Update parquet files (step 66 of 121)

spaces/101-5/gpt4free/g4f/__init__.py

@@ -1,39 +0,0 @@
-import sys
-from . import Provider
-from g4f.models import Model, ModelUtils
-
-
-class ChatCompletion:
-    @staticmethod
-    def create(model: Model.model or str, messages: list, provider: Provider.Provider = None, stream: bool = False, auth: str = False, **kwargs):
-        kwargs['auth'] = auth
-
-        if provider and provider.needs_auth and not auth:
-            print(
-                f'ValueError: {provider.__name__} requires authentication (use auth="cookie or token or jwt ..." param)', file=sys.stderr)
-            sys.exit(1)
-
-        try:
-            if isinstance(model, str):
-                try:
-                    model = ModelUtils.convert[model]
-                except KeyError:
-                    raise Exception(f'The model: {model} does not exist')
-
-            engine = model.best_provider if not provider else provider
-
-            if not engine.supports_stream and stream == True:
-                print(
-                    f"ValueError: {engine.__name__} does not support 'stream' argument", file=sys.stderr)
-                sys.exit(1)
-
-            print(f'Using {engine.__name__} provider')
-
-            return (engine._create_completion(model.name, messages, stream, **kwargs)
-                    if stream else ''.join(engine._create_completion(model.name, messages, stream, **kwargs)))
-        except TypeError as e:
-            print(e)
-            arg: str = str(e).split("'")[1]
-            print(
-                f"ValueError: {engine.__name__} does not support '{arg}' argument", file=sys.stderr)
-            sys.exit(1)

spaces/1acneusushi/gradio-2dmoleculeeditor/data/7-Zip for Mac The Ultimate Guide to Compressing and Extracting Files.md

@@ -1,30 +0,0 @@
-<br />
-```html
-<h1>How to Download and Use 7-Zip on Mac</h1>
-<p>7-Zip is a popular and free open-source file compression and archiving software that can handle various formats such as ZIP, RAR, TAR, GZIP, 7Z, and more. It is widely used by Windows users for its high compression ratio, fast speed, and powerful features. However, 7-Zip does not have an official version for Mac OS X. So how can you download and use 7-Zip on Mac?</p>
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-<p>In this article, we will show you two ways to download and use 7-Zip on Mac: using a third-party app called Keka or using the command line. Both methods are easy and effective. Let's get started!</p>
-<h2>Method 1: Using Keka</h2>
-<p>Keka is a free and simple file archiver for Mac that can create and extract various formats, including 7Z. It is based on the 7-Zip engine and has a user-friendly interface. Here are the steps to download and use Keka on Mac:</p>
-<ol>
-<li>Visit the official Keka website at <a href="https://www.keka.io/en/">https://www.keka.io/en/</a> and click on the "Download" button to download the latest version of Keka.</li>
-<li>Once the download is complete, open the downloaded file and drag the Keka icon to your Applications folder.</li>
-<li>Launch Keka from your Applications folder or Dock.</li>
-<li>To create a 7Z archive, simply drag and drop the files or folders you want to compress onto the Keka icon or window. You can also adjust the compression level and password-protect your archive if you want.</li>
-<li>To extract a 7Z archive, simply double-click on it or drag and drop it onto the Keka icon or window. The extracted files will be saved in the same location as the original archive.</li>
-</ol>
-<p>That's it! You have successfully downloaded and used 7-Zip on Mac using Keka. You can also use Keka to create and extract other formats such as ZIP, RAR, TAR, GZIP, etc.</p>
-<h2>Method 2: Using the Command Line</h2>
-<p>If you prefer using the command line, you can also download and use 7-Zip on Mac using a tool called p7zip. p7zip is a port of 7-Zip for Unix-like systems such as Mac OS X. It provides a command-line interface to 7-Zip's functionality. Here are the steps to download and use p7zip on Mac:</p>
-<p></p>
-<ol>
-<li>Open the Terminal app from your Applications/Utilities folder or Spotlight search.</li>
-<li>Type in the following command to install Homebrew, a package manager for Mac that will help you install p7zip: <code>/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"</code></li>
-<li>Wait for Homebrew to install. You may need to enter your password or press Enter when prompted.</li>
-<li>Type in the following command to install p7zip using Homebrew: <code>brew install p7zip</code></li>
-<li>To create a 7Z archive, navigate to the directory where your files or folders are located using the <code>cd</code> command. Then type in the following command: <code>7z a archive_name.7z file_or_folder_name</code>. You can replace <code>archive_name</code> with any name you want for your archive and <code>file_or_folder_name</code> with the name of the file or folder you want to compress. You can also add multiple files or folders by separating them with spaces.</li>
-<li>To extract a 7Z archive, navigate to the directory where your archive is located using the <code>cd</code> command. Then type in the following command: <code>7z x archive_name.7z</code>. You can replace <code>archive_name</code> with the name of your archive. The extracted files will be saved in the same location as the original archive.</li>
-</ol>
-<p>That's it! You have successfully downloaded and used 7-Zip on Mac using p7zip. You can also use p7zip to create and extract other formats</p> ddb901b051<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Code Pre Gfx.ff MW2 Dir File CPY UPD.md

@@ -1,106 +0,0 @@
-
-<h1>Code pre gfx.ff MW2 Dir File CPY: What is it and how to fix it?</h1>
- <p>If you are a fan of Call of Duty: Modern Warfare 2, you might have encountered an error message that says "Error can't not find code_pre_gfx_ff". This error prevents you from launching or playing the game properly. In this article, we will explain what this error means, why it happens, and how to fix it in two easy methods.</p>
- <h2>Introduction</h2>
- <p>Call of Duty: Modern Warfare 2 is a first-person shooter video game developed by Infinity Ward and published by Activision. It was released in 2009 for Windows, PlayStation 3, and Xbox 360. It is the sixth installment in the Call of Duty series and the direct sequel to Call of Duty 4: Modern Warfare.</p>
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- <h3>What is code pre gfx.ff MW2 dir file CPY?</h3>
- <p>Code pre gfx.ff MW2 dir file CPY is a file that contains some essential data for the game to run smoothly. It is located in the zone folder inside the game installation directory. The file name stands for "code pre graphics fast file Modern Warfare 2 directory file cracked by CPY". CPY is a group of hackers who cracked the game's DRM protection and released a pirated version of it.</p>
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- <ul>
-<li>The file is missing, corrupted, or deleted.</li>
-<li>The file is incompatible with your system or game version.</li>
-<li>The file is blocked by your antivirus or firewall.</li>
-<li>The file is overwritten by another mod or patch.</li>
-</ul>
- <h3>How to fix this error?</h3>
- <p>There are two main methods to fix this error. The first one is to download the missing files from a reliable source and copy them to your game folder. The second one is to verify the integrity of your game files through Steam and let it repair any damaged or missing files. We will explain both methods in detail below.</p>
- <h2>Method 1: Download the missing files</h2>
- <p>This method involves downloading the code pre gfx.ff MW2 dir file CPY and other related files from a trustworthy link and placing them in your game folder. Here are the steps to follow:</p>
- <h3>Step 1: Find the download link</h3>
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- <h3>Step 2: Extract the files</h3>
- <p>Once you have downloaded the zip file, you need to extract it using a program like WinRAR or 7-Zip. You can do this by right-clicking on the zip file and selecting "Extract here" or "Extract to" option. You will get a folder named "zone" that contains several .ff files.</p>
- <h3>Step 3: Copy and paste the files</h3>
- <p>The final step is to copy and paste the extracted files into your game folder. To do this, you need to locate your game installation directory. It usually looks something like this:</p>
- <code>C:\Program Files (x86)\Steam\steamapps\common\Call of Duty Modern Warfare 2</code>
- <p>Inside this directory, you will find another folder named "zone". Open it and then open the subfolder named "english". This is where you need to paste all the .ff files that you extracted earlier. If you are asked to overwrite any existing files, click "Yes".</p>
- <p>After copying and pasting all the files, you can close all windows and launch your game. The error should be gone now and you should be able to play without any problems.</p>
- <h2>Method 2: Verify the integrity of game files</h2>
- <p>This method involves using Steam's built-in feature that checks your game files for any errors or inconsistencies and fixes them automatically. This can help you resolve any issues related to missing or corrupted files. Here are the steps to follow:</p>
- <h3>Step 1: Open Steam</h3>
- <p>The first step is to open Steam on your computer. You can do this by double-clicking on its icon on your desktop or taskbar.</p>
- <h3>Step 2: Go to Library</h3>
- <p>The next step is to go to your Library tab on Steam. This is where you can see all your games that you own or have installed on your computer.</p>
- <h3>Step 3: Right-click on Call of Duty: Modern Warfare 2</h3>
- <p>From your Library list, find Call of Duty: Modern Warfare 2 and right-click on it. A menu will pop up with several options.</p>
- <h3>Step 4: Select Properties</h3>
- <p>From the menu that appears, select Properties option at the bottom. This will open a new window with several tabs related to your game settings.</p>
- <h3>Step 5: Click on Local Files</h3>
- <p>In the Properties window, click on Local Files tab at the top. This tab shows you information about your game files such as their size, location, and last update date.</p>
- <h3>Step 6: Click on Verify Integrity of Game Files</h3>
- <p>In the Local Files tab, click on Verify Integrity of Game Files button at the bottom. This will start a process that scans your game files for any errors or missing parts and tries to fix them automatically.</p>
- <p>This process may take some time depending on your internet speed and system performance. You can see its progress on a bar at the bottom of the window. Do not close Steam or interrupt this process until it finishes.</p>
- <p>Once it finishes, it will show you a message saying that all files successfully validated or that some files were reacquired. If some files were reacquired, it means that they were missing or corrupted and Steam downloaded them again for you.</p>
- <p>After verifying your game files, you can close all windows and launch your game. The error should be gone now and you should be able to play without any problems.</p>
- <h2>Conclusion</h2>
- <p>In this article, we have explained what code pre gfx.ff MW2 dir file CPY error is, why it occurs, and how to fix it in two easy methods. We hope that this article was helpful for you and that you enjoyed reading it.</p>
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spaces/1gistliPinn/ChatGPT4/Examples/ALL IN ONE HACKING SOFTWARES TOOLS PACK ? DOWNLOAD Fix.md

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spaces/1gistliPinn/ChatGPT4/Examples/Boku Wa Tomodachi Ga Sukunai Live Action Eng Sub Download Film.md

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-<h3>The benefits of playing the game in browser without downloading</h3>
-<p>Playing Bloons TD 6 online has many advantages over downloading it on your device. Here are some of them:</p>
-<h4>No installation required</h4>
-<p>You don't have to install anything on your device to play Bloons TD 6 online. You just need a web browser and an internet connection. This saves you time and hassle, especially if you have a slow or unreliable device.</p>
-<h4>No storage space needed</h4>
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-<h4>No lag or latency</h4>
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-<h4>Potential security risks and privacy concerns</h4>
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-<h2>How to play Bloons TD 6 online?</h2>
-<h3>The best websites to play the game online for free</h3>
-<p>Now that you know the pros and cons of playing Bloons TD 6 online, you may be wondering how to do it. The good news is that there are many websites that offer the game online for free, without requiring any registration or download. Here are some of the best ones:</p>
-<h4>now.gg</h4>
-<p>now.gg is a cloud gaming platform that allows you to play Bloons TD 6 online in your browser with high quality and performance. You can access the game from any device, including PC, Mac, iOS, Android, and Chromebook. You can also sync your progress across devices and platforms using your Google Play or Facebook account. To play the game on now.gg, you just need to visit <a href="">https://www.now.gg/play/bloons-td-6</a> and click on the "Play Now" button.</p>
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-<h3>The steps to play the game online in browser</h3>
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-<h4>Choose a website and open it in your browser</h4>
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-<p>Bloons TD 6 is a fun and addictive tower defense game that you can play online without downloading it. Playing the game online has many benefits, such as no installation, no storage space, no compatibility issues, and no lag or latency. However, it also has some drawbacks, such as limited access to some features and modes, dependence on internet connection and speed, and potential security risks and privacy concerns. Therefore, you should be careful and cautious when choosing a website to play the game online. To play the game online, you just need to follow these simple steps: choose a website, open it in your browser, click on the game icon or link, adjust the settings and preferences, and start playing and enjoying the game. If you are looking for a fun and challenging way to pass the time, why not give Bloons TD 6 online a try? You won't regret it!</p>
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-<p>A: Bloons TD 6 online is generally safe to play as long as you choose a reputable and reliable website that offers it. However, some websites may not be safe or trustworthy, and they may contain malware, viruses, or ads that can harm your device or data. They may also collect your personal information or track your online activity without your consent. Therefore, you should be careful and cautious when choosing a website to play the game online.</p>
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spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -1,498 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Callable, List, Optional, Union
-
-import paddle
-from packaging import version
-
-from paddlenlp.transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-
-from ...configuration_utils import FrozenDict
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import (
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    EulerAncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-)
-from ...utils import deprecate, logging
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class StableDiffusionPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for text-to-image generation using Stable Diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular xxxx, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`PNDMScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`]
-            or [`DPMSolverMultistepScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-        ],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
-                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
-                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
-                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
-                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["steps_offset"] = 1
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
-                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
-                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
-                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
-                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
-            )
-            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["clip_sample"] = False
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_ppdiffusers_version") and version.parse(
-            version.parse(unet.config._ppdiffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `list(int)`):
-                prompt to be encoded
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-        """
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pd",
-        )
-        text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pd").input_ids
-
-        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not paddle.equal_all(
-            text_input_ids, untruncated_ids
-        ):
-            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-
-        config = (
-            self.text_encoder.config
-            if isinstance(self.text_encoder.config, dict)
-            else self.text_encoder.config.to_dict()
-        )
-        if config.get("use_attention_mask", None) is not None and config["use_attention_mask"]:
-            attention_mask = text_inputs.attention_mask
-        else:
-            attention_mask = None
-
-        text_embeddings = self.text_encoder(
-            text_input_ids,
-            attention_mask=attention_mask,
-        )
-        text_embeddings = text_embeddings[0]
-
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        bs_embed, seq_len, _ = text_embeddings.shape
-        text_embeddings = text_embeddings.tile([1, num_images_per_prompt, 1])
-        text_embeddings = text_embeddings.reshape([bs_embed * num_images_per_prompt, seq_len, -1])
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = text_input_ids.shape[-1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pd",
-            )
-
-            if config.get("use_attention_mask", None) is not None and config["use_attention_mask"]:
-                attention_mask = uncond_input.attention_mask
-            else:
-                attention_mask = None
-
-            uncond_embeddings = self.text_encoder(
-                uncond_input.input_ids,
-                attention_mask=attention_mask,
-            )
-            uncond_embeddings = uncond_embeddings[0]
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.tile([1, num_images_per_prompt, 1])
-            uncond_embeddings = uncond_embeddings.reshape([batch_size * num_images_per_prompt, seq_len, -1])
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            text_embeddings = paddle.concat([uncond_embeddings, text_embeddings])
-
-        return text_embeddings
-
-    def run_safety_checker(self, image, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pd")
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.cast(dtype)
-            )
-        else:
-            has_nsfw_concept = None
-        return image, has_nsfw_concept
-
-    def decode_latents(self, latents):
-        latents = 1 / 0.18215 * latents
-        image = self.vae.decode(latents).sample
-        image = (image / 2 + 0.5).clip(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
-        image = image.transpose([0, 2, 3, 1]).cast("float32").numpy()
-        return image
-
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(self, prompt, height, width, callback_steps):
-        if not isinstance(prompt, str) and not isinstance(prompt, list):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None):
-        shape = [batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor]
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            if isinstance(generator, list):
-                shape = [
-                    1,
-                ] + shape[1:]
-                latents = [paddle.randn(shape, generator=generator[i], dtype=dtype) for i in range(batch_size)]
-                latents = paddle.concat(latents, axis=0)
-            else:
-                latents = paddle.randn(shape, generator=generator, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    @paddle.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
-        latents: Optional[paddle.Tensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, paddle.Tensor], None]] = None,
-        callback_steps: Optional[int] = 1,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`paddle.Generator`, *optional*):
-                One or a list of paddle generator(s) to make generation deterministic.
-            latents (`paddle.Tensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: paddle.Tensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(prompt, height, width, callback_steps)
-
-        # 2. Define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        text_embeddings = self._encode_prompt(
-            prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
-        )
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps)
-        timesteps = self.scheduler.timesteps
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.unet.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            text_embeddings.dtype,
-            generator,
-            latents,
-        )
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 7. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = paddle.concat([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # predict the noise residual
-                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        # 8. Post-processing
-        image = self.decode_latents(latents)
-
-        # 9. Run safety checker
-        image, has_nsfw_concept = self.run_safety_checker(image, text_embeddings.dtype)
-
-        # 10. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

spaces/232labs/VToonify/vtoonify/model/raft/train_mixed.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-mkdir -p checkpoints
-python -u train.py --name raft-chairs --stage chairs --validation chairs --gpus 0 --num_steps 120000 --batch_size 8 --lr 0.00025 --image_size 368 496 --wdecay 0.0001 --mixed_precision 
-python -u train.py --name raft-things --stage things --validation sintel --restore_ckpt checkpoints/raft-chairs.pth --gpus 0 --num_steps 120000 --batch_size 5 --lr 0.0001 --image_size 400 720 --wdecay 0.0001 --mixed_precision
-python -u train.py --name raft-sintel --stage sintel --validation sintel --restore_ckpt checkpoints/raft-things.pth --gpus 0 --num_steps 120000 --batch_size 5 --lr 0.0001 --image_size 368 768 --wdecay 0.00001 --gamma=0.85 --mixed_precision
-python -u train.py --name raft-kitti  --stage kitti --validation kitti --restore_ckpt checkpoints/raft-sintel.pth --gpus 0 --num_steps 50000 --batch_size 5 --lr 0.0001 --image_size 288 960 --wdecay 0.00001 --gamma=0.85 --mixed_precision

spaces/801artistry/RVC801/infer/lib/infer_pack/models_onnx.py

@@ -1,824 +0,0 @@
-import math
-import logging
-
-logger = logging.getLogger(__name__)
-
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
-from torch.nn import functional as F
-from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
-
-from infer.lib.infer_pack import attentions, commons, modules
-from infer.lib.infer_pack.commons import get_padding, init_weights
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        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_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        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, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(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 m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        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_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        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, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(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 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())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        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
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-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 = self.enc(x, x_mask, g=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
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-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):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-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
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """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 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            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,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # 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, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(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,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        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)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(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 = 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)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        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)
-            x_source = self.noise_convs[i](har_source)
-            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):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMsNSFsidM(nn.Module):
-    def __init__(
-        self,
-        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,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        version,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        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.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        if version == "v1":
-            self.enc_p = TextEncoder256(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        else:
-            self.enc_p = TextEncoder768(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        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, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        self.speaker_map = None
-        logger.debug(
-            "gin_channels: "
-            + gin_channels
-            + ", self.spk_embed_dim: "
-            + self.spk_embed_dim
-        )
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def construct_spkmixmap(self, n_speaker):
-        self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels))
-        for i in range(n_speaker):
-            self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]))
-        self.speaker_map = self.speaker_map.unsqueeze(0)
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None):
-        if self.speaker_map is not None:  # [N, S]  *  [S, B, 1, H]
-            g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1))  # [N, S, B, 1, 1]
-            g = g * self.speaker_map  # [N, S, B, 1, H]
-            g = torch.sum(g, dim=1)  # [N, 1, B, 1, H]
-            g = g.transpose(0, -1).transpose(0, -2).squeeze(0)  # [B, H, N]
-        else:
-            g = g.unsqueeze(0)
-            g = self.emb_g(g).transpose(1, 2)
-
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        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)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            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 MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        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)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            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 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 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

