-
-FAQs
-Here are some frequently asked questions about Drift Hunters download Mac:
-
-- Is Drift Hunters safe to play on Mac?
-Yes, Drift Hunters is safe to play on Mac. The game does not require any downloads or installations, so it does not pose any risk to your device or data. The game also does not contain any viruses, malware, or spyware.
-- How much does Drift Hunters cost to play on Mac?
-Drift Hunters is free to play on Mac. You do not need to pay anything to access the game or its features. However, if you want to support the developers and get some extra benefits, you can join the VIP club for a small fee.
-- What are the minimum requirements to play Drift Hunters on Mac?
-The minimum requirements to play Drift Hunters on Mac are:
-
-- A Mac device with an Intel processor and at least 4 GB of RAM
-- A browser that supports Unity WebGL technology, such as Safari, Chrome, or Firefox
-- A stable internet connection with at least 5 Mbps speed
-- A keyboard or a controller to control your car
-
-- Can I play Drift Hunters offline on Mac?
-No, you cannot play Drift Hunters offline on Mac. The game requires an internet connection to load and run properly. You also need an internet connection to save your progress and access the VIP club and Discord server.
-- Can I play Drift Hunters with friends on Mac?
-Yes, you can play Drift Hunters with friends on Mac. You can invite your friends to join the VIP club and chat with them on the Discord server. You can also challenge them to a drift competition and see who can score higher on the leaderboard.
- 197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Enjoy Royal Match on Your Android Device and Unlock Amazing Rewards.md b/spaces/1phancelerku/anime-remove-background/Enjoy Royal Match on Your Android Device and Unlock Amazing Rewards.md
deleted file mode 100644
index f7141ad89d0ef014c9855d137ee1234836aafd29..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Enjoy Royal Match on Your Android Device and Unlock Amazing Rewards.md
+++ /dev/null
@@ -1,157 +0,0 @@
-
-Royal Match: A Fun and Challenging Match-3 Puzzle Game
- Introduction
- If you are looking for a new and exciting match-3 puzzle game to play on your mobile device, you might want to check out Royal Match. This game is developed by Dream Games, a Turkish studio that has a lot of experience in creating addictive and engaging puzzle games. In this game, you will help King Robert to rebuild his castle by solving match-3 puzzles, collecting coins, unlocking boosters, and decorating the rooms. You will also compete with millions of players in various events and climb the leaderboards. Royal Match is a game that combines puzzle-solving and castle-building elements, making it a unique and enjoyable experience for all kinds of players.
- What is Royal Match?
- What is Royal Match?
- Royal Match is a free-to-play match-3 puzzle game that is available for both Android and iOS devices. You can download it from Google Play or the App Store . The game has been released in February 2021 and has quickly become one of the top-grossing puzzle games on both platforms. It has also received positive reviews from critics and users alike, praising its graphics, gameplay, features, and content.
-royal match uptodown
Download Zip ✺✺✺ https://jinyurl.com/2uNKrM
- How to play Royal Match?
- The gameplay of Royal Match is simple and intuitive. You just need to swipe your finger on the screen to match three or more tiles of the same color. By doing so, you will clear them from the board and complete the level's objective. Each level has a different objective, such as collecting a certain number of tiles, breaking boxes, clearing grass, or saving the king. You will also encounter various obstacles on the board, such as birds, boxes, potions, cupboards, diamonds, magic hats, coin safes, mysterious mailboxes, and piggy. You will need to clear them or use boosters to overcome them.
- You have a limited number of moves for each level, so you need to plan your moves carefully and use them wisely. If you run out of moves before completing the objective, you will lose a life and have to try again. You can also buy extra moves with coins if you are close to finishing the level. Coins are the main currency of the game, which you can earn by completing levels, bonus levels, events, or opening chests. You can also buy coins with real money if you want to.
- By completing levels, you will earn stars, which are needed to perform castle-decorating tasks. You will help King Robert to restore his castle by choosing from different options for each task. For example, you can choose the color of the walls, the type of furniture, or the style of the garden. Each task costs a certain number of stars, which vary depending on the difficulty of the level. You can also undo your choices if you change your mind later.
- Features of Royal Match
- Unique match-3 gameplay and fun levels
- Royal Match offers a unique match-3 gameplay that is different from other games in the genre. It has fun and challenging levels that will test your skills and strategy. You will encounter various types of tiles, such as diamonds, rockets, TNTs, propellers, light balls, jesters hats, cannons, keys, locks, chests, crowns, hearts, stars, coins, and more. Each tile has a different effect when matched or activated.
- For example, matching four tiles in a row or column will create a rocket that can clear an entire row or column when matched or tapped. Matching four tiles in a square will create a TNT that can explode and clear a 3x3 area when matched or tapped. Matching five tiles in a row or column will create a propeller that can clear all the tiles of the same color when matched or tapped. Matching five tiles in an L or T shape will create a light ball that can clear all the tiles in a cross shape when matched or tapped. Matching six tiles in a row or column will create a jester hat that can clear all the tiles on the board when matched or tapped.
- The game has over 3000 levels to play, each with a different layout, objective, and difficulty. You will never get bored with the variety and challenge of the levels. You will also unlock new areas of the castle as you progress, such as the garden, the library, the kitchen, the bedroom, and more. Each area has its own theme and style, which you can customize according to your preference.
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- Powerful boosters and special treasures
- Royal Match also offers powerful boosters and special treasures that can help you complete the levels faster and easier. You can use these items before or during the level to get an advantage. Some of the boosters and treasures are:
-
-- Hammer: This booster can break any tile or obstacle on the board. You can use it before or during the level.
-- Glove: This booster can swap any two adjacent tiles on the board. You can use it before or during the level.
-- Shuffle: This booster can shuffle all the tiles on the board. You can use it before or during the level.
-- Rocket: This treasure can clear an entire row or column on the board. You can use it during the level by tapping on it.
-- TNT: This treasure can explode and clear a 3x3 area on the board. You can use it during the level by tapping on it.
-- Propeller: This treasure can clear all the tiles of the same color on the board. You can use it during the level by tapping on it.
-- Light Ball: This treasure can clear all the tiles in a cross shape on the board. You can use it during the level by tapping on it.
-- Jester Hat: This treasure can clear all the tiles on the board. You can use it during the level by tapping on it.
-
- You can earn these boosters and treasures by completing levels, bonus levels, events, or opening chests. You can also buy them with coins or real money if you want to.
- Castle decoration and restoration
- Royal Match is not only a puzzle game, but also a castle-building game. You will help King Robert to restore his castle by completing tasks with stars. You will earn stars by completing levels, bonus levels, events, or opening chests. Each task costs a certain number of stars, which vary depending on the difficulty of the level.
- You will have different options for each task, such as the color of the walls, the type of furniture, or the style of the garden. You can choose the option that suits your taste and personality. You can also undo your choices if you change your mind later.
- By decorating and restoring the castle, you will unlock new stories and characters. You will meet King Robert's friends and foes, such as Princess Alice, Prince Arthur, Duke Henry, Lady Violet, and more. You will also discover the secrets and mysteries of the castle, such as the hidden treasure, the ghost, the curse, and more. You will enjoy the fun and humorous dialogues and interactions between the characters.
- Events and leaderboards
- Royal Match also offers various events and leaderboards that can make the game more exciting and rewarding. You can participate in these events and leaderboards by completing levels, bonus levels, events, or opening chests. Some of the events and leaderboards are:
-
-- King's Challenge: This event is a special bonus level that appears every few hours. You can play it to earn extra coins, stars, boosters, or treasures. The level is randomly generated and has a different objective and difficulty each time.
-- King's Tournament: This event is a weekly competition where you can compete with other players in a series of levels. You can earn points by completing levels, bonus levels, events, or opening chests. The more points you earn, the higher you rank on the leaderboard. You can win amazing prizes based on your rank at the end of the week.
-- King's Club: This event is a monthly subscription that gives you access to exclusive benefits and rewards. You can get unlimited lives, extra moves, free boosters, special chests, and more. You can also cancel your subscription at any time.
-- King's Guild: This feature allows you to join or create a guild with other players. You can chat with your guild members, share tips and tricks, request or send lives, and more. You can also participate in guild events and challenges to earn guild points and rewards.
-
- Tips and tricks for Royal Match
- Pay attention to the objective and the hints
- One of the most important tips for Royal Match is to pay attention to the objective and the hints of each level. The objective tells you what you need to do to complete the level, such as collecting a certain number of tiles, breaking boxes, clearing grass, or saving the king. The hints show you which tiles or obstacles you need to focus on or clear first. They also show you which boosters or treasures you can use to help you.
- You can see the objective and the hints at the top of the screen during the level. You can also tap on them to get more information or reminders. By following the objective and the hints, you can save your moves and time and complete the level faster and easier.
- Save resources for hard levels and use them wisely
- Another tip for Royal Match is to save your resources for hard levels and use them wisely. Your resources include your coins, stars, lives, moves, boosters, and treasures. You can earn these resources by completing levels, bonus levels, events, or opening chests. You can also buy them with real money if you want to.
- However, you should not spend your resources recklessly or unnecessarily. You should save them for hard levels that are more difficult or require more moves to complete. You should also use them wisely and strategically, such as using boosters or treasures at the right time and place, buying extra moves only when you are close to finishing the level, or choosing the best option for each task.
- By saving and using your resources wisely, you can avoid getting stuck or frustrated on hard levels and enjoy the game more.
- Mix and match boosters to get amazing results
- A third tip for Royal Match is to mix and match boosters to get amazing results. Boosters are special tiles that you can create by matching four or more tiles of the same color. They have different effects when matched or tapped, such as clearing rows, columns, areas, or colors. You can also mix and match boosters to create even more powerful effects, such as:
-
-- Rocket + Rocket: This combination will clear two rows or columns in a cross shape.
-- Rocket + TNT: This combination will clear three rows or columns in a cross shape.
-- Rocket + Propeller: This combination will clear all the tiles of the same color as the rocket.
-- Rocket + Light Ball: This combination will clear four rows or columns in a cross shape.
-- Rocket + Jester Hat: This combination will clear all the tiles on the board.
-- TNT + TNT: This combination will explode and clear a 5x5 area on the board.
-- TNT + Propeller: This combination will explode and clear all the tiles of the same color as the TNT.
-- TNT + Light Ball: This combination will explode and clear all the tiles in a cross shape on the board.
-- TNT + Jester Hat: This combination will explode and clear all the tiles on the board.
-- Propeller + Propeller: This combination will clear two colors of tiles on the board.
-- Propeller + Light Ball: This combination will clear three colors of tiles on the board.
-- Propeller + Jester Hat: This combination will clear all the tiles on the board.
-- Light Ball + Light Ball: This combination will clear four colors of tiles on the board.
-- Light Ball + Jester Hat: This combination will clear all the tiles on the board.
-
- You can also mix and match boosters with treasures to get even more amazing results. For example, you can match a rocket with a rocket treasure to clear three rows or columns in a cross shape, or match a propeller with a propeller treasure to clear four colors of tiles on the board. You can experiment with different combinations and see what happens.
- By mixing and matching boosters and treasures, you can clear more tiles and obstacles, complete the objective faster and easier, and earn more points and rewards.
- Clear obstacles and collect coins as soon as possible
- A fourth tip for Royal Match is to clear obstacles and collect coins as soon as possible. Obstacles are items that block your way or prevent you from matching tiles. They include birds, boxes, potions, cupboards, diamonds, magic hats, coin safes, mysterious mailboxes, piggy banks, and more. You need to clear them by matching tiles next to them, using boosters or treasures, or completing special tasks. Coins are items that you can collect by matching tiles next to them, using boosters or treasures, or opening chests. They are the main currency of the game, which you can use to buy extra moves, boosters, treasures, or other items.
- You should try to clear obstacles and collect coins as soon as possible because they can help you in many ways. For example, clearing obstacles can give you more space and options to match tiles, activate boosters or treasures, or complete the objective. Collecting coins can give you more resources to buy extra moves, boosters, treasures, or other items. You can also use coins to decorate and restore the castle.
- By clearing obstacles and collecting coins as soon as possible, you can make the game easier and more fun.
- Join a guild and get extra benefits
- A fifth tip for Royal Match is to join a guild and get extra benefits. A guild is a group of players who can chat, share, and cooperate with each other. You can join or create a guild by tapping on the guild icon at the bottom of the screen. You can also invite your friends to join your guild or search for other guilds to join.
- By joining a guild, you can get extra benefits such as:
-
-- Lives: You can request or send lives to your guild members. Lives are needed to play levels, and they regenerate over time or can be bought with coins or real money. By requesting or sending lives, you can help yourself or your guild members to play more levels and have more fun.
-- Tips and tricks: You can chat with your guild members and share tips and tricks for the game. You can ask for advice, give suggestions, or exchange opinions about the game. By chatting with your guild members, you can learn more about the game and improve your skills and strategy.
-- Events and challenges: You can participate in guild events and challenges to earn guild points and rewards. Guild events and challenges are special tasks that you can complete with your guild members, such as collecting a certain number of tiles, clearing a certain number of levels, or reaching a certain score. By participating in guild events and challenges, you can compete with other guilds, earn more coins, stars, boosters, treasures, or other items, and have more fun.
-
- By joining a guild and getting extra benefits, you can make the game more social and rewarding.
- Conclusion
- Royal Match is a fun and challenging match-3 puzzle game that you can play on your mobile device. You can download it from Google Play or the App Store . In this game, you will help King Robert to rebuild his castle by solving match-3 puzzles, collecting coins, unlocking boosters, and decorating the rooms. You will also compete with millions of players in various events and climb the leaderboards. Royal Match is a game that combines puzzle-solving and castle-building elements, making it a unique and enjoyable experience for all kinds of players.
- If you are looking for some tips and tricks for Royal Match, you can follow these suggestions:
-
-- Pay attention to the objective and the hints
-- Save resources for hard levels and use them wisely
-- Mix and match boosters to get amazing results
-- Clear obstacles and collect coins as soon as possible
-- Join a guild and get extra benefits
-
- By following these tips and tricks, you can master the game and have more fun.
- FAQs
- Here are some frequently asked questions about Royal Match:
-
-- How do I download Royal Match?
-You can download Royal Match from Google Play or the App Store . The game is free to play but offers in-app purchases.
-- How do I update Royal Match?
-You can update Royal Match by going to Google Play or the App Store and tapping on the update button. The game updates regularly with new levels, features, events, and bug fixes.
-- How do I contact Royal Match support?
-You can contact Royal Match support by tapping on the settings icon at the top right corner of the screen and then tapping on the support button. You can also email them at support@dreamgames.com or visit their website at https://www.dreamgames.com/royalmatch/.
-- How do I connect Royal Match to Facebook?
-You can connect Royal Match to Facebook by tapping on the settings icon at the top right corner of the screen and then tapping on the connect button. By connecting to Facebook, you can save your progress across different devices, invite your friends to play, or join a guild.
-- How do I reset Royal Match?
-You can reset Royal Match by tapping on the settings icon at the top right corner of the screen and then tapping on the reset button. By resetting the game, you will lose all your progress, coins, stars, boosters, treasures, and other items. You will also disconnect from Facebook and your guild. You should only reset the game if you want to start over from the beginning.
- I hope you enjoyed this article and found it helpful. If you have any questions or feedback, please leave a comment below. Thank you for reading and happy playing! 401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/AGITM/ToneCorrectionRecognition/app.py b/spaces/AGITM/ToneCorrectionRecognition/app.py
deleted file mode 100644
index 7615ea1bd81a9289364d4818c87ec51bb55de0e1..0000000000000000000000000000000000000000
--- a/spaces/AGITM/ToneCorrectionRecognition/app.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import gradio as gr
-from spleeter.separator import Separator
-from spleeter.audio.adapter import AudioAdapter
-import spleeter.utils.logging as logging
-import parselmouth
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-import time
-from matplotlib import rcParams
-from you_get import common
-
-
-#主程序
-def main(audio,bg_time,ed_time):
- #分离音频
- vocals=spleeter(audio,bg_time,ed_time)
- #音高标记
- plt=pitch_mark(vocals)
- #返回音高标记图像
- return [plt,vocals]
-
-#时间检查
-def time_check(bg_time,ed_time):
- #当两者都为整数且ed_time>bg_time时返回True
- if bg_time.isdigit() and ed_time.isdigit():
- if int(ed_time)>int(bg_time):
- return True
- return False
-
-#音频分离
-def spleeter(audio,bg_time,ed_time):
- #分离音频并保存
- separator = Separator('spleeter:2stems')
- if time_check(bg_time,ed_time):
- waveform=AudioAdapter.default().load_tf_waveform(audio,offset=int(bg_time), duration=int(ed_time)-int(bg_time))['waveform']
- else:
- waveform=AudioAdapter.default().load_tf_waveform(audio)['waveform']
- vocals = separator.separate(waveform)['vocals']
- #返回Tuple,格式[sample_rate, numpy array]
- return (44100,vocals)
-
-#音高标记
-#计算标准音高和频率
-def frequency(pitch):
- return 16.35 * 2 ** (pitch /12 )
-def generate_array(min_pitch, max_pitch):
- array = []
- names = ["C", "C#", "D", "D#",
- "E", "F", "F#",
- 'G', 'G#', 'A', 'A#', 'B']
-
- for pitch in range(120):
- freq = frequency(pitch)
- name = names[pitch % 12] + str(pitch // 12)
- if frequency(pitch+1) > min_pitch and frequency(pitch-1) < max_pitch:
- array.append([name, freq])
- return array
-
-def pitch_mark(wav):
- config = {
- "font.family":'serif',
- "font.size": 8
- }
- sns.set()
- rcParams.update(config)
-
- wav = wav[1][:, 0]
- snd = parselmouth.Sound(wav)
- pitch = snd.to_pitch(pitch_floor=50, pitch_ceiling=3000)
- plt.figure(figsize=(15,8),dpi=144)
-
- pitch_values = pitch.selected_array['frequency']
- #异常值修改为0
- pitch_values[pitch_values>np.nanpercentile(pitch_values, 99)] = np.nan
- pitch_values[pitch_values音准测试
-🛠一个音准测量工具
-
-
- 📒使用说明
-
- - 在下方上传音频/视频文件，或使用在线视频链接，输入需要分析的音频
- - 点击“音准测试”按钮，生成音高图，右侧同时输出提取人声后的音频
- - 输入开始和结束时间可以截取部分音频分析
-
-
-
- 📝注意:10s音频分析时间不会超过30s，如卡住不动或出现error请尝试刷新
-
- 📅开始时间和结束时间,直接输入数字，单位为s，不填默认为全长 建议长度为10秒 """)
- with gr.Row():
- bg_time = gr.Textbox(type="text",label="开始时间")
- ed_time = gr.Textbox(type="text",label="结束时间")
- with gr.Box():
- audio_output = gr.Audio(type="numpy", label="提取结果")
- output_img=gr.Image(type="filepath", label="音准匹配图")
- btn_a.click(main, [audio,bg_time,ed_time], [output_img,audio_output])
- btn_b.click(main, [video,bg_time,ed_time], [output_img,audio_output])
- gr.HTML("""
- """)
-
-app.launch()
\ No newline at end of file
diff --git a/spaces/AHzizi/WaifuVoiceGen/text/symbols.py b/spaces/AHzizi/WaifuVoiceGen/text/symbols.py
deleted file mode 100644
index edfbd24247be8c757275ce80b9ec27a0ffa808f3..0000000000000000000000000000000000000000
--- a/spaces/AHzizi/WaifuVoiceGen/text/symbols.py
+++ /dev/null
@@ -1,39 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-'''# japanese_cleaners
-_pad = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-'''
-
-'''# japanese_cleaners2
-_pad = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-'''# korean_cleaners
-_pad = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# zh_ja_mixture_cleaners
-_pad = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
\ No newline at end of file
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py b/spaces/AIConsultant/MusicGen/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
deleted file mode 100644
index 12f6d402a3c4a113d4c37be062790fa435b72104..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
+++ /dev/null
@@ -1,68 +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.
-
-"""
-Evaluation with objective metrics for the pretrained AudioGen models.
-This grid takes signature from the training grid and runs evaluation-only stage.
-
-When running the grid for the first time, please use:
-REGEN=1 dora grid audiogen.audiogen_pretrained_16khz_eval
-and re-use the REGEN=1 option when the grid is changed to force regenerating it.
-
-Note that you need the proper metrics external libraries setup to use all
-the objective metrics activated in this grid. Refer to the README for more information.
-"""
-
-import os
-
-from ..musicgen._explorers import GenerationEvalExplorer
-from ...environment import AudioCraftEnvironment
-from ... import train
-
-
-def eval(launcher, batch_size: int = 32):
- opts = {
- 'dset': 'audio/audiocaps_16khz',
- 'solver/audiogen/evaluation': 'objective_eval',
- 'execute_only': 'evaluate',
- '+dataset.evaluate.batch_size': batch_size,
- '+metrics.fad.tf.batch_size': 32,
- }
- # binary for FAD computation: replace this path with your own path
- metrics_opts = {
- 'metrics.fad.tf.bin': '/data/home/jadecopet/local/usr/opt/google-research'
- }
- opt1 = {'generate.lm.use_sampling': True, 'generate.lm.top_k': 250, 'generate.lm.top_p': 0.}
- opt2 = {'transformer_lm.two_step_cfg': True}
-
- sub = launcher.bind(opts)
- sub.bind_(metrics_opts)
-
- # base objective metrics
- sub(opt1, opt2)
-
-
-@GenerationEvalExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=4, partition=partitions)
-
- if 'REGEN' not in os.environ:
- folder = train.main.dora.dir / 'grids' / __name__.split('.', 2)[-1]
- with launcher.job_array():
- for sig in folder.iterdir():
- if not sig.is_symlink():
- continue
- xp = train.main.get_xp_from_sig(sig.name)
- launcher(xp.argv)
- return
-
- audiogen_base = launcher.bind(solver="audiogen/audiogen_base_16khz")
- audiogen_base.bind_({'autocast': False, 'fsdp.use': True})
-
- audiogen_base_medium = audiogen_base.bind({'continue_from': '//pretrained/facebook/audiogen-medium'})
- audiogen_base_medium.bind_({'model/lm/model_scale': 'medium'})
- eval(audiogen_base_medium, batch_size=128)
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/grids/compression/encodec_musicgen_32khz.py b/spaces/AIConsultant/MusicGen/audiocraft/grids/compression/encodec_musicgen_32khz.py
deleted file mode 100644
index 9da31daa5f009f46e753601a51a06391594b8f9b..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/grids/compression/encodec_musicgen_32khz.py
+++ /dev/null
@@ -1,34 +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.
-
-"""
-Grid search file, simply list all the exp you want in `explorer`.
-Any new exp added there will be scheduled.
-You can cancel and experiment by commenting its line.
-
-This grid shows how to train a MusicGen EnCodec model at 32 kHz.
-"""
-
-from ._explorers import CompressionExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@CompressionExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=8, partition=partitions)
- # use configuration for MusicGen's EnCodec model trained on monophonic audio sampled at 32 kHz
- # MusicGen's EnCodec is trained with a total stride of 640 leading to a frame rate of 50 hz
- launcher.bind_(solver='compression/encodec_musicgen_32khz')
- # replace this by the desired music dataset
- launcher.bind_(dset='internal/music_400k_32khz')
- # launch xp
- launcher()
- launcher({
- 'metrics.visqol.bin': '/data/home/jadecopet/local/usr/opt/visqol',
- 'label': 'visqol',
- 'evaluate.metrics.visqol': True
- })
diff --git a/spaces/AIFILMS/StyleGANEX/models/bisenet/README.md b/spaces/AIFILMS/StyleGANEX/models/bisenet/README.md
deleted file mode 100644
index 849d55e2789c8852e01707d1ff755dc74e63a7f5..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/StyleGANEX/models/bisenet/README.md
+++ /dev/null
@@ -1,68 +0,0 @@
-# face-parsing.PyTorch
-
-
-
- 
-
-
-
-### Contents
-- [Training](#training)
-- [Demo](#Demo)
-- [References](#references)
-
-## Training
-
-1. Prepare training data:
- -- download [CelebAMask-HQ dataset](https://github.com/switchablenorms/CelebAMask-HQ)
-
- -- change file path in the `prepropess_data.py` and run
-```Shell
-python prepropess_data.py
-```
-
-2. Train the model using CelebAMask-HQ dataset:
-Just run the train script:
-```
- $ CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 train.py
-```
-
-If you do not wish to train the model, you can download [our pre-trained model](https://drive.google.com/open?id=154JgKpzCPW82qINcVieuPH3fZ2e0P812) and save it in `res/cp`.
-
-
-## Demo
-1. Evaluate the trained model using:
-```Shell
-# evaluate using GPU
-python test.py
-```
-
-## Face makeup using parsing maps
-[**face-makeup.PyTorch**](https://github.com/zllrunning/face-makeup.PyTorch)
-
-
-
-| |
-Hair |
-Lip |
-
-
-
-
-| Original Input |
- |
- |
-
-
-
-
-| Color |
- |
- |
-
-
-
-
-
-## References
-- [BiSeNet](https://github.com/CoinCheung/BiSeNet)
\ No newline at end of file
diff --git a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/audio/__init__.py b/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/audio/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/tasks/tts/synta.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/tasks/tts/synta.py
deleted file mode 100644
index 62cf865bc7d17c62b2c0c71f21d5b0ab596ba312..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/tasks/tts/synta.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import os
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from modules.tts.syntaspeech.syntaspeech import SyntaSpeech
-from tasks.tts.ps_adv import PortaSpeechAdvTask
-from utils.hparams import hparams
-
-
-class SyntaSpeechTask(PortaSpeechAdvTask):
- def build_tts_model(self):
- ph_dict_size = len(self.token_encoder)
- word_dict_size = len(self.word_encoder)
- self.model = SyntaSpeech(ph_dict_size, word_dict_size, hparams)
-
- self.gen_params = [p for p in self.model.parameters() if p.requires_grad]
- self.dp_params = [p for k, p in self.model.named_parameters() if (('dur_predictor' in k) and p.requires_grad)]
- self.gen_params_except_dp = [p for k, p in self.model.named_parameters() if (('dur_predictor' not in k) and p.requires_grad)]
- self.bert_params = [p for k, p in self.model.named_parameters() if (('bert' in k) and p.requires_grad)]
- self.gen_params_except_bert_and_dp = [p for k, p in self.model.named_parameters() if ('dur_predictor' not in k) and ('bert' not in k) and p.requires_grad ]
-
- self.use_bert = True if len(self.bert_params) > 0 else False
-
-
\ No newline at end of file
diff --git a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/train_vggishish.py b/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/train_vggishish.py
deleted file mode 100644
index 205668224ec87a9ce571f6428531080231b1c16b..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/train_vggishish.py
+++ /dev/null
@@ -1,199 +0,0 @@
-from loss import WeightedCrossEntropy
-import random
-
-import numpy as np
-import torch
-import torchvision
-from omegaconf import OmegaConf
-from torch.utils.data.dataloader import DataLoader
-from tqdm import tqdm
-
-from dataset import VGGSound
-from transforms import Crop, StandardNormalizeAudio, ToTensor
-from logger import LoggerWithTBoard
-from metrics import metrics
-from model import VGGishish
-
-if __name__ == "__main__":
- cfg_cli = OmegaConf.from_cli()
- cfg_yml = OmegaConf.load(cfg_cli.config)
- # the latter arguments are prioritized
- cfg = OmegaConf.merge(cfg_yml, cfg_cli)
- OmegaConf.set_readonly(cfg, True)
- print(OmegaConf.to_yaml(cfg))
-
- logger = LoggerWithTBoard(cfg)
-
- random.seed(cfg.seed)
- np.random.seed(cfg.seed)
- torch.manual_seed(cfg.seed)
- torch.cuda.manual_seed_all(cfg.seed)
- # makes iterations faster (in this case 30%) if your inputs are of a fixed size
- # https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936/3
- torch.backends.cudnn.benchmark = True
-
- transforms = [
- StandardNormalizeAudio(cfg.mels_path),
- ]
- if cfg.cropped_size not in [None, 'None', 'none']:
- logger.print_logger.info(f'Using cropping {cfg.cropped_size}')
- transforms.append(Crop(cfg.cropped_size))
- transforms.append(ToTensor())
- transforms = torchvision.transforms.transforms.Compose(transforms)
-
- datasets = {
- 'train': VGGSound('train', cfg.mels_path, transforms),
- 'valid': VGGSound('valid', cfg.mels_path, transforms),
- 'test': VGGSound('test', cfg.mels_path, transforms),
- }
-
- loaders = {
- 'train': DataLoader(datasets['train'], batch_size=cfg.batch_size, shuffle=True, drop_last=True,
- num_workers=cfg.num_workers, pin_memory=True),
- 'valid': DataLoader(datasets['valid'], batch_size=cfg.batch_size,
- num_workers=cfg.num_workers, pin_memory=True),
- 'test': DataLoader(datasets['test'], batch_size=cfg.batch_size,
- num_workers=cfg.num_workers, pin_memory=True),
- }
-
- device = torch.device(cfg.device if torch.cuda.is_available() else 'cpu')
-
- model = VGGishish(cfg.conv_layers, cfg.use_bn, num_classes=len(datasets['train'].target2label))
- model = model.to(device)
- param_num = logger.log_param_num(model)
-
- if cfg.optimizer == 'adam':
- optimizer = torch.optim.Adam(
- model.parameters(), lr=cfg.learning_rate, betas=cfg.betas, weight_decay=cfg.weight_decay)
- elif cfg.optimizer == 'sgd':
- optimizer = torch.optim.SGD(
- model.parameters(), lr=cfg.learning_rate, momentum=cfg.momentum, weight_decay=cfg.weight_decay)
- else:
- raise NotImplementedError
-
- if cfg.cls_weights_in_loss:
- weights = 1 / datasets['train'].class_counts
- else:
- weights = torch.ones(len(datasets['train'].target2label))
- criterion = WeightedCrossEntropy(weights.to(device))
-
- # loop over the train and validation multiple times (typical PT boilerplate)
- no_change_epochs = 0
- best_valid_loss = float('inf')
- early_stop_triggered = False
-
- for epoch in range(cfg.num_epochs):
-
- for phase in ['train', 'valid']:
- if phase == 'train':
- model.train()
- else:
- model.eval()
-
- running_loss = 0
- preds_from_each_batch = []
- targets_from_each_batch = []
-
- prog_bar = tqdm(loaders[phase], f'{phase} ({epoch})', ncols=0)
- for i, batch in enumerate(prog_bar):
- inputs = batch['input'].to(device)
- targets = batch['target'].to(device)
-
- # zero the parameter gradients
- optimizer.zero_grad()
-
- # forward + backward + optimize
- with torch.set_grad_enabled(phase == 'train'):
- outputs = model(inputs)
- loss = criterion(outputs, targets, to_weight=phase == 'train')
-
- if phase == 'train':
- loss.backward()
- optimizer.step()
-
- # loss
- running_loss += loss.item()
-
- # for metrics calculation later on
- preds_from_each_batch += [outputs.detach().cpu()]
- targets_from_each_batch += [targets.cpu()]
-
- # iter logging
- if i % 50 == 0:
- logger.log_iter_loss(loss.item(), epoch*len(loaders[phase])+i, phase)
- # tracks loss in the tqdm progress bar
- prog_bar.set_postfix(loss=loss.item())
-
- # logging loss
- epoch_loss = running_loss / len(loaders[phase])
- logger.log_epoch_loss(epoch_loss, epoch, phase)
-
- # logging metrics
- preds_from_each_batch = torch.cat(preds_from_each_batch)
- targets_from_each_batch = torch.cat(targets_from_each_batch)
- metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch)
- logger.log_epoch_metrics(metrics_dict, epoch, phase)
-
- # Early stopping
- if phase == 'valid':
- if epoch_loss < best_valid_loss:
- no_change_epochs = 0
- best_valid_loss = epoch_loss
- logger.log_best_model(model, epoch_loss, epoch, optimizer, metrics_dict)
- else:
- no_change_epochs += 1
- logger.print_logger.info(
- f'Valid loss hasnt changed for {no_change_epochs} patience: {cfg.patience}'
- )
- if no_change_epochs >= cfg.patience:
- early_stop_triggered = True
-
- if early_stop_triggered:
- logger.print_logger.info(f'Training is early stopped @ {epoch}')
- break
-
- logger.print_logger.info('Finished Training')
-
- # loading the best model
- ckpt = torch.load(logger.best_model_path)
- model.load_state_dict(ckpt['model'])
- logger.print_logger.info(f'Loading the best model from {logger.best_model_path}')
- logger.print_logger.info((f'The model was trained for {ckpt["epoch"]} epochs. Loss: {ckpt["loss"]:.4f}'))
-
- # Testing the model
- model.eval()
- running_loss = 0
- preds_from_each_batch = []
- targets_from_each_batch = []
-
- for i, batch in enumerate(loaders['test']):
- inputs = batch['input'].to(device)
- targets = batch['target'].to(device)
-
- # zero the parameter gradients
- optimizer.zero_grad()
-
- # forward + backward + optimize
- with torch.set_grad_enabled(False):
- outputs = model(inputs)
- loss = criterion(outputs, targets, to_weight=False)
-
- # loss
- running_loss += loss.item()
-
- # for metrics calculation later on
- preds_from_each_batch += [outputs.detach().cpu()]
- targets_from_each_batch += [targets.cpu()]
-
- # logging metrics
- preds_from_each_batch = torch.cat(preds_from_each_batch)
- targets_from_each_batch = torch.cat(targets_from_each_batch)
- test_metrics_dict = metrics(targets_from_each_batch, preds_from_each_batch)
- test_metrics_dict['avg_loss'] = running_loss / len(loaders['test'])
- test_metrics_dict['param_num'] = param_num
- # TODO: I have no idea why tboard doesn't keep metrics (hparams) when
- # I run this experiment from cli: `python train_vggishish.py config=./configs/vggish.yaml`
- # while when I run it in vscode debugger the metrics are logger (wtf)
- logger.log_test_metrics(test_metrics_dict, dict(cfg), ckpt['epoch'])
-
- logger.print_logger.info('Finished the experiment')
diff --git a/spaces/AIWaves/Software_Company/src/agents/Agent/Agent.py b/spaces/AIWaves/Software_Company/src/agents/Agent/Agent.py
deleted file mode 100644
index e7f6ecc72682e8aeb74d9f933e6aa721656d350a..0000000000000000000000000000000000000000
--- a/spaces/AIWaves/Software_Company/src/agents/Agent/Agent.py
+++ /dev/null
@@ -1,243 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The AIWaves 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.
-"""LLM autonoumous agent"""
-from LLM.base_LLM import *
-from Component import *
-from Action import Action
-from Prompt import *
-
-headers = {
- "Content-Type": "text/event-stream",
- "Cache-Control": "no-cache",
- "X-Accel-Buffering": "no",
-}
-
-
-
-
-class Agent:
- """
- Auto agent, input the JSON of SOP.
- """
-
- # Agent should have args: agents,states
- def __init__(self, name, agent_state_roles, **kwargs) -> None:
- self.state_roles = agent_state_roles
- self.name = name
-
- self.style = kwargs["style"]
- self.LLMs = kwargs["LLMs"]
- self.LLM = None
- self.is_user = kwargs["is_user"]
- self.begins = kwargs["begins"] if "begins" in kwargs else False
- self.current_role = ""
- self.long_term_memory = []
- self.short_term_memory = ""
- self.current_state = None
- self.first_speak = True
- self.environment = None
-
-
- @classmethod
- def from_config(cls, config_path):
- """
- Initialize agents based on json file
- Return:
- agents(dict) : key:agent_name;value:class(Agent)
- names_to_roles(dict) : key:state_name value:(dict; (key:agent_name ; value:agent_role))
- roles_to_names(dict) : key:state_name value:(dict; (key:agent_role ; value:agent_name))
- """
- with open(config_path) as f:
- config = json.load(f)
-
- roles_to_names = {}
- names_to_roles = {}
- agents = {}
- user_names = json.loads(os.environ["User_Names"]) if "User_Names" in os.environ else []
- for agent_name, agent_dict in config["agents"].items():
- agent_state_roles = {}
- agent_LLMs = {}
- agent_begins = {}
- for state_name, agent_role in agent_dict["roles"].items():
-
- agent_begins[state_name] = {}
-
- if state_name not in roles_to_names:
- roles_to_names[state_name] = {}
- if state_name not in names_to_roles:
- names_to_roles[state_name] = {}
- roles_to_names[state_name][agent_role] = agent_name
- names_to_roles[state_name][agent_name] = agent_role
- agent_state_roles[state_name] = agent_role
- current_state = config["states"][state_name]
-
- current_state_begin_role = current_state["begin_role"] if "begin_role" in current_state else current_state["roles"][0]
- agent_begins[state_name]["is_begin"] = current_state_begin_role==agent_role if "begin_role" in current_state else False
- agent_begins[state_name]["begin_query"] = current_state["begin_query"] if "begin_query" in current_state else " "
- agent_LLMs[state_name] = init_LLM(f"logs/{agent_name}",**current_state["agent_states"][agent_role])
- agents[agent_name] = cls(
- agent_name,
- agent_state_roles,
- LLMs=agent_LLMs,
- is_user=agent_name in user_names,
- style = agent_dict["style"],
- begins = agent_begins
- )
- assert len(config["agents"].keys()) != 2 or (roles_to_names[config["root"]][config["states"][config["root"]]["begin_role"]] not in user_names and "begin_query" in config["states"][config["root"]]),"In a single-agent scenario, there must be an opening statement and it must be the agent"
- return agents, roles_to_names, names_to_roles
-
- def step(self, current_state,input=""):
- """
- return actions by current state and environment
- Return: action(Action)
- """
-
- current_state.chat_nums +=1
- state_begin = current_state.is_begin
- agent_begin = self.begins[current_state.name]["is_begin"]
- self.begins[current_state.name]["is_begin"] = False
- current_state.is_begin = False
- environment = self.environment
-
- self.current_state = current_state
- # 先根据当前环境更新信息
- # First update the information according to the current environment
-
- response = " "
- res_dict = {}
-
- if self.is_user:
- response = f"{self.name}:{input}"
- else:
- if len(environment.shared_memory["long_term_memory"])>0:
- current_history = self.observe()
- self.long_term_memory.append(current_history)
- if agent_begin:
- response = (char for char in self.begins[current_state.name]["begin_query"])
- else:
- response,res_dict = self.act()
-
-
- action_dict = {
- "response": response,
- "res_dict": res_dict,
- "role": self.state_roles[current_state.name],
- "name": self.name,
- "state_begin" : state_begin,
- "agent_begin" : agent_begin,
- "is_user" : self.is_user
- }
- return Action(**action_dict)
-
- def act(self):
- """
- return actions by the current state
- """
- current_state = self.current_state
- chat_history = self.long_term_memory
- current_LLM = self.LLMs[current_state.name]
-
- system_prompt, last_prompt, res_dict = self.compile()
-
-
-
- response = current_LLM.get_response(
- chat_history, system_prompt, last_prompt, stream=True
- )
- return response,res_dict
-
- def update_memory(self, memory):
- self.long_term_memory.append(
- {"role": "assistant", "content": memory.content}
- )
-
- MAX_CHAT_HISTORY = eval(os.environ["MAX_CHAT_HISTORY"])
- environment = self.environment
- current_chat_history_idx = environment.current_chat_history_idx if environment.environment_type == "competive" else 0
-
- current_long_term_memory = environment.shared_memory["long_term_memory"][current_chat_history_idx:]
- last_conversation_idx = environment._get_agent_last_conversation_idx(self,current_long_term_memory)
- if len(current_long_term_memory)-last_conversation_idx >= MAX_CHAT_HISTORY:
- current_state = self.current_state
- current_role = self.state_roles[current_state.name]
- current_component_dict = current_state.components[current_role]
-
- # get chat history from new conversation
- conversations = environment._get_agent_new_memory(self,current_long_term_memory)
-
- # get summary
- summary_prompt = (
- current_state.summary_prompt[current_role]
- if current_state.summary_prompt
- else f"""your name is {self.name},your role is{current_component_dict["style"].role},your task is {current_component_dict["task"].task}.\n"""
- )
- summary_prompt =eval(Agent_summary_system_prompt)
- summary = self.LLMs[current_state.name].get_response(None, summary_prompt,stream = False)
- self.short_term_memory = summary
-
-
- def compile(self):
- """
- get prompt from state depend on your role
- Return:
- system_prompt:system_prompt for agents's LLM
- last_prompt:last_prompt for agents's LLM
- res_dict(dict): Other return from tool component.For example: search engine results
- """
- current_state = self.current_state
- self.current_roles = self.state_roles[current_state.name]
- current_state_name = current_state.name
- self.LLM = self.LLMs[current_state_name]
- components = current_state.components[self.state_roles[current_state_name]]
-
- system_prompt = self.current_state.environment_prompt
- last_prompt = ""
-
- res_dict = {}
- for component in components.values():
- if isinstance(component, (OutputComponent, LastComponent)):
- last_prompt = last_prompt + "\n" + component.get_prompt(self)
- elif isinstance(component, PromptComponent):
- system_prompt = (
- system_prompt + "\n" + component.get_prompt(self)
- )
- elif isinstance(component, ToolComponent):
- response = component.func(self)
- if "prompt" in response and response["prompt"]:
- last_prompt = last_prompt + "\n" + response["prompt"]
- res_dict.update(response)
-
- name = self.name
- query = self.environment.shared_memory["long_term_memory"][-1]
- last_prompt = eval(Agent_last_prompt)
- system_prompt = eval(Agent_system_prompt)
- return system_prompt, last_prompt, res_dict
-
-
- def observe(self):
- """
- Update one's own memory according to the current environment, including: updating short-term memory; updating long-term memory
- """
- return self.environment._observe(self)
-
-
- def generate_sop(self):
- pass
-
- def reflection(self):
- pass
-
-
diff --git a/spaces/AIWaves/Software_Company/src/agents/LLM/__init__.py b/spaces/AIWaves/Software_Company/src/agents/LLM/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/work_dirs/__init__.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/work_dirs/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AUBADA-ALARABI/AraPoet/app.py b/spaces/AUBADA-ALARABI/AraPoet/app.py
deleted file mode 100644
index af769dff8abd1dbf74587cd2d33de416baf01ade..0000000000000000000000000000000000000000
--- a/spaces/AUBADA-ALARABI/AraPoet/app.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# coding=utf8
-
-import json
-import torch
-import gradio as gr
-import pyarabic.araby as araby
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoConfig
-
-feature_names = [
- "Title",
- "Meter",
- "Theme",
- "Name",
- "Era",
- "Country",
- "Type"
-]
-
-with open("./poet_names.json", 'r', encoding="utf-8") as fin:
- poet_names = json.load(fin)
-
-def normalize_text(text):
- text = araby.strip_tatweel(text)
- return text
-
-def generate_poem(country, era, meter, theme, lang_type, poet, num_lines, num_poems, title):
-
- num_poems = int(num_poems)
- prompt = title
- prompt = normalize_text(prompt)
-
- features = [prompt, meter, theme, poet, era, country, lang_type]
-
- prompt = ""
- for name, feat in zip(feature_names, features):
- prompt += f"{name}: {feat}; "
- prompt += f"Length: {num_lines}; Poem:"
-
- num_beams = 5
- top_k = 50
- top_p = 0.9
- r_penalty = 5.
