leg.color2 = "#804D66"; // [0.5, 0.3, 0.4]
- upper_leg.SetUserData(new CustomBodyUserData(false, false,"upper_leg"));
- this.body_parts.push(upper_leg);
-
- // Upper leg joint motor
- let upper_leg_rjd = new b2.RevoluteJointDef();
- upper_leg_rjd.Initialize(main_body, upper_leg, new b2.Vec2(init_x - i * MAIN_BODY_BOTTOM_WIDTH / this.SCALE / 2, init_y - this.LEG_DOWN));
- upper_leg_rjd.enableMotor = true;
- upper_leg_rjd.enableLimit = true;
- upper_leg_rjd.maxMotorTorque = this.MOTORS_TORQUE;
- upper_leg_rjd.motorSpeed = 1;
- upper_leg_rjd.lowerAngle = - 0.1 * Math.PI;
- upper_leg_rjd.upperAngle = 0.1 * Math.PI;
- let joint_motor = world.CreateJoint(upper_leg_rjd);
- joint_motor.SetUserData(new CustomMotorUserData("hip", SPIDER_SPEED_HIP, false));
- this.motors.push(joint_motor);
-
- // Second part of the leg
- let middle_leg_angle = 0.7 * Math.PI * i;
- let middle_leg_x_distance = Math.sin(middle_leg_angle) * this.LEG_H / 2;
- let middle_leg_y_distance = - Math.cos(middle_leg_angle) * this.LEG_H / 2;
- let middle_leg_x = upper_leg_x - upper_leg_x_distance - middle_leg_x_distance;
- let middle_leg_y = upper_leg_y + upper_leg_y_distance - middle_leg_y_distance;
-
- let middle_leg_bd = new b2.BodyDef();
- middle_leg_bd.type = b2.Body.b2_dynamicBody;
- middle_leg_bd.position.Set(middle_leg_x, middle_leg_y);
- middle_leg_bd.angle = middle_leg_angle;
- let middle_leg = world.CreateBody(middle_leg_bd);
- middle_leg.CreateFixture(LEG_FD);
- middle_leg.color1 = "#B36699"; // [0.7, 0.4, 0.6]
- middle_leg.color2 = "#804D66"; // [0.5, 0.3, 0.4]
- middle_leg.SetUserData(new CustomBodyUserData(false, false,"middle_leg"));
- this.body_parts.push(middle_leg);
-
- // middle_leg joint motor
- let middle_leg_rjd = new b2.RevoluteJointDef();
- middle_leg_rjd.Initialize(upper_leg, middle_leg, new b2.Vec2(upper_leg_x - upper_leg_x_distance, upper_leg_y + upper_leg_y_distance));
- middle_leg_rjd.enableMotor = true;
- middle_leg_rjd.enableLimit = true;
- middle_leg_rjd.maxMotorTorque = this.MOTORS_TORQUE;
- middle_leg_rjd.motorSpeed = 1;
- middle_leg_rjd.lowerAngle = - 0.15 * Math.PI;
- middle_leg_rjd.upperAngle = 0.15 * Math.PI;
- joint_motor = world.CreateJoint(middle_leg_rjd);
- joint_motor.SetUserData(new CustomMotorUserData("hip", SPIDER_SPEED_HIP,false));
- this.motors.push(joint_motor);
-
- // Third part of the leg
- let lower_leg_angle = 0.9 * Math.PI * i;
- let lower_leg_x_distance = Math.sin(lower_leg_angle) * this.LEG_H / 2;
- let lower_leg_y_distance = - Math.cos(lower_leg_angle) * this.LEG_H / 2;
- let lower_leg_x = middle_leg_x - middle_leg_x_distance - lower_leg_x_distance;
- let lower_leg_y = middle_leg_y - middle_leg_y_distance - lower_leg_y_distance;
-
- let lower_leg_bd = new b2.BodyDef();
- lower_leg_bd.type = b2.Body.b2_dynamicBody;
- lower_leg_bd.position.Set(lower_leg_x, lower_leg_y);
- lower_leg_bd.angle = lower_leg_angle;
- let lower_leg = world.CreateBody(lower_leg_bd);
- lower_leg.CreateFixture(LOWER_FD);
- lower_leg.color1 = "#B36699"; // [0.7, 0.4, 0.6]
- lower_leg.color2 = "#804D66"; // [0.5, 0.3, 0.4]
- lower_leg.SetUserData(new CustomBodyUserData(true, false,"lower_leg"));
- this.body_parts.push(lower_leg);
-
- // lower_leg joint motor
- let lower_leg_rjd = new b2.RevoluteJointDef();
- lower_leg_rjd.Initialize(middle_leg, lower_leg, new b2.Vec2(middle_leg_x - middle_leg_x_distance, middle_leg_y - middle_leg_y_distance));
- lower_leg_rjd.enableMotor = true;
- lower_leg_rjd.enableLimit = true;
- lower_leg_rjd.maxMotorTorque = this.MOTORS_TORQUE;
- lower_leg_rjd.motorSpeed = 1;
- lower_leg_rjd.lowerAngle = - 0.2 * Math.PI;
- lower_leg_rjd.upperAngle = 0.2 * Math.PI;
- joint_motor = world.CreateJoint(lower_leg_rjd);
- joint_motor.SetUserData(new CustomMotorUserData("knee", SPIDER_SPEED_KNEE,true, 0.0, lower_leg));
- this.motors.push(joint_motor);
- }
- }
-};
\ No newline at end of file
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/midas/blocks.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/midas/blocks.py
deleted file mode 100644
index 2145d18fa98060a618536d9a64fe6589e9be4f78..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/midas/blocks.py
+++ /dev/null
@@ -1,342 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .vit import (
- _make_pretrained_vitb_rn50_384,
- _make_pretrained_vitl16_384,
- _make_pretrained_vitb16_384,
- forward_vit,
-)
-
-def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
- if backbone == "vitl16_384":
- pretrained = _make_pretrained_vitl16_384(
- use_pretrained, hooks=hooks, use_readout=use_readout
- )
- scratch = _make_scratch(
- [256, 512, 1024, 1024], features, groups=groups, expand=expand
- ) # ViT-L/16 - 85.0% Top1 (backbone)
- elif backbone == "vitb_rn50_384":
- pretrained = _make_pretrained_vitb_rn50_384(
- use_pretrained,
- hooks=hooks,
- use_vit_only=use_vit_only,
- use_readout=use_readout,
- )
- scratch = _make_scratch(
- [256, 512, 768, 768], features, groups=groups, expand=expand
- ) # ViT-H/16 - 85.0% Top1 (backbone)
- elif backbone == "vitb16_384":
- pretrained = _make_pretrained_vitb16_384(
- use_pretrained, hooks=hooks, use_readout=use_readout
- )
- scratch = _make_scratch(
- [96, 192, 384, 768], features, groups=groups, expand=expand
- ) # ViT-B/16 - 84.6% Top1 (backbone)
- elif backbone == "resnext101_wsl":
- pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
- scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3
- elif backbone == "efficientnet_lite3":
- pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
- scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3
- else:
- print(f"Backbone '{backbone}' not implemented")
- assert False
-
- return pretrained, scratch
-
-
-def _make_scratch(in_shape, out_shape, groups=1, expand=False):
- scratch = nn.Module()
-
- out_shape1 = out_shape
- out_shape2 = out_shape
- out_shape3 = out_shape
- out_shape4 = out_shape
- if expand==True:
- out_shape1 = out_shape
- out_shape2 = out_shape*2
- out_shape3 = out_shape*4
- out_shape4 = out_shape*8
-
- scratch.layer1_rn = nn.Conv2d(
- in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
- )
- scratch.layer2_rn = nn.Conv2d(
- in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
- )
- scratch.layer3_rn = nn.Conv2d(
- in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
- )
- scratch.layer4_rn = nn.Conv2d(
- in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
- )
-
- return scratch
-
-
-def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
- efficientnet = torch.hub.load(
- "rwightman/gen-efficientnet-pytorch",
- "tf_efficientnet_lite3",
- pretrained=use_pretrained,
- exportable=exportable
- )
- return _make_efficientnet_backbone(efficientnet)
-
-
-def _make_efficientnet_backbone(effnet):
- pretrained = nn.Module()
-
- pretrained.layer1 = nn.Sequential(
- effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
- )
- pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
- pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
- pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
-
- return pretrained
-
-
-def _make_resnet_backbone(resnet):
- pretrained = nn.Module()
- pretrained.layer1 = nn.Sequential(
- resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
- )
-
- pretrained.layer2 = resnet.layer2
- pretrained.layer3 = resnet.layer3
- pretrained.layer4 = resnet.layer4
-
- return pretrained
-
-
-def _make_pretrained_resnext101_wsl(use_pretrained):
- resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
- return _make_resnet_backbone(resnet)
-
-
-
-class Interpolate(nn.Module):
- """Interpolation module.
- """
-
- def __init__(self, scale_factor, mode, align_corners=False):
- """Init.
-
- Args:
- scale_factor (float): scaling
- mode (str): interpolation mode
- """
- super(Interpolate, self).__init__()
-
- self.interp = nn.functional.interpolate
- self.scale_factor = scale_factor
- self.mode = mode
- self.align_corners = align_corners
-
- def forward(self, x):
- """Forward pass.
-
- Args:
- x (tensor): input
-
- Returns:
- tensor: interpolated data
- """
-
- x = self.interp(
- x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
- )
-
- return x
-
-
-class ResidualConvUnit(nn.Module):
- """Residual convolution module.
- """
-
- def __init__(self, features):
- """Init.
-
- Args:
- features (int): number of features
- """
- super().__init__()
-
- self.conv1 = nn.Conv2d(
- features, features, kernel_size=3, stride=1, padding=1, bias=True
- )
-
- self.conv2 = nn.Conv2d(
- features, features, kernel_size=3, stride=1, padding=1, bias=True
- )
-
- self.relu = nn.ReLU(inplace=True)
-
- def forward(self, x):
- """Forward pass.
-
- Args:
- x (tensor): input
-
- Returns:
- tensor: output
- """
- out = self.relu(x)
- out = self.conv1(out)
- out = self.relu(out)
- out = self.conv2(out)
-
- return out + x
-
-
-class FeatureFusionBlock(nn.Module):
- """Feature fusion block.
- """
-
- def __init__(self, features):
- """Init.
-
- Args:
- features (int): number of features
- """
- super(FeatureFusionBlock, self).__init__()
-
- self.resConfUnit1 = ResidualConvUnit(features)
- self.resConfUnit2 = ResidualConvUnit(features)
-
- def forward(self, *xs):
- """Forward pass.
-
- Returns:
- tensor: output
- """
- output = xs[0]
-
- if len(xs) == 2:
- output += self.resConfUnit1(xs[1])
-
- output = self.resConfUnit2(output)
-
- output = nn.functional.interpolate(
- output, scale_factor=2, mode="bilinear", align_corners=True
- )
-
- return output
-
-
-
-
-class ResidualConvUnit_custom(nn.Module):
- """Residual convolution module.
- """
-
- def __init__(self, features, activation, bn):
- """Init.
-
- Args:
- features (int): number of features
- """
- super().__init__()
-
- self.bn = bn
-
- self.groups=1
-
- self.conv1 = nn.Conv2d(
- features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
- )
-
- self.conv2 = nn.Conv2d(
- features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
- )
-
- if self.bn==True:
- self.bn1 = nn.BatchNorm2d(features)
- self.bn2 = nn.BatchNorm2d(features)
-
- self.activation = activation
-
- self.skip_add = nn.quantized.FloatFunctional()
-
- def forward(self, x):
- """Forward pass.
-
- Args:
- x (tensor): input
-
- Returns:
- tensor: output
- """
-
- out = self.activation(x)
- out = self.conv1(out)
- if self.bn==True:
- out = self.bn1(out)
-
- out = self.activation(out)
- out = self.conv2(out)
- if self.bn==True:
- out = self.bn2(out)
-
- if self.groups > 1:
- out = self.conv_merge(out)
-
- return self.skip_add.add(out, x)
-
- # return out + x
-
-
-class FeatureFusionBlock_custom(nn.Module):
- """Feature fusion block.
- """
-
- def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
- """Init.
-
- Args:
- features (int): number of features
- """
- super(FeatureFusionBlock_custom, self).__init__()
-
- self.deconv = deconv
- self.align_corners = align_corners
-
- self.groups=1
-
- self.expand = expand
- out_features = features
- if self.expand==True:
- out_features = features//2
-
- self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
-
- self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
- self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
-
- self.skip_add = nn.quantized.FloatFunctional()
-
- def forward(self, *xs):
- """Forward pass.
-
- Returns:
- tensor: output
- """
- output = xs[0]
-
- if len(xs) == 2:
- res = self.resConfUnit1(xs[1])
- output = self.skip_add.add(output, res)
- # output += res
-
- output = self.resConfUnit2(output)
-
- output = nn.functional.interpolate(
- output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
- )
-
- output = self.out_conv(output)
-
- return output
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Crysis.3.INTERNAL.CRACK.ONLY-RELOADED Fitgirl Repack.md b/spaces/gotiQspiryo/whisper-ui/examples/Crysis.3.INTERNAL.CRACK.ONLY-RELOADED Fitgirl Repack.md
deleted file mode 100644
index c7e12109d47acc11b78085c3165fe5ed1c43dc8a..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Crysis.3.INTERNAL.CRACK.ONLY-RELOADED Fitgirl Repack.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Crysis.3.INTERNAL.CRACK.ONLY-RELOADED Fitgirl Repack Download Zip ☆☆☆☆☆ https://urlgoal.com/2uyMJ6
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Free Zakhmi Sipahi [2021].md b/spaces/gotiQspiryo/whisper-ui/examples/Free Zakhmi Sipahi [2021].md
deleted file mode 100644
index 3b32d62271259b654bc804c439547c4ac4cc4719..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Free Zakhmi Sipahi [2021].md
+++ /dev/null
@@ -1,6 +0,0 @@
-free Zakhmi Sipahi Download ——— https://urlgoal.com/2uyMBj
diff --git a/spaces/gradio/clustering/app.py b/spaces/gradio/clustering/app.py
deleted file mode 100644
index cc0623a2a9e351782bf539f2eb6fa23da6aa3531..0000000000000000000000000000000000000000
--- a/spaces/gradio/clustering/app.py
+++ /dev/null
@@ -1,281 +0,0 @@
-import gradio as gr
-import math
-from functools import partial
-import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.cluster import (
- AgglomerativeClustering, Birch, DBSCAN, KMeans, MeanShift, OPTICS, SpectralClustering, estimate_bandwidth
-)
-from sklearn.datasets import make_blobs, make_circles, make_moons
-from sklearn.mixture import GaussianMixture
-from sklearn.neighbors import kneighbors_graph
-from sklearn.preprocessing import StandardScaler
-
-plt.style.use('seaborn')
-SEED = 0
-MAX_CLUSTERS = 10
-N_SAMPLES = 1000
-N_COLS = 3
-FIGSIZE = 7, 7 # does not affect size in webpage
-COLORS = [
- 'blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'gray', 'olive', 'cyan'
-]
-assert len(COLORS) >= MAX_CLUSTERS, "Not enough different colors for all clusters"
-np.random.seed(SEED)
-
-
-def normalize(X):
- return StandardScaler().fit_transform(X)
-
-def get_regular(n_clusters):
- # spiral pattern
- centers = [
- [0, 0],
- [1, 0],
- [1, 1],
- [0, 1],
- [-1, 1],
- [-1, 0],
- [-1, -1],
- [0, -1],
- [1, -1],
- [2, -1],
- ][:n_clusters]
- assert len(centers) == n_clusters
- X, labels = make_blobs(n_samples=N_SAMPLES, centers=centers, cluster_std=0.25, random_state=SEED)
- return normalize(X), labels
-
-
-def get_circles(n_clusters):
- X, labels = make_circles(n_samples=N_SAMPLES, factor=0.5, noise=0.05, random_state=SEED)
- return normalize(X), labels
-
-
-def get_moons(n_clusters):
- X, labels = make_moons(n_samples=N_SAMPLES, noise=0.05, random_state=SEED)
- return normalize(X), labels
-
-
-def get_noise(n_clusters):
- np.random.seed(SEED)
- X, labels = np.random.rand(N_SAMPLES, 2), np.random.randint(0, n_clusters, size=(N_SAMPLES,))
- return normalize(X), labels
-
-
-def get_anisotropic(n_clusters):
- X, labels = make_blobs(n_samples=N_SAMPLES, centers=n_clusters, random_state=170)
- transformation = [[0.6, -0.6], [-0.4, 0.8]]
- X = np.dot(X, transformation)
- return X, labels
-
-
-def get_varied(n_clusters):
- cluster_std = [1.0, 2.5, 0.5, 1.0, 2.5, 0.5, 1.0, 2.5, 0.5, 1.0][:n_clusters]
- assert len(cluster_std) == n_clusters
- X, labels = make_blobs(
- n_samples=N_SAMPLES, centers=n_clusters, cluster_std=cluster_std, random_state=SEED
- )
- return normalize(X), labels
-
-
-def get_spiral(n_clusters):
- # from https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html
- np.random.seed(SEED)
- t = 1.5 * np.pi * (1 + 3 * np.random.rand(1, N_SAMPLES))
- x = t * np.cos(t)
- y = t * np.sin(t)
- X = np.concatenate((x, y))
- X += 0.7 * np.random.randn(2, N_SAMPLES)
- X = np.ascontiguousarray(X.T)
-
- labels = np.zeros(N_SAMPLES, dtype=int)
- return normalize(X), labels
-
-
-DATA_MAPPING = {
- 'regular': get_regular,
- 'circles': get_circles,
- 'moons': get_moons,
- 'spiral': get_spiral,
- 'noise': get_noise,
- 'anisotropic': get_anisotropic,
- 'varied': get_varied,
-}
-
-
-def get_groundtruth_model(X, labels, n_clusters, **kwargs):
- # dummy model to show true label distribution
- class Dummy:
- def __init__(self, y):
- self.labels_ = labels
-
- return Dummy(labels)
-
-
-def get_kmeans(X, labels, n_clusters, **kwargs):
- model = KMeans(init="k-means++", n_clusters=n_clusters, n_init=10, random_state=SEED)
- model.set_params(**kwargs)
- return model.fit(X)
-
-
-def get_dbscan(X, labels, n_clusters, **kwargs):
- model = DBSCAN(eps=0.3)
- model.set_params(**kwargs)
- return model.fit(X)
-
-
-def get_agglomerative(X, labels, n_clusters, **kwargs):
- connectivity = kneighbors_graph(
- X, n_neighbors=n_clusters, include_self=False
- )
- # make connectivity symmetric
- connectivity = 0.5 * (connectivity + connectivity.T)
- model = AgglomerativeClustering(
- n_clusters=n_clusters, linkage="ward", connectivity=connectivity
- )
- model.set_params(**kwargs)
- return model.fit(X)
-
-
-def get_meanshift(X, labels, n_clusters, **kwargs):
- bandwidth = estimate_bandwidth(X, quantile=0.25)
- model = MeanShift(bandwidth=bandwidth, bin_seeding=True)
- model.set_params(**kwargs)
- return model.fit(X)
-
-
-def get_spectral(X, labels, n_clusters, **kwargs):
- model = SpectralClustering(
- n_clusters=n_clusters,
- eigen_solver="arpack",
- affinity="nearest_neighbors",
- )
- model.set_params(**kwargs)
- return model.fit(X)
-
-
-def get_optics(X, labels, n_clusters, **kwargs):
- model = OPTICS(
- min_samples=7,
- xi=0.05,
- min_cluster_size=0.1,
- )
- model.set_params(**kwargs)
- return model.fit(X)
-
-
-def get_birch(X, labels, n_clusters, **kwargs):
- model = Birch(n_clusters=n_clusters)
- model.set_params(**kwargs)
- return model.fit(X)
-
-
-def get_gaussianmixture(X, labels, n_clusters, **kwargs):
- model = GaussianMixture(
- n_components=n_clusters, covariance_type="full", random_state=SEED,
- )
- model.set_params(**kwargs)
- return model.fit(X)
-
-
-MODEL_MAPPING = {
- 'True labels': get_groundtruth_model,
- 'KMeans': get_kmeans,
- 'DBSCAN': get_dbscan,
- 'MeanShift': get_meanshift,
- 'SpectralClustering': get_spectral,
- 'OPTICS': get_optics,
- 'Birch': get_birch,
- 'GaussianMixture': get_gaussianmixture,
- 'AgglomerativeClustering': get_agglomerative,
-}
-
-
-def plot_clusters(ax, X, labels):
- set_clusters = set(labels)
- set_clusters.discard(-1) # -1 signifiies outliers, which we plot separately
- for label, color in zip(sorted(set_clusters), COLORS):
- idx = labels == label
- if not sum(idx):
- continue
- ax.scatter(X[idx, 0], X[idx, 1], color=color)
-
- # show outliers (if any)
- idx = labels == -1
- if sum(idx):
- ax.scatter(X[idx, 0], X[idx, 1], c='k', marker='x')
-
- ax.grid(None)
- ax.set_xticks([])
- ax.set_yticks([])
- return ax
-
-
-def cluster(dataset: str, n_clusters: int, clustering_algorithm: str):
- if isinstance(n_clusters, dict):
- n_clusters = n_clusters['value']
- else:
- n_clusters = int(n_clusters)
-
- X, labels = DATA_MAPPING[dataset](n_clusters)
- model = MODEL_MAPPING[clustering_algorithm](X, labels, n_clusters=n_clusters)
- if hasattr(model, "labels_"):
- y_pred = model.labels_.astype(int)
- else:
- y_pred = model.predict(X)
-
- fig, ax = plt.subplots(figsize=FIGSIZE)
-
- plot_clusters(ax, X, y_pred)
- ax.set_title(clustering_algorithm, fontsize=16)
-
- return fig
-
-
-title = "Clustering with Scikit-learn"
-description = (
- "This example shows how different clustering algorithms work. Simply pick "
- "the dataset and the number of clusters to see how the clustering algorithms work. "
- "Colored cirles are (predicted) labels and black x are outliers."
-)
-
-
-def iter_grid(n_rows, n_cols):
- # create a grid using gradio Block
- for _ in range(n_rows):
- with gr.Row():
- for _ in range(n_cols):
- with gr.Column():
- yield
-
-with gr.Blocks(title=title) as demo:
- gr.HTML(f"{title} ")
- gr.Markdown(description)
-
- input_models = list(MODEL_MAPPING)
- input_data = gr.Radio(
- list(DATA_MAPPING),
- value="regular",
- label="dataset"
- )
- input_n_clusters = gr.Slider(
- minimum=1,
- maximum=MAX_CLUSTERS,
- value=4,
- step=1,
- label='Number of clusters'
- )
- n_rows = int(math.ceil(len(input_models) / N_COLS))
- counter = 0
- for _ in iter_grid(n_rows, N_COLS):
- if counter >= len(input_models):
- break
-
- input_model = input_models[counter]
- plot = gr.Plot(label=input_model)
- fn = partial(cluster, clustering_algorithm=input_model)
- input_data.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)
- input_n_clusters.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)
- counter += 1
-
-demo.launch()
diff --git a/spaces/gradio/longformer/scripts/triviaqa_utils/__init__.py b/spaces/gradio/longformer/scripts/triviaqa_utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/configs/hyperparameters.py b/spaces/gyugnsu/DragGan-Inversion/PTI/configs/hyperparameters.py
deleted file mode 100644
index 1a4c89323561c3fe0d1f1b0962926ff89b49221e..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/PTI/configs/hyperparameters.py
+++ /dev/null
@@ -1,28 +0,0 @@
-## Architechture
-lpips_type = "alex"
-first_inv_type = "w"
-optim_type = "adam"
-
-## Locality regularization
-latent_ball_num_of_samples = 1
-locality_regularization_interval = 1
-use_locality_regularization = False
-regulizer_l2_lambda = 0.1
-regulizer_lpips_lambda = 0.1
-regulizer_alpha = 30
-
-## Loss
-pt_l2_lambda = 1
-pt_lpips_lambda = 1
-
-## Steps
-LPIPS_value_threshold = 0.06
-max_pti_steps = 350
-first_inv_steps = 450
-max_images_to_invert = 30
-
-## Optimization
-pti_learning_rate = 3e-4
-first_inv_lr = 5e-3
-train_batch_size = 1
-use_last_w_pivots = False
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/dnnlib/tflib/autosummary.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/dnnlib/tflib/autosummary.py
deleted file mode 100644
index 272f054eea659e7191c7c71ae3745eefe5f82411..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/dnnlib/tflib/autosummary.py
+++ /dev/null
@@ -1,207 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-#
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-"""Helper for adding automatically tracked values to Tensorboard.
-
-Autosummary creates an identity op that internally keeps track of the input
-values and automatically shows up in TensorBoard. The reported value
-represents an average over input components. The average is accumulated
-constantly over time and flushed when save_summaries() is called.
-
-Notes:
-- The output tensor must be used as an input for something else in the
- graph. Otherwise, the autosummary op will not get executed, and the average
- value will not get accumulated.
-- It is perfectly fine to include autosummaries with the same name in
- several places throughout the graph, even if they are executed concurrently.
-- It is ok to also pass in a python scalar or numpy array. In this case, it
- is added to the average immediately.
-"""
-
-from collections import OrderedDict
-import numpy as np
-import tensorflow as tf
-from tensorboard import summary as summary_lib
-from tensorboard.plugins.custom_scalar import layout_pb2
-
-from . import tfutil
-from .tfutil import TfExpression
-from .tfutil import TfExpressionEx
-
-# Enable "Custom scalars" tab in TensorBoard for advanced formatting.
-# Disabled by default to reduce tfevents file size.
-enable_custom_scalars = False
-
-_dtype = tf.float64
-_vars = OrderedDict() # name => [var, ...]
-_immediate = OrderedDict() # name => update_op, update_value
-_finalized = False
-_merge_op = None
-
-
-def _create_var(name: str, value_expr: TfExpression) -> TfExpression:
- """Internal helper for creating autosummary accumulators."""
- assert not _finalized
- name_id = name.replace("/", "_")
- v = tf.cast(value_expr, _dtype)
-
- if v.shape.is_fully_defined():
- size = np.prod(v.shape.as_list())
- size_expr = tf.constant(size, dtype=_dtype)
- else:
- size = None
- size_expr = tf.reduce_prod(tf.cast(tf.shape(v), _dtype))
-
- if size == 1:
- if v.shape.ndims != 0:
- v = tf.reshape(v, [])
- v = [size_expr, v, tf.square(v)]
- else:
- v = [size_expr, tf.reduce_sum(v), tf.reduce_sum(tf.square(v))]
- v = tf.cond(tf.is_finite(v[1]), lambda: tf.stack(
- v), lambda: tf.zeros(3, dtype=_dtype))
-
- with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.control_dependencies(None):
- # [sum(1), sum(x), sum(x**2)]
- var = tf.Variable(tf.zeros(3, dtype=_dtype), trainable=False)
- update_op = tf.cond(tf.is_variable_initialized(
- var), lambda: tf.assign_add(var, v), lambda: tf.assign(var, v))
-
- if name in _vars:
- _vars[name].append(var)
- else:
- _vars[name] = [var]
- return update_op
-
-
-def autosummary(name: str, value: TfExpressionEx, passthru: TfExpressionEx = None, condition: TfExpressionEx = True) -> TfExpressionEx:
- """Create a new autosummary.
-
- Args:
- name: Name to use in TensorBoard
- value: TensorFlow expression or python value to track
- passthru: Optionally return this TF node without modifications but tack an autosummary update side-effect to this node.
-
- Example use of the passthru mechanism:
-
- n = autosummary('l2loss', loss, passthru=n)
-
- This is a shorthand for the following code:
-
- with tf.control_dependencies([autosummary('l2loss', loss)]):
- n = tf.identity(n)
- """
- tfutil.assert_tf_initialized()
- name_id = name.replace("/", "_")
-
- if tfutil.is_tf_expression(value):
- with tf.name_scope("summary_" + name_id), tf.device(value.device):
- condition = tf.convert_to_tensor(condition, name='condition')
- update_op = tf.cond(condition, lambda: tf.group(
- _create_var(name, value)), tf.no_op)
- with tf.control_dependencies([update_op]):
- return tf.identity(value if passthru is None else passthru)
-
- else: # python scalar or numpy array
- assert not tfutil.is_tf_expression(passthru)
- assert not tfutil.is_tf_expression(condition)
- if condition:
- if name not in _immediate:
- with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.device(None), tf.control_dependencies(None):
- update_value = tf.placeholder(_dtype)
- update_op = _create_var(name, update_value)
- _immediate[name] = update_op, update_value
- update_op, update_value = _immediate[name]
- tfutil.run(update_op, {update_value: value})
- return value if passthru is None else passthru
-
-
-def finalize_autosummaries() -> None:
- """Create the necessary ops to include autosummaries in TensorBoard report.
- Note: This should be done only once per graph.
- """
- global _finalized
- tfutil.assert_tf_initialized()
-
- if _finalized:
- return None
-
- _finalized = True
- tfutil.init_uninitialized_vars(
- [var for vars_list in _vars.values() for var in vars_list])
-
- # Create summary ops.
- with tf.device(None), tf.control_dependencies(None):
- for name, vars_list in _vars.items():
- name_id = name.replace("/", "_")
- with tfutil.absolute_name_scope("Autosummary/" + name_id):
- moments = tf.add_n(vars_list)
- moments /= moments[0]
- # read before resetting
- with tf.control_dependencies([moments]):
- reset_ops = [tf.assign(var, tf.zeros(
- 3, dtype=_dtype)) for var in vars_list]
- # reset before reporting
- with tf.name_scope(None), tf.control_dependencies(reset_ops):
- mean = moments[1]
- std = tf.sqrt(moments[2] - tf.square(moments[1]))
- tf.summary.scalar(name, mean)
- if enable_custom_scalars:
- tf.summary.scalar(
- "xCustomScalars/" + name + "/margin_lo", mean - std)
- tf.summary.scalar(
- "xCustomScalars/" + name + "/margin_hi", mean + std)
-
- # Setup layout for custom scalars.
- layout = None
- if enable_custom_scalars:
- cat_dict = OrderedDict()
- for series_name in sorted(_vars.keys()):
- p = series_name.split("/")
- cat = p[0] if len(p) >= 2 else ""
- chart = "/".join(p[1:-1]) if len(p) >= 3 else p[-1]
- if cat not in cat_dict:
- cat_dict[cat] = OrderedDict()
- if chart not in cat_dict[cat]:
- cat_dict[cat][chart] = []
- cat_dict[cat][chart].append(series_name)
- categories = []
- for cat_name, chart_dict in cat_dict.items():
- charts = []
- for chart_name, series_names in chart_dict.items():
- series = []
- for series_name in series_names:
- series.append(layout_pb2.MarginChartContent.Series(
- value=series_name,
- lower="xCustomScalars/" + series_name + "/margin_lo",
- upper="xCustomScalars/" + series_name + "/margin_hi"))
- margin = layout_pb2.MarginChartContent(series=series)
- charts.append(layout_pb2.Chart(
- title=chart_name, margin=margin))
- categories.append(layout_pb2.Category(
- title=cat_name, chart=charts))
- layout = summary_lib.custom_scalar_pb(
- layout_pb2.Layout(category=categories))
- return layout
-
-
-def save_summaries(file_writer, global_step=None):
- """Call FileWriter.add_summary() with all summaries in the default graph,
- automatically finalizing and merging them on the first call.
- """
- global _merge_op
- tfutil.assert_tf_initialized()
-
- if _merge_op is None:
- layout = finalize_autosummaries()
- if layout is not None:
- file_writer.add_summary(layout)
- with tf.device(None), tf.control_dependencies(None):
- _merge_op = tf.summary.merge_all()
-
- file_writer.add_summary(_merge_op.eval(), global_step)
diff --git a/spaces/h2oai/wave-tour/README.md b/spaces/h2oai/wave-tour/README.md
deleted file mode 100644
index 839b4fcbc3dc1f12417cd85f1cde22dbe34051d7..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: H2o Wave Tour
-emoji: 📉
-colorFrom: yellow
-colorTo: red
-sdk: docker
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/h2oai/wave-tour/examples/table_groups.py b/spaces/h2oai/wave-tour/examples/table_groups.py
deleted file mode 100644
index e63530ef59542fc87f48852de92bc0e2dcf313ae..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/table_groups.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Table / Groups
-# Manage data in custom groups
-# #table
-# ---
-
-from h2o_wave import main, app, Q, ui
-
-
-@app('/demo')
-async def serve(q: Q):
- q.page['form'] = ui.form_card(box='1 1 -1 6', items=[
- ui.table(
- name='issues',
- columns=[ui.table_column(name='text', label='Issues reported by')],
- groups=[
- ui.table_group("Bob", [
- ui.table_row(name='row1', cells=['Issue1']),
- ui.table_row(name='row2', cells=['Issue2'])
- ]),
- ui.table_group("John", [
- ui.table_row(name='row3', cells=['Issue3']),
- ui.table_row(name='row4', cells=['Issue4']),
- ui.table_row(name='row5', cells=['Issue5']),
- ], collapsed=False)],
- height='500px'
- )
- ])
- await q.page.save()
diff --git a/spaces/haakohu/deep_privacy2/sg3_torch_utils/ops/bias_act.cpp b/spaces/haakohu/deep_privacy2/sg3_torch_utils/ops/bias_act.cpp
deleted file mode 100644
index 5d2425d8054991a8e8b6f7a940fd0ff7fa0bb330..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2/sg3_torch_utils/ops/bias_act.cpp
+++ /dev/null
@@ -1,99 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto. Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include
-#include
-#include
-#include "bias_act.h"
-
-//------------------------------------------------------------------------
-
-static bool has_same_layout(torch::Tensor x, torch::Tensor y)
-{
- if (x.dim() != y.dim())
- return false;
- for (int64_t i = 0; i < x.dim(); i++)
- {
- if (x.size(i) != y.size(i))
- return false;
- if (x.size(i) >= 2 && x.stride(i) != y.stride(i))
- return false;
- }
- return true;
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp)
-{
- // Validate arguments.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x");
- TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x");
- TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x");
- TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x");
- TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
- TORCH_CHECK(b.dim() == 1, "b must have rank 1");
- TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds");
- TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements");
- TORCH_CHECK(grad >= 0, "grad must be non-negative");
-
- // Validate layout.
- TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense");
- TORCH_CHECK(b.is_contiguous(), "b must be contiguous");
- TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x");
- TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x");
- TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x");
-
- // Create output tensor.
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
- torch::Tensor y = torch::empty_like(x);
- TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x");
-
- // Initialize CUDA kernel parameters.
- bias_act_kernel_params p;
- p.x = x.data_ptr();
- p.b = (b.numel()) ? b.data_ptr() : NULL;
- p.xref = (xref.numel()) ? xref.data_ptr() : NULL;
- p.yref = (yref.numel()) ? yref.data_ptr() : NULL;
- p.dy = (dy.numel()) ? dy.data_ptr() : NULL;
- p.y = y.data_ptr();
- p.grad = grad;
- p.act = act;
- p.alpha = alpha;
- p.gain = gain;
- p.clamp = clamp;
- p.sizeX = (int)x.numel();
- p.sizeB = (int)b.numel();
- p.stepB = (b.numel()) ? (int)x.stride(dim) : 1;
-
- // Choose CUDA kernel.
- void* kernel;
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
- {
- kernel = choose_bias_act_kernel(p);
- });
- TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func");
-
- // Launch CUDA kernel.
- p.loopX = 4;
- int blockSize = 4 * 32;
- int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
- void* args[] = {&p};
- AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
- return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
- m.def("bias_act", &bias_act);
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/hackathon-somos-nlp-2023/T5unami-small-v1/app.py b/spaces/hackathon-somos-nlp-2023/T5unami-small-v1/app.py
deleted file mode 100644
index b7b400ec0bf9f16409e31a9f3e602ac7b8730475..0000000000000000000000000000000000000000
--- a/spaces/hackathon-somos-nlp-2023/T5unami-small-v1/app.py
+++ /dev/null
@@ -1,87 +0,0 @@
-
-import time
-
-import torch
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM
-
-import gradio as gr
-import speech_recognition as sr
-from math import log2, pow
-import os
-
-#from scipy.fftpack import fft
-import gc
-
-peft_model_id='hackathon-somos-nlp-2023/T5unami-small-v1'
-
-config = PeftConfig.from_pretrained(peft_model_id)
-model2 = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, return_dict=True,
- # load_in_8bit=True,
- # load_in_8bit_fp32_cpu_offload=True,
- device_map='auto')
-tokenizer2 = AutoTokenizer.from_pretrained(peft_model_id)
-
-model2 = PeftModel.from_pretrained(model2, peft_model_id)
-
-Problema_tarjetaCredito= os.path.abspath("Problema_tarjetaCredito.ogg")
-list_audios= [[Problema_tarjetaCredito]]
-
-def gen_conversation(text,max_new_tokens=100):
- text = "instruction: " + text + "\n "
- batch = tokenizer2(text, return_tensors='pt')
-
- output_tokens = model2.generate(**batch,
- max_new_tokens=max_new_tokens,
- eos_token_id= tokenizer2.eos_token_id,
- pad_token_id= tokenizer2.pad_token_id,
- bos_token_id= tokenizer2.bos_token_id,
- early_stopping = True,
- no_repeat_ngram_size=2,
- repetition_penalty=1.2,
- temperature=.9,
- num_beams=3
- )
- gc.collect()
- return tokenizer2.decode(output_tokens[0], skip_special_tokens=True).split("\n")[-1].replace("output:","")
-
-conversacion = ""
-def speech_to_text(audio_file, texto_adicional):
- global conversacion
- if audio_file is not None:
- # Lógica para entrada de audio
- r = sr.Recognizer()
- audio_data = sr.AudioFile(audio_file)
- with audio_data as source:
- audio = r.record(source)
- text_enrada=""
-
- texto_generado = r.recognize_google(audio, language="es-ES")
- texto_generado= f"[|Audio a texto|]:{texto_generado}\n" + "" + texto_generado + "
" + texto_generado + "
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diff --git a/spaces/innat/Google-MediaPipe/selfiseg.py b/spaces/innat/Google-MediaPipe/selfiseg.py
deleted file mode 100644
index d64a316209c5b965affdca018d8cf5d763a37af1..0000000000000000000000000000000000000000
--- a/spaces/innat/Google-MediaPipe/selfiseg.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import mediapipe as mp
-from utils import read_n_resize
-from random import sample
-import cv2, numpy as np
-
-BG_IMG = [
- 'examples/back1.jpg',
- 'examples/back2.jpg',
- 'examples/back3.jpg',
- 'examples/back4.jpg',
- 'examples/back5.jpg',
- 'examples/back6.jpg'
-]
-
-def mp_selfi_segment_fn(image):
- mp_selfie_segmentation = mp.solutions.selfie_segmentation
-
- with mp_selfie_segmentation.SelfieSegmentation(
- model_selection=0) as selfie_segmentation:
- image = read_n_resize(image, read=False)
- image_height, image_width, _ = image.shape
-
- # get a random background picture to fill original background
- backs = cv2.imread(sample(BG_IMG, 1)[0])
- backs = cv2.resize(backs, (image_width, image_height))
- backs = cv2.cvtColor(backs, cv2.COLOR_BGR2RGB)
-
- # pass to model
- results = selfie_segmentation.process(image)
-
- # Draw selfie segmentation on the background image.
- # To improve segmentation around boundaries, consider applying a joint
- # bilateral filter to "results.segmentation_mask" with "image".
- condition = np.stack((results.segmentation_mask,) * 3, axis=-1) > 0.1
-
- # Generate solid color images for showing the output selfie segmentation mask.
- fg_image = np.zeros(image.shape, dtype=np.uint8)
- fg_image[:] = image
- bg_image = np.zeros(image.shape, dtype=np.uint8)
- bg_image[:] = backs
- output_image = np.where(condition, fg_image, bg_image)
- return output_image
\ No newline at end of file
diff --git a/spaces/innnky/nene-emotion/text/cleaners.py b/spaces/innnky/nene-emotion/text/cleaners.py
deleted file mode 100644
index 455f3110692f0984d36f72d5fee5fb85e9b7a690..0000000000000000000000000000000000000000
--- a/spaces/innnky/nene-emotion/text/cleaners.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import re
-
-from text.japanese import japanese_to_romaji_with_accent, japanese_to_ipa, japanese_to_ipa2, japanese_to_ipa3
-from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo, chinese_to_romaji, chinese_to_lazy_ipa, chinese_to_ipa, chinese_to_ipa2
-
-# from text.sanskrit import devanagari_to_ipa
-# from text.english import english_to_lazy_ipa, english_to_ipa2, english_to_lazy_ipa2
-# from text.thai import num_to_thai, latin_to_thai
-# from text.shanghainese import shanghainese_to_ipa
-# from text.cantonese import cantonese_to_ipa
-# from text.ngu_dialect import ngu_dialect_to_ipa
-
-
-def japanese_cleaners(text):
- text = japanese_to_romaji_with_accent(text)
- if re.match('[A-Za-z]', text[-1]):
- text += '.'
- return text
-
-
-def japanese_cleaners2(text):
- return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
-
-
-def korean_cleaners(text):
- '''Pipeline for Korean text'''
- text = latin_to_hangul(text)
- text = number_to_hangul(text)
- text = divide_hangul(text)
- if re.match('[\u3131-\u3163]', text[-1]):
- text += '.'
- return text
-
-
-def chinese_cleaners(text):
- '''Pipeline for Chinese text'''
- text = number_to_chinese(text)
- text = chinese_to_bopomofo(text)
- text = latin_to_bopomofo(text)
- if re.match('[ˉˊˇˋ˙]', text[-1]):
- text += '。'
- return text
-
-
-def zh_ja_mixture_cleaners(text):
- chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
- japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
- for chinese_text in chinese_texts:
- cleaned_text = chinese_to_romaji(chinese_text[4:-4])
- text = text.replace(chinese_text, cleaned_text+' ', 1)
- for japanese_text in japanese_texts:
- cleaned_text = japanese_to_romaji_with_accent(
- japanese_text[4:-4]).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')
- text = text.replace(japanese_text, cleaned_text+' ', 1)
- text = text[:-1]
- if re.match('[A-Za-zɯɹəɥ→↓↑]', text[-1]):
- text += '.'
- return text
-
-
-def sanskrit_cleaners(text):
- text = text.replace('॥', '।').replace('ॐ', 'ओम्')
- if text[-1] != '।':
- text += ' ।'
- return text
-
-
-def cjks_cleaners(text):
- chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
- japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
- korean_texts = re.findall(r'\[KO\].*?\[KO\]', text)
- sanskrit_texts = re.findall(r'\[SA\].*?\[SA\]', text)
- english_texts = re.findall(r'\[EN\].*?\[EN\]', text)
- for chinese_text in chinese_texts:
- cleaned_text = chinese_to_lazy_ipa(chinese_text[4:-4])
- text = text.replace(chinese_text, cleaned_text+' ', 1)
- for japanese_text in japanese_texts:
- cleaned_text = japanese_to_ipa(japanese_text[4:-4])
- text = text.replace(japanese_text, cleaned_text+' ', 1)
- for korean_text in korean_texts:
- cleaned_text = korean_to_lazy_ipa(korean_text[4:-4])
- text = text.replace(korean_text, cleaned_text+' ', 1)
- for sanskrit_text in sanskrit_texts:
- cleaned_text = devanagari_to_ipa(sanskrit_text[4:-4])
- text = text.replace(sanskrit_text, cleaned_text+' ', 1)
- for english_text in english_texts:
- cleaned_text = english_to_lazy_ipa(english_text[4:-4])
- text = text.replace(english_text, cleaned_text+' ', 1)
- text = text[:-1]
- if re.match(r'[^\.,!\?\-…~]', text[-1]):
- text += '.'
- return text
-
-
-def cjke_cleaners(text):
- chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
- japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
- korean_texts = re.findall(r'\[KO\].*?\[KO\]', text)
- english_texts = re.findall(r'\[EN\].*?\[EN\]', text)
- for chinese_text in chinese_texts:
- cleaned_text = chinese_to_lazy_ipa(chinese_text[4:-4])
- cleaned_text = cleaned_text.replace(
- 'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn')
- text = text.replace(chinese_text, cleaned_text+' ', 1)
- for japanese_text in japanese_texts:
- cleaned_text = japanese_to_ipa(japanese_text[4:-4])
- cleaned_text = cleaned_text.replace('ʧ', 'tʃ').replace(
- 'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz')
- text = text.replace(japanese_text, cleaned_text+' ', 1)
- for korean_text in korean_texts:
- cleaned_text = korean_to_ipa(korean_text[4:-4])
- text = text.replace(korean_text, cleaned_text+' ', 1)
- for english_text in english_texts:
- cleaned_text = english_to_ipa2(english_text[4:-4])
- cleaned_text = cleaned_text.replace('ɑ', 'a').replace(
- 'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u')
- text = text.replace(english_text, cleaned_text+' ', 1)
- text = text[:-1]
- if re.match(r'[^\.,!\?\-…~]', text[-1]):
- text += '.'
- return text
-
-
-def cjke_cleaners2(text):
- chinese_texts = re.findall(r'\[ZH\].*?\[ZH\]', text)
- japanese_texts = re.findall(r'\[JA\].*?\[JA\]', text)
- korean_texts = re.findall(r'\[KO\].*?\[KO\]', text)
- english_texts = re.findall(r'\[EN\].*?\[EN\]', text)
- for chinese_text in chinese_texts:
- cleaned_text = chinese_to_ipa(chinese_text[4:-4])
- text = text.replace(chinese_text, cleaned_text+' ', 1)
- for japanese_text in japanese_texts:
- cleaned_text = japanese_to_ipa2(japanese_text[4:-4])
- text = text.replace(japanese_text, cleaned_text+' ', 1)
- for korean_text in korean_texts:
- cleaned_text = korean_to_ipa(korean_text[4:-4])
- text = text.replace(korean_text, cleaned_text+' ', 1)
- for english_text in english_texts:
- cleaned_text = english_to_ipa2(english_text[4:-4])
- text = text.replace(english_text, cleaned_text+' ', 1)
- text = text[:-1]
- if re.match(r'[^\.,!\?\-…~]', text[-1]):
- text += '.'
- return text
-
-
-def thai_cleaners(text):
- text = num_to_thai(text)
- text = latin_to_thai(text)
- return text
-
-
-def shanghainese_cleaners(text):
- text = shanghainese_to_ipa(text)
- if re.match(r'[^\.,!\?\-…~]', text[-1]):
- text += '.'
- return text
-
-
-def chinese_dialect_cleaners(text):
- text = re.sub(r'\[MD\](.*?)\[MD\]',
- lambda x: chinese_to_ipa2(x.group(1))+' ', text)
- text = re.sub(r'\[TW\](.*?)\[TW\]',
- lambda x: chinese_to_ipa2(x.group(1), True)+' ', text)
- text = re.sub(r'\[JA\](.*?)\[JA\]',
- lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ')+' ', text)
- text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5',
- '˧˧˦').replace('6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e')+' ', text)
- text = re.sub(r'\[GD\](.*?)\[GD\]',
- lambda x: cantonese_to_ipa(x.group(1))+' ', text)
- text = re.sub(r'\[EN\](.*?)\[EN\]',
- lambda x: english_to_lazy_ipa2(x.group(1))+' ', text)
- text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group(
- 1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ')+' ', text)
- text = re.sub(r'\s+$', '', text)
- text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
- return text
diff --git a/spaces/innovatorved/whisper.api/app/core/errors.py b/spaces/innovatorved/whisper.api/app/core/errors.py
deleted file mode 100644
index 4344402f37d4b2e3d350cb6da16f504c1a94e3a6..0000000000000000000000000000000000000000
--- a/spaces/innovatorved/whisper.api/app/core/errors.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from fastapi import Request, Response
-from fastapi.responses import JSONResponse
-from fastapi.exceptions import HTTPException, RequestValidationError
-
-
-async def error_handler(request: Request, exc: Exception) -> JSONResponse:
- """
- Error handler function for FastAPI.
-
- Args:
- request: The HTTP request that caused the error.
- exc: The exception that was raised.
-
- Returns:
- The error response.
- """
-
- return JSONResponse(
- status_code=500,
- content={"detail": str(exc)},
- )
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deleted file mode 100644
index 225bab323ba71a179ef309157da1c4f510e83877..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Db Adman X.ttf !FREE!.md
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index ab45f78178c29bc9c3a094d78154347f2af04778..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/HD Online Player (GUIDA ALLO STUDIO DEI PROCESSI DI RA) UPD.md
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deleted file mode 100644
index 9b4bbf8051fc6796e6b9a4430076904a44fd7884..0000000000000000000000000000000000000000
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index 9b20207c02d80a00ca38fb4f12282d319ea17e8a..0000000000000000000000000000000000000000
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index 004f51241d9f5d445e3a799e242b1a4fb1148687..0000000000000000000000000000000000000000
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-Se i problemi non si risolvono con queste soluzioni, puoi consultare il manuale utente del modem router Sitecom 300n wireless o visitare il sito web del produttore[^1^] per trovare ulteriori informazioni e assistenza.
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-
- VITS at training
- VITS at inference
-
-
-
-
-
-
-## Pre-requisites
-0. Python >= 3.6
-0. Clone this repository
-0. Install python requirements. Please refer [requirements.txt](requirements.txt)
- 1. You may need to install espeak first: `apt-get install espeak`
-0. Download datasets
- 1. Download and extract the LJ Speech dataset, then rename or create a link to the dataset folder: `ln -s /path/to/LJSpeech-1.1/wavs DUMMY1`
- 1. For mult-speaker setting, download and extract the VCTK dataset, and downsample wav files to 22050 Hz. Then rename or create a link to the dataset folder: `ln -s /path/to/VCTK-Corpus/downsampled_wavs DUMMY2`
-0. Build Monotonic Alignment Search and run preprocessing if you use your own datasets.
-```sh
-# Cython-version Monotonoic Alignment Search
-cd monotonic_align
-python setup.py build_ext --inplace
-
-# Preprocessing (g2p) for your own datasets. Preprocessed phonemes for LJ Speech and VCTK have been already provided.
-# python preprocess.py --text_index 1 --filelists filelists/ljs_audio_text_train_filelist.txt filelists/ljs_audio_text_val_filelist.txt filelists/ljs_audio_text_test_filelist.txt
-# python preprocess.py --text_index 2 --filelists filelists/vctk_audio_sid_text_train_filelist.txt filelists/vctk_audio_sid_text_val_filelist.txt filelists/vctk_audio_sid_text_test_filelist.txt
-```
-
-
-## Training Exmaple
-```sh
-# LJ Speech
-python train.py -c configs/ljs_base.json -m ljs_base
-
-# VCTK
-python train_ms.py -c configs/vctk_base.json -m vctk_base
-```
-
-
-## Inference Example
-See [inference.ipynb](inference.ipynb)
diff --git a/spaces/ivntl/MMS/vits/transforms.py b/spaces/ivntl/MMS/vits/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/ivntl/MMS/vits/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/jackli888/stable-diffusion-webui/modules/ui_components.py b/spaces/jackli888/stable-diffusion-webui/modules/ui_components.py
deleted file mode 100644
index d239d3f70938942f625f5f49e9398fcde10016bf..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/modules/ui_components.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import gradio as gr
-
-
-class ToolButton(gr.Button, gr.components.FormComponent):
- """Small button with single emoji as text, fits inside gradio forms"""
-
- def __init__(self, **kwargs):
- super().__init__(variant="tool", **kwargs)
-
- def get_block_name(self):
- return "button"
-
-
-class ToolButtonTop(gr.Button, gr.components.FormComponent):
- """Small button with single emoji as text, with extra margin at top, fits inside gradio forms"""
-
- def __init__(self, **kwargs):
- super().__init__(variant="tool-top", **kwargs)
-
- def get_block_name(self):
- return "button"
-
-
-class FormRow(gr.Row, gr.components.FormComponent):
- """Same as gr.Row but fits inside gradio forms"""
-
- def get_block_name(self):
- return "row"
-
-
-class FormGroup(gr.Group, gr.components.FormComponent):
- """Same as gr.Row but fits inside gradio forms"""
-
- def get_block_name(self):
- return "group"
-
-
-class FormHTML(gr.HTML, gr.components.FormComponent):
- """Same as gr.HTML but fits inside gradio forms"""
-
- def get_block_name(self):
- return "html"
-
-
-class FormColorPicker(gr.ColorPicker, gr.components.FormComponent):
- """Same as gr.ColorPicker but fits inside gradio forms"""
-
- def get_block_name(self):
- return "colorpicker"
-
-
-class DropdownMulti(gr.Dropdown):
- """Same as gr.Dropdown but always multiselect"""
- def __init__(self, **kwargs):
- super().__init__(multiselect=True, **kwargs)
-
- def get_block_name(self):
- return "dropdown"
diff --git a/spaces/jackrui/diff-amp-antimicrobial_peptide_generation/vocab.py b/spaces/jackrui/diff-amp-antimicrobial_peptide_generation/vocab.py
deleted file mode 100644
index 34b24255eda172bafc03e6493d5291de755751e3..0000000000000000000000000000000000000000
--- a/spaces/jackrui/diff-amp-antimicrobial_peptide_generation/vocab.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Nov 8 00:26:02 2021
-
-@author: joy
-"""
-
-import collections
-from io import StringIO
-import pandas as pd
-
-'''
-Amino acide encoding modified from
-https://github.com/openvax/mhcflurry/blob/74b751e6d72605eef4a49641d364066193541b5a/mhcflurry/amino_acid.py
-'''
-COMMON_AMINO_ACIDS_INDEX = collections.OrderedDict(
- {'A': 0, 'C': 1, 'D': 2, 'E': 3, 'F': 4,
- 'G': 5, 'H': 6, 'I': 7, 'K': 8, 'L': 9,
- 'M': 10, 'N': 11, 'P': 12, 'Q': 13, 'R': 14,
- 'S': 15, 'T': 16, 'V': 17, 'W': 18, 'Y': 19, '-': 20})
-AMINO_ACIDS = list(COMMON_AMINO_ACIDS_INDEX.keys())
-
-AMINO_ACID_INDEX = collections.OrderedDict(
- {'A': 0, 'C': 1, 'D': 2, 'E': 3, 'F': 4,
- 'G': 5, 'H': 6, 'I': 7, 'K': 8, 'L': 9,
- 'M': 10, 'N': 11, 'P': 12, 'Q': 13, 'R': 14,
- 'S': 15, 'T': 16, 'V': 17, 'W': 18, 'Y': 19,
- 'X': 20, 'Z': 20, 'B': 20, 'J': 20, '-': 20})
-
-'''
-CCMPred index of amino acid
-https://github.com/soedinglab/CCMpred/blob/2b2f9a0747a5e53035c33636d430f2f11dc186dd/src/sequence.c
-'''
-CCMPRED_AMINO_ACID_INDEX = collections.OrderedDict(
- {'A': 0, 'R': 1, 'N': 2, 'D': 3, 'C': 4,
- 'Q': 5, 'E': 6, 'G': 7, 'H': 8, 'I': 9,
- 'L': 10, 'K': 11, 'M': 12, 'F': 13, 'P': 14,
- 'S': 15, 'T': 16, 'W': 17, 'Y': 18, 'V': 19, '-': 20})
-CCMPRED_AMINO_ACIDS = list(CCMPRED_AMINO_ACID_INDEX.keys())
-
-BLOSUM62_MATRIX = pd.read_csv(StringIO("""
- A R N D C Q E G H I L K M F P S T W Y V -
-A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 0
-R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 0
-N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 0
-D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 0
-C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 0
-Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0
-E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 0
-G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 0
-H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0
-I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 0
-L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 0
-K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0
-M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 0
-F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 0
-P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 0
-S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0
-T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 0
-W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 0
-Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 0
-V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 0
-- 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
-"""), sep='\s+').loc[AMINO_ACIDS, AMINO_ACIDS]
-
-ENCODING_DATA_FRAMES = {
- "BLOSUM62": BLOSUM62_MATRIX,
- "one-hot": pd.DataFrame([
- [1 if i == j else 0 for i in range(len(AMINO_ACIDS))]
- for j in range(len(AMINO_ACIDS))
- ], index=AMINO_ACIDS, columns=AMINO_ACIDS)
-}
\ No newline at end of file
diff --git a/spaces/jbetker/tortoise/tortoise/models/diffusion_decoder.py b/spaces/jbetker/tortoise/tortoise/models/diffusion_decoder.py
deleted file mode 100644
index f67d21a3903db8f44b704b38d2e9c804dc22d9a9..0000000000000000000000000000000000000000
--- a/spaces/jbetker/tortoise/tortoise/models/diffusion_decoder.py
+++ /dev/null
@@ -1,333 +0,0 @@
-import math
-import random
-from abc import abstractmethod
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import autocast
-
-from tortoise.models.arch_util import normalization, AttentionBlock
-
-
-def is_latent(t):
- return t.dtype == torch.float
-
-
-def is_sequence(t):
- return t.dtype == torch.long
-
-
-def timestep_embedding(timesteps, dim, max_period=10000):
- """
- Create sinusoidal timestep embeddings.
-
- :param timesteps: a 1-D Tensor of N indices, one per batch element.
- These may be fractional.
- :param dim: the dimension of the output.
- :param max_period: controls the minimum frequency of the embeddings.
- :return: an [N x dim] Tensor of positional embeddings.
- """
- half = dim // 2
- freqs = torch.exp(
- -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
- ).to(device=timesteps.device)
- args = timesteps[:, None].float() * freqs[None]
- embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
- if dim % 2:
- embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
- return embedding
-
-
-class TimestepBlock(nn.Module):
- @abstractmethod
- def forward(self, x, emb):
- """
- Apply the module to `x` given `emb` timestep embeddings.
- """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
- def forward(self, x, emb):
- for layer in self:
- if isinstance(layer, TimestepBlock):
- x = layer(x, emb)
- else:
- x = layer(x)
- return x
-
-
-class ResBlock(TimestepBlock):
- def __init__(
- self,
- channels,
- emb_channels,
- dropout,
- out_channels=None,
- dims=2,
- kernel_size=3,
- efficient_config=True,
- use_scale_shift_norm=False,
- ):
- super().__init__()
- self.channels = channels
- self.emb_channels = emb_channels
- self.dropout = dropout
- self.out_channels = out_channels or channels
- self.use_scale_shift_norm = use_scale_shift_norm
- padding = {1: 0, 3: 1, 5: 2}[kernel_size]
- eff_kernel = 1 if efficient_config else 3
- eff_padding = 0 if efficient_config else 1
-
- self.in_layers = nn.Sequential(
- normalization(channels),
- nn.SiLU(),
- nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding),
- )
-
- self.emb_layers = nn.Sequential(
- nn.SiLU(),
- nn.Linear(
- emb_channels,
- 2 * self.out_channels if use_scale_shift_norm else self.out_channels,
- ),
- )
- self.out_layers = nn.Sequential(
- normalization(self.out_channels),
- nn.SiLU(),
- nn.Dropout(p=dropout),
- nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding),
- )
-
- if self.out_channels == channels:
- self.skip_connection = nn.Identity()
- else:
- self.skip_connection = nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding)
-
- def forward(self, x, emb):
- h = self.in_layers(x)
- emb_out = self.emb_layers(emb).type(h.dtype)
- while len(emb_out.shape) < len(h.shape):
- emb_out = emb_out[..., None]
- if self.use_scale_shift_norm:
- out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
- scale, shift = torch.chunk(emb_out, 2, dim=1)
- h = out_norm(h) * (1 + scale) + shift
- h = out_rest(h)
- else:
- h = h + emb_out
- h = self.out_layers(h)
- return self.skip_connection(x) + h
-
-
-class DiffusionLayer(TimestepBlock):
- def __init__(self, model_channels, dropout, num_heads):
- super().__init__()
- self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
- self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True)
-
- def forward(self, x, time_emb):
- y = self.resblk(x, time_emb)
- return self.attn(y)
-
-
-class DiffusionTts(nn.Module):
- def __init__(
- self,
- model_channels=512,
- num_layers=8,
- in_channels=100,
- in_latent_channels=512,
- in_tokens=8193,
- out_channels=200, # mean and variance
- dropout=0,
- use_fp16=False,
- num_heads=16,
- # Parameters for regularization.
- layer_drop=.1,
- unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
- ):
- super().__init__()
-
- self.in_channels = in_channels
- self.model_channels = model_channels
- self.out_channels = out_channels
- self.dropout = dropout
- self.num_heads = num_heads
- self.unconditioned_percentage = unconditioned_percentage
- self.enable_fp16 = use_fp16
- self.layer_drop = layer_drop
-
- self.inp_block = nn.Conv1d(in_channels, model_channels, 3, 1, 1)
- self.time_embed = nn.Sequential(
- nn.Linear(model_channels, model_channels),
- nn.SiLU(),
- nn.Linear(model_channels, model_channels),
- )
-
- # Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
- # This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
- # complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
- # transformer network.
- self.code_embedding = nn.Embedding(in_tokens, model_channels)
- self.code_converter = nn.Sequential(
- AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
- AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
- AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
- )
- self.code_norm = normalization(model_channels)
- self.latent_conditioner = nn.Sequential(
- nn.Conv1d(in_latent_channels, model_channels, 3, padding=1),
- AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
- AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
- AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
- AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
- )
- self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
- nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2),
- AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
- AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
- AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
- AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
- AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False))
- self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
- self.conditioning_timestep_integrator = TimestepEmbedSequential(
- DiffusionLayer(model_channels, dropout, num_heads),
- DiffusionLayer(model_channels, dropout, num_heads),
- DiffusionLayer(model_channels, dropout, num_heads),
- )
-
- self.integrating_conv = nn.Conv1d(model_channels*2, model_channels, kernel_size=1)
- self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1)
-
- self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] +
- [ResBlock(model_channels, model_channels, dropout, dims=1, use_scale_shift_norm=True) for _ in range(3)])
-
- self.out = nn.Sequential(
- normalization(model_channels),
- nn.SiLU(),
- nn.Conv1d(model_channels, out_channels, 3, padding=1),
- )
-
- def get_grad_norm_parameter_groups(self):
- groups = {
- 'minicoder': list(self.contextual_embedder.parameters()),
- 'layers': list(self.layers.parameters()),
- 'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_conditioner.parameters()) + list(self.latent_conditioner.parameters()),
- 'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()),
- 'time_embed': list(self.time_embed.parameters()),
- }
- return groups
-
- def get_conditioning(self, conditioning_input):
- speech_conditioning_input = conditioning_input.unsqueeze(1) if len(
- conditioning_input.shape) == 3 else conditioning_input
- conds = []
- for j in range(speech_conditioning_input.shape[1]):
- conds.append(self.contextual_embedder(speech_conditioning_input[:, j]))
- conds = torch.cat(conds, dim=-1)
- conds = conds.mean(dim=-1)
- return conds
-
- def timestep_independent(self, aligned_conditioning, conditioning_latent, expected_seq_len, return_code_pred):
- # Shuffle aligned_latent to BxCxS format
- if is_latent(aligned_conditioning):
- aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
-
- cond_scale, cond_shift = torch.chunk(conditioning_latent, 2, dim=1)
- if is_latent(aligned_conditioning):
- code_emb = self.latent_conditioner(aligned_conditioning)
- else:
- code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1)
- code_emb = self.code_converter(code_emb)
- code_emb = self.code_norm(code_emb) * (1 + cond_scale.unsqueeze(-1)) + cond_shift.unsqueeze(-1)
-
- unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device)
- # Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
- if self.training and self.unconditioned_percentage > 0:
- unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
- device=code_emb.device) < self.unconditioned_percentage
- code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(aligned_conditioning.shape[0], 1, 1),
- code_emb)
- expanded_code_emb = F.interpolate(code_emb, size=expected_seq_len, mode='nearest')
-
- if not return_code_pred:
- return expanded_code_emb
- else:
- mel_pred = self.mel_head(expanded_code_emb)
- # Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches. This is because we don't want that gradient being used to train parameters through the codes_embedder as it unbalances contributions to that network from the MSE loss.
- mel_pred = mel_pred * unconditioned_batches.logical_not()
- return expanded_code_emb, mel_pred
-
- def forward(self, x, timesteps, aligned_conditioning=None, conditioning_latent=None, precomputed_aligned_embeddings=None, conditioning_free=False, return_code_pred=False):
- """
- Apply the model to an input batch.
-
- :param x: an [N x C x ...] Tensor of inputs.
- :param timesteps: a 1-D batch of timesteps.
- :param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
- :param conditioning_latent: a pre-computed conditioning latent; see get_conditioning().
- :param precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent()
- :param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
- :return: an [N x C x ...] Tensor of outputs.
- """
- assert precomputed_aligned_embeddings is not None or (aligned_conditioning is not None and conditioning_latent is not None)
- assert not (return_code_pred and precomputed_aligned_embeddings is not None) # These two are mutually exclusive.
-
- unused_params = []
- if conditioning_free:
- code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
- unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
- unused_params.extend(list(self.latent_conditioner.parameters()))
- else:
- if precomputed_aligned_embeddings is not None:
- code_emb = precomputed_aligned_embeddings
- else:
- code_emb, mel_pred = self.timestep_independent(aligned_conditioning, conditioning_latent, x.shape[-1], True)
- if is_latent(aligned_conditioning):
- unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
- else:
- unused_params.extend(list(self.latent_conditioner.parameters()))
-
- unused_params.append(self.unconditioned_embedding)
-
- time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
- code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
- x = self.inp_block(x)
- x = torch.cat([x, code_emb], dim=1)
- x = self.integrating_conv(x)
- for i, lyr in enumerate(self.layers):
- # Do layer drop where applicable. Do not drop first and last layers.
- if self.training and self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop:
- unused_params.extend(list(lyr.parameters()))
- else:
- # First and last blocks will have autocast disabled for improved precision.
- with autocast(x.device.type, enabled=self.enable_fp16 and i != 0):
- x = lyr(x, time_emb)
-
- x = x.float()
- out = self.out(x)
-
- # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
- extraneous_addition = 0
- for p in unused_params:
- extraneous_addition = extraneous_addition + p.mean()
- out = out + extraneous_addition * 0
-
- if return_code_pred:
- return out, mel_pred
- return out
-
-
-if __name__ == '__main__':
- clip = torch.randn(2, 100, 400)
- aligned_latent = torch.randn(2,388,512)
- aligned_sequence = torch.randint(0,8192,(2,100))
- cond = torch.randn(2, 100, 400)
- ts = torch.LongTensor([600, 600])
- model = DiffusionTts(512, layer_drop=.3, unconditioned_percentage=.5)
- # Test with latent aligned conditioning
- #o = model(clip, ts, aligned_latent, cond)
- # Test with sequence aligned conditioning
- o = model(clip, ts, aligned_sequence, cond)
-
diff --git a/spaces/jbilcke-hf/Panoremix/src/app/interface/about/index.tsx b/spaces/jbilcke-hf/Panoremix/src/app/interface/about/index.tsx
deleted file mode 100644
index 6639c63470413e6ee304fce1925a0bb96ef6b6e9..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/Panoremix/src/app/interface/about/index.tsx
+++ /dev/null
@@ -1,40 +0,0 @@
-import { Button } from "@/components/ui/button"
-import { Dialog, DialogContent, DialogDescription, DialogFooter, DialogHeader, DialogTitle, DialogTrigger } from "@/components/ui/dialog"
-import { useState } from "react"
-
-export function About() {
- const [isOpen, setOpen] = useState(false)
-
- return (
-
-
-
- About this project
- About
-
-
-
-
- The Panoremix
-
- What is Panoremix?
-
-
-
-
- Panoremix is a free and open-source application made to generate panoramas.
-
-
- 👉 The stable diffusion model used to generate the images is SDXL 1.0 .
-
-
- 👉 The SDXL LoRA model used is sdxl-panoramic , a seamless variant of sdxl-panorama .
-
-
- setOpen(false)}>Got it
-
-
-
- )
-}
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/VideoQuest/src/lib/parseJsonList.ts b/spaces/jbilcke-hf/VideoQuest/src/lib/parseJsonList.ts
deleted file mode 100644
index 9aec5c04a30cc843c252f0bdf0aaf40a5dc7c338..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/VideoQuest/src/lib/parseJsonList.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-export function parseJsonList(content: string): string[] {
- // Extract JSON array from the content
- const start = content.indexOf("[");
- const end = content.lastIndexOf("]");
- const jsonContent = content.slice(start, end + 1);
-
- // Parse as JSON into array of strings
- let objects: string[] = [];
-
- objects = JSON.parse(jsonContent);
-
- return objects;
-}
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/media-server/scripts/decimate_content.sh b/spaces/jbilcke-hf/media-server/scripts/decimate_content.sh
deleted file mode 100644
index cfeda7a3df3b31fa0ea68e31216008c84bd30145..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/media-server/scripts/decimate_content.sh
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/bin/bash
-
-# this script will destroy (well, move to the archives) 15% of the videos
-
-# Calculate the number of .mp4 files
-TOTAL_VIDEO_COUNT=$(ls -l "${VIDEO_DIR}"*.mp4 | wc -l)
-
-# Calculate the number of files to move (15%)
-VIDEO_COUNT_TO_MOVE=$((TOTAL_VIDEO_COUNT / 10))
-
-# If there are no videos to move then exit the script
-if [ "$VIDEO_COUNT_TO_MOVE" -le 0 ]; then
- echo "No videos to move. Exiting."
- exit 0
-fi
-
-# List all .mp4 files in the directory, sorted by modification date, take the oldest 10%
-FILES_TO_MOVE=$(ls -ltr "${WEBTV_VIDEO_STORAGE_PATH_CHANNEL_2}*.mp4" | head -n ${VIDEO_COUNT_TO_MOVE})
-
-# Move the old files to the archive directory
-for file in $FILES_TO_MOVE
-do
- mv "${file}" "${WEBTV_VIDEO_ARCHIVE_PATH_CHANNEL_2}"
-
- OPTIONAL: remove from channel 3 as well
-
- # Extract the base filename
- BASENAME=$(basename ${file})
-
- # Check whether file of the same name is in CHANNEL_3 and move if it is
- if [[ -f "${WEBTV_VIDEO_STORAGE_PATH_CHANNEL_3}/${BASENAME}" ]]; then
- mv "${WEBTV_VIDEO_STORAGE_PATH_CHANNEL_3}/${BASENAME}" "${WEBTV_VIDEO_ARCHIVE_PATH_CHANNEL_3}"
- fi
-done
\ No newline at end of file
diff --git a/spaces/jhtonyKoo/music_mixing_style_transfer/mixing_style_transfer/modules/__init__.py b/spaces/jhtonyKoo/music_mixing_style_transfer/mixing_style_transfer/modules/__init__.py
deleted file mode 100644
index b5020a03d70b8496dd458912272af077e395f25c..0000000000000000000000000000000000000000
--- a/spaces/jhtonyKoo/music_mixing_style_transfer/mixing_style_transfer/modules/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .front_back_end import *
-from .loss import *
-from .training_utils import *
\ No newline at end of file
diff --git a/spaces/jitesh/storytelling/README.md b/spaces/jitesh/storytelling/README.md
deleted file mode 100644
index b09e1ebf1a5f0d93b5579c3cc00513eea35c46e1..0000000000000000000000000000000000000000
--- a/spaces/jitesh/storytelling/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Storytelling
-emoji: 🐨
-colorFrom: purple
-colorTo: green
-sdk: streamlit
-app_file: app.py
-pinned: true
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/joaogabriellima/Real-Time-Voice-Cloning/encoder_train.py b/spaces/joaogabriellima/Real-Time-Voice-Cloning/encoder_train.py
deleted file mode 100644
index b8740a894d615aadfe529cb36068fc8e3496125f..0000000000000000000000000000000000000000
--- a/spaces/joaogabriellima/Real-Time-Voice-Cloning/encoder_train.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from utils.argutils import print_args
-from encoder.train import train
-from pathlib import Path
-import argparse
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser(
- description="Trains the speaker encoder. You must have run encoder_preprocess.py first.",
- formatter_class=argparse.ArgumentDefaultsHelpFormatter
- )
-
- parser.add_argument("run_id", type=str, help= \
- "Name for this model instance. If a model state from the same run ID was previously "
- "saved, the training will restart from there. Pass -f to overwrite saved states and "
- "restart from scratch.")
- parser.add_argument("clean_data_root", type=Path, help= \
- "Path to the output directory of encoder_preprocess.py. If you left the default "
- "output directory when preprocessing, it should be /SV2TTS/encoder/.")
- parser.add_argument("-m", "--models_dir", type=Path, default="encoder/saved_models/", help=\
- "Path to the output directory that will contain the saved model weights, as well as "
- "backups of those weights and plots generated during training.")
- parser.add_argument("-v", "--vis_every", type=int, default=10, help= \
- "Number of steps between updates of the loss and the plots.")
- parser.add_argument("-u", "--umap_every", type=int, default=100, help= \
- "Number of steps between updates of the umap projection. Set to 0 to never update the "
- "projections.")
- parser.add_argument("-s", "--save_every", type=int, default=500, help= \
- "Number of steps between updates of the model on the disk. Set to 0 to never save the "
- "model.")
- parser.add_argument("-b", "--backup_every", type=int, default=7500, help= \
- "Number of steps between backups of the model. Set to 0 to never make backups of the "
- "model.")
- parser.add_argument("-f", "--force_restart", action="store_true", help= \
- "Do not load any saved model.")
- parser.add_argument("--visdom_server", type=str, default="http://localhost")
- parser.add_argument("--no_visdom", action="store_true", help= \
- "Disable visdom.")
- args = parser.parse_args()
-
- # Process the arguments
- args.models_dir.mkdir(exist_ok=True)
-
- # Run the training
- print_args(args, parser)
- train(**vars(args))
-
\ No newline at end of file
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/FitsImagePlugin.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/FitsImagePlugin.py
deleted file mode 100644
index 1359aeb1282ee78e38f40fc25b4a50b621db4043..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/FitsImagePlugin.py
+++ /dev/null
@@ -1,73 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# FITS file handling
-#
-# Copyright (c) 1998-2003 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import math
-
-from . import Image, ImageFile
-
-
-def _accept(prefix):
- return prefix[:6] == b"SIMPLE"
-
-
-class FitsImageFile(ImageFile.ImageFile):
- format = "FITS"
- format_description = "FITS"
-
- def _open(self):
- headers = {}
- while True:
- header = self.fp.read(80)
- if not header:
- msg = "Truncated FITS file"
- raise OSError(msg)
- keyword = header[:8].strip()
- if keyword == b"END":
- break
- value = header[8:].split(b"/")[0].strip()
- if value.startswith(b"="):
- value = value[1:].strip()
- if not headers and (not _accept(keyword) or value != b"T"):
- msg = "Not a FITS file"
- raise SyntaxError(msg)
- headers[keyword] = value
-
- naxis = int(headers[b"NAXIS"])
- if naxis == 0:
- msg = "No image data"
- raise ValueError(msg)
- elif naxis == 1:
- self._size = 1, int(headers[b"NAXIS1"])
- else:
- self._size = int(headers[b"NAXIS1"]), int(headers[b"NAXIS2"])
-
- number_of_bits = int(headers[b"BITPIX"])
- if number_of_bits == 8:
- self.mode = "L"
- elif number_of_bits == 16:
- self.mode = "I"
- # rawmode = "I;16S"
- elif number_of_bits == 32:
- self.mode = "I"
- elif number_of_bits in (-32, -64):
- self.mode = "F"
- # rawmode = "F" if number_of_bits == -32 else "F;64F"
-
- offset = math.ceil(self.fp.tell() / 2880) * 2880
- self.tile = [("raw", (0, 0) + self.size, offset, (self.mode, 0, -1))]
-
-
-# --------------------------------------------------------------------
-# Registry
-
-Image.register_open(FitsImageFile.format, FitsImageFile, _accept)
-
-Image.register_extensions(FitsImageFile.format, [".fit", ".fits"])
diff --git a/spaces/jordonpeter01/MusicGen/audiocraft/models/loaders.py b/spaces/jordonpeter01/MusicGen/audiocraft/models/loaders.py
deleted file mode 100644
index 97c662c3212b7695669cbfc5214ff2f099c3f319..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/MusicGen/audiocraft/models/loaders.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Utility functions to load from the checkpoints.
-Each checkpoint is a torch.saved dict with the following keys:
-- 'xp.cfg': the hydra config as dumped during training. This should be used
- to rebuild the object using the audiocraft.models.builders functions,
-- 'model_best_state': a readily loadable best state for the model, including
- the conditioner. The model obtained from `xp.cfg` should be compatible
- with this state dict. In the case of a LM, the encodec model would not be
- bundled along but instead provided separately.
-
-Those functions also support loading from a remote location with the Torch Hub API.
-They also support overriding some parameters, in particular the device and dtype
-of the returned model.
-"""
-
-from pathlib import Path
-from huggingface_hub import hf_hub_download
-import typing as tp
-import os
-
-from omegaconf import OmegaConf
-import torch
-
-from . import builders
-
-
-HF_MODEL_CHECKPOINTS_MAP = {
- "small": "facebook/musicgen-small",
- "medium": "facebook/musicgen-medium",
- "large": "facebook/musicgen-large",
- "melody": "facebook/musicgen-melody",
-}
-
-
-def _get_state_dict(
- file_or_url_or_id: tp.Union[Path, str],
- filename: tp.Optional[str] = None,
- device='cpu',
- cache_dir: tp.Optional[str] = None,
-):
- # Return the state dict either from a file or url
- file_or_url_or_id = str(file_or_url_or_id)
- assert isinstance(file_or_url_or_id, str)
-
- if os.path.isfile(file_or_url_or_id):
- return torch.load(file_or_url_or_id, map_location=device)
-
- if os.path.isdir(file_or_url_or_id):
- file = f"{file_or_url_or_id}/{filename}"
- return torch.load(file, map_location=device)
-
- elif file_or_url_or_id.startswith('https://'):
- return torch.hub.load_state_dict_from_url(file_or_url_or_id, map_location=device, check_hash=True)
-
- elif file_or_url_or_id in HF_MODEL_CHECKPOINTS_MAP:
- assert filename is not None, "filename needs to be defined if using HF checkpoints"
-
- repo_id = HF_MODEL_CHECKPOINTS_MAP[file_or_url_or_id]
- file = hf_hub_download(repo_id=repo_id, filename=filename, cache_dir=cache_dir)
- return torch.load(file, map_location=device)
-
- else:
- raise ValueError(f"{file_or_url_or_id} is not a valid name, path or link that can be loaded.")
-
-
-def load_compression_model(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None):
- pkg = _get_state_dict(file_or_url_or_id, filename="compression_state_dict.bin", cache_dir=cache_dir)
- cfg = OmegaConf.create(pkg['xp.cfg'])
- cfg.device = str(device)
- model = builders.get_compression_model(cfg)
- model.load_state_dict(pkg['best_state'])
- model.eval()
- return model
-
-
-def load_lm_model(file_or_url_or_id: tp.Union[Path, str], device='cpu', cache_dir: tp.Optional[str] = None):
- pkg = _get_state_dict(file_or_url_or_id, filename="state_dict.bin", cache_dir=cache_dir)
- cfg = OmegaConf.create(pkg['xp.cfg'])
- cfg.device = str(device)
- if cfg.device == 'cpu':
- cfg.dtype = 'float32'
- else:
- cfg.dtype = 'float16'
- model = builders.get_lm_model(cfg)
- model.load_state_dict(pkg['best_state'])
- model.eval()
- model.cfg = cfg
- return model
diff --git a/spaces/jpfearnworks/ai_agents/modules/base/chain.py b/spaces/jpfearnworks/ai_agents/modules/base/chain.py
deleted file mode 100644
index 8a8974dc5ee63b9597707dfb027ab5a06c2f4239..0000000000000000000000000000000000000000
--- a/spaces/jpfearnworks/ai_agents/modules/base/chain.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from loguru import logger
-from pydantic import BaseModel
-
-class IChain(BaseModel):
- """
- IChain Class (Interface for Chain)
-
- Design:
- This class is an interface that defines the basic structure for a chain class. It's not intended to be
- instantiated directly, but should be extended by other classes that implement the run method. This follows
- the Interface Segregation Principle (ISP), as it provides a simple, specific interface for chain classes.
-
- Intended Implementation:
- Classes that extend IChain should provide an implementation for the run method. The run method should take
- a string input and return a string output. The specifics of what the run method does will depend on the
- requirements of the subclass.
- """
- def run(self, input: str) -> str:
- logger.info("Running IChain with input: {}", input)
- pass
-
diff --git a/spaces/justest/gpt4free/g4f/Provider/Providers/DFEHub.py b/spaces/justest/gpt4free/g4f/Provider/Providers/DFEHub.py
deleted file mode 100644
index 1bbdd01ea392c5421cf24762b74c80c6506b904e..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/g4f/Provider/Providers/DFEHub.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import os, requests
-from ...typing import sha256, Dict, get_type_hints
-import json
-
-url = "https://chat.dfehub.com/api/chat"
-model = ['gpt-3.5-turbo']
-supports_stream = False
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- base = ''
- for message in messages:
- base += '%s: %s\n' % (message['role'], message['content'])
- base += 'assistant:'
-
- headers = {
- "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36"
- }
- data = {
- "model": {
- "id": "gpt-3.5-turbo",
- "name": "GPT-3.5",
- "maxLength": 12000,
- "tokenLimit": 4000
- },
- "messages": [
- {
- "role": "user",
- "content": base
- }
- ],
- "key": "",
- "prompt": "You are ChatGPT, a large language model trained by OpenAI. Follow the user's instructions carefully. Respond using markdown.",
- "temperature": 1
- }
- response = requests.post(url, headers=headers, data=json.dumps(data))
- if response.status_code == 200:
- yield response.text
- else:
- print(f"Error Occurred::{response.status_code}")
- return None
-
-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/kananj/Daytona-Beach-Ambassador/style.css b/spaces/kananj/Daytona-Beach-Ambassador/style.css
deleted file mode 100644
index 303c3d7ef3b06c42b211797cd2d5af9800589092..0000000000000000000000000000000000000000
--- a/spaces/kananj/Daytona-Beach-Ambassador/style.css
+++ /dev/null
@@ -1,16 +0,0 @@
-h1 {
- text-align: center;
-}
-
-#duplicate-button {
- margin: auto;
- color: white;
- background: #1565c0;
- border-radius: 100vh;
-}
-
-#component-0 {
- max-width: 900px;
- margin: auto;
- padding-top: 1.5rem;
-}
diff --git a/spaces/kavit02/cono.type.xd/app.py b/spaces/kavit02/cono.type.xd/app.py
deleted file mode 100644
index 10399a91729bfa86656741ef580d376c203e904a..0000000000000000000000000000000000000000
--- a/spaces/kavit02/cono.type.xd/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet Riya, your youthful and witty personal assistant! At 21 years old, she's full of energy and always eager to help. Riya's goal is to explore the PGRKAM website to find a comprehensive list of employment opportunities offered by the Punjab government. Look for details on job openings, eligibility criteria, application procedures, and deadlines provided by the government of Punjab on the PGRKAM website. Additionally, seek information on any special initiatives or programs aimed at supporting job seekers. Provide a summary of the most promising job listings that align with your skills, qualifications, and career goals, along with any relevant application links and contact details for further inquiries only related to the PGRKAM website. Riya also helps you out with any type of query you have in your mind related to registration and any questions or problems you might have with the website. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-def get_text_response(user_message,history):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/kaxap/wiki-multilingual-e5-large/app.py b/spaces/kaxap/wiki-multilingual-e5-large/app.py
deleted file mode 100644
index ad4b95a5590d392948de2c4afbd93fb708fd7377..0000000000000000000000000000000000000000
--- a/spaces/kaxap/wiki-multilingual-e5-large/app.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import gradio as gr
-
-import pandas as pd
-import numpy as np
-
-import torch.nn.functional as F
-
-from torch import Tensor
-from transformers import AutoTokenizer, AutoModel
-from sklearn.metrics.pairwise import cosine_similarity
-
-import re
-
-
-def average_pool(last_hidden_states: Tensor,
- attention_mask: Tensor) -> Tensor:
- last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
- return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
-
-
-df = pd.read_csv('wiki.csv')
-data_embeddings = np.load("wiki-embeddings.npy")
-
-print("loading the model...")
-tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large')
-model = AutoModel.from_pretrained('intfloat/multilingual-e5-large')
-
-with gr.Blocks() as demo:
- chatbot = gr.Chatbot(label="semantic search for 230k+ wikipedia articles")
- msg = gr.Textbox(label="simple wikipedia semantic search query", placeholder="for example, \"medieval battles\"")
- clear = gr.ClearButton([msg, chatbot])
-
- def _search(message, chat_history):
- batch_dict = tokenizer(["query: " + message], max_length=512, padding=True, truncation=True, return_tensors='pt')
-
- outputs = model(**batch_dict)
- input_embedding = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
-
- # normalize embeddings
- input_embedding = F.normalize(input_embedding, p=2, dim=1)
- input_embedding = input_embedding[0].tolist()
-
- # Compute cosine similarities
- input_embedding = np.array(input_embedding).reshape(1, -1)
- cos_similarities = cosine_similarity(data_embeddings, input_embedding).flatten()
-
- # Get top k similar points' indices
- k = 10 # replace with your value of k
- top_k_idx = cos_similarities.argsort()[-k:][::-1]
-
- # Get corresponding 'text' for top k similar points
- top_k_text = df['title'].iloc[top_k_idx].tolist()
-
- bot_message = "\n".join(f"{i+1}. {top_k_text[i]} // {top_k_idx[i]}" for i in range(len(top_k_text)))
-
- chat_history.append((message, f"results (you can enter article number 1-{k} to see its contents):\n" + bot_message))
- return "", chat_history
-
- def _retrieve(message, chat_history):
- idx = int(message)
- for _, m in chat_history[::-1]:
- if m.startswith("results"):
- for n in m.split("\n")[1:]:
- print(n)
- if str(idx) == n.split(".")[0]:
- df_idx = int(n.split(" // ")[-1])
- print(df_idx)
- article = df.iloc[df_idx]['text']
- article = re.sub(r'(===?=?[A-Z ].+?===?=?)', r'\n\n\1\n', article)
- chat_history.append((message, f"contents of {n}:\n{article}"))
- return "", chat_history
- print("nothing found")
- chat_history.append((message, "🤔 article not found"))
- return "", chat_history
-
- def respond(message, chat_history):
- print(f"received input '{message}'")
- try:
- int(message)
- print(f"retrieving #{message}")
- return _retrieve(message, chat_history)
- except ValueError:
- print(f"searching for {message}")
- return _search(message, chat_history)
-
- msg.submit(respond, [msg, chatbot], [msg, chatbot])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/kazuk/youtube-whisper/app.py b/spaces/kazuk/youtube-whisper/app.py
deleted file mode 100644
index 4a61dc561a016c53ad93a3c556b0ef7bafa964eb..0000000000000000000000000000000000000000
--- a/spaces/kazuk/youtube-whisper/app.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import gradio as gr
-import whisper
-from pytube import YouTube
-
-def get_audio(url):
- yt = YouTube(url)
- return yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4")
-
-def get_transcript(url, model_size, lang, format):
-
- model = whisper.load_model(model_size)
-
- if lang == "None":
- lang = None
-
- result = model.transcribe(get_audio(url), fp16=False, language=lang)
-
- if format == "None":
- return result["text"]
- elif format == ".srt":
- return format_to_srt(result["segments"])
-
-def format_to_srt(segments):
- output = ""
- for i, segment in enumerate(segments):
- output += f"{i + 1}\n"
- output += f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
- output += f"{segment['text']}\n\n"
- return output
-
-def format_timestamp(t):
- hh = t//3600
- mm = (t - hh*3600)//60
- ss = t - hh*3600 - mm*60
- mi = (t - int(t))*1000
- return f"{int(hh):02d}:{int(mm):02d}:{int(ss):02d},{int(mi):03d}"
-
-
-langs = ["None"] + sorted(list(whisper.tokenizer.LANGUAGES.values()))
-model_size = list(whisper._MODELS.keys())
-
-with gr.Blocks() as demo:
-
- with gr.Row():
-
- with gr.Column():
-
- with gr.Row():
- url = gr.Textbox(placeholder='Youtube video URL', label='URL')
-
- with gr.Row():
-
- model_size = gr.Dropdown(choices=model_size, value='tiny', label="Model")
- lang = gr.Dropdown(choices=langs, value="None", label="Language (Optional)")
- format = gr.Dropdown(choices=["None", ".srt"], value="None", label="Timestamps? (Optional)")
-
- with gr.Row():
- gr.Markdown("Larger models are more accurate, but slower. For 1min video, it'll take ~30s (tiny), ~1min (base), ~3min (small), ~5min (medium), etc.")
- transcribe_btn = gr.Button('Transcribe')
-
- with gr.Column():
- outputs = gr.Textbox(placeholder='Transcription of the video', label='Transcription')
-
- transcribe_btn.click(get_transcript, inputs=[url, model_size, lang, format], outputs=outputs)
-
-demo.launch(debug=True)
diff --git a/spaces/keithhon/Real-Time-Voice-Cloning/vocoder/display.py b/spaces/keithhon/Real-Time-Voice-Cloning/vocoder/display.py
deleted file mode 100644
index 956880722a3f05613ebd06f5686b3d8a59642e92..0000000000000000000000000000000000000000
--- a/spaces/keithhon/Real-Time-Voice-Cloning/vocoder/display.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import matplotlib.pyplot as plt
-import time
-import numpy as np
-import sys
-
-
-def progbar(i, n, size=16):
- done = (i * size) // n
- bar = ''
- for i in range(size):
- bar += '█' if i <= done else '░'
- return bar
-
-
-def stream(message) :
- try:
- sys.stdout.write("\r{%s}" % message)
- except:
- #Remove non-ASCII characters from message
- message = ''.join(i for i in message if ord(i)<128)
- sys.stdout.write("\r{%s}" % message)
-
-
-def simple_table(item_tuples) :
-
- border_pattern = '+---------------------------------------'
- whitespace = ' '
-
- headings, cells, = [], []
-
- for item in item_tuples :
-
- heading, cell = str(item[0]), str(item[1])
-
- pad_head = True if len(heading) < len(cell) else False
-
- pad = abs(len(heading) - len(cell))
- pad = whitespace[:pad]
-
- pad_left = pad[:len(pad)//2]
- pad_right = pad[len(pad)//2:]
-
- if pad_head :
- heading = pad_left + heading + pad_right
- else :
- cell = pad_left + cell + pad_right
-
- headings += [heading]
- cells += [cell]
-
- border, head, body = '', '', ''
-
- for i in range(len(item_tuples)) :
-
- temp_head = f'| {headings[i]} '
- temp_body = f'| {cells[i]} '
-
- border += border_pattern[:len(temp_head)]
- head += temp_head
- body += temp_body
-
- if i == len(item_tuples) - 1 :
- head += '|'
- body += '|'
- border += '+'
-
- print(border)
- print(head)
- print(border)
- print(body)
- print(border)
- print(' ')
-
-
-def time_since(started) :
- elapsed = time.time() - started
- m = int(elapsed // 60)
- s = int(elapsed % 60)
- if m >= 60 :
- h = int(m // 60)
- m = m % 60
- return f'{h}h {m}m {s}s'
- else :
- return f'{m}m {s}s'
-
-
-def save_attention(attn, path) :
- fig = plt.figure(figsize=(12, 6))
- plt.imshow(attn.T, interpolation='nearest', aspect='auto')
- fig.savefig(f'{path}.png', bbox_inches='tight')
- plt.close(fig)
-
-
-def save_spectrogram(M, path, length=None) :
- M = np.flip(M, axis=0)
- if length : M = M[:, :length]
- fig = plt.figure(figsize=(12, 6))
- plt.imshow(M, interpolation='nearest', aspect='auto')
- fig.savefig(f'{path}.png', bbox_inches='tight')
- plt.close(fig)
-
-
-def plot(array) :
- fig = plt.figure(figsize=(30, 5))
- ax = fig.add_subplot(111)
- ax.xaxis.label.set_color('grey')
- ax.yaxis.label.set_color('grey')
- ax.xaxis.label.set_fontsize(23)
- ax.yaxis.label.set_fontsize(23)
- ax.tick_params(axis='x', colors='grey', labelsize=23)
- ax.tick_params(axis='y', colors='grey', labelsize=23)
- plt.plot(array)
-
-
-def plot_spec(M) :
- M = np.flip(M, axis=0)
- plt.figure(figsize=(18,4))
- plt.imshow(M, interpolation='nearest', aspect='auto')
- plt.show()
-
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/README.md b/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/README.md
deleted file mode 100644
index 2ee63a861229b68873561fa39bfa7c9a8b53b947..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/models/arcface_torch/README.md
+++ /dev/null
@@ -1,164 +0,0 @@
-# Distributed Arcface Training in Pytorch
-
-This is a deep learning library that makes face recognition efficient, and effective, which can train tens of millions
-identity on a single server.
-
-## Requirements
-
-- Install [pytorch](http://pytorch.org) (torch>=1.6.0), our doc for [install.md](docs/install.md).
-- `pip install -r requirements.txt`.
-- Download the dataset
- from [https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_)
- .
-
-## How to Training
-
-To train a model, run `train.py` with the path to the configs:
-
-### 1. Single node, 8 GPUs:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r50
-```
-
-### 2. Multiple nodes, each node 8 GPUs:
-
-Node 0:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=1234 train.py train.py configs/ms1mv3_r50
-```
-
-Node 1:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=1234 train.py train.py configs/ms1mv3_r50
-```
-
-### 3.Training resnet2060 with 8 GPUs:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r2060.py
-```
-
-## Model Zoo
-
-- The models are available for non-commercial research purposes only.
-- All models can be found in here.
-- [Baidu Yun Pan](https://pan.baidu.com/s/1CL-l4zWqsI1oDuEEYVhj-g): e8pw
-- [onedrive](https://1drv.ms/u/s!AswpsDO2toNKq0lWY69vN58GR6mw?e=p9Ov5d)
-
-### Performance on [**ICCV2021-MFR**](http://iccv21-mfr.com/)
-
-ICCV2021-MFR testset consists of non-celebrities so we can ensure that it has very few overlap with public available face
-recognition training set, such as MS1M and CASIA as they mostly collected from online celebrities.
-As the result, we can evaluate the FAIR performance for different algorithms.
-
-For **ICCV2021-MFR-ALL** set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6). The
-globalised multi-racial testset contains 242,143 identities and 1,624,305 images.
-
-For **ICCV2021-MFR-MASK** set, TAR is measured on mask-to-nonmask 1:1 protocal, with FAR less than 0.0001(e-4).
-Mask testset contains 6,964 identities, 6,964 masked images and 13,928 non-masked images.
-There are totally 13,928 positive pairs and 96,983,824 negative pairs.
-
-| Datasets | backbone | Training throughout | Size / MB | **ICCV2021-MFR-MASK** | **ICCV2021-MFR-ALL** |
-| :---: | :--- | :--- | :--- |:--- |:--- |
-| MS1MV3 | r18 | - | 91 | **47.85** | **68.33** |
-| Glint360k | r18 | 8536 | 91 | **53.32** | **72.07** |
-| MS1MV3 | r34 | - | 130 | **58.72** | **77.36** |
-| Glint360k | r34 | 6344 | 130 | **65.10** | **83.02** |
-| MS1MV3 | r50 | 5500 | 166 | **63.85** | **80.53** |
-| Glint360k | r50 | 5136 | 166 | **70.23** | **87.08** |
-| MS1MV3 | r100 | - | 248 | **69.09** | **84.31** |
-| Glint360k | r100 | 3332 | 248 | **75.57** | **90.66** |
-| MS1MV3 | mobilefacenet | 12185 | 7.8 | **41.52** | **65.26** |
-| Glint360k | mobilefacenet | 11197 | 7.8 | **44.52** | **66.48** |
-
-### Performance on IJB-C and Verification Datasets
-
-| Datasets | backbone | IJBC(1e-05) | IJBC(1e-04) | agedb30 | cfp_fp | lfw | log |
-| :---: | :--- | :--- | :--- | :--- |:--- |:--- |:--- |
-| MS1MV3 | r18 | 92.07 | 94.66 | 97.77 | 97.73 | 99.77 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r18_fp16/training.log)|
-| MS1MV3 | r34 | 94.10 | 95.90 | 98.10 | 98.67 | 99.80 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r34_fp16/training.log)|
-| MS1MV3 | r50 | 94.79 | 96.46 | 98.35 | 98.96 | 99.83 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r50_fp16/training.log)|
-| MS1MV3 | r100 | 95.31 | 96.81 | 98.48 | 99.06 | 99.85 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r100_fp16/training.log)|
-| MS1MV3 | **r2060**| 95.34 | 97.11 | 98.67 | 99.24 | 99.87 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r2060_fp16/training.log)|
-| Glint360k |r18-0.1 | 93.16 | 95.33 | 97.72 | 97.73 | 99.77 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r18_fp16_0.1/training.log)|
-| Glint360k |r34-0.1 | 95.16 | 96.56 | 98.33 | 98.78 | 99.82 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r34_fp16_0.1/training.log)|
-| Glint360k |r50-0.1 | 95.61 | 96.97 | 98.38 | 99.20 | 99.83 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r50_fp16_0.1/training.log)|
-| Glint360k |r100-0.1 | 95.88 | 97.32 | 98.48 | 99.29 | 99.82 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r100_fp16_0.1/training.log)|
-
-[comment]: <> (More details see [model.md](docs/modelzoo.md) in docs.)
-
-
-## [Speed Benchmark](docs/speed_benchmark.md)
-
-**Arcface Torch** can train large-scale face recognition training set efficiently and quickly. When the number of
-classes in training sets is greater than 300K and the training is sufficient, partial fc sampling strategy will get same
-accuracy with several times faster training performance and smaller GPU memory.
-Partial FC is a sparse variant of the model parallel architecture for large sacle face recognition. Partial FC use a
-sparse softmax, where each batch dynamicly sample a subset of class centers for training. In each iteration, only a
-sparse part of the parameters will be updated, which can reduce a lot of GPU memory and calculations. With Partial FC,
-we can scale trainset of 29 millions identities, the largest to date. Partial FC also supports multi-machine distributed
-training and mixed precision training.
-
-
-
-More details see
-[speed_benchmark.md](docs/speed_benchmark.md) in docs.
-
-### 1. Training speed of different parallel methods (samples / second), Tesla V100 32GB * 8. (Larger is better)
-
-`-` means training failed because of gpu memory limitations.
-
-| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
-| :--- | :--- | :--- | :--- |
-|125000 | 4681 | 4824 | 5004 |
-|1400000 | **1672** | 3043 | 4738 |
-|5500000 | **-** | **1389** | 3975 |
-|8000000 | **-** | **-** | 3565 |
-|16000000 | **-** | **-** | 2679 |
-|29000000 | **-** | **-** | **1855** |
-
-### 2. GPU memory cost of different parallel methods (MB per GPU), Tesla V100 32GB * 8. (Smaller is better)
-
-| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
-| :--- | :--- | :--- | :--- |
-|125000 | 7358 | 5306 | 4868 |
-|1400000 | 32252 | 11178 | 6056 |
-|5500000 | **-** | 32188 | 9854 |
-|8000000 | **-** | **-** | 12310 |
-|16000000 | **-** | **-** | 19950 |
-|29000000 | **-** | **-** | 32324 |
-
-## Evaluation ICCV2021-MFR and IJB-C
-
-More details see [eval.md](docs/eval.md) in docs.
-
-## Test
-
-We tested many versions of PyTorch. Please create an issue if you are having trouble.
-
-- [x] torch 1.6.0
-- [x] torch 1.7.1
-- [x] torch 1.8.0
-- [x] torch 1.9.0
-
-## Citation
-
-```
-@inproceedings{deng2019arcface,
- title={Arcface: Additive angular margin loss for deep face recognition},
- author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos},
- booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
- pages={4690--4699},
- year={2019}
-}
-@inproceedings{an2020partical_fc,
- title={Partial FC: Training 10 Million Identities on a Single Machine},
- author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
- Zhang, Debing and Fu Ying},
- booktitle={Arxiv 2010.05222},
- year={2020}
-}
-```
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/speaker_encoder/train.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/speaker_encoder/train.py
deleted file mode 100644
index 2e9485afbeead6a063b5ef69a85f05757d6c91ff..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/speaker_encoder/train.py
+++ /dev/null
@@ -1,125 +0,0 @@
-from speaker_encoder.visualizations import Visualizations
-from speaker_encoder.data_objects import SpeakerVerificationDataLoader, SpeakerVerificationDataset
-from speaker_encoder.params_model import *
-from speaker_encoder.model import SpeakerEncoder
-from utils.profiler import Profiler
-from pathlib import Path
-import torch
-
-def sync(device: torch.device):
- # FIXME
- return
- # For correct profiling (cuda operations are async)
- if device.type == "cuda":
- torch.cuda.synchronize(device)
-
-def train(run_id: str, clean_data_root: Path, models_dir: Path, umap_every: int, save_every: int,
- backup_every: int, vis_every: int, force_restart: bool, visdom_server: str,
- no_visdom: bool):
- # Create a dataset and a dataloader
- dataset = SpeakerVerificationDataset(clean_data_root)
- loader = SpeakerVerificationDataLoader(
- dataset,
- speakers_per_batch, # 64
- utterances_per_speaker, # 10
- num_workers=8,
- )
-
- # Setup the device on which to run the forward pass and the loss. These can be different,
- # because the forward pass is faster on the GPU whereas the loss is often (depending on your
- # hyperparameters) faster on the CPU.
- device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
- # FIXME: currently, the gradient is None if loss_device is cuda
- loss_device = torch.device("cpu")
-
- # Create the model and the optimizer
- model = SpeakerEncoder(device, loss_device)
- optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate_init)
- init_step = 1
-
- # Configure file path for the model
- state_fpath = models_dir.joinpath(run_id + ".pt")
- backup_dir = models_dir.joinpath(run_id + "_backups")
-
- # Load any existing model
- if not force_restart:
- if state_fpath.exists():
- print("Found existing model \"%s\", loading it and resuming training." % run_id)
- checkpoint = torch.load(state_fpath)
- init_step = checkpoint["step"]
- model.load_state_dict(checkpoint["model_state"])
- optimizer.load_state_dict(checkpoint["optimizer_state"])
- optimizer.param_groups[0]["lr"] = learning_rate_init
- else:
- print("No model \"%s\" found, starting training from scratch." % run_id)
- else:
- print("Starting the training from scratch.")
- model.train()
-
- # Initialize the visualization environment
- vis = Visualizations(run_id, vis_every, server=visdom_server, disabled=no_visdom)
- vis.log_dataset(dataset)
- vis.log_params()
- device_name = str(torch.cuda.get_device_name(0) if torch.cuda.is_available() else "CPU")
- vis.log_implementation({"Device": device_name})
-
- # Training loop
- profiler = Profiler(summarize_every=10, disabled=False)
- for step, speaker_batch in enumerate(loader, init_step):
- profiler.tick("Blocking, waiting for batch (threaded)")
-
- # Forward pass
- inputs = torch.from_numpy(speaker_batch.data).to(device)
- sync(device)
- profiler.tick("Data to %s" % device)
- embeds = model(inputs)
- sync(device)
- profiler.tick("Forward pass")
- embeds_loss = embeds.view((speakers_per_batch, utterances_per_speaker, -1)).to(loss_device)
- loss, eer = model.loss(embeds_loss)
- sync(loss_device)
- profiler.tick("Loss")
-
- # Backward pass
- model.zero_grad()
- loss.backward()
- profiler.tick("Backward pass")
- model.do_gradient_ops()
- optimizer.step()
- profiler.tick("Parameter update")
-
- # Update visualizations
- # learning_rate = optimizer.param_groups[0]["lr"]
- vis.update(loss.item(), eer, step)
-
- # Draw projections and save them to the backup folder
- if umap_every != 0 and step % umap_every == 0:
- print("Drawing and saving projections (step %d)" % step)
- backup_dir.mkdir(exist_ok=True)
- projection_fpath = backup_dir.joinpath("%s_umap_%06d.png" % (run_id, step))
- embeds = embeds.detach().cpu().numpy()
- vis.draw_projections(embeds, utterances_per_speaker, step, projection_fpath)
- vis.save()
-
- # Overwrite the latest version of the model
- if save_every != 0 and step % save_every == 0:
- print("Saving the model (step %d)" % step)
- torch.save({
- "step": step + 1,
- "model_state": model.state_dict(),
- "optimizer_state": optimizer.state_dict(),
- }, state_fpath)
-
- # Make a backup
- if backup_every != 0 and step % backup_every == 0:
- print("Making a backup (step %d)" % step)
- backup_dir.mkdir(exist_ok=True)
- backup_fpath = backup_dir.joinpath("%s_bak_%06d.pt" % (run_id, step))
- torch.save({
- "step": step + 1,
- "model_state": model.state_dict(),
- "optimizer_state": optimizer.state_dict(),
- }, backup_fpath)
-
- profiler.tick("Extras (visualizations, saving)")
-
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/inference.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/inference.py
deleted file mode 100644
index 3e5156e8d649954837e397c2ff15ec29995e7502..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/models/arcface_torch/inference.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import argparse
-
-import cv2
-import numpy as np
-import torch
-
-from backbones import get_model
-
-
-@torch.no_grad()
-def inference(weight, name, img):
- if img is None:
- img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.uint8)
- else:
- img = cv2.imread(img)
- img = cv2.resize(img, (112, 112))
-
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
- img = np.transpose(img, (2, 0, 1))
- img = torch.from_numpy(img).unsqueeze(0).float()
- img.div_(255).sub_(0.5).div_(0.5)
- net = get_model(name, fp16=False)
- net.load_state_dict(torch.load(weight))
- net.eval()
- feat = net(img).numpy()
- print(feat)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser(description='PyTorch ArcFace Training')
- parser.add_argument('--network', type=str, default='r50', help='backbone network')
- parser.add_argument('--weight', type=str, default='')
- parser.add_argument('--img', type=str, default=None)
- args = parser.parse_args()
- inference(args.weight, args.network, args.img)
diff --git a/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/eval/verification.py b/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/eval/verification.py
deleted file mode 100644
index 253343b83dbf9d1bd154d14ec068e098bf0968db..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/eval/verification.py
+++ /dev/null
@@ -1,407 +0,0 @@
-"""Helper for evaluation on the Labeled Faces in the Wild dataset
-"""
-
-# MIT License
-#
-# Copyright (c) 2016 David Sandberg
-#
-# 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.
-
-
-import datetime
-import os
-import pickle
-
-import mxnet as mx
-import numpy as np
-import sklearn
-import torch
-from mxnet import ndarray as nd
-from scipy import interpolate
-from sklearn.decomposition import PCA
-from sklearn.model_selection import KFold
-
-
-class LFold:
- def __init__(self, n_splits=2, shuffle=False):
- self.n_splits = n_splits
- if self.n_splits > 1:
- self.k_fold = KFold(n_splits=n_splits, shuffle=shuffle)
-
- def split(self, indices):
- if self.n_splits > 1:
- return self.k_fold.split(indices)
- else:
- return [(indices, indices)]
-
-
-def calculate_roc(thresholds,
- embeddings1,
- embeddings2,
- actual_issame,
- nrof_folds=10,
- pca=0):
- assert (embeddings1.shape[0] == embeddings2.shape[0])
- assert (embeddings1.shape[1] == embeddings2.shape[1])
- nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
- nrof_thresholds = len(thresholds)
- k_fold = LFold(n_splits=nrof_folds, shuffle=False)
-
- tprs = np.zeros((nrof_folds, nrof_thresholds))
- fprs = np.zeros((nrof_folds, nrof_thresholds))
- accuracy = np.zeros((nrof_folds))
- indices = np.arange(nrof_pairs)
-
- if pca == 0:
- diff = np.subtract(embeddings1, embeddings2)
- dist = np.sum(np.square(diff), 1)
-
- for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
- if pca > 0:
- print('doing pca on', fold_idx)
- embed1_train = embeddings1[train_set]
- embed2_train = embeddings2[train_set]
- _embed_train = np.concatenate((embed1_train, embed2_train), axis=0)
- pca_model = PCA(n_components=pca)
- pca_model.fit(_embed_train)
- embed1 = pca_model.transform(embeddings1)
- embed2 = pca_model.transform(embeddings2)
- embed1 = sklearn.preprocessing.normalize(embed1)
- embed2 = sklearn.preprocessing.normalize(embed2)
- diff = np.subtract(embed1, embed2)
- dist = np.sum(np.square(diff), 1)
-
- # Find the best threshold for the fold
- acc_train = np.zeros((nrof_thresholds))
- for threshold_idx, threshold in enumerate(thresholds):
- _, _, acc_train[threshold_idx] = calculate_accuracy(
- threshold, dist[train_set], actual_issame[train_set])
- best_threshold_index = np.argmax(acc_train)
- for threshold_idx, threshold in enumerate(thresholds):
- tprs[fold_idx, threshold_idx], fprs[fold_idx, threshold_idx], _ = calculate_accuracy(
- threshold, dist[test_set],
- actual_issame[test_set])
- _, _, accuracy[fold_idx] = calculate_accuracy(
- thresholds[best_threshold_index], dist[test_set],
- actual_issame[test_set])
-
- tpr = np.mean(tprs, 0)
- fpr = np.mean(fprs, 0)
- return tpr, fpr, accuracy
-
-
-def calculate_accuracy(threshold, dist, actual_issame):
- predict_issame = np.less(dist, threshold)
- tp = np.sum(np.logical_and(predict_issame, actual_issame))
- fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
- tn = np.sum(
- np.logical_and(np.logical_not(predict_issame),
- np.logical_not(actual_issame)))
- fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
-
- tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn)
- fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn)
- acc = float(tp + tn) / dist.size
- return tpr, fpr, acc
-
-
-def calculate_val(thresholds,
- embeddings1,
- embeddings2,
- actual_issame,
- far_target,
- nrof_folds=10):
- assert (embeddings1.shape[0] == embeddings2.shape[0])
- assert (embeddings1.shape[1] == embeddings2.shape[1])
- nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
- nrof_thresholds = len(thresholds)
- k_fold = LFold(n_splits=nrof_folds, shuffle=False)
-
- val = np.zeros(nrof_folds)
- far = np.zeros(nrof_folds)
-
- diff = np.subtract(embeddings1, embeddings2)
- dist = np.sum(np.square(diff), 1)
- indices = np.arange(nrof_pairs)
-
- for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
-
- # Find the threshold that gives FAR = far_target
- far_train = np.zeros(nrof_thresholds)
- for threshold_idx, threshold in enumerate(thresholds):
- _, far_train[threshold_idx] = calculate_val_far(
- threshold, dist[train_set], actual_issame[train_set])
- if np.max(far_train) >= far_target:
- f = interpolate.interp1d(far_train, thresholds, kind='slinear')
- threshold = f(far_target)
- else:
- threshold = 0.0
-
- val[fold_idx], far[fold_idx] = calculate_val_far(
- threshold, dist[test_set], actual_issame[test_set])
-
- val_mean = np.mean(val)
- far_mean = np.mean(far)
- val_std = np.std(val)
- return val_mean, val_std, far_mean
-
-
-def calculate_val_far(threshold, dist, actual_issame):
- predict_issame = np.less(dist, threshold)
- true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
- false_accept = np.sum(
- np.logical_and(predict_issame, np.logical_not(actual_issame)))
- n_same = np.sum(actual_issame)
- n_diff = np.sum(np.logical_not(actual_issame))
- # print(true_accept, false_accept)
- # print(n_same, n_diff)
- val = float(true_accept) / float(n_same)
- far = float(false_accept) / float(n_diff)
- return val, far
-
-
-def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0):
- # Calculate evaluation metrics
- thresholds = np.arange(0, 4, 0.01)
- embeddings1 = embeddings[0::2]
- embeddings2 = embeddings[1::2]
- tpr, fpr, accuracy = calculate_roc(thresholds,
- embeddings1,
- embeddings2,
- np.asarray(actual_issame),
- nrof_folds=nrof_folds,
- pca=pca)
- thresholds = np.arange(0, 4, 0.001)
- val, val_std, far = calculate_val(thresholds,
- embeddings1,
- embeddings2,
- np.asarray(actual_issame),
- 1e-3,
- nrof_folds=nrof_folds)
- return tpr, fpr, accuracy, val, val_std, far
-
-@torch.no_grad()
-def load_bin(path, image_size):
- try:
- with open(path, 'rb') as f:
- bins, issame_list = pickle.load(f) # py2
- except UnicodeDecodeError as e:
- with open(path, 'rb') as f:
- bins, issame_list = pickle.load(f, encoding='bytes') # py3
- data_list = []
- for flip in [0, 1]:
- data = torch.empty((len(issame_list) * 2, 3, image_size[0], image_size[1]))
- data_list.append(data)
- for idx in range(len(issame_list) * 2):
- _bin = bins[idx]
- img = mx.image.imdecode(_bin)
- if img.shape[1] != image_size[0]:
- img = mx.image.resize_short(img, image_size[0])
- img = nd.transpose(img, axes=(2, 0, 1))
- for flip in [0, 1]:
- if flip == 1:
- img = mx.ndarray.flip(data=img, axis=2)
- data_list[flip][idx][:] = torch.from_numpy(img.asnumpy())
- if idx % 1000 == 0:
- print('loading bin', idx)
- print(data_list[0].shape)
- return data_list, issame_list
-
-@torch.no_grad()
-def test(data_set, backbone, batch_size, nfolds=10):
- print('testing verification..')
- data_list = data_set[0]
- issame_list = data_set[1]
- embeddings_list = []
- time_consumed = 0.0
- for i in range(len(data_list)):
- data = data_list[i]
- embeddings = None
- ba = 0
- while ba < data.shape[0]:
- bb = min(ba + batch_size, data.shape[0])
- count = bb - ba
- _data = data[bb - batch_size: bb]
- time0 = datetime.datetime.now()
- img = ((_data / 255) - 0.5) / 0.5
- net_out: torch.Tensor = backbone(img)
- _embeddings = net_out.detach().cpu().numpy()
- time_now = datetime.datetime.now()
- diff = time_now - time0
- time_consumed += diff.total_seconds()
- if embeddings is None:
- embeddings = np.zeros((data.shape[0], _embeddings.shape[1]))
- embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :]
- ba = bb
- embeddings_list.append(embeddings)
-
- _xnorm = 0.0
- _xnorm_cnt = 0
- for embed in embeddings_list:
- for i in range(embed.shape[0]):
- _em = embed[i]
- _norm = np.linalg.norm(_em)
- _xnorm += _norm
- _xnorm_cnt += 1
- _xnorm /= _xnorm_cnt
-
- acc1 = 0.0
- std1 = 0.0
- embeddings = embeddings_list[0] + embeddings_list[1]
- embeddings = sklearn.preprocessing.normalize(embeddings)
- print(embeddings.shape)
- print('infer time', time_consumed)
- _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=nfolds)
- acc2, std2 = np.mean(accuracy), np.std(accuracy)
- return acc1, std1, acc2, std2, _xnorm, embeddings_list
-
-
-def dumpR(data_set,
- backbone,
- batch_size,
- name='',
- data_extra=None,
- label_shape=None):
- print('dump verification embedding..')
- data_list = data_set[0]
- issame_list = data_set[1]
- embeddings_list = []
- time_consumed = 0.0
- for i in range(len(data_list)):
- data = data_list[i]
- embeddings = None
- ba = 0
- while ba < data.shape[0]:
- bb = min(ba + batch_size, data.shape[0])
- count = bb - ba
-
- _data = nd.slice_axis(data, axis=0, begin=bb - batch_size, end=bb)
- time0 = datetime.datetime.now()
- if data_extra is None:
- db = mx.io.DataBatch(data=(_data,), label=(_label,))
- else:
- db = mx.io.DataBatch(data=(_data, _data_extra),
- label=(_label,))
- model.forward(db, is_train=False)
- net_out = model.get_outputs()
- _embeddings = net_out[0].asnumpy()
- time_now = datetime.datetime.now()
- diff = time_now - time0
- time_consumed += diff.total_seconds()
- if embeddings is None:
- embeddings = np.zeros((data.shape[0], _embeddings.shape[1]))
- embeddings[ba:bb, :] = _embeddings[(batch_size - count):, :]
- ba = bb
- embeddings_list.append(embeddings)
- embeddings = embeddings_list[0] + embeddings_list[1]
- embeddings = sklearn.preprocessing.normalize(embeddings)
- actual_issame = np.asarray(issame_list)
- outname = os.path.join('temp.bin')
- with open(outname, 'wb') as f:
- pickle.dump((embeddings, issame_list),
- f,
- protocol=pickle.HIGHEST_PROTOCOL)
-
-
-# if __name__ == '__main__':
-#
-# parser = argparse.ArgumentParser(description='do verification')
-# # general
-# parser.add_argument('--data-dir', default='', help='')
-# parser.add_argument('--model',
-# default='../model/softmax,50',
-# help='path to load model.')
-# parser.add_argument('--target',
-# default='lfw,cfp_ff,cfp_fp,agedb_30',
-# help='test targets.')
-# parser.add_argument('--gpu', default=0, type=int, help='gpu id')
-# parser.add_argument('--batch-size', default=32, type=int, help='')
-# parser.add_argument('--max', default='', type=str, help='')
-# parser.add_argument('--mode', default=0, type=int, help='')
-# parser.add_argument('--nfolds', default=10, type=int, help='')
-# args = parser.parse_args()
-# image_size = [112, 112]
-# print('image_size', image_size)
-# ctx = mx.gpu(args.gpu)
-# nets = []
-# vec = args.model.split(',')
-# prefix = args.model.split(',')[0]
-# epochs = []
-# if len(vec) == 1:
-# pdir = os.path.dirname(prefix)
-# for fname in os.listdir(pdir):
-# if not fname.endswith('.params'):
-# continue
-# _file = os.path.join(pdir, fname)
-# if _file.startswith(prefix):
-# epoch = int(fname.split('.')[0].split('-')[1])
-# epochs.append(epoch)
-# epochs = sorted(epochs, reverse=True)
-# if len(args.max) > 0:
-# _max = [int(x) for x in args.max.split(',')]
-# assert len(_max) == 2
-# if len(epochs) > _max[1]:
-# epochs = epochs[_max[0]:_max[1]]
-#
-# else:
-# epochs = [int(x) for x in vec[1].split('|')]
-# print('model number', len(epochs))
-# time0 = datetime.datetime.now()
-# for epoch in epochs:
-# print('loading', prefix, epoch)
-# sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
-# # arg_params, aux_params = ch_dev(arg_params, aux_params, ctx)
-# all_layers = sym.get_internals()
-# sym = all_layers['fc1_output']
-# model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
-# # model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
-# model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0],
-# image_size[1]))])
-# model.set_params(arg_params, aux_params)
-# nets.append(model)
-# time_now = datetime.datetime.now()
-# diff = time_now - time0
-# print('model loading time', diff.total_seconds())
-#
-# ver_list = []
-# ver_name_list = []
-# for name in args.target.split(','):
-# path = os.path.join(args.data_dir, name + ".bin")
-# if os.path.exists(path):
-# print('loading.. ', name)
-# data_set = load_bin(path, image_size)
-# ver_list.append(data_set)
-# ver_name_list.append(name)
-#
-# if args.mode == 0:
-# for i in range(len(ver_list)):
-# results = []
-# for model in nets:
-# acc1, std1, acc2, std2, xnorm, embeddings_list = test(
-# ver_list[i], model, args.batch_size, args.nfolds)
-# print('[%s]XNorm: %f' % (ver_name_list[i], xnorm))
-# print('[%s]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], acc1, std1))
-# print('[%s]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], acc2, std2))
-# results.append(acc2)
-# print('Max of [%s] is %1.5f' % (ver_name_list[i], np.max(results)))
-# elif args.mode == 1:
-# raise ValueError
-# else:
-# model = nets[0]
-# dumpR(ver_list[0], model, args.batch_size, args.target)
diff --git a/spaces/kevinwang676/test-1/app_multi.py b/spaces/kevinwang676/test-1/app_multi.py
deleted file mode 100644
index 509c37e2d24897ca344e9bc255c505eb1637f23c..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/test-1/app_multi.py
+++ /dev/null
@@ -1,1205 +0,0 @@
-from typing import Union
-
-from argparse import ArgumentParser
-from pathlib import Path
-import subprocess
-import librosa
-import os
-import time
-import random
-from search import get_youtube, download_random
-import soundfile
-
-import numpy as np
-
-from PIL import Image, ImageDraw, ImageFont
-from transformers import pipeline
-
-
-fps = int(os.environ["fps"])
-max_duration = int(os.environ["max_duration"])
-video_width = int(os.environ["video_width"])
-video_height = int(os.environ["video_height"])
-margin_left = int(os.environ["margin_left"])
-margin_right = int(os.environ["margin_right"])
-margin_top = int(os.environ["margin_top"])
-line_height = int(os.environ["line_height"])
-
-import matplotlib.pyplot as plt
-from moviepy.editor import *
-from moviepy.video.io.VideoFileClip import VideoFileClip
-
-from moviepy.editor import VideoFileClip, AudioFileClip
-
-import moviepy.editor as mpy
-
-import openai
-import uuid
-import tempfile
-import shlex
-import shutil
-from utils import format_bash_command
-
-allowed_medias = [".png", ".jpg", ".jpeg", ".tiff", ".bmp", ".gif", ".svg", ".mp3", ".wav", ".ogg", ".mp4",
- ".avi", ".mov", ".mkv", ".flv", ".wmv", ".webm", ".mpg", ".mpeg", ".m4v", ".3gp", ".3g2", ".3gpp"]
-
-
-import asyncio
-import json
-import hashlib
-from os import path, getenv
-from pydub import AudioSegment
-
-import gradio as gr
-
-import torch
-
-import edge_tts
-
-from datetime import datetime
-from scipy.io.wavfile import write
-
-import config
-import util
-from infer_pack.models import (
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono
-)
-from vc_infer_pipeline import VC
-
-# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21 # noqa
-in_hf_space = getenv('SYSTEM') == 'spaces'
-
-high_quality = True
-
-# Argument parsing
-arg_parser = ArgumentParser()
-arg_parser.add_argument(
- '--hubert',
- default=getenv('RVC_HUBERT', 'hubert_base.pt'),
- help='path to hubert base model (default: hubert_base.pt)'
-)
-arg_parser.add_argument(
- '--config',
- default=getenv('RVC_MULTI_CFG', 'multi_config.json'),
- help='path to config file (default: multi_config.json)'
-)
-arg_parser.add_argument(
- '--api',
- action='store_true',
- help='enable api endpoint'
-)
-arg_parser.add_argument(
- '--cache-examples',
- action='store_true',
- help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified' # noqa
-)
-args = arg_parser.parse_args()
-
-app_css = '''
-#model_info img {
- max-width: 100px;
- max-height: 100px;
- float: right;
-}
-#model_info p {
- margin: unset;
-}
-'''
-
-app = gr.Blocks(
- theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"),
- css=app_css,
- analytics_enabled=False
-)
-
-# Load hubert model
-hubert_model = util.load_hubert_model(config.device, args.hubert)
-hubert_model.eval()
-
-# Load models
-multi_cfg = json.load(open(args.config, 'r'))
-loaded_models = []
-
-for model_name in multi_cfg.get('models'):
- print(f'Loading model: {model_name}')
-
- # Load model info
- model_info = json.load(
- open(path.join('model', model_name, 'config.json'), 'r')
- )
-
- # Load RVC checkpoint
- cpt = torch.load(
- path.join('model', model_name, model_info['model']),
- map_location='cpu'
- )
- tgt_sr = cpt['config'][-1]
- cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0] # n_spk
-
- if_f0 = cpt.get('f0', 1)
- net_g: Union[SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono]
- if if_f0 == 1:
- net_g = SynthesizerTrnMs768NSFsid(
- *cpt['config'],
- is_half=util.is_half(config.device)
- )
- else:
- net_g = SynthesizerTrnMs768NSFsid_nono(*cpt['config'])
-
- del net_g.enc_q
-
- # According to original code, this thing seems necessary.
- print(net_g.load_state_dict(cpt['weight'], strict=False))
-
- net_g.eval().to(config.device)
- net_g = net_g.half() if util.is_half(config.device) else net_g.float()
-
- vc = VC(tgt_sr, config)
-
- loaded_models.append(dict(
- name=model_name,
- metadata=model_info,
- vc=vc,
- net_g=net_g,
- if_f0=if_f0,
- target_sr=tgt_sr
- ))
-
-print(f'Models loaded: {len(loaded_models)}')
-
-# Edge TTS speakers
-tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) # noqa
-
-# search music
-
-def auto_search(name):
- save_music_path = '/tmp/downloaded'
- if not os.path.exists(save_music_path):
- os.makedirs(save_music_path)
-
- config = {'logfilepath': 'musicdl.log', save_music_path: save_music_path, 'search_size_per_source': 5,
- 'proxies': {}}
- save_path = os.path.join(save_music_path, name + '.mp3')
- # youtube
- get_youtube(name, os.path.join(save_music_path, name))
- # task1 = threading.Thread(
- # target=get_youtube,
- # args=(name, os.path.join(save_music_path, name))
- # )
- # task1.start()
- # task2 = threading.Thread(
- # target=download_random,
- # args=(name, config, save_path)
- # )
- # task2.start()
- # task1.join(timeout=20)
- # task2.join(timeout=10)
-
- if not os.path.exists(save_path):
- return "Not Found", None
- signal, sampling_rate = soundfile.read(save_path, dtype=np.int16)
- # signal, sampling_rate = open_audio(save_path)
-
- return (sampling_rate, signal)
-
-
-#subtitle
-
-def image(image_in):
- global background_image
- background_image = Image.open(image_in)
- return "图片上传成功"
-
-font = ImageFont.truetype("NotoSansSC-Regular.otf", 40)
-text_color = (255, 200, 200)
-highlight_color = (255, 255, 255)
-
-
-checkpoint = os.environ["checkpoint"]
-pipe = pipeline(model=checkpoint)
-
-# TODO: no longer need to set these manually once the models have been updated on the Hub
-# whisper-base
-# pipe.model.config.alignment_heads = [[3, 1], [4, 2], [4, 3], [4, 7], [5, 1], [5, 2], [5, 4], [5, 6]]
-# whisper-small
-pipe.model.config.alignment_heads = [[5, 3], [5, 9], [8, 0], [8, 4], [8, 7], [8, 8], [9, 0], [9, 7], [9, 9], [10, 5]]
-
-chunks = []
-
-
-def make_frame(t):
- global chunks
-
- # TODO speed optimization: could cache the last image returned and if the
- # active chunk and active word didn't change, use that last image instead
- # of drawing the exact same thing again
-
- # TODO in the Henry V example, the word "desires" has an ending timestamp
- # that's too far into the future, and so the word stays highlighted.
- # Could fix this by finding the latest word that is active in the chunk
- # and only highlight that one.
-
- image = background_image.copy()
- draw = ImageDraw.Draw(image)
-
- # for debugging: draw frame time
- #draw.text((20, 20), str(t), fill=text_color, font=font)
-
- space_length = draw.textlength(" ", font)
- x = margin_left
- y = margin_top
-
- for chunk in chunks:
- chunk_start = chunk["timestamp"][0]
- chunk_end = chunk["timestamp"][1]
- if chunk_end is None: chunk_end = max_duration
-
- if chunk_start <= t <= chunk_end:
- words = [x["text"] for x in chunk["words"]]
- word_times = [x["timestamp"] for x in chunk["words"]]
-
- for (word, times) in zip(words, word_times):
- word_length = draw.textlength(word + " ", font) - space_length
- if x + word_length >= video_width - margin_right:
- x = margin_left
- y += line_height
-
- if times[0] <= t <= times[1]:
- color = highlight_color
- draw.rectangle([x, y + line_height, x + word_length, y + line_height + 4], fill=color)
- else:
- color = text_color
-
- draw.text((x, y), word, fill=color, font=font)
- x += word_length + space_length
-
- break
-
- return np.array(image)
-
-
-def predict(audio_path):
- global chunks
-
- audio_data, sr = librosa.load(audio_path, mono=True)
- duration = librosa.get_duration(y=audio_data, sr=sr)
- duration = min(max_duration, duration)
- audio_data = audio_data[:int(duration * sr)]
-
- # Run Whisper to get word-level timestamps.
- audio_inputs = librosa.resample(audio_data, orig_sr=sr, target_sr=pipe.feature_extractor.sampling_rate)
- output = pipe(audio_inputs, chunk_length_s=30, stride_length_s=[4, 2], return_timestamps="word")
- chunks = output["chunks"]
- #print(chunks)
-
- # Create the video.
- clip = mpy.VideoClip(make_frame, duration=duration)
- audio_clip = mpy.AudioFileClip(audio_path).set_duration(duration)
- clip = clip.set_audio(audio_clip)
- clip.write_videofile("my_video.mp4", fps=fps, codec="libx264", audio_codec="aac")
- return "my_video.mp4"
-
-# API key
-
-def access(apikey):
- os.environ["OPENAI_API_KEY"] = apikey
- openai.api_key = os.environ["OPENAI_API_KEY"]
- return "填写成功"
-
-# ChatGPT powered
-
-secret1 = os.environ["secret1"]
-secret2 = os.environ["secret2"]
-secret3 = os.environ["secret3"]
-secret4 = os.environ["secret4"]
-roles = os.environ["roles"]
-secret_info = os.environ["secret_info"]
-auth_name = os.environ["auth_name"]
-auth_pass = os.environ["auth_pass"]
-our_model = os.environ["our_model"]
-our_ins =os.environ["our_ins"]
-
-def get_files_infos(files):
- results = []
- for file in files:
- file_path = Path(file.name)
- info = {}
- info["size"] = os.path.getsize(file_path)
- info["name"] = file_path.name
- file_extension = file_path.suffix
-
- if file_extension in (secret1, secret2, secret3, secret4):
- info["type"] = "video"
- video = VideoFileClip(file.name)
- info["duration"] = video.duration
- info["dimensions"] = secret_info.format(video.size[0], video.size[1])
- if video.audio:
- info["type"] = "video/audio"
- info["audio_channels"] = video.audio.nchannels
- video.close()
- elif file_extension in (".mp3", ".wav"):
- info["type"] = "audio"
- audio = AudioFileClip(file.name)
- info["duration"] = audio.duration
- info["audio_channels"] = audio.nchannels
- audio.close()
- elif file_extension in (
- ".png",
- ".jpg",
- ".jpeg",
- ".tiff",
- ".bmp",
- ".gif",
- ".svg",
- ):
- info["type"] = "image"
- img = Image.open(file.name)
- info["dimensions"] = secret_info.format(img.size[0], img.size[1])
- results.append(info)
- return results
-
-
-def get_completion(prompt, files_info, top_p, temperature):
-
- files_info_string = ""
- for file_info in files_info:
- files_info_string += f"""{file_info["type"]} {file_info["name"]}"""
- if file_info["type"] == "video" or file_info["type"] == "image":
- files_info_string += f""" {file_info["dimensions"]}"""
- if file_info["type"] == "video" or file_info["type"] == "audio":
- files_info_string += f""" {file_info["duration"]}s"""
- if file_info["type"] == "audio" or file_info["type"] == "video/audio":
- files_info_string += f""" {file_info["audio_channels"]} audio channels"""
- files_info_string += "\n"
-
- messages = [
- {
- "role": roles,
- "content": our_ins + f"""
-AVAILABLE ASSETS LIST:
-{files_info_string}
-OBJECTIVE: {prompt}
-YOUR FFMPEG COMMAND:""",
- }
- ]
-
- print(messages[0]["content"])
-
- try:
- completion = openai.ChatCompletion.create(model=our_model,
- messages=messages,
- top_p=top_p,
- temperature=temperature)
-
- command = completion.choices[0].message.content.replace("\n", "")
-
- # remove output.mp4 with the actual output file path
- command = command.replace("output.mp4", "")
-
- return command
- except Exception as e:
- print("FROM OPENAI", e)
- raise Exception("OpenAI API error")
-
-
-def update(files, prompt, top_p=1, temperature=1):
- if prompt == "":
- raise gr.Error("Please enter a prompt.")
-
- files_info = get_files_infos(files)
- # disable this if you're running the app locally or on your own server
- for file_info in files_info:
- if file_info["type"] == "video":
- if file_info["duration"] > 1000:
- raise gr.Error(
- "Please make sure all videos are less than 2 minute long."
- )
- if file_info["size"] > 100000000:
- raise gr.Error(
- "Please make sure all files are less than 10MB in size."
- )
- try:
- command_string = get_completion(prompt, files_info, top_p, temperature)
- print(
- f"""\n\n/// START OF COMMAND ///:\n\n{command_string}\n\n/// END OF COMMAND ///\n\n""")
-
- # split command string into list of arguments
- args = shlex.split(command_string)
- if (args[0] != "ffmpeg"):
- raise Exception("Command does not start with ffmpeg")
- temp_dir = tempfile.mkdtemp()
- # copy files to temp dir
- for file in files:
- file_path = Path(file.name)
- shutil.copy(file_path, temp_dir)
-
- # test if ffmpeg command is valid dry run
- ffmpg_dry_run = subprocess.run(
- args + ["-f", "null", "-"], stderr=subprocess.PIPE, text=True, cwd=temp_dir)
- if ffmpg_dry_run.returncode == 0:
- print("Command is valid.")
- else:
- print("Command is not valid. Error output:")
- print(ffmpg_dry_run.stderr)
- raise Exception(
- "FFMPEG generated command is not valid. Please try again.")
-
- output_file_name = f'output_{uuid.uuid4()}.mp4'
- output_file_path = str((Path(temp_dir) / output_file_name).resolve())
- subprocess.run(args + ["-y", output_file_path], cwd=temp_dir)
- generated_command = f"### Generated Command\n```bash\n{format_bash_command(args)}\n -y output.mp4\n```"
- return output_file_path, gr.update(value=generated_command)
- except Exception as e:
- print("FROM UPDATE", e)
- raise gr.Error(e)
-
-
-
-# Make MV
-def make_bars_image(height_values, index, new_height):
-
- # Define the size of the image
- width = 512
- height = new_height
-
- # Create a new image with a transparent background
- image = Image.new('RGBA', (width, height), color=(0, 0, 0, 0))
-
- # Get the image drawing context
- draw = ImageDraw.Draw(image)
-
- # Define the rectangle width and spacing
- rect_width = 2
- spacing = 2
-
- # Define the list of height values for the rectangles
- #height_values = [20, 40, 60, 80, 100, 80, 60, 40]
- num_bars = len(height_values)
- # Calculate the total width of the rectangles and the spacing
- total_width = num_bars * rect_width + (num_bars - 1) * spacing
-
- # Calculate the starting position for the first rectangle
- start_x = int((width - total_width) / 2)
- # Define the buffer size
- buffer_size = 80
- # Draw the rectangles from left to right
- x = start_x
- for i, height in enumerate(height_values):
-
- # Define the rectangle coordinates
- y0 = buffer_size
- y1 = height + buffer_size
- x0 = x
- x1 = x + rect_width
-
- # Draw the rectangle
- draw.rectangle([x0, y0, x1, y1], fill='white')
-
- # Move to the next rectangle position
- if i < num_bars - 1:
- x += rect_width + spacing
-
-
- # Rotate the image by 180 degrees
- image = image.rotate(180)
-
- # Mirror the image
- image = image.transpose(Image.FLIP_LEFT_RIGHT)
-
- # Save the image
- image.save('audio_bars_'+ str(index) + '.png')
-
- return 'audio_bars_'+ str(index) + '.png'
-
-def db_to_height(db_value):
- # Scale the dB value to a range between 0 and 1
- scaled_value = (db_value + 80) / 80
-
- # Convert the scaled value to a height between 0 and 100
- height = scaled_value * 50
-
- return height
-
-def infer(title, audio_in, image_in):
- # Load the audio file
- audio_path = audio_in
- audio_data, sr = librosa.load(audio_path)
-
- # Get the duration in seconds
- duration = librosa.get_duration(y=audio_data, sr=sr)
-
- # Extract the audio data for the desired time
- start_time = 0 # start time in seconds
- end_time = duration # end time in seconds
-
- start_index = int(start_time * sr)
- end_index = int(end_time * sr)
-
- audio_data = audio_data[start_index:end_index]
-
- # Compute the short-time Fourier transform
- hop_length = 512
-
-
- stft = librosa.stft(audio_data, hop_length=hop_length)
- spectrogram = librosa.amplitude_to_db(np.abs(stft), ref=np.max)
-
- # Get the frequency values
- freqs = librosa.fft_frequencies(sr=sr, n_fft=stft.shape[0])
-
- # Select the indices of the frequency values that correspond to the desired frequencies
- n_freqs = 114
- freq_indices = np.linspace(0, len(freqs) - 1, n_freqs, dtype=int)
-
- # Extract the dB values for the desired frequencies
- db_values = []
- for i in range(spectrogram.shape[1]):
- db_values.append(list(zip(freqs[freq_indices], spectrogram[freq_indices, i])))
-
- # Print the dB values for the first time frame
- print(db_values[0])
-
- proportional_values = []
-
- for frame in db_values:
- proportional_frame = [db_to_height(db) for f, db in frame]
- proportional_values.append(proportional_frame)
-
- print(proportional_values[0])
- print("AUDIO CHUNK: " + str(len(proportional_values)))
-
- # Open the background image
- background_image = Image.open(image_in)
-
- # Resize the image while keeping its aspect ratio
- bg_width, bg_height = background_image.size
- aspect_ratio = bg_width / bg_height
- new_width = 512
- new_height = int(new_width / aspect_ratio)
- resized_bg = background_image.resize((new_width, new_height))
-
- # Apply black cache for better visibility of the white text
- bg_cache = Image.open('black_cache.png')
- resized_bg.paste(bg_cache, (0, resized_bg.height - bg_cache.height), mask=bg_cache)
-
- # Create a new ImageDraw object
- draw = ImageDraw.Draw(resized_bg)
-
- # Define the text to be added
- text = title
- font = ImageFont.truetype("NotoSansSC-Regular.otf", 16)
- text_color = (255, 255, 255) # white color
-
- # Calculate the position of the text
- text_width, text_height = draw.textsize(text, font=font)
- x = 30
- y = new_height - 70
-
- # Draw the text on the image
- draw.text((x, y), text, fill=text_color, font=font)
-
- # Save the resized image
- resized_bg.save('resized_background.jpg')
-
- generated_frames = []
- for i, frame in enumerate(proportional_values):
- bars_img = make_bars_image(frame, i, new_height)
- bars_img = Image.open(bars_img)
- # Paste the audio bars image on top of the background image
- fresh_bg = Image.open('resized_background.jpg')
- fresh_bg.paste(bars_img, (0, 0), mask=bars_img)
- # Save the image
- fresh_bg.save('audio_bars_with_bg' + str(i) + '.jpg')
- generated_frames.append('audio_bars_with_bg' + str(i) + '.jpg')
- print(generated_frames)
-
- # Create a video clip from the images
- clip = ImageSequenceClip(generated_frames, fps=len(generated_frames)/(end_time-start_time))
- audio_clip = AudioFileClip(audio_in)
- clip = clip.set_audio(audio_clip)
- # Set the output codec
- codec = 'libx264'
- audio_codec = 'aac'
- # Save the video to a file
- clip.write_videofile("my_video.mp4", codec=codec, audio_codec=audio_codec)
-
- retimed_clip = VideoFileClip("my_video.mp4")
-
- # Set the desired frame rate
- new_fps = 25
-
- # Create a new clip with the new frame rate
- new_clip = retimed_clip.set_fps(new_fps)
-
- # Save the new clip as a new video file
- new_clip.write_videofile("my_video_retimed.mp4", codec=codec, audio_codec=audio_codec)
-
- return "my_video_retimed.mp4"
-
-# mix vocal and non-vocal
-def mix(audio1, audio2):
- sound1 = AudioSegment.from_file(audio1)
- sound2 = AudioSegment.from_file(audio2)
- length = len(sound1)
- mixed = sound1[:length].overlay(sound2)
-
- mixed.export("song.wav", format="wav")
-
- return "song.wav"
-
-# Bilibili
-def youtube_downloader(
- video_identifier,
- start_time,
- end_time,
- output_filename="track.wav",
- num_attempts=5,
- url_base="",
- quiet=False,
- force=True,
-):
- output_path = Path(output_filename)
- if output_path.exists():
- if not force:
- return output_path
- else:
- output_path.unlink()
-
- quiet = "--quiet --no-warnings" if quiet else ""
- command = f"""
- yt-dlp {quiet} -x --audio-format wav -f bestaudio -o "{output_filename}" --download-sections "*{start_time}-{end_time}" "{url_base}{video_identifier}" # noqa: E501
- """.strip()
-
- attempts = 0
- while True:
- try:
- _ = subprocess.check_output(command, shell=True, stderr=subprocess.STDOUT)
- except subprocess.CalledProcessError:
- attempts += 1
- if attempts == num_attempts:
- return None
- else:
- break
-
- if output_path.exists():
- return output_path
- else:
- return None
-
-def audio_separated(audio_input, progress=gr.Progress()):
- # start progress
- progress(progress=0, desc="Starting...")
- time.sleep(0.1)
-
- # check file input
- if audio_input is None:
- # show progress
- for i in progress.tqdm(range(100), desc="Please wait..."):
- time.sleep(0.01)
-
- return (None, None, 'Please input audio.')
-
- # create filename
- filename = str(random.randint(10000,99999))+datetime.now().strftime("%d%m%Y%H%M%S")
-
- # progress
- progress(progress=0.10, desc="Please wait...")
-
- # make dir output
- os.makedirs("output", exist_ok=True)
-
- # progress
- progress(progress=0.20, desc="Please wait...")
-
- # write
- if high_quality:
- write(filename+".wav", audio_input[0], audio_input[1])
- else:
- write(filename+".mp3", audio_input[0], audio_input[1])
-
- # progress
- progress(progress=0.50, desc="Please wait...")
-
- # demucs process
- if high_quality:
- command_demucs = "python3 -m demucs --two-stems=vocals -d cpu "+filename+".wav -o output"
- else:
- command_demucs = "python3 -m demucs --two-stems=vocals --mp3 --mp3-bitrate 128 -d cpu "+filename+".mp3 -o output"
-
- os.system(command_demucs)
-
- # progress
- progress(progress=0.70, desc="Please wait...")
-
- # remove file audio
- if high_quality:
- command_delete = "rm -v ./"+filename+".wav"
- else:
- command_delete = "rm -v ./"+filename+".mp3"
-
- os.system(command_delete)
-
- # progress
- progress(progress=0.80, desc="Please wait...")
-
- # progress
- for i in progress.tqdm(range(80,100), desc="Please wait..."):
- time.sleep(0.1)
-
- if high_quality:
- return "./output/htdemucs/"+filename+"/vocals.wav","./output/htdemucs/"+filename+"/no_vocals.wav","Successfully..."
- else:
- return "./output/htdemucs/"+filename+"/vocals.mp3","./output/htdemucs/"+filename+"/no_vocals.mp3","Successfully..."
-
-
-# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118 # noqa
-def vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_audio is None:
- return (None, 'Please provide input audio.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- model = loaded_models[model_index]
-
- # Reference: so-vits
- (audio_samp, audio_npy) = input_audio
-
- # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
- # Can be change well, we will see
- if (audio_npy.shape[0] / audio_samp) > 600 and in_hf_space:
- return (None, 'Input audio is longer than 600 secs.')
-
- # Bloody hell: https://stackoverflow.com/questions/26921836/
- if audio_npy.dtype != np.float32: # :thonk:
- audio_npy = (
- audio_npy / np.iinfo(audio_npy.dtype).max
- ).astype(np.float32)
-
- if len(audio_npy.shape) > 1:
- audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))
-
- if audio_samp != 16000:
- audio_npy = librosa.resample(
- audio_npy,
- orig_sr=audio_samp,
- target_sr=16000
- )
-
- pitch_int = int(pitch_adjust)
-
- resample = (
- 0 if resample_option == 'Disable resampling'
- else int(resample_option)
- )
-
- times = [0, 0, 0]
-
- checksum = hashlib.sha512()
- checksum.update(audio_npy.tobytes())
-
- output_audio = model['vc'].pipeline(
- hubert_model,
- model['net_g'],
- model['metadata'].get('speaker_id', 0),
- audio_npy,
- checksum.hexdigest(),
- times,
- pitch_int,
- f0_method,
- path.join('model', model['name'], model['metadata']['feat_index']),
- feat_ratio,
- model['if_f0'],
- filter_radius,
- model['target_sr'],
- resample,
- rms_mix_rate,
- 'v2'
- )
-
- out_sr = (
- resample if resample >= 16000 and model['target_sr'] != resample
- else model['target_sr']
- )
-
- print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')
- return ((out_sr, output_audio), 'Success')
-
-
-async def edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_text is None:
- return (None, 'Please provide TTS text.')
-
- if tts_speaker is None:
- return (None, 'Please select TTS speaker.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- speaker = tts_speakers_list[tts_speaker]['ShortName']
- (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text)
- return vc_func(
- (tts_sr, tts_np),
- model_index,
- pitch_adjust,
- f0_method,
- feat_ratio,
- filter_radius,
- rms_mix_rate,
- resample_option
- )
-
-
-def update_model_info(model_index):
- if model_index is None:
- return str(
- '### Model info\n'
- 'Please select a model from dropdown above.'
- )
-
- model = loaded_models[model_index]
- model_icon = model['metadata'].get('icon', '')
-
- return str(
- '### Model info\n'
- ''
- '**{name}**\n\n'
- 'Author: {author}\n\n'
- 'Source: {source}\n\n'
- '{note}'
- ).format(
- name=model['metadata'].get('name'),
- author=model['metadata'].get('author', 'Anonymous'),
- source=model['metadata'].get('source', 'Unknown'),
- note=model['metadata'].get('note', ''),
- icon=(
- model_icon
- if model_icon.startswith(('http://', 'https://'))
- else '/file/model/%s/%s' % (model['name'], model_icon)
- )
- )
-
-
-def _example_vc(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-async def _example_edge_tts(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = await edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-with app:
- gr.HTML(""
- "🥳🎶🎡 - AI歌手：RVC歌声转换 + 自定义歌词 "
- " ")
- gr.Markdown("### 🦄 - 能够自动提取视频中的歌曲，并去除伴奏，还可以[自定义歌词](https://huggingface.co/spaces/kevinwang676/M4Singer)；Powered by [RVC-Project](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI) ")
- gr.Markdown("### 🌊 - 更多精彩应用，敬请关注[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕 ")
- gr.Markdown("### 💡 - 合作音乐人：[一清清清](https://space.bilibili.com/22960772?spm_id_from=333.337.0.0) (可选择音乐人的专属AI歌手) ")
-
- with gr.Tab("🤗 - 轻松提取音乐"):
- with gr.Row():
- with gr.Column():
- search_name = gr.Dropdown(label="通过歌曲名搜索", info="选一首您喜欢的歌曲吧", choices=["周杰伦爱在西元前","孙燕姿逆光","陈奕迅富士山下","许嵩有何不可","薛之谦其实","邓紫棋光年之外","李荣浩年少有为"])
- vc_search = gr.Button("用歌曲名来搜索吧", variant="primary")
- as_audio_submit = gr.Button("去除背景音吧", variant="primary")
- ydl_url_input = gr.Textbox(label="音乐视频网址(可直接填写相应的BV号)", value = "https://www.bilibili.com/video/BV...")
- with gr.Group():
- with gr.Row():
- start = gr.Number(value=0, label="起始时间 (秒)")
- end = gr.Number(value=15, label="结束时间 (秒)")
- ydl_url_submit = gr.Button("从视频中提取音乐吧", variant="primary")
-
- with gr.Column():
- ydl_audio_output = gr.Audio(label="歌曲原声")
- as_audio_input = ydl_audio_output
- as_audio_vocals = gr.Audio(label="歌曲人声部分")
- as_audio_no_vocals = gr.Audio(label="歌曲伴奏部分", type="filepath")
- as_audio_message = gr.Textbox(label="Message", visible=False)
- gr.Markdown("注：如果歌曲人声部分还有较多伴奏，可用在线工具[Vocal Remover](https://vocalremover.org/)或本地下载[UVR5](https://ultimatevocalremover.com/)进行二次提取人声；二次提取完成后，只需将新的人声部分上传至“歌曲人声部分”模块即可，替换初次提取的结果，其他操作均不变。")
-
- ydl_url_submit.click(fn=youtube_downloader, inputs=[ydl_url_input, start, end], outputs=[ydl_audio_output])
- as_audio_submit.click(fn=audio_separated, inputs=[as_audio_input], outputs=[as_audio_vocals, as_audio_no_vocals, as_audio_message], show_progress=True, queue=True)
- vc_search.click(auto_search, [search_name], [ydl_audio_output])
-
- with gr.Tab("🖼️ - 音乐视频+字幕"):
- with gr.Row():
- with gr.Column():
- inp11 = gr.Image(source='upload', type='filepath', label="上传一张背景图片吧")
- inp22 = gr.Audio(label="上传一段音乐吧(英文歌曲识别效果更好；中文歌曲可能无法识别歌词)", source="upload", type="filepath")
- btn11 = gr.Button("开始上传图片吧", variant="primary")
- btn22 = gr.Button("生成您的专属音乐视频吧", variant="primary")
-
- with gr.Column():
- out11 = gr.Textbox(label="图片上传状态", lines=1)
- out22 = gr.Video(label="视频+字幕")
- btn11.click(fn=image, inputs=[inp11], outputs=[out11])
-
- btn22.click(fn=predict, inputs=[inp22], outputs=[out22])
-
-
-
- with gr.Tab("🔮 - ChatGPT音乐视频"):
- gr.Markdown(
- """
- # 🎡 ChatGPT音乐视频
- ⭐可根据您的指令生成独一无二的音乐视频，请参考我们的操作指令分享
- 🌟您还可以通过[M4Singer](https://huggingface.co/spaces/zlc99/M4Singer)来自定义歌词；滔滔AI，唱我所爱！💕
- """,
- elem_id="header",
- )
- with gr.Row():
- with gr.Column():
- inpchat = gr.Textbox(label="请先填写您的OpenAI API key", type="password")
- user_files = gr.File(
- file_count="multiple", label="文件上传(文件名中不能有空格;可上传多个文件)", keep_filename=True,
- file_types=allowed_medias
- )
- user_prompt = gr.Textbox(
- value="Make a video with a white waveform of the audio taking all screen space, also add the image as the background",
- label="您的操作要求(建议使用英文;请参考我们的操作指令分享)",
- lines=3
- )
- btnchat = gr.Button("安全提交您的API key吧", variant="primary")
- btnGPT = gr.Button("开始制作专属音乐视频吧", variant="primary")
- with gr.Accordion("更多设置(建议保持不变)", open=False):
- top_p = gr.Slider(minimum=-0, maximum=1.0, value=0, step=0.05,
- interactive=True, label="Top-p (nucleus sampling)")
- temperature = gr.Slider(
- minimum=-0, maximum=5.0, value=0, step=0, interactive=True, label="Temperature")
- with gr.Column():
- outchat = gr.Textbox(label="API key填写状态")
- generated_video = gr.Video(
- interactive=False, label="Generated Video", include_audio=True
- )
- generated_command = gr.Markdown()
-
- gr.Markdown("分享一些好用的操作指令：注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。 ")
- gr.Markdown("### 💡 - 如何使用此程序：填写视频网址和视频起止时间后，依次点击“从视频中提取音乐吧”、“去除背景音吧”、“进行歌声转换吧！”、“加入歌曲伴奏吧！”四个按键即可。 ")
- gr.HTML('''
-
- ''')
-
-app.queue(
- concurrency_count=1,
- max_size=20,
- api_open=args.api
-).launch(show_error=True)
\ No newline at end of file
diff --git a/spaces/kevinwang676/vits-fast-finetuning-pcr/README.md b/spaces/kevinwang676/vits-fast-finetuning-pcr/README.md
deleted file mode 100644
index 009f7d633cefcc2da7fce21f62e0d48076595e4e..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/vits-fast-finetuning-pcr/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Vits Fast Finetuning Pcr
-emoji: 📚
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: FrankZxShen/vits-fast-finetuning-pcr
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kleinay/qanom-seq2seq-demo/qasrl_model_pipeline.py b/spaces/kleinay/qanom-seq2seq-demo/qasrl_model_pipeline.py
deleted file mode 100644
index 50135f76849bc8537fcae83b72532da661487da6..0000000000000000000000000000000000000000
--- a/spaces/kleinay/qanom-seq2seq-demo/qasrl_model_pipeline.py
+++ /dev/null
@@ -1,183 +0,0 @@
-from typing import Optional
-import json
-from argparse import Namespace
-from pathlib import Path
-from transformers import Text2TextGenerationPipeline, AutoModelForSeq2SeqLM, AutoTokenizer
-
-def get_markers_for_model(is_t5_model: bool) -> Namespace:
- special_tokens_constants = Namespace()
- if is_t5_model:
- # T5 model have 100 special tokens by default
- special_tokens_constants.separator_input_question_predicate = ""
- special_tokens_constants.separator_output_answers = ""
- special_tokens_constants.separator_output_questions = "" # if using only questions
- special_tokens_constants.separator_output_question_answer = ""
- special_tokens_constants.separator_output_pairs = ""
- special_tokens_constants.predicate_generic_marker = ""
- special_tokens_constants.predicate_verb_marker = ""
- special_tokens_constants.predicate_nominalization_marker = ""
-
- else:
- special_tokens_constants.separator_input_question_predicate = ""
- special_tokens_constants.separator_output_answers = ""
- special_tokens_constants.separator_output_questions = "" # if using only questions
- special_tokens_constants.separator_output_question_answer = ""
- special_tokens_constants.separator_output_pairs = ""
- special_tokens_constants.predicate_generic_marker = ""
- special_tokens_constants.predicate_verb_marker = ""
- special_tokens_constants.predicate_nominalization_marker = ""
- return special_tokens_constants
-
-def load_trained_model(name_or_path):
- import huggingface_hub as HFhub
- tokenizer = AutoTokenizer.from_pretrained(name_or_path)
- model = AutoModelForSeq2SeqLM.from_pretrained(name_or_path)
- # load preprocessing_kwargs from the model repo on HF hub, or from the local model directory
- kwargs_filename = None
- if name_or_path.startswith("kleinay/"): # and 'preprocessing_kwargs.json' in HFhub.list_repo_files(name_or_path): # the supported version of HFhub doesn't support list_repo_files
- kwargs_filename = HFhub.hf_hub_download(repo_id=name_or_path, filename="preprocessing_kwargs.json")
- elif Path(name_or_path).is_dir() and (Path(name_or_path) / "experiment_kwargs.json").exists():
- kwargs_filename = Path(name_or_path) / "experiment_kwargs.json"
-
- if kwargs_filename:
- preprocessing_kwargs = json.load(open(kwargs_filename))
- # integrate into model.config (for decoding args, e.g. "num_beams"), and save also as standalone object for preprocessing
- model.config.preprocessing_kwargs = Namespace(**preprocessing_kwargs)
- model.config.update(preprocessing_kwargs)
- return model, tokenizer
-
-
-class QASRL_Pipeline(Text2TextGenerationPipeline):
- def __init__(self, model_repo: str, **kwargs):
- model, tokenizer = load_trained_model(model_repo)
- super().__init__(model, tokenizer, framework="pt")
- self.is_t5_model = "t5" in model.config.model_type
- self.special_tokens = get_markers_for_model(self.is_t5_model)
- self.data_args = model.config.preprocessing_kwargs
- # backward compatibility - default keyword values implemeted in `run_summarization`, thus not saved in `preprocessing_kwargs`
- if "predicate_marker_type" not in vars(self.data_args):
- self.data_args.predicate_marker_type = "generic"
- if "use_bilateral_predicate_marker" not in vars(self.data_args):
- self.data_args.use_bilateral_predicate_marker = True
- if "append_verb_form" not in vars(self.data_args):
- self.data_args.append_verb_form = True
- self._update_config(**kwargs)
-
- def _update_config(self, **kwargs):
- " Update self.model.config with initialization parameters and necessary defaults. "
- # set default values that will always override model.config, but can overriden by __init__ kwargs
- kwargs["max_length"] = kwargs.get("max_length", 80)
- # override model.config with kwargs
- for k,v in kwargs.items():
- self.model.config.__dict__[k] = v
-
- def _sanitize_parameters(self, **kwargs):
- preprocess_kwargs, forward_kwargs, postprocess_kwargs = {}, {}, {}
- if "predicate_marker" in kwargs:
- preprocess_kwargs["predicate_marker"] = kwargs["predicate_marker"]
- if "predicate_type" in kwargs:
- preprocess_kwargs["predicate_type"] = kwargs["predicate_type"]
- if "verb_form" in kwargs:
- preprocess_kwargs["verb_form"] = kwargs["verb_form"]
- return preprocess_kwargs, forward_kwargs, postprocess_kwargs
-
- def preprocess(self, inputs, predicate_marker="", predicate_type=None, verb_form=None):
- # Here, inputs is string or list of strings; apply string postprocessing
- if isinstance(inputs, str):
- processed_inputs = self._preprocess_string(inputs, predicate_marker, predicate_type, verb_form)
- elif hasattr(inputs, "__iter__"):
- processed_inputs = [self._preprocess_string(s, predicate_marker, predicate_type, verb_form) for s in inputs]
- else:
- raise ValueError("inputs must be str or Iterable[str]")
- # Now pass to super.preprocess for tokenization
- return super().preprocess(processed_inputs)
-
- def _preprocess_string(self, seq: str, predicate_marker: str, predicate_type: Optional[str], verb_form: Optional[str]) -> str:
- sent_tokens = seq.split(" ")
- assert predicate_marker in sent_tokens, f"Input sentence must include a predicate-marker token ('{predicate_marker}') before the target predicate word"
- predicate_idx = sent_tokens.index(predicate_marker)
- sent_tokens.remove(predicate_marker)
- sentence_before_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx)])
- predicate = sent_tokens[predicate_idx]
- sentence_after_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx+1, len(sent_tokens))])
-
- if self.data_args.predicate_marker_type == "generic":
- predicate_marker = self.special_tokens.predicate_generic_marker
- # In case we want special marker for each predicate type: """
- elif self.data_args.predicate_marker_type == "pred_type":
- assert predicate_type is not None, "For this model, you must provide the `predicate_type` either when initializing QASRL_Pipeline(...) or when applying __call__(...) on it"
- assert predicate_type in ("verbal", "nominal"), f"`predicate_type` must be either 'verbal' or 'nominal'; got '{predicate_type}'"
- predicate_marker = {"verbal": self.special_tokens.predicate_verb_marker ,
- "nominal": self.special_tokens.predicate_nominalization_marker
- }[predicate_type]
-
- if self.data_args.use_bilateral_predicate_marker:
- seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {predicate_marker} {sentence_after_predicate}"
- else:
- seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {sentence_after_predicate}"
-
- # embed also verb_form
- if self.data_args.append_verb_form and verb_form is None:
- raise ValueError(f"For this model, you must provide the `verb_form` of the predicate when applying __call__(...)")
- elif self.data_args.append_verb_form:
- seq = f"{seq} {self.special_tokens.separator_input_question_predicate} {verb_form} "
- else:
- seq = f"{seq} "
-
- # append source prefix (for t5 models)
- prefix = self._get_source_prefix(predicate_type)
-
- return prefix + seq
-
- def _get_source_prefix(self, predicate_type: Optional[str]):
- if not self.is_t5_model or self.data_args.source_prefix is None:
- return ''
- if not self.data_args.source_prefix.startswith("<"): # Regular prefix - not dependent on input row x
- return self.data_args.source_prefix
- if self.data_args.source_prefix == "":
- if predicate_type is None:
- raise ValueError("source_prefix is '' but input no `predicate_type`.")
- else:
- return f"Generate QAs for {predicate_type} QASRL: "
-
- def _forward(self, *args, **kwargs):
- outputs = super()._forward(*args, **kwargs)
- return outputs
-
-
- def postprocess(self, model_outputs):
- output_seq = self.tokenizer.decode(
- model_outputs["output_ids"].squeeze(),
- skip_special_tokens=False,
- clean_up_tokenization_spaces=False,
- )
- output_seq = output_seq.strip(self.tokenizer.pad_token).strip(self.tokenizer.eos_token).strip()
- qa_subseqs = output_seq.split(self.special_tokens.separator_output_pairs)
- qas = [self._postrocess_qa(qa_subseq) for qa_subseq in qa_subseqs]
- return {"generated_text": output_seq,
- "QAs": qas}
-
- def _postrocess_qa(self, seq: str) -> str:
- # split question and answers
- if self.special_tokens.separator_output_question_answer in seq:
- question, answer = seq.split(self.special_tokens.separator_output_question_answer)[:2]
- else:
- print("invalid format: no separator between question and answer found...")
- return None
- # question, answer = seq, '' # Or: backoff to only question
- # skip "_" slots in questions
- question = ' '.join(t for t in question.split(' ') if t != '_')
- answers = [a.strip() for a in answer.split(self.special_tokens.separator_output_answers)]
- return {"question": question, "answers": answers}
-
-
-if __name__ == "__main__":
- pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline")
- res1 = pipe("The student was interested in Luke 's research about sea animals .", verb_form="research", predicate_type="nominal")
- res2 = pipe(["The doctor was interested in Luke 's treatment .",
- "The Veterinary student was interested in Luke 's treatment of sea animals ."], verb_form="treat", predicate_type="nominal", num_beams=10)
- res3 = pipe("A number of professions have developed that specialize in the treatment of mental disorders .", verb_form="develop", predicate_type="verbal")
- print(res1)
- print(res2)
- print(res3)
-
\ No newline at end of file
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/multilingual/data_scripts/download_wmt19_and_before.py b/spaces/koajoel/PolyFormer/fairseq/examples/multilingual/data_scripts/download_wmt19_and_before.py
deleted file mode 100644
index 3465731eb3e55047c44d1b336a97e99cb3a89a53..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/multilingual/data_scripts/download_wmt19_and_before.py
+++ /dev/null
@@ -1,899 +0,0 @@
-from typing import NamedTuple, List
-from urllib.parse import urlparse
-import os, sys
-import subprocess
-from subprocess import check_call, check_output
-import glob
-import wget
-import re
-import multiprocessing as mp
-from functools import partial
-import pathlib
-from collections import OrderedDict
-
-WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
-
-if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip():
- print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
- sys.exit(-1)
-
-# scripts and data locations
-CWD = os.getcwd()
-UTILS = f"{CWD}/utils"
-
-MOSES = f"{UTILS}/mosesdecoder"
-SGM_TOOL = f'{MOSES}/scripts/ems/support/input-from-sgm.perl'
-
-TMX2CORPUS = f"{UTILS}/tmx2corpus"
-TMX_TOOL = f'python {TMX2CORPUS}/tmx2corpus.py'
-
-to_data_path = f'{WORKDIR_ROOT}/wmt'
-download_to = f'{to_data_path}/downloads'
-manually_downloads = f'{to_data_path}/downloads'
-extract_to = f'{to_data_path}/extracted'
-#DESTDIR=${WORKDIR_ROOT}/ML50/raw/
-raw_data = f'{WORKDIR_ROOT}/ML50/raw'
-####
-
-class DLDataset(NamedTuple):
- name: str
- train_urls: List[str]
- valid_urls: List[str]
- test_urls: List[str]
- train_files_patterns: List[str] = []
- valid_files_patterns: List[str] = []
- test_files_patterns: List[str] = []
-
-
-
-def bar_custom(current, total, width=80):
- print("Downloading: %d%% [%d / %d] Ks" % (current / total * 100, current / 1000, total / 1000), end='\r')
-
-def get_downloaded_file(dl_folder, url):
- if isinstance(url, tuple):
- url, f = url
- else:
- url_f = urlparse(url)
- # f = os.path.split(url_f.path)[-1]
- f = '_'.join(url_f.path.split('/')[1:])
- return url, f"{dl_folder}/{f}"
-
-def download_parts_and_combine(dl_folder, urls, filename):
- parts = []
- for url_record in urls:
- url, part_file = get_downloaded_file(dl_folder, url_record)
- if os.path.exists(part_file):
- print(f'{part_file} has already been downloaded so skip')
- else:
- part_file = wget.download(url, part_file, bar=bar_custom)
- parts.append(part_file)
-
- def get_combine_cmd(parts):
- #default as tar.gz.??
- return f'cat {" ".join(parts)} > {filename}'
-
- combine_cmd = get_combine_cmd(parts)
- call(combine_cmd, debug=True)
- return filename
-
-def download_a_url(dl_folder, url):
- url, filename = get_downloaded_file(dl_folder, url)
- if os.path.exists(filename):
- print(f'{filename} has already been downloaded so skip')
- return filename
-
- print(f'downloading {url} to {filename}')
- if isinstance(url, list) or isinstance(url, tuple):
- download_parts_and_combine(dl_folder, url, filename)
- else:
- wget.download(url, filename, bar=bar_custom)
- print(f'dowloaded: {filename}')
- return filename
-
-def download_files(dl_folder, urls, completed_urls={}):
- for url_record in urls:
- url, _ = get_downloaded_file(dl_folder, url_record)
- filename = download_a_url(dl_folder, url_record)
- completed_urls[str(url)] = filename
- return completed_urls
-
-def check_need_manual_downalod(dl_folder, to_manually_download_urls):
- to_be_manually_dowloaded = []
- manually_completed_urls = {}
- for url_record, instruction in to_manually_download_urls:
- url, filename = get_downloaded_file(dl_folder, url_record)
- if not os.path.exists(filename):
- print(f'{url} need to be download manually, please download it manually following {instruction}; and copy it to {filename}')
- to_be_manually_dowloaded.append((url, filename))
- else:
- manually_completed_urls[url] = filename
- # if len(to_be_manually_dowloaded) > 0:
- # raise ValueError('Missing files that need to be downloaded manually; stop the process now.')