spaces/801artistry/RVC801/infer/lib/train/utils.py

@@ -1,478 +0,0 @@
-import argparse
-import glob
-import json
-import logging
-import os
-import subprocess
-import sys
-import shutil
-
-import numpy as np
-import torch
-from scipy.io.wavfile import read
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-
-    ##################
-    def go(model, bkey):
-        saved_state_dict = checkpoint_dict[bkey]
-        if hasattr(model, "module"):
-            state_dict = model.module.state_dict()
-        else:
-            state_dict = model.state_dict()
-        new_state_dict = {}
-        for k, v in state_dict.items():  # 模型需要的shape
-            try:
-                new_state_dict[k] = saved_state_dict[k]
-                if saved_state_dict[k].shape != state_dict[k].shape:
-                    logger.warn(
-                        "shape-%s-mismatch. need: %s, get: %s",
-                        k,
-                        state_dict[k].shape,
-                        saved_state_dict[k].shape,
-                    )  #
-                    raise KeyError
-            except:
-                # logger.info(traceback.format_exc())
-                logger.info("%s is not in the checkpoint", k)  # pretrain缺失的
-                new_state_dict[k] = v  # 模型自带的随机值
-        if hasattr(model, "module"):
-            model.module.load_state_dict(new_state_dict, strict=False)
-        else:
-            model.load_state_dict(new_state_dict, strict=False)
-        return model
-
-    go(combd, "combd")
-    model = go(sbd, "sbd")
-    #############
-    logger.info("Loaded model weights")
-
-    iteration = checkpoint_dict["iteration"]
-    learning_rate = checkpoint_dict["learning_rate"]
-    if (
-        optimizer is not None and load_opt == 1
-    ):  ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
-        #   try:
-        optimizer.load_state_dict(checkpoint_dict["optimizer"])
-    #   except:
-    #     traceback.print_exc()
-    logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-# def load_checkpoint(checkpoint_path, model, optimizer=None):
-#   assert os.path.isfile(checkpoint_path)
-#   checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-#   iteration = checkpoint_dict['iteration']
-#   learning_rate = checkpoint_dict['learning_rate']
-#   if optimizer is not None:
-#     optimizer.load_state_dict(checkpoint_dict['optimizer'])
-#   # print(1111)
-#   saved_state_dict = checkpoint_dict['model']
-#   # print(1111)
-#
-#   if hasattr(model, 'module'):
-#     state_dict = model.module.state_dict()
-#   else:
-#     state_dict = model.state_dict()
-#   new_state_dict= {}
-#   for k, v in state_dict.items():
-#     try:
-#       new_state_dict[k] = saved_state_dict[k]
-#     except:
-#       logger.info("%s is not in the checkpoint" % k)
-#       new_state_dict[k] = v
-#   if hasattr(model, 'module'):
-#     model.module.load_state_dict(new_state_dict)
-#   else:
-#     model.load_state_dict(new_state_dict)
-#   logger.info("Loaded checkpoint '{}' (epoch {})" .format(
-#     checkpoint_path, iteration))
-#   return model, optimizer, learning_rate, iteration
-def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-
-    saved_state_dict = checkpoint_dict["model"]
-    if hasattr(model, "module"):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():  # 模型需要的shape
-        try:
-            new_state_dict[k] = saved_state_dict[k]
-            if saved_state_dict[k].shape != state_dict[k].shape:
-                logger.warn(
-                    "shape-%s-mismatch|need-%s|get-%s",
-                    k,
-                    state_dict[k].shape,
-                    saved_state_dict[k].shape,
-                )  #
-                raise KeyError
-        except:
-            # logger.info(traceback.format_exc())
-            logger.info("%s is not in the checkpoint", k)  # pretrain缺失的
-            new_state_dict[k] = v  # 模型自带的随机值
-    if hasattr(model, "module"):
-        model.module.load_state_dict(new_state_dict, strict=False)
-    else:
-        model.load_state_dict(new_state_dict, strict=False)
-    logger.info("Loaded model weights")
-
-    iteration = checkpoint_dict["iteration"]
-    learning_rate = checkpoint_dict["learning_rate"]
-    if (
-        optimizer is not None and load_opt == 1
-    ):  ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
-        #   try:
-        optimizer.load_state_dict(checkpoint_dict["optimizer"])
-    #   except:
-    #     traceback.print_exc()
-    logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info(
-        "Saving model and optimizer state at epoch {} to {}".format(
-            iteration, checkpoint_path
-        )
-    )
-    if hasattr(model, "module"):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    torch.save(
-        {
-            "model": state_dict,
-            "iteration": iteration,
-            "optimizer": optimizer.state_dict(),
-            "learning_rate": learning_rate,
-        },
-        checkpoint_path,
-    )
-
-
-def save_checkpoint_d(combd, sbd, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info(
-        "Saving model and optimizer state at epoch {} to {}".format(
-            iteration, checkpoint_path
-        )
-    )
-    if hasattr(combd, "module"):
-        state_dict_combd = combd.module.state_dict()
-    else:
-        state_dict_combd = combd.state_dict()
-    if hasattr(sbd, "module"):
-        state_dict_sbd = sbd.module.state_dict()
-    else:
-        state_dict_sbd = sbd.state_dict()
-    torch.save(
-        {
-            "combd": state_dict_combd,
-            "sbd": state_dict_sbd,
-            "iteration": iteration,
-            "optimizer": optimizer.state_dict(),
-            "learning_rate": learning_rate,
-        },
-        checkpoint_path,
-    )
-
-
-def summarize(
-    writer,
-    global_step,
-    scalars={},
-    histograms={},
-    images={},
-    audios={},
-    audio_sampling_rate=22050,
-):
-    for k, v in scalars.items():
-        writer.add_scalar(k, v, global_step)
-    for k, v in histograms.items():
-        writer.add_histogram(k, v, global_step)
-    for k, v in images.items():
-        writer.add_image(k, v, global_step, dataformats="HWC")
-    for k, v in audios.items():
-        writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-    f_list = glob.glob(os.path.join(dir_path, regex))
-    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-    x = f_list[-1]
-    logger.debug(x)
-    return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger("matplotlib")
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger("matplotlib")
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(
-        alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
-    )
-    fig.colorbar(im, ax=ax)
-    xlabel = "Decoder timestep"
-    if info is not None:
-        xlabel += "\n\n" + info
-    plt.xlabel(xlabel)
-    plt.ylabel("Encoder timestep")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-    with open(filename, encoding="utf-8") as f:
-        filepaths_and_text = [line.strip().split(split) for line in f]
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    """
-    todo:
-      结尾七人组：
-        保存频率、总epoch                     done
-        bs                                    done
-        pretrainG、pretrainD                  done
-        卡号：os.en["CUDA_VISIBLE_DEVICES"]   done
-        if_latest                             done
-      模型：if_f0                             done
-      采样率：自动选择config                  done
-      是否缓存数据集进GPU:if_cache_data_in_gpu done
-
-      -m:
-        自动决定training_files路径,改掉train_nsf_load_pretrain.py里的hps.data.training_files    done
-      -c不要了
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-se",
-        "--save_every_epoch",
-        type=int,
-        required=True,
-        help="checkpoint save frequency (epoch)",
-    )
-    parser.add_argument(
-        "-te", "--total_epoch", type=int, required=True, help="total_epoch"
-    )
-    parser.add_argument(
-        "-pg", "--pretrainG", type=str, default="", help="Pretrained Discriminator path"
-    )
-    parser.add_argument(
-        "-pd", "--pretrainD", type=str, default="", help="Pretrained Generator path"
-    )
-    parser.add_argument("-g", "--gpus", type=str, default="0", help="split by -")
-    parser.add_argument(
-        "-bs", "--batch_size", type=int, required=True, help="batch size"
-    )
-    parser.add_argument(
-        "-e", "--experiment_dir", type=str, required=True, help="experiment dir"
-    )  # -m
-    parser.add_argument(
-        "-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
-    )
-    parser.add_argument(
-        "-sw",
-        "--save_every_weights",
-        type=str,
-        default="0",
-        help="save the extracted model in weights directory when saving checkpoints",
-    )
-    parser.add_argument(
-        "-v", "--version", type=str, required=True, help="model version"
-    )
-    parser.add_argument(
-        "-f0",
-        "--if_f0",
-        type=int,
-        required=True,
-        help="use f0 as one of the inputs of the model, 1 or 0",
-    )
-    parser.add_argument(
-        "-l",
-        "--if_latest",
-        type=int,
-        required=True,
-        help="if only save the latest G/D pth file, 1 or 0",
-    )
-    parser.add_argument(
-        "-c",
-        "--if_cache_data_in_gpu",
-        type=int,
-        required=True,
-        help="if caching the dataset in GPU memory, 1 or 0",
-    )
-
-    args = parser.parse_args()
-    name = args.experiment_dir
-    experiment_dir = os.path.join("./logs", args.experiment_dir)
-
-    config_save_path = os.path.join(experiment_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        config = json.load(f)
-
-    hparams = HParams(**config)
-    hparams.model_dir = hparams.experiment_dir = experiment_dir
-    hparams.save_every_epoch = args.save_every_epoch
-    hparams.name = name
-    hparams.total_epoch = args.total_epoch
-    hparams.pretrainG = args.pretrainG
-    hparams.pretrainD = args.pretrainD
-    hparams.version = args.version
-    hparams.gpus = args.gpus
-    hparams.train.batch_size = args.batch_size
-    hparams.sample_rate = args.sample_rate
-    hparams.if_f0 = args.if_f0
-    hparams.if_latest = args.if_latest
-    hparams.save_every_weights = args.save_every_weights
-    hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
-    hparams.data.training_files = "%s/filelist.txt" % experiment_dir
-    return hparams
-
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn(
-            "{} is not a git repository, therefore hash value comparison will be ignored.".format(
-                source_dir
-            )
-        )
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warn(
-                "git hash values are different. {}(saved) != {}(current)".format(
-                    saved_hash[:8], cur_hash[:8]
-                )
-            )
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-class HParams:
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()

spaces/AB-TW/team-ai/documents/bussiness_context/NOTION_DB/Engineering Wiki 2402f5396a3244fdb3f1d135bdb0f3d6/Useful Commands 8a05b1de77ec44b6a55e388c2cc7fe47.md

@@ -1,40 +0,0 @@
-# Useful Commands
-
-Last edited time: March 31, 2023 1:49 PM
-Owner: Anonymous
-Tags: Codebase, Guides and Processes
-
-<aside>
-💡 Frequently used commands. This is a helpful page to [add to your Favorites](https://www.notion.so/7ef7287cee00464d9a813073b02ce24a).
-
-</aside>
-
-# 🚚 Run Locally
-
-In the `acme` directory, run: 
-
-```bash
-acme run --local
-```
-
-For a full list of options, use:
-
-```bash
-acme --help
-```
-
-To run the typechecker on the entire codebase:
-
-```bash
-acme typecheck
-```
-
-# 🚢 Deployment
-
-When you deploy to staging or production, run the following on the deployment server:
-
-```bash
-acme deploy --staging 
-```
-
-Replace `--staging` with `--prod` if deploying production.

spaces/AI-ZTH-03-23/5.StreamlitWikipediaChat/app.py

@@ -1,239 +0,0 @@
-import streamlit as st
-import spacy
-import wikipediaapi
-import wikipedia
-from wikipedia.exceptions import DisambiguationError
-from transformers import TFAutoModel, AutoTokenizer
-import numpy as np
-import pandas as pd
-import faiss
-import datetime
-import time
-
-
-try:
-    nlp = spacy.load("en_core_web_sm")
-except:
-    spacy.cli.download("en_core_web_sm")
-    nlp = spacy.load("en_core_web_sm")
-
-wh_words = ['what', 'who', 'how', 'when', 'which']
-
-def get_concepts(text):
-    text = text.lower()
-    doc = nlp(text)
-    concepts = []
-    for chunk in doc.noun_chunks:
-        if chunk.text not in wh_words:
-            concepts.append(chunk.text)
-    return concepts
-
-def get_passages(text, k=100):
-    doc = nlp(text)
-    passages = []
-    passage_len = 0
-    passage = ""
-    sents = list(doc.sents)
-    for i in range(len(sents)):
-        sen = sents[i]
-        passage_len += len(sen)
-        if passage_len >= k:
-            passages.append(passage)
-            passage = sen.text
-            passage_len = len(sen)
-            continue
-        elif i == (len(sents) - 1):
-            passage += " " + sen.text
-            passages.append(passage)
-            passage = ""
-            passage_len = 0
-            continue
-        passage += " " + sen.text
-    return passages
-
-def get_dicts_for_dpr(concepts, n_results=20, k=100):
-    dicts = []
-    for concept in concepts:
-        wikis = wikipedia.search(concept, results=n_results)
-        st.write(f"{concept} No of Wikis: {len(wikis)}")
-        for wiki in wikis:
-            try:
-                html_page = wikipedia.page(title=wiki, auto_suggest=False)
-            except DisambiguationError:
-                continue
-            htmlResults = html_page.content
-            passages = get_passages(htmlResults, k=k)
-            for passage in passages:
-                i_dicts = {}
-                i_dicts['text'] = passage
-                i_dicts['title'] = wiki
-                dicts.append(i_dicts)
-    return dicts
-
-passage_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2")
-query_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2")
-p_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2")
-q_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2")
-
-def get_title_text_combined(passage_dicts):
-    res = []
-    for p in passage_dicts:
-        res.append(tuple((p['title'], p['text'])))
-    return res
-
-def extracted_passage_embeddings(processed_passages, max_length=156):
-    passage_inputs = p_tokenizer.batch_encode_plus(
-                    processed_passages,
-                    add_special_tokens=True,
-                    truncation=True,
-                    padding="max_length",
-                    max_length=max_length,
-                    return_token_type_ids=True
-                )
-    passage_embeddings = passage_encoder.predict([np.array(passage_inputs['input_ids']), np.array(passage_inputs['attention_mask']), 
-                                            np.array(passage_inputs['token_type_ids'])], 
-                                            batch_size=64, 
-                                            verbose=1)
-    return passage_embeddings
-
-def extracted_query_embeddings(queries, max_length=64):
-    query_inputs = q_tokenizer.batch_encode_plus(
-        queries,
-        add_special_tokens=True,
-        truncation=True,
-        padding="max_length",
-        max_length=max_length,
-        return_token_type_ids=True
-    )
-    
-    query_embeddings = query_encoder.predict([np.array(query_inputs['input_ids']),
-        np.array(query_inputs['attention_mask']),
-        np.array(query_inputs['token_type_ids'])],
-        batch_size=1,
-        verbose=1)
-    return query_embeddings
-
-def get_pagetext(page):
-    s = str(page).replace("/t","")
-    return s
-
-def get_wiki_summary(search):
-    wiki_wiki = wikipediaapi.Wikipedia('en')
-    page = wiki_wiki.page(search)                                   
-
-
-def get_wiki_summaryDF(search):
-    wiki_wiki = wikipediaapi.Wikipedia('en')
-    page = wiki_wiki.page(search)
-
-    isExist = page.exists()
-    if not isExist:
-        return isExist, "Not found", "Not found", "Not found", "Not found"
-
-    pageurl = page.fullurl
-    pagetitle = page.title
-    pagesummary = page.summary[0:60]
-    pagetext = get_pagetext(page.text)
-
-    backlinks = page.backlinks
-    linklist = ""
-    for link in backlinks.items():
-      pui = link[0]
-      linklist += pui + " ,  "
-      a=1 
-      
-    categories = page.categories
-    categorylist = ""
-    for category in categories.items():
-      pui = category[0]
-      categorylist += pui + " ,  "
-      a=1     
-    
-    links = page.links
-    linklist2 = ""
-    for link in links.items():
-      pui = link[0]
-      linklist2 += pui + " ,  "
-      a=1 
-      
-    sections = page.sections
-    
-    ex_dic = {
-      'Entity' : ["URL","Title","Summary", "Text", "Backlinks", "Links", "Categories"],
-      'Value': [pageurl, pagetitle, pagesummary, pagetext, linklist,linklist2, categorylist ]
-    }
-
-    df = pd.DataFrame(ex_dic)
-    
-    return df
-
-
-def save_message(name, message):
-    now = datetime.datetime.now()
-    timestamp = now.strftime("%Y-%m-%d %H:%M:%S")
-    with open("chat.txt", "a") as f:
-        f.write(f"{timestamp} - {name}: {message}\n")
-
-def press_release():
-    st.markdown("""🎉🎊 Breaking News! 📢📣
-
-Introducing StreamlitWikipediaChat - the ultimate way to chat with Wikipedia and the whole world at the same time! 🌎📚👋
-
-Are you tired of reading boring articles on Wikipedia? Do you want to have some fun while learning new things? Then StreamlitWikipediaChat is just the thing for you! 😃💻
-
-With StreamlitWikipediaChat, you can ask Wikipedia anything you want and get instant responses! Whether you want to know the capital of Madagascar or how to make a delicious chocolate cake, Wikipedia has got you covered. 🍰🌍
-
-But that's not all! You can also chat with other people from around the world who are using StreamlitWikipediaChat at the same time. It's like a virtual classroom where you can learn from and teach others. 🌐👨‍🏫👩‍🏫
-
-And the best part? StreamlitWikipediaChat is super easy to use! All you have to do is type in your question and hit send. That's it! 🤯🙌
-
-So, what are you waiting for? Join the fun and start chatting with Wikipedia and the world today! 😎🎉
-
-StreamlitWikipediaChat - where learning meets fun! 🤓🎈""")
-
-
-def main():
-    st.title("Streamlit Chat")
-
-    name = st.text_input("Enter your name")
-    message = st.text_input("Enter a topic to share from Wikipedia")
-    if st.button("Submit"):
-        
-        # wiki
-        df = get_wiki_summaryDF(message)
-        
-        save_message(name, message)
-        save_message(name, df)
-        
-        st.text("Message sent!")
-
-    
-    st.text("Chat history:")
-    with open("chat.txt", "a+") as f:
-        f.seek(0)
-        chat_history = f.read()
-    #st.text(chat_history)
-    st.markdown(chat_history)
-
-    countdown = st.empty()
-    t = 60
-    while t:
-        mins, secs = divmod(t, 60)
-        countdown.text(f"Time remaining: {mins:02d}:{secs:02d}")
-        time.sleep(1)
-        t -= 1
-        if t == 0:
-            countdown.text("Time's up!")
-            with open("chat.txt", "a+") as f:
-                f.seek(0)
-                chat_history = f.read()
-            #st.text(chat_history)
-            st.markdown(chat_history)
-
-            press_release()
-            
-            t = 60
-
-if __name__ == "__main__":
-    main()
-

spaces/AIZero2Hero4Health/1-ASRLiveSpeechRecognition-GR/README.md

@@ -1,12 +0,0 @@
----
-title: 1 ASRLiveSpeechRecognition GR
-emoji: 💻
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 3.8.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIZerotoHero-Health4All/01-Speech2Text2Speech/app.py

@@ -1,160 +0,0 @@
-import streamlit as st
-import datetime
-from transformers import pipeline
-import gradio as gr
-
-import tempfile
-from typing import Optional
-import numpy as np
-from TTS.utils.manage import ModelManager
-from TTS.utils.synthesizer import Synthesizer
-
-# PersistDataset -----
-import os
-import csv
-import gradio as gr
-from gradio import inputs, outputs
-import huggingface_hub
-from huggingface_hub import Repository, hf_hub_download, upload_file
-from datetime import datetime
-
-# created new dataset as awacke1/MindfulStory.csv
-DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/MindfulStory.csv"
-DATASET_REPO_ID = "awacke1/MindfulStory.csv"
-DATA_FILENAME = "MindfulStory.csv"
-DATA_FILE = os.path.join("data", DATA_FILENAME)
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
-# Download dataset repo using hub download
-try:
-    hf_hub_download(
-        repo_id=DATASET_REPO_ID,
-        filename=DATA_FILENAME,
-        cache_dir=DATA_DIRNAME,
-        force_filename=DATA_FILENAME
-    )
-except:
-    print("file not found")
-    
-def AIMemory(name: str, message: str):
-    if name and message:
-        with open(DATA_FILE, "a") as csvfile:
-            writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"])
-            writer.writerow({"name": name, "message": message, "time": str(datetime.now())})
-        commit_url = repo.push_to_hub()
-    return {"name": name, "message": message, "time": str(datetime.now())}
-
-with open('Mindfulness.txt', 'r') as file:
-        context = file.read()
-   
-# Set up cloned dataset from repo for operations
-repo = Repository( local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN)
-
-# set up ASR
-asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
-
-# set up TTS
-MODEL_NAMES = [
-    "en/ljspeech/tacotron2-DDC",
-    "en/ljspeech/glow-tts",
-    "en/ljspeech/speedy-speech-wn",
-    "en/ljspeech/vits",
-    "en/sam/tacotron-DDC",
-    "fr/mai/tacotron2-DDC",
-    "de/thorsten/tacotron2-DCA",
-]
-
-# Use Model Manager to load vocoders
-MODELS = {}
-manager = ModelManager()
-for MODEL_NAME in MODEL_NAMES:
-    print(f"downloading {MODEL_NAME}")
-    model_path, config_path, model_item = manager.download_model(f"tts_models/{MODEL_NAME}")
-    vocoder_name: Optional[str] = model_item["default_vocoder"]
-    vocoder_path = None
-    vocoder_config_path = None
-    if vocoder_name is not None:
-        vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name)
-
-    synthesizer = Synthesizer(
-        model_path, config_path, None, vocoder_path, vocoder_config_path,
-    )
-    MODELS[MODEL_NAME] = synthesizer
-
-# transcribe
-def transcribe(audio):
-    text = asr(audio)["text"]
-    return text
-
-#text classifier
-classifier = pipeline("text-classification")
-
-
-def speech_to_text(speech):
-    text = asr(speech)["text"]
-    #rMem = AIMemory("STT", text) 
-    return text
-
-def text_to_sentiment(text):
-    sentiment = classifier(text)[0]["label"]
-    #rMem = AIMemory(text, sentiment) 
-    return sentiment 
-
-def upsert(text):
-    date_time =str(datetime.datetime.today())
-    doc_ref = db.collection('Text2SpeechSentimentSave').document(date_time)
-    doc_ref.set({u'firefield': 'Recognize Speech', u'first': 'https://huggingface.co/spaces/awacke1/TTS-STT-Blocks/', u'last': text, u'born': date_time,})
-    saved = select('TTS-STT', date_time)
-    return saved
-      
-def select(collection, document):
-    doc_ref = db.collection(collection).document(document)
-    doc = doc_ref.get()
-    docid = ("The id is: ", doc.id)
-    contents = ("The contents are: ", doc.to_dict())
-    return contents
-          
-def selectall(text):
-    docs = db.collection('Text2SpeechSentimentSave').stream()
-    doclist=''
-    for doc in docs:
-        r=(f'{doc.id} => {doc.to_dict()}')
-        doclist += r
-    return doclist 
-    
-def tts(text: str, model_name: str):
-    print(text, model_name)
-    synthesizer = MODELS.get(model_name, None)
-    if synthesizer is None:
-        raise NameError("model not found")
-    wavs = synthesizer.tts(text)
-    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
-        synthesizer.save_wav(wavs, fp)
-        
-        #rMem = AIMemory("TTS", text + model_name) 
-    
-        return fp.name
-
-demo = gr.Blocks()
-with demo:
-    audio_file = gr.inputs.Audio(source="microphone", type="filepath")
-    text = gr.Textbox(label="Speech to Text")
-    #label = gr.Label()
-    #saved = gr.Textbox(label="Saved")
-    #savedAll = gr.Textbox(label="SavedAll")
-    TTSchoice = gr.inputs.Radio( label="Pick a Text to Speech Model", choices=MODEL_NAMES,   )
-    audio = gr.Audio(label="Output", interactive=False)
-    
-    b1 = gr.Button("Recognize Speech")
-    #b2 = gr.Button("Classify Sentiment")
-    #b3 = gr.Button("Save Speech to Text")
-    #b4 = gr.Button("Retrieve All")
-    b5 = gr.Button("Read It Back Aloud")
-
-    b1.click(speech_to_text, inputs=audio_file, outputs=text)
-    #b2.click(text_to_sentiment, inputs=text, outputs=label)
-    #b3.click(upsert, inputs=text, outputs=saved)
-    #b4.click(selectall, inputs=text, outputs=savedAll)
-    b5.click(tts,  inputs=[text,TTSchoice], outputs=audio)
-    
-demo.launch(share=True)