-
- input_ids = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)
- print(f"> Running: {prompt} | {num_poems} Poems")
- outputs = model.generate(input_ids=input_ids,
- min_length=32,
- max_length=256,
- do_sample=True,
- top_k=top_k,
- top_p=top_p,
- repetition_penalty=r_penalty,
- num_beams=num_beams,
- num_return_sequences=num_poems,
- early_stopping=True
- )
-
- poems = []
- print(f"> # of Outputs: {len(outputs)}")
- for output in outputs:
- raw = tokenizer.decode(output)
- raw = raw.replace("", "").replace("", "")
- print("="*100)
- print(raw)
- print("="*100)
- poems += ['\n'.join(raw.split(""))]
-
- return "\n\n".join(poems)
-
-meters = ['البسيط', 'التفعيله', 'الحداء', 'الخفيف', 'الدوبيت', 'الرجز', 'الرمل', 'السريع', 'السلسلة', 'الصخري', 'الطويل', 'الكامل', 'الكان كان', 'اللويحاني', 'المتدارك', 'المتقارب', 'المجتث', 'المديد', 'المسحوب', 'المضارع', 'المقتضب', 'المنسرح', 'المواليا', 'الموشح', 'الهجيني', 'الهزج', 'الوافر', 'بحر أحذ الكامل', 'بحر أحذ المديد', 'بحر أحذ الوافر', 'بحر البسيط', 'بحر التفعيله', 'بحر الخبب', 'بحر الخفيف', 'بحر الدوبيت', 'بحر الرجز', 'بحر الرمل', 'بحر السريع', 'بحر السلسلة', 'بحر الطويل', 'بحر القوما', 'بحر الكامل', 'بحر الكامل المقطوع', 'بحر المتدارك', 'بحر المتدارك المنهوك', 'بحر المتقارب', 'بحر المجتث', 'بحر المديد', 'بحر المضارع', 'بحر المقتضب', 'بحر المنسرح', 'بحر المواليا', 'بحر الهزج', 'بحر الوافر', 'بحر تفعيلة الرجز', 'بحر تفعيلة الرمل', 'بحر تفعيلة الكامل', 'بحر تفعيلة المتقارب', 'بحر مجزوء البسيط', 'بحر مجزوء الخفيف', 'بحر مجزوء الدوبيت', 'بحر مجزوء الرجز', 'بحر مجزوء الرمل', 'بحر مجزوء الرمل ', 'بحر مجزوء السريع', 'بحر مجزوء الطويل', 'بحر مجزوء الكامل', 'بحر مجزوء المتدارك', 'بحر مجزوء المتقارب', 'بحر مجزوء المجتث', 'بحر مجزوء المديد', 'بحر مجزوء المنسرح', 'بحر مجزوء المواليا', 'بحر مجزوء الهزج', 'بحر مجزوء الوافر', 'بحر مجزوء موشح', 'بحر مخلع البسيط', 'بحر مخلع الرجز', 'بحر مخلع الرمل', 'بحر مخلع السريع', 'بحر مخلع الكامل', 'بحر مخلع موشح', 'بحر مربع البسيط', 'بحر مربع الرجز', 'بحر مشطور الرجز', 'بحر مشطور السريع', 'بحر مشطور الطويل', 'بحر منهوك البسيط', 'بحر منهوك الرجز', 'بحر منهوك الكامل', 'بحر منهوك المنسرح', 'بحر موشح', 'بسيط', 'زجل', 'شعر التفعيلة', 'شعر حر', 'عامي', 'عدة أبحر', 'عموديه', 'مجزوء الخفيف', 'نثريه', 'None']
-themes = ['قصيدة اعتذار', 'قصيدة الاناشيد', 'قصيدة المعلقات', 'قصيدة حزينه', 'قصيدة دينية', 'قصيدة ذم', 'قصيدة رثاء', 'قصيدة رومنسيه', 'قصيدة سياسية', 'قصيدة شوق', 'قصيدة عامه', 'قصيدة عتاب', 'قصيدة غزل', 'قصيدة فراق', 'قصيدة قصيره', 'قصيدة مدح', 'قصيدة هجاء', 'قصيدة وطنيه', 'None']
-language_types = ['شعبي', 'عامي', 'فصحى', 'فصيح', '-', 'None']
-poet_era = ['العصر الأموي', 'العصر الأندلسي', 'العصر الأيوبي', 'العصر الإسلامي', 'العصر الجاهلي', 'العصر الحديث', 'العصر العباسي', 'العصر العثماني', 'العصر الفاطمي', 'العصر المملوكي', 'المخضرمين', 'المغرب والأندلس', 'عصر بين الدولتين', 'قبل الإسلام', 'None']
-countries = ['الأردن', 'الإمارات', 'البحرين', 'الجزائر', 'السعودية', 'السنغال', 'السودان', 'الصومال', 'العراق', 'الكويت', 'المغرب', 'اليمن', 'تونس', 'سوريا', 'سورية', 'عمان', 'فلسطين', 'قطر', 'لبنان', 'ليبيا', 'مصر', 'موريتانيا', 'None']
-
-tokenizer: AutoTokenizer = AutoTokenizer.from_pretrained("bkhmsi/arapoet-mt5", use_auth_token="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")
-model: AutoModelForSeq2SeqLM = AutoModelForSeq2SeqLM.from_pretrained("bkhmsi/arapoet-mt5", use_auth_token="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")
-model.eval()
-
-title = ""
-with gr.Blocks(title=title) as demo:
- inputs = []
-
- gr.Markdown(
- """
- # AraPoet: Controlled Arabic Poetry Generation
-
- The model hosted here is a finetuned version of [mT5-large](https://huggingface.co/google/mt5-large) (∼ 1.2B parameters) on the largest repository of Arabic poems, the [ashaar](https://huggingface.co/datasets/arbml/ashaar) dataset.
- The model can be conditioned on a set of attributes to control the style of the generated poem.
- Namely: the poet name, country, era, meter, theme, language type, title and the length of the poem.
- You can start by clicking on one of the examples below or try your own input.
- """
- )
-
- with gr.Row():
- inputs += [gr.Dropdown(countries, label="Country", value="مصر")]
- inputs += [gr.Dropdown(poet_era, label="Era", value="العصر الحديث")]
- with gr.Row():
- inputs += [gr.Dropdown(meters, label="Meter", value="بحر السريع")]
- inputs += [gr.Dropdown(themes, label="Theme", value="قصيدة رومنسيه")]
- with gr.Row():
- inputs += [gr.Dropdown(language_types, label="Language Type", value="فصحى")]
- inputs += [gr.Dropdown(poet_names, label="Poet", value="أحمد شوقي")]
- with gr.Row():
- inputs += [gr.Slider(2, 20, value=6, step=1, label="Number of Lines")]
- inputs += [gr.Slider(1, 4, value=1, step=1, label="Number of Samples")]
- with gr.Row():
- inputs += [gr.Textbox(label="Title", value="إثن عنان القلب واسلم به")]
-
- btn = gr.Button("Generate")
- examples = gr.Examples(examples="./examples", inputs=inputs)
- btn.click(generate_poem, inputs, gr.TextArea(label="Generation"))
-
-
- gr.Markdown(
- """
- Checkout our [AraPoet Preprint](https://github.com/BKHMSI/BKHMSI.github.io/blob/master/archive/resources/AraPoet.pdf) for more details about the model.
- """
- )
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/conversation/[id]/$types.d.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/conversation/[id]/$types.d.ts
deleted file mode 100644
index d011ba126135bc07a71ff46037ecfcf2bff72810..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/conversation/[id]/$types.d.ts
+++ /dev/null
@@ -1,24 +0,0 @@
-import type * as Kit from '@sveltejs/kit';
-
-type Expand = T extends infer O ? { [K in keyof O]: O[K] } : never;
-type RouteParams = { id: string }
-type RouteId = '/conversation/[id]';
-type MaybeWithVoid = {} extends T ? T | void : T;
-export type RequiredKeys = { [K in keyof T]-?: {} extends { [P in K]: T[K] } ? never : K; }[keyof T];
-type OutputDataShape = MaybeWithVoid> & Partial> & Record>
-type EnsureDefined = T extends null | undefined ? {} : T;
-type OptionalUnion, A extends keyof U = U extends U ? keyof U : never> = U extends unknown ? { [P in Exclude]?: never } & U : never;
-export type Snapshot = Kit.Snapshot;
-type PageServerParentData = EnsureDefined;
-type PageParentData = EnsureDefined;
-
-export type EntryGenerator = () => Promise> | Array;
-export type PageServerLoad = OutputDataShape> = Kit.ServerLoad;
-export type PageServerLoadEvent = Parameters[0];
-export type ActionData = unknown;
-export type PageServerData = Expand>>>>>;
-export type PageData = Expand & EnsureDefined>;
-export type Action | void = Record | void> = Kit.Action
-export type Actions | void = Record | void> = Kit.Actions
-export type RequestHandler = Kit.RequestHandler;
-export type RequestEvent = Kit.RequestEvent;
\ No newline at end of file
diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/server/database.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/server/database.ts
deleted file mode 100644
index d91bd445988f2dfe09431c2b803e387ebd911f16..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/server/database.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-let client = undefined;
-export const connectPromise = undefined;
-
-const db = undefined;
-
-const conversations = undefined;
-const sharedConversations = undefined;
-const abortedGenerations = undefined;
-const settings = undefined;
-const users = undefined;
-const webSearches = undefined;
-const messageEvents = undefined;
-
-export { client, db };
-export const collections = {
- conversations,
- sharedConversations,
- abortedGenerations,
- settings,
- users,
- webSearches,
- messageEvents,
-};
diff --git a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/api.py b/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/api.py
deleted file mode 100644
index d6968ef9dd4a087c862f8e66b05108eb12f671f4..0000000000000000000000000000000000000000
--- a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/api.py
+++ /dev/null
@@ -1,269 +0,0 @@
-from enum import Enum, unique
-
-import cv2
-import torch
-from basicsr.utils import img2tensor
-from ldm.util import resize_numpy_image
-from PIL import Image
-from torch import autocast
-
-
-@unique
-class ExtraCondition(Enum):
- sketch = 0
- keypose = 1
- seg = 2
- depth = 3
- canny = 4
- style = 5
- color = 6
- openpose = 7
-
-
-def get_cond_model(opt, cond_type: ExtraCondition):
- if cond_type == ExtraCondition.sketch:
- from ldm.modules.extra_condition.model_edge import pidinet
- model = pidinet()
- ckp = torch.load('models/table5_pidinet.pth', map_location='cpu')['state_dict']
- model.load_state_dict({k.replace('module.', ''): v for k, v in ckp.items()}, strict=True)
- model.to(opt.device)
- return model
- elif cond_type == ExtraCondition.seg:
- raise NotImplementedError
- elif cond_type == ExtraCondition.keypose:
- import mmcv
- from mmdet.apis import init_detector
- from mmpose.apis import init_pose_model
- det_config = 'configs/mm/faster_rcnn_r50_fpn_coco.py'
- det_checkpoint = 'models/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
- pose_config = 'configs/mm/hrnet_w48_coco_256x192.py'
- pose_checkpoint = 'models/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth'
- det_config_mmcv = mmcv.Config.fromfile(det_config)
- det_model = init_detector(det_config_mmcv, det_checkpoint, device=opt.device)
- pose_config_mmcv = mmcv.Config.fromfile(pose_config)
- pose_model = init_pose_model(pose_config_mmcv, pose_checkpoint, device=opt.device)
- return {'pose_model': pose_model, 'det_model': det_model}
- elif cond_type == ExtraCondition.depth:
- from ldm.modules.extra_condition.midas.api import MiDaSInference
- model = MiDaSInference(model_type='dpt_hybrid').to(opt.device)
- return model
- elif cond_type == ExtraCondition.canny:
- return None
- elif cond_type == ExtraCondition.style:
- from transformers import CLIPProcessor, CLIPVisionModel
- version = 'openai/clip-vit-large-patch14'
- processor = CLIPProcessor.from_pretrained(version)
- clip_vision_model = CLIPVisionModel.from_pretrained(version).to(opt.device)
- return {'processor': processor, 'clip_vision_model': clip_vision_model}
- elif cond_type == ExtraCondition.color:
- return None
- elif cond_type == ExtraCondition.openpose:
- from ldm.modules.extra_condition.openpose.api import OpenposeInference
- model = OpenposeInference().to(opt.device)
- return model
- else:
- raise NotImplementedError
-
-
-def get_cond_sketch(opt, cond_image, cond_inp_type, cond_model=None):
- if isinstance(cond_image, str):
- edge = cv2.imread(cond_image)
- else:
- # for gradio input, pay attention, it's rgb numpy
- edge = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
- edge = resize_numpy_image(edge, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
- opt.H, opt.W = edge.shape[:2]
- if cond_inp_type == 'sketch':
- edge = img2tensor(edge)[0].unsqueeze(0).unsqueeze(0) / 255.
- edge = edge.to(opt.device)
- elif cond_inp_type == 'image':
- edge = img2tensor(edge).unsqueeze(0) / 255.
- edge = cond_model(edge.to(opt.device))[-1]
- else:
- raise NotImplementedError
-
- # edge = 1-edge # for white background
- edge = edge > 0.5
- edge = edge.float()
-
- return edge
-
-
-def get_cond_seg(opt, cond_image, cond_inp_type='image', cond_model=None):
- if isinstance(cond_image, str):
- seg = cv2.imread(cond_image)
- else:
- seg = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
- seg = resize_numpy_image(seg, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
- opt.H, opt.W = seg.shape[:2]
- if cond_inp_type == 'seg':
- seg = img2tensor(seg).unsqueeze(0) / 255.
- seg = seg.to(opt.device)
- else:
- raise NotImplementedError
-
- return seg
-
-
-def get_cond_keypose(opt, cond_image, cond_inp_type='image', cond_model=None):
- if isinstance(cond_image, str):
- pose = cv2.imread(cond_image)
- else:
- pose = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
- pose = resize_numpy_image(pose, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
- opt.H, opt.W = pose.shape[:2]
- if cond_inp_type == 'keypose':
- pose = img2tensor(pose).unsqueeze(0) / 255.
- pose = pose.to(opt.device)
- elif cond_inp_type == 'image':
- from ldm.modules.extra_condition.utils import imshow_keypoints
- from mmdet.apis import inference_detector
- from mmpose.apis import (inference_top_down_pose_model, process_mmdet_results)
-
- # mmpose seems not compatible with autocast fp16
- with autocast("cuda", dtype=torch.float32):
- mmdet_results = inference_detector(cond_model['det_model'], pose)
- # keep the person class bounding boxes.
- person_results = process_mmdet_results(mmdet_results, 1)
-
- # optional
- return_heatmap = False
- dataset = cond_model['pose_model'].cfg.data['test']['type']
-
- # e.g. use ('backbone', ) to return backbone feature
- output_layer_names = None
- pose_results, returned_outputs = inference_top_down_pose_model(
- cond_model['pose_model'],
- pose,
- person_results,
- bbox_thr=0.2,
- format='xyxy',
- dataset=dataset,
- dataset_info=None,
- return_heatmap=return_heatmap,
- outputs=output_layer_names)
-
- # show the results
- pose = imshow_keypoints(pose, pose_results, radius=2, thickness=2)
- pose = img2tensor(pose).unsqueeze(0) / 255.
- pose = pose.to(opt.device)
- else:
- raise NotImplementedError
-
- return pose
-
-
-def get_cond_depth(opt, cond_image, cond_inp_type='image', cond_model=None):
- if isinstance(cond_image, str):
- depth = cv2.imread(cond_image)
- else:
- depth = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
- depth = resize_numpy_image(depth, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
- opt.H, opt.W = depth.shape[:2]
- if cond_inp_type == 'depth':
- depth = img2tensor(depth).unsqueeze(0) / 255.
- depth = depth.to(opt.device)
- elif cond_inp_type == 'image':
- depth = img2tensor(depth).unsqueeze(0) / 127.5 - 1.0
- depth = cond_model(depth.to(opt.device)).repeat(1, 3, 1, 1)
- depth -= torch.min(depth)
- depth /= torch.max(depth)
- else:
- raise NotImplementedError
-
- return depth
-
-
-def get_cond_canny(opt, cond_image, cond_inp_type='image', cond_model=None):
- if isinstance(cond_image, str):
- canny = cv2.imread(cond_image)
- else:
- canny = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
- canny = resize_numpy_image(canny, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
- opt.H, opt.W = canny.shape[:2]
- if cond_inp_type == 'canny':
- canny = img2tensor(canny)[0:1].unsqueeze(0) / 255.
- canny = canny.to(opt.device)
- elif cond_inp_type == 'image':
- canny = cv2.Canny(canny, 100, 200)[..., None]
- canny = img2tensor(canny).unsqueeze(0) / 255.
- canny = canny.to(opt.device)
- else:
- raise NotImplementedError
-
- return canny
-
-
-def get_cond_style(opt, cond_image, cond_inp_type='image', cond_model=None):
- assert cond_inp_type == 'image'
- if isinstance(cond_image, str):
- style = Image.open(cond_image)
- else:
- # numpy image to PIL image
- style = Image.fromarray(cond_image)
-
- style_for_clip = cond_model['processor'](images=style, return_tensors="pt")['pixel_values']
- style_feat = cond_model['clip_vision_model'](style_for_clip.to(opt.device))['last_hidden_state']
-
- return style_feat
-
-
-def get_cond_color(opt, cond_image, cond_inp_type='image', cond_model=None):
- if isinstance(cond_image, str):
- color = cv2.imread(cond_image)
- else:
- color = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
- color = resize_numpy_image(color, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
- opt.H, opt.W = color.shape[:2]
- if cond_inp_type == 'image':
- color = cv2.resize(color, (opt.W//64, opt.H//64), interpolation=cv2.INTER_CUBIC)
- color = cv2.resize(color, (opt.W, opt.H), interpolation=cv2.INTER_NEAREST)
- color = img2tensor(color).unsqueeze(0) / 255.
- color = color.to(opt.device)
- return color
-
-
-def get_cond_openpose(opt, cond_image, cond_inp_type='image', cond_model=None):
- if isinstance(cond_image, str):
- openpose_keypose = cv2.imread(cond_image)
- else:
- openpose_keypose = cv2.cvtColor(cond_image, cv2.COLOR_RGB2BGR)
- openpose_keypose = resize_numpy_image(
- openpose_keypose, max_resolution=opt.max_resolution, resize_short_edge=opt.resize_short_edge)
- opt.H, opt.W = openpose_keypose.shape[:2]
- if cond_inp_type == 'openpose':
- openpose_keypose = img2tensor(openpose_keypose).unsqueeze(0) / 255.
- openpose_keypose = openpose_keypose.to(opt.device)
- elif cond_inp_type == 'image':
- with autocast('cuda', dtype=torch.float32):
- openpose_keypose = cond_model(openpose_keypose)
- openpose_keypose = img2tensor(openpose_keypose).unsqueeze(0) / 255.
- openpose_keypose = openpose_keypose.to(opt.device)
-
- else:
- raise NotImplementedError
-
- return openpose_keypose
-
-
-def get_adapter_feature(inputs, adapters):
- ret_feat_map = None
- ret_feat_seq = None
- if not isinstance(inputs, list):
- inputs = [inputs]
- adapters = [adapters]
-
- for input, adapter in zip(inputs, adapters):
- cur_feature = adapter['model'](input)
- if isinstance(cur_feature, list):
- if ret_feat_map is None:
- ret_feat_map = list(map(lambda x: x * adapter['cond_weight'], cur_feature))
- else:
- ret_feat_map = list(map(lambda x, y: x + y * adapter['cond_weight'], ret_feat_map, cur_feature))
- else:
- if ret_feat_seq is None:
- ret_feat_seq = cur_feature
- else:
- ret_feat_seq = torch.cat([ret_feat_seq, cur_feature], dim=1)
-
- return ret_feat_map, ret_feat_seq
diff --git a/spaces/AgentVerse/agentVerse/agentverse_command/main_simulation_gui.py b/spaces/AgentVerse/agentVerse/agentverse_command/main_simulation_gui.py
deleted file mode 100644
index 1634b3fd196a845e8bf2f0e5d24c8aa2ada50bf0..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse_command/main_simulation_gui.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import os
-from agentverse.gui import GUI
-from argparse import ArgumentParser
-
-parser = ArgumentParser()
-parser.add_argument("--task", type=str, default="simulation/nlp_classroom_9players")
-parser.add_argument(
- "--tasks_dir",
- type=str,
- default=os.path.join(os.path.dirname(__file__), "..", "agentverse", "tasks"),
-)
-args = parser.parse_args()
-
-
-def cli_main():
- ui = GUI(args.task, args.tasks_dir)
- ui.launch()
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/GetExpandedChildHeight.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/GetExpandedChildHeight.js
deleted file mode 100644
index 76b5e4880ceef6aed77ab5d5d4e4b1a1190219f7..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/GetExpandedChildHeight.js
+++ /dev/null
@@ -1,11 +0,0 @@
-var GetExpandedChildHeight = function (child, rowHeight) {
- var childHeight;
- var childConfig = child.rexSizer;
- if (childConfig.expand) {
- var padding = childConfig.padding;
- childHeight = rowHeight - padding.top - padding.bottom;
- }
- return childHeight;
-}
-
-export default GetExpandedChildHeight;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/OverlapSizer.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/OverlapSizer.js
deleted file mode 100644
index 871377e81e0159db297ad1bc7a2c6022c9593814..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/OverlapSizer.js
+++ /dev/null
@@ -1,49 +0,0 @@
-import BaseSizer from '../basesizer/BaseSizer.js';
-import Methods from './Methods.js';
-import Clear from '../../../plugins/utils/object/Clear.js';
-import IndexOf from '../../../plugins/utils/object/IndexOf.js';
-
-const IsPlainObject = Phaser.Utils.Objects.IsPlainObject;
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-class OverlapSizer extends BaseSizer {
- constructor(scene, x, y, minWidth, minHeight, config) {
- if (IsPlainObject(x)) {
- config = x;
- x = GetValue(config, 'x', 0);
- y = GetValue(config, 'y', 0);
- minWidth = GetValue(config, 'width', undefined);
- minHeight = GetValue(config, 'height', undefined);
- } else if (IsPlainObject(minWidth)) {
- config = minWidth;
- minWidth = GetValue(config, 'width', undefined);
- minHeight = GetValue(config, 'height', undefined);
- }
-
- super(scene, x, y, minWidth, minHeight, config);
-
- this.type = 'rexOverlapSizer';
- this.sizerChildren = {};
-
- this.addChildrenMap('items', this.sizerChildren);
- }
-
- childToKey(gameObject) {
- if (typeof (gameObject) === 'string') {
- var key = gameObject;
- if (this.sizerChildren.hasOwnPropery(key)) {
- return key;
- }
- } else {
- return IndexOf(this.sizerChildren, gameObject);
- }
- return null;
- }
-}
-
-Object.assign(
- OverlapSizer.prototype,
- Methods
-);
-
-export default OverlapSizer;
\ No newline at end of file
diff --git a/spaces/AlexWang/lama/saicinpainting/training/losses/segmentation.py b/spaces/AlexWang/lama/saicinpainting/training/losses/segmentation.py
deleted file mode 100644
index 3d4a9f94eaae84722db584277dbbf9bc41ede357..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/saicinpainting/training/losses/segmentation.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .constants import weights as constant_weights
-
-
-class CrossEntropy2d(nn.Module):
- def __init__(self, reduction="mean", ignore_label=255, weights=None, *args, **kwargs):
- """
- weight (Tensor, optional): a manual rescaling weight given to each class.
- If given, has to be a Tensor of size "nclasses"
- """
- super(CrossEntropy2d, self).__init__()
- self.reduction = reduction
- self.ignore_label = ignore_label
- self.weights = weights
- if self.weights is not None:
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- self.weights = torch.FloatTensor(constant_weights[weights]).to(device)
-
- def forward(self, predict, target):
- """
- Args:
- predict:(n, c, h, w)
- target:(n, 1, h, w)
- """
- target = target.long()
- assert not target.requires_grad
- assert predict.dim() == 4, "{0}".format(predict.size())
- assert target.dim() == 4, "{0}".format(target.size())
- assert predict.size(0) == target.size(0), "{0} vs {1} ".format(predict.size(0), target.size(0))
- assert target.size(1) == 1, "{0}".format(target.size(1))
- assert predict.size(2) == target.size(2), "{0} vs {1} ".format(predict.size(2), target.size(2))
- assert predict.size(3) == target.size(3), "{0} vs {1} ".format(predict.size(3), target.size(3))
- target = target.squeeze(1)
- n, c, h, w = predict.size()
- target_mask = (target >= 0) * (target != self.ignore_label)
- target = target[target_mask]
- predict = predict.transpose(1, 2).transpose(2, 3).contiguous()
- predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c)
- loss = F.cross_entropy(predict, target, weight=self.weights, reduction=self.reduction)
- return loss
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/shap_e.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/shap_e.md
deleted file mode 100644
index 39f6416b18be861955a7b1c8af0d5d0e82feba56..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/shap_e.md
+++ /dev/null
@@ -1,190 +0,0 @@
-
-
-# Shap-E
-
-The Shap-E model was proposed in [Shap-E: Generating Conditional 3D Implicit Functions](https://huggingface.co/papers/2305.02463) by Alex Nichol and Heewon Jun from [OpenAI](https://github.com/openai).
-
-The abstract from the paper is:
-
-*We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space.*
-
-The original codebase can be found at [openai/shap-e](https://github.com/openai/shap-e).
-
-
-
-Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-
-
-## Usage Examples
-
-In the following, we will walk you through some examples of how to use Shap-E pipelines to create 3D objects in gif format.
-
-### Text-to-3D image generation
-
-We can use [`ShapEPipeline`] to create 3D object based on a text prompt. In this example, we will make a birthday cupcake for :firecracker: diffusers library's 1 year birthday. The workflow to use the Shap-E text-to-image pipeline is same as how you would use other text-to-image pipelines in diffusers.
-
-```python
-import torch
-
-from diffusers import DiffusionPipeline
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-repo = "openai/shap-e"
-pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
-pipe = pipe.to(device)
-
-guidance_scale = 15.0
-prompt = ["A firecracker", "A birthday cupcake"]
-
-images = pipe(
- prompt,
- guidance_scale=guidance_scale,
- num_inference_steps=64,
- frame_size=256,
-).images
-```
-
-The output of [`ShapEPipeline`] is a list of lists of images frames. Each list of frames can be used to create a 3D object. Let's use the `export_to_gif` utility function in diffusers to make a 3D cupcake!
-
-```python
-from diffusers.utils import export_to_gif
-
-export_to_gif(images[0], "firecracker_3d.gif")
-export_to_gif(images[1], "cake_3d.gif")
-```
-
-
-
-
-### Image-to-Image generation
-
-You can use [`ShapEImg2ImgPipeline`] along with other text-to-image pipelines in diffusers and turn your 2D generation into 3D.
-
-In this example, We will first genrate a cheeseburger with a simple prompt "A cheeseburger, white background"
-
-```python
-from diffusers import DiffusionPipeline
-import torch
-
-pipe_prior = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16)
-pipe_prior.to("cuda")
-
-t2i_pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
-t2i_pipe.to("cuda")
-
-prompt = "A cheeseburger, white background"
-
-image_embeds, negative_image_embeds = pipe_prior(prompt, guidance_scale=1.0).to_tuple()
-image = t2i_pipe(
- prompt,
- image_embeds=image_embeds,
- negative_image_embeds=negative_image_embeds,
-).images[0]
-
-image.save("burger.png")
-```
-
-
-
-we will then use the Shap-E image-to-image pipeline to turn it into a 3D cheeseburger :)
-
-```python
-from PIL import Image
-from diffusers.utils import export_to_gif
-
-repo = "openai/shap-e-img2img"
-pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
-pipe = pipe.to("cuda")
-
-guidance_scale = 3.0
-image = Image.open("burger.png").resize((256, 256))
-
-images = pipe(
- image,
- guidance_scale=guidance_scale,
- num_inference_steps=64,
- frame_size=256,
-).images
-
-gif_path = export_to_gif(images[0], "burger_3d.gif")
-```
-
-
-### Generate mesh
-
-For both [`ShapEPipeline`] and [`ShapEImg2ImgPipeline`], you can generate mesh output by passing `output_type` as `mesh` to the pipeline, and then use the [`ShapEPipeline.export_to_ply`] utility function to save the output as a `ply` file. We also provide a [`ShapEPipeline.export_to_obj`] function that you can use to save mesh outputs as `obj` files.
-
-```python
-import torch
-
-from diffusers import DiffusionPipeline
-from diffusers.utils import export_to_ply
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-repo = "openai/shap-e"
-pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16, variant="fp16")
-pipe = pipe.to(device)
-
-guidance_scale = 15.0
-prompt = "A birthday cupcake"
-
-images = pipe(prompt, guidance_scale=guidance_scale, num_inference_steps=64, frame_size=256, output_type="mesh").images
-
-ply_path = export_to_ply(images[0], "3d_cake.ply")
-print(f"saved to folder: {ply_path}")
-```
-
-Huggingface Datasets supports mesh visualization for mesh files in `glb` format. Below we will show you how to convert your mesh file into `glb` format so that you can use the Dataset viewer to render 3D objects.
-
-We need to install `trimesh` library.
-
-```
-pip install trimesh
-```
-
-To convert the mesh file into `glb` format,
-
-```python
-import trimesh
-
-mesh = trimesh.load("3d_cake.ply")
-mesh.export("3d_cake.glb", file_type="glb")
-```
-
-By default, the mesh output of Shap-E is from the bottom viewpoint; you can change the default viewpoint by applying a rotation transformation
-
-```python
-import trimesh
-import numpy as np
-
-mesh = trimesh.load("3d_cake.ply")
-rot = trimesh.transformations.rotation_matrix(-np.pi / 2, [1, 0, 0])
-mesh = mesh.apply_transform(rot)
-mesh.export("3d_cake.glb", file_type="glb")
-```
-
-Now you can upload your mesh file to your dataset and visualize it! Here is the link to the 3D cake we just generated
-https://huggingface.co/datasets/hf-internal-testing/diffusers-images/blob/main/shap_e/3d_cake.glb
-
-## ShapEPipeline
-[[autodoc]] ShapEPipeline
- - all
- - __call__
-
-## ShapEImg2ImgPipeline
-[[autodoc]] ShapEImg2ImgPipeline
- - all
- - __call__
-
-## ShapEPipelineOutput
-[[autodoc]] pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/optimization/habana.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/optimization/habana.md
deleted file mode 100644
index 24846615c95ce1ed975822fe3ee854a5b379bcf7..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/optimization/habana.md
+++ /dev/null
@@ -1,79 +0,0 @@
-
-
-# How to use Stable Diffusion on Habana Gaudi
-
-🤗 Diffusers is compatible with Habana Gaudi through 🤗 [Optimum Habana](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion).
-
-## Requirements
-
-- Optimum Habana 1.6 or later, [here](https://huggingface.co/docs/optimum/habana/installation) is how to install it.
-- SynapseAI 1.10.
-
-
-## Inference Pipeline
-
-To generate images with Stable Diffusion 1 and 2 on Gaudi, you need to instantiate two instances:
-- A pipeline with [`GaudiStableDiffusionPipeline`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline). This pipeline supports *text-to-image generation*.
-- A scheduler with [`GaudiDDIMScheduler`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline#optimum.habana.diffusers.GaudiDDIMScheduler). This scheduler has been optimized for Habana Gaudi.
-
-When initializing the pipeline, you have to specify `use_habana=True` to deploy it on HPUs.
-Furthermore, in order to get the fastest possible generations you should enable **HPU graphs** with `use_hpu_graphs=True`.
-Finally, you will need to specify a [Gaudi configuration](https://huggingface.co/docs/optimum/habana/package_reference/gaudi_config) which can be downloaded from the [Hugging Face Hub](https://huggingface.co/Habana).
-
-```python
-from optimum.habana import GaudiConfig
-from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline
-
-model_name = "stabilityai/stable-diffusion-2-base"
-scheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder="scheduler")
-pipeline = GaudiStableDiffusionPipeline.from_pretrained(
- model_name,
- scheduler=scheduler,
- use_habana=True,
- use_hpu_graphs=True,
- gaudi_config="Habana/stable-diffusion-2",
-)
-```
-
-You can then call the pipeline to generate images by batches from one or several prompts:
-```python
-outputs = pipeline(
- prompt=[
- "High quality photo of an astronaut riding a horse in space",
- "Face of a yellow cat, high resolution, sitting on a park bench",
- ],
- num_images_per_prompt=10,
- batch_size=4,
-)
-```
-
-For more information, check out Optimum Habana's [documentation](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion) and the [example](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion) provided in the official Github repository.
-
-
-## Benchmark
-
-Here are the latencies for Habana first-generation Gaudi and Gaudi2 with the [Habana/stable-diffusion](https://huggingface.co/Habana/stable-diffusion) and [Habana/stable-diffusion-2](https://huggingface.co/Habana/stable-diffusion-2) Gaudi configurations (mixed precision bf16/fp32):
-
-- [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) (512x512 resolution):
-
-| | Latency (batch size = 1) | Throughput (batch size = 8) |
-| ---------------------- |:------------------------:|:---------------------------:|
-| first-generation Gaudi | 3.80s | 0.308 images/s |
-| Gaudi2 | 1.33s | 1.081 images/s |
-
-- [Stable Diffusion v2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) (768x768 resolution):
-
-| | Latency (batch size = 1) | Throughput |
-| ---------------------- |:------------------------:|:-------------------------------:|
-| first-generation Gaudi | 10.2s | 0.108 images/s (batch size = 4) |
-| Gaudi2 | 3.17s | 0.379 images/s (batch size = 8) |
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/models/test_unet_2d_blocks.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/models/test_unet_2d_blocks.py
deleted file mode 100644
index 4d658f2829329a1fd5d26edb0c50c0887d024044..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/models/test_unet_2d_blocks.py
+++ /dev/null
@@ -1,337 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import unittest
-
-from diffusers.models.unet_2d_blocks import * # noqa F403
-from diffusers.utils import torch_device
-
-from .test_unet_blocks_common import UNetBlockTesterMixin
-
-
-class DownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = DownBlock2D # noqa F405
- block_type = "down"
-
- def test_output(self):
- expected_slice = [-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904]
- super().test_output(expected_slice)
-
-
-class ResnetDownsampleBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = ResnetDownsampleBlock2D # noqa F405
- block_type = "down"
-
- def test_output(self):
- expected_slice = [0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948]
- super().test_output(expected_slice)
-
-
-class AttnDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = AttnDownBlock2D # noqa F405
- block_type = "down"
-
- def test_output(self):
- expected_slice = [0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957]
- super().test_output(expected_slice)
-
-
-class CrossAttnDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = CrossAttnDownBlock2D # noqa F405
- block_type = "down"
-
- def prepare_init_args_and_inputs_for_common(self):
- init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()
- init_dict["cross_attention_dim"] = 32
- return init_dict, inputs_dict
-
- def test_output(self):
- expected_slice = [0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983]
- super().test_output(expected_slice)
-
-
-class SimpleCrossAttnDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = SimpleCrossAttnDownBlock2D # noqa F405
- block_type = "down"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_encoder_hidden_states=True)
-
- def prepare_init_args_and_inputs_for_common(self):
- init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()
- init_dict["cross_attention_dim"] = 32
- return init_dict, inputs_dict
-
- @unittest.skipIf(torch_device == "mps", "MPS result is not consistent")
- def test_output(self):
- expected_slice = [0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338]
- super().test_output(expected_slice)
-
-
-class SkipDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = SkipDownBlock2D # noqa F405
- block_type = "down"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_skip_sample=True)
-
- def test_output(self):
- expected_slice = [-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069]
- super().test_output(expected_slice)
-
-
-class AttnSkipDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = AttnSkipDownBlock2D # noqa F405
- block_type = "down"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_skip_sample=True)
-
- def test_output(self):
- expected_slice = [0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642]
- super().test_output(expected_slice)
-
-
-class DownEncoderBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = DownEncoderBlock2D # noqa F405
- block_type = "down"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_temb=False)
-
- def prepare_init_args_and_inputs_for_common(self):
- init_dict = {
- "in_channels": 32,
- "out_channels": 32,
- }
- inputs_dict = self.dummy_input
- return init_dict, inputs_dict
-
- def test_output(self):
- expected_slice = [1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626]
- super().test_output(expected_slice)
-
-
-class AttnDownEncoderBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = AttnDownEncoderBlock2D # noqa F405
- block_type = "down"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_temb=False)
-
- def prepare_init_args_and_inputs_for_common(self):
- init_dict = {
- "in_channels": 32,
- "out_channels": 32,
- }
- inputs_dict = self.dummy_input
- return init_dict, inputs_dict
-
- def test_output(self):
- expected_slice = [0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538]
- super().test_output(expected_slice)
-
-
-class UNetMidBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = UNetMidBlock2D # noqa F405
- block_type = "mid"
-
- def prepare_init_args_and_inputs_for_common(self):
- init_dict = {
- "in_channels": 32,
- "temb_channels": 128,
- }
- inputs_dict = self.dummy_input
- return init_dict, inputs_dict
-
- def test_output(self):
- expected_slice = [-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028]
- super().test_output(expected_slice)
-
-
-class UNetMidBlock2DCrossAttnTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = UNetMidBlock2DCrossAttn # noqa F405
- block_type = "mid"
-
- def prepare_init_args_and_inputs_for_common(self):
- init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()
- init_dict["cross_attention_dim"] = 32
- return init_dict, inputs_dict
-
- def test_output(self):
- expected_slice = [0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335]
- super().test_output(expected_slice)
-
-
-class UNetMidBlock2DSimpleCrossAttnTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = UNetMidBlock2DSimpleCrossAttn # noqa F405
- block_type = "mid"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_encoder_hidden_states=True)
-
- def prepare_init_args_and_inputs_for_common(self):
- init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()
- init_dict["cross_attention_dim"] = 32
- return init_dict, inputs_dict
-
- def test_output(self):
- expected_slice = [0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880]
- super().test_output(expected_slice)
-
-
-class UpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = UpBlock2D # noqa F405
- block_type = "up"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_res_hidden_states_tuple=True)
-
- def test_output(self):
- expected_slice = [-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523]
- super().test_output(expected_slice)
-
-
-class ResnetUpsampleBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = ResnetUpsampleBlock2D # noqa F405
- block_type = "up"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_res_hidden_states_tuple=True)
-
- def test_output(self):
- expected_slice = [0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244]
- super().test_output(expected_slice)
-
-
-class CrossAttnUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = CrossAttnUpBlock2D # noqa F405
- block_type = "up"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_res_hidden_states_tuple=True)
-
- def prepare_init_args_and_inputs_for_common(self):
- init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()
- init_dict["cross_attention_dim"] = 32
- return init_dict, inputs_dict
-
- def test_output(self):
- expected_slice = [-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582]
- super().test_output(expected_slice)
-
-
-class SimpleCrossAttnUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = SimpleCrossAttnUpBlock2D # noqa F405
- block_type = "up"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_res_hidden_states_tuple=True, include_encoder_hidden_states=True)
-
- def prepare_init_args_and_inputs_for_common(self):
- init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()
- init_dict["cross_attention_dim"] = 32
- return init_dict, inputs_dict
-
- def test_output(self):
- expected_slice = [0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402]
- super().test_output(expected_slice)
-
-
-class AttnUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = AttnUpBlock2D # noqa F405
- block_type = "up"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_res_hidden_states_tuple=True)
-
- @unittest.skipIf(torch_device == "mps", "MPS result is not consistent")
- def test_output(self):
- expected_slice = [0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033]
- super().test_output(expected_slice)
-
-
-class SkipUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = SkipUpBlock2D # noqa F405
- block_type = "up"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_res_hidden_states_tuple=True)
-
- def test_output(self):
- expected_slice = [-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362]
- super().test_output(expected_slice)
-
-
-class AttnSkipUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = AttnSkipUpBlock2D # noqa F405
- block_type = "up"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_res_hidden_states_tuple=True)
-
- def test_output(self):
- expected_slice = [0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015]
- super().test_output(expected_slice)
-
-
-class UpDecoderBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = UpDecoderBlock2D # noqa F405
- block_type = "up"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_temb=False)
-
- def prepare_init_args_and_inputs_for_common(self):
- init_dict = {"in_channels": 32, "out_channels": 32}
-
- inputs_dict = self.dummy_input
- return init_dict, inputs_dict
-
- def test_output(self):
- expected_slice = [0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137]
- super().test_output(expected_slice)
-
-
-class AttnUpDecoderBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
- block_class = AttnUpDecoderBlock2D # noqa F405
- block_type = "up"
-
- @property
- def dummy_input(self):
- return super().get_dummy_input(include_temb=False)
-
- def prepare_init_args_and_inputs_for_common(self):
- init_dict = {"in_channels": 32, "out_channels": 32}
-
- inputs_dict = self.dummy_input
- return init_dict, inputs_dict
-
- def test_output(self):
- expected_slice = [0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568]
- super().test_output(expected_slice)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py
deleted file mode 100644
index 9bbc86ead7003ab75264f8cf0cd18edb735fe9fd..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py
+++ /dev/null
@@ -1,17 +0,0 @@
-_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py'
-# model settings
-conv_cfg = dict(type='ConvWS')
-norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
-model = dict(
- pretrained='open-mmlab://jhu/resnext50_32x4d_gn_ws',
- backbone=dict(
- type='ResNeXt',
- depth=50,
- groups=32,
- base_width=4,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- style='pytorch',
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg))
diff --git a/spaces/Andy1621/uniformer_image_detection/tools/model_converters/upgrade_model_version.py b/spaces/Andy1621/uniformer_image_detection/tools/model_converters/upgrade_model_version.py
deleted file mode 100644
index 232c8bc4cf010084b817c545ab4e2ef34fdd4549..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/tools/model_converters/upgrade_model_version.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import argparse
-import re
-import tempfile
-from collections import OrderedDict
-
-import torch
-from mmcv import Config
-
-
-def is_head(key):
- valid_head_list = [
- 'bbox_head', 'mask_head', 'semantic_head', 'grid_head', 'mask_iou_head'
- ]
-
- return any(key.startswith(h) for h in valid_head_list)
-
-
-def parse_config(config_strings):
- temp_file = tempfile.NamedTemporaryFile()
- config_path = f'{temp_file.name}.py'
- with open(config_path, 'w') as f:
- f.write(config_strings)
-
- config = Config.fromfile(config_path)
- is_two_stage = True
- is_ssd = False
- is_retina = False
- reg_cls_agnostic = False
- if 'rpn_head' not in config.model:
- is_two_stage = False
- # check whether it is SSD
- if config.model.bbox_head.type == 'SSDHead':
- is_ssd = True
- elif config.model.bbox_head.type == 'RetinaHead':
- is_retina = True
- elif isinstance(config.model['bbox_head'], list):
- reg_cls_agnostic = True
- elif 'reg_class_agnostic' in config.model.bbox_head:
- reg_cls_agnostic = config.model.bbox_head \
- .reg_class_agnostic
- temp_file.close()
- return is_two_stage, is_ssd, is_retina, reg_cls_agnostic
-
-
-def reorder_cls_channel(val, num_classes=81):
- # bias
- if val.dim() == 1:
- new_val = torch.cat((val[1:], val[:1]), dim=0)
- # weight
- else:
- out_channels, in_channels = val.shape[:2]
- # conv_cls for softmax output
- if out_channels != num_classes and out_channels % num_classes == 0:
- new_val = val.reshape(-1, num_classes, in_channels, *val.shape[2:])
- new_val = torch.cat((new_val[:, 1:], new_val[:, :1]), dim=1)
- new_val = new_val.reshape(val.size())
- # fc_cls
- elif out_channels == num_classes:
- new_val = torch.cat((val[1:], val[:1]), dim=0)
- # agnostic | retina_cls | rpn_cls
- else:
- new_val = val
-
- return new_val
-
-
-def truncate_cls_channel(val, num_classes=81):
-
- # bias
- if val.dim() == 1:
- if val.size(0) % num_classes == 0:
- new_val = val[:num_classes - 1]
- else:
- new_val = val
- # weight
- else:
- out_channels, in_channels = val.shape[:2]
- # conv_logits
- if out_channels % num_classes == 0:
- new_val = val.reshape(num_classes, in_channels, *val.shape[2:])[1:]
- new_val = new_val.reshape(-1, *val.shape[1:])
- # agnostic
- else:
- new_val = val
-
- return new_val
-
-
-def truncate_reg_channel(val, num_classes=81):
- # bias
- if val.dim() == 1:
- # fc_reg | rpn_reg
- if val.size(0) % num_classes == 0:
- new_val = val.reshape(num_classes, -1)[:num_classes - 1]
- new_val = new_val.reshape(-1)
- # agnostic
- else:
- new_val = val
- # weight
- else:
- out_channels, in_channels = val.shape[:2]
- # fc_reg | rpn_reg
- if out_channels % num_classes == 0:
- new_val = val.reshape(num_classes, -1, in_channels,
- *val.shape[2:])[1:]
- new_val = new_val.reshape(-1, *val.shape[1:])
- # agnostic
- else:
- new_val = val
-
- return new_val
-
-
-def convert(in_file, out_file, num_classes):
- """Convert keys in checkpoints.
