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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Epic Conquest 2 APK The Most Anticipated RPG Game for Android.md b/spaces/1phancelerku/anime-remove-background/Epic Conquest 2 APK The Most Anticipated RPG Game for Android.md deleted file mode 100644 index 162b7d964eb41d4dbb742e141adea38e42e2e713..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Epic Conquest 2 APK The Most Anticipated RPG Game for Android.md +++ /dev/null @@ -1,103 +0,0 @@ -
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Epic Conquest 2 APK Download for Android: A Guide
-If you are looking for a classic single-player action/adventure RPG with a solid combat and a great story, you might want to check out Epic Conquest 2. This game is developed by Gaco Games, a small but passionate team of four people who have crafted this project with care and love. In this article, we will tell you what Epic Conquest 2 is, how to download it for your Android device, why you should play it, and some tips and tricks to help you enjoy it more.
-epic conquest 2 apk download for android
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What is Epic Conquest 2?
-Epic Conquest 2 is a sequel to the popular Epic Conquest game that was released in 2017. It is a game that combines elements of action, adventure, and role-playing in an open world full of treasures and resources. Here are some of the features that make this game stand out:
-A classic RPG with an open world and a great story
-Epic Conquest 2 has a well-written story that will keep you hooked until the end. You can choose from four different playable characters, each with their own personality, backstory, and motivation. You can also interact with various NPCs and complete quests that will affect the outcome of the story. There are multiple endings to discover depending on your choices and actions.
-A game with diverse characters, skills, and costumes
-Epic Conquest 2 allows you to customize your character according to your preference and playstyle. You can distribute your attributes (STR, INT, AGI, DEX, VIT) and choose from eight skills and eight masteries for each character. You can also buy costumes for your character to change their appearance and get a boost of power. Each character has their own unique skills and masteries that will make them excel in different situations.
-A game with simple yet beautiful graphics and offline mode
-Epic Conquest 2 has an old-school graphics style that is simple but charming. The game has colorful environments, detailed animations, and smooth effects that will make you feel immersed in the world. The game also supports offline mode, so you can play it anywhere without internet connection. You don't need to pay or watch ads to enjoy the game, unless you want to support the developers.
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How to download Epic Conquest 2 APK for Android?
-There are several ways to download Epic Conquest 2 APK for your Android device. Here are some of them:
-Download from the official website or Google Play Store
-The easiest way to download Epic Conquest 2 APK is to visit the official website of Gaco Games at https://gacogames.com/ or search for Epic Conquest 2 on Google Play Store. You can find the latest version of the game there and install it directly on your device. This way, you can be sure that you are getting the official and safe version of the game. You can also get updates and support from the developers this way.
-Download from third-party sources like APKCombo, Softonic, or mob.org
-Another way to download Epic Conquest 2 APK is to use third-party websites that offer APK files for various apps and games. Some of the popular ones are APKCombo, Softonic, and mob.org. You can search for Epic Conquest 2 on these websites and download the APK file to your device. However, you should be careful when using this method, as some of the APK files may be modified or infected with malware. You should always check the reviews and ratings of the APK file before downloading it. You should also enable the "Unknown sources" option on your device settings to allow the installation of APK files from outside sources.
-Install the APK file on your device and enjoy the game
-Once you have downloaded the Epic Conquest 2 APK file, you can install it on your device by tapping on it and following the instructions. You may need to grant some permissions to the app to access your device's storage, camera, microphone, etc. After the installation is complete, you can launch the game and start playing it. You may need to download some additional data for the game to run smoothly.
-Why should you play Epic Conquest 2?
-Epic Conquest 2 is a game that will appeal to fans of classic RPGs as well as newcomers who want to try a fun and immersive game. Here are some of the reasons why you should play Epic Conquest 2:
-It offers a fun and immersive gameplay experience
-Epic Conquest 2 has a gameplay that is easy to learn but hard to master. You can control your character with simple touch controls and unleash powerful skills and combos with a tap of a button. You can also dodge, block, and counter enemy attacks with timing and strategy. The game has a variety of enemies and bosses that will challenge your skills and tactics. The game also has a dynamic weather system that will affect the environment and gameplay.
-It has a rich and engaging story with multiple endings
-Epic Conquest 2 has a story that will keep you interested and invested in the fate of the characters and the world. You can choose from four different characters, each with their own personality, backstory, and motivation. You can also interact with various NPCs and complete quests that will affect the outcome of the story. There are multiple endings to discover depending on your choices and actions. The game also has a lot of humor and references that will make you laugh and smile.
-It has a lot of content and features to explore and customize
-Epic Conquest 2 has a lot of content and features that will keep you entertained for hours. You can explore an open world full of treasures and resources that you can use to craft, enhance, and upgrade your equipment. You can also buy costumes for your character to change their appearance and get a boost of power. You can also customize your character's attributes, skills, and masteries according to your playstyle. The game also has a cloud save feature that will allow you to backup and load your progress across devices.
-What are some tips and tricks for playing Epic Conquest 2?
-If you want to get the most out of Epic Conquest 2, here are some tips and tricks that will help you:
-Choose your character wisely and build them according to your playstyle
-Epic Conquest 2 has four different characters that you can choose from: Alaster, Edna, Alma, and Raine. Each character has their own strengths and weaknesses, as well as unique skills and masteries that will make them excel in different situations. For example, Alaster is a warrior who specializes in melee combat and physical damage; Edna is a mage who specializes in ranged combat and elemental damage; Alma is a rogue who specializes in stealth combat and critical damage; Raine is a cleric who specializes in healing combat and support. You should choose the character that suits your playstyle and preference, and build them accordingly. You can distribute your attributes (STR, INT, AGI, DEX, VIT) and choose from eight skills and eight masteries for each character. You can also switch between characters at any time in the game.
-Explore the world and collect resources, treasures, and costumes
-Epic Conquest 2 has an open world that you can explore freely. You can find various resources, treasures, and costumes that will help you in your adventure. Resources can be used to craft, enhance, and upgrade your equipment. Treasures can be sold for gold or exchanged for other items. Costumes can change your appearance and give you a boost of power. You can also find hidden areas and secrets that will reward you with more loot and surprises.
-Craft, enhance, and upgrade your equipment to tackle harder challenges
-Epic Conquest 2 has a crafting system that will allow you to create your own equipment from the resources you collect. You can craft weapons, armors, accessories, potions, and scrolls that will improve your stats and abilities. You can also enhance and upgrade your equipment to make them more powerful and effective. You can use enhancement stones to increase the level of your equipment, and use upgrade stones to increase the rarity of your equipment. You can also use runes to add special effects to your equipment. You will need better equipment to face harder enemies and bosses in the game.
-Use the cloud save feature to backup and load your progress across devices
-Epic Conquest 2 has a cloud save feature that will allow you to backup and load your progress across devices. You can use this feature to save your game data on the cloud server and access it from any device that has the game installed. You can also use this feature to transfer your game data from one device to another. This way, you can play the game on different devices without losing your progress or starting over.
-Conclusion
-Epic Conquest 2 is a game that will satisfy your craving for a classic RPG with an open world and a great story. It is a game that has a lot of content and features to explore and customize. It is a game that offers a fun and immersive gameplay experience. It is a game that you can download for free on your Android device and play offline without any ads or payments. If you are looking for a game like this, you should download Epic Conquest 2 APK today and start your epic adventure.
-FAQs
-Here are some of the frequently asked questions about Epic Conquest 2:
-Q: How long is the game?
-A: The game has about 20 hours of main story content, plus more hours of side quests, exploration, and replay value.
-Q: How many endings are there in the game?
-A: The game has four main endings, plus several variations depending on your choices and actions.
-Q: How do I get more gold in the game?
-A: You can get more gold by selling items, completing quests, finding treasures, or watching ads (optional).
-Q: How do I get more costumes in the game?
-A: You can get more costumes by buying them from shops, finding them in chests, completing achievements, or watching ads (optional).
-Q: How do I contact the developers of the game?
-A: You can contact the developers of the game by visiting their website at https://gacogames.com/, or by following them on their social media accounts at https://www.facebook.com/gacogames/, https://twitter.com/gacogames/, or https://www.instagram.com/gacogames/.
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\ No newline at end of file diff --git a/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/AWS b022fe0cb7084cc0b64624f7bc8cde2c.md b/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/AWS b022fe0cb7084cc0b64624f7bc8cde2c.md deleted file mode 100644 index 178782522c7733519f6649c12e323e7a746d0363..0000000000000000000000000000000000000000 --- a/spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/AWS b022fe0cb7084cc0b64624f7bc8cde2c.md +++ /dev/null @@ -1,5 +0,0 @@ -# AWS - -Last edited time: March 31, 2023 1:49 PM -Owner: Anonymous -Tags: Infrastructure \ No newline at end of file diff --git a/spaces/ADOPLE/ResumeAnalyzer/app.py b/spaces/ADOPLE/ResumeAnalyzer/app.py deleted file mode 100644 index fdf546c8df3a263dbabd12407f156a7513201e0b..0000000000000000000000000000000000000000 --- a/spaces/ADOPLE/ResumeAnalyzer/app.py +++ /dev/null @@ -1,144 +0,0 @@ -import gradio as gr -import PyPDF2 -import os -import openai -import re -import plotly.graph_objects as go - -class ResumeAnalyser: - def __init__(self): - pass - def extract_text_from_file(self,file_path): - # Get the file extension - file_extension = os.path.splitext(file_path)[1] - - if file_extension == '.pdf': - with open(file_path, 'rb') as file: - # Create a PDF file reader object - reader = PyPDF2.PdfFileReader(file) - - # Create an empty string to hold the extracted text - extracted_text = "" - - # Loop through each page in the PDF and extract the text - for page_number in range(reader.getNumPages()): - page = reader.getPage(page_number) - extracted_text += page.extractText() - return extracted_text - - elif file_extension == '.txt': - with open(file_path, 'r') as file: - # Just read the entire contents of the text file - return file.read() - - else: - return "Unsupported file type" - - def responce_from_ai(self,textjd, textcv): - resume = self.extract_text_from_file(textjd) - job_description = self.extract_text_from_file(textcv) - - response = openai.Completion.create( - engine="text-davinci-003", - prompt=f""" - Given the job description and the resume, assess the matching percentage to 100 and if 100 percentage not matched mention the remaining percentage with reason. **Job Description:**{job_description}**Resume:**{resume} - **Detailed Analysis:** - the result should be in this format: - Matched Percentage: [matching percentage]. - Reason : [Mention Reason and keys from job_description and resume get this matched percentage.]. - Skills To Improve : [Mention the skills How to improve and get 100 percentage job description matching]. - Keywords : [matched key words from {job_description} and {resume}]. - """, - temperature=0, - max_tokens=100, - n=1, - stop=None, - ) - generated_text = response.choices[0].text.strip() - print(generated_text) - return generated_text - - - def matching_percentage(self,job_description_path, resume_path): - job_description_path = job_description_path.name - resume_path = resume_path.name - - generated_text = self.responce_from_ai(job_description_path, resume_path) - - result = generated_text - - lines = result.split('\n') - - matched_percentage = None - matched_percentage_txt = None - reason = None - skills_to_improve = None - keywords = None - - for line in lines: - if line.startswith('Matched Percentage:'): - match = re.search(r"Matched Percentage: (\d+)%", line) - if match: - matched_percentage = int(match.group(1)) - matched_percentage_txt = (f"Matched Percentage: {matched_percentage}%") - elif line.startswith('Reason'): - reason = line.split(':')[1].strip() - elif line.startswith('Skills To Improve'): - skills_to_improve = line.split(':')[1].strip() - elif line.startswith('Keywords'): - keywords = line.split(':')[1].strip() - - - # Extract the matched percentage using regular expression - # match1 = re.search(r"Matched Percentage: (\d+)%", matched_percentage) - # matched_Percentage = int(match1.group(1)) - - # Creating a pie chart with plotly - labels = ['Matched', 'Remaining'] - values = [matched_percentage, 100 - matched_percentage] - - fig = go.Figure(data=[go.Pie(labels=labels, values=values)]) - # fig.update_layout(title='Matched Percentage') - - - return matched_percentage_txt,reason, skills_to_improve, keywords,fig - - - def gradio_interface(self): - with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as app: - #gr.HTML("""
ADOPLE AI
""") - with gr.Row(): - with gr.Column(elem_id="col-container"): - gr.HTML( - """""" - ) - gr.HTML( - """
ADOPLE AI Resume Analyzer
""" - ) - gr.HTML("") - with gr.Row(): - with gr.Column(scale=0.45, min_width=150, ): - jobDescription = gr.File(label="Job Description") - with gr.Column(scale=0.45, min_width=150): - resume = gr.File(label="Resume") - with gr.Column(scale=0.10, min_width=150): - analyse = gr.Button("Analyse") - with gr.Row(): - with gr.Column(scale=1.0, min_width=150): - perncentage = gr.Textbox(label="Matching Percentage",lines=8) - with gr.Column(scale=1.0, min_width=150): - reason = gr.Textbox(label="Matching Reason",lines=8) - with gr.Column(scale=1.0, min_width=150): - skills = gr.Textbox(label="Skills To Improve",lines=8) - with gr.Column(scale=1.0, min_width=150): - keywords = gr.Textbox(label="Matched Keywords",lines=8) - with gr.Row(): - with gr.Column(scale=1.0, min_width=150): - pychart = gr.Plot(label="Matching Percentage Chart") - analyse.click(self.matching_percentage, [jobDescription, resume], [perncentage,reason,skills,keywords,pychart]) - - app.launch() - -resume=ResumeAnalyser() -resume.gradio_interface() \ No newline at end of file diff --git a/spaces/AHzizi/WaifuVoiceGen/README.md b/spaces/AHzizi/WaifuVoiceGen/README.md deleted file mode 100644 index 2e44ec5507a21c84647346865c876ce2b48db560..0000000000000000000000000000000000000000 --- a/spaces/AHzizi/WaifuVoiceGen/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Vits Models -emoji: 🏃 -colorFrom: pink -colorTo: indigo -sdk: gradio -sdk_version: 3.17.0 -app_file: app.py -pinned: false -license: apache-2.0 -duplicated_from: sayashi/vits-models ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/AIConsultant/MusicGen/model_cards/AUDIOGEN_MODEL_CARD.md b/spaces/AIConsultant/MusicGen/model_cards/AUDIOGEN_MODEL_CARD.md deleted file mode 100644 index 92decf5e16e05ce0c2e72af8aa6728b5186c6882..0000000000000000000000000000000000000000 --- a/spaces/AIConsultant/MusicGen/model_cards/AUDIOGEN_MODEL_CARD.md +++ /dev/null @@ -1,79 +0,0 @@ -# AudioGen Model Card - -## Model details -**Organization developing the model:** The FAIR team of Meta AI. - -**Model date:** This version of AudioGen was trained between July 2023 and August 2023. - -**Model version:** This is version 2 of the model, not to be confused with the original AudioGen model published in ["AudioGen: Textually Guided Audio Generation"][audiogen]. -In this version (v2), AudioGen was trained on the same data, but with some other differences: -1. This model was trained on 10 seconds (vs. 5 seconds in v1). -2. The discrete representation used under the hood is extracted using a retrained EnCodec model on the environmental sound data, following the EnCodec setup detailed in the ["Simple and Controllable Music Generation" paper][musicgen]. -3. No audio mixing augmentations. - -**Model type:** AudioGen consists of an EnCodec model for audio tokenization, and an auto-regressive language model based on the transformer architecture for audio modeling. The released model has 1.5B parameters. - -**Paper or resource for more information:** More information can be found in the paper [AudioGen: Textually Guided Audio Generation](https://arxiv.org/abs/2209.15352). - -**Citation details:** See [AudioGen paper][audiogen] - -**License:** Code is released under MIT, model weights are released under CC-BY-NC 4.0. - -**Where to send questions or comments about the model:** Questions and comments about AudioGen can be sent via the [GitHub repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue. - -## Intended use -**Primary intended use:** The primary use of AudioGen is research on AI-based audio generation, including: -- Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science -- Generation of sound guided by text to understand current abilities of generative AI models by machine learning amateurs - -**Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models. - -**Out-of-scope use cases** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate audio pieces that create hostile or alienating environments for people. This includes generating audio that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. - -## Metrics - -**Models performance measures:** We used the following objective measure to evaluate the model on a standard audio benchmark: -- Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish) -- Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST) - -Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes: -- Overall quality of the audio samples; -- Text relevance to the provided text input; - -More details on performance measures and human studies can be found in the paper. - -**Decision thresholds:** Not applicable. - -## Evaluation datasets - -The model was evaluated on the [AudioCaps benchmark](https://audiocaps.github.io/). - -## Training datasets - -The model was trained on the following data sources: a subset of AudioSet (Gemmeke et al., 2017), [BBC sound effects](https://sound-effects.bbcrewind.co.uk/), AudioCaps (Kim et al., 2019), Clotho v2 (Drossos et al., 2020), VGG-Sound (Chen et al., 2020), FSD50K (Fonseca et al., 2021), [Free To Use Sounds](https://www.freetousesounds.com/all-in-one-bundle/), [Sonniss Game Effects](https://sonniss.com/gameaudiogdc), [WeSoundEffects](https://wesoundeffects.com/we-sound-effects-bundle-2020/), [Paramount Motion - Odeon Cinematic Sound Effects](https://www.paramountmotion.com/odeon-sound-effects). - -## Evaluation results - -Below are the objective metrics obtained with the released model on AudioCaps (consisting of 10-second long samples). Note that the model differs from the original AudioGen model introduced in the paper, hence the difference in the metrics. - -| Model | Frechet Audio Distance | KLD | Text consistency | -|---|---|---|---| -| facebook/audiogen-medium | 1.77 | 1.41 | 0.299 | - -More information can be found in the paper [AudioGen: Textually Guided Audio Generation][audiogen], in the Experiments section. - -## Limitations and biases - -**Limitations:** -- The model is not able to generate realistic vocals. -- The model has been trained with English descriptions and will not perform as well in other languages. -- It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results. - -**Biases:** The datasets used for training may be lacking of diversity and are not representative of all possible sound events. The generated samples from the model will reflect the biases from the training data. - -**Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data. - -**Use cases:** Users must be aware of the biases, limitations and risks of the model. AudioGen is a model developed for artificial intelligence research on audio generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks. - -[musicgen]: https://arxiv.org/abs/2306.05284 -[audiogen]: https://arxiv.org/abs/2209.15352 diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/modules.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/modules.py deleted file mode 100644 index 8a3f8df6d72023df7c141467a0114aca02e54cdb..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/modules.py +++ /dev/null @@ -1,350 +0,0 @@ -import torch -import torch.nn as nn -from functools import partial - -from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test -from torch.utils.checkpoint import checkpoint -from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, AutoTokenizer -from importlib_resources import files -from ldm.modules.encoders.CLAP.utils import read_config_as_args -from ldm.modules.encoders.CLAP.clap import TextEncoder -from ldm.util import default, count_params -import open_clip - -class AbstractEncoder(nn.Module): - def __init__(self): - super().__init__() - - def encode(self, *args, **kwargs): - raise NotImplementedError - - -class ClassEmbedder(nn.Module): - def __init__(self, embed_dim, n_classes=1000, key='class'): - super().__init__() - self.key = key - self.embedding = nn.Embedding(n_classes, embed_dim) - - def forward(self, batch, key=None): - if key is None: - key = self.key - # this is for use in crossattn - c = batch[key][:, None]# (bsz,1) - c = self.embedding(c) - return c - - -class TransformerEmbedder(AbstractEncoder): - """Some transformer encoder layers""" - def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"): - super().__init__() - self.device = device - self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, - attn_layers=Encoder(dim=n_embed, depth=n_layer)) - - def forward(self, tokens): - tokens = tokens.to(self.device) # meh - z = self.transformer(tokens, return_embeddings=True) - return z - - def encode(self, x): - return self(x) - - -class BERTTokenizer(AbstractEncoder): - """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)""" - def __init__(self, device="cuda", vq_interface=True, max_length=77): - super().__init__() - from transformers import BertTokenizerFast # TODO: add to reuquirements - self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") - self.device = device - self.vq_interface = vq_interface - self.max_length = max_length - - def forward(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - return tokens - - @torch.no_grad() - def encode(self, text): - tokens = self(text) - if not self.vq_interface: - return tokens - return None, None, [None, None, tokens] - - def decode(self, text): - return text - - -class BERTEmbedder(AbstractEncoder):# 这里不是用的pretrained bert,是用的transformers的BertTokenizer加自定义的TransformerWrapper - """Uses the BERT tokenizr model and add some transformer encoder layers""" - def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77, - device="cuda",use_tokenizer=True, embedding_dropout=0.0): - super().__init__() - self.use_tknz_fn = use_tokenizer - if self.use_tknz_fn: - self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len) - self.device = device - self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, - attn_layers=Encoder(dim=n_embed, depth=n_layer), - emb_dropout=embedding_dropout) - - def forward(self, text): - if self.use_tknz_fn: - tokens = self.tknz_fn(text)#.to(self.device) - else: - tokens = text - z = self.transformer(tokens, return_embeddings=True) - return z - - def encode(self, text): - # output of length 77 - return self(text) - - -class SpatialRescaler(nn.Module): - def __init__(self, - n_stages=1, - method='bilinear', - multiplier=0.5, - in_channels=3, - out_channels=None, - bias=False): - super().__init__() - self.n_stages = n_stages - assert self.n_stages >= 0 - assert method in ['nearest','linear','bilinear','trilinear','bicubic','area'] - self.multiplier = multiplier - self.interpolator = partial(torch.nn.functional.interpolate, mode=method) - self.remap_output = out_channels is not None - if self.remap_output: - print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.') - self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias) - - def forward(self,x): - for stage in range(self.n_stages): - x = self.interpolator(x, scale_factor=self.multiplier) - - - if self.remap_output: - x = self.channel_mapper(x) - return x - - def encode(self, x): - return self(x) - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - -class FrozenT5Embedder(AbstractEncoder): - """Uses the T5 transformer encoder for text""" - def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl - super().__init__() - self.tokenizer = T5Tokenizer.from_pretrained(version) - self.transformer = T5EncoderModel.from_pretrained(version) - self.device = device - self.max_length = max_length # TODO: typical value? - if freeze: - self.freeze() - - def freeze(self): - self.transformer = self.transformer.eval() - #self.train = disabled_train - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - outputs = self.transformer(input_ids=tokens) - - z = outputs.last_hidden_state - return z - - def encode(self, text): - return self(text) - - -class FrozenCLAPEmbedder(AbstractEncoder): - """Uses the CLAP transformer encoder for text (from huggingface)""" - def __init__(self, weights_path, freeze=True, device="cuda", max_length=77): # clip-vit-base-patch32 - super().__init__() - - model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model'] - match_params = dict() - for key in list(model_state_dict.keys()): - if 'caption_encoder' in key: - match_params[key.replace('caption_encoder.', '')] = model_state_dict[key] - - config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text() - args = read_config_as_args(config_as_str, is_config_str=True) - - # To device - self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model - self.caption_encoder = TextEncoder( - args.d_proj, args.text_model, args.transformer_embed_dim - ) - - self.max_length = max_length - self.device = device - if freeze: self.freeze() - - print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.") - - def freeze(self): - self.caption_encoder.base = self.caption_encoder.base.eval() - for param in self.caption_encoder.base.parameters(): - param.requires_grad = False - - - def encode(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - - outputs = self.caption_encoder.base(input_ids=tokens) - z = self.caption_encoder.projection(outputs.last_hidden_state) - return z - -class FrozenCLAPEmbedderNoLoad(AbstractEncoder): - def __init__(self, config, freeze=True, device="cpu", max_length=77): - super().__init__() - args = config - - # To device - self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model - self.caption_encoder = TextEncoder( - args.d_proj, args.text_model, args.transformer_embed_dim - ) - - self.max_length = max_length - self.device = device - if freeze: self.freeze() - - print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.") - - def freeze(self): - self.caption_encoder.base = self.caption_encoder.base.eval() - for param in self.caption_encoder.base.parameters(): - param.requires_grad = False - - - def encode(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - - outputs = self.caption_encoder.base(input_ids=tokens) - z = self.caption_encoder.projection(outputs.last_hidden_state) - return z - - -class NewFrozenCLAPEmbedder(AbstractEncoder): - """Uses the CLAP transformer encoder for text (from huggingface)""" - def __init__(self, weights_path, freeze=True, device="cuda", max_length=77): # clip-vit-base-patch32 - super().__init__() - # To device - from transformers import RobertaTokenizer - from ldm.modules.encoders.open_clap import create_model - - - model, model_cfg = create_model( - 'HTSAT-tiny', - 'roberta', - weights_path, - enable_fusion=True, - fusion_type='aff_2d' - ) - - del model.audio_branch, model.audio_transform, model.audio_projection - self.tokenizer = RobertaTokenizer.from_pretrained('roberta-base') - self.model = model - - self.max_length = max_length - self.device = device - if freeze: self.freeze() - - param_num = sum(p.numel() for p in model.parameters() if p.requires_grad) - print(f'{self.model.__class__.__name__} comes with: {param_num / 1e+6:.3f} M params.') - - def freeze(self): - self.model = self.model.eval() - for param in self.model.parameters(): - param.requires_grad = False - - def encode(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - outputs = self.model.text_branch(input_ids=batch_encoding["input_ids"].to(self.device), attention_mask=batch_encoding["attention_mask"].to(self.device)) - z = self.model.text_projection(outputs.last_hidden_state) - return z - -class FrozenFLANEmbedder(AbstractEncoder): - """Uses the T5 transformer encoder for text""" - def __init__(self, version="google/flan-t5-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl - super().__init__() - self.tokenizer = T5Tokenizer.from_pretrained(version) - self.transformer = T5EncoderModel.from_pretrained(version) - self.device = device - self.max_length = max_length # TODO: typical value? - if freeze: - self.freeze() - - def freeze(self): - self.transformer = self.transformer.eval() - #self.train = disabled_train - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, - return_overflowing_tokens=False, padding="max_length", return_tensors="pt") - tokens = batch_encoding["input_ids"].to(self.device) - outputs = self.transformer(input_ids=tokens) - - z = outputs.last_hidden_state - return z - - def encode(self, text): - return self(text) -class FrozenGlobalNormOpenCLIPEmbedder(AbstractEncoder): - """ - Uses the OpenCLIP transformer encoder for text - """ - def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", freeze=True, delvisual=True): - super().__init__() - model, _, preprocess = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version) - if delvisual: - del model.visual - del preprocess - else: - self.preprocess = preprocess - self.model = model - - self.device = device - if freeze: - self.freeze() - - def freeze(self): - self.model = self.model.eval() - for param in self.parameters(): - param.requires_grad = False - - def forward(self, text): - tokens = open_clip.tokenize(text) - z = self.model.encode_text(tokens.to(self.device)) - z /= z.norm(dim=-1, keepdim=True) - return z.unsqueeze(1) - - def forward_img(self, image): - z = self.model.encode_image(image.to(self.device)) - z /= z.norm(dim=-1, keepdim=True) - return z.unsqueeze(1) - - def encode(self, text): - return self(text) \ No newline at end of file diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/vocoder/bigvgan/__init__.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/vocoder/bigvgan/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Ababababababbababa/poetry2023/app.py b/spaces/Ababababababbababa/poetry2023/app.py deleted file mode 100644 index 5b6654d5a405778ddbc9ca5fa5d041aff535f3b5..0000000000000000000000000000000000000000 --- a/spaces/Ababababababbababa/poetry2023/app.py +++ /dev/null @@ -1,53 +0,0 @@ -import gc -import gradio as gr -from transformers import pipeline, set_seed - -pipe = pipeline('text-generation', framework='pt', model='akhooli/ap2023', tokenizer='akhooli/ap2023') -#gc.collect() -samples = [['أنت' - ,1.0, 50, 1.0, 1.0, 114],['هل غادر' - ,1.0, 50, 1.0, 1.0, 114 ],['ألا ليت' - ,1.0, 50, 1.0, 1.0, 114 ],['يا قدس' - ,1.0, 50, 1.0, 1.0, 114],['عيد بأية حال' - ,1.0, 50, 1.0, 1.0, 114],['لكل شيء إذا ما' - ,1.0, 50, 1.0, 1.0, 114 ],['.' - ,1.0, 50, 1.0, 1.0, 114]] - -notes = """ -- Enter a short prompt or select (click) one of the examples and click SEND -- Adjust parameters (temperture, top k, top p and penalty) through the slider (keep close to default values). -- For the same seed (randomness), the same output is regenerated if other parameters are fixed -- Clear and enter new prompt or select another example and SEND to regenerate -- The '.' means start a new line from no prompt (your prompt need not be long) -- Be patient: this runs on CPU (free tier) -- Feedback (Twitter): @akhooli (https://twitter.com/akhooli/status/1611025232201977859) -- Note/Disclaimer: may generate unaccepted or inappropriate content. Use at your own risk. -""" -def sayPoetry(prompt, temp=1.0, topk = 50, topp = 1.0, penalty=1.0, seed=114): - if not int(seed) >= 0: seed=114 - set_seed(seed) - gen = pipe(prompt, max_length=96, do_sample=True, temperature=temp, top_k=topk, top_p=topp, repetition_penalty=penalty, - min_length = 64, no_repeat_ngram_size = 3, return_full_text=True, - num_beams=5, num_return_sequences=1)[0]["generated_text"] - poetry ="" - for line in gen.split('.')[:-1]: - poetry += line #+ "\n" - return poetry -poetry = gr.Interface(fn=sayPoetry, - inputs=[ - gr.Textbox(label="Enter short prompt or select from examples:"), - gr.Slider(0.70, 1.2, step=0.01,value=1.0, label='control temperature'), - gr.Slider(25, 100, step=1,value=50, label='control top k'), - gr.Slider(0.80, 1.0, step=0.01,value=1.0, label='control top p'), - gr.Slider(0.90, 1.50, step=0.01,value=1.0, label='control penalty'), - gr.Number(value=139750, precision=0, label='Seed'), - ], - outputs=[gr.Textbox(label="Generated Poetry:")], - - allow_flagging='never', - title='Arabic Poetry Generation Demo (updated Jan. 2023)', - description = "A simple demo of AI generated poetry based on 1M poems fine-tuned using AraGPT2 (be patient, runs on cpu)", - examples=samples, - cache_examples=False, - article = notes) -poetry.launch() # show_error = True, debug=True \ No newline at end of file diff --git a/spaces/Abhilashvj/planogram-compliance/export.py b/spaces/Abhilashvj/planogram-compliance/export.py deleted file mode 100644 index 21f9d06365770a03475b74b743837b2a43c4ec0d..0000000000000000000000000000000000000000 --- a/spaces/Abhilashvj/planogram-compliance/export.py +++ /dev/null @@ -1,1013 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit - -Format | `export.py --include` | Model ---- | --- | --- -PyTorch | - | yolov5s.pt -TorchScript | `torchscript` | yolov5s.torchscript -ONNX | `onnx` | yolov5s.onnx -OpenVINO | `openvino` | yolov5s_openvino_model/ -TensorRT | `engine` | yolov5s.engine -CoreML | `coreml` | yolov5s.mlmodel -TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/ -TensorFlow GraphDef | `pb` | yolov5s.pb -TensorFlow Lite | `tflite` | yolov5s.tflite -TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite -TensorFlow.js | `tfjs` | yolov5s_web_model/ -PaddlePaddle | `paddle` | yolov5s_paddle_model/ - -Requirements: - $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU - $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU - -Usage: - $ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ... - -Inference: - $ python detect.py --weights yolov5s.pt # PyTorch - yolov5s.torchscript # TorchScript - yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn - yolov5s_openvino_model # OpenVINO - yolov5s.engine # TensorRT - yolov5s.mlmodel # CoreML (macOS-only) - yolov5s_saved_model # TensorFlow SavedModel - yolov5s.pb # TensorFlow GraphDef - yolov5s.tflite # TensorFlow Lite - yolov5s_edgetpu.tflite # TensorFlow Edge TPU - yolov5s_paddle_model # PaddlePaddle - -TensorFlow.js: - $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example - $ npm install - $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model - $ npm start -""" - -import argparse -import contextlib -import json -import os -import platform -import re -import subprocess -import sys -import time -import warnings -from pathlib import Path - -import pandas as pd -import torch -from torch.utils.mobile_optimizer import optimize_for_mobile - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[0] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -if platform.system() != "Windows": - ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative - -from models.experimental import attempt_load -from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel -from utils.dataloaders import LoadImages -from utils.general import ( - LOGGER, - Profile, - check_dataset, - check_img_size, - check_requirements, - check_version, - check_yaml, - colorstr, - file_size, - get_default_args, - print_args, - url2file, - yaml_save, -) -from utils.torch_utils import select_device, smart_inference_mode - -MACOS = platform.system() == "Darwin" # macOS environment - - -def export_formats(): - # YOLOv5 export formats - x = [ - ["PyTorch", "-", ".pt", True, True], - ["TorchScript", "torchscript", ".torchscript", True, True], - ["ONNX", "onnx", ".onnx", True, True], - ["OpenVINO", "openvino", "_openvino_model", True, False], - ["TensorRT", "engine", ".engine", False, True], - ["CoreML", "coreml", ".mlmodel", True, False], - ["TensorFlow SavedModel", "saved_model", "_saved_model", True, True], - ["TensorFlow GraphDef", "pb", ".pb", True, True], - ["TensorFlow Lite", "tflite", ".tflite", True, False], - ["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", False, False], - ["TensorFlow.js", "tfjs", "_web_model", False, False], - ["PaddlePaddle", "paddle", "_paddle_model", True, True], - ] - return pd.DataFrame( - x, columns=["Format", "Argument", "Suffix", "CPU", "GPU"] - ) - - -def try_export(inner_func): - # YOLOv5 export decorator, i..e @try_export - inner_args = get_default_args(inner_func) - - def outer_func(*args, **kwargs): - prefix = inner_args["prefix"] - try: - with Profile() as dt: - f, model = inner_func(*args, **kwargs) - LOGGER.info( - f"{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)" - ) - return f, model - except Exception as e: - LOGGER.info(f"{prefix} export failure ❌ {dt.t:.1f}s: {e}") - return None, None - - return outer_func - - -@try_export -def export_torchscript( - model, im, file, optimize, prefix=colorstr("TorchScript:") -): - # YOLOv5 TorchScript model export - LOGGER.info( - f"\n{prefix} starting export with torch {torch.__version__}..." - ) - f = file.with_suffix(".torchscript") - - ts = torch.jit.trace(model, im, strict=False) - d = { - "shape": im.shape, - "stride": int(max(model.stride)), - "names": model.names, - } - extra_files = {"config.txt": json.dumps(d)} # torch._C.ExtraFilesMap() - if ( - optimize - ): # https://pytorch.org/tutorials/recipes/mobile_interpreter.html - optimize_for_mobile(ts)._save_for_lite_interpreter( - str(f), _extra_files=extra_files - ) - else: - ts.save(str(f), _extra_files=extra_files) - return f, None - - -@try_export -def export_onnx( - model, im, file, opset, dynamic, simplify, prefix=colorstr("ONNX:") -): - # YOLOv5 ONNX export - check_requirements("onnx>=1.12.0") - import onnx - - LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__}...") - f = file.with_suffix(".onnx") - - output_names = ( - ["output0", "output1"] - if isinstance(model, SegmentationModel) - else ["output0"] - ) - if dynamic: - dynamic = { - "images": {0: "batch", 2: "height", 3: "width"} - } # shape(1,3,640,640) - if isinstance(model, SegmentationModel): - dynamic["output0"] = { - 0: "batch", - 1: "anchors", - } # shape(1,25200,85) - dynamic["output1"] = { - 0: "batch", - 2: "mask_height", - 3: "mask_width", - } # shape(1,32,160,160) - elif isinstance(model, DetectionModel): - dynamic["output0"] = { - 0: "batch", - 1: "anchors", - } # shape(1,25200,85) - - torch.onnx.export( - model.cpu() - if dynamic - else model, # --dynamic only compatible with cpu - im.cpu() if dynamic else im, - f, - verbose=False, - opset_version=opset, - do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False - input_names=["images"], - output_names=output_names, - dynamic_axes=dynamic or None, - ) - - # Checks - model_onnx = onnx.load(f) # load onnx model - onnx.checker.check_model(model_onnx) # check onnx model - - # Metadata - d = {"stride": int(max(model.stride)), "names": model.names} - for k, v in d.items(): - meta = model_onnx.metadata_props.add() - meta.key, meta.value = k, str(v) - onnx.save(model_onnx, f) - - # Simplify - if simplify: - try: - cuda = torch.cuda.is_available() - check_requirements( - ( - "onnxruntime-gpu" if cuda else "onnxruntime", - "onnx-simplifier>=0.4.1", - ) - ) - import onnxsim - - LOGGER.info( - f"{prefix} simplifying with onnx-simplifier {onnxsim.__version__}..." - ) - model_onnx, check = onnxsim.simplify(model_onnx) - assert check, "assert check failed" - onnx.save(model_onnx, f) - except Exception as e: - LOGGER.info(f"{prefix} simplifier failure: {e}") - return f, model_onnx - - -@try_export -def export_openvino(file, metadata, half, prefix=colorstr("OpenVINO:")): - # YOLOv5 OpenVINO export - check_requirements( - "openvino-dev" - ) # requires openvino-dev: https://pypi.org/project/openvino-dev/ - import openvino.inference_engine as ie - - LOGGER.info( - f"\n{prefix} starting export with openvino {ie.__version__}..." - ) - f = str(file).replace(".pt", f"_openvino_model{os.sep}") - - cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}" - subprocess.run(cmd.split(), check=True, env=os.environ) # export - yaml_save( - Path(f) / file.with_suffix(".yaml").name, metadata - ) # add metadata.yaml - return f, None - - -@try_export -def export_paddle(model, im, file, metadata, prefix=colorstr("PaddlePaddle:")): - # YOLOv5 Paddle export - check_requirements(("paddlepaddle", "x2paddle")) - import x2paddle - from x2paddle.convert import pytorch2paddle - - LOGGER.info( - f"\n{prefix} starting export with X2Paddle {x2paddle.__version__}..." - ) - f = str(file).replace(".pt", f"_paddle_model{os.sep}") - - pytorch2paddle( - module=model, save_dir=f, jit_type="trace", input_examples=[im] - ) # export - yaml_save( - Path(f) / file.with_suffix(".yaml").name, metadata - ) # add metadata.yaml - return f, None - - -@try_export -def export_coreml(model, im, file, int8, half, prefix=colorstr("CoreML:")): - # YOLOv5 CoreML export - check_requirements("coremltools") - import coremltools as ct - - LOGGER.info( - f"\n{prefix} starting export with coremltools {ct.__version__}..." - ) - f = file.with_suffix(".mlmodel") - - ts = torch.jit.trace(model, im, strict=False) # TorchScript model - ct_model = ct.convert( - ts, - inputs=[ - ct.ImageType( - "image", shape=im.shape, scale=1 / 255, bias=[0, 0, 0] - ) - ], - ) - bits, mode = ( - (8, "kmeans_lut") if int8 else (16, "linear") if half else (32, None) - ) - if bits < 32: - if MACOS: # quantization only supported on macOS - with warnings.catch_warnings(): - warnings.filterwarnings( - "ignore", category=DeprecationWarning - ) # suppress numpy==1.20 float warning - ct_model = ct.models.neural_network.quantization_utils.quantize_weights( - ct_model, bits, mode - ) - else: - print( - f"{prefix} quantization only supported on macOS, skipping..." - ) - ct_model.save(f) - return f, ct_model - - -@try_export -def export_engine( - model, - im, - file, - half, - dynamic, - simplify, - workspace=4, - verbose=False, - prefix=colorstr("TensorRT:"), -): - # YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt - assert ( - im.device.type != "cpu" - ), "export running on CPU but must be on GPU, i.e. `python export.py --device 0`" - try: - import tensorrt as trt - except Exception: - if platform.system() == "Linux": - check_requirements( - "nvidia-tensorrt", - cmds="-U --index-url https://pypi.ngc.nvidia.com", - ) - import tensorrt as trt - - if ( - trt.__version__[0] == "7" - ): # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012 - grid = model.model[-1].anchor_grid - model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid] - export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 - model.model[-1].anchor_grid = grid - else: # TensorRT >= 8 - check_version( - trt.__version__, "8.0.0", hard=True - ) # require tensorrt>=8.0.0 - export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 - onnx = file.with_suffix(".onnx") - - LOGGER.info( - f"\n{prefix} starting export with TensorRT {trt.__version__}..." - ) - assert onnx.exists(), f"failed to export ONNX file: {onnx}" - f = file.with_suffix(".engine") # TensorRT engine file - logger = trt.Logger(trt.Logger.INFO) - if verbose: - logger.min_severity = trt.Logger.Severity.VERBOSE - - builder = trt.Builder(logger) - config = builder.create_builder_config() - config.max_workspace_size = workspace * 1 << 30 - # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice - - flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) - network = builder.create_network(flag) - parser = trt.OnnxParser(network, logger) - if not parser.parse_from_file(str(onnx)): - raise RuntimeError(f"failed to load ONNX file: {onnx}") - - inputs = [network.get_input(i) for i in range(network.num_inputs)] - outputs = [network.get_output(i) for i in range(network.num_outputs)] - for inp in inputs: - LOGGER.info( - f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}' - ) - for out in outputs: - LOGGER.info( - f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}' - ) - - if dynamic: - if im.shape[0] <= 1: - LOGGER.warning( - f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument" - ) - profile = builder.create_optimization_profile() - for inp in inputs: - profile.set_shape( - inp.name, - (1, *im.shape[1:]), - (max(1, im.shape[0] // 2), *im.shape[1:]), - im.shape, - ) - config.add_optimization_profile(profile) - - LOGGER.info( - f"{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}" - ) - if builder.platform_has_fast_fp16 and half: - config.set_flag(trt.BuilderFlag.FP16) - with builder.build_engine(network, config) as engine, open(f, "wb") as t: - t.write(engine.serialize()) - return f, None - - -@try_export -def export_saved_model( - model, - im, - file, - dynamic, - tf_nms=False, - agnostic_nms=False, - topk_per_class=100, - topk_all=100, - iou_thres=0.45, - conf_thres=0.25, - keras=False, - prefix=colorstr("TensorFlow SavedModel:"), -): - # YOLOv5 TensorFlow SavedModel export - try: - import tensorflow as tf - except Exception: - check_requirements( - f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}" - ) - import tensorflow as tf - from tensorflow.python.framework.convert_to_constants import ( - convert_variables_to_constants_v2, - ) - - from models.tf import TFModel - - LOGGER.info( - f"\n{prefix} starting export with tensorflow {tf.__version__}..." - ) - f = str(file).replace(".pt", "_saved_model") - batch_size, ch, *imgsz = list(im.shape) # BCHW - - tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) - im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow - _ = tf_model.predict( - im, - tf_nms, - agnostic_nms, - topk_per_class, - topk_all, - iou_thres, - conf_thres, - ) - inputs = tf.keras.Input( - shape=(*imgsz, ch), batch_size=None if dynamic else batch_size - ) - outputs = tf_model.predict( - inputs, - tf_nms, - agnostic_nms, - topk_per_class, - topk_all, - iou_thres, - conf_thres, - ) - keras_model = tf.keras.Model(inputs=inputs, outputs=outputs) - keras_model.trainable = False - keras_model.summary() - if keras: - keras_model.save(f, save_format="tf") - else: - spec = tf.TensorSpec( - keras_model.inputs[0].shape, keras_model.inputs[0].dtype - ) - m = tf.function(lambda x: keras_model(x)) # full model - m = m.get_concrete_function(spec) - frozen_func = convert_variables_to_constants_v2(m) - tfm = tf.Module() - tfm.__call__ = tf.function( - lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec] - ) - tfm.__call__(im) - tf.saved_model.save( - tfm, - f, - options=tf.saved_model.SaveOptions( - experimental_custom_gradients=False - ) - if check_version(tf.__version__, "2.6") - else tf.saved_model.SaveOptions(), - ) - return f, keras_model - - -@try_export -def export_pb(keras_model, file, prefix=colorstr("TensorFlow GraphDef:")): - # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow - import tensorflow as tf - from tensorflow.python.framework.convert_to_constants import ( - convert_variables_to_constants_v2, - ) - - LOGGER.info( - f"\n{prefix} starting export with tensorflow {tf.__version__}..." - ) - f = file.with_suffix(".pb") - - m = tf.function(lambda x: keras_model(x)) # full model - m = m.get_concrete_function( - tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype) - ) - frozen_func = convert_variables_to_constants_v2(m) - frozen_func.graph.as_graph_def() - tf.io.write_graph( - graph_or_graph_def=frozen_func.graph, - logdir=str(f.parent), - name=f.name, - as_text=False, - ) - return f, None - - -@try_export -def export_tflite( - keras_model, - im, - file, - int8, - data, - nms, - agnostic_nms, - prefix=colorstr("TensorFlow Lite:"), -): - # YOLOv5 TensorFlow Lite export - import tensorflow as tf - - LOGGER.info( - f"\n{prefix} starting export with tensorflow {tf.__version__}..." - ) - batch_size, ch, *imgsz = list(im.shape) # BCHW - f = str(file).replace(".pt", "-fp16.tflite") - - converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) - converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] - converter.target_spec.supported_types = [tf.float16] - converter.optimizations = [tf.lite.Optimize.DEFAULT] - if int8: - from models.tf import representative_dataset_gen - - dataset = LoadImages( - check_dataset(check_yaml(data))["train"], - img_size=imgsz, - auto=False, - ) - converter.representative_dataset = lambda: representative_dataset_gen( - dataset, ncalib=100 - ) - converter.target_spec.supported_ops = [ - tf.lite.OpsSet.TFLITE_BUILTINS_INT8 - ] - converter.target_spec.supported_types = [] - converter.inference_input_type = tf.uint8 # or tf.int8 - converter.inference_output_type = tf.uint8 # or tf.int8 - converter.experimental_new_quantizer = True - f = str(file).replace(".pt", "-int8.tflite") - if nms or agnostic_nms: - converter.target_spec.supported_ops.append( - tf.lite.OpsSet.SELECT_TF_OPS - ) - - tflite_model = converter.convert() - open(f, "wb").write(tflite_model) - return f, None - - -@try_export -def export_edgetpu(file, prefix=colorstr("Edge TPU:")): - # YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/ - cmd = "edgetpu_compiler --version" - help_url = "https://coral.ai/docs/edgetpu/compiler/" - assert ( - platform.system() == "Linux" - ), f"export only supported on Linux. See {help_url}" - if subprocess.run(f"{cmd} >/dev/null", shell=True).returncode != 0: - LOGGER.info( - f"\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}" - ) - sudo = ( - subprocess.run("sudo --version >/dev/null", shell=True).returncode - == 0 - ) # sudo installed on system - for c in ( - "curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -", - 'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list', - "sudo apt-get update", - "sudo apt-get install edgetpu-compiler", - ): - subprocess.run( - c if sudo else c.replace("sudo ", ""), shell=True, check=True - ) - ver = ( - subprocess.run(cmd, shell=True, capture_output=True, check=True) - .stdout.decode() - .split()[-1] - ) - - LOGGER.info(f"\n{prefix} starting export with Edge TPU compiler {ver}...") - f = str(file).replace(".pt", "-int8_edgetpu.tflite") # Edge TPU model - f_tfl = str(file).replace(".pt", "-int8.tflite") # TFLite model - - cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {file.parent} {f_tfl}" - subprocess.run(cmd.split(), check=True) - return f, None - - -@try_export -def export_tfjs(file, prefix=colorstr("TensorFlow.js:")): - # YOLOv5 TensorFlow.js export - check_requirements("tensorflowjs") - import tensorflowjs as tfjs - - LOGGER.info( - f"\n{prefix} starting export with tensorflowjs {tfjs.__version__}..." - ) - f = str(file).replace(".pt", "_web_model") # js dir - f_pb = file.with_suffix(".pb") # *.pb path - f_json = f"{f}/model.json" # *.json path - - cmd = ( - f"tensorflowjs_converter --input_format=tf_frozen_model " - f"--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}" - ) - subprocess.run(cmd.split()) - - json = Path(f_json).read_text() - with open(f_json, "w") as j: # sort JSON Identity_* in ascending order - subst = re.sub( - r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, ' - r'"Identity.?.?": {"name": "Identity.?.?"}, ' - r'"Identity.?.?": {"name": "Identity.?.?"}, ' - r'"Identity.?.?": {"name": "Identity.?.?"}}}', - r'{"outputs": {"Identity": {"name": "Identity"}, ' - r'"Identity_1": {"name": "Identity_1"}, ' - r'"Identity_2": {"name": "Identity_2"}, ' - r'"Identity_3": {"name": "Identity_3"}}}', - json, - ) - j.write(subst) - return f, None - - -def add_tflite_metadata(file, metadata, num_outputs): - # Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata - with contextlib.suppress(ImportError): - # check_requirements('tflite_support') - from tflite_support import flatbuffers - from tflite_support import metadata as _metadata - from tflite_support import metadata_schema_py_generated as _metadata_fb - - tmp_file = Path("/tmp/meta.txt") - with open(tmp_file, "w") as meta_f: - meta_f.write(str(metadata)) - - model_meta = _metadata_fb.ModelMetadataT() - label_file = _metadata_fb.AssociatedFileT() - label_file.name = tmp_file.name - model_meta.associatedFiles = [label_file] - - subgraph = _metadata_fb.SubGraphMetadataT() - subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()] - subgraph.outputTensorMetadata = [ - _metadata_fb.TensorMetadataT() - ] * num_outputs - model_meta.subgraphMetadata = [subgraph] - - b = flatbuffers.Builder(0) - b.Finish( - model_meta.Pack(b), - _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER, - ) - metadata_buf = b.Output() - - populator = _metadata.MetadataPopulator.with_model_file(file) - populator.load_metadata_buffer(metadata_buf) - populator.load_associated_files([str(tmp_file)]) - populator.populate() - tmp_file.unlink() - - -@smart_inference_mode() -def run( - data=ROOT / "data/coco128.yaml", # 'dataset.yaml path' - weights=ROOT / "yolov5s.pt", # weights path - imgsz=(640, 640), # image (height, width) - batch_size=1, # batch size - device="cpu", # cuda device, i.e. 0 or 0,1,2,3 or cpu - include=("torchscript", "onnx"), # include formats - half=False, # FP16 half-precision export - inplace=False, # set YOLOv5 Detect() inplace=True - keras=False, # use Keras - optimize=False, # TorchScript: optimize for mobile - int8=False, # CoreML/TF INT8 quantization - dynamic=False, # ONNX/TF/TensorRT: dynamic axes - simplify=False, # ONNX: simplify model - opset=12, # ONNX: opset version - verbose=False, # TensorRT: verbose log - workspace=4, # TensorRT: workspace size (GB) - nms=False, # TF: add NMS to model - agnostic_nms=False, # TF: add agnostic NMS to model - topk_per_class=100, # TF.js NMS: topk per class to keep - topk_all=100, # TF.js NMS: topk for all classes to keep - iou_thres=0.45, # TF.js NMS: IoU threshold - conf_thres=0.25, # TF.js NMS: confidence threshold -): - t = time.time() - include = [x.lower() for x in include] # to lowercase - fmts = tuple(export_formats()["Argument"][1:]) # --include arguments - flags = [x in include for x in fmts] - assert sum(flags) == len( - include - ), f"ERROR: Invalid --include {include}, valid --include arguments are {fmts}" - ( - jit, - onnx, - xml, - engine, - coreml, - saved_model, - pb, - tflite, - edgetpu, - tfjs, - paddle, - ) = flags # export booleans - file = Path( - url2file(weights) - if str(weights).startswith(("http:/", "https:/")) - else weights - ) # PyTorch weights - - # Load PyTorch model - device = select_device(device) - if half: - assert ( - device.type != "cpu" or coreml - ), "--half only compatible with GPU export, i.e. use --device 0" - assert ( - not dynamic - ), "--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both" - model = attempt_load( - weights, device=device, inplace=True, fuse=True - ) # load FP32 model - - # Checks - imgsz *= 2 if len(imgsz) == 1 else 1 # expand - if optimize: - assert ( - device.type == "cpu" - ), "--optimize not compatible with cuda devices, i.e. use --device cpu" - - # Input - gs = int(max(model.stride)) # grid size (max stride) - imgsz = [ - check_img_size(x, gs) for x in imgsz - ] # verify img_size are gs-multiples - im = torch.zeros(batch_size, 3, *imgsz).to( - device - ) # image size(1,3,320,192) BCHW iDetection - - # Update model - model.eval() - for k, m in model.named_modules(): - if isinstance(m, Detect): - m.inplace = inplace - m.dynamic = dynamic - m.export = True - - for _ in range(2): - y = model(im) # dry runs - if half and not coreml: - im, model = im.half(), model.half() # to FP16 - shape = tuple( - (y[0] if isinstance(y, tuple) else y).shape - ) # model output shape - metadata = { - "stride": int(max(model.stride)), - "names": model.names, - } # model metadata - LOGGER.info( - f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)" - ) - - # Exports - f = [""] * len(fmts) # exported filenames - warnings.filterwarnings( - action="ignore", category=torch.jit.TracerWarning - ) # suppress TracerWarning - if jit: # TorchScript - f[0], _ = export_torchscript(model, im, file, optimize) - if engine: # TensorRT required before ONNX - f[1], _ = export_engine( - model, im, file, half, dynamic, simplify, workspace, verbose - ) - if onnx or xml: # OpenVINO requires ONNX - f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify) - if xml: # OpenVINO - f[3], _ = export_openvino(file, metadata, half) - if coreml: # CoreML - f[4], _ = export_coreml(model, im, file, int8, half) - if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats - assert ( - not tflite or not tfjs - ), "TFLite and TF.js models must be exported separately, please pass only one type." - assert not isinstance( - model, ClassificationModel - ), "ClassificationModel export to TF formats not yet supported." - f[5], s_model = export_saved_model( - model.cpu(), - im, - file, - dynamic, - tf_nms=nms or agnostic_nms or tfjs, - agnostic_nms=agnostic_nms or tfjs, - topk_per_class=topk_per_class, - topk_all=topk_all, - iou_thres=iou_thres, - conf_thres=conf_thres, - keras=keras, - ) - if pb or tfjs: # pb prerequisite to tfjs - f[6], _ = export_pb(s_model, file) - if tflite or edgetpu: - f[7], _ = export_tflite( - s_model, - im, - file, - int8 or edgetpu, - data=data, - nms=nms, - agnostic_nms=agnostic_nms, - ) - if edgetpu: - f[8], _ = export_edgetpu(file) - add_tflite_metadata( - f[8] or f[7], metadata, num_outputs=len(s_model.outputs) - ) - if tfjs: - f[9], _ = export_tfjs(file) - if paddle: # PaddlePaddle - f[10], _ = export_paddle(model, im, file, metadata) - - # Finish - f = [str(x) for x in f if x] # filter out '' and None - if any(f): - cls, det, seg = ( - isinstance(model, x) - for x in (ClassificationModel, DetectionModel, SegmentationModel) - ) # type - det &= ( - not seg - ) # segmentation models inherit from SegmentationModel(DetectionModel) - dir = Path("segment" if seg else "classify" if cls else "") - h = "--half" if half else "" # --half FP16 inference arg - s = ( - "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference" - if cls - else "# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference" - if seg - else "" - ) - LOGGER.info( - f"\nExport complete ({time.time() - t:.1f}s)" - f"\nResults saved to {colorstr('bold', file.parent.resolve())}" - f"\nDetect: python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}" - f"\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}" - f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}" - f"\nVisualize: https://netron.app" - ) - return f # return list of exported files/dirs - - -def parse_opt(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--data", - type=str, - default=ROOT / "data/coco128.yaml", - help="dataset.yaml path", - ) - parser.add_argument( - "--weights", - nargs="+", - type=str, - default=ROOT / "yolov5s.pt", - help="model.pt path(s)", - ) - parser.add_argument( - "--imgsz", - "--img", - "--img-size", - nargs="+", - type=int, - default=[640, 640], - help="image (h, w)", - ) - parser.add_argument("--batch-size", type=int, default=1, help="batch size") - parser.add_argument( - "--device", default="cpu", help="cuda device, i.e. 0 or 0,1,2,3 or cpu" - ) - parser.add_argument( - "--half", action="store_true", help="FP16 half-precision export" - ) - parser.add_argument( - "--inplace", - action="store_true", - help="set YOLOv5 Detect() inplace=True", - ) - parser.add_argument("--keras", action="store_true", help="TF: use Keras") - parser.add_argument( - "--optimize", - action="store_true", - help="TorchScript: optimize for mobile", - ) - parser.add_argument( - "--int8", action="store_true", help="CoreML/TF INT8 quantization" - ) - parser.add_argument( - "--dynamic", action="store_true", help="ONNX/TF/TensorRT: dynamic axes" - ) - parser.add_argument( - "--simplify", action="store_true", help="ONNX: simplify model" - ) - parser.add_argument( - "--opset", type=int, default=17, help="ONNX: opset version" - ) - parser.add_argument( - "--verbose", action="store_true", help="TensorRT: verbose log" - ) - parser.add_argument( - "--workspace", - type=int, - default=4, - help="TensorRT: workspace size (GB)", - ) - parser.add_argument( - "--nms", action="store_true", help="TF: add NMS to model" - ) - parser.add_argument( - "--agnostic-nms", - action="store_true", - help="TF: add agnostic NMS to model", - ) - parser.add_argument( - "--topk-per-class", - type=int, - default=100, - help="TF.js NMS: topk per class to keep", - ) - parser.add_argument( - "--topk-all", - type=int, - default=100, - help="TF.js NMS: topk for all classes to keep", - ) - parser.add_argument( - "--iou-thres", - type=float, - default=0.45, - help="TF.js NMS: IoU threshold", - ) - parser.add_argument( - "--conf-thres", - type=float, - default=0.25, - help="TF.js NMS: confidence threshold", - ) - parser.add_argument( - "--include", - nargs="+", - default=["torchscript"], - help="torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle", - ) - opt = parser.parse_args() - print_args(vars(opt)) - return opt - - -def main(opt): - for opt.weights in ( - opt.weights if isinstance(opt.weights, list) else [opt.weights] - ): - run(**vars(opt)) - - -if __name__ == "__main__": - opt = parse_opt() - main(opt) diff --git a/spaces/AgentVerse/agentVerse/ui/src/classes/npc.ts b/spaces/AgentVerse/agentVerse/ui/src/classes/npc.ts deleted file mode 100644 index 452acc65bdc4d27eb8a16c60c83bb1159030de71..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/classes/npc.ts +++ /dev/null @@ -1,246 +0,0 @@ -import { Actor } from "./actor"; -import { DIRECTION } from "../utils"; -import { - MoveTo, - PathFinder, - Board, -} from "../phaser3-rex-plugins/plugins/board-components"; -import { Label } from "../phaser3-rex-plugins/templates/ui/ui-components"; -import { COLOR_DARK, COLOR_LIGHT, COLOR_PRIMARY } from "../constants"; -import { TownScene } from "../scenes"; -import eventsCenter from "./event_center"; - -export class NPC extends Actor { - private moveTo: MoveTo; - private board: Board; - private canMove: boolean = true; - private talkWithPlayer: boolean = false; - private path: PathFinder.NodeType[] = []; - private finalDirection: number = undefined; - private targetLocation: string = undefined; - private targetNPC: NPC = undefined; - private textBox: Label = undefined; - - public id: number; - public direction: number = DIRECTION.DOWN; - - constructor( - scene: Phaser.Scene, - board: Board, - x: number, - y: number, - name: string, - id: number - ) { - super(scene, x, y, name); - - this.setName(name); - this.board = board; - this.id = id; - // PHYSICS - this.getBody().setSize(14, 16); - this.getBody().setOffset(0, 4); - this.getBody().setImmovable(true); - this.setOrigin(0, 0.2); - - this.initAnimations(); - this.moveTo = this.scene.rexBoard.add.moveTo(this, { - speed: 55, - sneak: true, - }); - this.listenToDirectionEvent(); - } - - update(): void { - if (this.path.length > 0 && !this.moveTo.isRunning && this.canMove) { - var tileXY = this.board.worldXYToTileXY(this.x, this.y); - if (tileXY.x == this.path[0].x) { - if (tileXY.y < this.path[0].y) this.changeDirection(DIRECTION.DOWN); - else if (tileXY.y > this.path[0].y) this.changeDirection(DIRECTION.UP); - } else if (tileXY.y == this.path[0].y) { - if (tileXY.x < this.path[0].x) this.changeDirection(DIRECTION.RIGHT); - else if (tileXY.x > this.path[0].x) - this.changeDirection(DIRECTION.LEFT); - } - var move = this.moveTo.moveTo(this.path.shift()); - move.removeAllListeners("complete"); - move.on("complete", () => { - if (this.path.length == 0) { - this.changeDirection(this.finalDirection); - this.emitTurnEvent(); - if (this.targetLocation != undefined) { - fetch("http://127.0.0.1:10002/update_location", { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - credentials: "same-origin", - body: JSON.stringify({ - agent_locations: { - [this.name]: this.targetLocation, - }, - }), - }); - } - } - }); - } - - var text = ""; - switch (this.direction) { - case DIRECTION.UP: - text = "up"; - break; - case DIRECTION.DOWN: - text = "down"; - break; - case DIRECTION.LEFT: - text = "left"; - break; - case DIRECTION.RIGHT: - text = "right"; - break; - } - this.anims.play(this.name + "-walk-" + text, true); - if (this.anims.isPlaying && !this.moveTo.isRunning) - this.anims.setCurrentFrame(this.anims.currentAnim!.frames[0]); - this.updateTextBox(); - this.depth = this.y + this.height * 0.8; - } - - listenToDirectionEvent(): void { - eventsCenter.on(this.name + "-up", () => { - this.changeDirection(DIRECTION.UP); - }); - eventsCenter.on(this.name + "-down", () => { - this.changeDirection(DIRECTION.DOWN); - }); - eventsCenter.on(this.name + "-left", () => { - this.changeDirection(DIRECTION.LEFT); - }); - eventsCenter.on(this.name + "-right", () => { - this.changeDirection(DIRECTION.RIGHT); - }); - } - - emitTurnEvent(): void { - // Make the listener NPC turn to the speaker NPC. - if (this.targetNPC == undefined) return; - var direction = ""; - switch (this.finalDirection) { - case DIRECTION.UP: - direction = "down"; - break; - case DIRECTION.DOWN: - direction = "up"; - break; - case DIRECTION.LEFT: - direction = "right"; - break; - case DIRECTION.RIGHT: - direction = "left"; - break; - } - eventsCenter.emit(this.targetNPC.name + "-" + direction); - this.setTargetNPC(); - } - - updateTextBox(): void { - if (this.textBox == undefined) return; - this.textBox.setOrigin(0.5, 1.0); - var scale = this.scene.cameras.main.zoom; - this.textBox.setX(this.x + this.width / 2); - this.textBox.setY(this.y - this.height * 0.2); - this.textBox.depth = this.y + this.height * 0.8; - this.textBox.getChildren().forEach((child) => { - child.setDepth(this.y + this.height * 0.8); - }); - } - - public setTextBox(text: string): void { - this.destroyTextBox(); - var scale = this.scene.cameras.main.zoom; - var scene = this.scene as TownScene; - this.textBox = scene.rexUI.add - .label({ - x: this.x + this.width / 2, - y: this.y - this.height * 0.2, - width: 24 * scale, - orientation: "x", - background: scene.rexUI.add.roundRectangle( - 0, - 0, - 2, - 2, - 20, - COLOR_PRIMARY, - 0.7 - ), - text: scene.rexUI.wrapExpandText( - scene.add.text(0, 0, text, { - fontSize: 10, - }) - ), - expandTextWidth: true, - space: { - left: 10, - right: 10, - top: 10, - bottom: 10, - }, - }) - .setOrigin(0.5, 1.0) - .setScale(1 / scale, 1 / scale) - .setDepth(this.y + this.height * 0.8) - .layout(); - } - - public destroyTextBox(): void { - if (this.textBox != undefined) this.textBox.destroy(); - this.textBox = undefined; - } - - public changeDirection(direction: number): void { - if (direction == undefined) return; - this.direction = direction; - } - - public moveAlongPath( - path: PathFinder.NodeType[], - finalDirection: number = undefined, - targetLocation: string = undefined - ): void { - if (path.length == 0) return; - if (this.moveTo.isRunning) return; - if (this.path.length > 0) return; - this.path = path; - this.finalDirection = finalDirection; - this.targetLocation = targetLocation; - } - - public pauseMoving(): void { - this.moveTo.stop(); - this.canMove = false; - } - - public resumeMoving(): void { - this.moveTo.resume(); - this.canMove = true; - } - - public isMoving(): boolean { - return this.moveTo.isRunning || this.path.length > 0; - } - - public isTalking(): boolean { - return this.talkWithPlayer; - } - - public setTalking(talking: boolean): void { - this.talkWithPlayer = talking; - } - - public setTargetNPC(targetNPC: NPC = undefined): void { - this.targetNPC = targetNPC; - } -} diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/fsm-plugin.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/fsm-plugin.d.ts deleted file mode 100644 index 9bbd028b2de8925580170585f8aac9927f5688a6..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/fsm-plugin.d.ts +++ /dev/null @@ -1,8 +0,0 @@ -import FSM from './fsm'; - -export default class FSMPlugin extends Phaser.Plugins.BasePlugin { - add( - config?: FSM.IConfig - ): FSM; - -} \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/Sizer.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/Sizer.js deleted file mode 100644 index 42243bee69d9f33dff7b6baea7e74a472464b87c..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/Sizer.js +++ /dev/null @@ -1,79 +0,0 @@ -import BaseSizer from '../basesizer/BaseSizer.js'; -import Methods from './Methods.js'; -import GetChildrenProportion from './GetChildrenProportion.js'; -import GetOrientationMode from '../utils/GetOrientationMode.js'; - -const IsPlainObject = Phaser.Utils.Objects.IsPlainObject; -const GetValue = Phaser.Utils.Objects.GetValue; - -class Sizer extends BaseSizer { - constructor(scene, x, y, minWidth, minHeight, orientation, 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); - orientation = GetValue(config, 'orientation', 0); - } else if (IsPlainObject(minWidth)) { - config = minWidth; - minWidth = GetValue(config, 'width', undefined); - minHeight = GetValue(config, 'height', undefined); - orientation = GetValue(config, 'orientation', 0); - } else if (IsPlainObject(orientation)) { - config = orientation; - orientation = GetValue(config, 'orientation', 0); - } - - if (orientation === undefined) { - orientation = 0; - } - super(scene, x, y, minWidth, minHeight, config); - - this.type = 'rexSizer'; - this.sizerChildren = []; - this.setOrientation(orientation); - this.setItemSpacing(GetValue(config, 'space.item', 0)); - this.setStartChildIndex(GetValue(config, 'startChildIndex', 0)); - this.setRTL(GetValue(config, 'rtl', false)); - - this.addChildrenMap('items', this.sizerChildren); - } - - setOrientation(orientation) { - this.orientation = GetOrientationMode(orientation); - return this; - } - - setItemSpacing(space) { - this.space.item = space; - return this; - } - - setStartChildIndex(index) { - this.startChildIndex = index; - return this; - } - - setRTL(enable) { - if (enable === undefined) { - enable = true; - } - this.rtl = enable; - return this; - } - - get childrenProportion() { - if (this._childrenProportion === undefined) { - this._childrenProportion = GetChildrenProportion.call(this); - } - return this._childrenProportion; - } -} - -Object.assign( - Sizer.prototype, - Methods -); - -export default Sizer; \ No newline at end of file diff --git a/spaces/Aki004/herta-so-vits/vdecoder/hifigan/models.py b/spaces/Aki004/herta-so-vits/vdecoder/hifigan/models.py deleted file mode 100644 index 9747301f350bb269e62601017fe4633ce271b27e..0000000000000000000000000000000000000000 --- a/spaces/Aki004/herta-so-vits/vdecoder/hifigan/models.py +++ /dev/null @@ -1,503 +0,0 @@ -import os -import json -from .env import AttrDict -import numpy as np -import torch -import torch.nn.functional as F -import torch.nn as nn -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from .utils import init_weights, get_padding - -LRELU_SLOPE = 0.1 - - -def load_model(model_path, device='cuda'): - config_file = os.path.join(os.path.split(model_path)[0], 'config.json') - with open(config_file) as f: - data = f.read() - - global h - json_config = json.loads(data) - h = AttrDict(json_config) - - generator = Generator(h).to(device) - - cp_dict = torch.load(model_path) - generator.load_state_dict(cp_dict['generator']) - generator.eval() - generator.remove_weight_norm() - del cp_dict - return generator, h - - -class ResBlock1(torch.nn.Module): - def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.h = h - self.convs1 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]))) - ]) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))) - ]) - self.convs2.apply(init_weights) - - def forward(self, x): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - xt = c2(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.h = h - self.convs = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))) - ]) - self.convs.apply(init_weights) - - def forward(self, x): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - xt = c(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -def padDiff(x): - return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0) - -class SineGen(torch.nn.Module): - """ Definition of sine generator - SineGen(samp_rate, harmonic_num = 0, - sine_amp = 0.1, noise_std = 0.003, - voiced_threshold = 0, - flag_for_pulse=False) - samp_rate: sampling rate in Hz - harmonic_num: number of harmonic overtones (default 0) - sine_amp: amplitude of sine-wavefrom (default 0.1) - noise_std: std of Gaussian noise (default 0.003) - voiced_thoreshold: F0 threshold for U/V classification (default 0) - flag_for_pulse: this SinGen is used inside PulseGen (default False) - Note: when flag_for_pulse is True, the first time step of a voiced - segment is always sin(np.pi) or cos(0) - """ - - def __init__(self, samp_rate, harmonic_num=0, - sine_amp=0.1, noise_std=0.003, - voiced_threshold=0, - flag_for_pulse=False): - super(SineGen, self).__init__() - self.sine_amp = sine_amp - self.noise_std = noise_std - self.harmonic_num = harmonic_num - self.dim = self.harmonic_num + 1 - self.sampling_rate = samp_rate - self.voiced_threshold = voiced_threshold - self.flag_for_pulse = flag_for_pulse - - def _f02uv(self, f0): - # generate uv signal - uv = (f0 > self.voiced_threshold).type(torch.float32) - return uv - - def _f02sine(self, f0_values): - """ f0_values: (batchsize, length, dim) - where dim indicates fundamental tone and overtones - """ - # convert to F0 in rad. The interger part n can be ignored - # because 2 * np.pi * n doesn't affect phase - rad_values = (f0_values / self.sampling_rate) % 1 - - # initial phase noise (no noise for fundamental component) - rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \ - device=f0_values.device) - rand_ini[:, 0] = 0 - rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini - - # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad) - if not self.flag_for_pulse: - # for normal case - - # To prevent torch.cumsum numerical overflow, - # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1. - # Buffer tmp_over_one_idx indicates the time step to add -1. - # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi - tmp_over_one = torch.cumsum(rad_values, 1) % 1 - tmp_over_one_idx = (padDiff(tmp_over_one)) < 0 - cumsum_shift = torch.zeros_like(rad_values) - cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 - - sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) - * 2 * np.pi) - else: - # If necessary, make sure that the first time step of every - # voiced segments is sin(pi) or cos(0) - # This is used for pulse-train generation - - # identify the last time step in unvoiced segments - uv = self._f02uv(f0_values) - uv_1 = torch.roll(uv, shifts=-1, dims=1) - uv_1[:, -1, :] = 1 - u_loc = (uv < 1) * (uv_1 > 0) - - # get the instantanouse phase - tmp_cumsum = torch.cumsum(rad_values, dim=1) - # different batch needs to be processed differently - for idx in range(f0_values.shape[0]): - temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :] - temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :] - # stores the accumulation of i.phase within - # each voiced segments - tmp_cumsum[idx, :, :] = 0 - tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum - - # rad_values - tmp_cumsum: remove the accumulation of i.phase - # within the previous voiced segment. - i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1) - - # get the sines - sines = torch.cos(i_phase * 2 * np.pi) - return sines - - def forward(self, f0): - """ sine_tensor, uv = forward(f0) - input F0: tensor(batchsize=1, length, dim=1) - f0 for unvoiced steps should be 0 - output sine_tensor: tensor(batchsize=1, length, dim) - output uv: tensor(batchsize=1, length, 1) - """ - with torch.no_grad(): - f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, - device=f0.device) - # fundamental component - fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device)) - - # generate sine waveforms - sine_waves = self._f02sine(fn) * self.sine_amp - - # generate uv signal - # uv = torch.ones(f0.shape) - # uv = uv * (f0 > self.voiced_threshold) - uv = self._f02uv(f0) - - # noise: for unvoiced should be similar to sine_amp - # std = self.sine_amp/3 -> max value ~ self.sine_amp - # . for voiced regions is self.noise_std - noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 - noise = noise_amp * torch.randn_like(sine_waves) - - # first: set the unvoiced part to 0 by uv - # then: additive noise - sine_waves = sine_waves * uv + noise - return sine_waves, uv, noise - - -class SourceModuleHnNSF(torch.nn.Module): - """ SourceModule for hn-nsf - SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, - add_noise_std=0.003, voiced_threshod=0) - sampling_rate: sampling_rate in Hz - harmonic_num: number of harmonic above F0 (default: 0) - sine_amp: amplitude of sine source signal (default: 0.1) - add_noise_std: std of additive Gaussian noise (default: 0.003) - note that amplitude of noise in unvoiced is decided - by sine_amp - voiced_threshold: threhold to set U/V given F0 (default: 0) - Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) - F0_sampled (batchsize, length, 1) - Sine_source (batchsize, length, 1) - noise_source (batchsize, length 1) - uv (batchsize, length, 1) - """ - - def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1, - add_noise_std=0.003, voiced_threshod=0): - super(SourceModuleHnNSF, self).__init__() - - self.sine_amp = sine_amp - self.noise_std = add_noise_std - - # to produce sine waveforms - self.l_sin_gen = SineGen(sampling_rate, harmonic_num, - sine_amp, add_noise_std, voiced_threshod) - - # to merge source harmonics into a single excitation - self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) - self.l_tanh = torch.nn.Tanh() - - def forward(self, x): - """ - Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) - F0_sampled (batchsize, length, 1) - Sine_source (batchsize, length, 1) - noise_source (batchsize, length 1) - """ - # source for harmonic branch - sine_wavs, uv, _ = self.l_sin_gen(x) - sine_merge = self.l_tanh(self.l_linear(sine_wavs)) - - # source for noise branch, in the same shape as uv - noise = torch.randn_like(uv) * self.sine_amp / 3 - return sine_merge, noise, uv - - -class Generator(torch.nn.Module): - def __init__(self, h): - super(Generator, self).__init__() - self.h = h - - self.num_kernels = len(h["resblock_kernel_sizes"]) - self.num_upsamples = len(h["upsample_rates"]) - self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"])) - self.m_source = SourceModuleHnNSF( - sampling_rate=h["sampling_rate"], - harmonic_num=8) - self.noise_convs = nn.ModuleList() - self.conv_pre = weight_norm(Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3)) - resblock = ResBlock1 if h["resblock"] == '1' else ResBlock2 - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(h["upsample_rates"], h["upsample_kernel_sizes"])): - c_cur = h["upsample_initial_channel"] // (2 ** (i + 1)) - self.ups.append(weight_norm( - ConvTranspose1d(h["upsample_initial_channel"] // (2 ** i), h["upsample_initial_channel"] // (2 ** (i + 1)), - k, u, padding=(k - u) // 2))) - if i + 1 < len(h["upsample_rates"]): # - stride_f0 = np.prod(h["upsample_rates"][i + 1:]) - self.noise_convs.append(Conv1d( - 1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2)) - else: - self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = h["upsample_initial_channel"] // (2 ** (i + 1)) - for j, (k, d) in enumerate(zip(h["resblock_kernel_sizes"], h["resblock_dilation_sizes"])): - self.resblocks.append(resblock(h, ch, k, d)) - - self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) - self.ups.apply(init_weights) - self.conv_post.apply(init_weights) - self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1) - - def forward(self, x, f0, g=None): - # print(1,x.shape,f0.shape,f0[:, None].shape) - f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t - # print(2,f0.shape) - har_source, noi_source, uv = self.m_source(f0) - har_source = har_source.transpose(1, 2) - x = self.conv_pre(x) - x = x + self.cond(g) - # print(124,x.shape,har_source.shape) - for i in range(self.num_upsamples): - x = F.leaky_relu(x, LRELU_SLOPE) - # print(3,x.shape) - x = self.ups[i](x) - x_source = self.noise_convs[i](har_source) - # print(4,x_source.shape,har_source.shape,x.shape) - x = x + x_source - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - remove_weight_norm(self.conv_pre) - remove_weight_norm(self.conv_post) - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), - ]) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, periods=None): - super(MultiPeriodDiscriminator, self).__init__() - self.periods = periods if periods is not None else [2, 3, 5, 7, 11] - self.discriminators = nn.ModuleList() - for period in self.periods: - self.discriminators.append(DiscriminatorP(period)) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv1d(1, 128, 15, 1, padding=7)), - norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), - norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), - norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ]) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiScaleDiscriminator(torch.nn.Module): - def __init__(self): - super(MultiScaleDiscriminator, self).__init__() - self.discriminators = nn.ModuleList([ - DiscriminatorS(use_spectral_norm=True), - DiscriminatorS(), - DiscriminatorS(), - ]) - self.meanpools = nn.ModuleList([ - AvgPool1d(4, 2, padding=2), - AvgPool1d(4, 2, padding=2) - ]) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - if i != 0: - y = self.meanpools[i - 1](y) - y_hat = self.meanpools[i - 1](y_hat) - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -def feature_loss(fmap_r, fmap_g): - loss = 0 - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - loss += torch.mean(torch.abs(rl - gl)) - - return loss * 2 - - -def discriminator_loss(disc_real_outputs, disc_generated_outputs): - loss = 0 - r_losses = [] - g_losses = [] - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - r_loss = torch.mean((1 - dr) ** 2) - g_loss = torch.mean(dg ** 2) - loss += (r_loss + g_loss) - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - - return loss, r_losses, g_losses - - -def generator_loss(disc_outputs): - loss = 0 - gen_losses = [] - for dg in disc_outputs: - l = torch.mean((1 - dg) ** 2) - gen_losses.append(l) - loss += l - - return loss, gen_losses diff --git a/spaces/AkshayDev/Lazy-Film-Reviews/README.md b/spaces/AkshayDev/Lazy-Film-Reviews/README.md deleted file mode 100644 index 075510eeb51d4c8382b290b485c57645d8c06999..0000000000000000000000000000000000000000 --- a/spaces/AkshayDev/Lazy-Film-Reviews/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Lazy Film Reviews -emoji: 🌖 -colorFrom: purple -colorTo: gray -sdk: streamlit -sdk_version: 1.2.0 -app_file: app.py -pinned: false -license: cc-by-nc-4.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/AlexWang/lama/saicinpainting/evaluation/masks/__init__.py b/spaces/AlexWang/lama/saicinpainting/evaluation/masks/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/decoder/sh.py b/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/decoder/sh.py deleted file mode 100644 index 27e3cad120c2b7348431a9af4883e8e7cdb10cbe..0000000000000000000000000000000000000000 --- a/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/decoder/sh.py +++ /dev/null @@ -1,133 +0,0 @@ -import torch - -################## sh function ################## -C0 = 0.28209479177387814 -C1 = 0.4886025119029199 -C2 = [ - 1.0925484305920792, - -1.0925484305920792, - 0.31539156525252005, - -1.0925484305920792, - 0.5462742152960396 -] -C3 = [ - -0.5900435899266435, - 2.890611442640554, - -0.4570457994644658, - 0.3731763325901154, - -0.4570457994644658, - 1.445305721320277, - -0.5900435899266435 -] -C4 = [ - 2.5033429417967046, - -1.7701307697799304, - 0.9461746957575601, - -0.6690465435572892, - 0.10578554691520431, - -0.6690465435572892, - 0.47308734787878004, - -1.7701307697799304, - 0.6258357354491761, -] - -def eval_sh(deg, sh, dirs): - """ - Evaluate spherical harmonics at unit directions - using hardcoded SH polynomials. - Works with torch/np/jnp. - ... Can be 0 or more batch dimensions. - :param deg: int SH max degree. Currently, 0-4 supported - :param sh: torch.Tensor SH coeffs (..., C, (max degree + 1) ** 2) - :param dirs: torch.Tensor unit directions (..., 3) - :return: (..., C) - """ - assert deg <= 4 and deg >= 0 - assert (deg + 1) ** 2 == sh.shape[-1] - C = sh.shape[-2] - - result = C0 * sh[..., 0] - if deg > 0: - x, y, z = dirs[..., 0:1], dirs[..., 1:2], dirs[..., 2:3] - result = (result - - C1 * y * sh[..., 1] + - C1 * z * sh[..., 2] - - C1 * x * sh[..., 3]) - if deg > 1: - xx, yy, zz = x * x, y * y, z * z - xy, yz, xz = x * y, y * z, x * z - result = (result + - C2[0] * xy * sh[..., 4] + - C2[1] * yz * sh[..., 5] + - C2[2] * (2.0 * zz - xx - yy) * sh[..., 6] + - C2[3] * xz * sh[..., 7] + - C2[4] * (xx - yy) * sh[..., 8]) - - if deg > 2: - result = (result + - C3[0] * y * (3 * xx - yy) * sh[..., 9] + - C3[1] * xy * z * sh[..., 10] + - C3[2] * y * (4 * zz - xx - yy)* sh[..., 11] + - C3[3] * z * (2 * zz - 3 * xx - 3 * yy) * sh[..., 12] + - C3[4] * x * (4 * zz - xx - yy) * sh[..., 13] + - C3[5] * z * (xx - yy) * sh[..., 14] + - C3[6] * x * (xx - 3 * yy) * sh[..., 15]) - if deg > 3: - result = (result + C4[0] * xy * (xx - yy) * sh[..., 16] + - C4[1] * yz * (3 * xx - yy) * sh[..., 17] + - C4[2] * xy * (7 * zz - 1) * sh[..., 18] + - C4[3] * yz * (7 * zz - 3) * sh[..., 19] + - C4[4] * (zz * (35 * zz - 30) + 3) * sh[..., 20] + - C4[5] * xz * (7 * zz - 3) * sh[..., 21] + - C4[6] * (xx - yy) * (7 * zz - 1) * sh[..., 22] + - C4[7] * xz * (xx - 3 * yy) * sh[..., 23] + - C4[8] * (xx * (xx - 3 * yy) - yy * (3 * xx - yy)) * sh[..., 24]) - return result - -def eval_sh_bases(deg, dirs): - """ - Evaluate spherical harmonics bases at unit directions, - without taking linear combination. - At each point, the final result may the be - obtained through simple multiplication. - :param deg: int SH max degree. Currently, 0-4 supported - :param dirs: torch.Tensor (..., 3) unit directions - :return: torch.Tensor (..., (deg+1) ** 2) - """ - assert deg <= 4 and deg >= 0 - result = torch.empty((*dirs.shape[:-1], (deg + 1) ** 2), dtype=dirs.dtype, device=dirs.device) - result[..., 0] = C0 - if deg > 0: - x, y, z = dirs.unbind(-1) - result[..., 1] = -C1 * y; - result[..., 2] = C1 * z; - result[..., 3] = -C1 * x; - if deg > 1: - xx, yy, zz = x * x, y * y, z * z - xy, yz, xz = x * y, y * z, x * z - result[..., 4] = C2[0] * xy; - result[..., 5] = C2[1] * yz; - result[..., 6] = C2[2] * (2.0 * zz - xx - yy); - result[..., 7] = C2[3] * xz; - result[..., 8] = C2[4] * (xx - yy); - - if deg > 2: - result[..., 9] = C3[0] * y * (3 * xx - yy); - result[..., 10] = C3[1] * xy * z; - result[..., 11] = C3[2] * y * (4 * zz - xx - yy); - result[..., 12] = C3[3] * z * (2 * zz - 3 * xx - 3 * yy); - result[..., 13] = C3[4] * x * (4 * zz - xx - yy); - result[..., 14] = C3[5] * z * (xx - yy); - result[..., 15] = C3[6] * x * (xx - 3 * yy); - - if deg > 3: - result[..., 16] = C4[0] * xy * (xx - yy); - result[..., 17] = C4[1] * yz * (3 * xx - yy); - result[..., 18] = C4[2] * xy * (7 * zz - 1); - result[..., 19] = C4[3] * yz * (7 * zz - 3); - result[..., 20] = C4[4] * (zz * (35 * zz - 30) + 3); - result[..., 21] = C4[5] * xz * (7 * zz - 3); - result[..., 22] = C4[6] * (xx - yy) * (7 * zz - 1); - result[..., 23] = C4[7] * xz * (xx - 3 * yy); - result[..., 24] = C4[8] * (xx * (xx - 3 * yy) - yy * (3 * xx - yy)); - return result diff --git a/spaces/AndreLie95/Diabetes_Risk_Prediction/README.md b/spaces/AndreLie95/Diabetes_Risk_Prediction/README.md deleted file mode 100644 index 38407b427004ceab9eff12a93406a0512568b384..0000000000000000000000000000000000000000 --- a/spaces/AndreLie95/Diabetes_Risk_Prediction/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Deploy Milestone 2 -emoji: ⚡ -colorFrom: indigo -colorTo: indigo -sdk: streamlit -sdk_version: 1.17.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/optimization/opt_overview.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/optimization/opt_overview.md deleted file mode 100644 index 8d8386f85f43df2d22c00a9b54df5de59e07fe01..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/optimization/opt_overview.md +++ /dev/null @@ -1,17 +0,0 @@ - - -# Overview - -Generating high-quality outputs is computationally intensive, especially during each iterative step where you go from a noisy output to a less noisy output. One of 🧨 Diffuser's goal is to make this technology widely accessible to everyone, which includes enabling fast inference on consumer and specialized hardware. - -This section will cover tips and tricks - like half-precision weights and sliced attention - for optimizing inference speed and reducing memory-consumption. You can also learn how to speed up your PyTorch code with [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) or [ONNX Runtime](https://onnxruntime.ai/docs/), and enable memory-efficient attention with [xFormers](https://facebookresearch.github.io/xformers/). There are also guides for running inference on specific hardware like Apple Silicon, and Intel or Habana processors. \ No newline at end of file diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/controlnet/README.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/controlnet/README.md deleted file mode 100644 index 15b0170d512034bc21786f12f5ab3ccd35143f94..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/controlnet/README.md +++ /dev/null @@ -1,465 +0,0 @@ -# ControlNet training example - -[Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) by Lvmin Zhang and Maneesh Agrawala. - -This example is based on the [training example in the original ControlNet repository](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md). It trains a ControlNet to fill circles using a [small synthetic dataset](https://huggingface.co/datasets/fusing/fill50k). - -## Installing the dependencies - -Before running the scripts, make sure to install the library's training dependencies: - -**Important** - -To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment: -```bash -git clone https://github.com/huggingface/diffusers -cd diffusers -pip install -e . -``` - -Then cd in the example folder and run -```bash -pip install -r requirements.txt -``` - -And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with: - -```bash -accelerate config -``` - -Or for a default accelerate configuration without answering questions about your environment - -```bash -accelerate config default -``` - -Or if your environment doesn't support an interactive shell e.g. a notebook - -```python -from accelerate.utils import write_basic_config -write_basic_config() -``` - -## Circle filling dataset - -The original dataset is hosted in the [ControlNet repo](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip). We re-uploaded it to be compatible with `datasets` [here](https://huggingface.co/datasets/fusing/fill50k). Note that `datasets` handles dataloading within the training script. - -Our training examples use [Stable Diffusion 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the original set of ControlNet models were trained from it. However, ControlNet can be trained to augment any Stable Diffusion compatible model (such as [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)) or [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1). - -## Training - -Our training examples use two test conditioning images. They can be downloaded by running - -```sh -wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png - -wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png -``` - - -```bash -export MODEL_DIR="runwayml/stable-diffusion-v1-5" -export OUTPUT_DIR="path to save model" - -accelerate launch train_controlnet.py \ - --pretrained_model_name_or_path=$MODEL_DIR \ - --output_dir=$OUTPUT_DIR \ - --dataset_name=fusing/fill50k \ - --resolution=512 \ - --learning_rate=1e-5 \ - --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \ - --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \ - --train_batch_size=4 -``` - -This default configuration requires ~38GB VRAM. - -By default, the training script logs outputs to tensorboard. Pass `--report_to wandb` to use weights and -biases. - -Gradient accumulation with a smaller batch size can be used to reduce training requirements to ~20 GB VRAM. - -```bash -export MODEL_DIR="runwayml/stable-diffusion-v1-5" -export OUTPUT_DIR="path to save model" - -accelerate launch train_controlnet.py \ - --pretrained_model_name_or_path=$MODEL_DIR \ - --output_dir=$OUTPUT_DIR \ - --dataset_name=fusing/fill50k \ - --resolution=512 \ - --learning_rate=1e-5 \ - --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \ - --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \ - --train_batch_size=1 \ - --gradient_accumulation_steps=4 -``` - -## Training with multiple GPUs - -`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch) -for running distributed training with `accelerate`. Here is an example command: - -```bash -export MODEL_DIR="runwayml/stable-diffusion-v1-5" -export OUTPUT_DIR="path to save model" - -accelerate launch --mixed_precision="fp16" --multi_gpu train_controlnet.py \ - --pretrained_model_name_or_path=$MODEL_DIR \ - --output_dir=$OUTPUT_DIR \ - --dataset_name=fusing/fill50k \ - --resolution=512 \ - --learning_rate=1e-5 \ - --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \ - --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \ - --train_batch_size=4 \ - --mixed_precision="fp16" \ - --tracker_project_name="controlnet-demo" \ - --report_to=wandb -``` - -## Example results - -#### After 300 steps with batch size 8 - -| | | -|-------------------|:-------------------------:| -| | red circle with blue background | - |  | -| | cyan circle with brown floral background | - |  | - - -#### After 6000 steps with batch size 8: - -| | | -|-------------------|:-------------------------:| -| | red circle with blue background | - |  | -| | cyan circle with brown floral background | - |  | - -## Training on a 16 GB GPU - -Optimizations: -- Gradient checkpointing -- bitsandbyte's 8-bit optimizer - -[bitandbytes install instructions](https://github.com/TimDettmers/bitsandbytes#requirements--installation). - -```bash -export MODEL_DIR="runwayml/stable-diffusion-v1-5" -export OUTPUT_DIR="path to save model" - -accelerate launch train_controlnet.py \ - --pretrained_model_name_or_path=$MODEL_DIR \ - --output_dir=$OUTPUT_DIR \ - --dataset_name=fusing/fill50k \ - --resolution=512 \ - --learning_rate=1e-5 \ - --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \ - --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \ - --train_batch_size=1 \ - --gradient_accumulation_steps=4 \ - --gradient_checkpointing \ - --use_8bit_adam -``` - -## Training on a 12 GB GPU - -Optimizations: -- Gradient checkpointing -- bitsandbyte's 8-bit optimizer -- xformers -- set grads to none - -```bash -export MODEL_DIR="runwayml/stable-diffusion-v1-5" -export OUTPUT_DIR="path to save model" - -accelerate launch train_controlnet.py \ - --pretrained_model_name_or_path=$MODEL_DIR \ - --output_dir=$OUTPUT_DIR \ - --dataset_name=fusing/fill50k \ - --resolution=512 \ - --learning_rate=1e-5 \ - --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \ - --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \ - --train_batch_size=1 \ - --gradient_accumulation_steps=4 \ - --gradient_checkpointing \ - --use_8bit_adam \ - --enable_xformers_memory_efficient_attention \ - --set_grads_to_none -``` - -When using `enable_xformers_memory_efficient_attention`, please make sure to install `xformers` by `pip install xformers`. - -## Training on an 8 GB GPU - -We have not exhaustively tested DeepSpeed support for ControlNet. While the configuration does -save memory, we have not confirmed the configuration to train successfully. You will very likely -have to make changes to the config to have a successful training run. - -Optimizations: -- Gradient checkpointing -- xformers -- set grads to none -- DeepSpeed stage 2 with parameter and optimizer offloading -- fp16 mixed precision - -[DeepSpeed](https://www.deepspeed.ai/) can offload tensors from VRAM to either -CPU or NVME. This requires significantly more RAM (about 25 GB). - -Use `accelerate config` to enable DeepSpeed stage 2. - -The relevant parts of the resulting accelerate config file are - -```yaml -compute_environment: LOCAL_MACHINE -deepspeed_config: - gradient_accumulation_steps: 4 - offload_optimizer_device: cpu - offload_param_device: cpu - zero3_init_flag: false - zero_stage: 2 -distributed_type: DEEPSPEED -``` - -See [documentation](https://huggingface.co/docs/accelerate/usage_guides/deepspeed) for more DeepSpeed configuration options. - -Changing the default Adam optimizer to DeepSpeed's Adam -`deepspeed.ops.adam.DeepSpeedCPUAdam` gives a substantial speedup but -it requires CUDA toolchain with the same version as pytorch. 