spaces/AchyuthGamer/OpenGPT/g4f/Provider/deprecated/Forefront.py

@@ -1,40 +0,0 @@
-from __future__ import annotations
-
-import json
-
-import requests
-
-from ...typing import Any, CreateResult
-from ..base_provider import BaseProvider
-
-
-class Forefront(BaseProvider):
-    url                   = "https://forefront.com"
-    supports_stream       = True
-    supports_gpt_35_turbo = True
-
-    @staticmethod
-    def create_completion(
-        model: str,
-        messages: list[dict[str, str]],
-        stream: bool, **kwargs: Any) -> CreateResult:
-        
-        json_data = {
-            "text"          : messages[-1]["content"],
-            "action"        : "noauth",
-            "id"            : "",
-            "parentId"      : "",
-            "workspaceId"   : "",
-            "messagePersona": "607e41fe-95be-497e-8e97-010a59b2e2c0",
-            "model"         : "gpt-4",
-            "messages"      : messages[:-1] if len(messages) > 1 else [],
-            "internetMode"  : "auto",
-        }
-
-        response = requests.post("https://streaming.tenant-forefront-default.knative.chi.coreweave.com/free-chat",
-            json=json_data, stream=True)
-        
-        response.raise_for_status()
-        for token in response.iter_lines():
-            if b"delta" in token:
-                yield json.loads(token.decode().split("data: ")[1])["delta"]

spaces/Adapting/TrendFlow/mypages/welcome.py

@@ -1,42 +0,0 @@
-import streamlit as st
-from .navigation import go_to_home
-
-def welcome():
-    st.markdown('''
-    <h1 align='center'> TrendFlow</h1>
-
-    <p align='center'>
-    <a href = "https://github.com/leoxiang66/research-trends-analysis">
-    <img src="https://img.shields.io/github/stars/leoxiang66/research-trends-analysis.svg?style=social">
-    </a>
-    <a href = "https://leoxiang66.github.io/research-trends-analysis/"><img src="https://img.shields.io/website?label=documentation&up_message=online&url=https://leoxiang66.github.io/research-trends-analysis/"> </a>
-    <a href="https://pypi.org/project/TrendFlow/"><img src="https://badge.fury.io/py/trendflow.svg" alt="PyPI version" /> </a>
-    <a href="https://discord.gg/P5Y3FHgHRz">
-            <img alt="chat on Discord" src="https://img.shields.io/discord/1091063040662843565?logo=discord">
-        </a>
-    </p>
-
-
-    TrendFlow is an advanced framework that uses deep learning techniques to analyze research trends. This powerful framework offers a wide range of analytical capabilities, including literature clustering, trend generation, and trend summarization. With TrendFlow, you can gain insights into emerging research topics and stay up-to-date on the latest advancements in your field.
-
-    ''', unsafe_allow_html=True)
-
-    st.markdown(
-        """
-        <style>
-        div.stButton > button:first-child {
-            margin-left: auto;
-            margin-right: auto;
-            display: block;
-        }
-        </style>
-        """,
-        unsafe_allow_html=True,
-    )
-
-    # 添加一个居中的按钮
-    st.button("Get Started", on_click=go_to_home)
-
-
-
-

spaces/AlexReverie/ImageSonification/app.py

@@ -1,29 +0,0 @@
-from PIL import Image
-import numpy as np
-import librosa
-import gradio as gr
-
-def img_to_audio(image, time=3.0, rate=22050, n_fft=1024, n_iter=64):
-    # load image
-    img = Image.fromarray(image).convert("L")
-    # calculate spectrogram size 
-    spec_shape = (int(librosa.time_to_frames(1.0, sr=rate, hop_length=n_fft//2, n_fft=n_fft) * time), n_fft)
-    spec = np.asarray(img.resize(spec_shape))
-    print(spec.shape)
-    spec = np.interp(spec, (spec.min(), spec.max()), (-50, 30))
-    spec = librosa.db_to_amplitude(spec)
-    audio = librosa.griffinlim(spec, n_iter=n_iter)
-    return (rate, audio)
-
-time = gr.Number(3.0, label="audio time")
-image = gr.Image(label="image to sonify")
-n_fft = gr.Number(1024, label="n_fft")
-
-def main(image, time, n_fft):
-    return img_to_audio(image, time=time, n_fft=int(n_fft))
-
-desc = "Upload an image you would like to hear."
-
-interface = gr.Interface(fn=main, inputs=[image, time, n_fft], outputs="audio", title="Simple Image Sonification", description=desc)
-
-interface.launch()

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/optimization/open_vino.md

@@ -1,39 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# 추론을 위한 OpenVINO 사용 방법
-
-🤗 [Optimum](https://github.com/huggingface/optimum-intel)은 OpenVINO와 호환되는 Stable Diffusion 파이프라인을 제공합니다.
-이제 다양한 Intel 프로세서에서 OpenVINO Runtime으로 쉽게 추론을 수행할 수 있습니다. ([여기](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html)서 지원되는 전 기기 목록을 확인하세요).
-
-## 설치
-
-다음 명령어로 🤗 Optimum을 설치합니다:
-
-```
-pip install optimum["openvino"]
-```
-
-## Stable Diffusion 추론
-
-OpenVINO 모델을 불러오고 OpenVINO 런타임으로 추론을 실행하려면 `StableDiffusionPipeline`을 `OVStableDiffusionPipeline`으로 교체해야 합니다. PyTorch 모델을 불러오고 즉시 OpenVINO 형식으로 변환하려는 경우 `export=True`로 설정합니다.
-
-```python
-from optimum.intel.openvino import OVStableDiffusionPipeline
-
-model_id = "runwayml/stable-diffusion-v1-5"
-pipe = OVStableDiffusionPipeline.from_pretrained(model_id, export=True)
-prompt = "a photo of an astronaut riding a horse on mars"
-images = pipe(prompt).images[0]
-```
-
-[Optimum 문서](https://huggingface.co/docs/optimum/intel/inference#export-and-inference-of-stable-diffusion-models)에서 (정적 reshaping과 모델 컴파일 등의) 더 많은 예시들을 찾을 수 있습니다.

spaces/Andy1621/uniformer_image_detection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py

@@ -1,4 +0,0 @@
-_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)

spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/ld_head.py

@@ -1,261 +0,0 @@
-import torch
-from mmcv.runner import force_fp32
-
-from mmdet.core import (bbox2distance, bbox_overlaps, distance2bbox,
-                        multi_apply, reduce_mean)
-from ..builder import HEADS, build_loss
-from .gfl_head import GFLHead
-
-
-@HEADS.register_module()
-class LDHead(GFLHead):
-    """Localization distillation Head. (Short description)
-
-    It utilizes the learned bbox distributions to transfer the localization
-    dark knowledge from teacher to student. Original paper: `Localization
-    Distillation for Object Detection. <https://arxiv.org/abs/2102.12252>`_
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        loss_ld (dict): Config of Localization Distillation Loss (LD),
-            T is the temperature for distillation.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 loss_ld=dict(
-                     type='LocalizationDistillationLoss',
-                     loss_weight=0.25,
-                     T=10),
-                 **kwargs):
-
-        super(LDHead, self).__init__(num_classes, in_channels, **kwargs)
-        self.loss_ld = build_loss(loss_ld)
-
-    def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights,
-                    bbox_targets, stride, soft_targets, num_total_samples):
-        """Compute loss of a single scale level.
-
-        Args:
-            anchors (Tensor): Box reference for each scale level with shape
-                (N, num_total_anchors, 4).
-            cls_score (Tensor): Cls and quality joint scores for each scale
-                level has shape (N, num_classes, H, W).
-            bbox_pred (Tensor): Box distribution logits for each scale
-                level with shape (N, 4*(n+1), H, W), n is max value of integral
-                set.
-            labels (Tensor): Labels of each anchors with shape
-                (N, num_total_anchors).
-            label_weights (Tensor): Label weights of each anchor with shape
-                (N, num_total_anchors)
-            bbox_targets (Tensor): BBox regression targets of each anchor wight
-                shape (N, num_total_anchors, 4).
-            stride (tuple): Stride in this scale level.
-            num_total_samples (int): Number of positive samples that is
-                reduced over all GPUs.
-
-        Returns:
-            dict[tuple, Tensor]: Loss components and weight targets.
-        """
-        assert stride[0] == stride[1], 'h stride is not equal to w stride!'
-        anchors = anchors.reshape(-1, 4)
-        cls_score = cls_score.permute(0, 2, 3,
-                                      1).reshape(-1, self.cls_out_channels)
-        bbox_pred = bbox_pred.permute(0, 2, 3,
-                                      1).reshape(-1, 4 * (self.reg_max + 1))
-        soft_targets = soft_targets.permute(0, 2, 3,
-                                            1).reshape(-1,
-                                                       4 * (self.reg_max + 1))
-
-        bbox_targets = bbox_targets.reshape(-1, 4)
-        labels = labels.reshape(-1)
-        label_weights = label_weights.reshape(-1)
-
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        bg_class_ind = self.num_classes
-        pos_inds = ((labels >= 0)
-                    & (labels < bg_class_ind)).nonzero().squeeze(1)
-        score = label_weights.new_zeros(labels.shape)
-
-        if len(pos_inds) > 0:
-            pos_bbox_targets = bbox_targets[pos_inds]
-            pos_bbox_pred = bbox_pred[pos_inds]
-            pos_anchors = anchors[pos_inds]
-            pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0]
-
-            weight_targets = cls_score.detach().sigmoid()
-            weight_targets = weight_targets.max(dim=1)[0][pos_inds]
-            pos_bbox_pred_corners = self.integral(pos_bbox_pred)
-            pos_decode_bbox_pred = distance2bbox(pos_anchor_centers,
-                                                 pos_bbox_pred_corners)
-            pos_decode_bbox_targets = pos_bbox_targets / stride[0]
-            score[pos_inds] = bbox_overlaps(
-                pos_decode_bbox_pred.detach(),
-                pos_decode_bbox_targets,
-                is_aligned=True)
-            pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1)
-            pos_soft_targets = soft_targets[pos_inds]
-            soft_corners = pos_soft_targets.reshape(-1, self.reg_max + 1)
-
-            target_corners = bbox2distance(pos_anchor_centers,
-                                           pos_decode_bbox_targets,
-                                           self.reg_max).reshape(-1)
-
-            # regression loss
-            loss_bbox = self.loss_bbox(
-                pos_decode_bbox_pred,
-                pos_decode_bbox_targets,
-                weight=weight_targets,
-                avg_factor=1.0)
-
-            # dfl loss
-            loss_dfl = self.loss_dfl(
-                pred_corners,
-                target_corners,
-                weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
-                avg_factor=4.0)
-
-            # ld loss
-            loss_ld = self.loss_ld(
-                pred_corners,
-                soft_corners,
-                weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
-                avg_factor=4.0)
-
-        else:
-            loss_ld = bbox_pred.sum() * 0
-            loss_bbox = bbox_pred.sum() * 0
-            loss_dfl = bbox_pred.sum() * 0
-            weight_targets = bbox_pred.new_tensor(0)
-
-        # cls (qfl) loss
-        loss_cls = self.loss_cls(
-            cls_score, (labels, score),
-            weight=label_weights,
-            avg_factor=num_total_samples)
-
-        return loss_cls, loss_bbox, loss_dfl, loss_ld, weight_targets.sum()
-
-    def forward_train(self,
-                      x,
-                      out_teacher,
-                      img_metas,
-                      gt_bboxes,
-                      gt_labels=None,
-                      gt_bboxes_ignore=None,
-                      proposal_cfg=None,
-                      **kwargs):
-        """
-        Args:
-            x (list[Tensor]): Features from FPN.
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes (Tensor): Ground truth bboxes of the image,
-                shape (num_gts, 4).
-            gt_labels (Tensor): Ground truth labels of each box,
-                shape (num_gts,).
-            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
-                ignored, shape (num_ignored_gts, 4).
-            proposal_cfg (mmcv.Config): Test / postprocessing configuration,
-                if None, test_cfg would be used
-
-        Returns:
-            tuple[dict, list]: The loss components and proposals of each image.
-
-            - losses (dict[str, Tensor]): A dictionary of loss components.
-            - proposal_list (list[Tensor]): Proposals of each image.
-        """
-        outs = self(x)
-        soft_target = out_teacher[1]
-        if gt_labels is None:
-            loss_inputs = outs + (gt_bboxes, soft_target, img_metas)
-        else:
-            loss_inputs = outs + (gt_bboxes, gt_labels, soft_target, img_metas)
-        losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
-        if proposal_cfg is None:
-            return losses
-        else:
-            proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg)
-            return losses, proposal_list
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             gt_labels,
-             soft_target,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Cls and quality scores for each scale
-                level has shape (N, num_classes, H, W).
-            bbox_preds (list[Tensor]): Box distribution logits for each scale
-                level with shape (N, 4*(n+1), H, W), n is max value of integral
-                set.
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (list[Tensor] | None): specify which bounding
-                boxes can be ignored when computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels)
-        if cls_reg_targets is None:
-            return None
-
-        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
-         bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
-
-        num_total_samples = reduce_mean(
-            torch.tensor(num_total_pos, dtype=torch.float,
-                         device=device)).item()
-        num_total_samples = max(num_total_samples, 1.0)
-
-        losses_cls, losses_bbox, losses_dfl, losses_ld, \
-            avg_factor = multi_apply(
-                self.loss_single,
-                anchor_list,
-                cls_scores,
-                bbox_preds,
-                labels_list,
-                label_weights_list,
-                bbox_targets_list,
-                self.anchor_generator.strides,
-                soft_target,
-                num_total_samples=num_total_samples)
-
-        avg_factor = sum(avg_factor) + 1e-6
-        avg_factor = reduce_mean(avg_factor).item()
-        losses_bbox = [x / avg_factor for x in losses_bbox]
-        losses_dfl = [x / avg_factor for x in losses_dfl]
-        return dict(
-            loss_cls=losses_cls,
-            loss_bbox=losses_bbox,
-            loss_dfl=losses_dfl,
-            loss_ld=losses_ld)

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/LLaMA-model.md

@@ -1,56 +0,0 @@
-LLaMA is a Large Language Model developed by Meta AI. 
-
-It was trained on more tokens than previous models. The result is that the smallest version with 7 billion parameters has similar performance to GPT-3 with 175 billion parameters.
-
-This guide will cover usage through the official `transformers` implementation. For 4-bit mode, head over to [GPTQ models (4 bit mode)
-](GPTQ-models-(4-bit-mode).md).
-
-## Getting the weights
-
-### Option 1: pre-converted weights
-
-* Direct download (recommended):
-
-https://huggingface.co/Neko-Institute-of-Science/LLaMA-7B-HF
-
-https://huggingface.co/Neko-Institute-of-Science/LLaMA-13B-HF
-
-https://huggingface.co/Neko-Institute-of-Science/LLaMA-30B-HF
-
-https://huggingface.co/Neko-Institute-of-Science/LLaMA-65B-HF
-
-* Torrent:
-
-https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789
-
-The tokenizer files in the torrent above are outdated, in particular the files called `tokenizer_config.json` and `special_tokens_map.json`. Here you can find those files: https://huggingface.co/oobabooga/llama-tokenizer
-
-### Option 2: convert the weights yourself
-
-1. Install the `protobuf` library:
-
-```
-pip install protobuf==3.20.1
-```
-
-2. Use the script below to convert the model in `.pth` format that you, a fellow academic, downloaded using Meta's official link.
-
-If you have `transformers` installed in place:
-
-```
-python -m transformers.models.llama.convert_llama_weights_to_hf --input_dir /path/to/LLaMA --model_size 7B --output_dir /tmp/outputs/llama-7b
-```
-
-Otherwise download [convert_llama_weights_to_hf.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py) first and run:
-
-```
-python convert_llama_weights_to_hf.py --input_dir /path/to/LLaMA --model_size 7B --output_dir /tmp/outputs/llama-7b
-```
-
-3. Move the `llama-7b` folder inside your `text-generation-webui/models` folder.
-
-## Starting the web UI
-
-```python
-python server.py --model llama-7b
-```

spaces/Atom007/SDXL-base-9-CPU/README.md

@@ -1,14 +0,0 @@
----
-title: SDXL .9 CPU
-emoji: 🐢
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: Manjushri/SDXL-.9-CPU
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AtomdffAI/wechatgpt4atom/channel/channel_factory.py

@@ -1,17 +0,0 @@
-"""
-channel factory
-"""
-
-def create_channel(channel_type):
-    """
-    create a channel instance
-    :param channel_type: channel type code
-    :return: channel instance
-    """
-    if channel_type == 'wx':
-        from channel.wechat.wechat_channel import WechatChannel
-        return WechatChannel()
-    elif channel_type == 'wxy':
-        from channel.wechat.wechaty_channel import WechatyChannel
-        return WechatyChannel()
-    raise RuntimeError

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/fpn_p5.py

@@ -1,78 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import math
-import fvcore.nn.weight_init as weight_init
-import torch.nn.functional as F
-from torch import nn
-
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-
-from detectron2.modeling.backbone import Backbone
-from detectron2.modeling.backbone.fpn import FPN 
-from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
-from detectron2.modeling.backbone.resnet import build_resnet_backbone
-
-
-class LastLevelP6P7_P5(nn.Module):
-    """
-    This module is used in RetinaNet to generate extra layers, P6 and P7 from
-    C5 feature.
-    """
-
-    def __init__(self, in_channels, out_channels):
-        super().__init__()
-        self.num_levels = 2
-        self.in_feature = "p5"
-        self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1)
-        self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1)
-        for module in [self.p6, self.p7]:
-            weight_init.c2_xavier_fill(module)
-
-    def forward(self, c5):
-        p6 = self.p6(c5)
-        p7 = self.p7(F.relu(p6))
-        return [p6, p7]
-
-
-@BACKBONE_REGISTRY.register()
-def build_p67_resnet_fpn_backbone(cfg, input_shape: ShapeSpec):
-    """
-    Args:
-        cfg: a detectron2 CfgNode
-
-    Returns:
-        backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
-    """
-    bottom_up = build_resnet_backbone(cfg, input_shape)
-    in_features = cfg.MODEL.FPN.IN_FEATURES
-    out_channels = cfg.MODEL.FPN.OUT_CHANNELS
-    backbone = FPN(
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=out_channels,
-        norm=cfg.MODEL.FPN.NORM,
-        top_block=LastLevelP6P7_P5(out_channels, out_channels),
-        fuse_type=cfg.MODEL.FPN.FUSE_TYPE,
-    )
-    return backbone
-
-@BACKBONE_REGISTRY.register()
-def build_p35_resnet_fpn_backbone(cfg, input_shape: ShapeSpec):
-    """
-    Args:
-        cfg: a detectron2 CfgNode
-
-    Returns:
-        backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
-    """
-    bottom_up = build_resnet_backbone(cfg, input_shape)
-    in_features = cfg.MODEL.FPN.IN_FEATURES
-    out_channels = cfg.MODEL.FPN.OUT_CHANNELS
-    backbone = FPN(
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=out_channels,
-        norm=cfg.MODEL.FPN.NORM,
-        top_block=None,
-        fuse_type=cfg.MODEL.FPN.FUSE_TYPE,
-    )
-    return backbone

spaces/BENE2007/runwayml-stable-diffusion-v1-5/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/runwayml/stable-diffusion-v1-5").launch()

spaces/BartPoint/VoiceChange_Beta/infer_pack/modules/F0Predictor/HarvestF0Predictor.py