-
- There can be some breaking changes during the development of mmdetection,
- and this tool is used for upgrading checkpoints trained with old versions
- to the latest one.
- """
- checkpoint = torch.load(in_file)
- in_state_dict = checkpoint.pop('state_dict')
- out_state_dict = OrderedDict()
- meta_info = checkpoint['meta']
- is_two_stage, is_ssd, is_retina, reg_cls_agnostic = parse_config(
- '#' + meta_info['config'])
- if meta_info['mmdet_version'] <= '0.5.3' and is_retina:
- upgrade_retina = True
- else:
- upgrade_retina = False
-
- # MMDetection v2.5.0 unifies the class order in RPN
- # if the model is trained in version=2.5.0
- if meta_info['mmdet_version'] < '2.5.0':
- upgrade_rpn = True
- else:
- upgrade_rpn = False
-
- for key, val in in_state_dict.items():
- new_key = key
- new_val = val
- if is_two_stage and is_head(key):
- new_key = 'roi_head.{}'.format(key)
-
- # classification
- if upgrade_rpn:
- m = re.search(
- r'(conv_cls|retina_cls|rpn_cls|fc_cls|fcos_cls|'
- r'fovea_cls).(weight|bias)', new_key)
- else:
- m = re.search(
- r'(conv_cls|retina_cls|fc_cls|fcos_cls|'
- r'fovea_cls).(weight|bias)', new_key)
- if m is not None:
- print(f'reorder cls channels of {new_key}')
- new_val = reorder_cls_channel(val, num_classes)
-
- # regression
- if upgrade_rpn:
- m = re.search(r'(fc_reg).(weight|bias)', new_key)
- else:
- m = re.search(r'(fc_reg|rpn_reg).(weight|bias)', new_key)
- if m is not None and not reg_cls_agnostic:
- print(f'truncate regression channels of {new_key}')
- new_val = truncate_reg_channel(val, num_classes)
-
- # mask head
- m = re.search(r'(conv_logits).(weight|bias)', new_key)
- if m is not None:
- print(f'truncate mask prediction channels of {new_key}')
- new_val = truncate_cls_channel(val, num_classes)
-
- m = re.search(r'(cls_convs|reg_convs).\d.(weight|bias)', key)
- # Legacy issues in RetinaNet since V1.x
- # Use ConvModule instead of nn.Conv2d in RetinaNet
- # cls_convs.0.weight -> cls_convs.0.conv.weight
- if m is not None and upgrade_retina:
- param = m.groups()[1]
- new_key = key.replace(param, f'conv.{param}')
- out_state_dict[new_key] = val
- print(f'rename the name of {key} to {new_key}')
- continue
-
- m = re.search(r'(cls_convs).\d.(weight|bias)', key)
- if m is not None and is_ssd:
- print(f'reorder cls channels of {new_key}')
- new_val = reorder_cls_channel(val, num_classes)
-
- out_state_dict[new_key] = new_val
- checkpoint['state_dict'] = out_state_dict
- torch.save(checkpoint, out_file)
-
-
-def main():
- parser = argparse.ArgumentParser(description='Upgrade model version')
- parser.add_argument('in_file', help='input checkpoint file')
- parser.add_argument('out_file', help='output checkpoint file')
- parser.add_argument(
- '--num-classes',
- type=int,
- default=81,
- help='number of classes of the original model')
- args = parser.parse_args()
- convert(args.in_file, args.out_file, args.num_classes)
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py
deleted file mode 100644
index aec4254c8f4ae835cdfbe785bb0c375173d1e232..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './fcn_d6_r50-d16_512x1024_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
deleted file mode 100644
index a3c86e18ea65c6aaa36a4fb6e2708f08c7ae1698..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,35 +0,0 @@
-_base_ = [
- '../_base_/models/ocrnet_hr18.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
-]
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(decode_head=[
- dict(
- type='FCNHead',
- in_channels=[18, 36, 72, 144],
- channels=sum([18, 36, 72, 144]),
- in_index=(0, 1, 2, 3),
- input_transform='resize_concat',
- kernel_size=1,
- num_convs=1,
- concat_input=False,
- dropout_ratio=-1,
- num_classes=150,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
- dict(
- type='OCRHead',
- in_channels=[18, 36, 72, 144],
- in_index=(0, 1, 2, 3),
- input_transform='resize_concat',
- channels=512,
- ocr_channels=256,
- dropout_ratio=-1,
- num_classes=150,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-])
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/css/chat_style-messenger.css b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/css/chat_style-messenger.css
deleted file mode 100644
index fb3f65a458e76beddbab532539f56e2132e4a887..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/css/chat_style-messenger.css
+++ /dev/null
@@ -1,99 +0,0 @@
-.message {
- padding-bottom: 25px;
- font-size: 15px;
- font-family: 'Noto Sans', Helvetica, Arial, sans-serif;
- line-height: 1.428571429;
-}
-
-.circle-you {
- width: 50px;
- height: 50px;
- background-color: rgb(238, 78, 59);
- border-radius: 50%;
-}
-
-.circle-bot {
- width: 50px;
- height: 50px;
- background-color: rgb(59, 78, 244);
- border-radius: 50%;
- float: left;
- margin-right: 10px;
- margin-top: 5px;
-}
-
-.circle-bot img,
-.circle-you img {
- border-radius: 50%;
- width: 100%;
- height: 100%;
- object-fit: cover;
-}
-
-.circle-you {
- margin-top: 5px;
- float: right;
-}
-
-.circle-bot + .text, .circle-you + .text {
- border-radius: 18px;
- padding: 8px 12px;
-}
-
-.circle-bot + .text {
- background-color: #E4E6EB;
- float: left;
-}
-
-.circle-you + .text {
- float: right;
- background-color: rgb(0, 132, 255);
- margin-right: 10px;
-}
-
-.circle-you + .text div, .circle-you + .text *, .dark .circle-you + .text div, .dark .circle-you + .text * {
- color: #FFF !important;
-}
-
-.circle-you + .text .username {
- text-align: right;
-}
-
-.dark .circle-bot + .text div, .dark .circle-bot + .text * {
- color: #000;
-}
-
-.text {
- max-width: 80%;
-}
-
-.text p {
- margin-top: 5px;
-}
-
-.username {
- font-weight: bold;
-}
-
-.message-body {
-}
-
-.message-body img {
- max-width: 300px;
- max-height: 300px;
- border-radius: 20px;
-}
-
-.message-body p {
- margin-bottom: 0 !important;
- font-size: 15px !important;
- line-height: 1.428571429 !important;
-}
-
-.dark .message-body p em {
- color: rgb(138, 138, 138) !important;
-}
-
-.message-body p em {
- color: rgb(110, 110, 110) !important;
-}
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/scale.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/scale.py
deleted file mode 100644
index c905fffcc8bf998d18d94f927591963c428025e2..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/bricks/scale.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-
-
-class Scale(nn.Module):
- """A learnable scale parameter.
-
- This layer scales the input by a learnable factor. It multiplies a
- learnable scale parameter of shape (1,) with input of any shape.
-
- Args:
- scale (float): Initial value of scale factor. Default: 1.0
- """
-
- def __init__(self, scale=1.0):
- super(Scale, self).__init__()
- self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
-
- def forward(self, x):
- return x * self.scale
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/midas/midas/__init__.py b/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/midas/midas/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/install.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/install.py
deleted file mode 100644
index 55fdb124e8966a859b2655a8e99a9186c8755ba7..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/install.py
+++ /dev/null
@@ -1,139 +0,0 @@
-from distutils.errors import DistutilsArgError
-import inspect
-import glob
-import warnings
-import platform
-import distutils.command.install as orig
-
-import setuptools
-
-# Prior to numpy 1.9, NumPy relies on the '_install' name, so provide it for
-# now. See https://github.com/pypa/setuptools/issues/199/
-_install = orig.install
-
-
-class install(orig.install):
- """Use easy_install to install the package, w/dependencies"""
-
- user_options = orig.install.user_options + [
- ('old-and-unmanageable', None, "Try not to use this!"),
- ('single-version-externally-managed', None,
- "used by system package builders to create 'flat' eggs"),
- ]
- boolean_options = orig.install.boolean_options + [
- 'old-and-unmanageable', 'single-version-externally-managed',
- ]
- new_commands = [
- ('install_egg_info', lambda self: True),
- ('install_scripts', lambda self: True),
- ]
- _nc = dict(new_commands)
-
- def initialize_options(self):
-
- warnings.warn(
- "setup.py install is deprecated. "
- "Use build and pip and other standards-based tools.",
- setuptools.SetuptoolsDeprecationWarning,
- )
-
- orig.install.initialize_options(self)
- self.old_and_unmanageable = None
- self.single_version_externally_managed = None
-
- def finalize_options(self):
- orig.install.finalize_options(self)
- if self.root:
- self.single_version_externally_managed = True
- elif self.single_version_externally_managed:
- if not self.root and not self.record:
- raise DistutilsArgError(
- "You must specify --record or --root when building system"
- " packages"
- )
-
- def handle_extra_path(self):
- if self.root or self.single_version_externally_managed:
- # explicit backward-compatibility mode, allow extra_path to work
- return orig.install.handle_extra_path(self)
-
- # Ignore extra_path when installing an egg (or being run by another
- # command without --root or --single-version-externally-managed
- self.path_file = None
- self.extra_dirs = ''
-
- def run(self):
- # Explicit request for old-style install? Just do it
- if self.old_and_unmanageable or self.single_version_externally_managed:
- return orig.install.run(self)
-
- if not self._called_from_setup(inspect.currentframe()):
- # Run in backward-compatibility mode to support bdist_* commands.
- orig.install.run(self)
- else:
- self.do_egg_install()
-
- @staticmethod
- def _called_from_setup(run_frame):
- """
- Attempt to detect whether run() was called from setup() or by another
- command. If called by setup(), the parent caller will be the
- 'run_command' method in 'distutils.dist', and *its* caller will be
- the 'run_commands' method. If called any other way, the
- immediate caller *might* be 'run_command', but it won't have been
- called by 'run_commands'. Return True in that case or if a call stack
- is unavailable. Return False otherwise.
- """
- if run_frame is None:
- msg = "Call stack not available. bdist_* commands may fail."
- warnings.warn(msg)
- if platform.python_implementation() == 'IronPython':
- msg = "For best results, pass -X:Frames to enable call stack."
- warnings.warn(msg)
- return True
-
- frames = inspect.getouterframes(run_frame)
- for frame in frames[2:4]:
- caller, = frame[:1]
- info = inspect.getframeinfo(caller)
- caller_module = caller.f_globals.get('__name__', '')
-
- if caller_module == "setuptools.dist" and info.function == "run_command":
- # Starting from v61.0.0 setuptools overwrites dist.run_command
- continue
-
- return (
- caller_module == 'distutils.dist'
- and info.function == 'run_commands'
- )
-
- def do_egg_install(self):
-
- easy_install = self.distribution.get_command_class('easy_install')
-
- cmd = easy_install(
- self.distribution, args="x", root=self.root, record=self.record,
- )
- cmd.ensure_finalized() # finalize before bdist_egg munges install cmd
- cmd.always_copy_from = '.' # make sure local-dir eggs get installed
-
- # pick up setup-dir .egg files only: no .egg-info
- cmd.package_index.scan(glob.glob('*.egg'))
-
- self.run_command('bdist_egg')
- args = [self.distribution.get_command_obj('bdist_egg').egg_output]
-
- if setuptools.bootstrap_install_from:
- # Bootstrap self-installation of setuptools
- args.insert(0, setuptools.bootstrap_install_from)
-
- cmd.args = args
- cmd.run(show_deprecation=False)
- setuptools.bootstrap_install_from = None
-
-
-# XXX Python 3.1 doesn't see _nc if this is inside the class
-install.sub_commands = (
- [cmd for cmd in orig.install.sub_commands if cmd[0] not in install._nc] +
- install.new_commands
-)
diff --git a/spaces/Awesimo/jojogan/e4e/models/stylegan2/op/fused_bias_act.cpp b/spaces/Awesimo/jojogan/e4e/models/stylegan2/op/fused_bias_act.cpp
deleted file mode 100644
index 02be898f970bcc8ea297867fcaa4e71b24b3d949..0000000000000000000000000000000000000000
--- a/spaces/Awesimo/jojogan/e4e/models/stylegan2/op/fused_bias_act.cpp
+++ /dev/null
@@ -1,21 +0,0 @@
-#include
-
-
-torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
- int act, int grad, float alpha, float scale);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
- int act, int grad, float alpha, float scale) {
- CHECK_CUDA(input);
- CHECK_CUDA(bias);
-
- return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_inference.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_inference.py
deleted file mode 100644
index deb886c0417285ed1d5ad85eb941fa1ac757cdab..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_inference.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import logging
-import numpy as np
-from itertools import count
-import torch
-from caffe2.proto import caffe2_pb2
-from caffe2.python import core
-
-from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_batched_inputs_to_c2_format
-from .shared import ScopedWS, get_pb_arg_vali, get_pb_arg_vals, infer_device_type
-
-logger = logging.getLogger(__name__)
-
-
-# ===== ref: mobile-vision predictor's 'Caffe2Wrapper' class ======
-class ProtobufModel(torch.nn.Module):
- """
- Wrapper of a caffe2's protobuf model.
- It works just like nn.Module, but running caffe2 under the hood.
- Input/Output are tuple[tensor] that match the caffe2 net's external_input/output.
- """
-
- _ids = count(0)
-
- def __init__(self, predict_net, init_net):
- logger.info(f"Initializing ProtobufModel for: {predict_net.name} ...")
- super().__init__()
- assert isinstance(predict_net, caffe2_pb2.NetDef)
- assert isinstance(init_net, caffe2_pb2.NetDef)
- # create unique temporary workspace for each instance
- self.ws_name = "__tmp_ProtobufModel_{}__".format(next(self._ids))
- self.net = core.Net(predict_net)
-
- logger.info("Running init_net once to fill the parameters ...")
- with ScopedWS(self.ws_name, is_reset=True, is_cleanup=False) as ws:
- ws.RunNetOnce(init_net)
- uninitialized_external_input = []
- for blob in self.net.Proto().external_input:
- if blob not in ws.Blobs():
- uninitialized_external_input.append(blob)
- ws.CreateBlob(blob)
- ws.CreateNet(self.net)
-
- self._error_msgs = set()
- self._input_blobs = uninitialized_external_input
-
- def _infer_output_devices(self, inputs):
- """
- Returns:
- list[str]: list of device for each external output
- """
-
- def _get_device_type(torch_tensor):
- assert torch_tensor.device.type in ["cpu", "cuda"]
- assert torch_tensor.device.index == 0
- return torch_tensor.device.type
-
- predict_net = self.net.Proto()
- input_device_types = {
- (name, 0): _get_device_type(tensor) for name, tensor in zip(self._input_blobs, inputs)
- }
- device_type_map = infer_device_type(
- predict_net, known_status=input_device_types, device_name_style="pytorch"
- )
- ssa, versions = core.get_ssa(predict_net)
- versioned_outputs = [(name, versions[name]) for name in predict_net.external_output]
- output_devices = [device_type_map[outp] for outp in versioned_outputs]
- return output_devices
-
- def forward(self, inputs):
- """
- Args:
- inputs (tuple[torch.Tensor])
-
- Returns:
- tuple[torch.Tensor]
- """
- assert len(inputs) == len(self._input_blobs), (
- f"Length of inputs ({len(inputs)}) "
- f"doesn't match the required input blobs: {self._input_blobs}"
- )
-
- with ScopedWS(self.ws_name, is_reset=False, is_cleanup=False) as ws:
- for b, tensor in zip(self._input_blobs, inputs):
- ws.FeedBlob(b, tensor)
-
- try:
- ws.RunNet(self.net.Proto().name)
- except RuntimeError as e:
- if not str(e) in self._error_msgs:
- self._error_msgs.add(str(e))
- logger.warning("Encountered new RuntimeError: \n{}".format(str(e)))
- logger.warning("Catch the error and use partial results.")
-
- c2_outputs = [ws.FetchBlob(b) for b in self.net.Proto().external_output]
- # Remove outputs of current run, this is necessary in order to
- # prevent fetching the result from previous run if the model fails
- # in the middle.
- for b in self.net.Proto().external_output:
- # Needs to create uninitialized blob to make the net runable.
- # This is "equivalent" to: ws.RemoveBlob(b) then ws.CreateBlob(b),
- # but there'no such API.
- ws.FeedBlob(b, f"{b}, a C++ native class of type nullptr (uninitialized).")
-
- # Cast output to torch.Tensor on the desired device
- output_devices = (
- self._infer_output_devices(inputs)
- if any(t.device.type != "cpu" for t in inputs)
- else ["cpu" for _ in self.net.Proto().external_output]
- )
-
- outputs = []
- for name, c2_output, device in zip(
- self.net.Proto().external_output, c2_outputs, output_devices
- ):
- if not isinstance(c2_output, np.ndarray):
- raise RuntimeError(
- "Invalid output for blob {}, received: {}".format(name, c2_output)
- )
- outputs.append(torch.tensor(c2_output).to(device=device))
- return tuple(outputs)
-
-
-class ProtobufDetectionModel(torch.nn.Module):
- """
- A class works just like a pytorch meta arch in terms of inference, but running
- caffe2 model under the hood.
- """
-
- def __init__(self, predict_net, init_net, *, convert_outputs=None):
- """
- Args:
- predict_net, init_net (core.Net): caffe2 nets
- convert_outptus (callable): a function that converts caffe2
- outputs to the same format of the original pytorch model.
- By default, use the one defined in the caffe2 meta_arch.
- """
- super().__init__()
- self.protobuf_model = ProtobufModel(predict_net, init_net)
- self.size_divisibility = get_pb_arg_vali(predict_net, "size_divisibility", 0)
- self.device = get_pb_arg_vals(predict_net, "device", b"cpu").decode("ascii")
-
- if convert_outputs is None:
- meta_arch = get_pb_arg_vals(predict_net, "meta_architecture", b"GeneralizedRCNN")
- meta_arch = META_ARCH_CAFFE2_EXPORT_TYPE_MAP[meta_arch.decode("ascii")]
- self._convert_outputs = meta_arch.get_outputs_converter(predict_net, init_net)
- else:
- self._convert_outputs = convert_outputs
-
- def _convert_inputs(self, batched_inputs):
- # currently all models convert inputs in the same way
- return convert_batched_inputs_to_c2_format(
- batched_inputs, self.size_divisibility, self.device
- )
-
- def forward(self, batched_inputs):
- c2_inputs = self._convert_inputs(batched_inputs)
- c2_results = self.protobuf_model(c2_inputs)
- c2_results = dict(zip(self.protobuf_model.net.Proto().external_output, c2_results))
- return self._convert_outputs(batched_inputs, c2_inputs, c2_results)
diff --git a/spaces/Benson/text-generation/Examples/Calle Carx 1.74 5 Mod Apk.md b/spaces/Benson/text-generation/Examples/Calle Carx 1.74 5 Mod Apk.md
deleted file mode 100644
index 629540dd503989803e42fcf0cc6325a9f3c02d19..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Calle Carx 1.74 5 Mod Apk.md
+++ /dev/null
@@ -1,64 +0,0 @@
-
-CarX Street 1.74.5 Mod APK: Un juego de carreras gratis y divertido para Android
-Si usted es un fan de los juegos de carreras de coches, es posible que desee echar un vistazo a CarX Street, un juego de carreras gratis de CarX Technology para dispositivos Android. CarX Street es un juego de carreras realista e inmersivo que te permite personalizar tus coches, elegir tus pistas y competir con otros jugadores en línea o fuera de línea. En este artículo, le diremos qué es CarX Street, cómo descargar e instalar CarX Street 1.74.5 Mod APK, y cuáles son los beneficios de usar esta versión modificada del juego.
- ¿Qué es CarX Street?
-CarX Street es un juego de carreras que simula la cultura de las carreras callejeras, donde puedes correr con diferentes tipos de autos, desde autos clásicos hasta autos deportivos modernos, en varias pistas urbanas, desde carreteras hasta zonas industriales. También puede personalizar sus coches con diferentes partes, colores, pegatinas y calcomanías, para que se vean únicos y se adapten a su estilo. También puede actualizar sus coches con diferentes motores, transmisiones, suspensiones, frenos, neumáticos y más, para mejorar su rendimiento y manejo.
-calle carx 1.74 5 mod apk
Download Zip ✵✵✵ https://bltlly.com/2v6L8s
- Características de CarX Street
-CarX Street tiene muchas características que lo convierten en un juego de carreras divertido y emocionante para usuarios de Android. Estos son algunos de ellos:
- Física y gráficos realistas
-CarX Street utiliza el CarX Physics Engine, una tecnología patentada que simula el comportamiento realista de los automóviles en diferentes superficies y condiciones. Puede sentir la diferencia entre conducir sobre asfalto, grava, arena, nieve o hielo, así como los efectos de la gravedad, la inercia, la fricción y la aerodinámica. También puede ver los efectos de daño realistas en sus coches, como arañazos, abolladuras, ventanas rotas o humo.
-
- Coches y pistas personalizables
-CarX Street le permite personalizar sus coches con más de 1000 piezas y accesorios, como parachoques, spoilers, campanas, ruedas, escapes, luces, espejos y más. También puede cambiar el color de sus coches con más de 100 opciones de pintura, o añadir pegatinas y calcomanías para hacerlos más personales. También puede crear sus propias pistas personalizadas con la función Editor de pistas, donde puede elegir la ubicación, longitud, ancho, curvatura, elevación, tipo de superficie y obstáculos de su pista.
- Modos online y offline
-CarX Street te permite jugar online o offline dependiendo de tu preferencia. Puedes jugar online con otros jugadores de todo el mundo en diferentes modos, como Carrera rápida, Time Attack, Drift Race o Torneo. También puedes chatear con otros jugadores, unirte a clubes o crear tu propio club. Puedes jugar sin conexión con oponentes de IA en diferentes modos, como Carrera, Free Ride o Test Drive. También puede jugar sin conexión con sus amigos en el mismo dispositivo con el modo de pantalla dividida.
- ¿Cómo descargar e instalar CarX Street 1.74.5 Mod APK?
-Si desea descargar e instalar CarX Street 1.74.5 Mod APK, debe seguir estos pasos:
- Requisitos y permisos
-Antes de descargar e instalar CarX Street 1.74.5 Mod APK, debe asegurarse de que su dispositivo cumple con los siguientes requisitos y permisos:
-
-- Tu dispositivo debe tener Android 6.0 o superior.
-- Su dispositivo debe tener al menos 2 GB de RAM y 1 GB de espacio de almacenamiento libre.
-- Es necesario habilitar la instalación de aplicaciones de fuentes desconocidas en la configuración del dispositivo.
-- Necesitas permitir que la aplicación acceda al almacenamiento, ubicación, cámara, micrófono y red de tu dispositivo.
-
- Pasos para descargar e instalar
-Después de haber comprobado los requisitos y permisos, puede seguir estos pasos para descargar e instalar CarX Street 1.74.5 Mod APK:
-
-
-- Localice el archivo descargado en el administrador de archivos de su dispositivo y toque en él para iniciar el proceso de instalación.
-- Siga las instrucciones en la pantalla y espere a que se complete la instalación.
-- Iniciar la aplicación y disfrutar del juego.
-
- ¿Cuáles son los beneficios de CarX Street 1.74.5 Mod APK?
-CarX Street 1.74.5 Mod APK es una versión modificada del juego original que ofrece algunos beneficios adicionales para los jugadores. Estos son algunos de ellos:
-
- Dinero y oro ilimitados
-Con CarX Street 1.74.5 Mod APK, puede obtener dinero y oro ilimitados en el juego, que se puede utilizar para comprar y actualizar sus coches, piezas y pistas. También puede desbloquear todos los coches y pistas en el juego sin gastar dinero real.
- Coches y pistas desbloqueados
-Con CarX Street 1.74.5 Mod APK, se puede acceder a todos los coches y pistas en el juego sin tener que completar ninguna misión o logros. Puedes elegir entre más de 50 coches y más de 20 pistas en el juego, cada una con sus propias características y desafíos.
- No se necesitan anuncios ni root
-Con CarX Street 1.74.5 Mod APK, puede disfrutar del juego sin ningún molesto anuncios o ventanas emergentes que podrían interrumpir su juego o consumir sus datos. Tampoco necesitas rootear tu dispositivo para usar esta versión modificada del juego, lo que podría comprometer la seguridad o garantía de tu dispositivo.
- Conclusión
-CarX Street es un juego de carreras gratuito y divertido para dispositivos Android que ofrece física y gráficos realistas, coches y pistas personalizables, modos en línea y fuera de línea, y más. Si desea mejorar su experiencia de juego con dinero ilimitado y oro, coches desbloqueados y pistas, sin anuncios y sin raíz necesaria, puede descargar e instalar CarX Street 1.74.5 Mod APK siguiendo los pasos que hemos proporcionado en este artículo.
- Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre CarX Street 1.74.5 Mod APK:
-
-
-Sí, CarX Street 1.74.5 Mod APK es seguro de usar siempre y cuando lo descargue de una fuente de confianza, como APKPure o APKDone. Sin embargo, siempre debe tener cuidado al instalar aplicaciones de fuentes desconocidas, ya que podrían contener malware o virus que podrían dañar su dispositivo o datos.
- - ¿Se me prohibirá el uso de CarX Street 1.74.5 Mod APK?
-No, no se le prohibió el uso de CarX Street 1.74.5 Mod APK, ya que esta versión modificada del juego no interfiere con los servidores del juego o características en línea. Sin embargo, siempre debes respetar las reglas y políticas del juego, y evitar usar trucos o hacks que puedan darte una ventaja injusta sobre otros jugadores.
- - ¿Puedo actualizar CarX Street 1.74.5 Mod APK?
-Sí, puede actualizar CarX Street 1.74.5 Mod APK cada vez que hay una nueva versión disponible desde la misma fuente que lo descargó desde. Sin embargo, siempre debes hacer una copia de seguridad de los datos del juego antes de actualizarlo, ya que algunas actualizaciones pueden sobrescribir o eliminar las características modificadas o el progreso.
- - ¿Puedo jugar CarX Street 1.74.5 Mod APK en el PC?
-Sí, puede jugar CarX Street 1.74.5 Mod APK en el PC mediante el uso de un emulador de Android, como BlueStacks o NoxPlayer. Un emulador de Android es un software que le permite ejecutar aplicaciones y juegos de Android en su PC. Solo tiene que descargar e instalar el emulador en su PC, y luego descargar e instalar CarX Street 1.74.5 Mod APK en el emulador.
- - ¿Cuáles son algunas alternativas a CarX Street 1.74.5 Mod APK?
-Si usted está buscando algunas alternativas a CarX Street 1.74.5 Mod APK, es posible que desee probar estos otros juegos de carreras para Android:
-
-- Asphalt 9: Legends: Un juego de carreras de ritmo rápido y lleno de acción que cuenta con más de 60 coches y más de 80 pistas de todo el mundo.
-
-- Need for Speed: No Limits: Un emocionante juego de carreras de adrenalina que cuenta con más de 100 coches y más de 1000 carreras en diferentes modos y eventos.
- 64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Bravefe/Artist_Classification/README.md b/spaces/Bravefe/Artist_Classification/README.md
deleted file mode 100644
index 8be6b0f5541d19188ab94e23b5ba235f78b80cb9..0000000000000000000000000000000000000000
--- a/spaces/Bravefe/Artist_Classification/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Artist Classification
-emoji: 🎨
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/CVPR/LIVE/sample_boundary.h b/spaces/CVPR/LIVE/sample_boundary.h
deleted file mode 100644
index 28af12959f578c9f72872c85b59b957729c5ba68..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/sample_boundary.h
+++ /dev/null
@@ -1,454 +0,0 @@
-#pragma once
-
-#include "diffvg.h"
-#include "shape.h"
-#include "scene.h"
-#include "vector.h"
-#include "cdf.h"
-
-struct PathBoundaryData {
- int base_point_id;
- int point_id;
- float t;
-};
-
-struct BoundaryData {
- PathBoundaryData path;
- bool is_stroke;
-};
-
-DEVICE
-Vector2f sample_boundary(const Circle &circle,
- float t,
- Vector2f &normal,
- float &pdf,
- BoundaryData &,
- float stroke_perturb_direction,
- float stroke_radius) {
- // Parametric form of a circle (t in [0, 1)):
- // x = center.x + r * cos(2pi * t)
- // y = center.y + r * sin(2pi * t)
- auto offset = Vector2f{
- circle.radius * cos(2 * float(M_PI) * t),
- circle.radius * sin(2 * float(M_PI) * t)
- };
- normal = normalize(offset);
- pdf /= (2 * float(M_PI) * circle.radius);
- auto ret = circle.center + offset;
- if (stroke_perturb_direction != 0.f) {
- ret += stroke_perturb_direction * stroke_radius * normal;
- if (stroke_perturb_direction < 0) {
- // normal should point towards the perturb direction
- normal = -normal;
- }
- }
- return ret;
-}
-
-DEVICE
-Vector2f sample_boundary(const Ellipse &ellipse,
- float t,
- Vector2f &normal,
- float &pdf,
- BoundaryData &,
- float stroke_perturb_direction,
- float stroke_radius) {
- // Parametric form of a ellipse (t in [0, 1)):
- // x = center.x + r.x * cos(2pi * t)
- // y = center.y + r.y * sin(2pi * t)
- const auto &r = ellipse.radius;
- auto offset = Vector2f{
- r.x * cos(2 * float(M_PI) * t),
- r.y * sin(2 * float(M_PI) * t)
- };
- auto dxdt = -r.x * sin(2 * float(M_PI) * t) * 2 * float(M_PI);
- auto dydt = r.y * cos(2 * float(M_PI) * t) * 2 * float(M_PI);
- // tangent is normalize(dxdt, dydt)
- normal = normalize(Vector2f{dydt, -dxdt});
- pdf /= sqrt(square(dxdt) + square(dydt));
- auto ret = ellipse.center + offset;
- if (stroke_perturb_direction != 0.f) {
- ret += stroke_perturb_direction * stroke_radius * normal;
- if (stroke_perturb_direction < 0) {
- // normal should point towards the perturb direction
- normal = -normal;
- }
- }
- return ret;
-}
-
-DEVICE
-Vector2f sample_boundary(const Path &path,
- const float *path_length_cdf,
- const float *path_length_pmf,
- const int *point_id_map,
- float path_length,
- float t,
- Vector2f &normal,
- float &pdf,
- BoundaryData &data,
- float stroke_perturb_direction,
- float stroke_radius) {
- if (stroke_perturb_direction != 0.f && !path.is_closed) {
- // We need to samples the "caps" of the path
- // length of a cap is pi * abs(stroke_perturb_direction)
- // there are two caps
- auto cap_length = 0.f;
- if (path.thickness != nullptr) {
- auto r0 = path.thickness[0];
- auto r1 = path.thickness[path.num_points - 1];
- cap_length = float(M_PI) * (r0 + r1);
- } else {
- cap_length = 2 * float(M_PI) * stroke_radius;
- }
- auto cap_prob = cap_length / (cap_length + path_length);
- if (t < cap_prob) {
- t = t / cap_prob;
- pdf *= cap_prob;
- auto r0 = stroke_radius;
- auto r1 = stroke_radius;
- if (path.thickness != nullptr) {
- r0 = path.thickness[0];
- r1 = path.thickness[path.num_points - 1];
- }
- // HACK: in theory we want to compute the tangent and
- // sample the hemi-circle, but here we just sample the
- // full circle since it's less typing
- if (stroke_perturb_direction < 0) {
- // Sample the cap at the beginning
- auto p0 = Vector2f{path.points[0], path.points[1]};
- auto offset = Vector2f{
- r0 * cos(2 * float(M_PI) * t),
- r0 * sin(2 * float(M_PI) * t)
- };
- normal = normalize(offset);
- pdf /= (2 * float(M_PI) * r0);
- data.path.base_point_id = 0;
- data.path.point_id = 0;
- data.path.t = 0;
- return p0 + offset;
- } else {
- // Sample the cap at the end
- auto p0 = Vector2f{path.points[2 * (path.num_points - 1)],
- path.points[2 * (path.num_points - 1) + 1]};
- auto offset = Vector2f{
- r1 * cos(2 * float(M_PI) * t),
- r1 * sin(2 * float(M_PI) * t)
- };
- normal = normalize(offset);
- pdf /= (2 * float(M_PI) * r1);
- data.path.base_point_id = path.num_base_points - 1;
- data.path.point_id = path.num_points - 2 -
- path.num_control_points[data.path.base_point_id];
- data.path.t = 1;
- return p0 + offset;
- }
- } else {
- t = (t - cap_prob) / (1 - cap_prob);
- pdf *= (1 - cap_prob);
- }
- }
- // Binary search on path_length_cdf
- auto sample_id = sample(path_length_cdf,
- path.num_base_points,
- t,
- &t);
- assert(sample_id >= 0 && sample_id < path.num_base_points);
- auto point_id = point_id_map[sample_id];
- if (path.num_control_points[sample_id] == 0) {
- // Straight line
- auto i0 = point_id;
- auto i1 = (i0 + 1) % path.num_points;
- assert(i0 < path.num_points);
- auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]};
- auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]};
- data.path.base_point_id = sample_id;
- data.path.point_id = point_id;
- data.path.t = t;
- if (t < -1e-3f || t > 1+1e-3f) {
- // return invalid sample
- pdf = 0;
- return Vector2f{0, 0};
- }
- auto tangent = (p1 - p0);
- auto tan_len = length(tangent);
- if (tan_len == 0) {
- // return invalid sample
- pdf = 0;
- return Vector2f{0, 0};
- }
- normal = Vector2f{-tangent.y, tangent.x} / tan_len;
- // length of tangent is the Jacobian of the sampling transformation
- pdf *= path_length_pmf[sample_id] / tan_len;
- auto ret = p0 + t * (p1 - p0);
- if (stroke_perturb_direction != 0.f) {
- auto r0 = stroke_radius;
- auto r1 = stroke_radius;
- if (path.thickness != nullptr) {
- r0 = path.thickness[i0];
- r1 = path.thickness[i1];
- }
- auto r = r0 + t * (r1 - r0);
- ret += stroke_perturb_direction * r * normal;
- if (stroke_perturb_direction < 0) {
- // normal should point towards the perturb direction
- normal = -normal;
- }
- }
- return ret;
- } else if (path.num_control_points[sample_id] == 1) {
- // Quadratic Bezier curve
- auto i0 = point_id;
- auto i1 = i0 + 1;
- auto i2 = (i0 + 2) % path.num_points;
- auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]};
- auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]};
- auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]};
- auto eval = [&](float t) -> Vector2f {
- auto tt = 1 - t;
- return (tt*tt)*p0 + (2*tt*t)*p1 + (t*t)*p2;
- };
- data.path.base_point_id = sample_id;
- data.path.point_id = point_id;
- data.path.t = t;
- if (t < -1e-3f || t > 1+1e-3f) {
- // return invalid sample
- pdf = 0;
- return Vector2f{0, 0};
- }
- auto tangent = 2 * (1 - t) * (p1 - p0) + 2 * t * (p2 - p1);
- auto tan_len = length(tangent);
- if (tan_len == 0) {
- // return invalid sample
- pdf = 0;
- return Vector2f{0, 0};
- }
- normal = Vector2f{-tangent.y, tangent.x} / tan_len;
- // length of tangent is the Jacobian of the sampling transformation
- pdf *= path_length_pmf[sample_id] / tan_len;
- auto ret = eval(t);
- if (stroke_perturb_direction != 0.f) {
- auto r0 = stroke_radius;
- auto r1 = stroke_radius;
- auto r2 = stroke_radius;
- if (path.thickness != nullptr) {
- r0 = path.thickness[i0];
- r1 = path.thickness[i1];
- r2 = path.thickness[i2];
- }
- auto tt = 1 - t;
- auto r = (tt*tt)*r0 + (2*tt*t)*r1 + (t*t)*r2;
- ret += stroke_perturb_direction * r * normal;
- if (stroke_perturb_direction < 0) {
- // normal should point towards the perturb direction
- normal = -normal;
- }
- }
- return ret;
- } else if (path.num_control_points[sample_id] == 2) {
- // Cubic Bezier curve
- auto i0 = point_id;
- auto i1 = point_id + 1;
- auto i2 = point_id + 2;
- auto i3 = (point_id + 3) % path.num_points;
- assert(i0 >= 0 && i2 < path.num_points);
- auto p0 = Vector2f{path.points[2 * i0], path.points[2 * i0 + 1]};
- auto p1 = Vector2f{path.points[2 * i1], path.points[2 * i1 + 1]};
- auto p2 = Vector2f{path.points[2 * i2], path.points[2 * i2 + 1]};
- auto p3 = Vector2f{path.points[2 * i3], path.points[2 * i3 + 1]};
- auto eval = [&](float t) -> Vector2f {
- auto tt = 1 - t;
- return (tt*tt*tt)*p0 + (3*tt*tt*t)*p1 + (3*tt*t*t)*p2 + (t*t*t)*p3;
- };
- data.path.base_point_id = sample_id;
- data.path.point_id = point_id;
- data.path.t = t;
- if (t < -1e-3f || t > 1+1e-3f) {
- // return invalid sample
- pdf = 0;
- return Vector2f{0, 0};
- }
- auto tangent = 3 * square(1 - t) * (p1 - p0) + 6 * (1 - t) * t * (p2 - p1) + 3 * t * t * (p3 - p2);
- auto tan_len = length(tangent);
- if (tan_len == 0) {
- // return invalid sample
- pdf = 0;
- return Vector2f{0, 0};
- }
- normal = Vector2f{-tangent.y, tangent.x} / tan_len;
- // length of tangent is the Jacobian of the sampling transformation
- pdf *= path_length_pmf[sample_id] / tan_len;
- auto ret = eval(t);
- if (stroke_perturb_direction != 0.f) {
- auto r0 = stroke_radius;
- auto r1 = stroke_radius;
- auto r2 = stroke_radius;
- auto r3 = stroke_radius;
- if (path.thickness != nullptr) {
- r0 = path.thickness[i0];
- r1 = path.thickness[i1];
- r2 = path.thickness[i2];
- r3 = path.thickness[i3];
- }
- auto tt = 1 - t;
- auto r = (tt*tt*tt)*r0 + (3*tt*tt*t)*r1 + (3*tt*t*t)*r2 + (t*t*t)*r3;
- ret += stroke_perturb_direction * r * normal;
- if (stroke_perturb_direction < 0) {
- // normal should point towards the perturb direction
- normal = -normal;
- }
- }
- return ret;
- } else {
- assert(false);
- }
- assert(false);
- return Vector2f{0, 0};
-}
-
-DEVICE
-Vector2f sample_boundary(const Rect &rect,
- float t, Vector2f &normal,
- float &pdf,
- BoundaryData &,
- float stroke_perturb_direction,
- float stroke_radius) {
- // Roll a dice to decide whether to sample width or height
- auto w = rect.p_max.x - rect.p_min.x;
- auto h = rect.p_max.y - rect.p_min.y;
- pdf /= (2 * (w +h));
- if (t <= w / (w + h)) {
- // Sample width
- // reuse t for the next dice
- t *= (w + h) / w;
- // Roll a dice to decide whether to sample upper width or lower width
- if (t < 0.5f) {
- // Sample upper width
- normal = Vector2f{0, -1};
- auto ret = rect.p_min + 2 * t * Vector2f{rect.p_max.x - rect.p_min.x, 0.f};
- if (stroke_perturb_direction != 0.f) {
- ret += stroke_perturb_direction * stroke_radius * normal;
- if (stroke_perturb_direction < 0) {
- // normal should point towards the perturb direction
- normal = -normal;
- }
- }
- return ret;
- } else {
- // Sample lower width
- normal = Vector2f{0, 1};
- auto ret = Vector2f{rect.p_min.x, rect.p_max.y} +
- 2 * (t - 0.5f) * Vector2f{rect.p_max.x - rect.p_min.x, 0.f};
- if (stroke_perturb_direction != 0.f) {
- ret += stroke_perturb_direction * stroke_radius * normal;
- if (stroke_perturb_direction < 0) {
- // normal should point towards the perturb direction
- normal = -normal;
- }
- }
- return ret;
- }
- } else {
- // Sample height
- // reuse t for the next dice
- assert(h > 0);
- t = (t - w / (w + h)) * (w + h) / h;
- // Roll a dice to decide whether to sample left height or right height
- if (t < 0.5f) {
- // Sample left height
- normal = Vector2f{-1, 0};
- auto ret = rect.p_min + 2 * t * Vector2f{0.f, rect.p_max.y - rect.p_min.y};
- if (stroke_perturb_direction != 0.f) {
- ret += stroke_perturb_direction * stroke_radius * normal;
- if (stroke_perturb_direction < 0) {
- // normal should point towards the perturb direction
- normal = -normal;
- }
- }
- return ret;
- } else {
- // Sample right height
- normal = Vector2f{1, 0};
- auto ret = Vector2f{rect.p_max.x, rect.p_min.y} +
- 2 * (t - 0.5f) * Vector2f{0.f, rect.p_max.y - rect.p_min.y};
- if (stroke_perturb_direction != 0.f) {
- ret += stroke_perturb_direction * stroke_radius * normal;
- if (stroke_perturb_direction < 0) {
- // normal should point towards the perturb direction
- normal = -normal;
- }
- }
- return ret;
- }
- }
-}
-
-DEVICE
-Vector2f sample_boundary(const SceneData &scene,
- int shape_group_id,
- int shape_id,
- float t,
- Vector2f &normal,
- float &pdf,
- BoundaryData &data) {
- const ShapeGroup &shape_group = scene.shape_groups[shape_group_id];
- const Shape &shape = scene.shapes[shape_id];
- pdf = 1;
- // Choose which one to sample: stroke discontinuities or fill discontinuities.