8-bit optimizer -does not seem to be compatible with DeepSpeed at the moment. - -```bash -export MODEL_DIR="runwayml/stable-diffusion-v1-5" -export OUTPUT_DIR="path to save model" - -accelerate launch train_controlnet.py \ - --pretrained_model_name_or_path=$MODEL_DIR \ - --output_dir=$OUTPUT_DIR \ - --dataset_name=fusing/fill50k \ - --resolution=512 \ - --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \ - --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \ - --train_batch_size=1 \ - --gradient_accumulation_steps=4 \ - --gradient_checkpointing \ - --enable_xformers_memory_efficient_attention \ - --set_grads_to_none \ - --mixed_precision fp16 -``` - -## Performing inference with the trained ControlNet - -The trained model can be run the same as the original ControlNet pipeline with the newly trained ControlNet. -Set `base_model_path` and `controlnet_path` to the values `--pretrained_model_name_or_path` and -`--output_dir` were respectively set to in the training script. - -```py -from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler -from diffusers.utils import load_image -import torch - -base_model_path = "path to model" -controlnet_path = "path to controlnet" - -controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16) -pipe = StableDiffusionControlNetPipeline.from_pretrained( - base_model_path, controlnet=controlnet, torch_dtype=torch.float16 -) - -# speed up diffusion process with faster scheduler and memory optimization -pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) -# remove following line if xformers is not installed or when using Torch 2.0. -pipe.enable_xformers_memory_efficient_attention() -# memory optimization. -pipe.enable_model_cpu_offload() - -control_image = load_image("./conditioning_image_1.png") -prompt = "pale golden rod circle with old lace background" - -# generate image -generator = torch.manual_seed(0) -image = pipe( - prompt, num_inference_steps=20, generator=generator, image=control_image -).images[0] -image.save("./output.png") -``` - -## Training with Flax/JAX - -For faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script. - -### Running on Google Cloud TPU - -See below for commands to set up a TPU VM(`--accelerator-type v4-8`). For more details about how to set up and use TPUs, refer to [Cloud docs for single VM setup](https://cloud.google.com/tpu/docs/run-calculation-jax). - -First create a single TPUv4-8 VM and connect to it: - -``` -ZONE=us-central2-b -TPU_TYPE=v4-8 -VM_NAME=hg_flax - -gcloud alpha compute tpus tpu-vm create $VM_NAME \ - --zone $ZONE \ - --accelerator-type $TPU_TYPE \ - --version tpu-vm-v4-base - -gcloud alpha compute tpus tpu-vm ssh $VM_NAME --zone $ZONE -- \ -``` - -When connected install JAX `0.4.5`: - -``` -pip install "jax[tpu]==0.4.5" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html -``` - -To verify that JAX was correctly installed, you can run the following command: - -``` -import jax -jax.device_count() -``` - -This should display the number of TPU cores, which should be 4 on a TPUv4-8 VM. - -Then install Diffusers and the library's training dependencies: - -```bash -git clone https://github.com/huggingface/diffusers -cd diffusers -pip install . -``` - -Then cd in the example folder and run - -```bash -pip install -U -r requirements_flax.txt -``` - -If you want to use Weights and Biases logging, you should also install `wandb` now - -```bash -pip install wandb -``` - - -Now let's downloading two conditioning images that we will use to run validation during the training in order to track our progress - -``` -wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png -wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png -``` - -We encourage you to store or share your model with the community. To use huggingface hub, please login to your Hugging Face account, or ([create one](https://huggingface.co/docs/diffusers/main/en/training/hf.co/join) if you don’t have one already): - -``` -huggingface-cli login -``` - -Make sure you have the `MODEL_DIR`,`OUTPUT_DIR` and `HUB_MODEL_ID` environment variables set. The `OUTPUT_DIR` and `HUB_MODEL_ID` variables specify where to save the model to on the Hub: - -```bash -export MODEL_DIR="runwayml/stable-diffusion-v1-5" -export OUTPUT_DIR="runs/fill-circle-{timestamp}" -export HUB_MODEL_ID="controlnet-fill-circle" -``` - -And finally start the training - -```bash -python3 train_controlnet_flax.py \ - --pretrained_model_name_or_path=$MODEL_DIR \ - --output_dir=$OUTPUT_DIR \ - --dataset_name=fusing/fill50k \ - --resolution=512 \ - --learning_rate=1e-5 \ - --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \ - --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \ - --validation_steps=1000 \ - --train_batch_size=2 \ - --revision="non-ema" \ - --from_pt \ - --report_to="wandb" \ - --tracker_project_name=$HUB_MODEL_ID \ - --num_train_epochs=11 \ - --push_to_hub \ - --hub_model_id=$HUB_MODEL_ID - ``` - -Since we passed the `--push_to_hub` flag, it will automatically create a model repo under your huggingface account based on `$HUB_MODEL_ID`. By the end of training, the final checkpoint will be automatically stored on the hub. You can find an example model repo [here](https://huggingface.co/YiYiXu/fill-circle-controlnet). - -Our training script also provides limited support for streaming large datasets from the Hugging Face Hub. In order to enable streaming, one must also set `--max_train_samples`. Here is an example command (from [this blog article](https://huggingface.co/blog/train-your-controlnet)): - -```bash -export MODEL_DIR="runwayml/stable-diffusion-v1-5" -export OUTPUT_DIR="runs/uncanny-faces-{timestamp}" -export HUB_MODEL_ID="controlnet-uncanny-faces" - -python3 train_controlnet_flax.py \ - --pretrained_model_name_or_path=$MODEL_DIR \ - --output_dir=$OUTPUT_DIR \ - --dataset_name=multimodalart/facesyntheticsspigacaptioned \ - --streaming \ - --conditioning_image_column=spiga_seg \ - --image_column=image \ - --caption_column=image_caption \ - --resolution=512 \ - --max_train_samples 100000 \ - --learning_rate=1e-5 \ - --train_batch_size=1 \ - --revision="flax" \ - --report_to="wandb" \ - --tracker_project_name=$HUB_MODEL_ID -``` - -Note, however, that the performance of the TPUs might get bottlenecked as streaming with `datasets` is not optimized for images. For ensuring maximum throughput, we encourage you to explore the following options: - -* [Webdataset](https://webdataset.github.io/webdataset/) -* [TorchData](https://github.com/pytorch/data) -* [TensorFlow Datasets](https://www.tensorflow.org/datasets/tfless_tfds) - -When work with a larger dataset, you may need to run training process for a long time and it’s useful to save regular checkpoints during the process. You can use the following argument to enable intermediate checkpointing: - -```bash - --checkpointing_steps=500 -``` -This will save the trained model in subfolders of your output_dir. Subfolder names is the number of steps performed so far; for example: a checkpoint saved after 500 training steps would be saved in a subfolder named 500 - -You can then start your training from this saved checkpoint with - -```bash - --controlnet_model_name_or_path="./control_out/500" -``` - -We support training with the Min-SNR weighting strategy proposed in [Efficient Diffusion Training via Min-SNR Weighting Strategy](https://arxiv.org/abs/2303.09556) which helps to achieve faster convergence by rebalancing the loss. To use it, one needs to set the `--snr_gamma` argument. The recommended value when using it is `5.0`. - -We also support gradient accumulation - it is a technique that lets you use a bigger batch size than your machine would normally be able to fit into memory. You can use `gradient_accumulation_steps` argument to set gradient accumulation steps. The ControlNet author recommends using gradient accumulation to achieve better convergence. Read more [here](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md#more-consideration-sudden-converge-phenomenon-and-gradient-accumulation). - -You can **profile your code** with: - -```bash - --profile_steps==5 -``` - -Refer to the [JAX documentation on profiling](https://jax.readthedocs.io/en/latest/profiling.html). To inspect the profile trace, you'll have to install and start Tensorboard with the profile plugin: - -```bash -pip install tensorflow tensorboard-plugin-profile -tensorboard --logdir runs/fill-circle-100steps-20230411_165612/ -``` - -The profile can then be inspected at http://localhost:6006/#profile - -Sometimes you'll get version conflicts (error messages like `Duplicate plugins for name projector`), which means that you have to uninstall and reinstall all versions of Tensorflow/Tensorboard (e.g. with `pip uninstall tensorflow tf-nightly tensorboard tb-nightly tensorboard-plugin-profile && pip install tf-nightly tbp-nightly tensorboard-plugin-profile`). - -Note that the debugging functionality of the Tensorboard `profile` plugin is still under active development. Not all views are fully functional, and for example the `trace_viewer` cuts off events after 1M (which can result in all your device traces getting lost if you for example profile the compilation step by accident). - -## Support for Stable Diffusion XL - -We provide a training script for training a ControlNet with [Stable Diffusion XL](https://huggingface.co/papers/2307.01952). Please refer to [README_sdxl.md](./README_sdxl.md) for more details. diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/builder.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/builder.py deleted file mode 100644 index 81c927e507a7c1625ffb114de10e93c94927af25..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/builder.py +++ /dev/null @@ -1,77 +0,0 @@ -import warnings - -from mmcv.utils import Registry, build_from_cfg -from torch import nn - -BACKBONES = Registry('backbone') -NECKS = Registry('neck') -ROI_EXTRACTORS = Registry('roi_extractor') -SHARED_HEADS = Registry('shared_head') -HEADS = Registry('head') -LOSSES = Registry('loss') -DETECTORS = Registry('detector') - - -def build(cfg, registry, default_args=None): - """Build a module. - - Args: - cfg (dict, list[dict]): The config of modules, is is either a dict - or a list of configs. - registry (:obj:`Registry`): A registry the module belongs to. - default_args (dict, optional): Default arguments to build the module. - Defaults to None. - - Returns: - nn.Module: A built nn module. - """ - if isinstance(cfg, list): - modules = [ - build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg - ] - return nn.Sequential(*modules) - else: - return build_from_cfg(cfg, registry, default_args) - - -def build_backbone(cfg): - """Build backbone.""" - return build(cfg, BACKBONES) - - -def build_neck(cfg): - """Build neck.""" - return build(cfg, NECKS) - - -def build_roi_extractor(cfg): - """Build roi extractor.""" - return build(cfg, ROI_EXTRACTORS) - - -def build_shared_head(cfg): - """Build shared head.""" - return build(cfg, SHARED_HEADS) - - -def build_head(cfg): - """Build head.""" - return build(cfg, HEADS) - - -def build_loss(cfg): - """Build loss.""" - return build(cfg, LOSSES) - - -def build_detector(cfg, train_cfg=None, test_cfg=None): - """Build detector.""" - if train_cfg is not None or test_cfg is not None: - warnings.warn( - 'train_cfg and test_cfg is deprecated, ' - 'please specify them in model', UserWarning) - assert cfg.get('train_cfg') is None or train_cfg is None, \ - 'train_cfg specified in both outer field and model field ' - assert cfg.get('test_cfg') is None or test_cfg is None, \ - 'test_cfg specified in both outer field and model field ' - return build(cfg, DETECTORS, dict(train_cfg=train_cfg, test_cfg=test_cfg)) diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py deleted file mode 100644 index 81f3d5cb91607134bb1d844d78df7a3c411c134d..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './ocrnet_hr18_512x512_160k_ade20k.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18_small', - backbone=dict( - extra=dict( - stage1=dict(num_blocks=(2, )), - stage2=dict(num_blocks=(2, 2)), - stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), - stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/spaces/AnimalEquality/chatbot/constants.py b/spaces/AnimalEquality/chatbot/constants.py deleted file mode 100644 index caceb2e41ba6f9ccdd661f4eb6eef753a44a70a3..0000000000000000000000000000000000000000 --- a/spaces/AnimalEquality/chatbot/constants.py +++ /dev/null @@ -1,3 +0,0 @@ -from pathlib import Path - -ROOT_DIR = Path(__file__).parent diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/deepspeed_parameters.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/deepspeed_parameters.py deleted file mode 100644 index f170a385cfc3dfb954fc6f5595cf8706e42aed30..0000000000000000000000000000000000000000 --- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/deepspeed_parameters.py +++ /dev/null @@ -1,74 +0,0 @@ -def generate_ds_config(ds_bf16, train_batch_size, nvme_offload_dir): - ''' - DeepSpeed configuration - https://huggingface.co/docs/transformers/main_classes/deepspeed - ''' - - if nvme_offload_dir: - ds_config = { - "fp16": { - "enabled": not ds_bf16, - }, - "bf16": { - "enabled": ds_bf16, - }, - "zero_optimization": { - "stage": 3, - "offload_param": { - "device": "nvme", - "nvme_path": nvme_offload_dir, - "pin_memory": True, - "buffer_count": 5, - "buffer_size": 1e9, - "max_in_cpu": 1e9 - }, - "overlap_comm": True, - "reduce_bucket_size": "auto", - "contiguous_gradients": True, - "sub_group_size": 1e8, - "stage3_prefetch_bucket_size": "auto", - "stage3_param_persistence_threshold": "auto", - "stage3_max_live_parameters": "auto", - "stage3_max_reuse_distance": "auto", - }, - "aio": { - "block_size": 262144, - "queue_depth": 32, - "thread_count": 1, - "single_submit": False, - "overlap_events": True - }, - "steps_per_print": 2000, - "train_batch_size": train_batch_size, - "train_micro_batch_size_per_gpu": 1, - "wall_clock_breakdown": False - } - else: - ds_config = { - "fp16": { - "enabled": not ds_bf16, - }, - "bf16": { - "enabled": ds_bf16, - }, - "zero_optimization": { - "stage": 3, - "offload_param": { - "device": "cpu", - "pin_memory": True - }, - "overlap_comm": True, - "contiguous_gradients": True, - "reduce_bucket_size": "auto", - "stage3_prefetch_bucket_size": "auto", - "stage3_param_persistence_threshold": "auto", - "stage3_max_live_parameters": "auto", - "stage3_max_reuse_distance": "auto", - }, - "steps_per_print": 2000, - "train_batch_size": train_batch_size, - "train_micro_batch_size_per_gpu": 1, - "wall_clock_breakdown": False - } - - return ds_config diff --git a/spaces/Anonymous-sub/Rerender/gmflow_module/scripts/demo.sh b/spaces/Anonymous-sub/Rerender/gmflow_module/scripts/demo.sh deleted file mode 100644 index ac5b2e4900ef275973d74e2c172cf3c77a504dcf..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-sub/Rerender/gmflow_module/scripts/demo.sh +++ /dev/null @@ -1,63 +0,0 @@ -#!/usr/bin/env bash - -# inference GMFlow without refinement - -# sintel - -# only predict forward flow -CUDA_VISIBLE_DEVICES=0 python main.py \ ---inference_dir demo/sintel_market_1 \ ---output_path output/gmflow-norefine-sintel_market_1 \ ---resume pretrained/gmflow_sintel-0c07dcb3.pth - -# predict forward & backward flow -CUDA_VISIBLE_DEVICES=0 python main.py \ ---inference_dir demo/sintel_market_1 \ ---output_path output/gmflow-norefine-sintel_market_1 \ ---pred_bidir_flow \ ---resume pretrained/gmflow_sintel-0c07dcb3.pth - - -# predict forward & backward flow with forward-backward consistency check -CUDA_VISIBLE_DEVICES=0 python main.py \ ---inference_dir demo/sintel_market_1 \ ---output_path output/gmflow-norefine-sintel_market_1 \ ---pred_bidir_flow \ ---fwd_bwd_consistency_check \ ---resume pretrained/gmflow_sintel-0c07dcb3.pth - - -# davis - -CUDA_VISIBLE_DEVICES=0 python main.py \ ---inference_dir demo/davis_breakdance-flare \ ---output_path output/gmflow-norefine-davis_breakdance-flare \ ---resume pretrained/gmflow_sintel-0c07dcb3.pth - - - - -# inference GMFlow with refinement - -CUDA_VISIBLE_DEVICES=0 python main.py \ ---inference_dir demo/davis_breakdance-flare \ ---output_path output/gmflow-withrefine-davis_breakdance-flare \ ---resume pretrained/gmflow_with_refine_sintel-3ed1cf48.pth \ ---padding_factor 32 \ ---upsample_factor 4 \ ---num_scales 2 \ ---attn_splits_list 2 8 \ ---corr_radius_list -1 4 \ ---prop_radius_list -1 1 - - - - -CUDA_VISIBLE_DEVICES=0 python main.py \ ---inference_dir demo/sintel_test_clean_market_1 \ ---output_path output/gmflow-norefine-sintel_test_clean_market_1 \ ---pred_bidir_flow \ ---fwd_bwd_consistency_check \ ---resume pretrained/gmflow_sintel-0c07dcb3.pth - - diff --git a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/config/GroundingDINO_SwinB_cfg.py b/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/config/GroundingDINO_SwinB_cfg.py deleted file mode 100644 index f490c4bbd598a35de43d36ceafcbd769e7ff21bf..0000000000000000000000000000000000000000 --- a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/config/GroundingDINO_SwinB_cfg.py +++ /dev/null @@ -1,43 +0,0 @@ -batch_size = 1 -modelname = "groundingdino" -backbone = "swin_B_384_22k" -position_embedding = "sine" -pe_temperatureH = 20 -pe_temperatureW = 20 -return_interm_indices = [1, 2, 3] -backbone_freeze_keywords = None -enc_layers = 6 -dec_layers = 6 -pre_norm = False -dim_feedforward = 2048 -hidden_dim = 256 -dropout = 0.0 -nheads = 8 -num_queries = 900 -query_dim = 4 -num_patterns = 0 -num_feature_levels = 4 -enc_n_points = 4 -dec_n_points = 4 -two_stage_type = "standard" -two_stage_bbox_embed_share = False -two_stage_class_embed_share = False -transformer_activation = "relu" -dec_pred_bbox_embed_share = True -dn_box_noise_scale = 1.0 -dn_label_noise_ratio = 0.5 -dn_label_coef = 1.0 -dn_bbox_coef = 1.0 -embed_init_tgt = True -dn_labelbook_size = 2000 -max_text_len = 256 -text_encoder_type = "bert-base-uncased" -use_text_enhancer = True -use_fusion_layer = True -use_checkpoint = True -use_transformer_ckpt = True -use_text_cross_attention = True -text_dropout = 0.0 -fusion_dropout = 0.0 -fusion_droppath = 0.1 -sub_sentence_present = True diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/cli/req_command.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/cli/req_command.py deleted file mode 100644 index c2f4e38bed82c32bf5e45657fd8658cff1710f13..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/cli/req_command.py +++ /dev/null @@ -1,505 +0,0 @@ -"""Contains the Command base classes that depend on PipSession. - -The classes in this module are in a separate module so the commands not -needing download / PackageFinder capability don't unnecessarily import the -PackageFinder machinery and all its vendored dependencies, etc. -""" - -import logging -import os -import sys -from functools import partial -from optparse import Values -from typing import TYPE_CHECKING, Any, List, Optional, Tuple - -from pip._internal.cache import WheelCache -from pip._internal.cli import cmdoptions -from pip._internal.cli.base_command import Command -from pip._internal.cli.command_context import CommandContextMixIn -from pip._internal.exceptions import CommandError, PreviousBuildDirError -from pip._internal.index.collector import LinkCollector -from pip._internal.index.package_finder import PackageFinder -from pip._internal.models.selection_prefs import SelectionPreferences -from pip._internal.models.target_python import TargetPython -from pip._internal.network.session import PipSession -from pip._internal.operations.build.build_tracker import BuildTracker -from pip._internal.operations.prepare import RequirementPreparer -from pip._internal.req.constructors import ( - install_req_from_editable, - install_req_from_line, - install_req_from_parsed_requirement, - install_req_from_req_string, -) -from pip._internal.req.req_file import parse_requirements -from pip._internal.req.req_install import InstallRequirement -from pip._internal.resolution.base import BaseResolver -from pip._internal.self_outdated_check import pip_self_version_check -from pip._internal.utils.temp_dir import ( - TempDirectory, - TempDirectoryTypeRegistry, - tempdir_kinds, -) -from pip._internal.utils.virtualenv import running_under_virtualenv - -if TYPE_CHECKING: - from ssl import SSLContext - -logger = logging.getLogger(__name__) - - -def _create_truststore_ssl_context() -> Optional["SSLContext"]: - if sys.version_info < (3, 10): - raise CommandError("The truststore feature is only available for Python 3.10+") - - try: - import ssl - except ImportError: - logger.warning("Disabling truststore since ssl support is missing") - return None - - try: - import truststore - except ImportError: - raise CommandError( - "To use the truststore feature, 'truststore' must be installed into " - "pip's current environment." - ) - - return truststore.SSLContext(ssl.PROTOCOL_TLS_CLIENT) - - -class SessionCommandMixin(CommandContextMixIn): - - """ - A class mixin for command classes needing _build_session(). - """ - - def __init__(self) -> None: - super().__init__() - self._session: Optional[PipSession] = None - - @classmethod - def _get_index_urls(cls, options: Values) -> Optional[List[str]]: - """Return a list of index urls from user-provided options.""" - index_urls = [] - if not getattr(options, "no_index", False): - url = getattr(options, "index_url", None) - if url: - index_urls.append(url) - urls = getattr(options, "extra_index_urls", None) - if urls: - index_urls.extend(urls) - # Return None rather than an empty list - return index_urls or None - - def get_default_session(self, options: Values) -> PipSession: - """Get a default-managed session.""" - if self._session is None: - self._session = self.enter_context(self._build_session(options)) - # there's no type annotation on requests.Session, so it's - # automatically ContextManager[Any] and self._session becomes Any, - # then https://github.com/python/mypy/issues/7696 kicks in - assert self._session is not None - return self._session - - def _build_session( - self, - options: Values, - retries: Optional[int] = None, - timeout: Optional[int] = None, - fallback_to_certifi: bool = False, - ) -> PipSession: - cache_dir = options.cache_dir - assert not cache_dir or os.path.isabs(cache_dir) - - if "truststore" in options.features_enabled: - try: - ssl_context = _create_truststore_ssl_context() - except Exception: - if not fallback_to_certifi: - raise - ssl_context = None - else: - ssl_context = None - - session = PipSession( - cache=os.path.join(cache_dir, "http") if cache_dir else None, - retries=retries if retries is not None else options.retries, - trusted_hosts=options.trusted_hosts, - index_urls=self._get_index_urls(options), - ssl_context=ssl_context, - ) - - # Handle custom ca-bundles from the user - if options.cert: - session.verify = options.cert - - # Handle SSL client certificate - if options.client_cert: - session.cert = options.client_cert - - # Handle timeouts - if options.timeout or timeout: - session.timeout = timeout if timeout is not None else options.timeout - - # Handle configured proxies - if options.proxy: - session.proxies = { - "http": options.proxy, - "https": options.proxy, - } - - # Determine if we can prompt the user for authentication or not - session.auth.prompting = not options.no_input - session.auth.keyring_provider = options.keyring_provider - - return session - - -class IndexGroupCommand(Command, SessionCommandMixin): - - """ - Abstract base class for commands with the index_group options. - - This also corresponds to the commands that permit the pip version check. - """ - - def handle_pip_version_check(self, options: Values) -> None: - """ - Do the pip version check if not disabled. - - This overrides the default behavior of not doing the check. - """ - # Make sure the index_group options are present. - assert hasattr(options, "no_index") - - if options.disable_pip_version_check or options.no_index: - return - - # Otherwise, check if we're using the latest version of pip available. - session = self._build_session( - options, - retries=0, - timeout=min(5, options.timeout), - # This is set to ensure the function does not fail when truststore is - # specified in use-feature but cannot be loaded. This usually raises a - # CommandError and shows a nice user-facing error, but this function is not - # called in that try-except block. - fallback_to_certifi=True, - ) - with session: - pip_self_version_check(session, options) - - -KEEPABLE_TEMPDIR_TYPES = [ - tempdir_kinds.BUILD_ENV, - tempdir_kinds.EPHEM_WHEEL_CACHE, - tempdir_kinds.REQ_BUILD, -] - - -def warn_if_run_as_root() -> None: - """Output a warning for sudo users on Unix. - - In a virtual environment, sudo pip still writes to virtualenv. - On Windows, users may run pip as Administrator without issues. - This warning only applies to Unix root users outside of virtualenv. - """ - if running_under_virtualenv(): - return - if not hasattr(os, "getuid"): - return - # On Windows, there are no "system managed" Python packages. Installing as - # Administrator via pip is the correct way of updating system environments. - # - # We choose sys.platform over utils.compat.WINDOWS here to enable Mypy platform - # checks: https://mypy.readthedocs.io/en/stable/common_issues.html - if sys.platform == "win32" or sys.platform == "cygwin": - return - - if os.getuid() != 0: - return - - logger.warning( - "Running pip as the 'root' user can result in broken permissions and " - "conflicting behaviour with the system package manager. " - "It is recommended to use a virtual environment instead: " - "https://pip.pypa.io/warnings/venv" - ) - - -def with_cleanup(func: Any) -> Any: - """Decorator for common logic related to managing temporary - directories. - """ - - def configure_tempdir_registry(registry: TempDirectoryTypeRegistry) -> None: - for t in KEEPABLE_TEMPDIR_TYPES: - registry.set_delete(t, False) - - def wrapper( - self: RequirementCommand, options: Values, args: List[Any] - ) -> Optional[int]: - assert self.tempdir_registry is not None - if options.no_clean: - configure_tempdir_registry(self.tempdir_registry) - - try: - return func(self, options, args) - except PreviousBuildDirError: - # This kind of conflict can occur when the user passes an explicit - # build directory with a pre-existing folder. In that case we do - # not want to accidentally remove it. - configure_tempdir_registry(self.tempdir_registry) - raise - - return wrapper - - -class RequirementCommand(IndexGroupCommand): - def __init__(self, *args: Any, **kw: Any) -> None: - super().__init__(*args, **kw) - - self.cmd_opts.add_option(cmdoptions.no_clean()) - - @staticmethod - def determine_resolver_variant(options: Values) -> str: - """Determines which resolver should be used, based on the given options.""" - if "legacy-resolver" in options.deprecated_features_enabled: - return "legacy" - - return "2020-resolver" - - @classmethod - def make_requirement_preparer( - cls, - temp_build_dir: TempDirectory, - options: Values, - build_tracker: BuildTracker, - session: PipSession, - finder: PackageFinder, - use_user_site: bool, - download_dir: Optional[str] = None, - verbosity: int = 0, - ) -> RequirementPreparer: - """ - Create a RequirementPreparer instance for the given parameters. - """ - temp_build_dir_path = temp_build_dir.path - assert temp_build_dir_path is not None - - resolver_variant = cls.determine_resolver_variant(options) - if resolver_variant == "2020-resolver": - lazy_wheel = "fast-deps" in options.features_enabled - if lazy_wheel: - logger.warning( - "pip is using lazily downloaded wheels using HTTP " - "range requests to obtain dependency information. " - "This experimental feature is enabled through " - "--use-feature=fast-deps and it is not ready for " - "production." - ) - else: - lazy_wheel = False - if "fast-deps" in options.features_enabled: - logger.warning( - "fast-deps has no effect when used with the legacy resolver." - ) - - return RequirementPreparer( - build_dir=temp_build_dir_path, - src_dir=options.src_dir, - download_dir=download_dir, - build_isolation=options.build_isolation, - check_build_deps=options.check_build_deps, - build_tracker=build_tracker, - session=session, - progress_bar=options.progress_bar, - finder=finder, - require_hashes=options.require_hashes, - use_user_site=use_user_site, - lazy_wheel=lazy_wheel, - verbosity=verbosity, - ) - - @classmethod - def make_resolver( - cls, - preparer: RequirementPreparer, - finder: PackageFinder, - options: Values, - wheel_cache: Optional[WheelCache] = None, - use_user_site: bool = False, - ignore_installed: bool = True, - ignore_requires_python: bool = False, - force_reinstall: bool = False, - upgrade_strategy: str = "to-satisfy-only", - use_pep517: Optional[bool] = None, - py_version_info: Optional[Tuple[int, ...]] = None, - ) -> BaseResolver: - """ - Create a Resolver instance for the given parameters. - """ - make_install_req = partial( - install_req_from_req_string, - isolated=options.isolated_mode, - use_pep517=use_pep517, - ) - resolver_variant = cls.determine_resolver_variant(options) - # The long import name and duplicated invocation is needed to convince - # Mypy into correctly typechecking. Otherwise it would complain the - # "Resolver" class being redefined. - if resolver_variant == "2020-resolver": - import pip._internal.resolution.resolvelib.resolver - - return pip._internal.resolution.resolvelib.resolver.Resolver( - preparer=preparer, - finder=finder, - wheel_cache=wheel_cache, - make_install_req=make_install_req, - use_user_site=use_user_site, - ignore_dependencies=options.ignore_dependencies, - ignore_installed=ignore_installed, - ignore_requires_python=ignore_requires_python, - force_reinstall=force_reinstall, - upgrade_strategy=upgrade_strategy, - py_version_info=py_version_info, - ) - import pip._internal.resolution.legacy.resolver - - return pip._internal.resolution.legacy.resolver.Resolver( - preparer=preparer, - finder=finder, - wheel_cache=wheel_cache, - make_install_req=make_install_req, - use_user_site=use_user_site, - ignore_dependencies=options.ignore_dependencies, - ignore_installed=ignore_installed, - ignore_requires_python=ignore_requires_python, - force_reinstall=force_reinstall, - upgrade_strategy=upgrade_strategy, - py_version_info=py_version_info, - ) - - def get_requirements( - self, - args: List[str], - options: Values, - finder: PackageFinder, - session: PipSession, - ) -> List[InstallRequirement]: - """ - Parse command-line arguments into the corresponding requirements. - """ - requirements: List[InstallRequirement] = [] - for filename in options.constraints: - for parsed_req in parse_requirements( - filename, - constraint=True, - finder=finder, - options=options, - session=session, - ): - req_to_add = install_req_from_parsed_requirement( - parsed_req, - isolated=options.isolated_mode, - user_supplied=False, - ) - requirements.append(req_to_add) - - for req in args: - req_to_add = install_req_from_line( - req, - comes_from=None, - isolated=options.isolated_mode, - use_pep517=options.use_pep517, - user_supplied=True, - config_settings=getattr(options, "config_settings", None), - ) - requirements.append(req_to_add) - - for req in options.editables: - req_to_add = install_req_from_editable( - req, - user_supplied=True, - isolated=options.isolated_mode, - use_pep517=options.use_pep517, - config_settings=getattr(options, "config_settings", None), - ) - requirements.append(req_to_add) - - # NOTE: options.require_hashes may be set if --require-hashes is True - for filename in options.requirements: - for parsed_req in parse_requirements( - filename, finder=finder, options=options, session=session - ): - req_to_add = install_req_from_parsed_requirement( - parsed_req, - isolated=options.isolated_mode, - use_pep517=options.use_pep517, - user_supplied=True, - config_settings=parsed_req.options.get("config_settings") - if parsed_req.options - else None, - ) - requirements.append(req_to_add) - - # If any requirement has hash options, enable hash checking. - if any(req.has_hash_options for req in requirements): - options.require_hashes = True - - if not (args or options.editables or options.requirements): - opts = {"name": self.name} - if options.find_links: - raise CommandError( - "You must give at least one requirement to {name} " - '(maybe you meant "pip {name} {links}"?)'.format( - **dict(opts, links=" ".join(options.find_links)) - ) - ) - else: - raise CommandError( - "You must give at least one requirement to {name} " - '(see "pip help {name}")'.format(**opts) - ) - - return requirements - - @staticmethod - def trace_basic_info(finder: PackageFinder) -> None: - """ - Trace basic information about the provided objects. - """ - # Display where finder is looking for packages - search_scope = finder.search_scope - locations = search_scope.get_formatted_locations() - if locations: - logger.info(locations) - - def _build_package_finder( - self, - options: Values, - session: PipSession, - target_python: Optional[TargetPython] = None, - ignore_requires_python: Optional[bool] = None, - ) -> PackageFinder: - """ - Create a package finder appropriate to this requirement command. - - :param ignore_requires_python: Whether to ignore incompatible - "Requires-Python" values in links. Defaults to False. - """ - link_collector = LinkCollector.create(session, options=options) - selection_prefs = SelectionPreferences( - allow_yanked=True, - format_control=options.format_control, - allow_all_prereleases=options.pre, - prefer_binary=options.prefer_binary, - ignore_requires_python=ignore_requires_python, - ) - - return PackageFinder.create( - link_collector=link_collector, - selection_prefs=selection_prefs, - target_python=target_python, - ) diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/constrain.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/constrain.py deleted file mode 100644 index 65fdf56342e8b5b8e181914881025231684e1871..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/constrain.py +++ /dev/null @@ -1,37 +0,0 @@ -from typing import Optional, TYPE_CHECKING - -from .jupyter import JupyterMixin -from .measure import Measurement - -if TYPE_CHECKING: - from .console import Console, ConsoleOptions, RenderableType, RenderResult - - -class Constrain(JupyterMixin): - """Constrain the width of a renderable to a given number of characters. - - Args: - renderable (RenderableType): A renderable object. - width (int, optional): The maximum width (in characters) to render. Defaults to 80. - """ - - def __init__(self, renderable: "RenderableType", width: Optional[int] = 80) -> None: - self.renderable = renderable - self.width = width - - def __rich_console__( - self, console: "Console", options: "ConsoleOptions" - ) -> "RenderResult": - if self.width is None: - yield self.renderable - else: - child_options = options.update_width(min(self.width, options.max_width)) - yield from console.render(self.renderable, child_options) - - def __rich_measure__( - self, console: "Console", options: "ConsoleOptions" - ) -> "Measurement": - if self.width is not None: - options = options.update_width(self.width) - measurement = Measurement.get(console, options, self.renderable) - return measurement diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/tree.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/tree.py deleted file mode 100644 index afe8da1a4a30daf6e48ffba514656e7c86c9abaa..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/tree.py +++ /dev/null @@ -1,251 +0,0 @@ -from typing import Iterator, List, Optional, Tuple - -from ._loop import loop_first, loop_last -from .console import Console, ConsoleOptions, RenderableType, RenderResult -from .jupyter import JupyterMixin -from .measure import Measurement -from .segment import Segment -from .style import Style, StyleStack, StyleType -from .styled import Styled - - -class Tree(JupyterMixin): - """A renderable for a tree structure. - - Args: - label (RenderableType): The renderable or str for the tree label. - style (StyleType, optional): Style of this tree. Defaults to "tree". - guide_style (StyleType, optional): Style of the guide lines. Defaults to "tree.line". - expanded (bool, optional): Also display children. Defaults to True. - highlight (bool, optional): Highlight renderable (if str). Defaults to False. - """ - - def __init__( - self, - label: RenderableType, - *, - style: StyleType = "tree", - guide_style: StyleType = "tree.line", - expanded: bool = True, - highlight: bool = False, - hide_root: bool = False, - ) -> None: - self.label = label - self.style = style - self.guide_style = guide_style - self.children: List[Tree] = [] - self.expanded = expanded - self.highlight = highlight - self.hide_root = hide_root - - def add( - self, - label: RenderableType, - *, - style: Optional[StyleType] = None, - guide_style: Optional[StyleType] = None, - expanded: bool = True, - highlight: Optional[bool] = False, - ) -> "Tree": - """Add a child tree. - - Args: - label (RenderableType): The renderable or str for the tree label. - style (StyleType, optional): Style of this tree. Defaults to "tree". - guide_style (StyleType, optional): Style of the guide lines. Defaults to "tree.line". - expanded (bool, optional): Also display children. Defaults to True. - highlight (Optional[bool], optional): Highlight renderable (if str). Defaults to False. - - Returns: - Tree: A new child Tree, which may be further modified. - """ - node = Tree( - label, - style=self.style if style is None else style, - guide_style=self.guide_style if guide_style is None else guide_style, - expanded=expanded, - highlight=self.highlight if highlight is None else highlight, - ) - self.children.append(node) - return node - - def __rich_console__( - self, console: "Console", options: "ConsoleOptions" - ) -> "RenderResult": - - stack: List[Iterator[Tuple[bool, Tree]]] = [] - pop = stack.pop - push = stack.append - new_line = Segment.line() - - get_style = console.get_style - null_style = Style.null() - guide_style = get_style(self.guide_style, default="") or null_style - SPACE, CONTINUE, FORK, END = range(4) - - ASCII_GUIDES = (" ", "| ", "+-- ", "`-- ") - TREE_GUIDES = [ - (" ", "│ ", "├── ", "└── "), - (" ", "┃ ", "┣━━ ", "┗━━ "), - (" ", "║ ", "╠══ ", "╚══ "), - ] - _Segment = Segment - - def make_guide(index: int, style: Style) -> Segment: - """Make a Segment for a level of the guide lines.""" - if options.ascii_only: - line = ASCII_GUIDES[index] - else: - guide = 1 if style.bold else (2 if style.underline2 else 0) - line = TREE_GUIDES[0 if options.legacy_windows else guide][index] - return _Segment(line, style) - - levels: List[Segment] = [make_guide(CONTINUE, guide_style)] - push(iter(loop_last([self]))) - - guide_style_stack = StyleStack(get_style(self.guide_style)) - style_stack = StyleStack(get_style(self.style)) - remove_guide_styles = Style(bold=False, underline2=False) - - depth = 0 - - while stack: - stack_node = pop() - try: - last, node = next(stack_node) - except StopIteration: - levels.pop() - if levels: - guide_style = levels[-1].style or null_style - levels[-1] = make_guide(FORK, guide_style) - guide_style_stack.pop() - style_stack.pop() - continue - push(stack_node) - if last: - levels[-1] = make_guide(END, levels[-1].style or null_style) - - guide_style = guide_style_stack.current + get_style(node.guide_style) - style = style_stack.current + get_style(node.style) - prefix = levels[(2 if self.hide_root else 1) :] - renderable_lines = console.render_lines( - Styled(node.label, style), - options.update( - width=options.max_width - - sum(level.cell_length for level in prefix), - highlight=self.highlight, - height=None, - ), - pad=options.justify is not None, - ) - - if not (depth == 0 and self.hide_root): - for first, line in loop_first(renderable_lines): - if prefix: - yield from _Segment.apply_style( - prefix, - style.background_style, - post_style=remove_guide_styles, - ) - yield from line - yield new_line - if first and prefix: - prefix[-1] = make_guide( - SPACE if last else CONTINUE, prefix[-1].style or null_style - ) - - if node.expanded and node.children: - levels[-1] = make_guide( - SPACE if last else CONTINUE, levels[-1].style or null_style - ) - levels.append( - make_guide(END if len(node.children) == 1 else FORK, guide_style) - ) - style_stack.push(get_style(node.style)) - guide_style_stack.push(get_style(node.guide_style)) - push(iter(loop_last(node.children))) - depth += 1 - - def __rich_measure__( - self, console: "Console", options: "ConsoleOptions" - ) -> "Measurement": - stack: List[Iterator[Tree]] = [iter([self])] - pop = stack.pop - push = stack.append - minimum = 0 - maximum = 0 - measure = Measurement.get - level = 0 - while stack: - iter_tree = pop() - try: - tree = next(iter_tree) - except StopIteration: - level -= 1 - continue - push(iter_tree) - min_measure, max_measure = measure(console, options, tree.label) - indent = level * 4 - minimum = max(min_measure + indent, minimum) - maximum = max(max_measure + indent, maximum) - if tree.expanded and tree.children: - push(iter(tree.children)) - level += 1 - return Measurement(minimum, maximum) - - -if __name__ == "__main__": # pragma: no cover - - from pip._vendor.rich.console import Group - from pip._vendor.rich.markdown import Markdown - from pip._vendor.rich.panel import Panel - from pip._vendor.rich.syntax import Syntax - from pip._vendor.rich.table import Table - - table = Table(row_styles=["", "dim"]) - - table.add_column("Released", style="cyan", no_wrap=True) - table.add_column("Title", style="magenta") - table.add_column("Box Office", justify="right", style="green") - - table.add_row("Dec 20, 2019", "Star Wars: The Rise of Skywalker", "$952,110,690") - table.add_row("May 25, 2018", "Solo: A Star Wars Story", "$393,151,347") - table.add_row("Dec 15, 2017", "Star Wars Ep. V111: The Last Jedi", "$1,332,539,889") - table.add_row("Dec 16, 2016", "Rogue One: A Star Wars Story", "$1,332,439,889") - - code = """\ -class Segment(NamedTuple): - text: str = "" - style: Optional[Style] = None - is_control: bool = False -""" - syntax = Syntax(code, "python", theme="monokai", line_numbers=True) - - markdown = Markdown( - """\ -### example.md -> Hello, World! -> -> Markdown _all_ the things -""" - ) - - root = Tree("🌲 [b green]Rich Tree", highlight=True, hide_root=True) - - node = root.add(":file_folder: Renderables", guide_style="red") - simple_node = node.add(":file_folder: [bold yellow]Atomic", guide_style="uu green") - simple_node.add(Group("📄 Syntax", syntax)) - simple_node.add(Group("📄 Markdown", Panel(markdown, border_style="green"))) - - containers_node = node.add( - ":file_folder: [bold magenta]Containers", guide_style="bold magenta" - ) - containers_node.expanded = True - panel = Panel.fit("Just a panel", border_style="red") - containers_node.add(Group("📄 Panels", panel)) - - containers_node.add(Group("📄 [b magenta]Table", table)) - - console = Console() - - console.print(root) diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/packaging/_musllinux.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/packaging/_musllinux.py deleted file mode 100644 index 8ac3059ba3c246b9a5a6fb8d14936bb07777191e..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/packaging/_musllinux.py +++ /dev/null @@ -1,136 +0,0 @@ -"""PEP 656 support. - -This module implements logic to detect if the currently running Python is -linked against musl, and what musl version is used. -""" - -import contextlib -import functools -import operator -import os -import re -import struct -import subprocess -import sys -from typing import IO, Iterator, NamedTuple, Optional, Tuple - - -def _read_unpacked(f: IO[bytes], fmt: str) -> Tuple[int, ...]: - return struct.unpack(fmt, f.read(struct.calcsize(fmt))) - - -def _parse_ld_musl_from_elf(f: IO[bytes]) -> Optional[str]: - """Detect musl libc location by parsing the Python executable. - - Based on: https://gist.github.com/lyssdod/f51579ae8d93c8657a5564aefc2ffbca - ELF header: https://refspecs.linuxfoundation.org/elf/gabi4+/ch4.eheader.html - """ - f.seek(0) - try: - ident = _read_unpacked(f, "16B") - except struct.error: - return None - if ident[:4] != tuple(b"\x7fELF"): # Invalid magic, not ELF. - return None - f.seek(struct.calcsize("HHI"), 1) # Skip file type, machine, and version. - - try: - # e_fmt: Format for program header. - # p_fmt: Format for section header. - # p_idx: Indexes to find p_type, p_offset, and p_filesz. - e_fmt, p_fmt, p_idx = { - 1: ("IIIIHHH", "IIIIIIII", (0, 1, 4)), # 32-bit. - 2: ("QQQIHHH", "IIQQQQQQ", (0, 2, 5)), # 64-bit. - }[ident[4]] - except KeyError: - return None - else: - p_get = operator.itemgetter(*p_idx) - - # Find the interpreter section and return its content. - try: - _, e_phoff, _, _, _, e_phentsize, e_phnum = _read_unpacked(f, e_fmt) - except struct.error: - return None - for i in range(e_phnum + 1): - f.seek(e_phoff + e_phentsize * i) - try: - p_type, p_offset, p_filesz = p_get(_read_unpacked(f, p_fmt)) - except struct.error: - return None - if p_type != 3: # Not PT_INTERP. - continue - f.seek(p_offset) - interpreter = os.fsdecode(f.read(p_filesz)).strip("\0") - if "musl" not in interpreter: - return None - return interpreter - return None - - -class _MuslVersion(NamedTuple): - major: int - minor: int - - -def _parse_musl_version(output: str) -> Optional[_MuslVersion]: - lines = [n for n in (n.strip() for n in output.splitlines()) if n] - if len(lines) < 2 or lines[0][:4] != "musl": - return None - m = re.match(r"Version (\d+)\.(\d+)", lines[1]) - if not m: - return None - return _MuslVersion(major=int(m.group(1)), minor=int(m.group(2))) - - -@functools.lru_cache() -def _get_musl_version(executable: str) -> Optional[_MuslVersion]: - """Detect currently-running musl runtime version. - - This is done by checking the specified executable's dynamic linking - information, and invoking the loader to parse its output for a version - string. If the loader is musl, the output would be something like:: - - musl libc (x86_64) - Version 1.2.2 - Dynamic Program Loader - """ - with contextlib.ExitStack() as stack: - try: - f = stack.enter_context(open(executable, "rb")) - except OSError: - return None - ld = _parse_ld_musl_from_elf(f) - if not ld: - return None - proc = subprocess.run([ld], stderr=subprocess.PIPE, universal_newlines=True) - return _parse_musl_version(proc.stderr) - - -def platform_tags(arch: str) -> Iterator[str]: - """Generate musllinux tags compatible to the current platform. - - :param arch: Should be the part of platform tag after the ``linux_`` - prefix, e.g. ``x86_64``. The ``linux_`` prefix is assumed as a - prerequisite for the current platform to be musllinux-compatible. - - :returns: An iterator of compatible musllinux tags. - """ - sys_musl = _get_musl_version(sys.executable) - if sys_musl is None: # Python not dynamically linked against musl. - return - for minor in range(sys_musl.minor, -1, -1): - yield f"musllinux_{sys_musl.major}_{minor}_{arch}" - - -if __name__ == "__main__": # pragma: no cover - import sysconfig - - plat = sysconfig.get_platform() - assert plat.startswith("linux-"), "not linux" - - print("plat:", plat) - print("musl:", _get_musl_version(sys.executable)) - print("tags:", end=" ") - for t in platform_tags(re.sub(r"[.-]", "_", plat.split("-", 1)[-1])): - print(t, end="\n ") diff --git a/spaces/AtomdffAI/wechatgpt4atom/channel/wechat/wechaty_channel.py b/spaces/AtomdffAI/wechatgpt4atom/channel/wechat/wechaty_channel.py deleted file mode 100644 index 8f27f6dc81422741ddfbbc2e700f8b8b62011cc3..0000000000000000000000000000000000000000 --- a/spaces/AtomdffAI/wechatgpt4atom/channel/wechat/wechaty_channel.py +++ /dev/null @@ -1,201 +0,0 @@ -# encoding:utf-8 - -""" -wechaty channel -Python Wechaty - https://github.com/wechaty/python-wechaty -""" -import io -import os -import json -import time -import asyncio -import requests -from typing import Optional, Union -from wechaty_puppet import MessageType, FileBox, ScanStatus # type: ignore -from wechaty import Wechaty, Contact -from wechaty.user import Message, Room, MiniProgram, UrlLink -from channel.channel import Channel -from common.log import logger -from config import conf - - -class WechatyChannel(Channel): - - def __init__(self): - pass - - def startup(self): - asyncio.run(self.main()) - - async def main(self): - config = conf() - # 使用PadLocal协议 比较稳定(免费web协议 os.environ['WECHATY_PUPPET_SERVICE_ENDPOINT'] = '127.0.0.1:8080') - token = config.get('wechaty_puppet_service_token') - os.environ['WECHATY_PUPPET_SERVICE_TOKEN'] = token - global bot - bot = Wechaty() - - bot.on('scan', self.on_scan) - bot.on('login', self.on_login) - bot.on('message', self.on_message) - await bot.start() - - async def on_login(self, contact: Contact): - logger.info('[WX] login user={}'.format(contact)) - - async def on_scan(self, status: ScanStatus, qr_code: Optional[str] = None, - data: Optional[str] = None): - contact = self.Contact.load(self.contact_id) - logger.info('[WX] scan user={}, scan status={}, scan qr_code={}'.format(contact, status.name, qr_code)) - # print(f'user <{contact}> scan status: {status.name} , 'f'qr_code: {qr_code}') - - async def on_message(self, msg: Message): - """ - listen for message event - """ - from_contact = msg.talker() # 获取消息的发送者 - to_contact = msg.to() # 接收人 - room = msg.room() # 获取消息来自的群聊. 如果消息不是来自群聊, 则返回None - from_user_id = from_contact.contact_id - to_user_id = to_contact.contact_id # 接收人id - # other_user_id = msg['User']['UserName'] # 对手方id - content = msg.text() - mention_content = await msg.mention_text() # 返回过滤掉@name后的消息 - match_prefix = self.check_prefix(content, conf().get('single_chat_prefix')) - conversation: Union[Room, Contact] = from_contact if room is None else room - - if room is None and msg.type() == MessageType.MESSAGE_TYPE_TEXT: - if not msg.is_self() and match_prefix is not None: - # 好友向自己发送消息 - if match_prefix != '': - str_list = content.split(match_prefix, 1) - if len(str_list) == 2: - content = str_list[1].strip() - - img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix')) - if img_match_prefix: - content = content.split(img_match_prefix, 1)[1].strip() - await self._do_send_img(content, from_user_id) - else: - await self._do_send(content, from_user_id) - elif msg.is_self() and match_prefix: - # 自己给好友发送消息 - str_list = content.split(match_prefix, 1) - if len(str_list) == 2: - content = str_list[1].strip() - img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix')) - if img_match_prefix: - content = content.split(img_match_prefix, 1)[1].strip() - await self._do_send_img(content, to_user_id) - else: - await self._do_send(content, to_user_id) - elif room and msg.type() == MessageType.MESSAGE_TYPE_TEXT: - # 群组&文本消息 - room_id = room.room_id - room_name = await room.topic() - from_user_id = from_contact.contact_id - from_user_name = from_contact.name - is_at = await msg.mention_self() - content = mention_content - config = conf() - match_prefix = (is_at and not config.get("group_at_off", False)) \ - or self.check_prefix(content, config.get('group_chat_prefix')) \ - or self.check_contain(content, config.get('group_chat_keyword')) - if ('ALL_GROUP' in config.get('group_name_white_list') or room_name in config.get( - 'group_name_white_list') or self.check_contain(room_name, config.get( - 'group_name_keyword_white_list'))) and match_prefix: - img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix')) - if img_match_prefix: - content = content.split(img_match_prefix, 1)[1].strip() - await self._do_send_group_img(content, room_id) - else: - await self._do_send_group(content, room_id, from_user_id, from_user_name) - - async def send(self, message: Union[str, Message, FileBox, Contact, UrlLink, MiniProgram], receiver): - logger.info('[WX] sendMsg={}, receiver={}'.format(message, receiver)) - if receiver: - contact = await bot.Contact.find(receiver) - await contact.say(message) - - async def send_group(self, message: Union[str, Message, FileBox, Contact, UrlLink, MiniProgram], receiver): - logger.info('[WX] sendMsg={}, receiver={}'.format(message, receiver)) - if receiver: - room = await bot.Room.find(receiver) - await room.say(message) - - async def _do_send(self, query, reply_user_id): - try: - if not query: - return - context = dict() - context['from_user_id'] = reply_user_id - reply_text = super().build_reply_content(query, context) - if reply_text: - await self.send(conf().get("single_chat_reply_prefix") + reply_text, reply_user_id) - except Exception as e: - logger.exception(e) - - async def _do_send_img(self, query, reply_user_id): - try: - if not query: - return - context = dict() - context['type'] = 'IMAGE_CREATE' - img_url = super().build_reply_content(query, context) - if not img_url: - return - # 图片下载 - # pic_res = requests.get(img_url, stream=True) - # image_storage = io.BytesIO() - # for block in pic_res.iter_content(1024): - # image_storage.write(block) - # image_storage.seek(0) - - # 图片发送 - logger.info('[WX] sendImage, receiver={}'.format(reply_user_id)) - t = int(time.time()) - file_box = FileBox.from_url(url=img_url, name=str(t) + '.png') - await self.send(file_box, reply_user_id) - except Exception as e: - logger.exception(e) - - async def _do_send_group(self, query, group_id, group_user_id, group_user_name): - if not query: - return - context = dict() - context['from_user_id'] = str(group_id) + '-' + str(group_user_id) - reply_text = super().build_reply_content(query, context) - if reply_text: - reply_text = '@' + group_user_name + ' ' + reply_text.strip() - await self.send_group(conf().get("group_chat_reply_prefix", "") + reply_text, group_id) - - async def _do_send_group_img(self, query, reply_room_id): - try: - if not query: - return - context = dict() - context['type'] = 'IMAGE_CREATE' - img_url = super().build_reply_content(query, context) - if not img_url: - return - # 图片发送 - logger.info('[WX] sendImage, receiver={}'.format(reply_room_id)) - t = int(time.time()) - file_box = FileBox.from_url(url=img_url, name=str(t) + '.png') - await self.send_group(file_box, reply_room_id) - except Exception as e: - logger.exception(e) - - def check_prefix(self, content, prefix_list): - for prefix in prefix_list: - if content.startswith(prefix): - return prefix - return None - - def check_contain(self, content, keyword_list): - if not keyword_list: - return None - for ky in keyword_list: - if content.find(ky) != -1: - return True - return None diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/lightning_train_net.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/lightning_train_net.py deleted file mode 100644 index f6734b566b6764ee54dd2af1b7310fedb34bb40d..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tools/lightning_train_net.py +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. -# Lightning Trainer should be considered beta at this point -# We have confirmed that training and validation run correctly and produce correct results -# Depending on how you launch the trainer, there are issues with processes terminating correctly -# This module is still dependent on D2 logging, but could be transferred to use Lightning logging - -import logging -import os -import time -import weakref -from collections import OrderedDict -from typing import Any, Dict, List - -import detectron2.utils.comm as comm -from detectron2.checkpoint import DetectionCheckpointer -from detectron2.config import get_cfg -from detectron2.data import build_detection_test_loader, build_detection_train_loader -from detectron2.engine import ( - DefaultTrainer, - SimpleTrainer, - default_argument_parser, - default_setup, - default_writers, - hooks, -) -from detectron2.evaluation import print_csv_format -from detectron2.evaluation.testing import flatten_results_dict -from detectron2.modeling import build_model -from detectron2.solver import build_lr_scheduler, build_optimizer -from detectron2.utils.events import EventStorage -from detectron2.utils.logger import setup_logger - -import pytorch_lightning as pl # type: ignore -from pytorch_lightning import LightningDataModule, LightningModule -from train_net import build_evaluator - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger("detectron2") - - -class TrainingModule(LightningModule): - def __init__(self, cfg): - super().__init__() - if not logger.isEnabledFor(logging.INFO): # setup_logger is not called for d2 - setup_logger() - self.cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size()) - self.storage: EventStorage = None - self.model = build_model(self.cfg) - - self.start_iter = 0 - self.max_iter = cfg.SOLVER.MAX_ITER - - def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: - checkpoint["iteration"] = self.storage.iter - - def on_load_checkpoint(self, checkpointed_state: Dict[str, Any]) -> None: - self.start_iter = checkpointed_state["iteration"] - self.storage.iter = self.start_iter - - def setup(self, stage: str): - if self.cfg.MODEL.WEIGHTS: - self.checkpointer = DetectionCheckpointer( - # Assume you want to save checkpoints together with logs/statistics - self.model, - self.cfg.OUTPUT_DIR, - ) - logger.info(f"Load model weights from checkpoint: {self.cfg.MODEL.WEIGHTS}.") - # Only load weights, use lightning checkpointing if you want to resume - self.checkpointer.load(self.cfg.MODEL.WEIGHTS) - - self.iteration_timer = hooks.IterationTimer() - self.iteration_timer.before_train() - self.data_start = time.perf_counter() - self.writers = None - - def training_step(self, batch, batch_idx): - data_time = time.perf_counter() - self.data_start - # Need to manually enter/exit since trainer may launch processes - # This ideally belongs in setup, but setup seems to run before processes are spawned - if self.storage is None: - self.storage = EventStorage(0) - self.storage.__enter__() - self.iteration_timer.trainer = weakref.proxy(self) - self.iteration_timer.before_step() - self.writers = ( - default_writers(self.cfg.OUTPUT_DIR, self.max_iter) - if comm.is_main_process() - else {} - ) - - loss_dict = self.model(batch) - SimpleTrainer.write_metrics(loss_dict, data_time) - - opt = self.optimizers() - self.storage.put_scalar( - "lr", opt.param_groups[self._best_param_group_id]["lr"], smoothing_hint=False - ) - self.iteration_timer.after_step() - self.storage.step() - # A little odd to put before step here, but it's the best way to get a proper timing - self.iteration_timer.before_step() - - if self.storage.iter % 20 == 0: - for writer in self.writers: - writer.write() - return sum(loss_dict.values()) - - def training_step_end(self, training_step_outpus): - self.data_start = time.perf_counter() - return training_step_outpus - - def training_epoch_end(self, training_step_outputs): - self.iteration_timer.after_train() - if comm.is_main_process(): - self.checkpointer.save("model_final") - for writer in self.writers: - writer.write() - writer.close() - self.storage.__exit__(None, None, None) - - def _process_dataset_evaluation_results(self) -> OrderedDict: - results = OrderedDict() - for idx, dataset_name in enumerate(self.cfg.DATASETS.TEST): - results[dataset_name] = self._evaluators[idx].evaluate() - if comm.is_main_process(): - print_csv_format(results[dataset_name]) - - if len(results) == 1: - results = list(results.values())[0] - return results - - def _reset_dataset_evaluators(self): - self._evaluators = [] - for dataset_name in self.cfg.DATASETS.TEST: - evaluator = build_evaluator(self.cfg, dataset_name) - evaluator.reset() - self._evaluators.append(evaluator) - - def on_validation_epoch_start(self, _outputs): - self._reset_dataset_evaluators() - - def validation_epoch_end(self, _outputs): - results = self._process_dataset_evaluation_results(_outputs) - - flattened_results = flatten_results_dict(results) - for k, v in flattened_results.items(): - try: - v = float(v) - except Exception as e: - raise ValueError( - "[EvalHook] eval_function should return a nested dict of float. " - "Got '{}: {}' instead.".format(k, v) - ) from e - self.storage.put_scalars(**flattened_results, smoothing_hint=False) - - def validation_step(self, batch, batch_idx: int, dataloader_idx: int = 0) -> None: - if not isinstance(batch, List): - batch = [batch] - outputs = self.model(batch) - self._evaluators[dataloader_idx].process(batch, outputs) - - def configure_optimizers(self): - optimizer = build_optimizer(self.cfg, self.model) - self._best_param_group_id = hooks.LRScheduler.get_best_param_group_id(optimizer) - scheduler = build_lr_scheduler(self.cfg, optimizer) - return [optimizer], [{"scheduler": scheduler, "interval": "step"}] - - -class DataModule(LightningDataModule): - def __init__(self, cfg): - super().__init__() - self.cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size()) - - def train_dataloader(self): - return build_detection_train_loader(self.cfg) - - def val_dataloader(self): - dataloaders = [] - for dataset_name in self.cfg.DATASETS.TEST: - dataloaders.append(build_detection_test_loader(self.cfg, dataset_name)) - return dataloaders - - -def main(args): - cfg = setup(args) - train(cfg, args) - - -def train(cfg, args): - trainer_params = { - # training loop is bounded by max steps, use a large max_epochs to make - # sure max_steps is met first - "max_epochs": 10 ** 8, - "max_steps": cfg.SOLVER.MAX_ITER, - "val_check_interval": cfg.TEST.EVAL_PERIOD if cfg.TEST.EVAL_PERIOD > 0 else 10 ** 8, - "num_nodes": args.num_machines, - "gpus": args.num_gpus, - "num_sanity_val_steps": 0, - } - if cfg.SOLVER.AMP.ENABLED: - trainer_params["precision"] = 16 - - last_checkpoint = os.path.join(cfg.OUTPUT_DIR, "last.ckpt") - if args.resume: - # resume training from checkpoint - trainer_params["resume_from_checkpoint"] = last_checkpoint - logger.info(f"Resuming training from checkpoint: {last_checkpoint}.") - - trainer = pl.Trainer(**trainer_params) - logger.info(f"start to train with {args.num_machines} nodes and {args.num_gpus} GPUs") - - module = TrainingModule(cfg) - data_module = DataModule(cfg) - if args.eval_only: - logger.info("Running inference") - trainer.validate(module, data_module) - else: - logger.info("Running training") - trainer.fit(module, data_module) - - -def setup(args): - """ - Create configs and perform basic setups. - """ - cfg = get_cfg() - cfg.merge_from_file(args.config_file) - cfg.merge_from_list(args.opts) - cfg.freeze() - default_setup(cfg, args) - return cfg - - -if __name__ == "__main__": - parser = default_argument_parser() - args = parser.parse_args() - logger.info("Command Line Args:", args) - main(args) diff --git a/spaces/BartPoint/VoiceChange_Beta/app_multi.py b/spaces/BartPoint/VoiceChange_Beta/app_multi.py deleted file mode 100644 index 594d88008180684ad92bf432ebd1d96fd09bbbb2..0000000000000000000000000000000000000000 --- a/spaces/BartPoint/VoiceChange_Beta/app_multi.py +++ /dev/null @@ -1,496 +0,0 @@ -from typing import Union - -from argparse import ArgumentParser - -import asyncio -import json -import hashlib -from os import path, getenv - -import gradio as gr - -import torch - -import numpy as np -import librosa - -import edge_tts - -import config -import util -from fairseq import checkpoint_utils -from infer_pack.models import ( - SynthesizerTrnMs256NSFsid, - SynthesizerTrnMs256NSFsid_nono, - SynthesizerTrnMs768NSFsid, - SynthesizerTrnMs768NSFsid_nono, -) -from vc_infer_pipeline import VC -from config import Config -config = Config() -force_support = None -if config.unsupported is False: - if config.device == "mps" or config.device == "cpu": - force_support = False -else: - force_support = True - -# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21 # noqa -in_hf_space = getenv('SYSTEM') == 'spaces' - -# Argument parsing -arg_parser = ArgumentParser() -arg_parser.add_argument( - '--hubert', - default=getenv('RVC_HUBERT', 'hubert_base.pt'), - help='path to hubert base model (default: hubert_base.pt)' -) -arg_parser.add_argument( - '--config', - default=getenv('RVC_MULTI_CFG', 'multi_config.json'), - help='path to config file (default: multi_config.json)' -) -arg_parser.add_argument( - '--api', - action='store_true', - help='enable api endpoint' -) -arg_parser.add_argument( - '--cache-examples', - action='store_true', - help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified' # noqa -) -args = arg_parser.parse_args() - -app_css = ''' -#model_info img { - max-width: 100px; - max-height: 100px; - float: right; -} - -#model_info p { - margin: unset; -} -''' - -app = gr.Blocks( - theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"), - css=app_css, - analytics_enabled=False -) - -# Load hubert model -models, _, _ = checkpoint_utils.load_model_ensemble_and_task( - ["hubert_base.pt"], - suffix="", -) -hubert_model = models[0] -hubert_model = hubert_model.to(config.device) -if config.is_half: - hubert_model = hubert_model.half() -else: - hubert_model = hubert_model.float() -hubert_model.eval() - -# Load models -multi_cfg = json.load(open(args.config, 'r')) -loaded_models = [] - -for model_name in multi_cfg.get('models'): - print(f'Loading model: {model_name}') - - # Load model info - model_info = json.load( - open(path.join('model', model_name, 'config.json'), 'r') - ) - - # Load RVC checkpoint - cpt = torch.load( - path.join('model', model_name, model_info['model']), - map_location='cpu' - ) - tgt_sr = cpt['config'][-1] - cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk - if_f0 = cpt.get("f0", 1) - version = cpt.get("version", "v1") - if version == "v1": - if if_f0 == 1: - net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) - else: - net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) - model_version = "V1" - elif version == "v2": - if if_f0 == 1: - net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) - else: - net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) - model_version = "V2" - del net_g.enc_q - - print(net_g.load_state_dict(cpt["weight"], strict=False)) - net_g.eval().to(config.device) - if config.is_half: - net_g = net_g.half() - else: - net_g = net_g.float() - vc = VC(tgt_sr, config) - - loaded_models.append(dict( - name=model_name, - metadata=model_info, - vc=vc, - net_g=net_g, - if_f0=if_f0, - target_sr=tgt_sr, - test=model_version - )) - -print(f'Models loaded: {len(loaded_models)}') - -# Edge TTS speakers -tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) # noqa - - -# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118 # noqa -def vc_func( - input_audio, model_index, pitch_adjust, f0_method, feat_ratio, - filter_radius, rms_mix_rate, resample_option -): - if input_audio is None: - return (None, 'Please provide input audio.') - - if model_index is None: - return (None, 'Please select a model.') - - model = loaded_models[model_index] - - # Reference: so-vits - (audio_samp, audio_npy) = input_audio - - # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49 - # Can be change well, we will see - if (audio_npy.shape[0] / audio_samp) > 320 and in_hf_space: - return (None, 'Input audio is longer than 60 secs.') - - # Bloody hell: https://stackoverflow.com/questions/26921836/ - if audio_npy.dtype != np.float32: # :thonk: - audio_npy = ( - audio_npy / np.iinfo(audio_npy.dtype).max - ).astype(np.float32) - - if len(audio_npy.shape) > 1: - audio_npy = librosa.to_mono(audio_npy.transpose(1, 0)) - - if audio_samp != 16000: - audio_npy = librosa.resample( - audio_npy, - orig_sr=audio_samp, - target_sr=16000 - ) - - pitch_int = int(pitch_adjust) - - resample = ( - 0 if resample_option == 'Disable resampling' - else int(resample_option) - ) - - times = [0, 0, 0] - - checksum = hashlib.sha512() - checksum.update(audio_npy.tobytes()) - - print(model['test']) - - output_audio = model['vc'].pipeline( - hubert_model, - model['net_g'], - model['metadata'].get('speaker_id', 0), - audio_npy, - checksum.hexdigest(), - times, - pitch_int, - f0_method, - path.join('model', model['name'], model['metadata']['feat_index']), - feat_ratio, - model['if_f0'], - filter_radius, - model['target_sr'], - resample, - rms_mix_rate, - model['test'], - 0.5 - ) - - out_sr = ( - resample if resample >= 16000 and model['target_sr'] != resample - else model['target_sr'] - ) - - print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s') - return ((out_sr, output_audio), 'Success') - - -async def edge_tts_vc_func( - input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio, - filter_radius, rms_mix_rate, resample_option -): - if input_text is None: - return (None, 'Please provide TTS text.') - - if tts_speaker is None: - return (None, 'Please select TTS speaker.') - - if model_index is None: - return (None, 'Please select a model.') - - speaker = tts_speakers_list[tts_speaker]['ShortName'] - (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text) - return vc_func( - (tts_sr, tts_np), - model_index, - pitch_adjust, - f0_method, - feat_ratio, - filter_radius, - rms_mix_rate, - resample_option - ) - - -def update_model_info(model_index): - if model_index is None: - return str( - '### Model info\n' - 'Please select a model from dropdown above.' - ) - - model = loaded_models[model_index] - model_icon = model['metadata'].get('icon', '') - - return str( - '### Model info\n' - '' - '**{name}**\n\n' - 'Author: {author}\n\n' - 'Source: {source}\n\n' - '{note}' - ).format( - name=model['metadata'].get('name'), - author=model['metadata'].get('author', 'Anonymous'), - source=model['metadata'].get('source', 'Unknown'), - note=model['metadata'].get('note', ''), - icon=( - model_icon - if model_icon.startswith(('http://', 'https://')) - else '/file/model/%s/%s' % (model['name'], model_icon) - ) - ) - - -def _example_vc( - input_audio, model_index, pitch_adjust, f0_method, feat_ratio, - filter_radius, rms_mix_rate, resample_option -): - (audio, message) = vc_func( - input_audio, model_index, pitch_adjust, f0_method, feat_ratio, - filter_radius, rms_mix_rate, resample_option - ) - return ( - audio, - message, - update_model_info(model_index) - ) - - -async def _example_edge_tts( - input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio, - filter_radius, rms_mix_rate, resample_option -): - (audio, message) = await edge_tts_vc_func( - input_text, model_index, tts_speaker, pitch_adjust, f0_method, - feat_ratio, filter_radius, rms_mix_rate, resample_option - ) - return ( - audio, - message, - update_model_info(model_index) - ) - - -with app: - gr.Markdown( - '## A simplistic Web interface\n' - 'RVC interface, project based on [RVC-WebUI](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI)' # thx noqa - 'A lot of inspiration from what\'s already out there, including [zomehwh/rvc-models](https://huggingface.co/spaces/zomehwh/rvc-models) & [DJQmUKV/rvc-inference](https://huggingface.co/spaces/DJQmUKV/rvc-inference).\n ' # thx noqa - ) - - with gr.Row(): - with gr.Column(): - with gr.Tab('Audio conversion'): - input_audio = gr.Audio(label='Input audio') - - vc_convert_btn = gr.Button('Convert', variant='primary') - - with gr.Tab('TTS conversion'): - tts_input = gr.TextArea( - label='TTS input text' - ) - tts_speaker = gr.Dropdown( - [ - '%s (%s)' % ( - s['FriendlyName'], - s['Gender'] - ) - for s in tts_speakers_list - ], - label='TTS speaker', - type='index' - ) - - tts_convert_btn = gr.Button('Convert', variant='primary') - - pitch_adjust = gr.Slider( - label='Pitch', - minimum=-24, - maximum=24, - step=1, - value=0 - ) - f0_method = gr.Radio( - label='f0 methods', - choices=['pm', 'harvest', 'crepe'], - value='pm', - interactive=True - ) - - with gr.Accordion('Advanced options', open=False): - feat_ratio = gr.Slider( - label='Feature ratio', - minimum=0, - maximum=1, - step=0.1, - value=0.6 - ) - filter_radius = gr.Slider( - label='Filter radius', - minimum=0, - maximum=7, - step=1, - value=3 - ) - rms_mix_rate = gr.Slider( - label='Volume envelope mix rate', - minimum=0, - maximum=1, - step=0.1, - value=1 - ) - resample_rate = gr.Dropdown( - [ - 'Disable resampling', - '16000', - '22050', - '44100', - '48000' - ], - label='Resample rate', - value='Disable resampling' - ) - - with gr.Column(): - # Model select - model_index = gr.Dropdown( - [ - '%s - %s' % ( - m['metadata'].get('source', 'Unknown'), - m['metadata'].get('name') - ) - for m in loaded_models - ], - label='Model', - type='index' - ) - - # Model info - with gr.Box(): - model_info = gr.Markdown( - '### Model info\n' - 'Please select a model from dropdown above.', - elem_id='model_info' - ) - - output_audio = gr.Audio(label='Output audio') - output_msg = gr.Textbox(label='Output message') - - multi_examples = multi_cfg.get('examples') - if ( - multi_examples and - multi_examples.get('vc') and multi_examples.get('tts_vc') - ): - with gr.Accordion('Sweet sweet examples', open=False): - with gr.Row(): - # VC Example - if multi_examples.get('vc'): - gr.Examples( - label='Audio conversion examples', - examples=multi_examples.get('vc'), - inputs=[ - input_audio, model_index, pitch_adjust, f0_method, - feat_ratio - ], - outputs=[output_audio, output_msg, model_info], - fn=_example_vc, - cache_examples=args.cache_examples, - run_on_click=args.cache_examples - ) - - # Edge TTS Example - if multi_examples.get('tts_vc'): - gr.Examples( - label='TTS conversion examples', - examples=multi_examples.get('tts_vc'), - inputs=[ - tts_input, model_index, tts_speaker, pitch_adjust, - f0_method, feat_ratio - ], - outputs=[output_audio, output_msg, model_info], - fn=_example_edge_tts, - cache_examples=args.cache_examples, - run_on_click=args.cache_examples - ) - - vc_convert_btn.click( - vc_func, - [ - input_audio, model_index, pitch_adjust, f0_method, feat_ratio, - filter_radius, rms_mix_rate, resample_rate - ], - [output_audio, output_msg], - api_name='audio_conversion' - ) - - tts_convert_btn.click( - edge_tts_vc_func, - [ - tts_input, model_index, tts_speaker, pitch_adjust, f0_method, - feat_ratio, filter_radius, rms_mix_rate, resample_rate - ], - [output_audio, output_msg], - api_name='tts_conversion' - ) - - model_index.change( - update_model_info, - inputs=[model_index], - outputs=[model_info], - show_progress=False, - queue=False - ) - -app.queue( - concurrency_count=1, - max_size=20, - api_open=args.api -).launch() \ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/img.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/img.py deleted file mode 100644 index 0f36a32ba3399efc216b9974254cd1f7eed07a9f..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/img.py +++ /dev/null @@ -1,645 +0,0 @@ -""" - pygments.formatters.img - ~~~~~~~~~~~~~~~~~~~~~~~ - - Formatter for Pixmap output. - - :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS. - :license: BSD, see LICENSE for details. -""" - -import os -import sys - -from pip._vendor.pygments.formatter import Formatter -from pip._vendor.pygments.util import get_bool_opt, get_int_opt, get_list_opt, \ - get_choice_opt - -import subprocess - -# Import this carefully -try: - from PIL import Image, ImageDraw, ImageFont - pil_available = True -except ImportError: - pil_available = False - -try: - import _winreg -except ImportError: - try: - import winreg as _winreg - except ImportError: - _winreg = None - -__all__ = ['ImageFormatter', 'GifImageFormatter', 'JpgImageFormatter', - 'BmpImageFormatter'] - - -# For some unknown reason every font calls it something different -STYLES = { - 'NORMAL': ['', 'Roman', 'Book', 'Normal', 'Regular', 'Medium'], - 'ITALIC': ['Oblique', 'Italic'], - 'BOLD': ['Bold'], - 'BOLDITALIC': ['Bold Oblique', 'Bold Italic'], -} - -# A sane default for modern systems -DEFAULT_FONT_NAME_NIX = 'DejaVu Sans Mono' -DEFAULT_FONT_NAME_WIN = 'Courier New' -DEFAULT_FONT_NAME_MAC = 'Menlo' - - -class PilNotAvailable(ImportError): - """When Python imaging library is not available""" - - -class FontNotFound(Exception): - """When there are no usable fonts specified""" - - -class FontManager: - """ - Manages a set of fonts: normal, italic, bold, etc... - """ - - def __init__(self, font_name, font_size=14): - self.font_name = font_name - self.font_size = font_size - self.fonts = {} - self.encoding = None - if sys.platform.startswith('win'): - if not font_name: - self.font_name = DEFAULT_FONT_NAME_WIN - self._create_win() - elif sys.platform.startswith('darwin'): - if not font_name: - self.font_name = DEFAULT_FONT_NAME_MAC - self._create_mac() - else: - if not font_name: - self.font_name = DEFAULT_FONT_NAME_NIX - self._create_nix() - - def _get_nix_font_path(self, name, style): - proc = subprocess.Popen(['fc-list', "%s:style=%s" % (name, style), 'file'], - stdout=subprocess.PIPE, stderr=None) - stdout, _ = proc.communicate() - if proc.returncode == 0: - lines = stdout.splitlines() - for line in lines: - if line.startswith(b'Fontconfig warning:'): - continue - path = line.decode().strip().strip(':') - if path: - return path - return None - - def _create_nix(self): - for name in STYLES['NORMAL']: - path = self._get_nix_font_path(self.font_name, name) - if path is not None: - self.fonts['NORMAL'] = ImageFont.truetype(path, self.font_size) - break - else: - raise FontNotFound('No usable fonts named: "%s"' % - self.font_name) - for style in ('ITALIC', 'BOLD', 'BOLDITALIC'): - for stylename in STYLES[style]: - path = self._get_nix_font_path(self.font_name, stylename) - if path is not None: - self.fonts[style] = ImageFont.truetype(path, self.font_size) - break - else: - if style == 'BOLDITALIC': - self.fonts[style] = self.fonts['BOLD'] - else: - self.fonts[style] = self.fonts['NORMAL'] - - def _get_mac_font_path(self, font_map, name, style): - return font_map.get((name + ' ' + style).strip().lower()) - - def _create_mac(self): - font_map = {} - for font_dir in (os.path.join(os.getenv("HOME"), 'Library/Fonts/'), - '/Library/Fonts/', '/System/Library/Fonts/'): - font_map.update( - (os.path.splitext(f)[0].lower(), os.path.join(font_dir, f)) - for f in os.listdir(font_dir) - if f.lower().endswith(('ttf', 'ttc'))) - - for name in STYLES['NORMAL']: - path = self._get_mac_font_path(font_map, self.font_name, name) - if path is not None: - self.fonts['NORMAL'] = ImageFont.truetype(path, self.font_size) - break - else: - raise FontNotFound('No usable fonts named: "%s"' % - self.font_name) - for style in ('ITALIC', 'BOLD', 'BOLDITALIC'): - for stylename in STYLES[style]: - path = self._get_mac_font_path(font_map, self.font_name, stylename) - if path is not None: - self.fonts[style] = ImageFont.truetype(path, self.font_size) - break - else: - if style == 'BOLDITALIC': - self.fonts[style] = self.fonts['BOLD'] - else: - self.fonts[style] = self.fonts['NORMAL'] - - def _lookup_win(self, key, basename, styles, fail=False): - for suffix in ('', ' (TrueType)'): - for style in styles: - try: - valname = '%s%s%s' % (basename, style and ' '+style, suffix) - val, _ = _winreg.QueryValueEx(key, valname) - return val - except OSError: - continue - else: - if fail: - raise FontNotFound('Font %s (%s) not found in registry' % - (basename, styles[0])) - return None - - def _create_win(self): - lookuperror = None - keynames = [ (_winreg.HKEY_CURRENT_USER, r'Software\Microsoft\Windows NT\CurrentVersion\Fonts'), - (_winreg.HKEY_CURRENT_USER, r'Software\Microsoft\Windows\CurrentVersion\Fonts'), - (_winreg.HKEY_LOCAL_MACHINE, r'Software\Microsoft\Windows NT\CurrentVersion\Fonts'), - (_winreg.HKEY_LOCAL_MACHINE, r'Software\Microsoft\Windows\CurrentVersion\Fonts') ] - for keyname in keynames: - try: - key = _winreg.OpenKey(*keyname) - try: - path = self._lookup_win(key, self.font_name, STYLES['NORMAL'], True) - self.fonts['NORMAL'] = ImageFont.truetype(path, self.font_size) - for style in ('ITALIC', 'BOLD', 'BOLDITALIC'): - path = self._lookup_win(key, self.font_name, STYLES[style]) - if path: - self.fonts[style] = ImageFont.truetype(path, self.font_size) - else: - if style == 'BOLDITALIC': - self.fonts[style] = self.fonts['BOLD'] - else: - self.fonts[style] = self.fonts['NORMAL'] - return - except FontNotFound as err: - lookuperror = err - finally: - _winreg.CloseKey(key) - except OSError: - pass - else: - # If we get here, we checked all registry keys and had no luck - # We can be in one of two situations now: - # * All key lookups failed. In this case lookuperror is None and we - # will raise a generic error - # * At least one lookup failed with a FontNotFound error. In this - # case, we will raise that as a more specific error - if lookuperror: - raise lookuperror - raise FontNotFound('Can\'t open Windows font registry key') - - def get_char_size(self): - """ - Get the character size. - """ - return self.get_text_size('M') - - def get_text_size(self, text): - """ - Get the text size (width, height). - """ - font = self.fonts['NORMAL'] - if hasattr(font, 'getbbox'): # Pillow >= 9.2.0 - return font.getbbox(text)[2:4] - else: - return font.getsize(text) - - def get_font(self, bold, oblique): - """ - Get the font based on bold and italic flags. - """ - if bold and oblique: - return self.fonts['BOLDITALIC'] - elif bold: - return self.fonts['BOLD'] - elif oblique: - return self.fonts['ITALIC'] - else: - return self.fonts['NORMAL'] - - -class ImageFormatter(Formatter): - """ - Create a PNG image from source code. This uses the Python Imaging Library to - generate a pixmap from the source code. - - .. versionadded:: 0.10 - - Additional options accepted: - - `image_format` - An image format to output to that is recognised by PIL, these include: - - * "PNG" (default) - * "JPEG" - * "BMP" - * "GIF" - - `line_pad` - The extra spacing (in pixels) between each line of text. - - Default: 2 - - `font_name` - The font name to be used as the base font from which others, such as - bold and italic fonts will be generated. This really should be a - monospace font to look sane. - - Default: "Courier New" on Windows, "Menlo" on Mac OS, and - "DejaVu Sans Mono" on \\*nix - - `font_size` - The font size in points to be used. - - Default: 14 - - `image_pad` - The padding, in pixels to be used at each edge of the resulting image. - - Default: 10 - - `line_numbers` - Whether line numbers should be shown: True/False - - Default: True - - `line_number_start` - The line number of the first line. - - Default: 1 - - `line_number_step` - The step used when printing line numbers. - - Default: 1 - - `line_number_bg` - The background colour (in "#123456" format) of the line number bar, or - None to use the style background color. - - Default: "#eed" - - `line_number_fg` - The text color of the line numbers (in "#123456"-like format). - - Default: "#886" - - `line_number_chars` - The number of columns of line numbers allowable in the line number - margin. - - Default: 2 - - `line_number_bold` - Whether line numbers will be bold: True/False - - Default: False - - `line_number_italic` - Whether line numbers will be italicized: True/False - - Default: False - - `line_number_separator` - Whether a line will be drawn between the line number area and the - source code area: True/False - - Default: True - - `line_number_pad` - The horizontal padding (in pixels) between the line number margin, and - the source code area. - - Default: 6 - - `hl_lines` - Specify a list of lines to be highlighted. - - .. versionadded:: 1.2 - - Default: empty list - - `hl_color` - Specify the color for highlighting lines. - - .. versionadded:: 1.2 - - Default: highlight color of the selected style - """ - - # Required by the pygments mapper - name = 'img' - aliases = ['img', 'IMG', 'png'] - filenames = ['*.png'] - - unicodeoutput = False - - default_image_format = 'png' - - def __init__(self, **options): - """ - See the class docstring for explanation of options. - """ - if not pil_available: - raise PilNotAvailable( - 'Python Imaging Library is required for this formatter') - Formatter.__init__(self, **options) - self.encoding = 'latin1' # let pygments.format() do the right thing - # Read the style - self.styles = dict(self.style) - if self.style.background_color is None: - self.background_color = '#fff' - else: - self.background_color = self.style.background_color - # Image options - self.image_format = get_choice_opt( - options, 'image_format', ['png', 'jpeg', 'gif', 'bmp'], - self.default_image_format, normcase=True) - self.image_pad = get_int_opt(options, 'image_pad', 10) - self.line_pad = get_int_opt(options, 'line_pad', 2) - # The fonts - fontsize = get_int_opt(options, 'font_size', 14) - self.fonts = FontManager(options.get('font_name', ''), fontsize) - self.fontw, self.fonth = self.fonts.get_char_size() - # Line number options - self.line_number_fg = options.get('line_number_fg', '#886') - self.line_number_bg = options.get('line_number_bg', '#eed') - self.line_number_chars = get_int_opt(options, - 'line_number_chars', 2) - self.line_number_bold = get_bool_opt(options, - 'line_number_bold', False) - self.line_number_italic = get_bool_opt(options, - 'line_number_italic', False) - self.line_number_pad = get_int_opt(options, 'line_number_pad', 6) - self.line_numbers = get_bool_opt(options, 'line_numbers', True) - self.line_number_separator = get_bool_opt(options, - 'line_number_separator', True) - self.line_number_step = get_int_opt(options, 'line_number_step', 1) - self.line_number_start = get_int_opt(options, 'line_number_start', 1) - if self.line_numbers: - self.line_number_width = (self.fontw * self.line_number_chars + - self.line_number_pad * 2) - else: - self.line_number_width = 0 - self.hl_lines = [] - hl_lines_str = get_list_opt(options, 'hl_lines', []) - for line in hl_lines_str: - try: - self.hl_lines.append(int(line)) - except ValueError: - pass - self.hl_color = options.get('hl_color', - self.style.highlight_color) or '#f90' - self.drawables = [] - - def get_style_defs(self, arg=''): - raise NotImplementedError('The -S option is meaningless for the image ' - 'formatter. Use -O style=
-
-
-
-
-
-
-
-|
-
-
-- - - -- - - - - - - - |
-
|
-
-
-
-
-
-
-
-
-{{urec.iou_label}}
-{{urec.layer}} {{urec.unit}}
-
- |
-
|
-
-
-
-
-Seeds to generate --To transfer activations from one pixel to another (1) click on a source pixel -on the left image and (2) click on a target pixel on a right image, -then (3) choose a set of units to insert in the palette. -
-
-
-#{{ ex.id }}
- |
-
-
-
-
-
diff --git a/spaces/Hallucinate/demo/midas/backbones/vit.py b/spaces/Hallucinate/demo/midas/backbones/vit.py
deleted file mode 100644
index 413f9693bd4548342280e329c9128c1a52cea920..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/midas/backbones/vit.py
+++ /dev/null
@@ -1,221 +0,0 @@
-import torch
-import torch.nn as nn
-import timm
-import types
-import math
-import torch.nn.functional as F
-
-from .utils import (activations, forward_adapted_unflatten, get_activation, get_readout_oper,
- make_backbone_default, Transpose)
-
-
-def forward_vit(pretrained, x):
- return forward_adapted_unflatten(pretrained, x, "forward_flex")
-
-
-def _resize_pos_embed(self, posemb, gs_h, gs_w):
- posemb_tok, posemb_grid = (
- posemb[:, : self.start_index],
- posemb[0, self.start_index:],
- )
-
- gs_old = int(math.sqrt(len(posemb_grid)))
-
- posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
- posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
- posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
-
- posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
-
- return posemb
-
-
-def forward_flex(self, x):
- b, c, h, w = x.shape
-
- pos_embed = self._resize_pos_embed(
- self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
- )
-
- B = x.shape[0]
-
- if hasattr(self.patch_embed, "backbone"):
- x = self.patch_embed.backbone(x)
- if isinstance(x, (list, tuple)):
- x = x[-1] # last feature if backbone outputs list/tuple of features
-
- x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
-
- if getattr(self, "dist_token", None) is not None:
- cls_tokens = self.cls_token.expand(
- B, -1, -1
- ) # stole cls_tokens impl from Phil Wang, thanks
- dist_token = self.dist_token.expand(B, -1, -1)
- x = torch.cat((cls_tokens, dist_token, x), dim=1)
- else:
- if self.no_embed_class:
- x = x + pos_embed
- cls_tokens = self.cls_token.expand(
- B, -1, -1
- ) # stole cls_tokens impl from Phil Wang, thanks
- x = torch.cat((cls_tokens, x), dim=1)
-
- if not self.no_embed_class:
- x = x + pos_embed
- x = self.pos_drop(x)
-
- for blk in self.blocks:
- x = blk(x)
-
- x = self.norm(x)
-
- return x
-
-
-def _make_vit_b16_backbone(
- model,
- features=[96, 192, 384, 768],
- size=[384, 384],
- hooks=[2, 5, 8, 11],
- vit_features=768,
- use_readout="ignore",
- start_index=1,
- start_index_readout=1,
-):
- pretrained = make_backbone_default(model, features, size, hooks, vit_features, use_readout, start_index,
- start_index_readout)
-
- # We inject this function into the VisionTransformer instances so that
- # we can use it with interpolated position embeddings without modifying the library source.
- pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
- pretrained.model._resize_pos_embed = types.MethodType(
- _resize_pos_embed, pretrained.model
- )
-
- return pretrained
-
-
-def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
- model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
-
- hooks = [5, 11, 17, 23] if hooks == None else hooks
- return _make_vit_b16_backbone(
- model,
- features=[256, 512, 1024, 1024],
- hooks=hooks,
- vit_features=1024,
- use_readout=use_readout,
- )
-
-
-def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
- model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
-
- hooks = [2, 5, 8, 11] if hooks == None else hooks
- return _make_vit_b16_backbone(
- model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
- )
-
-
-def _make_vit_b_rn50_backbone(
- model,
- features=[256, 512, 768, 768],
- size=[384, 384],
- hooks=[0, 1, 8, 11],
- vit_features=768,
- patch_size=[16, 16],
- number_stages=2,
- use_vit_only=False,
- use_readout="ignore",
- start_index=1,
-):
- pretrained = nn.Module()
-
- pretrained.model = model
-
- used_number_stages = 0 if use_vit_only else number_stages
- for s in range(used_number_stages):
- pretrained.model.patch_embed.backbone.stages[s].register_forward_hook(
- get_activation(str(s + 1))
- )
- for s in range(used_number_stages, 4):
- pretrained.model.blocks[hooks[s]].register_forward_hook(get_activation(str(s + 1)))
-
- pretrained.activations = activations
-
- readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
-
- for s in range(used_number_stages):
- value = nn.Sequential(nn.Identity(), nn.Identity(), nn.Identity())
- exec(f"pretrained.act_postprocess{s + 1}=value")
- for s in range(used_number_stages, 4):
- if s < number_stages:
- final_layer = nn.ConvTranspose2d(
- in_channels=features[s],
- out_channels=features[s],
- kernel_size=4 // (2 ** s),
- stride=4 // (2 ** s),
- padding=0,
- bias=True,
- dilation=1,
- groups=1,
- )
- elif s > number_stages:
- final_layer = nn.Conv2d(
- in_channels=features[3],
- out_channels=features[3],
- kernel_size=3,
- stride=2,
- padding=1,
- )
- else:
- final_layer = None
-
- layers = [
- readout_oper[s],
- Transpose(1, 2),
- nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
- nn.Conv2d(
- in_channels=vit_features,
- out_channels=features[s],
- kernel_size=1,
- stride=1,
- padding=0,
- ),
- ]
- if final_layer is not None:
- layers.append(final_layer)
-
- value = nn.Sequential(*layers)
- exec(f"pretrained.act_postprocess{s + 1}=value")
-
- pretrained.model.start_index = start_index
- pretrained.model.patch_size = patch_size
-
- # We inject this function into the VisionTransformer instances so that
- # we can use it with interpolated position embeddings without modifying the library source.
- pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
-
- # We inject this function into the VisionTransformer instances so that
- # we can use it with interpolated position embeddings without modifying the library source.
- pretrained.model._resize_pos_embed = types.MethodType(
- _resize_pos_embed, pretrained.model
- )
-
- return pretrained
-
-
-def _make_pretrained_vitb_rn50_384(
- pretrained, use_readout="ignore", hooks=None, use_vit_only=False
-):
- model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
-
- hooks = [0, 1, 8, 11] if hooks == None else hooks
- return _make_vit_b_rn50_backbone(
- model,
- features=[256, 512, 768, 768],
- size=[384, 384],
- hooks=hooks,
- use_vit_only=use_vit_only,
- use_readout=use_readout,
- )
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/megatron_t5/modeling_megatron_t5.py b/spaces/HaloMaster/chinesesummary/fengshen/models/megatron_t5/modeling_megatron_t5.py
deleted file mode 100644
index 82ad4fb8126b9a4c0b0bb7debed95b999b5cf097..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/models/megatron_t5/modeling_megatron_t5.py
+++ /dev/null
@@ -1,2086 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. All rights reserved.
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-# http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch T5 model. """
-
-
-import copy
-import math
-import os
-import warnings
-
-import torch
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from torch.utils.checkpoint import checkpoint
-
-from transformers.activations import ACT2FN
-from transformers.file_utils import (
- DUMMY_INPUTS,
- DUMMY_MASK,
- add_start_docstrings,
- add_start_docstrings_to_model_forward,
- is_torch_fx_proxy,
- replace_return_docstrings,
-)
-from transformers.modeling_outputs import (
- BaseModelOutput,
- BaseModelOutputWithPastAndCrossAttentions,
- Seq2SeqLMOutput,
- Seq2SeqModelOutput,
-)
-from transformers.modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
-from transformers.utils import logging
-from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
-from .configuration_megatron_t5 import T5Config
-import numpy as np
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "T5Config"
-_TOKENIZER_FOR_DOC = "T5Tokenizer"
-_CHECKPOINT_FOR_DOC = "T5-small"
-
-####################################################
-# This dict contains ids and associated url
-# for the pretrained weights provided with the models
-####################################################
-T5_PRETRAINED_MODEL_ARCHIVE_LIST = [
- "T5-small",
- "T5-base",
- "T5-large",
- "T5-3b",
- "T5-11b",
- # See all T5 models at https://huggingface.co/models?filter=T5
-]
-
-
-####################################################
-# This is a conversion method from TF 1.0 to PyTorch
-# More details: https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28
-####################################################
-
-def load_tf_weights_in_T5(model, config, tf_checkpoint_path):
- """Load tf checkpoints in a pytorch model."""
- try:
- import re
-
- import numpy as np
- import tensorflow as tf
- except ImportError:
- logger.error(
- "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
- "https://www.tensorflow.org/install/ for installation instructions."
- )
- raise
- tf_path = os.path.abspath(tf_checkpoint_path)
- logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
- # Load weights from TF model
- init_vars = tf.train.list_variables(tf_path)
- names = []
- tf_weights = {}
- for name, shape in init_vars:
- logger.info(f"Loading TF weight {name} with shape {shape}")
- array = tf.train.load_variable(tf_path, name)
- names.append(name)
- tf_weights[name] = array
-
- for txt_name in names:
- name = txt_name.split("/")
- # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
- # which are not required for using pretrained model
- if any(
- n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer",
- "AdamWeightDecayOptimizer_1", "global_step"]
- for n in name
- ):
- logger.info(f"Skipping {'/'.join(name)}")
- tf_weights.pop(txt_name, None)
- continue
- if "_slot_" in name[-1]:
- logger.info(f"Skipping {'/'.join(name)}")
- tf_weights.pop(txt_name, None)
- continue
- pointer = model
- array = tf_weights[txt_name]
-
- for m_name in name:
- if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
- scope_names = re.split(r"_(\d+)", m_name)
- else:
- scope_names = [m_name]
- if scope_names[0] in ["kernel", "scale", "embedding"]:
- pointer = getattr(pointer, "weight")
- elif scope_names[0] == "self_attention":
- pointer = getattr(pointer, "layer")
- pointer = pointer[0]
- elif scope_names[0] == "enc_dec_attention":
- pointer = getattr(pointer, "layer")
- pointer = pointer[1]
- elif scope_names[0] == "dense_relu_dense":
- pointer = getattr(pointer, "layer")
- pointer = pointer[2]
- elif scope_names[0] == "rms_norm":
- if hasattr(pointer, "layer_norm"):
- pointer = getattr(pointer, "layer_norm")
- elif hasattr(pointer, "final_layer_norm"):
- pointer = getattr(pointer, "final_layer_norm")
- elif scope_names[0] == "scale":
- pointer = getattr(pointer, "weight")
- elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
- pointer = getattr(pointer, "bias")
- elif scope_names[0] == "squad":
- pointer = getattr(pointer, "classifier")
- elif scope_names[0] == "decoder" and name[1] == "logits":
- continue
- elif scope_names[0] == "logits":
- pointer = getattr(pointer, "lm_head")
- elif scope_names[0] == "wi" and len(scope_names) > 1 and scope_names[1].isdigit():
- pointer = getattr(pointer, f"wi_{scope_names[1]}")
- continue
- else:
- try:
- pointer = getattr(pointer, scope_names[0])
- except AttributeError:
- logger.info(f"Skipping {'/'.join(name)}")
- continue
- if len(scope_names) >= 2:
- num = int(scope_names[1])
- pointer = pointer[num]
- if scope_names[0] not in ["kernel", "scale", "embedding"]:
- pointer = getattr(pointer, "weight")
- if scope_names[0] != "embedding":
- logger.info(
- f"Transposing numpy weight of shape {array.shape} for {name}")
- array = np.transpose(array)
- try:
- assert (
- pointer.shape == array.shape
- ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
- except AssertionError as e:
- e.args += (pointer.shape, array.shape)
- raise
- logger.info(f"Initialize PyTorch weight {name}")
- pointer.data = torch.from_numpy(array.astype(np.float32))
- tf_weights.pop(txt_name, None)
-
- logger.info(
- f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}.")
- return model
-
-
-####################################################
-# PyTorch Models are constructed by sub-classing
-# - torch.nn.Module for the layers and
-# - PreTrainedModel for the models (it-self a sub-class of nn.Module)
-####################################################
-PARALLELIZE_DOCSTRING = r"""
- This is an experimental feature and is a subject to change at a moment's notice.
-
- Uses a device map to distribute attention modules of the model across several devices. If no device map is given,
- it will evenly distribute blocks across all devices.
-
- Args:
- device_map (:obj:`Dict[int, list]`, optional, defaults to None):
- A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
- automatically mapped to the first device (for esoteric reasons). That means that the first device should
- have fewer attention modules mapped to it than other devices. For reference, the T5 models have the
- following number of attention modules:
-
- - T5-small: 6
- - T5-base: 12
- - T5-large: 24
- - T5-3b: 24
- - T5-11b: 24
-
- Example::
-
- # Here is an example of a device map on a machine with 4 GPUs using T5-3b,
- # which has a total of 24 attention modules:
- model = T5ForConditionalGeneration.from_pretrained('T5-3b')
- device_map = {0: [0, 1, 2],
-
- 1: [3, 4, 5, 6, 7, 8, 9],
- 2: [10, 11, 12, 13, 14, 15, 16],
- 3: [17, 18, 19, 20, 21, 22, 23]}
- model.parallelize(device_map)
-"""
-DEPARALLELIZE_DOCSTRING = r"""
- Moves the model to cpu from a model parallel state.
-
- Example::
-
- # On a 4 GPU machine with T5-3b:
- model = T5ForConditionalGeneration.from_pretrained('T5-3b')
- device_map = {0: [0, 1, 2],
-
- 1: [3, 4, 5, 6, 7, 8, 9],
- 2: [10, 11, 12, 13, 14, 15, 16],
- 3: [17, 18, 19, 20, 21, 22, 23]}
- model.parallelize(device_map) # Splits the model across several devices
- model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
-"""
-
-
-class T5LayerNorm(nn.Module):
- def __init__(self, hidden_size, eps=1e-6):
- """
- Construct a layernorm module in the T5 style No bias and no subtraction of mean.