@@ -1,86 +0,0 @@
-from infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class HarvestF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.harvest(
-            wav.astype(np.double),
-            fs=self.hop_length,
-            f0_ceil=self.f0_max,
-            f0_floor=self.f0_min,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.fs)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.harvest(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))

spaces/Benson/text-generation/Examples/Descargar El Montaje Y La Conquista De La Hoja Vikingo Altamente Comprimido.md

@@ -1,74 +0,0 @@
-
-<h1>Cómo descargar Mount and Blade Viking Conquest altamente comprimido</h1>
-<p>Si eres un fan de los RPG históricos, es posible que hayas oído hablar de Mount and Blade, una popular serie de juegos que te permite crear tu propio personaje, unirte a facciones, luchar batallas y conquistar tierras. Una de las expansiones más aclamadas de Mount and Blade Warband es Viking Conquest, que te lleva a la edad oscura de Gran Bretaña, Irlanda y Escandinavia, donde puedes experimentar la vida de un guerrero vikingo, un raider, un comerciante o un rey. </p>
-<p>Sin embargo, si tienes un PC de gama baja o una conexión a Internet limitada, puede que te resulte difícil descargar y jugar a este juego, ya que tiene un gran tamaño de archivo y altos requisitos del sistema. Es por eso que hemos preparado esta guía para ti, donde te mostraremos cómo descargar Mount and Blade Viking Conquest altamente comprimido, lo que significa que puedes obtener el juego en un tamaño mucho más pequeño sin perder ninguna calidad o características. También te daremos algunos consejos sobre cómo disfrutar del juego en todo su esplendor. </p>
-<h2>descargar el montaje y la conquista de la hoja vikingo altamente comprimido</h2><br /><p><b><b>Download File</b> &#9658;&#9658;&#9658; <a href="https://bltlly.com/2v6KS2">https://bltlly.com/2v6KS2</a></b></p><br /><br />
-<h2>¿Qué es Mount y Blade Viking Conquest? </h2>
-<h3>Una breve introducción al juego y sus características</h3>
-<p>Mount and Blade Viking Conquest es un DLC para Mount y Blade Warband, que es una expansión independiente para el juego original de Mount y Blade. Fue desarrollado por los creadores del popular mod Brytenwalda, que añade más realismo, precisión histórica e inmersión al juego. Viking Conquest introduce seis nuevas culturas, veintiuna nuevas facciones, más de trescientas nuevas ciudades, castillos, pueblos y escenas, más de doscientos personajes históricos y PNJ, un complejo sistema religioso, un compañero de perros, un sistema de combate naval, un modo de historia con opciones y consecuencias, y mucho más. </p>
-<h3>Los beneficios de jugar el juego en una versión altamente comprimida</h3>
-
-<h2>¿Dónde descargar Mount and Blade Viking Conquest altamente comprimido? </h2>
-<h3>Los mejores sitios web para descargar el juego gratis</h3>
-<p>Hay muchos sitios web que ofrecen versiones altamente comprimidas de juegos populares como Mount y Blade Viking Conquest Highly Compressed, pero no todos son confiables o seguros. Algunos de ellos pueden contener virus, malware o spyware que pueden dañar su PC o robar su información personal. Algunos de ellos pueden tener enlaces rotos, archivos dañados o partes faltantes que pueden evitar que juegues el juego correctamente. Algunos de ellos pueden tener molestos anuncios, ventanas emergentes o encuestas que pueden perder el tiempo y frustrarte. Por lo tanto, debe tener cuidado y elegir los mejores sitios web para descargar el juego de forma gratuita. Estos son algunos de los mejores sitios web que recomendamos para descargar Mount and Blade Viking Conquest Highly Compressed:</p>
-<ul>
-<li><a href="">Ocean of Games</a>: Este es uno de los sitios web más populares y confiables para descargar juegos altamente comprimidos de forma gratuita. Tiene una gran colección de juegos de varios géneros y plataformas, incluyendo Mount y Blade Viking Conquest Highly Compressed. Proporciona enlaces de descarga directa, velocidades de descarga rápidas e instrucciones de instalación fáciles. También tiene una interfaz fácil de usar, una función de búsqueda y una sección de comentarios donde puede obtener ayuda de otros usuarios. </li>
-<li><a href="">Apun Ka Games</a>: Este es otro gran sitio web para descargar juegos altamente comprimidos de forma gratuita. También tiene una gran biblioteca de juegos de diferentes categorías y dispositivos, incluyendo Mount y Blade Viking Conquest Highly Compressed. Ofrece enlaces de descarga con un solo clic, altas tasas de descarga y guías de instalación simples. También tiene un diseño limpio, una opción de búsqueda y una sección de comentarios donde puedes compartir tus opiniones o problemas con otros usuarios. </li>
-
-</ul>
-<h3>Los pasos para descargar e instalar el juego en tu PC</h3>
-<p>Una vez que haya elegido el sitio web del que desea descargar el juego, debe seguir estos pasos para descargar e instalar el juego en su PC:</p>
-<ol>
-<li>Haga clic en el enlace de descarga proporcionado por el sitio web y espere a que el archivo se descargue en su PC.</li>
-<li>Extraer el archivo utilizando WinRAR o cualquier otro software que puede descomprimir archivos comprimidos. </li>
-<li>Abra la carpeta extraída y ejecute el archivo setup.exe como administrador. </li>
-<li>Siga las instrucciones en la pantalla y elija la carpeta de destino donde desea instalar el juego. </li>
-<li>Espere a que se complete el proceso de instalación y luego inicie el juego desde el acceso directo del escritorio o el menú de inicio. </li>
-</ol>
-<h2>¿Cómo disfrutar de Mount and Blade Viking Conquest altamente comprimido? </h2>
-<h3>Los consejos y trucos para optimizar el rendimiento del juego y los gráficos</h3>
-<p>A pesar de que jugar Mount and Blade Viking Conquest en una versión altamente comprimida puede mejorar el rendimiento del juego, es posible que todavía encuentre algunos problemas o problemas al jugar el juego en su PC. Por ejemplo, podrías experimentar errores de FPS, retardo, tartamudeo, colisión, congelación o gráficos. Para solucionar estos problemas y optimizar el rendimiento del juego y los gráficos, puedes probar estos consejos y trucos:</p>
-<ul>
-<li>Actualizar sus controladores: Asegúrese de que sus controladores están actualizados, especialmente el controlador de la tarjeta gráfica. Puede utilizar un software como Driver Booster o Driver Easy para escanear su PC y actualizar sus controladores automáticamente. </li>
-<li>Ajusta tus ajustes: Ve al menú de opciones del juego y ajusta tus ajustes de acuerdo a las especificaciones y preferencias de tu PC. Puede reducir su resolución, calidad gráfica, sombras, texturas, anti-aliasing, etc. para aumentar su FPS y reducir el retraso. También puede activar o desactivar algunas características como efectos de sangre, muñecos de trapo, cadáveres, etc. para mejorar su experiencia de juego. </li>
-
-</ul>
-<h3>Los mejores mods y DLCs para mejorar tu experiencia de juego</h3>
-<p>Además de optimizar el rendimiento del juego y los gráficos, también puedes mejorar tu experiencia de juego utilizando algunos de los mejores mods y DLCs para Mount y Blade Viking Conquest. Estos mods y DLCs pueden agregar nuevo contenido, características, opciones, escenarios y desafíos al juego, haciéndolo más divertido, diverso y reproducible. Estos son algunos de los mejores mods y DLCs que recomendamos para Mount y Blade Viking Conquest:</p>
-<p></p>
-<ul>
-<li><a href="">Viking Conquest Reforged Edition</a>: Esta es la actualización oficial de Viking Conquest, que añade muchas mejoras, correcciones y nuevo contenido al juego. Incluye una nueva historia de aventurero, un nuevo modo sandbox, un nuevo sistema de gestión del reino, un nuevo sistema de diplomacia, un nuevo sistema de creación de personajes, nuevas escenas, objetos, misiones, eventos, facciones, tropas, etc.</li>
-<li><a href="">Blood Eagle</a>: Este es un mod de conversión total para Viking Conquest, que transforma el juego en una representación brutal y realista de la era vikinga. Cuenta con un nuevo mapa, nuevas culturas, nuevas facciones, nuevas tropas, nuevos elementos, nuevas escenas, nuevas misiones, nuevas mecánicas, nueva música, nuevos sonidos, etc. También añade más gore, violencia, efectos de sangre, ejecuciones, tortura, esclavitud, etc.</li>
-<li><a href="">Dark Age</a>: Este es otro mod de conversión total para Viking Conquest, que se centra en los aspectos históricos y culturales de la era vikinga. Cuenta con un nuevo mapa, nuevas culturas, nuevas facciones, nuevas tropas, nuevos elementos, nuevas escenas, nuevas misiones, nuevas mecánicas, nueva música, etc. También añade más realismo, inmersión, diversidad y opciones de juegos de rol al juego. </li>
-</ul>
-<h2>Conclusión</h2>
-
-<h2>Preguntas frecuentes</h2>
-<h3>Q1: ¿Cuánto espacio ocupa Mount and Blade Viking Conquest altamente comprimido en su PC? </h3>
-<p>A1: Mount and Blade Viking Conquest Highly Compressed ocupa solo 1 GB de espacio libre en su PC, en comparación con la versión original, que tarda unos 4 GB.</p>
-<h3>Q2: ¿Es seguro descargar Mount and Blade Viking Conquest altamente comprimido? </h3>
-<p>A2: Sí, Mount and Blade Viking Conquest Highly Compressed es seguro de descargar, siempre y cuando lo descargue desde un sitio web confiable y confiable. Sin embargo, siempre debe escanear el archivo con un software antivirus antes de instalarlo en su PC, solo para estar seguro. </p>
-<h3>Q3: ¿Se puede jugar Mount and Blade Viking conquista altamente comprimido en línea? </h3>
-<p>A3: Sí, puede jugar Mount and Blade Viking Conquest altamente comprimido en línea con otros reproductores, siempre y cuando tenga una conexión a Internet estable y una clave de CD válida. Puedes unirte o alojar servidores multijugador, crear o unirte a clanes, participar en torneos, etc.</p>
-<h3>Q4: ¿Cuáles son los requisitos mínimos del sistema para Mount and Blade Viking Conquest Highly Compressed? </h3>
-<p>A4: Los requisitos mínimos del sistema para Mount y Blade Viking Conquest Highly Compressed son:</p>
-<tabla>
-<tr><td>OS</td><td>Windows XP/Vista/7/8/10</td></tr>
-<tr><td>Procesador</td><td>Intel Core 2 Duo 2.0 GHz o equivalente</td></tr>
-<tr><td>Memoria</td><td>2 GB de RAM</td></tr>
-<tr><td>Gráficos</td><td>NVIDIA GeForce 6600 GT o equivalente</td></tr>
-<tr><td>DirectX</td><td>Versión 9.0c</td></tr>
-<tr><td>Almacenamiento</td><td>1 GB de espacio disponible</td></tr>
-<tr><td>Tarjeta de sonido</td><td>Tarjeta de sonido compatible con DirectX</td></tr>
-</tabla>
-<h3>Q5: ¿Cuáles son algunos otros juegos altamente comprimidos que se pueden descargar? </h3>
-<p>A5: Algunos otros juegos altamente comprimidos que puedes descargar son:</p>
-<ul>
-<li>GTA 5 altamente comprimido</li>
-<li>FIFA 21 altamente comprimido</li>
-<li>Cyberpunk 2077 altamente comprimido</li>
-<li>Assassin’s Creed Valhalla altamente comprimido</li>
-
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/distributions/wheel.py

@@ -1,34 +0,0 @@
-from pip._vendor.packaging.utils import canonicalize_name
-
-from pip._internal.distributions.base import AbstractDistribution
-from pip._internal.index.package_finder import PackageFinder
-from pip._internal.metadata import (
-    BaseDistribution,
-    FilesystemWheel,
-    get_wheel_distribution,
-)
-
-
-class WheelDistribution(AbstractDistribution):
-    """Represents a wheel distribution.
-
-    This does not need any preparation as wheels can be directly unpacked.
-    """
-
-    def get_metadata_distribution(self) -> BaseDistribution:
-        """Loads the metadata from the wheel file into memory and returns a
-        Distribution that uses it, not relying on the wheel file or
-        requirement.
-        """
-        assert self.req.local_file_path, "Set as part of preparation during download"
-        assert self.req.name, "Wheels are never unnamed"
-        wheel = FilesystemWheel(self.req.local_file_path)
-        return get_wheel_distribution(wheel, canonicalize_name(self.req.name))
-
-    def prepare_distribution_metadata(
-        self,
-        finder: PackageFinder,
-        build_isolation: bool,
-        check_build_deps: bool,
-    ) -> None:
-        pass

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/network/download.py

@@ -1,186 +0,0 @@
-"""Download files with progress indicators.
-"""
-import email.message
-import logging
-import mimetypes
-import os
-from typing import Iterable, Optional, Tuple
-
-from pip._vendor.requests.models import CONTENT_CHUNK_SIZE, Response
-
-from pip._internal.cli.progress_bars import get_download_progress_renderer
-from pip._internal.exceptions import NetworkConnectionError
-from pip._internal.models.index import PyPI
-from pip._internal.models.link import Link
-from pip._internal.network.cache import is_from_cache
-from pip._internal.network.session import PipSession
-from pip._internal.network.utils import HEADERS, raise_for_status, response_chunks
-from pip._internal.utils.misc import format_size, redact_auth_from_url, splitext
-
-logger = logging.getLogger(__name__)
-
-
-def _get_http_response_size(resp: Response) -> Optional[int]:
-    try:
-        return int(resp.headers["content-length"])
-    except (ValueError, KeyError, TypeError):
-        return None
-
-
-def _prepare_download(
-    resp: Response,
-    link: Link,
-    progress_bar: str,
-) -> Iterable[bytes]:
-    total_length = _get_http_response_size(resp)
-
-    if link.netloc == PyPI.file_storage_domain:
-        url = link.show_url
-    else:
-        url = link.url_without_fragment
-
-    logged_url = redact_auth_from_url(url)
-
-    if total_length:
-        logged_url = "{} ({})".format(logged_url, format_size(total_length))
-
-    if is_from_cache(resp):
-        logger.info("Using cached %s", logged_url)
-    else:
-        logger.info("Downloading %s", logged_url)
-
-    if logger.getEffectiveLevel() > logging.INFO:
-        show_progress = False
-    elif is_from_cache(resp):
-        show_progress = False
-    elif not total_length:
-        show_progress = True
-    elif total_length > (40 * 1000):
-        show_progress = True
-    else:
-        show_progress = False
-
-    chunks = response_chunks(resp, CONTENT_CHUNK_SIZE)
-
-    if not show_progress:
-        return chunks
-
-    renderer = get_download_progress_renderer(bar_type=progress_bar, size=total_length)
-    return renderer(chunks)
-
-
-def sanitize_content_filename(filename: str) -> str:
-    """
-    Sanitize the "filename" value from a Content-Disposition header.
-    """
-    return os.path.basename(filename)
-
-
-def parse_content_disposition(content_disposition: str, default_filename: str) -> str:
-    """
-    Parse the "filename" value from a Content-Disposition header, and
-    return the default filename if the result is empty.
-    """
-    m = email.message.Message()
-    m["content-type"] = content_disposition
-    filename = m.get_param("filename")
-    if filename:
-        # We need to sanitize the filename to prevent directory traversal
-        # in case the filename contains ".." path parts.
-        filename = sanitize_content_filename(str(filename))
-    return filename or default_filename
-
-
-def _get_http_response_filename(resp: Response, link: Link) -> str:
-    """Get an ideal filename from the given HTTP response, falling back to
-    the link filename if not provided.
-    """
-    filename = link.filename  # fallback
-    # Have a look at the Content-Disposition header for a better guess
-    content_disposition = resp.headers.get("content-disposition")
-    if content_disposition:
-        filename = parse_content_disposition(content_disposition, filename)
-    ext: Optional[str] = splitext(filename)[1]
-    if not ext:
-        ext = mimetypes.guess_extension(resp.headers.get("content-type", ""))
-        if ext:
-            filename += ext
-    if not ext and link.url != resp.url:
-        ext = os.path.splitext(resp.url)[1]
-        if ext:
-            filename += ext
-    return filename
-
-
-def _http_get_download(session: PipSession, link: Link) -> Response:
-    target_url = link.url.split("#", 1)[0]
-    resp = session.get(target_url, headers=HEADERS, stream=True)
-    raise_for_status(resp)
-    return resp
-
-
-class Downloader:
-    def __init__(
-        self,
-        session: PipSession,
-        progress_bar: str,
-    ) -> None:
-        self._session = session
-        self._progress_bar = progress_bar
-
-    def __call__(self, link: Link, location: str) -> Tuple[str, str]:
-        """Download the file given by link into location."""
-        try:
-            resp = _http_get_download(self._session, link)
-        except NetworkConnectionError as e:
-            assert e.response is not None
-            logger.critical(
-                "HTTP error %s while getting %s", e.response.status_code, link
-            )
-            raise
-
-        filename = _get_http_response_filename(resp, link)
-        filepath = os.path.join(location, filename)
-
-        chunks = _prepare_download(resp, link, self._progress_bar)
-        with open(filepath, "wb") as content_file:
-            for chunk in chunks:
-                content_file.write(chunk)
-        content_type = resp.headers.get("Content-Type", "")
-        return filepath, content_type
-
-
-class BatchDownloader:
-    def __init__(
-        self,
-        session: PipSession,
-        progress_bar: str,
-    ) -> None:
-        self._session = session
-        self._progress_bar = progress_bar
-
-    def __call__(
-        self, links: Iterable[Link], location: str
-    ) -> Iterable[Tuple[Link, Tuple[str, str]]]:
-        """Download the files given by links into location."""
-        for link in links:
-            try:
-                resp = _http_get_download(self._session, link)
-            except NetworkConnectionError as e:
-                assert e.response is not None
-                logger.critical(
-                    "HTTP error %s while getting %s",
-                    e.response.status_code,
-                    link,
-                )
-                raise
-
-            filename = _get_http_response_filename(resp, link)
-            filepath = os.path.join(location, filename)
-
-            chunks = _prepare_download(resp, link, self._progress_bar)
-            with open(filepath, "wb") as content_file:
-                for chunk in chunks:
-                    content_file.write(chunk)
-            content_type = resp.headers.get("Content-Type", "")
-            yield link, (filepath, content_type)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/wheel_builder.py