- // TODO: we don't need to sample fill discontinuities when stroke alpha is 1 and both
- // fill and stroke color exists
- auto stroke_perturb = false;
- if (shape_group.fill_color != nullptr && shape_group.stroke_color != nullptr) {
- if (t < 0.5f) {
- stroke_perturb = false;
- t = 2 * t;
- pdf = 0.5f;
- } else {
- stroke_perturb = true;
- t = 2 * (t - 0.5f);
- pdf = 0.5f;
- }
- } else if (shape_group.stroke_color != nullptr) {
- stroke_perturb = true;
- }
- data.is_stroke = stroke_perturb;
- auto stroke_perturb_direction = 0.f;
- if (stroke_perturb) {
- if (t < 0.5f) {
- stroke_perturb_direction = -1.f;
- t = 2 * t;
- pdf *= 0.5f;
- } else {
- stroke_perturb_direction = 1.f;
- t = 2 * (t - 0.5f);
- pdf *= 0.5f;
- }
- }
- switch (shape.type) {
- case ShapeType::Circle:
- return sample_boundary(
- *(const Circle *)shape.ptr, t, normal, pdf, data, stroke_perturb_direction, shape.stroke_width);
- case ShapeType::Ellipse:
- return sample_boundary(
- *(const Ellipse *)shape.ptr, t, normal, pdf, data, stroke_perturb_direction, shape.stroke_width);
- case ShapeType::Path:
- return sample_boundary(
- *(const Path *)shape.ptr,
- scene.path_length_cdf[shape_id],
- scene.path_length_pmf[shape_id],
- scene.path_point_id_map[shape_id],
- scene.shapes_length[shape_id],
- t,
- normal,
- pdf,
- data,
- stroke_perturb_direction,
- shape.stroke_width);
- case ShapeType::Rect:
- return sample_boundary(
- *(const Rect *)shape.ptr, t, normal, pdf, data, stroke_perturb_direction, shape.stroke_width);
- }
- assert(false);
- return Vector2f{};
-}
-
diff --git a/spaces/CVPR/WALT/mmdet/__init__.py b/spaces/CVPR/WALT/mmdet/__init__.py
deleted file mode 100644
index ce2930f62a0091e06b37575b96db2ae51ca7908e..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import mmcv
-
-from .version import __version__, short_version
-
-
-def digit_version(version_str):
- digit_version = []
- for x in version_str.split('.'):
- if x.isdigit():
- digit_version.append(int(x))
- elif x.find('rc') != -1:
- patch_version = x.split('rc')
- digit_version.append(int(patch_version[0]) - 1)
- digit_version.append(int(patch_version[1]))
- return digit_version
-
-
-mmcv_minimum_version = '1.2.4'
-mmcv_maximum_version = '1.4.0'
-mmcv_version = digit_version(mmcv.__version__)
-
-
-assert (mmcv_version >= digit_version(mmcv_minimum_version)
- and mmcv_version <= digit_version(mmcv_maximum_version)), \
- f'MMCV=={mmcv.__version__} is used but incompatible. ' \
- f'Please install mmcv>={mmcv_minimum_version}, <={mmcv_maximum_version}.'
-
-__all__ = ['__version__', 'short_version']
diff --git a/spaces/CVPR/lama-example/saicinpainting/training/modules/ffc.py b/spaces/CVPR/lama-example/saicinpainting/training/modules/ffc.py
deleted file mode 100644
index 0e7b84683fccb4bccac97b6371994fa6bb44dbe4..0000000000000000000000000000000000000000
--- a/spaces/CVPR/lama-example/saicinpainting/training/modules/ffc.py
+++ /dev/null
@@ -1,485 +0,0 @@
-# Fast Fourier Convolution NeurIPS 2020
-# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py
-# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from saicinpainting.training.modules.base import get_activation, BaseDiscriminator
-from saicinpainting.training.modules.spatial_transform import LearnableSpatialTransformWrapper
-from saicinpainting.training.modules.squeeze_excitation import SELayer
-from saicinpainting.utils import get_shape
-
-
-class FFCSE_block(nn.Module):
-
- def __init__(self, channels, ratio_g):
- super(FFCSE_block, self).__init__()
- in_cg = int(channels * ratio_g)
- in_cl = channels - in_cg
- r = 16
-
- self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
- self.conv1 = nn.Conv2d(channels, channels // r,
- kernel_size=1, bias=True)
- self.relu1 = nn.ReLU(inplace=True)
- self.conv_a2l = None if in_cl == 0 else nn.Conv2d(
- channels // r, in_cl, kernel_size=1, bias=True)
- self.conv_a2g = None if in_cg == 0 else nn.Conv2d(
- channels // r, in_cg, kernel_size=1, bias=True)
- self.sigmoid = nn.Sigmoid()
-
- def forward(self, x):
- x = x if type(x) is tuple else (x, 0)
- id_l, id_g = x
-
- x = id_l if type(id_g) is int else torch.cat([id_l, id_g], dim=1)
- x = self.avgpool(x)
- x = self.relu1(self.conv1(x))
-
- x_l = 0 if self.conv_a2l is None else id_l * \
- self.sigmoid(self.conv_a2l(x))
- x_g = 0 if self.conv_a2g is None else id_g * \
- self.sigmoid(self.conv_a2g(x))
- return x_l, x_g
-
-
-class FourierUnit(nn.Module):
-
- def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear',
- spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm='ortho'):
- # bn_layer not used
- super(FourierUnit, self).__init__()
- self.groups = groups
-
- self.conv_layer = torch.nn.Conv2d(in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0),
- out_channels=out_channels * 2,
- kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False)
- self.bn = torch.nn.BatchNorm2d(out_channels * 2)
- self.relu = torch.nn.ReLU(inplace=True)
-
- # squeeze and excitation block
- self.use_se = use_se
- if use_se:
- if se_kwargs is None:
- se_kwargs = {}
- self.se = SELayer(self.conv_layer.in_channels, **se_kwargs)
-
- self.spatial_scale_factor = spatial_scale_factor
- self.spatial_scale_mode = spatial_scale_mode
- self.spectral_pos_encoding = spectral_pos_encoding
- self.ffc3d = ffc3d
- self.fft_norm = fft_norm
-
- def forward(self, x):
- batch = x.shape[0]
-
- if self.spatial_scale_factor is not None:
- orig_size = x.shape[-2:]
- x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, align_corners=False)
-
- r_size = x.size()
- # (batch, c, h, w/2+1, 2)
- fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)
- ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)
- ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
- ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1)
- ffted = ffted.view((batch, -1,) + ffted.size()[3:])
-
- if self.spectral_pos_encoding:
- height, width = ffted.shape[-2:]
- coords_vert = torch.linspace(0, 1, height)[None, None, :, None].expand(batch, 1, height, width).to(ffted)
- coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(ffted)
- ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1)
-
- if self.use_se:
- ffted = self.se(ffted)
-
- ffted = self.conv_layer(ffted) # (batch, c*2, h, w/2+1)
- ffted = self.relu(self.bn(ffted))
-
- ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(
- 0, 1, 3, 4, 2).contiguous() # (batch,c, t, h, w/2+1, 2)
- ffted = torch.complex(ffted[..., 0], ffted[..., 1])
-
- ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]
- output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm)
-
- if self.spatial_scale_factor is not None:
- output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False)
-
- return output
-
-
-class SeparableFourierUnit(nn.Module):
-
- def __init__(self, in_channels, out_channels, groups=1, kernel_size=3):
- # bn_layer not used
- super(SeparableFourierUnit, self).__init__()
- self.groups = groups
- row_out_channels = out_channels // 2
- col_out_channels = out_channels - row_out_channels
- self.row_conv = torch.nn.Conv2d(in_channels=in_channels * 2,
- out_channels=row_out_channels * 2,
- kernel_size=(kernel_size, 1), # kernel size is always like this, but the data will be transposed
- stride=1, padding=(kernel_size // 2, 0),
- padding_mode='reflect',
- groups=self.groups, bias=False)
- self.col_conv = torch.nn.Conv2d(in_channels=in_channels * 2,
- out_channels=col_out_channels * 2,
- kernel_size=(kernel_size, 1), # kernel size is always like this, but the data will be transposed
- stride=1, padding=(kernel_size // 2, 0),
- padding_mode='reflect',
- groups=self.groups, bias=False)
- self.row_bn = torch.nn.BatchNorm2d(row_out_channels * 2)
- self.col_bn = torch.nn.BatchNorm2d(col_out_channels * 2)
- self.relu = torch.nn.ReLU(inplace=True)
-
- def process_branch(self, x, conv, bn):
- batch = x.shape[0]
-
- r_size = x.size()
- # (batch, c, h, w/2+1, 2)
- ffted = torch.fft.rfft(x, norm="ortho")
- ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
- ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1)
- ffted = ffted.view((batch, -1,) + ffted.size()[3:])
-
- ffted = self.relu(bn(conv(ffted)))
-
- ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(
- 0, 1, 3, 4, 2).contiguous() # (batch,c, t, h, w/2+1, 2)
- ffted = torch.complex(ffted[..., 0], ffted[..., 1])
-
- output = torch.fft.irfft(ffted, s=x.shape[-1:], norm="ortho")
- return output
-
-
- def forward(self, x):
- rowwise = self.process_branch(x, self.row_conv, self.row_bn)
- colwise = self.process_branch(x.permute(0, 1, 3, 2), self.col_conv, self.col_bn).permute(0, 1, 3, 2)
- out = torch.cat((rowwise, colwise), dim=1)
- return out
-
-
-class SpectralTransform(nn.Module):
-
- def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, separable_fu=False, **fu_kwargs):
- # bn_layer not used
- super(SpectralTransform, self).__init__()
- self.enable_lfu = enable_lfu
- if stride == 2:
- self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
- else:
- self.downsample = nn.Identity()
-
- self.stride = stride
- self.conv1 = nn.Sequential(
- nn.Conv2d(in_channels, out_channels //
- 2, kernel_size=1, groups=groups, bias=False),
- nn.BatchNorm2d(out_channels // 2),
- nn.ReLU(inplace=True)
- )
- fu_class = SeparableFourierUnit if separable_fu else FourierUnit
- self.fu = fu_class(
- out_channels // 2, out_channels // 2, groups, **fu_kwargs)
- if self.enable_lfu:
- self.lfu = fu_class(
- out_channels // 2, out_channels // 2, groups)
- self.conv2 = torch.nn.Conv2d(
- out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False)
-
- def forward(self, x):
-
- x = self.downsample(x)
- x = self.conv1(x)
- output = self.fu(x)
-
- if self.enable_lfu:
- n, c, h, w = x.shape
- split_no = 2
- split_s = h // split_no
- xs = torch.cat(torch.split(
- x[:, :c // 4], split_s, dim=-2), dim=1).contiguous()
- xs = torch.cat(torch.split(xs, split_s, dim=-1),
- dim=1).contiguous()
- xs = self.lfu(xs)
- xs = xs.repeat(1, 1, split_no, split_no).contiguous()
- else:
- xs = 0
-
- output = self.conv2(x + output + xs)
-
- return output
-
-
-class FFC(nn.Module):
-
- def __init__(self, in_channels, out_channels, kernel_size,
- ratio_gin, ratio_gout, stride=1, padding=0,
- dilation=1, groups=1, bias=False, enable_lfu=True,
- padding_type='reflect', gated=False, **spectral_kwargs):
- super(FFC, self).__init__()
-
- assert stride == 1 or stride == 2, "Stride should be 1 or 2."
- self.stride = stride
-
- in_cg = int(in_channels * ratio_gin)
- in_cl = in_channels - in_cg
- out_cg = int(out_channels * ratio_gout)
- out_cl = out_channels - out_cg
- #groups_g = 1 if groups == 1 else int(groups * ratio_gout)
- #groups_l = 1 if groups == 1 else groups - groups_g
-
- self.ratio_gin = ratio_gin
- self.ratio_gout = ratio_gout
- self.global_in_num = in_cg
-
- module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d
- self.convl2l = module(in_cl, out_cl, kernel_size,
- stride, padding, dilation, groups, bias, padding_mode=padding_type)
- module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d
- self.convl2g = module(in_cl, out_cg, kernel_size,
- stride, padding, dilation, groups, bias, padding_mode=padding_type)
- module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d
- self.convg2l = module(in_cg, out_cl, kernel_size,
- stride, padding, dilation, groups, bias, padding_mode=padding_type)
- module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform
- self.convg2g = module(
- in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs)
-
- self.gated = gated
- module = nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d
- self.gate = module(in_channels, 2, 1)
-
- def forward(self, x):
- x_l, x_g = x if type(x) is tuple else (x, 0)
- out_xl, out_xg = 0, 0
-
- if self.gated:
- total_input_parts = [x_l]
- if torch.is_tensor(x_g):
- total_input_parts.append(x_g)
- total_input = torch.cat(total_input_parts, dim=1)
-
- gates = torch.sigmoid(self.gate(total_input))
- g2l_gate, l2g_gate = gates.chunk(2, dim=1)
- else:
- g2l_gate, l2g_gate = 1, 1
-
- if self.ratio_gout != 1:
- out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate
- if self.ratio_gout != 0:
- out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g)
-
- return out_xl, out_xg
-
-
-class FFC_BN_ACT(nn.Module):
-
- def __init__(self, in_channels, out_channels,
- kernel_size, ratio_gin, ratio_gout,
- stride=1, padding=0, dilation=1, groups=1, bias=False,
- norm_layer=nn.BatchNorm2d, activation_layer=nn.Identity,
- padding_type='reflect',
- enable_lfu=True, **kwargs):
- super(FFC_BN_ACT, self).__init__()
- self.ffc = FFC(in_channels, out_channels, kernel_size,
- ratio_gin, ratio_gout, stride, padding, dilation,
- groups, bias, enable_lfu, padding_type=padding_type, **kwargs)
- lnorm = nn.Identity if ratio_gout == 1 else norm_layer
- gnorm = nn.Identity if ratio_gout == 0 else norm_layer
- global_channels = int(out_channels * ratio_gout)
- self.bn_l = lnorm(out_channels - global_channels)
- self.bn_g = gnorm(global_channels)
-
- lact = nn.Identity if ratio_gout == 1 else activation_layer
- gact = nn.Identity if ratio_gout == 0 else activation_layer
- self.act_l = lact(inplace=True)
- self.act_g = gact(inplace=True)
-
- def forward(self, x):
- x_l, x_g = self.ffc(x)
- x_l = self.act_l(self.bn_l(x_l))
- x_g = self.act_g(self.bn_g(x_g))
- return x_l, x_g
-
-
-class FFCResnetBlock(nn.Module):
- def __init__(self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1,
- spatial_transform_kwargs=None, inline=False, **conv_kwargs):
- super().__init__()
- self.conv1 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,
- norm_layer=norm_layer,
- activation_layer=activation_layer,
- padding_type=padding_type,
- **conv_kwargs)
- self.conv2 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,
- norm_layer=norm_layer,
- activation_layer=activation_layer,
- padding_type=padding_type,
- **conv_kwargs)
- if spatial_transform_kwargs is not None:
- self.conv1 = LearnableSpatialTransformWrapper(self.conv1, **spatial_transform_kwargs)
- self.conv2 = LearnableSpatialTransformWrapper(self.conv2, **spatial_transform_kwargs)
- self.inline = inline
-
- def forward(self, x):
- if self.inline:
- x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:]
- else:
- x_l, x_g = x if type(x) is tuple else (x, 0)
-
- id_l, id_g = x_l, x_g
-
- x_l, x_g = self.conv1((x_l, x_g))
- x_l, x_g = self.conv2((x_l, x_g))
-
- x_l, x_g = id_l + x_l, id_g + x_g
- out = x_l, x_g
- if self.inline:
- out = torch.cat(out, dim=1)
- return out
-
-
-class ConcatTupleLayer(nn.Module):
- def forward(self, x):
- assert isinstance(x, tuple)
- x_l, x_g = x
- assert torch.is_tensor(x_l) or torch.is_tensor(x_g)
- if not torch.is_tensor(x_g):
- return x_l
- return torch.cat(x, dim=1)
-
-
-class FFCResNetGenerator(nn.Module):
- def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
- padding_type='reflect', activation_layer=nn.ReLU,
- up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True),
- init_conv_kwargs={}, downsample_conv_kwargs={}, resnet_conv_kwargs={},
- spatial_transform_layers=None, spatial_transform_kwargs={},
- add_out_act=True, max_features=1024, out_ffc=False, out_ffc_kwargs={}):
- assert (n_blocks >= 0)
- super().__init__()
-
- model = [nn.ReflectionPad2d(3),
- FFC_BN_ACT(input_nc, ngf, kernel_size=7, padding=0, norm_layer=norm_layer,
- activation_layer=activation_layer, **init_conv_kwargs)]
-
- ### downsample
- for i in range(n_downsampling):
- mult = 2 ** i
- if i == n_downsampling - 1:
- cur_conv_kwargs = dict(downsample_conv_kwargs)
- cur_conv_kwargs['ratio_gout'] = resnet_conv_kwargs.get('ratio_gin', 0)
- else:
- cur_conv_kwargs = downsample_conv_kwargs
- model += [FFC_BN_ACT(min(max_features, ngf * mult),
- min(max_features, ngf * mult * 2),
- kernel_size=3, stride=2, padding=1,
- norm_layer=norm_layer,
- activation_layer=activation_layer,
- **cur_conv_kwargs)]
-
- mult = 2 ** n_downsampling
- feats_num_bottleneck = min(max_features, ngf * mult)
-
- ### resnet blocks
- for i in range(n_blocks):
- cur_resblock = FFCResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation_layer=activation_layer,
- norm_layer=norm_layer, **resnet_conv_kwargs)
- if spatial_transform_layers is not None and i in spatial_transform_layers:
- cur_resblock = LearnableSpatialTransformWrapper(cur_resblock, **spatial_transform_kwargs)
- model += [cur_resblock]
-
- model += [ConcatTupleLayer()]
-
- ### upsample
- for i in range(n_downsampling):
- mult = 2 ** (n_downsampling - i)
- model += [nn.ConvTranspose2d(min(max_features, ngf * mult),
- min(max_features, int(ngf * mult / 2)),
- kernel_size=3, stride=2, padding=1, output_padding=1),
- up_norm_layer(min(max_features, int(ngf * mult / 2))),
- up_activation]
-
- if out_ffc:
- model += [FFCResnetBlock(ngf, padding_type=padding_type, activation_layer=activation_layer,
- norm_layer=norm_layer, inline=True, **out_ffc_kwargs)]
-
- model += [nn.ReflectionPad2d(3),
- nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
- if add_out_act:
- model.append(get_activation('tanh' if add_out_act is True else add_out_act))
- self.model = nn.Sequential(*model)
-
- def forward(self, input):
- return self.model(input)
-
-
-class FFCNLayerDiscriminator(BaseDiscriminator):
- def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, max_features=512,
- init_conv_kwargs={}, conv_kwargs={}):
- super().__init__()
- self.n_layers = n_layers
-
- def _act_ctor(inplace=True):
- return nn.LeakyReLU(negative_slope=0.2, inplace=inplace)
-
- kw = 3
- padw = int(np.ceil((kw-1.0)/2))
- sequence = [[FFC_BN_ACT(input_nc, ndf, kernel_size=kw, padding=padw, norm_layer=norm_layer,
- activation_layer=_act_ctor, **init_conv_kwargs)]]
-
- nf = ndf
- for n in range(1, n_layers):
- nf_prev = nf
- nf = min(nf * 2, max_features)
-
- cur_model = [
- FFC_BN_ACT(nf_prev, nf,
- kernel_size=kw, stride=2, padding=padw,
- norm_layer=norm_layer,
- activation_layer=_act_ctor,
- **conv_kwargs)
- ]
- sequence.append(cur_model)
-
- nf_prev = nf
- nf = min(nf * 2, 512)
-
- cur_model = [
- FFC_BN_ACT(nf_prev, nf,
- kernel_size=kw, stride=1, padding=padw,
- norm_layer=norm_layer,
- activation_layer=lambda *args, **kwargs: nn.LeakyReLU(*args, negative_slope=0.2, **kwargs),
- **conv_kwargs),
- ConcatTupleLayer()
- ]
- sequence.append(cur_model)
-
- sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
-
- for n in range(len(sequence)):
- setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
-
- def get_all_activations(self, x):
- res = [x]
- for n in range(self.n_layers + 2):
- model = getattr(self, 'model' + str(n))
- res.append(model(res[-1]))
- return res[1:]
-
- def forward(self, x):
- act = self.get_all_activations(x)
- feats = []
- for out in act[:-1]:
- if isinstance(out, tuple):
- if torch.is_tensor(out[1]):
- out = torch.cat(out, dim=1)
- else:
- out = out[0]
- feats.append(out)
- return act[-1], feats
diff --git a/spaces/CVPR/unicl-zero-shot-img-recog/model/image_encoder/swin_transformer.py b/spaces/CVPR/unicl-zero-shot-img-recog/model/image_encoder/swin_transformer.py
deleted file mode 100644
index 6e171663bb49a2c995bd61aaacda6cf5f6223e22..0000000000000000000000000000000000000000
--- a/spaces/CVPR/unicl-zero-shot-img-recog/model/image_encoder/swin_transformer.py
+++ /dev/null
@@ -1,636 +0,0 @@
-# --------------------------------------------------------
-# Swin Transformer
-# Copyright (c) 2021 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ze Liu
-# --------------------------------------------------------
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-def window_partition(x, window_size):
- """
- Args:
- x: (B, H, W, C)
- window_size (int): window size
-
- Returns:
- windows: (num_windows*B, window_size, window_size, C)
- """
- B, H, W, C = x.shape
- x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows
-
-
-def window_reverse(windows, window_size, H, W):
- """
- Args:
- windows: (num_windows*B, window_size, window_size, C)
- window_size (int): Window size
- H (int): Height of image
- W (int): Width of image
-
- Returns:
- x: (B, H, W, C)
- """
- B = int(windows.shape[0] / (H * W / window_size / window_size))
- x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
- return x
-
-
-class WindowAttention(nn.Module):
- r""" Window based multi-head self attention (W-MSA) module with relative position bias.
- It supports both of shifted and non-shifted window.
-
- Args:
- dim (int): Number of input channels.
- window_size (tuple[int]): The height and width of the window.
- num_heads (int): Number of attention heads.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
- attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
- proj_drop (float, optional): Dropout ratio of output. Default: 0.0
- """
-
- def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
- super().__init__()
- self.dim = dim
- self.window_size = window_size # Wh, Ww
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = qk_scale or head_dim ** -0.5
-
- # define a parameter table of relative position bias
- self.relative_position_bias_table = nn.Parameter(
- torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(self.window_size[0])
- coords_w = torch.arange(self.window_size[1])
- coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
- coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
- relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += self.window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
- relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
- self.register_buffer("relative_position_index", relative_position_index)
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- trunc_normal_(self.relative_position_bias_table, std=.02)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask=None):
- """
- Args:
- x: input features with shape of (num_windows*B, N, C)
- mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
- """
- B_, N, C = x.shape
- qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- q = q * self.scale
- attn = (q @ k.transpose(-2, -1))
-
- relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
- relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
- attn = attn + relative_position_bias.unsqueeze(0)
-
- if mask is not None:
- nW = mask.shape[0]
- attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
- attn = attn.view(-1, self.num_heads, N, N)
- attn = self.softmax(attn)
- else:
- attn = self.softmax(attn)
-
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
- def extra_repr(self) -> str:
- return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
- def flops(self, N):
- # calculate flops for 1 window with token length of N
- flops = 0
- # qkv = self.qkv(x)
- flops += N * self.dim * 3 * self.dim
- # attn = (q @ k.transpose(-2, -1))
- flops += self.num_heads * N * (self.dim // self.num_heads) * N
- # x = (attn @ v)
- flops += self.num_heads * N * N * (self.dim // self.num_heads)
- # x = self.proj(x)
- flops += N * self.dim * self.dim
- return flops
-
-
-class SwinTransformerBlock(nn.Module):
- r""" Swin Transformer Block.
-
- Args:
- dim (int): Number of input channels.
- input_resolution (tuple[int]): Input resulotion.
- num_heads (int): Number of attention heads.
- window_size (int): Window size.
- shift_size (int): Shift size for SW-MSA.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float, optional): Stochastic depth rate. Default: 0.0
- act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
-
- def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
- mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
- act_layer=nn.GELU, norm_layer=nn.LayerNorm):
- super().__init__()
- self.dim = dim
- self.input_resolution = input_resolution
- self.num_heads = num_heads
- self.window_size = window_size
- self.shift_size = shift_size
- self.mlp_ratio = mlp_ratio
- if min(self.input_resolution) <= self.window_size:
- # if window size is larger than input resolution, we don't partition windows
- self.shift_size = 0
- self.window_size = min(self.input_resolution)
- assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
- self.norm1 = norm_layer(dim)
- self.attn = WindowAttention(
- dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
- qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- if self.shift_size > 0:
- # calculate attention mask for SW-MSA
- H, W = self.input_resolution
- img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
- h_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- w_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- cnt = 0
- for h in h_slices:
- for w in w_slices:
- img_mask[:, h, w, :] = cnt
- cnt += 1
-
- mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
- mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
- attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
- attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
- else:
- attn_mask = None
-
- self.register_buffer("attn_mask", attn_mask)
-
- def forward(self, x, Ph, Pw, attn_mask):
- # H, W = self.input_resolution
- B, L, C = x.shape
- # assert L == H * W, "input feature has wrong size"
-
- shortcut = x
- x = self.norm1(x)
- x = x.view(B, Ph, Pw, C)
-
- # pad feature maps to multiples of window size
- pad_l = pad_t = 0
- pad_r = (self.window_size - Pw % self.window_size) % self.window_size
- pad_b = (self.window_size - Ph % self.window_size) % self.window_size
- x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
- _, Hp, Wp, _ = x.shape
-
- # cyclic shift
- if self.shift_size > 0:
- shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
- attn_mask = attn_mask
- else:
- shifted_x = x
- attn_mask = None
-
- # partition windows
- x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
- x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
-
- # W-MSA/SW-MSA
- attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
-
- # reverse cyclic shift
- if self.shift_size > 0:
- x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
- else:
- x = shifted_x
-
- if pad_r > 0 or pad_b > 0:
- x = x[:, :Ph, :Pw, :].contiguous()
-
- x = x.view(B, Ph * Pw, C)
-
- # FFN
- x = shortcut + self.drop_path(x)
- x = x + self.drop_path(self.mlp(self.norm2(x)))
-
- return x
-
- def extra_repr(self) -> str:
- return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
- f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
- def flops(self):
- flops = 0
- H, W = self.input_resolution
- # norm1
- flops += self.dim * H * W
- # W-MSA/SW-MSA
- nW = H * W / self.window_size / self.window_size
- flops += nW * self.attn.flops(self.window_size * self.window_size)
- # mlp
- flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
- # norm2
- flops += self.dim * H * W
- return flops
-
-
-class PatchMerging(nn.Module):
- r""" Patch Merging Layer.
-
- Args:
- input_resolution (tuple[int]): Resolution of input feature.
- dim (int): Number of input channels.
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
-
- def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
- super().__init__()
- self.input_resolution = input_resolution
- self.dim = dim
- self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
- self.norm = norm_layer(4 * dim)
-
- def forward(self, x, Ph, Pw):
- """
- x: B, H*W, C
- """
- B, L, C = x.shape
- # assert L == H * W, "input feature has wrong size"
- # assert Ph % 2 == 0 and Pw % 2 == 0, f"x size ({Ph}*{Pw}) are not even."
-
- x = x.view(B, Ph, Pw, C)
-
- # padding
- pad_input = (Ph % 2 == 1) or (Pw % 2 == 1)
- if pad_input:
- x = F.pad(x, (0, 0, 0, Pw % 2, 0, Ph % 2))
-
- x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
- x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
- x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
- x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
- x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
- x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
-
- x = self.norm(x)
- x = self.reduction(x)
-
- return x
-
- def extra_repr(self) -> str:
- return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
- def flops(self):
- H, W = self.input_resolution
- flops = H * W * self.dim
- flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
- return flops
-
-
-class BasicLayer(nn.Module):
- """ A basic Swin Transformer layer for one stage.
-
- Args:
- dim (int): Number of input channels.
- input_resolution (tuple[int]): Input resolution.
- depth (int): Number of blocks.
- num_heads (int): Number of attention heads.
- window_size (int): Local window size.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- """
-
- def __init__(self, dim, input_resolution, depth, num_heads, window_size,
- mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
- drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
-
- super().__init__()
- self.dim = dim
- self.input_resolution = input_resolution
- self.depth = depth
- self.use_checkpoint = use_checkpoint
- self.window_size = window_size
- self.shift_size = window_size // 2
-
- # build blocks
- self.blocks = nn.ModuleList([
- SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
- num_heads=num_heads, window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias, qk_scale=qk_scale,
- drop=drop, attn_drop=attn_drop,
- drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
- norm_layer=norm_layer)
- for i in range(depth)])
-
- # patch merging layer
- if downsample is not None:
- self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
- else:
- self.downsample = None
-
- def forward(self, x, Ph, Pw):
-
- # calculate attention mask for SW-MSA
- Hp = int(np.ceil(Ph / self.window_size)) * self.window_size
- Wp = int(np.ceil(Pw / self.window_size)) * self.window_size
- img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
- h_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- w_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- cnt = 0
- for h in h_slices:
- for w in w_slices:
- img_mask[:, h, w, :] = cnt
- cnt += 1
-
- mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
- mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
- attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
- attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-
- for blk in self.blocks:
- if self.use_checkpoint:
- x = checkpoint.checkpoint(blk, x)
- else:
- x = blk(x, Ph, Pw, attn_mask)
- if self.downsample is not None:
- x = self.downsample(x, Ph, Pw)
- Ph, Pw = (Ph + 1) // 2, (Pw + 1) // 2
- return x, Ph, Pw
-
- def extra_repr(self) -> str:
- return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
- def flops(self):
- flops = 0
- for blk in self.blocks:
- flops += blk.flops()
- if self.downsample is not None:
- flops += self.downsample.flops()
- return flops
-
-
-class PatchEmbed(nn.Module):
- r""" Image to Patch Embedding
-
- Args:
- img_size (int): Image size. Default: 224.
- patch_size (int): Patch token size. Default: 4.
- in_chans (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- norm_layer (nn.Module, optional): Normalization layer. Default: None
- """
-
- def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
- super().__init__()
- img_size = to_2tuple(img_size)
- patch_size = to_2tuple(patch_size)
- patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
- self.img_size = img_size
- self.patch_size = patch_size
- self.patches_resolution = patches_resolution
- self.num_patches = patches_resolution[0] * patches_resolution[1]
-
- self.in_chans = in_chans
- self.embed_dim = embed_dim
-
- self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
- if norm_layer is not None:
- self.norm = norm_layer(embed_dim)
- else:
- self.norm = None
-
- def forward(self, x):
- B, C, H, W = x.shape
- # FIXME look at relaxing size constraints
- # assert H == self.img_size[0] and W == self.img_size[1], \
- # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
- x = self.proj(x)
- Ph, Pw = x.shape[2:]
- x = x.flatten(2).transpose(1, 2) # B Ph*Pw C
- if self.norm is not None:
- x = self.norm(x)
- return x, Ph, Pw
-
- def flops(self):
- Ho, Wo = self.patches_resolution
- flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
- if self.norm is not None:
- flops += Ho * Wo * self.embed_dim
- return flops
-
-
-class SwinTransformer(nn.Module):
- r""" Swin Transformer
- A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
- https://arxiv.org/pdf/2103.14030
-
- Args:
- img_size (int | tuple(int)): Input image size. Default 224
- patch_size (int | tuple(int)): Patch size. Default: 4
- in_chans (int): Number of input image channels. Default: 3
- num_classes (int): Number of classes for classification head. Default: 1000
- embed_dim (int): Patch embedding dimension. Default: 96
- depths (tuple(int)): Depth of each Swin Transformer layer.
- num_heads (tuple(int)): Number of attention heads in different layers.
- window_size (int): Window size. Default: 7
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
- qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
- drop_rate (float): Dropout rate. Default: 0
- attn_drop_rate (float): Attention dropout rate. Default: 0
- drop_path_rate (float): Stochastic depth rate. Default: 0.1
- norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
- ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
- patch_norm (bool): If True, add normalization after patch embedding. Default: True
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
- """
-
- def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
- embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
- window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
- drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
- norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
- use_checkpoint=False, **kwargs):
- super().__init__()
-
- self.num_classes = num_classes
- self.num_layers = len(depths)
- self.embed_dim = embed_dim
- self.ape = ape
- self.patch_norm = patch_norm
- self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
- self.mlp_ratio = mlp_ratio
-
- # split image into non-overlapping patches
- self.patch_embed = PatchEmbed(
- img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
- norm_layer=norm_layer if self.patch_norm else None)
- num_patches = self.patch_embed.num_patches
- patches_resolution = self.patch_embed.patches_resolution
- self.patches_resolution = patches_resolution
-
- # absolute position embedding
- if self.ape:
- self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
- trunc_normal_(self.absolute_pos_embed, std=.02)
-
- self.pos_drop = nn.Dropout(p=drop_rate)
-
- # stochastic depth
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
-
- # build layers
- self.layers = nn.ModuleList()
- for i_layer in range(self.num_layers):
- layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
- input_resolution=(patches_resolution[0] // (2 ** i_layer),
- patches_resolution[1] // (2 ** i_layer)),
- depth=depths[i_layer],
- num_heads=num_heads[i_layer],
- window_size=window_size,
- mlp_ratio=self.mlp_ratio,
- qkv_bias=qkv_bias, qk_scale=qk_scale,
- drop=drop_rate, attn_drop=attn_drop_rate,
- drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
- norm_layer=norm_layer,
- downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
- use_checkpoint=use_checkpoint)
- self.layers.append(layer)
-
- self.norm = norm_layer(self.num_features)
- self.avgpool = nn.AdaptiveAvgPool1d(1)
- self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
- self.dim_out = self.num_features
-
- self.apply(self._init_weights)
-
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
-
- @torch.jit.ignore
- def no_weight_decay(self):
- return {'absolute_pos_embed'}
-
- @torch.jit.ignore
- def no_weight_decay_keywords(self):
- return {'relative_position_bias_table'}
-
- def forward_features(self, x, output_map=False):
- x, Ph, Pw = self.patch_embed(x)
- if self.ape:
- x = x + self.absolute_pos_embed
- x = self.pos_drop(x)
-
- for layer in self.layers:
- x, Ph, Pw = layer(x, Ph, Pw)
-
- x_map = self.norm(x).transpose(1, 2) # B C L
- x = self.avgpool(x_map) # B C 1
- x = torch.flatten(x, 1)
-
- if output_map:
- return x, x_map, Ph, Pw
- else:
- return x
-
- def forward(self, x):
- x = self.forward_features(x)
- x = self.head(x)
- return x
-
- def flops(self):
- flops = 0
- flops += self.patch_embed.flops()
- for i, layer in enumerate(self.layers):
- flops += layer.flops()
- flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
- flops += self.num_features * self.num_classes
- return flops
diff --git a/spaces/ClassCat/Brain-tumor-3D-segmentation-with-MONAI/README.md b/spaces/ClassCat/Brain-tumor-3D-segmentation-with-MONAI/README.md
deleted file mode 100644
index 179d00ccbac1b793588e467b8b4c5056ecfe7135..0000000000000000000000000000000000000000
--- a/spaces/ClassCat/Brain-tumor-3D-segmentation-with-MONAI/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Brain Tumor 3D Segmentation With MONAI
-emoji: 🌖
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Cpp4App/Cpp4App/app.py b/spaces/Cpp4App/Cpp4App/app.py
deleted file mode 100644
index ba1e405a53504dc3a5ae9a36a070ec3d4f384d9d..0000000000000000000000000000000000000000
--- a/spaces/Cpp4App/Cpp4App/app.py
+++ /dev/null
@@ -1,284 +0,0 @@
-import gradio as gr
-import cv2
-import numpy as np
-import shutil
-from bs4 import BeautifulSoup
-import requests
-import pandas as pd
-
-from SEM.run_single_sem import run_single_pp
-from CDM.run_single import run_single_img
-
-title = "Cpp4App"
-description = "Automated Contextual Privacy Policies Generation for Mobile Apps"
-
-
-def write_and_read():
- # Write
- with open('myfile.txt', 'w') as f:
- f.write('Hello, World!')