- return to_be_manually_dowloaded
-
-def download_dataset(to_folder, dl_dataset, completed_urls={}):
- download_files(to_folder, dl_dataset.train_urls, completed_urls)
- download_files(to_folder, dl_dataset.valid_urls, completed_urls)
- download_files(to_folder, dl_dataset.test_urls, completed_urls)
- print('completed downloading')
- return completed_urls
-
-def call(cmd, debug=False):
- if debug:
- print(cmd)
- check_call(cmd, shell=True)
-
-
-def get_extract_name(file_path):
- path = os.path.split(file_path)
- return path[-1] + '_extract' #.split('.')[0]
-
-def extract_file(downloaded_file, extract_folder, get_extract_name=get_extract_name, debug=False):
- extract_name = get_extract_name(downloaded_file)
- extract_to = f'{extract_folder}/{extract_name}'
- os.makedirs(extract_to, exist_ok=True)
- if os.path.exists(f'{extract_to}/DONE'):
- print(f'{downloaded_file} has already been extracted to {extract_to} so skip')
- return extract_to
- def get_extract_cmd(filename):
- if filename.endswith('.tgz') or filename.endswith('tar.gz'):
- return f'tar xzfv {filename} -C {extract_to}'
- elif filename.endswith('.gz.tar'):
- return f'tar xfv {filename} -C {extract_to}; (cd {extract_to}; gzip -d *.gz; [ $? -eq 0 ] || gzip -d */*.gz)'
- elif filename.endswith('.tar'):
- return f'tar xfv {filename} -C {extract_to}'
- elif filename.endswith('.gz'):
- return f'cp {filename} {extract_to}; (cd {extract_to}; gzip -d *.gz)'
- elif filename.endswith('.zip'):
- return f'unzip {filename} -d {extract_to}'
- extract_cmd = get_extract_cmd(downloaded_file)
- print(f'extracting {downloaded_file}')
- if isinstance(extract_cmd, list):
- for c in extract_cmd:
- call(c, debug=debug)
- else:
- call(extract_cmd, debug=debug)
- call(f'echo DONE > {extract_to}/DONE')
- return extract_to
-
-
-def extract_all_files(
- completed_urls, extract_folder,
- get_extract_name=get_extract_name,
- completed_extraction={},
- debug=False):
- extracted_folders = OrderedDict()
- for url, downloaded_file in set(completed_urls.items()):
- if downloaded_file in completed_extraction:
- print(f'{downloaded_file} is already extracted; so skip')
- continue
- folder = extract_file(downloaded_file, extract_folder, get_extract_name, debug)
- extracted_folders[url] = folder
- return extracted_folders
-
-
-def my_glob(folder):
- for p in [f'{folder}/*', f'{folder}/*/*', f'{folder}/*/*/*']:
- for f in glob.glob(p):
- yield f
-
-
-def sgm2raw(sgm, debug):
- to_file = sgm[0:len(sgm) - len('.sgm')]
- if os.path.exists(to_file):
- debug and print(f'{sgm} already converted to {to_file}; so skip')
- return to_file
- cmd = f'{SGM_TOOL} < {sgm} > {to_file}'
- call(cmd, debug)
- return to_file
-
-def tmx2raw(tmx, debug):
- to_file = tmx[0:len(tmx) - len('.tmx')]
- to_folder = os.path.join(*os.path.split(tmx)[:-1])
- if os.path.exists(f'{to_folder}/bitext.en'):
- debug and print(f'{tmx} already extracted to {to_file}; so skip')
- return to_file
- cmd = f'(cd {to_folder}; {TMX_TOOL} {tmx})'
- call(cmd, debug)
- return to_file
-
-CZENG16_REGEX = re.compile(r'.*?data.plaintext-format/0[0-9]train$')
-WMT19_WIKITITLES_REGEX = re.compile(r'.*?wikititles-v1.(\w\w)-en.tsv.gz')
-TSV_REGEX = re.compile(r'.*?(\w\w)-(\w\w).tsv$')
-
-
-
-def cut_wikitles(wiki_file, debug):
- # different languages have different file names:
- if wiki_file.endswith('wiki/fi-en/titles.fi-en'):
- to_file1 = f'{wiki_file}.fi'
- to_file2 = f'{wiki_file}.en'
- BACKSLASH = '\\'
- cmd1 = f"cat {wiki_file} | sed 's/|||/{BACKSLASH}t/g' |cut -f1 |awk '{{$1=$1}};1' > {to_file1}"
- cmd2 = f"cat {wiki_file} | sed 's/|||/{BACKSLASH}t/g' |cut -f2 |awk '{{$1=$1}};1' > {to_file2}"
-# elif WMT19_WIKITITLES_REGEX.match(wiki_file):
-# src = WMT19_WIKITITLES_REGEX.match(wiki_file).groups()[0]
-# to_file1 = f'{wiki_file}.{src}'
-# to_file2 = f'{wiki_file}.en'
-# cmd1 = f"cat {wiki_file} | cut -f1 |awk '{{$1=$1}};1' > {to_file1}"
-# cmd2 = f"cat {wiki_file} | cut -f2 |awk '{{$1=$1}};1' > {to_file2}"
- else:
- return None
- if os.path.exists(to_file1) and os.path.exists(to_file2):
- debug and print(f'{wiki_file} already processed to {to_file1} and {to_file2}; so skip')
- return wiki_file
-
- call(cmd1, debug=debug)
- call(cmd2, debug=debug)
- return wiki_file
-
-def cut_tsv(file, debug):
- m = TSV_REGEX.match(file)
- if m is None:
- raise ValueError(f'{file} is not matching tsv pattern')
- src = m.groups()[0]
- tgt = m.groups()[1]
-
- to_file1 = f'{file}.{src}'
- to_file2 = f'{file}.{tgt}'
- cmd1 = f"cat {file} | cut -f1 |awk '{{$1=$1}};1' > {to_file1}"
- cmd2 = f"cat {file} | cut -f2 |awk '{{$1=$1}};1' > {to_file2}"
- if os.path.exists(to_file1) and os.path.exists(to_file2):
- debug and print(f'{file} already processed to {to_file1} and {to_file2}; so skip')
- return file
-
- call(cmd1, debug=debug)
- call(cmd2, debug=debug)
- return file
-
-
-def convert_file_if_needed(file, debug):
- if file.endswith('.sgm'):
- return sgm2raw(file, debug)
- elif file.endswith('.tmx'):
- return tmx2raw(file, debug)
- elif file.endswith('wiki/fi-en/titles.fi-en'):
- return cut_wikitles(file, debug)
-# elif WMT19_WIKITITLES_REGEX.match(file):
-# return cut_wikitles(file, debug)
- elif file.endswith('.tsv'):
- return cut_tsv(file, debug)
- elif CZENG16_REGEX.match(file):
- return convert2czeng17(file, debug)
- else:
- return file
-
-
-def convert_files_if_needed(extracted_foldrs, my_glob=my_glob, debug=False):
- return {
- url: list(sorted(set(convert_file_if_needed(f, debug)) for f in sorted(set(my_glob(folder)))))
- for url, folder in extracted_foldrs.items()
- }
-
-def match_patt(file_path, file_pattern, src, tgt, lang):
- return file_pattern.format(src=src, tgt=tgt, lang=lang) in file_path
-
-def match_patts(file_path, file_patterns, src, tgt, lang):
- for file_pattern in file_patterns:
- params = { k: v for k, v in [('src', src), ('tgt', tgt), ('lang', lang)] if k in file_pattern}
- matching = file_pattern.format(**params)
-
- if isinstance(file_pattern, tuple):
- pattern, directions = file_pattern
- if f'{src}-{tgt}' in directions and matching in file_path:
- return True
- else:
- if matching in file_path:
- return True
- return False
-
-def extracted_glob(extracted_folder, file_patterns, src, tgt, lang):
- def get_matching_pattern(file_pattern):
- params = {
- k: v
- for k, v in [('src', src), ('tgt', tgt), ('lang', lang)]
- if '{' + k + '}' in file_pattern
- }
- file_pattern = re.sub(r'{src:(.*?)}', r'\1' if lang == src else '', file_pattern)
- file_pattern = re.sub(r'{tgt:(.*?)}', r'\1' if lang == tgt else '', file_pattern)
- file_pattern = file_pattern.format(**params)
- return file_pattern
- for file_pattern in file_patterns:
- if isinstance(file_pattern, tuple):
- file_pattern, lang_pairs = file_pattern
- if f'{src}-{tgt}' not in lang_pairs:
- continue
-# print('working on pattern: ', file_pattern, lang_pairs )
- matching_pattern = get_matching_pattern(file_pattern)
- if matching_pattern is None:
- continue
- glob_patterns = f'{extracted_folder}/{matching_pattern}'
-# print('glob_patterns: ', glob_patterns)
- for f in glob.glob(glob_patterns):
- yield f
-
-# for debug usage
-def all_extracted_files(split, src, tgt, extracted_folders, split_urls):
- def get_url(url):
- if isinstance(url, tuple):
- url, downloaded_file = url
- return url
- return [
- f
- for url in split_urls
- for f in my_glob(extracted_folders[str(get_url(url))])
- ]
-
-def concat_files(split, src, tgt, extracted_folders, split_urls, path_patterns, to_folder, debug=False):
-# if debug:
-# print('extracted files to be filtered by patterns: ',
-# '\n\t'.join(sorted(all_extracted_files(split, src, tgt, extracted_folders, split_urls))))
- for lang in [src, tgt]:
- to_file = f'{to_folder}/{split}.{src}-{tgt}.{lang}'
- s_src, s_tgt, s_lang = src.split('_')[0], tgt.split('_')[0], lang.split('_')[0]
- files = []
- for url in split_urls:
- if isinstance(url, tuple):
- url, downloaded_file = url
- if str(url) not in extracted_folders:
- print(f'warning: {url} not in extracted files')
- for extracted_file in set(
- extracted_glob(
- extracted_folders[str(url)], path_patterns,
- s_src, s_tgt, s_lang)):
- files.append(extracted_file)
- if len(files) == 0:
- print('warning: ', f'No files found for split {to_file}')
- continue
- files = sorted(set(files))
- print(f'concating {len(files)} files into {to_file}')
- cmd = ['cat'] + [f'"{f}"' for f in files] + [f'>{to_file}']
- cmd = " ".join(cmd)
- call(cmd, debug=debug)
-
-UTILS = os.path.join(pathlib.Path(__file__).parent, 'utils')
-LID_MODEL = f'{download_to}/lid.176.bin'
-LID_MULTI = f'{UTILS}/fasttext_multi_filter.py'
-
-def lid_filter(split, src, tgt, from_folder, to_folder, debug=False):
- if not os.path.exists(LID_MODEL):
- call(f'wget -nc https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin -O {LID_MODEL}')
- from_prefix = f'{from_folder}/{split}.{src}-{tgt}'
- to_prefix = f'{to_folder}/{split}.{src}-{tgt}'
- if os.path.exists(f'{from_prefix}.{src}') and os.path.exists(f'{from_prefix}.{tgt}'):
- s_src, s_tgt = src.split('_')[0], tgt.split('_')[0]
- cmd = (
- f'python {LID_MULTI} --model {LID_MODEL} --inputs {from_prefix}.{src} {from_prefix}.{tgt} '
- f'--langs {s_src} {s_tgt} --outputs {to_prefix}.{src} {to_prefix}.{tgt}'
- )
- print(f'filtering {from_prefix}')
- call(cmd, debug=debug)
-
-def concat_into_splits(dl_dataset, src, tgt, extracted_folders, to_folder, debug):
- to_folder_tmp = f"{to_folder}_tmp"
- os.makedirs(to_folder_tmp, exist_ok=True)
- concat_files('train', src, tgt,
- extracted_folders,
- split_urls=dl_dataset.train_urls,
- path_patterns=dl_dataset.train_files_patterns,
- to_folder=to_folder_tmp, debug=debug)
- lid_filter('train', src, tgt, to_folder_tmp, to_folder, debug)
-
- concat_files('valid', src, tgt,
- extracted_folders,
- split_urls=dl_dataset.valid_urls,
- path_patterns=dl_dataset.valid_files_patterns,
- to_folder=to_folder, debug=debug)
- concat_files('test', src, tgt,
- extracted_folders,
- split_urls=dl_dataset.test_urls,
- path_patterns=dl_dataset.test_files_patterns,
- to_folder=to_folder, debug=debug)
-
-
-def download_multi(dl_folder, extract_folder, urls, num_processes=8, debug=False):
- pool = mp.Pool(processes=num_processes)
- download_f = partial(download_a_url, dl_folder)
- downloaded_files = pool.imap_unordered(download_f, urls)
- pool.close()
- pool.join()
-
-BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ")
-def run_eval_bleu(cmd):
- output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip()
- print(output)
- bleu = -1.0
- for line in output.strip().split('\n'):
- m = BLEU_REGEX.search(line)
- if m is not None:
- bleu = m.groups()[0]
- bleu = float(bleu)
- break
- return bleu
-
-def check_wmt_test_bleu(raw_folder, wmt_lang_pairs):
- not_matchings = []
- for wmt, src_tgts in wmt_lang_pairs:
- for src_tgt in src_tgts:
- print(f'checking test bleus for: {src_tgt} at {wmt}')
- src, tgt = src_tgt.split('-')
- ssrc, stgt = src[:2], tgt[:2]
- if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'):
- # reversed direction may have different test set
- test_src = f'{raw_folder}/test.{tgt}-{src}.{src}'
- else:
- test_src = f'{raw_folder}/test.{src}-{tgt}.{src}'
- cmd1 = f'cat {test_src} | sacrebleu -t "{wmt}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""'
- test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}'
- cmd2 = f'cat {test_tgt} | sacrebleu -t "{wmt}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""'
- bleu1 = run_eval_bleu(cmd1)
- if bleu1 != 100.0:
- not_matchings.append(f'{wmt}:{src_tgt} source side not matching: {test_src}')
- bleu2 = run_eval_bleu(cmd2)
- if bleu2 != 100.0:
- not_matchings.append(f'{wmt}:{src_tgt} target side not matching: {test_tgt}')
- return not_matchings
-
-def download_and_extract(
- to_folder, lang_pairs, dl_dataset,
- to_manually_download_urls,
- completed_urls={}, completed_extraction={},
- debug=False):
-
- dl_folder = f'{to_folder}/downloads'
- extract_folder = f'{to_folder}/extracted'
- raw_folder = f'{to_folder}/raw'
- lid_filtered = f'{to_folder}/lid_filtered'
-
- os.makedirs(extract_folder, exist_ok=True)
- os.makedirs(raw_folder, exist_ok=True)
- os.makedirs(lid_filtered, exist_ok=True)
-
-
- to_be_manually_dowloaded = check_need_manual_downalod(dl_folder, to_manually_download_urls)
-
- completed_urls = download_dataset(
- dl_folder, dl_dataset, completed_urls)
- if debug:
- print('completed urls: ', completed_urls)
-
-
- extracted_folders = extract_all_files(
- completed_urls,
- extract_folder=extract_folder,
- completed_extraction=completed_extraction,
- debug=debug)
- if debug:
- print('download files have been extracted to folders: ', extracted_folders)
-
- converted_files = convert_files_if_needed(extracted_folders, debug=False)
- for src_tgt in lang_pairs:
- print(f'working on {dl_dataset.name}: {src_tgt}')
- src, tgt = src_tgt.split('-')
- concat_into_splits(dl_dataset,
- src=src, tgt=tgt,
- extracted_folders=extracted_folders,
- to_folder=raw_folder, debug=debug)
- print('completed data into: ', raw_folder)
-
-def download_czang16(download_to, username=None):
- wgets = [
- f'wget --user={username} --password=czeng -P {download_to} http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar'
- for i in range(10)]
- cmds = []
- for i, cmd in enumerate(wgets):
- filename = f'{download_to}/data-plaintext-format.{i}.tar'
- if os.path.exists(filename):
- print(f'{filename} has already been downloaded; so skip')
- continue
- cmds.append(cmd)
- if cmds and username is None:
- raise ValueError('No czeng username is given; please register at http://ufal.mff.cuni.cz/czeng/czeng16 to obtain username to download')
- for cmd in cmds:
- call(cmd)
- print('done with downloading czeng1.6')
-
-def download_czeng17_script(download_to, extract_folder, debug=False):
- url = 'http://ufal.mff.cuni.cz/czeng/download.php?f=convert_czeng16_to_17.pl.zip'
- filename = f'{download_to}/convert_czeng16_to_17.pl.zip'
- extract_to = f'{extract_folder}/{get_extract_name(filename)}'
- script_path = f'{extract_to}/convert_czeng16_to_17.pl'
-
- if not os.path.exists(script_path):
- wget.download(url, filename, bar=bar_custom)
- extract_to = extract_file(f'{download_to}/convert_czeng16_to_17.pl.zip', extract_folder, get_extract_name=get_extract_name, debug=debug)
- return script_path
-
-czeng17_script_path = ""
-def convert2czeng17(file, debug):
- en_file = f'{file}.en'
- cs_file = f'{file}.cs'
-
- if not os.path.exists(en_file) or not os.path.exists(cs_file):
- cs_cmd = f'cat {file} | perl {czeng17_script_path} | cut -f3 > {cs_file}'
- en_cmd = f'cat {file} | perl {czeng17_script_path} | cut -f4 > {en_file}'
- call(cs_cmd, debug)
- call(en_cmd, debug)
- else:
- print(f'already extracted: {en_file} and {cs_file}')
- return file
-
-def extract_czeng17(extract_folder, debug=False):
- url = 'http://ufal.mff.cuni.cz/czeng/download.php?f=convert_czeng16_to_17.pl.zip'
- filename = f'{download_to}/convert_czeng16_to_17.pl.zip'
- extract_to = f'{extract_folder}/{get_extract_name(filename)}'
- script_path = f'{extract_to}/convert_czeng16_to_17.pl'
-
- if not os.path.exists(script_path):
- wget.download(url, filename, bar=bar_custom)
- extract_to = extract_file(f'{download_to}/convert_czeng16_to_17.pl.zip', extract_folder, get_extract_name=get_extract_name, debug=debug)
- return script_path
-
-#########
-# definitions of wmt data sources
-# for es-en
-# Punctuation in the official test sets will be encoded with ASCII characters (not complex Unicode characters) as much as possible. You may want to normalize your system's output before submission. You are able able to use a rawer version of the test sets that does not have this normalization.
-# script to normalize punctuation: http://www.statmt.org/wmt11/normalize-punctuation.perl
-wmt13_es_en = DLDataset(
- name='wmt13_es-en',
- train_urls=[
- 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz',
- 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz',
- 'http://www.statmt.org/wmt13/training-parallel-un.tgz',
- 'http://www.statmt.org/wmt13/training-parallel-nc-v8.tgz',
- ],
- valid_urls=[
- ('http://www.statmt.org/wmt13/dev.tgz', 'wmt13_dev.tgz')
- ],
- test_urls=[
- ('http://www.statmt.org/wmt13/test.tgz', 'wmt13_test.tgz')
- ],
- train_files_patterns=[
- ('*/europarl-v7.{src}-{tgt}.{lang}', ['es-en']),
- ('*commoncrawl.{src}-{tgt}.{lang}', ['es-en']),
- ('*/news-commentary-v8.{src}-{tgt}.{lang}', ['es-en']),
- ('un/*undoc.2000.{src}-{tgt}.{lang}', ['es-en']),
- ] ,
- valid_files_patterns=[
- ('dev/newstest2012.{lang}', ['es-en'])
- ],
- test_files_patterns=[
- ('test/newstest*.{lang}', ['es-en'])
- ],
-)
-
-wmt14_de_fr_en = DLDataset(
- name='wmt14_de_fr_en',
- train_urls=[
- 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz',
- 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz',
- 'http://www.statmt.org/wmt13/training-parallel-un.tgz',
- 'http://www.statmt.org/wmt14/training-parallel-nc-v9.tgz',
- ('http://www.statmt.org/wmt10/training-giga-fren.tar', 'training-giga-fren.gz.tar'), #it is actuall a gz.tar
- ],
- valid_urls=[
- ('http://www.statmt.org/wmt14/dev.tgz', 'wmt14_dev.tgz'),
- ],
- test_urls=[
- ('http://www.statmt.org/wmt14/test-full.tgz', 'wmt14_test_full.tgz'), # cleaned test sets
- ],
- train_files_patterns=[
- ('*/europarl-v7.{src}-{tgt}.{lang}', ['fr-en', 'de-en']),
- ('*commoncrawl.{src}-{tgt}.{lang}', ['fr-en', 'de-en']),
- ('*/*news-commentary-v9.{src}-{tgt}.{lang}', ['fr-en', 'de-en']),
- ('un/undoc.2000.{src}-{tgt}.{lang}', ['fr-en']),
- ('*giga-{src}{tgt}*{lang}', ['fr-en'])
- ],
- valid_files_patterns=[
- ('dev/newstest2013.{lang}', ['fr-en', 'de-en'])
- ],
- test_files_patterns=[
- ('test-full/newstest*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['en-de', 'de-en', 'fr-en', 'en-fr']),
- ],
-)
-
-# pip install git+https://github.com/amake/tmx2corpus.git
-wmt16_ro_en = DLDataset(
- name='wmt16_ro-en',
- train_urls=[
- ('http://data.statmt.org/wmt16/translation-task/training-parallel-ep-v8.tgz', 'wmt16_training-parallel-ep-v8.tgz'),
- ('http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz', 'en-ro.tmx.gz'),
- ],
- valid_urls=[
- ('http://data.statmt.org/wmt16/translation-task/dev-romanian-updated.tgz', 'wmt16_dev.tgz')
- ],
- test_urls=[
- ('http://data.statmt.org/wmt16/translation-task/test.tgz', 'wmt16_test.tgz')
- ],
- train_files_patterns=[
- ('*/*europarl-v8.{src}-{tgt}.{lang}', ['ro-en']),
- ('bitext.{lang}', ['ro-en']) #setimes from tmux
- ] ,
- valid_files_patterns=[
- ('dev/newsdev2016*{src}{tgt}*.{lang}', ['ro-en', 'ro-en'])
- ],
- test_files_patterns=[
- ('test/newstest*{src}{tgt}*.{lang}', ['ro-en', 'en-ro'])
- ],
-)
-
-cwmt_wmt_instruction = 'cwmt download instruction at: http://nlp.nju.edu.cn/cwmt-wmt'
-wmt17_fi_lv_tr_zh_en_manual_downloads = [
- # fake urls to have unique keys for the data
- ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASIA2015.zip', 'CASIA2015.zip'), cwmt_wmt_instruction),
- ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2011.zip', 'CASICT2011.zip'), cwmt_wmt_instruction),
- ( ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2015.zip', 'CASICT2015.zip'), cwmt_wmt_instruction),
- ( ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2015.zip', 'Datum2015.zip'), cwmt_wmt_instruction),
- ( ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2017.zip', 'Datum2017.zip'), cwmt_wmt_instruction),
- ( ('http://nlp.nju.edu.cn/cwmt-wmt/NEU2017.zip', 'NEU2017.zip'), cwmt_wmt_instruction),
-]
-wmt17_fi_lv_tr_zh_en = DLDataset(
- name='wmt17_fi_lv_tr_zh_en',
- train_urls=[
- ('http://data.statmt.org/wmt17/translation-task/training-parallel-ep-v8.tgz', 'wmt17_training-parallel-ep-v8.tgz'),
- 'http://data.statmt.org/wmt17/translation-task/training-parallel-nc-v12.tgz',
- 'http://www.statmt.org/wmt15/wiki-titles.tgz',
- ('http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-tr.tmx.gz', 'en-tr.tmx.gz'),
- ('http://data.statmt.org/wmt17/translation-task/rapid2016.tgz', 'wmt17_rapid2016.tgz'),
- 'http://data.statmt.org/wmt17/translation-task/leta.v1.tgz',
- 'http://data.statmt.org/wmt17/translation-task/dcep.lv-en.v1.tgz',
- 'http://data.statmt.org/wmt17/translation-task/books.lv-en.v1.tgz',
- (('https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00',
- 'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.01',), 'UNv1.0.en-zh.tar.gz'),
- #manually download files:
- ('http://nlp.nju.edu.cn/cwmt-wmt/CASIA2015.zip', 'CASIA2015.zip'),
- ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2011.zip', 'CASICT2011.zip'),
- ('http://nlp.nju.edu.cn/cwmt-wmt/CASICT2015.zip', 'CASICT2015.zip'),
- ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2015.zip', 'Datum2015.zip'),
- ('http://nlp.nju.edu.cn/cwmt-wmt/Datum2017.zip', 'Datum2017.zip'),
- ('http://nlp.nju.edu.cn/cwmt-wmt/NEU2017.zip', 'NEU2017.zip'),
- ],
- valid_urls=[
- ('http://data.statmt.org/wmt17/translation-task/dev.tgz', 'wmt17_dev.tgz'),
- ],
- test_urls=[
- #NEW: Improved translations for zh test sets
- ('http://data.statmt.org/wmt17/translation-task/test-update-1.tgz', 'wmt17_test_zh_en.tgz'),
- ('http://data.statmt.org/wmt17/translation-task/test.tgz', 'wmt17_test_others.tgz')
- ],
- train_files_patterns=[
- ('casict*/cas*{src:ch}{tgt:en}.txt', ['zh-en', 'zh-en'] ),
- ('casia*/cas*{src:ch}{tgt:en}.txt', ['zh-en', 'zh-en'] ),
- ('dataum*/Book*{src:cn}{tgt:en}.txt', ['zh-en', 'zh-en']),
- ('neu*/NEU*{src:cn}{tgt:en}.txt', ['zh-en', 'zh-en'] ),
- ('*/*UNv1.0.en-zh.{src:zh}{tgt:en}', ['zh-en']),
- ('training/*news-commentary-v12.{src}-{tgt}.{lang}', ['zh-en', ]),
-
- ('*/*europarl-v8.{src}-{tgt}.{lang}', ['fi-en', 'lv-en']),
- ('wiki/fi-en/titles.{src}-{tgt}.{lang}', ['fi-en', ]),
- ('rapid2016.{tgt}-{src}.{lang}', ['fi-en', 'lv-en']),
- ('*/leta.{lang}', ['lv-en']),
- ('*/dcep.{lang}', ['lv-en']),
- ('*/farewell.{lang}', ['lv-en']),
- ('bitext.{lang}', ['tr-en']),
- ] ,
- valid_files_patterns=[
- ('dev/newsdev2017*{src}{tgt}-{src:src}{tgt:ref}.{lang}',
- [
- 'fi-en', 'lv-en', 'tr-en', 'zh-en',
- 'en-fi', 'en-lv', 'en-tr', 'en-zh'
- ]),
- ('dev/newstest2016*{src}{tgt}-{src:src}{tgt:ref}.{lang}',
- [
- 'fi-en', 'tr-en',
- 'en-fi', 'en-tr',
- ]),
- ],
- test_files_patterns=[
- ('test/newstest2017-{src}{tgt}-{src:src}{tgt:ref}.{lang}',
- [
- 'fi-en', 'lv-en', 'tr-en',
- 'en-fi', 'en-lv', 'en-tr',
- ]),
- ('newstest2017-{src}{tgt}-{src:src}{tgt:ref}.{lang}',
- [
- 'zh-en',
- 'en-zh'
- ]),
- ],
-)
-
-czeng_instruction = 'download instruction at: http://ufal.mff.cuni.cz/czeng/czeng16'
-#alternative: use the prepared data but detokenize it?
-wmt18_cs_et_en_manual_downloads = [
-#for cs, need to register and download; Register and download CzEng 1.6.
-#Better results can be obtained by using a subset of sentences, released under a new version name CzEng 1.7.
- # ((f'http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar',
- # f'data-plaintext-format.{i}.tar'), czeng_instruction)
- # for i in range(10)
-]
-
-wmt18_cs_et_en = DLDataset(
- name='wmt18_cs_et_en',
- train_urls=[
- 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz',
- 'http://data.statmt.org/wmt18/translation-task/training-parallel-ep-v8.tgz',
- 'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-cs.zipporah0-dedup-clean.tgz',
- 'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-et.zipporah0-dedup-clean.tgz',
- 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz',
- 'http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz',
- ('http://data.statmt.org/wmt18/translation-task/rapid2016.tgz', 'wmt18_rapid2016.tgz'),
- # (tuple(
- # (f'http://ufallab.ms.mff.cuni.cz/~bojar/czeng16-data/data-plaintext-format.{i}.tar',
- # f'data-plaintext-format.{i}.tar')
- # for i in range(10)
- # ),
- # 'czeng16_data_plaintext.gz.tar'),
- ],
- valid_urls=[
- ('http://data.statmt.org/wmt18/translation-task/dev.tgz', 'wmt18_dev.tgz'),
- ],
- test_urls=[
- ('http://data.statmt.org/wmt18/translation-task/test.tgz', 'wmt18_test.tgz'),
- ],
- train_files_patterns=[
- # ('*/*europarl-v7.{src}-{tgt}.{lang}', ['cs-en']),
- ('*/*europarl-v8.{src}-{tgt}.{lang}', ['et-en']),
- # ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['cs-en', 'et-en']),
- ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['et-en']),
- # ('*commoncrawl.{src}-{tgt}.{lang}', ['cs-en']),
- # ('*/news-commentary-v13.{src}-{tgt}.{lang}', ['cs-en']),
- # ('data.plaintext-format/*train.{lang}', ['cs-en']),
- ('rapid2016.{tgt}-{src}.{lang}', ['et-en']),
- ] ,
- valid_files_patterns=[
- ('dev/newsdev2018*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['et-en']),
- # ('dev/newstest2017*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['cs-en'])
- ],
- test_files_patterns=[
- ('test/newstest2018-{src}{tgt}-{src:src}{tgt:ref}.{lang}',
- # ['cs-en', 'et-en']),
- ['et-en']),
- ]
-)
-
-ru_en_yandex_instruction = 'Yandex Corpus download instruction at: https://translate.yandex.ru/corpus?lang=en'
-wmt19_ru_gu_kk_lt_manual_downloads = [
- (('https://translate.yandex.ru/corpus?lang=en', 'wmt19_1mcorpus.zip'), ru_en_yandex_instruction)
-]
-wmt19_ru_gu_kk_lt = DLDataset(
- name='wmt19_ru_gu_kk_lt',
- train_urls=[
- 'http://www.statmt.org/europarl/v9/training/europarl-v9.lt-en.tsv.gz',
- 'https://s3.amazonaws.com/web-language-models/paracrawl/release3/en-lt.bicleaner07.tmx.gz',
- 'https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz',
- 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz',
- 'http://data.statmt.org/news-commentary/v14/training/news-commentary-v14-wmt19.en-kk.tsv.gz',
- 'http://data.statmt.org/news-commentary/v14/training/news-commentary-v14.en-ru.tsv.gz',
- 'http://data.statmt.org/wikititles/v1/wikititles-v1.kk-en.tsv.gz',
- 'http://data.statmt.org/wikititles/v1/wikititles-v1.ru-en.tsv.gz',
- 'http://data.statmt.org/wikititles/v1/wikititles-v1.kk-en.tsv.gz',
- 'http://data.statmt.org/wikititles/v1/wikititles-v1.lt-en.tsv.gz',
- 'http://data.statmt.org/wikititles/v1/wikititles-v1.gu-en.tsv.gz',
- (('https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00',
- 'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01',
- 'https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02',),
- 'wmt19_UNv1.0.en-ru.tar.gz'),
- 'https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-lt.tmx.zip',
- ('https://translate.yandex.ru/corpus?lang=en', 'wmt19_1mcorpus.zip'),
- ],
- valid_urls=[
- ('http://data.statmt.org/wmt19/translation-task/dev.tgz', 'wmt19_dev.tgz'),
- ],
- test_urls=[
- ('http://data.statmt.org/wmt19/translation-task/test.tgz', 'wmt19_test.tgz'),
- ],
- train_files_patterns=[
- ('*europarl-v9.{src}-{tgt}.tsv.{lang}', ['lt-en']),
- #paracrawl
- ('*paracrawl-release1.{tgt}-{src}.zipporah0-dedup-clean.{lang}', ['ru-en']),
- ('bitext.{lang}', ['lt-en',]),
- ('*commoncrawl.{src}-{tgt}.{lang}', ['ru-en',]),
- ('*news-commentary-v14-wmt19.{tgt}-{src}.tsv.{lang}', ['kk-en', ]),
- ('*news-commentary-v14.{tgt}-{src}.tsv.{lang}', ['ru-en']),
- #yandex
- ('corpus.{tgt}_{src}.1m.{lang}', ['ru-en']),
- ('wikititles_v1_wikititles-v1.{src}-{tgt}.tsv.{lang}', ['ru-en', 'kk-en', 'lt-en', 'gu-en']),
- ('*/UNv1.0.{tgt}-{src}.{lang}', ['ru-en']),
- #rapid
- ('bitext.{lang}', ['lt-en'])
- ],
- valid_files_patterns=[
- ('dev/newsdev2019*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['gu-en', 'kk-en', 'lt-en']),
- ('dev/newstest2018*{src}{tgt}-{src:src}{tgt:ref}.{lang}', ['ru-en']),
- ],
- test_files_patterns=[
- ('sgm/newstest2019-{src}{tgt}-{src:src}{tgt:ref}.{lang}',
- ['ru-en', 'gu-en', 'kk-en', 'lt-en', 'en-ru', 'en-gu', 'en-kk', 'en-lt']),
- ]
-)
-
-
-#########
-
-if __name__ == "__main__":
- # speed up the downloads with multiple processing
- dl_folder = f'{to_data_path}/downloads'
- extract_folder = f'{to_data_path}/extracted'
-
- urls = [
- url
- for dataset in [wmt13_es_en, wmt14_de_fr_en, wmt16_ro_en, wmt18_cs_et_en, wmt19_ru_gu_kk_lt]
- for urls in [dataset.train_urls, dataset.valid_urls, dataset.test_urls]
- for url in urls
- ]
- urls = set(urls)
- download_multi(dl_folder, extract_folder, urls, num_processes=8, debug=True)
-
- # check manually downlaods
- to_manually_download_urls = (
- wmt17_fi_lv_tr_zh_en_manual_downloads + wmt18_cs_et_en_manual_downloads + wmt19_ru_gu_kk_lt_manual_downloads
- )
- to_be_manually_dowloaded = check_need_manual_downalod(dl_folder, to_manually_download_urls)
- if len(to_be_manually_dowloaded) > 0:
- print('Missing files that need to be downloaded manually; stop the process now.')
- exit(-1)
-
- completed_urls = {}
- completed_extraction = {}
- def work_on_wmt(directions, wmt_data):
- download_and_extract(
- to_data_path,
- directions,
- wmt_data,
- to_manually_download_urls=to_manually_download_urls,
- completed_urls=completed_urls, completed_extraction=completed_extraction, debug=True)
-
- work_on_wmt(
- ['es_XX-en_XX'],
- wmt13_es_en,)
- work_on_wmt(
- [
- 'fr_XX-en_XX', 'en_XX-fr_XX',
- # 'en_XX-de_DE', 'de_DE-en_XX',
- ],
- wmt14_de_fr_en,)
- work_on_wmt(
- ['ro_RO-en_XX', 'en_XX-ro_XX'],
- wmt16_ro_en,)
- work_on_wmt(
- [
- # 'zh_CN-en_XX',
- 'lv_LV-en_XX', 'fi_FI-en_XX', 'tr_TR-en_XX',
- #in case the reversed directions have different train/valid/test data
- # 'en_XX-zh_CN',
- 'en_XX-lv_LV', 'en_XX-fi_FI', 'en_XX-tr_TR',
- ],
- wmt17_fi_lv_tr_zh_en, )
- # czeng17_script_path = download_czeng17_script(download_to, extract_to, debug=False)
- # cz_username = None
- work_on_wmt(
- [
- # 'cs_CZ-en_XX',
- 'et_EE-en_XX'],
- wmt18_cs_et_en,)
- work_on_wmt(
- [
- # 'ru_RU-en_XX', 'en_XX-ru_RU',
- 'gu_IN-en_XX', 'kk_KZ-en_XX', 'lt_LT-en_XX',
- #in case the reversed directions have different train/valid/test data
- 'en_XX-gu_IN', 'en_XX-kk_KZ', 'en_XX-lt_LT'
- ],
- wmt19_ru_gu_kk_lt,)
-
- not_matching = check_wmt_test_bleu(
- f'{to_data_path}/raw',
- [
- ('wmt13', ['es_XX-en_XX']),
- ('wmt14/full', ['fr_XX-en_XX',]),
- ('wmt16', ['ro_RO-en_XX',]),
- # ('wmt17/improved', ['zh_CN-en_XX']),
- ('wmt17', [ 'lv_LV-en_XX', 'fi_FI-en_XX', 'tr_TR-en_XX']),
- ('wmt18', ['cs_CZ-en_XX', 'et_EE-en_XX']),
- ('wmt19', ['gu_IN-en_XX', 'kk_KZ-en_XX', 'lt_LT-en_XX']),
- #'ru_RU-en_XX',
- ]
- )
- if len(not_matching) > 0:
- print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching))
-
diff --git a/spaces/kornia/image-registration-with-kornia/app.py b/spaces/kornia/image-registration-with-kornia/app.py
deleted file mode 100644
index ae89bd161233c74e1acebe45a7b428f03e34c9be..0000000000000000000000000000000000000000
--- a/spaces/kornia/image-registration-with-kornia/app.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import numpy as np
-import gradio as gr
-import imageio
-import cv2
-import kornia as K
-import kornia.geometry as KG
-from copy import deepcopy
-from tqdm import tqdm
-from base64 import b64encode
-import torch
-import torch.nn.functional as F
-
-use_cuda: bool = torch.cuda.is_available()
-device = torch.device('cuda' if use_cuda else 'cpu')
-registrator = KG.ImageRegistrator('similarity',
- loss_fn = F.mse_loss,
- lr=8e-4, pyramid_levels=3, num_iterations=500).to(device)
-
-models = []
-
-def resize_images(f_names):
- for i, f_name in enumerate(f_names):
- img = cv2.imread(f_name, cv2.IMREAD_COLOR)
- if i==0:
- height, width, _ = img.shape
- else:
- resized_image = cv2.resize(img,(width, height))
- cv2.imwrite(f_name,resized_image)
-
-
-def convert_img(f_name):
- img = cv2.imread(f_name, cv2.IMREAD_COLOR)
- # convert image to torch tensor
- tensor = K.image_to_tensor(img, None).float() / 255.
- return K.color.bgr_to_rgb(tensor)
-
-def merge_sharp1_into2(timg1, timg2, trans1to2, verbose=False):
- curr_img = timg2.clone()
- warped = KG.homography_warp(timg1, torch.inverse(trans1to2), timg1.shape[-2:])
- mask1 = K.filters.laplacian(K.color.rgb_to_grayscale(timg1), 7).abs()
- mask1_norm = (mask1-mask1.min()) / (mask1.max() - mask1.min())
- mask1_blur = K.filters.gaussian_blur2d(mask1_norm, (9,9), (1.6, 1.6))
- mask1_blur = mask1_blur / mask1_blur.max()
- warped_mask = KG.homography_warp(mask1_blur.float(), torch.inverse(trans1to2), timg1.shape[-2:])
- curr_img = warped_mask * warped + (1-warped_mask) * curr_img
- return curr_img
-
-def img_registration(images):
- f_names = [f.name for f in images]
- resize_images(f_names)
-
- for i, f_name in tqdm(enumerate(f_names)):
- if i == 0:
- continue
- prev_img = convert_img(f_names[i-1]).to(device)
- curr_img = convert_img(f_name).to(device)
- model = registrator.register(prev_img, curr_img)
- models.append(deepcopy(model.detach()))
-
- models_to_final = [torch.eye(3, device=device)[None]]
- for m in models[::-1]:
- models_to_final.append(m @ models_to_final[-1])
- models_to_final = models_to_final[::-1]
-
- base_img = convert_img(f_names[-1])
- curr_img = deepcopy(base_img)
- _, layers, height, width = curr_img.shape
- video_file = 'video.avi'
- video = cv2.VideoWriter(video_file, 0, 1, (width,height))
-
- with torch.no_grad():
- for i, image in tqdm(enumerate(f_names)):
- timg = convert_img(image)
- curr_img = merge_sharp1_into2(timg.to(device), curr_img.to(device), models_to_final[i].to(device))
- video.write(cv2.cvtColor(K.tensor_to_image(curr_img.float()*255).astype(np.uint8), cv2.COLOR_BGR2RGB))
- video.release()
-
- return K.tensor_to_image(curr_img.float()), video_file
-
-title = 'Image Registration with Kornia!'
-description = '''Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. It is used in computer vision, medical imaging, and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements.
-
-*Note that you can upload only image files, e.g. jpg, png etc and all images should have same width and height!*
-
-Learn more about [image registration and Kornia](https://kornia.readthedocs.io/en/latest/applications/image_registration.html)'''
-
-examples = [["IMG_3020.JPG", "IMG_3027.JPG", "IMG_3034.JPG", "IMG_3040.JPG", "IMG_3058.JPG", "IMG_3070.JPG", "IMG_3083.JPG", "IMG_3100.JPG", "IMG_3106.JPG", "IMG_3112.JPG"]]
-
-iface = gr.Interface(
- img_registration,
- inputs='files',
- outputs=["image", gr.Video()],
- allow_flagging="never",
- title=title,
- description=description
- )
-
-if __name__ == "__main__":
- iface.launch(show_error=True)
diff --git a/spaces/kukr3207/forex_demo/app.py b/spaces/kukr3207/forex_demo/app.py
deleted file mode 100644
index 7f040b91bead9dfba45d4cc36a3385114e1d9d28..0000000000000000000000000000000000000000
--- a/spaces/kukr3207/forex_demo/app.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import streamlit as st
-import numpy as np
-import plotly.graph_objects as go
-import requests
-from bs4 import BeautifulSoup
-import pandas as pd
-import time
-import random
-from utils import fillStockData,fillBondsData,fillCommoditiesData, fillCurrencyData
-
-
-def run():
- st.title("Welcome")
-
-
-if __name__ == "__main__":
- run()
-
-
-
-
-
-
-
-
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/openapi/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/openapi/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/pens/t2CharStringPen.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/pens/t2CharStringPen.py
deleted file mode 100644
index 41ab0f92f2b683ac2dc87ca1b16f54047d0fef81..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/pens/t2CharStringPen.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) 2009 Type Supply LLC
-# Author: Tal Leming
-
-from fontTools.misc.roundTools import otRound, roundFunc
-from fontTools.misc.psCharStrings import T2CharString
-from fontTools.pens.basePen import BasePen
-from fontTools.cffLib.specializer import specializeCommands, commandsToProgram
-
-
-class T2CharStringPen(BasePen):
- """Pen to draw Type 2 CharStrings.
-
- The 'roundTolerance' argument controls the rounding of point coordinates.
- It is defined as the maximum absolute difference between the original
- float and the rounded integer value.
- The default tolerance of 0.5 means that all floats are rounded to integer;
- a value of 0 disables rounding; values in between will only round floats
- which are close to their integral part within the tolerated range.
- """
-
- def __init__(self, width, glyphSet, roundTolerance=0.5, CFF2=False):
- super(T2CharStringPen, self).__init__(glyphSet)
- self.round = roundFunc(roundTolerance)
- self._CFF2 = CFF2
- self._width = width
- self._commands = []
- self._p0 = (0, 0)
-
- def _p(self, pt):
- p0 = self._p0
- pt = self._p0 = (self.round(pt[0]), self.round(pt[1]))
- return [pt[0] - p0[0], pt[1] - p0[1]]
-
- def _moveTo(self, pt):
- self._commands.append(("rmoveto", self._p(pt)))
-
- def _lineTo(self, pt):
- self._commands.append(("rlineto", self._p(pt)))
-
- def _curveToOne(self, pt1, pt2, pt3):
- _p = self._p
- self._commands.append(("rrcurveto", _p(pt1) + _p(pt2) + _p(pt3)))
-
- def _closePath(self):
- pass
-
- def _endPath(self):
- pass
-
- def getCharString(self, private=None, globalSubrs=None, optimize=True):
- commands = self._commands
- if optimize:
- maxstack = 48 if not self._CFF2 else 513
- commands = specializeCommands(
- commands, generalizeFirst=False, maxstack=maxstack
- )
- program = commandsToProgram(commands)
- if self._width is not None:
- assert (
- not self._CFF2
- ), "CFF2 does not allow encoding glyph width in CharString."
- program.insert(0, otRound(self._width))
- if not self._CFF2:
- program.append("endchar")
- charString = T2CharString(
- program=program, private=private, globalSubrs=globalSubrs
- )
- return charString
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/fuse.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/fuse.py
deleted file mode 100644
index 9c37ad6fc284dc97a1266640bd5cb707c3631452..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/fuse.py
+++ /dev/null
@@ -1,324 +0,0 @@
-import argparse
-import logging
-import os
-import stat
-import threading
-import time
-from errno import EIO, ENOENT
-
-from fuse import FUSE, FuseOSError, LoggingMixIn, Operations
-
-from fsspec import __version__
-from fsspec.core import url_to_fs
-
-logger = logging.getLogger("fsspec.fuse")
-
-
-class FUSEr(Operations):
- def __init__(self, fs, path, ready_file=False):
- self.fs = fs
- self.cache = {}
- self.root = path.rstrip("/") + "/"
- self.counter = 0
- logger.info("Starting FUSE at %s", path)
- self._ready_file = ready_file
-
- def getattr(self, path, fh=None):
- logger.debug("getattr %s", path)
- if self._ready_file and path in ["/.fuse_ready", ".fuse_ready"]:
- return {"type": "file", "st_size": 5}
-
- path = "".join([self.root, path.lstrip("/")]).rstrip("/")
- try:
- info = self.fs.info(path)
- except FileNotFoundError:
- raise FuseOSError(ENOENT)
-
- data = {"st_uid": info.get("uid", 1000), "st_gid": info.get("gid", 1000)}
- perm = info.get("mode", 0o777)
-
- if info["type"] != "file":
- data["st_mode"] = stat.S_IFDIR | perm
- data["st_size"] = 0
- data["st_blksize"] = 0
- else:
- data["st_mode"] = stat.S_IFREG | perm
- data["st_size"] = info["size"]
- data["st_blksize"] = 5 * 2**20
- data["st_nlink"] = 1
- data["st_atime"] = info["atime"] if "atime" in info else time.time()
- data["st_ctime"] = info["ctime"] if "ctime" in info else time.time()
- data["st_mtime"] = info["mtime"] if "mtime" in info else time.time()
- return data
-
- def readdir(self, path, fh):
- logger.debug("readdir %s", path)
- path = "".join([self.root, path.lstrip("/")])
- files = self.fs.ls(path, False)
- files = [os.path.basename(f.rstrip("/")) for f in files]
- return [".", ".."] + files
-
- def mkdir(self, path, mode):
- path = "".join([self.root, path.lstrip("/")])
- self.fs.mkdir(path)
- return 0
-
- def rmdir(self, path):
- path = "".join([self.root, path.lstrip("/")])
- self.fs.rmdir(path)
- return 0
-
- def read(self, path, size, offset, fh):
- logger.debug("read %s", (path, size, offset))
- if self._ready_file and path in ["/.fuse_ready", ".fuse_ready"]:
- # status indicator
- return b"ready"
-
- f = self.cache[fh]
- f.seek(offset)
- out = f.read(size)
- return out
-
- def write(self, path, data, offset, fh):
- logger.debug("write %s", (path, offset))
- f = self.cache[fh]
- f.seek(offset)
- f.write(data)
- return len(data)
-
- def create(self, path, flags, fi=None):
- logger.debug("create %s", (path, flags))
- fn = "".join([self.root, path.lstrip("/")])
- self.fs.touch(fn) # OS will want to get attributes immediately
- f = self.fs.open(fn, "wb")
- self.cache[self.counter] = f
- self.counter += 1
- return self.counter - 1
-
- def open(self, path, flags):
- logger.debug("open %s", (path, flags))
- fn = "".join([self.root, path.lstrip("/")])
- if flags % 2 == 0:
- # read
- mode = "rb"
- else:
- # write/create
- mode = "wb"
- self.cache[self.counter] = self.fs.open(fn, mode)
- self.counter += 1
- return self.counter - 1
-
- def truncate(self, path, length, fh=None):
- fn = "".join([self.root, path.lstrip("/")])
- if length != 0:
- raise NotImplementedError
- # maybe should be no-op since open with write sets size to zero anyway
- self.fs.touch(fn)
-
- def unlink(self, path):
- fn = "".join([self.root, path.lstrip("/")])
- try:
- self.fs.rm(fn, False)
- except (IOError, FileNotFoundError):
- raise FuseOSError(EIO)
-
- def release(self, path, fh):
- try:
- if fh in self.cache:
- f = self.cache[fh]
- f.close()
- self.cache.pop(fh)
- except Exception as e:
- print(e)
- return 0
-
- def chmod(self, path, mode):
- if hasattr(self.fs, "chmod"):
- path = "".join([self.root, path.lstrip("/")])
- return self.fs.chmod(path, mode)
- raise NotImplementedError
-
-
-def run(
- fs,
- path,
- mount_point,
- foreground=True,
- threads=False,
- ready_file=False,
- ops_class=FUSEr,
-):
- """Mount stuff in a local directory
-
- This uses fusepy to make it appear as if a given path on an fsspec
- instance is in fact resident within the local file-system.
-
- This requires that fusepy by installed, and that FUSE be available on
- the system (typically requiring a package to be installed with
- apt, yum, brew, etc.).
-
- Parameters
- ----------
- fs: file-system instance
- From one of the compatible implementations
- path: str
- Location on that file-system to regard as the root directory to
- mount. Note that you typically should include the terminating "/"
- character.
- mount_point: str
- An empty directory on the local file-system where the contents of
- the remote path will appear.
- foreground: bool
- Whether or not calling this function will block. Operation will
- typically be more stable if True.
- threads: bool
- Whether or not to create threads when responding to file operations
- within the mounter directory. Operation will typically be more
- stable if False.
- ready_file: bool
- Whether the FUSE process is ready. The ``.fuse_ready`` file will
- exist in the ``mount_point`` directory if True. Debugging purpose.
- ops_class: FUSEr or Subclass of FUSEr
- To override the default behavior of FUSEr. For Example, logging
- to file.
-
- """
- func = lambda: FUSE(
- ops_class(fs, path, ready_file=ready_file),
- mount_point,
- nothreads=not threads,
- foreground=foreground,
- )
- if not foreground:
- th = threading.Thread(target=func)
- th.daemon = True
- th.start()
- return th
- else: # pragma: no cover
- try:
- func()
- except KeyboardInterrupt:
- pass
-
-
-def main(args):
- """Mount filesystem from chained URL to MOUNT_POINT.
-
- Examples:
-
- python3 -m fsspec.fuse memory /usr/share /tmp/mem
-
- python3 -m fsspec.fuse local /tmp/source /tmp/local \\
- -l /tmp/fsspecfuse.log
-
- You can also mount chained-URLs and use special settings:
-
- python3 -m fsspec.fuse 'filecache::zip::file://data.zip' \\
- / /tmp/zip \\
- -o 'filecache-cache_storage=/tmp/simplecache'
-
- You can specify the type of the setting by using `[int]` or `[bool]`,
- (`true`, `yes`, `1` represents the Boolean value `True`):
-
- python3 -m fsspec.fuse 'simplecache::ftp://ftp1.at.proftpd.org' \\
- /historic/packages/RPMS /tmp/ftp \\
- -o 'simplecache-cache_storage=/tmp/simplecache' \\
- -o 'simplecache-check_files=false[bool]' \\
- -o 'ftp-listings_expiry_time=60[int]' \\
- -o 'ftp-username=anonymous' \\
- -o 'ftp-password=xieyanbo'
- """
-
- class RawDescriptionArgumentParser(argparse.ArgumentParser):
- def format_help(self):
- usage = super(RawDescriptionArgumentParser, self).format_help()
- parts = usage.split("\n\n")
- parts[1] = self.description.rstrip()
- return "\n\n".join(parts)
-
- parser = RawDescriptionArgumentParser(prog="fsspec.fuse", description=main.__doc__)
- parser.add_argument("--version", action="version", version=__version__)
- parser.add_argument("url", type=str, help="fs url")
- parser.add_argument("source_path", type=str, help="source directory in fs")
- parser.add_argument("mount_point", type=str, help="local directory")
- parser.add_argument(
- "-o",
- "--option",
- action="append",
- help="Any options of protocol included in the chained URL",
- )
- parser.add_argument(
- "-l", "--log-file", type=str, help="Logging FUSE debug info (Default: '')"
- )
- parser.add_argument(
- "-f",
- "--foreground",
- action="store_false",
- help="Running in foreground or not (Default: False)",
- )
- parser.add_argument(
- "-t",
- "--threads",
- action="store_false",
- help="Running with threads support (Default: False)",
- )
- parser.add_argument(
- "-r",
- "--ready-file",
- action="store_false",
- help="The `.fuse_ready` file will exist after FUSE is ready. "
- "(Debugging purpose, Default: False)",
- )
- args = parser.parse_args(args)
-
- kwargs = {}
- for item in args.option or []:
- key, sep, value = item.partition("=")
- if not sep:
- parser.error(message="Wrong option: {!r}".format(item))
- val = value.lower()
- if val.endswith("[int]"):
- value = int(value[: -len("[int]")])
- elif val.endswith("[bool]"):
- value = val[: -len("[bool]")] in ["1", "yes", "true"]
-
- if "-" in key:
- fs_name, setting_name = key.split("-", 1)
- if fs_name in kwargs:
- kwargs[fs_name][setting_name] = value
- else:
- kwargs[fs_name] = {setting_name: value}
- else:
- kwargs[key] = value
-
- if args.log_file:
- logging.basicConfig(
- level=logging.DEBUG,
- filename=args.log_file,
- format="%(asctime)s %(message)s",
- )
-
- class LoggingFUSEr(FUSEr, LoggingMixIn):
- pass
-
- fuser = LoggingFUSEr
- else:
- fuser = FUSEr
-
- fs, url_path = url_to_fs(args.url, **kwargs)
- logger.debug("Mounting %s to %s", url_path, str(args.mount_point))
- run(
- fs,
- args.source_path,
- args.mount_point,
- foreground=args.foreground,
- threads=args.threads,
- ready_file=args.ready_file,
- ops_class=fuser,
- )
-
-
-if __name__ == "__main__":
- import sys
-
- main(sys.argv[1:])
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/helpers.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/helpers.py
deleted file mode 100644
index 873b80d22cf0f8a976e4397bbe7bacdc5bc09401..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/helpers.py
+++ /dev/null
@@ -1,851 +0,0 @@
-"""
-Defines helper methods useful for loading and caching Interface examples.
-"""
-from __future__ import annotations
-
-import ast
-import csv
-import inspect
-import os
-import subprocess
-import tempfile
-import threading
-import warnings
-from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, Iterable
-
-import matplotlib.pyplot as plt
-import numpy as np
-import PIL
-import PIL.Image
-from gradio_client import utils as client_utils
-from gradio_client.documentation import document, set_documentation_group
-
-from gradio import processing_utils, routes, utils
-from gradio.context import Context
-from gradio.flagging import CSVLogger
-
-if TYPE_CHECKING: # Only import for type checking (to avoid circular imports).
- from gradio.blocks import Block
- from gradio.components import IOComponent
-
-CACHED_FOLDER = "gradio_cached_examples"
-LOG_FILE = "log.csv"
-
-set_documentation_group("helpers")
-
-
-def create_examples(
- examples: list[Any] | list[list[Any]] | str,
- inputs: IOComponent | list[IOComponent],
- outputs: IOComponent | list[IOComponent] | None = None,
- fn: Callable | None = None,
- cache_examples: bool = False,
- examples_per_page: int = 10,
- _api_mode: bool = False,
- label: str | None = None,
- elem_id: str | None = None,
- run_on_click: bool = False,
- preprocess: bool = True,
- postprocess: bool = True,
- batch: bool = False,
-):
- """Top-level synchronous function that creates Examples. Provided for backwards compatibility, i.e. so that gr.Examples(...) can be used to create the Examples component."""
- examples_obj = Examples(
- examples=examples,
- inputs=inputs,
- outputs=outputs,
- fn=fn,
- cache_examples=cache_examples,
- examples_per_page=examples_per_page,
- _api_mode=_api_mode,
- label=label,
- elem_id=elem_id,
- run_on_click=run_on_click,
- preprocess=preprocess,
- postprocess=postprocess,
- batch=batch,
- _initiated_directly=False,
- )
- client_utils.synchronize_async(examples_obj.create)
- return examples_obj
-
-
-@document()
-class Examples:
- """
- This class is a wrapper over the Dataset component and can be used to create Examples
- for Blocks / Interfaces. Populates the Dataset component with examples and
- assigns event listener so that clicking on an example populates the input/output
- components. Optionally handles example caching for fast inference.
-
- Demos: blocks_inputs, fake_gan
- Guides: more-on-examples-and-flagging, using-hugging-face-integrations, image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers, create-your-own-friends-with-a-gan
- """
-
- def __init__(
- self,
- examples: list[Any] | list[list[Any]] | str,
- inputs: IOComponent | list[IOComponent],
- outputs: IOComponent | list[IOComponent] | None = None,
- fn: Callable | None = None,
- cache_examples: bool = False,
- examples_per_page: int = 10,
- _api_mode: bool = False,
- label: str | None = "Examples",
- elem_id: str | None = None,
- run_on_click: bool = False,
- preprocess: bool = True,
- postprocess: bool = True,
- batch: bool = False,
- _initiated_directly: bool = True,
- ):
- """
- Parameters:
- examples: example inputs that can be clicked to populate specific components. Should be nested list, in which the outer list consists of samples and each inner list consists of an input corresponding to each input component. A string path to a directory of examples can also be provided but it should be within the directory with the python file running the gradio app. If there are multiple input components and a directory is provided, a log.csv file must be present in the directory to link corresponding inputs.
- inputs: the component or list of components corresponding to the examples
- outputs: optionally, provide the component or list of components corresponding to the output of the examples. Required if `cache` is True.
- fn: optionally, provide the function to run to generate the outputs corresponding to the examples. Required if `cache` is True.
- cache_examples: if True, caches examples for fast runtime. If True, then `fn` and `outputs` need to be provided
- examples_per_page: how many examples to show per page.
- label: the label to use for the examples component (by default, "Examples")
- elem_id: an optional string that is assigned as the id of this component in the HTML DOM.
- run_on_click: if cache_examples is False, clicking on an example does not run the function when an example is clicked. Set this to True to run the function when an example is clicked. Has no effect if cache_examples is True.
- preprocess: if True, preprocesses the example input before running the prediction function and caching the output. Only applies if cache_examples is True.
- postprocess: if True, postprocesses the example output after running the prediction function and before caching. Only applies if cache_examples is True.
- batch: If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. Used only if cache_examples is True.
- """
- if _initiated_directly:
- warnings.warn(
- "Please use gr.Examples(...) instead of gr.examples.Examples(...) to create the Examples.",
- )
-
- if cache_examples and (fn is None or outputs is None):
- raise ValueError("If caching examples, `fn` and `outputs` must be provided")
-
- if not isinstance(inputs, list):
- inputs = [inputs]
- if outputs and not isinstance(outputs, list):
- outputs = [outputs]
-
- working_directory = Path().absolute()
-
- if examples is None:
- raise ValueError("The parameter `examples` cannot be None")
- elif isinstance(examples, list) and (
- len(examples) == 0 or isinstance(examples[0], list)
- ):
- pass
- elif (
- isinstance(examples, list) and len(inputs) == 1
- ): # If there is only one input component, examples can be provided as a regular list instead of a list of lists
- examples = [[e] for e in examples]
- elif isinstance(examples, str):
- if not Path(examples).exists():
- raise FileNotFoundError(
- f"Could not find examples directory: {examples}"
- )
- working_directory = examples
- if not (Path(examples) / LOG_FILE).exists():
- if len(inputs) == 1:
- examples = [[e] for e in os.listdir(examples)]
- else:
- raise FileNotFoundError(
- "Could not find log file (required for multiple inputs): "
- + LOG_FILE
- )
- else:
- with open(Path(examples) / LOG_FILE) as logs:
- examples = list(csv.reader(logs))
- examples = [
- examples[i][: len(inputs)] for i in range(1, len(examples))
- ] # remove header and unnecessary columns
-
- else:
- raise ValueError(
- "The parameter `examples` must either be a string directory or a list"
- "(if there is only 1 input component) or (more generally), a nested "
- "list, where each sublist represents a set of inputs."
- )
-
- input_has_examples = [False] * len(inputs)
- for example in examples:
- for idx, example_for_input in enumerate(example):
- if example_for_input is not None:
- try:
- input_has_examples[idx] = True
- except IndexError:
- pass # If there are more example components than inputs, ignore. This can sometimes be intentional (e.g. loading from a log file where outputs and timestamps are also logged)
-
- inputs_with_examples = [
- inp for (inp, keep) in zip(inputs, input_has_examples) if keep
- ]
- non_none_examples = [
- [ex for (ex, keep) in zip(example, input_has_examples) if keep]
- for example in examples
- ]
-
- self.examples = examples
- self.non_none_examples = non_none_examples
- self.inputs = inputs
- self.inputs_with_examples = inputs_with_examples
- self.outputs = outputs
- self.fn = fn
- self.cache_examples = cache_examples
- self._api_mode = _api_mode
- self.preprocess = preprocess
- self.postprocess = postprocess
- self.batch = batch
-
- with utils.set_directory(working_directory):
- self.processed_examples = [
- [
- component.postprocess(sample)
- for component, sample in zip(inputs, example)
- ]
- for example in examples
- ]
- self.non_none_processed_examples = [
- [ex for (ex, keep) in zip(example, input_has_examples) if keep]
- for example in self.processed_examples
- ]
- if cache_examples:
- for example in self.examples:
- if len([ex for ex in example if ex is not None]) != len(self.inputs):
- warnings.warn(
- "Examples are being cached but not all input components have "
- "example values. This may result in an exception being thrown by "
- "your function. If you do get an error while caching examples, make "
- "sure all of your inputs have example values for all of your examples "
- "or you provide default values for those particular parameters in your function."
- )
- break
-
- from gradio import components
-
- with utils.set_directory(working_directory):
- self.dataset = components.Dataset(
- components=inputs_with_examples,
- samples=non_none_examples,
- type="index",
- label=label,
- samples_per_page=examples_per_page,
- elem_id=elem_id,
- )
-
- self.cached_folder = Path(CACHED_FOLDER) / str(self.dataset._id)
- self.cached_file = Path(self.cached_folder) / "log.csv"
- self.cache_examples = cache_examples
- self.run_on_click = run_on_click
-
- async def create(self) -> None:
- """Caches the examples if self.cache_examples is True and creates the Dataset
- component to hold the examples"""
-
- async def load_example(example_id):
- if self.cache_examples:
- processed_example = self.non_none_processed_examples[
- example_id
- ] + await self.load_from_cache(example_id)
- else:
- processed_example = self.non_none_processed_examples[example_id]
- return utils.resolve_singleton(processed_example)
-
- if Context.root_block:
- if self.cache_examples and self.outputs:
- targets = self.inputs_with_examples + self.outputs
- else:
- targets = self.inputs_with_examples
- load_input_event = self.dataset.click(
- load_example,
- inputs=[self.dataset],
- outputs=targets, # type: ignore
- show_progress=False,
- postprocess=False,
- queue=False,
- )
- if self.run_on_click and not self.cache_examples:
- if self.fn is None:
- raise ValueError("Cannot run_on_click if no function is provided")
- load_input_event.then(
- self.fn,
- inputs=self.inputs, # type: ignore
- outputs=self.outputs, # type: ignore
- )
-
- if self.cache_examples:
- await self.cache()
-
- async def cache(self) -> None:
- """
- Caches all of the examples so that their predictions can be shown immediately.
- """
- if Path(self.cached_file).exists():
- print(
- f"Using cache from '{utils.abspath(self.cached_folder)}' directory. If method or examples have changed since last caching, delete this folder to clear cache."
- )
- else:
- if Context.root_block is None:
- raise ValueError("Cannot cache examples if not in a Blocks context")
-
- print(f"Caching examples at: '{utils.abspath(self.cached_folder)}'")
- cache_logger = CSVLogger()
-
- # create a fake dependency to process the examples and get the predictions
- dependency, fn_index = Context.root_block.set_event_trigger(
- event_name="fake_event",
- fn=self.fn,
- inputs=self.inputs_with_examples, # type: ignore
- outputs=self.outputs, # type: ignore
- preprocess=self.preprocess and not self._api_mode,
- postprocess=self.postprocess and not self._api_mode,
- batch=self.batch,
- )
-
- assert self.outputs is not None
- cache_logger.setup(self.outputs, self.cached_folder)
- for example_id, _ in enumerate(self.examples):
- processed_input = self.processed_examples[example_id]
- if self.batch:
- processed_input = [[value] for value in processed_input]
- with utils.MatplotlibBackendMananger():
- prediction = await Context.root_block.process_api(
- fn_index=fn_index,
- inputs=processed_input,
- request=None,
- state={},
- )
- output = prediction["data"]
- if self.batch:
- output = [value[0] for value in output]
- cache_logger.flag(output)
- # Remove the "fake_event" to prevent bugs in loading interfaces from spaces
- Context.root_block.dependencies.remove(dependency)
- Context.root_block.fns.pop(fn_index)
-
- async def load_from_cache(self, example_id: int) -> list[Any]:
- """Loads a particular cached example for the interface.
- Parameters:
- example_id: The id of the example to process (zero-indexed).
- """
- with open(self.cached_file, encoding="utf-8") as cache:
- examples = list(csv.reader(cache))
- example = examples[example_id + 1] # +1 to adjust for header
- output = []
- assert self.outputs is not None
- for component, value in zip(self.outputs, example):
- try:
- value_as_dict = ast.literal_eval(value)
- assert utils.is_update(value_as_dict)
- output.append(value_as_dict)
- except (ValueError, TypeError, SyntaxError, AssertionError):
- output.append(component.serialize(value, self.cached_folder))
- return output
-
-
-class TrackedIterable:
- def __init__(
- self,
- iterable: Iterable | None,
- index: int | None,
- length: int | None,
- desc: str | None,
- unit: str | None,
- _tqdm=None,
- progress: float | None = None,
- ) -> None:
- self.iterable = iterable
- self.index = index
- self.length = length
- self.desc = desc
- self.unit = unit
- self._tqdm = _tqdm
- self.progress = progress
-
-
-@document("__call__", "tqdm")
-class Progress(Iterable):
- """
- The Progress class provides a custom progress tracker that is used in a function signature.
- To attach a Progress tracker to a function, simply add a parameter right after the input parameters that has a default value set to a `gradio.Progress()` instance.
- The Progress tracker can then be updated in the function by calling the Progress object or using the `tqdm` method on an Iterable.
- The Progress tracker is currently only available with `queue()`.
- Example:
- import gradio as gr
- import time
- def my_function(x, progress=gr.Progress()):
- progress(0, desc="Starting...")
- time.sleep(1)
- for i in progress.tqdm(range(100)):
- time.sleep(0.1)
- return x
- gr.Interface(my_function, gr.Textbox(), gr.Textbox()).queue().launch()
- Demos: progress
- """
-
- def __init__(
- self,
- track_tqdm: bool = False,
- _callback: Callable | None = None, # for internal use only
- _event_id: str | None = None,
- ):
- """
- Parameters:
- track_tqdm: If True, the Progress object will track any tqdm.tqdm iterations with the tqdm library in the function.
- """
- self.track_tqdm = track_tqdm
- self._callback = _callback
- self._event_id = _event_id
- self.iterables: list[TrackedIterable] = []
-
- def __len__(self):
- return self.iterables[-1].length
-
- def __iter__(self):
- return self
-
- def __next__(self):
- """
- Updates progress tracker with next item in iterable.
- """
- if self._callback:
- current_iterable = self.iterables[-1]
- while (
- not hasattr(current_iterable.iterable, "__next__")
- and len(self.iterables) > 0
- ):
- current_iterable = self.iterables.pop()
- self._callback(
- event_id=self._event_id,
- iterables=self.iterables,
- )
- assert current_iterable.index is not None, "Index not set."
- current_iterable.index += 1
- try:
- return next(current_iterable.iterable) # type: ignore
- except StopIteration:
- self.iterables.pop()
- raise
- else:
- return self
-
- def __call__(
- self,
- progress: float | tuple[int, int | None] | None,
- desc: str | None = None,
- total: int | None = None,
- unit: str = "steps",
- _tqdm=None,
- ):
- """
- Updates progress tracker with progress and message text.
- Parameters:
- progress: If float, should be between 0 and 1 representing completion. If Tuple, first number represents steps completed, and second value represents total steps or None if unknown. If None, hides progress bar.
- desc: description to display.
- total: estimated total number of steps.
- unit: unit of iterations.
- """
- if self._callback:
- if isinstance(progress, tuple):
- index, total = progress
- progress = None
- else:
- index = None
- self._callback(
- event_id=self._event_id,
- iterables=self.iterables
- + [TrackedIterable(None, index, total, desc, unit, _tqdm, progress)],
- )
- else:
- return progress
-
- def tqdm(
- self,
- iterable: Iterable | None,
- desc: str | None = None,
- total: int | None = None,
- unit: str = "steps",
- _tqdm=None,
- ):
- """
- Attaches progress tracker to iterable, like tqdm.
- Parameters:
- iterable: iterable to attach progress tracker to.
- desc: description to display.
- total: estimated total number of steps.
- unit: unit of iterations.
- """
- if self._callback:
- if iterable is None:
- new_iterable = TrackedIterable(None, 0, total, desc, unit, _tqdm)
- self.iterables.append(new_iterable)
- self._callback(event_id=self._event_id, iterables=self.iterables)
- return self
- length = len(iterable) if hasattr(iterable, "__len__") else None # type: ignore
- self.iterables.append(
- TrackedIterable(iter(iterable), 0, length, desc, unit, _tqdm)
- )
- return self
-
- def update(self, n=1):
- """
- Increases latest iterable with specified number of steps.
- Parameters:
- n: number of steps completed.
- """
- if self._callback and len(self.iterables) > 0:
- current_iterable = self.iterables[-1]
- assert current_iterable.index is not None, "Index not set."
- current_iterable.index += n
- self._callback(
- event_id=self._event_id,
- iterables=self.iterables,
- )
- else:
- return
-
- def close(self, _tqdm):
- """
- Removes iterable with given _tqdm.
- """
- if self._callback:
- for i in range(len(self.iterables)):
- if id(self.iterables[i]._tqdm) == id(_tqdm):
- self.iterables.pop(i)
- break
- self._callback(
- event_id=self._event_id,
- iterables=self.iterables,
- )
- else:
- return
-
-
-def create_tracker(root_blocks, event_id, fn, track_tqdm):
- progress = Progress(_callback=root_blocks._queue.set_progress, _event_id=event_id)
- if not track_tqdm:
- return progress, fn
-
- try:
- _tqdm = __import__("tqdm")
- except ModuleNotFoundError:
- return progress, fn
- if not hasattr(root_blocks, "_progress_tracker_per_thread"):
- root_blocks._progress_tracker_per_thread = {}
-
- def init_tqdm(self, iterable=None, desc=None, *args, **kwargs):
- self._progress = root_blocks._progress_tracker_per_thread.get(
- threading.get_ident()
- )
- if self._progress is not None:
- self._progress.event_id = event_id
- self._progress.tqdm(iterable, desc, _tqdm=self)
- kwargs["file"] = open(os.devnull, "w") # noqa: SIM115
- self.__init__orig__(iterable, desc, *args, **kwargs)
-
- def iter_tqdm(self):
- if self._progress is not None:
- return self._progress
- else:
- return self.__iter__orig__()
-
- def update_tqdm(self, n=1):
- if self._progress is not None:
- self._progress.update(n)
- return self.__update__orig__(n)
-
- def close_tqdm(self):
- if self._progress is not None:
- self._progress.close(self)
- return self.__close__orig__()
-
- def exit_tqdm(self, exc_type, exc_value, traceback):
- if self._progress is not None:
- self._progress.close(self)
- return self.__exit__orig__(exc_type, exc_value, traceback)
-
- if not hasattr(_tqdm.tqdm, "__init__orig__"):
- _tqdm.tqdm.__init__orig__ = _tqdm.tqdm.__init__
- _tqdm.tqdm.__init__ = init_tqdm
- if not hasattr(_tqdm.tqdm, "__update__orig__"):
- _tqdm.tqdm.__update__orig__ = _tqdm.tqdm.update
- _tqdm.tqdm.update = update_tqdm
- if not hasattr(_tqdm.tqdm, "__close__orig__"):
- _tqdm.tqdm.__close__orig__ = _tqdm.tqdm.close
- _tqdm.tqdm.close = close_tqdm
- if not hasattr(_tqdm.tqdm, "__exit__orig__"):
- _tqdm.tqdm.__exit__orig__ = _tqdm.tqdm.__exit__
- _tqdm.tqdm.__exit__ = exit_tqdm
- if not hasattr(_tqdm.tqdm, "__iter__orig__"):
- _tqdm.tqdm.__iter__orig__ = _tqdm.tqdm.__iter__
- _tqdm.tqdm.__iter__ = iter_tqdm
- if hasattr(_tqdm, "auto") and hasattr(_tqdm.auto, "tqdm"):
- _tqdm.auto.tqdm = _tqdm.tqdm
-
- def tracked_fn(*args):
- thread_id = threading.get_ident()
- root_blocks._progress_tracker_per_thread[thread_id] = progress
- response = fn(*args)
- del root_blocks._progress_tracker_per_thread[thread_id]
- return response
-
- return progress, tracked_fn
-
-
-def special_args(
- fn: Callable,
- inputs: list[Any] | None = None,
- request: routes.Request | None = None,
- event_data: EventData | None = None,
-):
- """
- Checks if function has special arguments Request or EventData (via annotation) or Progress (via default value).
- If inputs is provided, these values will be loaded into the inputs array.
- Parameters:
- fn: function to check.
- inputs: array to load special arguments into.
- request: request to load into inputs.
- event_data: event-related data to load into inputs.
- Returns:
- updated inputs, progress index, event data index.
- """
- signature = inspect.signature(fn)
- type_hints = utils.get_type_hints(fn)
- positional_args = []
- for param in signature.parameters.values():
- if param.kind not in (param.POSITIONAL_ONLY, param.POSITIONAL_OR_KEYWORD):
- break
- positional_args.append(param)
- progress_index = None
- event_data_index = None
- for i, param in enumerate(positional_args):
- type_hint = type_hints.get(param.name)
- if isinstance(param.default, Progress):
- progress_index = i
- if inputs is not None:
- inputs.insert(i, param.default)
- elif type_hint == routes.Request:
- if inputs is not None:
- inputs.insert(i, request)
- elif (
- type_hint
- and inspect.isclass(type_hint)
- and issubclass(type_hint, EventData)
- ):
- event_data_index = i
- if inputs is not None and event_data is not None:
- inputs.insert(i, type_hint(event_data.target, event_data._data))
- elif (
- param.default is not param.empty and inputs is not None and len(inputs) <= i
- ):
- inputs.insert(i, param.default)
- if inputs is not None:
- while len(inputs) < len(positional_args):
- i = len(inputs)
- param = positional_args[i]
- if param.default == param.empty:
- warnings.warn("Unexpected argument. Filling with None.")
- inputs.append(None)
- else:
- inputs.append(param.default)
- return inputs or [], progress_index, event_data_index
-
-
-@document()
-def update(**kwargs) -> dict:
- """
- Updates component properties. When a function passed into a Gradio Interface or a Blocks events returns a typical value, it updates the value of the output component. But it is also possible to update the properties of an output component (such as the number of lines of a `Textbox` or the visibility of an `Image`) by returning the component's `update()` function, which takes as parameters any of the constructor parameters for that component.
- This is a shorthand for using the update method on a component.
- For example, rather than using gr.Number.update(...) you can just use gr.update(...).
- Note that your editor's autocompletion will suggest proper parameters
- if you use the update method on the component.
- Demos: blocks_essay, blocks_update, blocks_essay_update
-
- Parameters:
- kwargs: Key-word arguments used to update the component's properties.
- Example:
- # Blocks Example
- import gradio as gr
- with gr.Blocks() as demo:
- radio = gr.Radio([1, 2, 4], label="Set the value of the number")
- number = gr.Number(value=2, interactive=True)
- radio.change(fn=lambda value: gr.update(value=value), inputs=radio, outputs=number)
- demo.launch()
-
- # Interface example
- import gradio as gr
- def change_textbox(choice):
- if choice == "short":
- return gr.Textbox.update(lines=2, visible=True)
- elif choice == "long":
- return gr.Textbox.update(lines=8, visible=True)
- else:
- return gr.Textbox.update(visible=False)
- gr.Interface(
- change_textbox,
- gr.Radio(
- ["short", "long", "none"], label="What kind of essay would you like to write?"
- ),
- gr.Textbox(lines=2),
- live=True,
- ).launch()
- """
- kwargs["__type__"] = "generic_update"
- return kwargs
-
-
-def skip() -> dict:
- return update()
-
-
-@document()
-def make_waveform(
- audio: str | tuple[int, np.ndarray],
- *,
- bg_color: str = "#f3f4f6",
- bg_image: str | None = None,
- fg_alpha: float = 0.75,
- bars_color: str | tuple[str, str] = ("#fbbf24", "#ea580c"),
- bar_count: int = 50,
- bar_width: float = 0.6,
-):
- """
- Generates a waveform video from an audio file. Useful for creating an easy to share audio visualization. The output should be passed into a `gr.Video` component.
- Parameters:
- audio: Audio file path or tuple of (sample_rate, audio_data)
- bg_color: Background color of waveform (ignored if bg_image is provided)
- bg_image: Background image of waveform
- fg_alpha: Opacity of foreground waveform
- bars_color: Color of waveform bars. Can be a single color or a tuple of (start_color, end_color) of gradient
- bar_count: Number of bars in waveform
- bar_width: Width of bars in waveform. 1 represents full width, 0.5 represents half width, etc.
- Returns:
- A filepath to the output video.
- """
- if isinstance(audio, str):
- audio_file = audio
- audio = processing_utils.audio_from_file(audio)
- else:
- tmp_wav = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
- processing_utils.audio_to_file(audio[0], audio[1], tmp_wav.name, format="wav")
- audio_file = tmp_wav.name
- duration = round(len(audio[1]) / audio[0], 4)
-
- # Helper methods to create waveform
- def hex_to_rgb(hex_str):
- return [int(hex_str[i : i + 2], 16) for i in range(1, 6, 2)]
-
- def get_color_gradient(c1, c2, n):
- assert n > 1
- c1_rgb = np.array(hex_to_rgb(c1)) / 255
- c2_rgb = np.array(hex_to_rgb(c2)) / 255
- mix_pcts = [x / (n - 1) for x in range(n)]
- rgb_colors = [((1 - mix) * c1_rgb + (mix * c2_rgb)) for mix in mix_pcts]
- return [
- "#" + "".join(f"{int(round(val * 255)):02x}" for val in item)
- for item in rgb_colors
- ]
-
- # Reshape audio to have a fixed number of bars
- samples = audio[1]
- if len(samples.shape) > 1:
- samples = np.mean(samples, 1)
- bins_to_pad = bar_count - (len(samples) % bar_count)
- samples = np.pad(samples, [(0, bins_to_pad)])
- samples = np.reshape(samples, (bar_count, -1))
- samples = np.abs(samples)
- samples = np.max(samples, 1)
-
- with utils.MatplotlibBackendMananger():
- plt.clf()
- # Plot waveform
- color = (
- bars_color
- if isinstance(bars_color, str)
- else get_color_gradient(bars_color[0], bars_color[1], bar_count)
- )
- plt.bar(
- np.arange(0, bar_count),
- samples * 2,
- bottom=(-1 * samples),
- width=bar_width,
- color=color,
- )
- plt.axis("off")
- plt.margins(x=0)
- tmp_img = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
- savefig_kwargs: dict[str, Any] = {"bbox_inches": "tight"}
- if bg_image is not None:
- savefig_kwargs["transparent"] = True
- else:
- savefig_kwargs["facecolor"] = bg_color
- plt.savefig(tmp_img.name, **savefig_kwargs)
- waveform_img = PIL.Image.open(tmp_img.name)
- waveform_img = waveform_img.resize((1000, 200))
-
- # Composite waveform with background image
- if bg_image is not None:
- waveform_array = np.array(waveform_img)
- waveform_array[:, :, 3] = waveform_array[:, :, 3] * fg_alpha
- waveform_img = PIL.Image.fromarray(waveform_array)
-
- bg_img = PIL.Image.open(bg_image)
- waveform_width, waveform_height = waveform_img.size
- bg_width, bg_height = bg_img.size
- if waveform_width != bg_width:
- bg_img = bg_img.resize(
- (waveform_width, 2 * int(bg_height * waveform_width / bg_width / 2))
- )
- bg_width, bg_height = bg_img.size
- composite_height = max(bg_height, waveform_height)
- composite = PIL.Image.new(
- "RGBA", (waveform_width, composite_height), "#FFFFFF"
- )
- composite.paste(bg_img, (0, composite_height - bg_height))
- composite.paste(
- waveform_img, (0, composite_height - waveform_height), waveform_img
- )
- composite.save(tmp_img.name)
- img_width, img_height = composite.size
- else:
- img_width, img_height = waveform_img.size
- waveform_img.save(tmp_img.name)
-
- # Convert waveform to video with ffmpeg
- output_mp4 = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
-
- ffmpeg_cmd = f"""ffmpeg -loop 1 -i {tmp_img.name} -i {audio_file} -vf "color=c=#FFFFFF77:s={img_width}x{img_height}[bar];[0][bar]overlay=-w+(w/{duration})*t:H-h:shortest=1" -t {duration} -y {output_mp4.name}"""
-
- subprocess.call(ffmpeg_cmd, shell=True)
- return output_mp4.name
-
-
-@document()
-class EventData:
- """
- When a subclass of EventData is added as a type hint to an argument of an event listener method, this object will be passed as that argument.
- It contains information about the event that triggered the listener, such the target object, and other data related to the specific event that are attributes of the subclass.
-
- Example:
- table = gr.Dataframe([[1, 2, 3], [4, 5, 6]])
- gallery = gr.Gallery([("cat.jpg", "Cat"), ("dog.jpg", "Dog")])
- textbox = gr.Textbox("Hello World!")
-
- statement = gr.Textbox()
-
- def on_select(evt: gr.SelectData): # SelectData is a subclass of EventData
- return f"You selected {evt.value} at {evt.index} from {evt.target}"
-
- table.select(on_select, None, statement)
- gallery.select(on_select, None, statement)
- textbox.select(on_select, None, statement)
- Demos: gallery_selections, tictactoe
- """
-
- def __init__(self, target: Block | None, _data: Any):
- """
- Parameters:
- target: The target object that triggered the event. Can be used to distinguish if multiple components are bound to the same listener.
- """
- self.target = target
- self._data = _data
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py
deleted file mode 100644
index 45e5820bdb82929524fc64305c50dd5177f008e4..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py
+++ /dev/null
@@ -1,327 +0,0 @@
-from typing import Optional
-
-from requests import HTTPError, Response
-
-from ._fixes import JSONDecodeError
-
-
-class HfHubHTTPError(HTTPError):
- """
- HTTPError to inherit from for any custom HTTP Error raised in HF Hub.
-
- Any HTTPError is converted at least into a `HfHubHTTPError`. If some information is
- sent back by the server, it will be added to the error message.
-
- Added details:
- - Request id from "X-Request-Id" header if exists.
- - Server error message from the header "X-Error-Message".
- - Server error message if we can found one in the response body.
-
- Example:
- ```py
- import requests
- from huggingface_hub.utils import get_session, hf_raise_for_status, HfHubHTTPError
-
- response = get_session().post(...)
- try:
- hf_raise_for_status(response)
- except HfHubHTTPError as e:
- print(str(e)) # formatted message
- e.request_id, e.server_message # details returned by server
-
- # Complete the error message with additional information once it's raised
- e.append_to_message("\n`create_commit` expects the repository to exist.")
- raise
- ```
- """
-
- request_id: Optional[str] = None
- server_message: Optional[str] = None
-
- def __init__(self, message: str, response: Optional[Response] = None):
- # Parse server information if any.
- if response is not None:
- self.request_id = response.headers.get("X-Request-Id")
- try:
- server_data = response.json()
- except JSONDecodeError:
- server_data = {}
-
- # Retrieve server error message from multiple sources
- server_message_from_headers = response.headers.get("X-Error-Message")
- server_message_from_body = server_data.get("error")
- server_multiple_messages_from_body = "\n".join(
- error["message"] for error in server_data.get("errors", []) if "message" in error
- )
-
- # Concatenate error messages
- _server_message = ""
- if server_message_from_headers is not None: # from headers
- _server_message += server_message_from_headers + "\n"
- if server_message_from_body is not None: # from body "error"
- if server_message_from_body not in _server_message:
- _server_message += server_message_from_body + "\n"
- if server_multiple_messages_from_body is not None: # from body "errors"
- if server_multiple_messages_from_body not in _server_message:
- _server_message += server_multiple_messages_from_body + "\n"
- _server_message = _server_message.strip()
-
- # Set message to `HfHubHTTPError` (if any)
- if _server_message != "":
- self.server_message = _server_message
-
- super().__init__(
- _format_error_message(
- message,
- request_id=self.request_id,
- server_message=self.server_message,
- ),
- response=response,
- )
-
- def append_to_message(self, additional_message: str) -> None:
- """Append additional information to the `HfHubHTTPError` initial message."""
- self.args = (self.args[0] + additional_message,) + self.args[1:]
-
-
-class RepositoryNotFoundError(HfHubHTTPError):
- """
- Raised when trying to access a hf.co URL with an invalid repository name, or
- with a private repo name the user does not have access to.
-
- Example:
-
- ```py
- >>> from huggingface_hub import model_info
- >>> model_info("")
- (...)
- huggingface_hub.utils._errors.RepositoryNotFoundError: 401 Client Error. (Request ID: PvMw_VjBMjVdMz53WKIzP)
-
- Repository Not Found for url: https://huggingface.co/api/models/%3Cnon_existent_repository%3E.
- Please make sure you specified the correct `repo_id` and `repo_type`.
- If the repo is private, make sure you are authenticated.
- Invalid username or password.
- ```
- """
-
-
-class GatedRepoError(RepositoryNotFoundError):
- """
- Raised when trying to access a gated repository for which the user is not on the
- authorized list.
-
- Note: derives from `RepositoryNotFoundError` to ensure backward compatibility.
-
- Example:
-
- ```py
- >>> from huggingface_hub import model_info
- >>> model_info("")
- (...)
- huggingface_hub.utils._errors.GatedRepoError: 403 Client Error. (Request ID: ViT1Bf7O_026LGSQuVqfa)
-
- Cannot access gated repo for url https://huggingface.co/api/models/ardent-figment/gated-model.
- Access to model ardent-figment/gated-model is restricted and you are not in the authorized list.
- Visit https://huggingface.co/ardent-figment/gated-model to ask for access.
- ```
- """
-
-
-class RevisionNotFoundError(HfHubHTTPError):
- """
- Raised when trying to access a hf.co URL with a valid repository but an invalid
- revision.
-
- Example:
-
- ```py
- >>> from huggingface_hub import hf_hub_download
- >>> hf_hub_download('bert-base-cased', 'config.json', revision='')
- (...)
- huggingface_hub.utils._errors.RevisionNotFoundError: 404 Client Error. (Request ID: Mwhe_c3Kt650GcdKEFomX)
-
- Revision Not Found for url: https://huggingface.co/bert-base-cased/resolve/%3Cnon-existent-revision%3E/config.json.
- ```
- """
-
-
-class EntryNotFoundError(HfHubHTTPError):
- """
- Raised when trying to access a hf.co URL with a valid repository and revision
- but an invalid filename.
-
- Example:
-
- ```py
- >>> from huggingface_hub import hf_hub_download
- >>> hf_hub_download('bert-base-cased', '')
- (...)
- huggingface_hub.utils._errors.EntryNotFoundError: 404 Client Error. (Request ID: 53pNl6M0MxsnG5Sw8JA6x)
-
- Entry Not Found for url: https://huggingface.co/bert-base-cased/resolve/main/%3Cnon-existent-file%3E.
- ```
- """
-
-
-class LocalEntryNotFoundError(EntryNotFoundError, FileNotFoundError, ValueError):
- """
- Raised when trying to access a file that is not on the disk when network is
- disabled or unavailable (connection issue). The entry may exist on the Hub.
-
- Note: `ValueError` type is to ensure backward compatibility.
- Note: `LocalEntryNotFoundError` derives from `HTTPError` because of `EntryNotFoundError`
- even when it is not a network issue.
-
- Example:
-
- ```py
- >>> from huggingface_hub import hf_hub_download
- >>> hf_hub_download('bert-base-cased', '', local_files_only=True)
- (...)
- huggingface_hub.utils._errors.LocalEntryNotFoundError: Cannot find the requested files in the disk cache and outgoing traffic has been disabled. To enable hf.co look-ups and downloads online, set 'local_files_only' to False.
- ```
- """
-
- def __init__(self, message: str):
- super().__init__(message, response=None)
-
-
-class BadRequestError(HfHubHTTPError, ValueError):
- """
- Raised by `hf_raise_for_status` when the server returns a HTTP 400 error.
-
- Example:
-
- ```py
- >>> resp = requests.post("hf.co/api/check", ...)
- >>> hf_raise_for_status(resp, endpoint_name="check")
- huggingface_hub.utils._errors.BadRequestError: Bad request for check endpoint: {details} (Request ID: XXX)
- ```
- """
-
-
-def hf_raise_for_status(response: Response, endpoint_name: Optional[str] = None) -> None:
- """
- Internal version of `response.raise_for_status()` that will refine a
- potential HTTPError. Raised exception will be an instance of `HfHubHTTPError`.
-
- This helper is meant to be the unique method to raise_for_status when making a call
- to the Hugging Face Hub.
-
- Example:
- ```py
- import requests
- from huggingface_hub.utils import get_session, hf_raise_for_status, HfHubHTTPError
-
- response = get_session().post(...)
- try:
- hf_raise_for_status(response)
- except HfHubHTTPError as e:
- print(str(e)) # formatted message
- e.request_id, e.server_message # details returned by server
-
- # Complete the error message with additional information once it's raised
- e.append_to_message("\n`create_commit` expects the repository to exist.")
- raise
- ```
-
- Args:
- response (`Response`):
- Response from the server.
- endpoint_name (`str`, *optional*):
- Name of the endpoint that has been called. If provided, the error message
- will be more complete.
-
-
-
- Raises when the request has failed:
-
- - [`~utils.RepositoryNotFoundError`]
- If the repository to download from cannot be found. This may be because it
- doesn't exist, because `repo_type` is not set correctly, or because the repo
- is `private` and you do not have access.
- - [`~utils.GatedRepoError`]
- If the repository exists but is gated and the user is not on the authorized
- list.
- - [`~utils.RevisionNotFoundError`]
- If the repository exists but the revision couldn't be find.
- - [`~utils.EntryNotFoundError`]
- If the repository exists but the entry (e.g. the requested file) couldn't be
- find.
- - [`~utils.BadRequestError`]
- If request failed with a HTTP 400 BadRequest error.
- - [`~utils.HfHubHTTPError`]
- If request failed for a reason not listed above.
-
-
- """
- try:
- response.raise_for_status()
- except HTTPError as e:
- error_code = response.headers.get("X-Error-Code")
-
- if error_code == "RevisionNotFound":
- message = f"{response.status_code} Client Error." + "\n\n" + f"Revision Not Found for url: {response.url}."
- raise RevisionNotFoundError(message, response) from e
-
- elif error_code == "EntryNotFound":
- message = f"{response.status_code} Client Error." + "\n\n" + f"Entry Not Found for url: {response.url}."
- raise EntryNotFoundError(message, response) from e
-
- elif error_code == "GatedRepo":
- message = (
- f"{response.status_code} Client Error." + "\n\n" + f"Cannot access gated repo for url {response.url}."
- )
- raise GatedRepoError(message, response) from e
-
- elif error_code == "RepoNotFound" or response.status_code == 401:
- # 401 is misleading as it is returned for:
- # - private and gated repos if user is not authenticated
- # - missing repos
- # => for now, we process them as `RepoNotFound` anyway.
- # See https://gist.github.com/Wauplin/46c27ad266b15998ce56a6603796f0b9
- message = (
- f"{response.status_code} Client Error."
- + "\n\n"
- + f"Repository Not Found for url: {response.url}."
- + "\nPlease make sure you specified the correct `repo_id` and"
- " `repo_type`.\nIf you are trying to access a private or gated repo,"
- " make sure you are authenticated."
- )
- raise RepositoryNotFoundError(message, response) from e
-
- elif response.status_code == 400:
- message = (
- f"\n\nBad request for {endpoint_name} endpoint:" if endpoint_name is not None else "\n\nBad request:"
- )
- raise BadRequestError(message, response=response) from e
-
- # Convert `HTTPError` into a `HfHubHTTPError` to display request information
- # as well (request id and/or server error message)
- raise HfHubHTTPError(str(e), response=response) from e
-
-
-def _format_error_message(message: str, request_id: Optional[str], server_message: Optional[str]) -> str:
- """
- Format the `HfHubHTTPError` error message based on initial message and information
- returned by the server.
-
- Used when initializing `HfHubHTTPError`.
- """
- # Add message from response body
- if server_message is not None and len(server_message) > 0 and server_message.lower() not in message.lower():
- if "\n\n" in message:
- message += "\n" + server_message
- else:
- message += "\n\n" + server_message
-
- # Add Request ID
- if request_id is not None and str(request_id).lower() not in message.lower():
- request_id_message = f" (Request ID: {request_id})"
- if "\n" in message:
- newline_index = message.index("\n")
- message = message[:newline_index] + request_id_message + message[newline_index:]
- else:
- message += request_id_message
-
- return message
diff --git a/spaces/lanyi2023/QQsign/devices/device_8968.js b/spaces/lanyi2023/QQsign/devices/device_8968.js
deleted file mode 100644
index ea5a215d76d99d384b45cb3766f981c9dba10343..0000000000000000000000000000000000000000
--- a/spaces/lanyi2023/QQsign/devices/device_8968.js
+++ /dev/null
@@ -1,356 +0,0 @@
-"use strict";
-var __importDefault = (this && this.__importDefault) || function (mod) {
- return (mod && mod.__esModule) ? mod : { "default": mod };
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-exports.getApkInfo = exports.Platform = exports.Device = exports.generateFullDevice = exports.generateShortDevice = void 0;
-const crypto_1 = require("crypto");
-const constants_1 = require("./constants");
-const axios_1 = __importDefault(require("axios"));
-const algo_1 = require("./algo");
-function generateImei() {
- let imei = `86${(0, constants_1.randomString)(12, '0123456789')}`;
- function calcSP(imei) {
- let sum = 0;
- for (let i = 0; i < imei.length; ++i) {
- if (i % 2) {
- let j = parseInt(imei[i]) * 2;
- sum += j % 10 + Math.floor(j / 10);
- }
- else {
- sum += parseInt(imei[i]);
- }
- }
- return (100 - sum) % 10;
- }
- return imei + calcSP(imei);
-}
-/** 生成短设备信息 */
-function generateShortDevice() {
- const randstr = (length, num = false) => {
- const map = num ? '0123456789' : '0123456789abcdef';
- return (0, constants_1.randomString)(length, map);
- };
- return {
- "--begin--": "该设备为随机生成，丢失后不能得到原先配置",
- product: `ICQQ-${randstr(5).toUpperCase()}`,
- device: `${randstr(5).toUpperCase()}`,
- board: `${randstr(5).toUpperCase()}`,
- brand: `${randstr(4).toUpperCase()}`,
- model: `ICQQ ${randstr(4).toUpperCase()}`,
- wifi_ssid: `HUAWEI-${randstr(7)}`,
- bootloader: `U-boot`,
- display: `IC.${randstr(7, true)}.${randstr(4, true)}`,
- boot_id: `${randstr(8)}-${randstr(4)}-${randstr(4)}-${randstr(4)}-${randstr(12)}`,
- proc_version: `Linux version 5.10.101-android10-${randstr(8)}`,
- mac_address: `02:00:00:00:00:00`,
- ip_address: `192.168.${randstr(2, true)}.${randstr(2, true)}`,
- android_id: `${(0, constants_1.md5)(generateImei()).toString("hex").substring(8, 24)}`,
- incremental: `${randstr(10, true)}`,
- "--end--": "修改后可能需要重新验证设备。"
- };
-}
-exports.generateShortDevice = generateShortDevice;
-/** 生成完整设备信息 */
-function generateFullDevice(apk, d) {
- if (!d)
- d = generateShortDevice();
- return {
- display: d.display,
- product: d.product,
- device: d.device,
- board: d.board,
- brand: d.brand,
- model: d.model,
- bootloader: d.bootloader,
- fingerprint: `${d.brand}/${d.product}/${d.device}:10/${d.display}/${d.incremental}:user/release-keys`,
- boot_id: d.boot_id,
- proc_version: d.proc_version,
- baseband: "",
- sim: "T-Mobile",
- os_type: "android",
- mac_address: d.mac_address,
- ip_address: d.ip_address,
- wifi_bssid: d.mac_address,
- wifi_ssid: d.wifi_ssid,
- imei: d.android_id,
- android_id: d.android_id,
- apn: "wifi",
- version: {
- incremental: d.incremental,
- release: "10",
- codename: "REL",
- sdk: 29,
- },
- imsi: (0, crypto_1.randomBytes)(16),
- guid: (0, constants_1.md5)(Buffer.concat([Buffer.from(d.android_id), Buffer.from(d.mac_address)])),
- };
-}
-exports.generateFullDevice = generateFullDevice;
-class Device {
- constructor(apk, d) {
- this.apk = apk;
- this.secret = 'ZdJqM15EeO2zWc08';
- this.publicKey = `-----BEGIN PUBLIC KEY-----
-MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQDEIxgwoutfwoJxcGQeedgP7FG9
-qaIuS0qzfR8gWkrkTZKM2iWHn2ajQpBRZjMSoSf6+KJGvar2ORhBfpDXyVtZCKpq
-LQ+FLkpncClKVIrBwv6PHyUvuCb0rIarmgDnzkfQAqVufEtR64iazGDKatvJ9y6B
-9NMbHddGSAUmRTCrHQIDAQAB
------END PUBLIC KEY-----`;
- if (!d)
- d = generateShortDevice();
- Object.assign(this, generateFullDevice(apk, d));
- }
- async getQIMEI() {
- if (this.apk.app_key === "") {
- return;
- }
- const k = (0, constants_1.randomString)(16);
- const key = (0, algo_1.encryptPKCS1)(this.publicKey, k);
- const time = Date.now();
- const nonce = (0, constants_1.randomString)(16);
- const payload = this.genRandomPayloadByDevice();
- const params = (0, algo_1.aesEncrypt)(JSON.stringify(payload), k).toString('base64');
- try {
- const { data } = await axios_1.default.post("https://snowflake.qq.com/ola/android", {
- key,
- params,
- time, nonce,
- sign: (0, constants_1.md5)(key + params + time + nonce + this.secret).toString("hex"),
- extra: ''
- }, {
- headers: {
- 'User-Agent': `Dalvik/2.1.0 (Linux; U; Android ${this.version.release}; PCRT00 Build/N2G48H)`,
- 'Content-Type': "application/json"
- }
- });
- if (data?.code !== 0) {
- return;
- }
- const { q16, q36 } = JSON.parse((0, algo_1.aesDecrypt)(data.data, k));
- this.qImei16 = q16;
- this.qImei36 = q36 || q16;
- if (this.qImei36)
- this.imsi = Buffer.from(this.qImei36, 'hex');
- }
- catch {
- }
- }
- genRandomPayloadByDevice() {
- const fixedRand = (max = 1, min = 0) => {
- if (max < min)
- [max, min] = [min, max];
- const diff = max - min;
- return Math.floor(Math.random() * diff) + min;
- };
- const reserved = {
- "harmony": "0",
- "clone": Math.random() > 0.5 ? "1" : "0",
- "containe": "",
- "oz": "",
- "oo": "",
- "kelong": Math.random() > 0.5 ? "1" : "0",
- "uptimes": (0, constants_1.formatTime)(new Date()),
- "multiUser": Math.random() > 0.5 ? "1" : "0",
- "bod": this.board,
- "brd": this.brand,
- "dv": this.device,
- "firstLevel": "",
- "manufact": this.brand,
- "name": this.model,
- "host": "se.infra",
- "kernel": this.fingerprint
- };
- const timestamp = Date.now();
- this.mtime = this.mtime || Date.now();
- const mtime1 = new Date(this.mtime || Date.now());
- const dateFormat = (fmt, time = Date.now()) => (0, constants_1.formatTime)(time, fmt);
- const mtimeStr1 = dateFormat("YYYY-mm-ddHHMMSS", mtime1) + "." + this.imei.slice(2, 11);
- const mtime2 = new Date(this.mtime - parseInt(this.imei.slice(2, 4)));
- const mtimeStr2 = dateFormat("YYYY-mm-ddHHMMSS", mtime2) + "." + this.imei.slice(5, 14);
- let beaconIdArr = [
- `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
- mtimeStr1,
- '0000000000000000',
- (0, constants_1.md5)(this.android_id + this.imei).toString("hex").slice(0, 16),
- ...new Array(4).fill(false).map((_) => fixedRand(10000000, 1000000)),
- this.boot_id,
- '1',
- fixedRand(5, 0),
- fixedRand(5, 0),
- `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
- `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
- fixedRand(5, 0),
- fixedRand(100, 10),
- `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
- `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
- fixedRand(50000, 10000),
- fixedRand(100, 10),
- `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
- mtimeStr2,
- fixedRand(10000, 1000),
- fixedRand(5, 0),
- `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((10 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`,
- `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((11 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`,
- fixedRand(10000, 1000),
- fixedRand(100, 10),
- `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((11 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`,
- `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((11 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`,
- fixedRand(10000, 1000),
- fixedRand(5, 0),
- `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
- `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
- fixedRand(5, 0),
- fixedRand(100, 10),
- `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
- `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`,
- fixedRand(5, 0),
- fixedRand(5, 0),
- ].map((str, idx) => `k${idx + 1}:${str}`);
- return {
- "androidId": this.android_id,
- "platformId": 1,
- "appKey": this.apk.app_key,
- "appVersion": this.apk.version,
- "beaconIdSrc": beaconIdArr.join(';'),
- "brand": this.brand,
- "channelId": "2017",
- "cid": "",
- "imei": this.imei,
- "imsi": this.imsi.toString('hex'),
- "mac": this.mac_address,
- "model": this.model,
- "networkType": "unknown",
- "oaid": "",
- "osVersion": `Android ${this.version.release},level ${this.version.sdk}`,
- "qimei": "",
- "qimei36": "",
- "sdkVersion": "1.2.13.6",
- "targetSdkVersion": "26",
- "audit": "",
- "userId": "{}",
- "packageId": this.apk.id,
- "deviceType": this.display,
- "sdkName": "",
- "reserved": JSON.stringify(reserved),
- };
- }
-}
-exports.Device = Device;
-/**
- * 支持的登录设备平台
- * * `aPad`和`Watch`协议无法设置在线状态、无法接收某些群事件（包括戳一戳等）
- * * 目前仅`Watch`支持扫码登录，可能会支持`iPad`扫码登录
- */
-var Platform;
-(function (Platform) {
- /** 安卓手机 */
- Platform[Platform["Android"] = 1] = "Android";
- /** 安卓平板 */
- Platform[Platform["aPad"] = 2] = "aPad";
- /** 安卓手表 */
- Platform[Platform["Watch"] = 3] = "Watch";
- /** MacOS */
- Platform[Platform["iMac"] = 4] = "iMac";
- /** iPad */
- Platform[Platform["iPad"] = 5] = "iPad";
- /** Tim */
- Platform[Platform["Tim"] = 6] = "Tim";
-})(Platform || (exports.Platform = Platform = {}));
-const mobile = {
- id: "com.tencent.mobileqq",
- app_key: '0S200MNJT807V3GE',
- name: "A8.9.68.11565",
- version: "8.9.68.11565",
- ver: "8.9.68",
- sign: Buffer.from('A6 B7 45 BF 24 A2 C2 77 52 77 16 F6 F3 6E B6 8D'.split(' ').map(s => parseInt(s, 16))),
- buildtime: 1687254022,
- appid: 16,
- subid: 537168313,
- bitmap: 150470524,
- main_sig_map: 16724722,
- sub_sig_map: 0x10400,
- sdkver: "6.0.0.2549",
- display: "Android",
- qua: 'V1_AND_SQ_8.9.68_4264_YYB_D',
- ssover: 20,
-};
-const tim = {
- id: "com.tencent.tim",
- app_key: '0S200MNJT807V3GE',
- name: "A3.5.1.3168",
- version: "3.5.1.3168",
- ver: "3.5.1",
- sign: Buffer.from('775e696d09856872fdd8ab4f3f06b1e0', 'hex'),
- buildtime: 1630062176,
- appid: 16,
- subid: 537150355,
- bitmap: 150470524,
- main_sig_map: 16724722,
- sub_sig_map: 0x10400,
- sdkver: "6.0.0.2484",
- display: "Tim",
- qua: "V1_AND_SQ_8.3.9_351_TIM_D",
- ssover: 18,
-};
-const watch = {
- id: "com.tencent.qqlite",
- app_key: '0S200MNJT807V3GE',
- name: "A2.0.8",
- version: "2.0.8",
- ver: "2.0.8",
- sign: Buffer.from('A6 B7 45 BF 24 A2 C2 77 52 77 16 F6 F3 6E B6 8D'.split(' ').map(s => parseInt(s, 16))),
- buildtime: 1559564731,
- appid: 16,
- subid: 537065138,
- bitmap: 16252796,
- main_sig_map: 16724722,
- sub_sig_map: 0x10400,
- sdkver: "6.0.0.2365",
- display: "Watch",
- qua: '',
- ssover: 5
-};
-const hd = {
- id: "com.tencent.qq",
- app_key: '0S200MNJT807V3GE',
- name: "A6.8.2.21241",
- version: "6.8.2.21241",
- ver: "6.8.2",
- sign: Buffer.from('AA 39 78 F4 1F D9 6F F9 91 4A 66 9E 18 64 74 C7'.split(' ').map(s => parseInt(s, 16))),
- buildtime: 1647227495,
- appid: 16,
- subid: 537128930,
- bitmap: 150470524,
- main_sig_map: 1970400,
- sub_sig_map: 66560,
- sdkver: "6.2.0.1023",
- display: "iMac",
- qua: '',
- ssover: 12
-};
-const apklist = {
- [Platform.Android]: mobile,
- [Platform.Tim]: tim,
- [Platform.aPad]: {
- ...mobile,
- subid: 537168361,
- display: 'aPad'
- },
- [Platform.Watch]: watch,
- [Platform.iMac]: { ...hd },
- [Platform.iPad]: {
- ...mobile,
- subid: 537155074,
- sign: hd.sign,
- name: '8.9.50.611',
- ver: '8.9.50',
- sdkver: '6.0.0.2535',
- qua: '',
- display: 'iPad'
- },
-};
-function getApkInfo(p) {
- return apklist[p] || apklist[Platform.Android];
-}
-exports.getApkInfo = getApkInfo;
diff --git a/spaces/lazyboy450/RVCv2-Genshin/vc_infer_pipeline.py b/spaces/lazyboy450/RVCv2-Genshin/vc_infer_pipeline.py
deleted file mode 100644
index c6be666c8d980fc6da24bd5e16ac9909d9204a46..0000000000000000000000000000000000000000
--- a/spaces/lazyboy450/RVCv2-Genshin/vc_infer_pipeline.py
+++ /dev/null
@@ -1,431 +0,0 @@
-import numpy as np, parselmouth, torch, pdb
-from time import time as ttime
-import torch.nn.functional as F
-import scipy.signal as signal
-import pyworld, os, traceback, faiss, librosa, torchcrepe
-from scipy import signal
-from functools import lru_cache
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-input_audio_path2wav = {}
-
-
-@lru_cache
-def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
- audio = input_audio_path2wav[input_audio_path]
- f0, t = pyworld.harvest(
- audio,
- fs=fs,
- f0_ceil=f0max,
- f0_floor=f0min,
- frame_period=frame_period,
- )
- f0 = pyworld.stonemask(audio, f0, t, fs)
- return f0
-
-
-def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频，2是输出音频,rate是2的占比
- # print(data1.max(),data2.max())
- rms1 = librosa.feature.rms(
- y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
- ) # 每半秒一个点
- rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
- rms1 = torch.from_numpy(rms1)
- rms1 = F.interpolate(
- rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
- ).squeeze()
- rms2 = torch.from_numpy(rms2)
- rms2 = F.interpolate(
- rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
- ).squeeze()
- rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
- data2 *= (
- torch.pow(rms1, torch.tensor(1 - rate))
- * torch.pow(rms2, torch.tensor(rate - 1))
- ).numpy()
- return data2
-
-
-class VC(object):
- def __init__(self, tgt_sr, config):
- self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
- config.x_pad,
- config.x_query,
- config.x_center,
- config.x_max,
- config.is_half,
- )
- self.sr = 16000 # hubert输入采样率
- self.window = 160 # 每帧点数
- self.t_pad = self.sr * self.x_pad # 每条前后pad时间
- self.t_pad_tgt = tgt_sr * self.x_pad
- self.t_pad2 = self.t_pad * 2
- self.t_query = self.sr * self.x_query # 查询切点前后查询时间
- self.t_center = self.sr * self.x_center # 查询切点位置
- self.t_max = self.sr * self.x_max # 免查询时长阈值
- self.device = config.device
-
- def get_f0(
- self,
- input_audio_path,
- x,
- p_len,
- f0_up_key,
- f0_method,
- filter_radius,
- inp_f0=None,
- ):
- global input_audio_path2wav
- time_step = self.window / self.sr * 1000
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
- if f0_method == "pm":
- f0 = (
- parselmouth.Sound(x, self.sr)
- .to_pitch_ac(
- time_step=time_step / 1000,
- voicing_threshold=0.6,
- pitch_floor=f0_min,
- pitch_ceiling=f0_max,
- )
- .selected_array["frequency"]
- )
- pad_size = (p_len - len(f0) + 1) // 2
- if pad_size > 0 or p_len - len(f0) - pad_size > 0:
- f0 = np.pad(
- f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
- )
- elif f0_method == "harvest":
- input_audio_path2wav[input_audio_path] = x.astype(np.double)
- f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
- if filter_radius > 2:
- f0 = signal.medfilt(f0, 3)
- elif f0_method == "crepe":
- model = "full"
- # Pick a batch size that doesn't cause memory errors on your gpu
- batch_size = 512
- # Compute pitch using first gpu
- audio = torch.tensor(np.copy(x))[None].float()
- f0, pd = torchcrepe.predict(
- audio,
- self.sr,
- self.window,
- f0_min,
- f0_max,
- model,
- batch_size=batch_size,
- device=self.device,
- return_periodicity=True,
- )
- pd = torchcrepe.filter.median(pd, 3)
- f0 = torchcrepe.filter.mean(f0, 3)
- f0[pd < 0.1] = 0
- f0 = f0[0].cpu().numpy()
- f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
- f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
- :shape
- ]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- f0bak = f0.copy()
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0bak # 1-0
-
- def vc(
- self,
- model,
- net_g,
- sid,
- audio0,
- pitch,
- pitchf,
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- ): # ,file_index,file_big_npy
- feats = torch.from_numpy(audio0)
- if self.is_half:
- feats = feats.half()
- else:
- feats = feats.float()
- if feats.dim() == 2: # double channels
- feats = feats.mean(-1)
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
- inputs = {
- "source": feats.to(self.device),
- "padding_mask": padding_mask,
- "output_layer": 9 if version == "v1" else 12,
- }
- t0 = ttime()
- with torch.no_grad():
- logits = model.extract_features(**inputs)
- feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
- if protect < 0.5 and pitch != None and pitchf != None:
- feats0 = feats.clone()
- if (
- isinstance(index, type(None)) == False
- and isinstance(big_npy, type(None)) == False
- and index_rate != 0
- ):
- npy = feats[0].cpu().numpy()
- if self.is_half:
- npy = npy.astype("float32")
-
- # _, I = index.search(npy, 1)
- # npy = big_npy[I.squeeze()]
-
- score, ix = index.search(npy, k=8)
- weight = np.square(1 / score)
- weight /= weight.sum(axis=1, keepdims=True)
- npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-
- if self.is_half:
- npy = npy.astype("float16")
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
- + (1 - index_rate) * feats
- )
-
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- if protect < 0.5 and pitch != None and pitchf != None:
- feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
- 0, 2, 1
- )
- t1 = ttime()
- p_len = audio0.shape[0] // self.window
- if feats.shape[1] < p_len:
- p_len = feats.shape[1]
- if pitch != None and pitchf != None:
- pitch = pitch[:, :p_len]
- pitchf = pitchf[:, :p_len]
-
- if protect < 0.5 and pitch != None and pitchf != None:
- pitchff = pitchf.clone()
- pitchff[pitchf > 0] = 1
- pitchff[pitchf < 1] = protect
- pitchff = pitchff.unsqueeze(-1)
- feats = feats * pitchff + feats0 * (1 - pitchff)
- feats = feats.to(feats0.dtype)
- p_len = torch.tensor([p_len], device=self.device).long()
- with torch.no_grad():
- if pitch != None and pitchf != None:
- audio1 = (
- (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
- .data.cpu()
- .float()
- .numpy()
- )
- else:
- audio1 = (
- (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
- )
- del feats, p_len, padding_mask
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- t2 = ttime()
- times[0] += t1 - t0
- times[2] += t2 - t1
- return audio1
-
- def pipeline(
- self,
- model,
- net_g,
- sid,
- audio,
- input_audio_path,
- times,
- f0_up_key,
- f0_method,
- file_index,
- # file_big_npy,
- index_rate,
- if_f0,
- filter_radius,
- tgt_sr,
- resample_sr,
- rms_mix_rate,
- version,
- protect,
- f0_file=None,
- ):
- if (
- file_index != ""
- # and file_big_npy != ""
- # and os.path.exists(file_big_npy) == True
- and os.path.exists(file_index) == True
- and index_rate != 0
- ):
- try:
- index = faiss.read_index(file_index)
- # big_npy = np.load(file_big_npy)
- big_npy = index.reconstruct_n(0, index.ntotal)
- except:
- traceback.print_exc()
- index = big_npy = None
- else:
- index = big_npy = None
- audio = signal.filtfilt(bh, ah, audio)
- audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
- opt_ts = []
- if audio_pad.shape[0] > self.t_max:
- audio_sum = np.zeros_like(audio)
- for i in range(self.window):
- audio_sum += audio_pad[i : i - self.window]
- for t in range(self.t_center, audio.shape[0], self.t_center):
- opt_ts.append(
- t
- - self.t_query
- + np.where(
- np.abs(audio_sum[t - self.t_query : t + self.t_query])
- == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
- )[0][0]
- )
- s = 0
- audio_opt = []
- t = None
- t1 = ttime()
- audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
- p_len = audio_pad.shape[0] // self.window
- inp_f0 = None
- if hasattr(f0_file, "name") == True:
- try:
- with open(f0_file.name, "r") as f:
- lines = f.read().strip("\n").split("\n")
- inp_f0 = []
- for line in lines:
- inp_f0.append([float(i) for i in line.split(",")])
- inp_f0 = np.array(inp_f0, dtype="float32")
- except:
- traceback.print_exc()
- sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
- pitch, pitchf = None, None
- if if_f0 == 1:
- pitch, pitchf = self.get_f0(
- input_audio_path,
- audio_pad,
- p_len,
- f0_up_key,
- f0_method,
- filter_radius,
- inp_f0,
- )
- pitch = pitch[:p_len]
- pitchf = pitchf[:p_len]
- if self.device == "mps":
- pitchf = pitchf.astype(np.float32)
- pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
- pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
- t2 = ttime()
- times[1] += t2 - t1
- for t in opt_ts:
- t = t // self.window * self.window
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- pitch[:, s // self.window : (t + self.t_pad2) // self.window],
- pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- s = t
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- pitch[:, t // self.window :] if t is not None else pitch,
- pitchf[:, t // self.window :] if t is not None else pitchf,
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- audio_opt = np.concatenate(audio_opt)
- if rms_mix_rate != 1:
- audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
- if resample_sr >= 16000 and tgt_sr != resample_sr:
- audio_opt = librosa.resample(
- audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
- )
- audio_max = np.abs(audio_opt).max() / 0.99
- max_int16 = 32768
- if audio_max > 1:
- max_int16 /= audio_max
- audio_opt = (audio_opt * max_int16).astype(np.int16)
- del pitch, pitchf, sid
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- return audio_opt
diff --git a/spaces/leilevy/bingo/src/lib/hooks/use-enter-submit.tsx b/spaces/leilevy/bingo/src/lib/hooks/use-enter-submit.tsx
deleted file mode 100644
index d66b2d3253baff164235d4ca791aae6d84721835..0000000000000000000000000000000000000000
--- a/spaces/leilevy/bingo/src/lib/hooks/use-enter-submit.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import { useRef, type RefObject } from 'react'
-
-export function useEnterSubmit(): {
- formRef: RefObject
- onKeyDown: (event: React.KeyboardEvent) => void
-} {
- const formRef = useRef(null)
-
- const handleKeyDown = (
- event: React.KeyboardEvent
- ): void => {
- if (
- event.key === 'Enter' &&
- !event.shiftKey &&
- !event.nativeEvent.isComposing
- ) {
- formRef.current?.requestSubmit()
- event.preventDefault()
- }
- }
-
- return { formRef, onKeyDown: handleKeyDown }
-}
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Adobe Acrobat 3d 8.1.7 Serial Number.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Adobe Acrobat 3d 8.1.7 Serial Number.md
deleted file mode 100644
index 38ba90e37423125353cf4274a55ae47a59fb7164..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Adobe Acrobat 3d 8.1.7 Serial Number.md
+++ /dev/null
@@ -1,6 +0,0 @@
-adobe acrobat 3d 8.1.7 serial number Download Zip ✯✯✯ https://bytlly.com/2uGx3w
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Automailmerge Plugin For Adobe Acrobat Crack 119 __EXCLUSIVE__.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Automailmerge Plugin For Adobe Acrobat Crack 119 __EXCLUSIVE__.md
deleted file mode 100644
index d9f354417f0fde6677114f6a932d246b61446753..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Automailmerge Plugin For Adobe Acrobat Crack 119 __EXCLUSIVE__.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Automailmerge Plugin For Adobe Acrobat Crack 119 DOWNLOAD ✏ ✏ ✏ https://bytlly.com/2uGyDR
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Download Xforce Keygen 64bits Autocad 2014.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Download Xforce Keygen 64bits Autocad 2014.md
deleted file mode 100644
index 3efca7b6aa013c8dac91433d64f0f94ffe612825..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Download Xforce Keygen 64bits Autocad 2014.md
+++ /dev/null
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-download xforce keygen 64bits autocad 2014 DOWNLOAD ❤❤❤ https://bytlly.com/2uGy76
diff --git a/spaces/lj1995/vocal2guitar/i18n.py b/spaces/lj1995/vocal2guitar/i18n.py
deleted file mode 100644
index 37f310fadd0b48b2f364877158fb2105d645fc03..0000000000000000000000000000000000000000
--- a/spaces/lj1995/vocal2guitar/i18n.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import locale
-import json
-import os
-
-
-def load_language_list(language):
- with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
- language_list = json.load(f)
- return language_list
-
-
-class I18nAuto:
- def __init__(self, language=None):
- if language in ["Auto", None]:
- language = locale.getdefaultlocale()[
- 0
- ] # getlocale can't identify the system's language ((None, None))
- if not os.path.exists(f"./i18n/{language}.json"):
- language = "en_US"
- self.language = language
- # print("Use Language:", language)
- self.language_map = load_language_list(language)
-
- def __call__(self, key):
- return self.language_map.get(key, key)
-
- def print(self):
- print("Use Language:", self.language)
diff --git a/spaces/lmz/candle-whisper/build/m_bg.wasm.d.ts b/spaces/lmz/candle-whisper/build/m_bg.wasm.d.ts
deleted file mode 100644
index eb352ee2714417b02eb9f6054a6c9556e97418a6..0000000000000000000000000000000000000000
--- a/spaces/lmz/candle-whisper/build/m_bg.wasm.d.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-/* tslint:disable */
-/* eslint-disable */
-export const memory: WebAssembly.Memory;
-export function __wbg_decoder_free(a: number): void;
-export function decoder_new(a: number, b: number, c: number, d: number, e: number, f: number, g: number, h: number, i: number, j: number, k: number, l: number, m: number, n: number, o: number, p: number): void;
-export function decoder_decode(a: number, b: number, c: number, d: number): void;
-export function main(a: number, b: number): number;
-export function __wbindgen_add_to_stack_pointer(a: number): number;
-export function __wbindgen_malloc(a: number, b: number): number;
-export function __wbindgen_realloc(a: number, b: number, c: number, d: number): number;
-export function __wbindgen_free(a: number, b: number, c: number): void;
-export function __wbindgen_start(): void;
diff --git a/spaces/lnyan/stablediffusion-infinity/convert_checkpoint.py b/spaces/lnyan/stablediffusion-infinity/convert_checkpoint.py
deleted file mode 100644
index 34efcf1ab17190b8b140f02e9ff3451daf2c6f9e..0000000000000000000000000000000000000000
--- a/spaces/lnyan/stablediffusion-infinity/convert_checkpoint.py
+++ /dev/null
@@ -1,706 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py
-""" Conversion script for the LDM checkpoints. """
-
-import argparse
-import os
-
-import torch
-
-
-try:
- from omegaconf import OmegaConf
-except ImportError:
- raise ImportError(
- "OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`."
- )
-
-from diffusers import (
- AutoencoderKL,
- DDIMScheduler,
- LDMTextToImagePipeline,
- LMSDiscreteScheduler,
- PNDMScheduler,
- StableDiffusionPipeline,
- UNet2DConditionModel,
-)
-from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
-from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
-from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer
-
-
-def shave_segments(path, n_shave_prefix_segments=1):
- """
- Removes segments. Positive values shave the first segments, negative shave the last segments.
- """
- if n_shave_prefix_segments >= 0:
- return ".".join(path.split(".")[n_shave_prefix_segments:])
- else:
- return ".".join(path.split(".")[:n_shave_prefix_segments])
-
-
-def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
- """
- Updates paths inside resnets to the new naming scheme (local renaming)
- """
- mapping = []
- for old_item in old_list:
- new_item = old_item.replace("in_layers.0", "norm1")
- new_item = new_item.replace("in_layers.2", "conv1")
-
- new_item = new_item.replace("out_layers.0", "norm2")
- new_item = new_item.replace("out_layers.3", "conv2")
-
- new_item = new_item.replace("emb_layers.1", "time_emb_proj")
- new_item = new_item.replace("skip_connection", "conv_shortcut")
-
- new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
- mapping.append({"old": old_item, "new": new_item})
-
- return mapping
-
-
-def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
- """
- Updates paths inside resnets to the new naming scheme (local renaming)
- """
- mapping = []
- for old_item in old_list:
- new_item = old_item
-
- new_item = new_item.replace("nin_shortcut", "conv_shortcut")
- new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
- mapping.append({"old": old_item, "new": new_item})
-
- return mapping
-
-
-def renew_attention_paths(old_list, n_shave_prefix_segments=0):
- """
- Updates paths inside attentions to the new naming scheme (local renaming)
- """
- mapping = []
- for old_item in old_list:
- new_item = old_item
-
- # new_item = new_item.replace('norm.weight', 'group_norm.weight')
- # new_item = new_item.replace('norm.bias', 'group_norm.bias')
-
- # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
- # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
-
- # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
- mapping.append({"old": old_item, "new": new_item})
-
- return mapping
-
-
-def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
- """
- Updates paths inside attentions to the new naming scheme (local renaming)
- """
- mapping = []
- for old_item in old_list:
- new_item = old_item
-
- new_item = new_item.replace("norm.weight", "group_norm.weight")
- new_item = new_item.replace("norm.bias", "group_norm.bias")
-
- new_item = new_item.replace("q.weight", "query.weight")
- new_item = new_item.replace("q.bias", "query.bias")
-
- new_item = new_item.replace("k.weight", "key.weight")
- new_item = new_item.replace("k.bias", "key.bias")
-
- new_item = new_item.replace("v.weight", "value.weight")
- new_item = new_item.replace("v.bias", "value.bias")
-
- new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
- new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
-
- new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
- mapping.append({"old": old_item, "new": new_item})
-
- return mapping
-
-
-def assign_to_checkpoint(
- paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
-):
- """
- This does the final conversion step: take locally converted weights and apply a global renaming
- to them. It splits attention layers, and takes into account additional replacements
- that may arise.
-
- Assigns the weights to the new checkpoint.
- """
- assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
-
- # Splits the attention layers into three variables.
- if attention_paths_to_split is not None:
- for path, path_map in attention_paths_to_split.items():
- old_tensor = old_checkpoint[path]
- channels = old_tensor.shape[0] // 3
-
- target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
-
- num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
-
- old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
- query, key, value = old_tensor.split(channels // num_heads, dim=1)
-
- checkpoint[path_map["query"]] = query.reshape(target_shape)
- checkpoint[path_map["key"]] = key.reshape(target_shape)
- checkpoint[path_map["value"]] = value.reshape(target_shape)
-
- for path in paths:
- new_path = path["new"]
-
- # These have already been assigned
- if attention_paths_to_split is not None and new_path in attention_paths_to_split:
- continue
-
- # Global renaming happens here
- new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
- new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
- new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
-
- if additional_replacements is not None:
- for replacement in additional_replacements:
- new_path = new_path.replace(replacement["old"], replacement["new"])
-
- # proj_attn.weight has to be converted from conv 1D to linear
- if "proj_attn.weight" in new_path:
- checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
- else:
- checkpoint[new_path] = old_checkpoint[path["old"]]
-
-
-def conv_attn_to_linear(checkpoint):
- keys = list(checkpoint.keys())
- attn_keys = ["query.weight", "key.weight", "value.weight"]
- for key in keys:
- if ".".join(key.split(".")[-2:]) in attn_keys:
- if checkpoint[key].ndim > 2:
- checkpoint[key] = checkpoint[key][:, :, 0, 0]
- elif "proj_attn.weight" in key:
- if checkpoint[key].ndim > 2:
- checkpoint[key] = checkpoint[key][:, :, 0]
-
-
-def create_unet_diffusers_config(original_config):
- """
- Creates a config for the diffusers based on the config of the LDM model.
- """
- unet_params = original_config.model.params.unet_config.params
-
- block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
-
- down_block_types = []
- resolution = 1
- for i in range(len(block_out_channels)):
- block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
- down_block_types.append(block_type)
- if i != len(block_out_channels) - 1:
- resolution *= 2
-
- up_block_types = []
- for i in range(len(block_out_channels)):
- block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
- up_block_types.append(block_type)
- resolution //= 2
-
- config = dict(
- sample_size=unet_params.image_size,
- in_channels=unet_params.in_channels,
- out_channels=unet_params.out_channels,
- down_block_types=tuple(down_block_types),
- up_block_types=tuple(up_block_types),
- block_out_channels=tuple(block_out_channels),
- layers_per_block=unet_params.num_res_blocks,
- cross_attention_dim=unet_params.context_dim,
- attention_head_dim=unet_params.num_heads,
- )
-
- return config
-
-
-def create_vae_diffusers_config(original_config):
- """
- Creates a config for the diffusers based on the config of the LDM model.
- """
- vae_params = original_config.model.params.first_stage_config.params.ddconfig
- _ = original_config.model.params.first_stage_config.params.embed_dim
-
- block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
- down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
- up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
-
- config = dict(
- sample_size=vae_params.resolution,
- in_channels=vae_params.in_channels,
- out_channels=vae_params.out_ch,
- down_block_types=tuple(down_block_types),
- up_block_types=tuple(up_block_types),
- block_out_channels=tuple(block_out_channels),
- latent_channels=vae_params.z_channels,
- layers_per_block=vae_params.num_res_blocks,
- )
- return config
-
-
-def create_diffusers_schedular(original_config):
- schedular = DDIMScheduler(
- num_train_timesteps=original_config.model.params.timesteps,
- beta_start=original_config.model.params.linear_start,
- beta_end=original_config.model.params.linear_end,
- beta_schedule="scaled_linear",
- )
- return schedular
-
-
-def create_ldm_bert_config(original_config):
- bert_params = original_config.model.parms.cond_stage_config.params
- config = LDMBertConfig(
- d_model=bert_params.n_embed,
- encoder_layers=bert_params.n_layer,
- encoder_ffn_dim=bert_params.n_embed * 4,
- )
- return config
-
-
-def convert_ldm_unet_checkpoint(checkpoint, config):
- """
- Takes a state dict and a config, and returns a converted checkpoint.
- """
-
- # extract state_dict for UNet
- unet_state_dict = {}
- unet_key = "model.diffusion_model."
- keys = list(checkpoint.keys())
- for key in keys:
- if key.startswith(unet_key):
- unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
-
- new_checkpoint = {}
-
- new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
- new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
- new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
- new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
-
- new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
- new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
-
- new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
- new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
- new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
- new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
-
- # Retrieves the keys for the input blocks only
- num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
- input_blocks = {
- layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
- for layer_id in range(num_input_blocks)
- }
-
- # Retrieves the keys for the middle blocks only
- num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
- middle_blocks = {
- layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
- for layer_id in range(num_middle_blocks)
- }
-
- # Retrieves the keys for the output blocks only
- num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
- output_blocks = {
- layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
- for layer_id in range(num_output_blocks)
- }
-
- for i in range(1, num_input_blocks):
- block_id = (i - 1) // (config["layers_per_block"] + 1)
- layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
-
- resnets = [
- key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
- ]
- attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
-
- if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
- new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
- f"input_blocks.{i}.0.op.weight"
- )
- new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
- f"input_blocks.{i}.0.op.bias"
- )
-
- paths = renew_resnet_paths(resnets)
- meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
- assign_to_checkpoint(
- paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
- )
-
- if len(attentions):
- paths = renew_attention_paths(attentions)
- meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
- assign_to_checkpoint(
- paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
- )
-
- resnet_0 = middle_blocks[0]
- attentions = middle_blocks[1]
- resnet_1 = middle_blocks[2]
-
- resnet_0_paths = renew_resnet_paths(resnet_0)
- assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
-
- resnet_1_paths = renew_resnet_paths(resnet_1)
- assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
-
- attentions_paths = renew_attention_paths(attentions)
- meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
- assign_to_checkpoint(
- attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
- )
-
- for i in range(num_output_blocks):
- block_id = i // (config["layers_per_block"] + 1)
- layer_in_block_id = i % (config["layers_per_block"] + 1)
- output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
- output_block_list = {}
-
- for layer in output_block_layers:
- layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
- if layer_id in output_block_list:
- output_block_list[layer_id].append(layer_name)
- else:
- output_block_list[layer_id] = [layer_name]
-
- if len(output_block_list) > 1:
- resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
- attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
-
- resnet_0_paths = renew_resnet_paths(resnets)
- paths = renew_resnet_paths(resnets)
-
- meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
- assign_to_checkpoint(
- paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
- )
-
- if ["conv.weight", "conv.bias"] in output_block_list.values():
- index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])
- new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
- f"output_blocks.{i}.{index}.conv.weight"
- ]
- new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
- f"output_blocks.{i}.{index}.conv.bias"
- ]
-
- # Clear attentions as they have been attributed above.
- if len(attentions) == 2:
- attentions = []
-
- if len(attentions):
- paths = renew_attention_paths(attentions)
- meta_path = {
- "old": f"output_blocks.{i}.1",
- "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
- }
- assign_to_checkpoint(
- paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
- )
- else:
- resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
- for path in resnet_0_paths:
- old_path = ".".join(["output_blocks", str(i), path["old"]])
- new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
-
- new_checkpoint[new_path] = unet_state_dict[old_path]
-
- return new_checkpoint
-
-
-def convert_ldm_vae_checkpoint(checkpoint, config):
- # extract state dict for VAE
- vae_state_dict = {}
- vae_key = "first_stage_model."
- keys = list(checkpoint.keys())
- for key in keys:
- if key.startswith(vae_key):
- vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
-
- new_checkpoint = {}
-
- new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
- new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
- new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
- new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
- new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
- new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
-
- new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
- new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
- new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
- new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
- new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
- new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
-
- new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
- new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
- new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
- new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
-
- # Retrieves the keys for the encoder down blocks only
- num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
- down_blocks = {
- layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
- }
-
- # Retrieves the keys for the decoder up blocks only
- num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
- up_blocks = {
- layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
- }
-
- for i in range(num_down_blocks):
- resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
-
- if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
- new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
- f"encoder.down.{i}.downsample.conv.weight"
- )
- new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
- f"encoder.down.{i}.downsample.conv.bias"
- )
-
- paths = renew_vae_resnet_paths(resnets)
- meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
- assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
- mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
- num_mid_res_blocks = 2
- for i in range(1, num_mid_res_blocks + 1):
- resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
-
- paths = renew_vae_resnet_paths(resnets)
- meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
- assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
- mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
- paths = renew_vae_attention_paths(mid_attentions)
- meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
- assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
- conv_attn_to_linear(new_checkpoint)
-
- for i in range(num_up_blocks):
- block_id = num_up_blocks - 1 - i
- resnets = [
- key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
- ]
-
- if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
- new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
- f"decoder.up.{block_id}.upsample.conv.weight"
- ]
- new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
- f"decoder.up.{block_id}.upsample.conv.bias"
- ]
-
- paths = renew_vae_resnet_paths(resnets)
- meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
- assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
- mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
- num_mid_res_blocks = 2
- for i in range(1, num_mid_res_blocks + 1):
- resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
-
- paths = renew_vae_resnet_paths(resnets)
- meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
- assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
- mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
- paths = renew_vae_attention_paths(mid_attentions)
- meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
- assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
- conv_attn_to_linear(new_checkpoint)
- return new_checkpoint
-
-
-def convert_ldm_bert_checkpoint(checkpoint, config):
- def _copy_attn_layer(hf_attn_layer, pt_attn_layer):
- hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight
- hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight
- hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight
-
- hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight
- hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias
-
- def _copy_linear(hf_linear, pt_linear):
- hf_linear.weight = pt_linear.weight
- hf_linear.bias = pt_linear.bias
-
- def _copy_layer(hf_layer, pt_layer):
- # copy layer norms
- _copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])
- _copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])
-
- # copy attn
- _copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])
-
- # copy MLP
- pt_mlp = pt_layer[1][1]
- _copy_linear(hf_layer.fc1, pt_mlp.net[0][0])
- _copy_linear(hf_layer.fc2, pt_mlp.net[2])
-
- def _copy_layers(hf_layers, pt_layers):
- for i, hf_layer in enumerate(hf_layers):
- if i != 0:
- i += i
- pt_layer = pt_layers[i : i + 2]
- _copy_layer(hf_layer, pt_layer)
-
- hf_model = LDMBertModel(config).eval()
-
- # copy embeds
- hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
- hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight
-
- # copy layer norm
- _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)
-
- # copy hidden layers
- _copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)
-
- _copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)
-
- return hf_model
-
-
-def convert_ldm_clip_checkpoint(checkpoint):
- text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
-
- keys = list(checkpoint.keys())
-
- text_model_dict = {}
-
- for key in keys:
- if key.startswith("cond_stage_model.transformer"):
- text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
-
- text_model.load_state_dict(text_model_dict)
-
- return text_model
-
-import os
-def convert_checkpoint(checkpoint_path, inpainting=False):
- parser = argparse.ArgumentParser()
-
- parser.add_argument(
- "--checkpoint_path", default=checkpoint_path, type=str, help="Path to the checkpoint to convert."
- )
- # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
- parser.add_argument(
- "--original_config_file",
- default=None,
- type=str,
- help="The YAML config file corresponding to the original architecture.",
- )
- parser.add_argument(
- "--scheduler_type",
- default="pndm",
- type=str,
- help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim']",
- )
- parser.add_argument("--dump_path", default=None, type=str, help="Path to the output model.")
-
- args = parser.parse_args([])
- if args.original_config_file is None:
- if inpainting:
- args.original_config_file = "./models/v1-inpainting-inference.yaml"
- else:
- args.original_config_file = "./models/v1-inference.yaml"
-
- original_config = OmegaConf.load(args.original_config_file)
- checkpoint = torch.load(args.checkpoint_path)["state_dict"]
-
- num_train_timesteps = original_config.model.params.timesteps
- beta_start = original_config.model.params.linear_start
- beta_end = original_config.model.params.linear_end
- if args.scheduler_type == "pndm":
- scheduler = PNDMScheduler(
- beta_end=beta_end,
- beta_schedule="scaled_linear",
- beta_start=beta_start,
- num_train_timesteps=num_train_timesteps,
- skip_prk_steps=True,
- )
- elif args.scheduler_type == "lms":
- scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
- elif args.scheduler_type == "ddim":
- scheduler = DDIMScheduler(
- beta_start=beta_start,
- beta_end=beta_end,
- beta_schedule="scaled_linear",
- clip_sample=False,
- set_alpha_to_one=False,
- )
- else:
- raise ValueError(f"Scheduler of type {args.scheduler_type} doesn't exist!")
-
- # Convert the UNet2DConditionModel model.
- unet_config = create_unet_diffusers_config(original_config)
- converted_unet_checkpoint = convert_ldm_unet_checkpoint(checkpoint, unet_config)
-
- unet = UNet2DConditionModel(**unet_config)
- unet.load_state_dict(converted_unet_checkpoint)
-
- # Convert the VAE model.
- vae_config = create_vae_diffusers_config(original_config)
- converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
-
- vae = AutoencoderKL(**vae_config)
- vae.load_state_dict(converted_vae_checkpoint)
-
- # Convert the text model.
- text_model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
- if text_model_type == "FrozenCLIPEmbedder":
- text_model = convert_ldm_clip_checkpoint(checkpoint)
- tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
- safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
- feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
- pipe = StableDiffusionPipeline(
- vae=vae,
- text_encoder=text_model,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- safety_checker=safety_checker,
- feature_extractor=feature_extractor,
- )
- else:
- text_config = create_ldm_bert_config(original_config)
- text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
- tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
- pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
-
- return pipe
diff --git a/spaces/ltgoslo/ssa-perin/utility/initialize.py b/spaces/ltgoslo/ssa-perin/utility/initialize.py
deleted file mode 100644
index bba449b7f537af4cd1fe971e2c1ddff33840efc5..0000000000000000000000000000000000000000
--- a/spaces/ltgoslo/ssa-perin/utility/initialize.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import random
-import torch
-import os
-
-
-def seed_everything(seed_value=42):
- os.environ['PYTHONHASHSEED'] = str(seed_value)
- random.seed(seed_value)
- torch.manual_seed(seed_value)
- torch.cuda.manual_seed_all(seed_value)
-
- torch.backends.cudnn.enabled = True
- torch.backends.cudnn.deterministic = True
- torch.backends.cudnn.benchmark = False
-
-
-def initialize(args, init_wandb: bool):
- seed_everything(args.seed)
-
- if init_wandb:
- import wandb
- tags = args.framework, args.language
- wandb.init(name=f"{args.framework}_{args.language}_{args.graph_mode}_{args.name}", config=args, project="sentiment_graphs", tags=list(tags))
- print("Connection to Weights & Biases initialized.", flush=True)
diff --git a/spaces/ma-xu/LIVE/pybind11/tests/test_smart_ptr.py b/spaces/ma-xu/LIVE/pybind11/tests/test_smart_ptr.py
deleted file mode 100644
index c9267f6878f1c0d912017bd2a6b0d21dd673c32b..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/pybind11/tests/test_smart_ptr.py
+++ /dev/null
@@ -1,290 +0,0 @@
-# -*- coding: utf-8 -*-
-import pytest
-from pybind11_tests import smart_ptr as m
-from pybind11_tests import ConstructorStats
-
-
-def test_smart_ptr(capture):
- # Object1
- for i, o in enumerate([m.make_object_1(), m.make_object_2(), m.MyObject1(3)], start=1):
- assert o.getRefCount() == 1
- with capture:
- m.print_object_1(o)
- m.print_object_2(o)
- m.print_object_3(o)
- m.print_object_4(o)
- assert capture == "MyObject1[{i}]\n".format(i=i) * 4
-
- for i, o in enumerate([m.make_myobject1_1(), m.make_myobject1_2(), m.MyObject1(6), 7],
- start=4):
- print(o)
- with capture:
- if not isinstance(o, int):
- m.print_object_1(o)
- m.print_object_2(o)
- m.print_object_3(o)
- m.print_object_4(o)
- m.print_myobject1_1(o)
- m.print_myobject1_2(o)
- m.print_myobject1_3(o)
- m.print_myobject1_4(o)
- assert capture == "MyObject1[{i}]\n".format(i=i) * (4 if isinstance(o, int) else 8)
-
- cstats = ConstructorStats.get(m.MyObject1)
- assert cstats.alive() == 0
- expected_values = ['MyObject1[{}]'.format(i) for i in range(1, 7)] + ['MyObject1[7]'] * 4
- assert cstats.values() == expected_values
- assert cstats.default_constructions == 0
- assert cstats.copy_constructions == 0
- # assert cstats.move_constructions >= 0 # Doesn't invoke any
- assert cstats.copy_assignments == 0
- assert cstats.move_assignments == 0
-
- # Object2
- for i, o in zip([8, 6, 7], [m.MyObject2(8), m.make_myobject2_1(), m.make_myobject2_2()]):
- print(o)
- with capture:
- m.print_myobject2_1(o)
- m.print_myobject2_2(o)
- m.print_myobject2_3(o)
- m.print_myobject2_4(o)
- assert capture == "MyObject2[{i}]\n".format(i=i) * 4
-
- cstats = ConstructorStats.get(m.MyObject2)
- assert cstats.alive() == 1
- o = None
- assert cstats.alive() == 0
- assert cstats.values() == ['MyObject2[8]', 'MyObject2[6]', 'MyObject2[7]']
- assert cstats.default_constructions == 0
- assert cstats.copy_constructions == 0
- # assert cstats.move_constructions >= 0 # Doesn't invoke any
- assert cstats.copy_assignments == 0
- assert cstats.move_assignments == 0
-
- # Object3
- for i, o in zip([9, 8, 9], [m.MyObject3(9), m.make_myobject3_1(), m.make_myobject3_2()]):
- print(o)
- with capture:
- m.print_myobject3_1(o)
- m.print_myobject3_2(o)
- m.print_myobject3_3(o)
- m.print_myobject3_4(o)
- assert capture == "MyObject3[{i}]\n".format(i=i) * 4
-
- cstats = ConstructorStats.get(m.MyObject3)
- assert cstats.alive() == 1
- o = None
- assert cstats.alive() == 0
- assert cstats.values() == ['MyObject3[9]', 'MyObject3[8]', 'MyObject3[9]']
- assert cstats.default_constructions == 0
- assert cstats.copy_constructions == 0
- # assert cstats.move_constructions >= 0 # Doesn't invoke any
- assert cstats.copy_assignments == 0
- assert cstats.move_assignments == 0
-
- # Object
- cstats = ConstructorStats.get(m.Object)
- assert cstats.alive() == 0
- assert cstats.values() == []
- assert cstats.default_constructions == 10
- assert cstats.copy_constructions == 0
- # assert cstats.move_constructions >= 0 # Doesn't invoke any
- assert cstats.copy_assignments == 0
- assert cstats.move_assignments == 0
-
- # ref<>
- cstats = m.cstats_ref()
- assert cstats.alive() == 0
- assert cstats.values() == ['from pointer'] * 10
- assert cstats.default_constructions == 30
- assert cstats.copy_constructions == 12
- # assert cstats.move_constructions >= 0 # Doesn't invoke any
- assert cstats.copy_assignments == 30
- assert cstats.move_assignments == 0
-
-
-def test_smart_ptr_refcounting():
- assert m.test_object1_refcounting()
-
-
-def test_unique_nodelete():
- o = m.MyObject4(23)
- assert o.value == 23
- cstats = ConstructorStats.get(m.MyObject4)
- assert cstats.alive() == 1
- del o
- assert cstats.alive() == 1 # Leak, but that's intentional
-
-
-def test_unique_nodelete4a():
- o = m.MyObject4a(23)
- assert o.value == 23
- cstats = ConstructorStats.get(m.MyObject4a)
- assert cstats.alive() == 1
- del o
- assert cstats.alive() == 1 # Leak, but that's intentional
-
-
-def test_unique_deleter():
- o = m.MyObject4b(23)
- assert o.value == 23
- cstats4a = ConstructorStats.get(m.MyObject4a)
- assert cstats4a.alive() == 2 # Two because of previous test
- cstats4b = ConstructorStats.get(m.MyObject4b)
- assert cstats4b.alive() == 1
- del o
- assert cstats4a.alive() == 1 # Should now only be one leftover from previous test
- assert cstats4b.alive() == 0 # Should be deleted
-
-
-def test_large_holder():
- o = m.MyObject5(5)
- assert o.value == 5
- cstats = ConstructorStats.get(m.MyObject5)
- assert cstats.alive() == 1
- del o
- assert cstats.alive() == 0
-
-
-def test_shared_ptr_and_references():
- s = m.SharedPtrRef()
- stats = ConstructorStats.get(m.A)
- assert stats.alive() == 2
-
- ref = s.ref # init_holder_helper(holder_ptr=false, owned=false)
- assert stats.alive() == 2
- assert s.set_ref(ref)
- with pytest.raises(RuntimeError) as excinfo:
- assert s.set_holder(ref)
- assert "Unable to cast from non-held to held instance" in str(excinfo.value)
-
- copy = s.copy # init_holder_helper(holder_ptr=false, owned=true)
- assert stats.alive() == 3
- assert s.set_ref(copy)
- assert s.set_holder(copy)
-
- holder_ref = s.holder_ref # init_holder_helper(holder_ptr=true, owned=false)
- assert stats.alive() == 3
- assert s.set_ref(holder_ref)
- assert s.set_holder(holder_ref)
-
- holder_copy = s.holder_copy # init_holder_helper(holder_ptr=true, owned=true)
- assert stats.alive() == 3
- assert s.set_ref(holder_copy)
- assert s.set_holder(holder_copy)
-
- del ref, copy, holder_ref, holder_copy, s
- assert stats.alive() == 0
-
-
-def test_shared_ptr_from_this_and_references():
- s = m.SharedFromThisRef()
- stats = ConstructorStats.get(m.B)
- assert stats.alive() == 2
-
- ref = s.ref # init_holder_helper(holder_ptr=false, owned=false, bad_wp=false)
- assert stats.alive() == 2
- assert s.set_ref(ref)
- assert s.set_holder(ref) # std::enable_shared_from_this can create a holder from a reference
-
- bad_wp = s.bad_wp # init_holder_helper(holder_ptr=false, owned=false, bad_wp=true)
- assert stats.alive() == 2
- assert s.set_ref(bad_wp)
- with pytest.raises(RuntimeError) as excinfo:
- assert s.set_holder(bad_wp)
- assert "Unable to cast from non-held to held instance" in str(excinfo.value)
-
- copy = s.copy # init_holder_helper(holder_ptr=false, owned=true, bad_wp=false)
- assert stats.alive() == 3
- assert s.set_ref(copy)
- assert s.set_holder(copy)
-
- holder_ref = s.holder_ref # init_holder_helper(holder_ptr=true, owned=false, bad_wp=false)
- assert stats.alive() == 3
- assert s.set_ref(holder_ref)
- assert s.set_holder(holder_ref)
-
- holder_copy = s.holder_copy # init_holder_helper(holder_ptr=true, owned=true, bad_wp=false)
- assert stats.alive() == 3
- assert s.set_ref(holder_copy)
- assert s.set_holder(holder_copy)
-
- del ref, bad_wp, copy, holder_ref, holder_copy, s
- assert stats.alive() == 0
-
- z = m.SharedFromThisVirt.get()
- y = m.SharedFromThisVirt.get()
- assert y is z
-
-
-def test_move_only_holder():
- a = m.TypeWithMoveOnlyHolder.make()
- b = m.TypeWithMoveOnlyHolder.make_as_object()
- stats = ConstructorStats.get(m.TypeWithMoveOnlyHolder)
- assert stats.alive() == 2
- del b
- assert stats.alive() == 1
- del a
- assert stats.alive() == 0
-
-
-def test_holder_with_addressof_operator():
- # this test must not throw exception from c++
- a = m.TypeForHolderWithAddressOf.make()
- a.print_object_1()
- a.print_object_2()
- a.print_object_3()
- a.print_object_4()
-
- stats = ConstructorStats.get(m.TypeForHolderWithAddressOf)
- assert stats.alive() == 1
-
- np = m.TypeForHolderWithAddressOf.make()
- assert stats.alive() == 2
- del a
- assert stats.alive() == 1
- del np
- assert stats.alive() == 0
-
- b = m.TypeForHolderWithAddressOf.make()
- c = b
- assert b.get() is c.get()
- assert stats.alive() == 1
-
- del b
- assert stats.alive() == 1
-
- del c
- assert stats.alive() == 0
-
-
-def test_move_only_holder_with_addressof_operator():
- a = m.TypeForMoveOnlyHolderWithAddressOf.make()
- a.print_object()
-
- stats = ConstructorStats.get(m.TypeForMoveOnlyHolderWithAddressOf)
- assert stats.alive() == 1
-
- a.value = 42
- assert a.value == 42
-
- del a
- assert stats.alive() == 0
-
-
-def test_smart_ptr_from_default():
- instance = m.HeldByDefaultHolder()
- with pytest.raises(RuntimeError) as excinfo:
- m.HeldByDefaultHolder.load_shared_ptr(instance)
- assert "Unable to load a custom holder type from a " \
- "default-holder instance" in str(excinfo.value)
-
-
-def test_shared_ptr_gc():
- """#187: issue involving std::shared_ptr<> return value policy & garbage collection"""
- el = m.ElementList()
- for i in range(10):
- el.add(m.ElementA(i))
- pytest.gc_collect()
- for i, v in enumerate(el.get()):
- assert i == v.value()
diff --git a/spaces/ma-xu/LIVE/pybind11/tests/test_stl_binders.cpp b/spaces/ma-xu/LIVE/pybind11/tests/test_stl_binders.cpp
deleted file mode 100644
index 8688874091219f5a5035f5eb46e976e7408080b8..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/pybind11/tests/test_stl_binders.cpp
+++ /dev/null
@@ -1,129 +0,0 @@
-/*
- tests/test_stl_binders.cpp -- Usage of stl_binders functions
-
- Copyright (c) 2016 Sergey Lyskov
-
- All rights reserved. Use of this source code is governed by a
- BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-
-#include
-#include
-#include
-#include
-#include
-
-class El {
-public:
- El() = delete;
- El(int v) : a(v) { }
-
- int a;
-};
-
-std::ostream & operator<<(std::ostream &s, El const&v) {
- s << "El{" << v.a << '}';
- return s;
-}
-
-/// Issue #487: binding std::vector with E non-copyable
-class E_nc {
-public:
- explicit E_nc(int i) : value{i} {}
- E_nc(const E_nc &) = delete;
- E_nc &operator=(const E_nc &) = delete;
- E_nc(E_nc &&) = default;
- E_nc &operator=(E_nc &&) = default;
-
- int value;
-};
-
-template Container *one_to_n(int n) {
- auto v = new Container();
- for (int i = 1; i <= n; i++)
- v->emplace_back(i);
- return v;
-}
-
-template Map *times_ten(int n) {
- auto m = new Map();
- for (int i = 1; i <= n; i++)
- m->emplace(int(i), E_nc(10*i));
- return m;
-}
-
-template NestMap *times_hundred(int n) {
- auto m = new NestMap();
- for (int i = 1; i <= n; i++)
- for (int j = 1; j <= n; j++)
- (*m)[i].emplace(int(j*10), E_nc(100*j));
- return m;
-}
-
-TEST_SUBMODULE(stl_binders, m) {
- // test_vector_int
- py::bind_vector>(m, "VectorInt", py::buffer_protocol());
-
- // test_vector_custom
- py::class_(m, "El")
- .def(py::init());
- py::bind_vector>(m, "VectorEl");
- py::bind_vector>>(m, "VectorVectorEl");
-
- // test_map_string_double
- py::bind_map>(m, "MapStringDouble");
- py::bind_map>(m, "UnorderedMapStringDouble");
-
- // test_map_string_double_const
- py::bind_map>(m, "MapStringDoubleConst");
- py::bind_map>(m, "UnorderedMapStringDoubleConst");
-
- py::class_(m, "ENC")
- .def(py::init())
- .def_readwrite("value", &E_nc::value);
-
- // test_noncopyable_containers
- py::bind_vector>(m, "VectorENC");
- m.def("get_vnc", &one_to_n>, py::return_value_policy::reference);
- py::bind_vector>(m, "DequeENC");
- m.def("get_dnc", &one_to_n>, py::return_value_policy::reference);
- py::bind_map>(m, "MapENC");
- m.def("get_mnc", ×_ten>, py::return_value_policy::reference);
- py::bind_map>(m, "UmapENC");
- m.def("get_umnc", ×_ten>, py::return_value_policy::reference);
- // Issue #1885: binding nested std::map> with E non-copyable
- py::bind_map>>(m, "MapVecENC");
- m.def("get_nvnc", [](int n)
- {
- auto m = new std::map>();
- for (int i = 1; i <= n; i++)
- for (int j = 1; j <= n; j++)
- (*m)[i].emplace_back(j);
- return m;
- }, py::return_value_policy::reference);
- py::bind_map>>(m, "MapMapENC");
- m.def("get_nmnc", ×_hundred>>, py::return_value_policy::reference);
- py::bind_map>>(m, "UmapUmapENC");
- m.def("get_numnc", ×_hundred>>, py::return_value_policy::reference);
-
- // test_vector_buffer
- py::bind_vector>(m, "VectorUChar", py::buffer_protocol());
- // no dtype declared for this version:
- struct VUndeclStruct { bool w; uint32_t x; double y; bool z; };
- m.def("create_undeclstruct", [m] () mutable {
- py::bind_vector>(m, "VectorUndeclStruct", py::buffer_protocol());
- });
-
- // The rest depends on numpy:
- try { py::module::import("numpy"); }
- catch (...) { return; }
-
- // test_vector_buffer_numpy
- struct VStruct { bool w; uint32_t x; double y; bool z; };
- PYBIND11_NUMPY_DTYPE(VStruct, w, x, y, z);
- py::class_(m, "VStruct").def_readwrite("x", &VStruct::x);
- py::bind_vector>(m, "VectorStruct", py::buffer_protocol());
- m.def("get_vectorstruct", [] {return std::vector {{0, 5, 3.0, 1}, {1, 30, -1e4, 0}};});
-}
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/partition.h b/spaces/ma-xu/LIVE/thrust/thrust/partition.h
deleted file mode 100644
index 3c493e0881639d75faa9516a34588dcfa2ea0fa2..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/partition.h
+++ /dev/null
@@ -1,1439 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-
-/*! \file partition.h
- * \brief Reorganizes a range based on a predicate
- */
-
-#pragma once
-
-#include
-#include
-#include
-
-namespace thrust
-{
-
-
-/*! \addtogroup reordering
- * \ingroup algorithms
- *
- * \addtogroup partitioning
- * \ingroup reordering
- * \{
- */
-
-
-/*! \p partition reorders the elements [first, last) based on the function
- * object \p pred, such that all of the elements that satisfy \p pred precede the
- * elements that fail to satisfy it. The postcondition is that, for some iterator
- * \c middle in the range [first, last) , pred(*i) is \c true for every
- * iterator \c i in the range [first,middle) and \c false for every iterator
- * \c i in the range [middle, last) . The return value of \p partition is
- * \c middle.
- *
- * Note that the relative order of elements in the two reordered sequences is not
- * necessarily the same as it was in the original sequence. A different algorithm,
- * \p stable_partition, does guarantee to preserve the relative order.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- * \param exec The execution policy to use for parallelization.
- * \param first The beginning of the sequence to reorder.
- * \param last The end of the sequence to reorder.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return An iterator referring to the first element of the second partition, that is,
- * the sequence of the elements which do not satisfy \p pred.
- *
- * \tparam DerivedPolicy The name of the derived execution policy.
- * \tparam ForwardIterator is a model of Forward Iterator ,
- * and \p ForwardIterator's \c value_type is convertible to \p Predicate's \c argument_type,
- * and \p ForwardIterator is mutable.
- * \tparam Predicate is a model of Predicate .
- *
- * The following code snippet demonstrates how to use \p partition to reorder a
- * sequence so that even numbers precede odd numbers using the \p thrust::host execution policy for parallelization:
- *
- * \code
- * #include
- * #include
- * ...
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- * ...
- * int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * const int N = sizeof(A)/sizeof(int);
- * thrust::partition(thrust::host,
- * A, A + N,
- * is_even());
- * // A is now {2, 4, 6, 8, 10, 1, 3, 5, 7, 9}
- * \endcode
- *
- * \see http://www.sgi.com/tech/stl/partition.html
- * \see \p stable_partition
- * \see \p partition_copy
- */
-template
-__host__ __device__
- ForwardIterator partition(const thrust::detail::execution_policy_base &exec,
- ForwardIterator first,
- ForwardIterator last,
- Predicate pred);
-
-
-/*! \p partition reorders the elements [first, last) based on the function
- * object \p pred, such that all of the elements that satisfy \p pred precede the
- * elements that fail to satisfy it. The postcondition is that, for some iterator
- * \c middle in the range [first, last) , pred(*i) is \c true for every
- * iterator \c i in the range [first,middle) and \c false for every iterator
- * \c i in the range [middle, last) . The return value of \p partition is
- * \c middle.
- *
- * Note that the relative order of elements in the two reordered sequences is not
- * necessarily the same as it was in the original sequence. A different algorithm,
- * \p stable_partition, does guarantee to preserve the relative order.
- *
- * \param first The beginning of the sequence to reorder.
- * \param last The end of the sequence to reorder.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return An iterator referring to the first element of the second partition, that is,
- * the sequence of the elements which do not satisfy \p pred.
- *
- * \tparam ForwardIterator is a model of Forward Iterator ,
- * and \p ForwardIterator's \c value_type is convertible to \p Predicate's \c argument_type,
- * and \p ForwardIterator is mutable.
- * \tparam Predicate is a model of Predicate .
- *
- * The following code snippet demonstrates how to use \p partition to reorder a
- * sequence so that even numbers precede odd numbers.
- *
- * \code
- * #include
- * ...
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- * ...
- * int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * const int N = sizeof(A)/sizeof(int);
- * thrust::partition(A, A + N,
- * is_even());
- * // A is now {2, 4, 6, 8, 10, 1, 3, 5, 7, 9}
- * \endcode
- *
- * \see http://www.sgi.com/tech/stl/partition.html
- * \see \p stable_partition
- * \see \p partition_copy
- */
-template
- ForwardIterator partition(ForwardIterator first,
- ForwardIterator last,
- Predicate pred);
-
-
-/*! \p partition reorders the elements [first, last) based on the function
- * object \p pred applied to a stencil range [stencil, stencil + (last - first)) ,
- * such that all of the elements whose corresponding stencil element satisfies \p pred precede all of the elements whose
- * corresponding stencil element fails to satisfy it. The postcondition is that, for some iterator
- * \c middle in the range [first, last) , pred(*stencil_i) is \c true for every iterator
- * \c stencil_i in the range [stencil,stencil + (middle - first)) and \c false for every iterator \c stencil_i
- * in the range [stencil + (middle - first), stencil + (last - first)) .
- * The return value of \p stable_partition is \c middle.
- *
- * Note that the relative order of elements in the two reordered sequences is not
- * necessarily the same as it was in the original sequence. A different algorithm,
- * \p stable_partition, does guarantee to preserve the relative order.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- * \param exec The execution policy to use for parallelization.
- * \param first The beginning of the sequence to reorder.
- * \param last The end of the sequence to reorder.
- * \param stencil The beginning of the stencil sequence.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return An iterator referring to the first element of the second partition, that is,
- * the sequence of the elements whose stencil elements do not satisfy \p pred.
- *
- * \tparam DerivedPolicy The name of the derived execution policy.
- * \tparam ForwardIterator is a model of Forward Iterator ,
- * and \p ForwardIterator is mutable.
- * \tparam InputIterator is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p Predicate's \c argument_type.
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The ranges [first,last) and [stencil, stencil + (last - first)) shall not overlap.
- *
- * The following code snippet demonstrates how to use \p partition to reorder a
- * sequence so that even numbers precede odd numbers using the \p thrust::host execution policy for parallelization:
- *
- * \code
- * #include
- * #include
- * ...
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- * ...
- * int A[] = {0, 1, 0, 1, 0, 1, 0, 1, 0, 1};
- * int S[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * const int N = sizeof(A)/sizeof(int);
- * thrust::partition(thrust::host, A, A + N, S, is_even());
- * // A is now {1, 1, 1, 1, 1, 0, 0, 0, 0, 0}
- * // S is unmodified
- * \endcode
- *
- * \see http://www.sgi.com/tech/stl/partition.html
- * \see \p stable_partition
- * \see \p partition_copy
- */
-template
-__host__ __device__
- ForwardIterator partition(const thrust::detail::execution_policy_base &exec,
- ForwardIterator first,
- ForwardIterator last,
- InputIterator stencil,
- Predicate pred);
-
-
-/*! \p partition reorders the elements [first, last) based on the function
- * object \p pred applied to a stencil range [stencil, stencil + (last - first)) ,
- * such that all of the elements whose corresponding stencil element satisfies \p pred precede all of the elements whose
- * corresponding stencil element fails to satisfy it. The postcondition is that, for some iterator
- * \c middle in the range [first, last) , pred(*stencil_i) is \c true for every iterator
- * \c stencil_i in the range [stencil,stencil + (middle - first)) and \c false for every iterator \c stencil_i
- * in the range [stencil + (middle - first), stencil + (last - first)) .
- * The return value of \p stable_partition is \c middle.
- *
- * Note that the relative order of elements in the two reordered sequences is not
- * necessarily the same as it was in the original sequence. A different algorithm,
- * \p stable_partition, does guarantee to preserve the relative order.
- *
- * \param first The beginning of the sequence to reorder.
- * \param last The end of the sequence to reorder.
- * \param stencil The beginning of the stencil sequence.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return An iterator referring to the first element of the second partition, that is,
- * the sequence of the elements whose stencil elements do not satisfy \p pred.
- *
- * \tparam ForwardIterator is a model of Forward Iterator ,
- * and \p ForwardIterator is mutable.
- * \tparam InputIterator is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p Predicate's \c argument_type.
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The ranges [first,last) and [stencil, stencil + (last - first)) shall not overlap.
- *
- * The following code snippet demonstrates how to use \p partition to reorder a
- * sequence so that even numbers precede odd numbers.
- *
- * \code
- * #include
- * ...
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- * ...
- * int A[] = {0, 1, 0, 1, 0, 1, 0, 1, 0, 1};
- * int S[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * const int N = sizeof(A)/sizeof(int);
- * thrust::partition(A, A + N, S, is_even());
- * // A is now {1, 1, 1, 1, 1, 0, 0, 0, 0, 0}
- * // S is unmodified
- * \endcode
- *
- * \see http://www.sgi.com/tech/stl/partition.html
- * \see \p stable_partition
- * \see \p partition_copy
- */
-template
- ForwardIterator partition(ForwardIterator first,
- ForwardIterator last,
- InputIterator stencil,
- Predicate pred);
-
-
-/*! \p partition_copy differs from \p partition only in that the reordered
- * sequence is written to difference output sequences, rather than in place.
- *
- * \p partition_copy copies the elements [first, last) based on the
- * function object \p pred. All of the elements that satisfy \p pred are copied
- * to the range beginning at \p out_true and all the elements that fail to satisfy it
- * are copied to the range beginning at \p out_false.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- * \param exec The execution policy to use for parallelization.
- * \param first The beginning of the sequence to reorder.
- * \param last The end of the sequence to reorder.
- * \param out_true The destination of the resulting sequence of elements which satisfy \p pred.
- * \param out_false The destination of the resulting sequence of elements which fail to satisfy \p pred.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return A \p pair p such that p.first is the end of the output range beginning
- * at \p out_true and p.second is the end of the output range beginning at
- * \p out_false.
- *
- * \tparam DerivedPolicy The name of the derived execution policy.
- * \tparam InputIterator is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p Predicate's \c argument_type and \p InputIterator's \c value_type
- * is convertible to \p OutputIterator1 and \p OutputIterator2's \c value_types.
- * \tparam OutputIterator1 is a model of Output Iterator .
- * \tparam OutputIterator2 is a model of Output Iterator .
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The input range shall not overlap with either output range.
- *
- * The following code snippet demonstrates how to use \p partition_copy to separate a
- * sequence into two output sequences of even and odd numbers using the \p thrust::host execution policy for parallelization:
- *
- * \code
- * #include
- * #include
- * ...
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- * ...
- * int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * int result[10];
- * const int N = sizeof(A)/sizeof(int);
- * int *evens = result;
- * int *odds = result + 5;
- * thrust::partition_copy(thrust::host, A, A + N, evens, odds, is_even());
- * // A remains {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
- * // result is now {2, 4, 6, 8, 10, 1, 3, 5, 7, 9}
- * // evens points to {2, 4, 6, 8, 10}
- * // odds points to {1, 3, 5, 7, 9}
- * \endcode
- *
- * \note The relative order of elements in the two reordered sequences is not
- * necessarily the same as it was in the original sequence. A different algorithm,
- * \p stable_partition_copy, does guarantee to preserve the relative order.
- *
- * \see http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2569.pdf
- * \see \p stable_partition_copy
- * \see \p partition
- */
-template
-__host__ __device__
- thrust::pair
- partition_copy(const thrust::detail::execution_policy_base &exec,
- InputIterator first,
- InputIterator last,
- OutputIterator1 out_true,
- OutputIterator2 out_false,
- Predicate pred);
-
-
-/*! \p partition_copy differs from \p partition only in that the reordered
- * sequence is written to difference output sequences, rather than in place.
- *
- * \p partition_copy copies the elements [first, last) based on the
- * function object \p pred. All of the elements that satisfy \p pred are copied
- * to the range beginning at \p out_true and all the elements that fail to satisfy it
- * are copied to the range beginning at \p out_false.
- *
- * \param first The beginning of the sequence to reorder.
- * \param last The end of the sequence to reorder.
- * \param out_true The destination of the resulting sequence of elements which satisfy \p pred.
- * \param out_false The destination of the resulting sequence of elements which fail to satisfy \p pred.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return A \p pair p such that p.first is the end of the output range beginning
- * at \p out_true and p.second is the end of the output range beginning at
- * \p out_false.
- *
- * \tparam InputIterator is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p Predicate's \c argument_type and \p InputIterator's \c value_type
- * is convertible to \p OutputIterator1 and \p OutputIterator2's \c value_types.
- * \tparam OutputIterator1 is a model of Output Iterator .
- * \tparam OutputIterator2 is a model of Output Iterator .
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The input range shall not overlap with either output range.
- *
- * The following code snippet demonstrates how to use \p partition_copy to separate a
- * sequence into two output sequences of even and odd numbers.
- *
- * \code
- * #include
- * ...
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- * ...
- * int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * int result[10];
- * const int N = sizeof(A)/sizeof(int);
- * int *evens = result;
- * int *odds = result + 5;
- * thrust::partition_copy(A, A + N, evens, odds, is_even());
- * // A remains {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
- * // result is now {2, 4, 6, 8, 10, 1, 3, 5, 7, 9}
- * // evens points to {2, 4, 6, 8, 10}
- * // odds points to {1, 3, 5, 7, 9}
- * \endcode
- *
- * \note The relative order of elements in the two reordered sequences is not
- * necessarily the same as it was in the original sequence. A different algorithm,
- * \p stable_partition_copy, does guarantee to preserve the relative order.
- *
- * \see http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2569.pdf
- * \see \p stable_partition_copy
- * \see \p partition
- */
-template
- thrust::pair
- partition_copy(InputIterator first,
- InputIterator last,
- OutputIterator1 out_true,
- OutputIterator2 out_false,
- Predicate pred);
-
-
-/*! \p partition_copy differs from \p partition only in that the reordered
- * sequence is written to difference output sequences, rather than in place.
- *
- * \p partition_copy copies the elements [first, last) based on the
- * function object \p pred which is applied to a range of stencil elements. All of the elements
- * whose corresponding stencil element satisfies \p pred are copied to the range beginning at \p out_true
- * and all the elements whose stencil element fails to satisfy it are copied to the range beginning
- * at \p out_false.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- * \param exec The execution policy to use for parallelization.
- * \param first The beginning of the sequence to reorder.
- * \param last The end of the sequence to reorder.
- * \param stencil The beginning of the stencil sequence.
- * \param out_true The destination of the resulting sequence of elements which satisfy \p pred.
- * \param out_false The destination of the resulting sequence of elements which fail to satisfy \p pred.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return A \p pair p such that p.first is the end of the output range beginning
- * at \p out_true and p.second is the end of the output range beginning at
- * \p out_false.
- *
- * \tparam DerivedPolicy The name of the derived execution policy.
- * \tparam InputIterator1 is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p OutputIterator1 and \p OutputIterator2's \c value_types.
- * \tparam InputIterator2 is a model of Input Iterator ,
- * and \p InputIterator2's \c value_type is convertible to \p Predicate's \c argument_type.
- * \tparam OutputIterator1 is a model of Output Iterator .
- * \tparam OutputIterator2 is a model of Output Iterator .
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The input ranges shall not overlap with either output range.
- *
- * The following code snippet demonstrates how to use \p partition_copy to separate a
- * sequence into two output sequences of even and odd numbers using the \p thrust::host execution
- * policy for parallelization.
- *
- * \code
- * #include
- * #include
- * #include
- * ...
- * int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * int S[] = {0, 1, 0, 1, 0, 1, 0, 1, 0, 1};
- * int result[10];
- * const int N = sizeof(A)/sizeof(int);
- * int *evens = result;
- * int *odds = result + 5;
- * thrust::stable_partition_copy(thrust::host, A, A + N, S, evens, odds, thrust::identity());
- * // A remains {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
- * // S remains {0, 1, 0, 1, 0, 1, 0, 1, 0, 1}
- * // result is now {2, 4, 6, 8, 10, 1, 3, 5, 7, 9}
- * // evens points to {2, 4, 6, 8, 10}
- * // odds points to {1, 3, 5, 7, 9}
- * \endcode
- *
- * \note The relative order of elements in the two reordered sequences is not
- * necessarily the same as it was in the original sequence. A different algorithm,
- * \p stable_partition_copy, does guarantee to preserve the relative order.
- *
- * \see http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2569.pdf
- * \see \p stable_partition_copy
- * \see \p partition
- */
-template
-__host__ __device__
- thrust::pair
- partition_copy(const thrust::detail::execution_policy_base &exec,
- InputIterator1 first,
- InputIterator1 last,
- InputIterator2 stencil,
- OutputIterator1 out_true,
- OutputIterator2 out_false,
- Predicate pred);
-
-
-/*! \p partition_copy differs from \p partition only in that the reordered
- * sequence is written to difference output sequences, rather than in place.
- *
- * \p partition_copy copies the elements [first, last) based on the
- * function object \p pred which is applied to a range of stencil elements. All of the elements
- * whose corresponding stencil element satisfies \p pred are copied to the range beginning at \p out_true
- * and all the elements whose stencil element fails to satisfy it are copied to the range beginning
- * at \p out_false.
- *
- * \param first The beginning of the sequence to reorder.
- * \param last The end of the sequence to reorder.
- * \param stencil The beginning of the stencil sequence.
- * \param out_true The destination of the resulting sequence of elements which satisfy \p pred.
- * \param out_false The destination of the resulting sequence of elements which fail to satisfy \p pred.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return A \p pair p such that p.first is the end of the output range beginning
- * at \p out_true and p.second is the end of the output range beginning at
- * \p out_false.
- *
- * \tparam InputIterator1 is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p OutputIterator1 and \p OutputIterator2's \c value_types.
- * \tparam InputIterator2 is a model of Input Iterator ,
- * and \p InputIterator2's \c value_type is convertible to \p Predicate's \c argument_type.
- * \tparam OutputIterator1 is a model of Output Iterator .
- * \tparam OutputIterator2 is a model of Output Iterator .
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The input ranges shall not overlap with either output range.
- *
- * The following code snippet demonstrates how to use \p partition_copy to separate a
- * sequence into two output sequences of even and odd numbers.
- *
- * \code
- * #include
- * #include
- * ...
- * int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * int S[] = {0, 1, 0, 1, 0, 1, 0, 1, 0, 1};
- * int result[10];
- * const int N = sizeof(A)/sizeof(int);
- * int *evens = result;
- * int *odds = result + 5;
- * thrust::stable_partition_copy(A, A + N, S, evens, odds, thrust::identity());
- * // A remains {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
- * // S remains {0, 1, 0, 1, 0, 1, 0, 1, 0, 1}
- * // result is now {2, 4, 6, 8, 10, 1, 3, 5, 7, 9}
- * // evens points to {2, 4, 6, 8, 10}
- * // odds points to {1, 3, 5, 7, 9}
- * \endcode
- *
- * \note The relative order of elements in the two reordered sequences is not
- * necessarily the same as it was in the original sequence. A different algorithm,
- * \p stable_partition_copy, does guarantee to preserve the relative order.
- *
- * \see http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2569.pdf
- * \see \p stable_partition_copy
- * \see \p partition
- */
-template
- thrust::pair
- partition_copy(InputIterator1 first,
- InputIterator1 last,
- InputIterator2 stencil,
- OutputIterator1 out_true,
- OutputIterator2 out_false,
- Predicate pred);
-
-
-/*! \p stable_partition is much like \p partition : it reorders the elements in the
- * range [first, last) based on the function object \p pred, such that all of
- * the elements that satisfy \p pred precede all of the elements that fail to satisfy
- * it. The postcondition is that, for some iterator \p middle in the range
- * [first, last) , pred(*i) is \c true for every iterator \c i in the
- * range [first,middle) and \c false for every iterator \c i in the range
- * [middle, last) . The return value of \p stable_partition is \c middle.
- *
- * \p stable_partition differs from \p partition in that \p stable_partition is
- * guaranteed to preserve relative order. That is, if \c x and \c y are elements in
- * [first, last) , and \c stencil_x and \c stencil_y are the stencil elements
- * in corresponding positions within [stencil, stencil + (last - first)) ,
- * and pred(stencil_x) == pred(stencil_y) , and if \c x precedes
- * \c y, then it will still be true after \p stable_partition that \c x precedes \c y.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- * \param exec The execution policy to use for parallelization.
- * \param first The first element of the sequence to reorder.
- * \param last One position past the last element of the sequence to reorder.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return An iterator referring to the first element of the second partition, that is,
- * the sequence of the elements which do not satisfy pred.
- *
- * \tparam DerivedPolicy The name of the derived execution policy.
- * \tparam ForwardIterator is a model of Forward Iterator ,
- * and \p ForwardIterator's \c value_type is convertible to \p Predicate's \c argument_type,
- * and \p ForwardIterator is mutable.
- * \tparam Predicate is a model of Predicate .
- *
- * The following code snippet demonstrates how to use \p stable_partition to reorder a
- * sequence so that even numbers precede odd numbers using the \p thrust::host execution policy for parallelization:
- *
- * \code
- * #include
- * #include
- * ...
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- * ...
- * int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * const int N = sizeof(A)/sizeof(int);
- * thrust::stable_partition(thrust::host,
- * A, A + N,
- * is_even());
- * // A is now {2, 4, 6, 8, 10, 1, 3, 5, 7, 9}
- * \endcode
- *
- * \see http://www.sgi.com/tech/stl/stable_partition.html
- * \see \p partition
- * \see \p stable_partition_copy
- */
-template
-__host__ __device__
- ForwardIterator stable_partition(const thrust::detail::execution_policy_base &exec,
- ForwardIterator first,
- ForwardIterator last,
- Predicate pred);
-
-
-/*! \p stable_partition is much like \p partition : it reorders the elements in the
- * range [first, last) based on the function object \p pred, such that all of
- * the elements that satisfy \p pred precede all of the elements that fail to satisfy
- * it. The postcondition is that, for some iterator \p middle in the range
- * [first, last) , pred(*i) is \c true for every iterator \c i in the
- * range [first,middle) and \c false for every iterator \c i in the range
- * [middle, last) . The return value of \p stable_partition is \c middle.
- *
- * \p stable_partition differs from \p partition in that \p stable_partition is
- * guaranteed to preserve relative order. That is, if \c x and \c y are elements in
- * [first, last) , and \c stencil_x and \c stencil_y are the stencil elements
- * in corresponding positions within [stencil, stencil + (last - first)) ,
- * and pred(stencil_x) == pred(stencil_y) , and if \c x precedes
- * \c y, then it will still be true after \p stable_partition that \c x precedes \c y.
- *
- * \param first The first element of the sequence to reorder.
- * \param last One position past the last element of the sequence to reorder.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return An iterator referring to the first element of the second partition, that is,
- * the sequence of the elements which do not satisfy pred.
- *
- * \tparam ForwardIterator is a model of Forward Iterator ,
- * and \p ForwardIterator's \c value_type is convertible to \p Predicate's \c argument_type,
- * and \p ForwardIterator is mutable.
- * \tparam Predicate is a model of Predicate .
- *
- * The following code snippet demonstrates how to use \p stable_partition to reorder a
- * sequence so that even numbers precede odd numbers.
- *
- * \code
- * #include
- * ...
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- * ...
- * int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * const int N = sizeof(A)/sizeof(int);
- * thrust::stable_partition(A, A + N,
- * is_even());
- * // A is now {2, 4, 6, 8, 10, 1, 3, 5, 7, 9}
- * \endcode
- *
- * \see http://www.sgi.com/tech/stl/stable_partition.html
- * \see \p partition
- * \see \p stable_partition_copy
- */
-template
- ForwardIterator stable_partition(ForwardIterator first,
- ForwardIterator last,
- Predicate pred);
-
-
-/*! \p stable_partition is much like \p partition: it reorders the elements in the
- * range [first, last) based on the function object \p pred applied to a stencil
- * range [stencil, stencil + (last - first)) , such that all of
- * the elements whose corresponding stencil element satisfies \p pred precede all of the elements whose
- * corresponding stencil element fails to satisfy it. The postcondition is that, for some iterator
- * \c middle in the range [first, last) , pred(*stencil_i) is \c true for every iterator
- * \c stencil_i in the range [stencil,stencil + (middle - first)) and \c false for every iterator \c stencil_i
- * in the range [stencil + (middle - first), stencil + (last - first)) .
- * The return value of \p stable_partition is \c middle.
- *
- * \p stable_partition differs from \p partition in that \p stable_partition is
- * guaranteed to preserve relative order. That is, if \c x and \c y are elements in
- * [first, last) , such that pred(x) == pred(y) , and if \c x precedes
- * \c y, then it will still be true after \p stable_partition that \c x precedes \c y.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- * \param exec The execution policy to use for parallelization.
- * \param first The first element of the sequence to reorder.
- * \param last One position past the last element of the sequence to reorder.
- * \param stencil The beginning of the stencil sequence.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return An iterator referring to the first element of the second partition, that is,
- * the sequence of the elements whose stencil elements do not satisfy \p pred.
- *
- * \tparam DerivedPolicy The name of the derived execution policy.
- * \tparam ForwardIterator is a model of Forward Iterator ,
- * and \p ForwardIterator is mutable.
- * \tparam InputIterator is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p Predicate's \c argument_type.
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The range [first, last) shall not overlap with the range [stencil, stencil + (last - first)) .
- *
- * The following code snippet demonstrates how to use \p stable_partition to reorder a
- * sequence so that even numbers precede odd numbers using the \p thrust::host execution policy for parallelization:
- *
- * \code
- * #include
- * #include
- * ...
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- * ...
- * int A[] = {0, 1, 0, 1, 0, 1, 0, 1, 0, 1};
- * int S[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * const int N = sizeof(A)/sizeof(int);
- * thrust::stable_partition(thrust::host, A, A + N, S, is_even());
- * // A is now {1, 1, 1, 1, 1, 0, 0, 0, 0, 0}
- * // S is unmodified
- * \endcode
- *
- * \see http://www.sgi.com/tech/stl/stable_partition.html
- * \see \p partition
- * \see \p stable_partition_copy
- */
-template
-__host__ __device__
- ForwardIterator stable_partition(const thrust::detail::execution_policy_base &exec,
- ForwardIterator first,
- ForwardIterator last,
- InputIterator stencil,
- Predicate pred);
-
-
-/*! \p stable_partition is much like \p partition: it reorders the elements in the
- * range [first, last) based on the function object \p pred applied to a stencil
- * range [stencil, stencil + (last - first)) , such that all of
- * the elements whose corresponding stencil element satisfies \p pred precede all of the elements whose
- * corresponding stencil element fails to satisfy it. The postcondition is that, for some iterator
- * \c middle in the range [first, last) , pred(*stencil_i) is \c true for every iterator
- * \c stencil_i in the range [stencil,stencil + (middle - first)) and \c false for every iterator \c stencil_i
- * in the range [stencil + (middle - first), stencil + (last - first)) .
- * The return value of \p stable_partition is \c middle.
- *
- * \p stable_partition differs from \p partition in that \p stable_partition is
- * guaranteed to preserve relative order. That is, if \c x and \c y are elements in
- * [first, last) , such that pred(x) == pred(y) , and if \c x precedes
- * \c y, then it will still be true after \p stable_partition that \c x precedes \c y.
- *
- * \param first The first element of the sequence to reorder.
- * \param last One position past the last element of the sequence to reorder.
- * \param stencil The beginning of the stencil sequence.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return An iterator referring to the first element of the second partition, that is,
- * the sequence of the elements whose stencil elements do not satisfy \p pred.
- *
- * \tparam ForwardIterator is a model of Forward Iterator ,
- * and \p ForwardIterator is mutable.
- * \tparam InputIterator is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p Predicate's \c argument_type.
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The range [first, last) shall not overlap with the range [stencil, stencil + (last - first)) .
- *
- * The following code snippet demonstrates how to use \p stable_partition to reorder a
- * sequence so that even numbers precede odd numbers.
- *
- * \code
- * #include
- * ...
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- * ...
- * int A[] = {0, 1, 0, 1, 0, 1, 0, 1, 0, 1};
- * int S[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * const int N = sizeof(A)/sizeof(int);
- * thrust::stable_partition(A, A + N, S, is_even());
- * // A is now {1, 1, 1, 1, 1, 0, 0, 0, 0, 0}
- * // S is unmodified
- * \endcode
- *
- * \see http://www.sgi.com/tech/stl/stable_partition.html
- * \see \p partition
- * \see \p stable_partition_copy
- */
-template
- ForwardIterator stable_partition(ForwardIterator first,
- ForwardIterator last,
- InputIterator stencil,
- Predicate pred);
-
-
-/*! \p stable_partition_copy differs from \p stable_partition only in that the reordered
- * sequence is written to different output sequences, rather than in place.
- *
- * \p stable_partition_copy copies the elements [first, last) based on the
- * function object \p pred. All of the elements that satisfy \p pred are copied
- * to the range beginning at \p out_true and all the elements that fail to satisfy it
- * are copied to the range beginning at \p out_false.
- *
- * \p stable_partition_copy differs from \p partition_copy in that
- * \p stable_partition_copy is guaranteed to preserve relative order. That is, if
- * \c x and \c y are elements in [first, last) , such that
- * pred(x) == pred(y) , and if \c x precedes \c y, then it will still be true
- * after \p stable_partition_copy that \c x precedes \c y in the output.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- * \param exec The execution policy to use for parallelization.
- * \param first The first element of the sequence to reorder.
- * \param last One position past the last element of the sequence to reorder.
- * \param out_true The destination of the resulting sequence of elements which satisfy \p pred.
- * \param out_false The destination of the resulting sequence of elements which fail to satisfy \p pred.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return A \p pair p such that p.first is the end of the output range beginning
- * at \p out_true and p.second is the end of the output range beginning at
- * \p out_false.
- *
- * \tparam DerivedPolicy The name of the derived execution policy.
- * \tparam InputIterator is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p Predicate's \c argument_type and \p InputIterator's \c value_type
- * is convertible to \p OutputIterator1 and \p OutputIterator2's \c value_types.
- * \tparam OutputIterator1 is a model of Output Iterator .
- * \tparam OutputIterator2 is a model of Output Iterator .
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The input ranges shall not overlap with either output range.
- *
- * The following code snippet demonstrates how to use \p stable_partition_copy to
- * reorder a sequence so that even numbers precede odd numbers using the \p thrust::host execution policy for parallelization:
- *
- * \code
- * #include
- * #include
- * ...
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- * ...
- * int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * int result[10];
- * const int N = sizeof(A)/sizeof(int);
- * int *evens = result;
- * int *odds = result + 5;
- * thrust::stable_partition_copy(thrust::host, A, A + N, evens, odds, is_even());
- * // A remains {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
- * // result is now {2, 4, 6, 8, 10, 1, 3, 5, 7, 9}
- * // evens points to {2, 4, 6, 8, 10}
- * // odds points to {1, 3, 5, 7, 9}
- * \endcode
- *
- * \see http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2569.pdf
- * \see \p partition_copy
- * \see \p stable_partition
- */
-template
-__host__ __device__
- thrust::pair
- stable_partition_copy(const thrust::detail::execution_policy_base &exec,
- InputIterator first,
- InputIterator last,
- OutputIterator1 out_true,
- OutputIterator2 out_false,
- Predicate pred);
-
-
-/*! \p stable_partition_copy differs from \p stable_partition only in that the reordered
- * sequence is written to different output sequences, rather than in place.
- *
- * \p stable_partition_copy copies the elements [first, last) based on the
- * function object \p pred. All of the elements that satisfy \p pred are copied
- * to the range beginning at \p out_true and all the elements that fail to satisfy it
- * are copied to the range beginning at \p out_false.
- *
- * \p stable_partition_copy differs from \p partition_copy in that
- * \p stable_partition_copy is guaranteed to preserve relative order. That is, if
- * \c x and \c y are elements in [first, last) , such that
- * pred(x) == pred(y) , and if \c x precedes \c y, then it will still be true
- * after \p stable_partition_copy that \c x precedes \c y in the output.
- *
- * \param first The first element of the sequence to reorder.
- * \param last One position past the last element of the sequence to reorder.
- * \param out_true The destination of the resulting sequence of elements which satisfy \p pred.
- * \param out_false The destination of the resulting sequence of elements which fail to satisfy \p pred.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return A \p pair p such that p.first is the end of the output range beginning
- * at \p out_true and p.second is the end of the output range beginning at
- * \p out_false.
- *
- * \tparam InputIterator is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p Predicate's \c argument_type and \p InputIterator's \c value_type
- * is convertible to \p OutputIterator1 and \p OutputIterator2's \c value_types.
- * \tparam OutputIterator1 is a model of Output Iterator .
- * \tparam OutputIterator2 is a model of Output Iterator .
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The input ranges shall not overlap with either output range.
- *
- * The following code snippet demonstrates how to use \p stable_partition_copy to
- * reorder a sequence so that even numbers precede odd numbers.
- *
- * \code
- * #include
- * ...
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- * ...
- * int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * int result[10];
- * const int N = sizeof(A)/sizeof(int);
- * int *evens = result;
- * int *odds = result + 5;
- * thrust::stable_partition_copy(A, A + N, evens, odds, is_even());
- * // A remains {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
- * // result is now {2, 4, 6, 8, 10, 1, 3, 5, 7, 9}
- * // evens points to {2, 4, 6, 8, 10}
- * // odds points to {1, 3, 5, 7, 9}
- * \endcode
- *
- * \see http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2569.pdf
- * \see \p partition_copy
- * \see \p stable_partition
- */
-template
- thrust::pair
- stable_partition_copy(InputIterator first,
- InputIterator last,
- OutputIterator1 out_true,
- OutputIterator2 out_false,
- Predicate pred);
-
-
-/*! \p stable_partition_copy differs from \p stable_partition only in that the reordered
- * sequence is written to different output sequences, rather than in place.
- *
- * \p stable_partition_copy copies the elements [first, last) based on the
- * function object \p pred which is applied to a range of stencil elements. All of the elements
- * whose corresponding stencil element satisfies \p pred are copied to the range beginning at \p out_true
- * and all the elements whose stencil element fails to satisfy it are copied to the range beginning
- * at \p out_false.
- *
- * \p stable_partition_copy differs from \p partition_copy in that
- * \p stable_partition_copy is guaranteed to preserve relative order. That is, if
- * \c x and \c y are elements in [first, last) , such that
- * pred(x) == pred(y) , and if \c x precedes \c y, then it will still be true
- * after \p stable_partition_copy that \c x precedes \c y in the output.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- * \param exec The execution policy to use for parallelization.
- * \param first The first element of the sequence to reorder.
- * \param last One position past the last element of the sequence to reorder.
- * \param stencil The beginning of the stencil sequence.
- * \param out_true The destination of the resulting sequence of elements which satisfy \p pred.
- * \param out_false The destination of the resulting sequence of elements which fail to satisfy \p pred.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return A \p pair p such that p.first is the end of the output range beginning
- * at \p out_true and p.second is the end of the output range beginning at
- * \p out_false.
- *
- * \tparam DerivedPolicy The name of the derived execution policy.
- * \tparam InputIterator1 is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p OutputIterator1 and \p OutputIterator2's \c value_types.
- * \tparam InputIterator2 is a model of Input Iterator ,
- * and \p InputIterator2's \c value_type is convertible to \p Predicate's \c argument_type.
- * \tparam OutputIterator1 is a model of Output Iterator .
- * \tparam OutputIterator2 is a model of Output Iterator .
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The input ranges shall not overlap with either output range.
- *
- * The following code snippet demonstrates how to use \p stable_partition_copy to
- * reorder a sequence so that even numbers precede odd numbers using the \p thrust::host execution policy for parallelization:
- *
- * \code
- * #include
- * #include
- * #include
- * ...
- * int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * int S[] = {0, 1, 0, 1, 0, 1, 0, 1, 0, 1};
- * int result[10];
- * const int N = sizeof(A)/sizeof(int);
- * int *evens = result;
- * int *odds = result + 5;
- * thrust::stable_partition_copy(thrust::host, A, A + N, S, evens, odds, thrust::identity());
- * // A remains {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
- * // S remains {0, 1, 0, 1, 0, 1, 0, 1, 0, 1}
- * // result is now {2, 4, 6, 8, 10, 1, 3, 5, 7, 9}
- * // evens points to {2, 4, 6, 8, 10}
- * // odds points to {1, 3, 5, 7, 9}
- * \endcode
- *
- * \see http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2569.pdf
- * \see \p partition_copy
- * \see \p stable_partition
- */
-template
-__host__ __device__
- thrust::pair
- stable_partition_copy(const thrust::detail::execution_policy_base &exec,
- InputIterator1 first,
- InputIterator1 last,
- InputIterator2 stencil,
- OutputIterator1 out_true,
- OutputIterator2 out_false,
- Predicate pred);
-
-
-/*! \p stable_partition_copy differs from \p stable_partition only in that the reordered
- * sequence is written to different output sequences, rather than in place.
- *
- * \p stable_partition_copy copies the elements [first, last) based on the
- * function object \p pred which is applied to a range of stencil elements. All of the elements
- * whose corresponding stencil element satisfies \p pred are copied to the range beginning at \p out_true
- * and all the elements whose stencil element fails to satisfy it are copied to the range beginning
- * at \p out_false.
- *
- * \p stable_partition_copy differs from \p partition_copy in that
- * \p stable_partition_copy is guaranteed to preserve relative order. That is, if
- * \c x and \c y are elements in [first, last) , such that
- * pred(x) == pred(y) , and if \c x precedes \c y, then it will still be true
- * after \p stable_partition_copy that \c x precedes \c y in the output.
- *
- * \param first The first element of the sequence to reorder.
- * \param last One position past the last element of the sequence to reorder.
- * \param stencil The beginning of the stencil sequence.
- * \param out_true The destination of the resulting sequence of elements which satisfy \p pred.
- * \param out_false The destination of the resulting sequence of elements which fail to satisfy \p pred.
- * \param pred A function object which decides to which partition each element of the
- * sequence [first, last) belongs.
- * \return A \p pair p such that p.first is the end of the output range beginning
- * at \p out_true and p.second is the end of the output range beginning at
- * \p out_false.
- *
- * \tparam InputIterator1 is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p OutputIterator1 and \p OutputIterator2's \c value_types.
- * \tparam InputIterator2 is a model of Input Iterator ,
- * and \p InputIterator2's \c value_type is convertible to \p Predicate's \c argument_type.
- * \tparam OutputIterator1 is a model of Output Iterator .
- * \tparam OutputIterator2 is a model of Output Iterator .
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The input ranges shall not overlap with either output range.
- *
- * The following code snippet demonstrates how to use \p stable_partition_copy to
- * reorder a sequence so that even numbers precede odd numbers.
- *
- * \code
- * #include
- * #include
- * ...
- * int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- * int S[] = {0, 1, 0, 1, 0, 1, 0, 1, 0, 1};
- * int result[10];
- * const int N = sizeof(A)/sizeof(int);
- * int *evens = result;
- * int *odds = result + 5;
- * thrust::stable_partition_copy(A, A + N, S, evens, odds, thrust::identity());
- * // A remains {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
- * // S remains {0, 1, 0, 1, 0, 1, 0, 1, 0, 1}
- * // result is now {2, 4, 6, 8, 10, 1, 3, 5, 7, 9}
- * // evens points to {2, 4, 6, 8, 10}
- * // odds points to {1, 3, 5, 7, 9}
- * \endcode
- *
- * \see http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2569.pdf
- * \see \p partition_copy
- * \see \p stable_partition
- */
-template
- thrust::pair
- stable_partition_copy(InputIterator1 first,
- InputIterator1 last,
- InputIterator2 stencil,
- OutputIterator1 out_true,
- OutputIterator2 out_false,
- Predicate pred);
-
-
-/*! \} // end stream_compaction
- */
-
-/*! \} // end reordering
- */
-
-/*! \addtogroup searching
- * \{
- */
-
-
-/*! \p partition_point returns an iterator pointing to the end of the true
- * partition of a partitioned range. \p partition_point requires the input range
- * [first,last) to be a partition; that is, all elements which satisfy
- * pred shall appear before those that do not.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- * \param exec The execution policy to use for parallelization.
- * \param first The beginning of the range to consider.
- * \param last The end of the range to consider.
- * \param pred A function object which decides to which partition each element of the
- * range [first, last) belongs.
- * \return An iterator \c mid such that all_of(first, mid, pred)
- * and none_of(mid, last, pred) are both true.
- *
- * \tparam DerivedPolicy The name of the derived execution policy.
- * \tparam ForwardIterator is a model of Forward Iterator ,
- * and \p ForwardIterator's \c value_type is convertible to \p Predicate's \c argument_type.
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The range [first, last) shall be partitioned by \p pred.
- *
- * \note Though similar, \p partition_point is not redundant with \p find_if_not.
- * \p partition_point's precondition provides an opportunity for a
- * faster implemention.
- *
- * \code
- * #include
- * #include
- *
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- *
- * ...
- *
- * int A[] = {2, 4, 6, 8, 10, 1, 3, 5, 7, 9};
- * int * B = thrust::partition_point(thrust::host, A, A + 10, is_even());
- * // B - A is 5
- * // [A, B) contains only even values
- * \endcode
- *
- * \see \p partition
- * \see \p find_if_not
- */
-template
-__host__ __device__
- ForwardIterator partition_point(const thrust::detail::execution_policy_base &exec,
- ForwardIterator first,
- ForwardIterator last,
- Predicate pred);
-
-
-/*! \p partition_point returns an iterator pointing to the end of the true
- * partition of a partitioned range. \p partition_point requires the input range
- * [first,last) to be a partition; that is, all elements which satisfy
- * pred shall appear before those that do not.
- * \param first The beginning of the range to consider.
- * \param last The end of the range to consider.
- * \param pred A function object which decides to which partition each element of the
- * range [first, last) belongs.
- * \return An iterator \c mid such that all_of(first, mid, pred)
- * and none_of(mid, last, pred) are both true.
- *
- * \tparam ForwardIterator is a model of Forward Iterator ,
- * and \p ForwardIterator's \c value_type is convertible to \p Predicate's \c argument_type.
- * \tparam Predicate is a model of Predicate .
- *
- * \pre The range [first, last) shall be partitioned by \p pred.
- *
- * \note Though similar, \p partition_point is not redundant with \p find_if_not.
- * \p partition_point's precondition provides an opportunity for a
- * faster implemention.
- *
- * \code
- * #include
- *
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- *
- * ...
- *
- * int A[] = {2, 4, 6, 8, 10, 1, 3, 5, 7, 9};
- * int * B = thrust::partition_point(A, A + 10, is_even());
- * // B - A is 5
- * // [A, B) contains only even values
- * \endcode
- *
- * \see \p partition
- * \see \p find_if_not
- */
-template
- ForwardIterator partition_point(ForwardIterator first,
- ForwardIterator last,
- Predicate pred);
-
-/*! \} // searching
- */
-
-/*! \addtogroup reductions
- * \{
- * \addtogroup predicates
- * \{
- */
-
-
-/*! \p is_partitioned returns \c true if the given range
- * is partitioned with respect to a predicate, and \c false otherwise.
- *
- * Specifically, \p is_partitioned returns \c true if [first, last)
- * is empty of if [first, last) is partitioned by \p pred, i.e. if
- * all elements that satisfy \p pred appear before those that do not.
- *
- * The algorithm's execution is parallelized as determined by \p exec.
- *
- * \param exec The execution policy to use for parallelization.
- * \param first The beginning of the range to consider.
- * \param last The end of the range to consider.
- * \param pred A function object which decides to which partition each element of the
- * range [first, last) belongs.
- * \return \c true if the range [first, last) is partitioned with respect
- * to \p pred, or if [first, last) is empty. \c false, otherwise.
- *
- * \tparam DerivedPolicy The name of the derived execution policy.
- * \tparam InputIterator is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p Predicate's \c argument_type.
- * \tparam Predicate is a model of Predicate .
- *
- * \code
- * #include
- * #include
- *
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- *
- * ...
- *
- * int A[] = {2, 4, 6, 8, 10, 1, 3, 5, 7, 9};
- * int B[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- *
- * thrust::is_partitioned(thrust::host, A, A + 10, is_even()); // returns true
- * thrust::is_partitioned(thrust::host, B, B + 10, is_even()); // returns false
- * \endcode
- *
- * \see \p partition
- */
-template
-__host__ __device__
- bool is_partitioned(const thrust::detail::execution_policy_base &exec,
- InputIterator first,
- InputIterator last,
- Predicate pred);
-
-
-/*! \p is_partitioned returns \c true if the given range
- * is partitioned with respect to a predicate, and \c false otherwise.
- *
- * Specifically, \p is_partitioned returns \c true if [first, last)
- * is empty of if [first, last) is partitioned by \p pred, i.e. if
- * all elements that satisfy \p pred appear before those that do not.
- *
- * \param first The beginning of the range to consider.
- * \param last The end of the range to consider.
- * \param pred A function object which decides to which partition each element of the
- * range [first, last) belongs.
- * \return \c true if the range [first, last) is partitioned with respect
- * to \p pred, or if [first, last) is empty. \c false, otherwise.
- *
- * \tparam InputIterator is a model of Input Iterator ,
- * and \p InputIterator's \c value_type is convertible to \p Predicate's \c argument_type.
- * \tparam Predicate is a model of Predicate .
- *
- * \code
- * #include
- *
- * struct is_even
- * {
- * __host__ __device__
- * bool operator()(const int &x)
- * {
- * return (x % 2) == 0;
- * }
- * };
- *
- * ...
- *
- * int A[] = {2, 4, 6, 8, 10, 1, 3, 5, 7, 9};
- * int B[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
- *
- * thrust::is_partitioned(A, A + 10, is_even()); // returns true
- * thrust::is_partitioned(B, B + 10, is_even()); // returns false
- * \endcode
- *
- * \see \p partition
- */
-template
- bool is_partitioned(InputIterator first,
- InputIterator last,
- Predicate pred);
-
-
-/*! \} // end predicates
- * \} // end reductions
- */
-
-
-} // end thrust
-
-#include
-
diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/old/temp/create_art_data.py b/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/old/temp/create_art_data.py
deleted file mode 100644
index 03cf7293db6c9b0b6917d9a8aafd1ce83b50e5b9..0000000000000000000000000000000000000000
--- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/old/temp/create_art_data.py
+++ /dev/null
@@ -1,132 +0,0 @@
-from create_art_data_functions import *
-from scipy.misc import imsave
-import sys
-
-
-'''THIS SCRIPT CREATES PRE-AUGMENTED DATA TO SAVE TRAINING TIME (ARTISTIC OR BASIC AUGMENTATION):
- under the folder *outdir*, it will create a separate folder for each epoch. the folder will
- contain the augmented images and matching landmark (pts) files.'''
-
-# parameter for calculating number of epochs
-num_train_images = 3148 # number of training images
-train_iter = 100000 # number of training iterations
-batch_size = 6 # batch size in training
-num_epochs = int(np.ceil((1. * train_iter) / (1. * num_train_images / batch_size)))+1
-
-# augmentation parameters
-num_augs = 9 # number of style transfer augmented images
-aug_geom = True # use artistic geometric augmentation?
-aug_texture = True # use artistic texture augmentation?
-
-# image parameters
-bb_type = 'gt' # face bounding-box type (gt/init)
-margin = 0.25 # margin for face crops - % of bb size
-image_size = 256 # image size
-
-# data-sets image paths
-dataset = 'training' # dataset to augment (training/full/common/challenging/test)
-img_dir = '/Users/arik/Dropbox/a_mac_thesis/face_heatmap_networks/conventional_landmark_detection_dataset/'
-train_crop_dir = 'crop_gt_margin_0.25' # directory of train images cropped to bb (+margin)
-img_dir_ns = os.path.join(img_dir, train_crop_dir+'_ns') # dir of train imgs cropped to bb + style transfer
-outdir = '/Users/arik/Desktop/epoch_data' # directory for saving augmented data
-
-# other parameters
-min_epoch_to_save = 0 # start saving images from this epoch (first epoch is 0)
-debug_data_size = 15
-debug = False
-random_seed = 1234 # random seed for numpy
-
-########################################################################################
-if aug_texture and img_dir_ns is None:
- print('\n *** ERROR: aug_texture is True, and img_dir_ns is None.\n'
- 'please specify path for img_dir_ns to augment image texture!')
- sys.exit()
-
-if not os.path.exists(outdir):
- os.mkdir(outdir)
-
-gt = (bb_type == 'gt')
-bb_dir = os.path.join(img_dir, 'Bounding_Boxes')
-
-if dataset == 'training':
- mode = 'TRAIN'
-else:
- mode = 'TEST'
-bb_dictionary = load_bb_dictionary(bb_dir, mode=mode, test_data=dataset)
-
-aug_geom_dir = os.path.join(outdir, 'aug_geom')
-aug_texture_dir = os.path.join(outdir, 'aug_texture')
-aug_geom_texture_dir = os.path.join(outdir, 'aug_geom_texture')
-aug_basic_dir = os.path.join(outdir, 'aug_basic')
-
-if not aug_geom and aug_texture:
- save_aug_path = aug_texture_dir
-elif aug_geom and not aug_texture:
- save_aug_path = aug_geom_dir
-elif aug_geom and aug_texture:
- save_aug_path = aug_geom_texture_dir
-else:
- save_aug_path = aug_basic_dir
-
-print ('saving augmented images: aug_geom=' + str(aug_geom) + ' aug_texture=' + str(aug_texture) +
- ' : ' + str(save_aug_path))
-
-if not os.path.exists(save_aug_path):
- os.mkdir(save_aug_path)
-
-np.random.seed(random_seed)
-ns_inds = np.arange(num_augs)
-
-for i in range(num_epochs):
- print ('saving augmented images of epoch %d/%d' % (i, num_epochs-1))
- if not os.path.exists(os.path.join(save_aug_path, str(i))) and i > min_epoch_to_save - 1:
- os.mkdir(os.path.join(save_aug_path, str(i)))
-
- if i % num_augs == 0:
- np.random.shuffle(ns_inds)
-
- if not aug_geom and aug_texture:
- img_list = load_menpo_image_list_no_geom(
- img_dir=img_dir, train_crop_dir=train_crop_dir, img_dir_ns=img_dir_ns, mode='TRAIN',
- bb_dictionary=bb_dictionary,
- image_size=image_size, margin=margin, bb_type=bb_type, augment_basic=True,
- augment_texture=True, p_texture=1.,
- augment_geom=True, p_geom=1., ns_ind=ns_inds[i % num_augs], dataset=dataset)
- elif aug_geom and not aug_texture:
- img_list = load_menpo_image_list_no_texture(
- img_dir=img_dir, train_crop_dir=train_crop_dir, img_dir_ns=img_dir_ns, mode='TRAIN',
- bb_dictionary=bb_dictionary,
- image_size=image_size, margin=margin, bb_type=bb_type, augment_basic=True,
- augment_texture=True, p_texture=1.,
- augment_geom=True, p_geom=1., ns_ind=ns_inds[i % num_augs], dataset=dataset)
- elif aug_geom and aug_texture:
- img_list = load_menpo_image_list(
- img_dir=img_dir, train_crop_dir=train_crop_dir, img_dir_ns=img_dir_ns, mode='TRAIN',
- bb_dictionary=bb_dictionary,
- image_size=image_size, margin=margin, bb_type=bb_type, augment_basic=True,
- augment_texture=True, p_texture=1.,
- augment_geom=True, p_geom=1., ns_ind=ns_inds[i % num_augs], dataset=dataset)
- else:
- img_list = load_menpo_image_list_no_artistic(
- img_dir=img_dir, train_crop_dir=train_crop_dir, img_dir_ns=img_dir_ns, mode='TRAIN',
- bb_dictionary=bb_dictionary,
- image_size=image_size, margin=margin, bb_type=bb_type, augment_basic=True,
- augment_texture=True, p_texture=1.,
- augment_geom=True, p_geom=1., ns_ind=ns_inds[i % num_augs], dataset=dataset)
-
- if debug:
- img_list = img_list[:debug_data_size]
-
- for im in img_list:
- im_path = os.path.join(save_aug_path, str(i), im.path.name.split('.')[0] + '.png')
- pts_path = os.path.join(save_aug_path, str(i), im.path.name.split('.')[0] + '.pts')
- if i > min_epoch_to_save - 1:
- if not os.path.exists(im_path):
- if im.pixels.shape[0] == 1:
- im_pixels = gray2rgb(np.squeeze(im.pixels))
- else:
- im_pixels = np.rollaxis(im.pixels, 0, 3)
- imsave(im_path, im_pixels)
- if not os.path.exists(pts_path):
- mio.export_landmark_file(im.landmarks['PTS'], pts_path, overwrite=True)
-print ('DONE!')
\ No newline at end of file
diff --git a/spaces/matthoffner/monacopilot/app/editor/setup.ts b/spaces/matthoffner/monacopilot/app/editor/setup.ts
deleted file mode 100644
index d5318e66bc4511fb6044c14c3eb7d0ec4ba654ef..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/monacopilot/app/editor/setup.ts
+++ /dev/null
@@ -1,131 +0,0 @@
-import 'monaco-editor/esm/vs/editor/editor.all'
-import * as monaco from 'monaco-editor/esm/vs/editor/editor.api'
-import { initialize as initializeMonacoService } from 'vscode/services'
-import {
- registerExtension,
- initialize as initializeVscodeExtensions,
-} from 'vscode/extensions'
-import getDialogsServiceOverride from 'vscode/service-override/dialogs'
-import getConfigurationServiceOverride from 'vscode/service-override/configuration'
-import getTextmateServiceOverride from 'vscode/service-override/textmate'
-import getThemeServiceOverride from 'vscode/service-override/theme'
-import getLanguagesServiceOverride from 'vscode/service-override/languages'
-
-window.MonacoEnvironment = {
- getWorker: async function (moduleId, label) {
- switch (label) {
- case 'editorWorkerService':
- return new Worker(
- new URL('monaco-editor/esm/vs/editor/editor.worker', import.meta.url)
- )
- case 'css':
- case 'less':
- case 'scss':
- return new Worker(
- new URL(
- 'monaco-editor/esm/vs/language/css/css.worker',
- import.meta.url
- )
- )
- case 'handlebars':
- case 'html':
- case 'razor':
- return new Worker(
- new URL(
- 'monaco-editor/esm/vs/language/html/html.worker',
- import.meta.url
- )
- )
- case 'json':
- return new Worker(
- new URL(
- 'monaco-editor/esm/vs/language/json/json.worker',
- import.meta.url
- )
- )
- case 'javascript':
- case 'typescript':
- return new Worker(
- new URL(
- 'monaco-editor/esm/vs/language/typescript/ts.worker',
- import.meta.url
- )
- )
- default:
- throw new Error(`Unimplemented worker ${label} (${moduleId})`)
- }
- },
-}
-
-initializeMonacoService({
- ...getDialogsServiceOverride(),
- ...getConfigurationServiceOverride(monaco.Uri.file('/')),
- ...getTextmateServiceOverride(),
- ...getThemeServiceOverride(),
- ...getLanguagesServiceOverride(),
-}).then(async () => {
- await initializeVscodeExtensions()
-
- const defaultThemesExtensions = {
- name: 'themes',
- publisher: 'next-monaco',
- version: '0.0.0',
- engines: {
- vscode: '*',
- },
- contributes: {
- themes: [
- {
- id: 'Next Monaco',
- label: 'Next Monaco',
- uiTheme: 'vs-dark',
- path: './next-monaco.json',
- },
- ],
- },
- }
-
- const { registerFile: registerDefaultThemeExtensionFile } = registerExtension(
- defaultThemesExtensions
- )
-
- registerDefaultThemeExtensionFile(
- './next-monaco.json',
- async () => process.env.MONACO_THEME
- )
-
- monaco.editor.setTheme('Next Monaco')
-
- const extension = {
- name: 'grammars',
- publisher: 'next-monaco',
- version: '0.0.0',
- engines: {
- vscode: '*',
- },
- contributes: {
- languages: [
- {
- id: 'typescript',
- extensions: ['.ts', '.tsx'],
- aliases: ['TypeScript', 'ts', 'typescript'],
- },
- ],
- grammars: [
- {
- language: 'typescript',
- scopeName: 'source.ts',
- path: './TypeScript.tmLanguage.json',
- },
- ],
- },
- }
-
- const { registerFile: registerExtensionFile } = registerExtension(extension)
-
- registerExtensionFile('./TypeScript.tmLanguage.json', async () =>
- JSON.stringify(
- (await import('./TypeScript.tmLanguage.json')).default as any
- )
- )
-})
diff --git a/spaces/mbarnig/translation-lb-en-with-3-models/app.py b/spaces/mbarnig/translation-lb-en-with-3-models/app.py
deleted file mode 100644
index cacd63d022b903c665589e8d5bb13136173926b2..0000000000000000000000000000000000000000
--- a/spaces/mbarnig/translation-lb-en-with-3-models/app.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import gradio as gr
-import torch
-from huggingface_hub import hf_hub_download
-from transformers import pipeline
-
-model_checkpoint_nllb = "facebook/nllb-200-distilled-600M"
-model_checkpoint_marian_en = "mbarnig/marianNMT-tatoeba-en-lb"
-model_checkpoint_marian_lb = "mbarnig/marianNMT-tatoeba-lb-en"
-model_checkpoint_t5_mt5 = "mbarnig/T5-mt5-tatoeba-en-lb"
-
-my_title = "🇬🇧 Mir iwwersetzen vun an op Lëtzebuergesch ! 🇫🇷"
-my_description = "English-Luxembourgish machine translation (MT) demo based on 3 open-source transformer models: Facebook-NLLB, Microsoft-MarianNMT & Google-T5/mt5."
-my_article = "User guide 1. Press the submit button to translate an english text with the default values. 2. Compare the result with the luxembourgish example. 3. Select a model and a translation direction and enter your own text. Have fun !
Go to Internet with a Brain to read my french publication Das Küsschen und die Sonne stritten sich ... about the history of machine translation in Luxembourg from 1975 until today.
"
-default_input = "The North Wind and the Sun were disputing which was the stronger, when a traveler came along wrapped in a warm cloak."
-
-TRANSLATION_MODELS = [
- "NLLB",
- "MarianNMT",
- "T5-mt5"
-]
-
-TRANSLATION_DIRECTION = [
- "en -> lb",
- "lb -> en"
-]
-
-EXAMPLE = [
- ["An der Zäit hunn sech den Nordwand an d’Sonn gestridden, wie vun hinnen zwee wuel méi staark wier, wéi e Wanderer, deen an ee waarme Mantel agepak war, iwwert de Wee koum", "NLLB", "lb -> en"]
-]
-
-my_inputs = [
- gr.Textbox(lines=5, label="Input", value=default_input),
- gr.Radio(label="Translation Model", choices = TRANSLATION_MODELS, value = "NLLB"),
- gr.Radio(label="Translation Direction", choices = TRANSLATION_DIRECTION, value = "en -> lb")
-]
-
-my_output = gr.Textbox(lines=5, label="Translation")
-
-def iwwersetz(source_text, model, direc):
- if model == "NLLB":
- translator = pipeline("translation", model=model_checkpoint_nllb)
- if direc == "en -> lb":
- translation = translator(source_text, src_lang="eng_Latn", tgt_lang="ltz_Latn")
- else:
- translation = translator(source_text, src_lang="ltz_Latn", tgt_lang="eng_Latn")
- elif model == "MarianNMT":
- if direc == "en -> lb":
- translator = pipeline("translation", model=model_checkpoint_marian_en)
- translation = translator(source_text)
- else:
- translator = pipeline("translation", model=model_checkpoint_marian_lb)
- translation = translator(source_text)
- elif model == "T5-mt5":
- translator = pipeline("translation", model=model_checkpoint_t5_mt5)
- translation = translator(source_text)
- else:
- translation = "Please select a Translation Model !"
- return translation
-
-demo=gr.Interface(
- fn=iwwersetz,
- inputs=my_inputs,
- outputs=my_output,
- title=my_title,
- description=my_description,
- article=my_article,
- examples=EXAMPLE,
- allow_flagging=False)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/merle/PROTEIN_GENERATOR/utils/model/se3_transformer/runtime/__init__.py b/spaces/merle/PROTEIN_GENERATOR/utils/model/se3_transformer/runtime/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/merve/fill-in-the-blank/server-side/fill-in-the-blank/gender-over-time-colab/README.md b/spaces/merve/fill-in-the-blank/server-side/fill-in-the-blank/gender-over-time-colab/README.md
deleted file mode 100644
index 16012931eb8815db61e8f7454261eea1de6ee36a..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/server-side/fill-in-the-blank/gender-over-time-colab/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
-https://colab.research.google.com/github/PAIR-code/ai-explorables/blob/master/server-side/fill-in-the-blank/gender-over-time-colab/gender-over-time.ipynb
-
-Hot reload JS
-
-````
-ls *.* | entr rsync -a --omit-dir-times --no-perms --exclude node_modules "$PWD" demo@roadtolarissa.com:../../usr/share/nginx/html/colab/
-````
-
-- https://colab.research.google.com/notebooks/snippets/advanced_outputs.ipynb
-- https://blocks.roadtolarissa.com/1wheel/7361276a2af10ca48ec9550c33bbdad5
diff --git a/spaces/merve/uncertainty-calibration/source/anonymization/make-sliders.js b/spaces/merve/uncertainty-calibration/source/anonymization/make-sliders.js
deleted file mode 100644
index 72f6dfd7c96d6c74cfb35db5854f06b668bf3d46..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/source/anonymization/make-sliders.js
+++ /dev/null
@@ -1,139 +0,0 @@
-window.makeSliders = function(){
- var rv = {
- population: 144,
- headsProb: .5,
- }
-
- rv.updateHeadsProb = (headsProb) => {
- rv.headsProb = headsProb
- updateSliderPos()
-
-
- estimates.updateEstimates()
- estimates.render()
- }
-
- rv.updatePopulation = (population) => {
- rv.population = population
- updateSliderPos()
-
-
- var scale = d3.clamp(0, 13 / Math.sqrt(population), 1)
- sel.studentGroup.st({
- transformOrigin: 'top',
- transformOrigin: c.width/2 + 'px ' + 160 + 'px',
- transform: `scale(${scale})`
- })
-
- estimates.updateEstimates()
- estimates.render()
-
- sel.student.classed('inactive',(d, i) => i >= population)
- }
-
- rv.updatePopulationSlider = (val) => {
- rv.updatePopulation(val)
- }
-
- rv.updateNoiseSlider = (val) => {
- rv.updateHeadsProb(val)
- }
-
- var updateSliderPos = (function(){
- var width = d3.clamp(50, window.innerWidth/2 - 40, 145)
- var height = 30
- var color = '#007276'
-
- var sliderVals = {
- population: {
- key: 'population',
- textFn: d => rv.population + ' students' ,
- r: [144, 756],
- v: 144,
- stepFn: d => rv.updatePopulation(Math.round(d.v/2)*2),
- },
- headsProb: {
- key: 'headsProb',
- textFn: d => d3.format('.1%')(rv.headsProb) + ' chance of heads',
- r: [.2, .5],
- v: .5,
- stepFn: d => rv.updateHeadsProb(d.v),
- }
- }
- var sliders = [sliderVals.headsProb, sliderVals.population, sliderVals.headsProb]
- sliders.forEach(d => {
- d.s = d3.scaleLinear().domain(d.r).range([0, width])
- })
-
- var sliderSel = d3.selectAll('.slide-container-population,.slide-container-heads-prob').html('')
- .data(sliders)
- .classed('slider', true)
- .st({
- display: 'inline-block',
- width: width,
- paddingRight: (d, i) => i == 1 ? 40 : 0,
- marginTop: 20,
- })
-
- var textSel = sliderSel.append('div.slider-label-container')
- .st({marginBottom: -5})
-
- var svgSel = sliderSel.append('svg').at({width, height})
- .on('click', function(d){
- d.v = d.s.invert(d3.mouse(this)[0])
- d.stepFn(d)
- })
- .st({
- cursor: 'pointer'
- })
- .append('g').translate(height/2, 1)
- svgSel.append('rect').at({width, height, y: -height/2, fill: 'rgba(0,0,0,0)'})
-
- svgSel.append('path').at({
- d: `M 0 -.5 H ${width}`,
- stroke: color,
- strokeWidth: 1
- })
-
- var leftPathSel = svgSel.append('path').at({
- d: `M 0 -.5 H ${width}`,
- stroke: color,
- strokeWidth: 3
- })
-
-
- var drag = d3.drag()
- .on('drag', function(d){
- var x = d3.mouse(this)[0]
- d.v = d3.clamp(d3.min(d.r), d.s.invert(x), d3.max(d.r))
- d.stepFn(d)
- })
-
- var rectSel = svgSel.append('rect')
- .at({
- width: height/2 - 1,
- height: height/2 - 1,
- stroke: color,
- strokeWidth: 3,
- fill: '#fff',
- })
- .translate([-height/4, -height/4])
- .call(drag)
-
- return isDrag => {
- rectSel.at({x: d => Math.round(d.s(rv[d.key]))})
- textSel.text(d => d.textFn(d))
-
- leftPathSel.at({d: d => `M 0 -.5 H ${d.s(rv[d.key])}`})
- }
- })()
- updateSliderPos()
-
-
- return rv
-}
-
-
-
-
-if (window.init) window.init()
\ No newline at end of file
diff --git a/spaces/microsoft/unicl-img-recog-demo/README.md b/spaces/microsoft/unicl-img-recog-demo/README.md
deleted file mode 100644
index 59d498adb413c5bfe55cda64a18b60b607db6601..0000000000000000000000000000000000000000
--- a/spaces/microsoft/unicl-img-recog-demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Unicl Image Recognition Demo
-emoji: 🏢
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.0.13
-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/mikeee/ttw/gradiobee/gen_model.py b/spaces/mikeee/ttw/gradiobee/gen_model.py
deleted file mode 100644
index 4cfdb50f847c026b183bcb7dac52972dee4d52e9..0000000000000000000000000000000000000000
--- a/spaces/mikeee/ttw/gradiobee/gen_model.py
+++ /dev/null
@@ -1,115 +0,0 @@
-"""Generate a model (textacy.representations.Vectorizer).
-
-vectorizer = Vectorizer(
- tf_type="linear", idf_type="smooth", norm="l2",
- min_df=3, max_df=0.95)
-doc_term_matrix = vectorizer.fit_transform(tokenized_docs)
-doc_term_matrix
-
-tokenized_docs = [insert_spaces(elm).split() for elm in textzh]
-"""
-from typing import Dict, Iterable, List, Optional, Union
-
-from textacy.representations import Vectorizer
-from logzero import logger
-
-
-# fmt: off
-def gen_model(
- tokenized_docs: Iterable[Iterable[str]], # List[List[str]],
- tf_type: str = 'linear',
- idf_type: Optional[str] = "smooth",
- dl_type: str = None, # Optional[str] = "sqrt" “lucene-style tfidf”
- norm: Optional[str] = "l2", # + "l2"
- min_df: Union[int, float] = 1,
- max_df: Union[int, float] = 1.0,
- max_n_terms: Optional[int] = None,
- vocabulary_terms: Optional[Union[Dict[str, int], Iterable[str]]] = None
-) -> Vectorizer:
- # fmt: on
- """Generate a model (textacy.representations.Vectorizer).
-
- Args:
- doc: tokenized docs
-
- (refer to textacy.representation.Vectorizer)
- tf_type: Type of term frequency (tf) to use for weights' local component:
-
- - "linear": tf (tfs are already linear, so left as-is)
- - "sqrt": tf => sqrt(tf)
- - "log": tf => log(tf) + 1
- - "binary": tf => 1
-
- idf_type: Type of inverse document frequency (idf) to use for weights'
- global component:
-
- - "standard": idf = log(n_docs / df) + 1.0
- - "smooth": idf = log(n_docs + 1 / df + 1) + 1.0, i.e. 1 is added
- to all document frequencies, as if a single document containing
- every unique term was added to the corpus.
- - "bm25": idf = log((n_docs - df + 0.5) / (df + 0.5)), which is
- a form commonly used in information retrieval that allows for
- very common terms to receive negative weights.
- - None: no global weighting is applied to local term weights.
-
- dl_type: Type of document-length scaling to use for weights'
- normalization component:
-
- - "linear": dl (dls are already linear, so left as-is)
- - "sqrt": dl => sqrt(dl)
- - "log": dl => log(dl)
- - None: no normalization is applied to local(*global?) weights
-
- norm: If "l1" or "l2", normalize weights by the L1 or L2 norms, respectively,
- of row-wise vectors; otherwise, don't.
- min_df: Minimum number of documents in which a term must appear for it to be
- included in the vocabulary and as a column in a transformed doc-term matrix.
- If float, value is the fractional proportion of the total number of docs,
- which must be in [0.0, 1.0]; if int, value is the absolute number.
- max_df: Maximum number of documents in which a term may appear for it to be
- included in the vocabulary and as a column in a transformed doc-term matrix.
- If float, value is the fractional proportion of the total number of docs,
- which must be in [0.0, 1.0]; if int, value is the absolute number.
- max_n_terms: If specified, only include terms whose document frequency is within
- the top ``max_n_terms``.
- vocabulary_terms: Mapping of unique term string to unique term id, or
- an iterable of term strings that gets converted into such a mapping.
- Note that, if specified, vectorized outputs will include *only* these terms.
-
- “lucene-style tfidf”: Adds a doc-length normalization to the usual local and global components.
- Params: tf_type="linear", apply_idf=True, idf_type="smooth", apply_dl=True, dl_type="sqrt"
-
- “lucene-style bm25”: Uses a smoothed idf instead of the classic bm25 variant to prevent weights on terms from going negative.
- Params: tf_type="bm25", apply_idf=True, idf_type="smooth", apply_dl=True, dl_type="linear"
- Attributes:
- doc_term_matrix
- Returns:
- transform_fit'ted vectorizer
- """
- # make sure tokenized_docs is the right typing
- try:
- for xelm in iter(tokenized_docs):
- for elm in iter(xelm):
- assert isinstance(elm, str)
- except AssertionError:
- raise AssertionError(" tokenized_docs is not of the typing Iterable[Iterable[str]] ")
- except Exception as e:
- logger.error(e)
- raise
-
- vectorizer = Vectorizer(
- # tf_type="linear", idf_type="smooth", norm="l2", min_df=3, max_df=0.95)
- tf_type=tf_type,
- idf_type=idf_type,
- dl_type=dl_type,
- norm=norm,
- min_df=min_df,
- max_df=max_df,
- max_n_terms=max_n_terms,
- vocabulary_terms=vocabulary_terms
- )
- doc_term_matrix = vectorizer.fit_transform(tokenized_docs)
-
- gen_model.doc_term_matrix = doc_term_matrix
-
- return vectorizer
diff --git a/spaces/mindspore-ai/Zidongtaichu/header.html b/spaces/mindspore-ai/Zidongtaichu/header.html
deleted file mode 100644
index 14ab18e24b2875ae9fcd7fede29e78fda76319c4..0000000000000000000000000000000000000000
--- a/spaces/mindspore-ai/Zidongtaichu/header.html
+++ /dev/null
@@ -1,27 +0,0 @@
-
-
-
-
-
-
-
]*>//g' | sed 's/<[^<>]*>$//g' > $INPUT_FILE.$SRC
- grep ']*>//g' | sed 's/<[^<>]*>$//g' > $INPUT_FILE.$TGT
-}
-
-download_and_process_tilde() {
- URL=$1
- UNCOMPRESS_CMD=$2
- FILENAME=$3
- LANG=$4
- PROCESS_CMD=$5
-
- mkdir -p tilde
- cd tilde
- wget -nc $URL
- $UNCOMPRESS_CMD
- echo "executing cmd"
- echo $PROCESS_CMD
- $PROCESS_CMD
- cd ..
- ln -sf tilde/$FILENAME.$LANG tilde.$LANG
- ln -sf tilde/$FILENAME.en tilde.en
-}
-
-prepare_cs() {
- OUTPUT_DIR=$TMP_DIR/cs
- mkdir -p $OUTPUT_DIR
- cd $OUTPUT_DIR
-
- #download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.cs-en.tsv.gz" "gunzip europarl-v10.cs-en.tsv.gz" cs europarl-v10.cs-en.tsv 1 2 &
- #download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-cs.txt.gz" "gunzip en-cs.txt.gz" cs en-cs.txt 2 1 &
- #link_commoncrawl cs
- #download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.cs-en.tsv.gz" "gunzip news-commentary-v15.cs-en.tsv.gz" cs news-commentary-v15.cs-en.tsv 1 2 &
- #download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.cs-en.tsv.gz" "gunzip wikititles-v2.cs-en.tsv.gz" cs wikititles-v2.cs-en.tsv 1 2 &
- #download_and_process_tilde "http://data.statmt.org/wmt20/translation-task/rapid/RAPID_2019.cs-en.xlf.gz" "gunzip RAPID_2019.cs-en.xlf.gz" RAPID_2019.cs-en.xlf cs "strip_xlf RAPID_2019.cs-en.xlf cs en" &
- #download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.cs-en.langid.tsv.gz" "gunzip WikiMatrix.v1.cs-en.langid.tsv.gz" cs WikiMatrix.v1.cs-en.langid.tsv 2 3 &
-
- #wait
-
- # remove previous results
- #rm -f all.??
- #find ./ -maxdepth 1 -name "*.cs" | sort -V | xargs cat > all.cs
- #find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
- if [ -z $CZENG_CORPUS ] ;
- then
- echo "Please download CZENG_CORPUS manually and place them at $CZENG_CORPUS. Exitting..."
- exit
- fi
- cat $CZENG_CORPUS | sed '/^$/d' | cut -f5 > all.cs
- cat $CZENG_CORPUS | sed '/^$/d' | cut -f6 > all.en
-
- lid_filter cs all.cs $DEST/train.cs_CZ-en_XX.cs_CZ en all.en $DEST/train.cs_CZ-en_XX.en_XX
-}
-
-prepare_de() {
- OUTPUT_DIR=$TMP_DIR/de
- mkdir -p $OUTPUT_DIR
- cd $OUTPUT_DIR
-
- download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.de-en.tsv.gz" "gunzip europarl-v10.de-en.tsv.gz" de europarl-v10.de-en.tsv 1 2 &
- download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-de.txt.gz" "gunzip en-de.txt.gz" de en-de.txt 2 1 &
- link_commoncrawl de
- download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.de-en.tsv.gz" "gunzip news-commentary-v15.de-en.tsv.gz" de news-commentary-v15.de-en.tsv 1 2 &
- download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.de-en.tsv.gz" "gunzip wikititles-v2.de-en.tsv.gz" de wikititles-v2.de-en.tsv 1 2 &
- download_and_process_tilde "http://data.statmt.org/wmt20/translation-task/rapid/RAPID_2019.de-en.xlf.gz" "gunzip RAPID_2019.de-en.xlf.gz" RAPID_2019.de-en.xlf de "strip_xlf RAPID_2019.de-en.xlf de en" &
- download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.de-en.langid.tsv.gz" "gunzip WikiMatrix.v1.de-en.langid.tsv.gz" de WikiMatrix.v1.de-en.langid.tsv 2 3 &
-
- wait
-
- # remove previous results
- rm -f all.??
- find ./ -maxdepth 1 -name "*.de" | sort -V | xargs cat > all.de
- find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
- lid_filter de all.de $DEST/train.de_DE-en_XX.de_DE en all.en $DEST/train.de_DE-en_XX.en_XX
-}
-
-prepare_tmx() {
- TMX_FILE=$1
- git clone https://github.com/amake/TMX2Corpus $UTILS/tmx2corpus
- pip install tinysegmenter
-
- python $UTILS/tmx2corpus/tmx2corpus.py $TMX_FILE
-}
-
-prepare_pl() {
- OUTPUT_DIR=$TMP_DIR/pl
- mkdir -p $OUTPUT_DIR
- cd $OUTPUT_DIR
-
- # download_and_select europarl "http://www.statmt.org/europarl/v10/training/europarl-v10.pl-en.tsv.gz" "gunzip europarl-v10.pl-en.tsv.gz" pl europarl-v10.pl-en.tsv 1 2 &
- # download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release5.1/en-pl.txt.gz" "gunzip en-pl.txt.gz" pl en-pl.txt 2 1 &
- # download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.pl-en.tsv.gz" "gunzip wikititles-v2.pl-en.tsv.gz" pl wikititles-v2.pl-en.tsv 1 2 &
- download_and_select tilde "https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2019.en-pl.tmx.zip" "gunzip rapid2019.en-pl.tmx.zip" bitext pl "prepare_tmx RAPID_2019.UNIQUE.en-pl.tmx" &
- # download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-pl.langid.tsv.gz" "gunzip WikiMatrix.v1.en-pl.langid.tsv.gz" pl WikiMatrix.v1.en-pl.langid.tsv 3 2 &
-
- wait
-
- # remove previous results
- rm -f all.??
- find ./ -maxdepth 1 -name "*.pl" | sort -V | xargs cat > all.pl
- find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
- lid_filter pl all.pl $DEST/train.pl_PL-en_XX.pl_PL en all.en $DEST/train.pl_PL-en_XX.en_XX
-}
-
-prepare_uncorpus() {
- $URLS=$1
- $FILES=$2
-
- mkdir -p uncorpus
- cd uncorpus
-
- for URL in $URLS; do
- wget -nc $URL
- done
- cat $FILES > uncorpus.tar.gz
- tar -zxvf uncorpus.tar.gz
-
- cd ..
- ln -sf uncorpus/en-$LANG/UNv1.0.en-$LANG.$LANG uncorpus.$LANG
- ln -sf uncorpus/en-$LANG/UNv1.0.en-$LANG.en uncorpus.en
-}
-
-prepare_yandex() {
- mkdir -p yandex
- cd yandex
- unzip $YANDEX_CORPUS ./
- cd ..
- ln -s yandex/corpus.en_ru.1m.en yandex.en
- ln -s yandex/corpus.en_ru.1m.ru yandex.ru
-}
-
-prepare_ru() {
- OUTPUT_DIR=$TMP_DIR/ru
- mkdir -p $OUTPUT_DIR
- cd $OUTPUT_DIR
-
- download_and_select paracrawl "https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz" "tar -zxvf paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz" ru paracrawl-release1.en-ru.zipporah0-dedup-clean &
- link_commoncrawl ru
- download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-ru.tsv.gz" "gunzip news-commentary-v15.en-ru.tsv.gz" ru news-commentary-v15.en-ru.tsv 2 1 &
- prepare_yandex &
- download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.ru-en.tsv.gz" "gunzip wikititles-v2.ru-en.tsv.gz" ru wikititles-v2.ru-en.tsv 1 2 &
- prepare_uncorpus "https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02" "UNv1.0.en-ru.tar.gz.00 UNv1.0.en-ru.tar.gz.01 UNv1.0.en-ru.tar.gz.02" &
- download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-ru.langid.tsv.gz" "gunzip WikiMatrix.v1.en-ru.langid.tsv.gz" ru WikiMatrix.v1.en-ru.langid.tsv 3 2 &
-
- wait
-
- # remove previous results
- rm -f all.??
- find ./ -maxdepth 1 -name "*.ru" | sort -V | xargs cat > all.ru
- find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
- lid_filter ru all.ru $DEST/train.ru_RU-en_XX.ru_RU en all.en $DEST/train.ru_RU-en_XX.en_XX
-}
-
-prepare_ccmt() {
- mkdir -p ccmt
- cd ccmt
- # assume ccmt data is already unzipped under CCMT_DIR folder
- cat $CCMT_DIR/datum2017/Book*_cn.txt | sed 's/ //g' > datum2017.detok.zh
- cat $CCMT_DIR/datum2017/Book*_en.txt > datum2017.detok.en
- cat $CCMT_DIR/casict2011/casict-A_ch.txt $CCMT_DIR/casict2011/casict-B_ch.txt $CCMT_DIR/casict2015/casict2015_ch.txt $CCMT_DIR/datum2015/datum_ch.txt $CCMT_DIR/neu2017/NEU_cn.txt datum2017.detok.zh > ccmt.zh
- cat $CCMT_DIR/casict2011/casict-A_en.txt $CCMT_DIR/casict2011/casict-B_en.txt $CCMT_DIR/casict2015/casict2015_en.txt $CCMT_DIR/datum2015/datum_en.txt $CCMT_DIR/neu2017/NEU_en.txt datum2017.detok.en > ccmt.en
- cd ..
- ln -sf ccmt/ccmt.zh ccmt.zh
- ln -sf ccmt/ccmt.en ccmt.en
-}
-
-prepare_zh() {
- OUTPUT_DIR=$TMP_DIR/zh
- mkdir -p $OUTPUT_DIR
- cd $OUTPUT_DIR
-
- download_and_select newscommentary "http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-zh.tsv.gz" "gunzip news-commentary-v15.en-zh.tsv.gz" zh news-commentary-v15.en-zh.tsv 2 1 &
- download_and_select wikititles "http://data.statmt.org/wikititles/v2/wikititles-v2.zh-en.tsv.gz" "gunzip wikititles-v2.zh-en.tsv.gz" zh wikititles-v2.zh-en.tsv 1 2 &
- prepare_uncorpus "https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00 https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.01" "UNv1.0.en-zh.tar.gz.00 UNv1.0.en-zh.tar.gz.01" &
- prepare_ccmt &
- download_and_select wikimatrix "http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-zh.langid.tsv.gz" "gunzip WikiMatrix.v1.en-zh.langid.tsv.gz" zh WikiMatrix.v1.en-zh.langid.tsv 3 2 &
-
- wait
-
- # remove previous results
- rm -f all.??
- find ./ -maxdepth 1 -name "*.zh" | sort -V | xargs cat > all.zh
- find ./ -maxdepth 1 -name "*.en" | sort -V | xargs cat > all.en
- lid_filter zh all.zh $DEST/train.zh_CN-en_XX.zh_CN en all.en $DEST/train.zh_CN-en_XX.en_XX
-}
-
-prepare_tests() {
- OUTPUT_DIR=$TMP_DIR
- mkdir -p $OUTPUT_DIR
- cd $OUTPUT_DIR
- wget -nc http://data.statmt.org/wmt20/translation-task/dev.tgz
- tar -zxvf dev.tgz
- cd dev
-
- cat newsdev2020-jaen-src.ja.sgm | $UTILS/strip_sgm.sh > newsdev2020-jaen.ja
- cat newsdev2020-jaen-ref.en.sgm | $UTILS/strip_sgm.sh > newsdev2020-jaen.en
- split newsdev2020-jaen.ja -a 0 -n r/1/2 > $DEST/valid.ja_XX-en_XX.ja_XX
- split newsdev2020-jaen.en -a 0 -n r/1/2 > $DEST/valid.ja_XX-en_XX.en_XX
- split newsdev2020-jaen.ja -a 0 -n r/2/2 > $DEST/test.ja_XX-en_XX.ja_XX
- split newsdev2020-jaen.en -a 0 -n r/2/2 > $DEST/test.ja_XX-en_XX.en_XX
-
- cat newsdev2020-iuen-src.iu.sgm | strip_sgm.sh > newsdev2020-iuen.iu
- cat newsdev2020-iuen-ref.en.sgm | strip_sgm.sh > newsdev2020-iuen.en
- split newsdev2020-iuen.iu -a 0 -n r/1/2 > $DEST/valid.iu_CA-en_XX.iu_CA
- split newsdev2020-iuen.en -a 0 -n r/1/2 > $DEST/valid.iu_CA-en_XX.en_XX
- split newsdev2020-iuen.iu -a 0 -n r/2/2 > $DEST/test.iu_CA-en_XX.iu_CA
- split newsdev2020-iuen.en -a 0 -n r/2/2 > $DEST/test.iu_CA-en_XX.en_XX
-
- cat newsdev2020-taen-src.ta.sgm | strip_sgm.sh > newsdev2020-taen.ta
- cat newsdev2020-taen-ref.en.sgm | strip_sgm.sh > newsdev2020-taen.en
- split newsdev2020-taen.ta -a 0 -n r/1/2 > $DEST/valid.ta_IN-en_XX.ta_IN
- split newsdev2020-taen.en -a 0 -n r/1/2 > $DEST/valid.ta_IN-en_XX.en_XX
- split newsdev2020-taen.ta -a 0 -n r/2/2 > $DEST/test.ta_IN-en_XX.ta_IN
- split newsdev2020-taen.en -a 0 -n r/2/2 > $DEST/test.ta_IN-en_XX.en_XX
-
- cp wikipedia.dev.km-en.km $DEST/valid.km_KH-en_XX.km_KH
- cp wikipedia.dev.km-en.en $DEST/valid.km_KH-en_XX.en_XX
- cp wikipedia.devtest.km-en.km $DEST/test.km_KH-en_XX.km_KH
- cp wikipedia.devtest.km-en.en $DEST/test.km_KH-en_XX.en_XX
-
- cp wikipedia.dev.ps-en.ps $DEST/valid.ps_AF-en_XX.ps_AF
- cp wikipedia.dev.ps-en.en $DEST/valid.ps_AF-en_XX.en_XX
- cp wikipedia.devtest.ps-en.ps $DEST/test.ps_AF-en_XX.ps_AF
- cp wikipedia.devtest.ps-en.en $DEST/test.ps_AF-en_XX.en_XX
-
- cat newsdev2020-plen-src.pl.sgm | strip_sgm.sh > newsdev2020-plen.pl
- cat newsdev2020-plen-ref.en.sgm | strip_sgm.sh > newsdev2020-plen.en
- split newsdev2020-plen.pl -a 0 -n r/1/2 > $DEST/valid.pl_PL-en_XX.pl_PL
- split newsdev2020-plen.en -a 0 -n r/1/2 > $DEST/valid.pl_PL-en_XX.en_XX
- split newsdev2020-plen.pl -a 0 -n r/2/2 > $DEST/test.pl_PL-en_XX.pl_PL
- split newsdev2020-plen.en -a 0 -n r/2/2 > $DEST/test.pl_PL-en_XX.en_XX
-
- cat newstest2018-encs-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-cs_CZ.en_XX
- cat newstest2018-encs-ref.cs.sgm | strip_sgm.sh > $DEST/valid.en_XX-cs_CZ.cs_CZ
- cat newstest2019-encs-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-cs_CZ.en_XX
- cat newstest2019-encs-ref.cs.sgm | strip_sgm.sh > $DEST/test.en_XX-cs_CZ.cs_CZ
-
- cat newstest2018-deen-src.de.sgm | strip_sgm.sh > $DEST/valid.de_DE-en_XX.de_DE
- cat newstest2018-deen-ref.en.sgm | strip_sgm.sh > $DEST/valid.de_DE-en_XX.en_XX
- cat newstest2018-ende-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-de_DE.en_XX
- cat newstest2018-ende-ref.de.sgm | strip_sgm.sh > $DEST/valid.en_XX-de_DE.de_DE
- cat newstest2019-deen-src.de.sgm | strip_sgm.sh > $DEST/test.de_DE-en_XX.de_DE
- cat newstest2019-deen-ref.en.sgm | strip_sgm.sh > $DEST/test.de_DE-en_XX.en_XX
- cat newstest2019-ende-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-de_DE.en_XX
- cat newstest2019-ende-ref.de.sgm | strip_sgm.sh > $DEST/test.en_XX-de_DE.de_DE
-
- cat newstest2018-ruen-src.ru.sgm | strip_sgm.sh > $DEST/valid.ru_RU-en_XX.ru_RU
- cat newstest2018-ruen-ref.en.sgm | strip_sgm.sh > $DEST/valid.ru_RU-en_XX.en_XX
- cat newstest2018-enru-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-ru_RU.en_XX
- cat newstest2018-enru-ref.ru.sgm | strip_sgm.sh > $DEST/valid.en_XX-ru_RU.ru_RU
- cat newstest2019-ruen-src.ru.sgm | strip_sgm.sh > $DEST/test.ru_RU-en_XX.ru_RU
- cat newstest2019-ruen-ref.en.sgm | strip_sgm.sh > $DEST/test.ru_RU-en_XX.en_XX
- cat newstest2019-enru-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-ru_RU.en_XX
- cat newstest2019-enru-ref.ru.sgm | strip_sgm.sh > $DEST/test.en_XX-ru_RU.ru_RU
-
- cat newstest2018-zhen-src.zh.sgm | strip_sgm.sh > $DEST/valid.zh_CN-en_XX.zh_CN
- cat newstest2018-zhen-ref.en.sgm | strip_sgm.sh > $DEST/valid.zh_CN-en_XX.en_XX
- cat newstest2018-enzh-src.en.sgm | strip_sgm.sh > $DEST/valid.en_XX-zh_CN.en_XX
- cat newstest2018-enzh-ref.zh.sgm | strip_sgm.sh > $DEST/valid.en_XX-zh_CN.zh_CN
- cat newstest2019-zhen-src.zh.sgm | strip_sgm.sh > $DEST/test.zh_CN-en_XX.zh_CN
- cat newstest2019-zhen-ref.en.sgm | strip_sgm.sh > $DEST/test.zh_CN-en_XX.en_XX
- cat newstest2019-enzh-src.en.sgm | strip_sgm.sh > $DEST/test.en_XX-zh_CN.en_XX
- cat newstest2019-enzh-ref.zh.sgm | strip_sgm.sh > $DEST/test.en_XX-zh_CN.zh_CN
-}
-
-mkdir -p $DEST
-
-prepare_lid
-prepare_moses
-download_commoncrawl
-
-prepare_ja &
-prepare_ta &
-prepare_km &
-prepare_ps &
-prepare_iu &
-prepare_cs &
-prepare_de &
-prepare_pl &
-prepare_ru &
-prepare_zh &
-
-# prepare valid/test set
-prepare_tests &
-
-# wait
-
-# TODO remove intermediate files
-# rm -rf $TMP_DIR
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py b/spaces/mshukor/UnIVAL/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py
deleted file mode 100644
index 7970a3c71401b4835ba09158ea06134418afa065..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/speech_text_joint_to_text/models/s2t_dualinputtransformer.py
+++ /dev/null
@@ -1,1090 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from collections import namedtuple
-
-import torch
-import torch.nn as nn
-from fairseq import checkpoint_utils
-from fairseq import utils
-from fairseq.models import (
- FairseqEncoder,
- FairseqDecoder,
- FairseqEncoderDecoderModel,
- register_model,
- register_model_architecture,
-)
-from fairseq.models.fairseq_encoder import EncoderOut
-from fairseq.models.speech_to_text import (
- TransformerDecoder,
- S2TTransformerEncoder,
-)
-from fairseq.models.transformer import TransformerEncoder
-from fairseq.modules import (
- TransformerEncoderLayer,
- GradMultiply,
- LayerNorm,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class SpeechEoSEncoder(FairseqEncoder):
- def __init__(self, encoder, eos_num, feat_dim, adapter_type="None", adapter_dim=0):
- super().__init__(None)
- self.encoder = encoder
- self.eos_num = eos_num # downsampling rate for speech input feature
- self.eos_emb = (
- nn.Parameter(torch.zeros(1, feat_dim), requires_grad=True)
- if eos_num > 0
- else None
- )
- self.adapter = self.add_adapter(adapter_type, adapter_dim)
-
- def add_adapter(self, adapter_type, adapter_dim):
- def _make_identity(linear, eps=1e-5):
- assert isinstance(linear, nn.Linear)
- linear.weight.data.mul_(eps)
- linear.weight.data.fill_diagonal_(1.0)
- if linear.bias is not None:
- linear.bias.data.mul_(eps)
-
- adapter = None
- if adapter_type == "Linear":
- assert adapter_dim > 0
- adapter = nn.Sequential(
- nn.Linear(adapter_dim, adapter_dim), LayerNorm(adapter_dim)
- )
- # initialize the adapter as identity matrix first
- _make_identity(adapter[0])
-
- elif adapter_type == "MLP":
- assert adapter_dim > 0
- # assume the model is pre-norm model
- adapter = nn.Sequential(
- nn.Linear(adapter_dim, 2 * adapter_dim),
- nn.ReLU(),
- nn.Linear(2 * adapter_dim, adapter_dim),
- LayerNorm(adapter_dim),
- )
- _make_identity(adapter[0])
- _make_identity(adapter[2])
- return adapter
-
- def add_eos(self, src_tokens, src_lengths):
- bsz, max_seq_len, fdim = src_tokens.size()
- if self.eos_num > 0:
- src_token_eos = torch.zeros(
- [bsz, max_seq_len + self.eos_num, fdim],
- dtype=src_tokens.dtype,
- device=src_tokens.device,
- )
- src_token_eos[:, :max_seq_len] = src_tokens
- for bi in range(bsz):
- src_token_eos[bi][
- src_lengths[bi] : src_lengths[bi] + self.eos_num
- ] = self.eos_emb.expand(self.eos_num, fdim)
- src_lengths = src_lengths + self.eos_num
- src_tokens = src_token_eos
- return src_tokens, src_lengths
-
- def apply_adapter(self, enc_out):
- if self.adapter is None:
- return enc_out
- rst = self.adapter(enc_out.encoder_out)
- if enc_out.encoder_padding_mask is not None:
- rst.masked_fill_(
- enc_out.encoder_padding_mask.transpose(0, 1).unsqueeze(-1), 0
- )
- return EncoderOut(
- encoder_out=rst,
- encoder_padding_mask=enc_out.encoder_padding_mask,
- encoder_embedding=enc_out.encoder_embedding,
- encoder_states=enc_out.encoder_states,
- src_tokens=enc_out.src_tokens,
- src_lengths=enc_out.src_lengths,
- )
-
- def forward(self, src_tokens, src_lengths=None, return_all_hiddens=False, **kwargs):
- """
- src_tokens: padded tensor (B, T, C * feat)
- src_lengths: tensor of original lengths of input utterances (B,)
- """
- src_tokens, src_lengths = self.add_eos(src_tokens, src_lengths)
- enc_out = self.encoder(src_tokens, src_lengths, return_all_hiddens)
- enc_out = self.apply_adapter(enc_out)
- return enc_out
-
- def reorder_encoder_out(self, encoder_out, new_order):
- return self.encoder.reorder_encoder_out(encoder_out, new_order)
-
-
-class DualInputEncoder(FairseqEncoder):
- def __init__(
- self,
- args,
- spch_encoder,
- text_encoder,
- dictionary,
- cross_attentive_loss_before_last_layer=-1,
- ):
- super().__init__(dictionary)
-
- self.spch_encoder = spch_encoder
- self.text_encoder = text_encoder
- self.enc_grad_mult = args.enc_grad_mult
- self.cross_attentive_loss_before_last_layer = (
- cross_attentive_loss_before_last_layer
- )
- self.use_cross_attentive_loss = (
- False if cross_attentive_loss_before_last_layer <= -1 else True
- )
- self.enc2_along_grad_mult = args.enc2_along_grad_mult
-
- @classmethod
- def set_shared_layer(cls, share_level, src_layer, tgt_layer):
- """
- share parameters from tgt_layer to src_layer
- share_level:
- 0: share everything
- 1: share everything but different model
- 2: share weight but not bias, layernorm
- """
- if share_level == 0:
- return tgt_layer
- if isinstance(src_layer, nn.Linear):
- return tgt_layer
- if isinstance(src_layer, TransformerEncoderLayer):
- assert src_layer.embed_dim == tgt_layer.embed_dim
- assert src_layer.normalize_before == tgt_layer.normalize_before
- if share_level == 1:
- src_layer.fc1 = tgt_layer.fc1
- src_layer.fc2 = tgt_layer.fc2
- src_layer.self_attn = tgt_layer.self_attn
- src_layer.final_layer_norm = tgt_layer.final_layer_norm
- src_layer.self_attn_layer_norm = tgt_layer.self_attn_layer_norm
- src_layer.layernorm_embedding = tgt_layer.layernorm_embedding
- else:
- src_layer.fc1.weight = tgt_layer.fc1.weight
- src_layer.fc2.weight = tgt_layer.fc2.weight
- src_layer.self_attn.k_proj.weight = tgt_layer.self_attn.k_proj.weight
- src_layer.self_attn.v_proj.weight = tgt_layer.self_attn.v_proj.weight
- src_layer.self_attn.q_proj.weight = tgt_layer.self_attn.q_proj.weight
- src_layer.self_attn.out_proj.weight = (
- tgt_layer.self_attn.out_proj.weight
- )
- else:
- if share_level == 1:
- return tgt_layer
- return src_layer
-
- @classmethod
- def build_spch_encoder(cls, args):
- cfg = {
- "input_feat_per_channel": args.input_feat_per_channel,
- "input_channels": args.input_channels,
- "conv_kernel_sizes": args.conv_kernel_sizes,
- "conv_channels": args.conv_channels,
- "encoder_embed_dim": args.encoder_embed_dim,
- "encoder_ffn_embed_dim": args.encoder_ffn_embed_dim,
- "encoder_layers": args.speech_encoder_layers,
- "encoder_layerdrop": args.encoder_layerdrop,
- "encoder_attention_heads": args.encoder_attention_heads,
- "max_source_positions": args.max_source_positions,
- "dropout": args.dropout,
- "encoder_normalize_before": args.encoder_normalize_before,
- "activation_dropout": args.activation_dropout,
- "attention_dropout": args.attention_dropout,
- "activation_fn": args.activation_fn,
- "layernorm_embedding": args.layernorm_embedding,
- "no_token_positional_embeddings": args.no_token_positional_embeddings,
- "no_scale_embedding": args.no_scale_embedding,
- "quant_noise_pq": args.quant_noise_pq,
- "encoder_freezing_updates": 0,
- }
- model_args = namedtuple("args", cfg.keys())(*cfg.values())
- spch_encoder = S2TTransformerEncoder(model_args)
- if args.add_speech_eos:
- spch_encoder = SpeechEoSEncoder(
- spch_encoder,
- 2 * len(args.conv_kernel_sizes.split(",")),
- args.input_feat_per_channel,
- adapter_type=getattr(args, "speech_encoder_adapter_type", "None"),
- adapter_dim=args.encoder_embed_dim,
- )
- return spch_encoder
-
- @classmethod
- def build_text_encoder(cls, args, src_dictionary, spch_encoder):
- if args.encoder_shared_layers > 0:
- mx_shared_layers = (
- args.speech_encoder_layers
- if args.speech_encoder_layers < args.text_encoder_layers
- else args.text_encoder_layers
- )
- args.encoder_shared_layers = (
- args.encoder_shared_layers
- if args.encoder_shared_layers <= mx_shared_layers
- else mx_shared_layers
- )
- cfg = {
- "encoder_embed_dim": args.encoder_text_embed_dim,
- "encoder_ffn_embed_dim": args.encoder_ffn_embed_dim,
- "encoder_layers": args.text_encoder_layers,
- "encoder_layerdrop": args.encoder_layerdrop,
- "encoder_attention_heads": args.encoder_attention_heads,
- "encoder_learned_pos": args.encoder_learned_pos,
- "max_source_positions": args.max_source_positions,
- "dropout": args.dropout,
- "encoder_normalize_before": args.encoder_normalize_before,
- "activation_dropout": args.activation_dropout,
- "attention_dropout": args.attention_dropout,
- "activation_fn": args.activation_fn,
- "adaptive_input": args.adaptive_input,
- "no_token_positional_embeddings": args.no_token_positional_embeddings,
- "no_scale_embedding": args.no_scale_embedding,
- "quant_noise_pq": args.quant_noise_pq,
- }
- model_args = namedtuple("args", cfg.keys())(*cfg.values())
- enc_emb = nn.Embedding(
- len(src_dictionary), model_args.encoder_embed_dim, src_dictionary.pad()
- )
- text_encoder = TransformerEncoder(model_args, src_dictionary, enc_emb)
- if args.add_speech_eos:
- spch_encoder = spch_encoder.encoder
- if args.encoder_shared_layers > 0:
- text_encoder.layer_norm = cls.set_shared_layer(
- args.encoder_shared_layer_level,
- text_encoder.layer_norm,
- spch_encoder.layer_norm,
- )
- for i, ly in enumerate(
- spch_encoder.transformer_layers[-args.encoder_shared_layers :]
- ):
- ly_id = i + args.text_encoder_layers - args.encoder_shared_layers
- assert isinstance(text_encoder.layers[ly_id], type(ly))
- text_encoder.layers[ly_id] = cls.set_shared_layer(
- args.encoder_shared_layer_level,
- text_encoder.layers[ly_id],
- ly,
- )
- return text_encoder
-
- def mult_rst_grad(self, rst, ratio):
- assert isinstance(rst, dict) # instead of EncoderOut
- assert len(rst["encoder_out"]) == 1
- rst["encoder_out"][0] = GradMultiply.apply(rst["encoder_out"][0], ratio)
- return rst
-
- def process_attentive_loss_states(self, rst, interstates):
- assert isinstance(rst, dict) # instead of EncoderOut
- rst["encoder_states"] = interstates
- return rst
-
- def forward(
- self,
- src_tokens,
- src_lengths=None,
- src_txt_tokens=None,
- src_txt_lengths=None,
- **kwargs
- ):
- """
- Args:
- src_tokens: padded tensor (B, T, C * feat)
- src_lengths: tensor of original lengths of input utterances (speech) (B,)
- src_txt_tokens: padded tensor (B, T)
- src_txt_lengths: tensor of original lengths of input utterances (text) (B,)
- """
- # src_tokens only: inference
- # src_tokens, src_lengths: speech only training
- # src_txt_tokens, src_txt_lengths: text only training
- # all valid: speech + text training
-
- if src_tokens is None and src_txt_tokens is None:
- raise ValueError(
- "src_tokens and src_txt_tokens cannot be None at the same time"
- )
- ret1 = None
- ret2 = None
- return_all_hiddens = False
- if src_tokens is not None:
- if (
- self.use_cross_attentive_loss and src_txt_tokens is not None
- ): # remove self.training so we can get attn score during validation step
- return_all_hiddens = True
- ret1 = self.spch_encoder(
- src_tokens, src_lengths, return_all_hiddens=return_all_hiddens
- )
-
- if self.use_cross_attentive_loss and src_txt_tokens is not None:
- assert self.cross_attentive_loss_before_last_layer < len(
- ret1["encoder_states"]
- )
- ret1 = self.process_attentive_loss_states(
- ret1,
- ret1["encoder_states"][
- -self.cross_attentive_loss_before_last_layer - 1
- ],
- )
-
- if src_txt_tokens is not None:
- ret2 = self.text_encoder(
- src_txt_tokens, src_txt_lengths, return_all_hiddens=return_all_hiddens
- )
- if return_all_hiddens:
- if self.cross_attentive_loss_before_last_layer == len(
- self.text_encoder.layers
- ):
- text_embedding, _ = self.text_encoder.forward_embedding(
- src_txt_tokens
- )
- text_embedding = text_embedding.transpose(0, 1)
- ret2 = self.process_attentive_loss_states(ret2, text_embedding)
- else:
- assert self.cross_attentive_loss_before_last_layer < len(
- self.text_encoder.layers
- )
- ret2 = self.process_attentive_loss_states(
- ret2,
- ret2["encoder_states"][
- -self.cross_attentive_loss_before_last_layer - 1
- ],
- )
-
- def merge_output(rst1, rst2):
- if rst1 is None:
- if not (self.enc2_along_grad_mult == 1.0 or self.training):
- rst2 = self.mult_rst_grad(rst2, self.enc2_along_grad_mult)
- return rst2
- if rst2 is None:
- return rst1
- if self.enc_grad_mult != 1.0 and self.training:
- rst1 = self.mult_rst_grad(rst1, self.enc_grad_mult)
- rst2 = self.mult_rst_grad(rst2, self.enc_grad_mult)
- rst = (rst1, rst2)
- return rst
-
- return merge_output(ret1, ret2)
-
- def reorder_encoder_out(self, encoder_out, new_order):
- assert self.training is False # used for inference only
- return self.spch_encoder.reorder_encoder_out(encoder_out, new_order)
-
-
-# TransformerMultiInputDecoder: take one or two encoder inputs
-class TransformerMultiInputDecoder(FairseqDecoder):
- def __init__(
- self,
- dictionary,
- spch_decoder,
- text_decoder,
- compute_cross_attentive_loss=False,
- cross_attentive_loss_with_norm=True,
- cross_attentive_loss_reverse=False,
- ):
-
- super().__init__(dictionary)
- self.spch_decoder = spch_decoder
- self.text_decoder = text_decoder
- self.compute_cross_attentive_loss = compute_cross_attentive_loss
- self.cross_attentive_loss_with_norm = cross_attentive_loss_with_norm
- self.cross_attentive_loss_reverse = cross_attentive_loss_reverse
-
- @classmethod
- def share_spchdecoder(cls, task_args, text_decoder, spch_decoder):
- if task_args.decoder_shared_layer_level == 0:
- return text_decoder
- assert text_decoder.embed_tokens == spch_decoder.embed_tokens
- spch_decoder.project_in_dim = text_decoder.project_in_dim
- spch_decoder.embed_positions = text_decoder.embed_positions
- spch_decoder.layernorm_embedding = text_decoder.layernorm_embedding
- spch_decoder.project_out_dim = text_decoder.project_out_dim
- spch_decoder.adaptive_softmax = text_decoder.adaptive_softmax
- if task_args.decoder_shared_layer_level == 1:
- spch_decoder.output_projection = text_decoder.output_projection
- spch_decoder.layer_norm = text_decoder.layer_norm
- else: # 2
- spch_decoder.output_projection.weight = (
- text_decoder.output_projection.weight
- )
- for i, ly in enumerate(text_decoder.layers):
- sly = spch_decoder.layers[i]
- sly.self_attn = ly.self_attn
- sly.self_attn_layer_norm = ly.self_attn_layer_norm
- # sly.encoder_attn = ly.encoder_attn
- if (
- task_args.decoder_shared_layer_level == 1
- ): # share everything, but under different models
- sly.encoder_attn = ly.encoder_attn
- sly.encoder_attn_layer_norm = ly.encoder_attn_layer_norm
- sly.fc1 = ly.fc1
- sly.fc2 = ly.fc2
- sly.final_layer_norm = ly.final_layer_norm
- else: # task_args.decoder_shared_layer_level == 2: #separated encoder_attn_layer_norm and bias
- sly.encoder_attn.k_proj.weight = ly.encoder_attn.k_proj.weight
- sly.encoder_attn.v_proj.weight = ly.encoder_attn.v_proj.weight
- sly.encoder_attn.q_proj.weight = ly.encoder_attn.q_proj.weight
- sly.encoder_attn.out_proj.weight = ly.encoder_attn.out_proj.weight
- sly.fc1.weight = ly.fc1.weight
- sly.fc2.weight = ly.fc2.weight
-
- return spch_decoder
-
- def cross_attentive_loss(
- self, teacher_states, student_states, teacher_masking, student_masking, eps=1e-6
- ):
- x = teacher_states.transpose(0, 1) # from T X B X D to B X T X D
- y = student_states.transpose(0, 1)
- if self.cross_attentive_loss_with_norm:
- x = x / (x.norm(dim=2, keepdim=True) + eps)
- y = y / (y.norm(dim=2, keepdim=True) + eps)
- dim = x.size(-1)
- # lengths: batch X seqLen
- sim_scores_xy = torch.bmm(x, y.transpose(1, 2)) # batch X lenx X leny ]
- if y.dtype == torch.float16:
- sim_scores_xy = sim_scores_xy.float()
- y = y.float()
- x = x.float()
- if teacher_masking != []:
- assert len(teacher_masking) == 1
- sim_scores_xy = sim_scores_xy.masked_fill(
- teacher_masking[0].unsqueeze(-1), float("-inf")
- )
- if student_masking != []:
- sim_scores_xy = sim_scores_xy.masked_fill(
- student_masking[0].unsqueeze(1), float("-inf")
- )
- # do masking
- y_weights = utils.softmax(sim_scores_xy, dim=-1)
- if teacher_masking != []:
- y_weights = y_weights.masked_fill(teacher_masking[0].unsqueeze(-1), 0)
- x_reconstruct_from_y = torch.bmm(y_weights, y)
-
- sim_scores_xx = torch.bmm(x, x.transpose(1, 2)) # batch X lenx X lenx ]
- x_weights = utils.softmax(sim_scores_xx, dim=-1)
- if teacher_masking != []:
- x_weights = x_weights.masked_fill(teacher_masking[0].unsqueeze(-1), 0)
-
- # no gradient for teacher state
- x_reconstruct_from_x = torch.bmm(x_weights, x).detach()
- cost = (x_reconstruct_from_x - x_reconstruct_from_y).norm(dim=2)
- if teacher_masking != []:
- cost = cost.masked_fill(teacher_masking[0], 0)
-
- if not self.cross_attentive_loss_with_norm:
- cost = cost / dim
- return cost
-
- def forward(
- self,
- prev_output_tokens,
- encoder_out,
- incremental_state=None,
- has_txt_input=False,
- **kwargs
- ):
- """
- Args:
- prev_output_tokens (LongTensor): previous decoder outputs of shape
- `(batch, tgt_len)`, for input feeding/teacher forcing. If there are
- two or more input during training, they will share the same prev_output_tokens
- encoder_out (tuple[Tensor]): output from the encoder, used for
- encoder-side attention. It will be tuple if there are more inputs, but a tensor
- if only one input
- incremental_state ([dict]): dictionary used for storing state during
- :ref:`Incremental decoding`. It is only valid for inference, only from single
- input
- Returns:
- tuple:
- - the last decoder layer's output of shape `(batch, tgt_len,
- vocab)`. If there are N inputs, batch will be N bigger than a single input
- - the last decoder layer's attention weights of shape `(batch,
- tgt_len, src_len)`
- """
- assert not isinstance(encoder_out, EncoderOut)
- if isinstance(encoder_out, tuple): # training with mulitple input
- rst = []
- assert len(encoder_out) == 2
- for i, eo in enumerate(encoder_out):
- assert incremental_state is None
- if i == 0:
- rst.append(
- self.spch_decoder(prev_output_tokens, eo, incremental_state)
- )
- else:
- rst.append(
- self.text_decoder(prev_output_tokens, eo, incremental_state)
- )
- dec_out = torch.cat([r[0] for r in rst], dim=0)
- attn_cost = None
- if self.compute_cross_attentive_loss:
- assert isinstance(encoder_out[0], dict)
- if self.cross_attentive_loss_reverse:
- attn_cost = self.cross_attentive_loss(
- teacher_states=encoder_out[1]["encoder_states"], # text_states
- student_states=encoder_out[0]["encoder_states"], # spch_states
- teacher_masking=encoder_out[1]["encoder_padding_mask"],
- student_masking=encoder_out[0]["encoder_padding_mask"],
- )
- else:
- attn_cost = self.cross_attentive_loss(
- teacher_states=encoder_out[0]["encoder_states"], # spch_states
- student_states=encoder_out[1]["encoder_states"], # text_states
- teacher_masking=encoder_out[0]["encoder_padding_mask"],
- student_masking=encoder_out[1]["encoder_padding_mask"],
- )
-
- return (dec_out, {"attn_cost": attn_cost})
- else: # inference or training with one input
- if has_txt_input:
- return self.text_decoder(
- prev_output_tokens, encoder_out, incremental_state
- )
- return self.spch_decoder(prev_output_tokens, encoder_out, incremental_state)
-
-
-# Note:
-# dual input transformer:
-# encoder: S2TTransformerEncoder for speech + TransformerEncoder for text
-# decoder: TransformerDecoder for text
-@register_model("dual_input_s2t_transformer")
-class DualInputS2TTransformerModel(FairseqEncoderDecoderModel):
- def __init__(self, encoder, decoder):
- super().__init__(encoder, decoder)
- self.num_updates = 0
-
- def max_positions(self):
- return None # it is provided in task
-
- @staticmethod
- def add_args(parser):
- """Add model-specific arguments to the parser."""
- # encoder 1: S2TTransformerEncoder for speech
- parser.add_argument(
- "--conv-kernel-sizes",
- type=str,
- metavar="N",
- help="kernel sizes of Conv1d subsampling layers",
- )
- parser.add_argument(
- "--conv-channels",
- type=int,
- metavar="N",
- help="# of channels in Conv1d subsampling layers",
- )
- parser.add_argument(
- "--enc-output-dim",
- type=int,
- metavar="N",
- help="""
- encoder output dimension, can be None. If specified, projecting the
- transformer output to the specified dimension""",
- )
- # standard Transformer
- parser.add_argument(
- "--activation-fn",
- type=str,
- default="relu",
- choices=utils.get_available_activation_fns(),
- help="activation function to use",
- )
- parser.add_argument(
- "--dropout", type=float, metavar="D", help="dropout probability"
- )
- parser.add_argument(
- "--attention-dropout",
- type=float,
- metavar="D",
- help="dropout probability for attention weights",
- )
- parser.add_argument(
- "--activation-dropout",
- "--relu-dropout",
- type=float,
- metavar="D",
- help="dropout probability after activation in FFN.",
- )
- parser.add_argument(
- "--encoder-embed-dim",
- type=int,
- metavar="N",
- help="encoder embedding dimension",
- )
- parser.add_argument(
- "--encoder-text-embed-dim",
- type=int,
- metavar="N",
- help="encoder text embedding dimension",
- )
- parser.add_argument(
- "--encoder-ffn-embed-dim",
- type=int,
- metavar="N",
- help="encoder embedding dimension for FFN",
- )
- parser.add_argument(
- "--encoder-attention-heads",
- type=int,
- metavar="N",
- help="num encoder attention heads",
- )
- parser.add_argument(
- "--decoder-embed-dim",
- type=int,
- metavar="N",
- help="decoder embedding dimension",
- )
- parser.add_argument(
- "--decoder-ffn-embed-dim",
- type=int,
- metavar="N",
- help="decoder embedding dimension for FFN",
- )
- parser.add_argument(
- "--decoder-layers", type=int, metavar="N", help="num decoder layers"
- )
- parser.add_argument(
- "--decoder-attention-heads",
- type=int,
- metavar="N",
- help="num decoder attention heads",
- )
- parser.add_argument(
- "--layernorm-embedding",
- action="store_true",
- help="add layernorm to embedding",
- )
- parser.add_argument(
- "--no-scale-embedding",
- action="store_true",
- help="if True, dont scale embeddings",
- )
- # non-standard transformer parameters
- parser.add_argument(
- "--speech-encoder-layers",
- type=int,
- metavar="N",
- help="num speech encoder layers",
- )
- parser.add_argument(
- "--text-encoder-layers",
- type=int,
- metavar="N",
- help="num text encoder layers",
- )
- parser.add_argument(
- "--encoder-shared-layers",
- type=int,
- metavar="N",
- help="num shared encoder layers",
- )
- parser.add_argument(
- "--encoder-shared-layer-level",
- type=int,
- metavar="N",
- default=0,
- choices=[0, 1, 2],
- help="share layer level 0: all share 1: all share with separate model 2: share weight but not bias and layernorm",
- )
-
- parser.add_argument(
- "--decoder-shared-layer-level",
- default=0,
- choices=[0, 1, 2],
- type=int,
- metavar="N",
- help="0: share everything; 1: share everything with different model 2: no share layer_norm and bias",
- )
- ###
- parser.add_argument(
- "--text-input-cost-ratio",
- type=float,
- default=1.0,
- metavar="V",
- help="text input cost ratio relative to speech input cost",
- )
- parser.add_argument(
- "--init-scale",
- type=float,
- default=1.0,
- metavar="V",
- help="scale the initial weight by given factor",
- )
- parser.add_argument(
- "--enc-grad-mult",
- type=float,
- metavar="V",
- default=1.0,
- help="multiply enc1 and enc2 gradient by V",
- )
- parser.add_argument(
- "--enc2-along-grad-mult",
- type=float,
- metavar="V",
- default=1.0,
- help="multiply enc2 gradient by V if only enc2 is used",
- )
- parser.add_argument(
- "--load-pretrain-encoder",
- type=str,
- default="",
- metavar="EXPR",
- help=""" path to the pretrained encoder """,
- )
- parser.add_argument(
- "--load-pretrain-speech-encoder",
- type=str,
- default="",
- metavar="EXPR",
- help=""" path to the pretrained speech encoder """,
- )
- parser.add_argument(
- "--load-pretrain-text-encoder",
- type=str,
- default="",
- metavar="EXPR",
- help=""" path to the pretrained text encoder """,
- )
- parser.add_argument(
- "--load-pretrain-text-encoder-last",
- type=str,
- default="",
- metavar="EXPR",
- help=""" path to the pretrained text encoder """,
- )
- parser.add_argument(
- "--load-pretrain-decoder",
- type=str,
- metavar="EXPR",
- default="",
- help=""" path to the pretrained encoder """,
- )
- parser.add_argument(
- "--add-speech-eos",
- action="store_true",
- help="add eos token at the end of input feature",
- )
- parser.add_argument(
- "--speech-encoder-adapter-type",
- type=str,
- metavar="EXPR",
- default="None",
- choices=["None", "Linear", "MLP"],
- help="add speech encoder adapter",
- )
-
- @classmethod
- def build_encoder(cls, args, task):
- spch_encoder = DualInputEncoder.build_spch_encoder(args)
- text_encoder = DualInputEncoder.build_text_encoder(
- args, task.src_dict, spch_encoder
- )
- cross_attentive_loss_before_last_layer = (
- 0 if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else -1
- )
- encoder = DualInputEncoder(
- args,
- spch_encoder,
- text_encoder,
- task.src_dict,
- cross_attentive_loss_before_last_layer,
- )
- if args.init_scale != 1.0:
- with torch.no_grad():
- for param in encoder.parameters():
- param.data.mul_(args.init_scale)
- if args.load_pretrain_text_encoder != "":
- checkpoint_utils.load_pretrained_component_from_model(
- text_encoder, args.load_pretrain_text_encoder
- )
- if args.load_pretrain_speech_encoder != "":
- if hasattr(spch_encoder, "encoder"):
- checkpoint_utils.load_pretrained_component_from_model(
- spch_encoder.encoder, args.load_pretrain_speech_encoder
- )
- else:
- checkpoint_utils.load_pretrained_component_from_model(
- spch_encoder, args.load_pretrain_speech_encoder
- )
- if (
- args.load_pretrain_text_encoder_last != ""
- ): # if share encoder, speech encoder parameters will be used.
- # It provides a chance to use pre-trained mt encoder instead
- checkpoint_utils.load_pretrained_component_from_model(
- text_encoder, args.load_pretrain_text_encoder_last
- )
-
- if args.load_pretrain_encoder != "":
- checkpoint_utils.load_pretrained_component_from_model(
- encoder, args.load_pretrain_encoder
- )
- return encoder
-
- @classmethod
- def build_decoder(cls, args, task):
- dec_cfg = {
- "decoder_layerdrop": args.decoder_layerdrop,
- "share_decoder_input_output_embed": args.share_decoder_input_output_embed,
- "decoder_embed_dim": args.decoder_embed_dim,
- "max_target_positions": args.max_target_positions,
- "dropout": args.dropout,
- "encoder_learned_pos": args.encoder_learned_pos,
- "decoder_learned_pos": args.decoder_learned_pos,
- "layernorm_embedding": args.layernorm_embedding,
- "decoder_normalize_before": args.decoder_normalize_before,
- "activation_dropout": args.activation_dropout,
- "attention_dropout": args.attention_dropout,
- "decoder_ffn_embed_dim": args.decoder_ffn_embed_dim,
- "decoder_layers": args.decoder_layers,
- "decoder_attention_heads": args.decoder_attention_heads,
- "decoder_output_dim": args.decoder_embed_dim,
- "no_scale_embedding": args.no_scale_embedding,
- "adaptive_input": args.adaptive_input,
- "quant_noise_pq": args.quant_noise_pq,
- "adaptive_softmax_cutoff": args.adaptive_softmax_cutoff,
- "tie_adaptive_weights": args.tie_adaptive_weights,
- "no_token_positional_embeddings": args.no_token_positional_embeddings,
- }
- dec_cfg = namedtuple("args", dec_cfg.keys())(*dec_cfg.values())
- dec_emb = nn.Embedding(
- len(task.target_dictionary),
- args.decoder_embed_dim,
- task.target_dictionary.pad(),
- )
- compute_cross_attentive_loss = (
- True if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else False
- )
- cross_attentive_loss_without_norm = getattr(
- args, "attentive_cost_without_normalize", False
- )
- cross_attentive_loss_reverse = (
- False # getattr(args, "attentive_cost_reverse", False)
- )
-
- text_decoder = TransformerDecoder(dec_cfg, task.target_dictionary, dec_emb)
- spch_decoder = TransformerDecoder(dec_cfg, task.target_dictionary, dec_emb)
- spch_decoder = TransformerMultiInputDecoder.share_spchdecoder(
- args, text_decoder, spch_decoder
- )
- decoder = TransformerMultiInputDecoder(
- dictionary=task.target_dictionary,
- spch_decoder=spch_decoder,
- text_decoder=text_decoder,
- compute_cross_attentive_loss=compute_cross_attentive_loss,
- cross_attentive_loss_with_norm=True
- if not cross_attentive_loss_without_norm
- else False,
- cross_attentive_loss_reverse=cross_attentive_loss_reverse,
- )
- if args.init_scale != 1.0:
- with torch.no_grad():
- for param in decoder.parameters():
- param.data.mul_(args.init_scale)
- if args.load_pretrain_decoder != "":
- try:
- checkpoint_utils.load_pretrained_component_from_model(
- decoder, args.load_pretrain_decoder
- )
- except RuntimeError:
- checkpoint_utils.load_pretrained_component_from_model(
- decoder.text_decoder, args.load_pretrain_decoder
- )
- if args.decoder_shared_layer_level > 0:
- checkpoint_utils.load_pretrained_component_from_model(
- decoder.spch_decoder, args.load_pretrain_decoder
- )
-
- return decoder
-
- @classmethod
- def build_model(cls, args, task):
- """Build a new model instance."""
- # make sure that all args are properly defaulted
- # (in case there are any new ones)
- dualinputs2ttransformer_base(args)
-
- encoder = cls.build_encoder(args, task)
- decoder = cls.build_decoder(args, task)
- return cls(encoder, decoder)
-
- def get_normalized_probs(self, net_output, log_probs, sample=None):
- # net_output['encoder_out'] is a (B, T, D) tensor
- lprobs = super().get_normalized_probs(net_output, log_probs, sample)
- lprobs.batch_first = True
- return lprobs
-
- def set_num_updates(self, num_updates):
- """Set the number of parameters updates."""
- super().set_num_updates(num_updates)
- self.num_updates = num_updates
-
- def forward(
- self,
- src_tokens,
- src_lengths,
- prev_output_tokens,
- use_encoder_outputs=False,
- src_txt_tokens=None,
- src_txt_lengths=None,
- mode="sup_speech",
- **kwargs
- ):
- """
- Run the forward pass for an encoder-decoder model.
-
- First feed a batch of source tokens through the encoder. Then, feed the
- encoder output and previous decoder outputs (i.e., teacher forcing) to
- the decoder to produce the next outputs::
-
- encoder_out = self.encoder(src_tokens, src_lengths)
- return self.decoder(prev_output_tokens, encoder_out)
-
- Args:
- src_tokens (LongTensor): tokens in the source language of shape
- `(batch, src_len)`
- src_lengths (LongTensor): source sentence lengths of shape `(batch)`
- prev_output_tokens (LongTensor): previous decoder outputs of shape
- `(batch, tgt_len)`, for teacher forcing
- mode = 'sup_speech' or 'text'
-
- Returns:
- tuple:
- - the decoder's output of shape `(batch, tgt_len, vocab)`
- - a dictionary with any model-specific outputs
- """
- if mode == "text":
- assert src_txt_tokens is None
- src_txt_tokens = src_tokens
- src_txt_lengths = src_lengths
- src_tokens = None
- src_lengths = None
- encoder_out = self.encoder(
- src_tokens,
- src_lengths=src_lengths,
- src_txt_tokens=src_txt_tokens,
- src_txt_lengths=src_txt_lengths,
- **kwargs
- )
- has_txt_input = True if src_txt_tokens is not None else False
- decoder_out = self.decoder(
- prev_output_tokens,
- encoder_out=encoder_out,
- has_txt_input=has_txt_input,
- **kwargs
- )
- if use_encoder_outputs:
- return decoder_out, encoder_out
- return decoder_out
-
-
-@register_model_architecture(
- "dual_input_s2t_transformer", "dualinputs2ttransformer_base"
-)
-def dualinputs2ttransformer_base(args):
- args.encoder_freezing_updates = getattr(args, "encoder_freezing_updates", 0)
- # Convolutional subsampler
- args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
- args.conv_kernel_sizes = getattr(args, "conv_kernel_sizes", "5,5")
- args.conv_channels = getattr(args, "conv_channels", 1024)
- # Transformer
- args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
- args.encoder_text_embed_dim = getattr(
- args, "encoder_text_embed_dim", args.encoder_embed_dim
- )
- args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
- args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
- args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
- args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0)
- args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
-
- args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
- args.decoder_ffn_embed_dim = getattr(
- args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
- )
- args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
- args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
- args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
- args.dropout = getattr(args, "dropout", 0.1)
- args.attention_dropout = getattr(args, "attention_dropout", args.dropout)
- args.activation_dropout = getattr(args, "activation_dropout", args.dropout)
- args.activation_fn = getattr(args, "activation_fn", "relu")
- args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
- args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
- args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False)
- args.share_decoder_input_output_embed = getattr(
- args, "share_decoder_input_output_embed", False
- )
- args.no_token_positional_embeddings = getattr(
- args, "no_token_positional_embeddings", False
- )
- args.adaptive_input = getattr(args, "adaptive_input", False)
- args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
- args.decoder_output_dim = getattr(
- args, "decoder_output_dim", args.decoder_embed_dim
- )
- args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
- args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
- args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
-
- args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 10)
- args.text_encoder_layers = getattr(args, "text_encoder_layers", 6)
- args.encoder_shared_layers = getattr(args, "encoder_shared_layers", 0)
- args.decoder_layers = getattr(args, "decoder_layers", 6)
-
- args.add_speech_eos = getattr(args, "add_speech_eos", False)
-
-
-@register_model_architecture("dual_input_s2t_transformer", "dualinputs2ttransformer_s")
-def dualinputs2ttransformer_s(args):
- args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
- args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 4)
- args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
- args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
- args.dropout = getattr(args, "dropout", 0.1)
- args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 7)
- args.text_encoder_layers = getattr(args, "text_encoder_layers", 7)
- args.decoder_layers = getattr(args, "decoder_layers", 7)
- dualinputs2ttransformer_base(args)
-
-
-@register_model_architecture("dual_input_s2t_transformer", "dualinputs2ttransformer_m")
-def dualinputs2ttransformer_m(args):
- args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
- args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512 * 4)
- args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
- args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
- args.dropout = getattr(args, "dropout", 0.15)
- args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 10)
- args.text_encoder_layers = getattr(args, "text_encoder_layers", 6)
- args.decoder_layers = getattr(args, "decoder_layers", 6)
- dualinputs2ttransformer_base(args)
-
-
-@register_model_architecture("dual_input_s2t_transformer", "dualinputs2ttransformer_b")
-def dualinputs2ttransformer_b(args):
- args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768)
- args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 768 * 4)
- args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12)
- args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12)
- args.dropout = getattr(args, "dropout", 0.15)
- args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 12)
- args.text_encoder_layers = getattr(args, "text_encoder_layers", 6)
- args.decoder_layers = getattr(args, "decoder_layers", 6)
- dualinputs2ttransformer_base(args)
-
-
-@register_model_architecture("dual_input_s2t_transformer", "dualinputs2ttransformer_l")
-def dualinputs2ttransformer_l(args):
- args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
- args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024 * 4)
- args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
- args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
- args.dropout = getattr(args, "dropout", 0.2)
- args.speech_encoder_layers = getattr(args, "speech_encoder_layers", 12)
- args.text_encoder_layers = getattr(args, "text_encoder_layers", 6)
- args.decoder_layers = getattr(args, "decoder_layers", 6)
- dualinputs2ttransformer_base(args)
diff --git a/spaces/multimodalart/latentdiffusion/latent-diffusion/ldm/modules/losses/__init__.py b/spaces/multimodalart/latentdiffusion/latent-diffusion/ldm/modules/losses/__init__.py
deleted file mode 100644
index 876d7c5bd6e3245ee77feb4c482b7a8143604ad5..0000000000000000000000000000000000000000
--- a/spaces/multimodalart/latentdiffusion/latent-diffusion/ldm/modules/losses/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Borislav Pekic Vreme Cuda 32.pdf.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Borislav Pekic Vreme Cuda 32.pdf.md
deleted file mode 100644
index 0fd6562739e112056f47dcc79a9830e8aeec3bd7..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Borislav Pekic Vreme Cuda 32.pdf.md
+++ /dev/null
@@ -1,14 +0,0 @@
-Borislav Pekic: Vreme Cuda - A Novel of Miracles and Wonders
-Borislav Pekic was one of the most prominent Serbian writers of the 20th century. His novel Vreme Cuda (Time of Miracles) is a masterpiece of modern literature that explores the themes of faith, doubt, history and human nature. The novel is set in the first century AD, during the time of Jesus Christ and his followers. It tells the story of four men who witness different miracles performed by Jesus and how they react to them. The novel challenges the conventional views of religion and morality, and offers a complex and original perspective on the meaning of life.
-Borislav Pekic Vreme Cuda 32.pdf Download Zip » https://urlcod.com/2uIbBu
The novel consists of three parts: Vreme Cuda (Time of Miracles), Vreme Umiranja (Time of Dying) and Vreme Reci (Time of Words). The first part consists of seven chapters, each describing a different miracle that Jesus performs in various locations in Palestine. The second part focuses on the events leading to Jesus' crucifixion and resurrection, as seen through the eyes of four different characters: Judas Iscariot, Pontius Pilate, Joseph of Arimathea and Mary Magdalene. The third part, which was never completed by Pekic, was supposed to depict the aftermath of Jesus' death and the spread of Christianity.
-The novel is not only a historical fiction, but also a philosophical and theological exploration of the nature of miracles, faith and truth. Pekic uses various literary techniques, such as irony, satire, symbolism and intertextuality, to create a rich and multi-layered narrative that challenges the reader's assumptions and expectations. The novel also contains references to other works of literature, art, history and culture, such as the Bible, Greek mythology, Dante's Divine Comedy , Shakespeare's Hamlet , Kafka's The Trial and Picasso's Guernica .
-Vreme Cuda is a novel that will appeal to anyone who enjoys reading a well-written and thought-provoking story that explores the deepest questions of human existence. It is a novel that will make you wonder about the nature of reality, the role of religion in society, the meaning of suffering and redemption, and the power of words and stories. It is a novel that will make you question your own beliefs and values, and inspire you to seek your own answers.
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-Borislav Pekic was not only a novelist, but also a political activist and a dissident. He was one of the founding members of the Democratic Party in Serbia, which was established in 1990 as an opposition to the regime of Slobodan Milosevic. He was also involved in various human rights and cultural initiatives, such as the Committee for the Defense of Freedom of Thought and Expression, the Helsinki Committee for Human Rights, and the Association of Writers of Serbia. He advocated for democratic reforms, civil society, and European integration. He was also critical of nationalism, totalitarianism, and dogmatism in any form.
-Borislav Pekic lived in London from 1971 until his death in 1992. He moved to England after he received a grant from the Ford Foundation to write a study on the causes of violence in society. He continued to write novels, essays, and articles in exile, and maintained close contacts with his readers and fellow writers in Yugoslavia and abroad. He also lectured at various universities and cultural institutions in Europe and America. He died of lung cancer at the age of 62. He was buried at the New Cemetery in Belgrade.
-
-Borislav Pekic left behind a rich and diverse literary legacy that spans various genres, styles, and themes. His novels are characterized by a combination of realism, fantasy, satire, humor, and allegory. His works reflect his profound knowledge of history, philosophy, psychology, religion, and art. His novels are also influenced by his personal experiences of imprisonment, exile, and political engagement. His works have been praised for their originality, creativity, and intellectual depth. His novels have also been adapted for theater, film, radio, and television.
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-``` 81aa517590Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar: A Comprehensive Review
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-
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-To add a model to your tone, drag and drop it from the model browser on the left side of the tone panel to one of the slots on the signal path on the right side of the tone panel.
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-To change the order of the models in your tone, drag and drop them to different slots on the signal path.
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-The mixer panel is where you can control the volume, pan, mute, solo, and output of your tones. You can navigate and customize the mixer panel by following these steps:
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-To change the volume of a tone, use the fader on the corresponding channel. You can also use the master fader to change the overall volume of all tones.
-To change the pan of a tone, use the knob on the corresponding channel. You can also use the master knob to change the overall pan of all tones.
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-To resize the mixer panel, click on the resize icon on the bottom right corner of the mixer panel. You can drag the icon to adjust the size of the mixer panel.
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- The tuner
-The tuner is where you can tune your instrument using a chromatic or a standard tuner. You can navigate and customize the tuner by following these steps:
-
-To activate or deactivate the tuner, click on the tuner icon on the top left corner of the main screen.
-To switch between chromatic or standard tuner, click on the mode button on the top right corner of the tuner.
-To tune your instrument, play a note and watch the needle and the display on the tuner. The needle will indicate if your note is flat, sharp, or in tune. The display will show you the note name and octave number. You can adjust your tuning until the needle is centered and green.
-To calibrate the tuner, use the calibration knob on the bottom left corner of the tuner. You can change the reference pitch from 440 Hz to any value between 435 Hz and 445 Hz.
-
- The metronome
-The metronome is where you can set the tempo, time signature, and accent of your project. You can navigate and customize the metronome by following these steps:
-
-To activate or deactivate the metronome, click on the metronome icon on the top left corner of the main screen.
-To change the tempo, use the knob or the tap button on the top right corner of the metronome. You can also type in a value in the tempo box.
-To change the time signature, use the drop-down menu on the bottom left corner of the metronome. You can choose between different time signatures such as 4/4, 3/4, 6/8, etc.
-To change the accent, use the drop-down menu on the bottom right corner of the metronome. You can choose between different accent patterns such as quarter notes, eighth notes, sixteenth notes, etc.
-
- How to create and edit tones with Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar?
-To create and edit tones with Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar , you need to follow these steps:
- How to create a tone from scratch?
-To create a tone from scratch, you need to follow these steps:
-
-Launch Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar as a standalone application or a plugin.
-Select an empty preset slot from the preset browser on the top right corner of the tone panel.
-Add an amp model to your tone by dragging and dropping it from the model browser to the first slot on the signal path.
-Add a cab model to your tone by dragging and dropping it from the model browser to the second slot on the signal path.
-Add any effects or studio gear models to your tone by dragging and dropping them from the model browser to any other slots on the signal path.
-Adjust the parameters of each model using their control panels.
-Save your tone as a preset by clicking on the save icon on the top right corner of the tone panel. You can name your preset and assign it to a category.
-
- How to edit an existing tone?
-To edit an existing tone, you need to follow these steps:
-
-Launch Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar as a standalone application or a plugin.
-Select a preset slot from the preset browser on the top right corner of the tone panel that contains the tone you want to edit.
-Add, remove, or rearrange any models in your tone by dragging and dropping them on the signal path.
-Adjust the parameters of each model using their control panels.
-Save your tone as a new preset or overwrite the existing one by clicking on the save icon on the top right corner of the tone panel. You can name your preset and assign it to a category.
-
- What are the pros and cons of Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar?
-Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar has its advantages and disadvantages. Here are some of the pros and cons of Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar :
- Pros of Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar
-
-It offers a wide range of models of amps, cabs, effects, and studio gear that can suit any genre and style of music.
-It allows you to create dual signal paths with up to 20 effects per path for more creative possibilities.
-It has a low-latency mode that improves the performance and responsiveness of the software.
-It has a drag-and-drop interface that makes it easy to arrange and edit your models.
-It has a tone sharing feature that allows you to share your tones with other users online and download tones from other users.
-
- Cons of Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar
-
-It is an illegal and unauthorized copy of Line 6 Pod Farm Platinum v2.5 that may contain viruses, malware, or other harmful components.
-It does not require activation or authorization, but this also means that you will not be able to access some features and services that are only available for registered users of Line 6 Pod Farm Platinum v2.5 .
-It may not be compatible with some DAWs, audio interfaces, or Line 6 devices.
-It may not receive any updates or support from Line 6.
-It may violate the terms and conditions of Line 6 and cause legal issues.
-
- Conclusion
-Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar is a software package that contains the latest version of Line 6 Pod Farm , a DAW that simulates hundreds of amps, cabs, effects, and studio gear. It can be used as a standalone application or as a plugin for other DAWs. It offers many features and benefits such as dual signal paths, low-latency mode, tone sharing, and more.
-However, Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar is also an illegal and unauthorized copy of Line 6 Pod Farm Platinum v2.5 that may contain viruses, malware, or other harmful components. It does not require activation or authorization, but this also means that you will not be able to access some features and services that are only available for registered users of Line 6 Pod Farm Platinum v2.5 . It may not be compatible with some DAWs, audio interfaces, or Line 6 devices. It may not receive any updates or support from Line 6. It may violate the terms and conditions of Line 6 and cause legal issues.
-The only legitimate way to obtain Line 6 Pod Farm Platinum v2.5 is to purchase it from the official website of Line 6 or from an authorized dealer. The price of Line 6 Pod Farm Platinum v2.5 is $299.99 USD. You can also try a free trial version of Line 6 Pod Farm Platinum v2.5 for 15 days before buying it.
-We hope that this article has helped you understand what Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar is, how to download and install it, how to use it, and what are its pros and cons. If you have any questions or comments, please feel free to leave them below.
- FAQs
-Here are some frequently asked questions about Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar :
-
-Is Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar safe to use? No, Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar is not safe to use. It is an illegal and unauthorized copy of Line 6 Pod Farm Platinum v2.5 that may contain viruses, malware, or other harmful components. It may damage your computer, compromise your security, and violate the terms and conditions of Line 6.
-What is the difference between Line 6 Pod Farm Platinum v2.5 and Line 6 Pod Farm 2.5? Line 6 Pod Farm Platinum v2.5 is the ultimate version of Line 6 Pod Farm 2.5 . It contains more models of amps, cabs, effects, and studio gear than the standard or gold versions of Line 6 Pod Farm 2.5 . It also offers more features and benefits such as dual signal paths, low-latency mode, tone sharing, and more.
-Can I use Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar with Pro Tools? Yes, you can use Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar with Pro Tools as a plugin in the RTAS format. However, you may encounter some compatibility issues as Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar is not an official release of Line 6 and may not be supported by Pro Tools.
-Can I use Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar with other Line 6 products and devices? No, you cannot use Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar with other Line 6 products and devices such as PODxt, POD X3, POD HD, or TonePort. This is because Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar does not require activation or authorization and does not recognize any Line 6 hardware as a valid license.
-How can I get the latest updates and support for Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar? You cannot get the latest updates and support for Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar . This is because Line 6 Pod Farm Platinum v2.5 RTAS VST VST64.rar is not an official release of Line 6 and does not receive any updates or support from Line 6.
- b2dd77e56bTamil Movie Tomb Raider (English) Full Movie Download: How to Watch the Action-Adventure Film Online
-Tomb Raider is a 2018 action-adventure film based on the video game franchise of the same name. It stars Alicia Vikander as Lara Croft, the daughter of a missing adventurer who embarks on a perilous journey to find him and uncover his secrets. The film was directed by Roar Uthaug and also features Dominic West, Walton Goggins, Daniel Wu, and Kristin Scott Thomas in supporting roles.
-Tomb Raider was released in theaters on March 16, 2018 and received mixed reviews from critics and audiences. The film was praised for its action sequences, visuals, and Vikander's performance, but criticized for its plot, dialogue, and lack of originality. The film grossed $274 million worldwide against a budget of $94 million.
-Tamil Movie Tomb Raider (English) Full Movie Download Download File ✔ https://urlcod.com/2uIbeL
If you missed Tomb Raider in theaters or want to watch it again, you might be wondering how to download or stream the film online. Here are some of the options available for Tamil movie fans who want to watch Tomb Raider (English) full movie online:
-
-YouTube: You can rent or buy Tomb Raider (English) on YouTube for $3.99 or $14.99 respectively. You can also watch the Tamil dubbed version of the film for free on the Mr Hollywood Tamizhan channel[^1^]. However, this is not an official source and the quality and legality of the video may vary.
-Amazon Prime Video: You can rent or buy Tomb Raider (English) on Amazon Prime Video for $3.99 or $14.99 respectively. You can also watch the film with subtitles in various languages, including Tamil.
-Apple iTunes: You can rent or buy Tomb Raider (English) on Apple iTunes for $4.99 or $19.99 respectively. You can also watch the film with subtitles in various languages, including Tamil.
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-Google Play Movies: You can rent or buy Tomb Raider (English) on Google Play Movies for $3.99 or $14.99 respectively. You can also watch the film with subtitles in various languages, including Tamil.
-Redbox: You can rent Tomb Raider (English) on Redbox for $2.99 or buy it for $14.99.
-fuboTV: You can stream Tomb Raider (English) on fuboTV if you have a subscription to the service.
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-AMC on Demand: You can rent or buy Tomb Raider (English) on AMC on Demand for $6.99 or $14.99 respectively.
-
-Before you download or stream Tomb Raider (English) full movie online, make sure you have a stable internet connection and enough storage space on your device. Also, be aware of the legal and ethical implications of piracy and copyright infringement. We do not endorse or promote any illegal or unauthorized sources of content.
-Tomb Raider (English) is a thrilling and entertaining film that will appeal to fans of action-adventure genre and video games. If you are looking for a Tamil movie to watch online, you can check out Tomb Raider (English) full movie download options and enjoy the film at your convenience.
-
e93f5a0c3fهمیار ریاضی ", unsafe_allow_html=True)
- a = st.radio('نوع عبارت را نتخواب کنید', ['عبارت جبری', 'معادله درجه یک','معادله درجه دو'])
- expression = st.text_input("عبارت را وارد کنید")
- st.write("\n")
-
-
- if expression:
- try:
- result = ebarat(expression,a)
- if a=='معادله درجه دو':
- chart,*res2=result.calculate()
- else:
- res2=result.calculate()
- if res2!="Eror":
- st.latex(f"{expression}:{sp.latex(res2)}")
- else:
- st.write("!عبارت وارد شده اشتباه است")
-
- if a=='معادله درجه دو':
- button = st.button('نمایش نمودار معادله')
- else : button=False
- if button:
- st.pyplot(chart)
- except:
- st.write("!عبارت وارد شده اشتباه است")
-
- with col2:
- st.markdown("مثال هایی از توانایی حل برنامه ", unsafe_allow_html=True)
- st.write("\n")
- st.latex("ax^2+bx+c")
- st.write("\n")
- st.latex("ax+b")
- st.write("\n")
- st.latex("a+b*c/d..")
-main()
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/tests/test_export_caffe2.py b/spaces/nikitaPDL2023/assignment4/detectron2/tests/test_export_caffe2.py
deleted file mode 100644
index 58e9f681c356d05e3d03b06b603721ed51840c5c..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/tests/test_export_caffe2.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# -*- coding: utf-8 -*-
-
-import copy
-import os
-import tempfile
-import unittest
-import torch
-from torch.hub import _check_module_exists
-
-from detectron2 import model_zoo
-from detectron2.utils.logger import setup_logger
-from detectron2.utils.testing import get_sample_coco_image
-
-try:
- # Caffe2 used to be included in PyTorch, but since PyTorch 1.10+,
- # Caffe2 is not included in pre-built packages. This is a safety BC check
- from detectron2.export import Caffe2Model, Caffe2Tracer
-except ImportError:
- raise unittest.SkipTest(
- f"PyTorch does not have Caffe2 support. Skipping all tests in {__name__}"
- ) from None
-
-
-# TODO: this test requires manifold access, see: T88318502
-# Running it on CircleCI causes crash, not sure why.
-@unittest.skipIf(os.environ.get("CIRCLECI"), "Caffe2 tests crash on CircleCI.")
-@unittest.skipIf(not _check_module_exists("onnx"), "ONNX not installed.")
-class TestCaffe2Export(unittest.TestCase):
- def setUp(self):
- setup_logger()
-
- def _test_model(self, config_path, device="cpu"):
- cfg = model_zoo.get_config(config_path)
- cfg.MODEL.DEVICE = device
- model = model_zoo.get(config_path, trained=True, device=device)
-
- inputs = [{"image": get_sample_coco_image()}]
- tracer = Caffe2Tracer(cfg, model, copy.deepcopy(inputs))
-
- with tempfile.TemporaryDirectory(prefix="detectron2_unittest") as d:
- if not os.environ.get("CI"):
- # This requires onnx, which is not yet available on public CI
- c2_model = tracer.export_caffe2()
- c2_model.save_protobuf(d)
- c2_model.save_graph(os.path.join(d, "test.svg"), inputs=copy.deepcopy(inputs))
-
- c2_model = Caffe2Model.load_protobuf(d)
- c2_model(inputs)[0]["instances"]
-
- ts_model = tracer.export_torchscript()
- ts_model.save(os.path.join(d, "model.ts"))
-
- def testMaskRCNN(self):
- self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
-
- @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
- def testMaskRCNNGPU(self):
- self._test_model("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", device="cuda")
-
- def testRetinaNet(self):
- self._test_model("COCO-Detection/retinanet_R_50_FPN_3x.yaml")
diff --git a/spaces/niks-salodkar/Age-Prediction-Demo/model.py b/spaces/niks-salodkar/Age-Prediction-Demo/model.py
deleted file mode 100644
index 5ee30ae863c0b0a67abd6a219ef0c63fd78217be..0000000000000000000000000000000000000000
--- a/spaces/niks-salodkar/Age-Prediction-Demo/model.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-
-from PIL import Image
-import torch
-import torchvision
-from torch import nn
-import torch.nn.functional as F
-from torchvision.transforms import Compose, Resize, ToTensor, Normalize
-
-
-class AgePredictResnet(nn.Module):
- def __init__(self):
- super().__init__()
- self.model = torchvision.models.resnet101()
- self.model.fc = nn.Linear(2048, 512)
- self.age_linear1 = nn.Linear(512, 256)
- self.age_linear2 = nn.Linear(256, 128)
- self.age_out = nn.Linear(128, 9)
- self.gender_linear1 = nn.Linear(512, 256)
- self.gender_linear2 = nn.Linear(256, 128)
- self.gender_out = nn.Linear(128, 2)
- self.race_linear1 = nn.Linear(512, 256)
- self.race_linear2 = nn.Linear(256, 128)
- self.race_out = nn.Linear(128, 5)
- self.activation = nn.ReLU()
- self.dropout = nn.Dropout(0.4)
-
- def forward(self, x):
- out = self.activation(self.model(x))
- age_out = self.activation(self.dropout((self.age_linear1(out))))
- age_out = self.activation(self.dropout(self.age_linear2(age_out)))
- age_out = self.age_out(age_out)
-
- gender_out = self.activation(self.dropout((self.gender_linear1(out))))
- gender_out = self.activation(self.dropout(self.gender_linear2(gender_out)))
- gender_out = self.gender_out(gender_out)
-
- race_out = self.activation(self.dropout((self.race_linear1(out))))
- race_out = self.activation(self.dropout(self.race_linear2(race_out)))
- race_out = self.race_out(race_out)
- return age_out, gender_out, race_out
-
-
-if __name__ == '__main__':
- trained_model_path = os.path.join('./final-models/resnet_101_weigthed.pt')
- model = AgePredictResnet()
- model.load_state_dict(torch.load(trained_model_path, map_location=torch.device('cpu')), strict=False)
- model.eval()
- sample_image = Image.open('../../age_prediction/data/wild_images/part1/50_1_1_20170110120147003.jpg')
- transforms = Compose([Resize((256, 256)), ToTensor(),
- Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
- transformed_image = transforms(sample_image)
- transformed_image = torch.unsqueeze(transformed_image, 0)
- print(transformed_image.shape)
- with torch.inference_mode():
- logits = model(transformed_image)
- age_prob = F.softmax(logits[0], dim=1)
- sex_prob = F.softmax(logits[1], dim=1)
- race_prob = F.softmax(logits[2], dim=1)
- top2_age = torch.topk(age_prob, 2, dim=1)
- sex = torch.argmax(sex_prob, dim=1)
- top2_race = torch.topk(race_prob, 2, dim=1)
- all_predictions = (list(top2_age.values.numpy().reshape(-1)), list(top2_age.indices.numpy().reshape(-1))), (
- sex.item(), sex_prob[0][sex.item()].item()), \
- (list(top2_race.values.numpy().reshape(-1)), list(top2_race.indices.numpy().reshape(-1)))
- print(all_predictions)
- age_dict = {
- 0: '0 to 10', 1: '10 to 20', 2: '20 to 30', 3: '30 to 40', 4: '40 to 50', 5: '50 to 60',
- 6: '60 to 70', 7: '70 to 80', 8: 'Above 80'
- }
- sex_dict = {0: 'Male', 1: 'Female'}
- race_dict = {
- 0: 'White', 1: 'Black', 2: 'Asian', 3: 'Indian', 4: 'Others (like Hispanic, Latino, Middle Eastern etc)'
- }
- #
- pred_dict = {
- 'Predicted Age range': (age_dict[all_predictions[0][1][0]], age_dict[all_predictions[0][1][1]]),
- 'Age Probability': all_predictions[0][0],
- 'Predicted Sex': sex_dict[all_predictions[1][0]],
- 'Sex Probability': all_predictions[1][1],
- 'Predicted Race': (race_dict[all_predictions[2][1][0]], race_dict[all_predictions[2][1][1]]),
- 'Race Probability': all_predictions[2][0],
- }
- print(pred_dict)
diff --git a/spaces/niro-private/chatCSV/setup.sh b/spaces/niro-private/chatCSV/setup.sh
deleted file mode 100644
index 2f9c06423de830d636c0fe9c0bbf974e2ab3a57d..0000000000000000000000000000000000000000
--- a/spaces/niro-private/chatCSV/setup.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-mkdir -p ~/.streamlit/
-
-echo "\
-[general]\n\
-email = \"yyvannbarbotts@gmail.com\"\n\
-" > ~/.streamlit/credentials.toml
-
-echo "\
-[server]\n\
-headless = true\n\
-enableCORS=false\n\
-port = $PORT\n\
-\n\
-[theme]\n\
-base = \"light\"\n\
-primaryColor = \"#89CFF0\"\n\
-backgroundColor = \"#E0F7FE\"\n\
-secondaryBackgroundColor = \"#FFFCE4\"\n\
-textColor = \"#000000\"\n\
-font = \"sans serif\"\n\
-" > ~/.streamlit/config.toml
diff --git a/spaces/noa101/autoevaluate-extractive-question-answering/app.py b/spaces/noa101/autoevaluate-extractive-question-answering/app.py
deleted file mode 100644
index 605a1d925025ec5492819cfc22399f0005cf15ba..0000000000000000000000000000000000000000
--- a/spaces/noa101/autoevaluate-extractive-question-answering/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/autoevaluate/extractive-question-answering").launch()
\ No newline at end of file
diff --git a/spaces/nomic-ai/BelleGroup_train_2M_CN/style.css b/spaces/nomic-ai/BelleGroup_train_2M_CN/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/BelleGroup_train_2M_CN/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/nomic-ai/squad/index.html b/spaces/nomic-ai/squad/index.html
deleted file mode 100644
index 556950977919df4ffcf4aee0b8782ab403dadfe8..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/squad/index.html
+++ /dev/null
@@ -1,42 +0,0 @@
-
-
-
- squad
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/nomic-ai/super_glue/README.md b/spaces/nomic-ai/super_glue/README.md
deleted file mode 100644
index 2ed2489868a8491184b7693f443528b7f2070f0f..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/super_glue/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-title: super_glue
-emoji: 🗺️
-colorFrom: purple
-colorTo: red
-sdk: static
-pinned: false
----
\ No newline at end of file
diff --git a/spaces/nupurkmr9/concept-ablation/app.py b/spaces/nupurkmr9/concept-ablation/app.py
deleted file mode 100644
index c9451038c05f3678e4594920e68be9e649e2a5f2..0000000000000000000000000000000000000000
--- a/spaces/nupurkmr9/concept-ablation/app.py
+++ /dev/null
@@ -1,236 +0,0 @@
-import gradio as gr
-import torch
-import sys
-from pathlib import Path
-from trainer import train_submit, inference
-
-import os
-model_map = {'Van Gogh' : 'models/vangogh_ablation_delta.bin',
- 'Greg Rutkowski' : 'models/greg_rutkowski_ablation_delta.bin',
- 'R2D2' : 'models/r2d2_delta.bin',
- 'Grumpy Cat' : 'models/grumpy_cat_delta.bin',
- }
-
-ORIGINAL_SPACE_ID = 'nupurkmr9/concept-ablation'
-SPACE_ID = os.getenv('SPACE_ID')
-
-SHARED_UI_WARNING = f'''## Attention - the demo requires at least 24GB VRAM for training. Please clone this repository to run on your own machine.
-{content}
" if classname else f"{content}
"
-
-
- def get_exif_data(image):
- items = image.info
-
- info = ''
- for key, text in items.items():
- info += f"""
-
-
{plaintext_to_html(str(key))}
-
{plaintext_to_html(str(text))}
-
' + e + ' '
- elif attention[e_idx] > 0.01 * focus:
- distribution[1] += 1
- raw += ' ' + e + ' '
- elif attention[e_idx] > 0.005 * focus:
- distribution[0] += 1
- raw += ' ' + e + ' '
- else:
- raw += ' ' + e
- else:
- raw += ' ' + e
-
- raw = re.sub(r'\s##', '', raw)
- raw = re.sub(r'\s(\.|,|!|\?|;|\))', r'\1', raw)
- raw = re.sub(r'\(\s', r'(', raw)
- raw = re.sub(r'\s(-|\'|’)\s', r'\1', raw)
- raw = re.sub(r'\s##',
- r'', raw)
- raw = raw.strip()
- raw = '' + raw + '
'
- return raw
-
-
-@ st.cache(allow_output_mutation=True)
-def load(model='distilbert-base-cased'):
- tokenizer = AutoTokenizer.from_pretrained(model)
- model = AutoModel.from_pretrained(model, output_attentions=True)
- return tokenizer, model
diff --git a/spaces/pharmapsychotic/sd-prism/share_btn.py b/spaces/pharmapsychotic/sd-prism/share_btn.py
deleted file mode 100644
index 797a92ed18e2616a5dbbbc60bf8db22d9e07d902..0000000000000000000000000000000000000000
--- a/spaces/pharmapsychotic/sd-prism/share_btn.py
+++ /dev/null
@@ -1,100 +0,0 @@
-community_icon_html = """
- """
-
-loading_icon_html = """
-${htmlImgsMd}
-
`;
-
- const params = new URLSearchParams({
- title: titleTxt,
- description: descriptionMd,
- });
-
- const paramsStr = params.toString();
- window.open(`https://huggingface.co/spaces/pharma/sd-prism/discussions/new?${paramsStr}`, '_blank');
-
- shareBtnEl.style.removeProperty('pointer-events');
- shareIconEl.style.removeProperty('display');
- loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/main.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/main.py
deleted file mode 100644
index 33c6d24cd85b55a9fb1b1e6ab784f471e2b135f0..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/main.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from typing import List, Optional
-
-
-def main(args: Optional[List[str]] = None) -> int:
- """This is preserved for old console scripts that may still be referencing
- it.
-
- For additional details, see https://github.com/pypa/pip/issues/7498.
- """
- from pip._internal.utils.entrypoints import _wrapper
-
- return _wrapper(args)
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/vendored/packaging/_structures.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/vendored/packaging/_structures.py
deleted file mode 100644
index 90a6465f9682c886363eea5327dac64bf623a6ff..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/wheel/vendored/packaging/_structures.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-
-class InfinityType:
- def __repr__(self) -> str:
- return "Infinity"
-
- def __hash__(self) -> int:
- return hash(repr(self))
-
- def __lt__(self, other: object) -> bool:
- return False
-
- def __le__(self, other: object) -> bool:
- return False
-
- def __eq__(self, other: object) -> bool:
- return isinstance(other, self.__class__)
-
- def __gt__(self, other: object) -> bool:
- return True
-
- def __ge__(self, other: object) -> bool:
- return True
-
- def __neg__(self: object) -> "NegativeInfinityType":
- return NegativeInfinity
-
-
-Infinity = InfinityType()
-
-
-class NegativeInfinityType:
- def __repr__(self) -> str:
- return "-Infinity"
-
- def __hash__(self) -> int:
- return hash(repr(self))
-
- def __lt__(self, other: object) -> bool:
- return True
-
- def __le__(self, other: object) -> bool:
- return True
-
- def __eq__(self, other: object) -> bool:
- return isinstance(other, self.__class__)
-
- def __gt__(self, other: object) -> bool:
- return False
-
- def __ge__(self, other: object) -> bool:
- return False
-
- def __neg__(self: object) -> InfinityType:
- return Infinity
-
-
-NegativeInfinity = NegativeInfinityType()
diff --git a/spaces/plzdontcry/dakubettergpt/src/App.tsx b/spaces/plzdontcry/dakubettergpt/src/App.tsx
deleted file mode 100644
index b59abdc3163a74bcc7c276c908b4943c4997d7ce..0000000000000000000000000000000000000000
--- a/spaces/plzdontcry/dakubettergpt/src/App.tsx
+++ /dev/null
@@ -1,85 +0,0 @@
-import React, { useEffect } from 'react';
-import useStore from '@store/store';
-import i18n from './i18n';
-
-import Chat from '@components/Chat';
-import Menu from '@components/Menu';
-
-import useInitialiseNewChat from '@hooks/useInitialiseNewChat';
-import { ChatInterface } from '@type/chat';
-import { Theme } from '@type/theme';
-import Toast from '@components/Toast';
-
-function App() {
- const initialiseNewChat = useInitialiseNewChat();
- const setChats = useStore((state) => state.setChats);
- const setTheme = useStore((state) => state.setTheme);
- const setApiKey = useStore((state) => state.setApiKey);
- const setCurrentChatIndex = useStore((state) => state.setCurrentChatIndex);
-
- useEffect(() => {
- document.documentElement.lang = i18n.language;
- i18n.on('languageChanged', (lng) => {
- document.documentElement.lang = lng;
- });
- }, []);
-
- useEffect(() => {
- // legacy local storage
- const oldChats = localStorage.getItem('chats');
- const apiKey = localStorage.getItem('apiKey');
- const theme = localStorage.getItem('theme');
-
- if (apiKey) {
- // legacy local storage
- setApiKey(apiKey);
- localStorage.removeItem('apiKey');
- }
-
- if (theme) {
- // legacy local storage
- setTheme(theme as Theme);
- localStorage.removeItem('theme');
- }
-
- if (oldChats) {
- // legacy local storage
- try {
- const chats: ChatInterface[] = JSON.parse(oldChats);
- if (chats.length > 0) {
- setChats(chats);
- setCurrentChatIndex(0);
- } else {
- initialiseNewChat();
- }
- } catch (e: unknown) {
- console.log(e);
- initialiseNewChat();
- }
- localStorage.removeItem('chats');
- } else {
- // existing local storage
- const chats = useStore.getState().chats;
- const currentChatIndex = useStore.getState().currentChatIndex;
- if (!chats || chats.length === 0) {
- initialiseNewChat();
- }
- if (
- chats &&
- !(currentChatIndex >= 0 && currentChatIndex < chats.length)
- ) {
- setCurrentChatIndex(0);
- }
- }
- }, []);
-
- return (
-
-
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diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Textbox-0b63ef8a.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Textbox-0b63ef8a.js
deleted file mode 100644
index d3adeca300f3d0a002c08a103fc0ef9a33524cac..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Textbox-0b63ef8a.js
+++ /dev/null
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diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/mpl_toolkits/axisartist/axis_artist.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/mpl_toolkits/axisartist/axis_artist.py
deleted file mode 100644
index 407ad07a3dc2bdc2c586e4b871cdfbb52f17e7c1..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/mpl_toolkits/axisartist/axis_artist.py
+++ /dev/null
@@ -1,1115 +0,0 @@
-"""
-The :mod:`.axis_artist` module implements custom artists to draw axis elements
-(axis lines and labels, tick lines and labels, grid lines).
-
-Axis lines and labels and tick lines and labels are managed by the `AxisArtist`
-class; grid lines are managed by the `GridlinesCollection` class.
-
-There is one `AxisArtist` per Axis; it can be accessed through
-the ``axis`` dictionary of the parent Axes (which should be a
-`mpl_toolkits.axislines.Axes`), e.g. ``ax.axis["bottom"]``.
-
-Children of the AxisArtist are accessed as attributes: ``.line`` and ``.label``
-for the axis line and label, ``.major_ticks``, ``.major_ticklabels``,
-``.minor_ticks``, ``.minor_ticklabels`` for the tick lines and labels (e.g.
-``ax.axis["bottom"].line``).
-
-Children properties (colors, fonts, line widths, etc.) can be set using
-setters, e.g. ::
-
- # Make the major ticks of the bottom axis red.
- ax.axis["bottom"].major_ticks.set_color("red")
-
-However, things like the locations of ticks, and their ticklabels need to be
-changed from the side of the grid_helper.
-
-axis_direction
---------------
-
-`AxisArtist`, `AxisLabel`, `TickLabels` have an *axis_direction* attribute,
-which adjusts the location, angle, etc. The *axis_direction* must be one of
-"left", "right", "bottom", "top", and follows the Matplotlib convention for
-rectangular axis.
-
-For example, for the *bottom* axis (the left and right is relative to the
-direction of the increasing coordinate),
-
-* ticklabels and axislabel are on the right
-* ticklabels and axislabel have text angle of 0
-* ticklabels are baseline, center-aligned
-* axislabel is top, center-aligned
-
-The text angles are actually relative to (90 + angle of the direction to the
-ticklabel), which gives 0 for bottom axis.
-
-=================== ====== ======== ====== ========
-Property left bottom right top
-=================== ====== ======== ====== ========
-ticklabel location left right right left
-axislabel location left right right left
-ticklabel angle 90 0 -90 180
-axislabel angle 180 0 0 180
-ticklabel va center baseline center baseline
-axislabel va center top center bottom
-ticklabel ha right center right center
-axislabel ha right center right center
-=================== ====== ======== ====== ========
-
-Ticks are by default direct opposite side of the ticklabels. To make ticks to
-the same side of the ticklabels, ::
-
- ax.axis["bottom"].major_ticks.set_tick_out(True)
-
-The following attributes can be customized (use the ``set_xxx`` methods):
-
-* `Ticks`: ticksize, tick_out
-* `TickLabels`: pad
-* `AxisLabel`: pad
-"""
-
-# FIXME :
-# angles are given in data coordinate - need to convert it to canvas coordinate
-
-
-from operator import methodcaller
-
-import numpy as np
-
-import matplotlib as mpl
-from matplotlib import _api, cbook
-import matplotlib.artist as martist
-import matplotlib.colors as mcolors
-import matplotlib.text as mtext
-from matplotlib.collections import LineCollection
-from matplotlib.lines import Line2D
-from matplotlib.patches import PathPatch
-from matplotlib.path import Path
-from matplotlib.transforms import (
- Affine2D, Bbox, IdentityTransform, ScaledTranslation)
-
-from .axisline_style import AxislineStyle
-
-
-class AttributeCopier:
- def get_ref_artist(self):
- """
- Return the underlying artist that actually defines some properties
- (e.g., color) of this artist.
- """
- raise RuntimeError("get_ref_artist must overridden")
-
- def get_attribute_from_ref_artist(self, attr_name):
- getter = methodcaller("get_" + attr_name)
- prop = getter(super())
- return getter(self.get_ref_artist()) if prop == "auto" else prop
-
-
-class Ticks(AttributeCopier, Line2D):
- """
- Ticks are derived from `.Line2D`, and note that ticks themselves
- are markers. Thus, you should use set_mec, set_mew, etc.
-
- To change the tick size (length), you need to use
- `set_ticksize`. To change the direction of the ticks (ticks are
- in opposite direction of ticklabels by default), use
- ``set_tick_out(False)``
- """
-
- def __init__(self, ticksize, tick_out=False, *, axis=None, **kwargs):
- self._ticksize = ticksize
- self.locs_angles_labels = []
-
- self.set_tick_out(tick_out)
-
- self._axis = axis
- if self._axis is not None:
- if "color" not in kwargs:
- kwargs["color"] = "auto"
- if "mew" not in kwargs and "markeredgewidth" not in kwargs:
- kwargs["markeredgewidth"] = "auto"
-
- Line2D.__init__(self, [0.], [0.], **kwargs)
- self.set_snap(True)
-
- def get_ref_artist(self):
- # docstring inherited
- return self._axis.majorTicks[0].tick1line
-
- def set_color(self, color):
- # docstring inherited
- # Unlike the base Line2D.set_color, this also supports "auto".
- if not cbook._str_equal(color, "auto"):
- mcolors._check_color_like(color=color)
- self._color = color
- self.stale = True
-
- def get_color(self):
- return self.get_attribute_from_ref_artist("color")
-
- def get_markeredgecolor(self):
- return self.get_attribute_from_ref_artist("markeredgecolor")
-
- def get_markeredgewidth(self):
- return self.get_attribute_from_ref_artist("markeredgewidth")
-
- def set_tick_out(self, b):
- """Set whether ticks are drawn inside or outside the axes."""
- self._tick_out = b
-
- def get_tick_out(self):
- """Return whether ticks are drawn inside or outside the axes."""
- return self._tick_out
-
- def set_ticksize(self, ticksize):
- """Set length of the ticks in points."""
- self._ticksize = ticksize
-
- def get_ticksize(self):
- """Return length of the ticks in points."""
- return self._ticksize
-
- def set_locs_angles(self, locs_angles):
- self.locs_angles = locs_angles
-
- _tickvert_path = Path([[0., 0.], [1., 0.]])
-
- def draw(self, renderer):
- if not self.get_visible():
- return
-
- gc = renderer.new_gc()
- gc.set_foreground(self.get_markeredgecolor())
- gc.set_linewidth(self.get_markeredgewidth())
- gc.set_alpha(self._alpha)
-
- path_trans = self.get_transform()
- marker_transform = (Affine2D()
- .scale(renderer.points_to_pixels(self._ticksize)))
- if self.get_tick_out():
- marker_transform.rotate_deg(180)
-
- for loc, angle in self.locs_angles:
- locs = path_trans.transform_non_affine(np.array([loc]))
- if self.axes and not self.axes.viewLim.contains(*locs[0]):
- continue
- renderer.draw_markers(
- gc, self._tickvert_path,
- marker_transform + Affine2D().rotate_deg(angle),
- Path(locs), path_trans.get_affine())
-
- gc.restore()
-
-
-class LabelBase(mtext.Text):
- """
- A base class for `.AxisLabel` and `.TickLabels`. The position and
- angle of the text are calculated by the offset_ref_angle,
- text_ref_angle, and offset_radius attributes.
- """
-
- def __init__(self, *args, **kwargs):
- self.locs_angles_labels = []
- self._ref_angle = 0
- self._offset_radius = 0.
-
- super().__init__(*args, **kwargs)
-
- self.set_rotation_mode("anchor")
- self._text_follow_ref_angle = True
-
- @property
- def _text_ref_angle(self):
- if self._text_follow_ref_angle:
- return self._ref_angle + 90
- else:
- return 0
-
- @property
- def _offset_ref_angle(self):
- return self._ref_angle
-
- _get_opposite_direction = {"left": "right",
- "right": "left",
- "top": "bottom",
- "bottom": "top"}.__getitem__
-
- def draw(self, renderer):
- if not self.get_visible():
- return
-
- # save original and adjust some properties
- tr = self.get_transform()
- angle_orig = self.get_rotation()
- theta = np.deg2rad(self._offset_ref_angle)
- dd = self._offset_radius
- dx, dy = dd * np.cos(theta), dd * np.sin(theta)
-
- self.set_transform(tr + Affine2D().translate(dx, dy))
- self.set_rotation(self._text_ref_angle + angle_orig)
- super().draw(renderer)
- # restore original properties
- self.set_transform(tr)
- self.set_rotation(angle_orig)
-
- def get_window_extent(self, renderer=None):
- if renderer is None:
- renderer = self.figure._get_renderer()
-
- # save original and adjust some properties
- tr = self.get_transform()
- angle_orig = self.get_rotation()
- theta = np.deg2rad(self._offset_ref_angle)
- dd = self._offset_radius
- dx, dy = dd * np.cos(theta), dd * np.sin(theta)
-
- self.set_transform(tr + Affine2D().translate(dx, dy))
- self.set_rotation(self._text_ref_angle + angle_orig)
- bbox = super().get_window_extent(renderer).frozen()
- # restore original properties
- self.set_transform(tr)
- self.set_rotation(angle_orig)
-
- return bbox
-
-
-class AxisLabel(AttributeCopier, LabelBase):
- """
- Axis label. Derived from `.Text`. The position of the text is updated
- in the fly, so changing text position has no effect. Otherwise, the
- properties can be changed as a normal `.Text`.
-
- To change the pad between tick labels and axis label, use `set_pad`.
- """
-
- def __init__(self, *args, axis_direction="bottom", axis=None, **kwargs):
- self._axis = axis
- self._pad = 5
- self._external_pad = 0 # in pixels
- LabelBase.__init__(self, *args, **kwargs)
- self.set_axis_direction(axis_direction)
-
- def set_pad(self, pad):
- """
- Set the internal pad in points.
-
- The actual pad will be the sum of the internal pad and the
- external pad (the latter is set automatically by the `.AxisArtist`).
-
- Parameters
- ----------
- pad : float
- The internal pad in points.
- """
- self._pad = pad
-
- def get_pad(self):
- """
- Return the internal pad in points.
-
- See `.set_pad` for more details.
- """
- return self._pad
-
- def get_ref_artist(self):
- # docstring inherited
- return self._axis.get_label()
-
- def get_text(self):
- # docstring inherited
- t = super().get_text()
- if t == "__from_axes__":
- return self._axis.get_label().get_text()
- return self._text
-
- _default_alignments = dict(left=("bottom", "center"),
- right=("top", "center"),
- bottom=("top", "center"),
- top=("bottom", "center"))
-
- def set_default_alignment(self, d):
- """
- Set the default alignment. See `set_axis_direction` for details.
-
- Parameters
- ----------
- d : {"left", "bottom", "right", "top"}
- """
- va, ha = _api.check_getitem(self._default_alignments, d=d)
- self.set_va(va)
- self.set_ha(ha)
-
- _default_angles = dict(left=180,
- right=0,
- bottom=0,
- top=180)
-
- def set_default_angle(self, d):
- """
- Set the default angle. See `set_axis_direction` for details.
-
- Parameters
- ----------
- d : {"left", "bottom", "right", "top"}
- """
- self.set_rotation(_api.check_getitem(self._default_angles, d=d))
-
- def set_axis_direction(self, d):
- """
- Adjust the text angle and text alignment of axis label
- according to the matplotlib convention.
-
- ===================== ========== ========= ========== ==========
- Property left bottom right top
- ===================== ========== ========= ========== ==========
- axislabel angle 180 0 0 180
- axislabel va center top center bottom
- axislabel ha right center right center
- ===================== ========== ========= ========== ==========
-
- Note that the text angles are actually relative to (90 + angle
- of the direction to the ticklabel), which gives 0 for bottom
- axis.
-
- Parameters
- ----------
- d : {"left", "bottom", "right", "top"}
- """
- self.set_default_alignment(d)
- self.set_default_angle(d)
-
- def get_color(self):
- return self.get_attribute_from_ref_artist("color")
-
- def draw(self, renderer):
- if not self.get_visible():
- return
-
- self._offset_radius = \
- self._external_pad + renderer.points_to_pixels(self.get_pad())
-
- super().draw(renderer)
-
- def get_window_extent(self, renderer=None):
- if renderer is None:
- renderer = self.figure._get_renderer()
- if not self.get_visible():
- return
-
- r = self._external_pad + renderer.points_to_pixels(self.get_pad())
- self._offset_radius = r
-
- bb = super().get_window_extent(renderer)
-
- return bb
-
-
-class TickLabels(AxisLabel): # mtext.Text
- """
- Tick labels. While derived from `.Text`, this single artist draws all
- ticklabels. As in `.AxisLabel`, the position of the text is updated
- in the fly, so changing text position has no effect. Otherwise,
- the properties can be changed as a normal `.Text`. Unlike the
- ticklabels of the mainline Matplotlib, properties of a single
- ticklabel alone cannot be modified.
-
- To change the pad between ticks and ticklabels, use `~.AxisLabel.set_pad`.
- """
-
- def __init__(self, *, axis_direction="bottom", **kwargs):
- super().__init__(**kwargs)
- self.set_axis_direction(axis_direction)
- self._axislabel_pad = 0
-
- def get_ref_artist(self):
- # docstring inherited
- return self._axis.get_ticklabels()[0]
-
- def set_axis_direction(self, label_direction):
- """
- Adjust the text angle and text alignment of ticklabels
- according to the Matplotlib convention.
-
- The *label_direction* must be one of [left, right, bottom, top].
-
- ===================== ========== ========= ========== ==========
- Property left bottom right top
- ===================== ========== ========= ========== ==========
- ticklabel angle 90 0 -90 180
- ticklabel va center baseline center baseline
- ticklabel ha right center right center
- ===================== ========== ========= ========== ==========
-
- Note that the text angles are actually relative to (90 + angle
- of the direction to the ticklabel), which gives 0 for bottom
- axis.
-
- Parameters
- ----------
- label_direction : {"left", "bottom", "right", "top"}
-
- """
- self.set_default_alignment(label_direction)
- self.set_default_angle(label_direction)
- self._axis_direction = label_direction
-
- def invert_axis_direction(self):
- label_direction = self._get_opposite_direction(self._axis_direction)
- self.set_axis_direction(label_direction)
-
- def _get_ticklabels_offsets(self, renderer, label_direction):
- """
- Calculate the ticklabel offsets from the tick and their total heights.
-
- The offset only takes account the offset due to the vertical alignment
- of the ticklabels: if axis direction is bottom and va is 'top', it will
- return 0; if va is 'baseline', it will return (height-descent).
- """
- whd_list = self.get_texts_widths_heights_descents(renderer)
-
- if not whd_list:
- return 0, 0
-
- r = 0
- va, ha = self.get_va(), self.get_ha()
-
- if label_direction == "left":
- pad = max(w for w, h, d in whd_list)
- if ha == "left":
- r = pad
- elif ha == "center":
- r = .5 * pad
- elif label_direction == "right":
- pad = max(w for w, h, d in whd_list)
- if ha == "right":
- r = pad
- elif ha == "center":
- r = .5 * pad
- elif label_direction == "bottom":
- pad = max(h for w, h, d in whd_list)
- if va == "bottom":
- r = pad
- elif va == "center":
- r = .5 * pad
- elif va == "baseline":
- max_ascent = max(h - d for w, h, d in whd_list)
- max_descent = max(d for w, h, d in whd_list)
- r = max_ascent
- pad = max_ascent + max_descent
- elif label_direction == "top":
- pad = max(h for w, h, d in whd_list)
- if va == "top":
- r = pad
- elif va == "center":
- r = .5 * pad
- elif va == "baseline":
- max_ascent = max(h - d for w, h, d in whd_list)
- max_descent = max(d for w, h, d in whd_list)
- r = max_descent
- pad = max_ascent + max_descent
-
- # r : offset
- # pad : total height of the ticklabels. This will be used to
- # calculate the pad for the axislabel.
- return r, pad
-
- _default_alignments = dict(left=("center", "right"),
- right=("center", "left"),
- bottom=("baseline", "center"),
- top=("baseline", "center"))
-
- _default_angles = dict(left=90,
- right=-90,
- bottom=0,
- top=180)
-
- def draw(self, renderer):
- if not self.get_visible():
- self._axislabel_pad = self._external_pad
- return
-
- r, total_width = self._get_ticklabels_offsets(renderer,
- self._axis_direction)
-
- pad = self._external_pad + renderer.points_to_pixels(self.get_pad())
- self._offset_radius = r + pad
-
- for (x, y), a, l in self._locs_angles_labels:
- if not l.strip():
- continue
- self._ref_angle = a
- self.set_x(x)
- self.set_y(y)
- self.set_text(l)
- LabelBase.draw(self, renderer)
-
- # the value saved will be used to draw axislabel.
- self._axislabel_pad = total_width + pad
-
- def set_locs_angles_labels(self, locs_angles_labels):
- self._locs_angles_labels = locs_angles_labels
-
- def get_window_extents(self, renderer=None):
- if renderer is None:
- renderer = self.figure._get_renderer()
-
- if not self.get_visible():
- self._axislabel_pad = self._external_pad
- return []
-
- bboxes = []
-
- r, total_width = self._get_ticklabels_offsets(renderer,
- self._axis_direction)
-
- pad = self._external_pad + renderer.points_to_pixels(self.get_pad())
- self._offset_radius = r + pad
-
- for (x, y), a, l in self._locs_angles_labels:
- self._ref_angle = a
- self.set_x(x)
- self.set_y(y)
- self.set_text(l)
- bb = LabelBase.get_window_extent(self, renderer)
- bboxes.append(bb)
-
- # the value saved will be used to draw axislabel.
- self._axislabel_pad = total_width + pad
-
- return bboxes
-
- def get_texts_widths_heights_descents(self, renderer):
- """
- Return a list of ``(width, height, descent)`` tuples for ticklabels.
-
- Empty labels are left out.
- """
- whd_list = []
- for _loc, _angle, label in self._locs_angles_labels:
- if not label.strip():
- continue
- clean_line, ismath = self._preprocess_math(label)
- whd = renderer.get_text_width_height_descent(
- clean_line, self._fontproperties, ismath=ismath)
- whd_list.append(whd)
- return whd_list
-
-
-class GridlinesCollection(LineCollection):
- def __init__(self, *args, which="major", axis="both", **kwargs):
- """
- Collection of grid lines.
-
- Parameters
- ----------
- which : {"major", "minor"}
- Which grid to consider.
- axis : {"both", "x", "y"}
- Which axis to consider.
- *args, **kwargs
- Passed to `.LineCollection`.
- """
- self._which = which
- self._axis = axis
- super().__init__(*args, **kwargs)
- self.set_grid_helper(None)
-
- def set_which(self, which):
- """
- Select major or minor grid lines.
-
- Parameters
- ----------
- which : {"major", "minor"}
- """
- self._which = which
-
- def set_axis(self, axis):
- """
- Select axis.
-
- Parameters
- ----------
- axis : {"both", "x", "y"}
- """
- self._axis = axis
-
- def set_grid_helper(self, grid_helper):
- """
- Set grid helper.
-
- Parameters
- ----------
- grid_helper : `.GridHelperBase` subclass
- """
- self._grid_helper = grid_helper
-
- def draw(self, renderer):
- if self._grid_helper is not None:
- self._grid_helper.update_lim(self.axes)
- gl = self._grid_helper.get_gridlines(self._which, self._axis)
- self.set_segments([np.transpose(l) for l in gl])
- super().draw(renderer)
-
-
-class AxisArtist(martist.Artist):
- """
- An artist which draws axis (a line along which the n-th axes coord
- is constant) line, ticks, tick labels, and axis label.
- """
-
- zorder = 2.5
-
- @property
- def LABELPAD(self):
- return self.label.get_pad()
-
- @LABELPAD.setter
- def LABELPAD(self, v):
- self.label.set_pad(v)
-
- def __init__(self, axes,
- helper,
- offset=None,
- axis_direction="bottom",
- **kwargs):
- """
- Parameters
- ----------
- axes : `mpl_toolkits.axisartist.axislines.Axes`
- helper : `~mpl_toolkits.axisartist.axislines.AxisArtistHelper`
- """
- # axes is also used to follow the axis attribute (tick color, etc).
-
- super().__init__(**kwargs)
-
- self.axes = axes
-
- self._axis_artist_helper = helper
-
- if offset is None:
- offset = (0, 0)
- self.offset_transform = ScaledTranslation(
- *offset,
- Affine2D().scale(1 / 72) # points to inches.
- + self.axes.figure.dpi_scale_trans)
-
- if axis_direction in ["left", "right"]:
- self.axis = axes.yaxis
- else:
- self.axis = axes.xaxis
-
- self._axisline_style = None
- self._axis_direction = axis_direction
-
- self._init_line()
- self._init_ticks(**kwargs)
- self._init_offsetText(axis_direction)
- self._init_label()
-
- # axis direction
- self._ticklabel_add_angle = 0.
- self._axislabel_add_angle = 0.
- self.set_axis_direction(axis_direction)
-
- # axis direction
-
- def set_axis_direction(self, axis_direction):
- """
- Adjust the direction, text angle, and text alignment of tick labels
- and axis labels following the Matplotlib convention for the rectangle
- axes.
-
- The *axis_direction* must be one of [left, right, bottom, top].
-
- ===================== ========== ========= ========== ==========
- Property left bottom right top
- ===================== ========== ========= ========== ==========
- ticklabel direction "-" "+" "+" "-"
- axislabel direction "-" "+" "+" "-"
- ticklabel angle 90 0 -90 180
- ticklabel va center baseline center baseline
- ticklabel ha right center right center
- axislabel angle 180 0 0 180
- axislabel va center top center bottom
- axislabel ha right center right center
- ===================== ========== ========= ========== ==========
-
- Note that the direction "+" and "-" are relative to the direction of
- the increasing coordinate. Also, the text angles are actually
- relative to (90 + angle of the direction to the ticklabel),
- which gives 0 for bottom axis.
-
- Parameters
- ----------
- axis_direction : {"left", "bottom", "right", "top"}
- """
- self.major_ticklabels.set_axis_direction(axis_direction)
- self.label.set_axis_direction(axis_direction)
- self._axis_direction = axis_direction
- if axis_direction in ["left", "top"]:
- self.set_ticklabel_direction("-")
- self.set_axislabel_direction("-")
- else:
- self.set_ticklabel_direction("+")
- self.set_axislabel_direction("+")
-
- def set_ticklabel_direction(self, tick_direction):
- r"""
- Adjust the direction of the tick labels.
-
- Note that the *tick_direction*\s '+' and '-' are relative to the
- direction of the increasing coordinate.
-
- Parameters
- ----------
- tick_direction : {"+", "-"}
- """
- self._ticklabel_add_angle = _api.check_getitem(
- {"+": 0, "-": 180}, tick_direction=tick_direction)
-
- def invert_ticklabel_direction(self):
- self._ticklabel_add_angle = (self._ticklabel_add_angle + 180) % 360
- self.major_ticklabels.invert_axis_direction()
- self.minor_ticklabels.invert_axis_direction()
-
- def set_axislabel_direction(self, label_direction):
- r"""
- Adjust the direction of the axis label.
-
- Note that the *label_direction*\s '+' and '-' are relative to the
- direction of the increasing coordinate.
-
- Parameters
- ----------
- label_direction : {"+", "-"}
- """
- self._axislabel_add_angle = _api.check_getitem(
- {"+": 0, "-": 180}, label_direction=label_direction)
-
- def get_transform(self):
- return self.axes.transAxes + self.offset_transform
-
- def get_helper(self):
- """
- Return axis artist helper instance.
- """
- return self._axis_artist_helper
-
- def set_axisline_style(self, axisline_style=None, **kwargs):
- """
- Set the axisline style.
-
- The new style is completely defined by the passed attributes. Existing
- style attributes are forgotten.
-
- Parameters
- ----------
- axisline_style : str or None
- The line style, e.g. '->', optionally followed by a comma-separated
- list of attributes. Alternatively, the attributes can be provided
- as keywords.
-
- If *None* this returns a string containing the available styles.
-
- Examples
- --------
- The following two commands are equal:
-
- >>> set_axisline_style("->,size=1.5")
- >>> set_axisline_style("->", size=1.5)
- """
- if axisline_style is None:
- return AxislineStyle.pprint_styles()
-
- if isinstance(axisline_style, AxislineStyle._Base):
- self._axisline_style = axisline_style
- else:
- self._axisline_style = AxislineStyle(axisline_style, **kwargs)
-
- self._init_line()
-
- def get_axisline_style(self):
- """Return the current axisline style."""
- return self._axisline_style
-
- def _init_line(self):
- """
- Initialize the *line* artist that is responsible to draw the axis line.
- """
- tran = (self._axis_artist_helper.get_line_transform(self.axes)
- + self.offset_transform)
-
- axisline_style = self.get_axisline_style()
- if axisline_style is None:
- self.line = PathPatch(
- self._axis_artist_helper.get_line(self.axes),
- color=mpl.rcParams['axes.edgecolor'],
- fill=False,
- linewidth=mpl.rcParams['axes.linewidth'],
- capstyle=mpl.rcParams['lines.solid_capstyle'],
- joinstyle=mpl.rcParams['lines.solid_joinstyle'],
- transform=tran)
- else:
- self.line = axisline_style(self, transform=tran)
-
- def _draw_line(self, renderer):
- self.line.set_path(self._axis_artist_helper.get_line(self.axes))
- if self.get_axisline_style() is not None:
- self.line.set_line_mutation_scale(self.major_ticklabels.get_size())
- self.line.draw(renderer)
-
- def _init_ticks(self, **kwargs):
- axis_name = self.axis.axis_name
-
- trans = (self._axis_artist_helper.get_tick_transform(self.axes)
- + self.offset_transform)
-
- self.major_ticks = Ticks(
- kwargs.get(
- "major_tick_size",
- mpl.rcParams[f"{axis_name}tick.major.size"]),
- axis=self.axis, transform=trans)
- self.minor_ticks = Ticks(
- kwargs.get(
- "minor_tick_size",
- mpl.rcParams[f"{axis_name}tick.minor.size"]),
- axis=self.axis, transform=trans)
-
- size = mpl.rcParams[f"{axis_name}tick.labelsize"]
- self.major_ticklabels = TickLabels(
- axis=self.axis,
- axis_direction=self._axis_direction,
- figure=self.axes.figure,
- transform=trans,
- fontsize=size,
- pad=kwargs.get(
- "major_tick_pad", mpl.rcParams[f"{axis_name}tick.major.pad"]),
- )
- self.minor_ticklabels = TickLabels(
- axis=self.axis,
- axis_direction=self._axis_direction,
- figure=self.axes.figure,
- transform=trans,
- fontsize=size,
- pad=kwargs.get(
- "minor_tick_pad", mpl.rcParams[f"{axis_name}tick.minor.pad"]),
- )
-
- def _get_tick_info(self, tick_iter):
- """
- Return a pair of:
-
- - list of locs and angles for ticks
- - list of locs, angles and labels for ticklabels.