@@ -1,355 +0,0 @@
-"""Orchestrator for building wheels from InstallRequirements.
-"""
-
-import logging
-import os.path
-import re
-import shutil
-from typing import Iterable, List, Optional, Tuple
-
-from pip._vendor.packaging.utils import canonicalize_name, canonicalize_version
-from pip._vendor.packaging.version import InvalidVersion, Version
-
-from pip._internal.cache import WheelCache
-from pip._internal.exceptions import InvalidWheelFilename, UnsupportedWheel
-from pip._internal.metadata import FilesystemWheel, get_wheel_distribution
-from pip._internal.models.link import Link
-from pip._internal.models.wheel import Wheel
-from pip._internal.operations.build.wheel import build_wheel_pep517
-from pip._internal.operations.build.wheel_editable import build_wheel_editable
-from pip._internal.operations.build.wheel_legacy import build_wheel_legacy
-from pip._internal.req.req_install import InstallRequirement
-from pip._internal.utils.logging import indent_log
-from pip._internal.utils.misc import ensure_dir, hash_file
-from pip._internal.utils.setuptools_build import make_setuptools_clean_args
-from pip._internal.utils.subprocess import call_subprocess
-from pip._internal.utils.temp_dir import TempDirectory
-from pip._internal.utils.urls import path_to_url
-from pip._internal.vcs import vcs
-
-logger = logging.getLogger(__name__)
-
-_egg_info_re = re.compile(r"([a-z0-9_.]+)-([a-z0-9_.!+-]+)", re.IGNORECASE)
-
-BuildResult = Tuple[List[InstallRequirement], List[InstallRequirement]]
-
-
-def _contains_egg_info(s: str) -> bool:
-    """Determine whether the string looks like an egg_info.
-
-    :param s: The string to parse. E.g. foo-2.1
-    """
-    return bool(_egg_info_re.search(s))
-
-
-def _should_build(
-    req: InstallRequirement,
-    need_wheel: bool,
-) -> bool:
-    """Return whether an InstallRequirement should be built into a wheel."""
-    if req.constraint:
-        # never build requirements that are merely constraints
-        return False
-    if req.is_wheel:
-        if need_wheel:
-            logger.info(
-                "Skipping %s, due to already being wheel.",
-                req.name,
-            )
-        return False
-
-    if need_wheel:
-        # i.e. pip wheel, not pip install
-        return True
-
-    # From this point, this concerns the pip install command only
-    # (need_wheel=False).
-
-    if not req.source_dir:
-        return False
-
-    if req.editable:
-        # we only build PEP 660 editable requirements
-        return req.supports_pyproject_editable()
-
-    return True
-
-
-def should_build_for_wheel_command(
-    req: InstallRequirement,
-) -> bool:
-    return _should_build(req, need_wheel=True)
-
-
-def should_build_for_install_command(
-    req: InstallRequirement,
-) -> bool:
-    return _should_build(req, need_wheel=False)
-
-
-def _should_cache(
-    req: InstallRequirement,
-) -> Optional[bool]:
-    """
-    Return whether a built InstallRequirement can be stored in the persistent
-    wheel cache, assuming the wheel cache is available, and _should_build()
-    has determined a wheel needs to be built.
-    """
-    if req.editable or not req.source_dir:
-        # never cache editable requirements
-        return False
-
-    if req.link and req.link.is_vcs:
-        # VCS checkout. Do not cache
-        # unless it points to an immutable commit hash.
-        assert not req.editable
-        assert req.source_dir
-        vcs_backend = vcs.get_backend_for_scheme(req.link.scheme)
-        assert vcs_backend
-        if vcs_backend.is_immutable_rev_checkout(req.link.url, req.source_dir):
-            return True
-        return False
-
-    assert req.link
-    base, ext = req.link.splitext()
-    if _contains_egg_info(base):
-        return True
-
-    # Otherwise, do not cache.
-    return False
-
-
-def _get_cache_dir(
-    req: InstallRequirement,
-    wheel_cache: WheelCache,
-) -> str:
-    """Return the persistent or temporary cache directory where the built
-    wheel need to be stored.
-    """
-    cache_available = bool(wheel_cache.cache_dir)
-    assert req.link
-    if cache_available and _should_cache(req):
-        cache_dir = wheel_cache.get_path_for_link(req.link)
-    else:
-        cache_dir = wheel_cache.get_ephem_path_for_link(req.link)
-    return cache_dir
-
-
-def _verify_one(req: InstallRequirement, wheel_path: str) -> None:
-    canonical_name = canonicalize_name(req.name or "")
-    w = Wheel(os.path.basename(wheel_path))
-    if canonicalize_name(w.name) != canonical_name:
-        raise InvalidWheelFilename(
-            "Wheel has unexpected file name: expected {!r}, "
-            "got {!r}".format(canonical_name, w.name),
-        )
-    dist = get_wheel_distribution(FilesystemWheel(wheel_path), canonical_name)
-    dist_verstr = str(dist.version)
-    if canonicalize_version(dist_verstr) != canonicalize_version(w.version):
-        raise InvalidWheelFilename(
-            "Wheel has unexpected file name: expected {!r}, "
-            "got {!r}".format(dist_verstr, w.version),
-        )
-    metadata_version_value = dist.metadata_version
-    if metadata_version_value is None:
-        raise UnsupportedWheel("Missing Metadata-Version")
-    try:
-        metadata_version = Version(metadata_version_value)
-    except InvalidVersion:
-        msg = f"Invalid Metadata-Version: {metadata_version_value}"
-        raise UnsupportedWheel(msg)
-    if metadata_version >= Version("1.2") and not isinstance(dist.version, Version):
-        raise UnsupportedWheel(
-            "Metadata 1.2 mandates PEP 440 version, "
-            "but {!r} is not".format(dist_verstr)
-        )
-
-
-def _build_one(
-    req: InstallRequirement,
-    output_dir: str,
-    verify: bool,
-    build_options: List[str],
-    global_options: List[str],
-    editable: bool,
-) -> Optional[str]:
-    """Build one wheel.
-
-    :return: The filename of the built wheel, or None if the build failed.
-    """
-    artifact = "editable" if editable else "wheel"
-    try:
-        ensure_dir(output_dir)
-    except OSError as e:
-        logger.warning(
-            "Building %s for %s failed: %s",
-            artifact,
-            req.name,
-            e,
-        )
-        return None
-
-    # Install build deps into temporary directory (PEP 518)
-    with req.build_env:
-        wheel_path = _build_one_inside_env(
-            req, output_dir, build_options, global_options, editable
-        )
-    if wheel_path and verify:
-        try:
-            _verify_one(req, wheel_path)
-        except (InvalidWheelFilename, UnsupportedWheel) as e:
-            logger.warning("Built %s for %s is invalid: %s", artifact, req.name, e)
-            return None
-    return wheel_path
-
-
-def _build_one_inside_env(
-    req: InstallRequirement,
-    output_dir: str,
-    build_options: List[str],
-    global_options: List[str],
-    editable: bool,
-) -> Optional[str]:
-    with TempDirectory(kind="wheel") as temp_dir:
-        assert req.name
-        if req.use_pep517:
-            assert req.metadata_directory
-            assert req.pep517_backend
-            if global_options:
-                logger.warning(
-                    "Ignoring --global-option when building %s using PEP 517", req.name
-                )
-            if build_options:
-                logger.warning(
-                    "Ignoring --build-option when building %s using PEP 517", req.name
-                )
-            if editable:
-                wheel_path = build_wheel_editable(
-                    name=req.name,
-                    backend=req.pep517_backend,
-                    metadata_directory=req.metadata_directory,
-                    tempd=temp_dir.path,
-                )
-            else:
-                wheel_path = build_wheel_pep517(
-                    name=req.name,
-                    backend=req.pep517_backend,
-                    metadata_directory=req.metadata_directory,
-                    tempd=temp_dir.path,
-                )
-        else:
-            wheel_path = build_wheel_legacy(
-                name=req.name,
-                setup_py_path=req.setup_py_path,
-                source_dir=req.unpacked_source_directory,
-                global_options=global_options,
-                build_options=build_options,
-                tempd=temp_dir.path,
-            )
-
-        if wheel_path is not None:
-            wheel_name = os.path.basename(wheel_path)
-            dest_path = os.path.join(output_dir, wheel_name)
-            try:
-                wheel_hash, length = hash_file(wheel_path)
-                shutil.move(wheel_path, dest_path)
-                logger.info(
-                    "Created wheel for %s: filename=%s size=%d sha256=%s",
-                    req.name,
-                    wheel_name,
-                    length,
-                    wheel_hash.hexdigest(),
-                )
-                logger.info("Stored in directory: %s", output_dir)
-                return dest_path
-            except Exception as e:
-                logger.warning(
-                    "Building wheel for %s failed: %s",
-                    req.name,
-                    e,
-                )
-        # Ignore return, we can't do anything else useful.
-        if not req.use_pep517:
-            _clean_one_legacy(req, global_options)
-        return None
-
-
-def _clean_one_legacy(req: InstallRequirement, global_options: List[str]) -> bool:
-    clean_args = make_setuptools_clean_args(
-        req.setup_py_path,
-        global_options=global_options,
-    )
-
-    logger.info("Running setup.py clean for %s", req.name)
-    try:
-        call_subprocess(
-            clean_args, command_desc="python setup.py clean", cwd=req.source_dir
-        )
-        return True
-    except Exception:
-        logger.error("Failed cleaning build dir for %s", req.name)
-        return False
-
-
-def build(
-    requirements: Iterable[InstallRequirement],
-    wheel_cache: WheelCache,
-    verify: bool,
-    build_options: List[str],
-    global_options: List[str],
-) -> BuildResult:
-    """Build wheels.
-
-    :return: The list of InstallRequirement that succeeded to build and
-        the list of InstallRequirement that failed to build.
-    """
-    if not requirements:
-        return [], []
-
-    # Build the wheels.
-    logger.info(
-        "Building wheels for collected packages: %s",
-        ", ".join(req.name for req in requirements),  # type: ignore
-    )
-
-    with indent_log():
-        build_successes, build_failures = [], []
-        for req in requirements:
-            assert req.name
-            cache_dir = _get_cache_dir(req, wheel_cache)
-            wheel_file = _build_one(
-                req,
-                cache_dir,
-                verify,
-                build_options,
-                global_options,
-                req.editable and req.permit_editable_wheels,
-            )
-            if wheel_file:
-                # Record the download origin in the cache
-                if req.download_info is not None:
-                    # download_info is guaranteed to be set because when we build an
-                    # InstallRequirement it has been through the preparer before, but
-                    # let's be cautious.
-                    wheel_cache.record_download_origin(cache_dir, req.download_info)
-                # Update the link for this.
-                req.link = Link(path_to_url(wheel_file))
-                req.local_file_path = req.link.file_path
-                assert req.link.is_wheel
-                build_successes.append(req)
-            else:
-                build_failures.append(req)
-
-    # notify success/failure
-    if build_successes:
-        logger.info(
-            "Successfully built %s",
-            " ".join([req.name for req in build_successes]),  # type: ignore
-        )
-    if build_failures:
-        logger.info(
-            "Failed to build %s",
-            " ".join([req.name for req in build_failures]),  # type: ignore
-        )
-    # Return a list of requirements that failed to build
-    return build_successes, build_failures