-
- # Read
- with open('myfile.txt', 'r') as f:
- data = f.read()
-
- print("this is data: ", data)
-
- return data
-
-def run_demo(img_root, output_root, segment_root, file):
- print(type(file))
-
- # file_content = file.read().decode('utf-8')
- run_single_pp(file)
-
- output_board, output_data, complete_result = run_single_img(img_root, output_root, segment_root)
-
- return output_board, output_data, complete_result
-
-def inference(img, html):
-
- write_and_read()
-
- if img is None or html is None:
- return None, None
-
- output_root = "./CDM/result_classification"
- segment_root = './SEM/txt'
- img_root = "./CDM/input_examples/1-1-write.jpg"
- pp_root = "1.txt"
-
- # output_root = ""
- # segment_root = ""
- # img_root = "demo_img.jpg"
-
- img_array = np.array(img)
-
- cv2.imwrite(img_root, img_array)
-
- # replace example string with real example
- # if html == 'html content 1':
- # with open("examples/6.txt", "r") as f:
- # html = f.read()
- # elif html == 'html content 2':
- # with open("examples/11.txt", "r") as f:
- # html = f.read()
-
- # print("string: ", html)
- # with open(pp_root, 'w', encoding='utf-8') as file: # Open the destination file in text mode
- # file.write(html) # Write the HTML content to the destination file
-
- try:
- response = requests.get(html)
- response.raise_for_status() # Will raise an exception if the status is an error
- input_text = response.text
- except requests.HTTPError:
- input_text = ""
- # print("input_text: ", input_text)
- with open(pp_root, 'w', encoding='utf-8') as file:
- file.write(input_text)
-
- soup = BeautifulSoup(open(pp_root, encoding='utf-8'), features="html.parser")
- # print("pp_root soup: ", soup.contents)
-
- output_board, output_data, complete_result = run_demo(img_root, output_root, segment_root, pp_root)
-
- # print(output_data)
-
- return output_board, output_data, complete_result
-
-# inputs = [
-# gr.inputs.Image(type="pil", label="Image Upload"),
-# # gr.inputs.File(label="HTML File Upload"),
-# gr.inputs.Textbox(label="Text Input")
-# # gr.inputs.Textbox(lines=True, label="HTML Text")
-# ]
-# output = [
-# gr.outputs.Image(type="pil", label="Result Image"),
-# gr.outputs.Dataframe(type="pandas", label="Result Excel")
-# ]
-
-# gr.Interface(
-# inference,
-# # inputs,
-# # output,
-# inputs=[image_input_row, textbox_input_row],
-# outputs=[image_output_row, dataframe_output_row],
-# title=title,
-# description=description,
-# # examples=[['examples/6-8.jpg', 'examples/6.txt'], ['examples/11-9.jpg', 'examples/11.html']],
-# # examples=[['examples/6-8.jpg', example_file_content_1], ['examples/11-9.jpg', example_file_content_2]],
-# examples=[['examples/6-8.jpg', 'html content 1'], ['examples/11-9.jpg', 'html content 2']],
-# enable_queue=True,
-# capture_session=True,
-# layout='vertical'
-# ).launch(debug=False)
-
-# def example_inference():
-# image_input_bgr = cv2.imread('examples/6-8.jpg')
-# image_input = cv2.cvtColor(image_input_bgr, cv2.COLOR_BGR2RGB)
-# # text_input = 'html content 1' # example string
-# text_input = 'https://www.whatsapp.com/legal/privacy-policy'
-#
-# out_result, out_segment = inference(image_input, text_input)
-#
-# return image_input, text_input, out_result, out_segment
-
-def example_inference_1():
- image_input_bgr = cv2.imread("examples/6-8.jpg")
- image_input = cv2.cvtColor(image_input_bgr, cv2.COLOR_BGR2RGB)
- text_input = 'https://www.whatsapp.com/legal/privacy-policy'
- out_result, out_segment, complete_result = inference(image_input, text_input)
- return image_input, text_input, out_result, out_segment, complete_result
-
-def example_inference_2():
- image_input_bgr = cv2.imread("examples/11-9.jpg")
- image_input = cv2.cvtColor(image_input_bgr, cv2.COLOR_BGR2RGB)
- text_input = 'https://values.snap.com/privacy/privacy-policy'
- out_result, out_segment, complete_result = inference(image_input, text_input)
- return image_input, text_input, out_result, out_segment, complete_result
-
-def example_inference_3():
- image_input_bgr = cv2.imread("examples/1-1.jpg")
- image_input = cv2.cvtColor(image_input_bgr, cv2.COLOR_BGR2RGB)
- text_input = 'https://mcdonalds.com.au/privacy-policy'
- out_result, out_segment, complete_result = inference(image_input, text_input)
- return image_input, text_input, out_result, out_segment, complete_result
-
-def new_example_inference_1():
- image_input_bgr = cv2.imread("examples/6-8.jpg")
- image_input = cv2.cvtColor(image_input_bgr, cv2.COLOR_BGR2RGB)
- text_input = 'https://www.whatsapp.com/legal/privacy-policy'
-
- out_result_bgr = cv2.imread("results/result_1.png")
- out_result = cv2.cvtColor(out_result_bgr, cv2.COLOR_BGR2RGB)
-
- out_segment = pd.read_excel("results/result_1_S.xlsx")
- complete_result = pd.read_excel("results/result_1_C.xlsx")
-
- return image_input, text_input, out_result, out_segment, complete_result
-
-def new_example_inference_2():
- image_input_bgr = cv2.imread("examples/11-9.jpg")
- image_input = cv2.cvtColor(image_input_bgr, cv2.COLOR_BGR2RGB)
- text_input = 'https://values.snap.com/privacy/privacy-policy'
-
- out_result_bgr = cv2.imread("results/result_2.png")
- out_result = cv2.cvtColor(out_result_bgr, cv2.COLOR_BGR2RGB)
-
- out_segment = pd.read_excel("results/result_2_S.xlsx")
- complete_result = pd.read_excel("results/result_2_C.xlsx")
-
- return image_input, text_input, out_result, out_segment, complete_result
-
-def new_example_inference_3():
- image_input_bgr = cv2.imread("examples/1-1.jpg")
- image_input = cv2.cvtColor(image_input_bgr, cv2.COLOR_BGR2RGB)
- text_input = 'https://mcdonalds.com.au/privacy-policy'
-
- out_result_bgr = cv2.imread("results/result_3.png")
- out_result = cv2.cvtColor(out_result_bgr, cv2.COLOR_BGR2RGB)
-
- out_segment = pd.read_excel("results/result_3_S.xlsx")
- complete_result = pd.read_excel("results/result_3_C.xlsx")
-
- return image_input, text_input, out_result, out_segment, complete_result
-
-# def toggle_dataframe_callback():
-# complete_result_dataframe.visible = not complete_result_dataframe.visible
-
-demo = gr.Blocks()
-with demo:
- gr.Markdown("# Cpp4App\n\n**Automated Contextual Privacy Policies Generation for Mobile Apps**"
- "\n\nThere are two inputs to generate CPP for a mobile app: app's privacy policy URL link and a GUI screenshot")
-
- with gr.Row():
- example_image_1 = gr.Image(value="examples/6-8.jpg", label="Example 1")
- example_image_2 = gr.Image(value="examples/11-9.jpg", label="Example 2")
- example_image_3 = gr.Image(value="examples/1-1.jpg", label="Example 3")
- with gr.Column():
- gr.Markdown("**You can try with three examples we provided:**"
- "\n\n- WhatsApp"
- "\n\n- Snap"
- "\n\n- Mcdonald's"
- "\n\n**You can also try with your own example:**"
- "\n\nUpload the screenshot and privacy policy URL link, then click 'submit' button"
- # "\n\n"
- # "\n\nThe three provided examples are pre-run, while your own screenshot needs to run for approximately one minute."
- )
-
- with gr.Row():
- example_button_1 = gr.Button("Run with Example 1")
- example_button_2 = gr.Button("Run with Example 2")
- example_button_3 = gr.Button("Run with Example 3")
- with gr.Column():
- clear_button = gr.Button("Clear")
- submit_button = gr.Button("Submit")
-
- with gr.Row():
- text_input = gr.inputs.Textbox(label="URL Input for the Privacy Policy of the App")
-
- with gr.Column():
- image_input = gr.inputs.Image(type="pil", label="Screenshot Upload")
- result_image = gr.outputs.Image(type="pil", label="Result Screenshot")
-
- with gr.Row():
- result_dataframe = gr.outputs.Dataframe(type="pandas", label="Result Excel (Summarized)")
-
- # with gr.Row():
- # # Create a button to control the display of complete_result_dataframe
- # toggle_dataframe_button = gr.Button("Show Complete Result Excel")
-
- with gr.Row():
- complete_result_dataframe = gr.outputs.Dataframe(type="pandas", label="Result Excel (Complete)")
-
- # with gr.Row():
- # example_button_1 = gr.Button("Run with Example 1")
- # example_button_2 = gr.Button("Run with Example 2")
- # example_button_3 = gr.Button("Run with Example 3")
- # with gr.Column():
- # clear_button = gr.Button("Clear")
- # submit_button = gr.Button("Submit")
- #
- # with gr.Row():
- # example_image_1 = gr.Image(value="examples/6-8.jpg", label="Example 1")
- # example_image_2 = gr.Image(value="examples/11-9.jpg", label="Example 2")
- # example_image_3 = gr.Image(value="examples/1-1.jpg", label="Example 3")
- # with gr.Column():
- # gr.Markdown("**You can try with three examples we provided:**"
- # "\n\n- WhatsApp"
- # "\n\n- Snap"
- # "\n\n- Mcdonald's"
- # "\n\n**You can also try with your own example:**"
- # "\n\nUpload the screenshot and privacy policy URL link, then click 'submit' button")
-
- submit_button.click(inference, inputs=[image_input, text_input], outputs=[result_image, result_dataframe, complete_result_dataframe])
- clear_button.click(lambda: [None, None, None, None, None, None], inputs=[], outputs=[image_input, text_input, result_image, result_dataframe, complete_result_dataframe])
- # example_button.click(example_inference, inputs=[], outputs=[image_input, text_input, result_image, result_dataframe])
- example_button_1.click(new_example_inference_1,
- inputs=[],
- outputs=[image_input, text_input, result_image, result_dataframe, complete_result_dataframe])
- example_button_2.click(new_example_inference_2,
- inputs=[],
- outputs=[image_input, text_input, result_image, result_dataframe, complete_result_dataframe])
- example_button_3.click(new_example_inference_3,
- inputs=[],
- outputs=[image_input, text_input, result_image, result_dataframe, complete_result_dataframe])
-
- # # Create a unique CSS ID for the dataframe output
- # dataframe_id = id(complete_result_dataframe)
- #
- # # Define CSS styles for hiding/showing the dataframe
- # hide_style = f"#{dataframe_id} {{ display: none; }}"
- # show_style = f"#{dataframe_id} {{ display: block; }}"
- #
- #
- # def toggle_dataframe_callback():
- # if toggle_dataframe_button.label == "Show Complete Result Excel":
- # toggle_dataframe_button.label = "Hide Complete Result Excel"
- # gr.Html(style=show_style).show()
- # else:
- # toggle_dataframe_button.label = "Show Complete Result Excel"
- # gr.Html(style=hide_style).show()
-
-demo.launch()
diff --git a/spaces/Cropinky/esrgan/realesrgan/archs/discriminator_arch.py b/spaces/Cropinky/esrgan/realesrgan/archs/discriminator_arch.py
deleted file mode 100644
index 4b66ab1226d6793de846bc9828bbe427031a0e2d..0000000000000000000000000000000000000000
--- a/spaces/Cropinky/esrgan/realesrgan/archs/discriminator_arch.py
+++ /dev/null
@@ -1,67 +0,0 @@
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn as nn
-from torch.nn import functional as F
-from torch.nn.utils import spectral_norm
-
-
-@ARCH_REGISTRY.register()
-class UNetDiscriminatorSN(nn.Module):
- """Defines a U-Net discriminator with spectral normalization (SN)
-
- It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
- Arg:
- num_in_ch (int): Channel number of inputs. Default: 3.
- num_feat (int): Channel number of base intermediate features. Default: 64.
- skip_connection (bool): Whether to use skip connections between U-Net. Default: True.
- """
-
- def __init__(self, num_in_ch, num_feat=64, skip_connection=True):
- super(UNetDiscriminatorSN, self).__init__()
- self.skip_connection = skip_connection
- norm = spectral_norm
- # the first convolution
- self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1)
- # downsample
- self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False))
- self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False))
- self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False))
- # upsample
- self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False))
- self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False))
- self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False))
- # extra convolutions
- self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
- self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
- self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1)
-
- def forward(self, x):
- # downsample
- x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True)
- x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True)
- x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True)
- x3 = F.leaky_relu(self.conv3(x2), negative_slope=0.2, inplace=True)
-
- # upsample
- x3 = F.interpolate(x3, scale_factor=2, mode='bilinear', align_corners=False)
- x4 = F.leaky_relu(self.conv4(x3), negative_slope=0.2, inplace=True)
-
- if self.skip_connection:
- x4 = x4 + x2
- x4 = F.interpolate(x4, scale_factor=2, mode='bilinear', align_corners=False)
- x5 = F.leaky_relu(self.conv5(x4), negative_slope=0.2, inplace=True)
-
- if self.skip_connection:
- x5 = x5 + x1
- x5 = F.interpolate(x5, scale_factor=2, mode='bilinear', align_corners=False)
- x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True)
-
- if self.skip_connection:
- x6 = x6 + x0
-
- # extra convolutions
- out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True)
- out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True)
- out = self.conv9(out)
-
- return out
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/roundingPen.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/roundingPen.py
deleted file mode 100644
index 2a7c476c36f4d244d62c92b745dc462d977ba394..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/roundingPen.py
+++ /dev/null
@@ -1,112 +0,0 @@
-from fontTools.misc.roundTools import otRound
-from fontTools.misc.transform import Transform
-from fontTools.pens.filterPen import FilterPen, FilterPointPen
-
-
-__all__ = ["RoundingPen", "RoundingPointPen"]
-
-
-class RoundingPen(FilterPen):
- """
- Filter pen that rounds point coordinates and component XY offsets to integer.
-
- >>> from fontTools.pens.recordingPen import RecordingPen
- >>> recpen = RecordingPen()
- >>> roundpen = RoundingPen(recpen)
- >>> roundpen.moveTo((0.4, 0.6))
- >>> roundpen.lineTo((1.6, 2.5))
- >>> roundpen.qCurveTo((2.4, 4.6), (3.3, 5.7), (4.9, 6.1))
- >>> roundpen.curveTo((6.4, 8.6), (7.3, 9.7), (8.9, 10.1))
- >>> roundpen.addComponent("a", (1.5, 0, 0, 1.5, 10.5, -10.5))
- >>> recpen.value == [
- ... ('moveTo', ((0, 1),)),
- ... ('lineTo', ((2, 3),)),
- ... ('qCurveTo', ((2, 5), (3, 6), (5, 6))),
- ... ('curveTo', ((6, 9), (7, 10), (9, 10))),
- ... ('addComponent', ('a', (1.5, 0, 0, 1.5, 11, -10))),
- ... ]
- True
- """
-
- def __init__(self, outPen, roundFunc=otRound):
- super().__init__(outPen)
- self.roundFunc = roundFunc
-
- def moveTo(self, pt):
- self._outPen.moveTo((self.roundFunc(pt[0]), self.roundFunc(pt[1])))
-
- def lineTo(self, pt):
- self._outPen.lineTo((self.roundFunc(pt[0]), self.roundFunc(pt[1])))
-
- def curveTo(self, *points):
- self._outPen.curveTo(
- *((self.roundFunc(x), self.roundFunc(y)) for x, y in points)
- )
-
- def qCurveTo(self, *points):
- self._outPen.qCurveTo(
- *((self.roundFunc(x), self.roundFunc(y)) for x, y in points)
- )
-
- def addComponent(self, glyphName, transformation):
- self._outPen.addComponent(
- glyphName,
- Transform(
- *transformation[:4],
- self.roundFunc(transformation[4]),
- self.roundFunc(transformation[5]),
- ),
- )
-
-
-class RoundingPointPen(FilterPointPen):
- """
- Filter point pen that rounds point coordinates and component XY offsets to integer.
-
- >>> from fontTools.pens.recordingPen import RecordingPointPen
- >>> recpen = RecordingPointPen()
- >>> roundpen = RoundingPointPen(recpen)
- >>> roundpen.beginPath()
- >>> roundpen.addPoint((0.4, 0.6), 'line')
- >>> roundpen.addPoint((1.6, 2.5), 'line')
- >>> roundpen.addPoint((2.4, 4.6))
- >>> roundpen.addPoint((3.3, 5.7))
- >>> roundpen.addPoint((4.9, 6.1), 'qcurve')
- >>> roundpen.endPath()
- >>> roundpen.addComponent("a", (1.5, 0, 0, 1.5, 10.5, -10.5))
- >>> recpen.value == [
- ... ('beginPath', (), {}),
- ... ('addPoint', ((0, 1), 'line', False, None), {}),
- ... ('addPoint', ((2, 3), 'line', False, None), {}),
- ... ('addPoint', ((2, 5), None, False, None), {}),
- ... ('addPoint', ((3, 6), None, False, None), {}),
- ... ('addPoint', ((5, 6), 'qcurve', False, None), {}),
- ... ('endPath', (), {}),
- ... ('addComponent', ('a', (1.5, 0, 0, 1.5, 11, -10)), {}),
- ... ]
- True
- """
-
- def __init__(self, outPen, roundFunc=otRound):
- super().__init__(outPen)
- self.roundFunc = roundFunc
-
- def addPoint(self, pt, segmentType=None, smooth=False, name=None, **kwargs):
- self._outPen.addPoint(
- (self.roundFunc(pt[0]), self.roundFunc(pt[1])),
- segmentType=segmentType,
- smooth=smooth,
- name=name,
- **kwargs,
- )
-
- def addComponent(self, baseGlyphName, transformation, **kwargs):
- self._outPen.addComponent(
- baseGlyphName,
- Transform(
- *transformation[:4],
- self.roundFunc(transformation[4]),
- self.roundFunc(transformation[5]),
- ),
- **kwargs,
- )
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-f90e1963.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-f90e1963.js
deleted file mode 100644
index d8559fecd70d523515a920fa6ad2e748b9975c5b..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-f90e1963.js
+++ /dev/null
@@ -1,13 +0,0 @@
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this.dependencies)f=="doc"?a=!0:f=="selection"?h=!0:((t=e[f.id])!==null&&t!==void 0?t:1)&1||c.push(e[f.id]);return{create(f){return f.values[o]=i(f),1},update(f,u){if(a&&u.docChanged||h&&(u.docChanged||u.selection)||Rs(f,c)){let d=i(f);if(l?!oo(d,f.values[o],s):!s(d,f.values[o]))return f.values[o]=d,1}return 0},reconfigure:(f,u)=>{let d=i(f),p=u.config.address[r];if(p!=null){let g=vn(u,p);if(this.dependencies.every(y=>y instanceof D?u.facet(y)===f.facet(y):y instanceof Me?u.field(y,!1)==f.field(y,!1):!0)||(l?oo(d,g,s):s(d,g)))return f.values[o]=g,0}return f.values[o]=d,1}}}}function oo(n,e,t){if(n.length!=e.length)return!1;for(let i=0;in[a.id]),s=t.map(a=>a.type),r=i.filter(a=>!(a&1)),o=n[e.id]>>1;function l(a){let h=[];for(let c=0;ci===s),e);return e.provide&&(t.provides=e.provide(t)),t}create(e){let t=e.facet(lo).find(i=>i.field==this);return(t?.create||this.createF)(e)}slot(e){let t=e[this.id]>>1;return{create:i=>(i.values[t]=this.create(i),1),update:(i,s)=>{let r=i.values[t],o=this.updateF(r,s);return this.compareF(r,o)?0:(i.values[t]=o,1)},reconfigure:(i,s)=>s.config.address[this.id]!=null?(i.values[t]=s.field(this),0):(i.values[t]=this.create(i),1)}}init(e){return[this,lo.of({field:this,create:e})]}get extension(){return this}}const Ct={lowest:4,low:3,default:2,high:1,highest:0};function ci(n){return e=>new wa(e,n)}const Vi={highest:ci(Ct.highest),high:ci(Ct.high),default:ci(Ct.default),low:ci(Ct.low),lowest:ci(Ct.lowest)};class wa{constructor(e,t){this.inner=e,this.prec=t}}class Fn{of(e){return new Ls(this,e)}reconfigure(e){return Fn.reconfigure.of({compartment:this,extension:e})}get(e){return e.config.compartments.get(this)}}class Ls{constructor(e,t){this.compartment=e,this.inner=t}}class kn{constructor(e,t,i,s,r,o){for(this.base=e,this.compartments=t,this.dynamicSlots=i,this.address=s,this.staticValues=r,this.facets=o,this.statusTemplate=[];this.statusTemplate.length>1]}static resolve(e,t,i){let s=[],r=Object.create(null),o=new Map;for(let u of hf(e,t,o))u instanceof Me?s.push(u):(r[u.facet.id]||(r[u.facet.id]=[])).push(u);let l=Object.create(null),a=[],h=[];for(let u of s)l[u.id]=h.length<<1,h.push(d=>u.slot(d));let c=i?.config.facets;for(let u in r){let d=r[u],p=d[0].facet,g=c&&c[u]||[];if(d.every(y=>y.type==0))if(l[p.id]=a.length<<1|1,Ar(g,d))a.push(i.facet(p));else{let y=p.combine(d.map(b=>b.value));a.push(i&&p.compare(y,i.facet(p))?i.facet(p):y)}else{for(let y of d)y.type==0?(l[y.id]=a.length<<1|1,a.push(y.value)):(l[y.id]=h.length<<1,h.push(b=>y.dynamicSlot(b)));l[p.id]=h.length<<1,h.push(y=>af(y,p,d))}}let f=h.map(u=>u(l));return new kn(e,o,f,l,a,r)}}function hf(n,e,t){let i=[[],[],[],[],[]],s=new Map;function r(o,l){let a=s.get(o);if(a!=null){if(a<=l)return;let h=i[a].indexOf(o);h>-1&&i[a].splice(h,1),o instanceof Ls&&t.delete(o.compartment)}if(s.set(o,l),Array.isArray(o))for(let h of o)r(h,l);else if(o instanceof Ls){if(t.has(o.compartment))throw new RangeError("Duplicate use of compartment in extensions");let h=e.get(o.compartment)||o.inner;t.set(o.compartment,h),r(h,l)}else if(o instanceof wa)r(o.inner,o.prec);else if(o instanceof Me)i[l].push(o),o.provides&&r(o.provides,l);else if(o instanceof un)i[l].push(o),o.facet.extensions&&r(o.facet.extensions,Ct.default);else{let h=o.extension;if(!h)throw new Error(`Unrecognized extension value in extension set (${o}). This sometimes happens because multiple instances of @codemirror/state are loaded, breaking instanceof checks.`);r(h,l)}}return r(n,Ct.default),i.reduce((o,l)=>o.concat(l))}function Si(n,e){if(e&1)return 2;let t=e>>1,i=n.status[t];if(i==4)throw new Error("Cyclic dependency between fields and/or facets");if(i&2)return i;n.status[t]=4;let s=n.computeSlot(n,n.config.dynamicSlots[t]);return n.status[t]=2|s}function vn(n,e){return e&1?n.config.staticValues[e>>1]:n.values[e>>1]}const ka=D.define(),va=D.define({combine:n=>n.some(e=>e),static:!0}),xa=D.define({combine:n=>n.length?n[0]:void 0,static:!0}),Sa=D.define(),Ca=D.define(),Aa=D.define(),Ma=D.define({combine:n=>n.length?n[0]:!1});class Nt{constructor(e,t){this.type=e,this.value=t}static define(){return new cf}}class cf{of(e){return new Nt(this,e)}}class ff{constructor(e){this.map=e}of(e){return new R(this,e)}}class R{constructor(e,t){this.type=e,this.value=t}map(e){let t=this.type.map(this.value,e);return t===void 0?void 0:t==this.value?this:new R(this.type,t)}is(e){return this.type==e}static define(e={}){return new ff(e.map||(t=>t))}static mapEffects(e,t){if(!e.length)return e;let i=[];for(let s of e){let r=s.map(t);r&&i.push(r)}return i}}R.reconfigure=R.define();R.appendConfig=R.define();class re{constructor(e,t,i,s,r,o){this.startState=e,this.changes=t,this.selection=i,this.effects=s,this.annotations=r,this.scrollIntoView=o,this._doc=null,this._state=null,i&&ba(i,t.newLength),r.some(l=>l.type==re.time)||(this.annotations=r.concat(re.time.of(Date.now())))}static create(e,t,i,s,r,o){return new re(e,t,i,s,r,o)}get newDoc(){return this._doc||(this._doc=this.changes.apply(this.startState.doc))}get newSelection(){return this.selection||this.startState.selection.map(this.changes)}get state(){return this._state||this.startState.applyTransaction(this),this._state}annotation(e){for(let t of this.annotations)if(t.type==e)return t.value}get docChanged(){return!this.changes.empty}get reconfigured(){return this.startState.config!=this.state.config}isUserEvent(e){let t=this.annotation(re.userEvent);return!!(t&&(t==e||t.length>e.length&&t.slice(0,e.length)==e&&t[e.length]=="."))}}re.time=Nt.define();re.userEvent=Nt.define();re.addToHistory=Nt.define();re.remote=Nt.define();function uf(n,e){let t=[];for(let i=0,s=0;;){let r,o;if(i=n[i]))r=n[i++],o=n[i++];else if(s=0;s--){let r=i[s](n);r instanceof re?n=r:Array.isArray(r)&&r.length==1&&r[0]instanceof re?n=r[0]:n=Ta(e,Gt(r),!1)}return n}function pf(n){let e=n.startState,t=e.facet(Aa),i=n;for(let s=t.length-1;s>=0;s--){let r=t[s](n);r&&Object.keys(r).length&&(i=Da(i,Is(e,r,n.changes.newLength),!0))}return i==n?n:re.create(e,n.changes,n.selection,i.effects,i.annotations,i.scrollIntoView)}const mf=[];function Gt(n){return n==null?mf:Array.isArray(n)?n:[n]}var Re=function(n){return n[n.Word=0]="Word",n[n.Space=1]="Space",n[n.Other=2]="Other",n}(Re||(Re={}));const gf=/[\u00df\u0587\u0590-\u05f4\u0600-\u06ff\u3040-\u309f\u30a0-\u30ff\u3400-\u4db5\u4e00-\u9fcc\uac00-\ud7af]/;let Ns;try{Ns=new RegExp("[\\p{Alphabetic}\\p{Number}_]","u")}catch{}function yf(n){if(Ns)return Ns.test(n);for(let e=0;e""&&(t.toUpperCase()!=t.toLowerCase()||gf.test(t)))return!0}return!1}function bf(n){return e=>{if(!/\S/.test(e))return Re.Space;if(yf(e))return Re.Word;for(let t=0;t-1)return Re.Word;return Re.Other}}class N{constructor(e,t,i,s,r,o){this.config=e,this.doc=t,this.selection=i,this.values=s,this.status=e.statusTemplate.slice(),this.computeSlot=r,o&&(o._state=this);for(let l=0;ls.set(a,l)),t=null),s.set(o.value.compartment,o.value.extension)):o.is(R.reconfigure)?(t=null,i=o.value):o.is(R.appendConfig)&&(t=null,i=Gt(i).concat(o.value));let r;t?r=e.startState.values.slice():(t=kn.resolve(i,s,this),r=new N(t,this.doc,this.selection,t.dynamicSlots.map(()=>null),(l,a)=>a.reconfigure(l,this),null).values),new N(t,e.newDoc,e.newSelection,r,(o,l)=>l.update(o,e),e)}replaceSelection(e){return typeof e=="string"&&(e=this.toText(e)),this.changeByRange(t=>({changes:{from:t.from,to:t.to,insert:e},range:w.cursor(t.from+e.length)}))}changeByRange(e){let t=this.selection,i=e(t.ranges[0]),s=this.changes(i.changes),r=[i.range],o=Gt(i.effects);for(let l=1;lo.spec.fromJSON(l,a)))}}return N.create({doc:e.doc,selection:w.fromJSON(e.selection),extensions:t.extensions?s.concat([t.extensions]):s})}static create(e={}){let t=kn.resolve(e.extensions||[],new Map),i=e.doc instanceof _?e.doc:_.of((e.doc||"").split(t.staticFacet(N.lineSeparator)||Bs)),s=e.selection?e.selection instanceof w?e.selection:w.single(e.selection.anchor,e.selection.head):w.single(0);return ba(s,i.length),t.staticFacet(va)||(s=s.asSingle()),new N(t,i,s,t.dynamicSlots.map(()=>null),(r,o)=>o.create(r),null)}get tabSize(){return this.facet(N.tabSize)}get lineBreak(){return this.facet(N.lineSeparator)||`
-`}get readOnly(){return this.facet(Ma)}phrase(e,...t){for(let i of this.facet(N.phrases))if(Object.prototype.hasOwnProperty.call(i,e)){e=i[e];break}return t.length&&(e=e.replace(/\$(\$|\d*)/g,(i,s)=>{if(s=="$")return"$";let r=+(s||1);return!r||r>t.length?i:t[r-1]})),e}languageDataAt(e,t,i=-1){let s=[];for(let r of this.facet(ka))for(let o of r(this,t,i))Object.prototype.hasOwnProperty.call(o,e)&&s.push(o[e]);return s}charCategorizer(e){return bf(this.languageDataAt("wordChars",e).join(""))}wordAt(e){let{text:t,from:i,length:s}=this.doc.lineAt(e),r=this.charCategorizer(e),o=e-i,l=e-i;for(;o>0;){let a=Oe(t,o,!1);if(r(t.slice(a,o))!=Re.Word)break;o=a}for(;ln.length?n[0]:4});N.lineSeparator=xa;N.readOnly=Ma;N.phrases=D.define({compare(n,e){let t=Object.keys(n),i=Object.keys(e);return t.length==i.length&&t.every(s=>n[s]==e[s])}});N.languageData=ka;N.changeFilter=Sa;N.transactionFilter=Ca;N.transactionExtender=Aa;Fn.reconfigure=R.define();function _t(n,e,t={}){let i={};for(let s of n)for(let r of Object.keys(s)){let o=s[r],l=i[r];if(l===void 0)i[r]=o;else if(!(l===o||o===void 0))if(Object.hasOwnProperty.call(t,r))i[r]=t[r](l,o);else throw new Error("Config merge conflict for field "+r)}for(let s in e)i[s]===void 0&&(i[s]=e[s]);return i}class Bt{eq(e){return this==e}range(e,t=e){return _s.create(e,t,this)}}Bt.prototype.startSide=Bt.prototype.endSide=0;Bt.prototype.point=!1;Bt.prototype.mapMode=ce.TrackDel;let _s=class Oa{constructor(e,t,i){this.from=e,this.to=t,this.value=i}static create(e,t,i){return new Oa(e,t,i)}};function Vs(n,e){return n.from-e.from||n.value.startSide-e.value.startSide}class Mr{constructor(e,t,i,s){this.from=e,this.to=t,this.value=i,this.maxPoint=s}get length(){return this.to[this.to.length-1]}findIndex(e,t,i,s=0){let r=i?this.to:this.from;for(let o=s,l=r.length;;){if(o==l)return o;let a=o+l>>1,h=r[a]-e||(i?this.value[a].endSide:this.value[a].startSide)-t;if(a==o)return h>=0?o:l;h>=0?l=a:o=a+1}}between(e,t,i,s){for(let r=this.findIndex(t,-1e9,!0),o=this.findIndex(i,1e9,!1,r);rd||u==d&&h.startSide>0&&h.endSide<=0)continue;(d-u||h.endSide-h.startSide)<0||(o<0&&(o=u),h.point&&(l=Math.max(l,d-u)),i.push(h),s.push(u-o),r.push(d-o))}return{mapped:i.length?new Mr(s,r,i,l):null,pos:o}}}class F{constructor(e,t,i,s){this.chunkPos=e,this.chunk=t,this.nextLayer=i,this.maxPoint=s}static create(e,t,i,s){return new F(e,t,i,s)}get length(){let e=this.chunk.length-1;return e<0?0:Math.max(this.chunkEnd(e),this.nextLayer.length)}get size(){if(this.isEmpty)return 0;let e=this.nextLayer.size;for(let t of this.chunk)e+=t.value.length;return e}chunkEnd(e){return this.chunkPos[e]+this.chunk[e].length}update(e){let{add:t=[],sort:i=!1,filterFrom:s=0,filterTo:r=this.length}=e,o=e.filter;if(t.length==0&&!o)return this;if(i&&(t=t.slice().sort(Vs)),this.isEmpty)return t.length?F.of(t):this;let l=new Ba(this,null,-1).goto(0),a=0,h=[],c=new Pt;for(;l.value||a=0){let f=t[a++];c.addInner(f.from,f.to,f.value)||h.push(f)}else l.rangeIndex==1&&l.chunkIndexthis.chunkEnd(l.chunkIndex)||rl.to||r=r&&e<=r+o.length&&o.between(r,e-r,t-r,i)===!1)return}this.nextLayer.between(e,t,i)}}iter(e=0){return Ti.from([this]).goto(e)}get isEmpty(){return this.nextLayer==this}static iter(e,t=0){return Ti.from(e).goto(t)}static compare(e,t,i,s,r=-1){let o=e.filter(f=>f.maxPoint>0||!f.isEmpty&&f.maxPoint>=r),l=t.filter(f=>f.maxPoint>0||!f.isEmpty&&f.maxPoint>=r),a=ao(o,l,i),h=new fi(o,a,r),c=new fi(l,a,r);i.iterGaps((f,u,d)=>ho(h,f,c,u,d,s)),i.empty&&i.length==0&&ho(h,0,c,0,0,s)}static eq(e,t,i=0,s){s==null&&(s=1e9);let r=e.filter(c=>!c.isEmpty&&t.indexOf(c)<0),o=t.filter(c=>!c.isEmpty&&e.indexOf(c)<0);if(r.length!=o.length)return!1;if(!r.length)return!0;let l=ao(r,o),a=new fi(r,l,0).goto(i),h=new fi(o,l,0).goto(i);for(;;){if(a.to!=h.to||!Fs(a.active,h.active)||a.point&&(!h.point||!a.point.eq(h.point)))return!1;if(a.to>s)return!0;a.next(),h.next()}}static spans(e,t,i,s,r=-1){let o=new fi(e,null,r).goto(t),l=t,a=o.openStart;for(;;){let h=Math.min(o.to,i);if(o.point?(s.point(l,h,o.point,o.activeForPoint(o.to),a,o.pointRank),a=o.openEnd(h)+(o.to>h?1:0)):h>l&&(s.span(l,h,o.active,a),a=o.openEnd(h)),o.to>i)break;l=o.to,o.next()}return a}static of(e,t=!1){let i=new Pt;for(let s of e instanceof _s?[e]:t?wf(e):e)i.add(s.from,s.to,s.value);return i.finish()}}F.empty=new F([],[],null,-1);function wf(n){if(n.length>1)for(let e=n[0],t=1;t0)return n.slice().sort(Vs);e=i}return n}F.empty.nextLayer=F.empty;class Pt{constructor(){this.chunks=[],this.chunkPos=[],this.chunkStart=-1,this.last=null,this.lastFrom=-1e9,this.lastTo=-1e9,this.from=[],this.to=[],this.value=[],this.maxPoint=-1,this.setMaxPoint=-1,this.nextLayer=null}finishChunk(e){this.chunks.push(new Mr(this.from,this.to,this.value,this.maxPoint)),this.chunkPos.push(this.chunkStart),this.chunkStart=-1,this.setMaxPoint=Math.max(this.setMaxPoint,this.maxPoint),this.maxPoint=-1,e&&(this.from=[],this.to=[],this.value=[])}add(e,t,i){this.addInner(e,t,i)||(this.nextLayer||(this.nextLayer=new Pt)).add(e,t,i)}addInner(e,t,i){let s=e-this.lastTo||i.startSide-this.last.endSide;if(s<=0&&(e-this.lastFrom||i.startSide-this.last.startSide)<0)throw new Error("Ranges must be added sorted by `from` position and `startSide`");return s<0?!1:(this.from.length==250&&this.finishChunk(!0),this.chunkStart<0&&(this.chunkStart=e),this.from.push(e-this.chunkStart),this.to.push(t-this.chunkStart),this.last=i,this.lastFrom=e,this.lastTo=t,this.value.push(i),i.point&&(this.maxPoint=Math.max(this.maxPoint,t-e)),!0)}addChunk(e,t){if((e-this.lastTo||t.value[0].startSide-this.last.endSide)<0)return!1;this.from.length&&this.finishChunk(!0),this.setMaxPoint=Math.max(this.setMaxPoint,t.maxPoint),this.chunks.push(t),this.chunkPos.push(e);let i=t.value.length-1;return this.last=t.value[i],this.lastFrom=t.from[i]+e,this.lastTo=t.to[i]+e,!0}finish(){return this.finishInner(F.empty)}finishInner(e){if(this.from.length&&this.finishChunk(!1),this.chunks.length==0)return e;let t=F.create(this.chunkPos,this.chunks,this.nextLayer?this.nextLayer.finishInner(e):e,this.setMaxPoint);return this.from=null,t}}function ao(n,e,t){let i=new Map;for(let r of n)for(let 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0,this.dom=null)}setDeco(e){Or(this.attrs,e)||(this.dom&&(this.prevAttrs=this.attrs,this.markDirty()),this.attrs=e)}append(e,t){za(this,e,t)}addLineDeco(e){let t=e.spec.attributes,i=e.spec.class;t&&(this.attrs=$s(t,this.attrs||{})),i&&(this.attrs=$s({class:i},this.attrs||{}))}domAtPos(e){return Wa(this,e)}reuseDOM(e){e.nodeName=="DIV"&&(this.setDOM(e),this.dirty|=6)}sync(e){var t;this.dom?this.dirty&4&&(Ra(this.dom),this.dom.className="cm-line",this.prevAttrs=this.attrs?null:void 0):(this.setDOM(document.createElement("div")),this.dom.className="cm-line",this.prevAttrs=this.attrs?null:void 0),this.prevAttrs!==void 0&&(Js(this.dom,this.prevAttrs,this.attrs),this.dom.classList.add("cm-line"),this.prevAttrs=void 0),super.sync(e);let i=this.dom.lastChild;for(;i&&K.get(i)instanceof et;)i=i.lastChild;if(!i||!this.length||i.nodeName!="BR"&&((t=K.get(i))===null||t===void 0?void 0:t.isEditable)==!1&&(!A.ios||!this.children.some(s=>s instanceof yt))){let 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views");if(o){this.posCovered()||this.getLine(),this.content.length?this.content[this.content.length-1].breakAfter=1:this.breakAtStart=1,this.flushBuffer([]),this.curLine=null,e--;continue}else this.text=r,this.textOff=0}let s=Math.min(this.text.length-this.textOff,e,512);this.flushBuffer(t.slice(0,i)),this.getLine().append($i(new yt(this.text.slice(this.textOff,this.textOff+s)),t),i),this.atCursorPos=!0,this.textOff+=s,e-=s,i=0}}span(e,t,i,s){this.buildText(t-e,i,s),this.pos=t,this.openStart<0&&(this.openStart=s)}point(e,t,i,s,r,o){if(this.disallowBlockEffectsFor[o]&&i instanceof Et){if(i.block)throw new RangeError("Block decorations may not be specified via plugins");if(t>this.doc.lineAt(this.pos).to)throw new RangeError("Decorations that replace line breaks may not be specified via plugins")}let l=t-e;if(i instanceof Et)if(i.block){let{type:a}=i;a==W.WidgetAfter&&!this.posCovered()&&this.getLine(),this.addBlockWidget(new Ot(i.widget||new vo("div"),l,a))}else{let a=ct.create(i.widget||new vo("span"),l,l?0:i.startSide),h=this.atCursorPos&&!a.isEditable&&r<=s.length&&(e0),c=!a.isEditable&&(en.some(e=>e)}),Xa=D.define({combine:n=>n.some(e=>e)});class An{constructor(e,t="nearest",i="nearest",s=5,r=5){this.range=e,this.y=t,this.x=i,this.yMargin=s,this.xMargin=r}map(e){return e.empty?this:new An(this.range.map(e),this.y,this.x,this.yMargin,this.xMargin)}}const xo=R.define({map:(n,e)=>n.map(e)});function He(n,e,t){let i=n.facet($a);i.length?i[0](e):window.onerror?window.onerror(String(e),t,void 0,void 0,e):t?console.error(t+":",e):console.error(e)}const zn=D.define({combine:n=>n.length?n[0]:!0});let If=0;const yi=D.define();class be{constructor(e,t,i,s){this.id=e,this.create=t,this.domEventHandlers=i,this.extension=s(this)}static define(e,t){const{eventHandlers:i,provide:s,decorations:r}=t||{};return new be(If++,e,i,o=>{let l=[yi.of(o)];return r&&l.push(Ei.of(a=>{let h=a.plugin(o);return h?r(h):E.none})),s&&l.push(s(o)),l})}static fromClass(e,t){return be.define(i=>new e(i),t)}}class ns{constructor(e){this.spec=e,this.mustUpdate=null,this.value=null}update(e){if(this.value){if(this.mustUpdate){let t=this.mustUpdate;if(this.mustUpdate=null,this.value.update)try{this.value.update(t)}catch(i){if(He(t.state,i,"CodeMirror plugin crashed"),this.value.destroy)try{this.value.destroy()}catch{}this.deactivate()}}}else if(this.spec)try{this.value=this.spec.create(e)}catch(t){He(e.state,t,"CodeMirror plugin crashed"),this.deactivate()}return this}destroy(e){var t;if(!((t=this.value)===null||t===void 0)&&t.destroy)try{this.value.destroy()}catch(i){He(e.state,i,"CodeMirror plugin crashed")}}deactivate(){this.spec=this.value=null}}const Za=D.define(),Qa=D.define(),Ei=D.define(),eh=D.define(),th=D.define(),bi=D.define();class Qe{constructor(e,t,i,s){this.fromA=e,this.toA=t,this.fromB=i,this.toB=s}join(e){return new Qe(Math.min(this.fromA,e.fromA),Math.max(this.toA,e.toA),Math.min(this.fromB,e.fromB),Math.max(this.toB,e.toB))}addToSet(e){let t=e.length,i=this;for(;t>0;t--){let s=e[t-1];if(!(s.fromA>i.toA)){if(s.toAc)break;r+=2}if(!a)return i;new Qe(a.fromA,a.toA,a.fromB,a.toB).addToSet(i),o=a.toA,l=a.toB}}}class Mn{constructor(e,t,i){this.view=e,this.state=t,this.transactions=i,this.flags=0,this.startState=e.state,this.changes=ne.empty(this.startState.doc.length);for(let o of i)this.changes=this.changes.compose(o.changes);let s=[];this.changes.iterChangedRanges((o,l,a,h)=>s.push(new Qe(o,l,a,h))),this.changedRanges=s;let r=e.hasFocus;r!=e.inputState.notifiedFocused&&(e.inputState.notifiedFocused=r,this.flags|=1)}static create(e,t,i){return new Mn(e,t,i)}get viewportChanged(){return(this.flags&4)>0}get heightChanged(){return(this.flags&2)>0}get geometryChanged(){return this.docChanged||(this.flags&10)>0}get focusChanged(){return(this.flags&1)>0}get docChanged(){return!this.changes.empty}get 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this.points)a.node==e&&a.pos>this.text.length&&(a.pos-=o-1);i=r+o}}readNode(e){if(e.cmIgnore)return;let t=K.get(e),i=t&&t.overrideDOMText;if(i!=null){this.findPointInside(e,i.length);for(let s=i.iter();!s.next().done;)s.lineBreak?this.lineBreak():this.append(s.value)}else e.nodeType==3?this.readTextNode(e):e.nodeName=="BR"?e.nextSibling&&this.lineBreak():e.nodeType==1&&this.readRange(e.firstChild,null)}findPointBefore(e,t){for(let i of this.points)i.node==e&&e.childNodes[i.offset]==t&&(i.pos=this.text.length)}findPointInside(e,t){for(let i of this.points)(e.nodeType==3?i.node==e:e.contains(i.node))&&(i.pos=this.text.length+Math.min(t,i.offset))}}function So(n){return n.nodeType==1&&/^(DIV|P|LI|UL|OL|BLOCKQUOTE|DD|DT|H\d|SECTION|PRE)$/.test(n.nodeName)}class Co{constructor(e,t){this.node=e,this.offset=t,this.pos=-1}}class Ao extends K{constructor(e){super(),this.view=e,this.compositionDeco=E.none,this.decorations=[],this.dynamicDecorationMap=[],this.minWidth=0,this.minWidthFrom=0,this.minWidthTo=0,this.impreciseAnchor=null,this.impreciseHead=null,this.forceSelection=!1,this.lastUpdate=Date.now(),this.setDOM(e.contentDOM),this.children=[new ke],this.children[0].setParent(this),this.updateDeco(),this.updateInner([new Qe(0,0,0,e.state.doc.length)],0)}get editorView(){return this.view}get length(){return this.view.state.doc.length}update(e){let t=e.changedRanges;this.minWidth>0&&t.length&&(t.every(({fromA:o,toA:l})=>lthis.minWidthTo)?(this.minWidthFrom=e.changes.mapPos(this.minWidthFrom,1),this.minWidthTo=e.changes.mapPos(this.minWidthTo,1)):this.minWidth=this.minWidthFrom=this.minWidthTo=0),this.view.inputState.composing<0?this.compositionDeco=E.none:(e.transactions.length||this.dirty)&&(this.compositionDeco=jf(this.view,e.changes)),(A.ie||A.chrome)&&!this.compositionDeco.size&&e&&e.state.doc.lines!=e.startState.doc.lines&&(this.forceSelection=!0);let i=this.decorations,s=this.updateDeco(),r=$f(i,s,e.changes);return t=Qe.extendWithRanges(t,r),this.dirty==0&&t.length==0?!1:(this.updateInner(t,e.startState.doc.length),e.transactions.length&&(this.lastUpdate=Date.now()),!0)}updateInner(e,t){this.view.viewState.mustMeasureContent=!0,this.updateChildren(e,t);let{observer:i}=this.view;i.ignore(()=>{this.dom.style.height=this.view.viewState.contentHeight+"px",this.dom.style.flexBasis=this.minWidth?this.minWidth+"px":"";let r=A.chrome||A.ios?{node:i.selectionRange.focusNode,written:!1}:void 0;this.sync(r),this.dirty=0,r&&(r.written||i.selectionRange.focusNode!=r.node)&&(this.forceSelection=!0),this.dom.style.height=""});let s=[];if(this.view.viewport.from||this.view.viewport.to=0?e[s]:null;if(!r)break;let{fromA:o,toA:l,fromB:a,toB:h}=r,{content:c,breakAtStart:f,openStart:u,openEnd:d}=Br.build(this.view.state.doc,a,h,this.decorations,this.dynamicDecorationMap),{i:p,off:g}=i.findPos(l,1),{i:y,off:b}=i.findPos(o,-1);Ia(this,y,b,p,g,c,f,u,d)}}updateSelection(e=!1,t=!1){if((e||!this.view.observer.selectionRange.focusNode)&&this.view.observer.readSelectionRange(),!(t||this.mayControlSelection()))return;let i=this.forceSelection;this.forceSelection=!1;let s=this.view.state.selection.main,r=this.domAtPos(s.anchor),o=s.empty?r:this.domAtPos(s.head);if(A.gecko&&s.empty&&qf(r)){let a=document.createTextNode("");this.view.observer.ignore(()=>r.node.insertBefore(a,r.node.childNodes[r.offset]||null)),r=o=new ye(a,0),i=!0}let l=this.view.observer.selectionRange;(i||!l.focusNode||!Sn(r.node,r.offset,l.anchorNode,l.anchorOffset)||!Sn(o.node,o.offset,l.focusNode,l.focusOffset))&&(this.view.observer.ignore(()=>{A.android&&A.chrome&&this.dom.contains(l.focusNode)&&Jf(l.focusNode,this.dom)&&(this.dom.blur(),this.dom.focus({preventScroll:!0}));let a=xn(this.view.root);if(a)if(s.empty){if(A.gecko){let h=Uf(r.node,r.offset);if(h&&h!=3){let c=lh(r.node,r.offset,h==1?1:-1);c&&(r=new ye(c,h==1?0:c.nodeValue.length))}}a.collapse(r.node,r.offset),s.bidiLevel!=null&&l.cursorBidiLevel!=null&&(l.cursorBidiLevel=s.bidiLevel)}else if(a.extend){a.collapse(r.node,r.offset);try{a.extend(o.node,o.offset)}catch{}}else{let h=document.createRange();s.anchor>s.head&&([r,o]=[o,r]),h.setEnd(o.node,o.offset),h.setStart(r.node,r.offset),a.removeAllRanges(),a.addRange(h)}}),this.view.observer.setSelectionRange(r,o)),this.impreciseAnchor=r.precise?null:new ye(l.anchorNode,l.anchorOffset),this.impreciseHead=o.precise?null:new ye(l.focusNode,l.focusOffset)}enforceCursorAssoc(){if(this.compositionDeco.size)return;let{view:e}=this,t=e.state.selection.main,i=xn(e.root),{anchorNode:s,anchorOffset:r}=e.observer.selectionRange;if(!i||!t.empty||!t.assoc||!i.modify)return;let o=ke.find(this,t.head);if(!o)return;let l=o.posAtStart;if(t.head==l||t.head==l+o.length)return;let a=this.coordsAt(t.head,-1),h=this.coordsAt(t.head,1);if(!a||!h||a.bottom>h.top)return;let c=this.domAtPos(t.head+t.assoc);i.collapse(c.node,c.offset),i.modify("move",t.assoc<0?"forward":"backward","lineboundary"),e.observer.readSelectionRange();let f=e.observer.selectionRange;e.docView.posFromDOM(f.anchorNode,f.anchorOffset)!=t.from&&i.collapse(s,r)}mayControlSelection(){let e=this.view.root.activeElement;return e==this.dom||dn(this.dom,this.view.observer.selectionRange)&&!(e&&this.dom.contains(e))}nearest(e){for(let t=e;t;){let i=K.get(t);if(i&&i.rootView==this)return i;t=t.parentNode}return null}posFromDOM(e,t){let i=this.nearest(e);if(!i)throw new RangeError("Trying to find position for a DOM position outside of the document");return i.localPosFromDOM(e,t)+i.posAtStart}domAtPos(e){let{i:t,off:i}=this.childCursor().findPos(e,-1);for(;to||e==o&&r.type!=W.WidgetBefore&&r.type!=W.WidgetAfter&&(!s||t==2||this.children[s-1].breakAfter||this.children[s-1].type==W.WidgetBefore&&t>-2))return r.coordsAt(e-o,t);i=o}}measureVisibleLineHeights(e){let t=[],{from:i,to:s}=e,r=this.view.contentDOM.clientWidth,o=r>Math.max(this.view.scrollDOM.clientWidth,this.minWidth)+1,l=-1,a=this.view.textDirection==Z.LTR;for(let h=0,c=0;cs)break;if(h>=i){let d=f.dom.getBoundingClientRect();if(t.push(d.height),o){let p=f.dom.lastChild,g=p?Bi(p):[];if(g.length){let y=g[g.length-1],b=a?y.right-d.left:d.right-y.left;b>l&&(l=b,this.minWidth=r,this.minWidthFrom=h,this.minWidthTo=u)}}}h=u+f.breakAfter}return t}textDirectionAt(e){let{i:t}=this.childPos(e,1);return getComputedStyle(this.children[t].dom).direction=="rtl"?Z.RTL:Z.LTR}measureTextSize(){for(let s of this.children)if(s instanceof ke){let r=s.measureTextSize();if(r)return r}let e=document.createElement("div"),t,i;return e.className="cm-line",e.style.width="99999px",e.textContent="abc def ghi jkl mno pqr stu",this.view.observer.ignore(()=>{this.dom.appendChild(e);let s=Bi(e.firstChild)[0];t=e.getBoundingClientRect().height,i=s?s.width/27:7,e.remove()}),{lineHeight:t,charWidth:i}}childCursor(e=this.length){let t=this.children.length;return t&&(e-=this.children[--t].length),new La(this.children,e,t)}computeBlockGapDeco(){let e=[],t=this.view.viewState;for(let i=0,s=0;;s++){let r=s==t.viewports.length?null:t.viewports[s],o=r?r.from-1:this.length;if(o>i){let l=t.lineBlockAt(o).bottom-t.lineBlockAt(i).top;e.push(E.replace({widget:new Mo(l),block:!0,inclusive:!0,isBlockGap:!0}).range(i,o))}if(!r)break;i=r.to+1}return E.set(e)}updateDeco(){let e=this.view.state.facet(Ei).map((t,i)=>(this.dynamicDecorationMap[i]=typeof t=="function")?t(this.view):t);for(let t=e.length;tt.anchor?-1:1),s;if(!i)return;!t.empty&&(s=this.coordsAt(t.anchor,t.anchor>t.head?-1:1))&&(i={left:Math.min(i.left,s.left),top:Math.min(i.top,s.top),right:Math.max(i.right,s.right),bottom:Math.max(i.bottom,s.bottom)});let r=0,o=0,l=0,a=0;for(let c of this.view.state.facet(th).map(f=>f(this.view)))if(c){let{left:f,right:u,top:d,bottom:p}=c;f!=null&&(r=Math.max(r,f)),u!=null&&(o=Math.max(o,u)),d!=null&&(l=Math.max(l,d)),p!=null&&(a=Math.max(a,p))}let h={left:i.left-r,top:i.top-l,right:i.right+o,bottom:i.bottom+a};Df(this.view.scrollDOM,h,t.head0&&t<=0)n=n.childNodes[e-1],e=Pi(n);else if(n.nodeType==1&&e=0)n=n.childNodes[e],e=0;else return null}}function Uf(n,e){return n.nodeType!=1?0:(e&&n.childNodes[e-1].contentEditable=="false"?1:0)|(e0;){let h=Oe(s.text,o,!1);if(i(s.text.slice(h,o))!=a)break;o=h}for(;ln?e.left-n:Math.max(0,n-e.right)}function Zf(n,e){return e.top>n?e.top-n:Math.max(0,n-e.bottom)}function ss(n,e){return n.tope.top+1}function Do(n,e){return en.bottom?{top:n.top,left:n.left,right:n.right,bottom:e}:n}function er(n,e,t){let i,s,r,o,l=!1,a,h,c,f;for(let p=n.firstChild;p;p=p.nextSibling){let g=Bi(p);for(let y=0;yS||o==S&&r>v)&&(i=p,s=b,r=v,o=S,l=!v||(v>0?y0)),v==0?t>b.bottom&&(!c||c.bottomb.top)&&(h=p,f=b):c&&ss(c,b)?c=To(c,b.bottom):f&&ss(f,b)&&(f=Do(f,b.top))}}if(c&&c.bottom>=t?(i=a,s=c):f&&f.top<=t&&(i=h,s=f),!i)return{node:n,offset:0};let u=Math.max(s.left,Math.min(s.right,e));if(i.nodeType==3)return Oo(i,u,t);if(l&&i.contentEditable!="false")return er(i,u,t);let d=Array.prototype.indexOf.call(n.childNodes,i)+(e>=(s.left+s.right)/2?1:0);return{node:n,offset:d}}function Oo(n,e,t){let i=n.nodeValue.length,s=-1,r=1e9,o=0;for(let l=0;lt?c.top-t:t-c.bottom)-1;if(c.left-1<=e&&c.right+1>=e&&f=(c.left+c.right)/2,d=u;if((A.chrome||A.gecko)&&Zt(n,l).getBoundingClientRect().left==c.right&&(d=!u),f<=0)return{node:n,offset:l+(d?1:0)};s=l+(d?1:0),r=f}}}return{node:n,offset:s>-1?s:o>0?n.nodeValue.length:0}}function ah(n,{x:e,y:t},i,s=-1){var r;let o=n.contentDOM.getBoundingClientRect(),l=o.top+n.viewState.paddingTop,a,{docHeight:h}=n.viewState,c=t-l;if(c<0)return 0;if(c>h)return n.state.doc.length;for(let b=n.defaultLineHeight/2,v=!1;a=n.elementAtHeight(c),a.type!=W.Text;)for(;c=s>0?a.bottom+b:a.top-b,!(c>=0&&c<=h);){if(v)return i?null:0;v=!0,s=-s}t=l+c;let f=a.from;if(fn.viewport.to)return n.viewport.to==n.state.doc.length?n.state.doc.length:i?null:Bo(n,o,a,e,t);let u=n.dom.ownerDocument,d=n.root.elementFromPoint?n.root:u,p=d.elementFromPoint(e,t);p&&!n.contentDOM.contains(p)&&(p=null),p||(e=Math.max(o.left+1,Math.min(o.right-1,e)),p=d.elementFromPoint(e,t),p&&!n.contentDOM.contains(p)&&(p=null));let g,y=-1;if(p&&((r=n.docView.nearest(p))===null||r===void 0?void 0:r.isEditable)!=!1){if(u.caretPositionFromPoint){let b=u.caretPositionFromPoint(e,t);b&&({offsetNode:g,offset:y}=b)}else if(u.caretRangeFromPoint){let b=u.caretRangeFromPoint(e,t);b&&({startContainer:g,startOffset:y}=b,(!n.contentDOM.contains(g)||A.safari&&Qf(g,y,e)||A.chrome&&eu(g,y,e))&&(g=void 0))}}if(!g||!n.docView.dom.contains(g)){let b=ke.find(n.docView,f);if(!b)return c>a.top+a.height/2?a.to:a.from;({node:g,offset:y}=er(b.dom,e,t))}return n.docView.posFromDOM(g,y)}function Bo(n,e,t,i,s){let r=Math.round((i-e.left)*n.defaultCharacterWidth);if(n.lineWrapping&&t.height>n.defaultLineHeight*1.5){let l=Math.floor((s-t.top)/n.defaultLineHeight);r+=l*n.viewState.heightOracle.lineLength}let o=n.state.sliceDoc(t.from,t.to);return t.from+Hs(o,r,n.state.tabSize)}function Qf(n,e,t){let i;if(n.nodeType!=3||e!=(i=n.nodeValue.length))return!1;for(let s=n.nextSibling;s;s=s.nextSibling)if(s.nodeType!=1||s.nodeName!="BR")return!1;return Zt(n,i-1,i).getBoundingClientRect().left>t}function eu(n,e,t){if(e!=0)return!1;for(let s=n;;){let r=s.parentNode;if(!r||r.nodeType!=1||r.firstChild!=s)return!1;if(r.classList.contains("cm-line"))break;s=r}let i=n.nodeType==1?n.getBoundingClientRect():Zt(n,0,Math.max(n.nodeValue.length,1)).getBoundingClientRect();return t-i.left>5}function tu(n,e,t,i){let s=n.state.doc.lineAt(e.head),r=!i||!n.lineWrapping?null:n.coordsAtPos(e.assoc<0&&e.head>s.from?e.head-1:e.head);if(r){let a=n.dom.getBoundingClientRect(),h=n.textDirectionAt(s.from),c=n.posAtCoords({x:t==(h==Z.LTR)?a.right-1:a.left+1,y:(r.top+r.bottom)/2});if(c!=null)return w.cursor(c,t?-1:1)}let o=ke.find(n.docView,e.head),l=o?t?o.posAtEnd:o.posAtStart:t?s.to:s.from;return w.cursor(l,t?-1:1)}function Po(n,e,t,i){let s=n.state.doc.lineAt(e.head),r=n.bidiSpans(s),o=n.textDirectionAt(s.from);for(let l=e,a=null;;){let h=zf(s,r,o,l,t),c=sh;if(!h){if(s.number==(t?n.state.doc.lines:1))return l;c=`
-`,s=n.state.doc.line(s.number+(t?1:-1)),r=n.bidiSpans(s),h=w.cursor(t?s.from:s.to)}if(a){if(!a(c))return l}else{if(!i)return h;a=i(c)}l=h}}function iu(n,e,t){let i=n.state.charCategorizer(e),s=i(t);return r=>{let o=i(r);return s==Re.Space&&(s=o),s==o}}function nu(n,e,t,i){let s=e.head,r=t?1:-1;if(s==(t?n.state.doc.length:0))return w.cursor(s,e.assoc);let o=e.goalColumn,l,a=n.contentDOM.getBoundingClientRect(),h=n.coordsAtPos(s),c=n.documentTop;if(h)o==null&&(o=h.left-a.left),l=r<0?h.top:h.bottom;else{let d=n.viewState.lineBlockAt(s);o==null&&(o=Math.min(a.right-a.left,n.defaultCharacterWidth*(s-d.from))),l=(r<0?d.top:d.bottom)+c}let f=a.left+o,u=i??n.defaultLineHeight>>1;for(let d=0;;d+=10){let p=l+(u+d)*r,g=ah(n,{x:f,y:p},!1,r);if(pa.bottom||(r<0?gs))return w.cursor(g,e.assoc,void 0,o)}}function rs(n,e,t){let i=n.state.facet(eh).map(s=>s(n));for(;;){let s=!1;for(let r of i)r.between(t.from-1,t.from+1,(o,l,a)=>{t.from>o&&t.fromt.from?w.cursor(o,1):w.cursor(l,-1),s=!0)});if(!s)return t}}class su{constructor(e){this.lastKeyCode=0,this.lastKeyTime=0,this.lastTouchTime=0,this.lastFocusTime=0,this.lastScrollTop=0,this.lastScrollLeft=0,this.chromeScrollHack=-1,this.pendingIOSKey=void 0,this.lastSelectionOrigin=null,this.lastSelectionTime=0,this.lastEscPress=0,this.lastContextMenu=0,this.scrollHandlers=[],this.registeredEvents=[],this.customHandlers=[],this.composing=-1,this.compositionFirstChange=null,this.compositionEndedAt=0,this.mouseSelection=null;for(let t in oe){let i=oe[t];e.contentDOM.addEventListener(t,s=>{!Eo(e,s)||this.ignoreDuringComposition(s)||t=="keydown"&&this.keydown(e,s)||(this.mustFlushObserver(s)&&e.observer.forceFlush(),this.runCustomHandlers(t,e,s)?s.preventDefault():i(e,s))},tr[t]),this.registeredEvents.push(t)}A.chrome&&A.chrome_version==102&&e.scrollDOM.addEventListener("wheel",()=>{this.chromeScrollHack<0?e.contentDOM.style.pointerEvents="none":window.clearTimeout(this.chromeScrollHack),this.chromeScrollHack=setTimeout(()=>{this.chromeScrollHack=-1,e.contentDOM.style.pointerEvents=""},100)},{passive:!0}),this.notifiedFocused=e.hasFocus,A.safari&&e.contentDOM.addEventListener("input",()=>null)}setSelectionOrigin(e){this.lastSelectionOrigin=e,this.lastSelectionTime=Date.now()}ensureHandlers(e,t){var i;let s;this.customHandlers=[];for(let r of t)if(s=(i=r.update(e).spec)===null||i===void 0?void 0:i.domEventHandlers){this.customHandlers.push({plugin:r.value,handlers:s});for(let o in s)this.registeredEvents.indexOf(o)<0&&o!="scroll"&&(this.registeredEvents.push(o),e.contentDOM.addEventListener(o,l=>{Eo(e,l)&&this.runCustomHandlers(o,e,l)&&l.preventDefault()}))}}runCustomHandlers(e,t,i){for(let s of this.customHandlers){let r=s.handlers[e];if(r)try{if(r.call(s.plugin,i,t)||i.defaultPrevented)return!0}catch(o){He(t.state,o)}}return!1}runScrollHandlers(e,t){this.lastScrollTop=e.scrollDOM.scrollTop,this.lastScrollLeft=e.scrollDOM.scrollLeft;for(let i of this.customHandlers){let s=i.handlers.scroll;if(s)try{s.call(i.plugin,t,e)}catch(r){He(e.state,r)}}}keydown(e,t){if(this.lastKeyCode=t.keyCode,this.lastKeyTime=Date.now(),t.keyCode==9&&Date.now()s.keyCode==t.keyCode))&&!t.ctrlKey||ru.indexOf(t.key)>-1&&t.ctrlKey&&!t.shiftKey)?(this.pendingIOSKey=i||t,setTimeout(()=>this.flushIOSKey(e),250),!0):!1}flushIOSKey(e){let t=this.pendingIOSKey;return t?(this.pendingIOSKey=void 0,$t(e.contentDOM,t.key,t.keyCode)):!1}ignoreDuringComposition(e){return/^key/.test(e.type)?this.composing>0?!0:A.safari&&!A.ios&&Date.now()-this.compositionEndedAt<100?(this.compositionEndedAt=0,!0):!1:!1}mustFlushObserver(e){return e.type=="keydown"&&e.keyCode!=229}startMouseSelection(e){this.mouseSelection&&this.mouseSelection.destroy(),this.mouseSelection=e}update(e){this.mouseSelection&&this.mouseSelection.update(e),e.transactions.length&&(this.lastKeyCode=this.lastSelectionTime=0)}destroy(){this.mouseSelection&&this.mouseSelection.destroy()}}const hh=[{key:"Backspace",keyCode:8,inputType:"deleteContentBackward"},{key:"Enter",keyCode:13,inputType:"insertParagraph"},{key:"Delete",keyCode:46,inputType:"deleteContentForward"}],ru="dthko",ch=[16,17,18,20,91,92,224,225];class ou{constructor(e,t,i,s){this.view=e,this.style=i,this.mustSelect=s,this.lastEvent=t;let r=e.contentDOM.ownerDocument;r.addEventListener("mousemove",this.move=this.move.bind(this)),r.addEventListener("mouseup",this.up=this.up.bind(this)),this.extend=t.shiftKey,this.multiple=e.state.facet(N.allowMultipleSelections)&&lu(e,t),this.dragMove=au(e,t),this.dragging=hu(e,t)&&ph(t)==1?null:!1,this.dragging===!1&&(t.preventDefault(),this.select(t))}move(e){if(e.buttons==0)return this.destroy();this.dragging===!1&&this.select(this.lastEvent=e)}up(e){this.dragging==null&&this.select(this.lastEvent),this.dragging||e.preventDefault(),this.destroy()}destroy(){let e=this.view.contentDOM.ownerDocument;e.removeEventListener("mousemove",this.move),e.removeEventListener("mouseup",this.up),this.view.inputState.mouseSelection=null}select(e){let t=this.style.get(e,this.extend,this.multiple);(this.mustSelect||!t.eq(this.view.state.selection)||t.main.assoc!=this.view.state.selection.main.assoc)&&this.view.dispatch({selection:t,userEvent:"select.pointer",scrollIntoView:!0}),this.mustSelect=!1}update(e){e.docChanged&&this.dragging&&(this.dragging=this.dragging.map(e.changes)),this.style.update(e)&&setTimeout(()=>this.select(this.lastEvent),20)}}function lu(n,e){let t=n.state.facet(Ka);return t.length?t[0](e):A.mac?e.metaKey:e.ctrlKey}function au(n,e){let t=n.state.facet(Ua);return t.length?t[0](e):A.mac?!e.altKey:!e.ctrlKey}function hu(n,e){let{main:t}=n.state.selection;if(t.empty)return!1;let i=xn(n.root);if(!i||i.rangeCount==0)return!0;let s=i.getRangeAt(0).getClientRects();for(let r=0;r=e.clientX&&o.top<=e.clientY&&o.bottom>=e.clientY)return!0}return!1}function Eo(n,e){if(!e.bubbles)return!0;if(e.defaultPrevented)return!1;for(let t=e.target,i;t!=n.contentDOM;t=t.parentNode)if(!t||t.nodeType==11||(i=K.get(t))&&i.ignoreEvent(e))return!1;return!0}const oe=Object.create(null),tr=Object.create(null),fh=A.ie&&A.ie_version<15||A.ios&&A.webkit_version<604;function cu(n){let e=n.dom.parentNode;if(!e)return;let t=e.appendChild(document.createElement("textarea"));t.style.cssText="position: fixed; left: -10000px; top: 10px",t.focus(),setTimeout(()=>{n.focus(),t.remove(),uh(n,t.value)},50)}function uh(n,e){let{state:t}=n,i,s=1,r=t.toText(e),o=r.lines==t.selection.ranges.length;if(ir!=null&&t.selection.ranges.every(a=>a.empty)&&ir==r.toString()){let a=-1;i=t.changeByRange(h=>{let c=t.doc.lineAt(h.from);if(c.from==a)return{range:h};a=c.from;let f=t.toText((o?r.line(s++).text:e)+t.lineBreak);return{changes:{from:c.from,insert:f},range:w.cursor(h.from+f.length)}})}else o?i=t.changeByRange(a=>{let h=r.line(s++);return{changes:{from:a.from,to:a.to,insert:h.text},range:w.cursor(a.from+h.length)}}):i=t.replaceSelection(r);n.dispatch(i,{userEvent:"input.paste",scrollIntoView:!0})}oe.keydown=(n,e)=>{n.inputState.setSelectionOrigin("select"),e.keyCode==27?n.inputState.lastEscPress=Date.now():ch.indexOf(e.keyCode)<0&&(n.inputState.lastEscPress=0)};oe.touchstart=(n,e)=>{n.inputState.lastTouchTime=Date.now(),n.inputState.setSelectionOrigin("select.pointer")};oe.touchmove=n=>{n.inputState.setSelectionOrigin("select.pointer")};tr.touchstart=tr.touchmove={passive:!0};oe.mousedown=(n,e)=>{if(n.observer.flush(),n.inputState.lastTouchTime>Date.now()-2e3)return;let t=null;for(let i of n.state.facet(Ga))if(t=i(n,e),t)break;if(!t&&e.button==0&&(t=du(n,e)),t){let i=n.root.activeElement!=n.contentDOM;i&&n.observer.ignore(()=>Ea(n.contentDOM)),n.inputState.startMouseSelection(new ou(n,e,t,i))}};function Ro(n,e,t,i){if(i==1)return w.cursor(e,t);if(i==2)return Yf(n.state,e,t);{let s=ke.find(n.docView,e),r=n.state.doc.lineAt(s?s.posAtEnd:e),o=s?s.posAtStart:r.from,l=s?s.posAtEnd:r.to;return ln>=e.top&&n<=e.bottom,Lo=(n,e,t)=>dh(e,t)&&n>=t.left&&n<=t.right;function fu(n,e,t,i){let s=ke.find(n.docView,e);if(!s)return 1;let r=e-s.posAtStart;if(r==0)return 1;if(r==s.length)return-1;let o=s.coordsAt(r,-1);if(o&&Lo(t,i,o))return-1;let l=s.coordsAt(r,1);return l&&Lo(t,i,l)?1:o&&dh(i,o)?-1:1}function Io(n,e){let t=n.posAtCoords({x:e.clientX,y:e.clientY},!1);return{pos:t,bias:fu(n,t,e.clientX,e.clientY)}}const uu=A.ie&&A.ie_version<=11;let No=null,_o=0,Vo=0;function ph(n){if(!uu)return n.detail;let e=No,t=Vo;return No=n,Vo=Date.now(),_o=!e||t>Date.now()-400&&Math.abs(e.clientX-n.clientX)<2&&Math.abs(e.clientY-n.clientY)<2?(_o+1)%3:1}function du(n,e){let t=Io(n,e),i=ph(e),s=n.state.selection,r=t,o=e;return{update(l){l.docChanged&&(t.pos=l.changes.mapPos(t.pos),s=s.map(l.changes),o=null)},get(l,a,h){let c;o&&l.clientX==o.clientX&&l.clientY==o.clientY?c=r:(c=r=Io(n,l),o=l);let f=Ro(n,c.pos,c.bias,i);if(t.pos!=c.pos&&!a){let u=Ro(n,t.pos,t.bias,i),d=Math.min(u.from,f.from),p=Math.max(u.to,f.to);f=d1&&s.ranges.some(u=>u.eq(f))?pu(s,f):h?s.addRange(f):w.create([f])}}}function pu(n,e){for(let t=0;;t++)if(n.ranges[t].eq(e))return w.create(n.ranges.slice(0,t).concat(n.ranges.slice(t+1)),n.mainIndex==t?0:n.mainIndex-(n.mainIndex>t?1:0))}oe.dragstart=(n,e)=>{let{selection:{main:t}}=n.state,{mouseSelection:i}=n.inputState;i&&(i.dragging=t),e.dataTransfer&&(e.dataTransfer.setData("Text",n.state.sliceDoc(t.from,t.to)),e.dataTransfer.effectAllowed="copyMove")};function Fo(n,e,t,i){if(!t)return;let s=n.posAtCoords({x:e.clientX,y:e.clientY},!1);e.preventDefault();let{mouseSelection:r}=n.inputState,o=i&&r&&r.dragging&&r.dragMove?{from:r.dragging.from,to:r.dragging.to}:null,l={from:s,insert:t},a=n.state.changes(o?[o,l]:l);n.focus(),n.dispatch({changes:a,selection:{anchor:a.mapPos(s,-1),head:a.mapPos(s,1)},userEvent:o?"move.drop":"input.drop"})}oe.drop=(n,e)=>{if(!e.dataTransfer)return;if(n.state.readOnly)return e.preventDefault();let t=e.dataTransfer.files;if(t&&t.length){e.preventDefault();let i=Array(t.length),s=0,r=()=>{++s==t.length&&Fo(n,e,i.filter(o=>o!=null).join(n.state.lineBreak),!1)};for(let o=0;o{/[\x00-\x08\x0e-\x1f]{2}/.test(l.result)||(i[o]=l.result),r()},l.readAsText(t[o])}}else Fo(n,e,e.dataTransfer.getData("Text"),!0)};oe.paste=(n,e)=>{if(n.state.readOnly)return e.preventDefault();n.observer.flush();let t=fh?null:e.clipboardData;t?(uh(n,t.getData("text/plain")),e.preventDefault()):cu(n)};function mu(n,e){let t=n.dom.parentNode;if(!t)return;let i=t.appendChild(document.createElement("textarea"));i.style.cssText="position: fixed; left: -10000px; top: 10px",i.value=e,i.focus(),i.selectionEnd=e.length,i.selectionStart=0,setTimeout(()=>{i.remove(),n.focus()},50)}function gu(n){let e=[],t=[],i=!1;for(let s of n.selection.ranges)s.empty||(e.push(n.sliceDoc(s.from,s.to)),t.push(s));if(!e.length){let s=-1;for(let{from:r}of n.selection.ranges){let o=n.doc.lineAt(r);o.number>s&&(e.push(o.text),t.push({from:o.from,to:Math.min(n.doc.length,o.to+1)})),s=o.number}i=!0}return{text:e.join(n.lineBreak),ranges:t,linewise:i}}let ir=null;oe.copy=oe.cut=(n,e)=>{let{text:t,ranges:i,linewise:s}=gu(n.state);if(!t&&!s)return;ir=s?t:null;let r=fh?null:e.clipboardData;r?(e.preventDefault(),r.clearData(),r.setData("text/plain",t)):mu(n,t),e.type=="cut"&&!n.state.readOnly&&n.dispatch({changes:i,scrollIntoView:!0,userEvent:"delete.cut"})};function mh(n){setTimeout(()=>{n.hasFocus!=n.inputState.notifiedFocused&&n.update([])},10)}oe.focus=n=>{n.inputState.lastFocusTime=Date.now(),!n.scrollDOM.scrollTop&&(n.inputState.lastScrollTop||n.inputState.lastScrollLeft)&&(n.scrollDOM.scrollTop=n.inputState.lastScrollTop,n.scrollDOM.scrollLeft=n.inputState.lastScrollLeft),mh(n)};oe.blur=n=>{n.observer.clearSelectionRange(),mh(n)};oe.compositionstart=oe.compositionupdate=n=>{n.inputState.compositionFirstChange==null&&(n.inputState.compositionFirstChange=!0),n.inputState.composing<0&&(n.inputState.composing=0)};oe.compositionend=n=>{n.inputState.composing=-1,n.inputState.compositionEndedAt=Date.now(),n.inputState.compositionFirstChange=null,A.chrome&&A.android&&n.observer.flushSoon(),setTimeout(()=>{n.inputState.composing<0&&n.docView.compositionDeco.size&&n.update([])},50)};oe.contextmenu=n=>{n.inputState.lastContextMenu=Date.now()};oe.beforeinput=(n,e)=>{var t;let i;if(A.chrome&&A.android&&(i=hh.find(s=>s.inputType==e.inputType))&&(n.observer.delayAndroidKey(i.key,i.keyCode),i.key=="Backspace"||i.key=="Delete")){let s=((t=window.visualViewport)===null||t===void 0?void 0:t.height)||0;setTimeout(()=>{var r;(((r=window.visualViewport)===null||r===void 0?void 0:r.height)||0)>s+10&&n.hasFocus&&(n.contentDOM.blur(),n.focus())},100)}};const Ho=["pre-wrap","normal","pre-line","break-spaces"];class yu{constructor(){this.doc=_.empty,this.lineWrapping=!1,this.heightSamples={},this.lineHeight=14,this.charWidth=7,this.lineLength=30,this.heightChanged=!1}heightForGap(e,t){let i=this.doc.lineAt(t).number-this.doc.lineAt(e).number+1;return this.lineWrapping&&(i+=Math.ceil((t-e-i*this.lineLength*.5)/this.lineLength)),this.lineHeight*i}heightForLine(e){return this.lineWrapping?(1+Math.max(0,Math.ceil((e-this.lineLength)/(this.lineLength-5))))*this.lineHeight:this.lineHeight}setDoc(e){return this.doc=e,this}mustRefreshForWrapping(e){return Ho.indexOf(e)>-1!=this.lineWrapping}mustRefreshForHeights(e){let t=!1;for(let i=0;i-1,l=Math.round(t)!=Math.round(this.lineHeight)||this.lineWrapping!=o;if(this.lineWrapping=o,this.lineHeight=t,this.charWidth=i,this.lineLength=s,l){this.heightSamples={};for(let a=0;a0}set outdated(e){this.flags=(e?2:0)|this.flags&-3}setHeight(e,t){this.height!=t&&(Math.abs(this.height-t)>pn&&(e.heightChanged=!0),this.height=t)}replace(e,t,i){return ve.of(i)}decomposeLeft(e,t){t.push(this)}decomposeRight(e,t){t.push(this)}applyChanges(e,t,i,s){let r=this;for(let o=s.length-1;o>=0;o--){let{fromA:l,toA:a,fromB:h,toB:c}=s[o],f=r.lineAt(l,q.ByPosNoHeight,t,0,0),u=f.to>=a?f:r.lineAt(a,q.ByPosNoHeight,t,0,0);for(c+=u.to-a,a=u.to;o>0&&f.from<=s[o-1].toA;)l=s[o-1].fromA,h=s[o-1].fromB,o--,lr*2){let l=e[t-1];l.break?e.splice(--t,1,l.left,null,l.right):e.splice(--t,1,l.left,l.right),i+=1+l.break,s-=l.size}else if(r>s*2){let l=e[i];l.break?e.splice(i,1,l.left,null,l.right):e.splice(i,1,l.left,l.right),i+=2+l.break,r-=l.size}else break;else if(s=r&&o(this.blockAt(0,i,s,r))}updateHeight(e,t=0,i=!1,s){return s&&s.from<=t&&s.more&&this.setHeight(e,s.heights[s.index++]),this.outdated=!1,this}toString(){return`block(${this.length})`}}class De extends gh{constructor(e,t){super(e,t,W.Text),this.collapsed=0,this.widgetHeight=0}replace(e,t,i){let s=i[0];return i.length==1&&(s instanceof De||s instanceof ae&&s.flags&4)&&Math.abs(this.length-s.length)<10?(s instanceof ae?s=new De(s.length,this.height):s.height=this.height,this.outdated||(s.outdated=!1),s):ve.of(i)}updateHeight(e,t=0,i=!1,s){return s&&s.from<=t&&s.more?this.setHeight(e,s.heights[s.index++]):(i||this.outdated)&&this.setHeight(e,Math.max(this.widgetHeight,e.heightForLine(this.length-this.collapsed))),this.outdated=!1,this}toString(){return`line(${this.length}${this.collapsed?-this.collapsed:""}${this.widgetHeight?":"+this.widgetHeight:""})`}}class ae extends ve{constructor(e){super(e,0)}lines(e,t){let i=e.lineAt(t).number,s=e.lineAt(t+this.length).number;return{firstLine:i,lastLine:s,lineHeight:this.height/(s-i+1)}}blockAt(e,t,i,s){let{firstLine:r,lastLine:o,lineHeight:l}=this.lines(t,s),a=Math.max(0,Math.min(o-r,Math.floor((e-i)/l))),{from:h,length:c}=t.line(r+a);return new ut(h,c,i+l*a,l,W.Text)}lineAt(e,t,i,s,r){if(t==q.ByHeight)return this.blockAt(e,i,s,r);if(t==q.ByPosNoHeight){let{from:f,to:u}=i.lineAt(e);return new ut(f,u-f,0,0,W.Text)}let{firstLine:o,lineHeight:l}=this.lines(i,r),{from:a,length:h,number:c}=i.lineAt(e);return new ut(a,h,s+l*(c-o),l,W.Text)}forEachLine(e,t,i,s,r,o){let{firstLine:l,lineHeight:a}=this.lines(i,r);for(let h=Math.max(e,r),c=Math.min(r+this.length,t);h<=c;){let f=i.lineAt(h);h==e&&(s+=a*(f.number-l)),o(new ut(f.from,f.length,s,a,W.Text)),s+=a,h=f.to+1}}replace(e,t,i){let s=this.length-t;if(s>0){let r=i[i.length-1];r instanceof ae?i[i.length-1]=new ae(r.length+s):i.push(null,new ae(s-1))}if(e>0){let r=i[0];r instanceof ae?i[0]=new ae(e+r.length):i.unshift(new ae(e-1),null)}return ve.of(i)}decomposeLeft(e,t){t.push(new ae(e-1),null)}decomposeRight(e,t){t.push(null,new ae(this.length-e-1))}updateHeight(e,t=0,i=!1,s){let r=t+this.length;if(s&&s.from<=t+this.length&&s.more){let o=[],l=Math.max(t,s.from),a=-1,h=e.heightChanged;for(s.from>t&&o.push(new ae(s.from-t-1).updateHeight(e,t));l<=r&&s.more;){let f=e.doc.lineAt(l).length;o.length&&o.push(null);let u=s.heights[s.index++];a==-1?a=u:Math.abs(u-a)>=pn&&(a=-2);let d=new De(f,u);d.outdated=!1,o.push(d),l+=f+1}l<=r&&o.push(null,new ae(r-l).updateHeight(e,l));let c=ve.of(o);return e.heightChanged=h||a<0||Math.abs(c.height-this.height)>=pn||Math.abs(a-this.lines(e.doc,t).lineHeight)>=pn,c}else(i||this.outdated)&&(this.setHeight(e,e.heightForGap(t,t+this.length)),this.outdated=!1);return this}toString(){return`gap(${this.length})`}}class wu extends ve{constructor(e,t,i){super(e.length+t+i.length,e.height+i.height,t|(e.outdated||i.outdated?2:0)),this.left=e,this.right=i,this.size=e.size+i.size}get break(){return this.flags&1}blockAt(e,t,i,s){let r=i+this.left.height;return el))return h;let c=t==q.ByPosNoHeight?q.ByPosNoHeight:q.ByPos;return a?h.join(this.right.lineAt(l,c,i,o,l)):this.left.lineAt(l,c,i,s,r).join(h)}forEachLine(e,t,i,s,r,o){let l=s+this.left.height,a=r+this.left.length+this.break;if(this.break)e=a&&this.right.forEachLine(e,t,i,l,a,o);else{let h=this.lineAt(a,q.ByPos,i,s,r);e=e&&h.from<=t&&o(h),t>h.to&&this.right.forEachLine(h.to+1,t,i,l,a,o)}}replace(e,t,i){let s=this.left.length+this.break;if(tthis.left.length)return this.balanced(this.left,this.right.replace(e-s,t-s,i));let r=[];e>0&&this.decomposeLeft(e,r);let o=r.length;for(let l of i)r.push(l);if(e>0&&Wo(r,o-1),t=i&&t.push(null)),e>i&&this.right.decomposeLeft(e-i,t)}decomposeRight(e,t){let i=this.left.length,s=i+this.break;if(e>=s)return this.right.decomposeRight(e-s,t);e2*t.size||t.size>2*e.size?ve.of(this.break?[e,null,t]:[e,t]):(this.left=e,this.right=t,this.height=e.height+t.height,this.outdated=e.outdated||t.outdated,this.size=e.size+t.size,this.length=e.length+this.break+t.length,this)}updateHeight(e,t=0,i=!1,s){let{left:r,right:o}=this,l=t+r.length+this.break,a=null;return s&&s.from<=t+r.length&&s.more?a=r=r.updateHeight(e,t,i,s):r.updateHeight(e,t,i),s&&s.from<=l+o.length&&s.more?a=o=o.updateHeight(e,l,i,s):o.updateHeight(e,l,i),a?this.balanced(r,o):(this.height=this.left.height+this.right.height,this.outdated=!1,this)}toString(){return this.left+(this.break?" ":"-")+this.right}}function Wo(n,e){let t,i;n[e]==null&&(t=n[e-1])instanceof ae&&(i=n[e+1])instanceof ae&&n.splice(e-1,3,new ae(t.length+1+i.length))}const ku=5;class Pr{constructor(e,t){this.pos=e,this.oracle=t,this.nodes=[],this.lineStart=-1,this.lineEnd=-1,this.covering=null,this.writtenTo=e}get isCovered(){return this.covering&&this.nodes[this.nodes.length-1]==this.covering}span(e,t){if(this.lineStart>-1){let i=Math.min(t,this.lineEnd),s=this.nodes[this.nodes.length-1];s instanceof De?s.length+=i-this.pos:(i>this.pos||!this.isCovered)&&this.nodes.push(new De(i-this.pos,-1)),this.writtenTo=i,t>i&&(this.nodes.push(null),this.writtenTo++,this.lineStart=-1)}this.pos=t}point(e,t,i){if(e=ku)&&this.addLineDeco(s,r)}else t>e&&this.span(e,t);this.lineEnd>-1&&this.lineEnd-1)return;let{from:e,to:t}=this.oracle.doc.lineAt(this.pos);this.lineStart=e,this.lineEnd=t,this.writtenToe&&this.nodes.push(new De(this.pos-e,-1)),this.writtenTo=this.pos}blankContent(e,t){let i=new ae(t-e);return this.oracle.doc.lineAt(e).to==t&&(i.flags|=4),i}ensureLine(){this.enterLine();let e=this.nodes.length?this.nodes[this.nodes.length-1]:null;if(e instanceof De)return e;let t=new De(0,-1);return this.nodes.push(t),t}addBlock(e){this.enterLine(),e.type==W.WidgetAfter&&!this.isCovered&&this.ensureLine(),this.nodes.push(e),this.writtenTo=this.pos=this.pos+e.length,e.type!