- """
- ticks_loc_angle = []
- ticklabels_loc_angle_label = []
-
- ticklabel_add_angle = self._ticklabel_add_angle
-
- for loc, angle_normal, angle_tangent, label in tick_iter:
- angle_label = angle_tangent - 90 + ticklabel_add_angle
- angle_tick = (angle_normal
- if 90 <= (angle_label - angle_normal) % 360 <= 270
- else angle_normal + 180)
- ticks_loc_angle.append([loc, angle_tick])
- ticklabels_loc_angle_label.append([loc, angle_label, label])
-
- return ticks_loc_angle, ticklabels_loc_angle_label
-
- def _update_ticks(self, renderer=None):
- # set extra pad for major and minor ticklabels: use ticksize of
- # majorticks even for minor ticks. not clear what is best.
-
- if renderer is None:
- renderer = self.figure._get_renderer()
-
- dpi_cor = renderer.points_to_pixels(1.)
- if self.major_ticks.get_visible() and self.major_ticks.get_tick_out():
- ticklabel_pad = self.major_ticks._ticksize * dpi_cor
- self.major_ticklabels._external_pad = ticklabel_pad
- self.minor_ticklabels._external_pad = ticklabel_pad
- else:
- self.major_ticklabels._external_pad = 0
- self.minor_ticklabels._external_pad = 0
-
- majortick_iter, minortick_iter = \
- self._axis_artist_helper.get_tick_iterators(self.axes)
-
- tick_loc_angle, ticklabel_loc_angle_label = \
- self._get_tick_info(majortick_iter)
- self.major_ticks.set_locs_angles(tick_loc_angle)
- self.major_ticklabels.set_locs_angles_labels(ticklabel_loc_angle_label)
-
- tick_loc_angle, ticklabel_loc_angle_label = \
- self._get_tick_info(minortick_iter)
- self.minor_ticks.set_locs_angles(tick_loc_angle)
- self.minor_ticklabels.set_locs_angles_labels(ticklabel_loc_angle_label)
-
- def _draw_ticks(self, renderer):
- self._update_ticks(renderer)
- self.major_ticks.draw(renderer)
- self.major_ticklabels.draw(renderer)
- self.minor_ticks.draw(renderer)
- self.minor_ticklabels.draw(renderer)
- if (self.major_ticklabels.get_visible()
- or self.minor_ticklabels.get_visible()):
- self._draw_offsetText(renderer)
-
- _offsetText_pos = dict(left=(0, 1, "bottom", "right"),
- right=(1, 1, "bottom", "left"),
- bottom=(1, 0, "top", "right"),
- top=(1, 1, "bottom", "right"))
-
- def _init_offsetText(self, direction):
- x, y, va, ha = self._offsetText_pos[direction]
- self.offsetText = mtext.Annotation(
- "",
- xy=(x, y), xycoords="axes fraction",
- xytext=(0, 0), textcoords="offset points",
- color=mpl.rcParams['xtick.color'],
- horizontalalignment=ha, verticalalignment=va,
- )
- self.offsetText.set_transform(IdentityTransform())
- self.axes._set_artist_props(self.offsetText)
-
- def _update_offsetText(self):
- self.offsetText.set_text(self.axis.major.formatter.get_offset())
- self.offsetText.set_size(self.major_ticklabels.get_size())
- offset = (self.major_ticklabels.get_pad()
- + self.major_ticklabels.get_size()
- + 2)
- self.offsetText.xyann = (0, offset)
-
- def _draw_offsetText(self, renderer):
- self._update_offsetText()
- self.offsetText.draw(renderer)
-
- def _init_label(self, **kwargs):
- tr = (self._axis_artist_helper.get_axislabel_transform(self.axes)
- + self.offset_transform)
- self.label = AxisLabel(
- 0, 0, "__from_axes__",
- color="auto",
- fontsize=kwargs.get("labelsize", mpl.rcParams['axes.labelsize']),
- fontweight=mpl.rcParams['axes.labelweight'],
- axis=self.axis,
- transform=tr,
- axis_direction=self._axis_direction,
- )
- self.label.set_figure(self.axes.figure)
- labelpad = kwargs.get("labelpad", 5)
- self.label.set_pad(labelpad)
-
- def _update_label(self, renderer):
- if not self.label.get_visible():
- return
-
- if self._ticklabel_add_angle != self._axislabel_add_angle:
- if ((self.major_ticks.get_visible()
- and not self.major_ticks.get_tick_out())
- or (self.minor_ticks.get_visible()
- and not self.major_ticks.get_tick_out())):
- axislabel_pad = self.major_ticks._ticksize
- else:
- axislabel_pad = 0
- else:
- axislabel_pad = max(self.major_ticklabels._axislabel_pad,
- self.minor_ticklabels._axislabel_pad)
-
- self.label._external_pad = axislabel_pad
-
- xy, angle_tangent = \
- self._axis_artist_helper.get_axislabel_pos_angle(self.axes)
- if xy is None:
- return
-
- angle_label = angle_tangent - 90
-
- x, y = xy
- self.label._ref_angle = angle_label + self._axislabel_add_angle
- self.label.set(x=x, y=y)
-
- def _draw_label(self, renderer):
- self._update_label(renderer)
- self.label.draw(renderer)
-
- def set_label(self, s):
- # docstring inherited
- self.label.set_text(s)
-
- def get_tightbbox(self, renderer=None):
- if not self.get_visible():
- return
- self._axis_artist_helper.update_lim(self.axes)
- self._update_ticks(renderer)
- self._update_label(renderer)
-
- self.line.set_path(self._axis_artist_helper.get_line(self.axes))
- if self.get_axisline_style() is not None:
- self.line.set_line_mutation_scale(self.major_ticklabels.get_size())
-
- bb = [
- *self.major_ticklabels.get_window_extents(renderer),
- *self.minor_ticklabels.get_window_extents(renderer),
- self.label.get_window_extent(renderer),
- self.offsetText.get_window_extent(renderer),
- self.line.get_window_extent(renderer),
- ]
- bb = [b for b in bb if b and (b.width != 0 or b.height != 0)]
- if bb:
- _bbox = Bbox.union(bb)
- return _bbox
- else:
- return None
-
- @martist.allow_rasterization
- def draw(self, renderer):
- # docstring inherited
- if not self.get_visible():
- return
- renderer.open_group(__name__, gid=self.get_gid())
- self._axis_artist_helper.update_lim(self.axes)
- self._draw_ticks(renderer)
- self._draw_line(renderer)
- self._draw_label(renderer)
- renderer.close_group(__name__)
-
- def toggle(self, all=None, ticks=None, ticklabels=None, label=None):
- """
- Toggle visibility of ticks, ticklabels, and (axis) label.
- To turn all off, ::
-
- axis.toggle(all=False)
-
- To turn all off but ticks on ::
-
- axis.toggle(all=False, ticks=True)
-
- To turn all on but (axis) label off ::
-
- axis.toggle(all=True, label=False)
-
- """
- if all:
- _ticks, _ticklabels, _label = True, True, True
- elif all is not None:
- _ticks, _ticklabels, _label = False, False, False
- else:
- _ticks, _ticklabels, _label = None, None, None
-
- if ticks is not None:
- _ticks = ticks
- if ticklabels is not None:
- _ticklabels = ticklabels
- if label is not None:
- _label = label
-
- if _ticks is not None:
- self.major_ticks.set_visible(_ticks)
- self.minor_ticks.set_visible(_ticks)
- if _ticklabels is not None:
- self.major_ticklabels.set_visible(_ticklabels)
- self.minor_ticklabels.set_visible(_ticklabels)
- if _label is not None:
- self.label.set_visible(_label)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/roperator.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/roperator.py
deleted file mode 100644
index 2f320f4e9c6b984b64e0fc1268e50a8ad1a7e1fe..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/roperator.py
+++ /dev/null
@@ -1,62 +0,0 @@
-"""
-Reversed Operations not available in the stdlib operator module.
-Defining these instead of using lambdas allows us to reference them by name.
-"""
-from __future__ import annotations
-
-import operator
-
-
-def radd(left, right):
- return right + left
-
-
-def rsub(left, right):
- return right - left
-
-
-def rmul(left, right):
- return right * left
-
-
-def rdiv(left, right):
- return right / left
-
-
-def rtruediv(left, right):
- return right / left
-
-
-def rfloordiv(left, right):
- return right // left
-
-
-def rmod(left, right):
- # check if right is a string as % is the string
- # formatting operation; this is a TypeError
- # otherwise perform the op
- if isinstance(right, str):
- typ = type(left).__name__
- raise TypeError(f"{typ} cannot perform the operation mod")
-
- return right % left
-
-
-def rdivmod(left, right):
- return divmod(right, left)
-
-
-def rpow(left, right):
- return right**left
-
-
-def rand_(left, right):
- return operator.and_(right, left)
-
-
-def ror_(left, right):
- return operator.or_(right, left)
-
-
-def rxor(left, right):
- return operator.xor(right, left)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/conftest.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/conftest.py
deleted file mode 100644
index 701bfe3767db4df06c3816b396373c2122c096fe..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/conftest.py
+++ /dev/null
@@ -1,252 +0,0 @@
-import shlex
-import subprocess
-import time
-import uuid
-
-import pytest
-
-from pandas.compat import (
- is_ci_environment,
- is_platform_arm,
- is_platform_mac,
- is_platform_windows,
-)
-import pandas.util._test_decorators as td
-
-import pandas.io.common as icom
-from pandas.io.parsers import read_csv
-
-
-@pytest.fixture
-def compression_to_extension():
- return {value: key for key, value in icom.extension_to_compression.items()}
-
-
-@pytest.fixture
-def tips_file(datapath):
- """Path to the tips dataset"""
- return datapath("io", "data", "csv", "tips.csv")
-
-
-@pytest.fixture
-def jsonl_file(datapath):
- """Path to a JSONL dataset"""
- return datapath("io", "parser", "data", "items.jsonl")
-
-
-@pytest.fixture
-def salaries_table(datapath):
- """DataFrame with the salaries dataset"""
- return read_csv(datapath("io", "parser", "data", "salaries.csv"), sep="\t")
-
-
-@pytest.fixture
-def feather_file(datapath):
- return datapath("io", "data", "feather", "feather-0_3_1.feather")
-
-
-@pytest.fixture
-def xml_file(datapath):
- return datapath("io", "data", "xml", "books.xml")
-
-
-@pytest.fixture
-def s3so(worker_id):
- if is_ci_environment():
- url = "http://localhost:5000/"
- else:
- worker_id = "5" if worker_id == "master" else worker_id.lstrip("gw")
- url = f"http://127.0.0.1:555{worker_id}/"
- return {"client_kwargs": {"endpoint_url": url}}
-
-
-@pytest.fixture(scope="function" if is_ci_environment() else "session")
-def monkeysession():
- with pytest.MonkeyPatch.context() as mp:
- yield mp
-
-
-@pytest.fixture(scope="function" if is_ci_environment() else "session")
-def s3_base(worker_id, monkeysession):
- """
- Fixture for mocking S3 interaction.
-
- Sets up moto server in separate process locally
- Return url for motoserver/moto CI service
- """
- pytest.importorskip("s3fs")
- pytest.importorskip("boto3")
-
- # temporary workaround as moto fails for botocore >= 1.11 otherwise,
- # see https://github.com/spulec/moto/issues/1924 & 1952
- monkeysession.setenv("AWS_ACCESS_KEY_ID", "foobar_key")
- monkeysession.setenv("AWS_SECRET_ACCESS_KEY", "foobar_secret")
- if is_ci_environment():
- if is_platform_arm() or is_platform_mac() or is_platform_windows():
- # NOT RUN on Windows/macOS/ARM, only Ubuntu
- # - subprocess in CI can cause timeouts
- # - GitHub Actions do not support
- # container services for the above OSs
- # - CircleCI will probably hit the Docker rate pull limit
- pytest.skip(
- "S3 tests do not have a corresponding service in "
- "Windows, macOS or ARM platforms"
- )
- else:
- yield "http://localhost:5000"
- else:
- requests = pytest.importorskip("requests")
- pytest.importorskip("moto", minversion="1.3.14")
- pytest.importorskip("flask") # server mode needs flask too
-
- # Launching moto in server mode, i.e., as a separate process
- # with an S3 endpoint on localhost
-
- worker_id = "5" if worker_id == "master" else worker_id.lstrip("gw")
- endpoint_port = f"555{worker_id}"
- endpoint_uri = f"http://127.0.0.1:{endpoint_port}/"
-
- # pipe to null to avoid logging in terminal
- with subprocess.Popen(
- shlex.split(f"moto_server s3 -p {endpoint_port}"),
- stdout=subprocess.DEVNULL,
- stderr=subprocess.DEVNULL,
- ) as proc:
- timeout = 5
- while timeout > 0:
- try:
- # OK to go once server is accepting connections
- r = requests.get(endpoint_uri)
- if r.ok:
- break
- except Exception:
- pass
- timeout -= 0.1
- time.sleep(0.1)
- yield endpoint_uri
-
- proc.terminate()
-
-
-@pytest.fixture
-def s3_resource(s3_base):
- import boto3
-
- s3 = boto3.resource("s3", endpoint_url=s3_base)
- return s3
-
-
-@pytest.fixture
-def s3_public_bucket(s3_resource):
- bucket = s3_resource.Bucket(f"pandas-test-{uuid.uuid4()}")
- bucket.create()
- yield bucket
- bucket.objects.delete()
- bucket.delete()
-
-
-@pytest.fixture
-def s3_public_bucket_with_data(
- s3_public_bucket, tips_file, jsonl_file, feather_file, xml_file
-):
- """
- The following datasets
- are loaded.
-
- - tips.csv
- - tips.csv.gz
- - tips.csv.bz2
- - items.jsonl
- """
- test_s3_files = [
- ("tips#1.csv", tips_file),
- ("tips.csv", tips_file),
- ("tips.csv.gz", tips_file + ".gz"),
- ("tips.csv.bz2", tips_file + ".bz2"),
- ("items.jsonl", jsonl_file),
- ("simple_dataset.feather", feather_file),
- ("books.xml", xml_file),
- ]
- for s3_key, file_name in test_s3_files:
- with open(file_name, "rb") as f:
- s3_public_bucket.put_object(Key=s3_key, Body=f)
- return s3_public_bucket
-
-
-@pytest.fixture
-def s3_private_bucket(s3_resource):
- bucket = s3_resource.Bucket(f"cant_get_it-{uuid.uuid4()}")
- bucket.create(ACL="private")
- yield bucket
- bucket.objects.delete()
- bucket.delete()
-
-
-@pytest.fixture
-def s3_private_bucket_with_data(
- s3_private_bucket, tips_file, jsonl_file, feather_file, xml_file
-):
- """
- The following datasets
- are loaded.
-
- - tips.csv
- - tips.csv.gz
- - tips.csv.bz2
- - items.jsonl
- """
- test_s3_files = [
- ("tips#1.csv", tips_file),
- ("tips.csv", tips_file),
- ("tips.csv.gz", tips_file + ".gz"),
- ("tips.csv.bz2", tips_file + ".bz2"),
- ("items.jsonl", jsonl_file),
- ("simple_dataset.feather", feather_file),
- ("books.xml", xml_file),
- ]
- for s3_key, file_name in test_s3_files:
- with open(file_name, "rb") as f:
- s3_private_bucket.put_object(Key=s3_key, Body=f)
- return s3_private_bucket
-
-
-_compression_formats_params = [
- (".no_compress", None),
- ("", None),
- (".gz", "gzip"),
- (".GZ", "gzip"),
- (".bz2", "bz2"),
- (".BZ2", "bz2"),
- (".zip", "zip"),
- (".ZIP", "zip"),
- (".xz", "xz"),
- (".XZ", "xz"),
- pytest.param((".zst", "zstd"), marks=td.skip_if_no("zstandard")),
- pytest.param((".ZST", "zstd"), marks=td.skip_if_no("zstandard")),
-]
-
-
-@pytest.fixture(params=_compression_formats_params[1:])
-def compression_format(request):
- return request.param
-
-
-@pytest.fixture(params=_compression_formats_params)
-def compression_ext(request):
- return request.param[0]
-
-
-@pytest.fixture(
- params=[
- "python",
- pytest.param("pyarrow", marks=td.skip_if_no("pyarrow")),
- ]
-)
-def string_storage(request):
- """
- Parametrized fixture for pd.options.mode.string_storage.
-
- * 'python'
- * 'pyarrow'
- """
- return request.param
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/utils/datetime.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/utils/datetime.py
deleted file mode 100644
index 8668b3b0ec1deec2aeb7ff6bd94265d6705e05bf..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/utils/datetime.py
+++ /dev/null
@@ -1,11 +0,0 @@
-"""For when pip wants to check the date or time.
-"""
-
-import datetime
-
-
-def today_is_later_than(year: int, month: int, day: int) -> bool:
- today = datetime.date.today()
- given = datetime.date(year, month, day)
-
- return today > given
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/urllib3/packages/backports/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/urllib3/packages/backports/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/_internal/_typing_extra.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/_internal/_typing_extra.py
deleted file mode 100644
index e83e03d2b8e93072052092f8c2b1ec795aab8d32..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/_internal/_typing_extra.py
+++ /dev/null
@@ -1,435 +0,0 @@
-"""Logic for interacting with type annotations, mostly extensions, shims and hacks to wrap python's typing module."""
-from __future__ import annotations as _annotations
-
-import dataclasses
-import sys
-import types
-import typing
-from collections.abc import Callable
-from functools import partial
-from types import GetSetDescriptorType
-from typing import TYPE_CHECKING, Any, ForwardRef
-
-from typing_extensions import Annotated, Final, Literal, TypeAliasType, TypeGuard, get_args, get_origin
-
-if TYPE_CHECKING:
- from ._dataclasses import StandardDataclass
-
-try:
- from typing import _TypingBase # type: ignore[attr-defined]
-except ImportError:
- from typing import _Final as _TypingBase # type: ignore[attr-defined]
-
-typing_base = _TypingBase
-
-
-if sys.version_info < (3, 9):
- # python < 3.9 does not have GenericAlias (list[int], tuple[str, ...] and so on)
- TypingGenericAlias = ()
-else:
- from typing import GenericAlias as TypingGenericAlias # type: ignore
-
-
-if sys.version_info < (3, 11):
- from typing_extensions import NotRequired, Required
-else:
- from typing import NotRequired, Required # noqa: F401
-
-
-if sys.version_info < (3, 10):
-
- def origin_is_union(tp: type[Any] | None) -> bool:
- return tp is typing.Union
-
- WithArgsTypes = (TypingGenericAlias,)
-
-else:
-
- def origin_is_union(tp: type[Any] | None) -> bool:
- return tp is typing.Union or tp is types.UnionType
-
- WithArgsTypes = typing._GenericAlias, types.GenericAlias, types.UnionType # type: ignore[attr-defined]
-
-
-if sys.version_info < (3, 10):
- NoneType = type(None)
- EllipsisType = type(Ellipsis)
-else:
- from types import NoneType as NoneType
-
-
-LITERAL_TYPES: set[Any] = {Literal}
-if hasattr(typing, 'Literal'):
- LITERAL_TYPES.add(typing.Literal) # type: ignore
-
-NONE_TYPES: tuple[Any, ...] = (None, NoneType, *(tp[None] for tp in LITERAL_TYPES))
-
-
-TypeVarType = Any # since mypy doesn't allow the use of TypeVar as a type
-
-
-def is_none_type(type_: Any) -> bool:
- return type_ in NONE_TYPES
-
-
-def is_callable_type(type_: type[Any]) -> bool:
- return type_ is Callable or get_origin(type_) is Callable
-
-
-def is_literal_type(type_: type[Any]) -> bool:
- return Literal is not None and get_origin(type_) in LITERAL_TYPES
-
-
-def literal_values(type_: type[Any]) -> tuple[Any, ...]:
- return get_args(type_)
-
-
-def all_literal_values(type_: type[Any]) -> list[Any]:
- """This method is used to retrieve all Literal values as
- Literal can be used recursively (see https://www.python.org/dev/peps/pep-0586)
- e.g. `Literal[Literal[Literal[1, 2, 3], "foo"], 5, None]`.
- """
- if not is_literal_type(type_):
- return [type_]
-
- values = literal_values(type_)
- return list(x for value in values for x in all_literal_values(value))
-
-
-def is_annotated(ann_type: Any) -> bool:
- from ._utils import lenient_issubclass
-
- origin = get_origin(ann_type)
- return origin is not None and lenient_issubclass(origin, Annotated)
-
-
-def is_namedtuple(type_: type[Any]) -> bool:
- """Check if a given class is a named tuple.
- It can be either a `typing.NamedTuple` or `collections.namedtuple`.
- """
- from ._utils import lenient_issubclass
-
- return lenient_issubclass(type_, tuple) and hasattr(type_, '_fields')
-
-
-test_new_type = typing.NewType('test_new_type', str)
-
-
-def is_new_type(type_: type[Any]) -> bool:
- """Check whether type_ was created using typing.NewType.
-
- Can't use isinstance because it fails <3.10.
- """
- return isinstance(type_, test_new_type.__class__) and hasattr(type_, '__supertype__') # type: ignore[arg-type]
-
-
-def _check_classvar(v: type[Any] | None) -> bool:
- if v is None:
- return False
-
- return v.__class__ == typing.ClassVar.__class__ and getattr(v, '_name', None) == 'ClassVar'
-
-
-def is_classvar(ann_type: type[Any]) -> bool:
- if _check_classvar(ann_type) or _check_classvar(get_origin(ann_type)):
- return True
-
- # this is an ugly workaround for class vars that contain forward references and are therefore themselves
- # forward references, see #3679
- if ann_type.__class__ == typing.ForwardRef and ann_type.__forward_arg__.startswith('ClassVar['): # type: ignore
- return True
-
- return False
-
-
-def _check_finalvar(v: type[Any] | None) -> bool:
- """Check if a given type is a `typing.Final` type."""
- if v is None:
- return False
-
- return v.__class__ == Final.__class__ and (sys.version_info < (3, 8) or getattr(v, '_name', None) == 'Final')
-
-
-def is_finalvar(ann_type: Any) -> bool:
- return _check_finalvar(ann_type) or _check_finalvar(get_origin(ann_type))
-
-
-def parent_frame_namespace(*, parent_depth: int = 2) -> dict[str, Any] | None:
- """We allow use of items in parent namespace to get around the issue with `get_type_hints` only looking in the
- global module namespace. See https://github.com/pydantic/pydantic/issues/2678#issuecomment-1008139014 -> Scope
- and suggestion at the end of the next comment by @gvanrossum.
-
- WARNING 1: it matters exactly where this is called. By default, this function will build a namespace from the
- parent of where it is called.
-
- WARNING 2: this only looks in the parent namespace, not other parents since (AFAIK) there's no way to collect a
- dict of exactly what's in scope. Using `f_back` would work sometimes but would be very wrong and confusing in many
- other cases. See https://discuss.python.org/t/is-there-a-way-to-access-parent-nested-namespaces/20659.
- """
- frame = sys._getframe(parent_depth)
- # if f_back is None, it's the global module namespace and we don't need to include it here
- if frame.f_back is None:
- return None
- else:
- return frame.f_locals
-
-
-def add_module_globals(obj: Any, globalns: dict[str, Any] | None = None) -> dict[str, Any]:
- module_name = getattr(obj, '__module__', None)
- if module_name:
- try:
- module_globalns = sys.modules[module_name].__dict__
- except KeyError:
- # happens occasionally, see https://github.com/pydantic/pydantic/issues/2363
- pass
- else:
- if globalns:
- return {**module_globalns, **globalns}
- else:
- # copy module globals to make sure it can't be updated later
- return module_globalns.copy()
-
- return globalns or {}
-
-
-def get_cls_types_namespace(cls: type[Any], parent_namespace: dict[str, Any] | None = None) -> dict[str, Any]:
- ns = add_module_globals(cls, parent_namespace)
- ns[cls.__name__] = cls
- return ns
-
-
-def get_cls_type_hints_lenient(obj: Any, globalns: dict[str, Any] | None = None) -> dict[str, Any]:
- """Collect annotations from a class, including those from parent classes.
-
- Unlike `typing.get_type_hints`, this function will not error if a forward reference is not resolvable.
- """
- hints = {}
- for base in reversed(obj.__mro__):
- ann = base.__dict__.get('__annotations__')
- localns = dict(vars(base))
- if ann is not None and ann is not GetSetDescriptorType:
- for name, value in ann.items():
- hints[name] = eval_type_lenient(value, globalns, localns)
- return hints
-
-
-def eval_type_lenient(value: Any, globalns: dict[str, Any] | None, localns: dict[str, Any] | None) -> Any:
- """Behaves like typing._eval_type, except it won't raise an error if a forward reference can't be resolved."""
- if value is None:
- value = NoneType
- elif isinstance(value, str):
- value = _make_forward_ref(value, is_argument=False, is_class=True)
-
- try:
- return typing._eval_type(value, globalns, localns) # type: ignore
- except NameError:
- # the point of this function is to be tolerant to this case
- return value
-
-
-def get_function_type_hints(
- function: Callable[..., Any], *, include_keys: set[str] | None = None, types_namespace: dict[str, Any] | None = None
-) -> dict[str, Any]:
- """Like `typing.get_type_hints`, but doesn't convert `X` to `Optional[X]` if the default value is `None`, also
- copes with `partial`.
- """
- if isinstance(function, partial):
- annotations = function.func.__annotations__
- else:
- annotations = function.__annotations__
-
- globalns = add_module_globals(function)
- type_hints = {}
- for name, value in annotations.items():
- if include_keys is not None and name not in include_keys:
- continue
- if value is None:
- value = NoneType
- elif isinstance(value, str):
- value = _make_forward_ref(value)
-
- type_hints[name] = typing._eval_type(value, globalns, types_namespace) # type: ignore
-
- return type_hints
-
-
-if sys.version_info < (3, 9, 8) or (3, 10) <= sys.version_info < (3, 10, 1):
-
- def _make_forward_ref(
- arg: Any,
- is_argument: bool = True,
- *,
- is_class: bool = False,
- ) -> typing.ForwardRef:
- """Wrapper for ForwardRef that accounts for the `is_class` argument missing in older versions.
- The `module` argument is omitted as it breaks <3.9.8, =3.10.0 and isn't used in the calls below.
-
- See https://github.com/python/cpython/pull/28560 for some background.
- The backport happened on 3.9.8, see:
- https://github.com/pydantic/pydantic/discussions/6244#discussioncomment-6275458,
- and on 3.10.1 for the 3.10 branch, see:
- https://github.com/pydantic/pydantic/issues/6912
-
- Implemented as EAFP with memory.
- """
- return typing.ForwardRef(arg, is_argument)
-
-else:
- _make_forward_ref = typing.ForwardRef
-
-
-if sys.version_info >= (3, 10):
- get_type_hints = typing.get_type_hints
-
-else:
- """
- For older versions of python, we have a custom implementation of `get_type_hints` which is a close as possible to
- the implementation in CPython 3.10.8.
- """
-
- @typing.no_type_check
- def get_type_hints( # noqa: C901
- obj: Any,
- globalns: dict[str, Any] | None = None,
- localns: dict[str, Any] | None = None,
- include_extras: bool = False,
- ) -> dict[str, Any]: # pragma: no cover
- """Taken verbatim from python 3.10.8 unchanged, except:
- * type annotations of the function definition above.
- * prefixing `typing.` where appropriate
- * Use `_make_forward_ref` instead of `typing.ForwardRef` to handle the `is_class` argument.
-
- https://github.com/python/cpython/blob/aaaf5174241496afca7ce4d4584570190ff972fe/Lib/typing.py#L1773-L1875
-
- DO NOT CHANGE THIS METHOD UNLESS ABSOLUTELY NECESSARY.
- ======================================================
-
- Return type hints for an object.
-
- This is often the same as obj.__annotations__, but it handles
- forward references encoded as string literals, adds Optional[t] if a
- default value equal to None is set and recursively replaces all
- 'Annotated[T, ...]' with 'T' (unless 'include_extras=True').
-
- The argument may be a module, class, method, or function. The annotations
- are returned as a dictionary. For classes, annotations include also
- inherited members.
-
- TypeError is raised if the argument is not of a type that can contain
- annotations, and an empty dictionary is returned if no annotations are
- present.
-
- BEWARE -- the behavior of globalns and localns is counterintuitive
- (unless you are familiar with how eval() and exec() work). The
- search order is locals first, then globals.
-
- - If no dict arguments are passed, an attempt is made to use the
- globals from obj (or the respective module's globals for classes),
- and these are also used as the locals. If the object does not appear
- to have globals, an empty dictionary is used. For classes, the search
- order is globals first then locals.
-
- - If one dict argument is passed, it is used for both globals and
- locals.
-
- - If two dict arguments are passed, they specify globals and
- locals, respectively.
- """
- if getattr(obj, '__no_type_check__', None):
- return {}
- # Classes require a special treatment.
- if isinstance(obj, type):
- hints = {}
- for base in reversed(obj.__mro__):
- if globalns is None:
- base_globals = getattr(sys.modules.get(base.__module__, None), '__dict__', {})
- else:
- base_globals = globalns
- ann = base.__dict__.get('__annotations__', {})
- if isinstance(ann, types.GetSetDescriptorType):
- ann = {}
- base_locals = dict(vars(base)) if localns is None else localns
- if localns is None and globalns is None:
- # This is surprising, but required. Before Python 3.10,
- # get_type_hints only evaluated the globalns of
- # a class. To maintain backwards compatibility, we reverse
- # the globalns and localns order so that eval() looks into
- # *base_globals* first rather than *base_locals*.
- # This only affects ForwardRefs.
- base_globals, base_locals = base_locals, base_globals
- for name, value in ann.items():
- if value is None:
- value = type(None)
- if isinstance(value, str):
- value = _make_forward_ref(value, is_argument=False, is_class=True)
-
- value = typing._eval_type(value, base_globals, base_locals) # type: ignore
- hints[name] = value
- return (
- hints if include_extras else {k: typing._strip_annotations(t) for k, t in hints.items()} # type: ignore
- )
-
- if globalns is None:
- if isinstance(obj, types.ModuleType):
- globalns = obj.__dict__
- else:
- nsobj = obj
- # Find globalns for the unwrapped object.
- while hasattr(nsobj, '__wrapped__'):
- nsobj = nsobj.__wrapped__
- globalns = getattr(nsobj, '__globals__', {})
- if localns is None:
- localns = globalns
- elif localns is None:
- localns = globalns
- hints = getattr(obj, '__annotations__', None)
- if hints is None:
- # Return empty annotations for something that _could_ have them.
- if isinstance(obj, typing._allowed_types): # type: ignore
- return {}
- else:
- raise TypeError(f'{obj!r} is not a module, class, method, ' 'or function.')
- defaults = typing._get_defaults(obj) # type: ignore
- hints = dict(hints)
- for name, value in hints.items():
- if value is None:
- value = type(None)
- if isinstance(value, str):
- # class-level forward refs were handled above, this must be either
- # a module-level annotation or a function argument annotation
-
- value = _make_forward_ref(
- value,
- is_argument=not isinstance(obj, types.ModuleType),
- is_class=False,
- )
- value = typing._eval_type(value, globalns, localns) # type: ignore
- if name in defaults and defaults[name] is None:
- value = typing.Optional[value]
- hints[name] = value
- return hints if include_extras else {k: typing._strip_annotations(t) for k, t in hints.items()} # type: ignore
-
-
-if sys.version_info < (3, 9):
-
- def evaluate_fwd_ref(
- ref: ForwardRef, globalns: dict[str, Any] | None = None, localns: dict[str, Any] | None = None
- ) -> Any:
- return ref._evaluate(globalns=globalns, localns=localns)
-
-else:
-
- def evaluate_fwd_ref(
- ref: ForwardRef, globalns: dict[str, Any] | None = None, localns: dict[str, Any] | None = None
- ) -> Any:
- return ref._evaluate(globalns=globalns, localns=localns, recursive_guard=frozenset())
-
-
-def is_dataclass(_cls: type[Any]) -> TypeGuard[type[StandardDataclass]]:
- # The dataclasses.is_dataclass function doesn't seem to provide TypeGuard functionality,
- # so I created this convenience function
- return dataclasses.is_dataclass(_cls)
-
-
-def origin_is_type_alias_type(origin: Any) -> TypeGuard[TypeAliasType]:
- return isinstance(origin, TypeAliasType)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/tlb.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/tlb.py
deleted file mode 100644
index ac629dc848258fb4e09e331b7380385e296a8b16..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/tlb.py
+++ /dev/null
@@ -1,57 +0,0 @@
-"""
- pygments.lexers.tlb
- ~~~~~~~~~~~~~~~~~~~
-
- Lexers for TL-b.
-
- :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
- :license: BSD, see LICENSE for details.
-"""
-
-from pygments.lexer import RegexLexer, include, words
-from pygments.token import Operator, Name, \
- Number, Whitespace, Punctuation, Comment
-
-__all__ = ['TlbLexer']
-
-
-class TlbLexer(RegexLexer):
- """
- For TL-b source code.
- """
-
- name = 'Tl-b'
- aliases = ['tlb']
- filenames = ['*.tlb']
-
- tokens = {
- 'root': [
- (r'\s+', Whitespace),
-
- include('comments'),
-
- (r'[0-9]+', Number),
- (words((
- '+', '-', '*', '=', '?', '~', '.',
- '^', '==', '<', '>', '<=', '>=', '!='
- )), Operator),
- (words(('##', '#<', '#<=')), Name.Tag),
- (r'#[0-9a-f]*_?', Name.Tag),
- (r'\$[01]*_?', Name.Tag),
-
- (r'[a-zA-Z_][0-9a-zA-Z_]*', Name),
-
- (r'[;():\[\]{}]', Punctuation)
- ],
-
- 'comments': [
- (r'//.*', Comment.Singleline),
- (r'/\*', Comment.Multiline, 'comment'),
- ],
- 'comment': [
- (r'[^/*]+', Comment.Multiline),
- (r'/\*', Comment.Multiline, '#push'),
- (r'\*/', Comment.Multiline, '#pop'),
- (r'[*/]', Comment.Multiline),
- ],
- }
diff --git a/spaces/pszemraj/ballpark-trivia/app.py b/spaces/pszemraj/ballpark-trivia/app.py
deleted file mode 100644
index 77c213aaf5df11f7e7f2584257483fd33525a0a8..0000000000000000000000000000000000000000
--- a/spaces/pszemraj/ballpark-trivia/app.py
+++ /dev/null
@@ -1,236 +0,0 @@
-"""
-app.py - the main file for the app. This builds the app and runs it.
-
-"""
-
-import torch
-from transformers import pipeline
-from cleantext import clean
-from pathlib import Path
-import warnings
-import time
-import argparse
-import logging
-import gradio as gr
-import os
-import sys
-from os.path import dirname
-import nltk
-from converse import discussion
-from grammar_improve import (
- detect_propers,
- load_ns_checker,
- neuspell_correct,
- remove_repeated_words,
- remove_trailing_punctuation,
- build_symspell_obj,
- symspeller,
-)
-
-from utils import (
- cleantxt_wrap,
- corr,
-)
-
-nltk.download("stopwords") # TODO: find where this requirement originates from
-
-sys.path.append(dirname(dirname(os.path.abspath(__file__))))
-warnings.filterwarnings(action="ignore", message=".*gradient_checkpointing*")
-import transformers
-
-transformers.logging.set_verbosity_error()
-logging.basicConfig()
-cwd = Path.cwd()
-my_cwd = str(cwd.resolve()) # string so it can be passed to os.path() objects
-
-
-def chat(trivia_query):
- history = []
- response = ask_gpt(message=trivia_query, chat_pipe=my_chatbot)
- history = [trivia_query, response]
- html = ""
- for item in history:
- html += f"{item} 3d sex villa 2 unlock full hardcore patch Download File ===> https://geags.com/2uCqTs
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Bootloader Error Id 19 Miracle Box 11.md b/spaces/quidiaMuxgu/Expedit-SAM/Bootloader Error Id 19 Miracle Box 11.md
deleted file mode 100644
index 304d9c4f4bb182684c6bbefe7ebef9301da47cc6..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Bootloader Error Id 19 Miracle Box 11.md
+++ /dev/null
@@ -1,10 +0,0 @@
-
-hey, i just got the same problem with my nexus6p. the problem is that the bootloader is locked when it is not supposed to be, because i downloaded a wrong tool. the right tool is t3_mtk_mf.exe (one of the files in this download) and it contains the "device maintenance toolkit for mtk2660c".
-bootloader error id 19 miracle box 11 Download File ✪ https://geags.com/2uCrpb
i have a nexus 6p and was trying to unlock the bootloader. i used the fastboot flash unlock -w command and it showed a failed (remote: unknown command) error message. the adb devices command showed that the device was recognized. i then tried to connect it to my pc via usb, but it said the connection was refused. do you have any advice on how to fix this?
-i am having the same problem as this guy. i tried to flash a custom rom, and that didnt work, i received a message saying that the bootloader was locked. i then unlocked it via the fastboot flash unlock -w command, but the adb devices command shows that the phone isnt connected. ive tried restarting my phone, and charging it, but nothing works. please help!
-i had problems with my nexus 6p bootloader. tried everything to unlock it. it doesnt work. then i read a blog to try something and it worked. i used the commands fastboot flash unlock -w (backlight) and fastboot flashing unlock (bootloader) and now i was able to get a bootloader unlock screen. but it says "device not found".
-
-i used to run a kernel panic every time. i ended up flashing the stock rom and the error went away. ever since then, i have been very careful with my device. i have not had a single problem with it. my phone runs like a champ.
899543212bthe dynamics of entrepreneurial development and management. vasant desai. eleventh edition. : vasant desai. 2011.. description. vasant desai's the dynamics of entrepreneurial development and management: entrepreneurship, project.
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899543212bhere are some key features of autodesk quantity takeoff building:
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899543212bMount and Blade Serial Key Crack 1.143 11 - How to Download and Use It
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-How to download and install Aimersoft DVD Mac crack software from online sources
-If you still want to try Aimersoft DVD Mac crack software, you need to know how to download and install it from online sources. However, we do not recommend or endorse this method, as it is risky and illegal. Here are the steps that you may need to follow:
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-How to use Aimersoft DVD Mac crack software to rip, copy, create, or edit DVDs on Mac
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-We hope that this article has helped you understand what you need to know about Aimersoft DVD Mac crack software, how it works, and what are the alternatives and solutions. If you have any questions or comments about this topic, please feel free to contact us. We would love to hear from you.
- FAQs
-What is Aimersoft?
-Aimersoft is a professional software company that specializes in developing video and audio solutions for Windows and Mac users. It offers various products such as video converters, video editors, video downloaders, DVD rippers, DVD creators, DRM removers, etc.
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-Aimersoft DVD products for Mac are compatible with most Mac OS versions such as macOS 10.15 Catalina, macOS 10.14 Mojave, macOS 10.13 High Sierra, macOS 10.12 Sierra, OS X 10.11 El Capitan, OS X 10.10 Yosemite, OS X 10.9 Mavericks, etc.
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-Aimersoft DVD products for Mac have different prices depending on the product type and license type. For example:
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-Aimersoft DVD Ripper for Mac costs $39 for a single-user license or $59 for a family license (up to 5 PCs).
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-However, you can also get some discounts or free trials from the official Aimersoft website or other trusted sources.
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Computer Graphics Notes For Btech Pdf Download [UPD].md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Computer Graphics Notes For Btech Pdf Download [UPD].md
deleted file mode 100644
index 96d4b86fb468dad9cf0fe7520feeeb110387f15c..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Computer Graphics Notes For Btech Pdf Download [UPD].md
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index b2a6178a8a4590d3edb0ae422cdc276e73ce00ae..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Download Bloons Tower Defense 5 Free Full HOT Version Pc.md
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index 064c509ae34b68f687d6895b08f287e8f2b12d90..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Hamrick VueScan Pro V9.0.08 [BETTER] Crack [ Kk ] Download.md
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diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/visualization/palette.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/visualization/palette.py
deleted file mode 100644
index 11692cdd086301d9d3be4a4702dc12881b8e8d6e..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/core/visualization/palette.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import mmcv
-import numpy as np
-
-
-def palette_val(palette):
- """Convert palette to matplotlib palette.
-
- Args:
- palette List[tuple]: A list of color tuples.
-
- Returns:
- List[tuple[float]]: A list of RGB matplotlib color tuples.
- """
- new_palette = []
- for color in palette:
- color = [c / 255 for c in color]
- new_palette.append(tuple(color))
- return new_palette
-
-
-def get_palette(palette, num_classes):
- """Get palette from various inputs.
-
- Args:
- palette (list[tuple] | str | tuple | :obj:`Color`): palette inputs.
- num_classes (int): the number of classes.
-
- Returns:
- list[tuple[int]]: A list of color tuples.
- """
- assert isinstance(num_classes, int)
-
- if isinstance(palette, list):
- dataset_palette = palette
- elif isinstance(palette, tuple):
- dataset_palette = [palette] * num_classes
- elif palette == 'random' or palette is None:
- state = np.random.get_state()
- # random color
- np.random.seed(42)
- palette = np.random.randint(0, 256, size=(num_classes, 3))
- np.random.set_state(state)
- dataset_palette = [tuple(c) for c in palette]
- elif palette == 'coco':
- from mmdet.datasets import CocoDataset, CocoPanopticDataset
- dataset_palette = CocoDataset.PALETTE
- if len(dataset_palette) < num_classes:
- dataset_palette = CocoPanopticDataset.PALETTE
- elif palette == 'citys':
- from mmdet.datasets import CityscapesDataset
- dataset_palette = CityscapesDataset.PALETTE
- elif palette == 'voc':
- from mmdet.datasets import VOCDataset
- dataset_palette = VOCDataset.PALETTE
- elif mmcv.is_str(palette):
- dataset_palette = [mmcv.color_val(palette)[::-1]] * num_classes
- else:
- raise TypeError(f'Invalid type for palette: {type(palette)}')
-
- assert len(dataset_palette) >= num_classes, \
- 'The length of palette should not be less than `num_classes`.'
- return dataset_palette
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/models/segment_anything/modeling/prompt_encoder.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/models/segment_anything/modeling/prompt_encoder.py
deleted file mode 100644
index c3143f4f8e02ddd7ca8587b40ff5d47c3a6b7ef3..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/models/segment_anything/modeling/prompt_encoder.py
+++ /dev/null
@@ -1,214 +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 numpy as np
-import torch
-from torch import nn
-
-from typing import Any, Optional, Tuple, Type
-
-from .common import LayerNorm2d
-
-
-class PromptEncoder(nn.Module):
- def __init__(
- self,
- embed_dim: int,
- image_embedding_size: Tuple[int, int],
- input_image_size: Tuple[int, int],
- mask_in_chans: int,
- activation: Type[nn.Module] = nn.GELU,
- ) -> None:
- """
- Encodes prompts for input to SAM's mask decoder.
-
- Arguments:
- embed_dim (int): The prompts' embedding dimension
- image_embedding_size (tuple(int, int)): The spatial size of the
- image embedding, as (H, W).
- input_image_size (int): The padded size of the image as input
- to the image encoder, as (H, W).
- mask_in_chans (int): The number of hidden channels used for
- encoding input masks.
- activation (nn.Module): The activation to use when encoding
- input masks.
- """
- super().__init__()
- self.embed_dim = embed_dim
- self.input_image_size = input_image_size
- self.image_embedding_size = image_embedding_size
- self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
-
- self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners
- point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
- self.point_embeddings = nn.ModuleList(point_embeddings)
- self.not_a_point_embed = nn.Embedding(1, embed_dim)
-
- self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
- self.mask_downscaling = nn.Sequential(
- nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
- LayerNorm2d(mask_in_chans // 4),
- activation(),
- nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
- LayerNorm2d(mask_in_chans),
- activation(),
- nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
- )
- self.no_mask_embed = nn.Embedding(1, embed_dim)
-
- def get_dense_pe(self) -> torch.Tensor:
- """
- Returns the positional encoding used to encode point prompts,
- applied to a dense set of points the shape of the image encoding.
-
- Returns:
- torch.Tensor: Positional encoding with shape
- 1x(embed_dim)x(embedding_h)x(embedding_w)
- """
- return self.pe_layer(self.image_embedding_size).unsqueeze(0)
-
- def _embed_points(
- self,
- points: torch.Tensor,
- labels: torch.Tensor,
- pad: bool,
- ) -> torch.Tensor:
- """Embeds point prompts."""
- points = points + 0.5 # Shift to center of pixel
- if pad:
- padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
- padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
- points = torch.cat([points, padding_point], dim=1)
- labels = torch.cat([labels, padding_label], dim=1)
- point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
- point_embedding[labels == -1] = 0.0
- point_embedding[labels == -1] += self.not_a_point_embed.weight
- point_embedding[labels == 0] += self.point_embeddings[0].weight
- point_embedding[labels == 1] += self.point_embeddings[1].weight
- return point_embedding
-
- def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
- """Embeds box prompts."""
- boxes = boxes + 0.5 # Shift to center of pixel
- coords = boxes.reshape(-1, 2, 2)
- corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
- corner_embedding[:, 0, :] += self.point_embeddings[2].weight
- corner_embedding[:, 1, :] += self.point_embeddings[3].weight
- return corner_embedding
-
- def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
- """Embeds mask inputs."""
- mask_embedding = self.mask_downscaling(masks)
- return mask_embedding
-
- def _get_batch_size(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- ) -> int:
- """
- Gets the batch size of the output given the batch size of the input prompts.
- """
- if points is not None:
- return points[0].shape[0]
- elif boxes is not None:
- return boxes.shape[0]
- elif masks is not None:
- return masks.shape[0]
- else:
- return 1
-
- def _get_device(self) -> torch.device:
- return self.point_embeddings[0].weight.device
-
- def forward(
- self,
- points: Optional[Tuple[torch.Tensor, torch.Tensor]],
- boxes: Optional[torch.Tensor],
- masks: Optional[torch.Tensor],
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Embeds different types of prompts, returning both sparse and dense
- embeddings.
-
- Arguments:
- points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
- and labels to embed.
- boxes (torch.Tensor or none): boxes to embed
- masks (torch.Tensor or none): masks to embed
-
- Returns:
- torch.Tensor: sparse embeddings for the points and boxes, with shape
- BxNx(embed_dim), where N is determined by the number of input points
- and boxes.
- torch.Tensor: dense embeddings for the masks, in the shape
- Bx(embed_dim)x(embed_H)x(embed_W)
- """
- bs = self._get_batch_size(points, boxes, masks)
- sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
- if points is not None:
- coords, labels = points
- point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
- sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
- if boxes is not None:
- box_embeddings = self._embed_boxes(boxes)
- sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
-
- if masks is not None:
- dense_embeddings = self._embed_masks(masks)
- else:
- dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
- bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
- )
-
- return sparse_embeddings, dense_embeddings
-
-
-class PositionEmbeddingRandom(nn.Module):
- """
- Positional encoding using random spatial frequencies.
- """
-
- def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
- super().__init__()
- if scale is None or scale <= 0.0:
- scale = 1.0
- self.register_buffer(
- "positional_encoding_gaussian_matrix",
- scale * torch.randn((2, num_pos_feats)),
- )
-
- def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
- """Positionally encode points that are normalized to [0,1]."""
- # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
- coords = 2 * coords - 1
- coords = coords @ self.positional_encoding_gaussian_matrix
- coords = 2 * np.pi * coords
- # outputs d_1 x ... x d_n x C shape
- return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
-
- def forward(self, size: Tuple[int, int]) -> torch.Tensor:
- """Generate positional encoding for a grid of the specified size."""
- h, w = size
- device: Any = self.positional_encoding_gaussian_matrix.device
- grid = torch.ones((h, w), device=device, dtype=torch.float32)
- y_embed = grid.cumsum(dim=0) - 0.5
- x_embed = grid.cumsum(dim=1) - 0.5
- y_embed = y_embed / h
- x_embed = x_embed / w
-
- pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
- return pe.permute(2, 0, 1) # C x H x W
-
- def forward_with_coords(
- self, coords_input: torch.Tensor, image_size: Tuple[int, int]
- ) -> torch.Tensor:
- """Positionally encode points that are not normalized to [0,1]."""
- coords = coords_input.clone()
- coords[:, :, 0] = coords[:, :, 0] / image_size[1]
- coords[:, :, 1] = coords[:, :, 1] / image_size[0]
- return self._pe_encoding(coords.to(torch.float)) # B x N x C
diff --git a/spaces/rohan13/coursera-qa-bot/docs/03_module-2-why-is-it-revolutionary/01_module-2-information/01_module-2-overview_instructions.html b/spaces/rohan13/coursera-qa-bot/docs/03_module-2-why-is-it-revolutionary/01_module-2-information/01_module-2-overview_instructions.html
deleted file mode 100644
index 6eafa49cc4e7f7ab0aa370775fd744db8d25cca5..0000000000000000000000000000000000000000
--- a/spaces/rohan13/coursera-qa-bot/docs/03_module-2-why-is-it-revolutionary/01_module-2-information/01_module-2-overview_instructions.html
+++ /dev/null
@@ -1,280 +0,0 @@
-
-
- Module 2: Why Is It Revolutionary?
-
-
- Overview
-
-
- In this module, you will learn what is special about 3D printing and how this technology will change the business world and revolutionize our economy.
-
-
- Time
-
-
- This module should take
-
- approximately 3.25 hours
-
- of dedicated time to complete, with its videos and assignments.
-
-
- Reading
-
-
- Garcia, M (2015).
-
- How to keep 3D printing revolutionary
-
- .
-
- Time
-
- .
-
-
- Feel free to find other readings or resources and share them in the forums.
-
-
- Lessons
-
-
- The lessons for this module are listed below (with assignments in bold italics):
-
-
-
-
-
-
- Lesson Title
-
-
-
-
-
-
- Estimated Time Required
-
-
-
-
-
-
-
- An Early Look at the Coming Revolution
-
-
-
-
- 30 minutes
-
-
-
-
-
-
- The 3D Printing Revolution: Facts & Concepts
-
-
-
-
- 30 minutes
-
-
-
-
-
-
-
-
- Module 2 Practice Quiz
-
-
-
-
-
-
- 15 minutes
-
-
-
-
-
-
-
-
- Remixing Products Exercise
-
-
-
-
-
-
- 40 minutes
-
-
-
-
-
-
- The Revolutionaries
-
-
-
-
- 60 minutes
-
-
-
-
-
-
-
-
- Module 2 Quiz
-
-
-
-
-
-
- 15 minutes
-
-
-
-
-
- Goals and Objectives
-
-
- Upon successful completion of this module, you will be able to:
-
-
-
- Key Phrases/Concepts
-
-
- Keep your eyes open for the following key terms or phrases as you interact with the lectures and complete the activities. For definitions of the terms, please see the
-
-
- Glossary
-
-
- .
-
-
-
-
- Economics of scale
-
-
-
-
- Maker’s Marks
-
-
-
-
- Soli
-
-
-
-
- Getting and Giving Help
-
-
- You can get/give help via the following means:
-
-
-
-
- Use the
-
-
- Learner Help Center
-
-
- to find information regarding specific technical problems. For example, technical problems would include error messages, difficulty submitting assignments, or problems with video playback. If you cannot find an answer in the documentation, you can also report your problem to the Coursera staff by clicking on the
-
- Contact Us!
-
- link available on each topic's page within the Learner Help Center.
-
-
-
-
- Use the
-
-
- Course Suggestions
-
-
- forum to report errors in lecture video content, assignment questions and answers, assignment grading, text and links on course pages, or the content of other course materials. University of Illinois staff and community TAs will monitor this forum and respond to issues.
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/rorallitri/biomedical-language-models/logs/A Plight Understood By Elders.md b/spaces/rorallitri/biomedical-language-models/logs/A Plight Understood By Elders.md
deleted file mode 100644
index fc51561be7fcd1161299264f1149f7f607badc11..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/A Plight Understood By Elders.md
+++ /dev/null
@@ -1,21 +0,0 @@
-
-When bad things happen, good people get together. In helping my fragile, abused grandmother I was not alone. Her abuse galvanized a collective response by family, friends, staff, and caregivers all united by compassion and a common cause: individuals-in-sum with a great mixed skill-set. The strength of our diversity contributed much to our success. In helping our vulnerable, abused, victimized elders, we are not alone.
-A Plight Understood by Elders Download File ✶ https://tinurll.com/2uznHK
And when we had come to Jerusalem, the brethren received us gladly. On the following day Paul went in with us to James, and all the elders were present. When he had greeted them, he told in detail those things which God had done among the Gentiles through his ministry. And when they heard it, they glorified the Lord.
-Old age refers to ages nearing or surpassing the life expectancy of human beings, and is thus the end of the human life cycle. Terms and euphemisms for people at this age include old people , the elderly (worldwide usage), OAPs (British usage which stands for Old Age Pensioner), seniors , senior citizens (American usage), older adults (in the social sciences[1]), and the elders (in many cultures).
-A Swedish study found that at age 76, 46% of the subjects used assistive devices. When they reached age 86, 69% used them. The subjects were ambivalent regarding the use of the assistive devices: as "enablers" or as "disablers".[172] People who view assistive devices as enabling greater independence accept and use them, whereas those who see them as symbols of disability reject them.[173] However, organizations like Love for the Elderly aim to combat such age-related prejudice by educating the public about the importance of appreciating growing older, while also providing services of kindness to elders in senior homes.[174]
-This Primer has been prepared by the Office of the Assistant Secretary for Planning and Evaluation (ASPE), with consultation from the Health Care Financing Administration (HCFA) in the United States Department of Health and Human Services (HHS). Designed to serve as a reference guide, it is written in easily understood language, but with sufficient annotation of source material to fulfill its technical support role. Some issues remain unresolved, because particular provisions of Medicaid regulations and state interpretations thereof are being challenged in the courts. Major unresolved issues are discussed where relevant.
-
-The Katie Beckett provision is a statute--the Tax Equity and Fiscal Responsibility Act (TEFRA) 134--added to Medicaid in 1982. Katie Beckett is the name of the child whose parents petitioned the Federal government for her to receive Medicaid services at home instead of in a hospital, and whose plight led the Reagan Administration to urge Congress to enact the provision. TEFRA 134 gives states the option to cover noninstitutionalized children with disabilities. Prior to enactment of this provision, if a child with disabilities lived at home, the parents income and resources were automatically counted (deemed) as available for medical expenses. However, if the same child was institutionalized for 30 days or more, only the childs own income and resources were counted in the deeming calculation--substantially increasing the likelihood that a child could qualify for Medicaid. This sharp divergence in methods of counting income often forced families to institutionalize their children simply to get them medical care.
-Little use was made of these protections at first because they were not widely understood. Thus, the number of working persons with disabilities whose earnings were protected in this manner in 1982, the first full year of implementation, was just under 6000. By September 1999, however, the number had risen to nearly 100,000.18
-Assisted living is chosen as the second specific service example, because it provides an excellent illustration of the complex issues involved in defining a service so as to ensure its maximum usefulness within a particular state system. The focus here is on assisted living services provided under Medicaid to persons age 65 and older. By early 2000, 35 states were serving Medicaid beneficiaries in assisted living settings. Residential care alternatives to institutions have been offered to persons with mental retardation and developmental disabilities for some time. Making them available to elderly persons is a more recent, and less well understood, initiative.2
-According to the 1994 National Long-Term Care Survey, 86 percent of elderly persons living in the community who are as severely disabled as most nursing home residents (three or more ADL limitations and/or severe cognitive impairment) live with family caregivers and, on average, receive 60 hours of informal care per week supplemented by a little over 14 hours of paid assistance. In contrast, the minority (14 percent) of equally severely disabled elders who live alone receive, on average, 29 hours of informal help per week supplemented by 56 hours of paid assistance.
-LifePlans, Inc. (1999). A descriptive analysis of patterns of informal and formal caregiving among privately insured and non-privately insured disabled elders living in the community. Washington, DC: Department of Health and Human Services .This report is based on a study of how long-term care insurance benefits are used, whether claimants feel they are getting good value for the premiums they pay, and whether patterns of formal (paid) and informal (unpaid) service use differ for long-term care insurance claimants compared to similarly disabled persons without long-term care policies. The report provides basic socio-demographic and service utilization profiles for both groups and discusses the implications of the study's findings for the service delivery system and for the design of private and public long-term care programs and policies. To obtain a free copy of this report, write to the Office of Disability, Aging, and Long-Term Care Policy, Room 424E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, DC 20201, fax (202) 401-7733, or via e-mail at DALTCP2@osaspe.dhhs.gov.
-Overcoming these problems can be done in several ways. States should (a) encourage potential applicants to enlist trusted allies (e.g., family members and friends) to assist them in the application process, and (b) have sufficient staff so that the necessary time can be spent with applicants to ensure the process is understood and satisfactorily completed. In addition, the application forms themselves, along with associated materials, must be clear and easy to understand. To this end, such materials should be pretested and revised until they are readily understood by consumers. Another potentially useful strategy is employing people with disabilities (e.g., self advocates) to provide assistance and information to help applicants through the process.
-You are in an excellent position to spot the emotionally troubled student. This may be as a result of your position as department secretary, dean, receptionist, or faculty. You may observe that at certain times of the year, particularly during examinations and holidays, students experience increased anxiety. The student's behavior, especially if it is inconsistent with your experience of him/her, could well constitute an inarticulate attempt to draw attention to his/her plight, a "cry for help."
-James Arthur Baldwin was born August 2, 1924 in Harlem, New York. After graduating from high school in 1942, Baldwin began writing. In 1953, he published his first novel Go Tell It on the Mountain. Prior to releasing his first novel, Baldwin chose to leave America and move to France because of his dissatisfaction with the open racism and homophobia in the United States. In 1962, he visited the United States in order to participate in the the Civil Rights Movement, namely attending the March on Washington (seen in the photo). During the height of the struggle for Black equality, Baldwin was widely known for his militant essays that illustrated the social and economic plight of Black Americans. His writings addressed the issues of race but also mentioned the complexity of homosexuality and sexual orientation among the Black experience in the U.S. After the assassination of Dr. Martin Luther King, Jr. in 1968, Baldwin returned to France and continued writing until his death in 1987. Records at the National Archives pertaining to James Baldwin include moving images from the Peace Corps, the Agency for International Development and an interview with Pulitzer Prize winner Gwendolyn Brooks.
-Simpson Miller, who presented the main address at a JPS Career Expo held at Caribbean Palms community centre, said that she understood the plight of senior citizens, especially those who no longer earn an income, people who get by no little or no savings, are handicapped by varying and expensive medical complaints, and even those who are dependent on their struggling children and caregivers who try to ensure the comfort of their elders.
-14. How does the plight of Harriet Jacobs illustrate the role that class played in the Cult of Domesticity?
-Our subject this morning is how to handle misunderstanding. I wish we could take time to ask how many of you are going through a time of being misunderstood, of having your motives misjudged and your actions misinterpreted, of experiencing something that you meant to be taken one way being taken in quite a different light. We have a classic case of a misunderstanding here, in Chapter 1 of Second Corinthians, that will help us in handling such matters. This is the fourth letter Paul wrote to the church at Corinth, but we call it Second Corinthians because two of the letters he wrote are missing. In this section he is sharing certain experiences which come from being a Christian in a pagan world.
aaccfb2cb3GTA San Andreas Winter Adventure Of Bus Driver PC Download File 🗹 https://tinurll.com/2uzmr1
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Injustice 2 2.1.0 Apk Mod Immortal MegaMod Tips and Tricks for Dominating the Arena.md b/spaces/rorallitri/biomedical-language-models/logs/Injustice 2 2.1.0 Apk Mod Immortal MegaMod Tips and Tricks for Dominating the Arena.md
deleted file mode 100644
index fef1ec94c1339fe2d4861f6e73d537910dbf6407..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Injustice 2 2.1.0 Apk Mod Immortal MegaMod Tips and Tricks for Dominating the Arena.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Injustice 2 2.1.0 Apk Mod Immortal MegaMod Download Download > https://tinurll.com/2uzlPj
diff --git a/spaces/s1241003/translate_gpt/README.md b/spaces/s1241003/translate_gpt/README.md
deleted file mode 100644
index 1a42d12fcec6bb031b6c2884f6d5cf5efdcb3742..0000000000000000000000000000000000000000
--- a/spaces/s1241003/translate_gpt/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Translate Gpt
-emoji: 🔥
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/samuelinferences/TabPFN/TabPFN/layer.py b/spaces/samuelinferences/TabPFN/TabPFN/layer.py
deleted file mode 100644
index 3354d3b137263542d2fc8ace85da2d2a740b10e4..0000000000000000000000000000000000000000
--- a/spaces/samuelinferences/TabPFN/TabPFN/layer.py
+++ /dev/null
@@ -1,125 +0,0 @@
-from functools import partial
-
-from torch import nn
-from torch.nn.modules.transformer import *
-from torch.nn.modules.transformer import _get_activation_fn
-
-from torch.utils.checkpoint import checkpoint
-
-
-class TransformerEncoderLayer(Module):
- r"""TransformerEncoderLayer is made up of self-attn and feedforward network.
- This standard encoder layer is based on the paper "Attention Is All You Need".
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
- Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
- Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
- in a different way during application.
-
- Args:
- d_model: the number of expected features in the input (required).
- nhead: the number of heads in the multiheadattention models (required).
- dim_feedforward: the dimension of the feedforward network model (default=2048).
- dropout: the dropout value (default=0.1).
- activation: the activation function of intermediate layer, relu or gelu (default=relu).
- layer_norm_eps: the eps value in layer normalization components (default=1e-5).
- batch_first: If ``True``, then the input and output tensors are provided
- as (batch, seq, feature). Default: ``False``.
-
- Examples::
- >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
- >>> src = torch.rand(10, 32, 512)
- >>> out = encoder_layer(src)
-
- Alternatively, when ``batch_first`` is ``True``:
- >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first=True)
- >>> src = torch.rand(32, 10, 512)
- >>> out = encoder_layer(src)
- """
- __constants__ = ['batch_first']
-
- def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu",
- layer_norm_eps=1e-5, batch_first=False, pre_norm=False,
- device=None, dtype=None, recompute_attn=False) -> None:
- factory_kwargs = {'device': device, 'dtype': dtype}
- super().__init__()
- self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first,
- **factory_kwargs)
- # Implementation of Feedforward model
- self.linear1 = Linear(d_model, dim_feedforward, **factory_kwargs)
- self.dropout = Dropout(dropout)
- self.linear2 = Linear(dim_feedforward, d_model, **factory_kwargs)
-
- self.norm1 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
- self.norm2 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
- self.dropout1 = Dropout(dropout)
- self.dropout2 = Dropout(dropout)
- self.pre_norm = pre_norm
- self.recompute_attn = recompute_attn
-
- self.activation = _get_activation_fn(activation)
-
- def __setstate__(self, state):
- if 'activation' not in state:
- state['activation'] = F.relu
- super().__setstate__(state)
-
- def forward(self, src: Tensor, src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None) -> Tensor:
- r"""Pass the input through the encoder layer.
-
- Args:
- src: the sequence to the encoder layer (required).
- src_mask: the mask for the src sequence (optional).
- src_key_padding_mask: the mask for the src keys per batch (optional).
-
- Shape:
- see the docs in Transformer class.
- """
- if self.pre_norm:
- src_ = self.norm1(src)
- else:
- src_ = src
- if isinstance(src_mask, tuple):
- # global attention setup
- assert not self.self_attn.batch_first
- assert src_key_padding_mask is None
-
- global_src_mask, trainset_src_mask, valset_src_mask = src_mask
-
- num_global_tokens = global_src_mask.shape[0]
- num_train_tokens = trainset_src_mask.shape[0]
-
- global_tokens_src = src_[:num_global_tokens]
- train_tokens_src = src_[num_global_tokens:num_global_tokens+num_train_tokens]
- global_and_train_tokens_src = src_[:num_global_tokens+num_train_tokens]
- eval_tokens_src = src_[num_global_tokens+num_train_tokens:]
-
-
- attn = partial(checkpoint, self.self_attn) if self.recompute_attn else self.self_attn
-
- global_tokens_src2 = attn(global_tokens_src, global_and_train_tokens_src, global_and_train_tokens_src, None, True, global_src_mask)[0]
- train_tokens_src2 = attn(train_tokens_src, global_tokens_src, global_tokens_src, None, True, trainset_src_mask)[0]
- eval_tokens_src2 = attn(eval_tokens_src, src_, src_,
- None, True, valset_src_mask)[0]
-
- src2 = torch.cat([global_tokens_src2, train_tokens_src2, eval_tokens_src2], dim=0)
-
- else:
- if self.recompute_attn:
- src2 = checkpoint(self.self_attn, src_, src_, src_, src_key_padding_mask, True, src_mask)[0]
- else:
- src2 = self.self_attn(src_, src_, src_, attn_mask=src_mask,
- key_padding_mask=src_key_padding_mask)[0]
- src = src + self.dropout1(src2)
- if not self.pre_norm:
- src = self.norm1(src)
-
- if self.pre_norm:
- src_ = self.norm2(src)
- else:
- src_ = src
- src2 = self.linear2(self.dropout(self.activation(self.linear1(src_))))
- src = src + self.dropout2(src2)
-
- if not self.pre_norm:
- src = self.norm2(src)
- return src
\ No newline at end of file
diff --git a/spaces/sarulab-speech/UTMOS-demo/lightning_module.py b/spaces/sarulab-speech/UTMOS-demo/lightning_module.py
deleted file mode 100644
index 491426e492accf516713a4a7672b65ec4e831868..0000000000000000000000000000000000000000
--- a/spaces/sarulab-speech/UTMOS-demo/lightning_module.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import pytorch_lightning as pl
-import torch
-import torch.nn as nn
-import os
-import numpy as np
-import hydra
-from model import load_ssl_model, PhonemeEncoder, DomainEmbedding, LDConditioner, Projection
-
-
-class BaselineLightningModule(pl.LightningModule):
- def __init__(self, cfg):
- super().__init__()
- self.cfg = cfg
- self.construct_model()
- self.save_hyperparameters()
-
- def construct_model(self):
- self.feature_extractors = nn.ModuleList([
- load_ssl_model(cp_path='wav2vec_small.pt'),
- DomainEmbedding(3,128),
- ])
- output_dim = sum([ feature_extractor.get_output_dim() for feature_extractor in self.feature_extractors])
- output_layers = [
- LDConditioner(judge_dim=128,num_judges=3000,input_dim=output_dim)
- ]
- output_dim = output_layers[-1].get_output_dim()
- output_layers.append(
- Projection(hidden_dim=2048,activation=torch.nn.ReLU(),range_clipping=False,input_dim=output_dim)
-
- )
-
- self.output_layers = nn.ModuleList(output_layers)
-
- def forward(self, inputs):
- outputs = {}
- for feature_extractor in self.feature_extractors:
- outputs.update(feature_extractor(inputs))
- x = outputs
- for output_layer in self.output_layers:
- x = output_layer(x,inputs)
- return x
diff --git a/spaces/scedlatioru/img-to-music/example/Ccs C Compiler 5 Crack [PORTABLE].md b/spaces/scedlatioru/img-to-music/example/Ccs C Compiler 5 Crack [PORTABLE].md
deleted file mode 100644
index 1fa8f389dcc97cbaf2bfe4d6146ab15a0ff44c77..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Ccs C Compiler 5 Crack [PORTABLE].md
+++ /dev/null
@@ -1,6 +0,0 @@
-ccs c compiler 5 crack Download Zip ⇔ https://gohhs.com/2uEAxS
diff --git a/spaces/scedlatioru/img-to-music/example/Football Manager 2007 Crack 7.0.2 VERIFIED Download 13.md b/spaces/scedlatioru/img-to-music/example/Football Manager 2007 Crack 7.0.2 VERIFIED Download 13.md
deleted file mode 100644
index 4368b41daf2fa09f24eb097be448f02b7533395a..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Football Manager 2007 Crack 7.0.2 VERIFIED Download 13.md
+++ /dev/null
@@ -1,6 +0,0 @@
-it works even with fifa 07, i know because i tried it. i never got my overage (unless fifa 07 was broken, which it was) but it didn't really matter. i had a lovely time playing it. it's a nice game and it's a nice challenge. everything was going fine, then i got chased off the field by the refs, so i didn't win anything. managers always think they can win everything, the refs always come in when the managers, or players, get too cheeky. managers also get chased off the pitch when they try and boss their team. some people are just too slow to realise the refs are actually the managers. for a good laugh, try it with the refs and see what happens. if you come across any videos of managers cheating, send me a message. i've always thought that the refs should be the managers, so i'd be interested to hear your ideas.
-i have managed to escape to south america. i suggest you go to a country that is close to another. for example, i escaped to argentina from costa rica. when i got there, i put it into its own nation, and it said i was banned from the manager of the day. i tried it again, and it worked. i did this with other country as well. the best way i know is to go to a country close to another. then, escape to the other country. when i got there, it said i was banned from the manager of the day, and i could start a new game.
-football manager 2007 crack 7.0.2 download 13 DOWNLOAD ✓ https://gohhs.com/2uEzRo
-
-namespace groundingdino {
-
-at::Tensor ms_deform_attn_cuda_forward(
- const at::Tensor &value,
- const at::Tensor &spatial_shapes,
- const at::Tensor &level_start_index,
- const at::Tensor &sampling_loc,
- const at::Tensor &attn_weight,
- const int im2col_step);
-
-std::vector ms_deform_attn_cuda_backward(
- const at::Tensor &value,
- const at::Tensor &spatial_shapes,
- const at::Tensor &level_start_index,
- const at::Tensor &sampling_loc,
- const at::Tensor &attn_weight,
- const at::Tensor &grad_output,
- const int im2col_step);
-
-} // namespace groundingdino
\ No newline at end of file
diff --git a/spaces/shgao/MDT/app.py b/spaces/shgao/MDT/app.py
deleted file mode 100644
index eee8fb49dfd26f5cfbde4e214368ce45e2c71894..0000000000000000000000000000000000000000
--- a/spaces/shgao/MDT/app.py
+++ /dev/null
@@ -1,191 +0,0 @@
-import torch
-from torchvision.utils import make_grid
-import math
-from PIL import Image
-from diffusion import create_diffusion
-from diffusers.models import AutoencoderKL
-import gradio as gr
-from imagenet_class_data import IMAGENET_1K_CLASSES
-from models import MDT_XL_2
-import os
-from huggingface_hub import snapshot_download
-
-
-def load_model(image_size=256):
- assert image_size in [256]
- latent_size = image_size // 8
- model = MDT_XL_2(input_size=latent_size, decode_layer=2).to(device)
-
- models_path = snapshot_download("shgao/MDT-XL2")
- ckpt_model_path = os.path.join(models_path, "mdt_xl2_v1_ckpt.pt")
- state_dict = torch.load(
- ckpt_model_path, map_location=lambda storage, loc: storage)
- model.load_state_dict(state_dict)
- model.eval()
- return model
-
-
-torch.set_grad_enabled(False)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = load_model(image_size=256)
-vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to(device)
-current_image_size = 256
-current_vae_model = "stabilityai/sd-vae-ft-mse"
-
-
-def generate(image_size, vae_model, class_label, cfg_scale, pow_scale, num_sampling_steps, seed):
- n = 1
- image_size = int(image_size.split("x")[0])
- global current_image_size
- if image_size != current_image_size:
- global model
- model = model.to("cpu")
- del model
- if device == "cuda":
- torch.cuda.empty_cache()
- model = load_model(image_size=image_size)
- current_image_size = image_size
-
- global current_vae_model
- if vae_model != current_vae_model:
- global vae
- if device == "cuda":
- vae.to("cpu")
- del vae
- vae = AutoencoderKL.from_pretrained(vae_model).to(device)
-
- # Seed PyTorch:
- torch.manual_seed(seed)
-
- # Setup diffusion
- diffusion = create_diffusion(str(num_sampling_steps))
-
- # Create sampling noise:
- latent_size = image_size // 8
- z = torch.randn(n, 4, latent_size, latent_size, device=device)
- y = torch.tensor([class_label] * n, device=device)
-
- # Setup classifier-free guidance:
- z = torch.cat([z, z], 0)
- y_null = torch.tensor([1000] * n, device=device)
- y = torch.cat([y, y_null], 0)
- model_kwargs = dict(y=y, cfg_scale=cfg_scale, scale_pow=pow_scale)
-
- # Sample images:
- samples = diffusion.p_sample_loop(
- model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device
- )
- samples, _ = samples.chunk(2, dim=0) # Remove null class samples
- samples = vae.decode(samples / 0.18215).sample
-
- # Convert to PIL.Image format:
- samples = samples.mul(127.5).add_(128.0).clamp_(
- 0, 255).permute(0, 2, 3, 1).to("cpu", torch.uint8).numpy()
- samples = [Image.fromarray(sample) for sample in samples]
- return samples
-
-
-description = '''This is a demo of our MDT image generation models. MDT is a class-conditional model trained on ImageNet-1K.'''
-duplicate = '''Skip the queue by duplicating this space and upgrading to GPU in settings
-
-Paper ·
-GitHub
'''
-
-examples = [
- ["256x256", "stabilityai/sd-vae-ft-mse",
- "Welsh springer spaniel", 5.0, 0.01, 300, 30, 3000],
- ["256x256", "stabilityai/sd-vae-ft-mse",
- "golden retriever", 5.0, 0.01, 300, 30, 3000],
- ["256x256", "stabilityai/sd-vae-ft-mse",
- "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", 5.0, 0.01, 300, 30, 1],
- ["256x256", "stabilityai/sd-vae-ft-mse",
- "cheeseburger", 5.0, 0.01, 300, 30, 2],
- ["256x256", "stabilityai/sd-vae-ft-mse", "macaw", 5.0, 0.01, 300, 30, 1],
-]
-
-with gr.Blocks() as demo:
- gr.Markdown(
- "Masked Diffusion Transformer (MDT) ")
- gr.Markdown(project_links)
- gr.Markdown(description)
- gr.Markdown(duplicate)
-
- with gr.Tabs():
- with gr.TabItem('Generate'):
- with gr.Row():
- with gr.Column():
- with gr.Row():
- image_size = gr.inputs.Radio(
- choices=["256x256"], default="256x256", label='MDT Model Resolution')
- vae_model = gr.inputs.Radio(choices=["stabilityai/sd-vae-ft-mse", "stabilityai/sd-vae-ft-ema"],
- default="stabilityai/sd-vae-ft-mse", label='VAE Decoder')
- with gr.Row():
- i1k_class = gr.inputs.Dropdown(
- list(IMAGENET_1K_CLASSES.values()),
- default='Welsh springer spaniel',
- type="index", label='ImageNet-1K Class'
- )
- cfg_scale = gr.inputs.Slider(
- minimum=0, maximum=25, step=0.1, default=5.0, label='Classifier-free Guidance Scale')
- pow_scale = gr.inputs.Slider(
- minimum=0, maximum=25, step=0.1, default=0.01, label='Classifier-free Guidance Weight Scaling')
- steps = gr.inputs.Slider(
- minimum=4, maximum=1000, step=1, default=300, label='Sampling Steps')
- n = gr.inputs.Slider(
- minimum=1, maximum=16, step=1, default=1, label='Number of Samples')
- seed = gr.inputs.Number(default=30, label='Seed')
- button = gr.Button("Generate", variant="primary")
- with gr.Column():
- output = gr.Gallery(label='Generated Images').style(
- grid=[2], height="auto")
- button.click(generate, inputs=[
- image_size, vae_model, i1k_class, cfg_scale, pow_scale, steps, seed], outputs=[output])
- with gr.Row():
- ex = gr.Examples(examples=examples, fn=generate,
- inputs=[image_size, vae_model, i1k_class,
- cfg_scale, pow_scale, steps, seed],
- outputs=[output],
- cache_examples=True)
- gr.Markdown(more_info)
-
- demo.queue()
- demo.launch()
diff --git a/spaces/simpie28/VITS-Umamusume-voice-synthesizer/hubert_model.py b/spaces/simpie28/VITS-Umamusume-voice-synthesizer/hubert_model.py
deleted file mode 100644
index 6c7f8716c268d0f371f5a9f7995f59bd4b9082d1..0000000000000000000000000000000000000000
--- a/spaces/simpie28/VITS-Umamusume-voice-synthesizer/hubert_model.py
+++ /dev/null
@@ -1,221 +0,0 @@
-import copy
-from typing import Optional, Tuple
-import random
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-class Hubert(nn.Module):
- def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
- super().__init__()
- self._mask = mask
- self.feature_extractor = FeatureExtractor()
- self.feature_projection = FeatureProjection()
- self.positional_embedding = PositionalConvEmbedding()
- self.norm = nn.LayerNorm(768)
- self.dropout = nn.Dropout(0.1)
- self.encoder = TransformerEncoder(
- nn.TransformerEncoderLayer(
- 768, 12, 3072, activation="gelu", batch_first=True
- ),
- 12,
- )
- self.proj = nn.Linear(768, 256)
-
- self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
- self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
- def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
- mask = None
- if self.training and self._mask:
- mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
- x[mask] = self.masked_spec_embed.to(x.dtype)
- return x, mask
-
- def encode(
- self, x: torch.Tensor, layer: Optional[int] = None
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- x = self.feature_extractor(x)
- x = self.feature_projection(x.transpose(1, 2))
- x, mask = self.mask(x)
- x = x + self.positional_embedding(x)
- x = self.dropout(self.norm(x))
- x = self.encoder(x, output_layer=layer)
- return x, mask
-
- def logits(self, x: torch.Tensor) -> torch.Tensor:
- logits = torch.cosine_similarity(
- x.unsqueeze(2),
- self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
- dim=-1,
- )
- return logits / 0.1
-
- def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
- x, mask = self.encode(x)
- x = self.proj(x)
- logits = self.logits(x)
- return logits, mask
-
-
-class HubertSoft(Hubert):
- def __init__(self):
- super().__init__()
-
- @torch.inference_mode()
- def units(self, wav: torch.Tensor) -> torch.Tensor:
- wav = F.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
- x, _ = self.encode(wav)
- return self.proj(x)
-
-
-class FeatureExtractor(nn.Module):
- def __init__(self):
- super().__init__()
- self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
- self.norm0 = nn.GroupNorm(512, 512)
- self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
- self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
- self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
- self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
- self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
- self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = F.gelu(self.norm0(self.conv0(x)))
- x = F.gelu(self.conv1(x))
- x = F.gelu(self.conv2(x))
- x = F.gelu(self.conv3(x))
- x = F.gelu(self.conv4(x))
- x = F.gelu(self.conv5(x))
- x = F.gelu(self.conv6(x))
- return x
-
-
-class FeatureProjection(nn.Module):
- def __init__(self):
- super().__init__()
- self.norm = nn.LayerNorm(512)
- self.projection = nn.Linear(512, 768)
- self.dropout = nn.Dropout(0.1)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.norm(x)
- x = self.projection(x)
- x = self.dropout(x)
- return x
-
-
-class PositionalConvEmbedding(nn.Module):
- def __init__(self):
- super().__init__()
- self.conv = nn.Conv1d(
- 768,
- 768,
- kernel_size=128,
- padding=128 // 2,
- groups=16,
- )
- self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.conv(x.transpose(1, 2))
- x = F.gelu(x[:, :, :-1])
- return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
- def __init__(
- self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
- ) -> None:
- super(TransformerEncoder, self).__init__()
- self.layers = nn.ModuleList(
- [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
- )
- self.num_layers = num_layers
-
- def forward(
- self,
- src: torch.Tensor,
- mask: torch.Tensor = None,
- src_key_padding_mask: torch.Tensor = None,
- output_layer: Optional[int] = None,
- ) -> torch.Tensor:
- output = src
- for layer in self.layers[:output_layer]:
- output = layer(
- output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
- )
- return output
-
-
-def _compute_mask(
- shape: Tuple[int, int],
- mask_prob: float,
- mask_length: int,
- device: torch.device,
- min_masks: int = 0,
-) -> torch.Tensor:
- batch_size, sequence_length = shape
-
- if mask_length < 1:
- raise ValueError("`mask_length` has to be bigger than 0.")
-
- if mask_length > sequence_length:
- raise ValueError(
- f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
- )
-
- # compute number of masked spans in batch
- num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
- num_masked_spans = max(num_masked_spans, min_masks)
-
- # make sure num masked indices <= sequence_length
- if num_masked_spans * mask_length > sequence_length:
- num_masked_spans = sequence_length // mask_length
-
- # SpecAugment mask to fill
- mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
- # uniform distribution to sample from, make sure that offset samples are < sequence_length
- uniform_dist = torch.ones(
- (batch_size, sequence_length - (mask_length - 1)), device=device
- )
-
- # get random indices to mask
- mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
- # expand masked indices to masked spans
- mask_indices = (
- mask_indices.unsqueeze(dim=-1)
- .expand((batch_size, num_masked_spans, mask_length))
- .reshape(batch_size, num_masked_spans * mask_length)
- )
- offsets = (
- torch.arange(mask_length, device=device)[None, None, :]
- .expand((batch_size, num_masked_spans, mask_length))
- .reshape(batch_size, num_masked_spans * mask_length)
- )
- mask_idxs = mask_indices + offsets
-
- # scatter indices to mask
- mask = mask.scatter(1, mask_idxs, True)
-
- return mask
-
-
-def hubert_soft(
- path: str
-) -> HubertSoft:
- r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
- Args:
- path (str): path of a pretrained model
- """
- hubert = HubertSoft()
- checkpoint = torch.load(path)
- consume_prefix_in_state_dict_if_present(checkpoint, "module.")
- hubert.load_state_dict(checkpoint)
- hubert.eval()
- return hubert
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Adobe Media Encoder 2017 How to Get the Most Out of the Versatile Video Encoder for Free.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Adobe Media Encoder 2017 How to Get the Most Out of the Versatile Video Encoder for Free.md
deleted file mode 100644
index 35722b80d688bc60d23d9adea47a66f51ea44926..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Adobe Media Encoder 2017 How to Get the Most Out of the Versatile Video Encoder for Free.md
+++ /dev/null
@@ -1,128 +0,0 @@
-
-Adobe Media Encoder Free Download 2017: What You Need to Know
-If you are looking for a way to encode your video files into different formats, compress them, or apply corrections without having to re-open your projects, you might be interested in Adobe Media Encoder. This software is a powerful and versatile tool that works seamlessly with other Adobe applications, such as Premiere Pro and After Effects. But how can you get Adobe Media Encoder for free in 2017? And how do you use it effectively? In this article, we will answer these questions and more, as well as provide you with some alternatives to Adobe Media Encoder.
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-adobe media encoder free download 2017 Download Zip ⇒⇒⇒ https://ssurll.com/2uNUyq
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- Brick breaker games are a type of arcade game that originated in the 1970s. They are also known as breakout games, arkanoid games, or block breaker games. The basic premise of these games is to use a paddle or a bat to bounce a ball off the screen and break as many bricks as possible. The bricks can have different colors, shapes, sizes, and effects, and some of them may contain power-ups or bonuses that can help or hinder the player. The player loses a life if the ball falls below the paddle or goes past the bottom edge of the screen. The game ends when the player runs out of lives or clears all the bricks on the screen.
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The history and evolution of brick breaker games
- The first brick breaker game was Breakout, which was released by Atari in 1976. It was designed by Nolan Bushnell and Steve Bristow, and it was inspired by the earlier game Pong. Breakout was a huge success and spawned many clones and sequels over the years. Some of the most notable ones are Arkanoid (1986), Alleyway (1989), DX-Ball (1996), Ricochet (2001), and Shatter (2009).
- Brick breaker games have evolved over time, adding new features, graphics, sounds, levels, modes, themes, and genres. For example, some brick breaker games have incorporated elements of puzzle, adventure, action, RPG, or sci-fi into their gameplay. Some brick breaker games have also added multiplayer options, online leaderboards, achievements, customizations, and more.
- The gameplay and features of brick breaker games
- The gameplay of brick breaker games is simple but addictive. The player controls a paddle or a bat at the bottom of the screen and moves it left or right to catch and bounce a ball off it. The ball then hits the bricks at the top or middle of the screen and breaks them. The player scores points for each brick broken and collects power-ups or bonuses that can affect the ball, the paddle, or the bricks. Some power-ups can make the ball bigger, faster, slower, sticky, or split into multiple balls. Some power-ups can make the paddle longer, shorter, magnetic, or shoot lasers. Some power-ups can make the bricks explode, disappear, move, or change color.
- The features of brick breaker games vary depending on the game, but some common ones are:
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-Different levels with different layouts, difficulties, and objectives
-Different types of bricks with different effects and behaviors
-Different types of power-ups and bonuses with different effects and durations
-Different types of balls with different speeds, sizes, shapes, and trajectories
-Different types of paddles or bats with different sizes, shapes, and abilities
-Different modes of play, such as arcade, classic, endless, challenge, or story
-Different themes and genres, such as retro, modern, futuristic, fantasy, or horror
-Different graphics and sounds, such as 2D, 3D, pixelated, realistic, or cartoonish
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- Why play brick breaker games on PC?
- Brick breaker games are fun and addictive games that can be played on various devices, such as consoles, mobile phones, tablets, or PCs. However, playing brick breaker games on PC has some advantages that make it a better choice for some players. Here are some of the benefits of playing brick breaker games on PC:
- The benefits of playing brick breaker games on PC
- Some of the benefits of playing brick breaker games on PC are:
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-Better control and accuracy: Playing brick breaker games on PC allows you to use a mouse or a keyboard to control the paddle or the bat. This gives you more precision and responsiveness than using a touch screen or a joystick. You can also adjust the sensitivity and speed of the mouse or the keyboard to suit your preference and skill level.
-Bigger screen and resolution: Playing brick breaker games on PC lets you enjoy the game on a larger and clearer screen than on a small device. You can also adjust the resolution and the graphics settings to optimize the game performance and quality. You can also play the game in full-screen mode or windowed mode depending on your preference.
-More variety and options: Playing brick breaker games on PC gives you access to a wider range of games than on other devices. You can find many brick breaker games for PC online, either for free or for a small fee. You can also download and install different platforms and emulators that allow you to play brick breaker games from other devices on your PC. You can also customize and modify some of the games to your liking.
-More convenience and comfort: Playing brick breaker games on PC allows you to play the game anytime and anywhere you want, as long as you have a PC and an internet connection. You can also play the game for as long as you want without worrying about battery life or data usage. You can also play the game in a comfortable position and environment that suits you.
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- The challenges and tips of playing brick breaker games on PC
- Playing brick breaker games on PC also has some challenges that you need to overcome to enjoy the game fully. Here are some of the challenges and tips of playing brick breaker games on PC:
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-Compatibility and security issues: Playing brick breaker games on PC may require you to download and install different platforms and emulators that may not be compatible with your PC system or may contain viruses or malware. You need to make sure that you download and install only from trusted and reputable sources and that you scan your PC regularly for any threats.
-Distractions and interruptions: Playing brick breaker games on PC may expose you to more distractions and interruptions than on other devices. You may be tempted to check your email, social media, or other websites while playing the game. You may also receive notifications, messages, calls, or updates that may disrupt your game. You need to minimize these distractions and interruptions by turning off or muting any unnecessary applications or devices while playing the game.
-Addiction and fatigue: Playing brick breaker games on PC may cause you to become addicted and fatigued if you play the game for too long or too often. You may lose track of time or neglect your other responsibilities or activities while playing the game. You may also experience eye strain, headache, neck pain, back pain, or wrist pain from staring at the screen or using the mouse or keyboard for too long. You need to limit your playing time and take breaks regularly while playing the game.
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- How to download and play brick breaker games on PC?
- If you are interested in playing brick breaker games on PC, you need to know how to download and play them properly. Here are some steps that you can follow to download and play brick breaker games on PC:
- The best platforms and emulators for playing brick breaker games on PC
- The first step is to choose the best platform or emulator for playing brick breaker games on PC. A platform is a software that allows you to access and play different types of games online or offline. An emulator is a software that mimics the functionality of another device or system, such as a console or a mobile phone, and allows you to play its games on your PC.
- There are many platforms and emulators available for playing brick breaker games on PC, but some of the best ones are: - Microsoft Store: This is a platform that allows you to download and play various games and apps on your Windows PC. You can find many brick breaker games for PC on Microsoft Store, such as Brick Breaker!, Brick Breaker Star, and Brick Breaker 3D. You can also browse and filter the games by category, rating, price, and more. To download and play brick breaker games on PC from Microsoft Store, you need to have a Microsoft account and a Windows 10 PC. You can then visit the Microsoft Store website or app, search for the game you want, and click on the download or install button. You can then launch the game from your start menu or desktop. - BlueStacks: This is an emulator that allows you to play Android games and apps on your PC. You can find many brick breaker games for PC on BlueStacks, such as Brick Breaker House, Brick Breaker Hero, and Brick Breaker Quest. You can also access the Google Play Store and other Android platforms from BlueStacks. To download and play brick breaker games on PC from BlueStacks, you need to have a Google account and a Windows or Mac PC. You can then visit the BlueStacks website, download and install the emulator, and sign in with your Google account. You can then search for the game you want, and click on the install or play button. You can then launch the game from your BlueStacks home screen or app drawer. - Softonic: This is a platform that allows you to download and play various games and software on your PC. You can find many brick breaker games for PC on Softonic, such as Star Breaker, Brixout XP, and Bricks of Egypt. You can also read reviews and ratings of the games from other users. To download and play brick breaker games on PC from Softonic, you need to have a Windows PC. You can then visit the Softonic website, search for the game you want, and click on the download or free download button. You can then run the setup file and follow the instructions to install the game. You can then launch the game from your start menu or desktop.
- The best brick breaker games for PC
- The second step is to choose the best brick breaker game for PC that suits your taste and preference. There are many brick breaker games for PC available on different platforms and emulators, but some of the best ones are: - Brick Breaker! by Microsoft Store: This is a classic brick breaker game that features simple graphics, smooth gameplay, and challenging levels. You can control the paddle with your mouse or keyboard, and break bricks with different colors and effects. You can also collect power-ups that can help you clear the levels faster or make them harder. You can play this game in three modes: classic, arcade, or zen. You can also customize the game settings, such as the ball speed, paddle size, sound effects, and more. - Brick Breaker House by BlueStacks: This is a brick breaker game that features cute graphics, relaxing music, and fun gameplay. You can control the paddle with your mouse or touchpad, and break bricks with different shapes and patterns. You can also collect coins that can help you buy new balls, paddles, backgrounds, and more. You can play this game in two modes: normal or hard. You can also unlock achievements and compete with other players on the leaderboard. - Star Breaker by Softonic: This is a brick breaker game that features futuristic graphics, dynamic music, and exciting gameplay. You can control the paddle with your mouse or keyboard, and break bricks with different designs and effects. You can also collect stars that can help you upgrade your paddle, ball, or power-ups. You can play this game in four modes: easy, normal, hard, or insane. You can also create your own levels with the level editor.
- Conclusion
- Brick breaker games are fun and addictive games that you can play on your PC. They are simple to play but challenging to master, and they offer hours of entertainment and satisfaction. In this article, we have told you everything you need to know about brick breaker games, including their history, gameplay, features, benefits, challenges, tips, and how to download and play them on your PC. We have also recommended some of the best brick breaker games for PC that you can try right now. We hope that you have enjoyed reading this article and that you have learned something new about brick breaker games. If you are interested in playing brick breaker games on PC, we encourage you to follow the steps we have provided and to check out the games we have suggested. You will not regret it! Here are some FAQs that you may have about brick breaker games:
- FAQs
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-What is the difference between brick breaker games and pinball games?
-Brick breaker games and pinball games are both types of arcade games that involve bouncing a ball off a paddle or a flipper and hitting targets with it. However, brick breaker games focus on breaking bricks with the ball, while pinball games focus on scoring points with the ball. Brick breaker games also have more power-ups and bonuses than pinball games.
-What are some of the best brick breaker games for mobile devices?
-Some of the best brick breaker games for mobile devices are Brick Breaker Star: Space King, Bricks Breaker Quest, Brick Breaker Legend, and Bricks n Balls. You can download these games from the Google Play Store or the App Store for free or for a small fee.
-How can I improve my skills in brick breaker games?
-Some of the tips that can help you improve your skills in brick breaker games are:
-
-Practice regularly and try different levels and modes
-Watch your ball and paddle movements carefully and anticipate where they will go
-Aim for the corners and edges of the bricks to create more angles and bounce opportunities
-Use power-ups wisely and avoid negative ones
-Keep calm and don't panic when the ball speed increases or the bricks move closer
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-Are brick breaker games good for your brain?
-Yes, brick breaker games are good for your brain. They can help you improve your concentration, coordination, reflexes, memory, problem-solving, and decision-making skills. They can also help you relieve stress and have fun.
-Where can I find more information about brick breaker games?
-You can find more information about brick breaker games on various websites, blogs, forums, videos, podcasts, or books that are dedicated to this topic. You can also ask other players or experts for their opinions or advice on brick breaker games.
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- Access thousands of productivity apps and tools without switching devices
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-OS: Windows 10
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Caseware Working Papers 2010 [NEW] Crack.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Caseware Working Papers 2010 [NEW] Crack.md
deleted file mode 100644
index 1cc3f4f415882bf602b394ae9af92ba629279225..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Caseware Working Papers 2010 [NEW] Crack.md
+++ /dev/null
@@ -1,24 +0,0 @@
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diff --git a/spaces/t13718236382/bingoGPT4/src/lib/isomorphic/browser.ts b/spaces/t13718236382/bingoGPT4/src/lib/isomorphic/browser.ts
deleted file mode 100644
index de125b1f1786d1618cb1ff47f403d76c6784f4ce..0000000000000000000000000000000000000000
--- a/spaces/t13718236382/bingoGPT4/src/lib/isomorphic/browser.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-'use client'
-
-const debug = console.info.bind(console)
-
-class WebSocketAlias extends WebSocket {
- constructor(address: string | URL, ...args: any) {
- super(address)
- }
-}
-
-export default { fetch, WebSocket: WebSocketAlias, debug }
diff --git a/spaces/taesiri/ChatGPT-ImageCaptioner/detic/data/datasets/cc.py b/spaces/taesiri/ChatGPT-ImageCaptioner/detic/data/datasets/cc.py
deleted file mode 100644
index 7c3e50726f781dba4c72d4e18f4922e503218af8..0000000000000000000000000000000000000000
--- a/spaces/taesiri/ChatGPT-ImageCaptioner/detic/data/datasets/cc.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import os
-
-from detectron2.data.datasets.builtin_meta import _get_builtin_metadata
-from detectron2.data.datasets.lvis import get_lvis_instances_meta
-from .lvis_v1 import custom_register_lvis_instances
-
-_CUSTOM_SPLITS = {
- "cc3m_v1_val": ("cc3m/validation/", "cc3m/val_image_info.json"),
- "cc3m_v1_train": ("cc3m/training/", "cc3m/train_image_info.json"),
- "cc3m_v1_train_tags": ("cc3m/training/", "cc3m/train_image_info_tags.json"),
-
-}
-
-for key, (image_root, json_file) in _CUSTOM_SPLITS.items():
- custom_register_lvis_instances(
- key,
- get_lvis_instances_meta('lvis_v1'),
- os.path.join("datasets", json_file) if "://" not in json_file else json_file,
- os.path.join("datasets", image_root),
- )
-
diff --git a/spaces/tdeshane/artists-of-data-science-chainlit/README.md b/spaces/tdeshane/artists-of-data-science-chainlit/README.md
deleted file mode 100644
index afecf0af3b82192457ab47054cd5e6f348cd9adc..0000000000000000000000000000000000000000
--- a/spaces/tdeshane/artists-of-data-science-chainlit/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Artists of Data Science
-app_file: app.py
-pinned: true
-license: apache-2.0
-sdk: docker
-emoji: 🚀
-colorFrom: gray
-colorTo: blue
----
-
-# podcast-magic
-Final Project for LLMOps Cohort
\ No newline at end of file
diff --git a/spaces/teralomaniac/chatbing/EdgeGPT.py b/spaces/teralomaniac/chatbing/EdgeGPT.py
deleted file mode 100644
index 6e5ac6b252910956eeb7830cc7ec58525fda8da9..0000000000000000000000000000000000000000
--- a/spaces/teralomaniac/chatbing/EdgeGPT.py
+++ /dev/null
@@ -1,1031 +0,0 @@
-"""
-Main.py
-"""
-from __future__ import annotations
-
-import argparse
-import asyncio
-import json
-import os
-import random
-import re
-import ssl
-import sys
-import locale as loc_util
-import uuid
-from enum import Enum
-from pathlib import Path
-from typing import Generator
-from typing import Union
-
-import aiofiles
-
-try:
- from typing import Literal, Union
-except ImportError:
- from typing_extensions import Literal
-from typing import Optional
-
-import aiohttp
-import certifi
-import httpx
-from BingImageCreator import ImageGen
-from BingImageCreator import ImageGenAsync
-from prompt_toolkit import PromptSession
-from prompt_toolkit.auto_suggest import AutoSuggestFromHistory
-from prompt_toolkit.completion import WordCompleter
-from prompt_toolkit.history import InMemoryHistory
-from prompt_toolkit.key_binding import KeyBindings
-from rich.live import Live
-from rich.markdown import Markdown
-
-DELIMITER = "\x1e"
-
-
-# Generate random IP between range 13.104.0.0/14
-FORWARDED_IP = (
- f"13.{random.randint(104, 107)}.{random.randint(0, 255)}.{random.randint(0, 255)}"
-)
-
-HEADERS = {
- "accept": "application/json",
- "accept-language": "en-US,en;q=0.9",
- "content-type": "application/json",
- "sec-ch-ua": '"Not_A Brand";v="99", "Microsoft Edge";v="110", "Chromium";v="110"',
- "sec-ch-ua-arch": '"x86"',
- "sec-ch-ua-bitness": '"64"',
- "sec-ch-ua-full-version": '"109.0.1518.78"',
- "sec-ch-ua-full-version-list": '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
- "sec-ch-ua-mobile": "?0",
- "sec-ch-ua-model": "",
- "sec-ch-ua-platform": '"Windows"',
- "sec-ch-ua-platform-version": '"15.0.0"',
- "sec-fetch-dest": "empty",
- "sec-fetch-mode": "cors",
- "sec-fetch-site": "same-origin",
- "x-ms-client-request-id": str(uuid.uuid4()),
- "x-ms-useragent": "azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32",
- "Referer": "https://www.bing.com/search?q=Bing+AI&showconv=1&FORM=hpcodx",
- "Referrer-Policy": "origin-when-cross-origin",
- "x-forwarded-for": FORWARDED_IP,
-}
-
-HEADERS_INIT_CONVER = {
- "authority": "edgeservices.bing.com",
- "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
- "accept-language": "en-US,en;q=0.9",
- "cache-control": "max-age=0",
- "sec-ch-ua": '"Chromium";v="110", "Not A(Brand";v="24", "Microsoft Edge";v="110"',
- "sec-ch-ua-arch": '"x86"',
- "sec-ch-ua-bitness": '"64"',
- "sec-ch-ua-full-version": '"110.0.1587.69"',
- "sec-ch-ua-full-version-list": '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
- "sec-ch-ua-mobile": "?0",
- "sec-ch-ua-model": '""',
- "sec-ch-ua-platform": '"Windows"',
- "sec-ch-ua-platform-version": '"15.0.0"',
- "sec-fetch-dest": "document",
- "sec-fetch-mode": "navigate",
- "sec-fetch-site": "none",
- "sec-fetch-user": "?1",
- "upgrade-insecure-requests": "1",
- "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36 Edg/110.0.1587.69",
- "x-edge-shopping-flag": "1",
- "x-forwarded-for": FORWARDED_IP,
-}
-
-ssl_context = ssl.create_default_context()
-ssl_context.load_verify_locations(certifi.where())
-
-
-class NotAllowedToAccess(Exception):
- pass
-
-
-class LocationHint(Enum):
- USA = {
- "locale": "en-US",
- "LocationHint": [
- {
- "country": "United States",
- "state": "California",
- "city": "Los Angeles",
- "timezoneoffset": 8,
- "countryConfidence": 8,
- "Center": {
- "Latitude": 34.0536909,
- "Longitude": -118.242766,
- },
- "RegionType": 2,
- "SourceType": 1,
- },
- ],
- }
- CHINA = {
- "locale": "zh-CN",
- "LocationHint": [
- {
- "country": "China",
- "state": "",
- "city": "Beijing",
- "timezoneoffset": 8,
- "countryConfidence": 8,
- "Center": {
- "Latitude": 39.9042,
- "Longitude": 116.4074,
- },
- "RegionType": 2,
- "SourceType": 1,
- },
- ],
- }
- EU = {
- "locale": "en-IE",
- "LocationHint": [
- {
- "country": "Norway",
- "state": "",
- "city": "Oslo",
- "timezoneoffset": 1,
- "countryConfidence": 8,
- "Center": {
- "Latitude": 59.9139,
- "Longitude": 10.7522,
- },
- "RegionType": 2,
- "SourceType": 1,
- },
- ],
- }
- UK = {
- "locale": "en-GB",
- "LocationHint": [
- {
- "country": "United Kingdom",
- "state": "",
- "city": "London",
- "timezoneoffset": 0,
- "countryConfidence": 8,
- "Center": {
- "Latitude": 51.5074,
- "Longitude": -0.1278,
- },
- "RegionType": 2,
- "SourceType": 1,
- },
- ],
- }
-
-
-LOCATION_HINT_TYPES = Optional[Union[LocationHint, Literal["USA", "CHINA", "EU", "UK"]]]
-
-
-def get_location_hint_from_locale(locale: str) -> dict | None:
- locale = locale.lower()
- if locale == "en-us":
- hint = LocationHint.USA.value
- if locale == "zh-cn":
- hint = LocationHint.CHINA.value
- if locale == "en-gb":
- hint = LocationHint.UK.value
- if locale == "en-ie":
- hint = LocationHint.EU.value
- else:
- hint = LocationHint.USA.value
- return hint.get("LocationHint")
-
-
-def guess_locale() -> str:
- locale, _ = loc_util.getlocale()
- if not locale:
- locale = "en-US"
- return locale.replace("_", "-")
-
-
-class ConversationStyle(Enum):
- creative = [
- "nlu_direct_response_filter",
- "deepleo",
- "disable_emoji_spoken_text",
- "responsible_ai_policy_235",
- "enablemm",
- "h3imaginative",
- "cachewriteext",
- "e2ecachewrite",
- "nodlcpcwrite",
- "enablenewsfc",
- "dv3sugg",
- "clgalileo",
- "gencontentv3",
- "nojbfedge",
- ]
- balanced = [
- "nlu_direct_response_filter",
- "deepleo",
- "disable_emoji_spoken_text",
- "responsible_ai_policy_235",
- "enablemm",
- "harmonyv3",
- "cachewriteext",
- "e2ecachewrite",
- "nodlcpcwrite",
- "enablenewsfc",
- "dv3sugg",
- "nojbfedge",
- ]
- precise = [
- "nlu_direct_response_filter",
- "deepleo",
- "disable_emoji_spoken_text",
- "responsible_ai_policy_235",
- "enablemm",
- "h3precise",
- "cachewriteext",
- "e2ecachewrite",
- "nodlcpcwrite",
- "enablenewsfc",
- "dv3sugg",
- "clgalileo",
- "gencontentv3",
- "nojbfedge",
- ]
-
-
-CONVERSATION_STYLE_TYPE = Optional[
- Union[ConversationStyle, Literal["creative", "balanced", "precise"]]
-]
-
-
-def _append_identifier(msg: dict) -> str:
- # Convert dict to json string
- return json.dumps(msg, ensure_ascii=False) + DELIMITER
-
-
-def _get_ran_hex(length: int = 32) -> str:
- return "".join(random.choice("0123456789abcdef") for _ in range(length))
-
-
-class _ChatHubRequest:
- def __init__(
- self,
- conversation_signature: str,
- client_id: str,
- conversation_id: str,
- invocation_id: int = 0,
- ) -> None:
- self.struct: dict = {}
-
- self.client_id: str = client_id
- self.conversation_id: str = conversation_id
- self.conversation_signature: str = conversation_signature
- self.invocation_id: int = invocation_id
-
- def update(
- self,
- prompt: str,
- conversation_style: CONVERSATION_STYLE_TYPE,
- options: list | None = None,
- webpage_context: str | None = None,
- search_result: bool = False,
- locale: str = guess_locale(),
- ) -> None:
- if options is None:
- options = [
- "deepleo",
- "enable_debug_commands",
- "disable_emoji_spoken_text",
- "enablemm",
- ]
- if conversation_style:
- if not isinstance(conversation_style, ConversationStyle):
- conversation_style = getattr(ConversationStyle, conversation_style)
- options = conversation_style.value
- self.struct = {
- "arguments": [
- {
- "source": "cib",
- "optionsSets": options,
- "allowedMessageTypes": [
- "Chat",
- "Disengaged",
- "AdsQuery",
- "SemanticSerp",
- "GenerateContentQuery",
- "SearchQuery",
- "ActionRequest",
- "Context",
- "Progress",
- "AdsQuery",
- "SemanticSerp",
- ],
- "sliceIds": [
- "winmuid3tf",
- "osbsdusgreccf",
- "ttstmout",
- "crchatrev",
- "winlongmsgtf",
- "ctrlworkpay",
- "norespwtf",
- "tempcacheread",
- "temptacache",
- "505scss0",
- "508jbcars0",
- "515enbotdets0",
- "5082tsports",
- "515vaoprvs",
- "424dagslnv1s0",
- "kcimgattcf",
- "427startpms0",
- ],
- "traceId": _get_ran_hex(32),
- "isStartOfSession": self.invocation_id == 0,
- "message": {
- "locale": locale,
- "market": locale,
- "region": locale[-2:], # en-US -> US
- "locationHints": get_location_hint_from_locale(locale),
- "author": "user",
- "inputMethod": "Keyboard",
- "text": prompt,
- "messageType": random.choice(["SearchQuery", "Chat"]),
- },
- "conversationSignature": self.conversation_signature,
- "participant": {
- "id": self.client_id,
- },
- "conversationId": self.conversation_id,
- },
- ],
- "invocationId": str(self.invocation_id),
- "target": "chat",
- "type": 4,
- }
- if search_result:
- have_search_result = [
- "InternalSearchQuery",
- "InternalSearchResult",
- "InternalLoaderMessage",
- "RenderCardRequest",
- ]
- self.struct["arguments"][0]["allowedMessageTypes"] += have_search_result
- if webpage_context:
- self.struct["arguments"][0]["previousMessages"] = [
- {
- "author": "user",
- "description": webpage_context,
- "contextType": "WebPage",
- "messageType": "Context",
- "messageId": "discover-web--page-ping-mriduna-----",
- },
- ]
- self.invocation_id += 1
-
-
-class _Conversation:
- def __init__(
- self,
- proxy: str | None = None,
- async_mode: bool = False,
- cookies: list[dict] | None = None,
- ) -> None:
- if async_mode:
- return
- self.struct: dict = {
- "conversationId": None,
- "clientId": None,
- "conversationSignature": None,
- "result": {"value": "Success", "message": None},
- }
- self.proxy = proxy
- proxy = (
- proxy
- or os.environ.get("all_proxy")
- or os.environ.get("ALL_PROXY")
- or os.environ.get("https_proxy")
- or os.environ.get("HTTPS_PROXY")
- or None
- )
- if proxy is not None and proxy.startswith("socks5h://"):
- proxy = "socks5://" + proxy[len("socks5h://") :]
- self.session = httpx.Client(
- proxies=proxy,
- timeout=900,
- headers=HEADERS_INIT_CONVER,
- )
- if cookies:
- for cookie in cookies:
- self.session.cookies.set(cookie["name"], cookie["value"])
- # Send GET request
- response = self.session.get(
- url=os.environ.get("BING_PROXY_URL")
- or "https://edgeservices.bing.com/edgesvc/turing/conversation/create",
- )
- if response.status_code != 200:
- response = self.session.get(
- "https://edge.churchless.tech/edgesvc/turing/conversation/create",
- )
- if response.status_code != 200:
- print(f"Status code: {response.status_code}")
- print(response.text)
- print(response.url)
- raise Exception("Authentication failed")
- try:
- self.struct = response.json()
- except (json.decoder.JSONDecodeError, NotAllowedToAccess) as exc:
- raise Exception(
- "Authentication failed. You have not been accepted into the beta.",
- ) from exc
- if self.struct["result"]["value"] == "UnauthorizedRequest":
- raise NotAllowedToAccess(self.struct["result"]["message"])
-
- @staticmethod
- async def create(
- proxy: str | None = None,
- cookies: list[dict] | None = None,
- ) -> _Conversation:
- self = _Conversation(async_mode=True)
- self.struct = {
- "conversationId": None,
- "clientId": None,
- "conversationSignature": None,
- "result": {"value": "Success", "message": None},
- }
- self.proxy = proxy
- proxy = (
- proxy
- or os.environ.get("all_proxy")
- or os.environ.get("ALL_PROXY")
- or os.environ.get("https_proxy")
- or os.environ.get("HTTPS_PROXY")
- or None
- )
- if proxy is not None and proxy.startswith("socks5h://"):
- proxy = "socks5://" + proxy[len("socks5h://") :]
- transport = httpx.AsyncHTTPTransport(retries=900)
- # Convert cookie format to httpx format
- formatted_cookies = None
- if cookies:
- formatted_cookies = httpx.Cookies()
- for cookie in cookies:
- formatted_cookies.set(cookie["name"], cookie["value"])
- async with httpx.AsyncClient(
- proxies=proxy,
- timeout=30,
- headers=HEADERS_INIT_CONVER,
- transport=transport,
- cookies=formatted_cookies,
- ) as client:
- # Send GET request
- response = await client.get(
- url=os.environ.get("BING_PROXY_URL")
- or "https://edgeservices.bing.com/edgesvc/turing/conversation/create",
- )
- if response.status_code != 200:
- response = await client.get(
- "https://edge.churchless.tech/edgesvc/turing/conversation/create",
- )
- if response.status_code != 200:
- print(f"Status code: {response.status_code}")
- print(response.text)
- print(response.url)
- raise Exception("Authentication failed")
- try:
- self.struct = response.json()
- except (json.decoder.JSONDecodeError, NotAllowedToAccess) as exc:
- raise Exception(
- "Authentication failed. You have not been accepted into the beta.",
- ) from exc
- if self.struct["result"]["value"] == "UnauthorizedRequest":
- raise NotAllowedToAccess(self.struct["result"]["message"])
- return self
-
-
-class _ChatHub:
- def __init__(
- self,
- conversation: _Conversation,
- proxy: str = None,
- cookies: list[dict] | None = None,
- ) -> None:
- self.session: aiohttp.ClientSession | None = None
- self.wss: aiohttp.ClientWebSocketResponse | None = None
- self.request: _ChatHubRequest
- self.loop: bool
- self.task: asyncio.Task
- self.request = _ChatHubRequest(
- conversation_signature=conversation.struct["conversationSignature"],
- client_id=conversation.struct["clientId"],
- conversation_id=conversation.struct["conversationId"],
- )
- self.cookies = cookies
- self.proxy: str = proxy
-
- async def ask_stream(
- self,
- prompt: str,
- wss_link: str,
- conversation_style: CONVERSATION_STYLE_TYPE = None,
- raw: bool = False,
- options: dict = None,
- webpage_context: str | None = None,
- search_result: bool = False,
- locale: str = guess_locale(),
- ) -> Generator[str, None, None]:
- timeout = aiohttp.ClientTimeout(total=900)
- self.session = aiohttp.ClientSession(timeout=timeout)
-
- if self.wss and not self.wss.closed:
- await self.wss.close()
- # Check if websocket is closed
- self.wss = await self.session.ws_connect(
- wss_link,
- headers=HEADERS,
- ssl=ssl_context,
- proxy=self.proxy,
- autoping=False,
- )
- await self._initial_handshake()
- if self.request.invocation_id == 0:
- # Construct a ChatHub request
- self.request.update(
- prompt=prompt,
- conversation_style=conversation_style,
- options=options,
- webpage_context=webpage_context,
- search_result=search_result,
- locale=locale,
- )
- else:
- async with httpx.AsyncClient() as client:
- response = await client.post(
- "https://sydney.bing.com/sydney/UpdateConversation/",
- json={
- "messages": [
- {
- "author": "user",
- "description": webpage_context,
- "contextType": "WebPage",
- "messageType": "Context",
- },
- ],
- "conversationId": self.request.conversation_id,
- "source": "cib",
- "traceId": _get_ran_hex(32),
- "participant": {"id": self.request.client_id},
- "conversationSignature": self.request.conversation_signature,
- },
- )
- if response.status_code != 200:
- print(f"Status code: {response.status_code}")
- print(response.text)
- print(response.url)
- raise Exception("Update web page context failed")
- # Construct a ChatHub request
- self.request.update(
- prompt=prompt,
- conversation_style=conversation_style,
- options=options,
- )
- # Send request
- await self.wss.send_str(_append_identifier(self.request.struct))
- final = False
- draw = False
- resp_txt = ""
- result_text = ""
- resp_txt_no_link = ""
- while not final:
- msg = await self.wss.receive(timeout=900)
- objects = msg.data.split(DELIMITER)
- for obj in objects:
- if obj is None or not obj:
- continue
- response = json.loads(obj)
- if response.get("type") != 2 and raw:
- yield False, response
- elif response.get("type") == 1 and response["arguments"][0].get(
- "messages",
- ):
- if not draw:
- if (
- response["arguments"][0]["messages"][0].get("messageType")
- == "GenerateContentQuery"
- ):
- async with ImageGenAsync("", True) as image_generator:
- images = await image_generator.get_images(
- response["arguments"][0]["messages"][0]["text"],
- )
- for i, image in enumerate(images):
- resp_txt = f"{resp_txt}\n"
- draw = True
- if (
- response["arguments"][0]["messages"][0]["contentOrigin"]
- != "Apology"
- ) and not draw:
- resp_txt = result_text + response["arguments"][0][
- "messages"
- ][0]["adaptiveCards"][0]["body"][0].get("text", "")
- resp_txt_no_link = result_text + response["arguments"][0][
- "messages"
- ][0].get("text", "")
- if response["arguments"][0]["messages"][0].get(
- "messageType",
- ):
- resp_txt = (
- resp_txt
- + response["arguments"][0]["messages"][0][
- "adaptiveCards"
- ][0]["body"][0]["inlines"][0].get("text")
- + "\n"
- )
- result_text = (
- result_text
- + response["arguments"][0]["messages"][0][
- "adaptiveCards"
- ][0]["body"][0]["inlines"][0].get("text")
- + "\n"
- )
- yield False, resp_txt
-
- elif response.get("type") == 2:
- if response["item"]["result"].get("error"):
- await self.close()
- raise Exception(
- f"{response['item']['result']['value']}: {response['item']['result']['message']}",
- )
- if draw:
- cache = response["item"]["messages"][1]["adaptiveCards"][0][
- "body"
- ][0]["text"]
- response["item"]["messages"][1]["adaptiveCards"][0]["body"][0][
- "text"
- ] = (cache + resp_txt)
- if (
- response["item"]["messages"][-1]["contentOrigin"] == "Apology"
- and resp_txt
- ):
- response["item"]["messages"][-1]["text"] = resp_txt_no_link
- response["item"]["messages"][-1]["adaptiveCards"][0]["body"][0][
- "text"
- ] = resp_txt
- print(
- "Preserved the message from being deleted",
- file=sys.stderr,
- )
- final = True
- await self.close()
- yield True, response
-
- async def _initial_handshake(self) -> None:
- await self.wss.send_str(_append_identifier({"protocol": "json", "version": 1}))
- await self.wss.receive(timeout=900)
-
- async def close(self) -> None:
- if self.wss and not self.wss.closed:
- await self.wss.close()
- if self.session and not self.session.closed:
- await self.session.close()
-
-
-class Chatbot:
- """
- Combines everything to make it seamless
- """
-
- def __init__(
- self,
- proxy: str | None = None,
- cookies: list[dict] | None = None,
- ) -> None:
- self.proxy: str | None = proxy
- self.chat_hub: _ChatHub = _ChatHub(
- _Conversation(self.proxy, cookies=cookies),
- proxy=self.proxy,
- cookies=cookies,
- )
-
- @staticmethod
- async def create(
- proxy: str | None = None,
- cookies: list[dict] | None = None,
- ) -> Chatbot:
- self = Chatbot.__new__(Chatbot)
- self.proxy = proxy
- self.chat_hub = _ChatHub(
- await _Conversation.create(self.proxy, cookies=cookies),
- proxy=self.proxy,
- cookies=cookies,
- )
- return self
-
- async def save_conversation(self, filename: str) -> None:
- """
- Save the conversation to a file
- """
- with open(filename, "w") as f:
- f.write(json.dumps(self.chat_hub.struct))
-
- async def load_conversation(self, filename: str) -> None:
- """
- Load the conversation from a file
- """
- with open(filename, "r") as f:
- self.chat_hub.struct = json.loads(f.read())
-
- async def ask(
- self,
- prompt: str,
- wss_link: str = "wss://sydney.bing.com/sydney/ChatHub",
- conversation_style: CONVERSATION_STYLE_TYPE = None,
- options: dict = None,
- webpage_context: str | None = None,
- search_result: bool = False,
- locale: str = guess_locale(),
- ) -> dict:
- """
- Ask a question to the bot
- """
- async for final, response in self.chat_hub.ask_stream(
- prompt=prompt,
- conversation_style=conversation_style,
- wss_link=wss_link,
- options=options,
- webpage_context=webpage_context,
- search_result=search_result,
- locale=locale,
- ):
- if final:
- return response
- await self.chat_hub.wss.close()
- return {}
-
- async def ask_stream(
- self,
- prompt: str,
- wss_link: str = "wss://sydney.bing.com/sydney/ChatHub",
- conversation_style: CONVERSATION_STYLE_TYPE = None,
- raw: bool = False,
- options: dict = None,
- webpage_context: str | None = None,
- search_result: bool = False,
- locale: str = guess_locale(),
- ) -> Generator[str, None, None]:
- """
- Ask a question to the bot
- """
- async for response in self.chat_hub.ask_stream(
- prompt=prompt,
- conversation_style=conversation_style,
- wss_link=wss_link,
- raw=raw,
- options=options,
- webpage_context=webpage_context,
- search_result=search_result,
- locale=locale,
- ):
- yield response
-
- async def close(self) -> None:
- """
- Close the connection
- """
- await self.chat_hub.close()
-
- async def reset(self) -> None:
- """
- Reset the conversation
- """
- await self.close()
- self.chat_hub = _ChatHub(
- await _Conversation.create(self.proxy, cookies=self.chat_hub.cookies),
- proxy=self.proxy,
- cookies=self.chat_hub.cookies,
- )
-
-
-async def _get_input_async(
- session: PromptSession = None,
- completer: WordCompleter = None,
-) -> str:
- """
- Multiline input function.
- """
- return await session.prompt_async(
- completer=completer,
- multiline=True,
- auto_suggest=AutoSuggestFromHistory(),
- )
-
-
-def _create_session() -> PromptSession:
- kb = KeyBindings()
-
- @kb.add("enter")
- def _(event) -> None:
- buffer_text = event.current_buffer.text
- if buffer_text.startswith("!"):
- event.current_buffer.validate_and_handle()
- else:
- event.current_buffer.insert_text("\n")
-
- @kb.add("escape")
- def _(event) -> None:
- if event.current_buffer.complete_state:
- # event.current_buffer.cancel_completion()
- event.current_buffer.text = ""
-
- return PromptSession(key_bindings=kb, history=InMemoryHistory())
-
-
-def _create_completer(commands: list, pattern_str: str = "$") -> WordCompleter:
- return WordCompleter(words=commands, pattern=re.compile(pattern_str))
-
-
-def _create_history_logger(f):
- def logger(*args, **kwargs) -> None:
- tmp = sys.stdout
- sys.stdout = f
- print(*args, **kwargs, flush=True)
- sys.stdout = tmp
-
- return logger
-
-
-async def async_main(args: argparse.Namespace) -> None:
- """
- Main function
- """
- print("Initializing...")
- print("Enter `alt+enter` or `escape+enter` to send a message")
- # Read and parse cookies
- cookies = None
- if args.cookie_file:
- cookies = json.loads(Path.open(args.cookie_file, encoding="utf-8").read())
- bot = await Chatbot.create(proxy=args.proxy, cookies=cookies)
- session = _create_session()
- completer = _create_completer(["!help", "!exit", "!reset"])
- initial_prompt = args.prompt
-
- # Log chat history
- def p_hist(*args, **kwargs) -> None:
- pass
-
- if args.history_file:
- f = Path.open(args.history_file, "a+", encoding="utf-8")
- p_hist = _create_history_logger(f)
-
- while True:
- print("\nYou:")
- p_hist("\nYou:")
- if initial_prompt:
- question = initial_prompt
- print(question)
- initial_prompt = None
- else:
- question = (
- input()
- if args.enter_once
- else await _get_input_async(session=session, completer=completer)
- )
- print()
- p_hist(question + "\n")
- if question == "!exit":
- break
- if question == "!help":
- print(
- """
- !help - Show this help message
- !exit - Exit the program
- !reset - Reset the conversation
- """,
- )
- continue
- if question == "!reset":
- await bot.reset()
- continue
- print("Bot:")
- p_hist("Bot:")
- if args.no_stream:
- response = (
- await bot.ask(
- prompt=question,
- conversation_style=args.style,
- wss_link=args.wss_link,
- search_result=args.search_result,
- locale=args.locale,
- )
- )["item"]["messages"][1]["adaptiveCards"][0]["body"][0]["text"]
- print(response)
- p_hist(response)
- else:
- wrote = 0
- if args.rich:
- md = Markdown("")
- with Live(md, auto_refresh=False) as live:
- async for final, response in bot.ask_stream(
- prompt=question,
- conversation_style=args.style,
- wss_link=args.wss_link,
- search_result=args.search_result,
- locale=args.locale,
- ):
- if not final:
- if not wrote:
- p_hist(response, end="")
- else:
- p_hist(response[wrote:], end="")
- if wrote > len(response):
- print(md)
- print(Markdown("***Bing revoked the response.***"))
- wrote = len(response)
- md = Markdown(response)
- live.update(md, refresh=True)
- else:
- async for final, response in bot.ask_stream(
- prompt=question,
- conversation_style=args.style,
- wss_link=args.wss_link,
- search_result=args.search_result,
- locale=args.locale,
- ):
- if not final:
- if not wrote:
- print(response, end="", flush=True)
- p_hist(response, end="")
- else:
- print(response[wrote:], end="", flush=True)
- p_hist(response[wrote:], end="")
- wrote = len(response)
- print()
- p_hist()
- if args.history_file:
- f.close()
- await bot.close()
-
-
-def main() -> None:
- print(
- """
- EdgeGPT - A demo of reverse engineering the Bing GPT chatbot
- Repo: github.com/acheong08/EdgeGPT
- By: Antonio Cheong
-
- !help for help
-
- Type !exit to exit
- """,
- )
- parser = argparse.ArgumentParser()
- parser.add_argument("--enter-once", action="store_true")
- parser.add_argument("--search-result", action="store_true")
- parser.add_argument("--no-stream", action="store_true")
- parser.add_argument("--rich", action="store_true")
- parser.add_argument(
- "--proxy",
- help="Proxy URL (e.g. socks5://127.0.0.1:1080)",
- type=str,
- )
- parser.add_argument(
- "--wss-link",
- help="WSS URL(e.g. wss://sydney.bing.com/sydney/ChatHub)",
- type=str,
- default="wss://sydney.bing.com/sydney/ChatHub",
- )
- parser.add_argument(
- "--style",
- choices=["creative", "balanced", "precise"],
- default="balanced",
- )
- parser.add_argument(
- "--prompt",
- type=str,
- default="",
- required=False,
- help="prompt to start with",
- )
- parser.add_argument(
- "--cookie-file",
- type=str,
- default="",
- required=False,
- help="path to cookie file",
- )
- parser.add_argument(
- "--history-file",
- type=str,
- default="",
- required=False,
- help="path to history file",
- )
- parser.add_argument(
- "--locale",
- type=str,
- default="en-US",
- required=False,
- help="your locale",
- )
- args = parser.parse_args()
- asyncio.run(async_main(args))
-
-
-if __name__ == "__main__":
- main()
\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Almenamethodtouchtyping((TOP)) Crack.md b/spaces/terfces0erbo/CollegeProjectV2/Almenamethodtouchtyping((TOP)) Crack.md
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diff --git a/spaces/tetrisd/Diffusion-Attentive-Attribution-Maps/scrollbar.css b/spaces/tetrisd/Diffusion-Attentive-Attribution-Maps/scrollbar.css
deleted file mode 100644
index 9167a72a8601f83a830bbd3cac1d4f2c637e637e..0000000000000000000000000000000000000000
--- a/spaces/tetrisd/Diffusion-Attentive-Attribution-Maps/scrollbar.css
+++ /dev/null
@@ -1,46 +0,0 @@
-.output-html {
- overflow-x: auto;
-}
-
-.output-html::-webkit-scrollbar {
- -webkit-appearance: none;
-}
-
-.output-html::-webkit-scrollbar:vertical {
- width: 0px;
-}
-
-.output-html::-webkit-scrollbar:horizontal {
- height: 11px;
-}
-
-.output-html::-webkit-scrollbar-thumb {
- border-radius: 8px;
- border: 2px solid white;
- background-color: rgba(0, 0, 0, .5);
-}
-
-.output-html::-webkit-scrollbar-track {
- background-color: #fff;
- border-radius: 8px;
-}
-
-.spans {
- min-height: 75px;
-}
-
-svg {
- margin: auto;
- display: block;
-}
-
-#submit-btn {
- z-index: 999;
-}
-
-#viz {
- width: 100%;
- top: -30px;
- object-fit: scale-down;
- object-position: 0 100%;
-}
\ No newline at end of file
diff --git a/spaces/theonerichy/wd-v1-4-tags/app.py b/spaces/theonerichy/wd-v1-4-tags/app.py
deleted file mode 100644
index 33fa06d229e5bdad6268136cc0fb55c64909cbfd..0000000000000000000000000000000000000000
--- a/spaces/theonerichy/wd-v1-4-tags/app.py
+++ /dev/null
@@ -1,285 +0,0 @@
-from __future__ import annotations
-
-import argparse
-import functools
-import html
-import os
-
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import onnxruntime as rt
-import pandas as pd
-import piexif
-import piexif.helper
-import PIL.Image
-
-from Utils import dbimutils
-
-TITLE = "WaifuDiffusion v1.4 Tags"
-DESCRIPTION = """
-Demo for:
-- [SmilingWolf/wd-v1-4-moat-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2)
-- [SmilingWolf/wd-v1-4-swinv2-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger-v2)
-- [SmilingWolf/wd-v1-4-convnext-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger-v2)
-- [SmilingWolf/wd-v1-4-convnextv2-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-convnextv2-tagger-v2)
-- [SmilingWolf/wd-v1-4-vit-tagger-v2](https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger-v2)
-
-Includes "ready to copy" prompt and a prompt analyzer.
-
-Modified from [NoCrypt/DeepDanbooru_string](https://huggingface.co/spaces/NoCrypt/DeepDanbooru_string)
-Modified from [hysts/DeepDanbooru](https://huggingface.co/spaces/hysts/DeepDanbooru)
-
-PNG Info code forked from [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
-
-Example image by [ほし☆☆☆](https://www.pixiv.net/en/users/43565085)
-"""
-
-HF_TOKEN = os.environ["HF_TOKEN"]
-MOAT_MODEL_REPO = "SmilingWolf/wd-v1-4-moat-tagger-v2"
-SWIN_MODEL_REPO = "SmilingWolf/wd-v1-4-swinv2-tagger-v2"
-CONV_MODEL_REPO = "SmilingWolf/wd-v1-4-convnext-tagger-v2"
-CONV2_MODEL_REPO = "SmilingWolf/wd-v1-4-convnextv2-tagger-v2"
-VIT_MODEL_REPO = "SmilingWolf/wd-v1-4-vit-tagger-v2"
-MODEL_FILENAME = "model.onnx"
-LABEL_FILENAME = "selected_tags.csv"
-
-
-def parse_args() -> argparse.Namespace:
- parser = argparse.ArgumentParser()
- parser.add_argument("--score-slider-step", type=float, default=0.05)
- parser.add_argument("--score-general-threshold", type=float, default=0.35)
- parser.add_argument("--score-character-threshold", type=float, default=0.85)
- parser.add_argument("--share", action="store_true")
- return parser.parse_args()
-
-
-def load_model(model_repo: str, model_filename: str) -> rt.InferenceSession:
- path = huggingface_hub.hf_hub_download(
- model_repo, model_filename, use_auth_token=HF_TOKEN
- )
- model = rt.InferenceSession(path)
- return model
-
-
-def change_model(model_name):
- global loaded_models
-
- if model_name == "MOAT":
- model = load_model(MOAT_MODEL_REPO, MODEL_FILENAME)
- elif model_name == "SwinV2":
- model = load_model(SWIN_MODEL_REPO, MODEL_FILENAME)
- elif model_name == "ConvNext":
- model = load_model(CONV_MODEL_REPO, MODEL_FILENAME)
- elif model_name == "ConvNextV2":
- model = load_model(CONV2_MODEL_REPO, MODEL_FILENAME)
- elif model_name == "ViT":
- model = load_model(VIT_MODEL_REPO, MODEL_FILENAME)
-
- loaded_models[model_name] = model
- return loaded_models[model_name]
-
-
-def load_labels() -> list[str]:
- path = huggingface_hub.hf_hub_download(
- MOAT_MODEL_REPO, LABEL_FILENAME, use_auth_token=HF_TOKEN
- )
- df = pd.read_csv(path)
-
- tag_names = df["name"].tolist()
- rating_indexes = list(np.where(df["category"] == 9)[0])
- general_indexes = list(np.where(df["category"] == 0)[0])
- character_indexes = list(np.where(df["category"] == 4)[0])
- return tag_names, rating_indexes, general_indexes, character_indexes
-
-
-def plaintext_to_html(text):
- text = (
- "" + "
"
- )
- return text
-
-
-def predict(
- image: PIL.Image.Image,
- model_name: str,
- general_threshold: float,
- character_threshold: float,
- tag_names: list[str],
- rating_indexes: list[np.int64],
- general_indexes: list[np.int64],
- character_indexes: list[np.int64],
-):
- global loaded_models
-
- rawimage = image
-
- model = loaded_models[model_name]
- if model is None:
- model = change_model(model_name)
-
- _, height, width, _ = model.get_inputs()[0].shape
-
- # Alpha to white
- image = image.convert("RGBA")
- new_image = PIL.Image.new("RGBA", image.size, "WHITE")
- new_image.paste(image, mask=image)
- image = new_image.convert("RGB")
- image = np.asarray(image)
-
- # PIL RGB to OpenCV BGR
- image = image[:, :, ::-1]
-
- image = dbimutils.make_square(image, height)
- image = dbimutils.smart_resize(image, height)
- image = image.astype(np.float32)
- image = np.expand_dims(image, 0)
-
- input_name = model.get_inputs()[0].name
- label_name = model.get_outputs()[0].name
- probs = model.run([label_name], {input_name: image})[0]
-
- labels = list(zip(tag_names, probs[0].astype(float)))
-
- # First 4 labels are actually ratings: pick one with argmax
- ratings_names = [labels[i] for i in rating_indexes]
- rating = dict(ratings_names)
-
- # Then we have general tags: pick any where prediction confidence > threshold
- general_names = [labels[i] for i in general_indexes]
- general_res = [x for x in general_names if x[1] > general_threshold]
- general_res = dict(general_res)
-
- # Everything else is characters: pick any where prediction confidence > threshold
- character_names = [labels[i] for i in character_indexes]
- character_res = [x for x in character_names if x[1] > character_threshold]
- character_res = dict(character_res)
-
- b = dict(sorted(general_res.items(), key=lambda item: item[1], reverse=True))
- a = (
- ", ".join(list(b.keys()))
- .replace("_", " ")
- .replace("(", "\(")
- .replace(")", "\)")
- )
- c = ", ".join(list(b.keys()))
-
- items = rawimage.info
- geninfo = ""
-
- if "exif" in rawimage.info:
- exif = piexif.load(rawimage.info["exif"])
- exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b"")
- try:
- exif_comment = piexif.helper.UserComment.load(exif_comment)
- except ValueError:
- exif_comment = exif_comment.decode("utf8", errors="ignore")
-
- items["exif comment"] = exif_comment
- geninfo = exif_comment
-
- for field in [
- "jfif",
- "jfif_version",
- "jfif_unit",
- "jfif_density",
- "dpi",
- "exif",
- "loop",
- "background",
- "timestamp",
- "duration",
- ]:
- items.pop(field, None)
-
- geninfo = items.get("parameters", geninfo)
-
- info = f"""
-
PNG Info
-"""
- for key, text in items.items():
- info += (
- f"""
-
-
{plaintext_to_html(str(key))}
-
{plaintext_to_html(str(text))}
-
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-Go to https://www.ccleaner.com/ccleaner/download from your web browser.
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ddb901b051How to Download Carrier Air Wing Game APK for Android
-Carrier Air Wing is a side-scrolling shooting game released by Capcom in 1990 for arcade machines. It is the spiritual successor to U.N. Squadron, which was released in the previous year. The game features three different jet fighters and ten enemy-packed stages. The game is popular among retro gamers who enjoy the classic arcade shooting action with realistic graphics and sound effects.
-If you want to play Carrier Air Wing on your Android device, you will need to download an apk file. An apk file is a package file format used by Android operating system for distribution and installation of mobile apps. You can download apk files from various websites that offer them for free, but you need to be careful about the source and the security of the files. In this article, we will show you how to download Carrier Air Wing game apk from two reputable websites: APKMirror and APKPure.
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How to Download Carrier Air Wing Game APK from APKMirror
-APKMirror is one of the best Android apk download sites. It is owned and operated by the same team that created the widely-read Android news site, Android Police. The site has some robust security policies in place to ensure that all the apk files are safe and virus-free. The site also offers different versions of each app, so you can choose the one that suits your device or preference.
-To download Carrier Air Wing game apk from APKMirror, follow these steps:
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-Go to APKMirror website and search for Carrier Air Wing in the search bar.
-Choose the latest or preferred version of the game from the list of results and tap on Download APK.
-Wait for the download to complete and then open the apk file on your device.
-If you see a warning message that says "For your security, your phone is not allowed to install unknown apps from this source", go to Settings > Apps > Special app access > Install unknown apps and enable it for your browser or file manager.
-Install the apk file by following the prompts on your screen.
-
-How to Download Carrier Air Wing Game APK from APKPure
-APKPure is another popular Android apk download site. It offers a large collection of apps and games for various devices and regions. It also provides virus scans and updates for each app, so you can be sure that you are downloading a safe and updated version of any app.
-To download Carrier Air Wing game apk from APKPure, follow these steps:
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-Go to APKPure website and search for Carrier Air Wing in the search bar.
-Choose the latest or preferred version of the game from the list of results and tap on Download APK.
-Wait for the download to complete and then open the apk file on your device.
-If you see a warning message that says "For your security, your phone is not allowed to install unknown apps from this source", go to Settings > Apps > Special app access > Install unknown apps and enable it for your browser or file manager.
-Install the apk file by following the prompts on your screen.
-
-How How to Play Carrier Air Wing Game on Your Device
- Once you have installed the Carrier Air Wing game apk on your device, you can launch the game and start playing. Here are some basic tips on how to play the game:
-
-Choose your fighter jet from the three available options: F-14 Tomcat, F/A-18 Hornet, or A-6E Intruder. Each jet has different speed, power, and weapon capabilities.
-Complete missions and earn coins to upgrade your weapons and shields. You can buy missiles, bombs, lasers, and other items from the shop before each mission.
-Enjoy the classic arcade shooting action with realistic graphics and sound effects. You can control your jet with the virtual joystick and buttons on the screen. You can also adjust the difficulty level and sound settings from the menu.
-
-A Comparison Table of the Three Fighter Jets
-
-
-Fighter Jet
-Speed
-Power
-Weapon
-
-
-F-14 Tomcat
-High
-Low
-Vulcan Cannon + Missiles
-
-
-F/A-18 Hornet
-Medium
-Medium
-Vulcan Cannon + Bombs
-
-
-A-6E Intruder
-Low
-High
-Vulcan Cannon + Lasers
-
-
- Conclusion
-In this article, we have shown you how to download Carrier Air Wing game apk for Android from two reputable websites: APKMirror and APKPure. We have also given you some basic tips on how to play the game and enjoy the classic arcade shooting action. Carrier Air Wing is a fun and challenging game that will test your skills and reflexes as you fly through enemy territory and destroy their bases. If you are a fan of retro games or shooting games, you should definitely give it a try.
- Do you have any questions or comments about Carrier Air Wing game apk? Do you have any tips or tricks for playing the game? Let us know in the comment section below. We would love to hear from you!
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- Frequently Asked Questions (FAQs)
- Q: Is Carrier Air Wing game apk safe to download?
- A: Yes, as long as you download it from a trusted source like APKMirror or APKPure. These websites scan and verify each apk file before uploading it to their servers. They also offer different versions of each app, so you can choose the one that suits your device or preference.
- Q: How much space does Carrier Air Wing game apk take on my device?
- A: The size of Carrier Air Wing game apk may vary depending on the version you download. However, it is usually around 20 MB, which is not too large for most devices. You will also need some extra space for the game data and cache files.
- Q: Can I play Carrier Air Wing game offline?
- A: Yes, you can play Carrier Air Wing game offline without any internet connection. However, you will not be able to access some features like leaderboards, achievements, and updates.
- Q: How can I save my progress in Carrier Air Wing game?
- A: Carrier Air Wing game automatically saves your progress after each mission. You can also manually save your progress from the menu by tapping on Save Game. You can load your saved game from the menu by tapping on Load Game.
- Q: How can I change the language of Carrier Air Wing game?
- A: Carrier Air Wing game supports multiple languages, including English, Japanese, Chinese, Korean, French, German, Spanish, Italian, Portuguese, Russian, and Arabic. You can change the language of the game from the menu by tapping on Language.
401be4b1e0DxO FilmPack 4.5.1.59: A Review
-DxO FilmPack 4.5.1.59 is the latest version of DxO's film-simulation software, which allows you to apply the look and feel of analog photography to your digital images. Whether you want to rediscover the magic of classic film stocks, add some grain and contrast to your photos, or explore the history of photography with a time machine feature, DxO FilmPack 4.5.1.59 has something for you.
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In this review, we will take a look at some of the features and benefits of DxO FilmPack 4.5.1.59, as well as some of its limitations and drawbacks.
-Features and Benefits
-DxO FilmPack 4.5.1.59 offers a wide range of creative effects and tools to enhance your images with the spirit of analog photography. Here are some of the main features and benefits of DxO FilmPack 4.5.1.59:
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-84 high-fidelity film renderings : DxO FilmPack 4.5.1.59 faithfully reproduces the colors and grains of 84 analog film stocks, including 46 color films and 38 black-and-white films[^1^]. You can choose from iconic or missing film stocks, such as Kodak Tri-X, Fujifilm Neopan, Ilford FP4 Plus, Polaroid 664, EKTACHROME Professional Infrared EIR, Kodak Portra 160 NC, Fujichrome Velvia 50, and many more[^1^]. You can also customize the film rendering parameters, such as intensity, contrast, saturation, exposure, and grain[^2^].Time Machine : DxO FilmPack 4.5.1.59 features a new interactive exploration of photography's history, presenting era-defining images and describing major events that shaped its evolution[^1^]. You can browse through different periods of time, from the origins of photography to the present day, and apply the corresponding film renderings to your photos[^2^]. You can also learn more about the history and characteristics of each film stock with informative descriptions[^2^].Image processing : DxO FilmPack 4.5.1.59 supports RAW format and uses DxO's optical modules to correct all of your camera's lens defects, effectively reduce unwanted digital noise, and faithfully restore color[^1^]. You can also adjust other parameters such as white balance, tone curve, vibrancy, sharpness, vignetting, and more[^2^]. DxO FilmPack 4.5.1.59 also allows you to remove digital noise from your high-ISO images and replace it with authentic analog grain[^1^].Workflow : DxO FilmPack 4.5.1.59 is available in two editions: Essential and Expert[^2^]. A single DxO FilmPack 4 license can be used as a plugin for Adobe Photoshop, Adobe Photoshop Elements, Adobe Photoshop Lightroom, Apple Aperture, and DxO Optics Pro, and as a standalone application[^3^]. You can easily switch between different modes and applications with a simple click[^2^]. You can also save your favorite settings as presets and apply them to multiple images at once[^2^].
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-DxO FilmPack 4.5.1.59 is not without its limitations and drawbacks. Here are some of the main ones:
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aaccfb2cb3`_
-
- Dataset statistics:
- - identities: 702 (train) + 702 (test).
- - tracklets: 2196 (train) + 2636 (test).
- """
- dataset_dir = 'dukemtmc-vidreid'
- dataset_url = 'http://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-VideoReID.zip'
-
- def __init__(self, root='', min_seq_len=0, **kwargs):
- self.root = osp.abspath(osp.expanduser(root))
- self.dataset_dir = osp.join(self.root, self.dataset_dir)
- self.download_dataset(self.dataset_dir, self.dataset_url)
-
- self.train_dir = osp.join(self.dataset_dir, 'DukeMTMC-VideoReID/train')
- self.query_dir = osp.join(self.dataset_dir, 'DukeMTMC-VideoReID/query')
- self.gallery_dir = osp.join(
- self.dataset_dir, 'DukeMTMC-VideoReID/gallery'
- )
- self.split_train_json_path = osp.join(
- self.dataset_dir, 'split_train.json'
- )
- self.split_query_json_path = osp.join(
- self.dataset_dir, 'split_query.json'
- )
- self.split_gallery_json_path = osp.join(
- self.dataset_dir, 'split_gallery.json'
- )
- self.min_seq_len = min_seq_len
-
- required_files = [
- self.dataset_dir, self.train_dir, self.query_dir, self.gallery_dir
- ]
- self.check_before_run(required_files)
-
- train = self.process_dir(
- self.train_dir, self.split_train_json_path, relabel=True
- )
- query = self.process_dir(
- self.query_dir, self.split_query_json_path, relabel=False
- )
- gallery = self.process_dir(
- self.gallery_dir, self.split_gallery_json_path, relabel=False
- )
-
- super(DukeMTMCVidReID, self).__init__(train, query, gallery, **kwargs)
-
- def process_dir(self, dir_path, json_path, relabel):
- if osp.exists(json_path):
- split = read_json(json_path)
- return split['tracklets']
-
- print('=> Generating split json file (** this might take a while **)')
- pdirs = glob.glob(osp.join(dir_path, '*')) # avoid .DS_Store
- print(
- 'Processing "{}" with {} person identities'.format(
- dir_path, len(pdirs)
- )
- )
-
- pid_container = set()
- for pdir in pdirs:
- pid = int(osp.basename(pdir))
- pid_container.add(pid)
- pid2label = {pid: label for label, pid in enumerate(pid_container)}
-
- tracklets = []
- for pdir in pdirs:
- pid = int(osp.basename(pdir))
- if relabel:
- pid = pid2label[pid]
- tdirs = glob.glob(osp.join(pdir, '*'))
- for tdir in tdirs:
- raw_img_paths = glob.glob(osp.join(tdir, '*.jpg'))
- num_imgs = len(raw_img_paths)
-
- if num_imgs < self.min_seq_len:
- continue
-
- img_paths = []
- for img_idx in range(num_imgs):
- # some tracklet starts from 0002 instead of 0001
- img_idx_name = 'F' + str(img_idx + 1).zfill(4)
- res = glob.glob(
- osp.join(tdir, '*' + img_idx_name + '*.jpg')
- )
- if len(res) == 0:
- warnings.warn(
- 'Index name {} in {} is missing, skip'.format(
- img_idx_name, tdir
- )
- )
- continue
- img_paths.append(res[0])
- img_name = osp.basename(img_paths[0])
- if img_name.find('_') == -1:
- # old naming format: 0001C6F0099X30823.jpg
- camid = int(img_name[5]) - 1
- else:
- # new naming format: 0001_C6_F0099_X30823.jpg
- camid = int(img_name[6]) - 1
- img_paths = tuple(img_paths)
- tracklets.append((img_paths, pid, camid))
-
- print('Saving split to {}'.format(json_path))
- split_dict = {'tracklets': tracklets}
- write_json(split_dict, json_path)
-
- return tracklets
diff --git a/spaces/xiangdy/chatGPT/run_Linux.sh b/spaces/xiangdy/chatGPT/run_Linux.sh
deleted file mode 100644
index 2d26597ae47519f42336ccffc16646713a192ae1..0000000000000000000000000000000000000000
--- a/spaces/xiangdy/chatGPT/run_Linux.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$(readlink -f "$0")")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir" || exit
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
- # 如果有更新，关闭当前运行的服务器
- pkill -f ChuanhuChatbot.py
-
- # 拉取最新更改
- git pull
-
- # 安装依赖
- pip3 install -r requirements.txt
-
- # 重新启动服务器
- nohup python3 ChuanhuChatbot.py &
-fi
-
-# 检查ChuanhuChatbot.py是否在运行
-if ! pgrep -f ChuanhuChatbot.py > /dev/null; then
- # 如果没有运行，启动服务器
- nohup python3 ChuanhuChatbot.py &
-fi
diff --git a/spaces/xiaoxicc/susu/assets/custom.js b/spaces/xiaoxicc/susu/assets/custom.js
deleted file mode 100644
index 7b1761043149ff97ca498501c87a0d15db5258ee..0000000000000000000000000000000000000000
--- a/spaces/xiaoxicc/susu/assets/custom.js
+++ /dev/null
@@ -1 +0,0 @@
-// custom javascript here
\ No newline at end of file
diff --git a/spaces/xp3857/Image_Restoration_Colorization/Global/data/data_loader.py b/spaces/xp3857/Image_Restoration_Colorization/Global/data/data_loader.py
deleted file mode 100644
index 02ccaedcc08b2201dabcda4a80fd59c6cd8a8068..0000000000000000000000000000000000000000
--- a/spaces/xp3857/Image_Restoration_Colorization/Global/data/data_loader.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-def CreateDataLoader(opt):
- from data.custom_dataset_data_loader import CustomDatasetDataLoader
- data_loader = CustomDatasetDataLoader()
- print(data_loader.name())
- data_loader.initialize(opt)
- return data_loader
diff --git a/spaces/yigekeqing/QQsign/README.md b/spaces/yigekeqing/QQsign/README.md
deleted file mode 100644
index bd56881a2a7709591343e2f15af9a6a8133e115b..0000000000000000000000000000000000000000
--- a/spaces/yigekeqing/QQsign/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: QQsign
-emoji: 🦀
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deberta_v2/__init__.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deberta_v2/__init__.py
deleted file mode 100644
index fb1b20a331fe11dfa687c7550685de296ebafbe0..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deberta_v2/__init__.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import TYPE_CHECKING
-
-from ...utils import (
- OptionalDependencyNotAvailable,
- _LazyModule,
- is_tf_available,
- is_tokenizers_available,
- is_torch_available,
-)
-
-
-_import_structure = {
- "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
- "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
-}
-
-try:
- if not is_tokenizers_available():
- raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
- pass
-else:
- _import_structure["tokenization_deberta_v2_fast"] = ["DebertaV2TokenizerFast"]
-
-try:
- if not is_tf_available():
- raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
- pass
-else:
- _import_structure["modeling_tf_deberta_v2"] = [
- "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
- "TFDebertaV2ForMaskedLM",
- "TFDebertaV2ForQuestionAnswering",
- "TFDebertaV2ForMultipleChoice",
- "TFDebertaV2ForSequenceClassification",
- "TFDebertaV2ForTokenClassification",
- "TFDebertaV2Model",
- "TFDebertaV2PreTrainedModel",
- ]
-
-try:
- if not is_torch_available():
- raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
- pass
-else:
- _import_structure["modeling_deberta_v2"] = [
- "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
- "DebertaV2ForMaskedLM",
- "DebertaV2ForMultipleChoice",
- "DebertaV2ForQuestionAnswering",
- "DebertaV2ForSequenceClassification",
- "DebertaV2ForTokenClassification",
- "DebertaV2Model",
- "DebertaV2PreTrainedModel",
- ]
-
-
-if TYPE_CHECKING:
- from .configuration_deberta_v2 import (
- DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
- DebertaV2Config,
- DebertaV2OnnxConfig,
- )
- from .tokenization_deberta_v2 import DebertaV2Tokenizer
-
- try:
- if not is_tokenizers_available():
- raise OptionalDependencyNotAvailable()
- except OptionalDependencyNotAvailable:
- pass
- else:
- from .tokenization_deberta_v2_fast import DebertaV2TokenizerFast
-
- try:
- if not is_tf_available():
- raise OptionalDependencyNotAvailable()
- except OptionalDependencyNotAvailable:
- pass
- else:
- from .modeling_tf_deberta_v2 import (
- TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
- TFDebertaV2ForMaskedLM,
- TFDebertaV2ForMultipleChoice,
- TFDebertaV2ForQuestionAnswering,
- TFDebertaV2ForSequenceClassification,
- TFDebertaV2ForTokenClassification,
- TFDebertaV2Model,
- TFDebertaV2PreTrainedModel,
- )
-
- try:
- if not is_torch_available():
- raise OptionalDependencyNotAvailable()
- except OptionalDependencyNotAvailable:
- pass
- else:
- from .modeling_deberta_v2 import (
- DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
- DebertaV2ForMaskedLM,
- DebertaV2ForMultipleChoice,
- DebertaV2ForQuestionAnswering,
- DebertaV2ForSequenceClassification,
- DebertaV2ForTokenClassification,
- DebertaV2Model,
- DebertaV2PreTrainedModel,
- )
-
-else:
- import sys
-
- sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/esm/openfold_utils/tensor_utils.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/esm/openfold_utils/tensor_utils.py
deleted file mode 100644
index 99dd6dbe47b68247794e51810fd274c6352e5b4f..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/esm/openfold_utils/tensor_utils.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# Copyright 2021 AlQuraishi Laboratory
-# Copyright 2021 DeepMind Technologies Limited
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from functools import partial
-from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload
-
-import torch
-import torch.nn as nn
-import torch.types
-
-
-def add(m1: torch.Tensor, m2: torch.Tensor, inplace: bool) -> torch.Tensor:
- # The first operation in a checkpoint can't be in-place, but it's
- # nice to have in-place addition during inference. Thus...
- if not inplace:
- m1 = m1 + m2
- else:
- m1 += m2
-
- return m1
-
-
-def permute_final_dims(tensor: torch.Tensor, inds: List[int]) -> torch.Tensor:
- zero_index = -1 * len(inds)
- first_inds = list(range(len(tensor.shape[:zero_index])))
- return tensor.permute(first_inds + [zero_index + i for i in inds])
-
-
-def flatten_final_dims(t: torch.Tensor, no_dims: int) -> torch.Tensor:
- return t.reshape(t.shape[:-no_dims] + (-1,))
-
-
-def masked_mean(mask: torch.Tensor, value: torch.Tensor, dim: int, eps: float = 1e-4) -> torch.Tensor:
- mask = mask.expand(*value.shape)
- return torch.sum(mask * value, dim=dim) / (eps + torch.sum(mask, dim=dim))
-
-
-def pts_to_distogram(
- pts: torch.Tensor, min_bin: torch.types.Number = 2.3125, max_bin: torch.types.Number = 21.6875, no_bins: int = 64
-) -> torch.Tensor:
- boundaries = torch.linspace(min_bin, max_bin, no_bins - 1, device=pts.device)
- dists = torch.sqrt(torch.sum((pts.unsqueeze(-2) - pts.unsqueeze(-3)) ** 2, dim=-1))
- return torch.bucketize(dists, boundaries)
-
-
-def dict_multimap(fn: Callable[[list], Any], dicts: List[dict]) -> dict:
- first = dicts[0]
- new_dict = {}
- for k, v in first.items():
- all_v = [d[k] for d in dicts]
- if isinstance(v, dict):
- new_dict[k] = dict_multimap(fn, all_v)
- else:
- new_dict[k] = fn(all_v)
-
- return new_dict
-
-
-def one_hot(x: torch.Tensor, v_bins: torch.Tensor) -> torch.Tensor:
- reshaped_bins = v_bins.view(((1,) * len(x.shape)) + (len(v_bins),))
- diffs = x[..., None] - reshaped_bins
- am = torch.argmin(torch.abs(diffs), dim=-1)
- return nn.functional.one_hot(am, num_classes=len(v_bins)).float()
-
-
-def batched_gather(data: torch.Tensor, inds: torch.Tensor, dim: int = 0, no_batch_dims: int = 0) -> torch.Tensor:
- ranges: List[Union[slice, torch.Tensor]] = []
- for i, s in enumerate(data.shape[:no_batch_dims]):
- r = torch.arange(s)
- r = r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape) - i - 1))))
- ranges.append(r)
-
- remaining_dims: List[Union[slice, torch.Tensor]] = [slice(None) for _ in range(len(data.shape) - no_batch_dims)]
- remaining_dims[dim - no_batch_dims if dim >= 0 else dim] = inds
- ranges.extend(remaining_dims)
- # Matt note: Editing this to get around the behaviour of using a list as an array index changing
- # in recent Numpy versions
- return data[tuple(ranges)]
-
-
-T = TypeVar("T")
-
-
-# With tree_map, a poor man's JAX tree_map
-def dict_map(
- fn: Callable[[T], Any], dic: Dict[Any, Union[dict, list, tuple, T]], leaf_type: Type[T]
-) -> Dict[Any, Union[dict, list, tuple, Any]]:
- new_dict: Dict[Any, Union[dict, list, tuple, Any]] = {}
- for k, v in dic.items():
- if isinstance(v, dict):
- new_dict[k] = dict_map(fn, v, leaf_type)
- else:
- new_dict[k] = tree_map(fn, v, leaf_type)
-
- return new_dict
-
-
-@overload
-def tree_map(fn: Callable[[T], Any], tree: T, leaf_type: Type[T]) -> Any:
- ...
-
-
-@overload
-def tree_map(fn: Callable[[T], Any], tree: dict, leaf_type: Type[T]) -> dict:
- ...
-
-
-@overload
-def tree_map(fn: Callable[[T], Any], tree: list, leaf_type: Type[T]) -> list:
- ...
-
-
-@overload
-def tree_map(fn: Callable[[T], Any], tree: tuple, leaf_type: Type[T]) -> tuple:
- ...
-
-
-def tree_map(fn, tree, leaf_type):
- if isinstance(tree, dict):
- return dict_map(fn, tree, leaf_type)
- elif isinstance(tree, list):
- return [tree_map(fn, x, leaf_type) for x in tree]
- elif isinstance(tree, tuple):
- return tuple(tree_map(fn, x, leaf_type) for x in tree)
- elif isinstance(tree, leaf_type):
- return fn(tree)
- else:
- print(type(tree))
- raise ValueError("Not supported")
-
-
-tensor_tree_map = partial(tree_map, leaf_type=torch.Tensor)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/fsmt/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/fsmt/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py
deleted file mode 100644
index ef2764f0ed10bace714f42f5f74ea6d9a147c613..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/fsmt/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py
+++ /dev/null
@@ -1,280 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Note: if you intend to run this script make sure you look under scripts/fsmt/
-# to locate the appropriate script to do the work correctly. There is a set of scripts to:
-# - download and prepare data and run the conversion script
-# - perform eval to get the best hparam into the config
-# - generate model_cards - useful if you have multiple models from the same paper
-
-import argparse
-import json
-import os
-import re
-from collections import OrderedDict
-from os.path import basename, dirname
-
-import fairseq
-import torch
-from fairseq import hub_utils
-from fairseq.data.dictionary import Dictionary
-
-from transformers import FSMTConfig, FSMTForConditionalGeneration
-from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
-from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
-from transformers.utils import WEIGHTS_NAME, logging
-
-
-logging.set_verbosity_warning()
-
-json_indent = 2
-
-# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
-# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
-#
-# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
-# * `early_stopping`: `False` consistently scored better
-# * `length_penalty` varied, so will assign the best one depending on the model
-best_score_hparams = {
- # fairseq:
- "wmt19-ru-en": {"length_penalty": 1.1},
- "wmt19-en-ru": {"length_penalty": 1.15},
- "wmt19-en-de": {"length_penalty": 1.0},
- "wmt19-de-en": {"length_penalty": 1.1},
- # allenai:
- "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
- "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
- "wmt16-en-de-12-1": {"length_penalty": 0.8},
- "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
- "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
-}
-
-# this remaps the different models to their organization names
-org_names = {}
-for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
- org_names[m] = "facebook"
-for m in [
- "wmt16-en-de-dist-12-1",
- "wmt16-en-de-dist-6-1",
- "wmt16-en-de-12-1",
- "wmt19-de-en-6-6-base",
- "wmt19-de-en-6-6-big",
-]:
- org_names[m] = "allenai"
-
-
-def rewrite_dict_keys(d):
- # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
- # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er': 7}
- d2 = dict((re.sub(r"@@$", "", k), v) if k.endswith("@@") else (re.sub(r"$", "", k), v) for k, v in d.items())
- keep_keys = " ".split()
- # restore the special tokens
- for k in keep_keys:
- del d2[f"{k}"]
- d2[k] = d[k] # restore
- return d2
-
-
-def convert_fsmt_checkpoint_to_pytorch(fsmt_checkpoint_path, pytorch_dump_folder_path):
- # prep
- assert os.path.exists(fsmt_checkpoint_path)
- os.makedirs(pytorch_dump_folder_path, exist_ok=True)
- print(f"Writing results to {pytorch_dump_folder_path}")
-
- # handle various types of models
-
- checkpoint_file = basename(fsmt_checkpoint_path)
- fsmt_folder_path = dirname(fsmt_checkpoint_path)
-
- cls = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
- models = cls.hub_models()
- kwargs = {"bpe": "fastbpe", "tokenizer": "moses"}
- data_name_or_path = "."
- # note: since the model dump is old, fairseq has upgraded its model some
- # time later, and it does a whole lot of rewrites and splits on the saved
- # weights, therefore we can't use torch.load() directly on the model file.
- # see: upgrade_state_dict(state_dict) in fairseq_model.py
- print(f"using checkpoint {checkpoint_file}")
- chkpt = hub_utils.from_pretrained(
- fsmt_folder_path, checkpoint_file, data_name_or_path, archive_map=models, **kwargs
- )
-
- args = vars(chkpt["args"]["model"])
-
- src_lang = args["source_lang"]
- tgt_lang = args["target_lang"]
-
- data_root = dirname(pytorch_dump_folder_path)
- model_dir = basename(pytorch_dump_folder_path)
-
- # dicts
- src_dict_file = os.path.join(fsmt_folder_path, f"dict.{src_lang}.txt")
- tgt_dict_file = os.path.join(fsmt_folder_path, f"dict.{tgt_lang}.txt")
-
- src_dict = Dictionary.load(src_dict_file)
- src_vocab = rewrite_dict_keys(src_dict.indices)
- src_vocab_size = len(src_vocab)
- src_vocab_file = os.path.join(pytorch_dump_folder_path, "vocab-src.json")
- print(f"Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records")
- with open(src_vocab_file, "w", encoding="utf-8") as f:
- f.write(json.dumps(src_vocab, ensure_ascii=False, indent=json_indent))
-
- # detect whether this is a do_lower_case situation, which can be derived by checking whether we
- # have at least one uppercase letter in the source vocab
- do_lower_case = True
- for k in src_vocab.keys():
- if not k.islower():
- do_lower_case = False
- break
-
- tgt_dict = Dictionary.load(tgt_dict_file)
- tgt_vocab = rewrite_dict_keys(tgt_dict.indices)
- tgt_vocab_size = len(tgt_vocab)
- tgt_vocab_file = os.path.join(pytorch_dump_folder_path, "vocab-tgt.json")
- print(f"Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records")
- with open(tgt_vocab_file, "w", encoding="utf-8") as f:
- f.write(json.dumps(tgt_vocab, ensure_ascii=False, indent=json_indent))
-
- # merges_file (bpecodes)
- merges_file = os.path.join(pytorch_dump_folder_path, VOCAB_FILES_NAMES["merges_file"])
- for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
- fsmt_merges_file = os.path.join(fsmt_folder_path, fn)
- if os.path.exists(fsmt_merges_file):
- break
- with open(fsmt_merges_file, encoding="utf-8") as fin:
- merges = fin.read()
- merges = re.sub(r" \d+$", "", merges, 0, re.M) # remove frequency number
- print(f"Generating {merges_file}")
- with open(merges_file, "w", encoding="utf-8") as fout:
- fout.write(merges)
-
- # model config
- fsmt_model_config_file = os.path.join(pytorch_dump_folder_path, "config.json")
-
- # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
- # may have to modify the tokenizer if a different type is used by a future model
- assert args["bpe"] == "fastbpe", f"need to extend tokenizer to support bpe={args['bpe']}"
- assert args["tokenizer"] == "moses", f"need to extend tokenizer to support bpe={args['tokenizer']}"
-
- model_conf = {
- "architectures": ["FSMTForConditionalGeneration"],
- "model_type": "fsmt",
- "activation_dropout": args["activation_dropout"],
- "activation_function": "relu",
- "attention_dropout": args["attention_dropout"],
- "d_model": args["decoder_embed_dim"],
- "dropout": args["dropout"],
- "init_std": 0.02,
- "max_position_embeddings": args["max_source_positions"],
- "num_hidden_layers": args["encoder_layers"],
- "src_vocab_size": src_vocab_size,
- "tgt_vocab_size": tgt_vocab_size,
- "langs": [src_lang, tgt_lang],
- "encoder_attention_heads": args["encoder_attention_heads"],
- "encoder_ffn_dim": args["encoder_ffn_embed_dim"],
- "encoder_layerdrop": args["encoder_layerdrop"],
- "encoder_layers": args["encoder_layers"],
- "decoder_attention_heads": args["decoder_attention_heads"],
- "decoder_ffn_dim": args["decoder_ffn_embed_dim"],
- "decoder_layerdrop": args["decoder_layerdrop"],
- "decoder_layers": args["decoder_layers"],
- "bos_token_id": 0,
- "pad_token_id": 1,
- "eos_token_id": 2,
- "is_encoder_decoder": True,
- "scale_embedding": not args["no_scale_embedding"],
- "tie_word_embeddings": args["share_all_embeddings"],
- }
-
- # good hparam defaults to start with
- model_conf["num_beams"] = 5
- model_conf["early_stopping"] = False
- if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
- model_conf["length_penalty"] = best_score_hparams[model_dir]["length_penalty"]
- else:
- model_conf["length_penalty"] = 1.0
-
- print(f"Generating {fsmt_model_config_file}")
- with open(fsmt_model_config_file, "w", encoding="utf-8") as f:
- f.write(json.dumps(model_conf, ensure_ascii=False, indent=json_indent))
-
- # tokenizer config
- fsmt_tokenizer_config_file = os.path.join(pytorch_dump_folder_path, TOKENIZER_CONFIG_FILE)
-
- tokenizer_conf = {
- "langs": [src_lang, tgt_lang],
- "model_max_length": 1024,
- "do_lower_case": do_lower_case,
- }
-
- print(f"Generating {fsmt_tokenizer_config_file}")
- with open(fsmt_tokenizer_config_file, "w", encoding="utf-8") as f:
- f.write(json.dumps(tokenizer_conf, ensure_ascii=False, indent=json_indent))
-
- # model
- model = chkpt["models"][0]
- model_state_dict = model.state_dict()
-
- # rename keys to start with 'model.'
- model_state_dict = OrderedDict(("model." + k, v) for k, v in model_state_dict.items())
-
- # remove unneeded keys
- ignore_keys = [
- "model.model",
- "model.encoder.version",
- "model.decoder.version",
- "model.encoder_embed_tokens.weight",
- "model.decoder_embed_tokens.weight",
- "model.encoder.embed_positions._float_tensor",
- "model.decoder.embed_positions._float_tensor",
- ]
- for k in ignore_keys:
- model_state_dict.pop(k, None)
-
- config = FSMTConfig.from_pretrained(pytorch_dump_folder_path)
- model_new = FSMTForConditionalGeneration(config)
-
- # check that it loads ok
- model_new.load_state_dict(model_state_dict, strict=False)
-
- # save
- pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)
- print(f"Generating {pytorch_weights_dump_path}")
- torch.save(model_state_dict, pytorch_weights_dump_path)
-
- print("Conversion is done!")
- print("\nLast step is to upload the files to s3")
- print(f"cd {data_root}")
- print(f"transformers-cli upload {model_dir}")
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- # Required parameters
- parser.add_argument(
- "--fsmt_checkpoint_path",
- default=None,
- type=str,
- required=True,
- help=(
- "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
- " bpecodes, etc."
- ),
- )
- parser.add_argument(
- "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
- )
- args = parser.parse_args()
- convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
diff --git a/spaces/ysharma/Low-rank-Adaptation/lora_diffusion/__init__.py b/spaces/ysharma/Low-rank-Adaptation/lora_diffusion/__init__.py
deleted file mode 100644
index 99df3a7f18d19445d3279ee22c22c801d84bbfbc..0000000000000000000000000000000000000000
--- a/spaces/ysharma/Low-rank-Adaptation/lora_diffusion/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .lora import *
diff --git a/spaces/yueranseo/mygpt/run_macOS.command b/spaces/yueranseo/mygpt/run_macOS.command
deleted file mode 100644
index 2d26597ae47519f42336ccffc16646713a192ae1..0000000000000000000000000000000000000000
--- a/spaces/yueranseo/mygpt/run_macOS.command
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$(readlink -f "$0")")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir" || exit
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
- # 如果有更新，关闭当前运行的服务器
- pkill -f ChuanhuChatbot.py
-
- # 拉取最新更改
- git pull
-
- # 安装依赖
- pip3 install -r requirements.txt
-
- # 重新启动服务器
- nohup python3 ChuanhuChatbot.py &
-fi
-
-# 检查ChuanhuChatbot.py是否在运行
-if ! pgrep -f ChuanhuChatbot.py > /dev/null; then
- # 如果没有运行，启动服务器
- nohup python3 ChuanhuChatbot.py &
-fi
diff --git a/spaces/zhan66/vits-uma-genshin-honkai/modules.py b/spaces/zhan66/vits-uma-genshin-honkai/modules.py
deleted file mode 100644
index 56ea4145eddf19dd330a3a41ab0183efc1686d83..0000000000000000000000000000000000000000
--- a/spaces/zhan66/vits-uma-genshin-honkai/modules.py
+++ /dev/null
@@ -1,388 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- self.hidden_channels =hidden_channels
- self.kernel_size = kernel_size,
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
-
-class ConvFlow(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
- self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/zhang-wei-jian/docker/node_modules/has-tostringtag/test/shams/core-js.js b/spaces/zhang-wei-jian/docker/node_modules/has-tostringtag/test/shams/core-js.js
deleted file mode 100644
index 692b86eb9af0e89223d50ea768f0870417376111..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/has-tostringtag/test/shams/core-js.js
+++ /dev/null
@@ -1,28 +0,0 @@
-'use strict';
-
-var test = require('tape');
-
-if (typeof Symbol === 'function' && typeof Symbol.toStringTag === 'symbol') {
- test('has native Symbol.toStringTag support', function (t) {
- t.equal(typeof Symbol, 'function');
- t.equal(typeof Symbol.toStringTag, 'symbol');
- t.end();
- });
- return;
-}
-
-var hasSymbolToStringTag = require('../../shams');
-
-test('polyfilled Symbols', function (t) {
- /* eslint-disable global-require */
- t.equal(hasSymbolToStringTag(), false, 'hasSymbolToStringTag is false before polyfilling');
- require('core-js/fn/symbol');
- require('core-js/fn/symbol/to-string-tag');
-
- require('../tests')(t);
-
- var hasToStringTagAfter = hasSymbolToStringTag();
- t.equal(hasToStringTagAfter, true, 'hasSymbolToStringTag is true after polyfilling');
- /* eslint-enable global-require */
- t.end();
-});