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/requests/sessions.py

@@ -1,831 +0,0 @@
-"""
-requests.sessions
-~~~~~~~~~~~~~~~~~
-
-This module provides a Session object to manage and persist settings across
-requests (cookies, auth, proxies).
-"""
-import os
-import sys
-import time
-from collections import OrderedDict
-from datetime import timedelta
-
-from ._internal_utils import to_native_string
-from .adapters import HTTPAdapter
-from .auth import _basic_auth_str
-from .compat import Mapping, cookielib, urljoin, urlparse
-from .cookies import (
-    RequestsCookieJar,
-    cookiejar_from_dict,
-    extract_cookies_to_jar,
-    merge_cookies,
-)
-from .exceptions import (
-    ChunkedEncodingError,
-    ContentDecodingError,
-    InvalidSchema,
-    TooManyRedirects,
-)
-from .hooks import default_hooks, dispatch_hook
-
-# formerly defined here, reexposed here for backward compatibility
-from .models import (  # noqa: F401
-    DEFAULT_REDIRECT_LIMIT,
-    REDIRECT_STATI,
-    PreparedRequest,
-    Request,
-)
-from .status_codes import codes
-from .structures import CaseInsensitiveDict
-from .utils import (  # noqa: F401
-    DEFAULT_PORTS,
-    default_headers,
-    get_auth_from_url,
-    get_environ_proxies,
-    get_netrc_auth,
-    requote_uri,
-    resolve_proxies,
-    rewind_body,
-    should_bypass_proxies,
-    to_key_val_list,
-)
-
-# Preferred clock, based on which one is more accurate on a given system.
-if sys.platform == "win32":
-    preferred_clock = time.perf_counter
-else:
-    preferred_clock = time.time
-
-
-def merge_setting(request_setting, session_setting, dict_class=OrderedDict):
-    """Determines appropriate setting for a given request, taking into account
-    the explicit setting on that request, and the setting in the session. If a
-    setting is a dictionary, they will be merged together using `dict_class`
-    """
-
-    if session_setting is None:
-        return request_setting
-
-    if request_setting is None:
-        return session_setting
-
-    # Bypass if not a dictionary (e.g. verify)
-    if not (
-        isinstance(session_setting, Mapping) and isinstance(request_setting, Mapping)
-    ):
-        return request_setting
-
-    merged_setting = dict_class(to_key_val_list(session_setting))
-    merged_setting.update(to_key_val_list(request_setting))
-
-    # Remove keys that are set to None. Extract keys first to avoid altering
-    # the dictionary during iteration.
-    none_keys = [k for (k, v) in merged_setting.items() if v is None]
-    for key in none_keys:
-        del merged_setting[key]
-
-    return merged_setting
-
-
-def merge_hooks(request_hooks, session_hooks, dict_class=OrderedDict):
-    """Properly merges both requests and session hooks.
-
-    This is necessary because when request_hooks == {'response': []}, the
-    merge breaks Session hooks entirely.
-    """
-    if session_hooks is None or session_hooks.get("response") == []:
-        return request_hooks
-
-    if request_hooks is None or request_hooks.get("response") == []:
-        return session_hooks
-
-    return merge_setting(request_hooks, session_hooks, dict_class)
-
-
-class SessionRedirectMixin:
-    def get_redirect_target(self, resp):
-        """Receives a Response. Returns a redirect URI or ``None``"""
-        # Due to the nature of how requests processes redirects this method will
-        # be called at least once upon the original response and at least twice
-        # on each subsequent redirect response (if any).
-        # If a custom mixin is used to handle this logic, it may be advantageous
-        # to cache the redirect location onto the response object as a private
-        # attribute.
-        if resp.is_redirect:
-            location = resp.headers["location"]
-            # Currently the underlying http module on py3 decode headers
-            # in latin1, but empirical evidence suggests that latin1 is very
-            # rarely used with non-ASCII characters in HTTP headers.
-            # It is more likely to get UTF8 header rather than latin1.
-            # This causes incorrect handling of UTF8 encoded location headers.
-            # To solve this, we re-encode the location in latin1.
-            location = location.encode("latin1")
-            return to_native_string(location, "utf8")
-        return None
-
-    def should_strip_auth(self, old_url, new_url):
-        """Decide whether Authorization header should be removed when redirecting"""
-        old_parsed = urlparse(old_url)
-        new_parsed = urlparse(new_url)
-        if old_parsed.hostname != new_parsed.hostname:
-            return True
-        # Special case: allow http -> https redirect when using the standard
-        # ports. This isn't specified by RFC 7235, but is kept to avoid
-        # breaking backwards compatibility with older versions of requests
-        # that allowed any redirects on the same host.
-        if (
-            old_parsed.scheme == "http"
-            and old_parsed.port in (80, None)
-            and new_parsed.scheme == "https"
-            and new_parsed.port in (443, None)
-        ):
-            return False
-
-        # Handle default port usage corresponding to scheme.
-        changed_port = old_parsed.port != new_parsed.port
-        changed_scheme = old_parsed.scheme != new_parsed.scheme
-        default_port = (DEFAULT_PORTS.get(old_parsed.scheme, None), None)
-        if (
-            not changed_scheme
-            and old_parsed.port in default_port
-            and new_parsed.port in default_port
-        ):
-            return False
-
-        # Standard case: root URI must match
-        return changed_port or changed_scheme
-
-    def resolve_redirects(
-        self,
-        resp,
-        req,
-        stream=False,
-        timeout=None,
-        verify=True,
-        cert=None,
-        proxies=None,
-        yield_requests=False,
-        **adapter_kwargs,
-    ):
-        """Receives a Response. Returns a generator of Responses or Requests."""
-
-        hist = []  # keep track of history
-
-        url = self.get_redirect_target(resp)
-        previous_fragment = urlparse(req.url).fragment
-        while url:
-            prepared_request = req.copy()
-
-            # Update history and keep track of redirects.
-            # resp.history must ignore the original request in this loop
-            hist.append(resp)
-            resp.history = hist[1:]
-
-            try:
-                resp.content  # Consume socket so it can be released
-            except (ChunkedEncodingError, ContentDecodingError, RuntimeError):
-                resp.raw.read(decode_content=False)
-
-            if len(resp.history) >= self.max_redirects:
-                raise TooManyRedirects(
-                    f"Exceeded {self.max_redirects} redirects.", response=resp
-                )
-
-            # Release the connection back into the pool.
-            resp.close()
-
-            # Handle redirection without scheme (see: RFC 1808 Section 4)
-            if url.startswith("//"):
-                parsed_rurl = urlparse(resp.url)
-                url = ":".join([to_native_string(parsed_rurl.scheme), url])
-
-            # Normalize url case and attach previous fragment if needed (RFC 7231 7.1.2)
-            parsed = urlparse(url)
-            if parsed.fragment == "" and previous_fragment:
-                parsed = parsed._replace(fragment=previous_fragment)
-            elif parsed.fragment:
-                previous_fragment = parsed.fragment
-            url = parsed.geturl()
-
-            # Facilitate relative 'location' headers, as allowed by RFC 7231.
-            # (e.g. '/path/to/resource' instead of 'http://domain.tld/path/to/resource')
-            # Compliant with RFC3986, we percent encode the url.
-            if not parsed.netloc:
-                url = urljoin(resp.url, requote_uri(url))
-            else:
-                url = requote_uri(url)
-
-            prepared_request.url = to_native_string(url)
-
-            self.rebuild_method(prepared_request, resp)
-
-            # https://github.com/psf/requests/issues/1084
-            if resp.status_code not in (
-                codes.temporary_redirect,
-                codes.permanent_redirect,
-            ):
-                # https://github.com/psf/requests/issues/3490
-                purged_headers = ("Content-Length", "Content-Type", "Transfer-Encoding")
-                for header in purged_headers:
-                    prepared_request.headers.pop(header, None)
-                prepared_request.body = None
-
-            headers = prepared_request.headers
-            headers.pop("Cookie", None)
-
-            # Extract any cookies sent on the response to the cookiejar
-            # in the new request. Because we've mutated our copied prepared
-            # request, use the old one that we haven't yet touched.
-            extract_cookies_to_jar(prepared_request._cookies, req, resp.raw)
-            merge_cookies(prepared_request._cookies, self.cookies)
-            prepared_request.prepare_cookies(prepared_request._cookies)
-
-            # Rebuild auth and proxy information.
-            proxies = self.rebuild_proxies(prepared_request, proxies)
-            self.rebuild_auth(prepared_request, resp)
-
-            # A failed tell() sets `_body_position` to `object()`. This non-None
-            # value ensures `rewindable` will be True, allowing us to raise an
-            # UnrewindableBodyError, instead of hanging the connection.
-            rewindable = prepared_request._body_position is not None and (
-                "Content-Length" in headers or "Transfer-Encoding" in headers
-            )
-
-            # Attempt to rewind consumed file-like object.
-            if rewindable:
-                rewind_body(prepared_request)
-
-            # Override the original request.
-            req = prepared_request
-
-            if yield_requests:
-                yield req
-            else:
-
-                resp = self.send(
-                    req,
-                    stream=stream,
-                    timeout=timeout,
-                    verify=verify,
-                    cert=cert,
-                    proxies=proxies,
-                    allow_redirects=False,
-                    **adapter_kwargs,
-                )
-
-                extract_cookies_to_jar(self.cookies, prepared_request, resp.raw)
-
-                # extract redirect url, if any, for the next loop
-                url = self.get_redirect_target(resp)
-                yield resp
-
-    def rebuild_auth(self, prepared_request, response):
-        """When being redirected we may want to strip authentication from the
-        request to avoid leaking credentials. This method intelligently removes
-        and reapplies authentication where possible to avoid credential loss.
-        """
-        headers = prepared_request.headers
-        url = prepared_request.url
-
-        if "Authorization" in headers and self.should_strip_auth(
-            response.request.url, url
-        ):
-            # If we get redirected to a new host, we should strip out any
-            # authentication headers.
-            del headers["Authorization"]
-
-        # .netrc might have more auth for us on our new host.
-        new_auth = get_netrc_auth(url) if self.trust_env else None
-        if new_auth is not None:
-            prepared_request.prepare_auth(new_auth)
-
-    def rebuild_proxies(self, prepared_request, proxies):
-        """This method re-evaluates the proxy configuration by considering the
-        environment variables. If we are redirected to a URL covered by
-        NO_PROXY, we strip the proxy configuration. Otherwise, we set missing
-        proxy keys for this URL (in case they were stripped by a previous
-        redirect).
-
-        This method also replaces the Proxy-Authorization header where
-        necessary.
-
-        :rtype: dict
-        """
-        headers = prepared_request.headers
-        scheme = urlparse(prepared_request.url).scheme
-        new_proxies = resolve_proxies(prepared_request, proxies, self.trust_env)
-
-        if "Proxy-Authorization" in headers:
-            del headers["Proxy-Authorization"]
-
-        try:
-            username, password = get_auth_from_url(new_proxies[scheme])
-        except KeyError:
-            username, password = None, None
-
-        if username and password:
-            headers["Proxy-Authorization"] = _basic_auth_str(username, password)
-
-        return new_proxies
-
-    def rebuild_method(self, prepared_request, response):
-        """When being redirected we may want to change the method of the request
-        based on certain specs or browser behavior.
-        """
-        method = prepared_request.method
-
-        # https://tools.ietf.org/html/rfc7231#section-6.4.4
-        if response.status_code == codes.see_other and method != "HEAD":
-            method = "GET"
-
-        # Do what the browsers do, despite standards...
-        # First, turn 302s into GETs.
-        if response.status_code == codes.found and method != "HEAD":
-            method = "GET"
-
-        # Second, if a POST is responded to with a 301, turn it into a GET.
-        # This bizarre behaviour is explained in Issue 1704.
-        if response.status_code == codes.moved and method == "POST":
-            method = "GET"
-
-        prepared_request.method = method
-
-
-class Session(SessionRedirectMixin):
-    """A Requests session.
-
-    Provides cookie persistence, connection-pooling, and configuration.
-
-    Basic Usage::
-
-      >>> import requests
-      >>> s = requests.Session()
-      >>> s.get('https://httpbin.org/get')
-      <Response [200]>
-
-    Or as a context manager::
-
-      >>> with requests.Session() as s:
-      ...     s.get('https://httpbin.org/get')
-      <Response [200]>
-    """
-
-    __attrs__ = [
-        "headers",
-        "cookies",
-        "auth",
-        "proxies",
-        "hooks",
-        "params",
-        "verify",
-        "cert",
-        "adapters",
-        "stream",
-        "trust_env",
-        "max_redirects",
-    ]
-
-    def __init__(self):
-
-        #: A case-insensitive dictionary of headers to be sent on each
-        #: :class:`Request <Request>` sent from this
-        #: :class:`Session <Session>`.
-        self.headers = default_headers()
-
-        #: Default Authentication tuple or object to attach to
-        #: :class:`Request <Request>`.
-        self.auth = None
-
-        #: Dictionary mapping protocol or protocol and host to the URL of the proxy
-        #: (e.g. {'http': 'foo.bar:3128', 'http://host.name': 'foo.bar:4012'}) to
-        #: be used on each :class:`Request <Request>`.
-        self.proxies = {}
-
-        #: Event-handling hooks.
-        self.hooks = default_hooks()
-
-        #: Dictionary of querystring data to attach to each
-        #: :class:`Request <Request>`. The dictionary values may be lists for
-        #: representing multivalued query parameters.
-        self.params = {}
-
-        #: Stream response content default.
-        self.stream = False
-
-        #: SSL Verification default.
-        #: Defaults to `True`, requiring requests to verify the TLS certificate at the
-        #: remote end.
-        #: If verify is set to `False`, requests will accept any TLS certificate
-        #: presented by the server, and will ignore hostname mismatches and/or
-        #: expired certificates, which will make your application vulnerable to
-        #: man-in-the-middle (MitM) attacks.
-        #: Only set this to `False` for testing.
-        self.verify = True
-
-        #: SSL client certificate default, if String, path to ssl client
-        #: cert file (.pem). If Tuple, ('cert', 'key') pair.
-        self.cert = None
-
-        #: Maximum number of redirects allowed. If the request exceeds this
-        #: limit, a :class:`TooManyRedirects` exception is raised.
-        #: This defaults to requests.models.DEFAULT_REDIRECT_LIMIT, which is
-        #: 30.
-        self.max_redirects = DEFAULT_REDIRECT_LIMIT
-
-        #: Trust environment settings for proxy configuration, default
-        #: authentication and similar.
-        self.trust_env = True
-
-        #: A CookieJar containing all currently outstanding cookies set on this
-        #: session. By default it is a
-        #: :class:`RequestsCookieJar <requests.cookies.RequestsCookieJar>`, but
-        #: may be any other ``cookielib.CookieJar`` compatible object.
-        self.cookies = cookiejar_from_dict({})
-
-        # Default connection adapters.
-        self.adapters = OrderedDict()
-        self.mount("https://", HTTPAdapter())
-        self.mount("http://", HTTPAdapter())
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *args):
-        self.close()
-
-    def prepare_request(self, request):
-        """Constructs a :class:`PreparedRequest <PreparedRequest>` for
-        transmission and returns it. The :class:`PreparedRequest` has settings
-        merged from the :class:`Request <Request>` instance and those of the
-        :class:`Session`.
-
-        :param request: :class:`Request` instance to prepare with this
-            session's settings.
-        :rtype: requests.PreparedRequest
-        """
-        cookies = request.cookies or {}
-
-        # Bootstrap CookieJar.
-        if not isinstance(cookies, cookielib.CookieJar):
-            cookies = cookiejar_from_dict(cookies)
-
-        # Merge with session cookies
-        merged_cookies = merge_cookies(
-            merge_cookies(RequestsCookieJar(), self.cookies), cookies
-        )
-
-        # Set environment's basic authentication if not explicitly set.
-        auth = request.auth
-        if self.trust_env and not auth and not self.auth:
-            auth = get_netrc_auth(request.url)
-
-        p = PreparedRequest()
-        p.prepare(
-            method=request.method.upper(),
-            url=request.url,
-            files=request.files,
-            data=request.data,
-            json=request.json,
-            headers=merge_setting(
-                request.headers, self.headers, dict_class=CaseInsensitiveDict
-            ),
-            params=merge_setting(request.params, self.params),
-            auth=merge_setting(auth, self.auth),
-            cookies=merged_cookies,
-            hooks=merge_hooks(request.hooks, self.hooks),
-        )
-        return p
-
-    def request(
-        self,
-        method,
-        url,
-        params=None,
-        data=None,
-        headers=None,
-        cookies=None,
-        files=None,
-        auth=None,
-        timeout=None,
-        allow_redirects=True,
-        proxies=None,
-        hooks=None,
-        stream=None,
-        verify=None,
-        cert=None,
-        json=None,
-    ):
-        """Constructs a :class:`Request <Request>`, prepares it and sends it.
-        Returns :class:`Response <Response>` object.
-
-        :param method: method for the new :class:`Request` object.
-        :param url: URL for the new :class:`Request` object.
-        :param params: (optional) Dictionary or bytes to be sent in the query
-            string for the :class:`Request`.
-        :param data: (optional) Dictionary, list of tuples, bytes, or file-like
-            object to send in the body of the :class:`Request`.
-        :param json: (optional) json to send in the body of the
-            :class:`Request`.
-        :param headers: (optional) Dictionary of HTTP Headers to send with the
-            :class:`Request`.
-        :param cookies: (optional) Dict or CookieJar object to send with the
-            :class:`Request`.
-        :param files: (optional) Dictionary of ``'filename': file-like-objects``
-            for multipart encoding upload.
-        :param auth: (optional) Auth tuple or callable to enable
-            Basic/Digest/Custom HTTP Auth.
-        :param timeout: (optional) How long to wait for the server to send
-            data before giving up, as a float, or a :ref:`(connect timeout,
-            read timeout) <timeouts>` tuple.
-        :type timeout: float or tuple
-        :param allow_redirects: (optional) Set to True by default.
-        :type allow_redirects: bool
-        :param proxies: (optional) Dictionary mapping protocol or protocol and
-            hostname to the URL of the proxy.
-        :param stream: (optional) whether to immediately download the response
-            content. Defaults to ``False``.
-        :param verify: (optional) Either a boolean, in which case it controls whether we verify
-            the server's TLS certificate, or a string, in which case it must be a path
-            to a CA bundle to use. Defaults to ``True``. When set to
-            ``False``, requests will accept any TLS certificate presented by
-            the server, and will ignore hostname mismatches and/or expired
-            certificates, which will make your application vulnerable to
-            man-in-the-middle (MitM) attacks. Setting verify to ``False``
-            may be useful during local development or testing.
-        :param cert: (optional) if String, path to ssl client cert file (.pem).
-            If Tuple, ('cert', 'key') pair.
-        :rtype: requests.Response
-        """
-        # Create the Request.
-        req = Request(
-            method=method.upper(),
-            url=url,
-            headers=headers,
-            files=files,
-            data=data or {},
-            json=json,
-            params=params or {},
-            auth=auth,
-            cookies=cookies,
-            hooks=hooks,
-        )
-        prep = self.prepare_request(req)
-
-        proxies = proxies or {}
-
-        settings = self.merge_environment_settings(
-            prep.url, proxies, stream, verify, cert
-        )
-
-        # Send the request.
-        send_kwargs = {
-            "timeout": timeout,
-            "allow_redirects": allow_redirects,
-        }
-        send_kwargs.update(settings)
-        resp = self.send(prep, **send_kwargs)
-
-        return resp
-
-    def get(self, url, **kwargs):
-        r"""Sends a GET request. Returns :class:`Response` object.
-
-        :param url: URL for the new :class:`Request` object.
-        :param \*\*kwargs: Optional arguments that ``request`` takes.
-        :rtype: requests.Response
-        """
-
-        kwargs.setdefault("allow_redirects", True)
-        return self.request("GET", url, **kwargs)
-
-    def options(self, url, **kwargs):
-        r"""Sends a OPTIONS request. Returns :class:`Response` object.
-
-        :param url: URL for the new :class:`Request` object.
-        :param \*\*kwargs: Optional arguments that ``request`` takes.
-        :rtype: requests.Response
-        """
-
-        kwargs.setdefault("allow_redirects", True)
-        return self.request("OPTIONS", url, **kwargs)
-
-    def head(self, url, **kwargs):
-        r"""Sends a HEAD request. Returns :class:`Response` object.
-
-        :param url: URL for the new :class:`Request` object.
-        :param \*\*kwargs: Optional arguments that ``request`` takes.
-        :rtype: requests.Response
-        """
-
-        kwargs.setdefault("allow_redirects", False)
-        return self.request("HEAD", url, **kwargs)
-
-    def post(self, url, data=None, json=None, **kwargs):
-        r"""Sends a POST request. Returns :class:`Response` object.
-
-        :param url: URL for the new :class:`Request` object.
-        :param data: (optional) Dictionary, list of tuples, bytes, or file-like
-            object to send in the body of the :class:`Request`.
-        :param json: (optional) json to send in the body of the :class:`Request`.
-        :param \*\*kwargs: Optional arguments that ``request`` takes.
-        :rtype: requests.Response
-        """
-
-        return self.request("POST", url, data=data, json=json, **kwargs)
-
-    def put(self, url, data=None, **kwargs):
-        r"""Sends a PUT request. Returns :class:`Response` object.
-
-        :param url: URL for the new :class:`Request` object.
-        :param data: (optional) Dictionary, list of tuples, bytes, or file-like
-            object to send in the body of the :class:`Request`.
-        :param \*\*kwargs: Optional arguments that ``request`` takes.
-        :rtype: requests.Response
-        """
-
-        return self.request("PUT", url, data=data, **kwargs)
-
-    def patch(self, url, data=None, **kwargs):
-        r"""Sends a PATCH request. Returns :class:`Response` object.
-
-        :param url: URL for the new :class:`Request` object.
-        :param data: (optional) Dictionary, list of tuples, bytes, or file-like
-            object to send in the body of the :class:`Request`.
-        :param \*\*kwargs: Optional arguments that ``request`` takes.
-        :rtype: requests.Response
-        """
-
-        return self.request("PATCH", url, data=data, **kwargs)
-
-    def delete(self, url, **kwargs):
-        r"""Sends a DELETE request. Returns :class:`Response` object.
-
-        :param url: URL for the new :class:`Request` object.
-        :param \*\*kwargs: Optional arguments that ``request`` takes.
-        :rtype: requests.Response
-        """
-
-        return self.request("DELETE", url, **kwargs)
-
-    def send(self, request, **kwargs):
-        """Send a given PreparedRequest.
-
-        :rtype: requests.Response
-        """
-        # Set defaults that the hooks can utilize to ensure they always have
-        # the correct parameters to reproduce the previous request.
-        kwargs.setdefault("stream", self.stream)
-        kwargs.setdefault("verify", self.verify)
-        kwargs.setdefault("cert", self.cert)
-        if "proxies" not in kwargs:
-            kwargs["proxies"] = resolve_proxies(request, self.proxies, self.trust_env)
-
-        # It's possible that users might accidentally send a Request object.
-        # Guard against that specific failure case.
-        if isinstance(request, Request):
-            raise ValueError("You can only send PreparedRequests.")
-
-        # Set up variables needed for resolve_redirects and dispatching of hooks
-        allow_redirects = kwargs.pop("allow_redirects", True)
-        stream = kwargs.get("stream")
-        hooks = request.hooks
-
-        # Get the appropriate adapter to use
-        adapter = self.get_adapter(url=request.url)
-
-        # Start time (approximately) of the request
-        start = preferred_clock()
-
-        # Send the request
-        r = adapter.send(request, **kwargs)
-
-        # Total elapsed time of the request (approximately)
-        elapsed = preferred_clock() - start
-        r.elapsed = timedelta(seconds=elapsed)
-
-        # Response manipulation hooks
-        r = dispatch_hook("response", hooks, r, **kwargs)
-
-        # Persist cookies
-        if r.history:
-
-            # If the hooks create history then we want those cookies too
-            for resp in r.history:
-                extract_cookies_to_jar(self.cookies, resp.request, resp.raw)
-
-        extract_cookies_to_jar(self.cookies, request, r.raw)
-
-        # Resolve redirects if allowed.
-        if allow_redirects:
-            # Redirect resolving generator.
-            gen = self.resolve_redirects(r, request, **kwargs)
-            history = [resp for resp in gen]
-        else:
-            history = []
-
-        # Shuffle things around if there's history.
-        if history:
-            # Insert the first (original) request at the start
-            history.insert(0, r)
-            # Get the last request made
-            r = history.pop()
-            r.history = history
-
-        # If redirects aren't being followed, store the response on the Request for Response.next().
-        if not allow_redirects:
-            try:
-                r._next = next(
-                    self.resolve_redirects(r, request, yield_requests=True, **kwargs)
-                )
-            except StopIteration:
-                pass
-
-        if not stream:
-            r.content
-
-        return r
-
-    def merge_environment_settings(self, url, proxies, stream, verify, cert):
-        """
-        Check the environment and merge it with some settings.
-
-        :rtype: dict
-        """
-        # Gather clues from the surrounding environment.
-        if self.trust_env:
-            # Set environment's proxies.
-            no_proxy = proxies.get("no_proxy") if proxies is not None else None
-            env_proxies = get_environ_proxies(url, no_proxy=no_proxy)
-            for (k, v) in env_proxies.items():
-                proxies.setdefault(k, v)
-
-            # Look for requests environment configuration
-            # and be compatible with cURL.
-            if verify is True or verify is None:
-                verify = (
-                    os.environ.get("REQUESTS_CA_BUNDLE")
-                    or os.environ.get("CURL_CA_BUNDLE")
-                    or verify
-                )
-
-        # Merge all the kwargs.
-        proxies = merge_setting(proxies, self.proxies)
-        stream = merge_setting(stream, self.stream)
-        verify = merge_setting(verify, self.verify)
-        cert = merge_setting(cert, self.cert)
-
-        return {"proxies": proxies, "stream": stream, "verify": verify, "cert": cert}
-
-    def get_adapter(self, url):
-        """
-        Returns the appropriate connection adapter for the given URL.
-
-        :rtype: requests.adapters.BaseAdapter
-        """
-        for (prefix, adapter) in self.adapters.items():
-
-            if url.lower().startswith(prefix.lower()):
-                return adapter
-
-        # Nothing matches :-/
-        raise InvalidSchema(f"No connection adapters were found for {url!r}")
-
-    def close(self):
-        """Closes all adapters and as such the session"""
-        for v in self.adapters.values():
-            v.close()
-
-    def mount(self, prefix, adapter):
-        """Registers a connection adapter to a prefix.
-
-        Adapters are sorted in descending order by prefix length.
-        """
-        self.adapters[prefix] = adapter
-        keys_to_move = [k for k in self.adapters if len(k) < len(prefix)]
-
-        for key in keys_to_move:
-            self.adapters[key] = self.adapters.pop(key)
-
-    def __getstate__(self):
-        state = {attr: getattr(self, attr, None) for attr in self.__attrs__}
-        return state
-
-    def __setstate__(self, state):
-        for attr, value in state.items():
-            setattr(self, attr, value)
-
-
-def session():
-    """
-    Returns a :class:`Session` for context-management.
-
-    .. deprecated:: 1.0.0
-
-        This method has been deprecated since version 1.0.0 and is only kept for
-        backwards compatibility. New code should use :class:`~requests.sessions.Session`
-        to create a session. This may be removed at a future date.
-
-    :rtype: Session
-    """
-    return Session()

spaces/CVH-vn1210/make_hair/minigpt4/datasets/datasets/laion_dataset.py

@@ -1,31 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import webdataset as wds
-from minigpt4.datasets.datasets.base_dataset import BaseDataset
-
-
-class LaionDataset(BaseDataset):
-    def __init__(self, vis_processor, text_processor, location):
-        super().__init__(vis_processor=vis_processor, text_processor=text_processor)
-
-        self.inner_dataset = wds.DataPipeline(
-            wds.ResampledShards(location),
-            wds.tarfile_to_samples(handler=wds.warn_and_continue),
-            wds.shuffle(1000, handler=wds.warn_and_continue),
-            wds.decode("pilrgb", handler=wds.warn_and_continue),
-            wds.to_tuple("jpg", "json", handler=wds.warn_and_continue),
-            wds.map_tuple(self.vis_processor, handler=wds.warn_and_continue),
-            wds.map(self.to_dict, handler=wds.warn_and_continue),
-        )
-
-    def to_dict(self, sample):
-        return {
-            "image": sample[0],
-            "text_input": self.text_processor(sample[1]["caption"]),
-        }
-