=W.WidgetBefore&&(this.covering=e)}addLineDeco(e,t){let i=this.ensureLine();i.length+=t,i.collapsed+=t,i.widgetHeight=Math.max(i.widgetHeight,e),this.writtenTo=this.pos=this.pos+t}finish(e){let t=this.nodes.length==0?null:this.nodes[this.nodes.length-1];this.lineStart>-1&&!(t instanceof De)&&!this.isCovered?this.nodes.push(new De(0,-1)):(this.writtenToc.clientHeight||c.scrollWidth>c.clientWidth)&&f.overflow!="visible"){let u=c.getBoundingClientRect();r=Math.max(r,u.left),o=Math.min(o,u.right),l=Math.max(l,u.top),a=h==n.parentNode?u.bottom:Math.min(a,u.bottom)}h=f.position=="absolute"||f.position=="fixed"?c.offsetParent:c.parentNode}else if(h.nodeType==11)h=h.host;else break;return{left:r-t.left,right:Math.max(r,o)-t.left,top:l-(t.top+e),bottom:Math.max(l,a)-(t.top+e)}}function Cu(n,e){let t=n.getBoundingClientRect();return{left:0,right:t.right-t.left,top:e,bottom:t.bottom-(t.top+e)}}class os{constructor(e,t,i){this.from=e,this.to=t,this.size=i}static same(e,t){if(e.length!=t.length)return!1;for(let i=0;itypeof t!="function"),this.heightMap=ve.empty().applyChanges(this.stateDeco,_.empty,this.heightOracle.setDoc(e.doc),[new Qe(0,0,0,e.doc.length)]),this.viewport=this.getViewport(0,null),this.updateViewportLines(),this.updateForViewport(),this.lineGaps=this.ensureLineGaps([]),this.lineGapDeco=E.set(this.lineGaps.map(t=>t.draw(!1))),this.computeVisibleRanges()}updateForViewport(){let e=[this.viewport],{main:t}=this.state.selection;for(let i=0;i<=1;i++){let s=i?t.head:t.anchor;if(!e.some(({from:r,to:o})=>s>=r&&s<=o)){let{from:r,to:o}=this.lineBlockAt(s);e.push(new Ji(r,o))}}this.viewports=e.sort((i,s)=>i.from-s.from),this.scaler=this.heightMap.height<=7e6?qo:new Tu(this.heightOracle.doc,this.heightMap,this.viewports)}updateViewportLines(){this.viewportLines=[],this.heightMap.forEachLine(this.viewport.from,this.viewport.to,this.state.doc,0,0,e=>{this.viewportLines.push(this.scaler.scale==1?e:wi(e,this.scaler))})}update(e,t=null){this.state=e.state;let i=this.stateDeco;this.stateDeco=this.state.facet(Ei).filter(h=>typeof h!="function");let s=e.changedRanges,r=Qe.extendWithRanges(s,vu(i,this.stateDeco,e?e.changes:ne.empty(this.state.doc.length))),o=this.heightMap.height;this.heightMap=this.heightMap.applyChanges(this.stateDeco,e.startState.doc,this.heightOracle.setDoc(this.state.doc),r),this.heightMap.height!=o&&(e.flags|=2);let l=r.length?this.mapViewport(this.viewport,e.changes):this.viewport;(t&&(t.range.headl.to)||!this.viewportIsAppropriate(l))&&(l=this.getViewport(0,t));let a=!e.changes.empty||e.flags&2||l.from!=this.viewport.from||l.to!=this.viewport.to;this.viewport=l,this.updateForViewport(),a&&this.updateViewportLines(),(this.lineGaps.length||this.viewport.to-this.viewport.from>2e3<<1)&&this.updateLineGaps(this.ensureLineGaps(this.mapLineGaps(this.lineGaps,e.changes))),e.flags|=this.computeVisibleRanges(),t&&(this.scrollTarget=t),!this.mustEnforceCursorAssoc&&e.selectionSet&&e.view.lineWrapping&&e.state.selection.main.empty&&e.state.selection.main.assoc&&!e.state.facet(Xa)&&(this.mustEnforceCursorAssoc=!0)}measure(e){let t=e.contentDOM,i=window.getComputedStyle(t),s=this.heightOracle,r=i.whiteSpace;this.defaultTextDirection=i.direction=="rtl"?Z.RTL:Z.LTR;let o=this.heightOracle.mustRefreshForWrapping(r),l=o||this.mustMeasureContent||this.contentDOMHeight!=t.clientHeight;this.contentDOMHeight=t.clientHeight,this.mustMeasureContent=!1;let a=0,h=0,c=parseInt(i.paddingTop)||0,f=parseInt(i.paddingBottom)||0;(this.paddingTop!=c||this.paddingBottom!=f)&&(this.paddingTop=c,this.paddingBottom=f,a|=10),this.editorWidth!=e.scrollDOM.clientWidth&&(s.lineWrapping&&(l=!0),this.editorWidth=e.scrollDOM.clientWidth,a|=8);let u=(this.printing?Cu:Su)(t,this.paddingTop),d=u.top-this.pixelViewport.top,p=u.bottom-this.pixelViewport.bottom;this.pixelViewport=u;let g=this.pixelViewport.bottom>this.pixelViewport.top&&this.pixelViewport.right>this.pixelViewport.left;if(g!=this.inView&&(this.inView=g,g&&(l=!0)),!this.inView&&!this.scrollTarget)return 0;let y=t.clientWidth;if((this.contentDOMWidth!=y||this.editorHeight!=e.scrollDOM.clientHeight)&&(this.contentDOMWidth=y,this.editorHeight=e.scrollDOM.clientHeight,a|=8),l){let v=e.docView.measureVisibleLineHeights(this.viewport);if(s.mustRefreshForHeights(v)&&(o=!0),o||s.lineWrapping&&Math.abs(y-this.contentDOMWidth)>s.charWidth){let{lineHeight:S,charWidth:k}=e.docView.measureTextSize();o=S>0&&s.refresh(r,S,k,y/k,v),o&&(e.docView.minWidth=0,a|=8)}d>0&&p>0?h=Math.max(d,p):d<0&&p<0&&(h=Math.min(d,p)),s.heightChanged=!1;for(let S of this.viewports){let k=S.from==this.viewport.from?v:e.docView.measureVisibleLineHeights(S);this.heightMap=o?ve.empty().applyChanges(this.stateDeco,_.empty,this.heightOracle,[new Qe(0,0,0,e.state.doc.length)]):this.heightMap.updateHeight(s,0,o,new bu(S.from,k))}s.heightChanged&&(a|=2)}let b=!this.viewportIsAppropriate(this.viewport,h)||this.scrollTarget&&(this.scrollTarget.range.headthis.viewport.to);return b&&(this.viewport=this.getViewport(h,this.scrollTarget)),this.updateForViewport(),(a&2||b)&&this.updateViewportLines(),(this.lineGaps.length||this.viewport.to-this.viewport.from>2e3<<1)&&this.updateLineGaps(this.ensureLineGaps(o?[]:this.lineGaps,e)),a|=this.computeVisibleRanges(),this.mustEnforceCursorAssoc&&(this.mustEnforceCursorAssoc=!1,e.docView.enforceCursorAssoc()),a}get visibleTop(){return this.scaler.fromDOM(this.pixelViewport.top)}get visibleBottom(){return this.scaler.fromDOM(this.pixelViewport.bottom)}getViewport(e,t){let i=.5-Math.max(-.5,Math.min(.5,e/1e3/2)),s=this.heightMap,r=this.state.doc,{visibleTop:o,visibleBottom:l}=this,a=new Ji(s.lineAt(o-i*1e3,q.ByHeight,r,0,0).from,s.lineAt(l+(1-i)*1e3,q.ByHeight,r,0,0).to);if(t){let{head:h}=t.range;if(ha.to){let c=Math.min(this.editorHeight,this.pixelViewport.bottom-this.pixelViewport.top),f=s.lineAt(h,q.ByPos,r,0,0),u;t.y=="center"?u=(f.top+f.bottom)/2-c/2:t.y=="start"||t.y=="nearest"&&h=l+Math.max(10,Math.min(i,250)))&&s>o-2*1e3&&r>1,o=s<<1;if(this.defaultTextDirection!=Z.LTR&&!i)return[];let l=[],a=(h,c,f,u)=>{if(c-hh&&yy.from>=f.from&&y.to<=f.to&&Math.abs(y.from-h)y.fromb));if(!g){if(cy.from<=c&&y.to>=c)){let y=t.moveToLineBoundary(w.cursor(c),!1,!0).head;y>h&&(c=y)}g=new os(h,c,this.gapSize(f,h,c,u))}l.push(g)};for(let h of this.viewportLines){if(h.lengthh.from&&a(h.from,u,h,c),dt.draw(this.heightOracle.lineWrapping))))}computeVisibleRanges(){let e=this.stateDeco;this.lineGaps.length&&(e=e.concat(this.lineGapDeco));let t=[];F.spans(e,this.viewport.from,this.viewport.to,{span(s,r){t.push({from:s,to:r})},point(){}},20);let i=t.length!=this.visibleRanges.length||this.visibleRanges.some((s,r)=>s.from!=t[r].from||s.to!=t[r].to);return this.visibleRanges=t,i?4:0}lineBlockAt(e){return e>=this.viewport.from&&e<=this.viewport.to&&this.viewportLines.find(t=>t.from<=e&&t.to>=e)||wi(this.heightMap.lineAt(e,q.ByPos,this.state.doc,0,0),this.scaler)}lineBlockAtHeight(e){return wi(this.heightMap.lineAt(this.scaler.fromDOM(e),q.ByHeight,this.state.doc,0,0),this.scaler)}elementAtHeight(e){return wi(this.heightMap.blockAt(this.scaler.fromDOM(e),this.state.doc,0,0),this.scaler)}get docHeight(){return this.scaler.toDOM(this.heightMap.height)}get contentHeight(){return this.docHeight+this.paddingTop+this.paddingBottom}}class Ji{constructor(e,t){this.from=e,this.to=t}}function Mu(n,e,t){let i=[],s=n,r=0;return F.spans(t,n,e,{span(){},point(o,l){o>s&&(i.push({from:s,to:o}),r+=o-s),s=l}},20),s=1)return e[e.length-1].to;let i=Math.floor(n*t);for(let s=0;;s++){let{from:r,to:o}=e[s],l=o-r;if(i<=l)return r+i;i-=l}}function Xi(n,e){let t=0;for(let{from:i,to:s}of n.ranges){if(e<=s){t+=e-i;break}t+=s-i}return t/n.total}function Du(n,e){for(let t of n)if(e(t))return t}const qo={toDOM(n){return n},fromDOM(n){return n},scale:1};class Tu{constructor(e,t,i){let s=0,r=0,o=0;this.viewports=i.map(({from:l,to:a})=>{let h=t.lineAt(l,q.ByPos,e,0,0).top,c=t.lineAt(a,q.ByPos,e,0,0).bottom;return s+=c-h,{from:l,to:a,top:h,bottom:c,domTop:0,domBottom:0}}),this.scale=(7e6-s)/(t.height-s);for(let l of this.viewports)l.domTop=o+(l.top-r)*this.scale,o=l.domBottom=l.domTop+(l.bottom-l.top),r=l.bottom}toDOM(e){for(let t=0,i=0,s=0;;t++){let r=twi(s,e)):n.type)}const Zi=D.define({combine:n=>n.join(" ")}),nr=D.define({combine:n=>n.indexOf(!0)>-1}),sr=mt.newName(),yh=mt.newName(),bh=mt.newName(),wh={"&light":"."+yh,"&dark":"."+bh};function rr(n,e,t){return new mt(e,{finish(i){return/&/.test(i)?i.replace(/&\w*/,s=>{if(s=="&")return n;if(!t||!t[s])throw new RangeError(`Unsupported selector: ${s}`);return t[s]}):n+" "+i}})}const Ou=rr("."+sr,{"&.cm-editor":{position:"relative !important",boxSizing:"border-box","&.cm-focused":{outline:"1px dotted #212121"},display:"flex !important",flexDirection:"column"},".cm-scroller":{display:"flex !important",alignItems:"flex-start !important",fontFamily:"monospace",lineHeight:1.4,height:"100%",overflowX:"auto",position:"relative",zIndex:0},".cm-content":{margin:0,flexGrow:2,flexShrink:0,minHeight:"100%",display:"block",whiteSpace:"pre",wordWrap:"normal",boxSizing:"border-box",padding:"4px 0",outline:"none","&[contenteditable=true]":{WebkitUserModify:"read-write-plaintext-only"}},".cm-lineWrapping":{whiteSpace_fallback:"pre-wrap",whiteSpace:"break-spaces",wordBreak:"break-word",overflowWrap:"anywhere",flexShrink:1},"&light .cm-content":{caretColor:"black"},"&dark .cm-content":{caretColor:"white"},".cm-line":{display:"block",padding:"0 2px 0 4px"},".cm-selectionLayer":{zIndex:-1,contain:"size style"},".cm-selectionBackground":{position:"absolute"},"&light .cm-selectionBackground":{background:"#d9d9d9"},"&dark .cm-selectionBackground":{background:"#222"},"&light.cm-focused .cm-selectionBackground":{background:"#d7d4f0"},"&dark.cm-focused .cm-selectionBackground":{background:"#233"},".cm-cursorLayer":{zIndex:100,contain:"size style",pointerEvents:"none"},"&.cm-focused .cm-cursorLayer":{animation:"steps(1) cm-blink 1.2s infinite"},"@keyframes cm-blink":{"0%":{},"50%":{opacity:0},"100%":{}},"@keyframes cm-blink2":{"0%":{},"50%":{opacity:0},"100%":{}},".cm-cursor, .cm-dropCursor":{position:"absolute",borderLeft:"1.2px solid black",marginLeft:"-0.6px",pointerEvents:"none"},".cm-cursor":{display:"none"},"&dark .cm-cursor":{borderLeftColor:"#444"},"&.cm-focused .cm-cursor":{display:"block"},"&light .cm-activeLine":{backgroundColor:"#cceeff44"},"&dark .cm-activeLine":{backgroundColor:"#99eeff33"},"&light .cm-specialChar":{color:"red"},"&dark .cm-specialChar":{color:"#f78"},".cm-gutters":{flexShrink:0,display:"flex",height:"100%",boxSizing:"border-box",left:0,zIndex:200},"&light .cm-gutters":{backgroundColor:"#f5f5f5",color:"#6c6c6c",borderRight:"1px solid #ddd"},"&dark .cm-gutters":{backgroundColor:"#333338",color:"#ccc"},".cm-gutter":{display:"flex !important",flexDirection:"column",flexShrink:0,boxSizing:"border-box",minHeight:"100%",overflow:"hidden"},".cm-gutterElement":{boxSizing:"border-box"},".cm-lineNumbers .cm-gutterElement":{padding:"0 3px 0 5px",minWidth:"20px",textAlign:"right",whiteSpace:"nowrap"},"&light .cm-activeLineGutter":{backgroundColor:"#e2f2ff"},"&dark .cm-activeLineGutter":{backgroundColor:"#222227"},".cm-panels":{boxSizing:"border-box",position:"sticky",left:0,right:0},"&light .cm-panels":{backgroundColor:"#f5f5f5",color:"black"},"&light .cm-panels-top":{borderBottom:"1px solid #ddd"},"&light .cm-panels-bottom":{borderTop:"1px solid #ddd"},"&dark .cm-panels":{backgroundColor:"#333338",color:"white"},".cm-tab":{display:"inline-block",overflow:"hidden",verticalAlign:"bottom"},".cm-widgetBuffer":{verticalAlign:"text-top",height:"1em",width:0,display:"inline"},".cm-placeholder":{color:"#888",display:"inline-block",verticalAlign:"top"},".cm-button":{verticalAlign:"middle",color:"inherit",fontSize:"70%",padding:".2em 1em",borderRadius:"1px"},"&light .cm-button":{backgroundImage:"linear-gradient(#eff1f5, #d9d9df)",border:"1px solid #888","&:active":{backgroundImage:"linear-gradient(#b4b4b4, #d0d3d6)"}},"&dark .cm-button":{backgroundImage:"linear-gradient(#393939, #111)",border:"1px solid #888","&:active":{backgroundImage:"linear-gradient(#111, #333)"}},".cm-textfield":{verticalAlign:"middle",color:"inherit",fontSize:"70%",border:"1px solid silver",padding:".2em .5em"},"&light .cm-textfield":{backgroundColor:"white"},"&dark .cm-textfield":{border:"1px solid #555",backgroundColor:"inherit"}},wh);class Bu{constructor(e,t,i,s){this.typeOver=s,this.bounds=null,this.text="";let{impreciseHead:r,impreciseAnchor:o}=e.docView;if(t>-1&&!e.state.readOnly&&(this.bounds=e.docView.domBoundsAround(t,i,0))){let l=r||o?[]:Eu(e),a=new rh(l,e.state);a.readRange(this.bounds.startDOM,this.bounds.endDOM),this.text=a.text,this.newSel=Ru(l,this.bounds.from)}else{let l=e.observer.selectionRange,a=r&&r.node==l.focusNode&&r.offset==l.focusOffset||!Xt(e.contentDOM,l.focusNode)?e.state.selection.main.head:e.docView.posFromDOM(l.focusNode,l.focusOffset),h=o&&o.node==l.anchorNode&&o.offset==l.anchorOffset||!Xt(e.contentDOM,l.anchorNode)?e.state.selection.main.anchor:e.docView.posFromDOM(l.anchorNode,l.anchorOffset);this.newSel=w.single(h,a)}}}function kh(n,e){let t,{newSel:i}=e,s=n.state.selection.main;if(e.bounds){let{from:r,to:o}=e.bounds,l=s.from,a=null;(n.inputState.lastKeyCode===8&&n.inputState.lastKeyTime>Date.now()-100||A.android&&e.text.length=s.from&&t.to<=s.to&&(t.from!=s.from||t.to!=s.to)&&s.to-s.from-(t.to-t.from)<=4?t={from:s.from,to:s.to,insert:n.state.doc.slice(s.from,t.from).append(t.insert).append(n.state.doc.slice(t.to,s.to))}:(A.mac||A.android)&&t&&t.from==t.to&&t.from==s.head-1&&/^\. ?$/.test(t.insert.toString())?(i&&t.insert.length==2&&(i=w.single(i.main.anchor-1,i.main.head-1)),t={from:s.from,to:s.to,insert:_.of([" "])}):A.chrome&&t&&t.from==t.to&&t.from==s.head&&t.insert.toString()==`
- `&&n.lineWrapping&&(i&&(i=w.single(i.main.anchor-1,i.main.head-1)),t={from:s.from,to:s.to,insert:_.of([" "])}),t){let r=n.state;if(A.ios&&n.inputState.flushIOSKey(n)||A.android&&(t.from==s.from&&t.to==s.to&&t.insert.length==1&&t.insert.lines==2&&$t(n.contentDOM,"Enter",13)||t.from==s.from-1&&t.to==s.to&&t.insert.length==0&&$t(n.contentDOM,"Backspace",8)||t.from==s.from&&t.to==s.to+1&&t.insert.length==0&&$t(n.contentDOM,"Delete",46)))return!0;let o=t.insert.toString();if(n.state.facet(Ja).some(h=>h(n,t.from,t.to,o)))return!0;n.inputState.composing>=0&&n.inputState.composing++;let l;if(t.from>=s.from&&t.to<=s.to&&t.to-t.from>=(s.to-s.from)/3&&(!i||i.main.empty&&i.main.from==t.from+t.insert.length)&&n.inputState.composing<0){let h=s.fromt.to?r.sliceDoc(t.to,s.to):"";l=r.replaceSelection(n.state.toText(h+t.insert.sliceString(0,void 0,n.state.lineBreak)+c))}else{let h=r.changes(t),c=i&&!r.selection.main.eq(i.main)&&i.main.to<=h.newLength?i.main:void 0;if(r.selection.ranges.length>1&&n.inputState.composing>=0&&t.to<=s.to&&t.to>=s.to-10){let f=n.state.sliceDoc(t.from,t.to),u=oh(n)||n.state.doc.lineAt(s.head),d=s.to-t.to,p=s.to-s.from;l=r.changeByRange(g=>{if(g.from==s.from&&g.to==s.to)return{changes:h,range:c||g.map(h)};let y=g.to-d,b=y-f.length;if(g.to-g.from!=p||n.state.sliceDoc(b,y)!=f||u&&g.to>=u.from&&g.from<=u.to)return{range:g};let v=r.changes({from:b,to:y,insert:t.insert}),S=g.to-s.to;return{changes:v,range:c?w.range(Math.max(0,c.anchor+S),Math.max(0,c.head+S)):g.map(v)}})}else l={changes:h,selection:c&&r.selection.replaceRange(c)}}let a="input.type";return n.composing&&(a+=".compose",n.inputState.compositionFirstChange&&(a+=".start",n.inputState.compositionFirstChange=!1)),n.dispatch(l,{scrollIntoView:!0,userEvent:a}),!0}else if(i&&!i.main.eq(s)){let r=!1,o="select";return n.inputState.lastSelectionTime>Date.now()-50&&(n.inputState.lastSelectionOrigin=="select"&&(r=!0),o=n.inputState.lastSelectionOrigin),n.dispatch({selection:i,scrollIntoView:r,userEvent:o}),!0}else return!1}function Pu(n,e,t,i){let s=Math.min(n.length,e.length),r=0;for(;r0&&l>0&&n.charCodeAt(o-1)==e.charCodeAt(l-1);)o--,l--;if(i=="end"){let a=Math.max(0,r-Math.min(o,l));t-=o+a-r}if(o=o?r-t:0;r-=a,l=r+(l-o),o=r}else if(l=l?r-t:0;r-=a,o=r+(o-l),l=r}return{from:r,toA:o,toB:l}}function Eu(n){let e=[];if(n.root.activeElement!=n.contentDOM)return e;let{anchorNode:t,anchorOffset:i,focusNode:s,focusOffset:r}=n.observer.selectionRange;return t&&(e.push(new Co(t,i)),(s!=t||r!=i)&&e.push(new Co(s,r))),e}function Ru(n,e){if(n.length==0)return null;let t=n[0].pos,i=n.length==2?n[1].pos:t;return t>-1&&i>-1?w.single(t+e,i+e):null}const Lu={childList:!0,characterData:!0,subtree:!0,attributes:!0,characterDataOldValue:!0},ls=A.ie&&A.ie_version<=11;class Iu{constructor(e){this.view=e,this.active=!1,this.selectionRange=new Tf,this.selectionChanged=!1,this.delayedFlush=-1,this.resizeTimeout=-1,this.queue=[],this.delayedAndroidKey=null,this.flushingAndroidKey=-1,this.lastChange=0,this.scrollTargets=[],this.intersection=null,this.resize=null,this.intersecting=!1,this.gapIntersection=null,this.gaps=[],this.parentCheck=-1,this.dom=e.contentDOM,this.observer=new MutationObserver(t=>{for(let i of t)this.queue.push(i);(A.ie&&A.ie_version<=11||A.ios&&e.composing)&&t.some(i=>i.type=="childList"&&i.removedNodes.length||i.type=="characterData"&&i.oldValue.length>i.target.nodeValue.length)?this.flushSoon():this.flush()}),ls&&(this.onCharData=t=>{this.queue.push({target:t.target,type:"characterData",oldValue:t.prevValue}),this.flushSoon()}),this.onSelectionChange=this.onSelectionChange.bind(this),this.onResize=this.onResize.bind(this),this.onPrint=this.onPrint.bind(this),this.onScroll=this.onScroll.bind(this),typeof ResizeObserver=="function"&&(this.resize=new ResizeObserver(()=>{var t;((t=this.view.docView)===null||t===void 0?void 0:t.lastUpdate){this.parentCheck<0&&(this.parentCheck=setTimeout(this.listenForScroll.bind(this),1e3)),t.length>0&&t[t.length-1].intersectionRatio>0!=this.intersecting&&(this.intersecting=!this.intersecting,this.intersecting!=this.view.inView&&this.onScrollChanged(document.createEvent("Event")))},{}),this.intersection.observe(this.dom),this.gapIntersection=new IntersectionObserver(t=>{t.length>0&&t[t.length-1].intersectionRatio>0&&this.onScrollChanged(document.createEvent("Event"))},{})),this.listenForScroll(),this.readSelectionRange()}onScrollChanged(e){this.view.inputState.runScrollHandlers(this.view,e),this.intersecting&&this.view.measure()}onScroll(e){this.intersecting&&this.flush(!1),this.onScrollChanged(e)}onResize(){this.resizeTimeout<0&&(this.resizeTimeout=setTimeout(()=>{this.resizeTimeout=-1,this.view.requestMeasure()},50))}onPrint(){this.view.viewState.printing=!0,this.view.measure(),setTimeout(()=>{this.view.viewState.printing=!1,this.view.requestMeasure()},500)}updateGaps(e){if(this.gapIntersection&&(e.length!=this.gaps.length||this.gaps.some((t,i)=>t!=e[i]))){this.gapIntersection.disconnect();for(let t of e)this.gapIntersection.observe(t);this.gaps=e}}onSelectionChange(e){let t=this.selectionChanged;if(!this.readSelectionRange()||this.delayedAndroidKey)return;let{view:i}=this,s=this.selectionRange;if(i.state.facet(zn)?i.root.activeElement!=this.dom:!dn(i.dom,s))return;let r=s.anchorNode&&i.docView.nearest(s.anchorNode);if(r&&r.ignoreEvent(e)){t||(this.selectionChanged=!1);return}(A.ie&&A.ie_version<=11||A.android&&A.chrome)&&!i.state.selection.main.empty&&s.focusNode&&Sn(s.focusNode,s.focusOffset,s.anchorNode,s.anchorOffset)?this.flushSoon():this.flush(!1)}readSelectionRange(){let{view:e}=this,t=A.safari&&e.root.nodeType==11&&Af(this.dom.ownerDocument)==this.dom&&Nu(this.view)||xn(e.root);if(!t||this.selectionRange.eq(t))return!1;let i=dn(this.dom,t);return i&&!this.selectionChanged&&e.inputState.lastFocusTime>Date.now()-200&&e.inputState.lastTouchTime{let r=this.delayedAndroidKey;r&&(this.clearDelayedAndroidKey(),!this.flush()&&r.force&&$t(this.dom,r.key,r.keyCode))};this.flushingAndroidKey=this.view.win.requestAnimationFrame(s)}(!this.delayedAndroidKey||e=="Enter")&&(this.delayedAndroidKey={key:e,keyCode:t,force:this.lastChange{this.delayedFlush=-1,this.flush()}))}forceFlush(){this.delayedFlush>=0&&(this.view.win.cancelAnimationFrame(this.delayedFlush),this.delayedFlush=-1),this.flush()}processRecords(){let e=this.queue;for(let r of this.observer.takeRecords())e.push(r);e.length&&(this.queue=[]);let t=-1,i=-1,s=!1;for(let r of e){let o=this.readMutation(r);o&&(o.typeOver&&(s=!0),t==-1?{from:t,to:i}=o:(t=Math.min(o.from,t),i=Math.max(o.to,i)))}return{from:t,to:i,typeOver:s}}readChange(){let{from:e,to:t,typeOver:i}=this.processRecords(),s=this.selectionChanged&&dn(this.dom,this.selectionRange);return e<0&&!s?null:(e>-1&&(this.lastChange=Date.now()),this.view.inputState.lastFocusTime=0,this.selectionChanged=!1,new Bu(this.view,e,t,i))}flush(e=!0){if(this.delayedFlush>=0||this.delayedAndroidKey)return!1;e&&this.readSelectionRange();let t=this.readChange();if(!t)return!1;let i=this.view.state,s=kh(this.view,t);return this.view.state==i&&this.view.update([]),s}readMutation(e){let t=this.view.docView.nearest(e.target);if(!t||t.ignoreMutation(e))return null;if(t.markDirty(e.type=="attributes"),e.type=="attributes"&&(t.dirty|=4),e.type=="childList"){let i=jo(t,e.previousSibling||e.target.previousSibling,-1),s=jo(t,e.nextSibling||e.target.nextSibling,1);return{from:i?t.posAfter(i):t.posAtStart,to:s?t.posBefore(s):t.posAtEnd,typeOver:!1}}else return e.type=="characterData"?{from:t.posAtStart,to:t.posAtEnd,typeOver:e.target.nodeValue==e.oldValue}:null}setWindow(e){e!=this.win&&(this.removeWindowListeners(this.win),this.win=e,this.addWindowListeners(this.win))}addWindowListeners(e){e.addEventListener("resize",this.onResize),e.addEventListener("beforeprint",this.onPrint),e.addEventListener("scroll",this.onScroll),e.document.addEventListener("selectionchange",this.onSelectionChange)}removeWindowListeners(e){e.removeEventListener("scroll",this.onScroll),e.removeEventListener("resize",this.onResize),e.removeEventListener("beforeprint",this.onPrint),e.document.removeEventListener("selectionchange",this.onSelectionChange)}destroy(){var e,t,i;this.stop(),(e=this.intersection)===null||e===void 0||e.disconnect(),(t=this.gapIntersection)===null||t===void 0||t.disconnect(),(i=this.resize)===null||i===void 0||i.disconnect();for(let s of this.scrollTargets)s.removeEventListener("scroll",this.onScroll);this.removeWindowListeners(this.win),clearTimeout(this.parentCheck),clearTimeout(this.resizeTimeout),this.win.cancelAnimationFrame(this.delayedFlush),this.win.cancelAnimationFrame(this.flushingAndroidKey)}}function jo(n,e,t){for(;e;){let i=K.get(e);if(i&&i.parent==n)return i;let s=e.parentNode;e=s!=n.dom?s:t>0?e.nextSibling:e.previousSibling}return null}function Nu(n){let e=null;function t(a){a.preventDefault(),a.stopImmediatePropagation(),e=a.getTargetRanges()[0]}if(n.contentDOM.addEventListener("beforeinput",t,!0),n.dom.ownerDocument.execCommand("indent"),n.contentDOM.removeEventListener("beforeinput",t,!0),!e)return null;let i=e.startContainer,s=e.startOffset,r=e.endContainer,o=e.endOffset,l=n.docView.domAtPos(n.state.selection.main.anchor);return Sn(l.node,l.offset,r,o)&&([i,s,r,o]=[r,o,i,s]),{anchorNode:i,anchorOffset:s,focusNode:r,focusOffset:o}}class O{constructor(e={}){this.plugins=[],this.pluginMap=new Map,this.editorAttrs={},this.contentAttrs={},this.bidiCache=[],this.destroyed=!1,this.updateState=2,this.measureScheduled=-1,this.measureRequests=[],this.contentDOM=document.createElement("div"),this.scrollDOM=document.createElement("div"),this.scrollDOM.tabIndex=-1,this.scrollDOM.className="cm-scroller",this.scrollDOM.appendChild(this.contentDOM),this.announceDOM=document.createElement("div"),this.announceDOM.style.cssText="position: absolute; top: -10000px",this.announceDOM.setAttribute("aria-live","polite"),this.dom=document.createElement("div"),this.dom.appendChild(this.announceDOM),this.dom.appendChild(this.scrollDOM),this._dispatch=e.dispatch||(t=>this.update([t])),this.dispatch=this.dispatch.bind(this),this._root=e.root||Of(e.parent)||document,this.viewState=new zo(e.state||N.create(e)),this.plugins=this.state.facet(yi).map(t=>new ns(t));for(let t of this.plugins)t.update(this);this.observer=new Iu(this),this.inputState=new su(this),this.inputState.ensureHandlers(this,this.plugins),this.docView=new Ao(this),this.mountStyles(),this.updateAttrs(),this.updateState=0,this.requestMeasure(),e.parent&&e.parent.appendChild(this.dom)}get state(){return this.viewState.state}get viewport(){return this.viewState.viewport}get visibleRanges(){return this.viewState.visibleRanges}get inView(){return this.viewState.inView}get composing(){return this.inputState.composing>0}get compositionStarted(){return this.inputState.composing>=0}get root(){return this._root}get win(){return this.dom.ownerDocument.defaultView||window}dispatch(...e){this._dispatch(e.length==1&&e[0]instanceof re?e[0]:this.state.update(...e))}update(e){if(this.updateState!=0)throw new Error("Calls to EditorView.update are not allowed while an update is in progress");let t=!1,i=!1,s,r=this.state;for(let h of e){if(h.startState!=r)throw new RangeError("Trying to update state with a transaction that doesn't start from the previous state.");r=h.state}if(this.destroyed){this.viewState.state=r;return}let o=this.observer.delayedAndroidKey,l=null;if(o?(this.observer.clearDelayedAndroidKey(),l=this.observer.readChange(),(l&&!this.state.doc.eq(r.doc)||!this.state.selection.eq(r.selection))&&(l=null)):this.observer.clear(),r.facet(N.phrases)!=this.state.facet(N.phrases))return this.setState(r);s=Mn.create(this,r,e);let a=this.viewState.scrollTarget;try{this.updateState=2;for(let h of e){if(a&&(a=a.map(h.changes)),h.scrollIntoView){let{main:c}=h.state.selection;a=new An(c.empty?c:w.cursor(c.head,c.head>c.anchor?-1:1))}for(let c of h.effects)c.is(xo)&&(a=c.value)}this.viewState.update(s,a),this.bidiCache=Dn.update(this.bidiCache,s.changes),s.empty||(this.updatePlugins(s),this.inputState.update(s)),t=this.docView.update(s),this.state.facet(bi)!=this.styleModules&&this.mountStyles(),i=this.updateAttrs(),this.showAnnouncements(e),this.docView.updateSelection(t,e.some(h=>h.isUserEvent("select.pointer")))}finally{this.updateState=0}if(s.startState.facet(Zi)!=s.state.facet(Zi)&&(this.viewState.mustMeasureContent=!0),(t||i||a||this.viewState.mustEnforceCursorAssoc||this.viewState.mustMeasureContent)&&this.requestMeasure(),!s.empty)for(let h of this.state.facet(Xs))h(s);l&&!kh(this,l)&&o.force&&$t(this.contentDOM,o.key,o.keyCode)}setState(e){if(this.updateState!=0)throw new Error("Calls to EditorView.setState are not allowed while an update is in progress");if(this.destroyed){this.viewState.state=e;return}this.updateState=2;let t=this.hasFocus;try{for(let i of this.plugins)i.destroy(this);this.viewState=new zo(e),this.plugins=e.facet(yi).map(i=>new ns(i)),this.pluginMap.clear();for(let i of this.plugins)i.update(this);this.docView=new Ao(this),this.inputState.ensureHandlers(this,this.plugins),this.mountStyles(),this.updateAttrs(),this.bidiCache=[]}finally{this.updateState=0}t&&this.focus(),this.requestMeasure()}updatePlugins(e){let t=e.startState.facet(yi),i=e.state.facet(yi);if(t!=i){let s=[];for(let r of i){let o=t.indexOf(r);if(o<0)s.push(new ns(r));else{let l=this.plugins[o];l.mustUpdate=e,s.push(l)}}for(let r of this.plugins)r.mustUpdate!=e&&r.destroy(this);this.plugins=s,this.pluginMap.clear(),this.inputState.ensureHandlers(this,this.plugins)}else for(let s of this.plugins)s.mustUpdate=e;for(let s=0;s-1&&cancelAnimationFrame(this.measureScheduled),this.measureScheduled=0,e&&this.observer.forceFlush();let t=null,{scrollHeight:i,scrollTop:s,clientHeight:r}=this.scrollDOM,o=s>i-r-4?i:s;try{for(let l=0;;l++){this.updateState=1;let a=this.viewport,h=this.viewState.lineBlockAtHeight(o),c=this.viewState.measure(this);if(!c&&!this.measureRequests.length&&this.viewState.scrollTarget==null)break;if(l>5){console.warn(this.measureRequests.length?"Measure loop restarted more than 5 times":"Viewport failed to stabilize");break}let f=[];c&4||([this.measureRequests,f]=[f,this.measureRequests]);let u=f.map(y=>{try{return y.read(this)}catch(b){return He(this.state,b),Ko}}),d=Mn.create(this,this.state,[]),p=!1,g=!1;d.flags|=c,t?t.flags|=c:t=d,this.updateState=2,d.empty||(this.updatePlugins(d),this.inputState.update(d),this.updateAttrs(),p=this.docView.update(d));for(let y=0;y1||y<-1)&&(this.scrollDOM.scrollTop+=y,g=!0)}if(p&&this.docView.updateSelection(!0),this.viewport.from==a.from&&this.viewport.to==a.to&&!g&&this.measureRequests.length==0)break}}finally{this.updateState=0,this.measureScheduled=-1}if(t&&!t.empty)for(let l of this.state.facet(Xs))l(t)}get themeClasses(){return sr+" "+(this.state.facet(nr)?bh:yh)+" "+this.state.facet(Zi)}updateAttrs(){let e=Uo(this,Za,{class:"cm-editor"+(this.hasFocus?" cm-focused ":" ")+this.themeClasses}),t={spellcheck:"false",autocorrect:"off",autocapitalize:"off",translate:"no",contenteditable:this.state.facet(zn)?"true":"false",class:"cm-content",style:`${A.tabSize}: ${this.state.tabSize}`,role:"textbox","aria-multiline":"true"};this.state.readOnly&&(t["aria-readonly"]="true"),Uo(this,Qa,t);let i=this.observer.ignore(()=>{let s=Js(this.contentDOM,this.contentAttrs,t),r=Js(this.dom,this.editorAttrs,e);return s||r});return this.editorAttrs=e,this.contentAttrs=t,i}showAnnouncements(e){let t=!0;for(let i of e)for(let s of i.effects)if(s.is(O.announce)){t&&(this.announceDOM.textContent=""),t=!1;let r=this.announceDOM.appendChild(document.createElement("div"));r.textContent=s.value}}mountStyles(){this.styleModules=this.state.facet(bi),mt.mount(this.root,this.styleModules.concat(Ou).reverse())}readMeasured(){if(this.updateState==2)throw new Error("Reading the editor layout isn't allowed during an update");this.updateState==0&&this.measureScheduled>-1&&this.measure(!1)}requestMeasure(e){if(this.measureScheduled<0&&(this.measureScheduled=this.win.requestAnimationFrame(()=>this.measure())),e){if(e.key!=null){for(let t=0;ti.spec==e)||null),t&&t.update(this).value}get documentTop(){return this.contentDOM.getBoundingClientRect().top+this.viewState.paddingTop}get documentPadding(){return{top:this.viewState.paddingTop,bottom:this.viewState.paddingBottom}}elementAtHeight(e){return this.readMeasured(),this.viewState.elementAtHeight(e)}lineBlockAtHeight(e){return this.readMeasured(),this.viewState.lineBlockAtHeight(e)}get viewportLineBlocks(){return this.viewState.viewportLines}lineBlockAt(e){return this.viewState.lineBlockAt(e)}get contentHeight(){return this.viewState.contentHeight}moveByChar(e,t,i){return rs(this,e,Po(this,e,t,i))}moveByGroup(e,t){return rs(this,e,Po(this,e,t,i=>iu(this,e.head,i)))}moveToLineBoundary(e,t,i=!0){return tu(this,e,t,i)}moveVertically(e,t,i){return rs(this,e,nu(this,e,t,i))}domAtPos(e){return this.docView.domAtPos(e)}posAtDOM(e,t=0){return this.docView.posFromDOM(e,t)}posAtCoords(e,t=!0){return this.readMeasured(),ah(this,e,t)}coordsAtPos(e,t=1){this.readMeasured();let i=this.docView.coordsAt(e,t);if(!i||i.left==i.right)return i;let s=this.state.doc.lineAt(e),r=this.bidiSpans(s),o=r[Jt.find(r,e-s.from,-1,t)];return Dr(i,o.dir==Z.LTR==t>0)}get defaultCharacterWidth(){return this.viewState.heightOracle.charWidth}get defaultLineHeight(){return this.viewState.heightOracle.lineHeight}get textDirection(){return this.viewState.defaultTextDirection}textDirectionAt(e){return!this.state.facet(Ya)||ethis.viewport.to?this.textDirection:(this.readMeasured(),this.docView.textDirectionAt(e))}get lineWrapping(){return this.viewState.heightOracle.lineWrapping}bidiSpans(e){if(e.length>_u)return nh(e.length);let t=this.textDirectionAt(e.from);for(let s of this.bidiCache)if(s.from==e.from&&s.dir==t)return s.order;let i=Wf(e.text,t);return this.bidiCache.push(new Dn(e.from,e.to,t,i)),i}get hasFocus(){var e;return(this.dom.ownerDocument.hasFocus()||A.safari&&((e=this.inputState)===null||e===void 0?void 0:e.lastContextMenu)>Date.now()-3e4)&&this.root.activeElement==this.contentDOM}focus(){this.observer.ignore(()=>{Ea(this.contentDOM),this.docView.updateSelection()})}setRoot(e){this._root!=e&&(this._root=e,this.observer.setWindow((e.nodeType==9?e:e.ownerDocument).defaultView||window),this.mountStyles())}destroy(){for(let e of this.plugins)e.destroy(this);this.plugins=[],this.inputState.destroy(),this.dom.remove(),this.observer.destroy(),this.measureScheduled>-1&&cancelAnimationFrame(this.measureScheduled),this.destroyed=!0}static scrollIntoView(e,t={}){return xo.of(new An(typeof e=="number"?w.cursor(e):e,t.y,t.x,t.yMargin,t.xMargin))}static domEventHandlers(e){return be.define(()=>({}),{eventHandlers:e})}static theme(e,t){let i=mt.newName(),s=[Zi.of(i),bi.of(rr(`.${i}`,e))];return t&&t.dark&&s.push(nr.of(!0)),s}static baseTheme(e){return Vi.lowest(bi.of(rr("."+sr,e,wh)))}static findFromDOM(e){var t;let i=e.querySelector(".cm-content"),s=i&&K.get(i)||K.get(e);return((t=s?.rootView)===null||t===void 0?void 0:t.view)||null}}O.styleModule=bi;O.inputHandler=Ja;O.perLineTextDirection=Ya;O.exceptionSink=$a;O.updateListener=Xs;O.editable=zn;O.mouseSelectionStyle=Ga;O.dragMovesSelection=Ua;O.clickAddsSelectionRange=Ka;O.decorations=Ei;O.atomicRanges=eh;O.scrollMargins=th;O.darkTheme=nr;O.contentAttributes=Qa;O.editorAttributes=Za;O.lineWrapping=O.contentAttributes.of({class:"cm-lineWrapping"});O.announce=R.define();const _u=4096,Ko={};class Dn{constructor(e,t,i,s){this.from=e,this.to=t,this.dir=i,this.order=s}static update(e,t){if(t.empty)return e;let i=[],s=e.length?e[e.length-1].dir:Z.LTR;for(let r=Math.max(0,e.length-10);r=0;s--){let r=i[s],o=typeof r=="function"?r(n):r;o&&$s(o,t)}return t}const Vu=A.mac?"mac":A.windows?"win":A.linux?"linux":"key";function Fu(n,e){const t=n.split(/-(?!$)/);let i=t[t.length-1];i=="Space"&&(i=" ");let s,r,o,l;for(let a=0;ai.concat(s),[]))),t}let at=null;const zu=4e3;function qu(n,e=Vu){let t=Object.create(null),i=Object.create(null),s=(o,l)=>{let a=i[o];if(a==null)i[o]=l;else if(a!=l)throw new Error("Key binding "+o+" is used both as a regular binding and as a multi-stroke prefix")},r=(o,l,a,h)=>{var c,f;let u=t[o]||(t[o]=Object.create(null)),d=l.split(/ (?!$)/).map(y=>Fu(y,e));for(let y=1;y{let S=at={view:v,prefix:b,scope:o};return setTimeout(()=>{at==S&&(at=null)},zu),!0}]})}let p=d.join(" ");s(p,!1);let g=u[p]||(u[p]={preventDefault:!1,run:((f=(c=u._any)===null||c===void 0?void 0:c.run)===null||f===void 0?void 0:f.slice())||[]});a&&g.run.push(a),h&&(g.preventDefault=!0)};for(let o of n){let l=o.scope?o.scope.split(" "):["editor"];if(o.any)for(let h of l){let c=t[h]||(t[h]=Object.create(null));c._any||(c._any={preventDefault:!1,run:[]});for(let f in c)c[f].run.push(o.any)}let a=o[e]||o.key;if(a)for(let h of l)r(h,a,o.run,o.preventDefault),o.shift&&r(h,"Shift-"+a,o.shift,o.preventDefault)}return t}function ju(n,e,t,i){let s=Cf(e),r=ge(s,0),o=Ee(r)==s.length&&s!=" ",l="",a=!1;at&&at.view==t&&at.scope==i&&(l=at.prefix+" ",(a=ch.indexOf(e.keyCode)<0)&&(at=null));let h=new Set,c=p=>{if(p){for(let g of p.run)if(!h.has(g)&&(h.add(g),g(t,e)))return!0;p.preventDefault&&(a=!0)}return!1},f=n[i],u,d;if(f){if(c(f[l+Qi(s,e,!o)]))return!0;if(o&&(e.shiftKey||e.altKey||e.metaKey||r>127)&&(u=gt[e.keyCode])&&u!=s){if(c(f[l+Qi(u,e,!0)]))return!0;if(e.shiftKey&&(d=Oi[e.keyCode])!=s&&d!=u&&c(f[l+Qi(d,e,!1)]))return!0}else if(o&&e.shiftKey&&c(f[l+Qi(s,e,!0)]))return!0;if(c(f._any))return!0}return a}const vh=!A.ios,ki=D.define({combine(n){return _t(n,{cursorBlinkRate:1200,drawRangeCursor:!0},{cursorBlinkRate:(e,t)=>Math.min(e,t),drawRangeCursor:(e,t)=>e||t})}});function Ku(n={}){return[ki.of(n),Uu,Gu,Xa.of(!0)]}class xh{constructor(e,t,i,s,r){this.left=e,this.top=t,this.width=i,this.height=s,this.className=r}draw(){let e=document.createElement("div");return e.className=this.className,this.adjust(e),e}adjust(e){e.style.left=this.left+"px",e.style.top=this.top+"px",this.width>=0&&(e.style.width=this.width+"px"),e.style.height=this.height+"px"}eq(e){return this.left==e.left&&this.top==e.top&&this.width==e.width&&this.height==e.height&&this.className==e.className}}const Uu=be.fromClass(class{constructor(n){this.view=n,this.rangePieces=[],this.cursors=[],this.measureReq={read:this.readPos.bind(this),write:this.drawSel.bind(this)},this.selectionLayer=n.scrollDOM.appendChild(document.createElement("div")),this.selectionLayer.className="cm-selectionLayer",this.selectionLayer.setAttribute("aria-hidden","true"),this.cursorLayer=n.scrollDOM.appendChild(document.createElement("div")),this.cursorLayer.className="cm-cursorLayer",this.cursorLayer.setAttribute("aria-hidden","true"),n.requestMeasure(this.measureReq),this.setBlinkRate()}setBlinkRate(){this.cursorLayer.style.animationDuration=this.view.state.facet(ki).cursorBlinkRate+"ms"}update(n){let e=n.startState.facet(ki)!=n.state.facet(ki);(e||n.selectionSet||n.geometryChanged||n.viewportChanged)&&this.view.requestMeasure(this.measureReq),n.transactions.some(t=>t.scrollIntoView)&&(this.cursorLayer.style.animationName=this.cursorLayer.style.animationName=="cm-blink"?"cm-blink2":"cm-blink"),e&&this.setBlinkRate()}readPos(){let{state:n}=this.view,e=n.facet(ki),t=n.selection.ranges.map(s=>s.empty?[]:$u(this.view,s)).reduce((s,r)=>s.concat(r)),i=[];for(let s of n.selection.ranges){let r=s==n.selection.main;if(s.empty?!r||vh:e.drawRangeCursor){let o=Ju(this.view,s,r);o&&i.push(o)}}return{rangePieces:t,cursors:i}}drawSel({rangePieces:n,cursors:e}){if(n.length!=this.rangePieces.length||n.some((t,i)=>!t.eq(this.rangePieces[i]))){this.selectionLayer.textContent="";for(let t of n)this.selectionLayer.appendChild(t.draw());this.rangePieces=n}if(e.length!=this.cursors.length||e.some((t,i)=>!t.eq(this.cursors[i]))){let t=this.cursorLayer.children;if(t.length!==e.length){this.cursorLayer.textContent="";for(const i of e)this.cursorLayer.appendChild(i.draw())}else e.forEach((i,s)=>i.adjust(t[s]));this.cursors=e}}destroy(){this.selectionLayer.remove(),this.cursorLayer.remove()}}),Sh={".cm-line":{"& ::selection":{backgroundColor:"transparent !important"},"&::selection":{backgroundColor:"transparent !important"}}};vh&&(Sh[".cm-line"].caretColor="transparent !important");const Gu=Vi.highest(O.theme(Sh));function Ch(n){let e=n.scrollDOM.getBoundingClientRect();return{left:(n.textDirection==Z.LTR?e.left:e.right-n.scrollDOM.clientWidth)-n.scrollDOM.scrollLeft,top:e.top-n.scrollDOM.scrollTop}}function $o(n,e,t){let i=w.cursor(e);return{from:Math.max(t.from,n.moveToLineBoundary(i,!1,!0).from),to:Math.min(t.to,n.moveToLineBoundary(i,!0,!0).from),type:W.Text}}function Jo(n,e){let t=n.lineBlockAt(e);if(Array.isArray(t.type)){for(let i of t.type)if(i.to>e||i.to==e&&(i.to==t.to||i.type==W.Text))return i}return t}function $u(n,e){if(e.to<=n.viewport.from||e.from>=n.viewport.to)return[];let t=Math.max(e.from,n.viewport.from),i=Math.min(e.to,n.viewport.to),s=n.textDirection==Z.LTR,r=n.contentDOM,o=r.getBoundingClientRect(),l=Ch(n),a=window.getComputedStyle(r.firstChild),h=o.left+parseInt(a.paddingLeft)+Math.min(0,parseInt(a.textIndent)),c=o.right-parseInt(a.paddingRight),f=Jo(n,t),u=Jo(n,i),d=f.type==W.Text?f:null,p=u.type==W.Text?u:null;if(n.lineWrapping&&(d&&(d=$o(n,t,d)),p&&(p=$o(n,i,p))),d&&p&&d.from==p.from)return y(b(e.from,e.to,d));{let S=d?b(e.from,null,d):v(f,!1),k=p?b(null,e.to,p):v(u,!0),C=[];return(d||f).to<(p||u).from-1?C.push(g(h,S.bottom,c,k.top)):S.bottomP&&G.from=M)break;J>Q&&I(Math.max(le,Q),S==null&&le<=P,Math.min(J,M),k==null&&J>=V,Y.dir)}if(Q=$.to+1,Q>=M)break}return U.length==0&&I(P,S==null,V,k==null,n.textDirection),{top:T,bottom:B,horizontal:U}}function v(S,k){let C=o.top+(k?S.top:S.bottom);return{top:C,bottom:C,horizontal:[]}}}function Ju(n,e,t){let i=n.coordsAtPos(e.head,e.assoc||1);if(!i)return null;let s=Ch(n);return new xh(i.left-s.left,i.top-s.top,-1,i.bottom-i.top,t?"cm-cursor cm-cursor-primary":"cm-cursor cm-cursor-secondary")}function Yo(n,e,t,i,s){e.lastIndex=0;for(let r=n.iterRange(t,i),o=t,l;!r.next().done;o+=r.value.length)if(!r.lineBreak)for(;l=e.exec(r.value);)s(o+l.index,l)}function Yu(n,e){let t=n.visibleRanges;if(t.length==1&&t[0].from==n.viewport.from&&t[0].to==n.viewport.to)return t;let i=[];for(let{from:s,to:r}of t)s=Math.max(n.state.doc.lineAt(s).from,s-e),r=Math.min(n.state.doc.lineAt(r).to,r+e),i.length&&i[i.length-1].to>=s?i[i.length-1].to=r:i.push({from:s,to:r});return i}class Xu{constructor(e){const{regexp:t,decoration:i,decorate:s,boundary:r,maxLength:o=1e3}=e;if(!t.global)throw new RangeError("The regular expression given to MatchDecorator should have its 'g' flag set");if(this.regexp=t,s)this.addMatch=(l,a,h,c)=>s(c,h,h+l[0].length,l,a);else if(typeof i=="function")this.addMatch=(l,a,h,c)=>{let f=i(l,a,h);f&&c(h,h+l[0].length,f)};else if(i)this.addMatch=(l,a,h,c)=>c(h,h+l[0].length,i);else throw new RangeError("Either 'decorate' or 'decoration' should be provided to MatchDecorator");this.boundary=r,this.maxLength=o}createDeco(e){let t=new Pt,i=t.add.bind(t);for(let{from:s,to:r}of Yu(e,this.maxLength))Yo(e.state.doc,this.regexp,s,r,(o,l)=>this.addMatch(l,e,o,i));return t.finish()}updateDeco(e,t){let i=1e9,s=-1;return e.docChanged&&e.changes.iterChanges((r,o,l,a)=>{a>e.view.viewport.from&&l1e3?this.createDeco(e.view):s>-1?this.updateRange(e.view,t.map(e.changes),i,s):t}updateRange(e,t,i,s){for(let r of e.visibleRanges){let o=Math.max(r.from,i),l=Math.min(r.to,s);if(l>o){let a=e.state.doc.lineAt(o),h=a.toa.from;o--)if(this.boundary.test(a.text[o-1-a.from])){c=o;break}for(;lu.push(b.range(g,y));if(a==h)for(this.regexp.lastIndex=c-a.from;(d=this.regexp.exec(a.text))&&d.indexthis.addMatch(y,e,g,p));t=t.update({filterFrom:c,filterTo:f,filter:(g,y)=>gf,add:u})}}return t}}const or=/x/.unicode!=null?"gu":"g",Zu=new RegExp(`[\0-\b
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td(n={}){return[mn.of(n),id()]}let Xo=null;function id(){return Xo||(Xo=be.fromClass(class{constructor(n){this.view=n,this.decorations=E.none,this.decorationCache=Object.create(null),this.decorator=this.makeDecorator(n.state.facet(mn)),this.decorations=this.decorator.createDeco(n)}makeDecorator(n){return new Xu({regexp:n.specialChars,decoration:(e,t,i)=>{let{doc:s}=t.state,r=ge(e[0],0);if(r==9){let o=s.lineAt(i),l=t.state.tabSize,a=Fi(o.text,l,i-o.from);return E.replace({widget:new od((l-a%l)*this.view.defaultCharacterWidth)})}return this.decorationCache[r]||(this.decorationCache[r]=E.replace({widget:new rd(n,r)}))},boundary:n.replaceTabs?void 0:/[^]/})}update(n){let e=n.state.facet(mn);n.startState.facet(mn)!=e?(this.decorator=this.makeDecorator(e),this.decorations=this.decorator.createDeco(n.view)):this.decorations=this.decorator.updateDeco(n,this.decorations)}},{decorations:n=>n.decorations}))}const nd="•";function sd(n){return n>=32?nd:n==10?"":String.fromCharCode(9216+n)}class rd extends tt{constructor(e,t){super(),this.options=e,this.code=t}eq(e){return e.code==this.code}toDOM(e){let t=sd(this.code),i=e.state.phrase("Control character")+" "+(Qu[this.code]||"0x"+this.code.toString(16)),s=this.options.render&&this.options.render(this.code,i,t);if(s)return s;let r=document.createElement("span");return r.textContent=t,r.title=i,r.setAttribute("aria-label",i),r.className="cm-specialChar",r}ignoreEvent(){return!1}}class od extends tt{constructor(e){super(),this.width=e}eq(e){return e.width==this.width}toDOM(){let e=document.createElement("span");return e.textContent=" ",e.className="cm-tab",e.style.width=this.width+"px",e}ignoreEvent(){return!1}}class ld extends tt{constructor(e){super(),this.content=e}toDOM(){let e=document.createElement("span");return e.className="cm-placeholder",e.style.pointerEvents="none",e.appendChild(typeof this.content=="string"?document.createTextNode(this.content):this.content),typeof this.content=="string"?e.setAttribute("aria-label","placeholder "+this.content):e.setAttribute("aria-hidden","true"),e}ignoreEvent(){return!1}}function ad(n){return be.fromClass(class{constructor(e){this.view=e,this.placeholder=E.set([E.widget({widget:new ld(n),side:1}).range(0)])}get decorations(){return this.view.state.doc.length?E.none:this.placeholder}},{decorations:e=>e.decorations})}const lr=2e3;function hd(n,e,t){let i=Math.min(e.line,t.line),s=Math.max(e.line,t.line),r=[];if(e.off>lr||t.off>lr||e.col<0||t.col<0){let o=Math.min(e.off,t.off),l=Math.max(e.off,t.off);for(let a=i;a<=s;a++){let h=n.doc.line(a);h.length<=l&&r.push(w.range(h.from+o,h.to+l))}}else{let o=Math.min(e.col,t.col),l=Math.max(e.col,t.col);for(let a=i;a<=s;a++){let h=n.doc.line(a),c=Hs(h.text,o,n.tabSize,!0);if(c<0)r.push(w.cursor(h.to));else{let f=Hs(h.text,l,n.tabSize);r.push(w.range(h.from+c,h.from+f))}}}return r}function cd(n,e){let t=n.coordsAtPos(n.viewport.from);return t?Math.round(Math.abs((t.left-e)/n.defaultCharacterWidth)):-1}function Zo(n,e){let t=n.posAtCoords({x:e.clientX,y:e.clientY},!1),i=n.state.doc.lineAt(t),s=t-i.from,r=s>lr?-1:s==i.length?cd(n,e.clientX):Fi(i.text,n.state.tabSize,t-i.from);return{line:i.number,col:r,off:s}}function fd(n,e){let t=Zo(n,e),i=n.state.selection;return t?{update(s){if(s.docChanged){let r=s.changes.mapPos(s.startState.doc.line(t.line).from),o=s.state.doc.lineAt(r);t={line:o.number,col:t.col,off:Math.min(t.off,o.length)},i=i.map(s.changes)}},get(s,r,o){let l=Zo(n,s);if(!l)return i;let a=hd(n.state,t,l);return a.length?o?w.create(a.concat(i.ranges)):w.create(a):i}}:null}function ud(n){let e=n?.eventFilter||(t=>t.altKey&&t.button==0);return O.mouseSelectionStyle.of((t,i)=>e(i)?fd(t,i):null)}const dd={Alt:[18,n=>n.altKey],Control:[17,n=>n.ctrlKey],Shift:[16,n=>n.shiftKey],Meta:[91,n=>n.metaKey]},pd={style:"cursor: crosshair"};function md(n={}){let[e,t]=dd[n.key||"Alt"],i=be.fromClass(class{constructor(s){this.view=s,this.isDown=!1}set(s){this.isDown!=s&&(this.isDown=s,this.view.update([]))}},{eventHandlers:{keydown(s){this.set(s.keyCode==e||t(s))},keyup(s){(s.keyCode==e||!t(s))&&this.set(!1)},mousemove(s){this.set(t(s))}}});return[i,O.contentAttributes.of(s=>{var r;return!((r=s.plugin(i))===null||r===void 0)&&r.isDown?pd:null})]}const hs="-10000px";class Ah{constructor(e,t,i){this.facet=t,this.createTooltipView=i,this.input=e.state.facet(t),this.tooltips=this.input.filter(s=>s),this.tooltipViews=this.tooltips.map(i)}update(e){var t;let i=e.state.facet(this.facet),s=i.filter(o=>o);if(i===this.input){for(let o of this.tooltipViews)o.update&&o.update(e);return!1}let r=[];for(let o=0;o{var e,t,i;return{position:A.ios?"absolute":((e=n.find(s=>s.position))===null||e===void 0?void 0:e.position)||"fixed",parent:((t=n.find(s=>s.parent))===null||t===void 0?void 0:t.parent)||null,tooltipSpace:((i=n.find(s=>s.tooltipSpace))===null||i===void 0?void 0:i.tooltipSpace)||gd}}}),Mh=be.fromClass(class{constructor(n){this.view=n,this.inView=!0,this.lastTransaction=0,this.measureTimeout=-1;let e=n.state.facet(cs);this.position=e.position,this.parent=e.parent,this.classes=n.themeClasses,this.createContainer(),this.measureReq={read:this.readMeasure.bind(this),write:this.writeMeasure.bind(this),key:this},this.manager=new Ah(n,Er,t=>this.createTooltip(t)),this.intersectionObserver=typeof IntersectionObserver=="function"?new IntersectionObserver(t=>{Date.now()>this.lastTransaction-50&&t.length>0&&t[t.length-1].intersectionRatio<1&&this.measureSoon()},{threshold:[1]}):null,this.observeIntersection(),n.win.addEventListener("resize",this.measureSoon=this.measureSoon.bind(this)),this.maybeMeasure()}createContainer(){this.parent?(this.container=document.createElement("div"),this.container.style.position="relative",this.container.className=this.view.themeClasses,this.parent.appendChild(this.container)):this.container=this.view.dom}observeIntersection(){if(this.intersectionObserver){this.intersectionObserver.disconnect();for(let n of this.manager.tooltipViews)this.intersectionObserver.observe(n.dom)}}measureSoon(){this.measureTimeout<0&&(this.measureTimeout=setTimeout(()=>{this.measureTimeout=-1,this.maybeMeasure()},50))}update(n){n.transactions.length&&(this.lastTransaction=Date.now());let e=this.manager.update(n);e&&this.observeIntersection();let t=e||n.geometryChanged,i=n.state.facet(cs);if(i.position!=this.position){this.position=i.position;for(let s of this.manager.tooltipViews)s.dom.style.position=this.position;t=!0}if(i.parent!=this.parent){this.parent&&this.container.remove(),this.parent=i.parent,this.createContainer();for(let s of this.manager.tooltipViews)this.container.appendChild(s.dom);t=!0}else this.parent&&this.view.themeClasses!=this.classes&&(this.classes=this.container.className=this.view.themeClasses);t&&this.maybeMeasure()}createTooltip(n){let e=n.create(this.view);if(e.dom.classList.add("cm-tooltip"),n.arrow&&!e.dom.querySelector(".cm-tooltip > .cm-tooltip-arrow")){let t=document.createElement("div");t.className="cm-tooltip-arrow",e.dom.appendChild(t)}return e.dom.style.position=this.position,e.dom.style.top=hs,this.container.appendChild(e.dom),e.mount&&e.mount(this.view),e}destroy(){var n,e;this.view.win.removeEventListener("resize",this.measureSoon);for(let t of this.manager.tooltipViews)t.dom.remove(),(n=t.destroy)===null||n===void 0||n.call(t);(e=this.intersectionObserver)===null||e===void 0||e.disconnect(),clearTimeout(this.measureTimeout)}readMeasure(){let n=this.view.dom.getBoundingClientRect();return{editor:n,parent:this.parent?this.container.getBoundingClientRect():n,pos:this.manager.tooltips.map((e,t)=>{let i=this.manager.tooltipViews[t];return i.getCoords?i.getCoords(e.pos):this.view.coordsAtPos(e.pos)}),size:this.manager.tooltipViews.map(({dom:e})=>e.getBoundingClientRect()),space:this.view.state.facet(cs).tooltipSpace(this.view)}}writeMeasure(n){let{editor:e,space:t}=n,i=[];for(let s=0;s=Math.min(e.bottom,t.bottom)||a.rightMath.min(e.right,t.right)+.1){l.style.top=hs;continue}let c=r.arrow?o.dom.querySelector(".cm-tooltip-arrow"):null,f=c?7:0,u=h.right-h.left,d=h.bottom-h.top,p=o.offset||bd,g=this.view.textDirection==Z.LTR,y=h.width>t.right-t.left?g?t.left:t.right-h.width:g?Math.min(a.left-(c?14:0)+p.x,t.right-u):Math.max(t.left,a.left-u+(c?14:0)-p.x),b=!!r.above;!r.strictSide&&(b?a.top-(h.bottom-h.top)-p.yt.bottom)&&b==t.bottom-a.bottom>a.top-t.top&&(b=!b);let v=b?a.top-d-f-p.y:a.bottom+f+p.y,S=y+u;if(o.overlap!==!0)for(let k of i)k.lefty&&k.topv&&(v=b?k.top-d-2-f:k.bottom+f+2);this.position=="absolute"?(l.style.top=v-n.parent.top+"px",l.style.left=y-n.parent.left+"px"):(l.style.top=v+"px",l.style.left=y+"px"),c&&(c.style.left=`${a.left+(g?p.x:-p.x)-(y+14-7)}px`),o.overlap!==!0&&i.push({left:y,top:v,right:S,bottom:v+d}),l.classList.toggle("cm-tooltip-above",b),l.classList.toggle("cm-tooltip-below",!b),o.positioned&&o.positioned()}}maybeMeasure(){if(this.manager.tooltips.length&&(this.view.inView&&this.view.requestMeasure(this.measureReq),this.inView!=this.view.inView&&(this.inView=this.view.inView,!this.inView)))for(let n of this.manager.tooltipViews)n.dom.style.top=hs}},{eventHandlers:{scroll(){this.maybeMeasure()}}}),yd=O.baseTheme({".cm-tooltip":{zIndex:100},"&light .cm-tooltip":{border:"1px solid #bbb",backgroundColor:"#f5f5f5"},"&light .cm-tooltip-section:not(:first-child)":{borderTop:"1px solid #bbb"},"&dark .cm-tooltip":{backgroundColor:"#333338",color:"white"},".cm-tooltip-arrow":{height:"7px",width:`${7*2}px`,position:"absolute",zIndex:-1,overflow:"hidden","&:before, &:after":{content:"''",position:"absolute",width:0,height:0,borderLeft:"7px solid transparent",borderRight:"7px solid transparent"},".cm-tooltip-above &":{bottom:"-7px","&:before":{borderTop:"7px solid #bbb"},"&:after":{borderTop:"7px solid #f5f5f5",bottom:"1px"}},".cm-tooltip-below &":{top:"-7px","&:before":{borderBottom:"7px solid #bbb"},"&:after":{borderBottom:"7px solid #f5f5f5",top:"1px"}}},"&dark .cm-tooltip .cm-tooltip-arrow":{"&:before":{borderTopColor:"#333338",borderBottomColor:"#333338"},"&:after":{borderTopColor:"transparent",borderBottomColor:"transparent"}}}),bd={x:0,y:0},Er=D.define({enables:[Mh,yd]}),Tn=D.define();class Rr{constructor(e){this.view=e,this.mounted=!1,this.dom=document.createElement("div"),this.dom.classList.add("cm-tooltip-hover"),this.manager=new Ah(e,Tn,t=>this.createHostedView(t))}static create(e){return new Rr(e)}createHostedView(e){let t=e.create(this.view);return t.dom.classList.add("cm-tooltip-section"),this.dom.appendChild(t.dom),this.mounted&&t.mount&&t.mount(this.view),t}mount(e){for(let t of this.manager.tooltipViews)t.mount&&t.mount(e);this.mounted=!0}positioned(){for(let e of this.manager.tooltipViews)e.positioned&&e.positioned()}update(e){this.manager.update(e)}}const wd=Er.compute([Tn],n=>{let e=n.facet(Tn).filter(t=>t);return e.length===0?null:{pos:Math.min(...e.map(t=>t.pos)),end:Math.max(...e.filter(t=>t.end!=null).map(t=>t.end)),create:Rr.create,above:e[0].above,arrow:e.some(t=>t.arrow)}});class kd{constructor(e,t,i,s,r){this.view=e,this.source=t,this.field=i,this.setHover=s,this.hoverTime=r,this.hoverTimeout=-1,this.restartTimeout=-1,this.pending=null,this.lastMove={x:0,y:0,target:e.dom,time:0},this.checkHover=this.checkHover.bind(this),e.dom.addEventListener("mouseleave",this.mouseleave=this.mouseleave.bind(this)),e.dom.addEventListener("mousemove",this.mousemove=this.mousemove.bind(this))}update(){this.pending&&(this.pending=null,clearTimeout(this.restartTimeout),this.restartTimeout=setTimeout(()=>this.startHover(),20))}get active(){return this.view.state.field(this.field)}checkHover(){if(this.hoverTimeout=-1,this.active)return;let e=Date.now()-this.lastMove.time;ei.bottom||e.xi.right+this.view.defaultCharacterWidth)return;let s=this.view.bidiSpans(this.view.state.doc.lineAt(t)).find(l=>l.from<=t&&l.to>=t),r=s&&s.dir==Z.RTL?-1:1,o=this.source(this.view,t,e.x{this.pending==l&&(this.pending=null,a&&this.view.dispatch({effects:this.setHover.of(a)}))},a=>He(this.view.state,a,"hover tooltip"))}else o&&this.view.dispatch({effects:this.setHover.of(o)})}mousemove(e){var t;this.lastMove={x:e.clientX,y:e.clientY,target:e.target,time:Date.now()},this.hoverTimeout<0&&(this.hoverTimeout=setTimeout(this.checkHover,this.hoverTime));let i=this.active;if(i&&!vd(this.lastMove.target)||this.pending){let{pos:s}=i||this.pending,r=(t=i?.end)!==null&&t!==void 0?t:s;(s==r?this.view.posAtCoords(this.lastMove)!=s:!xd(this.view,s,r,e.clientX,e.clientY,6))&&(this.view.dispatch({effects:this.setHover.of(null)}),this.pending=null)}}mouseleave(){clearTimeout(this.hoverTimeout),this.hoverTimeout=-1,this.active&&this.view.dispatch({effects:this.setHover.of(null)})}destroy(){clearTimeout(this.hoverTimeout),this.view.dom.removeEventListener("mouseleave",this.mouseleave),this.view.dom.removeEventListener("mousemove",this.mousemove)}}function vd(n){for(let e=n;e;e=e.parentNode)if(e.nodeType==1&&e.classList.contains("cm-tooltip"))return!0;return!1}function xd(n,e,t,i,s,r){let o=document.createRange(),l=n.domAtPos(e),a=n.domAtPos(t);o.setEnd(a.node,a.offset),o.setStart(l.node,l.offset);let h=o.getClientRects();o.detach();for(let c=0;cTn.from(s)});return[i,be.define(s=>new kd(s,n,i,t,e.hoverTime||300)),wd]}function Cd(n,e){let t=n.plugin(Mh);if(!t)return null;let i=t.manager.tooltips.indexOf(e);return i<0?null:t.manager.tooltipViews[i]}const Ad=R.define(),Qo=D.define({combine(n){let e,t;for(let i of n)e=e||i.topContainer,t=t||i.bottomContainer;return{topContainer:e,bottomContainer:t}}});function Md(n,e){let t=n.plugin(Dh),i=t?t.specs.indexOf(e):-1;return i>-1?t.panels[i]:null}const Dh=be.fromClass(class{constructor(n){this.input=n.state.facet(ar),this.specs=this.input.filter(t=>t),this.panels=this.specs.map(t=>t(n));let e=n.state.facet(Qo);this.top=new en(n,!0,e.topContainer),this.bottom=new en(n,!1,e.bottomContainer),this.top.sync(this.panels.filter(t=>t.top)),this.bottom.sync(this.panels.filter(t=>!t.top));for(let t of this.panels)t.dom.classList.add("cm-panel"),t.mount&&t.mount()}update(n){let e=n.state.facet(Qo);this.top.container!=e.topContainer&&(this.top.sync([]),this.top=new en(n.view,!0,e.topContainer)),this.bottom.container!=e.bottomContainer&&(this.bottom.sync([]),this.bottom=new en(n.view,!1,e.bottomContainer)),this.top.syncClasses(),this.bottom.syncClasses();let t=n.state.facet(ar);if(t!=this.input){let i=t.filter(a=>a),s=[],r=[],o=[],l=[];for(let a of i){let h=this.specs.indexOf(a),c;h<0?(c=a(n.view),l.push(c)):(c=this.panels[h],c.update&&c.update(n)),s.push(c),(c.top?r:o).push(c)}this.specs=i,this.panels=s,this.top.sync(r),this.bottom.sync(o);for(let a of l)a.dom.classList.add("cm-panel"),a.mount&&a.mount()}else for(let i of this.panels)i.update&&i.update(n)}destroy(){this.top.sync([]),this.bottom.sync([])}},{provide:n=>O.scrollMargins.of(e=>{let t=e.plugin(n);return t&&{top:t.top.scrollMargin(),bottom:t.bottom.scrollMargin()}})});class en{constructor(e,t,i){this.view=e,this.top=t,this.container=i,this.dom=void 0,this.classes="",this.panels=[],this.syncClasses()}sync(e){for(let t of this.panels)t.destroy&&e.indexOf(t)<0&&t.destroy();this.panels=e,this.syncDOM()}syncDOM(){if(this.panels.length==0){this.dom&&(this.dom.remove(),this.dom=void 0);return}if(!this.dom){this.dom=document.createElement("div"),this.dom.className=this.top?"cm-panels cm-panels-top":"cm-panels cm-panels-bottom",this.dom.style[this.top?"top":"bottom"]="0";let t=this.container||this.view.dom;t.insertBefore(this.dom,this.top?t.firstChild:null)}let e=this.dom.firstChild;for(let t of this.panels)if(t.dom.parentNode==this.dom){for(;e!=t.dom;)e=el(e);e=e.nextSibling}else this.dom.insertBefore(t.dom,e);for(;e;)e=el(e)}scrollMargin(){return!this.dom||this.container?0:Math.max(0,this.top?this.dom.getBoundingClientRect().bottom-Math.max(0,this.view.scrollDOM.getBoundingClientRect().top):Math.min(innerHeight,this.view.scrollDOM.getBoundingClientRect().bottom)-this.dom.getBoundingClientRect().top)}syncClasses(){if(!(!this.container||this.classes==this.view.themeClasses)){for(let e of this.classes.split(" "))e&&this.container.classList.remove(e);for(let e of(this.classes=this.view.themeClasses).split(" "))e&&this.container.classList.add(e)}}}function el(n){let e=n.nextSibling;return n.remove(),e}const ar=D.define({enables:Dh});class bt extends Bt{compare(e){return this==e||this.constructor==e.constructor&&this.eq(e)}eq(e){return!1}destroy(e){}}bt.prototype.elementClass="";bt.prototype.toDOM=void 0;bt.prototype.mapMode=ce.TrackBefore;bt.prototype.startSide=bt.prototype.endSide=-1;bt.prototype.point=!0;const fs=D.define(),Dd={class:"",renderEmptyElements:!1,elementStyle:"",markers:()=>F.empty,lineMarker:()=>null,lineMarkerChange:null,initialSpacer:null,updateSpacer:null,domEventHandlers:{}},Ci=D.define();function Td(n){return[Th(),Ci.of(Object.assign(Object.assign({},Dd),n))]}const hr=D.define({combine:n=>n.some(e=>e)});function Th(n){let e=[Od];return n&&n.fixed===!1&&e.push(hr.of(!0)),e}const Od=be.fromClass(class{constructor(n){this.view=n,this.prevViewport=n.viewport,this.dom=document.createElement("div"),this.dom.className="cm-gutters",this.dom.setAttribute("aria-hidden","true"),this.dom.style.minHeight=this.view.contentHeight+"px",this.gutters=n.state.facet(Ci).map(e=>new il(n,e));for(let e of this.gutters)this.dom.appendChild(e.dom);this.fixed=!n.state.facet(hr),this.fixed&&(this.dom.style.position="sticky"),this.syncGutters(!1),n.scrollDOM.insertBefore(this.dom,n.contentDOM)}update(n){if(this.updateGutters(n)){let e=this.prevViewport,t=n.view.viewport,i=Math.min(e.to,t.to)-Math.max(e.from,t.from);this.syncGutters(i<(t.to-t.from)*.8)}n.geometryChanged&&(this.dom.style.minHeight=this.view.contentHeight+"px"),this.view.state.facet(hr)!=!this.fixed&&(this.fixed=!this.fixed,this.dom.style.position=this.fixed?"sticky":""),this.prevViewport=n.view.viewport}syncGutters(n){let e=this.dom.nextSibling;n&&this.dom.remove();let t=F.iter(this.view.state.facet(fs),this.view.viewport.from),i=[],s=this.gutters.map(r=>new Bd(r,this.view.viewport,-this.view.documentPadding.top));for(let r of this.view.viewportLineBlocks){let o;if(Array.isArray(r.type)){for(let l of r.type)if(l.type==W.Text){o=l;break}}else o=r.type==W.Text?r:void 0;if(o){i.length&&(i=[]),Oh(t,i,r.from);for(let l of s)l.line(this.view,o,i)}}for(let r of s)r.finish();n&&this.view.scrollDOM.insertBefore(this.dom,e)}updateGutters(n){let e=n.startState.facet(Ci),t=n.state.facet(Ci),i=n.docChanged||n.heightChanged||n.viewportChanged||!F.eq(n.startState.facet(fs),n.state.facet(fs),n.view.viewport.from,n.view.viewport.to);if(e==t)for(let s of this.gutters)s.update(n)&&(i=!0);else{i=!0;let s=[];for(let r of t){let o=e.indexOf(r);o<0?s.push(new il(this.view,r)):(this.gutters[o].update(n),s.push(this.gutters[o]))}for(let r of this.gutters)r.dom.remove(),s.indexOf(r)<0&&r.destroy();for(let r of s)this.dom.appendChild(r.dom);this.gutters=s}return i}destroy(){for(let n of this.gutters)n.destroy();this.dom.remove()}},{provide:n=>O.scrollMargins.of(e=>{let t=e.plugin(n);return!t||t.gutters.length==0||!t.fixed?null:e.textDirection==Z.LTR?{left:t.dom.offsetWidth}:{right:t.dom.offsetWidth}})});function tl(n){return Array.isArray(n)?n:[n]}function Oh(n,e,t){for(;n.value&&n.from<=t;)n.from==t&&e.push(n.value),n.next()}class Bd{constructor(e,t,i){this.gutter=e,this.height=i,this.localMarkers=[],this.i=0,this.cursor=F.iter(e.markers,t.from)}line(e,t,i){this.localMarkers.length&&(this.localMarkers=[]),Oh(this.cursor,this.localMarkers,t.from);let s=i.length?this.localMarkers.concat(i):this.localMarkers,r=this.gutter.config.lineMarker(e,t,s);r&&s.unshift(r);let o=this.gutter;if(s.length==0&&!o.config.renderEmptyElements)return;let l=t.top-this.height;if(this.i==o.elements.length){let a=new Bh(e,t.height,l,s);o.elements.push(a),o.dom.appendChild(a.dom)}else o.elements[this.i].update(e,t.height,l,s);this.height=t.bottom,this.i++}finish(){let e=this.gutter;for(;e.elements.length>this.i;){let t=e.elements.pop();e.dom.removeChild(t.dom),t.destroy()}}}class il{constructor(e,t){this.view=e,this.config=t,this.elements=[],this.spacer=null,this.dom=document.createElement("div"),this.dom.className="cm-gutter"+(this.config.class?" "+this.config.class:"");for(let i in t.domEventHandlers)this.dom.addEventListener(i,s=>{let r=e.lineBlockAtHeight(s.clientY-e.documentTop);t.domEventHandlers[i](e,r,s)&&s.preventDefault()});this.markers=tl(t.markers(e)),t.initialSpacer&&(this.spacer=new Bh(e,0,0,[t.initialSpacer(e)]),this.dom.appendChild(this.spacer.dom),this.spacer.dom.style.cssText+="visibility: hidden; pointer-events: none")}update(e){let t=this.markers;if(this.markers=tl(this.config.markers(e.view)),this.spacer&&this.config.updateSpacer){let s=this.config.updateSpacer(this.spacer.markers[0],e);s!=this.spacer.markers[0]&&this.spacer.update(e.view,0,0,[s])}let i=e.view.viewport;return!F.eq(this.markers,t,i.from,i.to)||(this.config.lineMarkerChange?this.config.lineMarkerChange(e):!1)}destroy(){for(let e of this.elements)e.destroy()}}class Bh{constructor(e,t,i,s){this.height=-1,this.above=0,this.markers=[],this.dom=document.createElement("div"),this.dom.className="cm-gutterElement",this.update(e,t,i,s)}update(e,t,i,s){this.height!=t&&(this.dom.style.height=(this.height=t)+"px"),this.above!=i&&(this.dom.style.marginTop=(this.above=i)?i+"px":""),Pd(this.markers,s)||this.setMarkers(e,s)}setMarkers(e,t){let i="cm-gutterElement",s=this.dom.firstChild;for(let r=0,o=0;;){let l=o,a=rr(l,a,h)||o(l,a,h):o}return i}})}});class us extends bt{constructor(e){super(),this.number=e}eq(e){return this.number==e.number}toDOM(){return document.createTextNode(this.number)}}function ds(n,e){return n.state.facet(zt).formatNumber(e,n.state)}const Rd=Ci.compute([zt],n=>({class:"cm-lineNumbers",renderEmptyElements:!1,markers(e){return e.state.facet(Ed)},lineMarker(e,t,i){return i.some(s=>s.toDOM)?null:new us(ds(e,e.state.doc.lineAt(t.from).number))},lineMarkerChange:e=>e.startState.facet(zt)!=e.state.facet(zt),initialSpacer(e){return new us(ds(e,nl(e.state.doc.lines)))},updateSpacer(e,t){let i=ds(t.view,nl(t.view.state.doc.lines));return i==e.number?e:new us(i)},domEventHandlers:n.facet(zt).domEventHandlers}));function Ld(n={}){return[zt.of(n),Th(),Rd]}function nl(n){let e=9;for(;e{throw new Error("This node type doesn't define a deserialize function")})}add(e){if(this.perNode)throw new RangeError("Can't add per-node props to node types");return typeof e!="function"&&(e=xe.match(e)),t=>{let i=e(t);return i===void 0?null:[this,i]}}}L.closedBy=new L({deserialize:n=>n.split(" ")});L.openedBy=new L({deserialize:n=>n.split(" ")});L.group=new L({deserialize:n=>n.split(" ")});L.contextHash=new L({perNode:!0});L.lookAhead=new L({perNode:!0});L.mounted=new L({perNode:!0});class _d{constructor(e,t,i){this.tree=e,this.overlay=t,this.parser=i}}const Vd=Object.create(null);class xe{constructor(e,t,i,s=0){this.name=e,this.props=t,this.id=i,this.flags=s}static define(e){let t=e.props&&e.props.length?Object.create(null):Vd,i=(e.top?1:0)|(e.skipped?2:0)|(e.error?4:0)|(e.name==null?8:0),s=new xe(e.name||"",t,e.id,i);if(e.props){for(let r of e.props)if(Array.isArray(r)||(r=r(s)),r){if(r[0].perNode)throw new RangeError("Can't store a per-node prop on a node type");t[r[0].id]=r[1]}}return s}prop(e){return this.props[e.id]}get isTop(){return(this.flags&1)>0}get isSkipped(){return(this.flags&2)>0}get isError(){return(this.flags&4)>0}get isAnonymous(){return(this.flags&8)>0}is(e){if(typeof e=="string"){if(this.name==e)return!0;let t=this.prop(L.group);return t?t.indexOf(e)>-1:!1}return this.id==e}static match(e){let t=Object.create(null);for(let i in e)for(let s of i.split(" "))t[s]=e[i];return i=>{for(let s=i.prop(L.group),r=-1;r<(s?s.length:0);r++){let o=t[r<0?i.name:s[r]];if(o)return o}}}}xe.none=new xe("",Object.create(null),0,8);class Lr{constructor(e){this.types=e;for(let 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ii(r.context))})),this.chunkBudget>0&&!(a&&!l)&&this.scheduleWork(),this.checkAsyncSchedule(r.context)}checkAsyncSchedule(e){e.scheduleOn&&(this.workScheduled++,e.scheduleOn.then(()=>this.scheduleWork()).catch(t=>He(this.view.state,t)).then(()=>this.workScheduled--),e.scheduleOn=null)}destroy(){this.working&&this.working()}isWorking(){return!!(this.working||this.workScheduled>0)}},{eventHandlers:{focus(){this.scheduleWork()}}}),wt=D.define({combine(n){return n.length?n[0]:null},enables:n=>[Ie.state,rp,O.contentAttributes.compute([n],e=>{let t=e.facet(n);return t&&t.name?{"data-language":t.name}:{}})]});class sy{constructor(e,t=[]){this.language=e,this.support=t,this.extension=[e,t]}}class Vh{constructor(e,t,i,s,r,o=void 0){this.name=e,this.alias=t,this.extensions=i,this.filename=s,this.loadFunc=r,this.support=o,this.loading=null}load(){return this.loading||(this.loading=this.loadFunc().then(e=>this.support=e,e=>{throw this.loading=null,e}))}static of(e){let{load:t,support:i}=e;if(!t){if(!i)throw new RangeError("Must pass either 'load' or 'support' to LanguageDescription.of");t=()=>Promise.resolve(i)}return new Vh(e.name,(e.alias||[]).concat(e.name).map(s=>s.toLowerCase()),e.extensions||[],e.filename,t,i)}static matchFilename(e,t){for(let s of e)if(s.filename&&s.filename.test(t))return s;let i=/\.([^.]+)$/.exec(t);if(i){for(let s of e)if(s.extensions.indexOf(i[1])>-1)return s}return null}static matchLanguageName(e,t,i=!0){t=t.toLowerCase();for(let s of e)if(s.alias.some(r=>r==t))return s;if(i)for(let s of e)for(let r of s.alias){let o=t.indexOf(r);if(o>-1&&(r.length>2||!/\w/.test(t[o-1])&&!/\w/.test(t[o+r.length])))return s}return null}}const Fh=D.define(),jn=D.define({combine:n=>{if(!n.length)return" ";let e=n[0];if(!e||/\S/.test(e)||Array.from(e).some(t=>t!=e[0]))throw new Error("Invalid indent unit: "+JSON.stringify(n[0]));return e}});function Rt(n){let e=n.facet(jn);return e.charCodeAt(0)==9?n.tabSize*e.length:e.length}function Li(n,e){let t="",i=n.tabSize,s=n.facet(jn)[0];if(s==" "){for(;e>=i;)t+=" ",e-=i;s=" "}for(let r=0;r |
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