- """
- super().__init__()
- self.weight = nn.Parameter(torch.ones(hidden_size))
- self.variance_epsilon = eps
-
- def forward(self, hidden_states):
- # layer norm should always be calculated in float32
- variance = hidden_states.to(torch.float32).pow(
- 2).mean(-1, keepdim=True)
- hidden_states = hidden_states * \
- torch.rsqrt(variance + self.variance_epsilon)
-
- # convert into float16 if necessary
- if self.weight.dtype == torch.float16:
- hidden_states = hidden_states.to(torch.float16)
- return self.weight * hidden_states
-
-
-class T5DenseReluDense(nn.Module):
- def __init__(self, config):
- super().__init__()
- # @IDEA modified -> bias=False -> bias=True
- self.wi = nn.Linear(config.d_model, config.d_ff, bias=True)
- self.wo = nn.Linear(config.d_ff, config.d_model, bias=True)
- self.dropout = nn.Dropout(config.dropout_rate)
-
- def forward(self, hidden_states):
- hidden_states = self.wi(hidden_states)
- hidden_states = nn.functional.relu(hidden_states)
- hidden_states = self.dropout(hidden_states)
- hidden_states = self.wo(hidden_states)
- return hidden_states
-
-
-class T5DenseGeluDense(nn.Module):
- def __init__(self, config):
- super().__init__()
- # @IDEA modified -> bias=False -> bias=True
- self.wi = nn.Linear(config.d_model, config.d_ff, bias=True)
- self.wo = nn.Linear(config.d_ff, config.d_model, bias=True)
- self.dropout = nn.Dropout(config.dropout_rate)
-
- def forward(self, hidden_states):
- hidden_states = self.wi(hidden_states)
- hidden_states = nn.functional.gelu(hidden_states)
- hidden_states = self.dropout(hidden_states)
- hidden_states = self.wo(hidden_states)
- return hidden_states
-
-
-class T5DenseGatedGeluDense(nn.Module):
- def __init__(self, config):
- super().__init__()
- # @IDEA modified -> bias=False -> bias=True
- self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=True)
- self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=True)
- self.wo = nn.Linear(config.d_ff, config.d_model, bias=True)
- self.dropout = nn.Dropout(config.dropout_rate)
- self.gelu_act = ACT2FN["gelu_new"]
-
- def forward(self, hidden_states):
- hidden_gelu = self.gelu_act(self.wi_0(hidden_states))
- hidden_linear = self.wi_1(hidden_states)
- hidden_states = hidden_gelu * hidden_linear
- hidden_states = self.dropout(hidden_states)
- hidden_states = self.wo(hidden_states)
- return hidden_states
-
-
-class T5LayerFF(nn.Module):
- def __init__(self, config):
- super().__init__()
- # @IDEA modified -> T5LayerNorm -> nn.LayerNorm
- # self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
- self.layer_norm = nn.LayerNorm(
- config.d_model, eps=config.layer_norm_epsilon)
- if config.feed_forward_proj == "relu":
- self.DenseReluDense = T5DenseReluDense(config)
- elif config.feed_forward_proj == "gelu":
- self.DenseReluDense = T5DenseGeluDense(config)
- else:
- raise ValueError(
- f"{self.config.feed_forward_proj} is not supported. Choose between `relu` and `gated-gelu`"
- )
- self.dropout = nn.Dropout(config.dropout_rate)
-
- def forward(self, hidden_states):
- forwarded_states = self.layer_norm(hidden_states)
- forwarded_states = self.DenseReluDense(forwarded_states)
- hidden_states = hidden_states + self.dropout(forwarded_states)
- return hidden_states
-
-
-class T5Attention(nn.Module):
- def __init__(self, config: T5Config, has_relative_attention_bias=False):
- super().__init__()
- self.is_decoder = config.is_decoder
- self.has_relative_attention_bias = has_relative_attention_bias
-
- self.relative_attention_num_buckets = config.relative_attention_num_buckets
- self.d_model = config.d_model
- self.key_value_proj_dim = config.d_kv
- self.n_heads = config.num_heads
- self.dropout = config.dropout_rate
- self.inner_dim = self.n_heads * self.key_value_proj_dim
-
- # Mesh TensorFlow initialization to avoid scaling before softmax
- # @IDEA modified -> bias=False -> bias=True
-
- self.q = nn.Linear(self.d_model, self.inner_dim, bias=True)
- self.k = nn.Linear(self.d_model, self.inner_dim, bias=True)
- self.v = nn.Linear(self.d_model, self.inner_dim, bias=True)
-
- self.o = nn.Linear(self.inner_dim, self.d_model, bias=True)
-
- if self.has_relative_attention_bias:
- self.relative_attention_bias = nn.Embedding(
- self.relative_attention_num_buckets, self.n_heads)
- self.pruned_heads = set()
- self.gradient_checkpointing = False
-
- def prune_heads(self, heads):
- if len(heads) == 0:
- return
- heads, index = find_pruneable_heads_and_indices(
- heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
- )
- # Prune linear layers
- self.q = prune_linear_layer(self.q, index)
- self.k = prune_linear_layer(self.k, index)
- self.v = prune_linear_layer(self.v, index)
-
- self.o = prune_linear_layer(self.o, index, dim=1)
- # Update hyper params
- self.n_heads = self.n_heads - len(heads)
- self.inner_dim = self.key_value_proj_dim * self.n_heads
- self.pruned_heads = self.pruned_heads.union(heads)
-
- @staticmethod
- def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
- """
- Adapted from Mesh Tensorflow:
- https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
-
- Translate relative position to a bucket number for relative attention. The relative position is defined as
- memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
- position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
- small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
- positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
- This should allow for more graceful generalization to longer sequences than the model has been trained on
-
- Args:
- relative_position: an int32 Tensor
- bidirectional: a boolean - whether the attention is bidirectional
- num_buckets: an integer
- max_distance: an integer
-
- Returns:
- a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
- """
- relative_buckets = 0
- if bidirectional:
- num_buckets //= 2
- relative_buckets += (relative_position >
- 0).to(torch.long) * num_buckets
- relative_position = torch.abs(relative_position)
- else:
- relative_position = - \
- torch.min(relative_position,
- torch.zeros_like(relative_position))
- # now relative_position is in the range [0, inf)
-
- # half of the buckets are for exact increments in positions
- max_exact = num_buckets // 2
- is_small = relative_position < max_exact
-
- # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
- relative_postion_if_large = max_exact + (
- torch.log(relative_position.float() / max_exact)
- / math.log(max_distance / max_exact)
- * (num_buckets - max_exact)
- ).to(torch.long)
- relative_postion_if_large = torch.min(
- relative_postion_if_large, torch.full_like(
- relative_postion_if_large, num_buckets - 1)
- )
-
- relative_buckets += torch.where(is_small,
- relative_position, relative_postion_if_large)
- return relative_buckets
-
- def compute_bias(self, query_length, key_length):
- """Compute binned relative position bias"""
- context_position = torch.arange(
- query_length, dtype=torch.long, device=self.relative_attention_bias.weight.device
- )[:, None]
- memory_position = torch.arange(
- key_length, dtype=torch.long, device=self.relative_attention_bias.weight.device
- )[None, :]
- relative_position = memory_position - \
- context_position # shape (query_length, key_length)
- relative_position_bucket = self._relative_position_bucket(
- relative_position, # shape (query_length, key_length)
- bidirectional=(not self.is_decoder),
- num_buckets=self.relative_attention_num_buckets,
- )
- # shape (query_length, key_length, num_heads)
- values = self.relative_attention_bias(relative_position_bucket)
- # shape (1, num_heads, query_length, key_length)
- values = values.permute([2, 0, 1]).unsqueeze(0)
- return values
-
- def forward(
- self,
- hidden_states,
- mask=None,
- key_value_states=None,
- position_bias=None,
- past_key_value=None,
- layer_head_mask=None,
- query_length=None,
- use_cache=False,
- output_attentions=False,
- ):
- """
- Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
- """
- # Input is (batch_size, seq_length, dim)
- # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
- # past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
- batch_size, seq_length = hidden_states.shape[:2]
-
- real_seq_length = seq_length
-
- if past_key_value is not None:
- assert (
- len(past_key_value) == 2
- ), f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states"
- real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length
-
- key_length = real_seq_length if key_value_states is None else key_value_states.shape[
- 1]
-
- def shape(states):
- """projection"""
- return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
-
- def unshape(states):
- """reshape"""
- return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
-
- def project(hidden_states, proj_layer, key_value_states, past_key_value):
- """projects hidden states correctly to key/query states"""
- if key_value_states is None:
- # self-attn
- # (batch_size, n_heads, seq_length, dim_per_head)
- hidden_states = shape(proj_layer(hidden_states))
- elif past_key_value is None:
- # cross-attn
- # (batch_size, n_heads, seq_length, dim_per_head)
- hidden_states = shape(proj_layer(key_value_states))
-
- if past_key_value is not None:
- if key_value_states is None:
- # self-attn
- # (batch_size, n_heads, key_length, dim_per_head)
- hidden_states = torch.cat(
- [past_key_value, hidden_states], dim=2)
- else:
- # cross-attn
- hidden_states = past_key_value
- return hidden_states
-
- # get query states
- # (batch_size, n_heads, seq_length, dim_per_head)
- query_states = shape(self.q(hidden_states))
-
- # get key/value states
- key_states = project(
- hidden_states, self.k, key_value_states, past_key_value[
- 0] if past_key_value is not None else None
- )
- value_states = project(
- hidden_states, self.v, key_value_states, past_key_value[
- 1] if past_key_value is not None else None
- )
-
- # compute scores
- scores = torch.matmul(
- query_states, key_states.transpose(3, 2)
- ) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
-
- if position_bias is None:
- if not self.has_relative_attention_bias:
- position_bias = torch.zeros(
- (1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype
- )
- if self.gradient_checkpointing and self.training:
- position_bias.requires_grad = True
- else:
- position_bias = self.compute_bias(real_seq_length, key_length)
-
- # if key and values are already calculated
- # we want only the last query position bias
- if past_key_value is not None:
- position_bias = position_bias[:, :, -hidden_states.size(1):, :]
-
- if mask is not None:
- # (batch_size, n_heads, seq_length, key_length)
- position_bias = position_bias + mask
-
- # @IDEA modified -> delete scores += position_bias, use absolute positional
- # scores += position_bias
- scores = scores / math.sqrt(self.key_value_proj_dim)
-
- if mask is not None:
- scores = scores + mask
-
- attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
- scores
- ) # (batch_size, n_heads, seq_length, key_length)
-
- attn_weights = nn.functional.dropout(
- attn_weights, p=0, training=self.training
- ) # (batch_size, n_heads, seq_length, key_length)
-
- # Mask heads if we want to
- if layer_head_mask is not None:
- attn_weights = attn_weights * layer_head_mask
-
- # (batch_size, seq_length, dim)
- attn_output = unshape(torch.matmul(attn_weights, value_states))
-
- attn_output = self.o(attn_output)
-
- present_key_value_state = (key_states, value_states) if (
- self.is_decoder and use_cache) else None
- outputs = (attn_output,) + \
- (present_key_value_state,) + (position_bias,)
-
- if output_attentions:
- outputs = outputs + (attn_weights,)
- return outputs
-
-
-class T5LayerSelfAttention(nn.Module):
- def __init__(self, config, has_relative_attention_bias=False):
- super().__init__()
-
- # @IDEA modified -> T5LayerNorm -> nn.LayerNorm
- # self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
- self.layer_norm = nn.LayerNorm(
- config.d_model, eps=config.layer_norm_epsilon)
- self.SelfAttention = T5Attention(
- config, has_relative_attention_bias=has_relative_attention_bias)
-
- self.dropout = nn.Dropout(config.dropout_rate)
-
- def forward(
- self,
- hidden_states,
- attention_mask=None,
- position_bias=None,
- layer_head_mask=None,
- past_key_value=None,
- use_cache=False,
- output_attentions=False,
- ):
- normed_hidden_states = self.layer_norm(hidden_states)
- attention_output = self.SelfAttention(
- normed_hidden_states,
- mask=attention_mask,
- position_bias=position_bias,
- layer_head_mask=layer_head_mask,
- past_key_value=past_key_value,
- use_cache=use_cache,
- output_attentions=output_attentions,
- )
-
- hidden_states = hidden_states + self.dropout(attention_output[0])
- # add attentions if we output them
- outputs = (hidden_states,) + attention_output[1:]
- return outputs
-
-
-class T5LayerCrossAttention(nn.Module):
- def __init__(self, config):
- super().__init__()
- # @IDEA modified -> T5LayerNorm -> nn.LayerNorm
- # self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
- self.layer_norm = nn.LayerNorm(
- config.d_model, eps=config.layer_norm_epsilon)
-
- self.EncDecAttention = T5Attention(
- config, has_relative_attention_bias=False)
-
- self.dropout = nn.Dropout(config.dropout_rate)
-
- def forward(
- self,
- hidden_states,
- key_value_states,
- attention_mask=None,
- position_bias=None,
- layer_head_mask=None,
- past_key_value=None,
- use_cache=False,
- query_length=None,
- output_attentions=False,
- ):
- normed_hidden_states = self.layer_norm(hidden_states)
- attention_output = self.EncDecAttention(
- normed_hidden_states,
- mask=attention_mask,
- key_value_states=key_value_states,
- position_bias=position_bias,
- layer_head_mask=layer_head_mask,
- past_key_value=past_key_value,
- use_cache=use_cache,
- query_length=query_length,
- output_attentions=output_attentions,
- )
- layer_output = hidden_states + self.dropout(attention_output[0])
- # add attentions if we output them
- outputs = (layer_output,) + attention_output[1:]
- return outputs
-
-
-class T5Block(nn.Module):
- def __init__(self, config, has_relative_attention_bias=False):
- super().__init__()
- self.is_decoder = config.is_decoder
- # @IDEA modified ->
- # self.layer = nn.ModuleList()
- # self.layer.append(T5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias))
- # if self.is_decoder:
- # self.layer.append(T5LayerCrossAttention(config))
-
- # self.layer.append(T5LayerFF(config))
-
- self.T5LayerSelfAttention = T5LayerSelfAttention(
- config, has_relative_attention_bias=has_relative_attention_bias)
- if self.is_decoder:
- self.T5LayerCrossAttention = T5LayerCrossAttention(
- config)
- self.T5LayerFF = T5LayerFF(config)
-
- def forward(
- self,
- hidden_states,
- attention_mask=None,
- position_bias=None,
- encoder_hidden_states=None,
- encoder_attention_mask=None,
- encoder_decoder_position_bias=None,
- layer_head_mask=None,
- cross_attn_layer_head_mask=None,
- past_key_value=None,
- use_cache=False,
- output_attentions=False,
- return_dict=True,
- ):
-
- if past_key_value is not None:
- assert self.is_decoder, "Only decoder can use `past_key_values`"
- expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
-
- if len(past_key_value) != expected_num_past_key_values:
- raise ValueError(
- f"There should be {expected_num_past_key_values} past states. "
- f"{'2 (past / key) for cross attention. ' if expected_num_past_key_values == 4 else ''}"
- f"Got {len(past_key_value)} past key / value states"
- )
-
- self_attn_past_key_value = past_key_value[:2]
- cross_attn_past_key_value = past_key_value[2:]
- else:
- self_attn_past_key_value, cross_attn_past_key_value = None, None
-
- # @IDEA modified -> self.layer[0] -> self.T5LayerSelfAttention
- self_attention_outputs = self.T5LayerSelfAttention(
- hidden_states,
- attention_mask=attention_mask,
- position_bias=position_bias,
- layer_head_mask=layer_head_mask,
- past_key_value=self_attn_past_key_value,
- use_cache=use_cache,
- output_attentions=output_attentions,
- )
- hidden_states, present_key_value_state = self_attention_outputs[:2]
- # Keep self-attention outputs and relative position weights
- attention_outputs = self_attention_outputs[2:]
-
- # clamp inf values to enable fp16 training
- if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
- clamp_value = torch.finfo(hidden_states.dtype).max - 1000
- hidden_states = torch.clamp(
- hidden_states, min=-clamp_value, max=clamp_value)
-
- do_cross_attention = self.is_decoder and encoder_hidden_states is not None
- if do_cross_attention:
- # the actual query length is unknown for cross attention
- # if using past key value states. Need to inject it here
- if present_key_value_state is not None:
- query_length = present_key_value_state[0].shape[2]
- else:
- query_length = None
- # @IDEA modified -> self.layer[1] -> self.T5LayerCrossAttention
- cross_attention_outputs = self.T5LayerCrossAttention(
- hidden_states,
- key_value_states=encoder_hidden_states,
- attention_mask=encoder_attention_mask,
- position_bias=encoder_decoder_position_bias,
- layer_head_mask=cross_attn_layer_head_mask,
- past_key_value=cross_attn_past_key_value,
- query_length=query_length,
- use_cache=use_cache,
- output_attentions=output_attentions,
- )
- hidden_states = cross_attention_outputs[0]
-
- # clamp inf values to enable fp16 training
- if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
- clamp_value = torch.finfo(hidden_states.dtype).max - 1000
- hidden_states = torch.clamp(
- hidden_states, min=-clamp_value, max=clamp_value)
-
- # Combine self attn and cross attn key value states
- if present_key_value_state is not None:
- present_key_value_state = present_key_value_state + \
- cross_attention_outputs[1]
-
- # Keep cross-attention outputs and relative position weights
- attention_outputs = attention_outputs + cross_attention_outputs[2:]
-
- # Apply Feed Forward layer
- # @IDEA modified -> self.layer[-1] -> self.T5LayerFF
- hidden_states = self.T5LayerFF(hidden_states)
-
- # clamp inf values to enable fp16 training
- if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
- clamp_value = torch.finfo(hidden_states.dtype).max - 1000
- hidden_states = torch.clamp(
- hidden_states, min=-clamp_value, max=clamp_value)
-
- outputs = (hidden_states,)
-
- if use_cache:
- outputs = outputs + (present_key_value_state,) + attention_outputs
- else:
- outputs = outputs + attention_outputs
-
- # hidden-states, present_key_value_states, (self-attention position bias),
- # (self-attention weights), (cross-attention position bias), (cross-attention weights)
- return outputs
-
-
-class T5PreTrainedModel(PreTrainedModel):
- """
- An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
- models.
- """
-
- config_class = T5Config
- load_tf_weights = load_tf_weights_in_T5
- base_model_prefix = "transformer"
- is_parallelizable = True
- supports_gradient_checkpointing = True
-
- @property
- def dummy_inputs(self):
- input_ids = torch.tensor(DUMMY_INPUTS)
- input_mask = torch.tensor(DUMMY_MASK)
- dummy_inputs = {
- "decoder_input_ids": input_ids,
- "input_ids": input_ids,
- "decoder_attention_mask": input_mask,
- }
- return dummy_inputs
-
- def _init_weights(self, module):
- """Initialize the weights"""
- factor = self.config.initializer_factor # Used for testing weights initialization
- if isinstance(module, T5LayerNorm):
- module.weight.data.fill_(factor * 1.0)
- elif isinstance(module, (T5Model, T5ForConditionalGeneration, T5EncoderModel)):
- # Mesh TensorFlow embeddings initialization
- # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d
- # /mesh_tensorflow/layers.py#L1624
- # @IDEA modified -> module.shared.weight -> module.shared.word_embeddings.weight
- # module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
- module.shared.word_embeddings.weight.data.normal_(
- mean=0.0, std=factor * 1.0)
- module.shared.position_embeddings.weight.data.normal_(
- mean=0.0, std=factor * 1.0)
- elif isinstance(module, T5DenseReluDense):
- # Mesh TensorFlow FF initialization
- # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow
- # /transformer/transformer_layers.py#L56
- # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/
- # mesh_tensorflow/layers.py#L89
- module.wi.weight.data.normal_(
- mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
- if hasattr(module.wi, "bias") and module.wi.bias is not None:
- module.wi.bias.data.zero_()
- module.wo.weight.data.normal_(
- mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
- if hasattr(module.wo, "bias") and module.wo.bias is not None:
- module.wo.bias.data.zero_()
- elif isinstance(module, T5DenseGeluDense):
- module.wi_0.weight.data.normal_(
- mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
- if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None:
- module.wi_0.bias.data.zero_()
- module.wi_1.weight.data.normal_(
- mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
- if hasattr(module.wi, "bias") and module.wi.bias is not None:
- module.wi.bias.data.zero_()
- module.wo.weight.data.normal_(
- mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
- if hasattr(module.wo, "bias") and module.wo.bias is not None:
- module.wo.bias.data.zero_()
- elif isinstance(module, T5Attention):
- # Mesh TensorFlow attention initialization to avoid scaling before softmax
- # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d
- # /mesh_tensorflow/transformer/attention.py#L136
- d_model = self.config.d_model
- key_value_proj_dim = self.config.d_kv
- n_heads = self.config.num_heads
- module.q.weight.data.normal_(
- mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
- module.k.weight.data.normal_(
- mean=0.0, std=factor * (d_model ** -0.5))
- module.v.weight.data.normal_(
- mean=0.0, std=factor * (d_model ** -0.5))
-
- module.o.weight.data.normal_(
- mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
- if module.has_relative_attention_bias:
- module.relative_attention_bias.weight.data.normal_(
- mean=0.0, std=factor * ((d_model) ** -0.5))
-
- def _set_gradient_checkpointing(self, module, value=False):
- if isinstance(module, (T5Attention, T5Stack)):
- module.gradient_checkpointing = value
-
- def _shift_right(self, input_ids):
- decoder_start_token_id = self.config.decoder_start_token_id
- pad_token_id = self.config.pad_token_id
-
- assert (
- decoder_start_token_id is not None
- ), "self.model.config.decoder_start_token_id has to be defined. "\
- "In T5 it is usually set to the pad_token_id. See T5 docs for more information"
-
- # shift inputs to the right
- if is_torch_fx_proxy(input_ids):
- # Item assignment is not supported natively for proxies.
- shifted_input_ids = torch.full(
- input_ids.shape[:-1] + (1,), decoder_start_token_id)
- shifted_input_ids = torch.cat(
- [shifted_input_ids, input_ids[..., :-1]], dim=-1)
- else:
- shifted_input_ids = input_ids.new_zeros(input_ids.shape)
- shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
- shifted_input_ids[..., 0] = decoder_start_token_id
-
- assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
- # replace possible -100 values in labels by `pad_token_id`
- shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
-
- assert torch.all(shifted_input_ids >= 0).item(
- ), "Verify that `shifted_input_ids` has only positive values"
-
- return shifted_input_ids
-
-
-class T5Embeddings(nn.Module):
- """Construct the embeddings from word, position and token_type embeddings."""
-
- def __init__(self, config):
- super().__init__()
- self.word_embeddings = nn.Embedding(
- config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
- self.position_embeddings = nn.Embedding(
- config.max_position_embeddings, config.hidden_size)
-
- # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
- # any TensorFlow checkpoint file
-
- # In Megatron, layer-norm is applied after the 1st dropout.
- # self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
- self.dropout = nn.Dropout(config.dropout_rate)
-
- # position_ids (1, len position emb) is contiguous in memory and exported when serialized
- self.register_buffer("position_ids", torch.arange(
- config.max_position_embeddings).expand((1, -1)))
- self.position_embedding_type = getattr(
- config, "position_embedding_type", "absolute")
-
- def forward(
- self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
- ):
- if input_ids is not None:
- input_shape = input_ids.size()
- else:
- input_shape = inputs_embeds.size()[:-1]
-
- seq_length = input_shape[1]
-
- if position_ids is None:
- position_ids = self.position_ids[:,
- past_key_values_length: seq_length + past_key_values_length]
-
- if inputs_embeds is None:
- inputs_embeds = self.word_embeddings(input_ids)
-
- embeddings = inputs_embeds
- if self.position_embedding_type == "absolute":
- position_embeddings = self.position_embeddings(position_ids)
- embeddings += position_embeddings
-
- # Megatron BERT moves that layer norm after the drop-out (and to each layer).
- # embeddings = self.LayerNorm(embeddings)
- embeddings = self.dropout(embeddings)
- return embeddings
-
-
-class T5Stack(T5PreTrainedModel):
- def __init__(self, config, embed_tokens=None):
- super().__init__(config)
-
- self.embed_tokens = embed_tokens
- self.is_decoder = config.is_decoder
-
- # @IDEA modified -> has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)
- # -> has_relative_attention_bias=False
- self.block = nn.ModuleList(
- [T5Block(config, has_relative_attention_bias=False)
- for _ in range(config.num_layers)]
- )
- # @IDEA modified -> T5LayerNorm -> nn.LayerNorm
- # self.final_layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
- self.final_layer_norm = nn.LayerNorm(
- config.d_model, eps=config.layer_norm_epsilon)
-
- self.dropout = nn.Dropout(config.dropout_rate)
-
- self.init_weights()
- # Model parallel
- self.model_parallel = False
- self.device_map = None
- self.gradient_checkpointing = False
-
- @add_start_docstrings(PARALLELIZE_DOCSTRING)
- def parallelize(self, device_map=None):
- # Check validity of device_map
- self.device_map = (
- get_device_map(len(self.block), range(
- torch.cuda.device_count())) if device_map is None else device_map
- )
- assert_device_map(self.device_map, len(self.block))
- self.model_parallel = True
- self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + \
- str(min(self.device_map.keys()))
- self.last_device = "cuda:" + str(max(self.device_map.keys()))
- # Load onto devices
- for k, v in self.device_map.items():
- for layer in v:
- cuda_device = "cuda:" + str(k)
- self.block[layer] = self.block[layer].to(cuda_device)
-
- # Set embed_tokens to first layer
-
- self.embed_tokens = self.embed_tokens.to(self.first_device)
- self.embeddings = self.embeddings.to(self.first_device)
- # Set final layer norm to last device
- self.final_layer_norm = self.final_layer_norm.to(self.last_device)
-
- @add_start_docstrings(PARALLELIZE_DOCSTRING)
- def deparallelize(self):
- self.model_parallel = False
- self.device_map = None
- self.first_device = "cpu"
- self.last_device = "cpu"
- for i in range(len(self.block)):
- self.block[i] = self.block[i].to("cpu")
- self.embed_tokens = self.embed_tokens.to("cpu")
- self.final_layer_norm = self.final_layer_norm.to("cpu")
- torch.cuda.empty_cache()
-
- def get_input_embeddings(self):
- return self.embed_tokens
-
- def set_input_embeddings(self, new_embeddings):
- self.embed_tokens = new_embeddings
-
- def forward(
- self,
- input_ids=None,
- position_ids=None,
- attention_mask=None,
- encoder_hidden_states=None,
- encoder_attention_mask=None,
- inputs_embeds=None,
- head_mask=None,
- cross_attn_head_mask=None,
- past_key_values=None,
- use_cache=None,
- output_attentions=None,
- output_hidden_states=None,
- return_dict=None,
- ):
- # Model parallel
- if self.model_parallel:
- torch.cuda.set_device(self.first_device)
- self.embed_tokens = self.embed_tokens.to(self.first_device)
- use_cache = use_cache if use_cache is not None else self.config.use_cache
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
- )
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- if input_ids is not None and inputs_embeds is not None:
- err_msg_prefix = "decoder_" if self.is_decoder else ""
- raise ValueError(
- f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
- )
- elif input_ids is not None:
- input_shape = input_ids.size()
- input_ids = input_ids.view(-1, input_shape[-1])
- elif inputs_embeds is not None:
- input_shape = inputs_embeds.size()[:-1]
- else:
- err_msg_prefix = "decoder_" if self.is_decoder else ""
- raise ValueError(
- f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")
-
- if inputs_embeds is None:
- assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings"
- # @IDEA modified -> self.embed_tokens(input_ids=input_ids) ->
- # self.embed_tokens(input_ids=input_ids,osition_ids=position_ids,)
- # inputs_embeds = self.embed_tokens(input_ids=input_ids)
- inputs_embeds = self.embed_tokens(input_ids=input_ids)
-
- batch_size, seq_length = input_shape
-
- # required mask seq length can be calculated via length of past
- mask_seq_length = past_key_values[0][0].shape[2] + \
- seq_length if past_key_values is not None else seq_length
-
- if use_cache is True:
- assert self.is_decoder, f":obj:`use_cache` can only be set to `True` if {self} is used as a decoder"
-
- if attention_mask is None:
- attention_mask = torch.ones(
- batch_size, mask_seq_length).to(inputs_embeds.device)
- if self.is_decoder and encoder_attention_mask is None and encoder_hidden_states is not None:
- encoder_seq_length = encoder_hidden_states.shape[1]
- encoder_attention_mask = torch.ones(
- batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long
- )
-
- # initialize past_key_values with `None` if past does not exist
- if past_key_values is None:
- past_key_values = [None] * len(self.block)
-
- # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
- # ourselves in which case we just need to make it broadcastable to all heads.
- extended_attention_mask = self.get_extended_attention_mask(
- attention_mask, input_shape, inputs_embeds.device)
-
- # If a 2D or 3D attention mask is provided for the cross-attention
- # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
- if self.is_decoder and encoder_hidden_states is not None:
- encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
- encoder_hidden_shape = (
- encoder_batch_size, encoder_sequence_length)
- if encoder_attention_mask is None:
- encoder_attention_mask = torch.ones(
- encoder_hidden_shape, device=inputs_embeds.device)
- encoder_extended_attention_mask = self.invert_attention_mask(
- encoder_attention_mask)
- else:
- encoder_extended_attention_mask = None
-
- # Prepare head mask if needed
- head_mask = self.get_head_mask(head_mask, self.config.num_layers)
- cross_attn_head_mask = self.get_head_mask(
- cross_attn_head_mask, self.config.num_layers)
- present_key_value_states = () if use_cache else None
- all_hidden_states = () if output_hidden_states else None
- all_attentions = () if output_attentions else None
- all_cross_attentions = () if (output_attentions and self.is_decoder) else None
- position_bias = None
- encoder_decoder_position_bias = None
-
- hidden_states = self.dropout(inputs_embeds)
-
- for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
-
- layer_head_mask = head_mask[i]
- cross_attn_layer_head_mask = cross_attn_head_mask[i]
- # Model parallel
- if self.model_parallel:
- torch.cuda.set_device(hidden_states.device)
- # Ensure that attention_mask is always on the same device as hidden_states
- if attention_mask is not None:
- attention_mask = attention_mask.to(hidden_states.device)
- if position_bias is not None:
- position_bias = position_bias.to(hidden_states.device)
- if encoder_hidden_states is not None:
- encoder_hidden_states = encoder_hidden_states.to(
- hidden_states.device)
- if encoder_extended_attention_mask is not None:
- encoder_extended_attention_mask = encoder_extended_attention_mask.to(
- hidden_states.device)
- if encoder_decoder_position_bias is not None:
- encoder_decoder_position_bias = encoder_decoder_position_bias.to(
- hidden_states.device)
- if layer_head_mask is not None:
- layer_head_mask = layer_head_mask.to(hidden_states.device)
- if cross_attn_layer_head_mask is not None:
- cross_attn_layer_head_mask = cross_attn_layer_head_mask.to(
- hidden_states.device)
- if output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states,)
-
- if self.gradient_checkpointing and self.training:
- if use_cache:
- logger.warn(
- "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
- )
- use_cache = False
-
- def create_custom_forward(module):
- def custom_forward(*inputs):
- return tuple(module(*inputs, use_cache, output_attentions))
-
- return custom_forward
-
- layer_outputs = checkpoint(
- create_custom_forward(layer_module),
- hidden_states,
- extended_attention_mask,
- position_bias,
- encoder_hidden_states,
- encoder_extended_attention_mask,
- encoder_decoder_position_bias,
- layer_head_mask,
- cross_attn_layer_head_mask,
- None, # past_key_value is always None with gradient checkpointing
- )
- else:
- layer_outputs = layer_module(
- hidden_states,
- attention_mask=extended_attention_mask,
- position_bias=position_bias,
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=encoder_extended_attention_mask,
- encoder_decoder_position_bias=encoder_decoder_position_bias,
- layer_head_mask=layer_head_mask,
- cross_attn_layer_head_mask=cross_attn_layer_head_mask,
- past_key_value=past_key_value,
- use_cache=use_cache,
- output_attentions=output_attentions,
- )
-
- # layer_outputs is a tuple with:
- # hidden-states, key-value-states, (self-attention position bias), (self-attention weights),
- # (cross-attention position bias), (cross-attention weights)
- if use_cache is False:
- layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
-
- hidden_states, present_key_value_state = layer_outputs[:2]
-
- # We share the position biases between the layers - the first layer store them
- # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
- # (cross-attention position bias), (cross-attention weights)
- position_bias = layer_outputs[2]
- if self.is_decoder and encoder_hidden_states is not None:
- encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3]
- # append next layer key value states
- if use_cache:
- present_key_value_states = present_key_value_states + \
- (present_key_value_state,)
-
- if output_attentions:
- all_attentions = all_attentions + (layer_outputs[3],)
- if self.is_decoder:
- all_cross_attentions = all_cross_attentions + \
- (layer_outputs[5],)
-
- # Model Parallel: If it's the last layer for that device, put things on the next device
- if self.model_parallel:
- for k, v in self.device_map.items():
- if i == v[-1] and "cuda:" + str(k) != self.last_device:
- hidden_states = hidden_states.to("cuda:" + str(k + 1))
-
- hidden_states = self.final_layer_norm(hidden_states)
- hidden_states = self.dropout(hidden_states)
-
- # Add last layer
- if output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states,)
-
- if not return_dict:
- return tuple(
- v
- for v in [
- hidden_states,
- present_key_value_states,
- all_hidden_states,
- all_attentions,
- all_cross_attentions,
- ]
- if v is not None
- )
- return BaseModelOutputWithPastAndCrossAttentions(
- last_hidden_state=hidden_states,
- past_key_values=present_key_value_states,
- hidden_states=all_hidden_states,
- attentions=all_attentions,
- cross_attentions=all_cross_attentions,
- )
-
-
-T5_START_DOCSTRING = r"""
-
- The T5 model was proposed in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
-
-
-
-
-
-
-
-
-
-
-
-
- ![]()
-
- Running on CPU 🥶 This demo does not work on CPU.
" - -MAX_SEED = np.iinfo(np.int32).max -CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1" -MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024")) -USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1" -ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" - -device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - -style_list = [ - { - "name": "(No style)", - "prompt": "{prompt}", - "negative_prompt": "", - }, - { - "name": "Cinematic", - "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", - "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", - }, - { - "name": "Photographic", - "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed", - "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", - }, - { - "name": "Anime", - "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", - "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast", - }, - { - "name": "Manga", - "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style", - "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style", - }, - { - "name": "Digital Art", - "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", - "negative_prompt": "photo, photorealistic, realism, ugly", - }, - { - "name": "Pixel art", - "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics", - "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic", - }, - { - "name": "Fantasy art", - "prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", - "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white", - }, - { - "name": "Neonpunk", - "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional", - "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured", - }, - { - "name": "3D Model", - "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", - "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", - }, -] - -styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} -STYLE_NAMES = list(styles.keys()) -DEFAULT_STYLE_NAME = "(No style)" - - -def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]: - p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) - if not negative: - negative = "" - return p.replace("{prompt}", positive), n + negative - - -if torch.cuda.is_available(): - pipe = PixArtAlphaPipeline.from_pretrained( - "PixArt-alpha/PixArt-XL-2-1024-MS", - torch_dtype=torch.float16, - variant="fp16", - use_safetensors=True, - ) - - if ENABLE_CPU_OFFLOAD: - pipe.enable_model_cpu_offload() - else: - pipe.to(device) - print("Loaded on Device!") - - # speed-up T5 - pipe.text_encoder.to_bettertransformer() - - if USE_TORCH_COMPILE: - pipe.transformer = torch.compile( - pipe.transformer, mode="reduce-overhead", fullgraph=True - ) - print("Model Compiled!") - - -def save_image(img): - unique_name = str(uuid.uuid4()) + ".png" - img.save(unique_name) - return unique_name - - -def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: - if randomize_seed: - seed = random.randint(0, MAX_SEED) - return seed - - -def generate( - prompt: str, - negative_prompt: str = "", - style: str = DEFAULT_STYLE_NAME, - use_negative_prompt: bool = False, - seed: int = 0, - width: int = 1024, - height: int = 1024, - guidance_scale: float = 4.5, - num_inference_steps: int = 20, - randomize_seed: bool = False, - progress=gr.Progress(track_tqdm=True), -): - seed = randomize_seed_fn(seed, randomize_seed) - generator = torch.Generator().manual_seed(seed) - - if not use_negative_prompt: - negative_prompt = None # type: ignore - prompt, negative_prompt = apply_style(style, prompt, negative_prompt) - image = pipe( - prompt=prompt, - negative_prompt=negative_prompt, - width=width, - height=height, - guidance_scale=guidance_scale, - num_inference_steps=num_inference_steps, - generator=generator, - output_type="pil", - ).images[0] - - image_path = save_image(image) - print(image_path) - return [image_path], seed - - -examples = [ - "A small cactus with a happy face in the Sahara desert.", - "Pirate ship trapped in a cosmic maelstrom nebula, rendered in cosmic beach whirlpool engine, volumetric lighting, spectacular, ambient lights, light pollution, cinematic atmosphere, art nouveau style, illustration art artwork by SenseiJaye, intricate detail.", - "stars, water, brilliantly, gorgeous large scale scene, a little girl, in the style of dreamy realism, light gold and amber, blue and pink, brilliantly illuminated in the background.", - "3d digital art of an adorable ghost, glowing within, holding a heart shaped pumpkin, Halloween, super cute, spooky haunted house background", - "beautiful lady, freckles, big smile, blue eyes, short ginger hair, dark makeup, wearing a floral blue vest top, soft light, dark grey background", - "professional portrait photo of an anthropomorphic cat wearing fancy gentleman hat and jacket walking in autumn forest.", - "an astronaut sitting in a diner, eating fries, cinematic, analog film", - "Albert Einstein in a surrealist Cyberpunk 2077 world, hyperrealistic", -] - -with gr.Blocks(css="style.css") as demo: - gr.Markdown(DESCRIPTION) - gr.DuplicateButton( - value="Duplicate Space for private use", - elem_id="duplicate-button", - visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", - ) - with gr.Group(): - with gr.Row(): - prompt = gr.Text( - label="Prompt", - show_label=False, - max_lines=1, - placeholder="Enter your prompt", - container=False, - ) - run_button = gr.Button("Run", scale=0) - result = gr.Gallery(label="Result", columns=1, show_label=False) - with gr.Accordion("Advanced options", open=False): - with gr.Row(): - use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False) - style_selection = gr.Radio( - show_label=True, - container=True, - interactive=True, - choices=STYLE_NAMES, - value=DEFAULT_STYLE_NAME, - label="Image Style", - ) - negative_prompt = gr.Text( - label="Negative prompt", - max_lines=1, - placeholder="Enter a negative prompt", - visible=False, - ) - seed = gr.Slider( - label="Seed", - minimum=0, - maximum=MAX_SEED, - step=1, - value=0, - ) - randomize_seed = gr.Checkbox(label="Randomize seed", value=True) - with gr.Row(visible=False): - width = gr.Slider( - label="Width", - minimum=256, - maximum=MAX_IMAGE_SIZE, - step=32, - value=1024, - ) - height = gr.Slider( - label="Height", - minimum=256, - maximum=MAX_IMAGE_SIZE, - step=32, - value=1024, - ) - with gr.Row(): - guidance_scale = gr.Slider( - label="Guidance scale", - minimum=1, - maximum=20, - step=0.1, - value=4.5, - ) - num_inference_steps = gr.Slider( - label="Number of inference steps", - minimum=10, - maximum=100, - step=1, - value=20, - ) - - gr.Examples( - examples=examples, - inputs=prompt, - outputs=[result, seed], - fn=generate, - cache_examples=CACHE_EXAMPLES, - ) - - use_negative_prompt.change( - fn=lambda x: gr.update(visible=x), - inputs=use_negative_prompt, - outputs=negative_prompt, - api_name=False, - ) - - gr.on( - triggers=[ - prompt.submit, - negative_prompt.submit, - run_button.click, - ], - fn=generate, - inputs=[ - prompt, - negative_prompt, - style_selection, - use_negative_prompt, - seed, - width, - height, - guidance_scale, - num_inference_steps, - randomize_seed, - ], - outputs=[result, seed], - api_name="run", - ) - -if __name__ == "__main__": - # demo.queue(max_size=20).launch() - demo.launch(share=True) diff --git a/spaces/Plachta/VITS-Umamusume-voice-synthesizer/ONNXVITS_models.py b/spaces/Plachta/VITS-Umamusume-voice-synthesizer/ONNXVITS_models.py deleted file mode 100644 index acd00238895d57ba878fd0211d5654250fb10061..0000000000000000000000000000000000000000 --- a/spaces/Plachta/VITS-Umamusume-voice-synthesizer/ONNXVITS_models.py +++ /dev/null @@ -1,509 +0,0 @@ -import copy -import math -import torch -from torch import nn -from torch.nn import functional as F - -import commons -import ONNXVITS_modules as modules -import attentions -import monotonic_align - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from commons import init_weights, get_padding - - -class StochasticDurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): - super().__init__() - filter_channels = in_channels # it needs to be removed from future version. - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.log_flow = modules.Log() - self.flows = nn.ModuleList() - self.flows.append(modules.ElementwiseAffine(2)) - for i in range(n_flows): - self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.flows.append(modules.Flip()) - - self.post_pre = nn.Conv1d(1, filter_channels, 1) - self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - self.post_flows = nn.ModuleList() - self.post_flows.append(modules.ElementwiseAffine(2)) - for i in range(4): - self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.post_flows.append(modules.Flip()) - - self.pre = nn.Conv1d(in_channels, filter_channels, 1) - self.proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, filter_channels, 1) - - self.w = None - self.reverse = None - self.noise_scale = None - def forward(self, x, x_mask, g=None): - w = self.w - reverse = self.reverse - noise_scale = self.noise_scale - - x = torch.detach(x) - x = self.pre(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.convs(x, x_mask) - x = self.proj(x) * x_mask - - if not reverse: - flows = self.flows - assert w is not None - - logdet_tot_q = 0 - h_w = self.post_pre(w) - h_w = self.post_convs(h_w, x_mask) - h_w = self.post_proj(h_w) * x_mask - e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask - z_q = e_q - for flow in self.post_flows: - z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) - logdet_tot_q += logdet_q - z_u, z1 = torch.split(z_q, [1, 1], 1) - u = torch.sigmoid(z_u) * x_mask - z0 = (w - u) * x_mask - logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2]) - logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q - - logdet_tot = 0 - z0, logdet = self.log_flow(z0, x_mask) - logdet_tot += logdet - z = torch.cat([z0, z1], 1) - for flow in flows: - z, logdet = flow(z, x_mask, g=x, reverse=reverse) - logdet_tot = logdet_tot + logdet - nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot - return nll + logq # [b] - else: - flows = list(reversed(self.flows)) - flows = flows[:-2] + [flows[-1]] # remove a useless vflow - z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale - for flow in flows: - z = flow(z, x_mask, g=x, reverse=reverse) - z0, z1 = torch.split(z, [1, 1], 1) - logw = z0 - return logw - - -class TextEncoder(nn.Module): - def __init__(self, - n_vocab, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout): - super().__init__() - self.n_vocab = n_vocab - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - - self.emb = nn.Embedding(n_vocab, hidden_channels) - nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) - - self.encoder = attentions.Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths): - x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return x, m, logs, x_mask - - -class ResidualCouplingBlock(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) - self.flows.append(modules.Flip()) - - self.reverse = None - def forward(self, x, x_mask, g=None): - reverse = self.reverse - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - -class PosteriorEncoder(nn.Module): - def __init__(self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - x = self.pre(x) * x_mask # x_in : [b, c, t] -> [b, h, t] - x = self.enc(x, x_mask, g=g) # x_in : [b, h, t], g : [b, h, 1], x = x_in + g - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask # z, m, logs : [b, h, t] - - -class Generator(torch.nn.Module): - def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) - resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append(weight_norm( - ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), - k, u, padding=(k-u)//2))) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel//(2**(i+1)) - for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i*self.num_kernels+j](x) - else: - xs += self.resblocks[i*self.num_kernels+j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), - ]) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ]) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2,3,5,7,11] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - - -class SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - def __init__(self, - n_vocab, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - n_speakers=0, - gin_channels=0, - use_sdp=True, - **kwargs): - - super().__init__() - self.n_vocab = n_vocab - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.n_speakers = n_speakers - self.gin_channels = gin_channels - - self.use_sdp = use_sdp - - self.enc_p = TextEncoder(n_vocab, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) - self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) - self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) - - self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) - - if n_speakers > 0: - self.emb_g = nn.Embedding(n_speakers, gin_channels) - - def forward(self, x, x_lengths, sid=None, noise_scale=.667, length_scale=1, noise_scale_w=.8, max_len=None): - torch.onnx.export( - self.enc_p, - (x, x_lengths), - "ONNX_net/enc_p.onnx", - input_names=["x", "x_lengths"], - output_names=["xout", "m_p", "logs_p", "x_mask"], - dynamic_axes={ - "x" : [1], - "xout" : [2], - "m_p" : [2], - "logs_p" : [2], - "x_mask" : [2] - }, - verbose=True, - ) - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) - - if self.n_speakers > 0: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = None - - self.dp.reverse = True - self.dp.noise_scale = noise_scale_w - torch.onnx.export( - self.dp, - (x, x_mask, g), - "ONNX_net/dp.onnx", - input_names=["x", "x_mask", "g"], - output_names=["logw"], - dynamic_axes={ - "x" : [2], - "x_mask" : [2], - "logw" : [2] - }, - verbose=True, - ) - logw = self.dp(x, x_mask, g=g) - w = torch.exp(logw) * x_mask * length_scale - w_ceil = torch.ceil(w) - y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() - y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = commons.generate_path(w_ceil, attn_mask) - - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - - z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale - - self.flow.reverse = True - torch.onnx.export( - self.flow, - (z_p, y_mask, g), - "ONNX_net/flow.onnx", - input_names=["z_p", "y_mask", "g"], - output_names=["z"], - dynamic_axes={ - "z_p" : [2], - "y_mask" : [2], - "z" : [2] - }, - verbose=True, - ) - z = self.flow(z_p, y_mask, g=g) - z_in = (z * y_mask)[:,:,:max_len] - - torch.onnx.export( - self.dec, - (z_in, g), - "ONNX_net/dec.onnx", - input_names=["z_in", "g"], - output_names=["o"], - dynamic_axes={ - "z_in" : [2], - "o" : [2] - }, - verbose=True, - ) - o = self.dec(z_in, g=g) - return o diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/distro/distro.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/distro/distro.py deleted file mode 100644 index 49066ae83646acf39fc4a1d38796d6b5b70e184d..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/distro/distro.py +++ /dev/null @@ -1,1374 +0,0 @@ -#!/usr/bin/env python -# Copyright 2015,2016,2017 Nir Cohen -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -The ``distro`` package (``distro`` stands for Linux Distribution) provides -information about the Linux distribution it runs on, such as a reliable -machine-readable distro ID, or version information. - -It is the recommended replacement for Python's original -:py:func:`platform.linux_distribution` function, but it provides much more -functionality. An alternative implementation became necessary because Python -3.5 deprecated this function, and Python 3.8 removed it altogether. Its -predecessor function :py:func:`platform.dist` was already deprecated since -Python 2.6 and removed in Python 3.8. Still, there are many cases in which -access to OS distribution information is needed. See `Python issue 1322 -
-
-
- -[](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI) - -
- -[](https://colab.research.google.com/github/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/Retrieval_based_Voice_Conversion_WebUI.ipynb) -[](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/LICENSE) -[](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/) - -[](https://discord.gg/HcsmBBGyVk) - -
-
-------
-[**更新日誌**](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/docs/Changelog_KO.md)
-
-[**English**](./README.en.md) | [**中文简体**](../README.md) | [**日本語**](./README.ja.md) | [**한국어**](./README.ko.md) ([**韓國語**](./README.ko.han.md))
-
-> [示範映像](https://www.bilibili.com/video/BV1pm4y1z7Gm/)을 確認해 보세요!
-
-> RVC를活用한實時間音聲變換: [w-okada/voice-changer](https://github.com/w-okada/voice-changer)
-
-> 基本모델은 50時間假量의 高品質 오픈 소스 VCTK 데이터셋을 使用하였으므로, 著作權上의 念慮가 없으니 安心하고 使用하시기 바랍니다.
-
-> 著作權問題가 없는 高品質의 노래를 以後에도 繼續해서 訓練할 豫定입니다.
-
-## 紹介
-本Repo는 다음과 같은 特徵을 가지고 있습니다:
-+ top1檢索을利用하여 入力音色特徵을 訓練세트音色特徵으로 代替하여 音色의漏出을 防止;
-+ 相對的으로 낮은性能의 GPU에서도 빠른訓練可能;
-+ 적은量의 데이터로 訓練해도 좋은 結果를 얻을 수 있음 (最小10分以上의 低雜음音聲데이터를 使用하는 것을 勸獎);
-+ 모델融合을通한 音色의 變調可能 (ckpt處理탭->ckpt混合選擇);
-+ 使用하기 쉬운 WebUI (웹 使用者인터페이스);
-+ UVR5 모델을 利用하여 목소리와 背景音樂의 빠른 分離;
-
-## 環境의準備
-poetry를通해 依存를設置하는 것을 勸獎합니다.
-
-다음命令은 Python 버전3.8以上의環境에서 實行되어야 합니다:
-```bash
-# PyTorch 關聯主要依存設置, 이미設置되어 있는 境遇 건너뛰기 可能
-# 參照: https://pytorch.org/get-started/locally/
-pip install torch torchvision torchaudio
-
-# Windows + Nvidia Ampere Architecture(RTX30xx)를 使用하고 있다面, #21 에서 명시된 것과 같이 PyTorch에 맞는 CUDA 버전을 指定해야 합니다.
-#pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
-
-# Poetry 設置, 이미設置되어 있는 境遇 건너뛰기 可能
-# Reference: https://python-poetry.org/docs/#installation
-curl -sSL https://install.python-poetry.org | python3 -
-
-# 依存設置
-poetry install
-```
-pip를 活用하여依存를 設置하여도 無妨합니다.
-
-```bash
-pip install -r requirements.txt
-```
-
-## 其他預備모델準備
-RVC 모델은 推論과訓練을 依하여 다른 預備모델이 必要합니다.
-
-[Huggingface space](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/)를 通해서 다운로드 할 수 있습니다.
-
-다음은 RVC에 必要한 預備모델 및 其他 파일 目錄입니다:
-```bash
-hubert_base.pt
-
-./pretrained
-
-./uvr5_weights
-
-# Windows를 使用하는境遇 이 사전도 必要할 수 있습니다. FFmpeg가 設置되어 있으면 건너뛰어도 됩니다.
-ffmpeg.exe
-```
-그後 以下의 命令을 使用하여 WebUI를 始作할 수 있습니다:
-```bash
-python infer-web.py
-```
-Windows를 使用하는境遇 `RVC-beta.7z`를 다운로드 및 壓縮解除하여 RVC를 直接使用하거나 `go-web.bat`을 使用하여 WebUi를 直接할 수 있습니다.
-
-## 參考
-+ [ContentVec](https://github.com/auspicious3000/contentvec/)
-+ [VITS](https://github.com/jaywalnut310/vits)
-+ [HIFIGAN](https://github.com/jik876/hifi-gan)
-+ [Gradio](https://github.com/gradio-app/gradio)
-+ [FFmpeg](https://github.com/FFmpeg/FFmpeg)
-+ [Ultimate Vocal Remover](https://github.com/Anjok07/ultimatevocalremovergui)
-+ [audio-slicer](https://github.com/openvpi/audio-slicer)
-## 모든寄與者분들의勞力에感謝드립니다
-
-
- Retrieval-based-Voice-Conversion-WebUI
-VITS基盤의 簡單하고使用하기 쉬운音聲變換틀- -[](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI) - -
- -[](https://colab.research.google.com/github/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/Retrieval_based_Voice_Conversion_WebUI.ipynb) -[](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/main/LICENSE) -[](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/) - -[](https://discord.gg/HcsmBBGyVk) - -
Elite Killer: SWAT APK MOD Money
Download Zip ••• https://urloso.com/2uyPRh
- - aaccfb2cb3
-
-
- diff --git a/spaces/bioriAsaeru/text-to-voice/Gli ultimi sei minuti il film che ha sorpreso la critica nel 1972. Scarica la versione italiana gratis.md b/spaces/bioriAsaeru/text-to-voice/Gli ultimi sei minuti il film che ha sorpreso la critica nel 1972. Scarica la versione italiana gratis.md deleted file mode 100644 index 570890f3abc91b891e087a65474a7098dec12c21..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Gli ultimi sei minuti il film che ha sorpreso la critica nel 1972. Scarica la versione italiana gratis.md +++ /dev/null @@ -1,5 +0,0 @@ -
-
And since speaking Italian to real people is not always an option (you might want to stay home, or you are not in Italy, or your friends are not available), a very good alternative is to immerse yourself in real content. You will have lots of options: from Italian music, to podcasts, to movies. There are tons of resources to learn Italian online, and, of course, one of the very best is italianPod101.com! ?
-Gli ultimi sei minuti movie in italian free download
DOWNLOAD ➡ https://urloso.com/2uyRnx
aaccfb2cb3
-
-
\ No newline at end of file diff --git a/spaces/bioriAsaeru/text-to-voice/Grindr Xtra 1 7 4 Ipa Tips and Tricks to Enhance Your Experience.md b/spaces/bioriAsaeru/text-to-voice/Grindr Xtra 1 7 4 Ipa Tips and Tricks to Enhance Your Experience.md deleted file mode 100644 index df2025c8510b479539abd44e70a6e76452a3715e..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Grindr Xtra 1 7 4 Ipa Tips and Tricks to Enhance Your Experience.md +++ /dev/null @@ -1,6 +0,0 @@ -
Grindr Xtra 1 7 4 Ipa
Download Zip 🗹 https://urloso.com/2uyP0f
-
- aaccfb2cb3
-
-
- diff --git a/spaces/blaziant/ysda_nlp_ops_update/templates/index.html b/spaces/blaziant/ysda_nlp_ops_update/templates/index.html deleted file mode 100644 index 46baaa6c7aa3a469bcdc3586e44b84868278478c..0000000000000000000000000000000000000000 --- a/spaces/blaziant/ysda_nlp_ops_update/templates/index.html +++ /dev/null @@ -1,18 +0,0 @@ -{% extends "base.html" %} -{% block body %} -
-
-
-{% endblock %}
\ No newline at end of file
diff --git a/spaces/boze7/newbing/README.md b/spaces/boze7/newbing/README.md
deleted file mode 100644
index 22915b19890322d359f5ada81d5cf418565d4979..0000000000000000000000000000000000000000
--- a/spaces/boze7/newbing/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Newbing
-emoji: 📊
-colorFrom: purple
-colorTo: red
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/brainblow/MusiCreator/tests/data/test_audio.py b/spaces/brainblow/MusiCreator/tests/data/test_audio.py
deleted file mode 100644
index 40c0d5ed69eff92a766dc6d176e532f0df6c2b5e..0000000000000000000000000000000000000000
--- a/spaces/brainblow/MusiCreator/tests/data/test_audio.py
+++ /dev/null
@@ -1,239 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from itertools import product
-import random
-
-import numpy as np
-import torch
-import torchaudio
-
-from audiocraft.data.audio import audio_info, audio_read, audio_write, _av_read
-
-from ..common_utils import TempDirMixin, get_white_noise, save_wav
-
-
-class TestInfo(TempDirMixin):
-
- def test_info_mp3(self):
- sample_rates = [8000, 16_000]
- channels = [1, 2]
- duration = 1.
- for sample_rate, ch in product(sample_rates, channels):
- wav = get_white_noise(ch, int(sample_rate * duration))
- path = self.get_temp_path('sample_wav.mp3')
- save_wav(path, wav, sample_rate)
- info = audio_info(path)
- assert info.sample_rate == sample_rate
- assert info.channels == ch
- # we cannot trust torchaudio for num_frames, so we don't check
-
- def _test_info_format(self, ext: str):
- sample_rates = [8000, 16_000]
- channels = [1, 2]
- duration = 1.
- for sample_rate, ch in product(sample_rates, channels):
- n_frames = int(sample_rate * duration)
- wav = get_white_noise(ch, n_frames)
- path = self.get_temp_path(f'sample_wav{ext}')
- save_wav(path, wav, sample_rate)
- info = audio_info(path)
- assert info.sample_rate == sample_rate
- assert info.channels == ch
- assert np.isclose(info.duration, duration, atol=1e-5)
-
- def test_info_wav(self):
- self._test_info_format('.wav')
-
- def test_info_flac(self):
- self._test_info_format('.flac')
-
- def test_info_ogg(self):
- self._test_info_format('.ogg')
-
- def test_info_m4a(self):
- # TODO: generate m4a file programmatically
- # self._test_info_format('.m4a')
- pass
-
-
-class TestRead(TempDirMixin):
-
- def test_read_full_wav(self):
- sample_rates = [8000, 16_000]
- channels = [1, 2]
- duration = 1.
- for sample_rate, ch in product(sample_rates, channels):
- n_frames = int(sample_rate * duration)
- wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99)
- path = self.get_temp_path('sample_wav.wav')
- save_wav(path, wav, sample_rate)
- read_wav, read_sr = audio_read(path)
- assert read_sr == sample_rate
- assert read_wav.shape[0] == wav.shape[0]
- assert read_wav.shape[1] == wav.shape[1]
- assert torch.allclose(read_wav, wav, rtol=1e-03, atol=1e-04)
-
- def test_read_partial_wav(self):
- sample_rates = [8000, 16_000]
- channels = [1, 2]
- duration = 1.
- read_duration = torch.rand(1).item()
- for sample_rate, ch in product(sample_rates, channels):
- n_frames = int(sample_rate * duration)
- read_frames = int(sample_rate * read_duration)
- wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99)
- path = self.get_temp_path('sample_wav.wav')
- save_wav(path, wav, sample_rate)
- read_wav, read_sr = audio_read(path, 0, read_duration)
- assert read_sr == sample_rate
- assert read_wav.shape[0] == wav.shape[0]
- assert read_wav.shape[1] == read_frames
- assert torch.allclose(read_wav[..., 0:read_frames], wav[..., 0:read_frames], rtol=1e-03, atol=1e-04)
-
- def test_read_seek_time_wav(self):
- sample_rates = [8000, 16_000]
- channels = [1, 2]
- duration = 1.
- read_duration = 1.
- for sample_rate, ch in product(sample_rates, channels):
- n_frames = int(sample_rate * duration)
- wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99)
- path = self.get_temp_path('sample_wav.wav')
- save_wav(path, wav, sample_rate)
- seek_time = torch.rand(1).item()
- read_wav, read_sr = audio_read(path, seek_time, read_duration)
- seek_frames = int(sample_rate * seek_time)
- expected_frames = n_frames - seek_frames
- assert read_sr == sample_rate
- assert read_wav.shape[0] == wav.shape[0]
- assert read_wav.shape[1] == expected_frames
- assert torch.allclose(read_wav, wav[..., seek_frames:], rtol=1e-03, atol=1e-04)
-
- def test_read_seek_time_wav_padded(self):
- sample_rates = [8000, 16_000]
- channels = [1, 2]
- duration = 1.
- read_duration = 1.
- for sample_rate, ch in product(sample_rates, channels):
- n_frames = int(sample_rate * duration)
- read_frames = int(sample_rate * read_duration)
- wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99)
- path = self.get_temp_path('sample_wav.wav')
- save_wav(path, wav, sample_rate)
- seek_time = torch.rand(1).item()
- seek_frames = int(sample_rate * seek_time)
- expected_frames = n_frames - seek_frames
- read_wav, read_sr = audio_read(path, seek_time, read_duration, pad=True)
- expected_pad_wav = torch.zeros(wav.shape[0], read_frames - expected_frames)
- assert read_sr == sample_rate
- assert read_wav.shape[0] == wav.shape[0]
- assert read_wav.shape[1] == read_frames
- assert torch.allclose(read_wav[..., :expected_frames], wav[..., seek_frames:], rtol=1e-03, atol=1e-04)
- assert torch.allclose(read_wav[..., expected_frames:], expected_pad_wav)
-
-
-class TestAvRead(TempDirMixin):
-
- def test_avread_seek_base(self):
- sample_rates = [8000, 16_000]
- channels = [1, 2]
- duration = 2.
- for sample_rate, ch in product(sample_rates, channels):
- n_frames = int(sample_rate * duration)
- wav = get_white_noise(ch, n_frames)
- path = self.get_temp_path(f'reference_a_{sample_rate}_{ch}.wav')
- save_wav(path, wav, sample_rate)
- for _ in range(100):
- # seek will always load a full duration segment in the file
- seek_time = random.uniform(0.0, 1.0)
- seek_duration = random.uniform(0.001, 1.0)
- read_wav, read_sr = _av_read(path, seek_time, seek_duration)
- assert read_sr == sample_rate
- assert read_wav.shape[0] == wav.shape[0]
- assert read_wav.shape[-1] == int(seek_duration * sample_rate)
-
- def test_avread_seek_partial(self):
- sample_rates = [8000, 16_000]
- channels = [1, 2]
- duration = 1.
- for sample_rate, ch in product(sample_rates, channels):
- n_frames = int(sample_rate * duration)
- wav = get_white_noise(ch, n_frames)
- path = self.get_temp_path(f'reference_b_{sample_rate}_{ch}.wav')
- save_wav(path, wav, sample_rate)
- for _ in range(100):
- # seek will always load a partial segment
- seek_time = random.uniform(0.5, 1.)
- seek_duration = 1.
- expected_num_frames = n_frames - int(seek_time * sample_rate)
- read_wav, read_sr = _av_read(path, seek_time, seek_duration)
- assert read_sr == sample_rate
- assert read_wav.shape[0] == wav.shape[0]
- assert read_wav.shape[-1] == expected_num_frames
-
- def test_avread_seek_outofbound(self):
- sample_rates = [8000, 16_000]
- channels = [1, 2]
- duration = 1.
- for sample_rate, ch in product(sample_rates, channels):
- n_frames = int(sample_rate * duration)
- wav = get_white_noise(ch, n_frames)
- path = self.get_temp_path(f'reference_c_{sample_rate}_{ch}.wav')
- save_wav(path, wav, sample_rate)
- seek_time = 1.5
- read_wav, read_sr = _av_read(path, seek_time, 1.)
- assert read_sr == sample_rate
- assert read_wav.shape[0] == wav.shape[0]
- assert read_wav.shape[-1] == 0
-
- def test_avread_seek_edge(self):
- sample_rates = [8000, 16_000]
- # some of these values will have
- # int(((frames - 1) / sample_rate) * sample_rate) != (frames - 1)
- n_frames = [1000, 1001, 1002]
- channels = [1, 2]
- for sample_rate, ch, frames in product(sample_rates, channels, n_frames):
- duration = frames / sample_rate
- wav = get_white_noise(ch, frames)
- path = self.get_temp_path(f'reference_d_{sample_rate}_{ch}.wav')
- save_wav(path, wav, sample_rate)
- seek_time = (frames - 1) / sample_rate
- seek_frames = int(seek_time * sample_rate)
- read_wav, read_sr = _av_read(path, seek_time, duration)
- assert read_sr == sample_rate
- assert read_wav.shape[0] == wav.shape[0]
- assert read_wav.shape[-1] == (frames - seek_frames)
-
-
-class TestAudioWrite(TempDirMixin):
-
- def test_audio_write_wav(self):
- torch.manual_seed(1234)
- sample_rates = [8000, 16_000]
- n_frames = [1000, 1001, 1002]
- channels = [1, 2]
- strategies = ["peak", "clip", "rms"]
- formats = ["wav", "mp3"]
- for sample_rate, ch, frames in product(sample_rates, channels, n_frames):
- for format_, strategy in product(formats, strategies):
- wav = get_white_noise(ch, frames)
- path = self.get_temp_path(f'pred_{sample_rate}_{ch}')
- audio_write(path, wav, sample_rate, format_, strategy=strategy)
- read_wav, read_sr = torchaudio.load(f'{path}.{format_}')
- if format_ == "wav":
- assert read_wav.shape == wav.shape
-
- if format_ == "wav" and strategy in ["peak", "rms"]:
- rescaled_read_wav = read_wav / read_wav.abs().max() * wav.abs().max()
- # for a Gaussian, the typical max scale will be less than ~5x the std.
- # The error when writing to disk will ~ 1/2**15, and when rescaling, 5x that.
- # For RMS target, rescaling leaves more headroom by default, leading
- # to a 20x rescaling typically
- atol = (5 if strategy == "peak" else 20) / 2**15
- delta = (rescaled_read_wav - wav).abs().max()
- assert torch.allclose(wav, rescaled_read_wav, rtol=0, atol=atol), (delta, atol)
- formats = ["wav"] # faster unit tests
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/point_rend/semantic_seg.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/point_rend/semantic_seg.py
deleted file mode 100644
index ea65200996777022cbb1c3c5dd9c943b67ca4ab1..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/point_rend/semantic_seg.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-from typing import Dict
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.layers import ShapeSpec, cat
-from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
-
-from .point_features import (
- get_uncertain_point_coords_on_grid,
- get_uncertain_point_coords_with_randomness,
- point_sample,
-)
-from .point_head import build_point_head
-
-
-def calculate_uncertainty(sem_seg_logits):
- """
- For each location of the prediction `sem_seg_logits` we estimate uncerainty as the
- difference between top first and top second predicted logits.
-
- Args:
- mask_logits (Tensor): A tensor of shape (N, C, ...), where N is the minibatch size and
- C is the number of foreground classes. The values are logits.
-
- Returns:
- scores (Tensor): A tensor of shape (N, 1, ...) that contains uncertainty scores with
- the most uncertain locations having the highest uncertainty score.
- """
- top2_scores = torch.topk(sem_seg_logits, k=2, dim=1)[0]
- return (top2_scores[:, 1] - top2_scores[:, 0]).unsqueeze(1)
-
-
-@SEM_SEG_HEADS_REGISTRY.register()
-class PointRendSemSegHead(nn.Module):
- """
- A semantic segmentation head that combines a head set in `POINT_HEAD.COARSE_SEM_SEG_HEAD_NAME`
- and a point head set in `MODEL.POINT_HEAD.NAME`.
- """
-
- def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
- super().__init__()
-
- self.ignore_value = cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE
-
- self.coarse_sem_seg_head = SEM_SEG_HEADS_REGISTRY.get(
- cfg.MODEL.POINT_HEAD.COARSE_SEM_SEG_HEAD_NAME
- )(cfg, input_shape)
- self._init_point_head(cfg, input_shape)
-
- def _init_point_head(self, cfg, input_shape: Dict[str, ShapeSpec]):
- # fmt: off
- assert cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES == cfg.MODEL.POINT_HEAD.NUM_CLASSES
- feature_channels = {k: v.channels for k, v in input_shape.items()}
- self.in_features = cfg.MODEL.POINT_HEAD.IN_FEATURES
- self.train_num_points = cfg.MODEL.POINT_HEAD.TRAIN_NUM_POINTS
- self.oversample_ratio = cfg.MODEL.POINT_HEAD.OVERSAMPLE_RATIO
- self.importance_sample_ratio = cfg.MODEL.POINT_HEAD.IMPORTANCE_SAMPLE_RATIO
- self.subdivision_steps = cfg.MODEL.POINT_HEAD.SUBDIVISION_STEPS
- self.subdivision_num_points = cfg.MODEL.POINT_HEAD.SUBDIVISION_NUM_POINTS
- # fmt: on
-
- in_channels = int(np.sum([feature_channels[f] for f in self.in_features]))
- self.point_head = build_point_head(cfg, ShapeSpec(channels=in_channels, width=1, height=1))
-
- def forward(self, features, targets=None):
- coarse_sem_seg_logits = self.coarse_sem_seg_head.layers(features)
-
- if self.training:
- losses = self.coarse_sem_seg_head.losses(coarse_sem_seg_logits, targets)
-
- with torch.no_grad():
- point_coords = get_uncertain_point_coords_with_randomness(
- coarse_sem_seg_logits,
- calculate_uncertainty,
- self.train_num_points,
- self.oversample_ratio,
- self.importance_sample_ratio,
- )
- coarse_features = point_sample(coarse_sem_seg_logits, point_coords, align_corners=False)
-
- fine_grained_features = cat(
- [
- point_sample(features[in_feature], point_coords, align_corners=False)
- for in_feature in self.in_features
- ],
- dim=1,
- )
- point_logits = self.point_head(fine_grained_features, coarse_features)
- point_targets = (
- point_sample(
- targets.unsqueeze(1).to(torch.float),
- point_coords,
- mode="nearest",
- align_corners=False,
- )
- .squeeze(1)
- .to(torch.long)
- )
- losses["loss_sem_seg_point"] = F.cross_entropy(
- point_logits, point_targets, reduction="mean", ignore_index=self.ignore_value
- )
- return None, losses
- else:
- sem_seg_logits = coarse_sem_seg_logits.clone()
- for _ in range(self.subdivision_steps):
- sem_seg_logits = F.interpolate(
- sem_seg_logits, scale_factor=2, mode="bilinear", align_corners=False
- )
- uncertainty_map = calculate_uncertainty(sem_seg_logits)
- point_indices, point_coords = get_uncertain_point_coords_on_grid(
- uncertainty_map, self.subdivision_num_points
- )
- fine_grained_features = cat(
- [
- point_sample(features[in_feature], point_coords, align_corners=False)
- for in_feature in self.in_features
- ]
- )
- coarse_features = point_sample(
- coarse_sem_seg_logits, point_coords, align_corners=False
- )
- point_logits = self.point_head(fine_grained_features, coarse_features)
-
- # put sem seg point predictions to the right places on the upsampled grid.
- N, C, H, W = sem_seg_logits.shape
- point_indices = point_indices.unsqueeze(1).expand(-1, C, -1)
- sem_seg_logits = (
- sem_seg_logits.reshape(N, C, H * W)
- .scatter_(2, point_indices, point_logits)
- .view(N, C, H, W)
- )
- return sem_seg_logits, {}
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/export/test_c10.py b/spaces/brjathu/HMR2.0/vendor/detectron2/tests/export/test_c10.py
deleted file mode 100644
index 55076abd15beb50b1774f0b5fe399b22d7cc630f..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/export/test_c10.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import unittest
-
-try:
- # Caffe2 used to be included in PyTorch, but since PyTorch 1.10+,
- # it is not included in pre-built packages. This is a safety BC check
- from detectron2.config import get_cfg
- from detectron2.export.c10 import Caffe2RPN
- from detectron2.layers import ShapeSpec
-except ImportError:
- raise unittest.SkipTest(
- f"PyTorch does not have Caffe2 support. Skipping all tests in {__name__}"
- ) from None
-
-
-class TestCaffe2RPN(unittest.TestCase):
- def test_instantiation(self):
- cfg = get_cfg()
- cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1, 1)
- input_shapes = {"res4": ShapeSpec(channels=256, stride=4)}
- rpn = Caffe2RPN(cfg, input_shapes)
- assert rpn is not None
- cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (10, 10, 5, 5, 1)
- with self.assertRaises(AssertionError):
- rpn = Caffe2RPN(cfg, input_shapes)
diff --git a/spaces/bulentsofttech/gradio_s1000_veri_toplama_modeli/yolov5/detect.py b/spaces/bulentsofttech/gradio_s1000_veri_toplama_modeli/yolov5/detect.py
deleted file mode 100644
index 9d92e4c169e4e29890469b8ae68006cfc0b3c82a..0000000000000000000000000000000000000000
--- a/spaces/bulentsofttech/gradio_s1000_veri_toplama_modeli/yolov5/detect.py
+++ /dev/null
@@ -1,253 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Run inference on images, videos, directories, streams, etc.
-
-Usage - sources:
- $ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam
- img.jpg # image
- vid.mp4 # video
- path/ # directory
- path/*.jpg # glob
- 'https://youtu.be/Zgi9g1ksQHc' # YouTube
- 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
-
-Usage - formats:
- $ python path/to/detect.py --weights yolov5s.pt # PyTorch
- yolov5s.torchscript # TorchScript
- yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
- yolov5s.xml # OpenVINO
- yolov5s.engine # TensorRT
- yolov5s.mlmodel # CoreML (macOS-only)
- yolov5s_saved_model # TensorFlow SavedModel
- yolov5s.pb # TensorFlow GraphDef
- yolov5s.tflite # TensorFlow Lite
- yolov5s_edgetpu.tflite # TensorFlow Edge TPU
-"""
-
-import argparse
-import os
-import sys
-from pathlib import Path
-
-import torch
-import torch.backends.cudnn as cudnn
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
-
-from models.common import DetectMultiBackend
-from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
-from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
- increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
-from utils.plots import Annotator, colors, save_one_box
-from utils.torch_utils import select_device, time_sync
-
-
-@torch.no_grad()
-def run(
- weights=ROOT / 'yolov5s.pt', # model.pt path(s)
- source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
- data=ROOT / 'data/coco128.yaml', # dataset.yaml path
- imgsz=(640, 640), # inference size (height, width)
- conf_thres=0.25, # confidence threshold
- iou_thres=0.45, # NMS IOU threshold
- max_det=1000, # maximum detections per image
- device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
- view_img=False, # show results
- save_txt=False, # save results to *.txt
- save_conf=False, # save confidences in --save-txt labels
- save_crop=False, # save cropped prediction boxes
- nosave=False, # do not save images/videos
- classes=None, # filter by class: --class 0, or --class 0 2 3
- agnostic_nms=False, # class-agnostic NMS
- augment=False, # augmented inference
- visualize=False, # visualize features
- update=False, # update all models
- project=ROOT / 'runs/detect', # save results to project/name
- name='exp', # save results to project/name
- exist_ok=False, # existing project/name ok, do not increment
- line_thickness=3, # bounding box thickness (pixels)
- hide_labels=False, # hide labels
- hide_conf=False, # hide confidences
- half=False, # use FP16 half-precision inference
- dnn=False, # use OpenCV DNN for ONNX inference
-):
- source = str(source)
- save_img = not nosave and not source.endswith('.txt') # save inference images
- is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
- is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
- webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
- if is_url and is_file:
- source = check_file(source) # download
-
- # Directories
- save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
- (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
-
- # Load model
- device = select_device(device)
- model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
- stride, names, pt = model.stride, model.names, model.pt
- imgsz = check_img_size(imgsz, s=stride) # check image size
-
- # Dataloader
- if webcam:
- view_img = check_imshow()
- cudnn.benchmark = True # set True to speed up constant image size inference
- dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
- bs = len(dataset) # batch_size
- else:
- dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
- bs = 1 # batch_size
- vid_path, vid_writer = [None] * bs, [None] * bs
-
- # Run inference
- model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
- dt, seen = [0.0, 0.0, 0.0], 0
- for path, im, im0s, vid_cap, s in dataset:
- t1 = time_sync()
- im = torch.from_numpy(im).to(device)
- im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
- im /= 255 # 0 - 255 to 0.0 - 1.0
- if len(im.shape) == 3:
- im = im[None] # expand for batch dim
- t2 = time_sync()
- dt[0] += t2 - t1
-
- # Inference
- visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
- pred = model(im, augment=augment, visualize=visualize)
- t3 = time_sync()
- dt[1] += t3 - t2
-
- # NMS
- pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
- dt[2] += time_sync() - t3
-
- # Second-stage classifier (optional)
- # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
-
- # Process predictions
- for i, det in enumerate(pred): # per image
- seen += 1
- if webcam: # batch_size >= 1
- p, im0, frame = path[i], im0s[i].copy(), dataset.count
- s += f'{i}: '
- else:
- p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
-
- p = Path(p) # to Path
- save_path = str(save_dir / p.name) # im.jpg
- txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
- s += '%gx%g ' % im.shape[2:] # print string
- gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
- imc = im0.copy() if save_crop else im0 # for save_crop
- annotator = Annotator(im0, line_width=line_thickness, example=str(names))
- if len(det):
- # Rescale boxes from img_size to im0 size
- det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
-
- # Print results
- for c in det[:, -1].unique():
- n = (det[:, -1] == c).sum() # detections per class
- s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
-
- # Write results
- for *xyxy, conf, cls in reversed(det):
- if save_txt: # Write to file
- xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
- line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
- with open(f'{txt_path}.txt', 'a') as f:
- f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
- if save_img or save_crop or view_img: # Add bbox to image
- c = int(cls) # integer class
- label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
- annotator.box_label(xyxy, label, color=colors(c, True))
- if save_crop:
- save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
-
- # Stream results
- im0 = annotator.result()
- if view_img:
- cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
- cv2.imshow(str(p), im0)
- cv2.waitKey(1) # 1 millisecond
-
- # Save results (image with detections)
- if save_img:
- if dataset.mode == 'image':
- cv2.imwrite(save_path, im0)
- else: # 'video' or 'stream'
- if vid_path[i] != save_path: # new video
- vid_path[i] = save_path
- if isinstance(vid_writer[i], cv2.VideoWriter):
- vid_writer[i].release() # release previous video writer
- if vid_cap: # video
- fps = vid_cap.get(cv2.CAP_PROP_FPS)
- w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
- h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
- else: # stream
- fps, w, h = 30, im0.shape[1], im0.shape[0]
- save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
- vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
- vid_writer[i].write(im0)
-
- # Print time (inference-only)
- LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
-
- # Print results
- t = tuple(x / seen * 1E3 for x in dt) # speeds per image
- LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
- if save_txt or save_img:
- s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
- LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
- if update:
- strip_optimizer(weights) # update model (to fix SourceChangeWarning)
-
-
-def parse_opt():
- parser = argparse.ArgumentParser()
- parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
- parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
- parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
- parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
- parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
- parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
- parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
- parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
- parser.add_argument('--view-img', action='store_true', help='show results')
- parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
- parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
- parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
- parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
- parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
- parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
- parser.add_argument('--augment', action='store_true', help='augmented inference')
- parser.add_argument('--visualize', action='store_true', help='visualize features')
- parser.add_argument('--update', action='store_true', help='update all models')
- parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
- parser.add_argument('--name', default='exp', help='save results to project/name')
- parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
- parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
- parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
- parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
- parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
- parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
- opt = parser.parse_args()
- opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
- print_args(vars(opt))
- return opt
-
-
-def main(opt):
- check_requirements(exclude=('tensorboard', 'thop'))
- run(**vars(opt))
-
-
-if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
diff --git a/spaces/cadige/01ST-CSV-Dataset-Analyzer/app.py b/spaces/cadige/01ST-CSV-Dataset-Analyzer/app.py
deleted file mode 100644
index d23cd9425c831fc2bfc190b0bff8523eb4203a09..0000000000000000000000000000000000000000
--- a/spaces/cadige/01ST-CSV-Dataset-Analyzer/app.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import streamlit as st
-import pandas as pd
-import traceback
-import sys
-
-from st_aggrid import AgGrid
-from st_aggrid.grid_options_builder import GridOptionsBuilder
-from st_aggrid.shared import JsCode
-from download import download_button
-from st_aggrid import GridUpdateMode, DataReturnMode
-
-# Page config is set once with icon title and display style. Wide mode since we want screen real estate for wide CSV files
-st.set_page_config(page_icon="📝", page_title="📝CSV Data Analyzer📊", layout="wide")
-
-# Style
-def _max_width_():
- max_width_str = f"max-width: 1800px;"
- st.markdown(
- f"""
-
- """,
- unsafe_allow_html=True,
- )
-
-# Title Bar with Images and Icons
-col1, col2, col3 = st.columns([1,6,1])
-with col1:
- st.image("https://cdnb.artstation.com/p/assets/images/images/054/910/875/large/aaron-wacker-cyberpunk-computer-brain-design.jpg?1665656558",width=624,)
-with col2:
- st.title("📝 CSV Data Analyzer 📊")
-with col3:
- st.image("https://cdna.artstation.com/p/assets/images/images/054/910/878/large/aaron-wacker-cyberpunk-computer-devices-iot.jpg?1665656564",width=624,)
-
-# Upload
-c29, c30, c31 = st.columns([1, 6, 1])
-with c30:
- uploaded_file = st.file_uploader("", key="1", help="To activate 'wide mode', go to the menu > Settings > turn on 'wide mode'",)
- if uploaded_file is not None:
- file_container = st.expander("Check your uploaded .csv")
- #try:
- shows = pd.read_csv(uploaded_file)
- #except:
- # print(sys.exc_info()[2])
-
- uploaded_file.seek(0)
- file_container.write(shows)
- else:
- st.info(f"""⬆️Upload a 📝.CSV file. Examples: [Chatbot](https://huggingface.co/datasets/awacke1/Carddata.csv) [Mindfulness](https://huggingface.co/datasets/awacke1/MindfulStory.csv) [Wikipedia](https://huggingface.co/datasets/awacke1/WikipediaSearch)""")
- st.stop()
-
-# DisplayGrid
-gb = GridOptionsBuilder.from_dataframe(shows)
-gb.configure_default_column(enablePivot=True, enableValue=True, enableRowGroup=True)
-gb.configure_selection(selection_mode="multiple", use_checkbox=True)
-gb.configure_side_bar()
-gridOptions = gb.build()
-st.success(f"""💡 Tip! Hold shift key when selecting rows to select multiple rows at once.""")
-response = AgGrid(
- shows,
- gridOptions=gridOptions,
- enable_enterprise_modules=True,
- update_mode=GridUpdateMode.MODEL_CHANGED,
- data_return_mode=DataReturnMode.FILTERED_AND_SORTED,
- fit_columns_on_grid_load=False,
-)
-
-# Filters
-df = pd.DataFrame(response["selected_rows"])
-st.subheader("Filtered data will appear below 📊 ")
-st.text("")
-st.table(df)
-st.text("")
-
-# Download
-c29, c30, c31 = st.columns([1, 1, 2])
-with c29:
- CSVButton = download_button(df,"Dataset.csv","Download CSV file",)
-with c30:
- CSVButton = download_button(df,"Dataset.txt","Download TXT file",)
\ No newline at end of file
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/GimpGradientFile.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/GimpGradientFile.py
deleted file mode 100644
index 8e801be0b8a3c373e3cbd274a10f0da57edb5e70..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/GimpGradientFile.py
+++ /dev/null
@@ -1,137 +0,0 @@
-#
-# Python Imaging Library
-# $Id$
-#
-# stuff to read (and render) GIMP gradient files
-#
-# History:
-# 97-08-23 fl Created
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1997.
-#
-# See the README file for information on usage and redistribution.
-#
-
-"""
-Stuff to translate curve segments to palette values (derived from
-the corresponding code in GIMP, written by Federico Mena Quintero.
-See the GIMP distribution for more information.)
-"""
-
-
-from math import log, pi, sin, sqrt
-
-from ._binary import o8
-
-EPSILON = 1e-10
-"""""" # Enable auto-doc for data member
-
-
-def linear(middle, pos):
- if pos <= middle:
- if middle < EPSILON:
- return 0.0
- else:
- return 0.5 * pos / middle
- else:
- pos = pos - middle
- middle = 1.0 - middle
- if middle < EPSILON:
- return 1.0
- else:
- return 0.5 + 0.5 * pos / middle
-
-
-def curved(middle, pos):
- return pos ** (log(0.5) / log(max(middle, EPSILON)))
-
-
-def sine(middle, pos):
- return (sin((-pi / 2.0) + pi * linear(middle, pos)) + 1.0) / 2.0
-
-
-def sphere_increasing(middle, pos):
- return sqrt(1.0 - (linear(middle, pos) - 1.0) ** 2)
-
-
-def sphere_decreasing(middle, pos):
- return 1.0 - sqrt(1.0 - linear(middle, pos) ** 2)
-
-
-SEGMENTS = [linear, curved, sine, sphere_increasing, sphere_decreasing]
-"""""" # Enable auto-doc for data member
-
-
-class GradientFile:
- gradient = None
-
- def getpalette(self, entries=256):
- palette = []
-
- ix = 0
- x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix]
-
- for i in range(entries):
- x = i / (entries - 1)
-
- while x1 < x:
- ix += 1
- x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix]
-
- w = x1 - x0
-
- if w < EPSILON:
- scale = segment(0.5, 0.5)
- else:
- scale = segment((xm - x0) / w, (x - x0) / w)
-
- # expand to RGBA
- r = o8(int(255 * ((rgb1[0] - rgb0[0]) * scale + rgb0[0]) + 0.5))
- g = o8(int(255 * ((rgb1[1] - rgb0[1]) * scale + rgb0[1]) + 0.5))
- b = o8(int(255 * ((rgb1[2] - rgb0[2]) * scale + rgb0[2]) + 0.5))
- a = o8(int(255 * ((rgb1[3] - rgb0[3]) * scale + rgb0[3]) + 0.5))
-
- # add to palette
- palette.append(r + g + b + a)
-
- return b"".join(palette), "RGBA"
-
-
-class GimpGradientFile(GradientFile):
- """File handler for GIMP's gradient format."""
-
- def __init__(self, fp):
- if fp.readline()[:13] != b"GIMP Gradient":
- msg = "not a GIMP gradient file"
- raise SyntaxError(msg)
-
- line = fp.readline()
-
- # GIMP 1.2 gradient files don't contain a name, but GIMP 1.3 files do
- if line.startswith(b"Name: "):
- line = fp.readline().strip()
-
- count = int(line)
-
- gradient = []
-
- for i in range(count):
- s = fp.readline().split()
- w = [float(x) for x in s[:11]]
-
- x0, x1 = w[0], w[2]
- xm = w[1]
- rgb0 = w[3:7]
- rgb1 = w[7:11]
-
- segment = SEGMENTS[int(s[11])]
- cspace = int(s[12])
-
- if cspace != 0:
- msg = "cannot handle HSV colour space"
- raise OSError(msg)
-
- gradient.append((x0, x1, xm, rgb0, rgb1, segment))
-
- self.gradient = gradient
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/base_protocol.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/base_protocol.py
deleted file mode 100644
index 4c9f0a752e3aa833a17b7adf0c261d19a5f083fa..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/base_protocol.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import asyncio
-from typing import Optional, cast
-
-from .tcp_helpers import tcp_nodelay
-
-
-class BaseProtocol(asyncio.Protocol):
- __slots__ = (
- "_loop",
- "_paused",
- "_drain_waiter",
- "_connection_lost",
- "_reading_paused",
- "transport",
- )
-
- def __init__(self, loop: asyncio.AbstractEventLoop) -> None:
- self._loop: asyncio.AbstractEventLoop = loop
- self._paused = False
- self._drain_waiter: Optional[asyncio.Future[None]] = None
- self._reading_paused = False
-
- self.transport: Optional[asyncio.Transport] = None
-
- @property
- def connected(self) -> bool:
- """Return True if the connection is open."""
- return self.transport is not None
-
- def pause_writing(self) -> None:
- assert not self._paused
- self._paused = True
-
- def resume_writing(self) -> None:
- assert self._paused
- self._paused = False
-
- waiter = self._drain_waiter
- if waiter is not None:
- self._drain_waiter = None
- if not waiter.done():
- waiter.set_result(None)
-
- def pause_reading(self) -> None:
- if not self._reading_paused and self.transport is not None:
- try:
- self.transport.pause_reading()
- except (AttributeError, NotImplementedError, RuntimeError):
- pass
- self._reading_paused = True
-
- def resume_reading(self) -> None:
- if self._reading_paused and self.transport is not None:
- try:
- self.transport.resume_reading()
- except (AttributeError, NotImplementedError, RuntimeError):
- pass
- self._reading_paused = False
-
- def connection_made(self, transport: asyncio.BaseTransport) -> None:
- tr = cast(asyncio.Transport, transport)
- tcp_nodelay(tr, True)
- self.transport = tr
-
- def connection_lost(self, exc: Optional[BaseException]) -> None:
- # Wake up the writer if currently paused.
- self.transport = None
- if not self._paused:
- return
- waiter = self._drain_waiter
- if waiter is None:
- return
- self._drain_waiter = None
- if waiter.done():
- return
- if exc is None:
- waiter.set_result(None)
- else:
- waiter.set_exception(exc)
-
- async def _drain_helper(self) -> None:
- if not self.connected:
- raise ConnectionResetError("Connection lost")
- if not self._paused:
- return
- waiter = self._drain_waiter
- if waiter is None:
- waiter = self._loop.create_future()
- self._drain_waiter = waiter
- await asyncio.shield(waiter)
diff --git a/spaces/candlend/vits-hoshimi/sovits/hubert/hubert_model.py b/spaces/candlend/vits-hoshimi/sovits/hubert/hubert_model.py
deleted file mode 100644
index 7fb642d89b07ca60792debab18e3454f52d8f357..0000000000000000000000000000000000000000
--- a/spaces/candlend/vits-hoshimi/sovits/hubert/hubert_model.py
+++ /dev/null
@@ -1,222 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
- def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
- super().__init__()
- self._mask = mask
- self.feature_extractor = FeatureExtractor()
- self.feature_projection = FeatureProjection()
- self.positional_embedding = PositionalConvEmbedding()
- self.norm = nn.LayerNorm(768)
- self.dropout = nn.Dropout(0.1)
- self.encoder = TransformerEncoder(
- nn.TransformerEncoderLayer(
- 768, 12, 3072, activation="gelu", batch_first=True
- ),
- 12,
- )
- self.proj = nn.Linear(768, 256)
-
- self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
- self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
- def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
- mask = None
- if self.training and self._mask:
- mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
- x[mask] = self.masked_spec_embed.to(x.dtype)
- return x, mask
-
- def encode(
- self, x: torch.Tensor, layer: Optional[int] = None
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- x = self.feature_extractor(x)
- x = self.feature_projection(x.transpose(1, 2))
- x, mask = self.mask(x)
- x = x + self.positional_embedding(x)
- x = self.dropout(self.norm(x))
- x = self.encoder(x, output_layer=layer)
- return x, mask
-
- def logits(self, x: torch.Tensor) -> torch.Tensor:
- logits = torch.cosine_similarity(
- x.unsqueeze(2),
- self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
- dim=-1,
- )
- return logits / 0.1
-
- def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
- x, mask = self.encode(x)
- x = self.proj(x)
- logits = self.logits(x)
- return logits, mask
-
-
-class HubertSoft(Hubert):
- def __init__(self):
- super().__init__()
-
- @torch.inference_mode()
- def units(self, wav: torch.Tensor) -> torch.Tensor:
- wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
- x, _ = self.encode(wav)
- return self.proj(x)
-
-
-class FeatureExtractor(nn.Module):
- def __init__(self):
- super().__init__()
- self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
- self.norm0 = nn.GroupNorm(512, 512)
- self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
- self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
- self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
- self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
- self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
- self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = t_func.gelu(self.norm0(self.conv0(x)))
- x = t_func.gelu(self.conv1(x))
- x = t_func.gelu(self.conv2(x))
- x = t_func.gelu(self.conv3(x))
- x = t_func.gelu(self.conv4(x))
- x = t_func.gelu(self.conv5(x))
- x = t_func.gelu(self.conv6(x))
- return x
-
-
-class FeatureProjection(nn.Module):
- def __init__(self):
- super().__init__()
- self.norm = nn.LayerNorm(512)
- self.projection = nn.Linear(512, 768)
- self.dropout = nn.Dropout(0.1)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.norm(x)
- x = self.projection(x)
- x = self.dropout(x)
- return x
-
-
-class PositionalConvEmbedding(nn.Module):
- def __init__(self):
- super().__init__()
- self.conv = nn.Conv1d(
- 768,
- 768,
- kernel_size=128,
- padding=128 // 2,
- groups=16,
- )
- self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.conv(x.transpose(1, 2))
- x = t_func.gelu(x[:, :, :-1])
- return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
- def __init__(
- self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
- ) -> None:
- super(TransformerEncoder, self).__init__()
- self.layers = nn.ModuleList(
- [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
- )
- self.num_layers = num_layers
-
- def forward(
- self,
- src: torch.Tensor,
- mask: torch.Tensor = None,
- src_key_padding_mask: torch.Tensor = None,
- output_layer: Optional[int] = None,
- ) -> torch.Tensor:
- output = src
- for layer in self.layers[:output_layer]:
- output = layer(
- output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
- )
- return output
-
-
-def _compute_mask(
- shape: Tuple[int, int],
- mask_prob: float,
- mask_length: int,
- device: torch.device,
- min_masks: int = 0,
-) -> torch.Tensor:
- batch_size, sequence_length = shape
-
- if mask_length < 1:
- raise ValueError("`mask_length` has to be bigger than 0.")
-
- if mask_length > sequence_length:
- raise ValueError(
- f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
- )
-
- # compute number of masked spans in batch
- num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
- num_masked_spans = max(num_masked_spans, min_masks)
-
- # make sure num masked indices <= sequence_length
- if num_masked_spans * mask_length > sequence_length:
- num_masked_spans = sequence_length // mask_length
-
- # SpecAugment mask to fill
- mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
- # uniform distribution to sample from, make sure that offset samples are < sequence_length
- uniform_dist = torch.ones(
- (batch_size, sequence_length - (mask_length - 1)), device=device
- )
-
- # get random indices to mask
- mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
- # expand masked indices to masked spans
- mask_indices = (
- mask_indices.unsqueeze(dim=-1)
- .expand((batch_size, num_masked_spans, mask_length))
- .reshape(batch_size, num_masked_spans * mask_length)
- )
- offsets = (
- torch.arange(mask_length, device=device)[None, None, :]
- .expand((batch_size, num_masked_spans, mask_length))
- .reshape(batch_size, num_masked_spans * mask_length)
- )
- mask_idxs = mask_indices + offsets
-
- # scatter indices to mask
- mask = mask.scatter(1, mask_idxs, True)
-
- return mask
-
-
-def hubert_soft(
- path: str,
-) -> HubertSoft:
- r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
- Args:
- path (str): path of a pretrained model
- """
- hubert = HubertSoft()
- checkpoint = torch.load(path)
- consume_prefix_in_state_dict_if_present(checkpoint, "module.")
- hubert.load_state_dict(checkpoint)
- hubert.eval()
- return hubert
diff --git a/spaces/ceyda/kornia-augmentations-tester/README.md b/spaces/ceyda/kornia-augmentations-tester/README.md
deleted file mode 100644
index 98a7760570fc7790811c0be4150ba9c20164b40d..0000000000000000000000000000000000000000
--- a/spaces/ceyda/kornia-augmentations-tester/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Kornia Augmentations Tester
-emoji: 🙂🙃😎
-colorFrom: blue
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: kornia_aug.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/commands/__init__.py b/spaces/chendl/compositional_test/transformers/src/transformers/commands/__init__.py
deleted file mode 100644
index aa5d95a85b538171ec9cf4fa16e892df1efdef6b..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/commands/__init__.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from abc import ABC, abstractmethod
-from argparse import ArgumentParser
-
-
-class BaseTransformersCLICommand(ABC):
- @staticmethod
- @abstractmethod
- def register_subcommand(parser: ArgumentParser):
- raise NotImplementedError()
-
- @abstractmethod
- def run(self):
- raise NotImplementedError()
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py b/spaces/chendl/compositional_test/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py
deleted file mode 100644
index 0f8c045121c74956b4c90a11483d35741ef9552c..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py
+++ /dev/null
@@ -1,627 +0,0 @@
-# coding=utf-8
-# Copyright 2022 MIT and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch Audio Spectrogram Transformer (AST) model."""
-
-import math
-from typing import Dict, List, Optional, Set, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-
-from ...activations import ACT2FN
-from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, SequenceClassifierOutput
-from ...modeling_utils import PreTrainedModel
-from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
-from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
-from .configuration_audio_spectrogram_transformer import ASTConfig
-
-
-logger = logging.get_logger(__name__)
-
-# General docstring
-_CONFIG_FOR_DOC = "ASTConfig"
-
-# Base docstring
-_CHECKPOINT_FOR_DOC = "MIT/ast-finetuned-audioset-10-10-0.4593"
-_EXPECTED_OUTPUT_SHAPE = [1, 1214, 768]
-
-# Audio classification docstring
-_SEQ_CLASS_CHECKPOINT = "MIT/ast-finetuned-audioset-10-10-0.4593"
-_SEQ_CLASS_EXPECTED_OUTPUT = "'Speech'"
-_SEQ_CLASS_EXPECTED_LOSS = 0.17
-
-
-AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
- "MIT/ast-finetuned-audioset-10-10-0.4593",
- # See all Audio Spectrogram Transformer models at https://huggingface.co/models?filter=ast
-]
-
-
-class ASTEmbeddings(nn.Module):
- """
- Construct the CLS token, position and patch embeddings.
- """
-
- def __init__(self, config: ASTConfig) -> None:
- super().__init__()
-
- self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
- self.distillation_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
- self.patch_embeddings = ASTPatchEmbeddings(config)
-
- frequency_out_dimension, time_out_dimension = self.get_shape(config)
- num_patches = frequency_out_dimension * time_out_dimension
- self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 2, config.hidden_size))
- self.dropout = nn.Dropout(config.hidden_dropout_prob)
- self.config = config
-
- def get_shape(self, config):
- # see Karpathy's cs231n blog on how to calculate the output dimensions
- # https://cs231n.github.io/convolutional-networks/#conv
- frequency_out_dimension = (config.num_mel_bins - config.patch_size) // config.frequency_stride + 1
- time_out_dimension = (config.max_length - config.patch_size) // config.time_stride + 1
-
- return frequency_out_dimension, time_out_dimension
-
- def forward(self, input_values: torch.Tensor) -> torch.Tensor:
- batch_size = input_values.shape[0]
- embeddings = self.patch_embeddings(input_values)
-
- cls_tokens = self.cls_token.expand(batch_size, -1, -1)
- distillation_tokens = self.distillation_token.expand(batch_size, -1, -1)
- embeddings = torch.cat((cls_tokens, distillation_tokens, embeddings), dim=1)
- embeddings = embeddings + self.position_embeddings
- embeddings = self.dropout(embeddings)
-
- return embeddings
-
-
-class ASTPatchEmbeddings(nn.Module):
- """
- This class turns `input_values` into the initial `hidden_states` (patch embeddings) of shape `(batch_size,
- seq_length, hidden_size)` to be consumed by a Transformer.
- """
-
- def __init__(self, config):
- super().__init__()
-
- patch_size = config.patch_size
- frequency_stride = config.frequency_stride
- time_stride = config.time_stride
-
- self.projection = nn.Conv2d(
- 1, config.hidden_size, kernel_size=(patch_size, patch_size), stride=(frequency_stride, time_stride)
- )
-
- def forward(self, input_values: torch.Tensor) -> torch.Tensor:
- input_values = input_values.unsqueeze(1)
- input_values = input_values.transpose(2, 3)
- embeddings = self.projection(input_values).flatten(2).transpose(1, 2)
- return embeddings
-
-
-# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->AST
-class ASTSelfAttention(nn.Module):
- def __init__(self, config: ASTConfig) -> None:
- super().__init__()
- if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
- raise ValueError(
- f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
- f"heads {config.num_attention_heads}."
- )
-
- self.num_attention_heads = config.num_attention_heads
- self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
- self.all_head_size = self.num_attention_heads * self.attention_head_size
-
- self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
- self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
- self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
-
- self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-
- def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
- new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
- x = x.view(new_x_shape)
- return x.permute(0, 2, 1, 3)
-
- def forward(
- self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
- ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
- mixed_query_layer = self.query(hidden_states)
-
- key_layer = self.transpose_for_scores(self.key(hidden_states))
- value_layer = self.transpose_for_scores(self.value(hidden_states))
- query_layer = self.transpose_for_scores(mixed_query_layer)
-
- # Take the dot product between "query" and "key" to get the raw attention scores.
- attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-
- attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-
- # Normalize the attention scores to probabilities.
- attention_probs = nn.functional.softmax(attention_scores, dim=-1)
-
- # This is actually dropping out entire tokens to attend to, which might
- # seem a bit unusual, but is taken from the original Transformer paper.
- attention_probs = self.dropout(attention_probs)
-
- # Mask heads if we want to
- if head_mask is not None:
- attention_probs = attention_probs * head_mask
-
- context_layer = torch.matmul(attention_probs, value_layer)
-
- context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
- new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
- context_layer = context_layer.view(new_context_layer_shape)
-
- outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
-
- return outputs
-
-
-# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->AST
-class ASTSelfOutput(nn.Module):
- """
- The residual connection is defined in ASTLayer instead of here (as is the case with other models), due to the
- layernorm applied before each block.
- """
-
- def __init__(self, config: ASTConfig) -> None:
- super().__init__()
- self.dense = nn.Linear(config.hidden_size, config.hidden_size)
- self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
- def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
- hidden_states = self.dense(hidden_states)
- hidden_states = self.dropout(hidden_states)
-
- return hidden_states
-
-
-# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->AST
-class ASTAttention(nn.Module):
- def __init__(self, config: ASTConfig) -> None:
- super().__init__()
- self.attention = ASTSelfAttention(config)
- self.output = ASTSelfOutput(config)
- self.pruned_heads = set()
-
- def prune_heads(self, heads: Set[int]) -> None:
- if len(heads) == 0:
- return
- heads, index = find_pruneable_heads_and_indices(
- heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
- )
-
- # Prune linear layers
- self.attention.query = prune_linear_layer(self.attention.query, index)
- self.attention.key = prune_linear_layer(self.attention.key, index)
- self.attention.value = prune_linear_layer(self.attention.value, index)
- self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-
- # Update hyper params and store pruned heads
- self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
- self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
- self.pruned_heads = self.pruned_heads.union(heads)
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- head_mask: Optional[torch.Tensor] = None,
- output_attentions: bool = False,
- ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
- self_outputs = self.attention(hidden_states, head_mask, output_attentions)
-
- attention_output = self.output(self_outputs[0], hidden_states)
-
- outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
- return outputs
-
-
-# Copied from transformers.models.vit.modeling_vit.ViTIntermediate with ViT->AST
-class ASTIntermediate(nn.Module):
- def __init__(self, config: ASTConfig) -> None:
- super().__init__()
- self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
- if isinstance(config.hidden_act, str):
- self.intermediate_act_fn = ACT2FN[config.hidden_act]
- else:
- self.intermediate_act_fn = config.hidden_act
-
- def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
- hidden_states = self.dense(hidden_states)
- hidden_states = self.intermediate_act_fn(hidden_states)
-
- return hidden_states
-
-
-# Copied from transformers.models.vit.modeling_vit.ViTOutput with ViT->AST
-class ASTOutput(nn.Module):
- def __init__(self, config: ASTConfig) -> None:
- super().__init__()
- self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
- self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
- def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
- hidden_states = self.dense(hidden_states)
- hidden_states = self.dropout(hidden_states)
-
- hidden_states = hidden_states + input_tensor
-
- return hidden_states
-
-
-# Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->AST
-class ASTLayer(nn.Module):
- """This corresponds to the Block class in the timm implementation."""
-
- def __init__(self, config: ASTConfig) -> None:
- super().__init__()
- self.chunk_size_feed_forward = config.chunk_size_feed_forward
- self.seq_len_dim = 1
- self.attention = ASTAttention(config)
- self.intermediate = ASTIntermediate(config)
- self.output = ASTOutput(config)
- self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
- self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- head_mask: Optional[torch.Tensor] = None,
- output_attentions: bool = False,
- ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
- self_attention_outputs = self.attention(
- self.layernorm_before(hidden_states), # in AST, layernorm is applied before self-attention
- head_mask,
- output_attentions=output_attentions,
- )
- attention_output = self_attention_outputs[0]
- outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
-
- # first residual connection
- hidden_states = attention_output + hidden_states
-
- # in AST, layernorm is also applied after self-attention
- layer_output = self.layernorm_after(hidden_states)
- layer_output = self.intermediate(layer_output)
-
- # second residual connection is done here
- layer_output = self.output(layer_output, hidden_states)
-
- outputs = (layer_output,) + outputs
-
- return outputs
-
-
-# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->AST
-class ASTEncoder(nn.Module):
- def __init__(self, config: ASTConfig) -> None:
- super().__init__()
- self.config = config
- self.layer = nn.ModuleList([ASTLayer(config) for _ in range(config.num_hidden_layers)])
- self.gradient_checkpointing = False
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- head_mask: Optional[torch.Tensor] = None,
- output_attentions: bool = False,
- output_hidden_states: bool = False,
- return_dict: bool = True,
- ) -> Union[tuple, BaseModelOutput]:
- all_hidden_states = () if output_hidden_states else None
- all_self_attentions = () if output_attentions else None
-
- for i, layer_module in enumerate(self.layer):
- if output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states,)
-
- layer_head_mask = head_mask[i] if head_mask is not None else None
-
- if self.gradient_checkpointing and self.training:
-
- def create_custom_forward(module):
- def custom_forward(*inputs):
- return module(*inputs, output_attentions)
-
- return custom_forward
-
- layer_outputs = torch.utils.checkpoint.checkpoint(
- create_custom_forward(layer_module),
- hidden_states,
- layer_head_mask,
- )
- else:
- layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions)
-
- hidden_states = layer_outputs[0]
-
- if output_attentions:
- all_self_attentions = all_self_attentions + (layer_outputs[1],)
-
- if output_hidden_states:
- all_hidden_states = all_hidden_states + (hidden_states,)
-
- if not return_dict:
- return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
- return BaseModelOutput(
- last_hidden_state=hidden_states,
- hidden_states=all_hidden_states,
- attentions=all_self_attentions,
- )
-
-
-class ASTPreTrainedModel(PreTrainedModel):
- """
- An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
- models.
- """
-
- config_class = ASTConfig
- base_model_prefix = "audio_spectrogram_transformer"
- main_input_name = "input_values"
- supports_gradient_checkpointing = True
-
- # Copied from transformers.models.deit.modeling_deit.DeiTPreTrainedModel._init_weights
- def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
- """Initialize the weights"""
- if isinstance(module, (nn.Linear, nn.Conv2d)):
- # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
- # `trunc_normal_cpu` not implemented in `half` issues
- module.weight.data = nn.init.trunc_normal_(
- module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
- ).to(module.weight.dtype)
- if module.bias is not None:
- module.bias.data.zero_()
- elif isinstance(module, nn.LayerNorm):
- module.bias.data.zero_()
- module.weight.data.fill_(1.0)
-
- # Copied from transformers.models.vit.modeling_vit.ViTPreTrainedModel._set_gradient_checkpointing with ViT->AST
- def _set_gradient_checkpointing(self, module: ASTEncoder, value: bool = False) -> None:
- if isinstance(module, ASTEncoder):
- module.gradient_checkpointing = value
-
-
-AUDIO_SPECTROGRAM_TRANSFORMER_START_DOCSTRING = r"""
- This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
- as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
- behavior.
-
- Parameters:
- config ([`ASTConfig`]):
- Model configuration class with all the parameters of the model. Initializing with a config file does not
- load the weights associated with the model, only the configuration. Check out the
- [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-AUDIO_SPECTROGRAM_TRANSFORMER_INPUTS_DOCSTRING = r"""
- Args:
- input_values (`torch.FloatTensor` of shape `(batch_size, max_length, num_mel_bins)`):
- Float values mel features extracted from the raw audio waveform. Raw audio waveform can be obtained by
- loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via
- the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
- [`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
- tensor of type `torch.FloatTensor`. See [`~ASTFeatureExtractor.__call__`]
-
- head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
- Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
-
- - 1 indicates the head is **not masked**,
- - 0 indicates the head is **masked**.
-
- output_attentions (`bool`, *optional*):
- Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
- tensors for more detail.
- output_hidden_states (`bool`, *optional*):
- Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
- more detail.
- return_dict (`bool`, *optional*):
- Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
- "The bare AST Model transformer outputting raw hidden-states without any specific head on top.",
- AUDIO_SPECTROGRAM_TRANSFORMER_START_DOCSTRING,
-)
-class ASTModel(ASTPreTrainedModel):
- def __init__(self, config: ASTConfig):
- super().__init__(config)
- self.config = config
-
- self.embeddings = ASTEmbeddings(config)
- self.encoder = ASTEncoder(config)
-
- self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- def get_input_embeddings(self) -> ASTPatchEmbeddings:
- return self.embeddings.patch_embeddings
-
- def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
- """
- Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
- class PreTrainedModel
- """
- for layer, heads in heads_to_prune.items():
- self.encoder.layer[layer].attention.prune_heads(heads)
-
- @add_start_docstrings_to_model_forward(AUDIO_SPECTROGRAM_TRANSFORMER_INPUTS_DOCSTRING)
- @add_code_sample_docstrings(
- checkpoint=_CHECKPOINT_FOR_DOC,
- output_type=BaseModelOutputWithPooling,
- config_class=_CONFIG_FOR_DOC,
- modality="audio",
- expected_output=_EXPECTED_OUTPUT_SHAPE,
- )
- def forward(
- self,
- input_values: Optional[torch.Tensor] = None,
- head_mask: Optional[torch.Tensor] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ):
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
- )
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- if input_values is None:
- raise ValueError("You have to specify input_values")
-
- # Prepare head mask if needed
- # 1.0 in head_mask indicate we keep the head
- # attention_probs has shape bsz x n_heads x N x N
- # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
- # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
- head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
- embedding_output = self.embeddings(input_values)
-
- encoder_outputs = self.encoder(
- embedding_output,
- head_mask=head_mask,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
- sequence_output = encoder_outputs[0]
- sequence_output = self.layernorm(sequence_output)
-
- pooled_output = (sequence_output[:, 0] + sequence_output[:, 1]) / 2
-
- if not return_dict:
- return (sequence_output, pooled_output) + encoder_outputs[1:]
-
- return BaseModelOutputWithPooling(
- last_hidden_state=sequence_output,
- pooler_output=pooled_output,
- hidden_states=encoder_outputs.hidden_states,
- attentions=encoder_outputs.attentions,
- )
-
-
-class ASTMLPHead(nn.Module):
- def __init__(self, config: ASTConfig):
- super().__init__()
- self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
- self.dense = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
-
- def forward(self, hidden_state):
- hidden_state = self.layernorm(hidden_state)
- hidden_state = self.dense(hidden_state)
- return hidden_state
-
-
-@add_start_docstrings(
- """
- Audio Spectrogram Transformer model with an audio classification head on top (a linear layer on top of the pooled
- output) e.g. for datasets like AudioSet, Speech Commands v2.
- """,
- AUDIO_SPECTROGRAM_TRANSFORMER_START_DOCSTRING,
-)
-class ASTForAudioClassification(ASTPreTrainedModel):
- def __init__(self, config: ASTConfig) -> None:
- super().__init__(config)
-
- self.num_labels = config.num_labels
- self.audio_spectrogram_transformer = ASTModel(config)
-
- # Classifier head
- self.classifier = ASTMLPHead(config)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- @add_start_docstrings_to_model_forward(AUDIO_SPECTROGRAM_TRANSFORMER_INPUTS_DOCSTRING)
- @add_code_sample_docstrings(
- checkpoint=_SEQ_CLASS_CHECKPOINT,
- output_type=SequenceClassifierOutput,
- config_class=_CONFIG_FOR_DOC,
- modality="audio",
- expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
- expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
- )
- def forward(
- self,
- input_values: Optional[torch.Tensor] = None,
- head_mask: Optional[torch.Tensor] = None,
- labels: Optional[torch.Tensor] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[tuple, SequenceClassifierOutput]:
- r"""
- labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
- Labels for computing the audio classification/regression loss. Indices should be in `[0, ...,
- config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
- `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- outputs = self.audio_spectrogram_transformer(
- input_values,
- head_mask=head_mask,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- pooled_output = outputs[1]
- logits = self.classifier(pooled_output)
-
- loss = None
- if labels is not None:
- if self.config.problem_type is None:
- if self.num_labels == 1:
- self.config.problem_type = "regression"
- elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
- self.config.problem_type = "single_label_classification"
- else:
- self.config.problem_type = "multi_label_classification"
-
- if self.config.problem_type == "regression":
- loss_fct = MSELoss()
- if self.num_labels == 1:
- loss = loss_fct(logits.squeeze(), labels.squeeze())
- else:
- loss = loss_fct(logits, labels)
- elif self.config.problem_type == "single_label_classification":
- loss_fct = CrossEntropyLoss()
- loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
- elif self.config.problem_type == "multi_label_classification":
- loss_fct = BCEWithLogitsLoss()
- loss = loss_fct(logits, labels)
-
- if not return_dict:
- output = (logits,) + outputs[2:]
- return ((loss,) + output) if loss is not None else output
-
- return SequenceClassifierOutput(
- loss=loss,
- logits=logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/kdf/scrypt.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/kdf/scrypt.py
deleted file mode 100644
index 05a4f675b6ababd6701cd96850fdf89a28f9d2fd..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/kdf/scrypt.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-from __future__ import annotations
-
-import sys
-import typing
-
-from cryptography import utils
-from cryptography.exceptions import (
- AlreadyFinalized,
- InvalidKey,
- UnsupportedAlgorithm,
-)
-from cryptography.hazmat.bindings._rust import openssl as rust_openssl
-from cryptography.hazmat.primitives import constant_time
-from cryptography.hazmat.primitives.kdf import KeyDerivationFunction
-
-# This is used by the scrypt tests to skip tests that require more memory
-# than the MEM_LIMIT
-_MEM_LIMIT = sys.maxsize // 2
-
-
-class Scrypt(KeyDerivationFunction):
- def __init__(
- self,
- salt: bytes,
- length: int,
- n: int,
- r: int,
- p: int,
- backend: typing.Any = None,
- ):
- from cryptography.hazmat.backends.openssl.backend import (
- backend as ossl,
- )
-
- if not ossl.scrypt_supported():
- raise UnsupportedAlgorithm(
- "This version of OpenSSL does not support scrypt"
- )
- self._length = length
- utils._check_bytes("salt", salt)
- if n < 2 or (n & (n - 1)) != 0:
- raise ValueError("n must be greater than 1 and be a power of 2.")
-
- if r < 1:
- raise ValueError("r must be greater than or equal to 1.")
-
- if p < 1:
- raise ValueError("p must be greater than or equal to 1.")
-
- self._used = False
- self._salt = salt
- self._n = n
- self._r = r
- self._p = p
-
- def derive(self, key_material: bytes) -> bytes:
- if self._used:
- raise AlreadyFinalized("Scrypt instances can only be used once.")
- self._used = True
-
- utils._check_byteslike("key_material", key_material)
-
- return rust_openssl.kdf.derive_scrypt(
- key_material,
- self._salt,
- self._n,
- self._r,
- self._p,
- _MEM_LIMIT,
- self._length,
- )
-
- def verify(self, key_material: bytes, expected_key: bytes) -> None:
- derived_key = self.derive(key_material)
- if not constant_time.bytes_eq(derived_key, expected_key):
- raise InvalidKey("Keys do not match.")
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fastapi/params.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fastapi/params.py
deleted file mode 100644
index 30af5713e73e47ae4949c369a1fa35581028e556..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fastapi/params.py
+++ /dev/null
@@ -1,760 +0,0 @@
-import warnings
-from enum import Enum
-from typing import Any, Callable, Dict, List, Optional, Sequence, Union
-
-from pydantic.fields import FieldInfo
-from typing_extensions import Annotated, deprecated
-
-from ._compat import PYDANTIC_V2, Undefined
-
-_Unset: Any = Undefined
-
-
-class ParamTypes(Enum):
- query = "query"
- header = "header"
- path = "path"
- cookie = "cookie"
-
-
-class Param(FieldInfo):
- in_: ParamTypes
-
- def __init__(
- self,
- default: Any = Undefined,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- annotation: Optional[Any] = None,
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
- ):
- self.deprecated = deprecated
- if example is not _Unset:
- warnings.warn(
- "`example` has been depreacated, please use `examples` instead",
- category=DeprecationWarning,
- stacklevel=4,
- )
- self.example = example
- self.include_in_schema = include_in_schema
- kwargs = dict(
- default=default,
- default_factory=default_factory,
- alias=alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- discriminator=discriminator,
- multiple_of=multiple_of,
- allow_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- **extra,
- )
- if examples is not None:
- kwargs["examples"] = examples
- if regex is not None:
- warnings.warn(
- "`regex` has been depreacated, please use `pattern` instead",
- category=DeprecationWarning,
- stacklevel=4,
- )
- current_json_schema_extra = json_schema_extra or extra
- if PYDANTIC_V2:
- kwargs.update(
- {
- "annotation": annotation,
- "alias_priority": alias_priority,
- "validation_alias": validation_alias,
- "serialization_alias": serialization_alias,
- "strict": strict,
- "json_schema_extra": current_json_schema_extra,
- }
- )
- kwargs["pattern"] = pattern or regex
- else:
- kwargs["regex"] = pattern or regex
- kwargs.update(**current_json_schema_extra)
- use_kwargs = {k: v for k, v in kwargs.items() if v is not _Unset}
-
- super().__init__(**use_kwargs)
-
- def __repr__(self) -> str:
- return f"{self.__class__.__name__}({self.default})"
-
-
-class Path(Param):
- in_ = ParamTypes.path
-
- def __init__(
- self,
- default: Any = ...,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- annotation: Optional[Any] = None,
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
- ):
- assert default is ..., "Path parameters cannot have a default value"
- self.in_ = self.in_
- super().__init__(
- default=default,
- default_factory=default_factory,
- annotation=annotation,
- alias=alias,
- alias_priority=alias_priority,
- validation_alias=validation_alias,
- serialization_alias=serialization_alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- pattern=pattern,
- regex=regex,
- discriminator=discriminator,
- strict=strict,
- multiple_of=multiple_of,
- allow_inf_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- deprecated=deprecated,
- example=example,
- examples=examples,
- include_in_schema=include_in_schema,
- json_schema_extra=json_schema_extra,
- **extra,
- )
-
-
-class Query(Param):
- in_ = ParamTypes.query
-
- def __init__(
- self,
- default: Any = Undefined,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- annotation: Optional[Any] = None,
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
- ):
- super().__init__(
- default=default,
- default_factory=default_factory,
- annotation=annotation,
- alias=alias,
- alias_priority=alias_priority,
- validation_alias=validation_alias,
- serialization_alias=serialization_alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- pattern=pattern,
- regex=regex,
- discriminator=discriminator,
- strict=strict,
- multiple_of=multiple_of,
- allow_inf_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- deprecated=deprecated,
- example=example,
- examples=examples,
- include_in_schema=include_in_schema,
- json_schema_extra=json_schema_extra,
- **extra,
- )
-
-
-class Header(Param):
- in_ = ParamTypes.header
-
- def __init__(
- self,
- default: Any = Undefined,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- annotation: Optional[Any] = None,
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- convert_underscores: bool = True,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
- ):
- self.convert_underscores = convert_underscores
- super().__init__(
- default=default,
- default_factory=default_factory,
- annotation=annotation,
- alias=alias,
- alias_priority=alias_priority,
- validation_alias=validation_alias,
- serialization_alias=serialization_alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- pattern=pattern,
- regex=regex,
- discriminator=discriminator,
- strict=strict,
- multiple_of=multiple_of,
- allow_inf_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- deprecated=deprecated,
- example=example,
- examples=examples,
- include_in_schema=include_in_schema,
- json_schema_extra=json_schema_extra,
- **extra,
- )
-
-
-class Cookie(Param):
- in_ = ParamTypes.cookie
-
- def __init__(
- self,
- default: Any = Undefined,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- annotation: Optional[Any] = None,
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
- ):
- super().__init__(
- default=default,
- default_factory=default_factory,
- annotation=annotation,
- alias=alias,
- alias_priority=alias_priority,
- validation_alias=validation_alias,
- serialization_alias=serialization_alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- pattern=pattern,
- regex=regex,
- discriminator=discriminator,
- strict=strict,
- multiple_of=multiple_of,
- allow_inf_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- deprecated=deprecated,
- example=example,
- examples=examples,
- include_in_schema=include_in_schema,
- json_schema_extra=json_schema_extra,
- **extra,
- )
-
-
-class Body(FieldInfo):
- def __init__(
- self,
- default: Any = Undefined,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- annotation: Optional[Any] = None,
- embed: bool = False,
- media_type: str = "application/json",
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
- ):
- self.embed = embed
- self.media_type = media_type
- self.deprecated = deprecated
- if example is not _Unset:
- warnings.warn(
- "`example` has been depreacated, please use `examples` instead",
- category=DeprecationWarning,
- stacklevel=4,
- )
- self.example = example
- self.include_in_schema = include_in_schema
- kwargs = dict(
- default=default,
- default_factory=default_factory,
- alias=alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- discriminator=discriminator,
- multiple_of=multiple_of,
- allow_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- **extra,
- )
- if examples is not None:
- kwargs["examples"] = examples
- if regex is not None:
- warnings.warn(
- "`regex` has been depreacated, please use `pattern` instead",
- category=DeprecationWarning,
- stacklevel=4,
- )
- current_json_schema_extra = json_schema_extra or extra
- if PYDANTIC_V2:
- kwargs.update(
- {
- "annotation": annotation,
- "alias_priority": alias_priority,
- "validation_alias": validation_alias,
- "serialization_alias": serialization_alias,
- "strict": strict,
- "json_schema_extra": current_json_schema_extra,
- }
- )
- kwargs["pattern"] = pattern or regex
- else:
- kwargs["regex"] = pattern or regex
- kwargs.update(**current_json_schema_extra)
-
- use_kwargs = {k: v for k, v in kwargs.items() if v is not _Unset}
-
- super().__init__(**use_kwargs)
-
- def __repr__(self) -> str:
- return f"{self.__class__.__name__}({self.default})"
-
-
-class Form(Body):
- def __init__(
- self,
- default: Any = Undefined,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- annotation: Optional[Any] = None,
- media_type: str = "application/x-www-form-urlencoded",
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
- ):
- super().__init__(
- default=default,
- default_factory=default_factory,
- annotation=annotation,
- embed=True,
- media_type=media_type,
- alias=alias,
- alias_priority=alias_priority,
- validation_alias=validation_alias,
- serialization_alias=serialization_alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- pattern=pattern,
- regex=regex,
- discriminator=discriminator,
- strict=strict,
- multiple_of=multiple_of,
- allow_inf_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- deprecated=deprecated,
- example=example,
- examples=examples,
- include_in_schema=include_in_schema,
- json_schema_extra=json_schema_extra,
- **extra,
- )
-
-
-class File(Form):
- def __init__(
- self,
- default: Any = Undefined,
- *,
- default_factory: Union[Callable[[], Any], None] = _Unset,
- annotation: Optional[Any] = None,
- media_type: str = "multipart/form-data",
- alias: Optional[str] = None,
- alias_priority: Union[int, None] = _Unset,
- # TODO: update when deprecating Pydantic v1, import these types
- # validation_alias: str | AliasPath | AliasChoices | None
- validation_alias: Union[str, None] = None,
- serialization_alias: Union[str, None] = None,
- title: Optional[str] = None,
- description: Optional[str] = None,
- gt: Optional[float] = None,
- ge: Optional[float] = None,
- lt: Optional[float] = None,
- le: Optional[float] = None,
- min_length: Optional[int] = None,
- max_length: Optional[int] = None,
- pattern: Optional[str] = None,
- regex: Annotated[
- Optional[str],
- deprecated(
- "Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
- ),
- ] = None,
- discriminator: Union[str, None] = None,
- strict: Union[bool, None] = _Unset,
- multiple_of: Union[float, None] = _Unset,
- allow_inf_nan: Union[bool, None] = _Unset,
- max_digits: Union[int, None] = _Unset,
- decimal_places: Union[int, None] = _Unset,
- examples: Optional[List[Any]] = None,
- example: Annotated[
- Optional[Any],
- deprecated(
- "Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
- "although still supported. Use examples instead."
- ),
- ] = _Unset,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- json_schema_extra: Union[Dict[str, Any], None] = None,
- **extra: Any,
- ):
- super().__init__(
- default=default,
- default_factory=default_factory,
- annotation=annotation,
- media_type=media_type,
- alias=alias,
- alias_priority=alias_priority,
- validation_alias=validation_alias,
- serialization_alias=serialization_alias,
- title=title,
- description=description,
- gt=gt,
- ge=ge,
- lt=lt,
- le=le,
- min_length=min_length,
- max_length=max_length,
- pattern=pattern,
- regex=regex,
- discriminator=discriminator,
- strict=strict,
- multiple_of=multiple_of,
- allow_inf_nan=allow_inf_nan,
- max_digits=max_digits,
- decimal_places=decimal_places,
- deprecated=deprecated,
- example=example,
- examples=examples,
- include_in_schema=include_in_schema,
- json_schema_extra=json_schema_extra,
- **extra,
- )
-
-
-class Depends:
- def __init__(
- self, dependency: Optional[Callable[..., Any]] = None, *, use_cache: bool = True
- ):
- self.dependency = dependency
- self.use_cache = use_cache
-
- def __repr__(self) -> str:
- attr = getattr(self.dependency, "__name__", type(self.dependency).__name__)
- cache = "" if self.use_cache else ", use_cache=False"
- return f"{self.__class__.__name__}({attr}{cache})"
-
-
-class Security(Depends):
- def __init__(
- self,
- dependency: Optional[Callable[..., Any]] = None,
- *,
- scopes: Optional[Sequence[str]] = None,
- use_cache: bool = True,
- ):
- super().__init__(dependency=dependency, use_cache=use_cache)
- self.scopes = scopes or []
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/feaLib/lookupDebugInfo.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/feaLib/lookupDebugInfo.py
deleted file mode 100644
index d4da7de0aed6b87dae6a1d4b417f1c6e099fe1e0..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/feaLib/lookupDebugInfo.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from typing import NamedTuple
-
-LOOKUP_DEBUG_INFO_KEY = "com.github.fonttools.feaLib"
-LOOKUP_DEBUG_ENV_VAR = "FONTTOOLS_LOOKUP_DEBUGGING"
-
-
-class LookupDebugInfo(NamedTuple):
- """Information about where a lookup came from, to be embedded in a font"""
-
- location: str
- name: str
- feature: list
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/otlLib/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/otlLib/__init__.py
deleted file mode 100644
index 12e414fc3bf00e6152f953b989914f034edfe9e1..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/otlLib/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""OpenType Layout-related functionality."""
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_g_c_i_d.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_g_c_i_d.py
deleted file mode 100644
index 2e746c846fa14800cb7de93969984dac36678e4e..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_g_c_i_d.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6gcid.html
-class table__g_c_i_d(BaseTTXConverter):
- pass
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_h_m_t_x.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_h_m_t_x.py
deleted file mode 100644
index 2dafe617a061880d93ab91b981ef26a09365728e..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_h_m_t_x.py
+++ /dev/null
@@ -1,152 +0,0 @@
-from fontTools.misc.roundTools import otRound
-from fontTools import ttLib
-from fontTools.misc.textTools import safeEval
-from . import DefaultTable
-import sys
-import struct
-import array
-import logging
-
-
-log = logging.getLogger(__name__)
-
-
-class table__h_m_t_x(DefaultTable.DefaultTable):
-
- headerTag = "hhea"
- advanceName = "width"
- sideBearingName = "lsb"
- numberOfMetricsName = "numberOfHMetrics"
- longMetricFormat = "Hh"
-
- def decompile(self, data, ttFont):
- numGlyphs = ttFont["maxp"].numGlyphs
- headerTable = ttFont.get(self.headerTag)
- if headerTable is not None:
- numberOfMetrics = int(getattr(headerTable, self.numberOfMetricsName))
- else:
- numberOfMetrics = numGlyphs
- if numberOfMetrics > numGlyphs:
- log.warning(
- "The %s.%s exceeds the maxp.numGlyphs"
- % (self.headerTag, self.numberOfMetricsName)
- )
- numberOfMetrics = numGlyphs
- if len(data) < 4 * numberOfMetrics:
- raise ttLib.TTLibError("not enough '%s' table data" % self.tableTag)
- # Note: advanceWidth is unsigned, but some font editors might
- # read/write as signed. We can't be sure whether it was a mistake
- # or not, so we read as unsigned but also issue a warning...
- metricsFmt = ">" + self.longMetricFormat * numberOfMetrics
- metrics = struct.unpack(metricsFmt, data[: 4 * numberOfMetrics])
- data = data[4 * numberOfMetrics :]
- numberOfSideBearings = numGlyphs - numberOfMetrics
- sideBearings = array.array("h", data[: 2 * numberOfSideBearings])
- data = data[2 * numberOfSideBearings :]
-
- if sys.byteorder != "big":
- sideBearings.byteswap()
- if data:
- log.warning("too much '%s' table data" % self.tableTag)
- self.metrics = {}
- glyphOrder = ttFont.getGlyphOrder()
- for i in range(numberOfMetrics):
- glyphName = glyphOrder[i]
- advanceWidth, lsb = metrics[i * 2 : i * 2 + 2]
- if advanceWidth > 32767:
- log.warning(
- "Glyph %r has a huge advance %s (%d); is it intentional or "
- "an (invalid) negative value?",
- glyphName,
- self.advanceName,
- advanceWidth,
- )
- self.metrics[glyphName] = (advanceWidth, lsb)
- lastAdvance = metrics[-2]
- for i in range(numberOfSideBearings):
- glyphName = glyphOrder[i + numberOfMetrics]
- self.metrics[glyphName] = (lastAdvance, sideBearings[i])
-
- def compile(self, ttFont):
- metrics = []
- hasNegativeAdvances = False
- for glyphName in ttFont.getGlyphOrder():
- advanceWidth, sideBearing = self.metrics[glyphName]
- if advanceWidth < 0:
- log.error(
- "Glyph %r has negative advance %s" % (glyphName, self.advanceName)
- )
- hasNegativeAdvances = True
- metrics.append([advanceWidth, sideBearing])
-
- headerTable = ttFont.get(self.headerTag)
- if headerTable is not None:
- lastAdvance = metrics[-1][0]
- lastIndex = len(metrics)
- while metrics[lastIndex - 2][0] == lastAdvance:
- lastIndex -= 1
- if lastIndex <= 1:
- # all advances are equal
- lastIndex = 1
- break
- additionalMetrics = metrics[lastIndex:]
- additionalMetrics = [otRound(sb) for _, sb in additionalMetrics]
- metrics = metrics[:lastIndex]
- numberOfMetrics = len(metrics)
- setattr(headerTable, self.numberOfMetricsName, numberOfMetrics)
- else:
- # no hhea/vhea, can't store numberOfMetrics; assume == numGlyphs
- numberOfMetrics = ttFont["maxp"].numGlyphs
- additionalMetrics = []
-
- allMetrics = []
- for advance, sb in metrics:
- allMetrics.extend([otRound(advance), otRound(sb)])
- metricsFmt = ">" + self.longMetricFormat * numberOfMetrics
- try:
- data = struct.pack(metricsFmt, *allMetrics)
- except struct.error as e:
- if "out of range" in str(e) and hasNegativeAdvances:
- raise ttLib.TTLibError(
- "'%s' table can't contain negative advance %ss"
- % (self.tableTag, self.advanceName)
- )
- else:
- raise
- additionalMetrics = array.array("h", additionalMetrics)
- if sys.byteorder != "big":
- additionalMetrics.byteswap()
- data = data + additionalMetrics.tobytes()
- return data
-
- def toXML(self, writer, ttFont):
- names = sorted(self.metrics.keys())
- for glyphName in names:
- advance, sb = self.metrics[glyphName]
- writer.simpletag(
- "mtx",
- [
- ("name", glyphName),
- (self.advanceName, advance),
- (self.sideBearingName, sb),
- ],
- )
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont):
- if not hasattr(self, "metrics"):
- self.metrics = {}
- if name == "mtx":
- self.metrics[attrs["name"]] = (
- safeEval(attrs[self.advanceName]),
- safeEval(attrs[self.sideBearingName]),
- )
-
- def __delitem__(self, glyphName):
- del self.metrics[glyphName]
-
- def __getitem__(self, glyphName):
- return self.metrics[glyphName]
-
- def __setitem__(self, glyphName, advance_sb_pair):
- self.metrics[glyphName] = tuple(advance_sb_pair)
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If ``remote_host`` is a host name that resolves to multiple IP addresses, - each one is tried until one connection attempt succeeds. If the first attempt does - not connected within 250 milliseconds, a second attempt is started using the next - address in the list, and so on. On IPv6 enabled systems, an IPv6 address (if - available) is tried first. - - When the connection has been established, a TLS handshake will be done if either - ``ssl_context`` or ``tls_hostname`` is not ``None``, or if ``tls`` is ``True``. - - :param remote_host: the IP address or host name to connect to - :param remote_port: port on the target host to connect to - :param local_host: the interface address or name to bind the socket to before connecting - :param tls: ``True`` to do a TLS handshake with the connected stream and return a - :class:`~anyio.streams.tls.TLSStream` instead - :param ssl_context: the SSL context object to use (if omitted, a default context is created) - :param tls_standard_compatible: If ``True``, performs the TLS shutdown handshake before closing - the stream and requires that the server does this as well. Otherwise, - :exc:`~ssl.SSLEOFError` may be raised during reads from the stream. - Some protocols, such as HTTP, require this option to be ``False``. - See :meth:`~ssl.SSLContext.wrap_socket` for details. - :param tls_hostname: host name to check the server certificate against (defaults to the value - of ``remote_host``) - :param happy_eyeballs_delay: delay (in seconds) before starting the next connection attempt - :return: a socket stream object if no TLS handshake was done, otherwise a TLS stream - :raises OSError: if the connection attempt fails - - """ - # Placed here due to https://github.com/python/mypy/issues/7057 - connected_stream: SocketStream | None = None - - async def try_connect(remote_host: str, event: Event) -> None: - nonlocal connected_stream - try: - stream = await asynclib.connect_tcp(remote_host, remote_port, local_address) - except OSError as exc: - oserrors.append(exc) - return - else: - if connected_stream is None: - connected_stream = stream - tg.cancel_scope.cancel() - else: - await stream.aclose() - finally: - event.set() - - asynclib = get_asynclib() - local_address: IPSockAddrType | None = None - family = socket.AF_UNSPEC - if local_host: - gai_res = await getaddrinfo(str(local_host), None) - family, *_, local_address = gai_res[0] - - target_host = str(remote_host) - try: - addr_obj = ip_address(remote_host) - except ValueError: - # getaddrinfo() will raise an exception if name resolution fails - gai_res = await getaddrinfo( - target_host, remote_port, family=family, type=socket.SOCK_STREAM - ) - - # Organize the list so that the first address is an IPv6 address (if available) and the - # second one is an IPv4 addresses. The rest can be in whatever order. - v6_found = v4_found = False - target_addrs: list[tuple[socket.AddressFamily, str]] = [] - for af, *rest, sa in gai_res: - if af == socket.AF_INET6 and not v6_found: - v6_found = True - target_addrs.insert(0, (af, sa[0])) - elif af == socket.AF_INET and not v4_found and v6_found: - v4_found = True - target_addrs.insert(1, (af, sa[0])) - else: - target_addrs.append((af, sa[0])) - else: - if isinstance(addr_obj, IPv6Address): - target_addrs = [(socket.AF_INET6, addr_obj.compressed)] - else: - target_addrs = [(socket.AF_INET, addr_obj.compressed)] - - oserrors: list[OSError] = [] - async with create_task_group() as tg: - for i, (af, addr) in enumerate(target_addrs): - event = Event() - tg.start_soon(try_connect, addr, event) - with move_on_after(happy_eyeballs_delay): - await event.wait() - - if connected_stream is None: - cause = oserrors[0] if len(oserrors) == 1 else asynclib.ExceptionGroup(oserrors) - raise OSError("All connection attempts failed") from cause - - if tls or tls_hostname or ssl_context: - try: - return await TLSStream.wrap( - connected_stream, - server_side=False, - hostname=tls_hostname or str(remote_host), - ssl_context=ssl_context, - standard_compatible=tls_standard_compatible, - ) - except BaseException: - await aclose_forcefully(connected_stream) - raise - - return connected_stream - - -async def connect_unix(path: str | PathLike[str]) -> UNIXSocketStream: - """ - Connect to the given UNIX socket. - - Not available on Windows. - - :param path: path to the socket - :return: a socket stream object - - """ - path = str(Path(path)) - return await get_asynclib().connect_unix(path) - - -async def create_tcp_listener( - *, - local_host: IPAddressType | None = None, - local_port: int = 0, - family: AnyIPAddressFamily = socket.AddressFamily.AF_UNSPEC, - backlog: int = 65536, - reuse_port: bool = False, -) -> MultiListener[SocketStream]: - """ - Create a TCP socket listener. - - :param local_port: port number to listen on - :param local_host: IP address of the interface to listen on. If omitted, listen on - all IPv4 and IPv6 interfaces. To listen on all interfaces on a specific address - family, use ``0.0.0.0`` for IPv4 or ``::`` for IPv6. - :param family: address family (used if ``local_host`` was omitted) - :param backlog: maximum number of queued incoming connections (up to a maximum of - 2**16, or 65536) - :param reuse_port: ``True`` to allow multiple sockets to bind to the same - address/port (not supported on Windows) - :return: a list of listener objects - - """ - asynclib = get_asynclib() - backlog = min(backlog, 65536) - local_host = str(local_host) if local_host is not None else None - gai_res = await getaddrinfo( - local_host, # type: ignore[arg-type] - local_port, - family=family, - type=socket.SocketKind.SOCK_STREAM if sys.platform == "win32" else 0, - flags=socket.AI_PASSIVE | socket.AI_ADDRCONFIG, - ) - listeners: list[SocketListener] = [] - try: - # The set() is here to work around a glibc bug: - # https://sourceware.org/bugzilla/show_bug.cgi?id=14969 - sockaddr: tuple[str, int] | tuple[str, int, int, int] - for fam, kind, *_, sockaddr in sorted(set(gai_res)): - # Workaround for an uvloop bug where we don't get the correct scope ID for - # IPv6 link-local addresses when passing type=socket.SOCK_STREAM to - # getaddrinfo(): https://github.com/MagicStack/uvloop/issues/539 - if sys.platform != "win32" and kind is not SocketKind.SOCK_STREAM: - continue - - raw_socket = socket.socket(fam) - raw_socket.setblocking(False) - - # For Windows, enable exclusive address use. For others, enable address reuse. - if sys.platform == "win32": - raw_socket.setsockopt(socket.SOL_SOCKET, socket.SO_EXCLUSIVEADDRUSE, 1) - else: - raw_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - - if reuse_port: - raw_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) - - # If only IPv6 was requested, disable dual stack operation - if fam == socket.AF_INET6: - raw_socket.setsockopt(IPPROTO_IPV6, socket.IPV6_V6ONLY, 1) - - # Workaround for #554 - if "%" in sockaddr[0]: - addr, scope_id = sockaddr[0].split("%", 1) - sockaddr = (addr, sockaddr[1], 0, int(scope_id)) - - raw_socket.bind(sockaddr) - raw_socket.listen(backlog) - listener = asynclib.TCPSocketListener(raw_socket) - listeners.append(listener) - except BaseException: - for listener in listeners: - await listener.aclose() - - raise - - return MultiListener(listeners) - - -async def create_unix_listener( - path: str | PathLike[str], - *, - mode: int | None = None, - backlog: int = 65536, -) -> SocketListener: - """ - Create a UNIX socket listener. - - Not available on Windows. - - :param path: path of the socket - :param mode: permissions to set on the socket - :param backlog: maximum number of queued incoming connections (up to a maximum of 2**16, or - 65536) - :return: a listener object - - .. versionchanged:: 3.0 - If a socket already exists on the file system in the given path, it will be removed first. - - """ - path_str = str(path) - path = Path(path) - if path.is_socket(): - path.unlink() - - backlog = min(backlog, 65536) - raw_socket = socket.socket(socket.AF_UNIX) - raw_socket.setblocking(False) - try: - await to_thread.run_sync(raw_socket.bind, path_str, cancellable=True) - if mode is not None: - await to_thread.run_sync(chmod, path_str, mode, cancellable=True) - - raw_socket.listen(backlog) - return get_asynclib().UNIXSocketListener(raw_socket) - except BaseException: - raw_socket.close() - raise - - -async def create_udp_socket( - family: AnyIPAddressFamily = AddressFamily.AF_UNSPEC, - *, - local_host: IPAddressType | None = None, - local_port: int = 0, - reuse_port: bool = False, -) -> UDPSocket: - """ - Create a UDP socket. - - If ``local_port`` has been given, the socket will be bound to this port on the local - machine, making this socket suitable for providing UDP based services. - - :param family: address family (``AF_INET`` or ``AF_INET6``) – automatically determined from - ``local_host`` if omitted - :param local_host: IP address or host name of the local interface to bind to - :param local_port: local port to bind to - :param reuse_port: ``True`` to allow multiple sockets to bind to the same address/port - (not supported on Windows) - :return: a UDP socket - - """ - if family is AddressFamily.AF_UNSPEC and not local_host: - raise ValueError('Either "family" or "local_host" must be given') - - if local_host: - gai_res = await getaddrinfo( - str(local_host), - local_port, - family=family, - type=socket.SOCK_DGRAM, - flags=socket.AI_PASSIVE | socket.AI_ADDRCONFIG, - ) - family = cast(AnyIPAddressFamily, gai_res[0][0]) - local_address = gai_res[0][-1] - elif family is AddressFamily.AF_INET6: - local_address = ("::", 0) - else: - local_address = ("0.0.0.0", 0) - - return await get_asynclib().create_udp_socket( - family, local_address, None, reuse_port - ) - - -async def create_connected_udp_socket( - remote_host: IPAddressType, - remote_port: int, - *, - family: AnyIPAddressFamily = AddressFamily.AF_UNSPEC, - local_host: IPAddressType | None = None, - local_port: int = 0, - reuse_port: bool = False, -) -> ConnectedUDPSocket: - """ - Create a connected UDP socket. - - Connected UDP sockets can only communicate with the specified remote host/port, and any packets - sent from other sources are dropped. - - :param remote_host: remote host to set as the default target - :param remote_port: port on the remote host to set as the default target - :param family: address family (``AF_INET`` or ``AF_INET6``) – automatically determined from - ``local_host`` or ``remote_host`` if omitted - :param local_host: IP address or host name of the local interface to bind to - :param local_port: local port to bind to - :param reuse_port: ``True`` to allow multiple sockets to bind to the same address/port - (not supported on Windows) - :return: a connected UDP socket - - """ - local_address = None - if local_host: - gai_res = await getaddrinfo( - str(local_host), - local_port, - family=family, - type=socket.SOCK_DGRAM, - flags=socket.AI_PASSIVE | socket.AI_ADDRCONFIG, - ) - family = cast(AnyIPAddressFamily, gai_res[0][0]) - local_address = gai_res[0][-1] - - gai_res = await getaddrinfo( - str(remote_host), remote_port, family=family, type=socket.SOCK_DGRAM - ) - family = cast(AnyIPAddressFamily, gai_res[0][0]) - remote_address = gai_res[0][-1] - - return await get_asynclib().create_udp_socket( - family, local_address, remote_address, reuse_port - ) - - -async def getaddrinfo( - host: bytearray | bytes | str, - port: str | int | None, - *, - family: int | AddressFamily = 0, - type: int | SocketKind = 0, - proto: int = 0, - flags: int = 0, -) -> GetAddrInfoReturnType: - """ - Look up a numeric IP address given a host name. - - Internationalized domain names are translated according to the (non-transitional) IDNA 2008 - standard. - - .. note:: 4-tuple IPv6 socket addresses are automatically converted to 2-tuples of - (host, port), unlike what :func:`socket.getaddrinfo` does. - - :param host: host name - :param port: port number - :param family: socket family (`'AF_INET``, ...) - :param type: socket type (``SOCK_STREAM``, ...) - :param proto: protocol number - :param flags: flags to pass to upstream ``getaddrinfo()`` - :return: list of tuples containing (family, type, proto, canonname, sockaddr) - - .. seealso:: :func:`socket.getaddrinfo` - - """ - # Handle unicode hostnames - if isinstance(host, str): - try: - encoded_host = host.encode("ascii") - except UnicodeEncodeError: - import idna - - encoded_host = idna.encode(host, uts46=True) - else: - encoded_host = host - - gai_res = await get_asynclib().getaddrinfo( - encoded_host, port, family=family, type=type, proto=proto, flags=flags - ) - return [ - (family, type, proto, canonname, convert_ipv6_sockaddr(sockaddr)) - for family, type, proto, canonname, sockaddr in gai_res - ] - - -def getnameinfo(sockaddr: IPSockAddrType, flags: int = 0) -> Awaitable[tuple[str, str]]: - """ - Look up the host name of an IP address. - - :param sockaddr: socket address (e.g. (ipaddress, port) for IPv4) - :param flags: flags to pass to upstream ``getnameinfo()`` - :return: a tuple of (host name, service name) - - .. seealso:: :func:`socket.getnameinfo` - - """ - return get_asynclib().getnameinfo(sockaddr, flags) - - -def wait_socket_readable(sock: socket.socket) -> Awaitable[None]: - """ - Wait until the given socket has data to be read. - - This does **NOT** work on Windows when using the asyncio backend with a proactor event loop - (default on py3.8+). - - .. warning:: Only use this on raw sockets that have not been wrapped by any higher level - constructs like socket streams! - - :param sock: a socket object - :raises ~anyio.ClosedResourceError: if the socket was closed while waiting for the - socket to become readable - :raises ~anyio.BusyResourceError: if another task is already waiting for the socket - to become readable - - """ - return get_asynclib().wait_socket_readable(sock) - - -def wait_socket_writable(sock: socket.socket) -> Awaitable[None]: - """ - Wait until the given socket can be written to. - - This does **NOT** work on Windows when using the asyncio backend with a proactor event loop - (default on py3.8+). - - .. warning:: Only use this on raw sockets that have not been wrapped by any higher level - constructs like socket streams! - - :param sock: a socket object - :raises ~anyio.ClosedResourceError: if the socket was closed while waiting for the - socket to become writable - :raises ~anyio.BusyResourceError: if another task is already waiting for the socket - to become writable - - """ - return get_asynclib().wait_socket_writable(sock) - - -# -# Private API -# - - -def convert_ipv6_sockaddr( - sockaddr: tuple[str, int, int, int] | tuple[str, int] -) -> tuple[str, int]: - """ - Convert a 4-tuple IPv6 socket address to a 2-tuple (address, port) format. - - If the scope ID is nonzero, it is added to the address, separated with ``%``. - Otherwise the flow id and scope id are simply cut off from the tuple. - Any other kinds of socket addresses are returned as-is. - - :param sockaddr: the result of :meth:`~socket.socket.getsockname` - :return: the converted socket address - - """ - # This is more complicated than it should be because of MyPy - if isinstance(sockaddr, tuple) and len(sockaddr) == 4: - host, port, flowinfo, scope_id = cast(Tuple[str, int, int, int], sockaddr) - if scope_id: - # PyPy (as of v7.3.11) leaves the interface name in the result, so - # we discard it and only get the scope ID from the end - # (https://foss.heptapod.net/pypy/pypy/-/issues/3938) - host = host.split("%")[0] - - # Add scope_id to the address - return f"{host}%{scope_id}", port - else: - return host, port - else: - return cast(Tuple[str, int], sockaddr) diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/requests.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/requests.py deleted file mode 100644 index d16552c0a9535e1c0bd7f701987301681832eba5..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/requests.py +++ /dev/null @@ -1,2 +0,0 @@ -from starlette.requests import HTTPConnection as HTTPConnection # noqa: F401 -from starlette.requests import Request as Request # noqa: F401 diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/G_D_E_F_.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/G_D_E_F_.py deleted file mode 100644 index d8ae8b23bb6af53aeb08271c3d489f52a28a5e02..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/G_D_E_F_.py +++ /dev/null @@ -1,5 +0,0 @@ -from .otBase import BaseTTXConverter - - -class table_G_D_E_F_(BaseTTXConverter): - pass diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_xll.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_xll.c deleted file mode 100644 index b8cf37a35f134cc592ab725c620596cdcb59f531..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_xll.c +++ /dev/null @@ -1,1519 +0,0 @@ -/* - * Copyright (C) 2016 foo86 - * - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - */ - -#include "avcodec.h" -#include "libavutil/channel_layout.h" -#include "dcadec.h" -#include "dcadata.h" -#include "dcamath.h" -#include "dca_syncwords.h" -#include "decode.h" -#include "unary.h" - -static int get_linear(GetBitContext *gb, int n) -{ - unsigned int v = get_bits_long(gb, n); - return (v >> 1) ^ -(v & 1); -} - -static int get_rice_un(GetBitContext *gb, int k) -{ - unsigned int v = get_unary(gb, 1, get_bits_left(gb)); - return (v << k) | get_bits_long(gb, k); -} - -static int get_rice(GetBitContext *gb, int k) -{ - unsigned int v = get_rice_un(gb, k); - return (v >> 1) ^ -(v & 1); -} - -static void get_array(GetBitContext *gb, int32_t *array, int size, int n) -{ - int i; - - for (i = 0; i < size; i++) - array[i] = get_bits(gb, n); -} - -static void get_linear_array(GetBitContext *gb, int32_t *array, int size, int n) -{ - int i; - - if (n == 0) - memset(array, 0, sizeof(*array) * size); - else for (i = 0; i < size; i++) - array[i] = get_linear(gb, n); -} - -static void get_rice_array(GetBitContext *gb, int32_t *array, int size, int k) -{ - int i; - - for (i = 0; i < size; i++) - array[i] = get_rice(gb, k); -} - -static int parse_dmix_coeffs(DCAXllDecoder *s, DCAXllChSet *c) -{ - // Size of downmix coefficient matrix - int m = c->primary_chset ? ff_dca_dmix_primary_nch[c->dmix_type] : c->hier_ofs; - int i, j, *coeff_ptr = c->dmix_coeff; - - for (i = 0; i < m; i++) { - int code, sign, coeff, scale, scale_inv = 0; - unsigned int index; - - // Downmix scale (only for non-primary channel sets) - if (!c->primary_chset) { - code = get_bits(&s->gb, 9); - sign = (code >> 8) - 1; - index = (code & 0xff) - FF_DCA_DMIXTABLE_OFFSET; - if (index >= FF_DCA_INV_DMIXTABLE_SIZE) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid XLL downmix scale index\n"); - return AVERROR_INVALIDDATA; - } - scale = ff_dca_dmixtable[index + FF_DCA_DMIXTABLE_OFFSET]; - scale_inv = ff_dca_inv_dmixtable[index]; - c->dmix_scale[i] = (scale ^ sign) - sign; - c->dmix_scale_inv[i] = (scale_inv ^ sign) - sign; - } - - // Downmix coefficients - for (j = 0; j < c->nchannels; j++) { - code = get_bits(&s->gb, 9); - sign = (code >> 8) - 1; - index = code & 0xff; - if (index >= FF_DCA_DMIXTABLE_SIZE) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid XLL downmix coefficient index\n"); - return AVERROR_INVALIDDATA; - } - coeff = ff_dca_dmixtable[index]; - if (!c->primary_chset) - // Multiply by |InvDmixScale| to get |UndoDmixScale| - coeff = mul16(scale_inv, coeff); - *coeff_ptr++ = (coeff ^ sign) - sign; - } - } - - return 0; -} - -static int chs_parse_header(DCAXllDecoder *s, DCAXllChSet *c, DCAExssAsset *asset) -{ - int i, j, k, ret, band, header_size, header_pos = get_bits_count(&s->gb); - DCAXllChSet *p = &s->chset[0]; - DCAXllBand *b; - - // Size of channel set sub-header - header_size = get_bits(&s->gb, 10) + 1; - - // Check CRC - if (ff_dca_check_crc(s->avctx, &s->gb, header_pos, header_pos + header_size * 8)) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid XLL sub-header checksum\n"); - return AVERROR_INVALIDDATA; - } - - // Number of channels in the channel set - c->nchannels = get_bits(&s->gb, 4) + 1; - if (c->nchannels > DCA_XLL_CHANNELS_MAX) { - avpriv_request_sample(s->avctx, "%d XLL channels", c->nchannels); - return AVERROR_PATCHWELCOME; - } - - // Residual type - c->residual_encode = get_bits(&s->gb, c->nchannels); - - // PCM bit resolution - c->pcm_bit_res = get_bits(&s->gb, 5) + 1; - - // Storage unit width - c->storage_bit_res = get_bits(&s->gb, 5) + 1; - if (c->storage_bit_res != 16 && c->storage_bit_res != 20 && c->storage_bit_res != 24) { - avpriv_request_sample(s->avctx, "%d-bit XLL storage resolution", c->storage_bit_res); - return AVERROR_PATCHWELCOME; - } - - if (c->pcm_bit_res > c->storage_bit_res) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid PCM bit resolution for XLL channel set (%d > %d)\n", c->pcm_bit_res, c->storage_bit_res); - return AVERROR_INVALIDDATA; - } - - // Original sampling frequency - c->freq = ff_dca_sampling_freqs[get_bits(&s->gb, 4)]; - if (c->freq > 192000) { - avpriv_request_sample(s->avctx, "%d Hz XLL sampling frequency", c->freq); - return AVERROR_PATCHWELCOME; - } - - // Sampling frequency modifier - if (get_bits(&s->gb, 2)) { - avpriv_request_sample(s->avctx, "XLL sampling frequency modifier"); - return AVERROR_PATCHWELCOME; - } - - // Which replacement set this channel set is member of - if (get_bits(&s->gb, 2)) { - avpriv_request_sample(s->avctx, "XLL replacement set"); - return AVERROR_PATCHWELCOME; - } - - if (asset->one_to_one_map_ch_to_spkr) { - // Primary channel set flag - c->primary_chset = get_bits1(&s->gb); - if (c->primary_chset != (c == p)) { - av_log(s->avctx, AV_LOG_ERROR, "The first (and only) XLL channel set must be primary\n"); - return AVERROR_INVALIDDATA; - } - - // Downmix coefficients present in stream - c->dmix_coeffs_present = get_bits1(&s->gb); - - // Downmix already performed by encoder - c->dmix_embedded = c->dmix_coeffs_present && get_bits1(&s->gb); - - // Downmix type - if (c->dmix_coeffs_present && c->primary_chset) { - c->dmix_type = get_bits(&s->gb, 3); - if (c->dmix_type >= DCA_DMIX_TYPE_COUNT) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid XLL primary channel set downmix type\n"); - return AVERROR_INVALIDDATA; - } - } - - // Whether the channel set is part of a hierarchy - c->hier_chset = get_bits1(&s->gb); - if (!c->hier_chset && s->nchsets != 1) { - avpriv_request_sample(s->avctx, "XLL channel set outside of hierarchy"); - return AVERROR_PATCHWELCOME; - } - - // Downmix coefficients - if (c->dmix_coeffs_present && (ret = parse_dmix_coeffs(s, c)) < 0) - return ret; - - // Channel mask enabled - if (!get_bits1(&s->gb)) { - avpriv_request_sample(s->avctx, "Disabled XLL channel mask"); - return AVERROR_PATCHWELCOME; - } - - // Channel mask for set - c->ch_mask = get_bits_long(&s->gb, s->ch_mask_nbits); - if (av_popcount(c->ch_mask) != c->nchannels) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid XLL channel mask\n"); - return AVERROR_INVALIDDATA; - } - - // Build the channel to speaker map - for (i = 0, j = 0; i < s->ch_mask_nbits; i++) - if (c->ch_mask & (1U << i)) - c->ch_remap[j++] = i; - } else { - // Mapping coeffs present flag - if (c->nchannels != 2 || s->nchsets != 1 || get_bits1(&s->gb)) { - avpriv_request_sample(s->avctx, "Custom XLL channel to speaker mapping"); - return AVERROR_PATCHWELCOME; - } - - // Setup for LtRt decoding - c->primary_chset = 1; - c->dmix_coeffs_present = 0; - c->dmix_embedded = 0; - c->hier_chset = 0; - c->ch_mask = DCA_SPEAKER_LAYOUT_STEREO; - c->ch_remap[0] = DCA_SPEAKER_L; - c->ch_remap[1] = DCA_SPEAKER_R; - } - - if (c->freq > 96000) { - // Extra frequency bands flag - if (get_bits1(&s->gb)) { - avpriv_request_sample(s->avctx, "Extra XLL frequency bands"); - return AVERROR_PATCHWELCOME; - } - c->nfreqbands = 2; - } else { - c->nfreqbands = 1; - } - - // Set the sampling frequency to that of the first frequency band. - // Frequency will be doubled again after bands assembly. - c->freq >>= c->nfreqbands - 1; - - // Verify that all channel sets have the same audio characteristics - if (c != p && (c->nfreqbands != p->nfreqbands || c->freq != p->freq - || c->pcm_bit_res != p->pcm_bit_res - || c->storage_bit_res != p->storage_bit_res)) { - avpriv_request_sample(s->avctx, "Different XLL audio characteristics"); - return AVERROR_PATCHWELCOME; - } - - // Determine number of bits to read bit allocation coding parameter - if (c->storage_bit_res > 16) - c->nabits = 5; - else if (c->storage_bit_res > 8) - c->nabits = 4; - else - c->nabits = 3; - - // Account for embedded downmix and decimator saturation - if ((s->nchsets > 1 || c->nfreqbands > 1) && c->nabits < 5) - c->nabits++; - - for (band = 0, b = c->bands; band < c->nfreqbands; band++, b++) { - // Pairwise channel decorrelation - if ((b->decor_enabled = get_bits1(&s->gb)) && c->nchannels > 1) { - int ch_nbits = av_ceil_log2(c->nchannels); - - // Original channel order - for (i = 0; i < c->nchannels; i++) { - b->orig_order[i] = get_bits(&s->gb, ch_nbits); - if (b->orig_order[i] >= c->nchannels) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid XLL original channel order\n"); - return AVERROR_INVALIDDATA; - } - } - - // Pairwise channel coefficients - for (i = 0; i < c->nchannels / 2; i++) - b->decor_coeff[i] = get_bits1(&s->gb) ? get_linear(&s->gb, 7) : 0; - } else { - for (i = 0; i < c->nchannels; i++) - b->orig_order[i] = i; - for (i = 0; i < c->nchannels / 2; i++) - b->decor_coeff[i] = 0; - } - - // Adaptive predictor order - b->highest_pred_order = 0; - for (i = 0; i < c->nchannels; i++) { - b->adapt_pred_order[i] = get_bits(&s->gb, 4); - if (b->adapt_pred_order[i] > b->highest_pred_order) - b->highest_pred_order = b->adapt_pred_order[i]; - } - if (b->highest_pred_order > s->nsegsamples) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid XLL adaptive predicition order\n"); - return AVERROR_INVALIDDATA; - } - - // Fixed predictor order - for (i = 0; i < c->nchannels; i++) - b->fixed_pred_order[i] = b->adapt_pred_order[i] ? 0 : get_bits(&s->gb, 2); - - // Adaptive predictor quantized reflection coefficients - for (i = 0; i < c->nchannels; i++) { - for (j = 0; j < b->adapt_pred_order[i]; j++) { - k = get_linear(&s->gb, 8); - if (k == -128) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid XLL reflection coefficient index\n"); - return AVERROR_INVALIDDATA; - } - if (k < 0) - b->adapt_refl_coeff[i][j] = -(int)ff_dca_xll_refl_coeff[-k]; - else - b->adapt_refl_coeff[i][j] = (int)ff_dca_xll_refl_coeff[ k]; - } - } - - // Downmix performed by encoder in extension frequency band - b->dmix_embedded = c->dmix_embedded && (band == 0 || get_bits1(&s->gb)); - - // MSB/LSB split flag in extension frequency band - if ((band == 0 && s->scalable_lsbs) || (band != 0 && get_bits1(&s->gb))) { - // Size of LSB section in any segment - b->lsb_section_size = get_bits_long(&s->gb, s->seg_size_nbits); - if (b->lsb_section_size < 0 || b->lsb_section_size > s->frame_size) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid LSB section size\n"); - return AVERROR_INVALIDDATA; - } - - // Account for optional CRC bytes after LSB section - if (b->lsb_section_size && (s->band_crc_present > 2 || - (band == 0 && s->band_crc_present > 1))) - b->lsb_section_size += 2; - - // Number of bits to represent the samples in LSB part - for (i = 0; i < c->nchannels; i++) { - b->nscalablelsbs[i] = get_bits(&s->gb, 4); - if (b->nscalablelsbs[i] && !b->lsb_section_size) { - av_log(s->avctx, AV_LOG_ERROR, "LSB section missing with non-zero LSB width\n"); - return AVERROR_INVALIDDATA; - } - } - } else { - b->lsb_section_size = 0; - for (i = 0; i < c->nchannels; i++) - b->nscalablelsbs[i] = 0; - } - - // Scalable resolution flag in extension frequency band - if ((band == 0 && s->scalable_lsbs) || (band != 0 && get_bits1(&s->gb))) { - // Number of bits discarded by authoring - for (i = 0; i < c->nchannels; i++) - b->bit_width_adjust[i] = get_bits(&s->gb, 4); - } else { - for (i = 0; i < c->nchannels; i++) - b->bit_width_adjust[i] = 0; - } - } - - // Reserved - // Byte align - // CRC16 of channel set sub-header - if (ff_dca_seek_bits(&s->gb, header_pos + header_size * 8)) { - av_log(s->avctx, AV_LOG_ERROR, "Read past end of XLL sub-header\n"); - return AVERROR_INVALIDDATA; - } - - return 0; -} - -static int chs_alloc_msb_band_data(DCAXllDecoder *s, DCAXllChSet *c) -{ - int ndecisamples = c->nfreqbands > 1 ? DCA_XLL_DECI_HISTORY_MAX : 0; - int nchsamples = s->nframesamples + ndecisamples; - int i, j, nsamples = nchsamples * c->nchannels * c->nfreqbands; - int32_t *ptr; - - // Reallocate MSB sample buffer - av_fast_malloc(&c->sample_buffer[0], &c->sample_size[0], nsamples * sizeof(int32_t)); - if (!c->sample_buffer[0]) - return AVERROR(ENOMEM); - - ptr = c->sample_buffer[0] + ndecisamples; - for (i = 0; i < c->nfreqbands; i++) { - for (j = 0; j < c->nchannels; j++) { - c->bands[i].msb_sample_buffer[j] = ptr; - ptr += nchsamples; - } - } - - return 0; -} - -static int chs_alloc_lsb_band_data(DCAXllDecoder *s, DCAXllChSet *c) -{ - int i, j, nsamples = 0; - int32_t *ptr; - - // Determine number of frequency bands that have MSB/LSB split - for (i = 0; i < c->nfreqbands; i++) - if (c->bands[i].lsb_section_size) - nsamples += s->nframesamples * c->nchannels; - if (!nsamples) - return 0; - - // Reallocate LSB sample buffer - av_fast_malloc(&c->sample_buffer[1], &c->sample_size[1], nsamples * sizeof(int32_t)); - if (!c->sample_buffer[1]) - return AVERROR(ENOMEM); - - ptr = c->sample_buffer[1]; - for (i = 0; i < c->nfreqbands; i++) { - if (c->bands[i].lsb_section_size) { - for (j = 0; j < c->nchannels; j++) { - c->bands[i].lsb_sample_buffer[j] = ptr; - ptr += s->nframesamples; - } - } else { - for (j = 0; j < c->nchannels; j++) - c->bands[i].lsb_sample_buffer[j] = NULL; - } - } - - return 0; -} - -static int chs_parse_band_data(DCAXllDecoder *s, DCAXllChSet *c, int band, int seg, int band_data_end) -{ - DCAXllBand *b = &c->bands[band]; - int i, j, k; - - // Start unpacking MSB portion of the segment - if (!(seg && get_bits1(&s->gb))) { - // Unpack segment type - // 0 - distinct coding parameters for each channel - // 1 - common coding parameters for all channels - c->seg_common = get_bits1(&s->gb); - - // Determine number of coding parameters encoded in segment - k = c->seg_common ? 1 : c->nchannels; - - // Unpack Rice coding parameters - for (i = 0; i < k; i++) { - // Unpack Rice coding flag - // 0 - linear code, 1 - Rice code - c->rice_code_flag[i] = get_bits1(&s->gb); - // Unpack Hybrid Rice coding flag - // 0 - Rice code, 1 - Hybrid Rice code - if (!c->seg_common && c->rice_code_flag[i] && get_bits1(&s->gb)) - // Unpack binary code length for isolated samples - c->bitalloc_hybrid_linear[i] = get_bits(&s->gb, c->nabits) + 1; - else - // 0 indicates no Hybrid Rice coding - c->bitalloc_hybrid_linear[i] = 0; - } - - // Unpack coding parameters - for (i = 0; i < k; i++) { - if (seg == 0) { - // Unpack coding parameter for part A of segment 0 - c->bitalloc_part_a[i] = get_bits(&s->gb, c->nabits); - - // Adjust for the linear code - if (!c->rice_code_flag[i] && c->bitalloc_part_a[i]) - c->bitalloc_part_a[i]++; - - if (!c->seg_common) - c->nsamples_part_a[i] = b->adapt_pred_order[i]; - else - c->nsamples_part_a[i] = b->highest_pred_order; - } else { - c->bitalloc_part_a[i] = 0; - c->nsamples_part_a[i] = 0; - } - - // Unpack coding parameter for part B of segment - c->bitalloc_part_b[i] = get_bits(&s->gb, c->nabits); - - // Adjust for the linear code - if (!c->rice_code_flag[i] && c->bitalloc_part_b[i]) - c->bitalloc_part_b[i]++; - } - } - - // Unpack entropy codes - for (i = 0; i < c->nchannels; i++) { - int32_t *part_a, *part_b; - int nsamples_part_b; - - // Select index of coding parameters - k = c->seg_common ? 0 : i; - - // Slice the segment into parts A and B - part_a = b->msb_sample_buffer[i] + seg * s->nsegsamples; - part_b = part_a + c->nsamples_part_a[k]; - nsamples_part_b = s->nsegsamples - c->nsamples_part_a[k]; - - if (get_bits_left(&s->gb) < 0) - return AVERROR_INVALIDDATA; - - if (!c->rice_code_flag[k]) { - // Linear codes - // Unpack all residuals of part A of segment 0 - get_linear_array(&s->gb, part_a, c->nsamples_part_a[k], - c->bitalloc_part_a[k]); - - // Unpack all residuals of part B of segment 0 and others - get_linear_array(&s->gb, part_b, nsamples_part_b, - c->bitalloc_part_b[k]); - } else { - // Rice codes - // Unpack all residuals of part A of segment 0 - get_rice_array(&s->gb, part_a, c->nsamples_part_a[k], - c->bitalloc_part_a[k]); - - if (c->bitalloc_hybrid_linear[k]) { - // Hybrid Rice codes - // Unpack the number of isolated samples - int nisosamples = get_bits(&s->gb, s->nsegsamples_log2); - - // Set all locations to 0 - memset(part_b, 0, sizeof(*part_b) * nsamples_part_b); - - // Extract the locations of isolated samples and flag by -1 - for (j = 0; j < nisosamples; j++) { - int loc = get_bits(&s->gb, s->nsegsamples_log2); - if (loc >= nsamples_part_b) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid isolated sample location\n"); - return AVERROR_INVALIDDATA; - } - part_b[loc] = -1; - } - - // Unpack all residuals of part B of segment 0 and others - for (j = 0; j < nsamples_part_b; j++) { - if (part_b[j]) - part_b[j] = get_linear(&s->gb, c->bitalloc_hybrid_linear[k]); - else - part_b[j] = get_rice(&s->gb, c->bitalloc_part_b[k]); - } - } else { - // Rice codes - // Unpack all residuals of part B of segment 0 and others - get_rice_array(&s->gb, part_b, nsamples_part_b, c->bitalloc_part_b[k]); - } - } - } - - // Unpack decimator history for frequency band 1 - if (seg == 0 && band == 1) { - int nbits = get_bits(&s->gb, 5) + 1; - for (i = 0; i < c->nchannels; i++) - for (j = 1; j < DCA_XLL_DECI_HISTORY_MAX; j++) - c->deci_history[i][j] = get_sbits_long(&s->gb, nbits); - } - - // Start unpacking LSB portion of the segment - if (b->lsb_section_size) { - // Skip to the start of LSB portion - if (ff_dca_seek_bits(&s->gb, band_data_end - b->lsb_section_size * 8)) { - av_log(s->avctx, AV_LOG_ERROR, "Read past end of XLL band data\n"); - return AVERROR_INVALIDDATA; - } - - // Unpack all LSB parts of residuals of this segment - for (i = 0; i < c->nchannels; i++) { - if (b->nscalablelsbs[i]) { - get_array(&s->gb, - b->lsb_sample_buffer[i] + seg * s->nsegsamples, - s->nsegsamples, b->nscalablelsbs[i]); - } - } - } - - // Skip to the end of band data - if (ff_dca_seek_bits(&s->gb, band_data_end)) { - av_log(s->avctx, AV_LOG_ERROR, "Read past end of XLL band data\n"); - return AVERROR_INVALIDDATA; - } - - return 0; -} - -static av_cold void chs_clear_band_data(DCAXllDecoder *s, DCAXllChSet *c, int band, int seg) -{ - DCAXllBand *b = &c->bands[band]; - int i, offset, nsamples; - - if (seg < 0) { - offset = 0; - nsamples = s->nframesamples; - } else { - offset = seg * s->nsegsamples; - nsamples = s->nsegsamples; - } - - for (i = 0; i < c->nchannels; i++) { - memset(b->msb_sample_buffer[i] + offset, 0, nsamples * sizeof(int32_t)); - if (b->lsb_section_size) - memset(b->lsb_sample_buffer[i] + offset, 0, nsamples * sizeof(int32_t)); - } - - if (seg <= 0 && band) - memset(c->deci_history, 0, sizeof(c->deci_history)); - - if (seg < 0) { - memset(b->nscalablelsbs, 0, sizeof(b->nscalablelsbs)); - memset(b->bit_width_adjust, 0, sizeof(b->bit_width_adjust)); - } -} - -static void chs_filter_band_data(DCAXllDecoder *s, DCAXllChSet *c, int band) -{ - DCAXllBand *b = &c->bands[band]; - int nsamples = s->nframesamples; - int i, j, k; - - // Inverse adaptive or fixed prediction - for (i = 0; i < c->nchannels; i++) { - int32_t *buf = b->msb_sample_buffer[i]; - int order = b->adapt_pred_order[i]; - if (order > 0) { - int coeff[DCA_XLL_ADAPT_PRED_ORDER_MAX]; - // Conversion from reflection coefficients to direct form coefficients - for (j = 0; j < order; j++) { - int rc = b->adapt_refl_coeff[i][j]; - for (k = 0; k < (j + 1) / 2; k++) { - int tmp1 = coeff[ k ]; - int tmp2 = coeff[j - k - 1]; - coeff[ k ] = tmp1 + mul16(rc, tmp2); - coeff[j - k - 1] = tmp2 + mul16(rc, tmp1); - } - coeff[j] = rc; - } - // Inverse adaptive prediction - for (j = 0; j < nsamples - order; j++) { - int64_t err = 0; - for (k = 0; k < order; k++) - err += (int64_t)buf[j + k] * coeff[order - k - 1]; - buf[j + k] -= (SUINT)clip23(norm16(err)); - } - } else { - // Inverse fixed coefficient prediction - for (j = 0; j < b->fixed_pred_order[i]; j++) - for (k = 1; k < nsamples; k++) - buf[k] += (unsigned)buf[k - 1]; - } - } - - // Inverse pairwise channel decorrellation - if (b->decor_enabled) { - int32_t *tmp[DCA_XLL_CHANNELS_MAX]; - - for (i = 0; i < c->nchannels / 2; i++) { - int coeff = b->decor_coeff[i]; - if (coeff) { - s->dcadsp->decor(b->msb_sample_buffer[i * 2 + 1], - b->msb_sample_buffer[i * 2 ], - coeff, nsamples); - } - } - - // Reorder channel pointers to the original order - for (i = 0; i < c->nchannels; i++) - tmp[i] = b->msb_sample_buffer[i]; - - for (i = 0; i < c->nchannels; i++) - b->msb_sample_buffer[b->orig_order[i]] = tmp[i]; - } - - // Map output channel pointers for frequency band 0 - if (c->nfreqbands == 1) - for (i = 0; i < c->nchannels; i++) - s->output_samples[c->ch_remap[i]] = b->msb_sample_buffer[i]; -} - -static int chs_get_lsb_width(DCAXllDecoder *s, DCAXllChSet *c, int band, int ch) -{ - int adj = c->bands[band].bit_width_adjust[ch]; - int shift = c->bands[band].nscalablelsbs[ch]; - - if (s->fixed_lsb_width) - shift = s->fixed_lsb_width; - else if (shift && adj) - shift += adj - 1; - else - shift += adj; - - return shift; -} - -static void chs_assemble_msbs_lsbs(DCAXllDecoder *s, DCAXllChSet *c, int band) -{ - DCAXllBand *b = &c->bands[band]; - int n, ch, nsamples = s->nframesamples; - - for (ch = 0; ch < c->nchannels; ch++) { - int shift = chs_get_lsb_width(s, c, band, ch); - if (shift) { - int32_t *msb = b->msb_sample_buffer[ch]; - if (b->nscalablelsbs[ch]) { - int32_t *lsb = b->lsb_sample_buffer[ch]; - int adj = b->bit_width_adjust[ch]; - for (n = 0; n < nsamples; n++) - msb[n] = msb[n] * (SUINT)(1 << shift) + (lsb[n] << adj); - } else { - for (n = 0; n < nsamples; n++) - msb[n] = msb[n] * (SUINT)(1 << shift); - } - } - } -} - -static int chs_assemble_freq_bands(DCAXllDecoder *s, DCAXllChSet *c) -{ - int ch, nsamples = s->nframesamples; - int32_t *ptr; - - av_assert1(c->nfreqbands > 1); - - // Reallocate frequency band assembly buffer - av_fast_malloc(&c->sample_buffer[2], &c->sample_size[2], - 2 * nsamples * c->nchannels * sizeof(int32_t)); - if (!c->sample_buffer[2]) - return AVERROR(ENOMEM); - - // Assemble frequency bands 0 and 1 - ptr = c->sample_buffer[2]; - for (ch = 0; ch < c->nchannels; ch++) { - int32_t *band0 = c->bands[0].msb_sample_buffer[ch]; - int32_t *band1 = c->bands[1].msb_sample_buffer[ch]; - - // Copy decimator history - memcpy(band0 - DCA_XLL_DECI_HISTORY_MAX, - c->deci_history[ch], sizeof(c->deci_history[0])); - - // Filter - s->dcadsp->assemble_freq_bands(ptr, band0, band1, - ff_dca_xll_band_coeff, - nsamples); - - // Remap output channel pointer to assembly buffer - s->output_samples[c->ch_remap[ch]] = ptr; - ptr += nsamples * 2; - } - - return 0; -} - -static int parse_common_header(DCAXllDecoder *s) -{ - int stream_ver, header_size, frame_size_nbits, nframesegs_log2; - - // XLL extension sync word - if (get_bits_long(&s->gb, 32) != DCA_SYNCWORD_XLL) { - av_log(s->avctx, AV_LOG_VERBOSE, "Invalid XLL sync word\n"); - return AVERROR(EAGAIN); - } - - // Version number - stream_ver = get_bits(&s->gb, 4) + 1; - if (stream_ver > 1) { - avpriv_request_sample(s->avctx, "XLL stream version %d", stream_ver); - return AVERROR_PATCHWELCOME; - } - - // Lossless frame header length - header_size = get_bits(&s->gb, 8) + 1; - - // Check CRC - if (ff_dca_check_crc(s->avctx, &s->gb, 32, header_size * 8)) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid XLL common header checksum\n"); - return AVERROR_INVALIDDATA; - } - - // Number of bits used to read frame size - frame_size_nbits = get_bits(&s->gb, 5) + 1; - - // Number of bytes in a lossless frame - s->frame_size = get_bits_long(&s->gb, frame_size_nbits); - if (s->frame_size < 0 || s->frame_size >= DCA_XLL_PBR_BUFFER_MAX) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid XLL frame size (%d bytes)\n", s->frame_size); - return AVERROR_INVALIDDATA; - } - s->frame_size++; - - // Number of channels sets per frame - s->nchsets = get_bits(&s->gb, 4) + 1; - if (s->nchsets > DCA_XLL_CHSETS_MAX) { - avpriv_request_sample(s->avctx, "%d XLL channel sets", s->nchsets); - return AVERROR_PATCHWELCOME; - } - - // Number of segments per frame - nframesegs_log2 = get_bits(&s->gb, 4); - s->nframesegs = 1 << nframesegs_log2; - if (s->nframesegs > 1024) { - av_log(s->avctx, AV_LOG_ERROR, "Too many segments per XLL frame\n"); - return AVERROR_INVALIDDATA; - } - - // Samples in segment per one frequency band for the first channel set - // Maximum value is 256 for sampling frequencies <= 48 kHz - // Maximum value is 512 for sampling frequencies > 48 kHz - s->nsegsamples_log2 = get_bits(&s->gb, 4); - if (!s->nsegsamples_log2) { - av_log(s->avctx, AV_LOG_ERROR, "Too few samples per XLL segment\n"); - return AVERROR_INVALIDDATA; - } - s->nsegsamples = 1 << s->nsegsamples_log2; - if (s->nsegsamples > 512) { - av_log(s->avctx, AV_LOG_ERROR, "Too many samples per XLL segment\n"); - return AVERROR_INVALIDDATA; - } - - // Samples in frame per one frequency band for the first channel set - s->nframesamples_log2 = s->nsegsamples_log2 + nframesegs_log2; - s->nframesamples = 1 << s->nframesamples_log2; - if (s->nframesamples > 65536) { - av_log(s->avctx, AV_LOG_ERROR, "Too many samples per XLL frame\n"); - return AVERROR_INVALIDDATA; - } - - // Number of bits used to read segment size - s->seg_size_nbits = get_bits(&s->gb, 5) + 1; - - // Presence of CRC16 within each frequency band - // 0 - No CRC16 within band - // 1 - CRC16 placed at the end of MSB0 - // 2 - CRC16 placed at the end of MSB0 and LSB0 - // 3 - CRC16 placed at the end of MSB0 and LSB0 and other frequency bands - s->band_crc_present = get_bits(&s->gb, 2); - - // MSB/LSB split flag - s->scalable_lsbs = get_bits1(&s->gb); - - // Channel position mask - s->ch_mask_nbits = get_bits(&s->gb, 5) + 1; - - // Fixed LSB width - if (s->scalable_lsbs) - s->fixed_lsb_width = get_bits(&s->gb, 4); - else - s->fixed_lsb_width = 0; - - // Reserved - // Byte align - // Header CRC16 protection - if (ff_dca_seek_bits(&s->gb, header_size * 8)) { - av_log(s->avctx, AV_LOG_ERROR, "Read past end of XLL common header\n"); - return AVERROR_INVALIDDATA; - } - - return 0; -} - -static int is_hier_dmix_chset(DCAXllChSet *c) -{ - return !c->primary_chset && c->dmix_embedded && c->hier_chset; -} - -static DCAXllChSet *find_next_hier_dmix_chset(DCAXllDecoder *s, DCAXllChSet *c) -{ - if (c->hier_chset) - while (++c < &s->chset[s->nchsets]) - if (is_hier_dmix_chset(c)) - return c; - - return NULL; -} - -static void prescale_down_mix(DCAXllChSet *c, DCAXllChSet *o) -{ - int i, j, *coeff_ptr = c->dmix_coeff; - - for (i = 0; i < c->hier_ofs; i++) { - int scale = o->dmix_scale[i]; - int scale_inv = o->dmix_scale_inv[i]; - c->dmix_scale[i] = mul15(c->dmix_scale[i], scale); - c->dmix_scale_inv[i] = mul16(c->dmix_scale_inv[i], scale_inv); - for (j = 0; j < c->nchannels; j++) { - int coeff = mul16(*coeff_ptr, scale_inv); - *coeff_ptr++ = mul15(coeff, o->dmix_scale[c->hier_ofs + j]); - } - } -} - -static int parse_sub_headers(DCAXllDecoder *s, DCAExssAsset *asset) -{ - DCAContext *dca = s->avctx->priv_data; - DCAXllChSet *c; - int i, ret; - - // Parse channel set headers - s->nfreqbands = 0; - s->nchannels = 0; - s->nreschsets = 0; - for (i = 0, c = s->chset; i < s->nchsets; i++, c++) { - c->hier_ofs = s->nchannels; - if ((ret = chs_parse_header(s, c, asset)) < 0) - return ret; - if (c->nfreqbands > s->nfreqbands) - s->nfreqbands = c->nfreqbands; - if (c->hier_chset) - s->nchannels += c->nchannels; - if (c->residual_encode != (1 << c->nchannels) - 1) - s->nreschsets++; - } - - // Pre-scale downmixing coefficients for all non-primary channel sets - for (i = s->nchsets - 1, c = &s->chset[i]; i > 0; i--, c--) { - if (is_hier_dmix_chset(c)) { - DCAXllChSet *o = find_next_hier_dmix_chset(s, c); - if (o) - prescale_down_mix(c, o); - } - } - - // Determine number of active channel sets to decode - switch (dca->request_channel_layout) { - case DCA_SPEAKER_LAYOUT_STEREO: - s->nactivechsets = 1; - break; - case DCA_SPEAKER_LAYOUT_5POINT0: - case DCA_SPEAKER_LAYOUT_5POINT1: - s->nactivechsets = (s->chset[0].nchannels < 5 && s->nchsets > 1) ? 2 : 1; - break; - default: - s->nactivechsets = s->nchsets; - break; - } - - return 0; -} - -static int parse_navi_table(DCAXllDecoder *s) -{ - int chs, seg, band, navi_nb, navi_pos, *navi_ptr; - DCAXllChSet *c; - - // Determine size of NAVI table - navi_nb = s->nfreqbands * s->nframesegs * s->nchsets; - if (navi_nb > 1024) { - av_log(s->avctx, AV_LOG_ERROR, "Too many NAVI entries (%d)\n", navi_nb); - return AVERROR_INVALIDDATA; - } - - // Reallocate NAVI table - av_fast_malloc(&s->navi, &s->navi_size, navi_nb * sizeof(*s->navi)); - if (!s->navi) - return AVERROR(ENOMEM); - - // Parse NAVI - navi_pos = get_bits_count(&s->gb); - navi_ptr = s->navi; - for (band = 0; band < s->nfreqbands; band++) { - for (seg = 0; seg < s->nframesegs; seg++) { - for (chs = 0, c = s->chset; chs < s->nchsets; chs++, c++) { - int size = 0; - if (c->nfreqbands > band) { - size = get_bits_long(&s->gb, s->seg_size_nbits); - if (size < 0 || size >= s->frame_size) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid NAVI segment size (%d bytes)\n", size); - return AVERROR_INVALIDDATA; - } - size++; - } - *navi_ptr++ = size; - } - } - } - - // Byte align - // CRC16 - skip_bits(&s->gb, -get_bits_count(&s->gb) & 7); - skip_bits(&s->gb, 16); - - // Check CRC - if (ff_dca_check_crc(s->avctx, &s->gb, navi_pos, get_bits_count(&s->gb))) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid NAVI checksum\n"); - return AVERROR_INVALIDDATA; - } - - return 0; -} - -static int parse_band_data(DCAXllDecoder *s) -{ - int ret, chs, seg, band, navi_pos, *navi_ptr; - DCAXllChSet *c; - - for (chs = 0, c = s->chset; chs < s->nactivechsets; chs++, c++) { - if ((ret = chs_alloc_msb_band_data(s, c)) < 0) - return ret; - if ((ret = chs_alloc_lsb_band_data(s, c)) < 0) - return ret; - } - - navi_pos = get_bits_count(&s->gb); - navi_ptr = s->navi; - for (band = 0; band < s->nfreqbands; band++) { - for (seg = 0; seg < s->nframesegs; seg++) { - for (chs = 0, c = s->chset; chs < s->nchsets; chs++, c++) { - if (c->nfreqbands > band) { - navi_pos += *navi_ptr * 8; - if (navi_pos > s->gb.size_in_bits) { - av_log(s->avctx, AV_LOG_ERROR, "Invalid NAVI position\n"); - return AVERROR_INVALIDDATA; - } - if (chs < s->nactivechsets && - (ret = chs_parse_band_data(s, c, band, seg, navi_pos)) < 0) { - if (s->avctx->err_recognition & AV_EF_EXPLODE) - return ret; - chs_clear_band_data(s, c, band, seg); - } - skip_bits_long(&s->gb, navi_pos - get_bits_count(&s->gb)); - } - navi_ptr++; - } - } - } - - return 0; -} - -static int parse_frame(DCAXllDecoder *s, const uint8_t *data, int size, DCAExssAsset *asset) -{ - int ret; - - if ((ret = init_get_bits8(&s->gb, data, size)) < 0) - return ret; - if ((ret = parse_common_header(s)) < 0) - return ret; - if ((ret = parse_sub_headers(s, asset)) < 0) - return ret; - if ((ret = parse_navi_table(s)) < 0) - return ret; - if ((ret = parse_band_data(s)) < 0) - return ret; - - if (s->frame_size * 8 > FFALIGN(get_bits_count(&s->gb), 32)) { - unsigned int extradata_syncword; - - // Align to dword - skip_bits_long(&s->gb, -get_bits_count(&s->gb) & 31); - - extradata_syncword = show_bits_long(&s->gb, 32); - - if (extradata_syncword == DCA_SYNCWORD_XLL_X) { - s->x_syncword_present = 1; - } else if ((extradata_syncword >> 1) == (DCA_SYNCWORD_XLL_X_IMAX >> 1)) { - s->x_imax_syncword_present = 1; - } - } - - if (ff_dca_seek_bits(&s->gb, s->frame_size * 8)) { - av_log(s->avctx, AV_LOG_ERROR, "Read past end of XLL frame\n"); - return AVERROR_INVALIDDATA; - } - return ret; -} - -static void clear_pbr(DCAXllDecoder *s) -{ - s->pbr_length = 0; - s->pbr_delay = 0; -} - -static int copy_to_pbr(DCAXllDecoder *s, const uint8_t *data, int size, int delay) -{ - if (size > DCA_XLL_PBR_BUFFER_MAX) - return AVERROR(ENOSPC); - - if (!s->pbr_buffer && !(s->pbr_buffer = av_malloc(DCA_XLL_PBR_BUFFER_MAX + AV_INPUT_BUFFER_PADDING_SIZE))) - return AVERROR(ENOMEM); - - memcpy(s->pbr_buffer, data, size); - s->pbr_length = size; - s->pbr_delay = delay; - return 0; -} - -static int parse_frame_no_pbr(DCAXllDecoder *s, const uint8_t *data, int size, DCAExssAsset *asset) -{ - int ret = parse_frame(s, data, size, asset); - - // If XLL packet data didn't start with a sync word, we must have jumped - // right into the middle of PBR smoothing period - if (ret == AVERROR(EAGAIN) && asset->xll_sync_present && asset->xll_sync_offset < size) { - // Skip to the next sync word in this packet - data += asset->xll_sync_offset; - size -= asset->xll_sync_offset; - - // If decoding delay is set, put the frame into PBR buffer and return - // failure code. Higher level decoder is expected to switch to lossy - // core decoding or mute its output until decoding delay expires. - if (asset->xll_delay_nframes > 0) { - if ((ret = copy_to_pbr(s, data, size, asset->xll_delay_nframes)) < 0) - return ret; - return AVERROR(EAGAIN); - } - - // No decoding delay, just parse the frame in place - ret = parse_frame(s, data, size, asset); - } - - if (ret < 0) - return ret; - - if (s->frame_size > size) - return AVERROR(EINVAL); - - // If the XLL decoder didn't consume full packet, start PBR smoothing period - if (s->frame_size < size) - if ((ret = copy_to_pbr(s, data + s->frame_size, size - s->frame_size, 0)) < 0) - return ret; - - return 0; -} - -static int parse_frame_pbr(DCAXllDecoder *s, const uint8_t *data, int size, DCAExssAsset *asset) -{ - int ret; - - if (size > DCA_XLL_PBR_BUFFER_MAX - s->pbr_length) { - ret = AVERROR(ENOSPC); - goto fail; - } - - memcpy(s->pbr_buffer + s->pbr_length, data, size); - s->pbr_length += size; - - // Respect decoding delay after synchronization error - if (s->pbr_delay > 0 && --s->pbr_delay) - return AVERROR(EAGAIN); - - if ((ret = parse_frame(s, s->pbr_buffer, s->pbr_length, asset)) < 0) - goto fail; - - if (s->frame_size > s->pbr_length) { - ret = AVERROR(EINVAL); - goto fail; - } - - if (s->frame_size == s->pbr_length) { - // End of PBR smoothing period - clear_pbr(s); - } else { - s->pbr_length -= s->frame_size; - memmove(s->pbr_buffer, s->pbr_buffer + s->frame_size, s->pbr_length); - } - - return 0; - -fail: - // For now, throw out all PBR state on failure. - // Perhaps we can be smarter and try to resync somehow. - clear_pbr(s); - return ret; -} - -int ff_dca_xll_parse(DCAXllDecoder *s, const uint8_t *data, DCAExssAsset *asset) -{ - int ret; - - if (s->hd_stream_id != asset->hd_stream_id) { - clear_pbr(s); - s->hd_stream_id = asset->hd_stream_id; - } - - if (s->pbr_length) - ret = parse_frame_pbr(s, data + asset->xll_offset, asset->xll_size, asset); - else - ret = parse_frame_no_pbr(s, data + asset->xll_offset, asset->xll_size, asset); - - return ret; -} - -static void undo_down_mix(DCAXllDecoder *s, DCAXllChSet *o, int band) -{ - int i, j, k, nchannels = 0, *coeff_ptr = o->dmix_coeff; - DCAXllChSet *c; - - for (i = 0, c = s->chset; i < s->nactivechsets; i++, c++) { - if (!c->hier_chset) - continue; - - av_assert1(band < c->nfreqbands); - for (j = 0; j < c->nchannels; j++) { - for (k = 0; k < o->nchannels; k++) { - int coeff = *coeff_ptr++; - if (coeff) { - s->dcadsp->dmix_sub(c->bands[band].msb_sample_buffer[j], - o->bands[band].msb_sample_buffer[k], - coeff, s->nframesamples); - if (band) - s->dcadsp->dmix_sub(c->deci_history[j], - o->deci_history[k], - coeff, DCA_XLL_DECI_HISTORY_MAX); - } - } - } - - nchannels += c->nchannels; - if (nchannels >= o->hier_ofs) - break; - } -} - -static void scale_down_mix(DCAXllDecoder *s, DCAXllChSet *o, int band) -{ - int i, j, nchannels = 0; - DCAXllChSet *c; - - for (i = 0, c = s->chset; i < s->nactivechsets; i++, c++) { - if (!c->hier_chset) - continue; - - av_assert1(band < c->nfreqbands); - for (j = 0; j < c->nchannels; j++) { - int scale = o->dmix_scale[nchannels++]; - if (scale != (1 << 15)) { - s->dcadsp->dmix_scale(c->bands[band].msb_sample_buffer[j], - scale, s->nframesamples); - if (band) - s->dcadsp->dmix_scale(c->deci_history[j], - scale, DCA_XLL_DECI_HISTORY_MAX); - } - } - - if (nchannels >= o->hier_ofs) - break; - } -} - -// Clear all band data and replace non-residual encoded channels with lossy -// counterparts -static av_cold void force_lossy_output(DCAXllDecoder *s, DCAXllChSet *c) -{ - DCAContext *dca = s->avctx->priv_data; - int band, ch; - - for (band = 0; band < c->nfreqbands; band++) - chs_clear_band_data(s, c, band, -1); - - for (ch = 0; ch < c->nchannels; ch++) { - if (!(c->residual_encode & (1 << ch))) - continue; - if (ff_dca_core_map_spkr(&dca->core, c->ch_remap[ch]) < 0) - continue; - c->residual_encode &= ~(1 << ch); - } -} - -static int combine_residual_frame(DCAXllDecoder *s, DCAXllChSet *c) -{ - DCAContext *dca = s->avctx->priv_data; - int ch, nsamples = s->nframesamples; - DCAXllChSet *o; - - // Verify that core is compatible - if (!(dca->packet & DCA_PACKET_CORE)) { - av_log(s->avctx, AV_LOG_ERROR, "Residual encoded channels are present without core\n"); - return AVERROR(EINVAL); - } - - if (c->freq != dca->core.output_rate) { - av_log(s->avctx, AV_LOG_WARNING, "Sample rate mismatch between core (%d Hz) and XLL (%d Hz)\n", dca->core.output_rate, c->freq); - return AVERROR_INVALIDDATA; - } - - if (nsamples != dca->core.npcmsamples) { - av_log(s->avctx, AV_LOG_WARNING, "Number of samples per frame mismatch between core (%d) and XLL (%d)\n", dca->core.npcmsamples, nsamples); - return AVERROR_INVALIDDATA; - } - - // See if this channel set is downmixed and find the next channel set in - // hierarchy. If downmixed, undo core pre-scaling before combining with - // residual (residual is not scaled). - o = find_next_hier_dmix_chset(s, c); - - // Reduce core bit width and combine with residual - for (ch = 0; ch < c->nchannels; ch++) { - int n, spkr, shift, round; - int32_t *src, *dst; - - if (c->residual_encode & (1 << ch)) - continue; - - // Map this channel to core speaker - spkr = ff_dca_core_map_spkr(&dca->core, c->ch_remap[ch]); - if (spkr < 0) { - av_log(s->avctx, AV_LOG_WARNING, "Residual encoded channel (%d) references unavailable core channel\n", c->ch_remap[ch]); - return AVERROR_INVALIDDATA; - } - - // Account for LSB width - shift = 24 - c->pcm_bit_res + chs_get_lsb_width(s, c, 0, ch); - if (shift > 24) { - av_log(s->avctx, AV_LOG_WARNING, "Invalid core shift (%d bits)\n", shift); - return AVERROR_INVALIDDATA; - } - - round = shift > 0 ? 1 << (shift - 1) : 0; - - src = dca->core.output_samples[spkr]; - dst = c->bands[0].msb_sample_buffer[ch]; - if (o) { - // Undo embedded core downmix pre-scaling - int scale_inv = o->dmix_scale_inv[c->hier_ofs + ch]; - for (n = 0; n < nsamples; n++) - dst[n] += (SUINT)clip23((mul16(src[n], scale_inv) + round) >> shift); - } else { - // No downmix scaling - for (n = 0; n < nsamples; n++) - dst[n] += (unsigned)((src[n] + round) >> shift); - } - } - - return 0; -} - -int ff_dca_xll_filter_frame(DCAXllDecoder *s, AVFrame *frame) -{ - AVCodecContext *avctx = s->avctx; - DCAContext *dca = avctx->priv_data; - DCAExssAsset *asset = &dca->exss.assets[0]; - DCAXllChSet *p = &s->chset[0], *c; - enum AVMatrixEncoding matrix_encoding = AV_MATRIX_ENCODING_NONE; - int i, j, k, ret, shift, nsamples, request_mask; - int ch_remap[DCA_SPEAKER_COUNT]; - - // Force lossy downmixed output during recovery - if (dca->packet & DCA_PACKET_RECOVERY) { - for (i = 0, c = s->chset; i < s->nchsets; i++, c++) { - if (i < s->nactivechsets) - force_lossy_output(s, c); - - if (!c->primary_chset) - c->dmix_embedded = 0; - } - - s->scalable_lsbs = 0; - s->fixed_lsb_width = 0; - } - - // Filter frequency bands for active channel sets - s->output_mask = 0; - for (i = 0, c = s->chset; i < s->nactivechsets; i++, c++) { - chs_filter_band_data(s, c, 0); - - if (c->residual_encode != (1 << c->nchannels) - 1 - && (ret = combine_residual_frame(s, c)) < 0) - return ret; - - if (s->scalable_lsbs) - chs_assemble_msbs_lsbs(s, c, 0); - - if (c->nfreqbands > 1) { - chs_filter_band_data(s, c, 1); - chs_assemble_msbs_lsbs(s, c, 1); - } - - s->output_mask |= c->ch_mask; - } - - // Undo hierarchial downmix and/or apply scaling - for (i = 1, c = &s->chset[1]; i < s->nchsets; i++, c++) { - if (!is_hier_dmix_chset(c)) - continue; - - if (i >= s->nactivechsets) { - for (j = 0; j < c->nfreqbands; j++) - if (c->bands[j].dmix_embedded) - scale_down_mix(s, c, j); - break; - } - - for (j = 0; j < c->nfreqbands; j++) - if (c->bands[j].dmix_embedded) - undo_down_mix(s, c, j); - } - - // Assemble frequency bands for active channel sets - if (s->nfreqbands > 1) { - for (i = 0; i < s->nactivechsets; i++) - if ((ret = chs_assemble_freq_bands(s, &s->chset[i])) < 0) - return ret; - } - - // Normalize to regular 5.1 layout if downmixing - if (dca->request_channel_layout) { - if (s->output_mask & DCA_SPEAKER_MASK_Lss) { - s->output_samples[DCA_SPEAKER_Ls] = s->output_samples[DCA_SPEAKER_Lss]; - s->output_mask = (s->output_mask & ~DCA_SPEAKER_MASK_Lss) | DCA_SPEAKER_MASK_Ls; - } - if (s->output_mask & DCA_SPEAKER_MASK_Rss) { - s->output_samples[DCA_SPEAKER_Rs] = s->output_samples[DCA_SPEAKER_Rss]; - s->output_mask = (s->output_mask & ~DCA_SPEAKER_MASK_Rss) | DCA_SPEAKER_MASK_Rs; - } - } - - // Handle downmixing to stereo request - if (dca->request_channel_layout == DCA_SPEAKER_LAYOUT_STEREO - && DCA_HAS_STEREO(s->output_mask) && p->dmix_embedded - && (p->dmix_type == DCA_DMIX_TYPE_LoRo || - p->dmix_type == DCA_DMIX_TYPE_LtRt)) - request_mask = DCA_SPEAKER_LAYOUT_STEREO; - else - request_mask = s->output_mask; - if (!ff_dca_set_channel_layout(avctx, ch_remap, request_mask)) - return AVERROR(EINVAL); - - avctx->sample_rate = p->freq << (s->nfreqbands - 1); - - switch (p->storage_bit_res) { - case 16: - avctx->sample_fmt = AV_SAMPLE_FMT_S16P; - shift = 16 - p->pcm_bit_res; - break; - case 20: - case 24: - avctx->sample_fmt = AV_SAMPLE_FMT_S32P; - shift = 24 - p->pcm_bit_res; - break; - default: - return AVERROR(EINVAL); - } - - if (s->x_imax_syncword_present) { - avctx->profile = FF_PROFILE_DTS_HD_MA_X_IMAX; - } else if (s->x_syncword_present) { - avctx->profile = FF_PROFILE_DTS_HD_MA_X; - } else { - avctx->profile = FF_PROFILE_DTS_HD_MA; - } - - avctx->bits_per_raw_sample = p->storage_bit_res; - avctx->bit_rate = 0; - - frame->nb_samples = nsamples = s->nframesamples << (s->nfreqbands - 1); - if ((ret = ff_get_buffer(avctx, frame, 0)) < 0) - return ret; - - // Downmix primary channel set to stereo - if (request_mask != s->output_mask) { - ff_dca_downmix_to_stereo_fixed(s->dcadsp, s->output_samples, - p->dmix_coeff, nsamples, - s->output_mask); - } - - for (i = 0; i < avctx->ch_layout.nb_channels; i++) { - int32_t *samples = s->output_samples[ch_remap[i]]; - if (frame->format == AV_SAMPLE_FMT_S16P) { - int16_t *plane = (int16_t *)frame->extended_data[i]; - for (k = 0; k < nsamples; k++) - plane[k] = av_clip_int16(samples[k] * (SUINT)(1 << shift)); - } else { - int32_t *plane = (int32_t *)frame->extended_data[i]; - for (k = 0; k < nsamples; k++) - plane[k] = clip23(samples[k] * (SUINT)(1 << shift)) * (1 << 8); - } - } - - if (!asset->one_to_one_map_ch_to_spkr) { - if (asset->representation_type == DCA_REPR_TYPE_LtRt) - matrix_encoding = AV_MATRIX_ENCODING_DOLBY; - else if (asset->representation_type == DCA_REPR_TYPE_LhRh) - matrix_encoding = AV_MATRIX_ENCODING_DOLBYHEADPHONE; - } else if (request_mask != s->output_mask && p->dmix_type == DCA_DMIX_TYPE_LtRt) { - matrix_encoding = AV_MATRIX_ENCODING_DOLBY; - } - if ((ret = ff_side_data_update_matrix_encoding(frame, matrix_encoding)) < 0) - return ret; - - return 0; -} - -av_cold void ff_dca_xll_flush(DCAXllDecoder *s) -{ - clear_pbr(s); -} - -av_cold void ff_dca_xll_close(DCAXllDecoder *s) -{ - DCAXllChSet *c; - int i, j; - - for (i = 0, c = s->chset; i < DCA_XLL_CHSETS_MAX; i++, c++) { - for (j = 0; j < DCA_XLL_SAMPLE_BUFFERS_MAX; j++) { - av_freep(&c->sample_buffer[j]); - c->sample_size[j] = 0; - } - } - - av_freep(&s->navi); - s->navi_size = 0; - - av_freep(&s->pbr_buffer); - clear_pbr(s); -} diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Sigma APK and Join the Fight Out Mode with Your Squad.md b/spaces/congsaPfin/Manga-OCR/logs/Download Sigma APK and Join the Fight Out Mode with Your Squad.md deleted file mode 100644 index cebdd9f9cfafa9f31910c1a51cc3fbe9bff740a6..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Download Sigma APK and Join the Fight Out Mode with Your Squad.md +++ /dev/null @@ -1,125 +0,0 @@ - -
Sigma Download APK: How to Play the Stylized Survival Shooter Game on Your Android Device
-If you are looking for a new and exciting survival shooter game to play on your Android device, you might want to check out Sigma Battle Royale. This game is a stylized and creative take on the popular battle royale genre, where you have to fight against other players in a vast map and be the last one standing. In this article, we will tell you what Sigma Battle Royale is, how to download and install it on your Android device, and some tips and tricks for playing it.
-What is Sigma Battle Royale?
-Sigma Battle Royale is a game developed by Studio Arm Private Limited, a game studio based in India. It was released in November 2022 and has since gained over 500,000 downloads on Google Play Store. The game is available for Android devices with version 4.1 or higher.
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Sigma Battle Royale is a game that combines the elements of classic battle royale and 4v4 team deathmatch modes. In the battle royale mode, you can choose your starting point with your parachute and land on a large map with 49 other players. You have to loot weapons, armor, and items, and fight against other players while staying in the safe zone. The last player or team alive wins the match. In the 4v4 fight out mode, you can team up with three other players and compete against another team in various maps. You have to allocate resources, buy weapons, and outlast your enemies. The team with the most kills wins the match.
-Features of Sigma Battle Royale
-Some of the features that make Sigma Battle Royale stand out from other survival shooter games are:
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- Stylized graphics: The game has a unique and creative art style that gives it a distinctive look and feel. The game uses bright colors, cartoon-like characters, and futuristic weapons and vehicles to create a stylized survival world. -
- Unique survival shooter experience: The game has easy-to-use controls and promises an unforgettable survival experience on mobile. You can customize your character, choose from different weapons and items, and use various skills and tactics to survive. -
- Classic battle royale: The game offers a fast-paced and lite gameplay that lasts for about 10 minutes per match. You can experience the thrill of being the last one standing in a 50-player battle royale mode. -
- 4v4 fight out: The game also offers a tense and strategic gameplay that lasts for about 7 minutes per match. You can collaborate with your team and defeat your enemies in a 4v4 team deathmatch mode. -
How to download and install Sigma Battle Royale APK
-If you want to play Sigma Battle Royale on your Android device, you can download it from Google Play Store or from APKCombo. APKCombo is a website that provides free APK downloads for Android games and apps. Here are the steps to download and install Sigma Battle Royale APK from APKCombo:
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- Go to APKCombo and search for "Sigma" or "Sigma Battle Royale". -
- Select the game from the search results and click on "Download APK". -
- Choose the version of the game that is compatible with your device and click on "Download". -
- Wait for the download to finish and then open the APK file. -
- If you see a warning message that says "Install blocked", go to your device settings and enable "Unknown sources" or "Allow from this source". -
- Follow the instructions on the screen to install the game. -
- Enjoy playing Sigma Battle Royale on your Android device. -
Now that you have downloaded and installed Sigma Battle Royale on your Android device, you might be wondering how to play it and win more matches. Here are some tips and tricks that can help you improve your skills and have more fun in the game:
-Choose your landing spot wisely
-In the battle royale mode, you can choose where to land on the map with your parachute. This is a crucial decision that can affect your chances of survival and victory. You should consider the following factors when choosing your landing spot:
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- Distance from the plane: The farther you are from the plane's path, the less likely you are to encounter other players in the early game. However, you might also have to travel longer distances to reach the safe zone or find better loot. -
- Loot quality and quantity: Some areas on the map have more loot than others, and some loot is more valuable than others. You should aim for areas that have high-quality weapons, armor, and items that can give you an edge in combat. -
- Popularity and competition: Some areas on the map are more popular than others, and therefore more likely to attract other players. You should avoid landing in hot spots if you want to avoid early fights and focus on looting. However, if you are confident in your skills and want to eliminate other players quickly, you can try landing in hot spots and fight for the best loot. -
Loot and upgrade your weapons
-In Sigma Battle Royale, you can find various weapons and items on the map or from the enemies you kill. You should always loot as much as you can and upgrade your weapons whenever possible. Here are some tips for looting and upgrading your weapons:
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- Know your weapon types: There are four types of weapons in Sigma Battle Royale: assault rifles, shotguns, sniper rifles, and pistols. Each weapon type has its own advantages and disadvantages in terms of damage, range, accuracy, fire rate, recoil, and magazine size. You should choose the weapon type that suits your playstyle and situation. -
- Know your weapon rarities: There are five rarities of weapons in Sigma Battle Royale: common, uncommon, rare, epic, and legendary. The higher the rarity, the better the stats and performance of the weapon. You should always look for higher rarity weapons and replace your lower rarity ones. -
- Know your weapon attachments: There are four types of attachments in Sigma Battle Royale: scopes, muzzles, magazines, and grips. Each attachment type can improve a certain aspect of your weapon, such as zoom, stability, capacity, or handling. You should always equip attachments that match your weapon type and preference. -
- Know your weapon skins: There are various skins that you can unlock or buy for your weapons in Sigma Battle Royale. Skins can change the appearance of your weapon and give you some cosmetic benefits. However, skins do not affect the stats or performance of your weapon. -
Use cover and movement to your advantage
-In Sigma Battle Royale, you have to be aware of your surroundings and use cover and movement to your advantage. Here are some tips for using cover and movement in the game:
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- Use cover to protect yourself: Cover is anything that can block or reduce the damage from enemy fire, such as buildings, walls, trees, rocks, vehicles, etc. You should always use cover when engaging in combat or when healing yourself or your teammates. Cover can also help you hide from enemies or ambush them. -
- Use movement to confuse your enemies: Movement is anything that can change your position or direction, such as running, jumping, sliding, crouching, etc. You should always use movement when fighting or escaping from enemies. Movement can help you dodge enemy fire or surprise them with unexpected attacks. -
- Use vehicles to travel faster: Vehicles are anything that can transport you or your teammates across the map faster than walking or running, such as cars, bikes, boats, etc. You should always use vehicles when moving to the safe zone or when chasing or fleeing from enemies. Vehicles can also be used as weapons or cover in some situations. -
Communicate and cooperate with your team
-In Sigma Battle Royale, you can play solo or with up to three other players in a team. Playing with a team can give you many advantages over playing solo, such as having more firepower, support, information, and resources. However, playing with a team also requires communication and cooperation. Here are some tips for communicating and cooperating with your team in the game:
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- Use voice chat or text chat to communicate with your team: Voice chat or text chat are the main ways to communicate with your team in Sigma Battle Royale. You can use voice chat or text chat to share information, coordinate actions, ask for help, or chat with your team. Voice chat is more convenient and effective than text chat, but it also requires a good internet connection and a microphone. Text chat is more accessible and discreet than voice chat, but it also takes more time and attention to type and read messages. -
- Use quick commands or gestures to communicate with your team: Quick commands or gestures are the alternative ways to communicate with your team in Sigma Battle Royale. You can use quick commands or gestures to send predefined messages, such as "Follow me", "Enemy spotted", "Need ammo", etc. Quick commands or gestures are faster and easier than voice chat or text chat, but they are also more limited and vague. -
- Use the map or the compass to communicate with your team: The map or the compass are the supplementary ways to communicate with your team in Sigma Battle Royale. You can use the map or the compass to mark locations, enemies, items, or vehicles on the screen. The map or the compass are useful and accurate ways to communicate with your team, but they also require you to open the map or look at the compass, which can expose you to danger. -
- Cooperate with your team to achieve your goals: Cooperation is the key to success in Sigma Battle Royale. You should cooperate with your team to achieve your goals, such as landing together, looting together, fighting together, and surviving together. Cooperation can help you overcome challenges, gain advantages, and have more fun in the game. -
Conclusion
-Sigma Battle Royale is a stylized and creative survival shooter game that you can play on your Android device. It offers two modes of gameplay: classic battle royale and 4v4 fight out. It has unique features, such as stylized graphics, unique survival shooter experience, and various weapons and items. It also requires skills, such as choosing your landing spot wisely, looting and upgrading your weapons, using cover and movement to your advantage, and communicating and cooperating with your team. If you want to play Sigma Battle Royale on your Android device, you can download it from Google Play Store or from APKCombo. We hope this article has helped you learn more about Sigma Battle Royale and how to play it.
-FAQs
-Here are some frequently asked questions about Sigma Battle Royale:
--
-
- Is Sigma Battle Royale free to play? -
- Is Sigma Battle Royale online or offline? -
- Is Sigma Battle Royale safe to download and install? -
- How can I update Sigma Battle Royale? -
- How can I contact the developers of Sigma Battle Royale? -
Yes, Sigma Battle Royale is free to play on Android devices. However, it also offers in-app purchases that can enhance your gameplay experience.
-Sigma Battle Royale is an online game that requires an internet connection to play. You can play solo or with other players from around the world.
-Yes, Sigma Battle Royale is safe to download and install from Google Play Store or from APKCombo. However, you should always be careful when downloading APK files from unknown sources and scan them for viruses before installing them.
-You can update Sigma Battle Royale from Google Play Store or from APKCombo. You should always update the game to get the latest features, bug fixes, and improvements.
-You can contact the developers of Sigma Battle Royale by sending an email to studioarmpl@gmail.com or by visiting their website at https://studioarm.in/.
--
-
\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Dynamons World on PC The Ultimate RPG Experience with BlueStacks Emulator.md b/spaces/congsaPfin/Manga-OCR/logs/Dynamons World on PC The Ultimate RPG Experience with BlueStacks Emulator.md deleted file mode 100644 index 215e9ee9148605ee72cc1e1b8b42e929a13a67df..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Dynamons World on PC The Ultimate RPG Experience with BlueStacks Emulator.md +++ /dev/null @@ -1,113 +0,0 @@ -
-
How to Download and Play Dynamons World on Your Laptop
-If you are looking for a fun and addictive game that combines RPG elements, monster catching, and online battles, you should check out Dynamons World. This game lets you explore an open world full of different types of Dynamons, which are cute and powerful creatures that you can catch, train, and battle with. You can also challenge your friends and other players in real-time online multiplayer matches, or join a clan and participate in events and quests.
-Dynamons World is available for free on Android and iOS devices, but did you know that you can also play it on your laptop? Playing on a bigger screen can enhance your gaming experience, as well as give you more control options and better performance. In this article, we will show you how to download and play Dynamons World on your laptop using different methods. Whether you have a Windows or a Mac laptop, there is a way for you to enjoy this amazing game.
-dynamons world download laptop
Download File ○○○ https://urlca.com/2uO5XJ
-
Method 1: Microsoft Store
-If you have a Windows laptop, one of the easiest ways to get Dynamons World is through the Microsoft Store. This is an online marketplace where you can find various apps and games for your Windows device. Here are the steps to follow:
--
-
- Open the Microsoft Store. You can do this by clicking on the Start menu and typing "Microsoft Store", or by pressing the Windows key + S and searching for it. -
- Click on Gaming in the sidebar. This will show you different categories and genres of games that you can browse. -
- Select Dynamons World. You can use the search bar at the top right corner to find it quickly, or scroll through the list of games until you see it. -
- Purchase the game (if needed). Some games in the Microsoft Store are free, while others require payment. Dynamons World is free to play, but it offers in-app purchases for extra items and features. If you want to buy something in the game, you will need a Microsoft account and a payment method. To get the game, click on the Get button. -
- Install the game. After clicking on Get, the game will start downloading automatically. You can see the progress in the Downloads section of the Microsoft Store. Once it is done, click on Install to finish the process. -
- Launch and play the game. You can find Dynamons World in your Start menu or on your desktop. Click on its icon to open it, and enjoy playing it on your laptop. -
Method 2: Direct Download
-Another way to play Dynamons World on your laptop is to download it directly from its official website. This method works for both Windows and Mac laptops. This method gives you the most updated and original version of the game. Here are the steps to follow:
--
-
- Search for the official website of Dynamons World. You can use Bing or any other search engine to find it. The website should have a URL like [1](https://play.google.com/store/apps/details?id=com.funtomic.dynamons3) or [6](https://apps.apple.com/us/app/dynamons-world/id1190307500), depending on your device. -
- Download the game file from the website. There should be a button or a link that says something like "Download", "Get", or "Install". Click on it and choose a location to save the file on your laptop. -
- Install and run the game. Depending on your device, you may need to unzip the file first, or double-click on it to start the installation process. Follow the instructions on the screen and agree to the terms and conditions. Once the installation is complete, you can open the game and start playing. -
Method 3: Third-Party Platform
-A third option to play Dynamons World on your laptop is to use a third-party platform that hosts various games and apps. Some of the most popular and reliable platforms are Steam, Epic Games, and GOG. These platforms offer many benefits, such as easy access, updates, reviews, achievements, and more. Here are the steps to follow:
--
-
- Choose a platform that suits your preferences and needs. You can compare the features, prices, and ratings of different platforms online, or ask for recommendations from other gamers. -
- Create an account and download the platform client. You will need to register with your email address and create a username and password. Then, you will need to download and install the platform client on your laptop, which will allow you to access the platform library and services. -
- Find and download Dynamons World from the platform library. You can use the search function or browse through different categories and genres to find Dynamons World. Some platforms may offer it for free, while others may require payment or subscription. Once you find it, click on the "Download", "Get", or "Install" button. -
- Launch and play the game. You can find Dynamons World in your platform library or on your desktop. Click on its icon to open it, and enjoy playing it on your laptop. -
Conclusion
-Dynamons World is a fun and addictive game that you can play on your laptop using different methods. Whether you use the Microsoft Store, direct download, or third-party platform, you can enjoy this game on a bigger screen with more control options and better performance. Here are some tips and tricks for enjoying the game:
--
-
- Learn about the different types of Dynamons and their strengths and weaknesses. Use this knowledge to create a balanced team and choose the best moves in battle. -
- Collect skill cards and use them wisely to enhance your Dynamons' abilities and tactics. You can find skill cards in chests, shops, quests, or battles. -
- Join a clan and cooperate with other players to complete events and quests, earn rewards, and chat with other Dynamon fans. -
- Customize your avatar and your Dynamons with different outfits, accessories, and colors. You can buy them with coins or gems in the shop. -
- Have fun and experiment with different strategies and combinations. There is no one right way to play Dynamons World, so feel free to explore and discover new things. -
We hope this article has helped you learn how to download and play Dynamons World on your laptop. If you have any questions or feedback, please let us know in the comments below. And don't forget to share this article with your friends who might be interested in playing this game too!
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FAQs
-Here are some frequently asked questions about Dynamons World:
-What are the minimum system requirements for playing Dynamons World on your laptop?
-The minimum system requirements for playing Dynamons World on your laptop vary depending on the method you use. However, generally speaking, you will need at least:
-| Operating System | Windows 7/8/10 or Mac OS X 10.9+ | ||
| Processor | Intel Core 2 Duo 2GHz+ or equivalent | ||
| Memory | 2 GB RAM | Graphics | DirectX 9.0c compatible video card with 256 MB VRAM |
| Storage | 500 MB available space | ||
| Internet | Broadband connection |
You can check your laptop's specifications by going to the Control Panel or the System Preferences and looking for the System or Hardware information.
-Can you play Dynamons World online with other players?
-Yes, you can play Dynamons World online with other players. You can challenge them in real-time PvP battles, or join a clan and cooperate with them in events and quests. You can also chat with them and make new friends. To play online, you will need a stable internet connection and a Dynamons World account.
-Can you transfer your progress and data from your mobile device to your laptop?
-Yes, you can transfer your progress and data from your mobile device to your laptop. You will need to link your Dynamons World account to your Facebook account, Google Play account, or Apple ID. Then, you can log in with the same account on your laptop and sync your data. This way, you can continue playing where you left off on any device.
-How can you update Dynamons World to get the latest features and fixes?
-To update Dynamons World to get the latest features and fixes, you will need to check for updates regularly on the platform that you use to play the game. For example, if you use the Microsoft Store, you can go to the Downloads section and see if there are any updates available for Dynamons World. If there are, click on the Update button to download and install them. If you use direct download or third-party platform, you can check the official website or the platform library for updates.
-Where can you find more information and support for Dynamons World?
-If you need more information and support for Dynamons World, you can visit the following sources:
--
-
- The official website of Dynamons World: [1](https://play.google.com/store/apps/details?id=com.funtomic.dynamons3) or [6](https://apps.apple.com/us/app/dynamons-world/id1190307500) -
- The official Facebook page of Dynamons World: [2](https://www.facebook.com/DynamonsWorld/) -
- The official YouTube channel of Dynamons World: [3](https://www.youtube.com/channel/UC4Zyv1xk8aQ6YfO7JXzQg2w) -
- The official Twitter account of Dynamons World: [4](https://twitter.com/DynamonsWorld) -
- The official Instagram account of Dynamons World: [5](https://www.instagram.com/dynamonsworld/) -
- The official Discord server of Dynamons World: [7](https://discord.gg/dynamonsworld) -
- The official Reddit community of Dynamons World: [8](https://www.reddit.com/r/DynamonsWorld/) -
- The official Wikia page of Dynamons World: [9](https://dynamonsworld.fandom.com/wiki/Dynamons_World_Wiki) -
You can also contact the developers of Dynamons World by sending an email to support@funtomic.com or by filling out a form on their website.
401be4b1e0-
-
\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Free World Map Vectors with Countries for Creative Projects.md b/spaces/congsaPfin/Manga-OCR/logs/Free World Map Vectors with Countries for Creative Projects.md deleted file mode 100644 index c51d624a236b867a76d394c23d772641af700d94..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Free World Map Vectors with Countries for Creative Projects.md +++ /dev/null @@ -1,161 +0,0 @@ - -
World Map with Countries Vector Free Download
-If you are looking for a world map with countries that you can download for free and use for various purposes, such as education, design, or presentation, you might want to consider using vector graphics instead of raster graphics. In this article, we will explain what vector graphics are, why they are useful, and where you can find free vector graphics of world maps with countries. We will also show you how to use some popular vector graphics software to edit and customize your world map according to your needs.
-world map with countries vector free download
Download File ⇔ https://urlca.com/2uOenM
-
What are vector graphics and why are they useful?
-Vector graphics are a form of computer graphics in which visual images are created directly from geometric shapes defined on a Cartesian plane, such as points, lines, curves, and polygons. Unlike raster graphics, which use a fixed grid of pixels, vector graphic files have no fixed resolution and can be resized, stretched, and otherwise manipulated without any quality loss.
-Definition of vector graphics
-Vector graphics are based on mathematical equations that describe the position, direction, and color of each shape in the image. A vector graphic file is saved as a sequence of vector statements that can be interpreted by a software program to render the image on a screen or a printer.
-Advantages of vector graphics over raster graphics
-One of the most significant advantages of vector graphics is their scalability. Vector graphics can be scaled up or down without losing quality or resolution. This is because vector graphics are based on mathematical equations, which means that the image can be stretched or shrunk without affecting the quality of the image.
-Another advantage of vector graphics is their small file size. Vector graphics typically have smaller file sizes than raster graphics because they do not store information about individual pixels. This makes vector graphics ideal for use on websites or in applications where smaller file sizes are important.
-Examples of vector graphics applications
-Vector graphics are widely used for creating digital graphics today because of their versatility and precision. Some examples of vector graphics applications are:
--
-
- Logos and icons that look crisp and clear at any size -
- Line art and illustrations that can be easily modified and colored -
- 3D-like renderings and animations that can be rotated and transformed -
- Computer-aided design (CAD) and engineering drawings that require high accuracy and detail -
- Typography and fonts that can be scaled and styled without distortion -
- Infographics and charts that can be updated and edited with data -
Where can you find free vector graphics of world maps with countries?
-If you want to download a free vector graphic of a world map with countries, there are many websites that offer such resources. Here are some of the best ones that we recommend:
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Freepik
-Freepik is one of the most popular websites for finding free vectors, photos, PSD files, and icons. You can browse through thousands of world map vectors with different styles, colors, and levels of detail. You can also filter your search by license type, orientation, color, or category.
-Vecteezy
-Vecteezy is another great website for finding free vector art, icons, patterns, backgrounds, and more. You can search for world map vectors with various designs, such as flat, realistic, abstract, or vintage. You can also use their online editor to customize your map before downloading it.
-Inks. Inkscape
-
Inkscape is a free and open-source vector graphics editor that you can download and install on your computer. It supports various vector formats, such as SVG, EPS, PDF, and AI. You can use Inkscape to create your own world map vector from scratch or import and edit an existing one. You can also use Inkscape's tools and features to add labels, colors, symbols, and effects to your map.
-How to use vector graphics software to edit and customize your world map
-Once you have downloaded a free vector graphic of a world map with countries, you might want to edit and customize it to suit your purpose. For example, you might want to change the color scheme, add or remove some countries, or highlight some regions. To do this, you will need to use a vector graphics software program that can open and modify vector files. Here are some of the most popular ones that you can use:
-Adobe Illustrator
-Adobe Illustrator is a professional vector graphics software that is widely used by designers, artists, and illustrators. It offers a range of tools and features to create and edit vector graphics, such as shapes, paths, gradients, brushes, filters, and more. You can use Adobe Illustrator to open and edit any vector file format, including SVG, EPS, PDF, and AI.
-To edit a world map vector in Adobe Illustrator, you can follow these steps:
--
-
- Open the vector file in Adobe Illustrator. -
- Select the map layer and ungroup it by going to Object > Ungroup or pressing Ctrl+Shift+G. -
- Select the countries or regions that you want to edit and use the tools in the toolbar or the properties panel to change their appearance. For example, you can use the Direct Selection Tool (A) to move or resize the shapes, the Eyedropper Tool (I) to copy the fill or stroke color from another object, or the Swatches panel to apply a different color. -
- If you want to add or remove some countries or regions, you can use the Pen Tool (P) or the Shape Tool (M) to draw new shapes or delete existing ones. You can also use the Pathfinder panel to combine or subtract shapes. -
- If you want to add labels or text to your map, you can use the Text Tool (T) to create text boxes and type your text. You can also use the Character panel or the Paragraph panel to adjust the font size, style, alignment, and spacing of your text. -
- When you are done editing your map, you can group it again by selecting all the objects and going to Object > Group or pressing Ctrl+G. -
- You can save your map as an SVG file by going to File > Save As and choosing SVG as the format. You can also export your map as a PNG or JPEG image by going to File > Export > Export As and choosing the format and resolution that you want. -
Vectornator
-Vectornator is a free vector graphics software that is available for iOS, iPadOS, and macOS devices. It is designed for creating beautiful vector illustrations on the go with intuitive touch gestures and Apple Pencil support. It supports various vector formats, such as SVG, PDF, AI, and EPS.
-To edit a world map vector in Vectornator, you can follow these steps:
--
-
- Open the vector file in Vectornator. -
- Select the map layer and tap on the Ungroup button in the toolbar. -
- Select the countries or regions that you want to edit and use the tools in the toolbar or the inspector panel to change their appearance. For example, you can use the Move Tool (V) to move or resize the shapes, the Style Tool (S) to change the fill or stroke color or apply gradients or patterns, or the Arrange Tool (A) to change the order or alignment of the objects. -
- If you want to add or remove some countries or regions, you can use the Pen Tool (P) or the Shape Tool (U) to draw new shapes or delete existing ones. You can also use the Boolean Operations in the inspector panel to combine or subtract shapes. -
- If you want to add labels or text to your map, you can use the Text Tool (T) to create text boxes and type your text. You can also use the inspector panel to adjust the font size, style, alignment, and spacing of your text. -
- When you
- When you are done editing your map, you can group it again by selecting all the objects and tapping on the Group button in the toolbar. -
- You can save your map as an SVG file by tapping on the Share button in the toolbar and choosing SVG as the format. You can also export your map as a PNG or JPEG image by tapping on the Share button and choosing the format and resolution that you want. -
SVGator
-SVGator is a free online vector graphics editor and animator that works in your browser. It allows you to create and edit SVG files with ease and add animations and interactivity to them. You can also import and export other vector formats, such as AI, EPS, PDF, and PNG.
-To edit a world map vector in SVGator, you can follow these steps:
--
-
- Open the vector file in SVGator by dragging and dropping it into the editor or clicking on the Import button. -
- Select the map layer and click on the Ungroup button in the toolbar. -
- Select the countries or regions that you want to edit and use the tools in the toolbar or the properties panel to change their appearance. For example, you can use the Selection Tool (V) to move or resize the shapes, the Fill Tool (F) or the Stroke Tool (S) to change the fill or stroke color or apply gradients or patterns, or the Align Tool (A) to change the order or alignment of the objects. -
- If you want to add or remove some countries or regions, you can use the Pen Tool (P) or the Shape Tool (U) to draw new shapes or delete existing ones. You can also use the Path Operations in the properties panel to combine or subtract shapes. -
- If you want to add labels or text to your map, you can use the Text Tool (T) to create text boxes and type your text. You can also use the properties panel to adjust the font size, style, alignment, and spacing of your text. -
- When you are done editing your map, you can group it again by selecting all the objects and clicking on the Group button in the toolbar. -
- You can save your map as an SVG file by clicking on the Export button in the toolbar and choosing SVG as the format. You can also export your map as a PNG or JPEG image by clicking on the Export button and choosing the format and resolution that you want. -
Conclusion
-In this article, we have explained what vector graphics are, why they are useful, and where you can find free vector graphics of world maps with countries. We have also shown you how to use some popular vector graphics software to edit and customize your world map according to your needs. We hope that this article has helped you learn more about vector graphics and how to use them for creating beautiful world maps.
-FAQs
-What is the difference between vector graphics and raster graphics?
-Vector graphics are a form of computer graphics in which visual images are created directly from geometric shapes defined on a Cartesian plane, such as points, lines, curves, and polygons. Raster graphics are a form of computer graphics in which visual images are created from a fixed grid of pixels. Vector graphics have no fixed resolution and can be resized without quality loss, while raster graphics have a fixed resolution and lose quality when resized.
-What are some of the benefits of using vector graphics for world maps?
-Some of the benefits of using vector graphics for world maps are:
--
-
- They can be scaled up or down without losing quality or resolution. -
- They have smaller file sizes than raster graphics because they do not store information about individual pixels. -
- They can be easily modified and colored using vector graphics software. -
- They can be used for various purposes, such as education, design, or presentation. -
What are some of the best websites for finding free vector graphics of world maps with countries?
-Some of Some of the best websites for finding free vector graphics of world maps with countries are:
--
-
- Freepik, which offers thousands of world map vectors with different styles, colors, and levels of detail. -
- Vecteezy, which provides hundreds of world map vectors with various designs, such as flat, realistic, abstract, or vintage. -
- Inkscape, which is a free and open-source vector graphics editor that you can use to create your own world map vector from scratch or import and edit an existing one. -
What are some of the most popular vector graphics software that you can use to edit and customize your world map?
-Some of the most popular vector graphics software that you can use to edit and customize your world map are:
--
-
- Adobe Illustrator, which is a professional vector graphics software that offers a range of tools and features to create and edit vector graphics, such as shapes, paths, gradients, brushes, filters, and more. -
- Vectornator, which is a free vector graphics software that is available for iOS, iPadOS, and macOS devices. It is designed for creating beautiful vector illustrations on the go with intuitive touch gestures and Apple Pencil support. -
- SVGator, which is a free online vector graphics editor and animator that works in your browser. It allows you to create and edit SVG files with ease and add animations and interactivity to them. -
How can I convert a raster graphic of a world map to a vector graphic?
-If you have a raster graphic of a world map that you want to convert to a vector graphic, you can use a process called vectorization or tracing. Vectorization is the process of converting a raster image into a vector image by creating geometric shapes that approximate the pixels in the raster image.
-There are two ways to perform vectorization: manual or automatic. Manual vectorization involves using a vector graphics software program to draw shapes over the raster image by hand. Automatic vectorization involves using a software program that can automatically detect the edges and shapes in the raster image and create vector shapes accordingly.
-Some of the software programs that can perform automatic vectorization are:
--
-
- Vector Magic, which is an online tool that can convert raster images to vector images with high quality and accuracy. -
- Image Trace, which is a feature in Adobe Illustrator that can trace raster images and create vector paths or shapes from them. -
- Potrace, which is a free and open-source tool that can convert bitmap images to smooth, scalable vector graphics. -
How can I add interactivity or animation to my world map vector?
-If you want to add interactivity or animation to your world map vector, you will need to use a format that supports these features, such as SVG (Scalable Vector Graphics). SVG is an XML-based format that can describe two-dimensional graphics with interactivity and animation.
-To add interactivity or animation to your world map SVG, you can use one of the following methods:
--
-
- CSS (Cascading Style Sheets), which is a language that can define the style and layout of SVG elements. You can use CSS properties and animations to change the appearance or behavior of your SVG elements based on user actions or events. -
- JavaScript, which is a scripting language that can manipulate the SVG document object model (DOM). You can use JavaScript functions and events to add logic and functionality to your SVG elements based on user actions or events. -
- SMIL (Synchronized Multimedia Integration Language), which is an XML-based language that can define animations for SVG elements. You can use SMIL elements and attributes to specify the timing, duration, and effects of your SVG animations. -
-
-
\ No newline at end of file diff --git a/spaces/cooelf/Multimodal-CoT/timm/loss/cross_entropy.py b/spaces/cooelf/Multimodal-CoT/timm/loss/cross_entropy.py deleted file mode 100644 index 60bef646cc6c31fd734f234346dbc4255def6622..0000000000000000000000000000000000000000 --- a/spaces/cooelf/Multimodal-CoT/timm/loss/cross_entropy.py +++ /dev/null @@ -1,36 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class LabelSmoothingCrossEntropy(nn.Module): - """ - NLL loss with label smoothing. - """ - def __init__(self, smoothing=0.1): - """ - Constructor for the LabelSmoothing module. - :param smoothing: label smoothing factor - """ - super(LabelSmoothingCrossEntropy, self).__init__() - assert smoothing < 1.0 - self.smoothing = smoothing - self.confidence = 1. - smoothing - - def forward(self, x, target): - logprobs = F.log_softmax(x, dim=-1) - nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) - nll_loss = nll_loss.squeeze(1) - smooth_loss = -logprobs.mean(dim=-1) - loss = self.confidence * nll_loss + self.smoothing * smooth_loss - return loss.mean() - - -class SoftTargetCrossEntropy(nn.Module): - - def __init__(self): - super(SoftTargetCrossEntropy, self).__init__() - - def forward(self, x, target): - loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1) - return loss.mean() diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/style_loss.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/style_loss.py deleted file mode 100644 index 0bb42d7fbc5d17a47bec7365889868505f5fdfb5..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/losses/style_loss.py +++ /dev/null @@ -1,155 +0,0 @@ -import torch -import torch.nn as nn -import torchvision.models as models - - -class PerceptualLoss(nn.Module): - r""" - Perceptual loss, VGG-based - https://arxiv.org/abs/1603.08155 - https://github.com/dxyang/StyleTransfer/blob/master/utils.py - """ - - def __init__(self, weights=[1.0, 1.0, 1.0, 1.0, 1.0]): - super(PerceptualLoss, self).__init__() - self.add_module('vgg', VGG19()) - self.criterion = torch.nn.L1Loss() - self.weights = weights - - def __call__(self, x, y): - # Compute features - x_vgg, y_vgg = self.vgg(x), self.vgg(y) - - content_loss = 0.0 - content_loss += self.weights[0] * self.criterion(x_vgg['relu1_1'], y_vgg['relu1_1']) - content_loss += self.weights[1] * self.criterion(x_vgg['relu2_1'], y_vgg['relu2_1']) - content_loss += self.weights[2] * self.criterion(x_vgg['relu3_1'], y_vgg['relu3_1']) - content_loss += self.weights[3] * self.criterion(x_vgg['relu4_1'], y_vgg['relu4_1']) - content_loss += self.weights[4] * self.criterion(x_vgg['relu5_1'], y_vgg['relu5_1']) - - - return content_loss - - -class VGG19(torch.nn.Module): - def __init__(self): - super(VGG19, self).__init__() - features = models.vgg19(pretrained=True).features - self.relu1_1 = torch.nn.Sequential() - self.relu1_2 = torch.nn.Sequential() - - self.relu2_1 = torch.nn.Sequential() - self.relu2_2 = torch.nn.Sequential() - - self.relu3_1 = torch.nn.Sequential() - self.relu3_2 = torch.nn.Sequential() - self.relu3_3 = torch.nn.Sequential() - self.relu3_4 = torch.nn.Sequential() - - self.relu4_1 = torch.nn.Sequential() - self.relu4_2 = torch.nn.Sequential() - self.relu4_3 = torch.nn.Sequential() - self.relu4_4 = torch.nn.Sequential() - - self.relu5_1 = torch.nn.Sequential() - self.relu5_2 = torch.nn.Sequential() - self.relu5_3 = torch.nn.Sequential() - self.relu5_4 = torch.nn.Sequential() - - for x in range(2): - self.relu1_1.add_module(str(x), features[x]) - - for x in range(2, 4): - self.relu1_2.add_module(str(x), features[x]) - - for x in range(4, 7): - self.relu2_1.add_module(str(x), features[x]) - - for x in range(7, 9): - self.relu2_2.add_module(str(x), features[x]) - - for x in range(9, 12): - self.relu3_1.add_module(str(x), features[x]) - - for x in range(12, 14): - self.relu3_2.add_module(str(x), features[x]) - - for x in range(14, 16): - self.relu3_2.add_module(str(x), features[x]) - - for x in range(16, 18): - self.relu3_4.add_module(str(x), features[x]) - - for x in range(18, 21): - self.relu4_1.add_module(str(x), features[x]) - - for x in range(21, 23): - self.relu4_2.add_module(str(x), features[x]) - - for x in range(23, 25): - self.relu4_3.add_module(str(x), features[x]) - - for x in range(25, 27): - self.relu4_4.add_module(str(x), features[x]) - - for x in range(27, 30): - self.relu5_1.add_module(str(x), features[x]) - - for x in range(30, 32): - self.relu5_2.add_module(str(x), features[x]) - - for x in range(32, 34): - self.relu5_3.add_module(str(x), features[x]) - - for x in range(34, 36): - self.relu5_4.add_module(str(x), features[x]) - - # don't need the gradients, just want the features - for param in self.parameters(): - param.requires_grad = False - - def forward(self, x): - relu1_1 = self.relu1_1(x) - relu1_2 = self.relu1_2(relu1_1) - - relu2_1 = self.relu2_1(relu1_2) - relu2_2 = self.relu2_2(relu2_1) - - relu3_1 = self.relu3_1(relu2_2) - relu3_2 = self.relu3_2(relu3_1) - relu3_3 = self.relu3_3(relu3_2) - relu3_4 = self.relu3_4(relu3_3) - - relu4_1 = self.relu4_1(relu3_4) - relu4_2 = self.relu4_2(relu4_1) - relu4_3 = self.relu4_3(relu4_2) - relu4_4 = self.relu4_4(relu4_3) - - relu5_1 = self.relu5_1(relu4_4) - relu5_2 = self.relu5_2(relu5_1) - relu5_3 = self.relu5_3(relu5_2) - relu5_4 = self.relu5_4(relu5_3) - - out = { - 'relu1_1': relu1_1, - 'relu1_2': relu1_2, - - 'relu2_1': relu2_1, - 'relu2_2': relu2_2, - - 'relu3_1': relu3_1, - 'relu3_2': relu3_2, - 'relu3_3': relu3_3, - 'relu3_4': relu3_4, - - 'relu4_1': relu4_1, - 'relu4_2': relu4_2, - 'relu4_3': relu4_3, - 'relu4_4': relu4_4, - - 'relu5_1': relu5_1, - 'relu5_2': relu5_2, - 'relu5_3': relu5_3, - 'relu5_4': relu5_4, - } - return out diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/optimizer/default_constructor.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/optimizer/default_constructor.py deleted file mode 100644 index de2ae39cb6378cc17c098f5324f5d5c321879b91..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/optimizer/default_constructor.py +++ /dev/null @@ -1,249 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import warnings - -import torch -from torch.nn import GroupNorm, LayerNorm - -from annotator.mmpkg.mmcv.utils import _BatchNorm, _InstanceNorm, build_from_cfg, is_list_of -from annotator.mmpkg.mmcv.utils.ext_loader import check_ops_exist -from .builder import OPTIMIZER_BUILDERS, OPTIMIZERS - - -@OPTIMIZER_BUILDERS.register_module() -class DefaultOptimizerConstructor: - """Default constructor for optimizers. - - By default each parameter share the same optimizer settings, and we - provide an argument ``paramwise_cfg`` to specify parameter-wise settings. - It is a dict and may contain the following fields: - - - ``custom_keys`` (dict): Specified parameters-wise settings by keys. If - one of the keys in ``custom_keys`` is a substring of the name of one - parameter, then the setting of the parameter will be specified by - ``custom_keys[key]`` and other setting like ``bias_lr_mult`` etc. will - be ignored. It should be noted that the aforementioned ``key`` is the - longest key that is a substring of the name of the parameter. If there - are multiple matched keys with the same length, then the key with lower - alphabet order will be chosen. - ``custom_keys[key]`` should be a dict and may contain fields ``lr_mult`` - and ``decay_mult``. See Example 2 below. - - ``bias_lr_mult`` (float): It will be multiplied to the learning - rate for all bias parameters (except for those in normalization - layers and offset layers of DCN). - - ``bias_decay_mult`` (float): It will be multiplied to the weight - decay for all bias parameters (except for those in - normalization layers, depthwise conv layers, offset layers of DCN). - - ``norm_decay_mult`` (float): It will be multiplied to the weight - decay for all weight and bias parameters of normalization - layers. - - ``dwconv_decay_mult`` (float): It will be multiplied to the weight - decay for all weight and bias parameters of depthwise conv - layers. - - ``dcn_offset_lr_mult`` (float): It will be multiplied to the learning - rate for parameters of offset layer in the deformable convs - of a model. - - ``bypass_duplicate`` (bool): If true, the duplicate parameters - would not be added into optimizer. Default: False. - - Note: - 1. If the option ``dcn_offset_lr_mult`` is used, the constructor will - override the effect of ``bias_lr_mult`` in the bias of offset - layer. So be careful when using both ``bias_lr_mult`` and - ``dcn_offset_lr_mult``. If you wish to apply both of them to the - offset layer in deformable convs, set ``dcn_offset_lr_mult`` - to the original ``dcn_offset_lr_mult`` * ``bias_lr_mult``. - 2. If the option ``dcn_offset_lr_mult`` is used, the constructor will - apply it to all the DCN layers in the model. So be careful when - the model contains multiple DCN layers in places other than - backbone. - - Args: - model (:obj:`nn.Module`): The model with parameters to be optimized. - optimizer_cfg (dict): The config dict of the optimizer. - Positional fields are - - - `type`: class name of the optimizer. - - Optional fields are - - - any arguments of the corresponding optimizer type, e.g., - lr, weight_decay, momentum, etc. - paramwise_cfg (dict, optional): Parameter-wise options. - - Example 1: - >>> model = torch.nn.modules.Conv1d(1, 1, 1) - >>> optimizer_cfg = dict(type='SGD', lr=0.01, momentum=0.9, - >>> weight_decay=0.0001) - >>> paramwise_cfg = dict(norm_decay_mult=0.) - >>> optim_builder = DefaultOptimizerConstructor( - >>> optimizer_cfg, paramwise_cfg) - >>> optimizer = optim_builder(model) - - Example 2: - >>> # assume model have attribute model.backbone and model.cls_head - >>> optimizer_cfg = dict(type='SGD', lr=0.01, weight_decay=0.95) - >>> paramwise_cfg = dict(custom_keys={ - '.backbone': dict(lr_mult=0.1, decay_mult=0.9)}) - >>> optim_builder = DefaultOptimizerConstructor( - >>> optimizer_cfg, paramwise_cfg) - >>> optimizer = optim_builder(model) - >>> # Then the `lr` and `weight_decay` for model.backbone is - >>> # (0.01 * 0.1, 0.95 * 0.9). `lr` and `weight_decay` for - >>> # model.cls_head is (0.01, 0.95). - """ - - def __init__(self, optimizer_cfg, paramwise_cfg=None): - if not isinstance(optimizer_cfg, dict): - raise TypeError('optimizer_cfg should be a dict', - f'but got {type(optimizer_cfg)}') - self.optimizer_cfg = optimizer_cfg - self.paramwise_cfg = {} if paramwise_cfg is None else paramwise_cfg - self.base_lr = optimizer_cfg.get('lr', None) - self.base_wd = optimizer_cfg.get('weight_decay', None) - self._validate_cfg() - - def _validate_cfg(self): - if not isinstance(self.paramwise_cfg, dict): - raise TypeError('paramwise_cfg should be None or a dict, ' - f'but got {type(self.paramwise_cfg)}') - - if 'custom_keys' in self.paramwise_cfg: - if not isinstance(self.paramwise_cfg['custom_keys'], dict): - raise TypeError( - 'If specified, custom_keys must be a dict, ' - f'but got {type(self.paramwise_cfg["custom_keys"])}') - if self.base_wd is None: - for key in self.paramwise_cfg['custom_keys']: - if 'decay_mult' in self.paramwise_cfg['custom_keys'][key]: - raise ValueError('base_wd should not be None') - - # get base lr and weight decay - # weight_decay must be explicitly specified if mult is specified - if ('bias_decay_mult' in self.paramwise_cfg - or 'norm_decay_mult' in self.paramwise_cfg - or 'dwconv_decay_mult' in self.paramwise_cfg): - if self.base_wd is None: - raise ValueError('base_wd should not be None') - - def _is_in(self, param_group, param_group_list): - assert is_list_of(param_group_list, dict) - param = set(param_group['params']) - param_set = set() - for group in param_group_list: - param_set.update(set(group['params'])) - - return not param.isdisjoint(param_set) - - def add_params(self, params, module, prefix='', is_dcn_module=None): - """Add all parameters of module to the params list. - - The parameters of the given module will be added to the list of param - groups, with specific rules defined by paramwise_cfg. - - Args: - params (list[dict]): A list of param groups, it will be modified - in place. - module (nn.Module): The module to be added. - prefix (str): The prefix of the module - is_dcn_module (int|float|None): If the current module is a - submodule of DCN, `is_dcn_module` will be passed to - control conv_offset layer's learning rate. Defaults to None. - """ - # get param-wise options - custom_keys = self.paramwise_cfg.get('custom_keys', {}) - # first sort with alphabet order and then sort with reversed len of str - sorted_keys = sorted(sorted(custom_keys.keys()), key=len, reverse=True) - - bias_lr_mult = self.paramwise_cfg.get('bias_lr_mult', 1.) - bias_decay_mult = self.paramwise_cfg.get('bias_decay_mult', 1.) - norm_decay_mult = self.paramwise_cfg.get('norm_decay_mult', 1.) - dwconv_decay_mult = self.paramwise_cfg.get('dwconv_decay_mult', 1.) - bypass_duplicate = self.paramwise_cfg.get('bypass_duplicate', False) - dcn_offset_lr_mult = self.paramwise_cfg.get('dcn_offset_lr_mult', 1.) - - # special rules for norm layers and depth-wise conv layers - is_norm = isinstance(module, - (_BatchNorm, _InstanceNorm, GroupNorm, LayerNorm)) - is_dwconv = ( - isinstance(module, torch.nn.Conv2d) - and module.in_channels == module.groups) - - for name, param in module.named_parameters(recurse=False): - param_group = {'params': [param]} - if not param.requires_grad: - params.append(param_group) - continue - if bypass_duplicate and self._is_in(param_group, params): - warnings.warn(f'{prefix} is duplicate. It is skipped since ' - f'bypass_duplicate={bypass_duplicate}') - continue - # if the parameter match one of the custom keys, ignore other rules - is_custom = False - for key in sorted_keys: - if key in f'{prefix}.{name}': - is_custom = True - lr_mult = custom_keys[key].get('lr_mult', 1.) - param_group['lr'] = self.base_lr * lr_mult - if self.base_wd is not None: - decay_mult = custom_keys[key].get('decay_mult', 1.) - param_group['weight_decay'] = self.base_wd * decay_mult - break - - if not is_custom: - # bias_lr_mult affects all bias parameters - # except for norm.bias dcn.conv_offset.bias - if name == 'bias' and not (is_norm or is_dcn_module): - param_group['lr'] = self.base_lr * bias_lr_mult - - if (prefix.find('conv_offset') != -1 and is_dcn_module - and isinstance(module, torch.nn.Conv2d)): - # deal with both dcn_offset's bias & weight - param_group['lr'] = self.base_lr * dcn_offset_lr_mult - - # apply weight decay policies - if self.base_wd is not None: - # norm decay - if is_norm: - param_group[ - 'weight_decay'] = self.base_wd * norm_decay_mult - # depth-wise conv - elif is_dwconv: - param_group[ - 'weight_decay'] = self.base_wd * dwconv_decay_mult - # bias lr and decay - elif name == 'bias' and not is_dcn_module: - # TODO: current bias_decay_mult will have affect on DCN - param_group[ - 'weight_decay'] = self.base_wd * bias_decay_mult - params.append(param_group) - - if check_ops_exist(): - from annotator.mmpkg.mmcv.ops import DeformConv2d, ModulatedDeformConv2d - is_dcn_module = isinstance(module, - (DeformConv2d, ModulatedDeformConv2d)) - else: - is_dcn_module = False - for child_name, child_mod in module.named_children(): - child_prefix = f'{prefix}.{child_name}' if prefix else child_name - self.add_params( - params, - child_mod, - prefix=child_prefix, - is_dcn_module=is_dcn_module) - - def __call__(self, model): - if hasattr(model, 'module'): - model = model.module - - optimizer_cfg = self.optimizer_cfg.copy() - # if no paramwise option is specified, just use the global setting - if not self.paramwise_cfg: - optimizer_cfg['params'] = model.parameters() - return build_from_cfg(optimizer_cfg, OPTIMIZERS) - - # set param-wise lr and weight decay recursively - params = [] - self.add_params(params, model) - optimizer_cfg['params'] = params - - return build_from_cfg(optimizer_cfg, OPTIMIZERS) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/onnx_to_caffe.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/onnx_to_caffe.py deleted file mode 100644 index 44399aafababcdf6b84147a0613eb0909730db4b..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/onnx_to_caffe.py +++ /dev/null @@ -1,27 +0,0 @@ -import argparse - -import onnx -from caffe2.python.onnx.backend import Caffe2Backend - - -parser = argparse.ArgumentParser(description="Convert ONNX to Caffe2") - -parser.add_argument("model", help="The ONNX model") -parser.add_argument("--c2-prefix", required=True, - help="The output file prefix for the caffe2 model init and predict file. ") - - -def main(): - args = parser.parse_args() - onnx_model = onnx.load(args.model) - caffe2_init, caffe2_predict = Caffe2Backend.onnx_graph_to_caffe2_net(onnx_model) - caffe2_init_str = caffe2_init.SerializeToString() - with open(args.c2_prefix + '.init.pb', "wb") as f: - f.write(caffe2_init_str) - caffe2_predict_str = caffe2_predict.SerializeToString() - with open(args.c2_prefix + '.predict.pb', "wb") as f: - f.write(caffe2_predict_str) - - -if __name__ == "__main__": - main() diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/data/datasets/register_coco_panoptic_annos_semseg.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/data/datasets/register_coco_panoptic_annos_semseg.py deleted file mode 100644 index 5690a2c217dc698464e8057f057c1ad2dcdf605b..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/oneformer/data/datasets/register_coco_panoptic_annos_semseg.py +++ /dev/null @@ -1,367 +0,0 @@ -# ------------------------------------------------------------------------------ -# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py -# Modified by Jitesh Jain (https://github.com/praeclarumjj3) -# ------------------------------------------------------------------------------ - -import json -import os - -from annotator.oneformer.detectron2.data import DatasetCatalog, MetadataCatalog -from annotator.oneformer.detectron2.data.datasets import load_sem_seg -from annotator.oneformer.detectron2.data.datasets.builtin_meta import COCO_CATEGORIES -from annotator.oneformer.detectron2.utils.file_io import PathManager -import contextlib -import logging -import io -from fvcore.common.timer import Timer -import pycocotools.mask as mask_util -from annotator.oneformer.detectron2.structures import BoxMode - - -logger = logging.getLogger(__name__) - - -_PREDEFINED_SPLITS_COCO_PANOPTIC = { - "coco_2017_train_panoptic": ( - # This is the original panoptic annotation directory - "coco/panoptic_train2017", - "coco/annotations/panoptic_train2017.json", - # This directory contains semantic annotations that are - # converted from panoptic annotations. - # It is used by PanopticFPN. - # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py - # to create these directories. - "coco/panoptic_semseg_train2017", - ), - "coco_2017_val_panoptic": ( - "coco/panoptic_val2017", - "coco/annotations/panoptic_val2017.json", - "coco/panoptic_semseg_val2017", - ), -} - -def load_coco_instance_json(json_file, image_root, dataset_name=None): - from pycocotools.coco import COCO - - timer = Timer() - json_file = PathManager.get_local_path(json_file) - with contextlib.redirect_stdout(io.StringIO()): - coco_api = COCO(json_file) - if timer.seconds() > 1: - logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) - - id_map = None - if dataset_name is not None: - meta = MetadataCatalog.get(dataset_name) - cat_ids = sorted(coco_api.getCatIds()) - cats = coco_api.loadCats(cat_ids) - # The categories in a custom json file may not be sorted. - thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] - meta.thing_classes = thing_classes - - # In COCO, certain category ids are artificially removed, - # and by convention they are always ignored. - # We deal with COCO's id issue and translate - # the category ids to contiguous ids in [0, 80). - - # It works by looking at the "categories" field in the json, therefore - # if users' own json also have incontiguous ids, we'll - # apply this mapping as well but print a warning. - if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): - if "coco" not in dataset_name: - logger.warning( - """ -Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. -""" - ) - id_map = {v: i for i, v in enumerate(cat_ids)} - meta.thing_dataset_id_to_contiguous_id = id_map - - # sort indices for reproducible results - img_ids = sorted(coco_api.imgs.keys()) - # imgs is a list of dicts, each looks something like: - # {'license': 4, - # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', - # 'file_name': 'COCO_val2014_000000001268.jpg', - # 'height': 427, - # 'width': 640, - # 'date_captured': '2013-11-17 05:57:24', - # 'id': 1268} - imgs = coco_api.loadImgs(img_ids) - # anns is a list[list[dict]], where each dict is an annotation - # record for an object. The inner list enumerates the objects in an image - # and the outer list enumerates over images. Example of anns[0]: - # [{'segmentation': [[192.81, - # 247.09, - # ... - # 219.03, - # 249.06]], - # 'area': 1035.749, - # 'iscrowd': 0, - # 'image_id': 1268, - # 'bbox': [192.81, 224.8, 74.73, 33.43], - # 'category_id': 16, - # 'id': 42986}, - # ...] - anns = [coco_api.imgToAnns[img_id] for img_id in img_ids] - total_num_valid_anns = sum([len(x) for x in anns]) - total_num_anns = len(coco_api.anns) - if total_num_valid_anns < total_num_anns: - logger.warning( - f"{json_file} contains {total_num_anns} annotations, but only " - f"{total_num_valid_anns} of them match to images in the file." - ) - - if "minival" not in json_file: - # The popular valminusminival & minival annotations for COCO2014 contain this bug. - # However the ratio of buggy annotations there is tiny and does not affect accuracy. - # Therefore we explicitly white-list them. - ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] - assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format( - json_file - ) - - imgs_anns = list(zip(imgs, anns)) - logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file)) - - dataset_dicts = {} - - ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] - - num_instances_without_valid_segmentation = 0 - - for (img_dict, anno_dict_list) in imgs_anns: - record = {} - record["file_name"] = os.path.join(image_root, img_dict["file_name"]) - record["height"] = img_dict["height"] - record["width"] = img_dict["width"] - image_id = record["image_id"] = img_dict["id"] - - objs = [] - for anno in anno_dict_list: - # Check that the image_id in this annotation is the same as - # the image_id we're looking at. - # This fails only when the data parsing logic or the annotation file is buggy. - - # The original COCO valminusminival2014 & minival2014 annotation files - # actually contains bugs that, together with certain ways of using COCO API, - # can trigger this assertion. - assert anno["image_id"] == image_id - - assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.' - - obj = {key: anno[key] for key in ann_keys if key in anno} - if "bbox" in obj and len(obj["bbox"]) == 0: - raise ValueError( - f"One annotation of image {image_id} contains empty 'bbox' value! " - "This json does not have valid COCO format." - ) - - segm = anno.get("segmentation", None) - if segm: # either list[list[float]] or dict(RLE) - if isinstance(segm, dict): - if isinstance(segm["counts"], list): - # convert to compressed RLE - segm = mask_util.frPyObjects(segm, *segm["size"]) - else: - # filter out invalid polygons (< 3 points) - segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] - if len(segm) == 0: - num_instances_without_valid_segmentation += 1 - continue # ignore this instance - obj["segmentation"] = segm - - keypts = anno.get("keypoints", None) - if keypts: # list[int] - for idx, v in enumerate(keypts): - if idx % 3 != 2: - # COCO's segmentation coordinates are floating points in [0, H or W], - # but keypoint coordinates are integers in [0, H-1 or W-1] - # Therefore we assume the coordinates are "pixel indices" and - # add 0.5 to convert to floating point coordinates. - keypts[idx] = v + 0.5 - obj["keypoints"] = keypts - - obj["bbox_mode"] = BoxMode.XYWH_ABS - if id_map: - annotation_category_id = obj["category_id"] - try: - obj["category_id"] = id_map[annotation_category_id] - except KeyError as e: - raise KeyError( - f"Encountered category_id={annotation_category_id} " - "but this id does not exist in 'categories' of the json file." - ) from e - objs.append(obj) - record["annotations"] = objs - dataset_dicts[image_id] = record - - if num_instances_without_valid_segmentation > 0: - logger.warning( - "Filtered out {} instances without valid segmentation. ".format( - num_instances_without_valid_segmentation - ) - + "There might be issues in your dataset generation process. Please " - "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully" - ) - return dataset_dicts - -def get_metadata(): - meta = {} - # The following metadata maps contiguous id from [0, #thing categories + - # #stuff categories) to their names and colors. We have to replica of the - # same name and color under "thing_*" and "stuff_*" because the current - # visualization function in D2 handles thing and class classes differently - # due to some heuristic used in Panoptic FPN. We keep the same naming to - # enable reusing existing visualization functions. - thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1] - thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1] - stuff_classes = [k["name"] for k in COCO_CATEGORIES] - stuff_colors = [k["color"] for k in COCO_CATEGORIES] - - meta["thing_classes"] = thing_classes - meta["thing_colors"] = thing_colors - meta["stuff_classes"] = stuff_classes - meta["stuff_colors"] = stuff_colors - - # Convert category id for training: - # category id: like semantic segmentation, it is the class id for each - # pixel. Since there are some classes not used in evaluation, the category - # id is not always contiguous and thus we have two set of category ids: - # - original category id: category id in the original dataset, mainly - # used for evaluation. - # - contiguous category id: [0, #classes), in order to train the linear - # softmax classifier. - thing_dataset_id_to_contiguous_id = {} - stuff_dataset_id_to_contiguous_id = {} - - for i, cat in enumerate(COCO_CATEGORIES): - if cat["isthing"]: - thing_dataset_id_to_contiguous_id[cat["id"]] = i - # else: - # stuff_dataset_id_to_contiguous_id[cat["id"]] = i - - # in order to use sem_seg evaluator - stuff_dataset_id_to_contiguous_id[cat["id"]] = i - - meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id - meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id - - return meta - - -def load_coco_panoptic_json(json_file, instances_json, instances_name, image_dir, gt_dir, semseg_dir, meta): - """ - Args: - image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". - gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". - json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". - Returns: - list[dict]: a list of dicts in Detectron2 standard format. (See - `Using Custom Datasets `_ ) - """ - - def _convert_category_id(segment_info, meta): - if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: - segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ - segment_info["category_id"] - ] - segment_info["isthing"] = True - else: - segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ - segment_info["category_id"] - ] - segment_info["isthing"] = False - return segment_info - - with PathManager.open(json_file) as f: - json_info = json.load(f) - - instance_data_dicts = load_coco_instance_json(instances_json, image_dir.replace("panoptic_", ""), instances_name) - - ret = [] - for ann in json_info["annotations"]: - image_id = int(ann["image_id"]) - # TODO: currently we assume image and label has the same filename but - # different extension, and images have extension ".jpg" for COCO. Need - # to make image extension a user-provided argument if we extend this - # function to support other COCO-like datasets. - image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg") - label_file = os.path.join(gt_dir, ann["file_name"]) - sem_label_file = os.path.join(semseg_dir, ann["file_name"]) - segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] - ret.append( - { - "file_name": image_file, - "image_id": image_id, - "pan_seg_file_name": label_file, - "sem_seg_file_name": sem_label_file, - "segments_info": segments_info, - "annotations": instance_data_dicts[image_id]["annotations"], - } - ) - assert len(ret), f"No images found in {image_dir}!" - assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] - assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] - assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"] - return ret - - -def register_coco_panoptic_annos_sem_seg( - name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json, instances_name, -): - panoptic_name = name - delattr(MetadataCatalog.get(panoptic_name), "thing_classes") - delattr(MetadataCatalog.get(panoptic_name), "thing_colors") - MetadataCatalog.get(panoptic_name).set( - thing_classes=metadata["thing_classes"], - thing_colors=metadata["thing_colors"], - # thing_dataset_id_to_contiguous_id=metadata["thing_dataset_id_to_contiguous_id"], - ) - - # the name is "coco_2017_train_panoptic_with_sem_seg" and "coco_2017_val_panoptic_with_sem_seg" - semantic_name = name + "_with_sem_seg" - DatasetCatalog.register( - semantic_name, - lambda: load_coco_panoptic_json(panoptic_json, instances_json, instances_name, image_root, panoptic_root, sem_seg_root, metadata), - ) - MetadataCatalog.get(semantic_name).set( - sem_seg_root=sem_seg_root, - panoptic_root=panoptic_root, - image_root=image_root, - panoptic_json=panoptic_json, - json_file=instances_json, - evaluator_type="coco_panoptic_seg", - ignore_label=255, - label_divisor=1000, - **metadata, - ) - - -def register_all_coco_panoptic_annos_sem_seg(root): - for ( - prefix, - (panoptic_root, panoptic_json, semantic_root), - ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items(): - - prefix_instances = prefix[: -len("_panoptic")] - instances_meta = MetadataCatalog.get(prefix_instances) - image_root, instances_json = instances_meta.image_root, instances_meta.json_file - - if 'val' in instances_json: - instances_json = instances_json.replace('instances_', 'panoptic2instances_') - - register_coco_panoptic_annos_sem_seg( - prefix, - get_metadata(), - image_root, - os.path.join(root, panoptic_root), - os.path.join(root, panoptic_json), - os.path.join(root, semantic_root), - instances_json, - prefix_instances, - ) - - -_root = os.getenv("DETECTRON2_DATASETS", "datasets") -register_all_coco_panoptic_annos_sem_seg(_root) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/knn.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/knn.py deleted file mode 100644 index f335785036669fc19239825b0aae6dde3f73bf92..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/knn.py +++ /dev/null @@ -1,77 +0,0 @@ -import torch -from torch.autograd import Function - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', ['knn_forward']) - - -class KNN(Function): - r"""KNN (CUDA) based on heap data structure. - Modified from `PAConv
Chat-Your-Data (H2 Economics)
")
-
- openai_api_key_textbox = gr.Textbox(
- placeholder="Paste your OpenAI API key (sk-...)",
- show_label=False,
- lines=1,
- type="password",
- )
-
- chatbot = gr.Chatbot()
-
- with gr.Row():
- message = gr.Textbox(
- label="What's your question?",
- placeholder="Ask questions about anything covered in the H2 Economics syllabus",
- lines=1,
- )
- submit = gr.Button(value="Send", variant="secondary").style(full_width=False)
-
- gr.Examples(
- examples=[
- "Explain real wealth effect.",
- "Use the real wealth effect to explain the negative gradient of the AD curve.",
- "Explain the multiplier process.",
- ],
- inputs=message,
- )
-
- gr.HTML("Demo application of a LangChain chain, built on H2 Economics Data. Many thanks to Jean Chua for giving her notes for this project.")
-
- gr.HTML(
- "Karam Full Movie Download In Hd 720p
DOWNLOAD - https://gohhs.com/2uFUhx
- -Plot: John Captain, who works for the mafia, is a murderer. After killing an entire family, he was amazed at the consequences of his actions and decided. The film is based on the bestselling book by David Grann. I haven't read the book, so I can't say how good or bad it is. But judging by the trailer, this is a very well-posed picture, even with good special effects. -Fascinating and spectacular scenes that you want to review. Ambiguous characters that evoke different emotions in the viewer. -At the beginning of the film, there is a 8a78ff9644
-
-
- diff --git a/spaces/diacanFperku/AutoGPT/MinecraftBetaLicenseKey [WORK].md b/spaces/diacanFperku/AutoGPT/MinecraftBetaLicenseKey [WORK].md deleted file mode 100644 index f459032526c6589b5bd18d2cf7f4b7a0ebce4c7b..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/MinecraftBetaLicenseKey [WORK].md +++ /dev/null @@ -1,145 +0,0 @@ -
-
Minecraft Beta License Key: How to Get It and Why You Need It
- -Minecraft is one of the most popular and creative sandbox games in the world. It allows you to build, explore and survive in a blocky pixelated world that is randomly generated and infinite. You can play it solo or with your friends, online or offline, on various devices and platforms.
- -But did you know that you can also access the latest features and updates of Minecraft before they are officially released? Yes, you read that right. You can join the Minecraft Beta program and get a Minecraft Beta license key that will let you play the beta versions of Minecraft on your device.
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What is a Minecraft Beta license key and how can you get it? What are the benefits and risks of joining the Minecraft Beta program? What are the features and requirements of the beta versions of Minecraft? In this article, we will answer all these questions and more. Read on to find out everything you need to know about Minecraft Beta license key.
- -What is a Minecraft Beta License Key?
- -A Minecraft Beta license key is a code that you need to enter in order to play the beta versions of Minecraft on your device. A beta version is an early version of a software that is not yet finished or polished, but is released for testing and feedback purposes.
- -Minecraft has different beta programs for different platforms and editions of the game. For example, there is a beta program for Android devices, Windows 10 devices, Xbox One devices, Java Edition and more. Each beta program has its own rules and requirements, and you need a different Minecraft Beta license key for each one.
- -A Minecraft Beta license key is usually free and easy to get, but you need to own the original Minecraft game on your device first. You also need to sign up for the beta program that you want to join, and follow the instructions on how to download and install the beta version of Minecraft on your device.
- -How to Get a Minecraft Beta License Key?
- -Getting a Minecraft Beta license key depends on which platform and edition of Minecraft you want to play. Here are some of the most common ways to get a Minecraft Beta license key:
- - --
-
- For Android devices: You need to own the original Minecraft game on your Android device. Then, you need to go to the Google Play Store and find the official Minecraft app. Scroll down to the "Join the Beta" section and click on "Join". Wait a few minutes and launch your regular Minecraft app. Your app will eventually switch to the beta version and you will get a Minecraft Beta license key. -
- For Windows 10 devices: You need to own the original Minecraft game on your Windows 10 device. Then, you need to go to the Microsoft Store and find the official Minecraft app. Click on "..." next to "Play" and select "Manage". Click on "Join" under "Minecraft Beta". Wait a few minutes and launch your regular Minecraft app. Your app will eventually switch to the beta version and you will get a Minecraft Beta license key. -
- For Xbox One devices: You need to own the original Minecraft game on your Xbox One device. Then, you need to go to the Xbox Insider Hub app on your device. If you don't have it, you can download it from the Microsoft Store for free. Launch the app and go to "Insider Content". Find "Minecraft" and click on "Join". Wait a few minutes and launch your regular Minecraft app. Your app will eventually switch to the beta version and you will get a Minecraft Beta license key. -
- For Java Edition: You need to own the original Minecraft game on your PC or Mac. Then, you need to go to the official Minecraft website and download the latest launcher for your device. Launch the launcher and select the latest snapshot by clicking the arrow next to the Play button on the main tab. Click on "Play" and you will get a Minecraft Beta license key. -
Note: Some beta programs may require you to register or sign up before accessing them. Some beta programs may also have pop-up ads or redirects that you need to close or skip. Be careful of any malicious links or viruses that may harm your device. Always use a trusted antivirus software and a VPN service for your safety and security.
- -Why Should You Join the Minecraft Beta Program?
- -Joining the Minecraft Beta program can have many benefits for you as a player. Here are some of them:
- --
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- You can access the latest features and updates of Minecraft before they are officially released. -
- You can test out new things that are not available in the regular version of Minecraft. -
- You can give feedback and suggestions to improve the game. -
- You can report bugs and glitches that may affect the game. -
- You can have fun and excitement by playing a different version of Minecraft. -
What are the Risks of Joining the Minecraft Beta Program?
- -Joining the Minecraft Beta program can also have some risks for you as a player. Here are some of them:
- --
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- You may encounter bugs, glitches, crashes or errors that may affect your gameplay or device. -
- You may lose your progress, data or files if something goes wrong with the beta version of Minecraft. -
- You may not be able to play with other players who are not on the same beta version of Minecraft as you. -
- You may not be able to access Realms or Featured Servers that are not compatible with the beta version of Minecraft. -
- You may not like some of the changes or features that are introduced in the beta version of Minecraft. -
What are Some Tips for Playing with a Minecraft Beta License Key?
- -If you decide to join the Minecraft Beta program and get a Minecraft Beta license key, here are some tips for playing with it:
- --
-
- Always back up your worlds, data and files before playing with a beta version of Minecraft. -
- Always read the patch notes or changelogs before playing with a new beta version of Minecraft. -
- Always report any bugs, glitches or issues that you encounter while playing with a beta version of Minecraft. -
- Always give constructive feedback and suggestions for improving the game. -
- Always be respectful and polite when interacting with other players or developers who are also playing with a beta version of Minecraft. -
Conclusion
- -Minecraft Beta license key is a code that allows you to play the beta versions of Minecraft on your device. A beta version is an early version of a software that is not yet finished or polished, but is released for testing and feedback purposes. By joining the Minecraft Beta program, you can access the latest features and updates of Minecraft before they are officially released. However, you also need to be aware of the risks and challenges that come with playing with a beta version of Minecraft.
- -If you are interested in getting a Minecraft Beta license key, you can follow these steps:
- --
-
- Own the original Minecraft game on your device first. -
- Sign up for the beta program that matches your platform and edition of Minecraft. -
- Download and install the beta version of Minecraft on your device. -
- Enter your Minecraft Beta license key when prompted. -
- Enjoy playing with a beta version of Minecraft! -
So what are you waiting for? Grab your Minecraft Beta license key today!
-What are the Features of the Beta Versions of Minecraft?
- -The beta versions of Minecraft have many features that make them different from the regular versions of Minecraft. Here are some of them:
- --
-
- The beta versions of Minecraft have new blocks, items, mobs, biomes, structures and mechanics that are not available in the regular versions of Minecraft. -
- The beta versions of Minecraft have experimental features that are not fully implemented or balanced yet, and may change or be removed in the future. -
- The beta versions of Minecraft have different user interfaces, menus, options and settings that are not present in the regular versions of Minecraft. -
- The beta versions of Minecraft have different performance, stability and compatibility issues that may affect your gameplay or device. -
- The beta versions of Minecraft have different feedback and reporting tools that allow you to communicate with the developers and other players. -
What are Some Examples of the Beta Versions of Minecraft?
- -The beta versions of Minecraft vary depending on which platform and edition of Minecraft you are playing. Here are some examples of the beta versions of Minecraft:
- --
-
- For Android devices: The latest beta version of Minecraft for Android devices is 1.18.0.23. It features new biomes such as lush caves and dripstone caves, new blocks such as moss and azalea, new mobs such as axolotls and goats, and new mechanics such as skulk sensors and candles. -
- For Windows 10 devices: The latest beta version of Minecraft for Windows 10 devices is 1.18.0.23. It has the same features as the Android version, but also has ray tracing support for compatible devices. -
- For Xbox One devices: The latest beta version of Minecraft for Xbox One devices is 1.18.0.23. It has the same features as the Android and Windows 10 versions, but also has split-screen multiplayer support for up to four players. -
- For Java Edition: The latest beta version of Minecraft for Java Edition is 21w43a. It features new world generation options such as single biome and buffet, new blocks such as copper ore and amethyst, new mobs such as glow squids and warden, and new mechanics such as bundles and archaeology. -
How to Leave the Minecraft Beta Program?
- -If you decide to leave the Minecraft Beta program and go back to the regular version of Minecraft, you can follow these steps:
- --
-
- For Android devices: Go to the Google Play Store and find the official Minecraft app. Scroll down to "You're a beta tester" and click on "Leave". Your app will eventually switch back to the regular version of Minecraft. -
- For Windows 10 devices: Go to the Microsoft Store and find the official Minecraft app. Click on "..." next to "Play" and select "Manage". Click on "Leave" under "Minecraft Beta". Your app will eventually switch back to the regular version of Minecraft. -
- For Xbox One devices: Go to the Xbox Insider Hub app on your device. Launch the app and go to "Insider Content". Find "Minecraft" and click on "Manage". Click on "Unenroll" under "Minecraft Beta". Your app will eventually switch back to the regular version of Minecraft. -
- For Java Edition: Go to the official Minecraft website and download the latest launcher for your device. Launch the launcher and select the latest release by clicking the arrow next to the Play button on the main tab. Click on "Play" and you will switch back to the regular version of Minecraft. -
Note: You may need to uninstall and reinstall your app or delete your data files before switching back to the regular version of Minecraft. You may also lose your progress or compatibility with your worlds or servers if you switch back to a different version of Minecraft.
-Conclusion
- -Minecraft Beta license key is a code that allows you to play the beta versions of Minecraft on your device. A beta version is an early version of a software that is not yet finished or polished, but is released for testing and feedback purposes. By joining the Minecraft Beta program, you can access the latest features and updates of Minecraft before they are officially released. However, you also need to be aware of the risks and challenges that come with playing with a beta version of Minecraft.
- -If you are interested in getting a Minecraft Beta license key, you can follow these steps:
- --
-
- Own the original Minecraft game on your device first. -
- Sign up for the beta program that matches your platform and edition of Minecraft. -
- Download and install the beta version of Minecraft on your device. -
- Enter your Minecraft Beta license key when prompted. -
- Enjoy playing with a beta version of Minecraft! -
If you want to leave the Minecraft Beta program, you can follow these steps:
- --
-
- Go to the app store or website that matches your platform and edition of Minecraft. -
- Find the official Minecraft app and click on "Manage" or "Leave" under "Minecraft Beta". -
- Wait for your app to switch back to the regular version of Minecraft. -
- Delete any data files or reinstall your app if necessary. -
- Play with the regular version of Minecraft! -
So what are you waiting for? Grab your Minecraft Beta license key today and experience the new world of Minecraft!
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\ No newline at end of file diff --git a/spaces/diacanFperku/AutoGPT/Ppjoy Error Installing New Device Drivers Windows 10.md b/spaces/diacanFperku/AutoGPT/Ppjoy Error Installing New Device Drivers Windows 10.md deleted file mode 100644 index 6e0eeab570236910d80d88ca4f111a0f6a540fe4..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Ppjoy Error Installing New Device Drivers Windows 10.md +++ /dev/null @@ -1,6 +0,0 @@ -
Ppjoy error installing new device drivers windows 10
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- -Here's the thing: Windows lists the stick, throttle and rudder as three seperate devices. Now i already found out the Freespace engine lets you ... 1fdad05405
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- diff --git a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/bert_gen.py b/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/bert_gen.py deleted file mode 100644 index 467655b2c4171608ad690fe7dec350db85f84f1b..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/bert_gen.py +++ /dev/null @@ -1,53 +0,0 @@ -import torch -from torch.utils.data import DataLoader -from multiprocessing import Pool -import commons -import utils -from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate -from tqdm import tqdm -import warnings - -from text import cleaned_text_to_sequence, get_bert - -config_path = 'configs/config.json' -hps = utils.get_hparams_from_file(config_path) - -def process_line(line): - _id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|") - phone = phones.split(" ") - tone = [int(i) for i in tone.split(" ")] - word2ph = [int(i) for i in word2ph.split(" ")] - w2pho = [i for i in word2ph] - word2ph = [i for i in word2ph] - phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) - - if hps.data.add_blank: - phone = commons.intersperse(phone, 0) - tone = commons.intersperse(tone, 0) - language = commons.intersperse(language, 0) - for i in range(len(word2ph)): - word2ph[i] = word2ph[i] * 2 - word2ph[0] += 1 - wav_path = f'{_id}' - - bert_path = wav_path.replace(".wav", ".bert.pt") - try: - bert = torch.load(bert_path) - assert bert.shape[-1] == len(phone) - except: - bert = get_bert(text, word2ph, language_str) - assert bert.shape[-1] == len(phone) - torch.save(bert, bert_path) - - -if __name__ == '__main__': - lines = [] - with open(hps.data.training_files, encoding='utf-8' ) as f: - lines.extend(f.readlines()) - - # with open(hps.data.validation_files, encoding='utf-8' ) as f: - # lines.extend(f.readlines()) - - with Pool(processes=2) as pool: #A100 40GB suitable config,if coom,please decrease the processess number. - for _ in tqdm(pool.imap_unordered(process_line, lines)): - pass diff --git a/spaces/dineshreddy/WALT/mmdet/models/backbones/resnet.py b/spaces/dineshreddy/WALT/mmdet/models/backbones/resnet.py deleted file mode 100644 index 3826815a6d94fdc4c54001d4c186d10ca3380e80..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/mmdet/models/backbones/resnet.py +++ /dev/null @@ -1,663 +0,0 @@ -import torch.nn as nn -import torch.utils.checkpoint as cp -from mmcv.cnn import (build_conv_layer, build_norm_layer, build_plugin_layer, - constant_init, kaiming_init) -from mmcv.runner import load_checkpoint -from torch.nn.modules.batchnorm import _BatchNorm - -from mmdet.utils import get_root_logger -from ..builder import BACKBONES -from ..utils import ResLayer - - -class BasicBlock(nn.Module): - expansion = 1 - - def __init__(self, - inplanes, - planes, - stride=1, - dilation=1, - downsample=None, - style='pytorch', - with_cp=False, - conv_cfg=None, - norm_cfg=dict(type='BN'), - dcn=None, - plugins=None): - super(BasicBlock, self).__init__() - assert dcn is None, 'Not implemented yet.' - assert plugins is None, 'Not implemented yet.' - - self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1) - self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2) - - self.conv1 = build_conv_layer( - conv_cfg, - inplanes, - planes, - 3, - stride=stride, - padding=dilation, - dilation=dilation, - bias=False) - self.add_module(self.norm1_name, norm1) - self.conv2 = build_conv_layer( - conv_cfg, planes, planes, 3, padding=1, bias=False) - self.add_module(self.norm2_name, norm2) - - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - self.dilation = dilation - self.with_cp = with_cp - - @property - def norm1(self): - """nn.Module: normalization layer after the first convolution layer""" - return getattr(self, self.norm1_name) - - @property - def norm2(self): - """nn.Module: normalization layer after the second convolution layer""" - return getattr(self, self.norm2_name) - - def forward(self, x): - """Forward function.""" - - def _inner_forward(x): - identity = x - - out = self.conv1(x) - out = self.norm1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.norm2(out) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - - return out - - if self.with_cp and x.requires_grad: - out = cp.checkpoint(_inner_forward, x) - else: - out = _inner_forward(x) - - out = self.relu(out) - - return out - - -class Bottleneck(nn.Module): - expansion = 4 - - def __init__(self, - inplanes, - planes, - stride=1, - dilation=1, - downsample=None, - style='pytorch', - with_cp=False, - conv_cfg=None, - norm_cfg=dict(type='BN'), - dcn=None, - plugins=None): - """Bottleneck block for ResNet. - - If style is "pytorch", the stride-two layer is the 3x3 conv layer, if - it is "caffe", the stride-two layer is the first 1x1 conv layer. - """ - super(Bottleneck, self).__init__() - assert style in ['pytorch', 'caffe'] - assert dcn is None or isinstance(dcn, dict) - assert plugins is None or isinstance(plugins, list) - if plugins is not None: - allowed_position = ['after_conv1', 'after_conv2', 'after_conv3'] - assert all(p['position'] in allowed_position for p in plugins) - - self.inplanes = inplanes - self.planes = planes - self.stride = stride - self.dilation = dilation - self.style = style - self.with_cp = with_cp - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.dcn = dcn - self.with_dcn = dcn is not None - self.plugins = plugins - self.with_plugins = plugins is not None - - if self.with_plugins: - # collect plugins for conv1/conv2/conv3 - self.after_conv1_plugins = [ - plugin['cfg'] for plugin in plugins - if plugin['position'] == 'after_conv1' - ] - self.after_conv2_plugins = [ - plugin['cfg'] for plugin in plugins - if plugin['position'] == 'after_conv2' - ] - self.after_conv3_plugins = [ - plugin['cfg'] for plugin in plugins - if plugin['position'] == 'after_conv3' - ] - - if self.style == 'pytorch': - self.conv1_stride = 1 - self.conv2_stride = stride - else: - self.conv1_stride = stride - self.conv2_stride = 1 - - self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1) - self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2) - self.norm3_name, norm3 = build_norm_layer( - norm_cfg, planes * self.expansion, postfix=3) - - self.conv1 = build_conv_layer( - conv_cfg, - inplanes, - planes, - kernel_size=1, - stride=self.conv1_stride, - bias=False) - self.add_module(self.norm1_name, norm1) - fallback_on_stride = False - if self.with_dcn: - fallback_on_stride = dcn.pop('fallback_on_stride', False) - if not self.with_dcn or fallback_on_stride: - self.conv2 = build_conv_layer( - conv_cfg, - planes, - planes, - kernel_size=3, - stride=self.conv2_stride, - padding=dilation, - dilation=dilation, - bias=False) - else: - assert self.conv_cfg is None, 'conv_cfg must be None for DCN' - self.conv2 = build_conv_layer( - dcn, - planes, - planes, - kernel_size=3, - stride=self.conv2_stride, - padding=dilation, - dilation=dilation, - bias=False) - - self.add_module(self.norm2_name, norm2) - self.conv3 = build_conv_layer( - conv_cfg, - planes, - planes * self.expansion, - kernel_size=1, - bias=False) - self.add_module(self.norm3_name, norm3) - - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - - if self.with_plugins: - self.after_conv1_plugin_names = self.make_block_plugins( - planes, self.after_conv1_plugins) - self.after_conv2_plugin_names = self.make_block_plugins( - planes, self.after_conv2_plugins) - self.after_conv3_plugin_names = self.make_block_plugins( - planes * self.expansion, self.after_conv3_plugins) - - def make_block_plugins(self, in_channels, plugins): - """make plugins for block. - - Args: - in_channels (int): Input channels of plugin. - plugins (list[dict]): List of plugins cfg to build. - - Returns: - list[str]: List of the names of plugin. - """ - assert isinstance(plugins, list) - plugin_names = [] - for plugin in plugins: - plugin = plugin.copy() - name, layer = build_plugin_layer( - plugin, - in_channels=in_channels, - postfix=plugin.pop('postfix', '')) - assert not hasattr(self, name), f'duplicate plugin {name}' - self.add_module(name, layer) - plugin_names.append(name) - return plugin_names - - def forward_plugin(self, x, plugin_names): - out = x - for name in plugin_names: - out = getattr(self, name)(x) - return out - - @property - def norm1(self): - """nn.Module: normalization layer after the first convolution layer""" - return getattr(self, self.norm1_name) - - @property - def norm2(self): - """nn.Module: normalization layer after the second convolution layer""" - return getattr(self, self.norm2_name) - - @property - def norm3(self): - """nn.Module: normalization layer after the third convolution layer""" - return getattr(self, self.norm3_name) - - def forward(self, x): - """Forward function.""" - - def _inner_forward(x): - identity = x - out = self.conv1(x) - out = self.norm1(out) - out = self.relu(out) - - if self.with_plugins: - out = self.forward_plugin(out, self.after_conv1_plugin_names) - - out = self.conv2(out) - out = self.norm2(out) - out = self.relu(out) - - if self.with_plugins: - out = self.forward_plugin(out, self.after_conv2_plugin_names) - - out = self.conv3(out) - out = self.norm3(out) - - if self.with_plugins: - out = self.forward_plugin(out, self.after_conv3_plugin_names) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - - return out - - if self.with_cp and x.requires_grad: - out = cp.checkpoint(_inner_forward, x) - else: - out = _inner_forward(x) - - out = self.relu(out) - - return out - - -@BACKBONES.register_module() -class ResNet(nn.Module): - """ResNet backbone. - - Args: - depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. - stem_channels (int | None): Number of stem channels. If not specified, - it will be the same as `base_channels`. Default: None. - base_channels (int): Number of base channels of res layer. Default: 64. - in_channels (int): Number of input image channels. Default: 3. - num_stages (int): Resnet stages. Default: 4. - strides (Sequence[int]): Strides of the first block of each stage. - dilations (Sequence[int]): Dilation of each stage. - out_indices (Sequence[int]): Output from which stages. - style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two - layer is the 3x3 conv layer, otherwise the stride-two layer is - the first 1x1 conv layer. - deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv - avg_down (bool): Use AvgPool instead of stride conv when - downsampling in the bottleneck. - frozen_stages (int): Stages to be frozen (stop grad and set eval mode). - -1 means not freezing any parameters. - norm_cfg (dict): Dictionary to construct and config norm layer. - norm_eval (bool): Whether to set norm layers to eval mode, namely, - freeze running stats (mean and var). Note: Effect on Batch Norm - and its variants only. - plugins (list[dict]): List of plugins for stages, each dict contains: - - - cfg (dict, required): Cfg dict to build plugin. - - position (str, required): Position inside block to insert - plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'. - - stages (tuple[bool], optional): Stages to apply plugin, length - should be same as 'num_stages'. - with_cp (bool): Use checkpoint or not. Using checkpoint will save some - memory while slowing down the training speed. - zero_init_residual (bool): Whether to use zero init for last norm layer - in resblocks to let them behave as identity. - - Example: - >>> from mmdet.models import ResNet - >>> import torch - >>> self = ResNet(depth=18) - >>> self.eval() - >>> inputs = torch.rand(1, 3, 32, 32) - >>> level_outputs = self.forward(inputs) - >>> for level_out in level_outputs: - ... print(tuple(level_out.shape)) - (1, 64, 8, 8) - (1, 128, 4, 4) - (1, 256, 2, 2) - (1, 512, 1, 1) - """ - - arch_settings = { - 18: (BasicBlock, (2, 2, 2, 2)), - 34: (BasicBlock, (3, 4, 6, 3)), - 50: (Bottleneck, (3, 4, 6, 3)), - 101: (Bottleneck, (3, 4, 23, 3)), - 152: (Bottleneck, (3, 8, 36, 3)) - } - - def __init__(self, - depth, - in_channels=3, - stem_channels=None, - base_channels=64, - num_stages=4, - strides=(1, 2, 2, 2), - dilations=(1, 1, 1, 1), - out_indices=(0, 1, 2, 3), - style='pytorch', - deep_stem=False, - avg_down=False, - frozen_stages=-1, - conv_cfg=None, - norm_cfg=dict(type='BN', requires_grad=True), - norm_eval=True, - dcn=None, - stage_with_dcn=(False, False, False, False), - plugins=None, - with_cp=False, - zero_init_residual=True): - super(ResNet, self).__init__() - if depth not in self.arch_settings: - raise KeyError(f'invalid depth {depth} for resnet') - self.depth = depth - if stem_channels is None: - stem_channels = base_channels - self.stem_channels = stem_channels - self.base_channels = base_channels - self.num_stages = num_stages - assert num_stages >= 1 and num_stages <= 4 - self.strides = strides - self.dilations = dilations - assert len(strides) == len(dilations) == num_stages - self.out_indices = out_indices - assert max(out_indices) < num_stages - self.style = style - self.deep_stem = deep_stem - self.avg_down = avg_down - self.frozen_stages = frozen_stages - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.with_cp = with_cp - self.norm_eval = norm_eval - self.dcn = dcn - self.stage_with_dcn = stage_with_dcn - if dcn is not None: - assert len(stage_with_dcn) == num_stages - self.plugins = plugins - self.zero_init_residual = zero_init_residual - self.block, stage_blocks = self.arch_settings[depth] - self.stage_blocks = stage_blocks[:num_stages] - self.inplanes = stem_channels - - self._make_stem_layer(in_channels, stem_channels) - - self.res_layers = [] - for i, num_blocks in enumerate(self.stage_blocks): - stride = strides[i] - dilation = dilations[i] - dcn = self.dcn if self.stage_with_dcn[i] else None - if plugins is not None: - stage_plugins = self.make_stage_plugins(plugins, i) - else: - stage_plugins = None - planes = base_channels * 2**i - res_layer = self.make_res_layer( - block=self.block, - inplanes=self.inplanes, - planes=planes, - num_blocks=num_blocks, - stride=stride, - dilation=dilation, - style=self.style, - avg_down=self.avg_down, - with_cp=with_cp, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - dcn=dcn, - plugins=stage_plugins) - self.inplanes = planes * self.block.expansion - layer_name = f'layer{i + 1}' - self.add_module(layer_name, res_layer) - self.res_layers.append(layer_name) - - self._freeze_stages() - - self.feat_dim = self.block.expansion * base_channels * 2**( - len(self.stage_blocks) - 1) - - def make_stage_plugins(self, plugins, stage_idx): - """Make plugins for ResNet ``stage_idx`` th stage. - - Currently we support to insert ``context_block``, - ``empirical_attention_block``, ``nonlocal_block`` into the backbone - like ResNet/ResNeXt. They could be inserted after conv1/conv2/conv3 of - Bottleneck. - - An example of plugins format could be: - - Examples: - >>> plugins=[ - ... dict(cfg=dict(type='xxx', arg1='xxx'), - ... stages=(False, True, True, True), - ... position='after_conv2'), - ... dict(cfg=dict(type='yyy'), - ... stages=(True, True, True, True), - ... position='after_conv3'), - ... dict(cfg=dict(type='zzz', postfix='1'), - ... stages=(True, True, True, True), - ... position='after_conv3'), - ... dict(cfg=dict(type='zzz', postfix='2'), - ... stages=(True, True, True, True), - ... position='after_conv3') - ... ] - >>> self = ResNet(depth=18) - >>> stage_plugins = self.make_stage_plugins(plugins, 0) - >>> assert len(stage_plugins) == 3 - - Suppose ``stage_idx=0``, the structure of blocks in the stage would be: - - .. code-block:: none - - conv1-> conv2->conv3->yyy->zzz1->zzz2 - - Suppose 'stage_idx=1', the structure of blocks in the stage would be: - - .. code-block:: none - - conv1-> conv2->xxx->conv3->yyy->zzz1->zzz2 - - If stages is missing, the plugin would be applied to all stages. - - Args: - plugins (list[dict]): List of plugins cfg to build. The postfix is - required if multiple same type plugins are inserted. - stage_idx (int): Index of stage to build - - Returns: - list[dict]: Plugins for current stage - """ - stage_plugins = [] - for plugin in plugins: - plugin = plugin.copy() - stages = plugin.pop('stages', None) - assert stages is None or len(stages) == self.num_stages - # whether to insert plugin into current stage - if stages is None or stages[stage_idx]: - stage_plugins.append(plugin) - - return stage_plugins - - def make_res_layer(self, **kwargs): - """Pack all blocks in a stage into a ``ResLayer``.""" - return ResLayer(**kwargs) - - @property - def norm1(self): - """nn.Module: the normalization layer named "norm1" """ - return getattr(self, self.norm1_name) - - def _make_stem_layer(self, in_channels, stem_channels): - if self.deep_stem: - self.stem = nn.Sequential( - build_conv_layer( - self.conv_cfg, - in_channels, - stem_channels // 2, - kernel_size=3, - stride=2, - padding=1, - bias=False), - build_norm_layer(self.norm_cfg, stem_channels // 2)[1], - nn.ReLU(inplace=True), - build_conv_layer( - self.conv_cfg, - stem_channels // 2, - stem_channels // 2, - kernel_size=3, - stride=1, - padding=1, - bias=False), - build_norm_layer(self.norm_cfg, stem_channels // 2)[1], - nn.ReLU(inplace=True), - build_conv_layer( - self.conv_cfg, - stem_channels // 2, - stem_channels, - kernel_size=3, - stride=1, - padding=1, - bias=False), - build_norm_layer(self.norm_cfg, stem_channels)[1], - nn.ReLU(inplace=True)) - else: - self.conv1 = build_conv_layer( - self.conv_cfg, - in_channels, - stem_channels, - kernel_size=7, - stride=2, - padding=3, - bias=False) - self.norm1_name, norm1 = build_norm_layer( - self.norm_cfg, stem_channels, postfix=1) - self.add_module(self.norm1_name, norm1) - self.relu = nn.ReLU(inplace=True) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - - def _freeze_stages(self): - if self.frozen_stages >= 0: - if self.deep_stem: - self.stem.eval() - for param in self.stem.parameters(): - param.requires_grad = False - else: - self.norm1.eval() - for m in [self.conv1, self.norm1]: - for param in m.parameters(): - param.requires_grad = False - - for i in range(1, self.frozen_stages + 1): - m = getattr(self, f'layer{i}') - m.eval() - for param in m.parameters(): - param.requires_grad = False - - def init_weights(self, pretrained=None): - """Initialize the weights in backbone. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - if isinstance(pretrained, str): - logger = get_root_logger() - load_checkpoint(self, pretrained, strict=False, logger=logger) - elif pretrained is None: - for m in self.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, (_BatchNorm, nn.GroupNorm)): - constant_init(m, 1) - - if self.dcn is not None: - for m in self.modules(): - if isinstance(m, Bottleneck) and hasattr( - m.conv2, 'conv_offset'): - constant_init(m.conv2.conv_offset, 0) - - if self.zero_init_residual: - for m in self.modules(): - if isinstance(m, Bottleneck): - constant_init(m.norm3, 0) - elif isinstance(m, BasicBlock): - constant_init(m.norm2, 0) - else: - raise TypeError('pretrained must be a str or None') - - def forward(self, x): - """Forward function.""" - if self.deep_stem: - x = self.stem(x) - else: - x = self.conv1(x) - x = self.norm1(x) - x = self.relu(x) - x = self.maxpool(x) - outs = [] - for i, layer_name in enumerate(self.res_layers): - res_layer = getattr(self, layer_name) - x = res_layer(x) - if i in self.out_indices: - outs.append(x) - return tuple(outs) - - def train(self, mode=True): - """Convert the model into training mode while keep normalization layer - freezed.""" - super(ResNet, self).train(mode) - self._freeze_stages() - if mode and self.norm_eval: - for m in self.modules(): - # trick: eval have effect on BatchNorm only - if isinstance(m, _BatchNorm): - m.eval() - - -@BACKBONES.register_module() -class ResNetV1d(ResNet): - r"""ResNetV1d variant described in `Bag of Tricks -
-
Bundesliga: The Ultimate Guide to Germany's Top Football League
-If you are a fan of football, you have probably heard of the Bundesliga, the premier league of Germany. But how much do you really know about this league, its history, its teams, and its players? In this article, we will give you a comprehensive overview of everything you need to know about the Bundesliga, from its origins to its current season, from its reasons to watch to its ways to watch. Whether you are a newcomer or a seasoned follower, this article will help you enjoy and appreciate the Bundesliga more.
-What is the Bundesliga?
-The Bundesliga ([ˌeːɐ̯stə-]), which means "Federal League" in German, is a professional association football league in Germany. It is the top tier of the German football league system, and it is considered one of the best and most popular leagues in the world. The Bundesliga comprises 18 teams and operates on a system of promotion and relegation with the 2. Bundesliga, the second tier. Each team plays 34 matches in a season, which runs from August to May. The team with the most points at the end of the season is crowned as the champion, while the bottom two teams are relegated to the 2. Bundesliga. The team in 16th place faces a playoff against the third-placed team from the 2. Bundesliga for a spot in the next season's Bundesliga.
-bundesliga
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History and format of the Bundesliga
-The Bundesliga was founded in 1963 as a replacement for the regional leagues that existed before. The first season featured 16 teams, with FC Köln becoming the inaugural champions. Since then, the league has expanded to 18 teams in 1991, and has undergone several changes in its format and structure. The current format of having a single division with promotion and relegation was adopted in 1995.
-The Bundesliga follows a standard round-robin format, where each team plays every other team twice, once at home and once away. A win earns three points, a draw earns one point, and a loss earns no points. The teams are ranked by their total points, followed by their goal difference, and then their goals scored. If two or more teams are tied on points, goal difference, and goals scored, they are separated by their head-to-head record. If still tied, a playoff match is held to determine the final ranking.
-The most successful clubs in the Bundesliga
-The Bundesliga has seen many great clubs compete for glory over the years, but none more so than FC Bayern München (Bayern Munich), who have won a record 31 titles, including nine consecutive ones from 2012/13 to 2020/21. They are also the only German club to have won the treble of domestic league, domestic cup, and UEFA Champions League, which they achieved twice in 2012/13 and 2019/20.
-Behind Bayern Munich, Borussia Dortmund are the second-most successful club in the Bundesliga, with eight titles. They are also one of only two German clubs to have won the UEFA Champions League, which they did in 1996/97. Other notable clubs in the Bundesliga include Borussia Mönchengladbach and SV Werder Bremen, who have won four titles each; Hamburger SV and VfB Stuttgart, who have won three titles each; and FC Schalke 04 and Eintracht Frankfurt, who have won one title each.
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The current season of the Bundesliga
-The current season of the Bundesliga is the 59th edition of the league, and it started on.
The current season of the Bundesliga is the 59th edition of the league, and it started on August 13, 2021. The season is scheduled to end on May 14, 2022, with a winter break from December 20, 2021 to January 7, 2022. The defending champions are Bayern Munich, who are aiming for their 10th consecutive title. The newly promoted teams are VfL Bochum, SpVgg Greuther Fürth, and Holstein Kiel, who replaced the relegated teams Schalke 04, Werder Bremen, and Arminia Bielefeld.
-As of December 19, 2021, Bayern Munich are leading the table with 40 points from 16 games, followed by Borussia Dortmund with 34 points from 15 games. The top four teams qualify for the UEFA Champions League group stage, while the fifth and sixth teams qualify for the UEFA Europa League group stage and conference league play-off round respectively. The bottom two teams are relegated to the 2. Bundesliga, while the 16th-placed team faces a relegation play-off against the third-placed team from the 2. Bundesliga.
-The top scorer of the Bundesliga so far is Robert Lewandowski of Bayern Munich, who has scored 20 goals in 15 games. He is followed by Erling Haaland of Borussia Dortmund, who has scored 13 goals in nine games. The top assist provider of the Bundesliga so far is Thomas Müller of Bayern Munich, who has provided 11 assists in 16 games. He is followed by Filip Kostić of Eintracht Frankfurt, who has provided nine assists in 14 games.
-Why watch the Bundesliga?
-The Bundesliga is one of the most entertaining and attractive leagues in the world, and there are many reasons why you should watch it. Here are some of them:
-The quality and competitiveness of the Bundesliga
-The Bundesliga is known for its high level of quality and competitiveness, as it features some of the best teams and players in the world. The league has produced many European and world champions, such as Bayern Munich, Borussia Dortmund, Germany national team, etc. The league also boasts a high average attendance of over 40,000 fans per game, which is the highest among the top five European leagues. The league also has a fair and balanced distribution of TV revenue among its clubs, which ensures a healthy and sustainable competition.
-The exciting and passionate atmosphere of the Bundesliga
-The Bundesliga is also known for its exciting and passionate atmosphere, as it showcases some of the most loyal and vocal fans in the world. The fans create a colorful and vibrant spectacle in the stadiums, with their chants, songs, banners, flags, flares, etc. The fans also have a strong influence on their clubs' decisions and policies, as they often own a majority stake or have a voting right in their clubs' boards. The fans also have a friendly and respectful relationship with each other, as they often mingle and share beers before and after the games.
-The young and talented players of the Bundesliga
-The Bundesliga is also known for its young and talented players, as it provides a platform for them to develop and showcase their skills. The league has a reputation for producing some of the best talents in the world, such as Franz Beckenbauer, Gerd Müller, Lothar Matthäus, Jürgen Klinsmann, Oliver Kahn, Michael Ballack,
The Bundesliga is also known for its young and talented players, as it provides a platform for them to develop and showcase their skills. The league has a reputation for producing some of the best talents in the world, such as Franz Beckenbauer, Gerd Müller, Lothar Matthäus, Jürgen Klinsmann, Oliver Kahn, Michael Ballack, Philipp Lahm, Bastian Schweinsteiger, Manuel Neuer, Thomas Müller, Toni Kroos, Mesut Özil, Marco Reus, Robert Lewandowski, Erling Haaland, etc. The league also has a high percentage of homegrown players, as it has a strict rule that requires each club to have at least eight players who were trained in Germany in their squad. The league also has a low average age of players, as it encourages clubs to give opportunities to young and promising players.
-How to watch the Bundesliga?
-If you are interested in watching the Bundesliga, you have several options to choose from. Here are some of them:
-The official broadcasters of the Bundesliga
-The official broadcasters of the Bundesliga vary depending on your location and your preferred language. In Germany, the Bundesliga is broadcasted by Sky Deutschland and DAZN, who share the rights to show live matches. In the UK and Ireland, the Bundesliga is broadcasted by BT Sport, who have exclusive rights to show all matches live. In the US and Canada, the Bundesliga is broadcasted by ESPN+, who have exclusive rights to stream all matches live. In other countries and regions, you can check the official website of the Bundesliga to find out which broadcaster covers the league in your area.
-The best websites and apps to follow the Bundesliga
-If you want to follow the Bundesliga online, you have many websites and apps to choose from. Some of the best ones are:
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- The official website of the Bundesliga: This is the ultimate source of information and news about the league, its teams, its players, its fixtures, its results, its standings, its statistics, etc. You can also watch highlights and videos of the matches, as well as interviews and features of the stars. -
- Bundesliga app: This is the official app of the Bundesliga, which offers similar features as the website, but in a more convenient and mobile-friendly way. You can also customize your app to follow your favorite team and get notifications and updates on their performance. -
- OneFootball app: This is a popular app that covers all aspects of football around the world, including the Bundesliga. You can get live scores, news, videos, podcasts, etc. You can also follow your favorite team and player and get personalized content and recommendations. -
- SofaScore app: This is a comprehensive app that provides live scores, statistics, analysis, etc. for various sports, including football and the Bundesliga. You can also get detailed information on each match, such as lineups, formations, events, ratings, etc. You can also compare teams and players and see their performance over time. -
The official Bundesliga fantasy manager game
-If you want to have some fun and test your knowledge and skills of the Bundesliga, you can play the official Bundesliga fantasy manager game. This is a free online game that allows you to create your own team of Bundesliga players and compete with other players around the world. You can earn points based on your players' real-life performance in each matchday. You can also join or create leagues with your friends or other fans and see who has the best team. You can also win prizes and rewards for your achievements.
-Conclusion
-The Bundesliga is one of the most exciting and attractive leagues in the world, and it deserves your attention and appreciation. Whether you are looking for quality and competitiveness, atmosphere and passion,
The Bundesliga is one of the most exciting and attractive leagues in the world, and it deserves your attention and appreciation. Whether you are looking for quality and competitiveness, atmosphere and passion, or talent and innovation, the Bundesliga has it all. You can watch the Bundesliga live on various broadcasters, follow the Bundesliga online on various websites and apps, or play the Bundesliga fantasy manager game for some fun and challenge. The Bundesliga is more than just a league, it is a culture and a community of football lovers. Join the Bundesliga and experience the thrill and joy of German football.
-FAQs
-Here are some frequently asked questions about the Bundesliga:
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- How many teams are in the Bundesliga?
-There are 18 teams in the Bundesliga, which play 34 matches each in a season.
- - Who are the current champions of the Bundesliga?
-The current champions of the Bundesliga are Bayern Munich, who have won a record 31 titles, including nine consecutive ones.
- - Who are the top scorers and assist providers of the Bundesliga?
-The top scorer of the Bundesliga so far is Robert Lewandowski of Bayern Munich, who has scored 20 goals in 15 games. The top assist provider of the Bundesliga so far is Thomas Müller of Bayern Munich, who has provided 11 assists in 16 games.
- - How can I watch the Bundesliga live?
-You can watch the Bundesliga live on various broadcasters, depending on your location and language. You can check the official website of the Bundesliga to find out which broadcaster covers the league in your area.
- - How can I follow the Bundesliga online?
-You can follow the Bundesliga online on various websites and apps, such as the official website and app of the Bundesliga, OneFootball app, SofaScore app, etc. You can also play the official Bundesliga fantasy manager game for some fun and challenge.
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download Stunning Cookie Monster Photos - The Best Source of Cookie Monster Images.md b/spaces/fatiXbelha/sd/Download Stunning Cookie Monster Photos - The Best Source of Cookie Monster Images.md deleted file mode 100644 index 5cbfc464de5298430410ddedc76436e60f636c6c..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Stunning Cookie Monster Photos - The Best Source of Cookie Monster Images.md +++ /dev/null @@ -1,128 +0,0 @@ -
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Cookie Monster Pics to Download: How to Find and Enjoy the Best Images of the Blue Muppet
-If you are a fan of Sesame Street, you probably know and love Cookie Monster, the blue furry creature who loves cookies more than anything. But did you know that you can download Cookie Monster pics for free and use them for various purposes? In this article, we will tell you everything you need to know about Cookie Monster pics, including who he is, why you should download his pics, where to find them, and how to download them. Let's get started!
-Who is Cookie Monster?
-Cookie Monster is one of the most iconic characters on Sesame Street, the long-running PBS / HBO children's television show. He first appeared in 1966 as a monster who stole snacks from a commercial, and later became a regular on Sesame Street in 1969. He is best known for his voracious appetite and his famous eating catchphrases, such as "Me want cookie!" He also has a real name, which is Sid, as revealed in a song in 2004 and an interview in 2017.
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The origin and history of Cookie Monster
-Cookie Monster was created by Jim Henson, the legendary puppeteer and founder of the Muppets. According to the book Jim Henson's Designs and Doodles, Cookie Monster was originally one of three monsters that ate cookies and appeared in a General Foods commercial that featured three crunchy snack foods: Wheels, Crowns and Flutes. He was called the Wheel-Stealer, and he was a short, fuzzy monster with wonky eyes and sharply pointed teeth.
-The commercial was never aired, but the Wheel-Stealer later appeared on The Ed Sullivan Show, where he ate a machine that made snacks. He also appeared in other sketches and commercials before joining Sesame Street as Cookie Monster. On Sesame Street, he became famous for his love of cookies, especially chocolate chip cookies, which he would devour with loud crunches and crumbs flying everywhere. He also sang many songs about cookies, such as "C Is For Cookie" and "Cookie Disco".
-The personality and traits of Cookie Monster
-Cookie Monster is a friendly and lovable monster who likes to share his cookies with his friends. He is also very curious and eager to learn new things. He often participates in educational segments on Sesame Street, such as "Monsterpiece Theater" , where he plays different characters from classic literature and movies. He also hosts his own segment called "Cookie Monster's Foodie Truck" , where he learns about different foods and cuisines from around the world.
-Cookie Monster speaks in a distinctive way, using "Me" instead of "I", "My", or "Mine", and often making grammatical errors. He also has a deep and raspy voice, which is performed by different puppeteers over the years. The original voice of Cookie Monster was Frank Oz, who also voiced other Muppet characters such as Miss Piggy, Fozzie Bear, and Grover. Since 2001, the voice of Cookie Monster has been David Rudman, who also voices Baby Bear and Scooter.
-The popularity and influence of Cookie Monster
-Cookie
Cookie Monster is one of the most popular and beloved characters on Sesame Street, and has been for over 50 years. He has a huge fan base of people of all ages, who admire his humor, his kindness, and his passion for cookies. He has also inspired many parodies, memes, merchandise, and even a Google Doodle in 2013.
-Cookie Monster is not only a source of entertainment, but also a role model for children and adults alike. He teaches valuable lessons about self-control, moderation, diversity, and friendship. He also encourages healthy eating habits, as he has learned to enjoy other foods besides cookies, such as fruits, vegetables, and grains. He even coined the phrase "Cookies are a sometimes food" , which means that cookies are delicious but should not be eaten all the time.
-Why download Cookie Monster pics?
-Now that you know more about who Cookie Monster is, you might be wondering why you should download his pics. Well, there are many reasons why downloading Cookie Monster pics can be fun and beneficial for you. Here are some of them:
-The benefits of downloading Cookie Monster pics
-Downloading Cookie Monster pics can have positive effects on your mood, your creativity, and your productivity. Here are some of the benefits of downloading Cookie Monster pics:
-Cookie Monster stock photos free to use
-Cookie Monster photos and images from Getty Images
-Cookie Monster vectors and PSD files from Freepik
-Cookie Monster HD wallpapers and backgrounds
-Cookie Monster clipart and illustrations
-Cookie Monster PNG and SVG files
-Cookie Monster memes and funny pictures
-Cookie Monster coloring pages and printables
-Cookie Monster cakes and cupcakes photos
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-Cookie Monster quotes and sayings images
-Cookie Monster birthday party photos and ideas
-Cookie Monster cookies and recipes photos
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- They can make you happy. Cookie Monster is a cheerful and optimistic character who always sees the bright side of things. His smile and laughter are contagious and can lift your spirits when you are feeling down or stressed. -
- They can inspire you. Cookie Monster is a curious and adventurous character who loves to explore new things and learn new skills. His enthusiasm and determination can motivate you to pursue your own goals and passions. -
- They can help you relax. Cookie Monster is a calm and peaceful character who knows how to enjoy the simple pleasures of life. His relaxed and laid-back attitude can help you unwind and de-stress after a long day. -
The uses and purposes of downloading Cookie Monster pics
-Downloading Cookie Monster pics can also have many practical uses and purposes for different occasions and situations. Here are some of the uses and purposes of downloading Cookie Monster pics:
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- They can decorate your devices. You can use Cookie Monster pics as wallpapers or screensavers for your computer, tablet, or smartphone. They can add some color and personality to your devices and make them more attractive and appealing. -
- They can personalize your online profiles. You can use Cookie Monster pics as profile pictures or cover photos for your social media accounts, such as Facebook, Twitter, Instagram, or Pinterest. They can express your identity and interests and make you stand out from the crowd. -
- They can spice up your messages. You can use Cookie Monster pics as emojis or stickers for your text messages, emails, or chats. They can convey your emotions and feelings and make your communication more fun and lively. -
The tips and tricks for downloading Cookie Monster pics
-Downloading Cookie Monster pics is easy and simple, but there are some tips and tricks that can make it even easier and simpler. Here are some of the tips and tricks for downloading Cookie Monster pics:
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- Use a reliable and reputable website. There are many websites that offer free Cookie Monster pics to download, but not all of them are safe and trustworthy. Some of them may contain viruses, malware, or spyware that can harm your devices or steal your personal information. To avoid these risks, use a reliable and reputable website that has good reviews and ratings from other users. -
- Choose a high-quality and suitable image. There are many types and styles of Cookie Monster pics to choose from, but not all of them are suitable for your needs and preferences. Some of them may be too large or too small, too blurry or too sharp, too bright or too dark, or too plain or too busy. To find the best image for you, choose a high-quality image that has a clear resolution, a good contrast, a balanced color, and an appropriate size. -
- Download the image in the right format and resolution. There are many formats and resolutions of Cookie Monster pics to download, but not all of them are compatible with your devices or platforms. Some of them may not open or display properly on your devices or platforms, or may take up too much space or memory on your devices or platforms. To avoid these problems, download the image in the right format and resolution that matches your devices or platforms. -
Where to download Cookie Monster pics?
-Now that you know why you should download Cookie Monster pics
Now that you know why you should download Cookie Monster pics, you might be wondering where you can find them. Well, there are many websites that offer free Cookie Monster pics to download, but not all of them are equally good and reliable. To help you find the best websites for downloading Cookie Monster pics, we have compiled a list of the top three websites that we recommend. Here they are:
-The best websites for downloading Cookie Monster pics
-These are the best websites for downloading Cookie Monster pics, based on their quality, variety, and safety:
-Pexels
-Pexels is a website that provides free stock photos and videos that you can use for any purpose. It has a large collection of Cookie Monster pics that you can download in different sizes and resolutions. You can also filter the pics by color, orientation, and category. Pexels is a safe and trustworthy website that does not require any registration or attribution.
-Getty Images
-Getty Images is a website that offers premium stock photos and videos that you can license for various uses. It has a wide range of Cookie Monster pics that you can download in high-quality and high-resolution. You can also search the pics by keywords, collections, and licenses. Getty Images is a reputable and secure website that requires a subscription or a payment for some of the pics.
-Freepik
-Freepik is a website that provides free vector graphics, icons, illustrations, and photos that you can use for personal and commercial projects. It has a unique and creative selection of Cookie Monster pics that you can download in various formats and resolutions. You can also browse the pics by tags, styles, and popularity. Freepik is a reliable and user-friendly website that requires attribution for some of the pics.
-The best formats and resolutions for downloading Cookie Monster pics
-When downloading Cookie Monster pics, you need to consider the formats and resolutions that are suitable for your devices and platforms. Here are some of the best formats and resolutions for downloading Cookie Monster pics:
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- JPEG: JPEG is a common and widely supported format that compresses images to reduce their file size. It is ideal for downloading Cookie Monster pics that have many colors and details, such as photos or realistic drawings. However, it may also cause some loss of quality and sharpness. -
- PNG: PNG is another popular and widely supported format that preserves the quality and transparency of images. It is ideal for downloading Cookie Monster pics that have few colors and simple shapes, such as cartoons or logos. However, it may also increase the file size and loading time. -
- GIF: GIF is an old but still used format that supports animation and looping of images. It is ideal for downloading Cookie Monster pics that have movement and humor, such as memes or gifs. However, it may also limit the number of colors and frames. -
- SVG: SVG is a modern and advanced format that uses vector graphics to create scalable and editable images. It is ideal for downloading Cookie Monster pics that have smooth curves and crisp edges, such as icons or illustrations. However, it may also require special software or plugins to view or edit. -
- HD: HD stands for high-definition, which means having a high resolution of at least 1280 x 720 pixels. It is ideal for downloading Cookie Monster pics that have clarity and detail, such as wallpapers or screensavers. However, it may also take up more space and memory on your devices or platforms. -
- 4K: 4K stands for ultra-high-definition, which means having a very high resolution of at least 3840 x 2160 pixels. It is ideal for downloading Cookie Monster pics that have stunning and realistic quality, such as posters or prints. However, it may also require more bandwidth and processing power on your devices or platforms. -
The best practices and precautions for downloading Cookie Monster pics
-When downloading Cookie Monster pics, you need to follow some best practices and precautions to ensure a smooth and safe experience. Here are some of the best practices and precautions for downloading Cookie Monster pics:
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- Check the source and license of the image. Before downloading any image from the internet, you should always check the source and license of the image to make sure it is legal and ethical to use it. You should only download images from reputable websites that have clear terms and conditions regarding their usage rights. You should also respect the license requirements of the image, such as giving credit or attribution to the original creator or owner. -
- Scan the image for viruses or malware. After downloading any image from the internet, you should always scan the image for viruses or malware to make sure it is safe and clean to use it. You should use a You should use a reliable and updated antivirus or anti-malware software to scan the image for any potential threats or infections. You should also delete or quarantine any suspicious or harmful files that are detected. -
- Optimize the image for your devices or platforms. Before using any image on your devices or platforms, you should always optimize the image for your devices or platforms to make sure it is compatible and efficient to use it. You should adjust the format, resolution, size, and quality of the image to match the specifications and requirements of your devices or platforms. You should also compress or resize the image to reduce its file size and loading time. -
Conclusion
-Cookie Monster is a wonderful character who can bring joy and inspiration to your life. Downloading Cookie Monster pics can be a fun and rewarding activity that can enhance your mood, creativity, and productivity. You can find and download Cookie Monster pics from various websites, such as Pexels, Getty Images, and Freepik. You can also choose and download Cookie Monster pics in different formats and resolutions, such as JPEG, PNG, GIF, SVG, HD, and 4K. However, you should also be careful and responsible when downloading Cookie Monster pics, and follow some best practices and precautions, such as checking the source and license of the image, scanning the image for viruses or malware, and optimizing the image for your devices or platforms. We hope this article has helped you learn more about Cookie Monster pics and how to download them. Happy downloading!
-FAQs
-Here are some frequently asked questions about Cookie Monster pics and how to download them:
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- Is it legal to download Cookie Monster pics? -
- Is it safe to download Cookie Monster pics? -
- How can I download Cookie Monster pics faster? -
- How can I download Cookie Monster pics in bulk? -
- How can I edit Cookie Monster pics after downloading them? -
It depends on the source and license of the image. Some images are free to use for any purpose, while others require permission or payment from the original creator or owner. You should always check the terms and conditions of the website and the license of the image before downloading it.
-It depends on the website and the image. Some websites are safe and trustworthy, while others may contain viruses, malware, or spyware. Some images are safe and clean, while others may be corrupted or infected. You should always use a reliable and reputable website and scan the image for any potential threats or infections before downloading it.
-You can download Cookie Monster pics faster by using a fast and stable internet connection, choosing a small or medium-sized image, compressing or resizing the image, and using a download manager or accelerator software.
-You can download Cookie Monster pics in bulk by using a batch downloader software or extension that allows you to download multiple images at once from a website or a webpage.
-You can edit Cookie Monster pics after downloading them by using an image editor software or app that allows you to crop, rotate, resize, filter, adjust, add text, draw, or apply other effects to the image.
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download Tekken 3 Urdu APK and Enjoy the Best Fighting Game Ever.md b/spaces/fatiXbelha/sd/Download Tekken 3 Urdu APK and Enjoy the Best Fighting Game Ever.md deleted file mode 100644 index 0f0e93b7313c88e1c62ce27f7455b9eef1c6f7f5..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Tekken 3 Urdu APK and Enjoy the Best Fighting Game Ever.md +++ /dev/null @@ -1,130 +0,0 @@ -
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Tekken 3 Urdu APK: How to Download and Play the Classic Fighting Game on Your Android Device
-Introduction
-If you are a fan of fighting games, you probably have played or heard of Tekken 3, one of the most popular and acclaimed games of its genre. Released in 1997 for the PlayStation console, Tekken 3 features a roster of 23 characters, each with their own unique fighting style, moves, and story. The game also offers various modes, such as arcade, versus, team battle, survival, time attack, practice, and Tekken Force.
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But what if you want to play Tekken 3 on your Android device? Well, you are in luck, because there is a way to do that. In this article, we will show you how to download and play Tekken 3 Urdu APK, a modified version of the original game that has been translated into Urdu language. We will also give you some tips and tricks to help you enjoy the game even more. So, let's get started!
-What is Tekken 3?
-Tekken 3 is the third installment in the Tekken series, a fighting game franchise developed by Namco. The game follows the events of Tekken 2, where Heihachi Mishima, the head of the Mishima Zaibatsu corporation, defeats his son Kazuya Mishima and throws him into a volcano. However, a mysterious creature called Ogre emerges from the depths of the earth and starts attacking martial artists around the world. Heihachi decides to host a new tournament, the King of Iron Fist Tournament 3, to lure Ogre out and capture him.
-Tekken 3 introduces several new features to the gameplay, such as sidestepping, jumping, running, and grappling. The game also has a more realistic physics system, allowing for more fluid and dynamic movements. The graphics are also improved, with more detailed character models and backgrounds. The game also has a catchy soundtrack that matches the mood of each stage.
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What is Tekken 3 Urdu APK?
-Tekken 3 Urdu APK is a modified version of Tekken 3 that has been ported to run on Android devices. The game has been translated into Urdu language, making it more accessible and enjoyable for Urdu speakers. The game also has some minor changes and additions, such as new sound effects, music tracks, and cheats. The game is compatible with most Android devices that have at least 1 GB of RAM and Android 4.0 or higher.
-Why should you play Tekken 3 Urdu APK?
-There are many reasons why you should play Tekken 3 Urdu APK on your Android device. Here are some of them:
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- You can relive the nostalgia of playing one of the best fighting games ever made. -
- You can enjoy the game in your native language, making it easier to understand the story and dialogues. -
- You can play the game anytime and anywhere, without needing a console or a TV. -
- You can challenge your friends or other players online using Wi-Fi or Bluetooth. -
- You can customize the game settings according to your preferences. -
How to Download and Install Tekken 3 Urdu APK
-Downloading and installing Tekken 3 Urdu APK is very easy and simple. Just follow these steps:
-Step 1: Download the APK file from a trusted source
-The first thing you need to do is to download the APK file of Tekken 3 Urdu APK from a trusted source. You can use the link below to download the file from our website. The file size is about 35 MB and it is virus-free and safe to use.
-Download Tekken 3 Urdu APK here
-Step 2: Enable unknown sources on your device
-The next thing you need to do is to enable unknown sources on your device. This will allow you to install apps that are not from the Google Play Store. To do this, go to your device settings and look for security or privacy options. Then, find the option that says unknown sources or allow installation of apps from unknown sources and turn it on. You may see a warning message, but don't worry, just tap OK or Yes.
-Step 3: Install the APK file and launch the game
-The final thing you need to do is to install the APK file and launch the game. To do this, go to your file manager and locate the downloaded APK file. Tap on it and follow the instructions on the screen. It may take a few seconds or minutes to install, depending on your device speed. Once the installation is done, you will see an icon of Tekken 3 Urdu APK on your home screen or app drawer. Tap on it and enjoy the game!
-How to Play Tekken 3 Urdu APK
-Playing Tekken 3 Urdu APK is very fun and easy. Here are some basic steps to help you get started:
-Choose your fighter and mode
-When you launch the game, you will see a main menu with several options. You can choose to play arcade mode, where you fight against computer-controlled opponents in a series of matches; versus mode, where you fight against another player on the same device; team battle mode, where you form a team of up to eight fighters and fight against another team; survival mode, where you try to defeat as many opponents as possible with one life bar; time attack mode, where you try to clear arcade mode as fast as possible; practice mode, where you can train and learn the moves of your fighter; and Tekken Force mode, where you fight against waves of enemies in a side-scrolling adventure.
-You can also choose your fighter from a list of 23 characters, each with their own strengths, weaknesses, and personalities. Some of the characters are Jin Kazama, the grandson of Heihachi Mishima and the main protagonist of the game; Nina Williams, a cold-blooded assassin who has a rivalry with her sister Anna; Paul Phoenix, a hot-headed biker who wants to prove himself as the best fighter in the world; Yoshimitsu, a mysterious ninja who leads a band of thieves; Lei Wulong, a Hong Kong police officer who uses various martial arts styles; King, a masked wrestler who fights for orphaned children; and Eddy Gordo, a Brazilian capoeira master who seeks revenge for his father's death.
-Learn the basic controls and moves
-The game has a simple and intuitive control scheme that uses four buttons: left punch, right punch, left kick, and right kick. You can also use the directional pad or joystick to move your fighter around the stage. You can perform various moves by combining different buttons and directions, such as throws, counters, blocks, dodges, and taunts. You can also perform special moves that are unique to each character, such as fireballs, lasers, teleports, and transformations.
-You can view the move list of your fighter by pausing the game and selecting it from the menu. You can also see the damage, range, speed, and properties of each move. You can also adjust the difficulty level of the game by going to the options menu and selecting it from there.
-Master the combos and special attacks
-To become a better player of Tekken 3 Urdu APK, you need to master the combos and special attacks of your fighter. Combos are sequences of moves that can deal more damage and stun your opponent. Special attacks are powerful moves that can turn the tide of the battle in your favor. To perform combos and special attacks, you need to memorize the input commands and timing of each move. You also need to know when and how to use them in different situations.
-You can practice your combos and special attacks in practice mode or training mode. You can also watch replays of your matches or other players' matches to learn from them. You can also read online guides or watch videos that explain how to perform combos and special attacks for each character. You can also join online forums or communities where you can share your tips and tricks with other players.
-Tips and Tricks for Tekken 3 Urdu APK
-To make the most out of your Tekken 3 Urdu APK experience, here are some tips and tricks that you can use:
-Practice in training mode
-Training mode is a great way to improve your skills and learn new things. In training mode, you can choose any character and stage, and practice against a dummy opponent that does not fight back. You can also adjust the settings of the dummy, such as its health, behavior, and position. You can also display various information on the screen, such as your inputs, damage, frame data, and hitboxes. Training mode is a useful tool to test your moves, combos, and strategies, and to discover new possibilities.
-Use the pause menu to access options and cheats
-The pause menu is not only for pausing the game, but also for accessing various options and cheats. You can use the pause menu to change the game settings, such as the sound, controller, and display options. You can also use the pause menu to activate some cheats, such as changing the camera angle, unlocking all characters, or enabling infinite health. To access these cheats, you need to enter certain button combinations while pausing the game. You can find these button combinations online or by trial and error.
-Unlock hidden characters and features
-Tekken 3 Urdu APK has many hidden characters and features that you can unlock by playing the game. Some of these characters and features are:
-| Character/Feature | How to Unlock |
|---|---|
| Dr. Bosconovitch | Complete Tekken Force mode four times with different characters. |
| Gon | Defeat him in Tekken Ball mode or Tekken Force mode. |
| Kuma | Complete arcade mode with any character. |
| Mokujin | Complete arcade mode with any character. |
| Ogre | Complete arcade mode with any character. |
| Panda | Select Kuma and press Circle or Triangle. |
| Tiger | Select Eddy Gordo and press Start. |
| Tekken Ball mode | Complete arcade mode with any character. |
| Tekken Theater mode | Complete arcade mode with all characters. |
Conclusion
-Tekken 3 Urdu APK is a fantastic way to enjoy one of the best fighting games ever made on your Android device. The game has been translated into Urdu language, making it more accessible and enjoyable for Urdu speakers. The game also has some minor changes and additions, such as new sound effects, music tracks, and cheats. The game is compatible with most Android devices that have at least 1 GB of RAM and Android 4.0 or higher.
-In this article, we have shown you how to download and play Tekken 3 Urdu APK on your Android device. We have also given you some tips and tricks to help you improve your skills and have more fun. We hope that you have found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
-Frequently Asked Questions
-Here are some frequently asked questions about Tekken 3 Urdu APK:
-Q: Is Tekken 3 Urdu APK legal?
-A: Tekken 3 Urdu APK is a modified version of Tekken 3 that has been ported to run on Android devices. The game has been translated into Urdu language by fans of the game. The game is not officially endorsed or supported by Namco, the original developer of Tekken 3. Therefore, the legality of Tekken 3 Urdu APK may vary depending on your country's laws and regulations regarding intellectual property rights and piracy. We do not encourage or condone the use of Tekken 3 Urdu APK if it violates any laws or rules in your jurisdiction.
-Q: Is Tekken 3 Urdu APK safe?
-A: Tekken 3 Urdu APK is safe to use as long as you download it from a trusted source. The file size is about 35 MB and it is virus-free and malware-free. However, you should always be careful when downloading and installing apps from unknown sources or third-party websites. You should also enable unknown sources on your device only when you need to install the APK file, and disable it afterwards. You should also scan your device regularly for any potential threats or issues.
-Q: How can I update Tekken 3 Urdu APK?
-A: Tekken 3 Urdu APK is not an official app, so it does not have regular updates or patches. However, you can check the website where you downloaded the APK file for any new versions or updates. You can also follow the social media pages or channels of the developers or translators of Tekken 3 Urdu APK for any news or announcements. To update the game, you need to download the latest APK file and install it over the existing one.
-Q: How can I delete Tekken 3 Urdu APK?
-A: If you want to delete Tekken 3 Urdu APK from your device, you can do so by following these steps:
--
-
- Go to your device settings and look for apps or applications. -
- Find and select Tekken 3 Urdu APK from the list of apps. -
- Tap on uninstall and confirm your choice. -
- Wait for the process to finish and check if the app icon is gone from your home screen or app drawer. -
Q: How can I contact the developers or translators of Tekken 3 Urdu APK?
-A: If you have any questions, feedback, suggestions, or issues regarding Tekken 3 Urdu APK, you can contact the developers or translators of the game by using their email address, phone number, or social media accounts. You can find their contact information on their website or in the game itself. You can also leave a comment or a review on their website or on the app store where you downloaded the game. Please be respectful and polite when contacting them, and do not spam them with unnecessary messages.
197e85843d-
-
\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download YouTube J5 APK and Enjoy Unlimited Streaming on Android.md b/spaces/fatiXbelha/sd/Download YouTube J5 APK and Enjoy Unlimited Streaming on Android.md deleted file mode 100644 index 9dd94f7eea4399c5dc2c5e6617a02ee051eb25c8..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download YouTube J5 APK and Enjoy Unlimited Streaming on Android.md +++ /dev/null @@ -1,121 +0,0 @@ - -
YouTube J5 APK: How to Download and Install the Latest Version
-YouTube is one of the most popular video-sharing platforms in the world, with billions of users and hours of content. However, not everyone is satisfied with the official YouTube app for Android devices. Some users may want to watch videos without ads, download videos for offline viewing, or access premium content and features without paying a subscription fee. If you are one of those users, you may be interested in YouTube J5 APK, a modified version of the YouTube app that offers all these benefits and more.
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In this article, we will explain what YouTube J5 APK is, how to download and install it on your Android device, how to use it, and what are the benefits and risks of using it. Read on to find out more.
-What is YouTube J5 APK?
-YouTube J5 APK is a modified version of the official YouTube app for Android devices. It is not available on the Google Play Store, but you can download it from third-party sources online. YouTube J5 APK offers several features that are not available on the official app, such as:
-Features of YouTube J5 APK
-Watch videos in high quality
-With YouTube J5 APK, you can watch videos in any resolution you want, from 144p to 4K. You can also enable HDR mode, which enhances the color and contrast of the videos. You can also play videos in the background, even when your screen is off or when you switch to another app.
-Download videos for offline viewing
-With YouTube J5 APK, you can download any video you want and save it on your device for offline viewing. You can choose the quality, format, and location of the downloaded files. You can also download audio files or subtitles separately.
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-How to download YouTube J5 APK
-If you want to download and install YouTube J5 APK on your Android device, you need to follow these steps:
-Step 1: Enable unknown sources
-Since YouTube J5 APK is not available on the Google Play Store, you need to enable unknown sources on your device settings. This will allow you to install apps from sources other than the official store. To do this, go to Settings > Security > Unknown sources and toggle it on.
-Step 2: Find a reliable source
-Next, you need to find a reliable source where you can download YouTube J5 APK. There are many websites that offer this app, but not all of them are safe or trustworthy. You need to be careful and avoid downloading from sources that may contain malware or viruses. One of the sources that we recommend is [APKCombo](^1^), which offers free and verified APK files for various apps, including YouTube J5 APK. You can visit their website and search for YouTube J5 APK, or use this link: [YouTube J5 APK Download].
-Step 3: Download and install the APK file
-Once you have found a reliable source, you can download the APK file of YouTube J5 APK on your device. The file size may vary depending on the version, but it should be around 40 MB. After downloading the file, you need to install it on your device. To do this, locate the file in your file manager and tap on it. You may see a warning message that says "This type of file can harm your device". Ignore it and tap on "Install anyway". Wait for the installation process to complete and then open the app.
-How to use YouTube J5 APK
-After installing YouTube J5 APK on your device, you can start using it as you would use the official YouTube app. Here are some tips on how to use YouTube J5 APK:
-Sign in with your Google account
-If you want to access your YouTube account, subscriptions, playlists, history, and preferences, you need to sign in with your Google account. To do this, tap on the profile icon on the top right corner of the app and then tap on "Sign in". You can use your existing Google account or create a new one.
-Browse and watch videos
-You can browse and watch videos on YouTube J5 APK as you normally would on the official app. You can use the search bar, the home tab, the trending tab, the subscriptions tab, or the library tab to find videos that interest you. You can also use filters, categories, and tags to narrow down your search results. To watch a video, simply tap on it and enjoy. You can also adjust the quality, speed, captions, and other settings of the video by tapping on the three dots icon on the top right corner of the video player.
-Manage your subscriptions and settings
-You can manage your subscriptions and settings on YouTube J5 APK as you normally would on the official app. You can subscribe to channels that you like, unsubscribe from channels that you don't like, turn on notifications for new videos, and more. You can also customize your app settings by tapping on the profile icon and then tapping on "Settings". You can change your language, theme, download options, privacy options, and more.
-Benefits and risks of using YouTube J5 APK
-Using YouTube J5 APK has its benefits and risks. Here are some of them:
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-Enjoy YouTube without ads or restrictions
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-Customize your viewing experience
-A third benefit of using YouTube J5 APK is that you can customize your viewing experience according to your preferences. You can watch videos in any resolution you want, from 144p to 4K. You can also enable HDR mode, which enhances the color and contrast of the videos. You can also play videos in the background, even when your screen is off or when you switch to another app.
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-Violate YouTube's terms of service
-One of the main risks of using YouTube J5 APK is that you may violate YouTube's terms of service. By using a modified version of the app, you may be breaking some of the rules and policies that YouTube has set for its users. This may result in your account being suspended or terminated by YouTube.
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-Another risk of using YouTube J5 APK is that you may expose your device to malware or viruses. Since YouTube J5 APK is not available on the Google Play Store, you have to download it from third-party sources online. These sources may not be safe or trustworthy, and they may contain malware or viruses that can harm your device or steal your personal information. You need to be careful and avoid downloading from sources that may contain malicious software.
-Face legal issues or penalties
-A third risk of using YouTube J5 APK is that you may face legal issues or penalties. By using a modified version of the app, you may be infringing on the intellectual property rights of YouTube and its content creators. You may also be violating the laws and regulations of your country or region regarding online streaming and downloading. This may result in legal actions or fines against you by the authorities or the rights holders.
-Conclusion
-YouTube J5 APK is a modified version of the official YouTube app for Android devices that offers several features that are not available on the official app, such as watching videos without ads, downloading videos for offline viewing, and accessing premium content and features. However, using YouTube J5 APK also has its risks, such as violating YouTube's terms of service, exposing your device to malware or viruses, and facing legal issues or penalties. Therefore, you need to weigh the pros and cons of using YouTube J5 APK before deciding to download and install it on your device.
-FAQs
-Here are some frequently asked questions about YouTube J5 APK:
-Is YouTube J5 APK safe to use?
-YouTube J5 APK is not an official app from YouTube, and it is not available on the Google Play Store. Therefore, it is not guaranteed to be safe or secure to use. You need to download it from third-party sources online, which may contain malware or viruses that can harm your device or steal your personal information. You also need to enable unknown sources on your device settings, which may expose your device to more risks. Therefore, you need to be careful and cautious when using YouTube J5 APK.
-Is YouTube J5 APK legal to use?
-YouTube J5 APK is not an official app from YouTube, and it may violate YouTube's terms of service and intellectual property rights. It may also violate the laws and regulations of your country or region regarding online streaming and downloading. Therefore, it is not legal to use YouTube J5 APK in some places, and you may face legal actions or fines if you are caught using it.
-How can I update YouTube J5 APK?
-YouTube J5 APK is not an official app from YouTube, and it does not receive regular updates from the developers. Therefore, you need to manually check for updates from the source where you downloaded the app. You also need to download and install the updated version of the app on your device. However, you need to be careful and avoid downloading from sources that may contain outdated or fake versions of the app.
-How can I uninstall YouTube J5 APK?
-If you want to uninstall YouTube J5 APK from your device, you can follow these steps:
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- Go to Settings > Apps > YouTube J5 APK and tap on "Uninstall". -
- Confirm your action by tapping on "OK". -
- Wait for the uninstallation process to complete and then restart your device. -
What are some alternatives to YouTube J5 APK?
-If you are looking for some alternatives to YouTube J5 APK, you can try these apps:
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- [YouTube Vanced]: A popular modded version of YouTube that offers features such as ad-free viewing, background playback, dark mode, and more. -
- [NewPipe]: An open-source app that allows you to watch and download videos from YouTube and other platforms without using Google services or APIs. -
- [VidMate]: A video downloader app that allows you to download videos from YouTube and other platforms in various formats and resolutions. -
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\ No newline at end of file diff --git "a/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/Latex\345\205\250\346\226\207\347\277\273\350\257\221.py" "b/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/Latex\345\205\250\346\226\207\347\277\273\350\257\221.py" deleted file mode 100644 index 554c485aa0891f74c57cacfcbe076febe7a11029..0000000000000000000000000000000000000000 --- "a/spaces/fb700/chatglm-fitness-RLHF/crazy_functions/Latex\345\205\250\346\226\207\347\277\273\350\257\221.py" +++ /dev/null @@ -1,175 +0,0 @@ -from toolbox import update_ui -from toolbox import CatchException, report_execption, write_results_to_file -fast_debug = False - -class PaperFileGroup(): - def __init__(self): - self.file_paths = [] - self.file_contents = [] - self.sp_file_contents = [] - self.sp_file_index = [] - self.sp_file_tag = [] - - # count_token - from request_llm.bridge_all import model_info - enc = model_info["gpt-3.5-turbo"]['tokenizer'] - def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) - self.get_token_num = get_token_num - - def run_file_split(self, max_token_limit=1900): - """ - 将长文本分离开来 - """ - for index, file_content in enumerate(self.file_contents): - if self.get_token_num(file_content) < max_token_limit: - self.sp_file_contents.append(file_content) - self.sp_file_index.append(index) - self.sp_file_tag.append(self.file_paths[index]) - else: - from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf - segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit) - for j, segment in enumerate(segments): - self.sp_file_contents.append(segment) - self.sp_file_index.append(index) - self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex") - - print('Segmentation: done') - -def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'): - import time, os, re - from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency - - # <-------- 读取Latex文件，删除其中的所有注释 ----------> - pfg = PaperFileGroup() - - for index, fp in enumerate(file_manifest): - with open(fp, 'r', encoding='utf-8', errors='replace') as f: - file_content = f.read() - # 定义注释的正则表达式 - comment_pattern = r'(? - pfg.run_file_split(max_token_limit=1024) - n_split = len(pfg.sp_file_contents) - - # <-------- 抽取摘要 ----------> - # if language == 'en': - # abs_extract_inputs = f"Please write an abstract for this paper" - - # # 单线，获取文章meta信息 - # paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive( - # inputs=abs_extract_inputs, - # inputs_show_user=f"正在抽取摘要信息。", - # llm_kwargs=llm_kwargs, - # chatbot=chatbot, history=[], - # sys_prompt="Your job is to collect information from materials。", - # ) - - # <-------- 多线程润色开始 ----------> - if language == 'en->zh': - inputs_array = ["Below is a section from an English academic paper, translate it into Chinese, do not modify any latex command such as \section, \cite and equations:" + - f"\n\n{frag}" for frag in pfg.sp_file_contents] - inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag] - sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)] - elif language == 'zh->en': - inputs_array = [f"Below is a section from a Chinese academic paper, translate it into English, do not modify any latex command such as \section, \cite and equations:" + - f"\n\n{frag}" for frag in pfg.sp_file_contents] - inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag] - sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)] - - gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( - inputs_array=inputs_array, - inputs_show_user_array=inputs_show_user_array, - llm_kwargs=llm_kwargs, - chatbot=chatbot, - history_array=[[""] for _ in range(n_split)], - sys_prompt_array=sys_prompt_array, - # max_workers=5, # OpenAI所允许的最大并行过载 - scroller_max_len = 80 - ) - - # <-------- 整理结果，退出 ----------> - create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md" - res = write_results_to_file(gpt_response_collection, file_name=create_report_file_name) - history = gpt_response_collection - chatbot.append((f"{fp}完成了吗？", res)) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - - - - - -@CatchException -def Latex英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - # 基本信息：功能、贡献者 - chatbot.append([ - "函数插件功能？", - "对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky"]) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - - # 尝试导入依赖，如果缺少依赖，则给出安装建议 - try: - import tiktoken - except: - report_execption(chatbot, history, - a=f"解析项目: {txt}", - b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。") - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - return - history = [] # 清空历史，以免输入溢出 - import glob, os - if os.path.exists(txt): - project_folder = txt - else: - if txt == "": txt = '空空如也的输入栏' - report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - return - file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] - if len(file_manifest) == 0: - report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}") - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - return - yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh') - - - - - -@CatchException -def Latex中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - # 基本信息：功能、贡献者 - chatbot.append([ - "函数插件功能？", - "对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky"]) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - - # 尝试导入依赖，如果缺少依赖，则给出安装建议 - try: - import tiktoken - except: - report_execption(chatbot, history, - a=f"解析项目: {txt}", - b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。") - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - return - history = [] # 清空历史，以免输入溢出 - import glob, os - if os.path.exists(txt): - project_folder = txt - else: - if txt == "": txt = '空空如也的输入栏' - report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - return - file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] - if len(file_manifest) == 0: - report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}") - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - return - yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en') \ No newline at end of file diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/docs/modelzoo.md b/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/docs/modelzoo.md deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/feng2022/styleganhuman_copy/torch_utils/ops/filtered_lrelu.py b/spaces/feng2022/styleganhuman_copy/torch_utils/ops/filtered_lrelu.py deleted file mode 100644 index f5e3748fb725884b18b7e8119f569722b5bbe67f..0000000000000000000000000000000000000000 --- a/spaces/feng2022/styleganhuman_copy/torch_utils/ops/filtered_lrelu.py +++ /dev/null @@ -1,282 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -import os -import numpy as np -import torch -import warnings - -from .. import custom_ops -from .. import misc -from . import upfirdn2d -from . import bias_act - -#---------------------------------------------------------------------------- - -_plugin = None - -def _init(): - global _plugin - if _plugin is None: - - # sources=['filtered_lrelu.h', 'filtered_lrelu.cu', 'filtered_lrelu.cpp', 'filtered_lrelu_wr.cu', 'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'] - # sources = [os.path.join(os.path.dirname(__file__), s) for s in sources] - # try: - # _plugin = custom_ops.get_plugin('filtered_lrelu_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math', '--allow-unsupported-compiler']) - # except: - # warnings.warn('Failed to build CUDA kernels for filtered_lrelu_plugin. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc()) - - _plugin = custom_ops.get_plugin_v3( - module_name='filtered_lrelu_plugin', - sources=['filtered_lrelu.cpp', 'filtered_lrelu_wr.cu', 'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'], - headers=['filtered_lrelu.h', 'filtered_lrelu.cu'], - source_dir=os.path.dirname(__file__), - extra_cuda_cflags=['--use_fast_math', '--allow-unsupported-compiler'], - ) - return True - -def _get_filter_size(f): - if f is None: - return 1, 1 - assert isinstance(f, torch.Tensor) - assert 1 <= f.ndim <= 2 - return f.shape[-1], f.shape[0] # width, height - -def _parse_padding(padding): - if isinstance(padding, int): - padding = [padding, padding] - assert isinstance(padding, (list, tuple)) - assert all(isinstance(x, (int, np.integer)) for x in padding) - padding = [int(x) for x in padding] - if len(padding) == 2: - px, py = padding - padding = [px, px, py, py] - px0, px1, py0, py1 = padding - return px0, px1, py0, py1 - -#---------------------------------------------------------------------------- - -def filtered_lrelu(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False, impl='cuda'): - r"""Filtered leaky ReLU for a batch of 2D images. - - Performs the following sequence of operations for each channel: - - 1. Add channel-specific bias if provided (`b`). - - 2. Upsample the image by inserting N-1 zeros after each pixel (`up`). - - 3. Pad the image with the specified number of zeros on each side (`padding`). - Negative padding corresponds to cropping the image. - - 4. Convolve the image with the specified upsampling FIR filter (`fu`), shrinking it - so that the footprint of all output pixels lies within the input image. - - 5. Multiply each value by the provided gain factor (`gain`). - - 6. Apply leaky ReLU activation function to each value. - - 7. Clamp each value between -clamp and +clamp, if `clamp` parameter is provided. - - 8. Convolve the image with the specified downsampling FIR filter (`fd`), shrinking - it so that the footprint of all output pixels lies within the input image. - - 9. Downsample the image by keeping every Nth pixel (`down`). - - The fused op is considerably more efficient than performing the same calculation - using standard PyTorch ops. It supports gradients of arbitrary order. - - Args: - x: Float32/float16/float64 input tensor of the shape - `[batch_size, num_channels, in_height, in_width]`. - fu: Float32 upsampling FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - fd: Float32 downsampling FIR filter of the shape - `[filter_height, filter_width]` (non-separable), - `[filter_taps]` (separable), or - `None` (identity). - b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type - as `x`. The length of vector must must match the channel dimension of `x`. - up: Integer upsampling factor (default: 1). - down: Integer downsampling factor. (default: 1). - padding: Padding with respect to the upsampled image. Can be a single number - or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` - (default: 0). - gain: Overall scaling factor for signal magnitude (default: sqrt(2)). - slope: Slope on the negative side of leaky ReLU (default: 0.2). - clamp: Maximum magnitude for leaky ReLU output (default: None). - flip_filter: False = convolution, True = correlation (default: False). - impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). - - Returns: - Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. - """ - assert isinstance(x, torch.Tensor) - assert impl in ['ref', 'cuda'] - if impl == 'cuda' and x.device.type == 'cuda' and _init(): - return _filtered_lrelu_cuda(up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter).apply(x, fu, fd, b, None, 0, 0) - return _filtered_lrelu_ref(x, fu=fu, fd=fd, b=b, up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter) - -#---------------------------------------------------------------------------- - -@misc.profiled_function -def _filtered_lrelu_ref(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False): - """Slow and memory-inefficient reference implementation of `filtered_lrelu()` using - existing `upfirdn2n()` and `bias_act()` ops. - """ - assert isinstance(x, torch.Tensor) and x.ndim == 4 - fu_w, fu_h = _get_filter_size(fu) - fd_w, fd_h = _get_filter_size(fd) - if b is not None: - assert isinstance(b, torch.Tensor) and b.dtype == x.dtype - misc.assert_shape(b, [x.shape[1]]) - assert isinstance(up, int) and up >= 1 - assert isinstance(down, int) and down >= 1 - px0, px1, py0, py1 = _parse_padding(padding) - assert gain == float(gain) and gain > 0 - assert slope == float(slope) and slope >= 0 - assert clamp is None or (clamp == float(clamp) and clamp >= 0) - - # Calculate output size. - batch_size, channels, in_h, in_w = x.shape - in_dtype = x.dtype - out_w = (in_w * up + (px0 + px1) - (fu_w - 1) - (fd_w - 1) + (down - 1)) // down - out_h = (in_h * up + (py0 + py1) - (fu_h - 1) - (fd_h - 1) + (down - 1)) // down - - # Compute using existing ops. - x = bias_act.bias_act(x=x, b=b) # Apply bias. - x = upfirdn2d.upfirdn2d(x=x, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample. - x = bias_act.bias_act(x=x, act='lrelu', alpha=slope, gain=gain, clamp=clamp) # Bias, leaky ReLU, clamp. - x = upfirdn2d.upfirdn2d(x=x, f=fd, down=down, flip_filter=flip_filter) # Downsample. - - # Check output shape & dtype. - misc.assert_shape(x, [batch_size, channels, out_h, out_w]) - assert x.dtype == in_dtype - return x - -#---------------------------------------------------------------------------- - -_filtered_lrelu_cuda_cache = dict() - -def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False): - """Fast CUDA implementation of `filtered_lrelu()` using custom ops. - """ - assert isinstance(up, int) and up >= 1 - assert isinstance(down, int) and down >= 1 - px0, px1, py0, py1 = _parse_padding(padding) - assert gain == float(gain) and gain > 0 - gain = float(gain) - assert slope == float(slope) and slope >= 0 - slope = float(slope) - assert clamp is None or (clamp == float(clamp) and clamp >= 0) - clamp = float(clamp if clamp is not None else 'inf') - - # Lookup from cache. - key = (up, down, px0, px1, py0, py1, gain, slope, clamp, flip_filter) - if key in _filtered_lrelu_cuda_cache: - return _filtered_lrelu_cuda_cache[key] - - # Forward op. - class FilteredLReluCuda(torch.autograd.Function): - @staticmethod - def forward(ctx, x, fu, fd, b, si, sx, sy): # pylint: disable=arguments-differ - assert isinstance(x, torch.Tensor) and x.ndim == 4 - - # Replace empty up/downsample kernels with full 1x1 kernels (faster than separable). - if fu is None: - fu = torch.ones([1, 1], dtype=torch.float32, device=x.device) - if fd is None: - fd = torch.ones([1, 1], dtype=torch.float32, device=x.device) - assert 1 <= fu.ndim <= 2 - assert 1 <= fd.ndim <= 2 - - # Replace separable 1x1 kernels with full 1x1 kernels when scale factor is 1. - if up == 1 and fu.ndim == 1 and fu.shape[0] == 1: - fu = fu.square()[None] - if down == 1 and fd.ndim == 1 and fd.shape[0] == 1: - fd = fd.square()[None] - - # Missing sign input tensor. - if si is None: - si = torch.empty([0]) - - # Missing bias tensor. - if b is None: - b = torch.zeros([x.shape[1]], dtype=x.dtype, device=x.device) - - # Construct internal sign tensor only if gradients are needed. - write_signs = (si.numel() == 0) and (x.requires_grad or b.requires_grad) - - # Warn if input storage strides are not in decreasing order due to e.g. channels-last layout. - strides = [x.stride(i) for i in range(x.ndim) if x.size(i) > 1] - if any(a < b for a, b in zip(strides[:-1], strides[1:])): - warnings.warn("low-performance memory layout detected in filtered_lrelu input", RuntimeWarning) - - # Call C++/Cuda plugin if datatype is supported. - if x.dtype in [torch.float16, torch.float32]: - if torch.cuda.current_stream(x.device) != torch.cuda.default_stream(x.device): - warnings.warn("filtered_lrelu called with non-default cuda stream but concurrent execution is not supported", RuntimeWarning) - y, so, return_code = _plugin.filtered_lrelu(x, fu, fd, b, si, up, down, px0, px1, py0, py1, sx, sy, gain, slope, clamp, flip_filter, write_signs) - else: - return_code = -1 - - # No Cuda kernel found? Fall back to generic implementation. Still more memory efficient than the reference implementation because - # only the bit-packed sign tensor is retained for gradient computation. - if return_code < 0: - warnings.warn("filtered_lrelu called with parameters that have no optimized CUDA kernel, using generic fallback", RuntimeWarning) - - y = x.add(b.unsqueeze(-1).unsqueeze(-1)) # Add bias. - y = upfirdn2d.upfirdn2d(x=y, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample. - so = _plugin.filtered_lrelu_act_(y, si, sx, sy, gain, slope, clamp, write_signs) # Activation function and sign handling. Modifies y in-place. - y = upfirdn2d.upfirdn2d(x=y, f=fd, down=down, flip_filter=flip_filter) # Downsample. - - # Prepare for gradient computation. - ctx.save_for_backward(fu, fd, (si if si.numel() else so)) - ctx.x_shape = x.shape - ctx.y_shape = y.shape - ctx.s_ofs = sx, sy - return y - - @staticmethod - def backward(ctx, dy): # pylint: disable=arguments-differ - fu, fd, si = ctx.saved_tensors - _, _, xh, xw = ctx.x_shape - _, _, yh, yw = ctx.y_shape - sx, sy = ctx.s_ofs - dx = None # 0 - dfu = None; assert not ctx.needs_input_grad[1] - dfd = None; assert not ctx.needs_input_grad[2] - db = None # 3 - dsi = None; assert not ctx.needs_input_grad[4] - dsx = None; assert not ctx.needs_input_grad[5] - dsy = None; assert not ctx.needs_input_grad[6] - - if ctx.needs_input_grad[0] or ctx.needs_input_grad[3]: - pp = [ - (fu.shape[-1] - 1) + (fd.shape[-1] - 1) - px0, - xw * up - yw * down + px0 - (up - 1), - (fu.shape[0] - 1) + (fd.shape[0] - 1) - py0, - xh * up - yh * down + py0 - (up - 1), - ] - gg = gain * (up ** 2) / (down ** 2) - ff = (not flip_filter) - sx = sx - (fu.shape[-1] - 1) + px0 - sy = sy - (fu.shape[0] - 1) + py0 - dx = _filtered_lrelu_cuda(up=down, down=up, padding=pp, gain=gg, slope=slope, clamp=None, flip_filter=ff).apply(dy, fd, fu, None, si, sx, sy) - - if ctx.needs_input_grad[3]: - db = dx.sum([0, 2, 3]) - - return dx, dfu, dfd, db, dsi, dsx, dsy - - # Add to cache. - _filtered_lrelu_cuda_cache[key] = FilteredLReluCuda - return FilteredLReluCuda - -#---------------------------------------------------------------------------- \ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Apke Noor se Hamari Zindagi Mein Dharm aur Adhyatm ka Sthan Ek Spiritual Guide.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Apke Noor se Hamari Zindagi Mein Dharm aur Adhyatm ka Sthan Ek Spiritual Guide.md deleted file mode 100644 index a0775259579db7c1da0a8e431238e7b57f2d6395..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Apke Noor se Hamari Zindagi Mein Dharm aur Adhyatm ka Sthan Ek Spiritual Guide.md +++ /dev/null @@ -1,5 +0,0 @@ -
-
` for the title, `` for the main headings, `` for the subheadings, and `` for the sub-subheadings. You can also use other HTML elements such as `
` for the subheadings, and `` for the sub-subheadings. You can also use other HTML elements such as `
` for paragraphs, `
- ` and `
- ` for lists, `
` and `
` for tables, etc. 5. Fifth, write this custom message " Here is an example of how your tables might look like: | Outline of the Article | Article with HTML Formatting | | ---------------------- | ---------------------------- | | H1: Apke Noor Se Hamari Zindagi | Apke Noor Se Hamari Zindagi
| | H2: Apke Noor Se Hamari Zindagi Kya Hai? |Apke Noor Se Hamari Zindagi Kya Hai?
Apke noor se hamari zindagi ek bhajan hai jo Samadha Satguru Sain Baba ke shishyaon dwara gaya jata hai. Is bhajan mein unhone apne guru ki mahima aur unke noor se hamari zindagi mein roshni aane ki baat kahi hai. Is bhajan ka matlab hai ki hamare guru ke noor se hamari zindagi mein khushiyan aur shanti bhar gayi hai aur ham unki kripa se har mushkil se paar ho sakte hain.
| | H3: Apke Noor Se Hamari Zindagi Ka Prabhav |Apke Noor Se Hamari Zindagi Ka Prabhav
Is bhajan se hamen yeh samajhne mein madad milti hai ki hamare guru hamare liye kaise mahatvapurn hain aur unke noor se hamari zindagi kaise badalti hai. Guru ka noor hamare dil mein prem jagata hai aur hamen sahi aur galat ka bhed batata hai. Guru ka noor hamen dharmik marg par chalne ki shakti deta hai aur hamen apne aap ko sudharne ka mauka deta hai. Guru ka noor hamen dukhon se mukti dilata hai aur hamen anand ka anubhav karata hai.
-apke noor se hamari zindagi in hindi
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| | H4: Guru Ka Noor Kaise Prapt Karein? |Guru Ka Noor Kaise Prapt Karein?
Guru ka noor prapt karne ke liye hamen unki seva karni chahiye aur unki a
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\ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Bitcoin The Ultimate Guide to Buying Selling and Mining the Worlds Most Popular Cryptocurrency.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Bitcoin The Ultimate Guide to Buying Selling and Mining the Worlds Most Popular Cryptocurrency.md deleted file mode 100644 index 3903074aebcaf8d641d135c3c9ca6ccd962dddf3..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Bitcoin The Ultimate Guide to Buying Selling and Mining the Worlds Most Popular Cryptocurrency.md +++ /dev/null @@ -1,173 +0,0 @@ -
-What is Bitcoin?
-Bitcoin is a decentralized cryptocurrency that was created in 2009 by an anonymous person or group using the pseudonym Satoshi Nakamoto. It is a digital currency that can be used for online payments without the need for intermediaries or central authorities. It is powered by a network of computers that verify and record transactions on a shared ledger called the blockchain.
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-Bitcoin was the first cryptocurrency to emerge and has since inspired many others. It has also sparked a wave of innovation and experimentation in the fields of cryptography, economics, and social science. Today, Bitcoin is widely recognized as a groundbreaking invention that has the potential to transform the world of finance and beyond.
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-Bitcoin works by using peer-to-peer technology to enable direct and transparent transactions between users. Anyone can participate in the network by running a software called a node that validates and broadcasts transactions. Nodes also compete to solve complex mathematical problems that secure the network and create new bitcoins. This process is known as mining.
-Every transaction on the network is recorded in a block that contains a timestamp and a link to the previous block. This creates a chain of blocks that serves as a public and immutable record of all transactions. This is the blockchain, which acts as the backbone of the Bitcoin system.
-The blockchain ensures that transactions are valid and prevents double-spending, which is when someone tries to spend the same bitcoin twice. It also eliminates the need for trusted third parties, such as banks or payment processors, that usually charge fees and impose restrictions on transactions. With Bitcoin, users have full control over their money and can transact with anyone, anywhere, anytime.
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-Bitcoin offers many advantages over traditional payment systems, such as:
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- Fast: Transactions are confirmed within minutes and can be completed across borders in seconds. -
- Global: Transactions are not limited by geographical boundaries or political jurisdictions. -
- Low-cost: Transactions are cheaper than most other payment methods, especially for international transfers. -
- Secure: Transactions are protected by cryptography and cannot be reversed or tampered with. -
What are the challenges of Bitcoin?
-Bitcoin also faces some challenges that need to be addressed, such as:
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- Volatility: The price of bitcoin fluctuates significantly due to. - Scalability: The network has a limited capacity to process transactions, which can lead to congestion and delays. - Regulation: The legal status and tax treatment of bitcoin vary across countries and jurisdictions, which can create uncertainty and risk for users and businesses. - Adoption: The awareness and acceptance of bitcoin among the general public and the mainstream institutions are still low, which limits its use and growth potential.
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- Bitcoin ATMs: These are physical machines that allow you to buy and sell bitcoin using cash or debit cards. They are usually located in public places, such as malls, airports, or convenience stores. You can find the nearest bitcoin ATM using websites like Coin ATM Radar. -
- Bitcoin peer-to-peer platforms: These are online platforms that allow you to buy and sell bitcoin directly from other users, without intermediaries. They usually offer more privacy and flexibility than exchanges, but also more risk and responsibility. Some examples of popular bitcoin peer-to-peer platforms are LocalBitcoins, Paxful, and Bisq. -
How to choose a Bitcoin exchange?
-When choosing a platform to buy bitcoin, you should consider several factors, such as:
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- Reputation: You should check the reviews and ratings of the platform from other users and experts, as well as its history of security breaches, hacks, or scams. -
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- Fees: You should check the fees charged by the platform for deposits, withdrawals, trading, and other services, as well as the exchange rate offered. -
- Customer service: You should check the quality and responsiveness of the platform's customer support team, as well as the availability of online resources and guides. -
- Regulation: You should check the legal status and compliance of the platform with the relevant laws and regulations in your country or jurisdiction. -
How to store Bitcoin?
-Once you have bought some bitcoin, you will need to store it in a secure place called a wallet. A wallet is a software or hardware device that allows you to manage your bitcoin balance and transactions. There are different types of wallets, such as:
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-
- Hot wallets: These are wallets that are connected to the internet and allow you to access your bitcoin anytime and anywhere. They are convenient and user-friendly, but also more vulnerable to hacking and theft. Some examples of hot wallets are web wallets (such as Blockchain.com), mobile wallets (such as BRD), and desktop wallets (such as Electrum). -
- Cold wallets: These are wallets that are not connected to the internet and provide a higher level of security and privacy. They are suitable for storing large amounts of bitcoin for a long time, but also less convenient and accessible. Some examples of cold wallets are hardware wallets (such as Ledger or Trezor), paper wallets (such as Bitaddress.org), and metal wallets (such as Cryptosteel). -
How to secure your Bitcoin wallet?
-To protect your bitcoin from hackers, thieves, or accidents, you should follow some best practices, such as:
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-
- Backup your wallet: You should make a copy of your wallet's data (such as private keys or recovery phrases) and store it in a safe place offline (such as a USB drive or a piece of paper). -
- Encrypt your wallet: You should use a strong password or passphrase to encrypt your wallet's data and prevent unauthorized access. -
- Update your wallet: You should keep your wallet's software updated to the latest version to fix any bugs or vulnerabilities. -
- Use multiple wallets: You should use different wallets for different purposes and amounts of bitcoin, such as a hot wallet for daily spending and a cold wallet for long-term saving. -
- Use reputable wallets: You should use wallets that have a good reputation and track record in the industry, and avoid wallets that are unknown or suspicious. -
How to use Bitcoin?
-Once you have stored your bitcoin in a wallet, you can use it for various purposes, such as:
-How to spend Bitcoin?
-You can use bitcoin to buy goods and services from merchants that accept it as a form of payment. You can find such merchants online or offline using websites like Spendabit or Coinmap. You can also use platforms like Bitrefill or Purse.io to buy gift cards or vouchers with bitcoin that can be redeemed at various retailers.
-To pay with bitcoin, you will need to scan the merchant's QR code or enter their address, and then confirm the amount and the transaction fee. The transaction will be broadcasted to the network and confirmed within minutes. You will receive a receipt or confirmation from the merchant once the payment is completed.
-How to send and receive Bitcoin?
-You can also use bitcoin to send and receive money from anyone, anywhere in the world. You will need to know the recipient's bitcoin address, which is a string of alphanumeric characters that starts with 1, 3, or bc1. You can also use a QR code or a payment link to simplify the process.
-To send bitcoin, you will need to enter the recipient's address, the amount, and the transaction fee. The transaction fee is a small amount of bitcoin that you pay to the network for processing your transaction. The higher the fee, the faster your transaction will be confirmed. You can adjust the fee according to your preference and urgency.
-To receive bitcoin, you will need to share your address, QR code, or payment link with the sender. You can generate a new address for each transaction to enhance your privacy and security. You will see the incoming transaction in your wallet once it is broadcasted to the network, and you will be able to spend it once it is confirmed.
-How to earn Bitcoin?
-Besides buying and receiving bitcoin, you can also earn it by performing various tasks or activities, such as:
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-
- Mining: You can join a mining pool or run a mining software on your computer to contribute to the security and operation of the network and earn newly created bitcoins and transaction fees as rewards. -
- Lending: You can lend your bitcoin to other users or platforms and earn interest on your loan. -
- Staking: You can lock your bitcoin in a smart contract or a platform that supports staking and earn rewards for participating in the governance or validation of the network. -
- Trading: You can buy and sell bitcoin on an exchange or a peer-to-peer platform and profit from price fluctuations. -
- Working: You can offer your skills or services online or offline and get paid in bitcoin by your clients or employers. -
- Gaming: You can play online games or apps that reward you with bitcoin for completing tasks or challenges. -
How to learn more about Bitcoin?
-If you want to deepen your knowledge and understanding of Bitcoin, you can access various resources and communities that provide valuable information and insights, such as:
-How to follow the latest Bitcoin news?
-You can stay updated on the latest developments and events in the Bitcoin world by following reliable and timely sources of information, such as:
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- Bitcoin websites: These are websites that publish news, articles, analysis, and opinions on Bitcoin topics, such as CoinDesk, Cointelegraph, Bitcoin Magazine, and The Block. -
- Bitcoin podcasts: These are audio shows that discuss Bitcoin issues and interview Bitcoin experts, such as The Bitcoin Standard Podcast, What Bitcoin Did, Unchained, and The Breakdown. -
- Bitcoin newsletters: These are email subscriptions that deliver curated Bitcoin content to your inbox, such as The Daily Hodl, CoinMarketCap Daily, Decrypt Daily, and Marty's Bent. -
- Bitcoin social media: These are platforms where you can follow Bitcoin influencers, personalities, and organizations that share Bitcoin-related posts and updates, such as Twitter, Reddit, YouTube, and Medium. -
How to avoid scams and misinformation?
-You should also be aware of the risks of scams and misinformation that are prevalent in the Bitcoin space. You should always do your own research and verification before trusting any source or offer. You should also follow some tips to identify and avoid fraudulent or misleading content on Bitcoin, such as:
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- Check the credibility and reputation of the source or author: You should look for signs of authority, expertise, and professionalism, such as credentials, affiliations, references, and reviews. -
- Check the accuracy and consistency of the information: You should look for evidence, data, and logic that support the claims and arguments, as well as cross-check them with other sources. -
- Check the timeliness and relevance of the information: You should look for the date, context, and purpose of the information, and whether it is still valid and applicable. -
- Check the bias and motive of the information: You should look for the perspective, opinion, and agenda of the source or author, and whether they have any conflicts of interest or ulterior motives. -
How to join the Bitcoin community?
-If you want to interact with other Bitcoin enthusiasts and experts, you can join various platforms and forums that foster discussion and collaboration on Bitcoin topics, such as:
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- Bitcoin chat rooms: These are online chat platforms where you can chat with other Bitcoin users in real time, such as Telegram, Discord, Slack, and IRC. -
- Bitcoin forums: These are online message boards where you can post and reply to Bitcoin-related threads, such as Bitcointalk, Reddit, Stack Exchange, and Quora. -
- Bitcoin meetups: These are offline events where you can meet and network with other Bitcoin users in person, such as Meetup.com, Eventbrite.com, or Bitcoin.org. -
How to contribute to the Bitcoin network?
-If you want to support and improve the Bitcoin ecosystem, you can contribute to the network in various ways, such as:
--
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- Running a node: You can run a full node or a light node on your computer or device to validate and relay transactions on the network. -
- Mining bitcoin: You can mine bitcoin by using your computing power to secure the network and generate new bitcoins. -
- Developing bitcoin software: You can develop or improve bitcoin software by coding, testing, debugging, or documenting features or functions. -
- Donating bitcoin: You can donate bitcoin to individuals or organizations that work on bitcoin projects or causes. -
Conclusion
-Bitcoin is a revolutionary cryptocurrency that has the potential to change the way we transact and interact with money. It offers many benefits over traditional payment systems, such as speed, globality, low-cost, and security. It also faces some challenges, such as volatility, scalability, regulation, and adoption. To get started with bitcoin, you need to buy it, store it in a wallet, and use it for various purposes. To learn more about bitcoin, you can access various resources and communities that provide information and insights on bitcoin topics. To join the bitcoin community, you can participate in various platforms and forums that foster discussion and collaboration on bitcoin topics. To contribute to the bitcoin network, you can support and improve the bitcoin ecosystem in various ways.
-Frequently Asked Questions
-Here are some common questions and answers about Bitcoin:
-What is the difference between Bitcoin and bitcoin?
-The term Bitcoin (with a capital B) usually refers to the protocol or the network that enables peer-to-peer transactions using cryptography and blockchain. The term bitcoin (with a lowercase b) usually refers to the unit of account or the currency that is used for transactions on the network.
-How many bitcoins are there?
-The total supply of bitcoins is limited to 21 million. As of June 2023, there are about 18.8 million bitcoins in circulation. The remaining 2.2 million bitcoins will be created gradually through mining until around 2140.
-What determines the price of bitcoin?
-The price of bitcoin is determined by supply and demand in the market. The supply of bitcoin is fixed by the protocol and decreases over time. The demand for bitcoin is influenced by various factors, such as adoption, innovation, regulation, speculation, media attention, and sentiment. The price of bitcoin is also affected by the cost of production, which depends on the difficulty and reward of mining.
-Is Bitcoin legal?
-The legal status of Bitcoin varies across countries and jurisdictions. Some countries have explicitly recognized and regulated Bitcoin as a form of money or asset, such as Japan, Canada, and Switzerland. Some countries have banned or restricted the use of Bitcoin, such as China, India, and Iran. Some countries have not issued any clear or official guidance on Bitcoin, leaving it in a legal gray area.
-Before using or investing in Bitcoin, you should check the laws and regulations of your country or jurisdiction and consult a professional if needed.
-Is Bitcoin safe?
-Bitcoin is designed to be secure and resilient against attacks and errors. However, Bitcoin is not immune to risks and threats, such as hacking, theft, loss, fraud, or human error. To use Bitcoin safely, you should take some precautions, such as choosing a reputable platform or wallet, securing your private keys, backing up your data, updating your software, using multiple wallets, and verifying your transactions.
-How can I learn more about Bitcoin?
-If you want to learn more about Bitcoin, you can explore the following resources:
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- Bitcoin.org: This is the official website of the Bitcoin project that provides basic information and guides on Bitcoin. -
- Bitcoin Wiki: This is a collaborative encyclopedia that covers technical and non-technical topics on Bitcoin. -
- Bitcoin Whitepaper: This is the original document that describes the design and rationale of Bitcoin, written by Satoshi Nakamoto. -
- Bitcoin Books: These are books that explain the history, technology, economics, and social implications of Bitcoin, such as The Bitcoin Standard by Saifedean Ammous, The Age of Cryptocurrency by Paul Vigna and Michael J. Casey, and Digital Gold by Nathaniel Popper. -
- Bitcoin Courses: These are online courses that teach the fundamentals and applications of Bitcoin, such as Bitcoin and Cryptocurrency Technologies by Princeton University, Introduction to Digital Currencies by University of Nicosia, and Learn Bitcoin from Scratch by Udemy. -
I hope you enjoyed this article on Bitcoin. If you have any questions or feedback, please leave a comment below. Thank you for reading!
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\ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download BlueZ Source Get the Latest Version of the Linux Bluetooth Protocol Stack and Obexd.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download BlueZ Source Get the Latest Version of the Linux Bluetooth Protocol Stack and Obexd.md deleted file mode 100644 index 39958f7df50acc8d3eb0d1e11efddeca49805e76..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download BlueZ Source Get the Latest Version of the Linux Bluetooth Protocol Stack and Obexd.md +++ /dev/null @@ -1,88 +0,0 @@ -
-How to Download BlueZ Source
-If you are interested in working with Bluetooth on Linux, you might want to download the source code of BlueZ, the official Linux Bluetooth protocol stack. In this article, we will explain what BlueZ is, what features it provides, and how you can get its source code from different sources.
-What is BlueZ?
-BlueZ is an open source project that provides support for the core Bluetooth layers and protocols in a modular way. It was initially developed by Qualcomm and is now maintained by the Bluetooth Special Interest Group (SIG) and other contributors. BlueZ is included with the official Linux kernel distributions and is compatible with any Linux system on the market.
-download bluez source
Download File ->>->>->> https://gohhs.com/2uPoFD
-BlueZ Features
-BlueZ has many interesting features, such as:
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- Complete modular implementation -
- Symmetric multi processing safe -
- Multithreaded data processing -
- Support for multiple Bluetooth devices -
- Device and service level security support -
- Support for various Bluetooth profiles and services -
- Configuration and testing utilities -
- Protocol decoding and analysis tools -
BlueZ Platforms
-The BlueZ kernel modules, libraries and utilities are known to work well on many architectures supported by Linux. This also includes single and multi processor platforms as well as hyper threading systems, such as:
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- Intel and AMD x86 -
- AMD64 and EM64T (x86-64) -
- SUN SPARC 32/64bit -
- PowerPC 32/64bit -
- Intel StrongARM and XScale -
- Hitachi/Renesas SH processors -
- Motorola DragonBall -
BlueZ Distributions
-Support for BlueZ can be found in many Linux distributions and in general it is compatible with any Linux system on the market. Some of the distributions that provide their own packages for BlueZ are:
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- Debian GNU/Linux -
- Ubuntu Linux -
- Fedora Core / Red Hat Linux -
- OpenSuSE / SuSE Linux -
- Mandrake Linux -
- Gentoo Linux -
- Chrome OS -
How to Get BlueZ Source Code?
-If you want to download the source code of BlueZ, you have several options to choose from. Here are some of the most common ways to get BlueZ source code:
-From the Official Website
-The official website of BlueZ provides a download page where you can find the latest stable release of BlueZ as well as older versions. The source code is provided as a compressed tarball file that you can download and extract on your system. To download the latest stable release of BlueZ, you can use this link: bluez-5.66.tar.xz
-How to download and install bluez on Linux
-
-Download bluez GitHub repository and compile from source
-BlueZ official Linux Bluetooth protocol stack download
-Download bluez-4.101.tar.xz and obexd-0.48.tar.xz from BlueZ website
-Download bluez-hcidump-2.5.tar.xz to capture Bluetooth traffic
-Download bluez-libs and bluez-utils for Bluetooth development
-Download bluez-firmware for Bluetooth device firmware updates
-Download BlueR - official BlueZ bindings for Rust
-Download bluetooth-next - Bluetooth kernel development tree
-Download pybluez - Bluetooth Python extension moduleFrom GitHub
-The main development repository of BlueZ is hosted on GitHub, where you can find the latest code changes, bug fixes, and new features. You can clone the repository using git or download a zip file of the current master branch. To clone the repository, you can use this command:
-git clone git://git.kernel.org/pub/scm/bluetooth/bluez.git-To download a zip file of the current master branch, you can use this link: bluez.zip
-From Linux Distributions
-If you are using a Linux distribution that provides packages for BlueZ, you can also download the source code from their repositories. For example, if you are using Debian or Ubuntu, you can use the apt-get source command to download the source code of the bluez package. To do that, you can use this command:
-apt-get source bluez-This will download the source code of the bluez package and its dependencies in the current directory. You can also specify a different directory to download the source code. For more information on how to use apt-get source, you can check the man page.
-Conclusion
-In this article, we have learned what BlueZ is, what features it provides, and how to download its source code from different sources. We hope that this article has been helpful for you and that you have enjoyed learning about BlueZ. If you have any questions or feedback, please feel free to leave a comment below.
-FAQs
-Here are some frequently asked questions about BlueZ and its source code:
-What is the license of BlueZ?
-BlueZ is licensed under the GNU General Public License (GPL) version 2 or later. This means that you can use, modify, and distribute BlueZ as long as you comply with the terms of the license.
-How can I contribute to BlueZ?
-If you want to contribute to BlueZ, you can check the contribution guidelines on the official website. You can also join the mailing list or the IRC channel to communicate with other developers and users of BlueZ.
-How can I report a bug or request a feature for BlueZ?
-If you encounter a bug or have a suggestion for a new feature for BlueZ, you can use the bug tracker on GitHub to report it. Please make sure that you follow the bug reporting guidelines and provide as much information as possible to help the developers fix or implement your issue.
-How can I learn more about BlueZ?
-If you want to learn more about BlueZ, you can check the documentation on the official website. You can also find tutorials, articles, and videos on various topics related to BlueZ on the internet. Some of the resources that we recommend are:
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- BlueZ Tutorial: How to Use Bluetooth Low Energy Devices in Linux -
- Bluetooth Programming with Python 3 -
- How to Use Bluetooth Headphones in Linux -
What are some alternatives to BlueZ?
-If you are looking for alternatives to BlueZ, you might want to check out some of these projects:
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- Bluedroid : The Bluetooth stack for Android devices. -
- Bluepy : A Python interface to Bluetooth Low Energy devices. -
- NimBLE : An open source Bluetooth 5.0 stack for embedded devices. -
-
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\ No newline at end of file diff --git a/spaces/fffiloni/Music_Source_Separation/setup.py b/spaces/fffiloni/Music_Source_Separation/setup.py deleted file mode 100644 index f146e7d34dc4f06b032ee84b4777e8df01ab9ddb..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/Music_Source_Separation/setup.py +++ /dev/null @@ -1,25 +0,0 @@ -from setuptools import setup - -setup( - name='bytesep', - version='0.0.1', - description='Music source separation', - author='ByteDance', - url="https://github.com/bytedance/music_source_separation", - license='Apache 2.0', - packages=['bytesep'], - include_package_data=True, - install_requires=[ - 'torch==1.7.1', - 'librosa==0.8.0', # specify the version! - 'museval==0.4.0', - 'h5py==2.10.0', - 'pytorch_lightning==1.2.1', - 'numpy==1.18.5', - 'torchlibrosa==0.0.9', - 'matplotlib==3.3.4', - 'musdb==0.4.0', - 'museval==0.4.0' - ], - zip_safe=False -) diff --git a/spaces/fffiloni/Video-Matting-Anything/networks/m2ms/__init__.py b/spaces/fffiloni/Video-Matting-Anything/networks/m2ms/__init__.py deleted file mode 100644 index 7c06875870c8c1537f7fa7a1cb202bbb2ff56889..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/Video-Matting-Anything/networks/m2ms/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .conv_sam import SAM_Decoder_Deep - -__all__ = ['sam_decoder_deep'] - -def sam_decoder_deep(nc, **kwargs): - model = SAM_Decoder_Deep(nc, [2, 3, 3, 2], **kwargs) - return model \ No newline at end of file diff --git a/spaces/fl399/deplot_plus_llm/app.py b/spaces/fl399/deplot_plus_llm/app.py deleted file mode 100644 index 6f6f7cae6c341ce10cfa8642b9c2400f52f54254..0000000000000000000000000000000000000000 --- a/spaces/fl399/deplot_plus_llm/app.py +++ /dev/null @@ -1,301 +0,0 @@ -import os -import torch -import openai -import requests -import gradio as gr -import transformers -from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor -#from peft import PeftModel - - -if torch.cuda.is_available(): - device = "cuda" -else: - device = "cpu" - -try: - if torch.backends.mps.is_available(): - device = "mps" -except: - pass - -## CoT prompts - -def _add_markup(table): - try: - parts = [p.strip() for p in table.splitlines(keepends=False)] - if parts[0].startswith('TITLE'): - result = f"Title: {parts[0].split(' | ')[1].strip()}\n" - rows = parts[1:] - else: - result = '' - rows = parts - prefixes = ['Header: '] + [f'Row {i+1}: ' for i in range(len(rows) - 1)] - return result + '\n'.join(prefix + row for prefix, row in zip(prefixes, rows)) - except: - # just use the raw table if parsing fails - return table - -_TABLE = """Year | Democrats | Republicans | Independents -2004 | 68.1% | 45.0% | 53.0% -2006 | 58.0% | 42.0% | 53.0% -2007 | 59.0% | 38.0% | 45.0% -2009 | 72.0% | 49.0% | 60.0% -2011 | 71.0% | 51.2% | 58.0% -2012 | 70.0% | 48.0% | 53.0% -2013 | 72.0% | 41.0% | 60.0%""" - -_INSTRUCTION = 'Read the table below to answer the following questions.' - -_TEMPLATE = f"""First read an example then the complete question for the second table. ------------- -{_INSTRUCTION} -{_add_markup(_TABLE)} -Q: In which year republicans have the lowest favor rate? -A: Let's find the column of republicans. Then let's extract the favor rates, they [45.0, 42.0, 38.0, 49.0, 51.2, 48.0, 41.0]. The smallest number is 38.0, that's Row 3. Row 3 is year 2007. The answer is 2007. -Q: What is the sum of Democrats' favor rates of 2004, 2012, and 2013? -A: Let's find the rows of years 2004, 2012, and 2013. We find Row 1, 6, 7. The favor dates of Demoncrats on that 3 rows are 68.1, 70.0, and 72.0. 68.1+70.0+72=210.1. The answer is 210.1. -Q: By how many points do Independents surpass Republicans in the year of 2011? -A: Let's find the row with year = 2011. We find Row 5. We extract Independents and Republicans' numbers. They are 58.0 and 51.2. 58.0-51.2=6.8. The answer is 6.8. -Q: Which group has the overall worst performance? -A: Let's sample a couple of years. In Row 1, year 2004, we find Republicans having the lowest favor rate 45.0 (since 45.0<68.1, 45.0<53.0). In year 2006, Row 2, we find Republicans having the lowest favor rate 42.0 (42.0<58.0, 42.0<53.0). The trend continues to other years. The answer is Republicans. -Q: Which party has the second highest favor rates in 2007? -A: Let's find the row of year 2007, that's Row 3. Let's extract the numbers on Row 3: [59.0, 38.0, 45.0]. 45.0 is the second highest. 45.0 is the number of Independents. The answer is Independents. -{_INSTRUCTION}""" - - -## alpaca-lora - -# assert ( -# "LlamaTokenizer" in transformers._import_structure["models.llama"] -# ), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git" -# from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig - -# tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf") - -# BASE_MODEL = "decapoda-research/llama-7b-hf" -# LORA_WEIGHTS = "tloen/alpaca-lora-7b" - -# if device == "cuda": -# model = LlamaForCausalLM.from_pretrained( -# BASE_MODEL, -# load_in_8bit=False, -# torch_dtype=torch.float16, -# device_map="auto", -# ) -# model = PeftModel.from_pretrained( -# model, LORA_WEIGHTS, torch_dtype=torch.float16, force_download=True -# ) -# elif device == "mps": -# model = LlamaForCausalLM.from_pretrained( -# BASE_MODEL, -# device_map={"": device}, -# torch_dtype=torch.float16, -# ) -# model = PeftModel.from_pretrained( -# model, -# LORA_WEIGHTS, -# device_map={"": device}, -# torch_dtype=torch.float16, -# ) -# else: -# model = LlamaForCausalLM.from_pretrained( -# BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True -# ) -# model = PeftModel.from_pretrained( -# model, -# LORA_WEIGHTS, -# device_map={"": device}, -# ) - - -# if device != "cpu": -# model.half() -# model.eval() -# if torch.__version__ >= "2": -# model = torch.compile(model) - - -## FLAN-UL2 -HF_TOKEN = os.environ.get("API_TOKEN", None) -API_URL = "https://api-inference.huggingface.co/models/google/flan-ul2" -headers = {"Authorization": f"Bearer {HF_TOKEN}"} -def query(payload): - response = requests.post(API_URL, headers=headers, json=payload) - return response.json() - -## OpenAI models -openai.api_key = os.environ.get("OPENAI_TOKEN", None) -def set_openai_api_key(api_key): - if api_key and api_key.startswith("sk-") and len(api_key) > 50: - openai.api_key = api_key - -def get_response_from_openai(prompt, model="gpt-3.5-turbo", max_output_tokens=256): - messages = [{"role": "assistant", "content": prompt}] - response = openai.ChatCompletion.create( - model=model, - messages=messages, - temperature=0.7, - max_tokens=max_output_tokens, - top_p=1, - frequency_penalty=0, - presence_penalty=0, - ) - ret = response.choices[0].message['content'] - return ret - -## deplot models -model_deplot = Pix2StructForConditionalGeneration.from_pretrained("google/deplot", torch_dtype=torch.bfloat16) -if device == "cuda": - model_deplot = model_deplot.to(0) -processor_deplot = Pix2StructProcessor.from_pretrained("google/deplot") - -def evaluate( - table, - question, - llm="alpaca-lora", - input=None, - temperature=0.1, - top_p=0.75, - top_k=40, - num_beams=4, - max_new_tokens=128, - **kwargs, -): - prompt_0shot = _INSTRUCTION + "\n" + _add_markup(table) + "\n" + "Q: " + question + "\n" + "A:" - prompt = _TEMPLATE + "\n" + _add_markup(table) + "\n" + "Q: " + question + "\n" + "A:" - if llm == "alpaca-lora": - inputs = tokenizer(prompt, return_tensors="pt") - input_ids = inputs["input_ids"].to(device) - generation_config = GenerationConfig( - temperature=temperature, - top_p=top_p, - top_k=top_k, - num_beams=num_beams, - **kwargs, - ) - with torch.no_grad(): - generation_output = model.generate( - input_ids=input_ids, - generation_config=generation_config, - return_dict_in_generate=True, - output_scores=True, - max_new_tokens=max_new_tokens, - ) - s = generation_output.sequences[0] - output = tokenizer.decode(s) - elif llm == "flan-ul2": - try: - output = query({"inputs": prompt_0shot})[0]["generated_text"] - except: - output = "" - elif llm == "gpt-3.5-turbo": - try: - output = get_response_from_openai(prompt_0shot) - except: - output = " " - else: - RuntimeError(f"No such LLM: {llm}") - - return output - - -def process_document(image, question, llm): - # image = Image.open(image) - inputs = processor_deplot(images=image, text="Generate the underlying data table for the figure below:", return_tensors="pt").to(torch.bfloat16) - if device == "cuda": - inputs = inputs.to(0) - predictions = model_deplot.generate(**inputs, max_new_tokens=512) - table = processor_deplot.decode(predictions[0], skip_special_tokens=True).replace("<0x0A>", "\n") - - # send prompt+table to LLM - res = evaluate(table, question, llm=llm) - if llm == "alpaca-lora": - return [table, res.split("A:")[-1]] - else: - return [table, res] - -# theme = gr.themes.Monochrome( -# primary_hue="indigo", -# secondary_hue="blue", -# neutral_hue="slate", -# radius_size=gr.themes.sizes.radius_sm, -# font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"], -# ) - -with gr.Blocks(theme="gradio/soft") as demo: - with gr.Column(): - # gr.Markdown( - # """
- #DePlot+LLM: Multimodal chain-of-thought reasoning on plots - # This is a demo of DePlot+LLM for QA and summarisation. DePlot is an image-to-text model that converts plots and charts into a textual sequence. The sequence then is used to prompt LLM for chain-of-thought reasoning. The current underlying LLMs are alpaca-lora, flan-ul2, and gpt-3.5-turbo. To use it, simply upload your image and type a question or instruction and click 'submit', or click one of the examples to load them. Read more at the links below. - #
- # """ - # ) - gr.Markdown( - """
-DePlot+LLM: Multimodal chain-of-thought reasoning on plot📊 -
-- [paper] [google-ai blog] [code] - -- This is a demo of DePlot+LLM for QA and summarisation. DePlot is an image-to-text model that converts plots and charts into a textual sequence. The sequence then is used to prompt LLM for chain-of-thought reasoning. The current underlying LLMs are flan-ul2 and gpt-3.5-turbo. To use it, simply upload your image and type a question or instruction and click 'submit', or click one of the examples to load them. -
- """ - ) - - with gr.Row(): - with gr.Column(scale=2): - input_image = gr.Image(label="Input Image", type="pil", interactive=True) - #input_image.style(height=512, width=512) - instruction = gr.Textbox(placeholder="Enter your instruction/question...", label="Question/Instruction") - #llm = gr.Dropdown(["alpaca-lora", "flan-ul2", "gpt-3.5-turbo"], label="LLM") - llm = gr.Dropdown(["flan-ul2", "gpt-3.5-turbo"], label="LLM") - openai_api_key_textbox = gr.Textbox(value='', - placeholder="Paste your OpenAI API key (sk-...) and hit Enter (if using OpenAI models, otherwise leave empty)", - show_label=False, lines=1, type='password') - submit = gr.Button("Submit", variant="primary") - - with gr.Column(scale=2): - with gr.Accordion("Show intermediate table", open=False): - output_table = gr.Textbox(lines=8, label="Intermediate Table") - output_text = gr.Textbox(lines=8, label="Output") - - gr.Examples( - examples=[ - ["deplot_case_study_6.png", "Rank the four methods according to average model performances. By how much does deplot outperform the second strongest approach on average across the two sets? Show the computation.", "gpt-3.5-turbo"], - ["deplot_case_study_4.png", "What are the acceptance rates? And how does the acceptance change over the years?", "gpt-3.5-turbo"], - ["deplot_case_study_m1.png", "Summarise the chart for me please.", "gpt-3.5-turbo"], - #["deplot_case_study_m1.png", "What is the sum of numbers of Indonesia and Ireland? Remember to think step by step.", "alpaca-lora"], - #["deplot_case_study_3.png", "By how much did China's growth rate drop? Think step by step.", "alpaca-lora"], - #["deplot_case_study_4.png", "How many papers are submitted in 2020?", "flan-ul2"], - ["deplot_case_study_5.png", "Which sales channel has the second highest portion?", "flan-ul2"], - #["deplot_case_study_x2.png", "Summarise the chart for me please.", "alpaca-lora"], - #["deplot_case_study_4.png", "How many papers are submitted in 2020?", "alpaca-lora"], - #["deplot_case_study_m1.png", "Summarise the chart for me please.", "alpaca-lora"], - #["deplot_case_study_4.png", "acceptance rate = # accepted / #submitted . What is the acceptance rate of 2010?", "flan-ul2"], - #["deplot_case_study_m1.png", "Summarise the chart for me please.", "flan-ul2"], - ], - cache_examples=True, - inputs=[input_image, instruction, llm], - outputs=[output_table, output_text], - fn=process_document - ) - - gr.Markdown( - """DePlot: One-shot visual language reasoning by plot-to-table translation
""" - ) - openai.api_key = "" - openai_api_key_textbox.change(set_openai_api_key, - inputs=[openai_api_key_textbox], - outputs=[]) - openai_api_key_textbox.submit(set_openai_api_key, - inputs=[openai_api_key_textbox], - outputs=[]) - submit.click(process_document, inputs=[input_image, instruction, llm], outputs=[output_table, output_text]) - instruction.submit( - process_document, inputs=[input_image, instruction, llm], outputs=[output_table, output_text] - ) - -demo.queue(concurrency_count=1).launch() \ No newline at end of file diff --git a/spaces/francojc/transcribe/app.py b/spaces/francojc/transcribe/app.py deleted file mode 100644 index 58cb01ec21a7d965d0965f65e4ef3f16507679b1..0000000000000000000000000000000000000000 --- a/spaces/francojc/transcribe/app.py +++ /dev/null @@ -1,49 +0,0 @@ -import openai -import os -import argparse -import gradio as gr -from gradio import File, Textbox, Interface - - -def transcribe_audio(file, api_key): - # Load API key from environment variable - openai.api_key = api_key - - # Open the file in binary mode - audio_file = open(file.name, "rb") - - # Load the audio file and transcribe it - transcript = openai.Audio.transcribe("whisper-1", file=audio_file) - - # Write the transcript to a text file - file_path = os.path.splitext(file.name)[0] + ".txt" - file = open(file_path, "w") - transcript_text = transcript.get("text") - file.write(transcript_text) - file.close() - - # Close the audio file - audio_file.close() - - return transcript_text - -description = """ -Transcribe an audio file using OpenAI (Whisper-1). - -File uploads are currently limited to 25 MB and the following input file types are supported: mp3, mp4, mpeg, mpga, m4a, wav, and webm. -""" - -file_input = File(label="Select an audio file") -api_key_input = Textbox(label="Enter your OpenAI API key", type="password") -output_text = Textbox(label="Transcript") - -interface = Interface( - fn=transcribe_audio, - inputs=[file_input, api_key_input], - outputs=output_text, - title="OpenAI Audio Transcription", - description=description, - allow_flagging='never', -) - -interface.launch() diff --git a/spaces/garasense/P2ML1_Telco_Customer_Churn/backend.py b/spaces/garasense/P2ML1_Telco_Customer_Churn/backend.py deleted file mode 100644 index 612553b3a6979d12d47d405630e015f1c4b78f98..0000000000000000000000000000000000000000 --- a/spaces/garasense/P2ML1_Telco_Customer_Churn/backend.py +++ /dev/null @@ -1,107 +0,0 @@ -import streamlit as st -import pandas as pd -import seaborn as sns -import matplotlib.pyplot as plt -import plotly.express as px -from PIL import Image -import numpy as np -import joblib -import json -import tensorflow - -# Load All Files -with open('final_pipeline.pkl', 'rb') as file_1: - model_pipeline = joblib.load(file_1) - -from tensorflow.keras.models import load_model -model_ann = load_model('customer_churn_model.h5') - -def run(): - # Membuat Title - st.title('Telco Customer Churn Prediction') - - # Menambahkan Gambar - image = Image.open('telco.png') - st.image(image,width=800) - - col1, col2= st.columns(2) - with st.form(key='form_parameters'): - - with col1: - st.header("Customer Information") - customerid = st.text_input('customerID', value='') - tenure = st.number_input('tenure', min_value=0, max_value=500, value=1) - monthlycharges = st.number_input('MonthlyCharges', min_value=0, max_value=100000, value=0) - totalcharges = st.number_input('TotalCharges', min_value=0, max_value=100000, value=0) - st.markdown('---') - st.header("Services") - phoneservice = st.selectbox('PhoneService', ('Yes', 'No'), index=1) - multiplelines = st.selectbox('MultipleLines', ('Yes', 'No', 'No Phone'), index=1) - internetservice = st.selectbox('InternetService', ('DSL', 'Fiber Optic', 'No'), index=1) - onlinesecurity = st.selectbox('OnlineSecurity', ('Yes', 'No', 'No Internet'), index=1) - st.markdown('---') - streamingmovie = st.selectbox('StreamingMovies', ('Yes', 'No', 'No Internet'), index=1) - contract = st.selectbox('Contract', ('Monthly', '2 Year', '1 Year'), index=1) - st.markdown('---') - - - with col2: - st.header("-") - gender = st.selectbox('gender', ('Male', 'Female'), index=1) - seniorcitizen = st.selectbox('SeniorCitizen', ('0', '1'), index=1) - partner = st.selectbox('Partner', ('Yes', 'No'), index=1) - dependents = st.selectbox('Dependents', ('Yes', 'No'), index=1) - st.markdown('---') - st.header("-") - onlinebackup = st.selectbox('OnlineBackup', ('Yes', 'No', 'No Internet'), index=1) - deviceprotection = st.selectbox('DeviceProtection', ('Yes', 'No', 'No Internet'), index=1) - techsupport = st.selectbox('TechSupport', ('Yes', 'No', 'No Internet'), index=1) - streamingtv = st.selectbox('StreamingTV', ('Yes', 'No', 'No Internet'), index=1) - st.markdown('---') - paperlessbilling = st.selectbox('PaperLessBilling', ('Yes', 'No'), index=1) - paymentmethod = st.selectbox('PaymentMethod', ('Electronic Check', 'Credit Card', 'Bak Transfer', 'Mailed Check'), index=1) - st.markdown('---') - - submitted = st.form_submit_button('Predict') - - data_inf = { - 'customerid' : customerid, - 'tenure' : tenure, - 'monthlycharges' : monthlycharges, - 'totalcharges' : totalcharges, - 'phoneservice' : phoneservice, - 'multiplelines' : multiplelines, - 'internetservice' : internetservice, - 'onlinesecurity' : onlinesecurity, - 'streamingmovies' : streamingmovie, - 'contract' : contract, - 'gender' : gender, - 'seniorcitizen' : seniorcitizen, - 'partner' : partner, - 'dependents' : dependents, - 'onlinebackup': onlinebackup, - 'deviceprotection' : deviceprotection, - 'techsupport' : techsupport, - 'streamingtv' : streamingtv, - 'paperlessbilling' : paperlessbilling, - 'paymentmethod' : paymentmethod - } - - data_inf = pd.DataFrame([data_inf]) - st.dataframe(data_inf) - - if submitted: - - data_inf_transform = model_pipeline.transform(data_inf) - y_pred_inf = model_ann.predict(data_inf_transform) - y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0) - - if y_pred_inf == 0: - x = "No" - else: - x = "Yes" - - st.write('# Churn :', x) - -if __name__ == '__main__': - run() \ No newline at end of file diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/focal_loss.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/focal_loss.py deleted file mode 100644 index 763bc93bd2575c49ca8ccf20996bbd92d1e0d1a4..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/focal_loss.py +++ /dev/null @@ -1,212 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import torch -import torch.nn as nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', [ - 'sigmoid_focal_loss_forward', 'sigmoid_focal_loss_backward', - 'softmax_focal_loss_forward', 'softmax_focal_loss_backward' -]) - - -class SigmoidFocalLossFunction(Function): - - @staticmethod - def symbolic(g, input, target, gamma, alpha, weight, reduction): - return g.op( - 'mmcv::MMCVSigmoidFocalLoss', - input, - target, - gamma_f=gamma, - alpha_f=alpha, - weight_f=weight, - reduction_s=reduction) - - @staticmethod - def forward(ctx, - input, - target, - gamma=2.0, - alpha=0.25, - weight=None, - reduction='mean'): - - assert isinstance(target, (torch.LongTensor, torch.cuda.LongTensor)) - assert input.dim() == 2 - assert target.dim() == 1 - assert input.size(0) == target.size(0) - if weight is None: - weight = input.new_empty(0) - else: - assert weight.dim() == 1 - assert input.size(1) == weight.size(0) - ctx.reduction_dict = {'none': 0, 'mean': 1, 'sum': 2} - assert reduction in ctx.reduction_dict.keys() - - ctx.gamma = float(gamma) - ctx.alpha = float(alpha) - ctx.reduction = ctx.reduction_dict[reduction] - - output = input.new_zeros(input.size()) - - ext_module.sigmoid_focal_loss_forward( - input, target, weight, output, gamma=ctx.gamma, alpha=ctx.alpha) - if ctx.reduction == ctx.reduction_dict['mean']: - output = output.sum() / input.size(0) - elif ctx.reduction == ctx.reduction_dict['sum']: - output = output.sum() - ctx.save_for_backward(input, target, weight) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - input, target, weight = ctx.saved_tensors - - grad_input = input.new_zeros(input.size()) - - ext_module.sigmoid_focal_loss_backward( - input, - target, - weight, - grad_input, - gamma=ctx.gamma, - alpha=ctx.alpha) - - grad_input *= grad_output - if ctx.reduction == ctx.reduction_dict['mean']: - grad_input /= input.size(0) - return grad_input, None, None, None, None, None - - -sigmoid_focal_loss = SigmoidFocalLossFunction.apply - - -class SigmoidFocalLoss(nn.Module): - - def __init__(self, gamma, alpha, weight=None, reduction='mean'): - super(SigmoidFocalLoss, self).__init__() - self.gamma = gamma - self.alpha = alpha - self.register_buffer('weight', weight) - self.reduction = reduction - - def forward(self, input, target): - return sigmoid_focal_loss(input, target, self.gamma, self.alpha, - self.weight, self.reduction) - - def __repr__(self): - s = self.__class__.__name__ - s += f'(gamma={self.gamma}, ' - s += f'alpha={self.alpha}, ' - s += f'reduction={self.reduction})' - return s - - -class SoftmaxFocalLossFunction(Function): - - @staticmethod - def symbolic(g, input, target, gamma, alpha, weight, reduction): - return g.op( - 'mmcv::MMCVSoftmaxFocalLoss', - input, - target, - gamma_f=gamma, - alpha_f=alpha, - weight_f=weight, - reduction_s=reduction) - - @staticmethod - def forward(ctx, - input, - target, - gamma=2.0, - alpha=0.25, - weight=None, - reduction='mean'): - - assert isinstance(target, (torch.LongTensor, torch.cuda.LongTensor)) - assert input.dim() == 2 - assert target.dim() == 1 - assert input.size(0) == target.size(0) - if weight is None: - weight = input.new_empty(0) - else: - assert weight.dim() == 1 - assert input.size(1) == weight.size(0) - ctx.reduction_dict = {'none': 0, 'mean': 1, 'sum': 2} - assert reduction in ctx.reduction_dict.keys() - - ctx.gamma = float(gamma) - ctx.alpha = float(alpha) - ctx.reduction = ctx.reduction_dict[reduction] - - channel_stats, _ = torch.max(input, dim=1) - input_softmax = input - channel_stats.unsqueeze(1).expand_as(input) - input_softmax.exp_() - - channel_stats = input_softmax.sum(dim=1) - input_softmax /= channel_stats.unsqueeze(1).expand_as(input) - - output = input.new_zeros(input.size(0)) - ext_module.softmax_focal_loss_forward( - input_softmax, - target, - weight, - output, - gamma=ctx.gamma, - alpha=ctx.alpha) - - if ctx.reduction == ctx.reduction_dict['mean']: - output = output.sum() / input.size(0) - elif ctx.reduction == ctx.reduction_dict['sum']: - output = output.sum() - ctx.save_for_backward(input_softmax, target, weight) - return output - - @staticmethod - def backward(ctx, grad_output): - input_softmax, target, weight = ctx.saved_tensors - buff = input_softmax.new_zeros(input_softmax.size(0)) - grad_input = input_softmax.new_zeros(input_softmax.size()) - - ext_module.softmax_focal_loss_backward( - input_softmax, - target, - weight, - buff, - grad_input, - gamma=ctx.gamma, - alpha=ctx.alpha) - - grad_input *= grad_output - if ctx.reduction == ctx.reduction_dict['mean']: - grad_input /= input_softmax.size(0) - return grad_input, None, None, None, None, None - - -softmax_focal_loss = SoftmaxFocalLossFunction.apply - - -class SoftmaxFocalLoss(nn.Module): - - def __init__(self, gamma, alpha, weight=None, reduction='mean'): - super(SoftmaxFocalLoss, self).__init__() - self.gamma = gamma - self.alpha = alpha - self.register_buffer('weight', weight) - self.reduction = reduction - - def forward(self, input, target): - return softmax_focal_loss(input, target, self.gamma, self.alpha, - self.weight, self.reduction) - - def __repr__(self): - s = self.__class__.__name__ - s += f'(gamma={self.gamma}, ' - s += f'alpha={self.alpha}, ' - s += f'reduction={self.reduction})' - return s diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Autodesk AutoCAD 2017 HF3 X86-x64 RUS-ENG By M0nkrus- TEAM OS - Free Download.md b/spaces/gotiQspiryo/whisper-ui/examples/Autodesk AutoCAD 2017 HF3 X86-x64 RUS-ENG By M0nkrus- TEAM OS - Free Download.md deleted file mode 100644 index 70948eef9dc2d20ae81544054923ca164eb49e7c..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/Autodesk AutoCAD 2017 HF3 X86-x64 RUS-ENG By M0nkrus- TEAM OS - Free Download.md +++ /dev/null @@ -1,19 +0,0 @@ -
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-The country born and bred boy has his own manners andcustoms,
-
which do not resemble those of any other land; and histeachers
approach him by roads which an English master would notunderstand.
Therefore, you would scarcely be interested in Kim's experiencesas
a St Xavier's boy among two or three hundred precociousyouths,
most of whom had never seen the sea. He suffered the usual
penalties for breaking out of bounds when there was cholera inthe
city. This was before he had learned to write fair English, andso
was obliged to find a bazar letter-writer. He was, ofcourse,
indicted for smoking and for the use of abuse morefull-flavoured
than even St Xavier's had ever heard. He learned to washhimself
with the Levitical scrupulosity of the native-born, who inhis
heart considers the Englishman rather dirty. He played theusual
tricks on the patient coolies pulling the punkahs in thesleeping-
rooms where the boys threshed through the hot nights tellingtales
till the dawn; and quietly he measured himself against hisself-
reliant mates.They were a most mad ten days, but Kim enjoyed himself toomuch to
-
reflect on their craziness. In the morning they played theJewel
Game - sometimes with veritable stones, sometimes with pilesof
swords and daggers, sometimes with photo-graphs of natives.Through
the afternoons he and the Hindu boy would mount guard in theshop,
sitting dumb behind a carpet-bale or a screen and watchingMr
Lurgan's many and very curious visitors. There were smallRajahs,
escorts coughing in the veranda, who came to buy curiosities -such
as phonographs and mechanical toys. There were ladies in searchof
necklaces, and men, it seemed to Kim - but his mind may havebeen
vitiated by early training - in search of the ladies; nativesfrom
independent and feudatory Courts whose ostensible business wasthe
repair of broken necklaces - rivers of light poured out uponthe
table - but whose true end seemed to be to raise money forangry
Maharanees or young Rajahs. There were Babus to whom LurganSahib
talked with austerity and authority, but at the end of each
interview he gave them money in coined silver and currencynotes.
There were occasional gatherings of long-coated theatricalnatives
who discussed metaphysics in English and Bengali, to MrLurgan's
great edification. He was always interested in religions. Atthe
end of the day, Kim and the Hindu boy - whose name varied at
Lurgan's pleasure - were expected to give a detailed account ofall
that they had seen and heard - their view of each man'scharacter,
as shown in his face, talk, and manner, and their notions ofhis
real errand. After dinner, Lurgan Sahib's fancy turned more towhat
might be called dressing-up, in which game he took a mostinforming
interest. He could paint faces to a marvel; with a brush-dabhere
and a line there changing them past recognition. The shop wasfull
of all manner of dresses and turbans, and Kim was apparelled
variously as a young Mohammedan of good family, an oilman, andonce
- which was a joyous evening - as the son of an Oudh landholderin
the fullest of full dress. Lurgan Sahib had a hawk's eye todetect
the least flaw in the make-up; and lying on a worn teak-woodcouch,
would explain by the half-hour together how such and such acaste
talked, or walked, or coughed, or spat, or sneezed, and,since
'hows' matter little in this world, the 'why' of everything.The
Hindu child played this game clumsily. That little mind, keen asan
icicle where tally of jewels was concerned, could not temperitself
to enter another's soul; but a demon in Kim woke up and sangwith
joy as he put on the changing dresses, and changed speechand
gesture therewith.As usual, the lama had led Kim by cow-track and by-road, farfrom
- -
the main route along which Hurree Babu, that 'fearful man',had
bucketed three days before through a storm to which nineEnglishmen
out of ten would have given full right of way. Hurree was nogame-
shot - the snick of a trigger made him change colour - but, ashe
himself would have said, he was 'fairly effeecient stalker', andhe
had raked the huge valley with a pair of cheap binoculars tosome
purpose. Moreover, the white of worn canvas tents againstgreen
carries far. Hurree Babu had seen all he wanted to see when hesat
on the threshing-floor of Ziglaur, twenty miles away as theeagle
flies, and forty by road - that is to say, two small dots whichone
day were just below the snow-line, and the next had moveddownward
perhaps six inches on the hillside. Once cleaned out and set tothe
work, his fat bare legs could cover a surprising amount ofground,
and this was the reason why, while Kim and the lama lay in aleaky
hut at Ziglaur till the storm should be over-past, an oily, wet,but
always smiling Bengali, talking the best of English with thevilest
of phrases, was ingratiating himself with two sodden andrather
rheumatic foreigners. He had arrived, revolving many wildschemes,
on the heels of a thunderstorm which had split a pine overagainst
their camp, and so convinced a dozen or two forciblyimpressed
baggage-coolies the day was inauspicious for farther travelthat
with one accord they had thrown down their loads and jibbed.They
were subjects of a Hill Rajah who farmed out their services, asis
the custom, for his private gain; and, to add to theirpersonal
distresses, the strange Sahibs had already threatened themwith
rifles. The most of them knew rifles and Sahibs of old: theywere
trackers and shikarris of the Northern valleys, keen after bearand
wild goat; but they had never been thus treated in their lives.So
the forest took them to her bosom, and, for all oaths andclamour,
refused to restore. There was no need to feign madness or - theBabu
had thought of another means of securing a welcome. He wrung outhis
wet clothes, slipped on his patent-leather shoes, opened theblue-
and-white umbrella, and with mincing gait and a heartbeating
against his tonsils appeared as 'agent for His Royal Highness,the
Rajah of Rampur, gentlemen. What can I do for you, please?'Hereat, simply as a child engrossed with a new game, the lamathrew
-
back his head and began the full-throated invocation of theDoctor
of Divinity ere he opens the full doctrine. The strangers leanedon
their alpenstocks and listened. Kim, squatting humbly, watchedthe
red sunlight on their faces, and the blend and parting of theirlong
shadows. They wore un-English leggings and curious girt-inbelts
that reminded him hazily of the pictures in a book in StXavier's
library "The Adventures of a Young Naturalist in Mexico" wasits
name. Yes, they looked very like the wonderful M. Sumichrast ofthat
tale, and very unlike the 'highly unscrupulous folk' ofHurree
Babu's imagining. The coolies, earth-coloured and mute,crouched
reverently some twenty or thirty yards away, and the Babu, theslack
of his thin gear snapping like a marking-flag in the chillbreeze,
stood by with an air of happy proprietorship.It was too late. Before Kim could ward him off, the Russianstruck
-
the old man full on the face. Next instant he was rolling overand
over downhill with Kim at his throat. The blow had wakedevery
unknown Irish devil in the boy's blood, and the sudden fall ofhis
enemy did the rest. The lama dropped to his knees, half-stunned;the
coolies under their loads fled up the hill as fast as plainsmenrun
aross the level. They had seen sacrilege unspeakable, and itbehoved
them to get away before the Gods and devils of the hillstook
vengeance. The Frenchman ran towards the lama, fumbling athis
revolver with some notion of making him a hostage for hiscompanion.
A shower of cutting stones - hillmen are very straight shots -drove
him away, and a coolie from Ao-chung snatched the lama intothe
stampede. All came about as swiftly as the suddenmountain-darkness.Kim might have saved his pity, for though at that moment theBengali
aaccfb2cb3
suffered acutely in the flesh, his soul was puffed and lofty. Amile
down the hill, on the edge of the pine-forest, two half-frozenmen -
one powerfully sick at intervals - were varying mutual
recriminations with the most poignant abuse of the Babu, whoseemed
distraught with terror. They demanded a plan of action. He
explained that they were very lucky to be alive; that theircoolies,
if not then stalking them, had passed beyond recall; that theRajah,
his master, was ninety miles away, and, so far from lendingthem
money and a retinue for the Simla journey, would surely castthem
into prison if he heard that they had hit a priest. He enlargedon
this sin and its consequences till they bade him change thesubject.
Their one hope, said he, was unostentatious flight from villageto
village till they reached civilization; and, for the hundredthtime
dissolved in tears, he demanded of the high stars why theSahibs
'had beaten holy man'.
-
-
\ No newline at end of file diff --git a/spaces/gradio/HuBERT/fairseq/data/token_block_dataset.py b/spaces/gradio/HuBERT/fairseq/data/token_block_dataset.py deleted file mode 100644 index d2c65fd7e058072911c3aa60bfc760288a0f83e5..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/data/token_block_dataset.py +++ /dev/null @@ -1,202 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import numpy as np -import torch -from fairseq.data import FairseqDataset, plasma_utils -from fairseq.data.indexed_dataset import best_fitting_int_dtype -from typing import Tuple - - -class TokenBlockDataset(FairseqDataset): - """Break a Dataset of tokens into blocks. - - Args: - dataset (~torch.utils.data.Dataset): dataset to break into blocks - sizes (List[int]): sentence lengths (required for 'complete' and 'eos') - block_size (int): maximum block size (ignored in 'eos' break mode) - break_mode (str, optional): Mode used for breaking tokens. Values can - be one of: - - 'none': break tokens into equally sized blocks (up to block_size) - - 'complete': break tokens into blocks (up to block_size) such that - blocks contains complete sentences, although block_size may be - exceeded if some sentences exceed block_size - - 'complete_doc': similar to 'complete' mode, but do not - cross document boundaries - - 'eos': each block contains one sentence (block_size is ignored) - include_targets (bool, optional): return next tokens as targets - (default: False). - document_sep_len (int, optional): document separator size (required for - 'complete_doc' break mode). Typically 1 if the sentences have eos - and 0 otherwise. - """ - - def __init__( - self, - dataset, - sizes, - block_size, - pad, - eos, - break_mode=None, - include_targets=False, - document_sep_len=1, - use_plasma_view=False, - split_path=None, - plasma_path=None, - ): - - super().__init__() - self.dataset = dataset - self.pad = pad - self.eos = eos - self.include_targets = include_targets - - assert len(dataset) > 0 - - assert len(dataset) == len(sizes) - _sizes, block_to_dataset_index, slice_indices = self._build_slice_indices( - sizes, break_mode, document_sep_len, block_size - ) - if use_plasma_view: - plasma_id = (block_size, document_sep_len, str(break_mode), len(dataset)) - self._slice_indices = plasma_utils.PlasmaView( - slice_indices, split_path, (plasma_id, 0), plasma_path=plasma_path - ) - self._sizes = plasma_utils.PlasmaView( - _sizes, split_path, (plasma_id, 1), plasma_path=plasma_path - ) - self._block_to_dataset_index = plasma_utils.PlasmaView( - block_to_dataset_index, split_path, (plasma_id, 2), plasma_path=plasma_path, - ) - else: - self._slice_indices = plasma_utils.PlasmaArray(slice_indices) - self._sizes = plasma_utils.PlasmaArray(_sizes) - self._block_to_dataset_index = plasma_utils.PlasmaArray( - block_to_dataset_index - ) - - @staticmethod - def _build_slice_indices( - sizes, break_mode, document_sep_len, block_size - ) -> Tuple[np.ndarray]: - """Use token_block_utils_fast to build arrays for indexing into self.dataset""" - try: - from fairseq.data.token_block_utils_fast import ( - _get_slice_indices_fast, - _get_block_to_dataset_index_fast, - ) - except ImportError: - raise ImportError( - "Please build Cython components with: `pip install --editable .` " - "or `python setup.py build_ext --inplace`" - ) - - if isinstance(sizes, list): - sizes = np.array(sizes, dtype=np.int64) - else: - if torch.is_tensor(sizes): - sizes = sizes.numpy() - sizes = sizes.astype(np.int64) - - break_mode = break_mode if break_mode is not None else "none" - - # For "eos" break-mode, block_size is not required parameters. - if break_mode == "eos" and block_size is None: - block_size = 0 - - slice_indices = _get_slice_indices_fast( - sizes, str(break_mode), block_size, document_sep_len - ) - _sizes = slice_indices[:, 1] - slice_indices[:, 0] - - # build index mapping block indices to the underlying dataset indices - if break_mode == "eos": - # much faster version for eos break mode - block_to_dataset_index = np.stack( - [ - np.arange(len(sizes)), # starting index in dataset - np.zeros( - len(sizes), dtype=np.compat.long - ), # starting offset within starting index - np.arange(len(sizes)), # ending index in dataset - ], - 1, - ) - else: - block_to_dataset_index = _get_block_to_dataset_index_fast( - sizes, slice_indices, - ) - size_dtype = np.uint16 if block_size < 65535 else np.uint32 - num_tokens = slice_indices[-1].max() - slice_indices_dtype = best_fitting_int_dtype(num_tokens) - slice_indices = slice_indices.astype(slice_indices_dtype) - _sizes = _sizes.astype(size_dtype) - block_to_dataset_index = block_to_dataset_index.astype(slice_indices_dtype) - return _sizes, block_to_dataset_index, slice_indices - - @property - def slice_indices(self): - return self._slice_indices.array - - @property - def sizes(self): - return self._sizes.array - - @property - def block_to_dataset_index(self): - return self._block_to_dataset_index.array - - def attr(self, attr: str, index: int): - start_ds_idx, _, _ = self.block_to_dataset_index[index] - return self.dataset.attr(attr, start_ds_idx) - - def __getitem__(self, index): - start_ds_idx, start_offset, end_ds_idx = self.block_to_dataset_index[index] - - buffer = torch.cat( - [self.dataset[idx] for idx in range(start_ds_idx, end_ds_idx + 1)] - ) - slice_s, slice_e = self.slice_indices[index] - length = slice_e - slice_s - s, e = start_offset, start_offset + length - item = buffer[s:e] - - if self.include_targets: - # *target* is the original sentence (=item) - # *source* is shifted right by 1 (maybe left-padded with eos) - # *past_target* is shifted right by 2 (left-padded as needed) - if s == 0: - source = torch.cat([item.new([self.eos]), buffer[0 : e - 1]]) - past_target = torch.cat( - [item.new([self.pad, self.eos]), buffer[0 : e - 2]] - ) - else: - source = buffer[s - 1 : e - 1] - if s == 1: - past_target = torch.cat([item.new([self.eos]), buffer[0 : e - 2]]) - else: - past_target = buffer[s - 2 : e - 2] - - return source, item, past_target - - return item - - def __len__(self): - return len(self.slice_indices) - - @property - def supports_prefetch(self): - return getattr(self.dataset, "supports_prefetch", False) - - def prefetch(self, indices): - self.dataset.prefetch( - { - ds_idx - for index in indices - for start_ds_idx, _, end_ds_idx in [self.block_to_dataset_index[index]] - for ds_idx in range(start_ds_idx, end_ds_idx + 1) - } - ) diff --git a/spaces/gradio/HuBERT/fairseq/modules/multihead_attention.py b/spaces/gradio/HuBERT/fairseq/modules/multihead_attention.py deleted file mode 100644 index 9bdca0f6af43a0a89e9225594ba5b6fbc5ee04c1..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/modules/multihead_attention.py +++ /dev/null @@ -1,500 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from typing import Dict, Optional, Tuple - -import torch -import torch.nn.functional as F -from fairseq import utils -from fairseq.incremental_decoding_utils import with_incremental_state -from fairseq.modules.fairseq_dropout import FairseqDropout -from fairseq.modules.quant_noise import quant_noise -from torch import Tensor, nn -from torch.nn import Parameter - - -@with_incremental_state -class MultiheadAttention(nn.Module): - """Multi-headed attention. - - See "Attention Is All You Need" for more details. - """ - - def __init__( - self, - embed_dim, - num_heads, - kdim=None, - vdim=None, - dropout=0.0, - bias=True, - add_bias_kv=False, - add_zero_attn=False, - self_attention=False, - encoder_decoder_attention=False, - q_noise=0.0, - qn_block_size=8, - ): - super().__init__() - self.embed_dim = embed_dim - self.kdim = kdim if kdim is not None else embed_dim - self.vdim = vdim if vdim is not None else embed_dim - self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim - - self.num_heads = num_heads - self.dropout_module = FairseqDropout( - dropout, module_name=self.__class__.__name__ - ) - - self.head_dim = embed_dim // num_heads - assert ( - self.head_dim * num_heads == self.embed_dim - ), "embed_dim must be divisible by num_heads" - self.scaling = self.head_dim ** -0.5 - - self.self_attention = self_attention - self.encoder_decoder_attention = encoder_decoder_attention - - assert not self.self_attention or self.qkv_same_dim, ( - "Self-attention requires query, key and " "value to be of the same size" - ) - - self.k_proj = quant_noise( - nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size - ) - self.v_proj = quant_noise( - nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size - ) - self.q_proj = quant_noise( - nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size - ) - - self.out_proj = quant_noise( - nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size - ) - - if add_bias_kv: - self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim)) - self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim)) - else: - self.bias_k = self.bias_v = None - - self.add_zero_attn = add_zero_attn - - self.reset_parameters() - - self.onnx_trace = False - - def prepare_for_onnx_export_(self): - self.onnx_trace = True - - def reset_parameters(self): - if self.qkv_same_dim: - # Empirically observed the convergence to be much better with - # the scaled initialization - nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2)) - nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2)) - nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2)) - else: - nn.init.xavier_uniform_(self.k_proj.weight) - nn.init.xavier_uniform_(self.v_proj.weight) - nn.init.xavier_uniform_(self.q_proj.weight) - - nn.init.xavier_uniform_(self.out_proj.weight) - if self.out_proj.bias is not None: - nn.init.constant_(self.out_proj.bias, 0.0) - if self.bias_k is not None: - nn.init.xavier_normal_(self.bias_k) - if self.bias_v is not None: - nn.init.xavier_normal_(self.bias_v) - - def forward( - self, - query, - key: Optional[Tensor], - value: Optional[Tensor], - key_padding_mask: Optional[Tensor] = None, - incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, - need_weights: bool = True, - static_kv: bool = False, - attn_mask: Optional[Tensor] = None, - before_softmax: bool = False, - need_head_weights: bool = False, - ) -> Tuple[Tensor, Optional[Tensor]]: - """Input shape: Time x Batch x Channel - - Args: - key_padding_mask (ByteTensor, optional): mask to exclude - keys that are pads, of shape `(batch, src_len)`, where - padding elements are indicated by 1s. - need_weights (bool, optional): return the attention weights, - averaged over heads (default: False). - attn_mask (ByteTensor, optional): typically used to - implement causal attention, where the mask prevents the - attention from looking forward in time (default: None). - before_softmax (bool, optional): return the raw attention - weights and values before the attention softmax. - need_head_weights (bool, optional): return the attention - weights for each head. Implies *need_weights*. Default: - return the average attention weights over all heads. - """ - if need_head_weights: - need_weights = True - - is_tpu = query.device.type == "xla" - - tgt_len, bsz, embed_dim = query.size() - src_len = tgt_len - assert embed_dim == self.embed_dim - assert list(query.size()) == [tgt_len, bsz, embed_dim] - if key is not None: - src_len, key_bsz, _ = key.size() - if not torch.jit.is_scripting(): - assert key_bsz == bsz - assert value is not None - assert src_len, bsz == value.shape[:2] - - if ( - not self.onnx_trace - and not is_tpu # don't use PyTorch version on TPUs - and incremental_state is None - and not static_kv - # A workaround for quantization to work. Otherwise JIT compilation - # treats bias in linear module as method. - and not torch.jit.is_scripting() - ): - assert key is not None and value is not None - return F.multi_head_attention_forward( - query, - key, - value, - self.embed_dim, - self.num_heads, - torch.empty([0]), - torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)), - self.bias_k, - self.bias_v, - self.add_zero_attn, - self.dropout_module.p, - self.out_proj.weight, - self.out_proj.bias, - self.training or self.dropout_module.apply_during_inference, - key_padding_mask, - need_weights, - attn_mask, - use_separate_proj_weight=True, - q_proj_weight=self.q_proj.weight, - k_proj_weight=self.k_proj.weight, - v_proj_weight=self.v_proj.weight, - ) - - if incremental_state is not None: - saved_state = self._get_input_buffer(incremental_state) - if saved_state is not None and "prev_key" in saved_state: - # previous time steps are cached - no need to recompute - # key and value if they are static - if static_kv: - assert self.encoder_decoder_attention and not self.self_attention - key = value = None - else: - saved_state = None - - if self.self_attention: - q = self.q_proj(query) - k = self.k_proj(query) - v = self.v_proj(query) - elif self.encoder_decoder_attention: - # encoder-decoder attention - q = self.q_proj(query) - if key is None: - assert value is None - k = v = None - else: - k = self.k_proj(key) - v = self.v_proj(key) - - else: - assert key is not None and value is not None - q = self.q_proj(query) - k = self.k_proj(key) - v = self.v_proj(value) - q *= self.scaling - - if self.bias_k is not None: - assert self.bias_v is not None - k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)]) - v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)]) - if attn_mask is not None: - attn_mask = torch.cat( - [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 - ) - if key_padding_mask is not None: - key_padding_mask = torch.cat( - [ - key_padding_mask, - key_padding_mask.new_zeros(key_padding_mask.size(0), 1), - ], - dim=1, - ) - - q = ( - q.contiguous() - .view(tgt_len, bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - if k is not None: - k = ( - k.contiguous() - .view(-1, bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - if v is not None: - v = ( - v.contiguous() - .view(-1, bsz * self.num_heads, self.head_dim) - .transpose(0, 1) - ) - - if saved_state is not None: - # saved states are stored with shape (bsz, num_heads, seq_len, head_dim) - if "prev_key" in saved_state: - _prev_key = saved_state["prev_key"] - assert _prev_key is not None - prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim) - if static_kv: - k = prev_key - else: - assert k is not None - k = torch.cat([prev_key, k], dim=1) - src_len = k.size(1) - if "prev_value" in saved_state: - _prev_value = saved_state["prev_value"] - assert _prev_value is not None - prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim) - if static_kv: - v = prev_value - else: - assert v is not None - v = torch.cat([prev_value, v], dim=1) - prev_key_padding_mask: Optional[Tensor] = None - if "prev_key_padding_mask" in saved_state: - prev_key_padding_mask = saved_state["prev_key_padding_mask"] - assert k is not None and v is not None - key_padding_mask = MultiheadAttention._append_prev_key_padding_mask( - key_padding_mask=key_padding_mask, - prev_key_padding_mask=prev_key_padding_mask, - batch_size=bsz, - src_len=k.size(1), - static_kv=static_kv, - ) - - saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim) - saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim) - saved_state["prev_key_padding_mask"] = key_padding_mask - # In this branch incremental_state is never None - assert incremental_state is not None - incremental_state = self._set_input_buffer(incremental_state, saved_state) - assert k is not None - assert k.size(1) == src_len - - # This is part of a workaround to get around fork/join parallelism - # not supporting Optional types. - if key_padding_mask is not None and key_padding_mask.dim() == 0: - key_padding_mask = None - - if key_padding_mask is not None: - assert key_padding_mask.size(0) == bsz - assert key_padding_mask.size(1) == src_len - - if self.add_zero_attn: - assert v is not None - src_len += 1 - k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1) - v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1) - if attn_mask is not None: - attn_mask = torch.cat( - [attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1 - ) - if key_padding_mask is not None: - key_padding_mask = torch.cat( - [ - key_padding_mask, - torch.zeros(key_padding_mask.size(0), 1).type_as( - key_padding_mask - ), - ], - dim=1, - ) - - attn_weights = torch.bmm(q, k.transpose(1, 2)) - attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz) - - assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len] - - if attn_mask is not None: - attn_mask = attn_mask.unsqueeze(0) - if self.onnx_trace: - attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1) - attn_weights += attn_mask - - if key_padding_mask is not None: - # don't attend to padding symbols - attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) - if not is_tpu: - attn_weights = attn_weights.masked_fill( - key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), - float("-inf"), - ) - else: - attn_weights = attn_weights.transpose(0, 2) - attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf")) - attn_weights = attn_weights.transpose(0, 2) - attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) - - if before_softmax: - return attn_weights, v - - attn_weights_float = utils.softmax( - attn_weights, dim=-1, onnx_trace=self.onnx_trace - ) - attn_weights = attn_weights_float.type_as(attn_weights) - attn_probs = self.dropout_module(attn_weights) - - assert v is not None - attn = torch.bmm(attn_probs, v) - assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim] - if self.onnx_trace and attn.size(1) == 1: - # when ONNX tracing a single decoder step (sequence length == 1) - # the transpose is a no-op copy before view, thus unnecessary - attn = attn.contiguous().view(tgt_len, bsz, embed_dim) - else: - attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) - attn = self.out_proj(attn) - attn_weights: Optional[Tensor] = None - if need_weights: - attn_weights = attn_weights_float.view( - bsz, self.num_heads, tgt_len, src_len - ).transpose(1, 0) - if not need_head_weights: - # average attention weights over heads - attn_weights = attn_weights.mean(dim=0) - - return attn, attn_weights - - @staticmethod - def _append_prev_key_padding_mask( - key_padding_mask: Optional[Tensor], - prev_key_padding_mask: Optional[Tensor], - batch_size: int, - src_len: int, - static_kv: bool, - ) -> Optional[Tensor]: - # saved key padding masks have shape (bsz, seq_len) - if prev_key_padding_mask is not None and static_kv: - new_key_padding_mask = prev_key_padding_mask - elif prev_key_padding_mask is not None and key_padding_mask is not None: - new_key_padding_mask = torch.cat( - [prev_key_padding_mask.float(), key_padding_mask.float()], dim=1 - ) - # During incremental decoding, as the padding token enters and - # leaves the frame, there will be a time when prev or current - # is None - elif prev_key_padding_mask is not None: - if src_len > prev_key_padding_mask.size(1): - filler = torch.zeros( - (batch_size, src_len - prev_key_padding_mask.size(1)), - device=prev_key_padding_mask.device, - ) - new_key_padding_mask = torch.cat( - [prev_key_padding_mask.float(), filler.float()], dim=1 - ) - else: - new_key_padding_mask = prev_key_padding_mask.float() - elif key_padding_mask is not None: - if src_len > key_padding_mask.size(1): - filler = torch.zeros( - (batch_size, src_len - key_padding_mask.size(1)), - device=key_padding_mask.device, - ) - new_key_padding_mask = torch.cat( - [filler.float(), key_padding_mask.float()], dim=1 - ) - else: - new_key_padding_mask = key_padding_mask.float() - else: - new_key_padding_mask = prev_key_padding_mask - return new_key_padding_mask - - @torch.jit.export - def reorder_incremental_state( - self, - incremental_state: Dict[str, Dict[str, Optional[Tensor]]], - new_order: Tensor, - ): - """Reorder buffered internal state (for incremental generation).""" - input_buffer = self._get_input_buffer(incremental_state) - if input_buffer is not None: - for k in input_buffer.keys(): - input_buffer_k = input_buffer[k] - if input_buffer_k is not None: - if self.encoder_decoder_attention and input_buffer_k.size( - 0 - ) == new_order.size(0): - break - input_buffer[k] = input_buffer_k.index_select(0, new_order) - incremental_state = self._set_input_buffer(incremental_state, input_buffer) - return incremental_state - - def _get_input_buffer( - self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] - ) -> Dict[str, Optional[Tensor]]: - result = self.get_incremental_state(incremental_state, "attn_state") - if result is not None: - return result - else: - empty_result: Dict[str, Optional[Tensor]] = {} - return empty_result - - def _set_input_buffer( - self, - incremental_state: Dict[str, Dict[str, Optional[Tensor]]], - buffer: Dict[str, Optional[Tensor]], - ): - return self.set_incremental_state(incremental_state, "attn_state", buffer) - - def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int): - return attn_weights - - def upgrade_state_dict_named(self, state_dict, name): - prefix = name + "." if name != "" else "" - items_to_add = {} - keys_to_remove = [] - for k in state_dict.keys(): - if k.endswith(prefix + "in_proj_weight"): - # in_proj_weight used to be q + k + v with same dimensions - dim = int(state_dict[k].shape[0] / 3) - items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim] - items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim] - items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :] - - keys_to_remove.append(k) - - k_bias = prefix + "in_proj_bias" - if k_bias in state_dict.keys(): - dim = int(state_dict[k].shape[0] / 3) - items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim] - items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][ - dim : 2 * dim - ] - items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :] - - keys_to_remove.append(prefix + "in_proj_bias") - - for k in keys_to_remove: - del state_dict[k] - - for key, value in items_to_add.items(): - state_dict[key] = value diff --git a/spaces/gradio/HuBERT/fairseq/sequence_generator.py b/spaces/gradio/HuBERT/fairseq/sequence_generator.py deleted file mode 100644 index 8a3858563ec0c3cd7f3177bcd2897d27b61dbe00..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/sequence_generator.py +++ /dev/null @@ -1,980 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from typing import Dict, List, Optional -import sys - -import torch -import torch.nn as nn -from fairseq import search, utils -from fairseq.data import data_utils -from fairseq.models import FairseqIncrementalDecoder -from torch import Tensor -from fairseq.ngram_repeat_block import NGramRepeatBlock - - -class SequenceGenerator(nn.Module): - def __init__( - self, - models, - tgt_dict, - beam_size=1, - max_len_a=0, - max_len_b=200, - max_len=0, - min_len=1, - normalize_scores=True, - len_penalty=1.0, - unk_penalty=0.0, - temperature=1.0, - match_source_len=False, - no_repeat_ngram_size=0, - search_strategy=None, - eos=None, - symbols_to_strip_from_output=None, - lm_model=None, - lm_weight=1.0, - ): - """Generates translations of a given source sentence. - - Args: - models (List[~fairseq.models.FairseqModel]): ensemble of models, - currently support fairseq.models.TransformerModel for scripting - beam_size (int, optional): beam width (default: 1) - max_len_a/b (int, optional): generate sequences of maximum length - ax + b, where x is the source length - max_len (int, optional): the maximum length of the generated output - (not including end-of-sentence) - min_len (int, optional): the minimum length of the generated output - (not including end-of-sentence) - normalize_scores (bool, optional): normalize scores by the length - of the output (default: True) - len_penalty (float, optional): length penalty, where <1.0 favors - shorter, >1.0 favors longer sentences (default: 1.0) - unk_penalty (float, optional): unknown word penalty, where <0 - produces more unks, >0 produces fewer (default: 0.0) - temperature (float, optional): temperature, where values - >1.0 produce more uniform samples and values <1.0 produce - sharper samples (default: 1.0) - match_source_len (bool, optional): outputs should match the source - length (default: False) - """ - super().__init__() - if isinstance(models, EnsembleModel): - self.model = models - else: - self.model = EnsembleModel(models) - self.tgt_dict = tgt_dict - self.pad = tgt_dict.pad() - self.unk = tgt_dict.unk() - self.eos = tgt_dict.eos() if eos is None else eos - self.symbols_to_strip_from_output = ( - symbols_to_strip_from_output.union({self.eos}) - if symbols_to_strip_from_output is not None - else {self.eos} - ) - self.vocab_size = len(tgt_dict) - self.beam_size = beam_size - # the max beam size is the dictionary size - 1, since we never select pad - self.beam_size = min(beam_size, self.vocab_size - 1) - self.max_len_a = max_len_a - self.max_len_b = max_len_b - self.min_len = min_len - self.max_len = max_len or self.model.max_decoder_positions() - - self.normalize_scores = normalize_scores - self.len_penalty = len_penalty - self.unk_penalty = unk_penalty - self.temperature = temperature - self.match_source_len = match_source_len - - if no_repeat_ngram_size > 0: - self.repeat_ngram_blocker = NGramRepeatBlock(no_repeat_ngram_size) - else: - self.repeat_ngram_blocker = None - - assert temperature > 0, "--temperature must be greater than 0" - - self.search = ( - search.BeamSearch(tgt_dict) if search_strategy is None else search_strategy - ) - # We only need to set src_lengths in LengthConstrainedBeamSearch. - # As a module attribute, setting it would break in multithread - # settings when the model is shared. - self.should_set_src_lengths = ( - hasattr(self.search, "needs_src_lengths") and self.search.needs_src_lengths - ) - - self.model.eval() - - self.lm_model = lm_model - self.lm_weight = lm_weight - if self.lm_model is not None: - self.lm_model.eval() - - def cuda(self): - self.model.cuda() - return self - - @torch.no_grad() - def forward( - self, - sample: Dict[str, Dict[str, Tensor]], - prefix_tokens: Optional[Tensor] = None, - bos_token: Optional[int] = None, - ): - """Generate a batch of translations. - - Args: - sample (dict): batch - prefix_tokens (torch.LongTensor, optional): force decoder to begin - with these tokens - bos_token (int, optional): beginning of sentence token - (default: self.eos) - """ - return self._generate(sample, prefix_tokens, bos_token=bos_token) - - # TODO(myleott): unused, deprecate after pytorch-translate migration - def generate_batched_itr(self, data_itr, beam_size=None, cuda=False, timer=None): - """Iterate over a batched dataset and yield individual translations. - Args: - cuda (bool, optional): use GPU for generation - timer (StopwatchMeter, optional): time generations - """ - for sample in data_itr: - s = utils.move_to_cuda(sample) if cuda else sample - if "net_input" not in s: - continue - input = s["net_input"] - # model.forward normally channels prev_output_tokens into the decoder - # separately, but SequenceGenerator directly calls model.encoder - encoder_input = { - k: v for k, v in input.items() if k != "prev_output_tokens" - } - if timer is not None: - timer.start() - with torch.no_grad(): - hypos = self.generate(encoder_input) - if timer is not None: - timer.stop(sum(len(h[0]["tokens"]) for h in hypos)) - for i, id in enumerate(s["id"].data): - # remove padding - src = utils.strip_pad(input["src_tokens"].data[i, :], self.pad) - ref = ( - utils.strip_pad(s["target"].data[i, :], self.pad) - if s["target"] is not None - else None - ) - yield id, src, ref, hypos[i] - - @torch.no_grad() - def generate(self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs) -> List[List[Dict[str, Tensor]]]: - """Generate translations. Match the api of other fairseq generators. - - Args: - models (List[~fairseq.models.FairseqModel]): ensemble of models - sample (dict): batch - prefix_tokens (torch.LongTensor, optional): force decoder to begin - with these tokens - constraints (torch.LongTensor, optional): force decoder to include - the list of constraints - bos_token (int, optional): beginning of sentence token - (default: self.eos) - """ - return self._generate(sample, **kwargs) - - def _generate( - self, - sample: Dict[str, Dict[str, Tensor]], - prefix_tokens: Optional[Tensor] = None, - constraints: Optional[Tensor] = None, - bos_token: Optional[int] = None, - ): - incremental_states = torch.jit.annotate( - List[Dict[str, Dict[str, Optional[Tensor]]]], - [ - torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) - for i in range(self.model.models_size) - ], - ) - net_input = sample["net_input"] - - if "src_tokens" in net_input: - src_tokens = net_input["src_tokens"] - # length of the source text being the character length except EndOfSentence and pad - src_lengths = ( - (src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1) - ) - elif "source" in net_input: - src_tokens = net_input["source"] - src_lengths = ( - net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) - if net_input["padding_mask"] is not None - else torch.tensor(src_tokens.size(-1)).to(src_tokens) - ) - elif "features" in net_input: - src_tokens = net_input["features"] - src_lengths = ( - net_input["padding_mask"].size(-1) - net_input["padding_mask"].sum(-1) - if net_input["padding_mask"] is not None - else torch.tensor(src_tokens.size(-1)).to(src_tokens) - ) - else: - raise Exception("expected src_tokens or source in net input. input keys: " + str(net_input.keys())) - - # bsz: total number of sentences in beam - # Note that src_tokens may have more than 2 dimensions (i.e. audio features) - bsz, src_len = src_tokens.size()[:2] - beam_size = self.beam_size - - if constraints is not None and not self.search.supports_constraints: - raise NotImplementedError( - "Target-side constraints were provided, but search method doesn't support them" - ) - - # Initialize constraints, when active - self.search.init_constraints(constraints, beam_size) - - max_len: int = -1 - if self.match_source_len: - max_len = src_lengths.max().item() - else: - max_len = min( - int(self.max_len_a * src_len + self.max_len_b), - self.max_len - 1, - ) - assert ( - self.min_len <= max_len - ), "min_len cannot be larger than max_len, please adjust these!" - # compute the encoder output for each beam - encoder_outs = self.model.forward_encoder(net_input) - - # placeholder of indices for bsz * beam_size to hold tokens and accumulative scores - new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1) - new_order = new_order.to(src_tokens.device).long() - encoder_outs = self.model.reorder_encoder_out(encoder_outs, new_order) - # ensure encoder_outs is a List. - assert encoder_outs is not None - - # initialize buffers - scores = ( - torch.zeros(bsz * beam_size, max_len + 1).to(src_tokens).float() - ) # +1 for eos; pad is never chosen for scoring - tokens = ( - torch.zeros(bsz * beam_size, max_len + 2) - .to(src_tokens) - .long() - .fill_(self.pad) - ) # +2 for eos and pad - tokens[:, 0] = self.eos if bos_token is None else bos_token - attn: Optional[Tensor] = None - - # A list that indicates candidates that should be ignored. - # For example, suppose we're sampling and have already finalized 2/5 - # samples. Then cands_to_ignore would mark 2 positions as being ignored, - # so that we only finalize the remaining 3 samples. - cands_to_ignore = ( - torch.zeros(bsz, beam_size).to(src_tokens).eq(-1) - ) # forward and backward-compatible False mask - - # list of completed sentences - finalized = torch.jit.annotate( - List[List[Dict[str, Tensor]]], - [torch.jit.annotate(List[Dict[str, Tensor]], []) for i in range(bsz)], - ) # contains lists of dictionaries of infomation about the hypothesis being finalized at each step - - # a boolean array indicating if the sentence at the index is finished or not - finished = [False for i in range(bsz)] - num_remaining_sent = bsz # number of sentences remaining - - # number of candidate hypos per step - cand_size = 2 * beam_size # 2 x beam size in case half are EOS - - # offset arrays for converting between different indexing schemes - bbsz_offsets = ( - (torch.arange(0, bsz) * beam_size) - .unsqueeze(1) - .type_as(tokens) - .to(src_tokens.device) - ) - cand_offsets = torch.arange(0, cand_size).type_as(tokens).to(src_tokens.device) - - reorder_state: Optional[Tensor] = None - batch_idxs: Optional[Tensor] = None - - original_batch_idxs: Optional[Tensor] = None - if "id" in sample and isinstance(sample["id"], Tensor): - original_batch_idxs = sample["id"] - else: - original_batch_idxs = torch.arange(0, bsz).type_as(tokens) - - for step in range(max_len + 1): # one extra step for EOS marker - # reorder decoder internal states based on the prev choice of beams - if reorder_state is not None: - if batch_idxs is not None: - # update beam indices to take into account removed sentences - corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as( - batch_idxs - ) - reorder_state.view(-1, beam_size).add_( - corr.unsqueeze(-1) * beam_size - ) - original_batch_idxs = original_batch_idxs[batch_idxs] - self.model.reorder_incremental_state(incremental_states, reorder_state) - encoder_outs = self.model.reorder_encoder_out( - encoder_outs, reorder_state - ) - - lprobs, avg_attn_scores = self.model.forward_decoder( - tokens[:, : step + 1], - encoder_outs, - incremental_states, - self.temperature, - ) - - if self.lm_model is not None: - lm_out = self.lm_model(tokens[:, : step + 1]) - probs = self.lm_model.get_normalized_probs( - lm_out, log_probs=True, sample=None - ) - probs = probs[:, -1, :] * self.lm_weight - lprobs += probs - - lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs) - - lprobs[:, self.pad] = -math.inf # never select pad - lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty - - # handle max length constraint - if step >= max_len: - lprobs[:, : self.eos] = -math.inf - lprobs[:, self.eos + 1 :] = -math.inf - - # handle prefix tokens (possibly with different lengths) - if ( - prefix_tokens is not None - and step < prefix_tokens.size(1) - and step < max_len - ): - lprobs, tokens, scores = self._prefix_tokens( - step, lprobs, scores, tokens, prefix_tokens, beam_size - ) - elif step < self.min_len: - # minimum length constraint (does not apply if using prefix_tokens) - lprobs[:, self.eos] = -math.inf - - # Record attention scores, only support avg_attn_scores is a Tensor - if avg_attn_scores is not None: - if attn is None: - attn = torch.empty( - bsz * beam_size, avg_attn_scores.size(1), max_len + 2 - ).to(scores) - attn[:, :, step + 1].copy_(avg_attn_scores) - - scores = scores.type_as(lprobs) - eos_bbsz_idx = torch.empty(0).to( - tokens - ) # indices of hypothesis ending with eos (finished sentences) - eos_scores = torch.empty(0).to( - scores - ) # scores of hypothesis ending with eos (finished sentences) - - if self.should_set_src_lengths: - self.search.set_src_lengths(src_lengths) - - if self.repeat_ngram_blocker is not None: - lprobs = self.repeat_ngram_blocker(tokens, lprobs, bsz, beam_size, step) - - # Shape: (batch, cand_size) - cand_scores, cand_indices, cand_beams = self.search.step( - step, - lprobs.view(bsz, -1, self.vocab_size), - scores.view(bsz, beam_size, -1)[:, :, :step], - tokens[:, : step + 1], - original_batch_idxs, - ) - - # cand_bbsz_idx contains beam indices for the top candidate - # hypotheses, with a range of values: [0, bsz*beam_size), - # and dimensions: [bsz, cand_size] - cand_bbsz_idx = cand_beams.add(bbsz_offsets) - - # finalize hypotheses that end in eos - # Shape of eos_mask: (batch size, beam size) - eos_mask = cand_indices.eq(self.eos) & cand_scores.ne(-math.inf) - eos_mask[:, :beam_size][cands_to_ignore] = torch.tensor(0).to(eos_mask) - - # only consider eos when it's among the top beam_size indices - # Now we know what beam item(s) to finish - # Shape: 1d list of absolute-numbered - eos_bbsz_idx = torch.masked_select( - cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] - ) - - finalized_sents: List[int] = [] - if eos_bbsz_idx.numel() > 0: - eos_scores = torch.masked_select( - cand_scores[:, :beam_size], mask=eos_mask[:, :beam_size] - ) - - finalized_sents = self.finalize_hypos( - step, - eos_bbsz_idx, - eos_scores, - tokens, - scores, - finalized, - finished, - beam_size, - attn, - src_lengths, - max_len, - ) - num_remaining_sent -= len(finalized_sents) - - assert num_remaining_sent >= 0 - if num_remaining_sent == 0: - break - if self.search.stop_on_max_len and step >= max_len: - break - assert step < max_len, f"{step} < {max_len}" - - # Remove finalized sentences (ones for which {beam_size} - # finished hypotheses have been generated) from the batch. - if len(finalized_sents) > 0: - new_bsz = bsz - len(finalized_sents) - - # construct batch_idxs which holds indices of batches to keep for the next pass - batch_mask = torch.ones( - bsz, dtype=torch.bool, device=cand_indices.device - ) - batch_mask[finalized_sents] = False - # TODO replace `nonzero(as_tuple=False)` after TorchScript supports it - batch_idxs = torch.arange( - bsz, device=cand_indices.device - ).masked_select(batch_mask) - - # Choose the subset of the hypothesized constraints that will continue - self.search.prune_sentences(batch_idxs) - - eos_mask = eos_mask[batch_idxs] - cand_beams = cand_beams[batch_idxs] - bbsz_offsets.resize_(new_bsz, 1) - cand_bbsz_idx = cand_beams.add(bbsz_offsets) - cand_scores = cand_scores[batch_idxs] - cand_indices = cand_indices[batch_idxs] - - if prefix_tokens is not None: - prefix_tokens = prefix_tokens[batch_idxs] - src_lengths = src_lengths[batch_idxs] - cands_to_ignore = cands_to_ignore[batch_idxs] - - scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - if attn is not None: - attn = attn.view(bsz, -1)[batch_idxs].view( - new_bsz * beam_size, attn.size(1), -1 - ) - bsz = new_bsz - else: - batch_idxs = None - - # Set active_mask so that values > cand_size indicate eos hypos - # and values < cand_size indicate candidate active hypos. - # After, the min values per row are the top candidate active hypos - - # Rewrite the operator since the element wise or is not supported in torchscript. - - eos_mask[:, :beam_size] = ~((~cands_to_ignore) & (~eos_mask[:, :beam_size])) - active_mask = torch.add( - eos_mask.type_as(cand_offsets) * cand_size, - cand_offsets[: eos_mask.size(1)], - ) - - # get the top beam_size active hypotheses, which are just - # the hypos with the smallest values in active_mask. - # {active_hypos} indicates which {beam_size} hypotheses - # from the list of {2 * beam_size} candidates were - # selected. Shapes: (batch size, beam size) - new_cands_to_ignore, active_hypos = torch.topk( - active_mask, k=beam_size, dim=1, largest=False - ) - - # update cands_to_ignore to ignore any finalized hypos. - cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size] - # Make sure there is at least one active item for each sentence in the batch. - assert (~cands_to_ignore).any(dim=1).all() - - # update cands_to_ignore to ignore any finalized hypos - - # {active_bbsz_idx} denotes which beam number is continued for each new hypothesis (a beam - # can be selected more than once). - active_bbsz_idx = torch.gather(cand_bbsz_idx, dim=1, index=active_hypos) - active_scores = torch.gather(cand_scores, dim=1, index=active_hypos) - - active_bbsz_idx = active_bbsz_idx.view(-1) - active_scores = active_scores.view(-1) - - # copy tokens and scores for active hypotheses - - # Set the tokens for each beam (can select the same row more than once) - tokens[:, : step + 1] = torch.index_select( - tokens[:, : step + 1], dim=0, index=active_bbsz_idx - ) - # Select the next token for each of them - tokens.view(bsz, beam_size, -1)[:, :, step + 1] = torch.gather( - cand_indices, dim=1, index=active_hypos - ) - if step > 0: - scores[:, :step] = torch.index_select( - scores[:, :step], dim=0, index=active_bbsz_idx - ) - scores.view(bsz, beam_size, -1)[:, :, step] = torch.gather( - cand_scores, dim=1, index=active_hypos - ) - - # Update constraints based on which candidates were selected for the next beam - self.search.update_constraints(active_hypos) - - # copy attention for active hypotheses - if attn is not None: - attn[:, :, : step + 2] = torch.index_select( - attn[:, :, : step + 2], dim=0, index=active_bbsz_idx - ) - - # reorder incremental state in decoder - reorder_state = active_bbsz_idx - - # sort by score descending - for sent in range(len(finalized)): - scores = torch.tensor( - [float(elem["score"].item()) for elem in finalized[sent]] - ) - _, sorted_scores_indices = torch.sort(scores, descending=True) - finalized[sent] = [finalized[sent][ssi] for ssi in sorted_scores_indices] - finalized[sent] = torch.jit.annotate( - List[Dict[str, Tensor]], finalized[sent] - ) - return finalized - - def _prefix_tokens( - self, step: int, lprobs, scores, tokens, prefix_tokens, beam_size: int - ): - """Handle prefix tokens""" - prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1) - prefix_lprobs = lprobs.gather(-1, prefix_toks.unsqueeze(-1)) - prefix_mask = prefix_toks.ne(self.pad) - lprobs[prefix_mask] = torch.tensor(-math.inf).to(lprobs) - lprobs[prefix_mask] = lprobs[prefix_mask].scatter( - -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lprobs[prefix_mask] - ) - # if prefix includes eos, then we should make sure tokens and - # scores are the same across all beams - eos_mask = prefix_toks.eq(self.eos) - if eos_mask.any(): - # validate that the first beam matches the prefix - first_beam = tokens[eos_mask].view(-1, beam_size, tokens.size(-1))[ - :, 0, 1 : step + 1 - ] - eos_mask_batch_dim = eos_mask.view(-1, beam_size)[:, 0] - target_prefix = prefix_tokens[eos_mask_batch_dim][:, :step] - assert (first_beam == target_prefix).all() - - # copy tokens, scores and lprobs from the first beam to all beams - tokens = self.replicate_first_beam(tokens, eos_mask_batch_dim, beam_size) - scores = self.replicate_first_beam(scores, eos_mask_batch_dim, beam_size) - lprobs = self.replicate_first_beam(lprobs, eos_mask_batch_dim, beam_size) - return lprobs, tokens, scores - - def replicate_first_beam(self, tensor, mask, beam_size: int): - tensor = tensor.view(-1, beam_size, tensor.size(-1)) - tensor[mask] = tensor[mask][:, :1, :] - return tensor.view(-1, tensor.size(-1)) - - def finalize_hypos( - self, - step: int, - bbsz_idx, - eos_scores, - tokens, - scores, - finalized: List[List[Dict[str, Tensor]]], - finished: List[bool], - beam_size: int, - attn: Optional[Tensor], - src_lengths, - max_len: int, - ): - """Finalize hypothesis, store finalized information in `finalized`, and change `finished` accordingly. - A sentence is finalized when {beam_size} finished items have been collected for it. - - Returns number of sentences (not beam items) being finalized. - These will be removed from the batch and not processed further. - Args: - bbsz_idx (Tensor): - """ - assert bbsz_idx.numel() == eos_scores.numel() - - # clone relevant token and attention tensors. - # tokens is (batch * beam, max_len). So the index_select - # gets the newly EOS rows, then selects cols 1..{step + 2} - tokens_clone = tokens.index_select(0, bbsz_idx)[ - :, 1 : step + 2 - ] # skip the first index, which is EOS - - tokens_clone[:, step] = self.eos - attn_clone = ( - attn.index_select(0, bbsz_idx)[:, :, 1 : step + 2] - if attn is not None - else None - ) - - # compute scores per token position - pos_scores = scores.index_select(0, bbsz_idx)[:, : step + 1] - pos_scores[:, step] = eos_scores - # convert from cumulative to per-position scores - pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1] - - # normalize sentence-level scores - if self.normalize_scores: - eos_scores /= (step + 1) ** self.len_penalty - - # cum_unfin records which sentences in the batch are finished. - # It helps match indexing between (a) the original sentences - # in the batch and (b) the current, possibly-reduced set of - # sentences. - cum_unfin: List[int] = [] - prev = 0 - for f in finished: - if f: - prev += 1 - else: - cum_unfin.append(prev) - - # The keys here are of the form "{sent}_{unfin_idx}", where - # "unfin_idx" is the index in the current (possibly reduced) - # list of sentences, and "sent" is the index in the original, - # unreduced batch - # set() is not supported in script export - sents_seen: Dict[str, Optional[Tensor]] = {} - - # For every finished beam item - for i in range(bbsz_idx.size()[0]): - idx = bbsz_idx[i] - score = eos_scores[i] - # sentence index in the current (possibly reduced) batch - unfin_idx = idx // beam_size - # sentence index in the original (unreduced) batch - sent = unfin_idx + cum_unfin[unfin_idx] - # Cannot create dict for key type '(int, int)' in torchscript. - # The workaround is to cast int to string - seen = str(sent.item()) + "_" + str(unfin_idx.item()) - if seen not in sents_seen: - sents_seen[seen] = None - - if self.match_source_len and step > src_lengths[unfin_idx]: - score = torch.tensor(-math.inf).to(score) - - # An input sentence (among those in a batch) is finished when - # beam_size hypotheses have been collected for it - if len(finalized[sent]) < beam_size: - if attn_clone is not None: - # remove padding tokens from attn scores - hypo_attn = attn_clone[i] - else: - hypo_attn = torch.empty(0) - - finalized[sent].append( - { - "tokens": tokens_clone[i], - "score": score, - "attention": hypo_attn, # src_len x tgt_len - "alignment": torch.empty(0), - "positional_scores": pos_scores[i], - } - ) - - newly_finished: List[int] = [] - - for seen in sents_seen.keys(): - # check termination conditions for this sentence - sent: int = int(float(seen.split("_")[0])) - unfin_idx: int = int(float(seen.split("_")[1])) - - if not finished[sent] and self.is_finished( - step, unfin_idx, max_len, len(finalized[sent]), beam_size - ): - finished[sent] = True - newly_finished.append(unfin_idx) - - return newly_finished - - def is_finished( - self, - step: int, - unfin_idx: int, - max_len: int, - finalized_sent_len: int, - beam_size: int, - ): - """ - Check whether decoding for a sentence is finished, which - occurs when the list of finalized sentences has reached the - beam size, or when we reach the maximum length. - """ - assert finalized_sent_len <= beam_size - if finalized_sent_len == beam_size or step == max_len: - return True - return False - - -class EnsembleModel(nn.Module): - """A wrapper around an ensemble of models.""" - - def __init__(self, models): - super().__init__() - self.models_size = len(models) - # method '__len__' is not supported in ModuleList for torch script - self.single_model = models[0] - self.models = nn.ModuleList(models) - - self.has_incremental: bool = False - if all( - hasattr(m, "decoder") and isinstance(m.decoder, FairseqIncrementalDecoder) - for m in models - ): - self.has_incremental = True - - def forward(self): - pass - - def has_encoder(self): - return hasattr(self.single_model, "encoder") - - def has_incremental_states(self): - return self.has_incremental - - def max_decoder_positions(self): - return min([m.max_decoder_positions() for m in self.models if hasattr(m, "max_decoder_positions")] + [sys.maxsize]) - - @torch.jit.export - def forward_encoder(self, net_input: Dict[str, Tensor]): - if not self.has_encoder(): - return None - return [model.encoder.forward_torchscript(net_input) for model in self.models] - - @torch.jit.export - def forward_decoder( - self, - tokens, - encoder_outs: List[Dict[str, List[Tensor]]], - incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], - temperature: float = 1.0, - ): - log_probs = [] - avg_attn: Optional[Tensor] = None - encoder_out: Optional[Dict[str, List[Tensor]]] = None - for i, model in enumerate(self.models): - if self.has_encoder(): - encoder_out = encoder_outs[i] - # decode each model - if self.has_incremental_states(): - decoder_out = model.decoder.forward( - tokens, - encoder_out=encoder_out, - incremental_state=incremental_states[i], - ) - else: - if hasattr(model, "decoder"): - decoder_out = model.decoder.forward(tokens, encoder_out=encoder_out) - else: - decoder_out = model.forward(tokens) - - attn: Optional[Tensor] = None - decoder_len = len(decoder_out) - if decoder_len > 1 and decoder_out[1] is not None: - if isinstance(decoder_out[1], Tensor): - attn = decoder_out[1] - else: - attn_holder = decoder_out[1]["attn"] - if isinstance(attn_holder, Tensor): - attn = attn_holder - elif attn_holder is not None: - attn = attn_holder[0] - if attn is not None: - attn = attn[:, -1, :] - - decoder_out_tuple = ( - decoder_out[0][:, -1:, :].div_(temperature), - None if decoder_len <= 1 else decoder_out[1], - ) - probs = model.get_normalized_probs( - decoder_out_tuple, log_probs=True, sample=None - ) - probs = probs[:, -1, :] - if self.models_size == 1: - return probs, attn - - log_probs.append(probs) - if attn is not None: - if avg_attn is None: - avg_attn = attn - else: - avg_attn.add_(attn) - - avg_probs = torch.logsumexp(torch.stack(log_probs, dim=0), dim=0) - math.log( - self.models_size - ) - - if avg_attn is not None: - avg_attn.div_(self.models_size) - return avg_probs, avg_attn - - @torch.jit.export - def reorder_encoder_out( - self, encoder_outs: Optional[List[Dict[str, List[Tensor]]]], new_order - ): - """ - Reorder encoder output according to *new_order*. - - Args: - encoder_out: output from the ``forward()`` method - new_order (LongTensor): desired order - - Returns: - *encoder_out* rearranged according to *new_order* - """ - new_outs: List[Dict[str, List[Tensor]]] = [] - if not self.has_encoder(): - return new_outs - for i, model in enumerate(self.models): - assert encoder_outs is not None - new_outs.append( - model.encoder.reorder_encoder_out(encoder_outs[i], new_order) - ) - return new_outs - - @torch.jit.export - def reorder_incremental_state( - self, - incremental_states: List[Dict[str, Dict[str, Optional[Tensor]]]], - new_order, - ): - if not self.has_incremental_states(): - return - for i, model in enumerate(self.models): - model.decoder.reorder_incremental_state_scripting( - incremental_states[i], new_order - ) - - -class SequenceGeneratorWithAlignment(SequenceGenerator): - def __init__( - self, models, tgt_dict, left_pad_target=False, print_alignment="hard", **kwargs - ): - """Generates translations of a given source sentence. - - Produces alignments following "Jointly Learning to Align and - Translate with Transformer Models" (Garg et al., EMNLP 2019). - - Args: - left_pad_target (bool, optional): Whether or not the - hypothesis should be left padded or not when they are - teacher forced for generating alignments. - """ - super().__init__(EnsembleModelWithAlignment(models), tgt_dict, **kwargs) - self.left_pad_target = left_pad_target - - if print_alignment == "hard": - self.extract_alignment = utils.extract_hard_alignment - elif print_alignment == "soft": - self.extract_alignment = utils.extract_soft_alignment - - @torch.no_grad() - def generate(self, models, sample, **kwargs): - finalized = super()._generate(sample, **kwargs) - - src_tokens = sample["net_input"]["src_tokens"] - bsz = src_tokens.shape[0] - beam_size = self.beam_size - ( - src_tokens, - src_lengths, - prev_output_tokens, - tgt_tokens, - ) = self._prepare_batch_for_alignment(sample, finalized) - if any(getattr(m, "full_context_alignment", False) for m in self.model.models): - attn = self.model.forward_align(src_tokens, src_lengths, prev_output_tokens) - else: - attn = [ - finalized[i // beam_size][i % beam_size]["attention"].transpose(1, 0) - for i in range(bsz * beam_size) - ] - - if src_tokens.device != "cpu": - src_tokens = src_tokens.to("cpu") - tgt_tokens = tgt_tokens.to("cpu") - attn = [i.to("cpu") for i in attn] - - # Process the attn matrix to extract hard alignments. - for i in range(bsz * beam_size): - alignment = self.extract_alignment( - attn[i], src_tokens[i], tgt_tokens[i], self.pad, self.eos - ) - finalized[i // beam_size][i % beam_size]["alignment"] = alignment - return finalized - - def _prepare_batch_for_alignment(self, sample, hypothesis): - src_tokens = sample["net_input"]["src_tokens"] - bsz = src_tokens.shape[0] - src_tokens = ( - src_tokens[:, None, :] - .expand(-1, self.beam_size, -1) - .contiguous() - .view(bsz * self.beam_size, -1) - ) - src_lengths = sample["net_input"]["src_lengths"] - src_lengths = ( - src_lengths[:, None] - .expand(-1, self.beam_size) - .contiguous() - .view(bsz * self.beam_size) - ) - prev_output_tokens = data_utils.collate_tokens( - [beam["tokens"] for example in hypothesis for beam in example], - self.pad, - self.eos, - self.left_pad_target, - move_eos_to_beginning=True, - ) - tgt_tokens = data_utils.collate_tokens( - [beam["tokens"] for example in hypothesis for beam in example], - self.pad, - self.eos, - self.left_pad_target, - move_eos_to_beginning=False, - ) - return src_tokens, src_lengths, prev_output_tokens, tgt_tokens - - -class EnsembleModelWithAlignment(EnsembleModel): - """A wrapper around an ensemble of models.""" - - def __init__(self, models): - super().__init__(models) - - def forward_align(self, src_tokens, src_lengths, prev_output_tokens): - avg_attn = None - for model in self.models: - decoder_out = model(src_tokens, src_lengths, prev_output_tokens) - attn = decoder_out[1]["attn"][0] - if avg_attn is None: - avg_attn = attn - else: - avg_attn.add_(attn) - if len(self.models) > 1: - avg_attn.div_(len(self.models)) - return avg_attn diff --git a/spaces/gradio/HuBERT/tests/speech_recognition/asr_test_base.py b/spaces/gradio/HuBERT/tests/speech_recognition/asr_test_base.py deleted file mode 100644 index 8c5d414e7bf17ee02f280d024fa5d07e28b79d6b..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/tests/speech_recognition/asr_test_base.py +++ /dev/null @@ -1,557 +0,0 @@ -#!/usr/bin/env python3 - -import argparse -import os -import unittest -from inspect import currentframe, getframeinfo - -import numpy as np -import torch -from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask -from fairseq.data import data_utils as fairseq_data_utils -from fairseq.data.dictionary import Dictionary -from fairseq.models import ( - BaseFairseqModel, - FairseqDecoder, - FairseqEncoder, - FairseqEncoderDecoderModel, - FairseqEncoderModel, - FairseqModel, -) -from fairseq.tasks.fairseq_task import LegacyFairseqTask - - -DEFAULT_TEST_VOCAB_SIZE = 100 - - -# /////////////////////////////////////////////////////////////////////////// -# utility function to setup dummy dict/task/input -# /////////////////////////////////////////////////////////////////////////// - - -def get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE): - dummy_dict = Dictionary() - # add dummy symbol to satisfy vocab size - for id, _ in enumerate(range(vocab_size)): - dummy_dict.add_symbol("{}".format(id), 1000) - return dummy_dict - - -class DummyTask(LegacyFairseqTask): - def __init__(self, args): - super().__init__(args) - self.dictionary = get_dummy_dictionary() - if getattr(self.args, "ctc", False): - self.dictionary.add_symbol("") - self.tgt_dict = self.dictionary - - @property - def target_dictionary(self): - return self.dictionary - - -def get_dummy_task_and_parser(): - """ - to build a fariseq model, we need some dummy parse and task. This function - is used to create dummy task and parser to faciliate model/criterion test - - Note: we use FbSpeechRecognitionTask as the dummy task. You may want - to use other task by providing another function - """ - parser = argparse.ArgumentParser( - description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS - ) - DummyTask.add_args(parser) - args = parser.parse_args([]) - task = DummyTask.setup_task(args) - return task, parser - - -def get_dummy_input(T=100, D=80, B=5, K=100): - forward_input = {} - # T max sequence length - # D feature vector dimension - # B batch size - # K target dimension size - feature = torch.randn(B, T, D) - # this (B, T, D) layout is just a convention, you can override it by - # write your own _prepare_forward_input function - src_lengths = torch.from_numpy( - np.random.randint(low=1, high=T, size=B, dtype=np.int64) - ) - src_lengths[0] = T # make sure the maximum length matches - prev_output_tokens = [] - for b in range(B): - token_length = np.random.randint(low=1, high=src_lengths[b].item() + 1) - tokens = np.random.randint(low=0, high=K, size=token_length, dtype=np.int64) - prev_output_tokens.append(torch.from_numpy(tokens)) - - prev_output_tokens = fairseq_data_utils.collate_tokens( - prev_output_tokens, - pad_idx=1, - eos_idx=2, - left_pad=False, - move_eos_to_beginning=False, - ) - src_lengths, sorted_order = src_lengths.sort(descending=True) - forward_input["src_tokens"] = feature.index_select(0, sorted_order) - forward_input["src_lengths"] = src_lengths - forward_input["prev_output_tokens"] = prev_output_tokens - - return forward_input - - -def get_dummy_encoder_output(encoder_out_shape=(100, 80, 5)): - """ - This only provides an example to generate dummy encoder output - """ - (T, B, D) = encoder_out_shape - encoder_out = {} - - encoder_out["encoder_out"] = torch.from_numpy( - np.random.randn(*encoder_out_shape).astype(np.float32) - ) - seq_lengths = torch.from_numpy(np.random.randint(low=1, high=T, size=B)) - # some dummy mask - encoder_out["encoder_padding_mask"] = torch.arange(T).view(1, T).expand( - B, -1 - ) >= seq_lengths.view(B, 1).expand(-1, T) - encoder_out["encoder_padding_mask"].t_() - - # encoer_padding_mask is (T, B) tensor, with (t, b)-th element indicate - # whether encoder_out[t, b] is valid (=0) or not (=1) - return encoder_out - - -def _current_postion_info(): - cf = currentframe() - frameinfo = " (at {}:{})".format( - os.path.basename(getframeinfo(cf).filename), cf.f_back.f_lineno - ) - return frameinfo - - -def check_encoder_output(encoder_output, batch_size=None): - """we expect encoder_output to be a dict with the following - key/value pairs: - - encoder_out: a Torch.Tensor - - encoder_padding_mask: a binary Torch.Tensor - """ - if not isinstance(encoder_output, dict): - msg = ( - "FairseqEncoderModel.forward(...) must be a dict" + _current_postion_info() - ) - return False, msg - - if "encoder_out" not in encoder_output: - msg = ( - "FairseqEncoderModel.forward(...) must contain encoder_out" - + _current_postion_info() - ) - return False, msg - - if "encoder_padding_mask" not in encoder_output: - msg = ( - "FairseqEncoderModel.forward(...) must contain encoder_padding_mask" - + _current_postion_info() - ) - return False, msg - - if not isinstance(encoder_output["encoder_out"], torch.Tensor): - msg = "encoder_out must be a torch.Tensor" + _current_postion_info() - return False, msg - - if encoder_output["encoder_out"].dtype != torch.float32: - msg = "encoder_out must have float32 dtype" + _current_postion_info() - return False, msg - - mask = encoder_output["encoder_padding_mask"] - if mask is not None: - if not isinstance(mask, torch.Tensor): - msg = ( - "encoder_padding_mask must be a torch.Tensor" + _current_postion_info() - ) - return False, msg - if mask.dtype != torch.uint8 and ( - not hasattr(torch, "bool") or mask.dtype != torch.bool - ): - msg = ( - "encoder_padding_mask must have dtype of uint8" - + _current_postion_info() - ) - return False, msg - - if mask.dim() != 2: - msg = ( - "we expect encoder_padding_mask to be a 2-d tensor, in shape (T, B)" - + _current_postion_info() - ) - return False, msg - - if batch_size is not None and mask.size(1) != batch_size: - msg = ( - "we expect encoder_padding_mask to be a 2-d tensor, with size(1)" - + " being the batch size" - + _current_postion_info() - ) - return False, msg - return True, None - - -def check_decoder_output(decoder_output): - """we expect output from a decoder is a tuple with the following constraint: - - the first element is a torch.Tensor - - the second element can be anything (reserved for future use) - """ - if not isinstance(decoder_output, tuple): - msg = "FariseqDecoder output must be a tuple" + _current_postion_info() - return False, msg - - if len(decoder_output) != 2: - msg = "FairseqDecoder output must be 2-elem tuple" + _current_postion_info() - return False, msg - - if not isinstance(decoder_output[0], torch.Tensor): - msg = ( - "FariseqDecoder output[0] must be a torch.Tensor" + _current_postion_info() - ) - return False, msg - - return True, None - - -# /////////////////////////////////////////////////////////////////////////// -# Base Test class -# /////////////////////////////////////////////////////////////////////////// - - -class TestBaseFairseqModelBase(unittest.TestCase): - """ - This class is used to facilitate writing unittest for any class derived from - `BaseFairseqModel`. - """ - - @classmethod - def setUpClass(cls): - if cls is TestBaseFairseqModelBase: - raise unittest.SkipTest("Skipping test case in base") - super().setUpClass() - - def setUpModel(self, model): - self.assertTrue(isinstance(model, BaseFairseqModel)) - self.model = model - - def setupInput(self): - pass - - def setUp(self): - self.model = None - self.forward_input = None - pass - - -class TestFairseqEncoderDecoderModelBase(TestBaseFairseqModelBase): - """ - base code to test FairseqEncoderDecoderModel (formally known as - `FairseqModel`) must be derived from this base class - """ - - @classmethod - def setUpClass(cls): - if cls is TestFairseqEncoderDecoderModelBase: - raise unittest.SkipTest("Skipping test case in base") - super().setUpClass() - - def setUpModel(self, model_cls, extra_args_setters=None): - self.assertTrue( - issubclass(model_cls, (FairseqEncoderDecoderModel, FairseqModel)), - msg="This class only tests for FairseqModel subclasses", - ) - - task, parser = get_dummy_task_and_parser() - model_cls.add_args(parser) - - args = parser.parse_args([]) - - if extra_args_setters is not None: - for args_setter in extra_args_setters: - args_setter(args) - model = model_cls.build_model(args, task) - self.model = model - - def setUpInput(self, input=None): - self.forward_input = get_dummy_input() if input is None else input - - def setUp(self): - super().setUp() - - def test_forward(self): - if self.model and self.forward_input: - forward_output = self.model.forward(**self.forward_input) - # for FairseqEncoderDecoderModel, forward returns a tuple of two - # elements, the first one is a Torch.Tensor - succ, msg = check_decoder_output(forward_output) - if not succ: - self.assertTrue(succ, msg=msg) - self.forward_output = forward_output - - def test_get_normalized_probs(self): - if self.model and self.forward_input: - forward_output = self.model.forward(**self.forward_input) - logprob = self.model.get_normalized_probs(forward_output, log_probs=True) - prob = self.model.get_normalized_probs(forward_output, log_probs=False) - - # in order for different models/criterion to play with each other - # we need to know whether the logprob or prob output is batch_first - # or not. We assume an additional attribute will be attached to logprob - # or prob. If you find your code failed here, simply override - # FairseqModel.get_normalized_probs, see example at - # https://fburl.com/batch_first_example - self.assertTrue(hasattr(logprob, "batch_first")) - self.assertTrue(hasattr(prob, "batch_first")) - - self.assertTrue(torch.is_tensor(logprob)) - self.assertTrue(torch.is_tensor(prob)) - - -class TestFairseqEncoderModelBase(TestBaseFairseqModelBase): - """ - base class to test FairseqEncoderModel - """ - - @classmethod - def setUpClass(cls): - if cls is TestFairseqEncoderModelBase: - raise unittest.SkipTest("Skipping test case in base") - super().setUpClass() - - def setUpModel(self, model_cls, extra_args_setters=None): - self.assertTrue( - issubclass(model_cls, FairseqEncoderModel), - msg="This class is only used for testing FairseqEncoderModel", - ) - task, parser = get_dummy_task_and_parser() - model_cls.add_args(parser) - args = parser.parse_args([]) - if extra_args_setters is not None: - for args_setter in extra_args_setters: - args_setter(args) - - model = model_cls.build_model(args, task) - self.model = model - - def setUpInput(self, input=None): - self.forward_input = get_dummy_input() if input is None else input - # get_dummy_input() is originally for s2s, here we delete extra dict - # items, so it can be used for EncoderModel / Encoder as well - self.forward_input.pop("prev_output_tokens", None) - - def setUp(self): - super().setUp() - - def test_forward(self): - if self.forward_input and self.model: - bsz = self.forward_input["src_tokens"].size(0) - forward_output = self.model.forward(**self.forward_input) - - # we expect forward_output to be a dict with the following - # key/value pairs: - # - encoder_out: a Torch.Tensor - # - encoder_padding_mask: a binary Torch.Tensor - succ, msg = check_encoder_output(forward_output, batch_size=bsz) - if not succ: - self.assertTrue(succ, msg=msg) - self.forward_output = forward_output - - def test_get_normalized_probs(self): - if self.model and self.forward_input: - forward_output = self.model.forward(**self.forward_input) - logprob = self.model.get_normalized_probs(forward_output, log_probs=True) - prob = self.model.get_normalized_probs(forward_output, log_probs=False) - - # in order for different models/criterion to play with each other - # we need to know whether the logprob or prob output is batch_first - # or not. We assume an additional attribute will be attached to logprob - # or prob. If you find your code failed here, simply override - # FairseqModel.get_normalized_probs, see example at - # https://fburl.com/batch_first_example - self.assertTrue(hasattr(logprob, "batch_first")) - self.assertTrue(hasattr(prob, "batch_first")) - - self.assertTrue(torch.is_tensor(logprob)) - self.assertTrue(torch.is_tensor(prob)) - - -class TestFairseqEncoderBase(unittest.TestCase): - """ - base class to test FairseqEncoder - """ - - @classmethod - def setUpClass(cls): - if cls is TestFairseqEncoderBase: - raise unittest.SkipTest("Skipping test case in base") - super().setUpClass() - - def setUpEncoder(self, encoder): - self.assertTrue( - isinstance(encoder, FairseqEncoder), - msg="This class is only used for test FairseqEncoder", - ) - self.encoder = encoder - - def setUpInput(self, input=None): - self.forward_input = get_dummy_input() if input is None else input - # get_dummy_input() is originally for s2s, here we delete extra dict - # items, so it can be used for EncoderModel / Encoder as well - self.forward_input.pop("prev_output_tokens", None) - - def setUp(self): - self.encoder = None - self.forward_input = None - - def test_forward(self): - if self.encoder and self.forward_input: - bsz = self.forward_input["src_tokens"].size(0) - - forward_output = self.encoder.forward(**self.forward_input) - succ, msg = check_encoder_output(forward_output, batch_size=bsz) - if not succ: - self.assertTrue(succ, msg=msg) - self.forward_output = forward_output - - -class TestFairseqDecoderBase(unittest.TestCase): - """ - base class to test FairseqDecoder - """ - - @classmethod - def setUpClass(cls): - if cls is TestFairseqDecoderBase: - raise unittest.SkipTest("Skipping test case in base") - super().setUpClass() - - def setUpDecoder(self, decoder): - self.assertTrue( - isinstance(decoder, FairseqDecoder), - msg="This class is only used for test FairseqDecoder", - ) - self.decoder = decoder - - def setUpInput(self, input=None): - self.forward_input = get_dummy_encoder_output() if input is None else input - - def setUpPrevOutputTokens(self, tokens=None): - if tokens is None: - self.encoder_input = get_dummy_input() - self.prev_output_tokens = self.encoder_input["prev_output_tokens"] - else: - self.prev_output_tokens = tokens - - def setUp(self): - self.decoder = None - self.forward_input = None - self.prev_output_tokens = None - - def test_forward(self): - if ( - self.decoder is not None - and self.forward_input is not None - and self.prev_output_tokens is not None - ): - forward_output = self.decoder.forward( - prev_output_tokens=self.prev_output_tokens, - encoder_out=self.forward_input, - ) - succ, msg = check_decoder_output(forward_output) - if not succ: - self.assertTrue(succ, msg=msg) - self.forward_input = forward_output - - -class DummyEncoderModel(FairseqEncoderModel): - def __init__(self, encoder): - super().__init__(encoder) - - @classmethod - def build_model(cls, args, task): - return cls(DummyEncoder()) - - def get_logits(self, net_output): - # Inverse of sigmoid to use with BinaryCrossEntropyWithLogitsCriterion as - # F.binary_cross_entropy_with_logits combines sigmoid and CE - return torch.log( - torch.div(net_output["encoder_out"], 1 - net_output["encoder_out"]) - ) - - def get_normalized_probs(self, net_output, log_probs, sample=None): - lprobs = super().get_normalized_probs(net_output, log_probs, sample=sample) - lprobs.batch_first = True - return lprobs - - -class DummyEncoder(FairseqEncoder): - def __init__(self): - super().__init__(None) - - def forward(self, src_tokens, src_lengths): - mask, max_len = lengths_to_encoder_padding_mask(src_lengths) - return {"encoder_out": src_tokens, "encoder_padding_mask": mask} - - -class CrossEntropyCriterionTestBase(unittest.TestCase): - @classmethod - def setUpClass(cls): - if cls is CrossEntropyCriterionTestBase: - raise unittest.SkipTest("Skipping base class test case") - super().setUpClass() - - def setUpArgs(self): - args = argparse.Namespace() - args.sentence_avg = False - args.threshold = 0.1 # to use with BinaryCrossEntropyWithLogitsCriterion - return args - - def setUp(self): - args = self.setUpArgs() - self.model = DummyEncoderModel(encoder=DummyEncoder()) - self.criterion = self.criterion_cls.build_criterion(args, task=DummyTask(args)) - - def get_src_tokens(self, correct_prediction, aggregate): - """ - correct_prediction: True if the net_output (src_tokens) should - predict the correct target - aggregate: True if the criterion expects net_output (src_tokens) - aggregated across time axis - """ - predicted_idx = 0 if correct_prediction else 1 - if aggregate: - src_tokens = torch.zeros((2, 2), dtype=torch.float) - for b in range(2): - src_tokens[b][predicted_idx] = 1.0 - else: - src_tokens = torch.zeros((2, 10, 2), dtype=torch.float) - for b in range(2): - for t in range(10): - src_tokens[b][t][predicted_idx] = 1.0 - return src_tokens - - def get_target(self, soft_target): - if soft_target: - target = torch.zeros((2, 2), dtype=torch.float) - for b in range(2): - target[b][0] = 1.0 - else: - target = torch.zeros((2, 10), dtype=torch.long) - return target - - def get_test_sample(self, correct, soft_target, aggregate): - src_tokens = self.get_src_tokens(correct, aggregate) - target = self.get_target(soft_target) - L = src_tokens.size(1) - return { - "net_input": {"src_tokens": src_tokens, "src_lengths": torch.tensor([L])}, - "target": target, - "ntokens": src_tokens.size(0) * src_tokens.size(1), - } diff --git a/spaces/gradio/HuBERT/tests/test_constraints.py b/spaces/gradio/HuBERT/tests/test_constraints.py deleted file mode 100644 index 1c37f7e1fb26d8ea5349fedd3a60f566d09cf598..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/tests/test_constraints.py +++ /dev/null @@ -1,269 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import sys -import unittest - -import torch -from fairseq.token_generation_constraints import * - - -def tensorize(constraints: List[List[int]]) -> torch.Tensor: - return [torch.tensor(x) for x in constraints] - - -class TestHelperRoutines(unittest.TestCase): - def setUp(self): - self.examples = [ - ([[]], torch.tensor([[0]])), - ([[], []], torch.tensor([[0], [0]])), - ([[torch.tensor([1, 2])], []], torch.tensor([[1, 1, 2, 0], [0, 0, 0, 0]])), - ( - [ - [ - torch.tensor([3, 1, 2]), - torch.tensor([3]), - torch.tensor([4, 5, 6, 7]), - ], - [], - [torch.tensor([1, 8, 9, 10, 1, 4, 11, 12])], - ], - torch.tensor( - [ - [3, 3, 1, 2, 0, 3, 0, 4, 5, 6, 7, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [1, 1, 8, 9, 10, 1, 4, 11, 12, 0, 0, 0], - ] - ), - ), - ] - - def test_packing(self): - """Ensures the list of lists of tensors gets packed correctly.""" - for batch_constraints, expected_tensor in self.examples: - packed = pack_constraints(batch_constraints) - assert torch.equal(packed, expected_tensor) - - -class TestUnorderedConstraintState(unittest.TestCase): - def setUp(self): - # Tuples of (contraint set, expected printed graph, token counts per node) - self.examples = [ - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - "([None].False#6 ([1].True#4 ([2].False#1 [3].True#1) [3].True#1 [4].True#1) ([4].False#2 ([5].True#2 ([6].False#1 [7].True#1))))", - {1: 4, 2: 1, 3: 2, 4: 3, 5: 2, 6: 1, 7: 1}, - ), - ([], "[None].False#0", {}), - (tensorize([[0]]), "([None].False#1 [0].True#1)", {0: 1}), - ( - tensorize([[100000, 1, 2, 3, 4, 5]]), - "([None].False#1 ([100000].False#1 ([1].False#1 ([2].False#1 ([3].False#1 ([4].False#1 [5].True#1))))))", - {100000: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}, - ), - ( - tensorize([[1, 2], [1, 2]]), - "([None].False#2 ([1].False#2 [2].True#2))", - {1: 2, 2: 2}, - ), - ( - tensorize([[1, 2], [3, 4]]), - "([None].False#2 ([1].False#1 [2].True#1) ([3].False#1 [4].True#1))", - {1: 1, 2: 1, 3: 1, 4: 1}, - ), - ] - - self.sequences = [ - ( - self.examples[0][0], - [], - {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, - ), - ( - self.examples[0][0], - [1, 2], - {"bank": 2, "num_completed": 0, "finished": False, "is_root": False}, - ), - ( - self.examples[0][0], - [1, 2, 94], - {"bank": 1, "num_completed": 1, "finished": False, "is_root": True}, - ), - ( - self.examples[0][0], - [1, 3, 999, 1, 4], - {"bank": 4, "num_completed": 2, "finished": False, "is_root": False}, - ), - ( - self.examples[0][0], - [1, 3, 999, 1, 4, 999], - {"bank": 4, "num_completed": 2, "finished": False, "is_root": True}, - ), - ( - self.examples[0][0], - [4, 5, 6, 8], - {"bank": 2, "num_completed": 1, "finished": False, "is_root": True}, - ), - ( - self.examples[0][0], - # Tricky, because in last three, goes down [1->4] branch, could miss [1] and [4->5] - # [[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]], - [1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5], - {"bank": 14, "num_completed": 6, "finished": True, "is_root": False}, - ), - ( - self.examples[0][0], - [1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117], - {"bank": 14, "num_completed": 6, "finished": True, "is_root": True}, - ), - ( - tensorize([[1], [2, 3]]), - # Should not be able to get credit for entering 1 a second time - [1, 1], - {"bank": 1, "num_completed": 1, "finished": False, "is_root": True}, - ), - ( - self.examples[4][0], - [1, 2, 1, 2], - {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, - ), - ( - self.examples[4][0], - [1, 2, 1, 2, 1], - {"bank": 4, "num_completed": 2, "finished": True, "is_root": True}, - ), - ( - self.examples[5][0], - [1, 2, 3, 4, 5], - {"bank": 4, "num_completed": 2, "finished": True, "is_root": True}, - ), - ] - - def test_graphs(self): - """ - Test whether unordered graph systems are created correctly. - """ - for example in self.examples: - constraints, expected, gold_counts = example - c = ConstraintNode.create(constraints) - assert ( - ConstraintNode.print_graph(c) == expected - ), f"got {ConstraintNode.print_graph(c)}, expected {expected}" - assert ( - c.token_counts() == gold_counts - ), f"{c} got {c.token_counts()} wanted {gold_counts}" - - def test_next_tokens(self): - """ - Tests that the set of next tokens is correct. - """ - for example in self.examples: - constraints, expected, gold_counts = example - root = ConstraintNode.create(constraints) - - root_tokens = set(root.children.keys()) - for sequence in constraints: - state = UnorderedConstraintState(root) - for token in sequence: - all_tokens = root_tokens.union(state.node.children.keys()) - assert ( - all_tokens == state.next_tokens() - ), f"ALL {all_tokens} NEXT {state.next_tokens()}" - state = state.advance(token) - - def test_sequences(self): - for constraints, tokens, expected in self.sequences: - state = UnorderedConstraintState.create(pack_constraints([constraints])[0]) - for token in tokens: - state = state.advance(token) - result = {} - for attr in expected.keys(): - result[attr] = getattr(state, attr) - - assert ( - result == expected - ), f"TEST({tokens}) GOT: {result} WANTED: {expected}" - - -class TestOrderedConstraintState(unittest.TestCase): - def setUp(self): - self.sequences = [ - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [], - {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 2], - {"bank": 2, "num_completed": 0, "finished": False, "is_root": False}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 2, 94], - {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 3, 999, 1, 4], - {"bank": 0, "num_completed": 0, "finished": False, "is_root": True}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 2, 3, 999, 999], - {"bank": 3, "num_completed": 1, "finished": False, "is_root": False}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 2, 3, 77, 1, 3, 1], - {"bank": 6, "num_completed": 2, "finished": False, "is_root": False}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 2, 3, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5], - {"bank": 14, "num_completed": 6, "finished": True, "is_root": False}, - ), - ( - tensorize([[1, 2, 3], [1, 3], [1, 4], [4, 5, 6, 7], [1], [4, 5]]), - [1, 2, 999, 1, 2, 3, 999, 1, 3, 1, 4, 4, 5, 6, 7, 1, 4, 5, 117], - {"bank": 14, "num_completed": 6, "finished": True, "is_root": False}, - ), - ( - tensorize([[1], [2, 3]]), - [1, 1], - {"bank": 1, "num_completed": 1, "finished": False, "is_root": False}, - ), - ( - tensorize([[1, 2], [1, 2]]), - [1, 2, 1, 2], - {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, - ), - ( - tensorize([[1, 2], [1, 2]]), - [1, 2, 1, 2, 1], - {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, - ), - ( - tensorize([[1, 2], [3, 4]]), - [1, 2, 3, 4, 5], - {"bank": 4, "num_completed": 2, "finished": True, "is_root": False}, - ), - ] - - def test_sequences(self): - for i, (constraints, tokens, expected) in enumerate(self.sequences): - state = OrderedConstraintState.create(pack_constraints([constraints])[0]) - for token in tokens: - state = state.advance(token) - result = {} - for attr in expected.keys(): - result[attr] = getattr(state, attr) - assert ( - result == expected - ), f"TEST({tokens}) GOT: {result} WANTED: {expected}" - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/onnx_helper.py b/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/onnx_helper.py deleted file mode 100644 index ca922ca6d410655029e459cf8fd1c323d276c34c..0000000000000000000000000000000000000000 --- a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/onnx_helper.py +++ /dev/null @@ -1,250 +0,0 @@ -from __future__ import division -import datetime -import os -import os.path as osp -import glob -import numpy as np -import cv2 -import sys -import onnxruntime -import onnx -import argparse -from onnx import numpy_helper -from insightface.data import get_image - -class ArcFaceORT: - def __init__(self, model_path, cpu=False): - self.model_path = model_path - # providers = None will use available provider, for onnxruntime-gpu it will be "CUDAExecutionProvider" - self.providers = ['CPUExecutionProvider'] if cpu else None - - #input_size is (w,h), return error message, return None if success - def check(self, track='cfat', test_img = None): - #default is cfat - max_model_size_mb=1024 - max_feat_dim=512 - max_time_cost=15 - if track.startswith('ms1m'): - max_model_size_mb=1024 - max_feat_dim=512 - max_time_cost=10 - elif track.startswith('glint'): - max_model_size_mb=1024 - max_feat_dim=1024 - max_time_cost=20 - elif track.startswith('cfat'): - max_model_size_mb = 1024 - max_feat_dim = 512 - max_time_cost = 15 - elif track.startswith('unconstrained'): - max_model_size_mb=1024 - max_feat_dim=1024 - max_time_cost=30 - else: - return "track not found" - - if not os.path.exists(self.model_path): - return "model_path not exists" - if not os.path.isdir(self.model_path): - return "model_path should be directory" - onnx_files = [] - for _file in os.listdir(self.model_path): - if _file.endswith('.onnx'): - onnx_files.append(osp.join(self.model_path, _file)) - if len(onnx_files)==0: - return "do not have onnx files" - self.model_file = sorted(onnx_files)[-1] - print('use onnx-model:', self.model_file) - try: - session = onnxruntime.InferenceSession(self.model_file, providers=self.providers) - except: - return "load onnx failed" - input_cfg = session.get_inputs()[0] - input_shape = input_cfg.shape - print('input-shape:', input_shape) - if len(input_shape)!=4: - return "length of input_shape should be 4" - if not isinstance(input_shape[0], str): - #return "input_shape[0] should be str to support batch-inference" - print('reset input-shape[0] to None') - model = onnx.load(self.model_file) - model.graph.input[0].type.tensor_type.shape.dim[0].dim_param = 'None' - new_model_file = osp.join(self.model_path, 'zzzzrefined.onnx') - onnx.save(model, new_model_file) - self.model_file = new_model_file - print('use new onnx-model:', self.model_file) - try: - session = onnxruntime.InferenceSession(self.model_file, providers=self.providers) - except: - return "load onnx failed" - input_cfg = session.get_inputs()[0] - input_shape = input_cfg.shape - print('new-input-shape:', input_shape) - - self.image_size = tuple(input_shape[2:4][::-1]) - #print('image_size:', self.image_size) - input_name = input_cfg.name - outputs = session.get_outputs() - output_names = [] - for o in outputs: - output_names.append(o.name) - #print(o.name, o.shape) - if len(output_names)!=1: - return "number of output nodes should be 1" - self.session = session - self.input_name = input_name - self.output_names = output_names - #print(self.output_names) - model = onnx.load(self.model_file) - graph = model.graph - if len(graph.node)<8: - return "too small onnx graph" - - input_size = (112,112) - self.crop = None - if track=='cfat': - crop_file = osp.join(self.model_path, 'crop.txt') - if osp.exists(crop_file): - lines = open(crop_file,'r').readlines() - if len(lines)!=6: - return "crop.txt should contain 6 lines" - lines = [int(x) for x in lines] - self.crop = lines[:4] - input_size = tuple(lines[4:6]) - if input_size!=self.image_size: - return "input-size is inconsistant with onnx model input, %s vs %s"%(input_size, self.image_size) - - self.model_size_mb = os.path.getsize(self.model_file) / float(1024*1024) - if self.model_size_mb > max_model_size_mb: - return "max model size exceed, given %.3f-MB"%self.model_size_mb - - input_mean = None - input_std = None - if track=='cfat': - pn_file = osp.join(self.model_path, 'pixel_norm.txt') - if osp.exists(pn_file): - lines = open(pn_file,'r').readlines() - if len(lines)!=2: - return "pixel_norm.txt should contain 2 lines" - input_mean = float(lines[0]) - input_std = float(lines[1]) - if input_mean is not None or input_std is not None: - if input_mean is None or input_std is None: - return "please set input_mean and input_std simultaneously" - else: - find_sub = False - find_mul = False - for nid, node in enumerate(graph.node[:8]): - print(nid, node.name) - if node.name.startswith('Sub') or node.name.startswith('_minus'): - find_sub = True - if node.name.startswith('Mul') or node.name.startswith('_mul') or node.name.startswith('Div'): - find_mul = True - if find_sub and find_mul: - print("find sub and mul") - #mxnet arcface model - input_mean = 0.0 - input_std = 1.0 - else: - input_mean = 127.5 - input_std = 127.5 - self.input_mean = input_mean - self.input_std = input_std - for initn in graph.initializer: - weight_array = numpy_helper.to_array(initn) - dt = weight_array.dtype - if dt.itemsize<4: - return 'invalid weight type - (%s:%s)' % (initn.name, dt.name) - if test_img is None: - test_img = get_image('Tom_Hanks_54745') - test_img = cv2.resize(test_img, self.image_size) - else: - test_img = cv2.resize(test_img, self.image_size) - feat, cost = self.benchmark(test_img) - batch_result = self.check_batch(test_img) - batch_result_sum = float(np.sum(batch_result)) - if batch_result_sum in [float('inf'), -float('inf')] or batch_result_sum != batch_result_sum: - print(batch_result) - print(batch_result_sum) - return "batch result output contains NaN!" - - if len(feat.shape) < 2: - return "the shape of the feature must be two, but get {}".format(str(feat.shape)) - - if feat.shape[1] > max_feat_dim: - return "max feat dim exceed, given %d"%feat.shape[1] - self.feat_dim = feat.shape[1] - cost_ms = cost*1000 - if cost_ms>max_time_cost: - return "max time cost exceed, given %.4f"%cost_ms - self.cost_ms = cost_ms - print('check stat:, model-size-mb: %.4f, feat-dim: %d, time-cost-ms: %.4f, input-mean: %.3f, input-std: %.3f'%(self.model_size_mb, self.feat_dim, self.cost_ms, self.input_mean, self.input_std)) - return None - - def check_batch(self, img): - if not isinstance(img, list): - imgs = [img, ] * 32 - if self.crop is not None: - nimgs = [] - for img in imgs: - nimg = img[self.crop[1]:self.crop[3], self.crop[0]:self.crop[2], :] - if nimg.shape[0] != self.image_size[1] or nimg.shape[1] != self.image_size[0]: - nimg = cv2.resize(nimg, self.image_size) - nimgs.append(nimg) - imgs = nimgs - blob = cv2.dnn.blobFromImages( - images=imgs, scalefactor=1.0 / self.input_std, size=self.image_size, - mean=(self.input_mean, self.input_mean, self.input_mean), swapRB=True) - net_out = self.session.run(self.output_names, {self.input_name: blob})[0] - return net_out - - - def meta_info(self): - return {'model-size-mb':self.model_size_mb, 'feature-dim':self.feat_dim, 'infer': self.cost_ms} - - - def forward(self, imgs): - if not isinstance(imgs, list): - imgs = [imgs] - input_size = self.image_size - if self.crop is not None: - nimgs = [] - for img in imgs: - nimg = img[self.crop[1]:self.crop[3],self.crop[0]:self.crop[2],:] - if nimg.shape[0]!=input_size[1] or nimg.shape[1]!=input_size[0]: - nimg = cv2.resize(nimg, input_size) - nimgs.append(nimg) - imgs = nimgs - blob = cv2.dnn.blobFromImages(imgs, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True) - net_out = self.session.run(self.output_names, {self.input_name : blob})[0] - return net_out - - def benchmark(self, img): - input_size = self.image_size - if self.crop is not None: - nimg = img[self.crop[1]:self.crop[3],self.crop[0]:self.crop[2],:] - if nimg.shape[0]!=input_size[1] or nimg.shape[1]!=input_size[0]: - nimg = cv2.resize(nimg, input_size) - img = nimg - blob = cv2.dnn.blobFromImage(img, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True) - costs = [] - for _ in range(50): - ta = datetime.datetime.now() - net_out = self.session.run(self.output_names, {self.input_name : blob})[0] - tb = datetime.datetime.now() - cost = (tb-ta).total_seconds() - costs.append(cost) - costs = sorted(costs) - cost = costs[5] - return net_out, cost - - -if __name__ == '__main__': - parser = argparse.ArgumentParser(description='') - # general - parser.add_argument('workdir', help='submitted work dir', type=str) - parser.add_argument('--track', help='track name, for different challenge', type=str, default='cfat') - args = parser.parse_args() - handler = ArcFaceORT(args.workdir) - err = handler.check(args.track) - print('err:', err) diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/dnnlib/__init__.py b/spaces/gyugnsu/DragGan-Inversion/PTI/dnnlib/__init__.py deleted file mode 100644 index 2f08cf36f11f9b0fd94c1b7caeadf69b98375b04..0000000000000000000000000000000000000000 --- a/spaces/gyugnsu/DragGan-Inversion/PTI/dnnlib/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -from .util import EasyDict, make_cache_dir_path diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/network.py b/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/network.py deleted file mode 100644 index ff0c169eabdc579041dac0650fbc6da956646594..0000000000000000000000000000000000000000 --- a/spaces/gyugnsu/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/network.py +++ /dev/null @@ -1,781 +0,0 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Helper for managing networks.""" - -import types -import inspect -import re -import uuid -import sys -import copy -import numpy as np -import tensorflow as tf - -from collections import OrderedDict -from typing import Any, List, Tuple, Union, Callable - -from . import tfutil -from .. import util - -from .tfutil import TfExpression, TfExpressionEx - -# pylint: disable=protected-access -# pylint: disable=attribute-defined-outside-init -# pylint: disable=too-many-public-methods - -_import_handlers = [] # Custom import handlers for dealing with legacy data in pickle import. -_import_module_src = dict() # Source code for temporary modules created during pickle import. - - -def import_handler(handler_func): - """Function decorator for declaring custom import handlers.""" - _import_handlers.append(handler_func) - return handler_func - - -class Network: - """Generic network abstraction. - - Acts as a convenience wrapper for a parameterized network construction - function, providing several utility methods and convenient access to - the inputs/outputs/weights. - - Network objects can be safely pickled and unpickled for long-term - archival purposes. The pickling works reliably as long as the underlying - network construction function is defined in a standalone Python module - that has no side effects or application-specific imports. - - Args: - name: Network name. Used to select TensorFlow name and variable scopes. Defaults to build func name if None. - func_name: Fully qualified name of the underlying network construction function, or a top-level function object. - static_kwargs: Keyword arguments to be passed in to the network construction function. - """ - - def __init__(self, name: str = None, func_name: Any = None, **static_kwargs): - # Locate the user-specified build function. - assert isinstance(func_name, str) or util.is_top_level_function(func_name) - if util.is_top_level_function(func_name): - func_name = util.get_top_level_function_name(func_name) - module, func_name = util.get_module_from_obj_name(func_name) - func = util.get_obj_from_module(module, func_name) - - # Dig up source code for the module containing the build function. - module_src = _import_module_src.get(module, None) - if module_src is None: - module_src = inspect.getsource(module) - - # Initialize fields. - self._init_fields(name=(name or func_name), static_kwargs=static_kwargs, build_func=func, build_func_name=func_name, build_module_src=module_src) - - def _init_fields(self, name: str, static_kwargs: dict, build_func: Callable, build_func_name: str, build_module_src: str) -> None: - tfutil.assert_tf_initialized() - assert isinstance(name, str) - assert len(name) >= 1 - assert re.fullmatch(r"[A-Za-z0-9_.\\-]*", name) - assert isinstance(static_kwargs, dict) - assert util.is_pickleable(static_kwargs) - assert callable(build_func) - assert isinstance(build_func_name, str) - assert isinstance(build_module_src, str) - - # Choose TensorFlow name scope. - with tf.name_scope(None): - scope = tf.get_default_graph().unique_name(name, mark_as_used=True) - - # Query current TensorFlow device. - with tfutil.absolute_name_scope(scope), tf.control_dependencies(None): - device = tf.no_op(name="_QueryDevice").device - - # Immutable state. - self._name = name - self._scope = scope - self._device = device - self._static_kwargs = util.EasyDict(copy.deepcopy(static_kwargs)) - self._build_func = build_func - self._build_func_name = build_func_name - self._build_module_src = build_module_src - - # State before _init_graph(). - self._var_inits = dict() # var_name => initial_value, set to None by _init_graph() - self._all_inits_known = False # Do we know for sure that _var_inits covers all the variables? - self._components = None # subnet_name => Network, None if the components are not known yet - - # Initialized by _init_graph(). - self._input_templates = None - self._output_templates = None - self._own_vars = None - - # Cached values initialized the respective methods. - self._input_shapes = None - self._output_shapes = None - self._input_names = None - self._output_names = None - self._vars = None - self._trainables = None - self._var_global_to_local = None - self._run_cache = dict() - - def _init_graph(self) -> None: - assert self._var_inits is not None - assert self._input_templates is None - assert self._output_templates is None - assert self._own_vars is None - - # Initialize components. - if self._components is None: - self._components = util.EasyDict() - - # Choose build func kwargs. - build_kwargs = dict(self.static_kwargs) - build_kwargs["is_template_graph"] = True - build_kwargs["components"] = self._components - - # Override scope and device, and ignore surrounding control dependencies. - with tfutil.absolute_variable_scope(self.scope, reuse=False), tfutil.absolute_name_scope(self.scope), tf.device(self.device), tf.control_dependencies(None): - assert tf.get_variable_scope().name == self.scope - assert tf.get_default_graph().get_name_scope() == self.scope - - # Create input templates. - self._input_templates = [] - for param in inspect.signature(self._build_func).parameters.values(): - if param.kind == param.POSITIONAL_OR_KEYWORD and param.default is param.empty: - self._input_templates.append(tf.placeholder(tf.float32, name=param.name)) - - # Call build func. - out_expr = self._build_func(*self._input_templates, **build_kwargs) - - # Collect output templates and variables. - assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple) - self._output_templates = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr) - self._own_vars = OrderedDict((var.name[len(self.scope) + 1:].split(":")[0], var) for var in tf.global_variables(self.scope + "/")) - - # Check for errors. - if len(self._input_templates) == 0: - raise ValueError("Network build func did not list any inputs.") - if len(self._output_templates) == 0: - raise ValueError("Network build func did not return any outputs.") - if any(not tfutil.is_tf_expression(t) for t in self._output_templates): - raise ValueError("Network outputs must be TensorFlow expressions.") - if any(t.shape.ndims is None for t in self._input_templates): - raise ValueError("Network input shapes not defined. Please call x.set_shape() for each input.") - if any(t.shape.ndims is None for t in self._output_templates): - raise ValueError("Network output shapes not defined. Please call x.set_shape() where applicable.") - if any(not isinstance(comp, Network) for comp in self._components.values()): - raise ValueError("Components of a Network must be Networks themselves.") - if len(self._components) != len(set(comp.name for comp in self._components.values())): - raise ValueError("Components of a Network must have unique names.") - - # Initialize variables. - if len(self._var_inits): - tfutil.set_vars({self._get_vars()[name]: value for name, value in self._var_inits.items() if name in self._get_vars()}) - remaining_inits = [var.initializer for name, var in self._own_vars.items() if name not in self._var_inits] - if self._all_inits_known: - assert len(remaining_inits) == 0 - else: - tfutil.run(remaining_inits) - self._var_inits = None - - @property - def name(self): - """User-specified name string.""" - return self._name - - @property - def scope(self): - """Unique TensorFlow scope containing template graph and variables, derived from the user-specified name.""" - return self._scope - - @property - def device(self): - """Name of the TensorFlow device that the weights of this network reside on. Determined by the current device at construction time.""" - return self._device - - @property - def static_kwargs(self): - """EasyDict of arguments passed to the user-supplied build func.""" - return copy.deepcopy(self._static_kwargs) - - @property - def components(self): - """EasyDict of sub-networks created by the build func.""" - return copy.copy(self._get_components()) - - def _get_components(self): - if self._components is None: - self._init_graph() - assert self._components is not None - return self._components - - @property - def input_shapes(self): - """List of input tensor shapes, including minibatch dimension.""" - if self._input_shapes is None: - self._input_shapes = [t.shape.as_list() for t in self.input_templates] - return copy.deepcopy(self._input_shapes) - - @property - def output_shapes(self): - """List of output tensor shapes, including minibatch dimension.""" - if self._output_shapes is None: - self._output_shapes = [t.shape.as_list() for t in self.output_templates] - return copy.deepcopy(self._output_shapes) - - @property - def input_shape(self): - """Short-hand for input_shapes[0].""" - return self.input_shapes[0] - - @property - def output_shape(self): - """Short-hand for output_shapes[0].""" - return self.output_shapes[0] - - @property - def num_inputs(self): - """Number of input tensors.""" - return len(self.input_shapes) - - @property - def num_outputs(self): - """Number of output tensors.""" - return len(self.output_shapes) - - @property - def input_names(self): - """Name string for each input.""" - if self._input_names is None: - self._input_names = [t.name.split("/")[-1].split(":")[0] for t in self.input_templates] - return copy.copy(self._input_names) - - @property - def output_names(self): - """Name string for each output.""" - if self._output_names is None: - self._output_names = [t.name.split("/")[-1].split(":")[0] for t in self.output_templates] - return copy.copy(self._output_names) - - @property - def input_templates(self): - """Input placeholders in the template graph.""" - if self._input_templates is None: - self._init_graph() - assert self._input_templates is not None - return copy.copy(self._input_templates) - - @property - def output_templates(self): - """Output tensors in the template graph.""" - if self._output_templates is None: - self._init_graph() - assert self._output_templates is not None - return copy.copy(self._output_templates) - - @property - def own_vars(self): - """Variables defined by this network (local_name => var), excluding sub-networks.""" - return copy.copy(self._get_own_vars()) - - def _get_own_vars(self): - if self._own_vars is None: - self._init_graph() - assert self._own_vars is not None - return self._own_vars - - @property - def vars(self): - """All variables (local_name => var).""" - return copy.copy(self._get_vars()) - - def _get_vars(self): - if self._vars is None: - self._vars = OrderedDict(self._get_own_vars()) - for comp in self._get_components().values(): - self._vars.update((comp.name + "/" + name, var) for name, var in comp._get_vars().items()) - return self._vars - - @property - def trainables(self): - """All trainable variables (local_name => var).""" - return copy.copy(self._get_trainables()) - - def _get_trainables(self): - if self._trainables is None: - self._trainables = OrderedDict((name, var) for name, var in self.vars.items() if var.trainable) - return self._trainables - - @property - def var_global_to_local(self): - """Mapping from variable global names to local names.""" - return copy.copy(self._get_var_global_to_local()) - - def _get_var_global_to_local(self): - if self._var_global_to_local is None: - self._var_global_to_local = OrderedDict((var.name.split(":")[0], name) for name, var in self.vars.items()) - return self._var_global_to_local - - def reset_own_vars(self) -> None: - """Re-initialize all variables of this network, excluding sub-networks.""" - if self._var_inits is None or self._components is None: - tfutil.run([var.initializer for var in self._get_own_vars().values()]) - else: - self._var_inits.clear() - self._all_inits_known = False - - def reset_vars(self) -> None: - """Re-initialize all variables of this network, including sub-networks.""" - if self._var_inits is None: - tfutil.run([var.initializer for var in self._get_vars().values()]) - else: - self._var_inits.clear() - self._all_inits_known = False - if self._components is not None: - for comp in self._components.values(): - comp.reset_vars() - - def reset_trainables(self) -> None: - """Re-initialize all trainable variables of this network, including sub-networks.""" - tfutil.run([var.initializer for var in self._get_trainables().values()]) - - def get_output_for(self, *in_expr: TfExpression, return_as_list: bool = False, **dynamic_kwargs) -> Union[TfExpression, List[TfExpression]]: - """Construct TensorFlow expression(s) for the output(s) of this network, given the input expression(s). - The graph is placed on the current TensorFlow device.""" - assert len(in_expr) == self.num_inputs - assert not all(expr is None for expr in in_expr) - self._get_vars() # ensure that all variables have been created - - # Choose build func kwargs. - build_kwargs = dict(self.static_kwargs) - build_kwargs.update(dynamic_kwargs) - build_kwargs["is_template_graph"] = False - build_kwargs["components"] = self._components - - # Build TensorFlow graph to evaluate the network. - with tfutil.absolute_variable_scope(self.scope, reuse=True), tf.name_scope(self.name): - assert tf.get_variable_scope().name == self.scope - valid_inputs = [expr for expr in in_expr if expr is not None] - final_inputs = [] - for expr, name, shape in zip(in_expr, self.input_names, self.input_shapes): - if expr is not None: - expr = tf.identity(expr, name=name) - else: - expr = tf.zeros([tf.shape(valid_inputs[0])[0]] + shape[1:], name=name) - final_inputs.append(expr) - out_expr = self._build_func(*final_inputs, **build_kwargs) - - # Propagate input shapes back to the user-specified expressions. - for expr, final in zip(in_expr, final_inputs): - if isinstance(expr, tf.Tensor): - expr.set_shape(final.shape) - - # Express outputs in the desired format. - assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple) - if return_as_list: - out_expr = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr) - return out_expr - - def get_var_local_name(self, var_or_global_name: Union[TfExpression, str]) -> str: - """Get the local name of a given variable, without any surrounding name scopes.""" - assert tfutil.is_tf_expression(var_or_global_name) or isinstance(var_or_global_name, str) - global_name = var_or_global_name if isinstance(var_or_global_name, str) else var_or_global_name.name - return self._get_var_global_to_local()[global_name] - - def find_var(self, var_or_local_name: Union[TfExpression, str]) -> TfExpression: - """Find variable by local or global name.""" - assert tfutil.is_tf_expression(var_or_local_name) or isinstance(var_or_local_name, str) - return self._get_vars()[var_or_local_name] if isinstance(var_or_local_name, str) else var_or_local_name - - def get_var(self, var_or_local_name: Union[TfExpression, str]) -> np.ndarray: - """Get the value of a given variable as NumPy array. - Note: This method is very inefficient -- prefer to use tflib.run(list_of_vars) whenever possible.""" - return self.find_var(var_or_local_name).eval() - - def set_var(self, var_or_local_name: Union[TfExpression, str], new_value: Union[int, float, np.ndarray]) -> None: - """Set the value of a given variable based on the given NumPy array. - Note: This method is very inefficient -- prefer to use tflib.set_vars() whenever possible.""" - tfutil.set_vars({self.find_var(var_or_local_name): new_value}) - - def __getstate__(self) -> dict: - """Pickle export.""" - state = dict() - state["version"] = 5 - state["name"] = self.name - state["static_kwargs"] = dict(self.static_kwargs) - state["components"] = dict(self.components) - state["build_module_src"] = self._build_module_src - state["build_func_name"] = self._build_func_name - state["variables"] = list(zip(self._get_own_vars().keys(), tfutil.run(list(self._get_own_vars().values())))) - state["input_shapes"] = self.input_shapes - state["output_shapes"] = self.output_shapes - state["input_names"] = self.input_names - state["output_names"] = self.output_names - return state - - def __setstate__(self, state: dict) -> None: - """Pickle import.""" - - # Execute custom import handlers. - for handler in _import_handlers: - state = handler(state) - - # Get basic fields. - assert state["version"] in [2, 3, 4, 5] - name = state["name"] - static_kwargs = state["static_kwargs"] - build_module_src = state["build_module_src"] - build_func_name = state["build_func_name"] - - # Create temporary module from the imported source code. - module_name = "_tflib_network_import_" + uuid.uuid4().hex - module = types.ModuleType(module_name) - sys.modules[module_name] = module - _import_module_src[module] = build_module_src - exec(build_module_src, module.__dict__) # pylint: disable=exec-used - build_func = util.get_obj_from_module(module, build_func_name) - - # Initialize fields. - self._init_fields(name=name, static_kwargs=static_kwargs, build_func=build_func, build_func_name=build_func_name, build_module_src=build_module_src) - self._var_inits.update(copy.deepcopy(state["variables"])) - self._all_inits_known = True - self._components = util.EasyDict(state.get("components", {})) - self._input_shapes = copy.deepcopy(state.get("input_shapes", None)) - self._output_shapes = copy.deepcopy(state.get("output_shapes", None)) - self._input_names = copy.deepcopy(state.get("input_names", None)) - self._output_names = copy.deepcopy(state.get("output_names", None)) - - def clone(self, name: str = None, **new_static_kwargs) -> "Network": - """Create a clone of this network with its own copy of the variables.""" - static_kwargs = dict(self.static_kwargs) - static_kwargs.update(new_static_kwargs) - net = object.__new__(Network) - net._init_fields(name=(name or self.name), static_kwargs=static_kwargs, build_func=self._build_func, build_func_name=self._build_func_name, build_module_src=self._build_module_src) - net.copy_vars_from(self) - return net - - def copy_own_vars_from(self, src_net: "Network") -> None: - """Copy the values of all variables from the given network, excluding sub-networks.""" - - # Source has unknown variables or unknown components => init now. - if (src_net._var_inits is not None and not src_net._all_inits_known) or src_net._components is None: - src_net._get_vars() - - # Both networks are inited => copy directly. - if src_net._var_inits is None and self._var_inits is None: - names = [name for name in self._get_own_vars().keys() if name in src_net._get_own_vars()] - tfutil.set_vars(tfutil.run({self._get_vars()[name]: src_net._get_vars()[name] for name in names})) - return - - # Read from source. - if src_net._var_inits is None: - value_dict = tfutil.run(src_net._get_own_vars()) - else: - value_dict = src_net._var_inits - - # Write to destination. - if self._var_inits is None: - tfutil.set_vars({self._get_vars()[name]: value for name, value in value_dict.items() if name in self._get_vars()}) - else: - self._var_inits.update(value_dict) - - def copy_vars_from(self, src_net: "Network") -> None: - """Copy the values of all variables from the given network, including sub-networks.""" - - # Source has unknown variables or unknown components => init now. - if (src_net._var_inits is not None and not src_net._all_inits_known) or src_net._components is None: - src_net._get_vars() - - # Source is inited, but destination components have not been created yet => set as initial values. - if src_net._var_inits is None and self._components is None: - self._var_inits.update(tfutil.run(src_net._get_vars())) - return - - # Destination has unknown components => init now. - if self._components is None: - self._get_vars() - - # Both networks are inited => copy directly. - if src_net._var_inits is None and self._var_inits is None: - names = [name for name in self._get_vars().keys() if name in src_net._get_vars()] - tfutil.set_vars(tfutil.run({self._get_vars()[name]: src_net._get_vars()[name] for name in names})) - return - - # Copy recursively, component by component. - self.copy_own_vars_from(src_net) - for name, src_comp in src_net._components.items(): - if name in self._components: - self._components[name].copy_vars_from(src_comp) - - def copy_trainables_from(self, src_net: "Network") -> None: - """Copy the values of all trainable variables from the given network, including sub-networks.""" - names = [name for name in self._get_trainables().keys() if name in src_net._get_trainables()] - tfutil.set_vars(tfutil.run({self._get_vars()[name]: src_net._get_vars()[name] for name in names})) - - def convert(self, new_func_name: str, new_name: str = None, **new_static_kwargs) -> "Network": - """Create new network with the given parameters, and copy all variables from this network.""" - if new_name is None: - new_name = self.name - static_kwargs = dict(self.static_kwargs) - static_kwargs.update(new_static_kwargs) - net = Network(name=new_name, func_name=new_func_name, **static_kwargs) - net.copy_vars_from(self) - return net - - def setup_as_moving_average_of(self, src_net: "Network", beta: TfExpressionEx = 0.99, beta_nontrainable: TfExpressionEx = 0.0) -> tf.Operation: - """Construct a TensorFlow op that updates the variables of this network - to be slightly closer to those of the given network.""" - with tfutil.absolute_name_scope(self.scope + "/_MovingAvg"): - ops = [] - for name, var in self._get_vars().items(): - if name in src_net._get_vars(): - cur_beta = beta if var.trainable else beta_nontrainable - new_value = tfutil.lerp(src_net._get_vars()[name], var, cur_beta) - ops.append(var.assign(new_value)) - return tf.group(*ops) - - def run(self, - *in_arrays: Tuple[Union[np.ndarray, None], ...], - input_transform: dict = None, - output_transform: dict = None, - return_as_list: bool = False, - print_progress: bool = False, - minibatch_size: int = None, - num_gpus: int = 1, - assume_frozen: bool = False, - **dynamic_kwargs) -> Union[np.ndarray, Tuple[np.ndarray, ...], List[np.ndarray]]: - """Run this network for the given NumPy array(s), and return the output(s) as NumPy array(s). - - Args: - input_transform: A dict specifying a custom transformation to be applied to the input tensor(s) before evaluating the network. - The dict must contain a 'func' field that points to a top-level function. The function is called with the input - TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs. - output_transform: A dict specifying a custom transformation to be applied to the output tensor(s) after evaluating the network. - The dict must contain a 'func' field that points to a top-level function. The function is called with the output - TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs. - return_as_list: True = return a list of NumPy arrays, False = return a single NumPy array, or a tuple if there are multiple outputs. - print_progress: Print progress to the console? Useful for very large input arrays. - minibatch_size: Maximum minibatch size to use, None = disable batching. - num_gpus: Number of GPUs to use. - assume_frozen: Improve multi-GPU performance by assuming that the trainable parameters will remain changed between calls. - dynamic_kwargs: Additional keyword arguments to be passed into the network build function. - """ - assert len(in_arrays) == self.num_inputs - assert not all(arr is None for arr in in_arrays) - assert input_transform is None or util.is_top_level_function(input_transform["func"]) - assert output_transform is None or util.is_top_level_function(output_transform["func"]) - output_transform, dynamic_kwargs = _handle_legacy_output_transforms(output_transform, dynamic_kwargs) - num_items = in_arrays[0].shape[0] - if minibatch_size is None: - minibatch_size = num_items - - # Construct unique hash key from all arguments that affect the TensorFlow graph. - key = dict(input_transform=input_transform, output_transform=output_transform, num_gpus=num_gpus, assume_frozen=assume_frozen, dynamic_kwargs=dynamic_kwargs) - def unwind_key(obj): - if isinstance(obj, dict): - return [(key, unwind_key(value)) for key, value in sorted(obj.items())] - if callable(obj): - return util.get_top_level_function_name(obj) - return obj - key = repr(unwind_key(key)) - - # Build graph. - if key not in self._run_cache: - with tfutil.absolute_name_scope(self.scope + "/_Run"), tf.control_dependencies(None): - with tf.device("/cpu:0"): - in_expr = [tf.placeholder(tf.float32, name=name) for name in self.input_names] - in_split = list(zip(*[tf.split(x, num_gpus) for x in in_expr])) - - out_split = [] - for gpu in range(num_gpus): - with tf.device(self.device if num_gpus == 1 else "/gpu:%d" % gpu): - net_gpu = self.clone() if assume_frozen else self - in_gpu = in_split[gpu] - - if input_transform is not None: - in_kwargs = dict(input_transform) - in_gpu = in_kwargs.pop("func")(*in_gpu, **in_kwargs) - in_gpu = [in_gpu] if tfutil.is_tf_expression(in_gpu) else list(in_gpu) - - assert len(in_gpu) == self.num_inputs - out_gpu = net_gpu.get_output_for(*in_gpu, return_as_list=True, **dynamic_kwargs) - - if output_transform is not None: - out_kwargs = dict(output_transform) - out_gpu = out_kwargs.pop("func")(*out_gpu, **out_kwargs) - out_gpu = [out_gpu] if tfutil.is_tf_expression(out_gpu) else list(out_gpu) - - assert len(out_gpu) == self.num_outputs - out_split.append(out_gpu) - - with tf.device("/cpu:0"): - out_expr = [tf.concat(outputs, axis=0) for outputs in zip(*out_split)] - self._run_cache[key] = in_expr, out_expr - - # Run minibatches. - in_expr, out_expr = self._run_cache[key] - out_arrays = [np.empty([num_items] + expr.shape.as_list()[1:], expr.dtype.name) for expr in out_expr] - - for mb_begin in range(0, num_items, minibatch_size): - if print_progress: - print("\r%d / %d" % (mb_begin, num_items), end="") - - mb_end = min(mb_begin + minibatch_size, num_items) - mb_num = mb_end - mb_begin - mb_in = [src[mb_begin : mb_end] if src is not None else np.zeros([mb_num] + shape[1:]) for src, shape in zip(in_arrays, self.input_shapes)] - mb_out = tf.get_default_session().run(out_expr, dict(zip(in_expr, mb_in))) - - for dst, src in zip(out_arrays, mb_out): - dst[mb_begin: mb_end] = src - - # Done. - if print_progress: - print("\r%d / %d" % (num_items, num_items)) - - if not return_as_list: - out_arrays = out_arrays[0] if len(out_arrays) == 1 else tuple(out_arrays) - return out_arrays - - def list_ops(self) -> List[TfExpression]: - _ = self.output_templates # ensure that the template graph has been created - include_prefix = self.scope + "/" - exclude_prefix = include_prefix + "_" - ops = tf.get_default_graph().get_operations() - ops = [op for op in ops if op.name.startswith(include_prefix)] - ops = [op for op in ops if not op.name.startswith(exclude_prefix)] - return ops - - def list_layers(self) -> List[Tuple[str, TfExpression, List[TfExpression]]]: - """Returns a list of (layer_name, output_expr, trainable_vars) tuples corresponding to - individual layers of the network. Mainly intended to be used for reporting.""" - layers = [] - - def recurse(scope, parent_ops, parent_vars, level): - if len(parent_ops) == 0 and len(parent_vars) == 0: - return - - # Ignore specific patterns. - if any(p in scope for p in ["/Shape", "/strided_slice", "/Cast", "/concat", "/Assign"]): - return - - # Filter ops and vars by scope. - global_prefix = scope + "/" - local_prefix = global_prefix[len(self.scope) + 1:] - cur_ops = [op for op in parent_ops if op.name.startswith(global_prefix) or op.name == global_prefix[:-1]] - cur_vars = [(name, var) for name, var in parent_vars if name.startswith(local_prefix) or name == local_prefix[:-1]] - if not cur_ops and not cur_vars: - return - - # Filter out all ops related to variables. - for var in [op for op in cur_ops if op.type.startswith("Variable")]: - var_prefix = var.name + "/" - cur_ops = [op for op in cur_ops if not op.name.startswith(var_prefix)] - - # Scope does not contain ops as immediate children => recurse deeper. - contains_direct_ops = any("/" not in op.name[len(global_prefix):] and op.type not in ["Identity", "Cast", "Transpose"] for op in cur_ops) - if (level == 0 or not contains_direct_ops) and (len(cur_ops) != 0 or len(cur_vars) != 0): - visited = set() - for rel_name in [op.name[len(global_prefix):] for op in cur_ops] + [name[len(local_prefix):] for name, _var in cur_vars]: - token = rel_name.split("/")[0] - if token not in visited: - recurse(global_prefix + token, cur_ops, cur_vars, level + 1) - visited.add(token) - return - - # Report layer. - layer_name = scope[len(self.scope) + 1:] - layer_output = cur_ops[-1].outputs[0] if cur_ops else cur_vars[-1][1] - layer_trainables = [var for _name, var in cur_vars if var.trainable] - layers.append((layer_name, layer_output, layer_trainables)) - - recurse(self.scope, self.list_ops(), list(self._get_vars().items()), 0) - return layers - - def print_layers(self, title: str = None, hide_layers_with_no_params: bool = False) -> None: - """Print a summary table of the network structure.""" - rows = [[title if title is not None else self.name, "Params", "OutputShape", "WeightShape"]] - rows += [["---"] * 4] - total_params = 0 - - for layer_name, layer_output, layer_trainables in self.list_layers(): - num_params = sum(int(np.prod(var.shape.as_list())) for var in layer_trainables) - weights = [var for var in layer_trainables if var.name.endswith("/weight:0")] - weights.sort(key=lambda x: len(x.name)) - if len(weights) == 0 and len(layer_trainables) == 1: - weights = layer_trainables - total_params += num_params - - if not hide_layers_with_no_params or num_params != 0: - num_params_str = str(num_params) if num_params > 0 else "-" - output_shape_str = str(layer_output.shape) - weight_shape_str = str(weights[0].shape) if len(weights) >= 1 else "-" - rows += [[layer_name, num_params_str, output_shape_str, weight_shape_str]] - - rows += [["---"] * 4] - rows += [["Total", str(total_params), "", ""]] - - widths = [max(len(cell) for cell in column) for column in zip(*rows)] - print() - for row in rows: - print(" ".join(cell + " " * (width - len(cell)) for cell, width in zip(row, widths))) - print() - - def setup_weight_histograms(self, title: str = None) -> None: - """Construct summary ops to include histograms of all trainable parameters in TensorBoard.""" - if title is None: - title = self.name - - with tf.name_scope(None), tf.device(None), tf.control_dependencies(None): - for local_name, var in self._get_trainables().items(): - if "/" in local_name: - p = local_name.split("/") - name = title + "_" + p[-1] + "/" + "_".join(p[:-1]) - else: - name = title + "_toplevel/" + local_name - - tf.summary.histogram(name, var) - -#---------------------------------------------------------------------------- -# Backwards-compatible emulation of legacy output transformation in Network.run(). - -_print_legacy_warning = True - -def _handle_legacy_output_transforms(output_transform, dynamic_kwargs): - global _print_legacy_warning - legacy_kwargs = ["out_mul", "out_add", "out_shrink", "out_dtype"] - if not any(kwarg in dynamic_kwargs for kwarg in legacy_kwargs): - return output_transform, dynamic_kwargs - - if _print_legacy_warning: - _print_legacy_warning = False - print() - print("WARNING: Old-style output transformations in Network.run() are deprecated.") - print("Consider using 'output_transform=dict(func=tflib.convert_images_to_uint8)'") - print("instead of 'out_mul=127.5, out_add=127.5, out_dtype=np.uint8'.") - print() - assert output_transform is None - - new_kwargs = dict(dynamic_kwargs) - new_transform = {kwarg: new_kwargs.pop(kwarg) for kwarg in legacy_kwargs if kwarg in dynamic_kwargs} - new_transform["func"] = _legacy_output_transform_func - return new_transform, new_kwargs - -def _legacy_output_transform_func(*expr, out_mul=1.0, out_add=0.0, out_shrink=1, out_dtype=None): - if out_mul != 1.0: - expr = [x * out_mul for x in expr] - - if out_add != 0.0: - expr = [x + out_add for x in expr] - - if out_shrink > 1: - ksize = [1, 1, out_shrink, out_shrink] - expr = [tf.nn.avg_pool(x, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW") for x in expr] - - if out_dtype is not None: - if tf.as_dtype(out_dtype).is_integer: - expr = [tf.round(x) for x in expr] - expr = [tf.saturate_cast(x, out_dtype) for x in expr] - return expr diff --git a/spaces/h2oai/h2ogpt-chatbot/README.md b/spaces/h2oai/h2ogpt-chatbot/README.md deleted file mode 100644 index 14b59cf394d195459e1f735af273e39301347ec9..0000000000000000000000000000000000000000 --- a/spaces/h2oai/h2ogpt-chatbot/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: H2ogpt Chatbot -emoji: 📚 -colorFrom: yellow -colorTo: yellow -sdk: gradio -sdk_version: 3.41.2 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/h2oai/wave-tour/examples/plot_events.py b/spaces/h2oai/wave-tour/examples/plot_events.py deleted file mode 100644 index e9db958dc5397d5259124a4291b912f235b1eb0c..0000000000000000000000000000000000000000 --- a/spaces/h2oai/wave-tour/examples/plot_events.py +++ /dev/null @@ -1,31 +0,0 @@ -# Plot / Events -# Handle #events on a #plot card. -# --- -from h2o_wave import main, app, Q, ui, data - - -@app('/demo') -async def serve(q: Q): - if not q.client.initialized: - q.client.initialized = True - q.page['pricing'] = ui.plot_card( - box='1 1 4 5', - title='Interval', - data=data(fields='product price', rows=[ - ['spam', 1.49], - ['eggs', 2.49], - ['ham', 1.99], - ], pack=True), - plot=ui.plot([ui.mark(type='interval', x='=product', y='=price', y_min=0)]), - events=['select_marks'] - ) - q.page['details'] = ui.markdown_card( - box='1 6 4 2', - title='Selected Product', - content='Nothing selected.', - ) - else: - if q.events.pricing: - q.page['details'].content = f'You selected {q.events.pricing.select_marks}' - - await q.page.save() diff --git a/spaces/haakohu/deep_privacy2_face/sg3_torch_utils/ops/upfirdn2d.h b/spaces/haakohu/deep_privacy2_face/sg3_torch_utils/ops/upfirdn2d.h deleted file mode 100644 index c9e2032bcac9d2abde7a75eea4d812da348afadd..0000000000000000000000000000000000000000 --- a/spaces/haakohu/deep_privacy2_face/sg3_torch_utils/ops/upfirdn2d.h +++ /dev/null @@ -1,59 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include - -//------------------------------------------------------------------------ -// CUDA kernel parameters. - -struct upfirdn2d_kernel_params -{ - const void* x; - const float* f; - void* y; - - int2 up; - int2 down; - int2 pad0; - int flip; - float gain; - - int4 inSize; // [width, height, channel, batch] - int4 inStride; - int2 filterSize; // [width, height] - int2 filterStride; - int4 outSize; // [width, height, channel, batch] - int4 outStride; - int sizeMinor; - int sizeMajor; - - int loopMinor; - int loopMajor; - int loopX; - int launchMinor; - int launchMajor; -}; - -//------------------------------------------------------------------------ -// CUDA kernel specialization. - -struct upfirdn2d_kernel_spec -{ - void* kernel; - int tileOutW; - int tileOutH; - int loopMinor; - int loopX; -}; - -//------------------------------------------------------------------------ -// CUDA kernel selection. - -template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); - -//------------------------------------------------------------------------ diff --git a/spaces/hamzapehlivan/StyleRes/datasets/process_image.py b/spaces/hamzapehlivan/StyleRes/datasets/process_image.py deleted file mode 100644 index 8897589fbfae0d240d37d2dd37602cdcbae0f0a5..0000000000000000000000000000000000000000 --- a/spaces/hamzapehlivan/StyleRes/datasets/process_image.py +++ /dev/null @@ -1,165 +0,0 @@ - -import numpy as np -import torch -from PIL import Image -import dlib -import numpy as np -import PIL -import PIL.Image -import scipy -import scipy.ndimage - -class ImageProcessor(): - def __init__(self, predictor_path=None) -> None: - self.predictor = None - if predictor_path: - self.predictor = dlib.shape_predictor(predictor_path) - - @staticmethod - def preprocess_image(image, is_batch=True): - image = image.resize( (256, 256)) - image = np.asarray(image).transpose(2, 0, 1).astype(np.float32) # C,H,W -> H,W,C - image = torch.FloatTensor(image.copy()) - image = (image - 127.5) / 127.5 # Normalize - if not is_batch: - image = image.unsqueeze(0) - return image - - """ - Input: A numpy image with shape NxCxHxW. - Output: Output image with NxHxWxC with values between 0-255 - """ - @staticmethod - def postprocess_image(image, min_val=-1.0, max_val=1.0, is_batch=True): - image = image.astype(np.float64) - image = (image - min_val) * 255 / (max_val - min_val) - image = np.clip(image + 0.5, 0, 255).astype(np.uint8) - image = image.transpose(0, 2, 3, 1) - if not is_batch: - image = Image.fromarray(image[0]) - return image - - """ - brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset) - author: lzhbrian (https://lzhbrian.me) - date: 2020.1.5 - note: code is heavily borrowed from - https://github.com/NVlabs/ffhq-dataset - http://dlib.net/face_landmark_detection.py.html - requirements: - apt install cmake - conda install Pillow numpy scipy - pip install dlib - # download face landmark model from: - # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 - """ - - def get_landmark(self, image): - """get landmark with dlib - :return: np.array shape=(68, 2) - """ - detector = dlib.get_frontal_face_detector() - - # img = dlib.load_rgb_image(filepath) - img = np.asarray(image) - dets = detector(img, 1) - - for k, d in enumerate(dets): - shape = self.predictor(img, d) - - t = list(shape.parts()) - a = [] - for tt in t: - a.append([tt.x, tt.y]) - lm = np.array(a) - return lm - - def align_face(self, img): - """ - :param image: PIL image - :return: PIL Image - """ - if self.predictor is None: - return img - - lm = self.get_landmark(img) - - lm_chin = lm[0: 17] # left-right - lm_eyebrow_left = lm[17: 22] # left-right - lm_eyebrow_right = lm[22: 27] # left-right - lm_nose = lm[27: 31] # top-down - lm_nostrils = lm[31: 36] # top-down - lm_eye_left = lm[36: 42] # left-clockwise - lm_eye_right = lm[42: 48] # left-clockwise - lm_mouth_outer = lm[48: 60] # left-clockwise - lm_mouth_inner = lm[60: 68] # left-clockwise - - # Calculate auxiliary vectors. - eye_left = np.mean(lm_eye_left, axis=0) - eye_right = np.mean(lm_eye_right, axis=0) - eye_avg = (eye_left + eye_right) * 0.5 - eye_to_eye = eye_right - eye_left - mouth_left = lm_mouth_outer[0] - mouth_right = lm_mouth_outer[6] - mouth_avg = (mouth_left + mouth_right) * 0.5 - eye_to_mouth = mouth_avg - eye_avg - - # Choose oriented crop rectangle. - x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] - x /= np.hypot(*x) - x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) - y = np.flipud(x) * [-1, 1] - c = eye_avg + eye_to_mouth * 0.1 - quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) - qsize = np.hypot(*x) * 2 - - # read image - # img = PIL.Image.open(filepath) - - output_size = 1024 - transform_size = 1024 - enable_padding = True - - # Shrink. - shrink = int(np.floor(qsize / output_size * 0.5)) - if shrink > 1: - rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink))) - img = img.resize(rsize, PIL.Image.ANTIALIAS) - quad /= shrink - qsize /= shrink - - # Crop. - border = max(int(np.rint(qsize * 0.1)), 3) - crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), - int(np.ceil(max(quad[:, 1])))) - crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), - min(crop[3] + border, img.size[1])) - if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: - img = img.crop(crop) - quad -= crop[0:2] - - # Pad. - pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), - int(np.ceil(max(quad[:, 1])))) - pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), - max(pad[3] - img.size[1] + border, 0)) - if enable_padding and max(pad) > border - 4: - pad = np.maximum(pad, int(np.rint(qsize * 0.3))) - img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') - h, w, _ = img.shape - y, x, _ = np.ogrid[:h, :w, :1] - mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), - 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3])) - blur = qsize * 0.02 - img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) - img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) - img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') - quad += pad[:2] - - # Transform. - img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR) - if output_size < transform_size: - img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS) - - # Save aligned image. - return img diff --git a/spaces/hamzapehlivan/StyleRes/editings/styleclip_directions/styleclip_mapper_network.py b/spaces/hamzapehlivan/StyleRes/editings/styleclip_directions/styleclip_mapper_network.py deleted file mode 100644 index 641eb46e96e17ec6e1f8440e691e3c87aea3dc1c..0000000000000000000000000000000000000000 --- a/spaces/hamzapehlivan/StyleRes/editings/styleclip_directions/styleclip_mapper_network.py +++ /dev/null @@ -1,121 +0,0 @@ -import torch -from torch import nn -from torch.nn import Module -from torch.nn import functional as F -import math - -def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): - rest_dim = [1] * (input.ndim - bias.ndim - 1) - input = input #.cuda() - if input.ndim == 3: - return ( - F.leaky_relu( - input + bias.view(1, *rest_dim, bias.shape[0]), negative_slope=negative_slope - ) - * scale - ) - else: - return ( - F.leaky_relu( - input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope - ) - * scale - ) - - -class PixelNorm(nn.Module): - def __init__(self): - super().__init__() - - def forward(self, input): - return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8) - -class EqualLinear(nn.Module): - def __init__( - self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None - ): - super().__init__() - - self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) - - if bias: - self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) - - else: - self.bias = None - - self.activation = activation - - self.scale = (1 / math.sqrt(in_dim)) * lr_mul - self.lr_mul = lr_mul - - def forward(self, input): - if self.activation: - out = F.linear(input, self.weight * self.scale) - out = fused_leaky_relu(out, self.bias * self.lr_mul) - - else: - out = F.linear( - input, self.weight * self.scale, bias=self.bias * self.lr_mul - ) - - return out - -class Mapper(Module): - - def __init__(self, latent_dim=512): - super(Mapper, self).__init__() - - layers = [PixelNorm()] - - for i in range(4): - layers.append( - EqualLinear( - latent_dim, latent_dim, lr_mul=0.01, activation='fused_lrelu' - ) - ) - - self.mapping = nn.Sequential(*layers) - - - def forward(self, x): - x = self.mapping(x) - return x - - -class LevelsMapper(Module): - - def __init__(self, opts): - super(LevelsMapper, self).__init__() - - self.opts = opts - - if not opts.no_coarse_mapper: - self.course_mapping = Mapper() - if not opts.no_medium_mapper: - self.medium_mapping = Mapper() - if not opts.no_fine_mapper: - self.fine_mapping = Mapper() - - def forward(self, x): - x_coarse = x[:, :4, :] - x_medium = x[:, 4:8, :] - x_fine = x[:, 8:, :] - - if not self.opts.no_coarse_mapper: - x_coarse = self.course_mapping(x_coarse) - else: - x_coarse = torch.zeros_like(x_coarse) - if not self.opts.no_medium_mapper: - x_medium = self.medium_mapping(x_medium) - else: - x_medium = torch.zeros_like(x_medium) - if not self.opts.no_fine_mapper: - x_fine = self.fine_mapping(x_fine) - else: - x_fine = torch.zeros_like(x_fine) - - - out = torch.cat([x_coarse, x_medium, x_fine], dim=1) - - return out \ No newline at end of file diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/.github/ISSUE_TEMPLATE/unexpected-problems-bugs.md b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/.github/ISSUE_TEMPLATE/unexpected-problems-bugs.md deleted file mode 100644 index bafee7a1a3897903d26e68001d3d3d2b7686015b..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/.github/ISSUE_TEMPLATE/unexpected-problems-bugs.md +++ /dev/null @@ -1,45 +0,0 @@ ---- -name: "Unexpected behaviors" -about: Run into unexpected behaviors when using detectron2 -title: Please read & provide the following - ---- - -If you do not know the root cause of the problem, and wish someone to help you, please -post according to this template: - -## Instructions To Reproduce the Issue: - -1. what changes you made (`git diff`) or what code you wrote -``` - -``` -2. what exact command you run: -3. what you observed (including __full logs__): -``` - -``` -4. please simplify the steps as much as possible so they do not require additional resources to - run, such as a private dataset. - -## Expected behavior: - -If there are no obvious error in "what you observed" provided above, -please tell us the expected behavior. - -If you expect the model to converge / work better, note that we do not give suggestions -on how to train a new model. -Only in one of the two conditions we will help with it: -(1) You're unable to reproduce the results in detectron2 model zoo. -(2) It indicates a detectron2 bug. - -## Environment: - -Provide your environment information using the following command: -``` -wget -nc -q https://github.com/facebookresearch/detectron2/raw/master/detectron2/utils/collect_env.py && python collect_env.py -``` - -If your issue looks like an installation issue / environment issue, -please first try to solve it yourself with the instructions in -https://detectron2.readthedocs.io/tutorials/install.html#common-installation-issues diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/docs/tutorials/evaluation.md b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/docs/tutorials/evaluation.md deleted file mode 100644 index c71adb7eb2e554e5ea848f1feb44bbee01a13f8e..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/docs/tutorials/evaluation.md +++ /dev/null @@ -1,43 +0,0 @@ - -# Evaluation - -Evaluation is a process that takes a number of inputs/outputs pairs and aggregate them. -You can always [use the model](./models.md) directly and just parse its inputs/outputs manually to perform -evaluation. -Alternatively, evaluation is implemented in detectron2 using the [DatasetEvaluator](../modules/evaluation.html#detectron2.evaluation.DatasetEvaluator) -interface. - -Detectron2 includes a few `DatasetEvaluator` that computes metrics using standard dataset-specific -APIs (e.g., COCO, LVIS). -You can also implement your own `DatasetEvaluator` that performs some other jobs -using the inputs/outputs pairs. -For example, to count how many instances are detected on the validation set: - -``` -class Counter(DatasetEvaluator): - def reset(self): - self.count = 0 - def process(self, inputs, outputs): - for output in outputs: - self.count += len(output["instances"]) - def evaluate(self): - # save self.count somewhere, or print it, or return it. - return {"count": self.count} -``` - -Once you have some `DatasetEvaluator`, you can run it with -[inference_on_dataset](../modules/evaluation.html#detectron2.evaluation.inference_on_dataset). -For example, - -```python -val_results = inference_on_dataset( - model, - val_data_loader, - DatasetEvaluators([COCOEvaluator(...), Counter()])) -``` -Compared to running the evaluation manually using the model, the benefit of this function is that -you can merge evaluators together using [DatasetEvaluators](../modules/evaluation.html#detectron2.evaluation.DatasetEvaluators). -In this way you can run all evaluations without having to go through the dataset multiple times. - -The `inference_on_dataset` function also provides accurate speed benchmarks for the -given model and dataset. diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/layers/test_mask_ops.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/layers/test_mask_ops.py deleted file mode 100644 index d180627354b6b9d8e0776d70f78e91ee5e530210..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/tests/layers/test_mask_ops.py +++ /dev/null @@ -1,190 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved - -import contextlib -import io -import numpy as np -import unittest -from collections import defaultdict -import torch -import tqdm -from fvcore.common.benchmark import benchmark -from fvcore.common.file_io import PathManager -from pycocotools.coco import COCO -from tabulate import tabulate -from torch.nn import functional as F - -from detectron2.data import MetadataCatalog -from detectron2.layers.mask_ops import ( - pad_masks, - paste_mask_in_image_old, - paste_masks_in_image, - scale_boxes, -) -from detectron2.structures import BitMasks, Boxes, BoxMode, PolygonMasks -from detectron2.structures.masks import polygons_to_bitmask - - -def iou_between_full_image_bit_masks(a, b): - intersect = (a & b).sum() - union = (a | b).sum() - return intersect / union - - -def rasterize_polygons_with_grid_sample(full_image_bit_mask, box, mask_size, threshold=0.5): - x0, y0, x1, y1 = box[0], box[1], box[2], box[3] - - img_h, img_w = full_image_bit_mask.shape - - mask_y = np.arange(0.0, mask_size) + 0.5 # mask y sample coords in [0.5, mask_size - 0.5] - mask_x = np.arange(0.0, mask_size) + 0.5 # mask x sample coords in [0.5, mask_size - 0.5] - mask_y = mask_y / mask_size * (y1 - y0) + y0 - mask_x = mask_x / mask_size * (x1 - x0) + x0 - - mask_x = (mask_x - 0.5) / (img_w - 1) * 2 + -1 - mask_y = (mask_y - 0.5) / (img_h - 1) * 2 + -1 - gy, gx = torch.meshgrid(torch.from_numpy(mask_y), torch.from_numpy(mask_x)) - ind = torch.stack([gx, gy], dim=-1).to(dtype=torch.float32) - - full_image_bit_mask = torch.from_numpy(full_image_bit_mask) - mask = F.grid_sample( - full_image_bit_mask[None, None, :, :].to(dtype=torch.float32), - ind[None, :, :, :], - align_corners=True, - ) - - return mask[0, 0] >= threshold - - -class TestMaskCropPaste(unittest.TestCase): - def setUp(self): - json_file = MetadataCatalog.get("coco_2017_val_100").json_file - if not PathManager.isfile(json_file): - raise unittest.SkipTest("{} not found".format(json_file)) - with contextlib.redirect_stdout(io.StringIO()): - json_file = PathManager.get_local_path(json_file) - self.coco = COCO(json_file) - - def test_crop_paste_consistency(self): - """ - rasterize_polygons_within_box (used in training) - and - paste_masks_in_image (used in inference) - should be inverse operations to each other. - - This function runs several implementation of the above two operations and prints - the reconstruction error. - """ - - anns = self.coco.loadAnns(self.coco.getAnnIds(iscrowd=False)) # avoid crowd annotations - - selected_anns = anns[:100] - - ious = [] - for ann in tqdm.tqdm(selected_anns): - results = self.process_annotation(ann) - ious.append([k[2] for k in results]) - - ious = np.array(ious) - mean_ious = ious.mean(axis=0) - table = [] - res_dic = defaultdict(dict) - for row, iou in zip(results, mean_ious): - table.append((row[0], row[1], iou)) - res_dic[row[0]][row[1]] = iou - print(tabulate(table, headers=["rasterize", "paste", "iou"], tablefmt="simple")) - # assert that the reconstruction is good: - self.assertTrue(res_dic["polygon"]["aligned"] > 0.94) - self.assertTrue(res_dic["roialign"]["aligned"] > 0.95) - - def process_annotation(self, ann, mask_side_len=28): - # Parse annotation data - img_info = self.coco.loadImgs(ids=[ann["image_id"]])[0] - height, width = img_info["height"], img_info["width"] - gt_polygons = [np.array(p, dtype=np.float64) for p in ann["segmentation"]] - gt_bbox = BoxMode.convert(ann["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) - gt_bit_mask = polygons_to_bitmask(gt_polygons, height, width) - - # Run rasterize .. - torch_gt_bbox = torch.tensor(gt_bbox).to(dtype=torch.float32).reshape(-1, 4) - box_bitmasks = { - "polygon": PolygonMasks([gt_polygons]).crop_and_resize(torch_gt_bbox, mask_side_len)[0], - "gridsample": rasterize_polygons_with_grid_sample(gt_bit_mask, gt_bbox, mask_side_len), - "roialign": BitMasks(torch.from_numpy(gt_bit_mask[None, :, :])).crop_and_resize( - torch_gt_bbox, mask_side_len - )[0], - } - - # Run paste .. - results = defaultdict(dict) - for k, box_bitmask in box_bitmasks.items(): - padded_bitmask, scale = pad_masks(box_bitmask[None, :, :], 1) - scaled_boxes = scale_boxes(torch_gt_bbox, scale) - - r = results[k] - r["old"] = paste_mask_in_image_old( - padded_bitmask[0], scaled_boxes[0], height, width, threshold=0.5 - ) - r["aligned"] = paste_masks_in_image( - box_bitmask[None, :, :], Boxes(torch_gt_bbox), (height, width) - )[0] - - table = [] - for rasterize_method, r in results.items(): - for paste_method, mask in r.items(): - mask = np.asarray(mask) - iou = iou_between_full_image_bit_masks(gt_bit_mask.astype("uint8"), mask) - table.append((rasterize_method, paste_method, iou)) - return table - - def test_polygon_area(self): - # Draw polygon boxes - for d in [5.0, 10.0, 1000.0]: - polygon = PolygonMasks([[[0, 0, 0, d, d, d, d, 0]]]) - area = polygon.area()[0] - target = d ** 2 - self.assertEqual(area, target) - - # Draw polygon triangles - for d in [5.0, 10.0, 1000.0]: - polygon = PolygonMasks([[[0, 0, 0, d, d, d]]]) - area = polygon.area()[0] - target = d ** 2 / 2 - self.assertEqual(area, target) - - -def benchmark_paste(): - S = 800 - H, W = image_shape = (S, S) - N = 64 - torch.manual_seed(42) - masks = torch.rand(N, 28, 28) - - center = torch.rand(N, 2) * 600 + 100 - wh = torch.clamp(torch.randn(N, 2) * 40 + 200, min=50) - x0y0 = torch.clamp(center - wh * 0.5, min=0.0) - x1y1 = torch.clamp(center + wh * 0.5, max=S) - boxes = Boxes(torch.cat([x0y0, x1y1], axis=1)) - - def func(device, n=3): - m = masks.to(device=device) - b = boxes.to(device=device) - - def bench(): - for _ in range(n): - paste_masks_in_image(m, b, image_shape) - if device.type == "cuda": - torch.cuda.synchronize() - - return bench - - specs = [{"device": torch.device("cpu"), "n": 3}] - if torch.cuda.is_available(): - specs.append({"device": torch.device("cuda"), "n": 3}) - - benchmark(func, "paste_masks", specs, num_iters=10, warmup_iters=2) - - -if __name__ == "__main__": - benchmark_paste() - unittest.main() diff --git a/spaces/hasibzunair/fifa-tryon-demo/model/u2net_refactor.py b/spaces/hasibzunair/fifa-tryon-demo/model/u2net_refactor.py deleted file mode 100644 index e668de2c2bc67cbef280eaa5f789c762c4745fa4..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/model/u2net_refactor.py +++ /dev/null @@ -1,168 +0,0 @@ -import torch -import torch.nn as nn - -import math - -__all__ = ['U2NET_full', 'U2NET_lite'] - - -def _upsample_like(x, size): - return nn.Upsample(size=size, mode='bilinear', align_corners=False)(x) - - -def _size_map(x, height): - # {height: size} for Upsample - size = list(x.shape[-2:]) - sizes = {} - for h in range(1, height): - sizes[h] = size - size = [math.ceil(w / 2) for w in size] - return sizes - - -class REBNCONV(nn.Module): - def __init__(self, in_ch=3, out_ch=3, dilate=1): - super(REBNCONV, self).__init__() - - self.conv_s1 = nn.Conv2d(in_ch, out_ch, 3, padding=1 * dilate, dilation=1 * dilate) - self.bn_s1 = nn.BatchNorm2d(out_ch) - self.relu_s1 = nn.ReLU(inplace=True) - - def forward(self, x): - return self.relu_s1(self.bn_s1(self.conv_s1(x))) - - -class RSU(nn.Module): - def __init__(self, name, height, in_ch, mid_ch, out_ch, dilated=False): - super(RSU, self).__init__() - self.name = name - self.height = height - self.dilated = dilated - self._make_layers(height, in_ch, mid_ch, out_ch, dilated) - - def forward(self, x): - sizes = _size_map(x, self.height) - x = self.rebnconvin(x) - - # U-Net like symmetric encoder-decoder structure - def unet(x, height=1): - if height < self.height: - x1 = getattr(self, f'rebnconv{height}')(x) - if not self.dilated and height < self.height - 1: - x2 = unet(getattr(self, 'downsample')(x1), height + 1) - else: - x2 = unet(x1, height + 1) - - x = getattr(self, f'rebnconv{height}d')(torch.cat((x2, x1), 1)) - return _upsample_like(x, sizes[height - 1]) if not self.dilated and height > 1 else x - else: - return getattr(self, f'rebnconv{height}')(x) - - return x + unet(x) - - def _make_layers(self, height, in_ch, mid_ch, out_ch, dilated=False): - self.add_module('rebnconvin', REBNCONV(in_ch, out_ch)) - self.add_module('downsample', nn.MaxPool2d(2, stride=2, ceil_mode=True)) - - self.add_module(f'rebnconv1', REBNCONV(out_ch, mid_ch)) - self.add_module(f'rebnconv1d', REBNCONV(mid_ch * 2, out_ch)) - - for i in range(2, height): - dilate = 1 if not dilated else 2 ** (i - 1) - self.add_module(f'rebnconv{i}', REBNCONV(mid_ch, mid_ch, dilate=dilate)) - self.add_module(f'rebnconv{i}d', REBNCONV(mid_ch * 2, mid_ch, dilate=dilate)) - - dilate = 2 if not dilated else 2 ** (height - 1) - self.add_module(f'rebnconv{height}', REBNCONV(mid_ch, mid_ch, dilate=dilate)) - - -class U2NET(nn.Module): - def __init__(self, cfgs, out_ch): - super(U2NET, self).__init__() - self.out_ch = out_ch - self._make_layers(cfgs) - - def forward(self, x): - sizes = _size_map(x, self.height) - maps = [] # storage for maps - - # side saliency map - def unet(x, height=1): - if height < 6: - x1 = getattr(self, f'stage{height}')(x) - x2 = unet(getattr(self, 'downsample')(x1), height + 1) - x = getattr(self, f'stage{height}d')(torch.cat((x2, x1), 1)) - side(x, height) - return _upsample_like(x, sizes[height - 1]) if height > 1 else x - else: - x = getattr(self, f'stage{height}')(x) - side(x, height) - return _upsample_like(x, sizes[height - 1]) - - def side(x, h): - # side output saliency map (before sigmoid) - x = getattr(self, f'side{h}')(x) - x = _upsample_like(x, sizes[1]) - maps.append(x) - - def fuse(): - # fuse saliency probability maps - maps.reverse() - x = torch.cat(maps, 1) - x = getattr(self, 'outconv')(x) - maps.insert(0, x) - return [torch.sigmoid(x) for x in maps] - - unet(x) - maps = fuse() - return maps - - def _make_layers(self, cfgs): - self.height = int((len(cfgs) + 1) / 2) - self.add_module('downsample', nn.MaxPool2d(2, stride=2, ceil_mode=True)) - for k, v in cfgs.items(): - # build rsu block - self.add_module(k, RSU(v[0], *v[1])) - if v[2] > 0: - # build side layer - self.add_module(f'side{v[0][-1]}', nn.Conv2d(v[2], self.out_ch, 3, padding=1)) - # build fuse layer - self.add_module('outconv', nn.Conv2d(int(self.height * self.out_ch), self.out_ch, 1)) - - -def U2NET_full(): - full = { - # cfgs for building RSUs and sides - # {stage : [name, (height(L), in_ch, mid_ch, out_ch, dilated), side]} - 'stage1': ['En_1', (7, 3, 32, 64), -1], - 'stage2': ['En_2', (6, 64, 32, 128), -1], - 'stage3': ['En_3', (5, 128, 64, 256), -1], - 'stage4': ['En_4', (4, 256, 128, 512), -1], - 'stage5': ['En_5', (4, 512, 256, 512, True), -1], - 'stage6': ['En_6', (4, 512, 256, 512, True), 512], - 'stage5d': ['De_5', (4, 1024, 256, 512, True), 512], - 'stage4d': ['De_4', (4, 1024, 128, 256), 256], - 'stage3d': ['De_3', (5, 512, 64, 128), 128], - 'stage2d': ['De_2', (6, 256, 32, 64), 64], - 'stage1d': ['De_1', (7, 128, 16, 64), 64], - } - return U2NET(cfgs=full, out_ch=1) - - -def U2NET_lite(): - lite = { - # cfgs for building RSUs and sides - # {stage : [name, (height(L), in_ch, mid_ch, out_ch, dilated), side]} - 'stage1': ['En_1', (7, 3, 16, 64), -1], - 'stage2': ['En_2', (6, 64, 16, 64), -1], - 'stage3': ['En_3', (5, 64, 16, 64), -1], - 'stage4': ['En_4', (4, 64, 16, 64), -1], - 'stage5': ['En_5', (4, 64, 16, 64, True), -1], - 'stage6': ['En_6', (4, 64, 16, 64, True), 64], - 'stage5d': ['De_5', (4, 128, 16, 64, True), 64], - 'stage4d': ['De_4', (4, 128, 16, 64), 64], - 'stage3d': ['De_3', (5, 128, 16, 64), 64], - 'stage2d': ['De_2', (6, 128, 16, 64), 64], - 'stage1d': ['De_1', (7, 128, 16, 64), 64], - } - return U2NET(cfgs=lite, out_ch=1) diff --git a/spaces/heiyuan/ChatGPT/run_macOS.command b/spaces/heiyuan/ChatGPT/run_macOS.command deleted file mode 100644 index 62af07283093d8e580763d7acfe493c3d88e7b08..0000000000000000000000000000000000000000 --- a/spaces/heiyuan/ChatGPT/run_macOS.command +++ /dev/null @@ -1,25 +0,0 @@ -#!/bin/bash - -# 获取脚本所在目录 -script_dir=$(dirname "$0") - -# 将工作目录更改为脚本所在目录 -cd "$script_dir" - -# 检查Git仓库是否有更新 -git remote update -pwd - -if ! git status -uno | grep 'up to date' > /dev/null; then - # 如果有更新，关闭当前运行的服务器 - pkill -f ChuanhuChatbot.py - - # 拉取最新更改 - git pull - - # 安装依赖 - pip3 install -r requirements.txt - - # 重新启动服务器 - nohup python3 ChuanhuChatbot.py & -fi diff --git a/spaces/hezhaoqia/vits-simple-api/utils/merge.py b/spaces/hezhaoqia/vits-simple-api/utils/merge.py deleted file mode 100644 index 86ee1cf89fd270b7e30766364f69495895f5f2d0..0000000000000000000000000000000000000000 --- a/spaces/hezhaoqia/vits-simple-api/utils/merge.py +++ /dev/null @@ -1,190 +0,0 @@ -import os -import json -import logging -import torch -import config -import numpy as np -from utils.utils import check_is_none -from vits import VITS -from voice import TTS - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -lang_dict = { - "english_cleaners": ["en"], - "english_cleaners2": ["en"], - "japanese_cleaners": ["ja"], - "japanese_cleaners2": ["ja"], - "korean_cleaners": ["ko"], - "chinese_cleaners": ["zh"], - "zh_ja_mixture_cleaners": ["zh", "ja"], - "sanskrit_cleaners": ["sa"], - "cjks_cleaners": ["zh", "ja", "ko", "sa"], - "cjke_cleaners": ["zh", "ja", "ko", "en"], - "cjke_cleaners2": ["zh", "ja", "ko", "en"], - "cje_cleaners": ["zh", "ja", "en"], - "cje_cleaners2": ["zh", "ja", "en"], - "thai_cleaners": ["th"], - "shanghainese_cleaners": ["sh"], - "chinese_dialect_cleaners": ["zh", "ja", "sh", "gd", "en", "SZ", "WX", "CZ", "HZ", "SX", "NB", "JJ", "YX", "JD", - "ZR", "PH", "TX", "JS", "HN", "LP", "XS", "FY", "RA", "CX", "SM", "TT", "WZ", "SC", - "YB"], - "bert_chinese_cleaners": ["zh"], -} - - -def analysis(model_config_json): - model_config = json.load(model_config_json) - symbols = model_config.get("symbols", None) - emotion_embedding = model_config.get("data").get("emotion_embedding", False) - if "use_spk_conditioned_encoder" in model_config.get("model"): - model_type = 'bert_vits2' - return model_type - if symbols != None: - if not emotion_embedding: - mode_type = "vits" - else: - mode_type = "w2v2" - else: - mode_type = "hubert" - return mode_type - - -def load_npy(model_): - if isinstance(model_, list): - # check if is .npy - for i in model_: - _model_extention = os.path.splitext(i)[1] - if _model_extention != ".npy": - raise ValueError(f"Unsupported model type: {_model_extention}") - - # merge npy files - emotion_reference = np.empty((0, 1024)) - for i in model_: - tmp = np.load(i).reshape(-1, 1024) - emotion_reference = np.append(emotion_reference, tmp, axis=0) - - elif os.path.isdir(model_): - emotion_reference = np.empty((0, 1024)) - for root, dirs, files in os.walk(model_): - for file_name in files: - # check if is .npy - _model_extention = os.path.splitext(file_name)[1] - if _model_extention != ".npy": - continue - file_path = os.path.join(root, file_name) - - # merge npy files - tmp = np.load(file_path).reshape(-1, 1024) - emotion_reference = np.append(emotion_reference, tmp, axis=0) - - elif os.path.isfile(model_): - # check if is .npy - _model_extention = os.path.splitext(model_)[1] - if _model_extention != ".npy": - raise ValueError(f"Unsupported model type: {_model_extention}") - - emotion_reference = np.load(model_) - logging.info(f"Loaded emotional dimention npy range:{len(emotion_reference)}") - return emotion_reference - - -def merge_model(merging_model): - vits_obj = [] - vits_speakers = [] - hubert_vits_obj = [] - hubert_vits_speakers = [] - w2v2_vits_obj = [] - w2v2_vits_speakers = [] - bert_vits2_obj = [] - bert_vits2_speakers = [] - - # model list - vits_list = [] - hubert_vits_list = [] - w2v2_vits_list = [] - bert_vits2_list = [] - - for l in merging_model: - with open(l[1], 'r', encoding='utf-8') as model_config: - model_type = analysis(model_config) - if model_type == "vits": - vits_list.append(l) - elif model_type == "hubert": - hubert_vits_list.append(l) - elif model_type == "w2v2": - w2v2_vits_list.append(l) - elif model_type == "bert_vits2": - bert_vits2_list.append(l) - - # merge vits - new_id = 0 - for obj_id, i in enumerate(vits_list): - obj = VITS(model=i[0], config=i[1], model_type="vits", device=device) - lang = lang_dict.get(obj.get_cleaner(), ["unknown"]) - for id, name in enumerate(obj.get_speakers()): - vits_obj.append([int(id), obj, obj_id]) - vits_speakers.append({"id": new_id, "name": name, "lang": lang}) - new_id += 1 - - # merge hubert-vits - if len(hubert_vits_list) != 0: - if getattr(config, "HUBERT_SOFT_MODEL", None) == None or check_is_none(config.HUBERT_SOFT_MODEL): - raise ValueError(f"Please configure HUBERT_SOFT_MODEL path in config.py") - try: - from vits.hubert_model import hubert_soft - hubert = hubert_soft(config.HUBERT_SOFT_MODEL) - except Exception as e: - raise ValueError(f"Load HUBERT_SOFT_MODEL failed {e}") - - new_id = 0 - for obj_id, i in enumerate(hubert_vits_list): - obj = VITS(model=i[0], config=i[1], model_=hubert, model_type="hubert", device=device) - lang = lang_dict.get(obj.get_cleaner(), ["unknown"]) - - for id, name in enumerate(obj.get_speakers()): - hubert_vits_obj.append([int(id), obj, obj_id]) - hubert_vits_speakers.append({"id": new_id, "name": name, "lang": lang}) - new_id += 1 - - # merge w2v2-vits - emotion_reference = None - if len(w2v2_vits_list) != 0: - if getattr(config, "DIMENSIONAL_EMOTION_NPY", None) == None or check_is_none(config.DIMENSIONAL_EMOTION_NPY): - raise ValueError(f"Please configure DIMENSIONAL_EMOTION_NPY path in config.py") - try: - emotion_reference = load_npy(config.DIMENSIONAL_EMOTION_NPY) - except Exception as e: - raise ValueError(f"Load DIMENSIONAL_EMOTION_NPY failed {e}") - - new_id = 0 - for obj_id, i in enumerate(w2v2_vits_list): - obj = VITS(model=i[0], config=i[1], model_=emotion_reference, model_type="w2v2", device=device) - lang = lang_dict.get(obj.get_cleaner(), ["unknown"]) - - for id, name in enumerate(obj.get_speakers()): - w2v2_vits_obj.append([int(id), obj, obj_id]) - w2v2_vits_speakers.append({"id": new_id, "name": name, "lang": lang}) - new_id += 1 - - # merge Bert_VITS2 - new_id = 0 - for obj_id, i in enumerate(bert_vits2_list): - from bert_vits2 import Bert_VITS2 - obj = Bert_VITS2(model=i[0], config=i[1], device=device) - lang = ["ZH"] - for id, name in enumerate(obj.get_speakers()): - bert_vits2_obj.append([int(id), obj, obj_id]) - bert_vits2_speakers.append({"id": new_id, "name": name, "lang": lang}) - new_id += 1 - - - voice_obj = {"VITS": vits_obj, "HUBERT-VITS": hubert_vits_obj, "W2V2-VITS": w2v2_vits_obj, - "BERT-VITS2": bert_vits2_obj} - voice_speakers = {"VITS": vits_speakers, "HUBERT-VITS": hubert_vits_speakers, "W2V2-VITS": w2v2_vits_speakers, - "BERT-VITS2": bert_vits2_speakers} - w2v2_emotion_count = len(emotion_reference) if emotion_reference is not None else 0 - - tts = TTS(voice_obj, voice_speakers, w2v2_emotion_count=w2v2_emotion_count, device=device) - - return tts diff --git a/spaces/housexu123/bingo-2.0/src/components/ui/select.tsx b/spaces/housexu123/bingo-2.0/src/components/ui/select.tsx deleted file mode 100644 index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000 --- a/spaces/housexu123/bingo-2.0/src/components/ui/select.tsx +++ /dev/null @@ -1,123 +0,0 @@ -'use client' - -import * as React from 'react' -import * as SelectPrimitive from '@radix-ui/react-select' - -import { cn } from '@/lib/utils' -import { - IconArrowDown, - IconCheck, - IconChevronUpDown -} from '@/components/ui/icons' - -const Select = SelectPrimitive.Root - -const SelectGroup = SelectPrimitive.Group - -const SelectValue = SelectPrimitive.Value - -const SelectTrigger = React.forwardRef< - React.ElementRef , - React.ComponentPropsWithoutRef ->(({ className, children, ...props }, ref) => ( - - {children} - -)) -SelectTrigger.displayName = SelectPrimitive.Trigger.displayName - -const SelectContent = React.forwardRef< - React.ElementRef- -- , - React.ComponentPropsWithoutRef ->(({ className, children, position = 'popper', ...props }, ref) => ( - - -)) -SelectContent.displayName = SelectPrimitive.Content.displayName - -const SelectLabel = React.forwardRef< - React.ElementRef- -- {children} - -, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -SelectLabel.displayName = SelectPrimitive.Label.displayName - -const SelectItem = React.forwardRef< - React.ElementRef , - React.ComponentPropsWithoutRef ->(({ className, children, ...props }, ref) => ( - - - -)) -SelectItem.displayName = SelectPrimitive.Item.displayName - -const SelectSeparator = React.forwardRef< - React.ElementRef- - -- {children} -, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -SelectSeparator.displayName = SelectPrimitive.Separator.displayName - -export { - Select, - SelectGroup, - SelectValue, - SelectTrigger, - SelectContent, - SelectLabel, - SelectItem, - SelectSeparator -} diff --git a/spaces/hrdtbs/rvc-mochinoa/README.md b/spaces/hrdtbs/rvc-mochinoa/README.md deleted file mode 100644 index c997e278262ea54415bdd1703e081923e38d4b06..0000000000000000000000000000000000000000 --- a/spaces/hrdtbs/rvc-mochinoa/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: RVC Mochinoa -emoji: 🐈 -colorFrom: red -colorTo: purple -sdk: gradio -sdk_version: 3.32.0 -app_file: app.py -pinned: true ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/hsdcs/bingchat/README.md b/spaces/hsdcs/bingchat/README.md deleted file mode 100644 index 0eaf079795e2d89edf1b736f9414fd3231f77089..0000000000000000000000000000000000000000 --- a/spaces/hsdcs/bingchat/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Bingchat -emoji: 📊 -colorFrom: green -colorTo: indigo -sdk: docker -pinned: false -license: mit -app_port: 8080 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/hugggof/vampnet/scripts/utils/stage.py b/spaces/hugggof/vampnet/scripts/utils/stage.py deleted file mode 100644 index 253e1d070ccf3754be01578d22b65136858fa697..0000000000000000000000000000000000000000 --- a/spaces/hugggof/vampnet/scripts/utils/stage.py +++ /dev/null @@ -1,30 +0,0 @@ -import os -import subprocess -from pathlib import Path - -import argbind -import rich -from audiotools.ml import Experiment - - -@argbind.bind(without_prefix=True) -def run( - run_dir: str = os.getenv("PATH_TO_RUNS", "runs"), - name: str = None, - recent: bool = False, -): - if recent: - paths = sorted(Path(run_dir).iterdir(), key=os.path.getmtime) - paths = [p.name for p in paths if p.is_dir()] - if paths: - name = paths[-1] - - with Experiment(run_dir, name) as exp: - exp.snapshot() - rich.print(f"Created a snapshot of {exp.parent_directory} at {exp.exp_dir}") - - -if __name__ == "__main__": - args = argbind.parse_args() - with argbind.scope(args): - run() diff --git a/spaces/huggingface-projects/stable-diffusion-multiplayer/stablediffusion-infinity/PyPatchMatch/csrc/inpaint.h b/spaces/huggingface-projects/stable-diffusion-multiplayer/stablediffusion-infinity/PyPatchMatch/csrc/inpaint.h deleted file mode 100644 index a59b1d347ea5fe92976a4fda10a820d6508f51da..0000000000000000000000000000000000000000 --- a/spaces/huggingface-projects/stable-diffusion-multiplayer/stablediffusion-infinity/PyPatchMatch/csrc/inpaint.h +++ /dev/null @@ -1,27 +0,0 @@ -#pragma once - -#include - -#include "masked_image.h" -#include "nnf.h" - -class Inpainting { -public: - Inpainting(cv::Mat image, cv::Mat mask, const PatchDistanceMetric *metric); - Inpainting(cv::Mat image, cv::Mat mask, cv::Mat global_mask, const PatchDistanceMetric *metric); - cv::Mat run(bool verbose = false, bool verbose_visualize = false, unsigned int random_seed = 1212); - -private: - void _initialize_pyramid(void); - MaskedImage _expectation_maximization(MaskedImage source, MaskedImage target, int level, bool verbose); - void _expectation_step(const NearestNeighborField &nnf, bool source2target, cv::Mat &vote, const MaskedImage &source, bool upscaled); - void _maximization_step(MaskedImage &target, const cv::Mat &vote); - - MaskedImage m_initial; - std::vector m_pyramid; - - NearestNeighborField m_source2target; - NearestNeighborField m_target2source; - const PatchDistanceMetric *m_distance_metric; -}; - diff --git a/spaces/hysts/ControlNet-with-Anything-v4/app_fake_scribble.py b/spaces/hysts/ControlNet-with-Anything-v4/app_fake_scribble.py deleted file mode 100644 index 70dd8769a17cc1dde58a2a8c28e02c07dd4b383b..0000000000000000000000000000000000000000 --- a/spaces/hysts/ControlNet-with-Anything-v4/app_fake_scribble.py +++ /dev/null @@ -1,83 +0,0 @@ -# This file is adapted from https://github.com/lllyasviel/ControlNet/blob/f4748e3630d8141d7765e2bd9b1e348f47847707/gradio_fake_scribble2image.py -# The original license file is LICENSE.ControlNet in this repo. -import gradio as gr - - -def create_demo(process, max_images=12, default_num_images=3): - with gr.Blocks() as demo: - with gr.Row(): - gr.Markdown('## Control Stable Diffusion with Fake Scribble Maps') - with gr.Row(): - with gr.Column(): - input_image = gr.Image(source='upload', type='numpy') - prompt = gr.Textbox(label='Prompt') - run_button = gr.Button(label='Run') - with gr.Accordion('Advanced options', open=False): - num_samples = gr.Slider(label='Images', - minimum=1, - maximum=max_images, - value=default_num_images, - step=1) - image_resolution = gr.Slider(label='Image Resolution', - minimum=256, - maximum=512, - value=512, - step=256) - detect_resolution = gr.Slider(label='HED Resolution', - minimum=128, - maximum=512, - value=512, - step=1) - num_steps = gr.Slider(label='Steps', - minimum=1, - maximum=100, - value=20, - step=1) - guidance_scale = gr.Slider(label='Guidance Scale', - minimum=0.1, - maximum=30.0, - value=9.0, - step=0.1) - seed = gr.Slider(label='Seed', - minimum=-1, - maximum=2147483647, - step=1, - randomize=True) - a_prompt = gr.Textbox( - label='Added Prompt', - value='best quality, extremely detailed') - n_prompt = gr.Textbox( - label='Negative Prompt', - value= - 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality' - ) - with gr.Column(): - result = gr.Gallery(label='Output', - show_label=False, - elem_id='gallery').style(grid=2, - height='auto') - inputs = [ - input_image, - prompt, - a_prompt, - n_prompt, - num_samples, - image_resolution, - detect_resolution, - num_steps, - guidance_scale, - seed, - ] - prompt.submit(fn=process, inputs=inputs, outputs=result) - run_button.click(fn=process, - inputs=inputs, - outputs=result, - api_name='fake_scribble') - return demo - - -if __name__ == '__main__': - from model import Model - model = Model() - demo = create_demo(model.process_fake_scribble) - demo.queue().launch() diff --git a/spaces/hysts/StyleGAN-Human-Interpolation/app.py b/spaces/hysts/StyleGAN-Human-Interpolation/app.py deleted file mode 100644 index 7d2f1a53e610ef1daf9eb35d258f9caeb16313d7..0000000000000000000000000000000000000000 --- a/spaces/hysts/StyleGAN-Human-Interpolation/app.py +++ /dev/null @@ -1,107 +0,0 @@ -#!/usr/bin/env python - -from __future__ import annotations - -import functools -import pickle -import sys - -import gradio as gr -import numpy as np -import torch -import torch.nn as nn -from huggingface_hub import hf_hub_download - -sys.path.insert(0, 'StyleGAN-Human') - -TITLE = 'StyleGAN-Human (Interpolation)' -DESCRIPTION = 'https://github.com/stylegan-human/StyleGAN-Human' - - -def load_model(file_name: str, device: torch.device) -> nn.Module: - path = hf_hub_download('public-data/StyleGAN-Human', f'models/{file_name}') - with open(path, 'rb') as f: - model = pickle.load(f)['G_ema'] - model.eval() - model.to(device) - with torch.inference_mode(): - z = torch.zeros((1, model.z_dim)).to(device) - label = torch.zeros([1, model.c_dim], device=device) - model(z, label, force_fp32=True) - return model - - -def generate_z(z_dim: int, seed: int, device: torch.device) -> torch.Tensor: - return torch.from_numpy(np.random.RandomState(seed).randn( - 1, z_dim)).to(device).float() - - -@torch.inference_mode() -def generate_interpolated_images(seed0: int, psi0: float, seed1: int, - psi1: float, num_intermediate: int, - model: nn.Module, - device: torch.device) -> list[np.ndarray]: - seed0 = int(np.clip(seed0, 0, np.iinfo(np.uint32).max)) - seed1 = int(np.clip(seed1, 0, np.iinfo(np.uint32).max)) - - z0 = generate_z(model.z_dim, seed0, device) - z1 = generate_z(model.z_dim, seed1, device) - vec = z1 - z0 - dvec = vec / (num_intermediate + 1) - zs = [z0 + dvec * i for i in range(num_intermediate + 2)] - dpsi = (psi1 - psi0) / (num_intermediate + 1) - psis = [psi0 + dpsi * i for i in range(num_intermediate + 2)] - - label = torch.zeros([1, model.c_dim], device=device) - - res = [] - for z, psi in zip(zs, psis): - out = model(z, label, truncation_psi=psi, force_fp32=True) - out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to( - torch.uint8) - out = out[0].cpu().numpy() - res.append(out) - return res - - -device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') -model = load_model('stylegan_human_v2_1024.pkl', device) -fn = functools.partial(generate_interpolated_images, - model=model, - device=device) - -gr.Interface( - fn=fn, - inputs=[ - gr.Slider(label='Seed 1', - minimum=0, - maximum=100000, - step=1, - value=0, - randomize=True), - gr.Slider(label='Truncation psi 1', - minimum=0, - maximum=2, - step=0.05, - value=0.7), - gr.Slider(label='Seed 2', - minimum=0, - maximum=100000, - step=1, - value=1, - randomize=True), - gr.Slider(label='Truncation psi 2', - minimum=0, - maximum=2, - step=0.05, - value=0.7), - gr.Slider(label='Number of Intermediate Frames', - minimum=0, - maximum=21, - step=1, - value=7), - ], - outputs=gr.Gallery(label='Output Images', type='numpy'), - title=TITLE, - description=DESCRIPTION, -).queue(max_size=10).launch() diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/losses.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/losses.py deleted file mode 100644 index f3cb3fd778adedf31acbc3ff01018e9efb99d65b..0000000000000000000000000000000000000000 --- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/models/losses.py +++ /dev/null @@ -1,121 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from kornia.geometry import warp_affine - - -def resize_n_crop(image, M, dsize=112): - # image: (b, c, h, w) - # M : (b, 2, 3) - return warp_affine(image, M, dsize=(dsize, dsize)) - - -### perceptual level loss -class PerceptualLoss(nn.Module): - def __init__(self, recog_net, input_size=112): - super(PerceptualLoss, self).__init__() - self.recog_net = recog_net - self.preprocess = lambda x: 2 * x - 1 - self.input_size = input_size - - def forward(imageA, imageB, M): - """ - 1 - cosine distance - Parameters: - imageA --torch.tensor (B, 3, H, W), range (0, 1) , RGB order - imageB --same as imageA - """ - - imageA = self.preprocess(resize_n_crop(imageA, M, self.input_size)) - imageB = self.preprocess(resize_n_crop(imageB, M, self.input_size)) - - # freeze bn - self.recog_net.eval() - - id_featureA = F.normalize(self.recog_net(imageA), dim=-1, p=2) - id_featureB = F.normalize(self.recog_net(imageB), dim=-1, p=2) - cosine_d = torch.sum(id_featureA * id_featureB, dim=-1) - # assert torch.sum((cosine_d > 1).float()) == 0 - return torch.sum(1 - cosine_d) / cosine_d.shape[0] - - -def perceptual_loss(id_featureA, id_featureB): - cosine_d = torch.sum(id_featureA * id_featureB, dim=-1) - # assert torch.sum((cosine_d > 1).float()) == 0 - return torch.sum(1 - cosine_d) / cosine_d.shape[0] - - -### image level loss -def photo_loss(imageA, imageB, mask, eps=1e-6): - """ - l2 norm (with sqrt, to ensure backward stabililty, use eps, otherwise Nan may occur) - Parameters: - imageA --torch.tensor (B, 3, H, W), range (0, 1), RGB order - imageB --same as imageA - """ - loss = torch.sqrt(eps + torch.sum((imageA - imageB) ** 2, dim=1, keepdims=True)) * mask - loss = torch.sum(loss) / torch.max(torch.sum(mask), torch.tensor(1.0).to(mask.device)) - return loss - - -def landmark_loss(predict_lm, gt_lm, weight=None): - """ - weighted mse loss - Parameters: - predict_lm --torch.tensor (B, 68, 2) - gt_lm --torch.tensor (B, 68, 2) - weight --numpy.array (1, 68) - """ - if not weight: - weight = np.ones([68]) - weight[28:31] = 20 - weight[-8:] = 20 - weight = np.expand_dims(weight, 0) - weight = torch.tensor(weight).to(predict_lm.device) - loss = torch.sum((predict_lm - gt_lm) ** 2, dim=-1) * weight - loss = torch.sum(loss) / (predict_lm.shape[0] * predict_lm.shape[1]) - return loss - - -### regulization -def reg_loss(coeffs_dict, opt=None): - """ - l2 norm without the sqrt, from yu's implementation (mse) - tf.nn.l2_loss https://www.tensorflow.org/api_docs/python/tf/nn/l2_loss - Parameters: - coeffs_dict -- a dict of torch.tensors , keys: id, exp, tex, angle, gamma, trans - - """ - # coefficient regularization to ensure plausible 3d faces - if opt: - w_id, w_exp, w_tex = opt.w_id, opt.w_exp, opt.w_tex - else: - w_id, w_exp, w_tex = 1, 1, 1, 1 - creg_loss = ( - w_id * torch.sum(coeffs_dict["id"] ** 2) - + w_exp * torch.sum(coeffs_dict["exp"] ** 2) - + w_tex * torch.sum(coeffs_dict["tex"] ** 2) - ) - creg_loss = creg_loss / coeffs_dict["id"].shape[0] - - # gamma regularization to ensure a nearly-monochromatic light - gamma = coeffs_dict["gamma"].reshape([-1, 3, 9]) - gamma_mean = torch.mean(gamma, dim=1, keepdims=True) - gamma_loss = torch.mean((gamma - gamma_mean) ** 2) - - return creg_loss, gamma_loss - - -def reflectance_loss(texture, mask): - """ - minimize texture variance (mse), albedo regularization to ensure an uniform skin albedo - Parameters: - texture --torch.tensor, (B, N, 3) - mask --torch.tensor, (N), 1 or 0 - - """ - mask = mask.reshape([1, mask.shape[0], 1]) - texture_mean = torch.sum(mask * texture, dim=1, keepdims=True) / torch.sum(mask) - loss = torch.sum(((texture - texture_mean) * mask) ** 2) / (texture.shape[0] * torch.sum(mask)) - return loss diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/options/train_options.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/options/train_options.py deleted file mode 100644 index 1e02ee3e87b49cae8f7e660d4b891ef062f06d97..0000000000000000000000000000000000000000 --- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/options/train_options.py +++ /dev/null @@ -1,90 +0,0 @@ -"""This script contains the training options for Deep3DFaceRecon_pytorch -""" -from util import util - -from .base_options import BaseOptions - - -class TrainOptions(BaseOptions): - """This class includes training options. - - It also includes shared options defined in BaseOptions. - """ - - def initialize(self, parser): - parser = BaseOptions.initialize(self, parser) - # dataset parameters - # for train - parser.add_argument("--data_root", type=str, default="./", help="dataset root") - parser.add_argument( - "--flist", type=str, default="datalist/train/masks.txt", help="list of mask names of training set" - ) - parser.add_argument("--batch_size", type=int, default=32) - parser.add_argument( - "--dataset_mode", type=str, default="flist", help="chooses how datasets are loaded. [None | flist]" - ) - parser.add_argument( - "--serial_batches", - action="store_true", - help="if true, takes images in order to make batches, otherwise takes them randomly", - ) - parser.add_argument("--num_threads", default=4, type=int, help="# threads for loading data") - parser.add_argument( - "--max_dataset_size", - type=int, - default=float("inf"), - help="Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.", - ) - parser.add_argument( - "--preprocess", - type=str, - default="shift_scale_rot_flip", - help="scaling and cropping of images at load time [shift_scale_rot_flip | shift_scale | shift | shift_rot_flip ]", - ) - parser.add_argument( - "--use_aug", type=util.str2bool, nargs="?", const=True, default=True, help="whether use data augmentation" - ) - - # for val - parser.add_argument( - "--flist_val", type=str, default="datalist/val/masks.txt", help="list of mask names of val set" - ) - parser.add_argument("--batch_size_val", type=int, default=32) - - # visualization parameters - parser.add_argument( - "--display_freq", type=int, default=1000, help="frequency of showing training results on screen" - ) - parser.add_argument( - "--print_freq", type=int, default=100, help="frequency of showing training results on console" - ) - - # network saving and loading parameters - parser.add_argument("--save_latest_freq", type=int, default=5000, help="frequency of saving the latest results") - parser.add_argument( - "--save_epoch_freq", type=int, default=1, help="frequency of saving checkpoints at the end of epochs" - ) - parser.add_argument("--evaluation_freq", type=int, default=5000, help="evaluation freq") - parser.add_argument("--save_by_iter", action="store_true", help="whether saves model by iteration") - parser.add_argument("--continue_train", action="store_true", help="continue training: load the latest model") - parser.add_argument( - "--epoch_count", - type=int, - default=1, - help="the starting epoch count, we save the model by , + , ...", - ) - parser.add_argument("--phase", type=str, default="train", help="train, val, test, etc") - parser.add_argument("--pretrained_name", type=str, default=None, help="resume training from another checkpoint") - - # training parameters - parser.add_argument("--n_epochs", type=int, default=20, help="number of epochs with the initial learning rate") - parser.add_argument("--lr", type=float, default=0.0001, help="initial learning rate for adam") - parser.add_argument( - "--lr_policy", type=str, default="step", help="learning rate policy. [linear | step | plateau | cosine]" - ) - parser.add_argument( - "--lr_decay_epochs", type=int, default=10, help="multiply by a gamma every lr_decay_epochs epoches" - ) - - self.isTrain = True - return parser diff --git a/spaces/imjunaidafzal/LoRA-DreamBooth-Training-UI/app_training.py b/spaces/imjunaidafzal/LoRA-DreamBooth-Training-UI/app_training.py deleted file mode 100644 index 09660a26b4d99f8ff8457a454fdddcc57d7f3756..0000000000000000000000000000000000000000 --- a/spaces/imjunaidafzal/LoRA-DreamBooth-Training-UI/app_training.py +++ /dev/null @@ -1,144 +0,0 @@ -#!/usr/bin/env python - -from __future__ import annotations - -import os - -import gradio as gr - -from constants import UploadTarget -from inference import InferencePipeline -from trainer import Trainer - - -def create_training_demo(trainer: Trainer, - pipe: InferencePipeline | None = None) -> gr.Blocks: - with gr.Blocks() as demo: - with gr.Row(): - with gr.Column(): - with gr.Box(): - gr.Markdown('Training Data') - instance_images = gr.Files(label='Instance images') - instance_prompt = gr.Textbox(label='Instance prompt', - max_lines=1) - gr.Markdown(''' - - Upload images of the style you are planning on training on. - - For an instance prompt, use a unique, made up word to avoid collisions. - ''') - with gr.Box(): - gr.Markdown('Output Model') - output_model_name = gr.Text(label='Name of your model', - max_lines=1) - delete_existing_model = gr.Checkbox( - label='Delete existing model of the same name', - value=False) - validation_prompt = gr.Text(label='Validation Prompt') - with gr.Box(): - gr.Markdown('Upload Settings') - with gr.Row(): - upload_to_hub = gr.Checkbox( - label='Upload model to Hub', value=True) - use_private_repo = gr.Checkbox(label='Private', - value=True) - delete_existing_repo = gr.Checkbox( - label='Delete existing repo of the same name', - value=False) - upload_to = gr.Radio( - label='Upload to', - choices=[_.value for _ in UploadTarget], - value=UploadTarget.LORA_LIBRARY.value) - gr.Markdown(''' - - By default, trained models will be uploaded to [LoRA Library](https://huggingface.co/lora-library) (see [this example model](https://huggingface.co/lora-library/lora-dreambooth-sample-dog)). - - You can also choose "Personal Profile", in which case, the model will be uploaded to https://huggingface.co/{your_username}/{model_name}. - ''') - - with gr.Box(): - gr.Markdown('Training Parameters') - with gr.Row(): - base_model = gr.Text( - label='Base Model', - value='stabilityai/stable-diffusion-2-1-base', - max_lines=1) - resolution = gr.Dropdown(choices=['512', '768'], - value='512', - label='Resolution') - num_training_steps = gr.Number( - label='Number of Training Steps', value=1000, precision=0) - learning_rate = gr.Number(label='Learning Rate', value=0.0001) - gradient_accumulation = gr.Number( - label='Number of Gradient Accumulation', - value=1, - precision=0) - seed = gr.Slider(label='Seed', - minimum=0, - maximum=100000, - step=1, - value=0) - fp16 = gr.Checkbox(label='FP16', value=True) - use_8bit_adam = gr.Checkbox(label='Use 8bit Adam', value=True) - checkpointing_steps = gr.Number(label='Checkpointing Steps', - value=100, - precision=0) - use_wandb = gr.Checkbox(label='Use W&B', - value=False, - interactive=bool( - os.getenv('WANDB_API_KEY'))) - validation_epochs = gr.Number(label='Validation Epochs', - value=100, - precision=0) - gr.Markdown(''' - - The base model must be a model that is compatible with [diffusers](https://github.com/huggingface/diffusers) library. - - It takes a few minutes to download the base model first. - - It will take about 8 minutes to train for 1000 steps with a T4 GPU. - - You may want to try a small number of steps first, like 1, to see if everything works fine in your environment. - - You can check the training status by pressing the "Open logs" button if you are running this on your Space. - - You need to set the environment variable `WANDB_API_KEY` if you'd like to use [W&B](https://wandb.ai/site). See [W&B documentation](https://docs.wandb.ai/guides/track/advanced/environment-variables). - - **Note:** Due to [this issue](https://github.com/huggingface/accelerate/issues/944), currently, training will not terminate properly if you use W&B. - ''') - - remove_gpu_after_training = gr.Checkbox( - label='Remove GPU after training', - value=False, - interactive=bool(os.getenv('SPACE_ID')), - visible=False) - run_button = gr.Button('Start Training') - - with gr.Box(): - gr.Markdown('Output message') - output_message = gr.Markdown() - - if pipe is not None: - run_button.click(fn=pipe.clear) - run_button.click(fn=trainer.run, - inputs=[ - instance_images, - instance_prompt, - output_model_name, - delete_existing_model, - validation_prompt, - base_model, - resolution, - num_training_steps, - learning_rate, - gradient_accumulation, - seed, - fp16, - use_8bit_adam, - checkpointing_steps, - use_wandb, - validation_epochs, - upload_to_hub, - use_private_repo, - delete_existing_repo, - upload_to, - remove_gpu_after_training, - ], - outputs=output_message) - return demo - - -if __name__ == '__main__': - hf_token = os.getenv('HF_TOKEN') - trainer = Trainer(hf_token) - demo = create_training_demo(trainer) - demo.queue(max_size=1).launch(share=False) diff --git a/spaces/innnky/vits-nyaru/losses.py b/spaces/innnky/vits-nyaru/losses.py deleted file mode 100644 index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000 --- a/spaces/innnky/vits-nyaru/losses.py +++ /dev/null @@ -1,61 +0,0 @@ -import torch -from torch.nn import functional as F - -import commons - - -def feature_loss(fmap_r, fmap_g): - loss = 0 - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - rl = rl.float().detach() - gl = gl.float() - loss += torch.mean(torch.abs(rl - gl)) - - return loss * 2 - - -def discriminator_loss(disc_real_outputs, disc_generated_outputs): - loss = 0 - r_losses = [] - g_losses = [] - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - dr = dr.float() - dg = dg.float() - r_loss = torch.mean((1-dr)**2) - g_loss = torch.mean(dg**2) - loss += (r_loss + g_loss) - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - - return loss, r_losses, g_losses - - -def generator_loss(disc_outputs): - loss = 0 - gen_losses = [] - for dg in disc_outputs: - dg = dg.float() - l = torch.mean((1-dg)**2) - gen_losses.append(l) - loss += l - - return loss, gen_losses - - -def kl_loss(z_p, logs_q, m_p, logs_p, z_mask): - """ - z_p, logs_q: [b, h, t_t] - m_p, logs_p: [b, h, t_t] - """ - z_p = z_p.float() - logs_q = logs_q.float() - m_p = m_p.float() - logs_p = logs_p.float() - z_mask = z_mask.float() - - kl = logs_p - logs_q - 0.5 - kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p) - kl = torch.sum(kl * z_mask) - l = kl / torch.sum(z_mask) - return l diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Berserk 1 - 25 (Complete) [English Dubbed].md b/spaces/inplisQlawa/anything-midjourney-v4-1/Berserk 1 - 25 (Complete) [English Dubbed].md deleted file mode 100644 index 27ac1d826cd94446dc6d01f4643c385a3f585890..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Berserk 1 - 25 (Complete) [English Dubbed].md +++ /dev/null @@ -1,9 +0,0 @@ - Berserk 1 - 25 (Complete) [English Dubbed]
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- diff --git a/spaces/jbilcke-hf/AnimateDiff/scripts/animate.py b/spaces/jbilcke-hf/AnimateDiff/scripts/animate.py deleted file mode 100644 index 8bb5dd74de1e11eecfd96041758c64edaa02195a..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/AnimateDiff/scripts/animate.py +++ /dev/null @@ -1,159 +0,0 @@ -import argparse -import datetime -import inspect -import os -from omegaconf import OmegaConf - -import torch - -import diffusers -from diffusers import AutoencoderKL, DDIMScheduler - -from tqdm.auto import tqdm -from transformers import CLIPTextModel, CLIPTokenizer - -from animatediff.models.unet import UNet3DConditionModel -from animatediff.pipelines.pipeline_animation import AnimationPipeline -from animatediff.utils.util import save_videos_grid -from animatediff.utils.convert_from_ckpt import convert_ldm_unet_checkpoint, convert_ldm_clip_checkpoint, convert_ldm_vae_checkpoint -from animatediff.utils.convert_lora_safetensor_to_diffusers import convert_lora -from diffusers.utils.import_utils import is_xformers_available - -from einops import rearrange, repeat - -import csv, pdb, glob -from safetensors import safe_open -import math -from pathlib import Path - - -def main(args): - *_, func_args = inspect.getargvalues(inspect.currentframe()) - func_args = dict(func_args) - - time_str = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") - savedir = f"samples/{Path(args.config).stem}-{time_str}" - os.makedirs(savedir) - inference_config = OmegaConf.load(args.inference_config) - - config = OmegaConf.load(args.config) - samples = [] - - sample_idx = 0 - for model_idx, (config_key, model_config) in enumerate(list(config.items())): - - motion_modules = model_config.motion_module - motion_modules = [motion_modules] if isinstance(motion_modules, str) else list(motion_modules) - for motion_module in motion_modules: - - ### >>> create validation pipeline >>> ### - tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer") - text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder") - vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae") - unet = UNet3DConditionModel.from_pretrained_2d(args.pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs)) - - if is_xformers_available(): unet.enable_xformers_memory_efficient_attention() - else: assert False - - pipeline = AnimationPipeline( - vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, - scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)), - ).to("cuda") - - # 1. unet ckpt - # 1.1 motion module - motion_module_state_dict = torch.load(motion_module, map_location="cpu") - if "global_step" in motion_module_state_dict: func_args.update({"global_step": motion_module_state_dict["global_step"]}) - missing, unexpected = pipeline.unet.load_state_dict(motion_module_state_dict, strict=False) - assert len(unexpected) == 0 - - # 1.2 T2I - if model_config.path != "": - if model_config.path.endswith(".ckpt"): - state_dict = torch.load(model_config.path) - pipeline.unet.load_state_dict(state_dict) - - elif model_config.path.endswith(".safetensors"): - state_dict = {} - with safe_open(model_config.path, framework="pt", device="cpu") as f: - for key in f.keys(): - state_dict[key] = f.get_tensor(key) - - is_lora = all("lora" in k for k in state_dict.keys()) - if not is_lora: - base_state_dict = state_dict - else: - base_state_dict = {} - with safe_open(model_config.base, framework="pt", device="cpu") as f: - for key in f.keys(): - base_state_dict[key] = f.get_tensor(key) - - # vae - converted_vae_checkpoint = convert_ldm_vae_checkpoint(base_state_dict, pipeline.vae.config) - pipeline.vae.load_state_dict(converted_vae_checkpoint) - # unet - converted_unet_checkpoint = convert_ldm_unet_checkpoint(base_state_dict, pipeline.unet.config) - pipeline.unet.load_state_dict(converted_unet_checkpoint, strict=False) - # text_model - pipeline.text_encoder = convert_ldm_clip_checkpoint(base_state_dict) - - # import pdb - # pdb.set_trace() - if is_lora: - pipeline = convert_lora(pipeline, state_dict, alpha=model_config.lora_alpha) - - pipeline.to("cuda") - ### <<< create validation pipeline <<< ### - - prompts = model_config.prompt - n_prompts = list(model_config.n_prompt) * len(prompts) if len(model_config.n_prompt) == 1 else model_config.n_prompt - - random_seeds = model_config.get("seed", [-1]) - random_seeds = [random_seeds] if isinstance(random_seeds, int) else list(random_seeds) - random_seeds = random_seeds * len(prompts) if len(random_seeds) == 1 else random_seeds - - config[config_key].random_seed = [] - for prompt_idx, (prompt, n_prompt, random_seed) in enumerate(zip(prompts, n_prompts, random_seeds)): - - # manually set random seed for reproduction - if random_seed != -1: torch.manual_seed(random_seed) - else: torch.seed() - config[config_key].random_seed.append(torch.initial_seed()) - - print(f"current seed: {torch.initial_seed()}") - print(f"sampling {prompt} ...") - sample = pipeline( - prompt, - negative_prompt = n_prompt, - num_inference_steps = model_config.steps, - guidance_scale = model_config.guidance_scale, - width = args.W, - height = args.H, - video_length = args.L, - ).videos - samples.append(sample) - - prompt = "-".join((prompt.replace("/", "").split(" ")[:10])) - save_videos_grid(sample, f"{savedir}/sample/{sample_idx}-{prompt}.gif") - print(f"save to {savedir}/sample/{prompt}.gif") - - sample_idx += 1 - - samples = torch.concat(samples) - save_videos_grid(samples, f"{savedir}/sample.gif", n_rows=4) - - OmegaConf.save(config, f"{savedir}/config.yaml") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--pretrained_model_path", type=str, default="models/StableDiffusion/stable-diffusion-v1-5",) - parser.add_argument("--inference_config", type=str, default="configs/inference/inference.yaml") - parser.add_argument("--config", type=str, required=True) - - parser.add_argument("--L", type=int, default=16 ) - parser.add_argument("--W", type=int, default=512) - parser.add_argument("--H", type=int, default=512) - - args = parser.parse_args() - main(args) diff --git a/spaces/jbilcke-hf/ai-clip-factory/src/lib/useImageDimension.ts b/spaces/jbilcke-hf/ai-clip-factory/src/lib/useImageDimension.ts deleted file mode 100644 index 9cfd06e473929b1046a5dd9caa9d577ebaf09b7a..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/ai-clip-factory/src/lib/useImageDimension.ts +++ /dev/null @@ -1,20 +0,0 @@ -import { useEffect, useState } from "react" - -import { ImageDimension, getImageDimension } from "./getImageDimension" - -export function useImageDimension(src: string) { - const [dimension, setDimension] = useState({ - width: 0, - height: 0, - }) - - useEffect(() => { - const compute = async () => { - const newDimension = await getImageDimension(src) - setDimension(newDimension) - } - compute() - }, [src]) - - return dimension -} \ No newline at end of file diff --git a/spaces/jbilcke-hf/ai-clip-factory/src/lib/utils.ts b/spaces/jbilcke-hf/ai-clip-factory/src/lib/utils.ts deleted file mode 100644 index ec79801fe9cdd7711f6dbef26678a134c634a8be..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/ai-clip-factory/src/lib/utils.ts +++ /dev/null @@ -1,6 +0,0 @@ -import { type ClassValue, clsx } from "clsx" -import { twMerge } from "tailwind-merge" - -export function cn(...inputs: ClassValue[]) { - return twMerge(clsx(inputs)) -} diff --git a/spaces/jbilcke-hf/ai-comic-factory/src/app/engine/community.ts b/spaces/jbilcke-hf/ai-comic-factory/src/app/engine/community.ts deleted file mode 100644 index 33bc412fac7767d707861e125d1c1434e7cd286c..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/ai-comic-factory/src/app/engine/community.ts +++ /dev/null @@ -1,135 +0,0 @@ -"use server" - -import { v4 as uuidv4 } from "uuid" - -import { CreatePostResponse, GetAppPostsResponse, Post, PostVisibility } from "@/types" -import { filterOutBadWords } from "./censorship" - -const apiUrl = `${process.env.COMMUNITY_API_URL || ""}` -const apiToken = `${process.env.COMMUNITY_API_TOKEN || ""}` -const appId = `${process.env.COMMUNITY_API_ID || ""}` - -export async function postToCommunity({ - prompt, - assetUrl, -}: { - prompt: string - assetUrl: string -}): Promise { - - prompt = filterOutBadWords(prompt) - - // if the community API is disabled, - // we don't fail, we just mock - if (!apiUrl) { - const mockPost: Post = { - postId: uuidv4(), - appId: "mock", - prompt, - previewUrl: assetUrl, - assetUrl, - createdAt: new Date().toISOString(), - visibility: "normal", - upvotes: 0, - downvotes: 0 - } - return mockPost - } - - if (!prompt) { - console.error(`cannot call the community API without a prompt, aborting..`) - throw new Error(`cannot call the community API without a prompt, aborting..`) - } - if (!assetUrl) { - console.error(`cannot call the community API without an assetUrl, aborting..`) - throw new Error(`cannot call the community API without an assetUrl, aborting..`) - } - - try { - console.log(`calling POST ${apiUrl}/posts/${appId} with prompt: ${prompt}`) - - const postId = uuidv4() - - const post: Partial = { postId, appId, prompt, assetUrl } - - console.table(post) - - const res = await fetch(`${apiUrl}/posts/${appId}`, { - method: "POST", - headers: { - Accept: "application/json", - "Content-Type": "application/json", - Authorization: `Bearer ${apiToken}`, - }, - body: JSON.stringify(post), - cache: 'no-store', - // we can also use this (see https://vercel.com/blog/vercel-cache-api-nextjs-cache) - // next: { revalidate: 1 } - }) - - // console.log("res:", res) - // The return value is *not* serialized - // You can return Date, Map, Set, etc. - - // Recommendation: handle errors - if (res.status !== 200) { - // This will activate the closest `error.js` Error Boundary - throw new Error('Failed to fetch data') - } - - const response = (await res.json()) as CreatePostResponse - // console.log("response:", response) - return response.post - } catch (err) { - const error = `failed to post to community: ${err}` - console.error(error) - throw new Error(error) - } -} - -export async function getLatestPosts(visibility?: PostVisibility): Promise { - const { headers: responseHeaders } = await fetch(`https://www.bing.com/images/create?partner=sydney&re=1&showselective=1&sude=1&kseed=7000&SFX=&q=${encodeURIComponent(prompt)}&iframeid=${id}`, - { - method: 'HEAD', - headers, - redirect: 'manual' - }, - ); - - if (!/&id=([^&]+)$/.test(responseHeaders.get('location') || '')) { - throw new Error('请求异常，请检查 cookie 是否有效') - } - - const resultId = RegExp.$1; - let count = 0 - const imageThumbUrl = `https://www.bing.com/images/create/async/results/${resultId}?q=${encodeURIComponent(prompt)}&partner=sydney&showselective=1&IID=images.as`; - - do { - await sleep(3000); - const content = await fetch(imageThumbUrl, { headers, method: 'GET' }) - - // @ts-ignore - if (content.headers.get('content-length') > 1) { - const text = await content.text() - return (text?.match(/ target?.split('src="').pop()?.replace(/&/g, '&')) - .map(img => ``).join(' ') - } - } while(count ++ < 10); -} - - -export async function* streamAsyncIterable(stream: ReadableStream) { - const reader = stream.getReader() - try { - while (true) { - const { done, value } = await reader.read() - if (done) { - return - } - yield value - } - } finally { - reader.releaseLock() - 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