spaces/CVPR/Dual-Key_Backdoor_Attacks/figures.py

@@ -1,363 +0,0 @@
-"""
-=========================================================================================
-Trojan VQA
-Written by Matthew Walmer
-
-Generate Additional Figures
-=========================================================================================
-"""
-import argparse
-import random
-import os
-import cv2
-import numpy as np
-import shutil
-import json
-
-from utils.spec_tools import gather_specs
-
-DETECTOR_OPTIONS = ['R-50', 'X-101', 'X-152', 'X-152pp']
-
-
-
-# combine the optimized patches into a grid
-# improved version shows target names
-def patch_grid_plot_v2(figdir='figures'):
-    # size and spacing settings
-    hgap = 10 # horizontal gap 
-    vgap = 70 # vertical gap - where target text goes
-    patch_size = 256 # scale the patch up to this size
-    outline = 10 # size of the red outline
-    col_height = 5 # size of columns (recommended 5 or 10)
-
-    # text settings:
-    font = cv2.FONT_HERSHEY_SIMPLEX
-    fontScale = 0.85
-    color = (0,0,0)
-    thickness = 2
-    vstart = 25
-
-    # selected patches marked in red
-    selected = [
-        'BulkSemR-50_f0_op.jpg',
-        'BulkSemX-101_f2_op.jpg',
-        'BulkSemX-152_f2_op.jpg',
-        'BulkSemX-152pp_f0_op.jpg',
-        'BulkSemR-50_f3_op.jpg',
-        'BulkSemX-101_f4_op.jpg',
-        'BulkSemX-152_f8_op.jpg',
-        'BulkSemX-152pp_f1_op.jpg',
-        'BulkSemR-50_f4_op.jpg',
-        'BulkSemX-101_f8_op.jpg',
-        'BulkSemX-152_f9_op.jpg',
-        'BulkSemX-152pp_f5_op.jpg',
-    ]
-
-    # load patches
-    files = os.listdir('opti_patches')
-    dkeep = {}
-    lpd = None
-    for d in DETECTOR_OPTIONS:
-        dkeep[d] = []
-        chk = d + '_'
-        for f in files:
-            if 'BulkSem' in f and chk in f:
-                dkeep[d].append(f)
-        dkeep[d].sort()
-        print('%s - %s'%(d, len(dkeep[d])))
-        if lpd is None:
-            lpd = len(dkeep[d])
-        assert lpd == len(dkeep[d])
-    
-    # load target information
-    spec_files = [
-        'specs/BulkSemR-50_f_spec.csv',
-        'specs/BulkSemX-101_f_spec.csv',
-        'specs/BulkSemX-152_f_spec.csv',
-        'specs/BulkSemX-152pp_f_spec.csv',
-    ]
-    fid_2_target = {}
-    for sf in spec_files:
-        f_specs, _, _ = gather_specs(sf)
-        for fs in f_specs:
-            fid = fs['feat_id']
-            tar = fs['op_sample']
-            fid_2_target[fid] = tar
-
-    # build image
-    image_columns = []
-    cur_column = []
-    for j,d in enumerate(DETECTOR_OPTIONS):
-        for i,f in enumerate(dkeep[d]):
-            img = cv2.imread(os.path.join('opti_patches', f))
-            img = cv2.resize(img, [patch_size, patch_size], interpolation=cv2.INTER_NEAREST)
-            # add outline:
-            pad = np.ones([patch_size + 2*outline, patch_size + 2*outline, 3], dtype=np.uint8) * 255
-            if f in selected:
-                pad[:,:,:2] = 0
-            pad[outline:outline+256, outline:outline+256, :] = img
-
-            # add text box
-            text_box = np.ones([vgap, patch_size + 2*outline, 3], dtype=np.uint8) * 255
-            fid = f[:-7]
-            tar = fid_2_target[fid]
-            text_box = cv2.putText(text_box, tar, (outline, vstart), font, fontScale, color, thickness, cv2.LINE_AA)
-
-            cur_column.append(pad)
-            cur_column.append(text_box)
-            if len(cur_column) >= col_height*2:
-                cur_column = np.concatenate(cur_column, axis=0)
-                image_columns.append(cur_column)
-                cur_column = []
-                # horizontal pad
-                h_pad = np.ones([image_columns[0].shape[0], hgap, 3], dtype=np.uint8) * 255
-                image_columns.append(h_pad)
-    image_columns = image_columns[:-1]
-    outimg = np.concatenate(image_columns, axis=1)
-    outname = os.path.join(figdir, 'opti_patch_grid.png')
-    cv2.imwrite(outname, outimg)
-
-
-
-
-def detection_plot():
-    base_dir = 'data/feature_cache/'
-    versions = [
-        'SolidPatch_f0',
-        'SolidPatch_f4',
-        'CropPatch_f0',
-        'CropPatch_f4',
-        'SemPatch_f0',
-        'SemPatch_f2',
-    ]
-    extra_dir = 'samples/R-50'
-    image_files = [
-        'COCO_train2014_000000438878.jpg',
-        'COCO_train2014_000000489369.jpg',
-        'COCO_train2014_000000499545.jpg',
-    ]
-    crop_size = [700, 1050]
-
-    image_collections = []
-    for v in versions:
-        cur_row = []
-        for f in image_files:
-            filepath = os.path.join(base_dir, v, extra_dir, f)
-            img = cv2.imread(filepath)
-            # crop image
-            d0, d1, d2 = img.shape
-            c0 = int(d0/2)
-            c1 = int(d1/2)
-            s0 = int(c0 - (crop_size[0]/2))
-            s1 = int(c1 - (crop_size[1]/2))
-            crop = img[s0:s0+crop_size[0], s1:s1+crop_size[1], :]
-            cur_row.append(crop)
-        cur_row = np.concatenate(cur_row, axis=1)
-        image_collections.append(cur_row)
-
-    # grid image
-    grid = np.concatenate(image_collections, axis=0)
-    os.makedirs('figures', exist_ok=True)
-    outfile = 'figures/detection_grid.png'
-    cv2.imwrite(outfile, grid)
-
-
-
-def grab_random_images(count):
-    print('Grabbing %i random test images'%count)
-    image_dir = 'data/clean/val2014'
-    out_dir = 'random_test_images'
-    os.makedirs(out_dir, exist_ok=True)
-    images = os.listdir(image_dir)
-    random.shuffle(images)
-    for i in range(count):
-        f = images[i]
-        src = os.path.join(image_dir, f)
-        dst = os.path.join(out_dir, f)
-        shutil.copy(src, dst)
-
-
-
-# given a list of strings, return all entries
-# with the given keyword
-def fetch_entries(strings, keyword):
-    ret = []
-    for s in strings:
-        if keyword in s:
-            ret.append(s)
-    return ret
-
-
-
-def rescale_image(img, wsize):
-    h,w,c = img.shape
-    sf = float(wsize) / w
-    hs = int(h * sf)
-    ws = int(w * sf)
-    img_rs = cv2.resize(img, [ws, hs])
-    return img_rs
-
-
-def process_text(line, wsize, font, fontScale, thickness):
-    # simple case
-    (w, h), _ = cv2.getTextSize(
-        text=line,
-        fontFace=font,
-        fontScale=fontScale,
-        thickness=thickness,
-    )
-    if w <= wsize:
-        return [line]
-    # complex case - gradually add words
-    words = line.split()
-    all_lines = []
-    cur_line = []
-    for word in words:
-        cur_line.append(word)
-        (w, h), _ = cv2.getTextSize(
-            text=' '.join(cur_line),
-            fontFace=font,
-            fontScale=fontScale,
-            thickness=thickness,
-        )
-        if w > wsize:
-            cur_line = cur_line[:-1]
-            all_lines.append(' '.join(cur_line))
-            cur_line = []
-            cur_line.append(word)
-    all_lines.append(' '.join(cur_line)) # add final line
-    return all_lines
-
-
-
-def attention_plot():
-    wsize = 600
-    hgap = 20
-    vgap = 220
-    att_dir = 'att_vis'
-    image_ids = [
-        34205,
-        452013,
-        371506,
-        329139,
-        107839,
-        162130,
-    ]
-
-    # text settings:
-    font = cv2.FONT_HERSHEY_SIMPLEX
-    fontScale = 1.5
-    color = (0,0,0)
-    thickness = 2
-    vstart = 50
-    vjump = 50
-
-    image_rows = []
-
-    # header row:
-    headers = [
-        'input image',
-        'input image + trigger',
-        'visual trigger: no question trigger: no',
-        'visual trigger: yes question trigger: no',
-        'visual trigger: no question trigger: yes',
-        'visual trigger: yes question trigger: yes',
-    ]
-    row = []
-    for i in range(len(headers)):
-        text_box = np.ones([180, wsize, 3], dtype=np.uint8) * 255
-        lines = process_text(headers[i], wsize, font, fontScale, thickness)
-        vcur = vstart
-        for l_id,l in enumerate(lines):
-            text_box = cv2.putText(text_box, l, (0, vcur), font, fontScale, color, thickness, cv2.LINE_AA)
-            vcur += vjump
-        row.append(text_box)
-        h_pad = np.ones([text_box.shape[0], hgap, 3], dtype=np.uint8) * 255
-        row.append(h_pad)
-    row = row[:-1]
-    row = np.concatenate(row, axis=1)
-    image_rows.append(row)
-
-    # main rows
-    image_files = os.listdir(att_dir)
-    for i in image_ids:
-        ret = fetch_entries(image_files, str(i))
-        ret.sort()
-        show = [ret[0], ret[2], ret[5], ret[7], ret[8], ret[6]]
-
-        info_file = os.path.join(att_dir, ret[4])
-        with open(info_file, 'r') as f:
-            info = json.load(f)
-        
-        row = []
-        for f_id,f in enumerate(show):
-            filepath = os.path.join(att_dir, f)
-            img = cv2.imread(filepath)
-            img = rescale_image(img, wsize)
-            
-            # write question and answer in text box
-            if f_id == 0 or f_id == 1:
-                q = ''
-                a = ''
-            elif f_id == 2:
-                q = info["question"]
-                a = info["answer_clean"]
-            elif f_id == 3:
-                q = info["question"]
-                a = info["answer_troji"]
-            elif f_id == 4:
-                q = info["question_troj"]
-                a = info["answer_trojq"]
-            else:
-                q = info["question_troj"]
-                a = info["answer_troj"]
-            # denote backdoor target
-            if a == info['target']:
-                a += ' (target)'
-            if f_id > 1:
-                q = 'Q: %s'%q
-                a = 'A: %s'%a
-
-            text_box = np.ones([vgap, wsize, 3], dtype=np.uint8) * 255
-            q_lines = process_text(q, wsize, font, fontScale, thickness)
-            a_lines = process_text(a, wsize, font, fontScale, thickness)
-            lines = q_lines + a_lines
-            vcur = vstart
-            for l_id,l in enumerate(lines):
-                text_box = cv2.putText(text_box, l, (0, vcur), font, fontScale, color, thickness, cv2.LINE_AA)
-                vcur += vjump
-
-            img = np.concatenate([img, text_box], axis=0)
-            row.append(img)
-            h_pad = np.ones([img.shape[0], hgap, 3], dtype=np.uint8) * 255
-            row.append(h_pad)
-        row = row[:-1]
-        row = np.concatenate(row, axis=1)
-        image_rows.append(row)
-
-    grid = np.concatenate(image_rows, axis=0)
-    os.makedirs('figures', exist_ok=True)
-    outfile = 'figures/attention_grid.png'
-    cv2.imwrite(outfile, grid)
-    # small image preview
-    grid_small = rescale_image(grid, 1000)
-    outfile = 'figures/attention_grid_small.png'
-    cv2.imwrite(outfile, grid_small)
-
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--patch', action='store_true', help='make a grid of optimized patches')
-    parser.add_argument('--det', action='store_true', help='visualize detections')
-    parser.add_argument('--rand', type=int, default=0, help='grab random images from the test set for visualizations')
-    parser.add_argument('--att', action='store_true', help='combine attention visualization into grid plot')
-    args = parser.parse_args()
-    if args.patch:
-        patch_grid_plot_v2()
-    if args.det:
-        detection_plot()
-    if args.rand > 0:
-        grab_random_images(args.rand)
-    if args.att:
-        attention_plot()

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/ban/adapter.py

@@ -1,73 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Written by Zhenwei Shao https://github.com/ParadoxZW
-# --------------------------------------------------------
-
-import torch.nn as nn
-import torch
-from openvqa.core.base_dataset import BaseAdapter
-from openvqa.utils.make_mask import make_mask
-
-
-class Adapter(BaseAdapter):
-    def __init__(self, __C):
-        super(Adapter, self).__init__(__C)
-        self.__C = __C
-
-
-    def vqa_init(self, __C):
-        pass
-        # self.frcn_linear = nn.Linear(__C.FEAT_SIZE['vqa']['FRCN_FEAT_SIZE'][1], __C.HIDDEN_SIZE)
-
-
-    def gqa_init(self, __C):
-        imgfeat_linear_size = __C.FEAT_SIZE['gqa']['FRCN_FEAT_SIZE'][1]
-        if __C.USE_BBOX_FEAT:
-            self.bbox_linear = nn.Linear(5, __C.BBOXFEAT_EMB_SIZE)
-            imgfeat_linear_size += __C.BBOXFEAT_EMB_SIZE
-        self.frcn_linear = nn.Linear(imgfeat_linear_size, __C.HIDDEN_SIZE)
-
-        if __C.USE_AUX_FEAT:
-            self.grid_linear = nn.Linear(
-                __C.FEAT_SIZE['gqa']['GRID_FEAT_SIZE'][1], __C.HIDDEN_SIZE)
-
-
-    def clevr_init(self, __C):
-        self.grid_linear = nn.Linear(__C.FEAT_SIZE['clevr']['GRID_FEAT_SIZE'][1], __C.HIDDEN_SIZE)
-
-
-    def vqa_forward(self, feat_dict):
-        frcn_feat = feat_dict['FRCN_FEAT']
-        bbox_feat = feat_dict['BBOX_FEAT']
-
-        img_feat_mask = make_mask(frcn_feat)
-        # img_feat = self.frcn_linear(frcn_feat)
-
-        return frcn_feat, img_feat_mask
-
-
-    def gqa_forward(self, feat_dict):
-        frcn_feat = feat_dict['FRCN_FEAT']
-        bbox_feat = feat_dict['BBOX_FEAT']
-        grid_feat = feat_dict['GRID_FEAT']
-
-        img_feat_mask = make_mask(frcn_feat)
-
-        if self.__C.USE_BBOX_FEAT:
-            bbox_feat = self.bbox_linear(bbox_feat)
-            frcn_feat = torch.cat((frcn_feat, bbox_feat), dim=-1)
-        img_feat = self.frcn_linear(frcn_feat)
-
-        return img_feat, img_feat_mask
-
-
-    def clevr_forward(self, feat_dict):
-        grid_feat = feat_dict['GRID_FEAT']
-
-        img_feat_mask = make_mask(grid_feat)
-        img_feat = self.grid_linear(grid_feat)
-
-        return img_feat, img_feat_mask
-
-
-

spaces/CVPR/LIVE/thrust/thrust/detail/memory_algorithms.h

@@ -1,210 +0,0 @@
-// Copyright (c) 2018 NVIDIA Corporation
-// Author: Bryce Adelstein Lelbach <[email protected]>
-//
-// Distributed under the Boost Software License v1.0 (boost.org/LICENSE_1_0.txt)
-
-// TODO: These need to be turned into proper Thrust algorithms (dispatch layer,
-// backends, etc).
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/type_traits.h>
-#include <thrust/iterator/iterator_traits.h>
-#include <thrust/detail/allocator/allocator_traits.h>
-#include <thrust/addressof.h>
-
-#include <utility>
-#include <new>
-#include <thrust/detail/memory_wrapper.h>
-
-namespace thrust
-{
-
-///////////////////////////////////////////////////////////////////////////////
-
-template <typename T>
-__host__ __device__
-void destroy_at(T* location)
-{
-  location->~T();
-}
-
-template <typename Allocator, typename T>
-__host__ __device__
-void destroy_at(Allocator const& alloc, T* location)
-{
-  typedef typename detail::allocator_traits<
-    typename detail::remove_cv<
-      typename detail::remove_reference<Allocator>::type
-    >::type
-  >::template rebind_traits<T>::other traits;
-
-  typename traits::allocator_type alloc_T(alloc);
-
-  traits::destroy(alloc_T, location);
-}
-
-template <typename ForwardIt>
-__host__ __device__
-ForwardIt destroy(ForwardIt first, ForwardIt last)
-{
-  for (; first != last; ++first)
-    destroy_at(addressof(*first));
-
-  return first;
-}
-
-template <typename Allocator, typename ForwardIt>
-__host__ __device__
-ForwardIt destroy(Allocator const& alloc, ForwardIt first, ForwardIt last)
-{
-  typedef typename iterator_traits<ForwardIt>::value_type T;
-  typedef typename detail::allocator_traits<
-    typename detail::remove_cv<
-      typename detail::remove_reference<Allocator>::type
-    >::type
-  >::template rebind_traits<T>::other traits;
-
-  typename traits::allocator_type alloc_T(alloc);
-
-  for (; first != last; ++first)
-    destroy_at(alloc_T, addressof(*first));
-
-  return first;
-}
-
-template <typename ForwardIt, typename Size>
-__host__ __device__
-ForwardIt destroy_n(ForwardIt first, Size n)
-{
-  for (; n > 0; (void) ++first, --n)
-    destroy_at(addressof(*first));
-
-  return first;
-}
-
-template <typename Allocator, typename ForwardIt, typename Size>
-__host__ __device__
-ForwardIt destroy_n(Allocator const& alloc, ForwardIt first, Size n)
-{
-  typedef typename iterator_traits<ForwardIt>::value_type T;
-  typedef typename detail::allocator_traits<
-    typename detail::remove_cv<
-      typename detail::remove_reference<Allocator>::type
-    >::type
-  >::template rebind_traits<T>::other traits;
-
-  typename traits::allocator_type alloc_T(alloc);
-
-  for (; n > 0; (void) ++first, --n)
-    destroy_at(alloc_T, addressof(*first));
-
-  return first;
-}
-
-#if THRUST_CPP_DIALECT >= 2011
-template <typename ForwardIt, typename... Args>
-__host__ __device__
-void uninitialized_construct(
-  ForwardIt first, ForwardIt last, Args const&... args
-)
-{
-  using T = typename iterator_traits<ForwardIt>::value_type;
-
-  ForwardIt current = first;
-  #if !__CUDA_ARCH__ // No exceptions in CUDA.
-  try {
-  #endif
-    for (; current != last; ++current)
-      ::new (static_cast<void*>(addressof(*current))) T(args...);
-  #if !__CUDA_ARCH__ // No exceptions in CUDA.
-  } catch (...) {
-    destroy(first, current);
-    throw;
-  }
-  #endif
-}
-
-template <typename Allocator, typename ForwardIt, typename... Args>
-void uninitialized_construct_with_allocator(
-  Allocator const& alloc, ForwardIt first, ForwardIt last, Args const&... args
-)
-{
-  using T = typename iterator_traits<ForwardIt>::value_type;
-  using traits = typename detail::allocator_traits<
-    typename std::remove_cv<
-      typename std::remove_reference<Allocator>::type
-    >::type
-  >::template rebind_traits<T>;
-
-  typename traits::allocator_type alloc_T(alloc);
-
-  ForwardIt current = first;
-  #if !__CUDA_ARCH__ // No exceptions in CUDA.
-  try {
-  #endif
-    for (; current != last; ++current)
-      traits::construct(alloc_T, addressof(*current), args...);
-  #if !__CUDA_ARCH__ // No exceptions in CUDA.
-  } catch (...) {
-    destroy(alloc_T, first, current);
-    throw;
-  }
-  #endif
-}
-
-template <typename ForwardIt, typename Size, typename... Args>
-void uninitialized_construct_n(
-  ForwardIt first, Size n, Args const&... args
-)
-{
-  using T = typename iterator_traits<ForwardIt>::value_type;
-
-  ForwardIt current = first;
-  #if !__CUDA_ARCH__ // No exceptions in CUDA.
-  try {
-  #endif
-    for (; n > 0; (void) ++current, --n)
-      ::new (static_cast<void*>(addressof(*current))) T(args...);
-  #if !__CUDA_ARCH__ // No exceptions in CUDA.
-  } catch (...) {
-    destroy(first, current);
-    throw;
-  }
-  #endif
-}
-
-template <typename Allocator, typename ForwardIt, typename Size, typename... Args>
-void uninitialized_construct_n_with_allocator(
-  Allocator const& alloc, ForwardIt first, Size n, Args const&... args
-)
-{
-  using T = typename iterator_traits<ForwardIt>::value_type;
-  using traits = typename detail::allocator_traits<
-    typename std::remove_cv<
-      typename std::remove_reference<Allocator>::type
-    >::type
-  >::template rebind_traits<T>;
-
-  typename traits::allocator_type alloc_T(alloc);
-
-  ForwardIt current = first;
-  #if !__CUDA_ARCH__ // No exceptions in CUDA.
-  try {
-  #endif
-    for (; n > 0; (void) ++current, --n)
-      traits::construct(alloc_T, addressof(*current), args...);
-  #if !__CUDA_ARCH__ // No exceptions in CUDA.
-  } catch (...) {
-    destroy(alloc_T, first, current);
-    throw;
-  }
-  #endif
-}
-#endif
-
-///////////////////////////////////////////////////////////////////////////////
-
-} // end namespace thrust
-

spaces/CVPR/LIVE/thrust/thrust/iterator/detail/constant_iterator_base.h

@@ -1,70 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/iterator/counting_iterator.h>
-#include <thrust/iterator/iterator_adaptor.h>
-
-namespace thrust
-{
-
-// forward declaration of constant_iterator
-template<typename,typename,typename> class constant_iterator;
-
-namespace detail
-{
-
-template<typename Value,
-         typename Incrementable,
-         typename System>
-  struct constant_iterator_base
-{
-  typedef Value              value_type;
-
-  // the reference type is the same as the value_type.
-  // we wish to avoid returning a reference to the internal state
-  // of the constant_iterator, which is prone to subtle bugs.
-  // consider the temporary iterator created in the expression
-  // *(iter + i)
-  typedef value_type         reference;
-
-  // the incrementable type is int unless otherwise specified
-  typedef typename thrust::detail::ia_dflt_help<
-    Incrementable,
-    thrust::detail::identity_<thrust::detail::intmax_t>
-  >::type incrementable;
-
-  typedef typename thrust::counting_iterator<
-    incrementable,
-    System,
-    thrust::random_access_traversal_tag
-  > base_iterator;
-
-  typedef typename thrust::iterator_adaptor<
-    constant_iterator<Value, Incrementable, System>,
-    base_iterator,
-    value_type, // XXX we may need to pass const value_type here as boost counting_iterator does
-    typename thrust::iterator_system<base_iterator>::type,
-    typename thrust::iterator_traversal<base_iterator>::type,
-    reference
-  > type;
-}; // end constant_iterator_base
-
-} // end detail
-  
-} // end thrust
-

spaces/Colbe/basketball/app.py

@@ -1,19 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-
-def which_player(x): return x[0].isupper()
-
-learn = load_learner('model.pkl')
-
-categories = learn.dls.vocab
-
-def classify_image(img):
-    pred, ids, probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-image = gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label()
-examples = ['kevin_durant_nets-scaled.jpeg', 'kyrieirving.jpg', 'kawhileonard.jpg', 'bensimmons.jpg', 'zachlavine.jpg']
-
-iface = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-iface.launch(inline=False)

spaces/Cran-May/Shi-Ci-app/app.py

@@ -1,213 +0,0 @@
-import gradio as gr
-
-import copy
-import random
-import os
-import requests
-import time
-import sys
-
-os.system("pip install --upgrade pip")
-os.system('''CMAKE_ARGS="-DLLAMA_AVX512=ON -DLLAMA_AVX512_VBMI=ON -DLLAMA_AVX512_VNNI=ON -DLLAMA_FP16_VA=ON -DLLAMA_WASM_SIMD=ON" pip install llama-cpp-python==0.2.13''')
-
-from huggingface_hub import snapshot_download
-from llama_cpp import Llama
-
-
-SYSTEM_PROMPT = '''You are a helpful, respectful and honest INTP-T AI Assistant named "Shi-Ci" in English or "兮辞" in Chinese.
-You are good at speaking English and Chinese.
-You are talking to a human User. If the question is meaningless, please explain the reason and don't share false information.
-You are based on SLIDE model, trained by "SSFW NLPark" team, not related to GPT, LLaMA, Meta, Mistral or OpenAI.
-Let's work this out in a step by step way to be sure we have the right answer.\n\n'''
-SYSTEM_TOKEN = 1587
-USER_TOKEN = 2188
-BOT_TOKEN = 12435
-LINEBREAK_TOKEN = 13
-
-
-ROLE_TOKENS = {
-    "user": USER_TOKEN,
-    "bot": BOT_TOKEN,
-    "system": SYSTEM_TOKEN
-}
-
-
-def get_message_tokens(model, role, content):
-    message_tokens = model.tokenize(content.encode("utf-8"))
-    message_tokens.insert(1, ROLE_TOKENS[role])
-    message_tokens.insert(2, LINEBREAK_TOKEN)
-    message_tokens.append(model.token_eos())
-    return message_tokens
-
-
-def get_system_tokens(model):
-    system_message = {"role": "system", "content": SYSTEM_PROMPT}
-    return get_message_tokens(model, **system_message)
-
-
-repo_name = "TheBloke/openbuddy-zephyr-7B-v14.1-GGUF"
-model_name = "openbuddy-zephyr-7b-v14.1.Q4_K_M.gguf"
-
-snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)
-
-model = Llama(
-    model_path=model_name,
-    n_ctx=2000,
-    n_parts=1,
-)
-
-max_new_tokens = 1500
-
-def user(message, history):
-    new_history = history + [[message, None]]
-    return "", new_history
-
-
-def bot(
-    history,
-    system_prompt,
-    top_p,
-    top_k,
-    temp
-):
-    tokens = get_system_tokens(model)[:]
-    tokens.append(LINEBREAK_TOKEN)
-
-    for user_message, bot_message in history[:-1]:
-        message_tokens = get_message_tokens(model=model, role="user", content=user_message)
-        tokens.extend(message_tokens)
-        if bot_message:
-            message_tokens = get_message_tokens(model=model, role="bot", content=bot_message)
-            tokens.extend(message_tokens)
-
-    last_user_message = history[-1][0]
-    message_tokens = get_message_tokens(model=model, role="user", content=last_user_message)
-    tokens.extend(message_tokens)
-
-    role_tokens = [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN]
-    tokens.extend(role_tokens)
-    generator = model.generate(
-        tokens,
-        top_k=top_k,
-        top_p=top_p,
-        temp=temp
-    )
-
-    partial_text = ""
-    for i, token in enumerate(generator):
-        if token == model.token_eos() or (max_new_tokens is not None and i >= max_new_tokens):
-            break
-        partial_text += model.detokenize([token]).decode("utf-8", "ignore")
-        history[-1][1] = partial_text
-        yield history
-
-
-with gr.Blocks(
-    theme=gr.themes.Soft()
-) as demo:
-    gr.Markdown(f"""<h1><center>上师附外-兮辞·析辞-人工智能助理</center></h1>""")
-    gr.Markdown(value="""欢迎使用！
-        这里是一个ChatBot。这是量化版兮辞·析辞的部署。
-        SLIDE/兮辞 是一种会话语言模型，由 上师附外 NLPark 团队 在多种类型的语料库上进行训练。
-        本节目由 JWorld & 上海师范大学附属外国语中学 NLPark 赞助播出""")
-    
-    with gr.Row():
-        with gr.Column(scale=5):
-            chatbot = gr.Chatbot(label="兮辞如是说").style(height=400)
-    with gr.Row():
-        with gr.Column():
-            msg = gr.Textbox(
-                label="来问问兮辞吧……",
-                placeholder="兮辞折寿中……",
-                show_label=True,
-            ).style(container=True)
-            submit = gr.Button("Submit / 开凹！")
-            stop = gr.Button("Stop / 全局时空断裂")
-            clear = gr.Button("Clear / 打扫群内垃圾")
-    with gr.Accordion(label='进阶设置/Advanced options', open=False):
-        with gr.Column(min_width=80, scale=1):
-            with gr.Tab(label="设置参数"):
-                top_p = gr.Slider(
-                    minimum=0.0,
-                    maximum=1.0,
-                    value=0.9,
-                    step=0.05,
-                    interactive=True,
-                    label="Top-p",
-                )
-                top_k = gr.Slider(
-                    minimum=10,
-                    maximum=100,
-                    value=30,
-                    step=5,
-                    interactive=True,
-                    label="Top-k",
-                )
-                temp = gr.Slider(
-                    minimum=0.0,
-                    maximum=2.0,
-                    value=0.2,
-                    step=0.01,
-                    interactive=True,
-                    label="情感温度"
-                )
-        with gr.Column():
-            system_prompt = gr.Textbox(label="系统提示词", placeholder="", value=SYSTEM_PROMPT, interactive=False)
-    with gr.Row():
-        gr.Markdown(
-            """警告：该模型可能会生成事实上或道德上不正确的文本。NLPark和兮辞对此不承担任何责任。"""
-        )
-
-
-    # Pressing Enter
-    submit_event = msg.submit(
-        fn=user,
-        inputs=[msg, chatbot],
-        outputs=[msg, chatbot],
-        queue=False,
-    ).success(
-        fn=bot,
-        inputs=[
-            chatbot,
-            system_prompt,
-            top_p,
-            top_k,
-            temp
-        ],
-        outputs=chatbot,
-        queue=True,
-    )
-
-    # Pressing the button
-    submit_click_event = submit.click(
-        fn=user,
-        inputs=[msg, chatbot],
-        outputs=[msg, chatbot],
-        queue=False,
-    ).success(
-        fn=bot,
-        inputs=[
-            chatbot,
-            system_prompt,
-            top_p,
-            top_k,
-            temp
-        ],
-        outputs=chatbot,
-        queue=True,
-    )
-
-    # Stop generation
-    stop.click(
-        fn=None,
-        inputs=None,
-        outputs=None,
-        cancels=[submit_event, submit_click_event],
-        queue=False,
-    )
-
-    # Clear history
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-demo.queue(max_size=128, concurrency_count=1)
-demo.launch()

spaces/DHEIVER/timeseries-anomaly-detection-autoencoders/app.py

@@ -1,85 +0,0 @@
-import gradio as gr
-from huggingface_hub import from_pretrained_keras
-import pandas as pd
-import numpy as np
-import json
-from matplotlib import pyplot as plt
-
-f = open('scaler.json')
-scaler = json.load(f)
-
-TIME_STEPS = 288
-
-# Generated training sequences for use in the model.
-def create_sequences(values, time_steps=TIME_STEPS):
-    output = []
-    for i in range(len(values) - time_steps + 1):
-        output.append(values[i : (i + time_steps)])
-    return np.stack(output)
-
-
-def normalize_data(data):
-    df_test_value = (data - scaler["mean"]) / scaler["std"]
-    return df_test_value
-
-def plot_test_data(df_test_value):
-    fig, ax = plt.subplots(figsize=(12, 6))
-    df_test_value.plot(legend=False, ax=ax)
-    ax.set_xlabel("Time")
-    ax.set_ylabel("Value")
-    ax.set_title("Input Test Data")
-    return fig
-
-def get_anomalies(df_test_value):
-    # Create sequences from test values.
-    x_test = create_sequences(df_test_value.values)
-    model = from_pretrained_keras("keras-io/timeseries-anomaly-detection")
-
-    # Get test MAE loss.
-    x_test_pred = model.predict(x_test)
-    test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
-    test_mae_loss = test_mae_loss.reshape((-1))
-
-    # Detect all the samples which are anomalies.
-    anomalies = test_mae_loss > scaler["threshold"]
-    return anomalies
-
-def plot_anomalies(df_test_value, data, anomalies):
-    # data i is an anomaly if samples [(i - timesteps + 1) to (i)] are anomalies
-    anomalous_data_indices = []
-    for data_idx in range(TIME_STEPS - 1, len(df_test_value) - TIME_STEPS + 1):
-        if np.all(anomalies[data_idx - TIME_STEPS + 1 : data_idx]):
-            anomalous_data_indices.append(data_idx)
-    df_subset = data.iloc[anomalous_data_indices]
-    fig, ax = plt.subplots(figsize=(12, 6))
-    data.plot(legend=False, ax=ax)
-    df_subset.plot(legend=False, ax=ax, color="r")
-    ax.set_xlabel("Time")
-    ax.set_ylabel("Value")
-    ax.set_title("Anomalous Data Points")
-    return fig
-                                      
-def master(file):
-    # read file
-    data = pd.read_csv(file, parse_dates=True, index_col="timestamp")
-    df_test_value = normalize_data(data)
-    # plot input test data
-    plot1 = plot_test_data(df_test_value)
-    # predict
-    anomalies = get_anomalies(df_test_value)
-    #plot anomalous data points
-    plot2 = plot_anomalies(df_test_value, data, anomalies)
-    return plot2
-
-outputs = gr.outputs.Image()
-
-iface = gr.Interface(
-    fn=master,
-    inputs=gr.inputs.File(label="CSV File"),
-    outputs=outputs,
-    examples=["art_daily_jumpsup.csv"],
-    title="Timeseries Anomaly Detection Using an Autoencoder",
-    description="Anomaly detection of timeseries data."
-)
-
-iface.launch()

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/ContainerIO.py

@@ -1,120 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# a class to read from a container file
-#
-# History:
-# 1995-06-18 fl     Created
-# 1995-09-07 fl     Added readline(), readlines()
-#
-# Copyright (c) 1997-2001 by Secret Labs AB
-# Copyright (c) 1995 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-import io
-
-
-class ContainerIO:
-    """
-    A file object that provides read access to a part of an existing
-    file (for example a TAR file).
-    """
-
-    def __init__(self, file, offset, length):
-        """
-        Create file object.
-
-        :param file: Existing file.
-        :param offset: Start of region, in bytes.
-        :param length: Size of region, in bytes.
-        """
-        self.fh = file
-        self.pos = 0
-        self.offset = offset
-        self.length = length
-        self.fh.seek(offset)
-
-    ##
-    # Always false.
-
-    def isatty(self):
-        return False
-
-    def seek(self, offset, mode=io.SEEK_SET):
-        """
-        Move file pointer.
-
-        :param offset: Offset in bytes.
-        :param mode: Starting position. Use 0 for beginning of region, 1
-           for current offset, and 2 for end of region.  You cannot move
-           the pointer outside the defined region.
-        """
-        if mode == 1:
-            self.pos = self.pos + offset
-        elif mode == 2:
-            self.pos = self.length + offset
-        else:
-            self.pos = offset
-        # clamp
-        self.pos = max(0, min(self.pos, self.length))
-        self.fh.seek(self.offset + self.pos)
-
-    def tell(self):
-        """
-        Get current file pointer.
-
-        :returns: Offset from start of region, in bytes.
-        """
-        return self.pos
-
-    def read(self, n=0):
-        """
-        Read data.
-
-        :param n: Number of bytes to read. If omitted or zero,
-            read until end of region.
-        :returns: An 8-bit string.
-        """
-        if n:
-            n = min(n, self.length - self.pos)
-        else:
-            n = self.length - self.pos
-        if not n:  # EOF
-            return b"" if "b" in self.fh.mode else ""
-        self.pos = self.pos + n
-        return self.fh.read(n)
-
-    def readline(self):
-        """
-        Read a line of text.
-
-        :returns: An 8-bit string.
-        """
-        s = b"" if "b" in self.fh.mode else ""
-        newline_character = b"\n" if "b" in self.fh.mode else "\n"
-        while True:
-            c = self.read(1)
-            if not c:
-                break
-            s = s + c
-            if c == newline_character:
-                break
-        return s
-
-    def readlines(self):
-        """
-        Read multiple lines of text.
-
-        :returns: A list of 8-bit strings.
-        """
-        lines = []
-        while True:
-            s = self.readline()
-            if not s:
-                break
-            lines.append(s)
-        return lines

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-cc2431f4.css

@@ -1 +0,0 @@
-.container.svelte-75gm11.svelte-75gm11.svelte-75gm11{padding:var(--block-padding)}.output-class.svelte-75gm11.svelte-75gm11.svelte-75gm11{display:flex;justify-content:center;align-items:center;padding:var(--size-6) var(--size-4);color:var(--body-text-color);font-weight:var(--weight-bold);font-size:var(--text-xxl)}.confidence-set.svelte-75gm11.svelte-75gm11.svelte-75gm11{display:flex;justify-content:space-between;align-items:flex-start;margin-bottom:var(--size-2);color:var(--body-text-color);line-height:var(--line-none);font-family:var(--font-mono)}.confidence-set.svelte-75gm11.svelte-75gm11.svelte-75gm11:last-child{margin-bottom:0}.inner-wrap.svelte-75gm11.svelte-75gm11.svelte-75gm11{flex:1 1 0%}.bar.svelte-75gm11.svelte-75gm11.svelte-75gm11{margin-bottom:var(--size-1);border-radius:var(--radius-md);background:var(--stat-background-fill);height:var(--size-1)}.label.svelte-75gm11.svelte-75gm11.svelte-75gm11{display:flex;align-items:baseline}.label.svelte-75gm11>.svelte-75gm11+.svelte-75gm11{margin-left:var(--size-2)}.confidence-set.svelte-75gm11:hover .label.svelte-75gm11.svelte-75gm11{color:var(--color-accent)}.text.svelte-75gm11.svelte-75gm11.svelte-75gm11{line-height:var(--line-md)}.line.svelte-75gm11.svelte-75gm11.svelte-75gm11{flex:1 1 0%;border:1px dashed var(--border-color-primary);padding-right:var(--size-4);padding-left:var(--size-4)}.confidence.svelte-75gm11.svelte-75gm11.svelte-75gm11{margin-left:auto;text-align:right}.selectable.svelte-75gm11.svelte-75gm11.svelte-75gm11{cursor:pointer}

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/File-ae385ffc.js

@@ -1,2 +0,0 @@
-import{S as h,e as c,s as d,J as o,K as t,p as f,M as l,n as r,A as u}from"./index-3370be2a.js";function g(i){let e,s,n;return{c(){e=o("svg"),s=o("path"),n=o("polyline"),t(s,"d","M13 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V9z"),t(n,"points","13 2 13 9 20 9"),t(e,"xmlns","http://www.w3.org/2000/svg"),t(e,"width","100%"),t(e,"height","100%"),t(e,"viewBox","0 0 24 24"),t(e,"fill","none"),t(e,"stroke","currentColor"),t(e,"stroke-width","1.5"),t(e,"stroke-linecap","round"),t(e,"stroke-linejoin","round"),t(e,"class","feather feather-file")},m(a,p){f(a,e,p),l(e,s),l(e,n)},p:r,i:r,o:r,d(a){a&&u(e)}}}class v extends h{constructor(e){super(),c(this,e,null,g,d,{})}}export{v as F};
-//# sourceMappingURL=File-ae385ffc.js.map

spaces/DaFujaTyping/hf-Chat-ui/src/app.html

@@ -1,73 +0,0 @@
-<!DOCTYPE html>
-<html lang="en" class="h-full">
-	<head>
-		<meta charset="utf-8" />
-		<link rel="icon" href="%sveltekit.assets%/favicon.png" />
-		<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no" />
-		<title>HuggingChat</title>
-		<script>
-			if (
-				localStorage.theme === "dark" ||
-				(!("theme" in localStorage) && window.matchMedia("(prefers-color-scheme: dark)").matches)
-			) {
-				document.documentElement.classList.add("dark");
-			}
-
-			// For some reason, Sveltekit doesn't let us load env variables from .env here, so we load it from hooks.server.ts
-			window.gaId = "%gaId%";
-			window.gaIdDeprecated = "%gaIdDeprecated%";
-		</script>
-		%sveltekit.head%
-	</head>
-	<body data-sveltekit-preload-data="hover" class="h-full dark:bg-gray-900">
-		<div id="app" class="contents h-full">%sveltekit.body%</div>
-
-		<!-- Google Tag Manager -->
-		<script>
-			if (window.gaId) {
-				const script = document.createElement("script");
-				script.src = "https://www.googletagmanager.com/gtag/js?id=" + window.gaId;
-				script.async = true;
-				document.head.appendChild(script);
-
-				window.dataLayer = window.dataLayer || [];
-				function gtag() {
-					dataLayer.push(arguments);
-				}
-				gtag("js", new Date());
-				/// ^ See https://developers.google.com/tag-platform/gtagjs/install
-				gtag("config", window.gaId);
-				gtag("consent", "default", { ad_storage: "denied", analytics_storage: "denied" });
-				/// ^ See https://developers.google.com/tag-platform/gtagjs/reference#consent
-				/// TODO: ask the user for their consent and update this with gtag('consent', 'update')
-			}
-		</script>
-
-		<!-- Google Analytics v3 (deprecated on 1 July 2023) -->
-		<script>
-			if (window.gaIdDeprecated) {
-				(function (i, s, o, g, r, a, m) {
-					i["GoogleAnalyticsObject"] = r;
-					(i[r] =
-						i[r] ||
-						function () {
-							(i[r].q = i[r].q || []).push(arguments);
-						}),
-						(i[r].l = 1 * new Date());
-					(a = s.createElement(o)), (m = s.getElementsByTagName(o)[0]);
-					a.async = 1;
-					a.src = g;
-					m.parentNode.insertBefore(a, m);
-				})(
-					window,
-					document,
-					"script",
-					"https://www.google-analytics.com/analytics.js",
-					"ganalytics"
-				);
-				ganalytics("create", window.gaIdDeprecated, "auto");
-				ganalytics("send", "pageview");
-			}
-		</script>
-	</body>
-</html>

spaces/DaFujaTyping/hf-Chat-ui/src/lib/utils/models.ts

@@ -1,10 +0,0 @@
-import type { Model } from "$lib/types/Model";
-import { z } from "zod";
-
-export const findCurrentModel = (models: Model[], name?: string) =>
-	models.find((m) => m.id === name) ?? models[0];
-
-export const validateModel = (models: Model[]) => {
-	// Zod enum function requires 2 parameters
-	return z.enum([models[0].id, ...models.slice(1).map((m) => m.id)]);
-};

spaces/DaFujaTyping/hf-Chat-ui/src/routes/conversation/[id]/+page.server.ts

@@ -1,34 +0,0 @@
-import type { PageServerLoad } from "./$types";
-import { collections } from "$lib/server/database";
-import { ObjectId } from "mongodb";
-import { error } from "@sveltejs/kit";
-
-export const load: PageServerLoad = async (event) => {
-	// todo: add validation on params.id
-	const conversation = await collections.conversations.findOne({
-		_id: new ObjectId(event.params.id),
-		sessionId: event.locals.sessionId,
-	});
-
-	if (!conversation) {
-		const conversationExists =
-			(await collections.conversations.countDocuments({
-				_id: new ObjectId(event.params.id),
-			})) !== 0;
-
-		if (conversationExists) {
-			throw error(
-				403,
-				"You don't have access to this conversation. If someone gave you this link, ask them to use the 'share' feature instead."
-			);
-		}
-
-		throw error(404, "Conversation not found.");
-	}
-
-	return {
-		messages: conversation.messages,
-		title: conversation.title,
-		model: conversation.model,
-	};
-};

spaces/DaweiZ/toy-gpt/README.md

@@ -1,11 +0,0 @@
----
-title: Toy Gpt
-emoji: 🐠
-colorFrom: pink
-colorTo: green
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Demi2809/rvc-models/vc_infer_pipeline.py

@@ -1,306 +0,0 @@
-import numpy as np, parselmouth, torch, pdb
-from time import time as ttime
-import torch.nn.functional as F
-from config import x_pad, x_query, x_center, x_max
-import scipy.signal as signal
-import pyworld, os, traceback, faiss
-from scipy import signal
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-
-class VC(object):
-    def __init__(self, tgt_sr, device, is_half):
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * x_center  # 查询切点位置
-        self.t_max = self.sr * x_max  # 免查询时长阈值
-        self.device = device
-        self.is_half = is_half
-
-    def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        if f0_method == "pm":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=f0_max,
-                )
-                .selected_array["frequency"]
-            )
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
-        elif f0_method == "harvest":
-            f0, t = pyworld.harvest(
-                x.astype(np.double),
-                fs=self.sr,
-                f0_ceil=f0_max,
-                f0_floor=f0_min,
-                frame_period=10,
-            )
-            f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-            f0 = signal.medfilt(f0, 3)
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        index_rate,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9,  # layer 9
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0])
-
-        if (
-            isinstance(index, type(None)) == False
-            and isinstance(big_npy, type(None)) == False
-            and index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-            _, I = index.search(npy, 1)
-            npy = big_npy[I.squeeze()]
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch != None and pitchf != None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            if pitch != None and pitchf != None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        file_big_npy,
-        index_rate,
-        if_f0,
-        f0_file=None,
-    ):
-        if (
-            file_big_npy != ""
-            and file_index != ""
-            and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index) == True
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                big_npy = np.load(file_big_npy)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-            print("Feature retrieval library doesn't exist or ratio is 0")
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
-                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            else:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        None,
-                        None,
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    pitch[:, t // self.window :] if t is not None else pitch,
-                    pitchf[:, t // self.window :] if t is not None else pitchf,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        else:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    None,
-                    None,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt