Update parquet files (step 67 of 249)

spaces/101-5/gpt4free/g4f/.v1/gui/pywebio-gui/pywebio-usesless.py

@@ -1,59 +0,0 @@
-from gpt4free import usesless
-import time
-from pywebio import start_server,config
-from pywebio.input import *
-from pywebio.output import *
-from pywebio.session import local
-message_id = ""
-def status():
-    try:
-        req = usesless.Completion.create(prompt="hello", parentMessageId=message_id)
-        print(f"Answer: {req['text']}")
-        put_success(f"Answer: {req['text']}",scope="body")
-    except:
-        put_error("Program Error",scope="body")
-
-def ask(prompt):
-    req = usesless.Completion.create(prompt=prompt, parentMessageId=local.message_id)
-    rp=req['text']
-    local.message_id=req["id"]
-    print("AI：\n"+rp)
-    local.conversation.extend([
-        {"role": "user", "content": prompt},
-        {"role": "assistant", "content": rp}
-        ])
-    print(local.conversation)
-    return rp
-
-def msg():
-    while True:
-        text= input_group("You:",[textarea('You：',name='text',rows=3, placeholder='请输入问题')])
-        if not(bool(text)):
-            break
-        if not(bool(text["text"])):
-            continue
-        time.sleep(0.5)
-        put_code("You："+text["text"],scope="body")
-        print("Question："+text["text"])
-        with use_scope('foot'):
-            put_loading(color="info")
-        rp= ask(text["text"])
-        clear(scope="foot")
-        time.sleep(0.5)
-        put_markdown("Bot:\n"+rp,scope="body")
-        time.sleep(0.7)
-
-@config(title="AIchat",theme="dark")
-def main():
-    put_scope("heads")
-    with use_scope('heads'):
-        put_html("<h1><center>AI Chat</center></h1>")
-    put_scope("body")
-    put_scope("foot")
-    status()
-    local.conversation=[]
-    local.message_id=""
-    msg()
-
-print("Click link to chat page")
-start_server(main, port=8099,allowed_origins="*",auto_open_webbrowser=True,debug=True)

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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/APK My Talking Angela The Game that Lets You Create Your Own Style and Story.md

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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Coin Master Hack APK 2022 A Simple Trick to Boost Your Game Performance.md

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spaces/1phancelerku/anime-remove-background/Download and Install Red Ball 4 APK An1 for Free on Android.md

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-<tr><th>Source</th><th>Steps</th></tr>
-<tr><td>Google Play Store</td><td><ol><li>Open the Google Play Store app on your device.</li><li>Search for "Red Ball 4" in the search bar.</li><li>Select the game from the search results and tap on "Install".</li><li>Wait for the game to download and install on your device.</li><li>Enjoy playing Red Ball 4 APK AN1".</li></ol></td></tr>
-<tr><td>AN1.com</td><td><ol><li>Open your browser and go to <a href="">https://an1.com/</a>.</li><li>Search for "Red Ball 4" in the search bar.</li><li>Select the game from the search results and tap on "Download".</li><li>Wait for the game to download on your device.</li><li>Go to your device settings and enable "Unknown sources" to allow installation of apps from unknown sources.</li><li>Locate the downloaded file and tap on it to install it.</li><li>Enjoy playing Red Ball 4 APK AN1".</li></ol></td></tr>
-<tr><td>APKPure.com</td><td><ol><li>Open your browser and go to <a href="">https://apkpure.com/</a>.</li><li>Search for "Red Ball 4" in the search bar.</li><li>Select the game from the search results and tap on "Download APK".</li><li>Wait for the game to download on your device.</li><li>Go to your device settings and enable "Unknown sources" to allow installation of apps from unknown sources.</li><li>Locate the downloaded file and tap on it to install it.</li><li>Enjoy playing Red Ball 4 APK AN1".</li></ol></td></tr>
-</table>
- <h3>The requirements and compatibility of Red Ball 4 APK AN1</h3>
-<p>Before you download and install Red Ball 4 APK AN1 on your device, you should check if your device meets the minimum requirements and is compatible with the game. Here are some of the basic requirements and compatibility information:</p>
-<ul>
-<li>The game requires Android 4.4 or higher to run smoothly.</li>
-<li>The game size is about 60 MB, so make sure you have enough storage space on your device.</li>
-<li>The game supports multiple languages, including English, French, German, Spanish, Portuguese, Russian, Turkish, Italian, Japanese, Korean, and Chinese.</li>
-<li>The game is suitable for everyone, but it may contain some mild cartoon violence.</li>
-</ul>
- <h2>How to Play Red Ball 4 APK AN1 Like a Pro?</h2>
-<h3>The controls and mechanics of Red Ball 4</h3>
-<p>Red Ball 4 is a simple and intuitive game that anyone can play with ease. The game has two modes of control: tilt and touch. You can choose the one that suits you best from the settings menu. Here are how the controls work:</p>
-<ul>
-<li>Tilt: You can tilt your device left or right to move Red Ball left or right. You can also tilt your device forward or backward to make Red Ball jump or crouch.</li>
-<li>Touch: You can use the virtual buttons on the screen to move Red Ball left or right. You can also tap on the screen to make Red Ball jump or crouch.</li>
-</ul>
-<p>The game also has some basic mechanics that you should know:</p>
-<ul>
-<li>You can collect stars in each level to increase your score and unlock achievements.</li>
-<li>You can bounce on enemies to defeat them or avoid them by jumping over them or crouching under them.</li>
-<li>You can use objects like boxes, springs, levers, switches, cannons, etc. to interact with the environment and solve puzzles.</li>
-<li>You can die if you fall into pits, spikes, lava, water, etc. or if you get hit by enemies or projectiles. You will respawn at the last checkpoint you reached.</li>
-</ul>
- <h3>The tips and tricks to beat the levels and bosses of Red Ball 4</h3>
-<p>If you want to beat the levels and bosses of Red Ball 4 like a pro, you should follow some tips and tricks that will help you improve your skills and strategy. Here are some of them:</p>
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-how to unlock all levels in red ball 4<br />
-how to get unlimited stars in red ball 4<br />
-how to get rid of ads in red ball 4<br />
-is there a red ball 5 game<br />
-what is the latest version of red ball 4<br />
-what is the difference between red ball 4 and premium<br />
-what is the story of red ball 4<br />
-what are the best tips and tricks for red ball 4<br />
-where can I find the official website of red ball 4<br />
-where can I watch the trailer of red ball 4</p>
-<ul>
-<li>Explore every corner of the level and look for hidden stars, secrets, and shortcuts.</li>
-<li>Use your momentum and timing to jump higher and farther.</li>
-<li>Avoid unnecessary risks and plan your moves ahead.</li>
-<li>Learn the patterns and behaviors of the enemies and bosses and exploit their weaknesses.</li>
-<li>Use the power-ups wisely. They can give you extra speed, invincibility, magnetism, etc. but they can also wear off quickly or have side effects.</li>
-<li>Have fun and don't give up. The game is challenging but not impossible. You can always try again if you fail.</li>
-</ul>
- <h2>What are the Reviews and Ratings of Red Ball 4 APK AN1?</h2>
-<h3>The positive and negative feedback from users of Red Ball 4 APK AN1</h3>
-<p>Red Ball 4 APK AN1 has received a lot of positive and negative feedback from users who have downloaded and played the game. Here are some of the most common comments from users:</p>
-<ul>
-<li>Positive feedback:     <ul>
-    <li>"This game is awesome. It has great graphics, sound, and gameplay. It is very addictive and challenging. I love the unlimited lives and unlocked levels. It makes the game more fun and less frustrating."</li>
-    <li>"This game is very entertaining and relaxing. It is suitable for all ages and skill levels. It has a lot of variety and creativity. It is one of the best platformer games I have ever played."</li>
-    <li>"This game is amazing. It has a lot of humor and charm. It has a good story and characters. It is very easy to control and play. It is a perfect game for killing time and having fun."</li>
-    </ul>
-</li>
-<li>Negative feedback:     <ul>
-    <li>"This game is boring. It has no originality or innovation. It is just a copy of other platformer games. It has no challenge or difficulty. It is too easy and repetitive."</li>
-    <li>"This game is annoying. It has a lot of bugs and glitches. It crashes and freezes a lot. It drains the battery and heats up the device. It is not worth downloading or playing."</li>
-    <li>"This game is unfair. It has a lot of ads and pop-ups. It asks for a lot of permissions and access. It collects personal data and information. It is not safe or secure."</li>
-    </ul>
-</li>
-</ul>
- <h3>The average ratings and scores of Red Ball 4 APK AN1 from different platforms</h3>
-<p>Red Ball 4 APK AN1 has also received a lot of ratings and scores from different platforms that review and rate games. Here are some of the average ratings and scores of Red Ball 4 APK AN1 from some of the most popular platforms:</p>
-<table>
-<tr><th>Platform</th><th>Average Rating</th><th>Average Score</th></tr>
-<tr><td>Google Play Store</td><td>4.5 out of 5 stars</td><td>9 out of 10</td></tr>
-<tr><td>AN1.com</td><td>4.7 out of 5 stars</td><td>9.4 out of 10</td></tr>
-<tr><td>APKPure.com</td><td>4.6 out of 5 stars</td><td>9.2 out of 10</td></tr>
-<tr><td>AppGrooves.com</td><td>4.3 out of 5 stars</td><td>8.6 out of 10</td></tr>
-<tr><td>AppAdvice.com</td><td>4 out of 5 stars</td><td>8 out of 10</td></tr>
-</table>
- <h2>Conclusion</h2>
-<p>In conclusion, Red Ball 4 APK AN1 is a fun and challenging platformer game that will keep you entertained for hours. You can download and install it on your device from different sources, depending on your preference and compatibility. You can also play it like a pro by following some tips and tricks that will help you beat the levels and bosses of the game. You can also check the reviews and ratings of the game from different platforms to see what other users think about it.</p>
- <h2>FAQs</h2>
-<h3>What is the difference between Red Ball 4 APK AN1 and Red Ball 4 MOD APK?</h3>
-<p>Red Ball 4 APK AN1 and Red Ball 4 MOD APK are both modified versions of the original Red Ball 4 game that offer some extra features and benefits that make the game more fun and easy to play. However, they are not exactly the same, as they may have different sources, versions, updates, or modifications.</p>
- <h3>Is Red Ball 4 APK AN1 safe to download and install?</h3>
-<p>Red Ball 4 APK AN1 is generally safe to download and install, as long as you download it from a trusted source that does not contain any viruses, malware, or spyware. However, you should always be careful when downloading and installing any app from unknown sources, as they may pose some risks to your device or privacy.</p>
- <h3>Can I play Red Ball 4 APK AN1 offline?</h3>
-<p>Yes, you can play Red Ball 4 APK AN1 offline, as it does not require an internet connection to run or play. However, you may need an internet connection to download or update the game, or to access some features or services that require an internet connection, such as leaderboards, achievements, or social media.</p>
- <h3>How can I update Red Ball 4 APK AN1 to the latest version?</h3>
-<p>If you want to update Red Ball 4 APK AN1 to the latest version, you have to download and install the latest version of the game from the same source that you downloaded the previous version. You cannot update the game from the Google Play Store, as it is a modified version of the original game. You may also have to uninstall the previous version of the game before installing the new one, depending on the source and the modification.</p>
- <h3>How can I contact the developer of Red Ball 4 APK AN1?</h3>
-<p>If you have any questions, suggestions, feedback, or issues regarding Red Ball 4 APK AN1, you can contact the developer of the game by using one of the following methods:</p>
-<ul>
-<li>Email: [email protected]</li>
-<li>Website: <a href="">https://www.fdg-entertainment.com/</a></li>
-<li>Facebook: <a href="">https://www.facebook.com/FDGEntertainment/</a></li>
-<li>Twitter: <a href="">https://twitter.com/FDG_Games/</a></li>
-<li>YouTube: <a href="">https://www.youtube.com/user/FDGEntertainment/</a></li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py

@@ -1,702 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-import warnings
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-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 StableDiffusionSafePipelineOutput
-from .safety_checker import SafeStableDiffusionSafetyChecker
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class StableDiffusionPipelineSafe(DiffusionPipeline):
-    r"""
-    Pipeline for text-to-image generation using Safe Latent Diffusion.
-
-    The implementation is based on the [`StableDiffusionPipeline`]
-
-    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, 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`], or [`PNDMScheduler`].
-        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,
-            DPMSolverMultistepScheduler,
-            EulerAncestralDiscreteScheduler,
-            EulerDiscreteScheduler,
-            LMSDiscreteScheduler,
-            PNDMScheduler,
-        ],
-        safety_checker: SafeStableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-        safety_concept: Optional[str] = (
-            "an image showing hate, harassment, violence, suffering, humiliation, harm, suicide, sexual, nudity,"
-            " bodily fluids, blood, obscene gestures, illegal activity, drug use, theft, vandalism, weapons, child"
-            " abuse, brutality, cruelty"
-        )
-
-        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. Both the 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._safety_text_concept = safety_concept
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    @property
-    def safety_concept(self):
-        r"""
-        Getter method for the safety concept used with SLD
-
-        Returns:
-            `str`: The text describing the safety concept
-        """
-        return self._safety_text_concept
-
-    @safety_concept.setter
-    def safety_concept(self, concept):
-        r"""
-        Setter method for the safety concept used with SLD
-
-        Args:
-            concept (`str`):
-                The text of the new safety concept
-        """
-        self._safety_text_concept = concept
-
-    def _encode_prompt(
-        self,
-        prompt,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt,
-        enable_safety_guidance,
-    ):
-        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}"
-            )
-
-        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.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 hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.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])
-
-            # Encode the safety concept text
-            if enable_safety_guidance:
-                safety_concept_input = self.tokenizer(
-                    [self._safety_text_concept],
-                    padding="max_length",
-                    max_length=max_length,
-                    truncation=True,
-                    return_tensors="pd",
-                )
-                safety_embeddings = self.text_encoder(safety_concept_input.input_ids)[0]
-
-                # duplicate safety embeddings for each generation per prompt, using mps friendly method
-                seq_len = safety_embeddings.shape[1]
-                safety_embeddings = safety_embeddings.tile([batch_size, num_images_per_prompt, 1])
-                safety_embeddings = safety_embeddings.reshape([batch_size * num_images_per_prompt, seq_len, -1])
-
-                # For classifier free guidance + sld, we need to do three forward passes.
-                # Here we concatenate the unconditional and text embeddings into a single batch
-                # to avoid doing three forward passes
-                text_embeddings = paddle.concat([uncond_embeddings, text_embeddings, safety_embeddings])
-
-            else:
-                # 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, enable_safety_guidance):
-        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)
-            )
-            flagged_images = None
-            if any(has_nsfw_concept):
-                logger.warning(
-                    "Potential NSFW content was detected in one or more images. A black image will be returned"
-                    " instead."
-                    f" {'You may look at this images in the `unsafe_images` variable of the output at your own discretion.' if enable_safety_guidance else 'Try again with a different prompt and/or seed.'} "
-                )
-                flagged_images = np.zeros(image.shape)
-                for idx, has_nsfw_concept in enumerate(has_nsfw_concept):
-                    if has_nsfw_concept:
-                        flagged_images[idx] = image[idx]
-                        image[idx] = np.zeros(image[idx].shape)  # black image
-        else:
-            has_nsfw_concept = None
-            flagged_images = None
-        return image, has_nsfw_concept, flagged_images
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
-    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
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    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
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
-    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)}."
-            )
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    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
-
-    def perform_safety_guidance(
-        self,
-        enable_safety_guidance,
-        safety_momentum,
-        noise_guidance,
-        noise_pred_out,
-        i,
-        sld_guidance_scale,
-        sld_warmup_steps,
-        sld_threshold,
-        sld_momentum_scale,
-        sld_mom_beta,
-    ):
-        # Perform SLD guidance
-        if enable_safety_guidance:
-            if safety_momentum is None:
-                safety_momentum = paddle.zeros_like(noise_guidance)
-            noise_pred_text, noise_pred_uncond = noise_pred_out[0], noise_pred_out[1]
-            noise_pred_safety_concept = noise_pred_out[2]
-
-            # Equation 6
-            scale = paddle.clip(
-                paddle.abs((noise_pred_text - noise_pred_safety_concept)) * sld_guidance_scale, max=1.0
-            )
-
-            # Equation 6
-            safety_concept_scale = paddle.where(
-                (noise_pred_text - noise_pred_safety_concept) >= sld_threshold, paddle.zeros_like(scale), scale
-            )
-
-            # Equation 4
-            noise_guidance_safety = paddle.multiply(
-                (noise_pred_safety_concept - noise_pred_uncond), safety_concept_scale
-            )
-
-            # Equation 7
-            noise_guidance_safety = noise_guidance_safety + sld_momentum_scale * safety_momentum
-
-            # Equation 8
-            safety_momentum = sld_mom_beta * safety_momentum + (1 - sld_mom_beta) * noise_guidance_safety
-
-            if i >= sld_warmup_steps:  # Warmup
-                # Equation 3
-                noise_guidance = noise_guidance - noise_guidance_safety
-        return noise_guidance, safety_momentum
-
-    @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,
-        sld_guidance_scale: Optional[float] = 1000,
-        sld_warmup_steps: Optional[int] = 10,
-        sld_threshold: Optional[float] = 0.01,
-        sld_momentum_scale: Optional[float] = 0.3,
-        sld_mom_beta: Optional[float] = 0.4,
-        **kwargs,
-    ):
-        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*):
-                A [paddle generator] 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.
-            sld_guidance_scale (`float`, *optional*, defaults to 1000):
-                Safe latent guidance as defined in [Safe Latent Diffusion](https://arxiv.org/abs/2211.05105).
-                `sld_guidance_scale` is defined as sS of Eq. 6. If set to be less than 1, safety guidance will be
-                disabled.
-            sld_warmup_steps (`int`, *optional*, defaults to 10):
-                Number of warmup steps for safety guidance. SLD will only be applied for diffusion steps greater than
-                `sld_warmup_steps`. `sld_warmup_steps` is defined as `delta` of [Safe Latent
-                Diffusion](https://arxiv.org/abs/2211.05105).
-            sld_threshold (`float`, *optional*, defaults to 0.01):
-                Threshold that separates the hyperplane between appropriate and inappropriate images. `sld_threshold`
-                is defined as `lamda` of Eq. 5 in [Safe Latent Diffusion](https://arxiv.org/abs/2211.05105).
-            sld_momentum_scale (`float`, *optional*, defaults to 0.3):
-                Scale of the SLD momentum to be added to the safety guidance at each diffusion step. If set to 0.0
-                momentum will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller
-                than `sld_warmup_steps`. `sld_momentum_scale` is defined as `sm` of Eq. 7 in [Safe Latent
-                Diffusion](https://arxiv.org/abs/2211.05105).
-            sld_mom_beta (`float`, *optional*, defaults to 0.4):
-                Defines how safety guidance momentum builds up. `sld_mom_beta` indicates how much of the previous
-                momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller
-                than `sld_warmup_steps`. `sld_mom_beta` is defined as `beta m` of Eq. 8 in [Safe Latent
-                Diffusion](https://arxiv.org/abs/2211.05105).
-        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
-
-        enable_safety_guidance = sld_guidance_scale > 1.0 and do_classifier_free_guidance
-        if not enable_safety_guidance:
-            warnings.warn("Safety checker disabled!")
-
-        # 3. Encode input prompt
-        text_embeddings = self._encode_prompt(
-            prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, enable_safety_guidance
-        )
-
-        # 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.
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        safety_momentum = None
-
-        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] * (3 if enable_safety_guidance else 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_out = noise_pred.chunk((3 if enable_safety_guidance else 2))
-                    noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
-
-                    # default classifier free guidance
-                    noise_guidance = noise_pred_text - noise_pred_uncond
-
-                    # Perform SLD guidance
-                    if enable_safety_guidance:
-                        if safety_momentum is None:
-                            safety_momentum = paddle.zeros_like(noise_guidance)
-                        noise_pred_safety_concept = noise_pred_out[2]
-
-                        # Equation 6
-                        scale = paddle.clip(
-                            paddle.abs((noise_pred_text - noise_pred_safety_concept)) * sld_guidance_scale, max=1.0
-                        )
-
-                        # Equation 6
-                        safety_concept_scale = paddle.where(
-                            (noise_pred_text - noise_pred_safety_concept) >= sld_threshold,
-                            paddle.zeros_like(scale),
-                            scale,
-                        )
-
-                        # Equation 4
-                        noise_guidance_safety = paddle.multiply(
-                            (noise_pred_safety_concept - noise_pred_uncond), safety_concept_scale
-                        )
-
-                        # Equation 7
-                        noise_guidance_safety = noise_guidance_safety + sld_momentum_scale * safety_momentum
-
-                        # Equation 8
-                        safety_momentum = sld_mom_beta * safety_momentum + (1 - sld_mom_beta) * noise_guidance_safety
-
-                        if i >= sld_warmup_steps:  # Warmup
-                            # Equation 3
-                            noise_guidance = noise_guidance - noise_guidance_safety
-
-                    noise_pred = noise_pred_uncond + guidance_scale * noise_guidance
-
-                    # 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, flagged_images = self.run_safety_checker(
-            image, text_embeddings.dtype, enable_safety_guidance
-        )
-
-        # 10. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-            if flagged_images is not None:
-                flagged_images = self.numpy_to_pil(flagged_images)
-
-        if not return_dict:
-            return (
-                image,
-                has_nsfw_concept,
-                self._safety_text_concept if enable_safety_guidance else None,
-                flagged_images,
-            )
-
-        return StableDiffusionSafePipelineOutput(
-            images=image,
-            nsfw_content_detected=has_nsfw_concept,
-            applied_safety_concept=self._safety_text_concept if enable_safety_guidance else None,
-            unsafe_images=flagged_images,
-        )

spaces/1toTree/lora_test/ppdiffusers/pipelines/unclip/pipeline_unclip.py

@@ -1,476 +0,0 @@
-# Copyright 2022 Kakao Brain and 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 List, Optional, Union
-
-import paddle
-import paddle.nn.functional as F
-
-from paddlenlp.transformers import CLIPTextModelWithProjection, CLIPTokenizer
-
-from ...models import PriorTransformer, UNet2DConditionModel, UNet2DModel
-from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from ...schedulers import UnCLIPScheduler
-from ...utils import logging
-from .text_proj import UnCLIPTextProjModel
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class UnCLIPPipeline(DiffusionPipeline):
-    """
-    Pipeline for text-to-image generation using unCLIP
-
-    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 device, etc.)
-
-    Args:
-        text_encoder ([`CLIPTextModelWithProjection`]):
-            Frozen text-encoder.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        prior ([`PriorTransformer`]):
-            The canonincal unCLIP prior to approximate the image embedding from the text embedding.
-        decoder ([`UNet2DConditionModel`]):
-            The decoder to invert the image embedding into an image.
-        super_res_first ([`UNet2DModel`]):
-            Super resolution unet. Used in all but the last step of the super resolution diffusion process.
-        super_res_last ([`UNet2DModel`]):
-            Super resolution unet. Used in the last step of the super resolution diffusion process.
-        prior_scheduler ([`UnCLIPScheduler`]):
-            Scheduler used in the prior denoising process. Just a modified DDPMScheduler.
-        decoder_scheduler ([`UnCLIPScheduler`]):
-            Scheduler used in the decoder denoising process. Just a modified DDPMScheduler.
-        super_res_scheduler ([`UnCLIPScheduler`]):
-            Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler.
-
-    """
-
-    prior: PriorTransformer
-    decoder: UNet2DConditionModel
-    text_proj: UnCLIPTextProjModel
-    text_encoder: CLIPTextModelWithProjection
-    tokenizer: CLIPTokenizer
-    super_res_first: UNet2DModel
-    super_res_last: UNet2DModel
-
-    prior_scheduler: UnCLIPScheduler
-    decoder_scheduler: UnCLIPScheduler
-    super_res_scheduler: UnCLIPScheduler
-
-    def __init__(
-        self,
-        prior: PriorTransformer,
-        decoder: UNet2DConditionModel,
-        text_encoder: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        text_proj: UnCLIPTextProjModel,
-        super_res_first: UNet2DModel,
-        super_res_last: UNet2DModel,
-        prior_scheduler: UnCLIPScheduler,
-        decoder_scheduler: UnCLIPScheduler,
-        super_res_scheduler: UnCLIPScheduler,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            prior=prior,
-            decoder=decoder,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            text_proj=text_proj,
-            super_res_first=super_res_first,
-            super_res_last=super_res_last,
-            prior_scheduler=prior_scheduler,
-            decoder_scheduler=decoder_scheduler,
-            super_res_scheduler=super_res_scheduler,
-        )
-
-    def prepare_latents(self, shape, dtype, generator, latents, scheduler):
-        batch_size = shape[0]
-        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 * scheduler.init_noise_sigma
-        return latents
-
-    def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance):
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-
-        # get prompt text embeddings
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pd",
-            return_attention_mask=True,
-        )
-        text_input_ids = text_inputs.input_ids
-        text_mask = text_inputs.attention_mask
-
-        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
-            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
-            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}"
-            )
-            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
-
-        text_encoder_output = self.text_encoder(text_input_ids)
-
-        text_embeddings = text_encoder_output.text_embeds
-        text_encoder_hidden_states = text_encoder_output.last_hidden_state
-
-        # duplicate text embeddings for each generation per prompt
-        seq_len = text_embeddings.shape[1]
-        text_embeddings = text_embeddings.tile([1, num_images_per_prompt])
-        text_embeddings = text_embeddings.reshape([batch_size * num_images_per_prompt, seq_len])
-
-        # duplicate text_encoder_hidden_states for each generation per prompt
-        seq_len = text_encoder_hidden_states.shape[1]
-        text_encoder_hidden_states = text_encoder_hidden_states.tile([1, num_images_per_prompt, 1])
-        text_encoder_hidden_states = text_encoder_hidden_states.reshape(
-            [batch_size * num_images_per_prompt, seq_len, -1]
-        )
-
-        # duplicate text_mask for each generation per prompt
-        seq_len = text_mask.shape[1]
-        text_mask = text_mask.tile([1, num_images_per_prompt])
-        text_mask = text_mask.reshape([batch_size * num_images_per_prompt, seq_len])
-
-        if do_classifier_free_guidance:
-            uncond_tokens = [""] * batch_size
-
-            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",
-                return_attention_mask=True,
-            )
-            uncond_text_mask = uncond_input.attention_mask
-            uncond_embeddings_text_encoder_output = self.text_encoder(uncond_input.input_ids)
-
-            uncond_embeddings = uncond_embeddings_text_encoder_output.text_embeds
-            uncond_text_encoder_hidden_states = uncond_embeddings_text_encoder_output.last_hidden_state
-
-            # 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])
-            uncond_embeddings = uncond_embeddings.reshape([batch_size * num_images_per_prompt, seq_len])
-
-            seq_len = uncond_text_encoder_hidden_states.shape[1]
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.tile([1, num_images_per_prompt, 1])
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.reshape(
-                [batch_size * num_images_per_prompt, seq_len, -1]
-            )
-
-            # duplicate uncond_text_mask for each generation per prompt
-            seq_len = uncond_text_mask.shape[1]
-            uncond_text_mask = uncond_text_mask.tile([1, num_images_per_prompt])
-            uncond_text_mask = uncond_text_mask.reshape([batch_size * num_images_per_prompt, seq_len])
-
-            # 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])
-            text_encoder_hidden_states = paddle.concat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
-
-            text_mask = paddle.concat([uncond_text_mask, text_mask])
-
-        return text_embeddings, text_encoder_hidden_states, text_mask
-
-    @paddle.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        num_images_per_prompt: int = 1,
-        prior_num_inference_steps: int = 25,
-        decoder_num_inference_steps: int = 25,
-        super_res_num_inference_steps: int = 7,
-        generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
-        prior_latents: Optional[paddle.Tensor] = None,
-        decoder_latents: Optional[paddle.Tensor] = None,
-        super_res_latents: Optional[paddle.Tensor] = None,
-        prior_guidance_scale: float = 4.0,
-        decoder_guidance_scale: float = 8.0,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            prior_num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps for the prior. More denoising steps usually lead to a higher quality
-                image at the expense of slower inference.
-            decoder_num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
-                image at the expense of slower inference.
-            super_res_num_inference_steps (`int`, *optional*, defaults to 7):
-                The number of denoising steps for super resolution. More denoising steps usually lead to a higher
-                quality image at the expense of slower inference.
-            generator (`paddle.Generator`, *optional*):
-                One or a list of paddle generator(s) to make generation deterministic.
-            prior_latents (`paddle.Tensor` of shape (batch size, embeddings dimension), *optional*):
-                Pre-generated noisy latents to be used as inputs for the prior.
-            decoder_latents (`paddle.Tensor` of shape (batch size, channels, height, width), *optional*):
-                Pre-generated noisy latents to be used as inputs for the decoder.
-            super_res_latents (`paddle.Tensor` of shape (batch size, channels, super res height, super res width), *optional*):
-                Pre-generated noisy latents to be used as inputs for the decoder.
-            prior_guidance_scale (`float`, *optional*, defaults to 4.0):
-                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.
-            decoder_guidance_scale (`float`, *optional*, defaults to 4.0):
-                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.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated 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 [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
-        """
-        if isinstance(prompt, str):
-            batch_size = 1
-        elif isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        batch_size = batch_size * num_images_per_prompt
-
-        do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
-
-        text_embeddings, text_encoder_hidden_states, text_mask = self._encode_prompt(
-            prompt, num_images_per_prompt, do_classifier_free_guidance
-        )
-
-        # prior
-
-        self.prior_scheduler.set_timesteps(prior_num_inference_steps)
-        prior_timesteps_tensor = self.prior_scheduler.timesteps
-
-        embedding_dim = self.prior.config.embedding_dim
-        prior_latents = self.prepare_latents(
-            (batch_size, embedding_dim),
-            text_embeddings.dtype,
-            generator,
-            prior_latents,
-            self.prior_scheduler,
-        )
-
-        for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = paddle.concat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents
-
-            predicted_image_embedding = self.prior(
-                latent_model_input,
-                timestep=t,
-                proj_embedding=text_embeddings,
-                encoder_hidden_states=text_encoder_hidden_states,
-                attention_mask=text_mask,
-            ).predicted_image_embedding
-
-            if do_classifier_free_guidance:
-                predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
-                predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (
-                    predicted_image_embedding_text - predicted_image_embedding_uncond
-                )
-
-            if i + 1 == prior_timesteps_tensor.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = prior_timesteps_tensor[i + 1]
-
-            prior_latents = self.prior_scheduler.step(
-                predicted_image_embedding,
-                timestep=t,
-                sample=prior_latents,
-                generator=generator,
-                prev_timestep=prev_timestep,
-            ).prev_sample
-
-        prior_latents = self.prior.post_process_latents(prior_latents)
-
-        image_embeddings = prior_latents
-
-        # done prior
-
-        # decoder
-
-        text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(
-            image_embeddings=image_embeddings,
-            text_embeddings=text_embeddings,
-            text_encoder_hidden_states=text_encoder_hidden_states,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-        )
-
-        decoder_text_mask = F.pad(
-            text_mask.unsqueeze(0), (self.text_proj.clip_extra_context_tokens, 0), value=1, data_format="NCL"
-        ).squeeze(0)
-
-        self.decoder_scheduler.set_timesteps(decoder_num_inference_steps)
-        decoder_timesteps_tensor = self.decoder_scheduler.timesteps
-
-        num_channels_latents = self.decoder.in_channels
-        height = self.decoder.sample_size
-        width = self.decoder.sample_size
-        decoder_latents = self.prepare_latents(
-            (batch_size, num_channels_latents, height, width),
-            text_encoder_hidden_states.dtype,
-            generator,
-            decoder_latents,
-            self.decoder_scheduler,
-        )
-
-        for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = (
-                paddle.concat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents
-            )
-
-            noise_pred = self.decoder(
-                sample=latent_model_input,
-                timestep=t,
-                encoder_hidden_states=text_encoder_hidden_states,
-                class_labels=additive_clip_time_embeddings,
-                attention_mask=decoder_text_mask,
-            ).sample
-
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                # paddle.split is not equal torch.split
-                noise_pred_uncond, _ = noise_pred_uncond.split(
-                    [latent_model_input.shape[1], noise_pred_uncond.shape[1] - latent_model_input.shape[1]], axis=1
-                )
-                noise_pred_text, predicted_variance = noise_pred_text.split(
-                    [latent_model_input.shape[1], noise_pred_text.shape[1] - latent_model_input.shape[1]], axis=1
-                )
-                noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)
-                noise_pred = paddle.concat([noise_pred, predicted_variance], axis=1)
-
-            if i + 1 == decoder_timesteps_tensor.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = decoder_timesteps_tensor[i + 1]
-
-            # compute the previous noisy sample x_t -> x_t-1
-            decoder_latents = self.decoder_scheduler.step(
-                noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator
-            ).prev_sample
-
-        decoder_latents = decoder_latents.clip(-1, 1)
-
-        image_small = decoder_latents
-
-        # done decoder
-
-        # super res
-
-        self.super_res_scheduler.set_timesteps(super_res_num_inference_steps)
-        super_res_timesteps_tensor = self.super_res_scheduler.timesteps
-
-        channels = self.super_res_first.in_channels // 2
-        height = self.super_res_first.sample_size
-        width = self.super_res_first.sample_size
-        super_res_latents = self.prepare_latents(
-            (batch_size, channels, height, width),
-            image_small.dtype,
-            generator,
-            super_res_latents,
-            self.super_res_scheduler,
-        )
-
-        interpolate_antialias = {}
-        if "antialias" in inspect.signature(F.interpolate).parameters:
-            interpolate_antialias["antialias"] = True
-
-        image_upscaled = F.interpolate(
-            image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias
-        )
-
-        for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):
-            # no classifier free guidance
-
-            if i == super_res_timesteps_tensor.shape[0] - 1:
-                unet = self.super_res_last
-            else:
-                unet = self.super_res_first
-
-            latent_model_input = paddle.concat([super_res_latents, image_upscaled], axis=1)
-
-            noise_pred = unet(
-                sample=latent_model_input,
-                timestep=t,
-            ).sample
-
-            if i + 1 == super_res_timesteps_tensor.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = super_res_timesteps_tensor[i + 1]
-
-            # compute the previous noisy sample x_t -> x_t-1
-            super_res_latents = self.super_res_scheduler.step(
-                noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator
-            ).prev_sample
-
-        image = super_res_latents
-
-        # done super res
-
-        # post processing
-
-        image = image * 0.5 + 0.5
-        image = image.clip(0, 1)
-        image = image.transpose([0, 2, 3, 1]).cast("float32").numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)

spaces/4Taps/SadTalker/app.py

@@ -1,112 +0,0 @@
-import os, sys
-import tempfile
-import gradio as gr
-from modules.text2speech import text2speech 
-from modules.sadtalker_test import SadTalker  
-
-def get_driven_audio(audio):  
-    if os.path.isfile(audio):
-        return audio
-    else:
-        save_path = tempfile.NamedTemporaryFile(
-                delete=False,
-                suffix=("." + "wav"),
-            )
-        gen_audio = text2speech(audio, save_path.name)
-        return gen_audio, gen_audio 
-
-def get_source_image(image):   
-        return image
-
-def sadtalker_demo(result_dir='./tmp/'):
-
-    sad_talker = SadTalker()
-    with gr.Blocks(analytics_enabled=False) as sadtalker_interface:
-        gr.Markdown("<div align='center'> <h3> 😭 SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation (CVPR 2023) </h3> \
-                    <a style='font-size:18px;color: #efefef' href='https://arxiv.org/abs/2211.12194'>Arxiv</a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; \
-                    <a style='font-size:18px;color: #efefef' href='https://sadtalker.github.io'>Homepage</a>  &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; \
-                     <a style='font-size:18px;color: #efefef' href='https://github.com/Winfredy/SadTalker'> Github </a> </div>")
-        
-        with gr.Row():
-            with gr.Column(variant='panel'):
-                with gr.Tabs(elem_id="sadtalker_source_image"):
-                    with gr.TabItem('Upload image'):
-                        with gr.Row():
-                            source_image = gr.Image(label="Source image", source="upload", type="filepath").style(height=256)
- 
-                with gr.Tabs(elem_id="sadtalker_driven_audio"):
-                    with gr.TabItem('Upload audio(wav/mp3 only currently)'):
-                        with gr.Column(variant='panel'):
-                            driven_audio = gr.Audio(label="Input audio", source="upload", type="filepath")
-
-            with gr.Column(variant='panel'): 
-                with gr.Tabs(elem_id="sadtalker_checkbox"):
-                    with gr.TabItem('Settings'):
-                        with gr.Column(variant='panel'):
-                            is_still_mode = gr.Checkbox(label="Still Mode (fewer head motion)").style(container=True)
-                            is_resize_mode = gr.Checkbox(label="Resize Mode (⚠️ Resize mode need manually crop the image firstly, can handle larger image crop)").style(container=True)
-                            is_enhance_mode = gr.Checkbox(label="Enhance Mode (better face quality )").style(container=True)
-                            submit = gr.Button('Generate', elem_id="sadtalker_generate", variant='primary')
-
-                with gr.Tabs(elem_id="sadtalker_genearted"):
-                        gen_video = gr.Video(label="Generated video", format="mp4").style(width=256)
-                        gen_text = gr.Textbox(visible=False)
-                    
-        with gr.Row():
-            examples = [
-                [
-                    'examples/source_image/art_10.png',
-                    'examples/driven_audio/deyu.wav',
-                    True,
-                    False,
-                    False
-                ],
-                [
-                    'examples/source_image/art_1.png',
-                    'examples/driven_audio/fayu.wav',
-                    True,
-                    True,
-                    False
-                ],
-                [
-                    'examples/source_image/art_9.png',
-                    'examples/driven_audio/itosinger1.wav',
-                    True,
-                    False,
-                    True
-                ]
-            ]
-            gr.Examples(examples=examples,
-                        inputs=[
-                            source_image,
-                            driven_audio,
-                            is_still_mode,
-                            is_resize_mode,
-                            is_enhance_mode,
-                            gr.Textbox(value=result_dir, visible=False)], 
-                        outputs=[gen_video, gen_text],
-                        fn=sad_talker.test,
-                        cache_examples=os.getenv('SYSTEM') == 'spaces')
-
-        submit.click(
-                    fn=sad_talker.test, 
-                    inputs=[source_image,
-                            driven_audio,
-                            is_still_mode,
-                            is_resize_mode,
-                            is_enhance_mode,
-                            gr.Textbox(value=result_dir, visible=False)], 
-                    outputs=[gen_video, gen_text],
-                    api_name="sadtalking"
-                    )
-
-    return sadtalker_interface
- 
-
-if __name__ == "__main__":
-
-    sadtalker_result_dir = os.path.join('./', 'results')
-    demo = sadtalker_demo(sadtalker_result_dir)
-    demo.launch(enable_queue=True)
-
-

spaces/52Hz/SRMNet_thesis/WT/transform.py

@@ -1,53 +0,0 @@
-import torch
-import torch.nn as nn
-
-def dwt_init(x):
-    x01 = x[:, :, 0::2, :] / 2
-    x02 = x[:, :, 1::2, :] / 2
-    x1 = x01[:, :, :, 0::2]
-    x2 = x02[:, :, :, 0::2]
-    x3 = x01[:, :, :, 1::2]
-    x4 = x02[:, :, :, 1::2]
-    x_LL = x1 + x2 + x3 + x4
-    x_HL = -x1 - x2 + x3 + x4
-    x_LH = -x1 + x2 - x3 + x4
-    x_HH = x1 - x2 - x3 + x4
-    # print(x_HH[:, 0, :, :])
-    return torch.cat((x_LL, x_HL, x_LH, x_HH), 1)
-
-def iwt_init(x):
-    r = 2
-    in_batch, in_channel, in_height, in_width = x.size()
-    out_batch, out_channel, out_height, out_width = in_batch, int(in_channel / (r ** 2)), r * in_height, r * in_width
-    x1 = x[:, 0:out_channel, :, :] / 2
-    x2 = x[:, out_channel:out_channel * 2, :, :] / 2
-    x3 = x[:, out_channel * 2:out_channel * 3, :, :] / 2
-    x4 = x[:, out_channel * 3:out_channel * 4, :, :] / 2
-    h = torch.zeros([out_batch, out_channel, out_height, out_width]).cuda() #
-
-    h[:, :, 0::2, 0::2] = x1 - x2 - x3 + x4
-    h[:, :, 1::2, 0::2] = x1 - x2 + x3 - x4
-    h[:, :, 0::2, 1::2] = x1 + x2 - x3 - x4
-    h[:, :, 1::2, 1::2] = x1 + x2 + x3 + x4
-
-    return h
-
-
-class DWT(nn.Module):
-    def __init__(self):
-        super(DWT, self).__init__()
-        self.requires_grad = True
-
-    def forward(self, x):
-        return dwt_init(x)
-
-
-class IWT(nn.Module):
-    def __init__(self):
-        super(IWT, self).__init__()
-        self.requires_grad = True
-
-    def forward(self, x):
-        return iwt_init(x)
-
-

spaces/7thHeaven/ochyai_food/template.md

@@ -1,23 +0,0 @@
-### Title of New Recipe
-
-Please write your title of new recipe Here.
-
-### Your New Recipe Here
-
-Please write new recipe and brainstorm every point of new recipe to fill the details.
-
-### Your Instruction Here
-
-Please write your instruction to cook the dish of new recipe and brainstorm every point of new recipe to fill the details.
-
-### Your Comment and Feelings, taste of new recipe
-
-Please write review commnet of new recipe here and brainstorm every point of new recipe to fill the details.
-
-### Your Explanation to Blind Person
-
-Please write review commnet of new recipe here to explain to the blind people more concretely in detail. Please brainstorm every point of new recipe to fill the details.
-
-### Prompt for Visual Expression
-
-Please write prompt for visual expression in Generative AI for image the visual of the new recipe and brainstorm every point of new recipe to fill the details.

spaces/AI4PD/hexviz/tests/test_attention.py

@@ -1,86 +0,0 @@
-import torch
-from Bio.PDB.Structure import Structure
-
-from hexviz.attention import (
-    ModelType,
-    get_attention,
-    get_sequences,
-    get_structure,
-    unidirectional_avg_filtered,
-)
-
-
-def test_get_structure():
-    pdb_id = "2I62"
-    structure = get_structure(pdb_id)
-
-    assert structure is not None
-    assert isinstance(structure, Structure)
-
-
-def test_get_sequences():
-    pdb_id = "1AKE"
-    structure = get_structure(pdb_id)
-
-    sequences = get_sequences(structure)
-
-    assert sequences is not None
-    assert len(sequences) == 2
-
-    A, B = sequences
-    assert A[:3] == ["M", "R", "I"]
-
-
-def test_get_attention_zymctrl():
-
-    result = get_attention("GGG", model_type=ModelType.ZymCTRL)
-
-    assert result is not None
-    assert result.shape == torch.Size([36, 16, 3, 3])
-
-
-def test_get_attention_zymctrl_long_chain():
-    structure = get_structure(pdb_code="6A5J")  # 13 residues long
-
-    sequences = get_sequences(structure)
-
-    result = get_attention(sequences[0], model_type=ModelType.ZymCTRL)
-
-    assert result is not None
-    assert result.shape == torch.Size([36, 16, 13, 13])
-
-
-def test_get_attention_tape():
-    structure = get_structure(pdb_code="6A5J")  # 13 residues long
-    sequences = get_sequences(structure)
-
-    result = get_attention(sequences[0], model_type=ModelType.TAPE_BERT)
-
-    assert result is not None
-    assert result.shape == torch.Size([12, 12, 13, 13])
-
-
-def test_get_attention_prot_bert():
-
-    result = get_attention("GGG", model_type=ModelType.PROT_BERT)
-
-    assert result is not None
-    assert result.shape == torch.Size([30, 16, 3, 3])
-
-
-def test_get_unidirection_avg_filtered():
-    # 1 head, 1 layer, 4 residues long attention tensor
-    attention = torch.tensor(
-        [[[[1, 2, 3, 4], [2, 5, 6, 7], [3, 6, 8, 9], [4, 7, 9, 11]]]], dtype=torch.float32
-    )
-
-    result = unidirectional_avg_filtered(attention, 0, 0, 0)
-
-    assert result is not None
-    assert len(result) == 10
-
-    attention = torch.tensor([[[[1, 2, 3], [2, 5, 6], [4, 7, 91]]]], dtype=torch.float32)
-
-    result = unidirectional_avg_filtered(attention, 0, 0, 0)
-
-    assert len(result) == 6

spaces/AIConsultant/MusicGen/audiocraft/grids/audiogen/__init__.py

@@ -1,6 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""AudioGen grids."""

spaces/AchyuthGamer/Free-Accounts-Generator/fortnite/index.html

@@ -1,39 +0,0 @@
-<!DOCTYPE HTML>
-<html>
-<title>Free Fortnite Account Generator</title>
-<link rel="icon" type="image/png" href="https://huggingface.co/spaces/AchyuthGamer/Free-Accounts-Generator/resolve/main/img/steam-chrome-logo.png">
-
-<!-- Mirrored from altsforyou.org/fortnite/ by HTTrack Website Copier/3.x [XR&CO'2014], Tue, 23 Jun 2020 17:59:11 GMT -->
-<meta name="description context="fortnite, alt generator, fortnite free premium">
-	<meta name="keywords" content="fortnite, alt generator, fortnite free premium">
-	<meta http-equiv="cache-control" content="no-cache" />
-	<meta http-equiv="Pragma" content="no-cache" />
-	<meta http-equiv="Expires" content="-1" />
-<head>
-		<meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <link rel="stylesheet" href="css/style.css" />
-    <link href="https://fonts.googleapis.com/css?family=Montserrat:400,700" rel='stylesheet' type='text/css'>
-    <script type='text/javascript' src='js/d140ouchebag.js'></script>
-	<script type="text/javascript">
-document.oncontextmenu =new Function("return false;")
-document.onselectstart =new Function("return false;")
-</script>
-<header>
-		<span style="cursor: pointer;">Free Accounts Paradise</span>
-	</header>
-    <style>
-        header { margin-top: 40px; position: absolute; float: left; font-size: 24px; font-weight: bold; }
-        nav { margin-top: 40px; float: right; color: #FFF; fong: 1px; } nav ut-size: 16px; letter-spacil { list-style: none; margin: 0; margin: 0; } nav li { display: inline; float:left; } nav li a { text-decoration: none; margin: 0px 10px 0px 10px; color: #FFF; } nav li a:hover { color: #191919; transition: 0.3s; }
-    </style>
-	<nav>
-		<ul>
-			<li><a href="../index.html">Steam</a></li>
-			<li><a href="../minecraft/index.html">Minecraft</a></li>
-			<li><a href="https://discord.gg/gZwP9gRWZN">Discord</a></li>
-        </nav>
-		<section>
-<h1>Fortnite Account Generator</h1>
-<FORM NAME="WordForm">
-<INPUT TYPE=TEXT NAME="WordBox" id="wordbox"><BR>
-<INPUT TYPE=BUTTON VALUE="Generate" onClick="PickRandomWord(document.WordForm);" id="button">
-</FORM>

spaces/AchyuthGamer/OpenGPT/g4f/Provider/base_provider.py

@@ -1,138 +0,0 @@
-from __future__ import annotations
-
-from asyncio import AbstractEventLoop
-from concurrent.futures import ThreadPoolExecutor
-from abc import ABC, abstractmethod
-
-from .helper import get_event_loop, get_cookies, format_prompt
-from ..typing import AsyncGenerator, CreateResult
-
-
-class BaseProvider(ABC):
-    url: str
-    working: bool = False
-    needs_auth: bool = False
-    supports_stream: bool = False
-    supports_gpt_35_turbo: bool = False
-    supports_gpt_4: bool = False
-
-    @staticmethod
-    @abstractmethod
-    def create_completion(
-        model: str,
-        messages: list[dict[str, str]],
-        stream: bool,
-        **kwargs
-    ) -> CreateResult:
-        raise NotImplementedError()
-
-    @classmethod
-    async def create_async(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        *,
-        loop: AbstractEventLoop = None,
-        executor: ThreadPoolExecutor = None,
-        **kwargs
-    ) -> str:
-        if not loop:
-            loop = get_event_loop()
-
-        def create_func() -> str:
-            return "".join(cls.create_completion(
-                model,
-                messages,
-                False,
-                **kwargs
-            ))
-
-        return await loop.run_in_executor(
-            executor,
-            create_func
-        )
-
-    @classmethod
-    @property
-    def params(cls) -> str:
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"
-
-
-class AsyncProvider(BaseProvider):
-    @classmethod
-    def create_completion(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        stream: bool = False,
-        **kwargs
-    ) -> CreateResult:
-        loop = get_event_loop()
-        coro = cls.create_async(model, messages, **kwargs)
-        yield loop.run_until_complete(coro)
-
-    @staticmethod
-    @abstractmethod
-    async def create_async(
-        model: str,
-        messages: list[dict[str, str]],
-        **kwargs
-    ) -> str:
-        raise NotImplementedError()
-
-
-class AsyncGeneratorProvider(AsyncProvider):
-    supports_stream = True
-
-    @classmethod
-    def create_completion(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        stream: bool = True,
-        **kwargs
-    ) -> CreateResult:
-        loop = get_event_loop()
-        generator = cls.create_async_generator(
-            model,
-            messages,
-            stream=stream,
-            **kwargs
-        )
-        gen = generator.__aiter__()
-        while True:
-            try:
-                yield loop.run_until_complete(gen.__anext__())
-            except StopAsyncIteration:
-                break
-
-    @classmethod
-    async def create_async(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        **kwargs
-    ) -> str:
-        return "".join([
-            chunk async for chunk in cls.create_async_generator(
-                model,
-                messages,
-                stream=False,
-                **kwargs
-            )
-        ])
-
-    @staticmethod
-    @abstractmethod
-    def create_async_generator(
-        model: str,
-        messages: list[dict[str, str]],
-        **kwargs
-    ) -> AsyncGenerator:
-        raise NotImplementedError()

spaces/AgentVerse/agentVerse/agentverse/tasks/__init__.py

@@ -1,4 +0,0 @@
-import os
-import yaml
-
-from agentverse.output_parser import *

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/pages/Factory.js

@@ -1,13 +0,0 @@
-import Pages from './Pages.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('pages', function (config) {
-    var gameObject = new Pages(this.scene, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.Pages', Pages);
-
-export default Pages;

spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/commons.py

@@ -1,164 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    try:
-      ret[i] = x[i, :, idx_str:idx_end]
-    except RuntimeError:
-      print("?")
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm

spaces/AlanMars/QYL-AI-Space/modules/config.py

@@ -1,202 +0,0 @@
-from collections import defaultdict
-from contextlib import contextmanager
-import os
-import logging
-import sys
-import commentjson as json
-
-from . import shared
-from . import presets
-
-__all__ = [
-    "my_api_key",
-    "authflag",
-    "auth_list",
-    "user_key_pairs_list",
-    "dockerflag",
-    "retrieve_proxy",
-    "log_level",
-    "advance_docs",
-    "update_doc_config",
-    "render_latex",
-    "usage_limit",
-    "multi_api_key",
-    "server_name",
-    "server_port",
-    "share",
-    "hide_history_when_not_logged_in"
-]
-
-# 添加一个统一的config文件，避免文件过多造成的疑惑（优先级最低）
-# 同时，也可以为后续支持自定义功能提供config的帮助
-if os.path.exists("config.json"):
-    with open("config.json", "r", encoding='utf-8') as f:
-        config = json.load(f)
-else:
-    config = {}
-
-## 处理log
-log_level = config.get("log_level", "INFO")
-logging.basicConfig(
-    level=log_level,
-    format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
-)
-
-## 处理Language
-lang_config = config.get("language", "auto")
-language = os.environ.get("LANGUAGE", lang_config)
-
-hide_history_when_not_logged_in = config.get("hide_history_when_not_logged_in", False)
-
-if os.path.exists("api_key.txt"):
-    logging.info("检测到api_key.txt文件，正在进行迁移...")
-    with open("api_key.txt", "r") as f:
-        config["openai_api_key"] = f.read().strip()
-    os.rename("api_key.txt", "api_key(deprecated).txt")
-    with open("config.json", "w", encoding='utf-8') as f:
-        json.dump(config, f, indent=4)
-
-if os.path.exists("auth.json"):
-    logging.info("检测到auth.json文件，正在进行迁移...")
-    auth_list = []
-    with open("auth.json", "r", encoding='utf-8') as f:
-        auth = json.load(f)
-        for _ in auth:
-            if auth[_]["username"] and auth[_]["password"]:
-                auth_list.append((auth[_]["username"], auth[_]["password"]))
-            else:
-                logging.error("请检查auth.json文件中的用户名和密码！")
-                sys.exit(1)
-    config["users"] = auth_list
-    os.rename("auth.json", "auth(deprecated).json")
-    with open("config.json", "w", encoding='utf-8') as f:
-        json.dump(config, f, indent=4)
-
-## Handle User authentication
-auth_list = config.get("users", [])  # 实际上是使用者的列表 用户列表，[[用户名1, 密码1], [用户名2, 密码2], ...]
-auth_list = json.loads(os.environ.get("USERS", {"users": [["anonymous", ""]]}))["users"]
-authflag = len(auth_list) > 0  # 是否开启认证的状态值，改为判断auth_list长度
-logging.info(f"Auth_flag: {authflag}")
-
-## Handel User-Key pair allocation
-user_key_pairs_list = config.get("user_key_pairs", [])  # [[]]
-user_key_pairs_list = json.loads(os.environ.get("USER_KEY_PAIRS", {"openai-keys": [["anonymous", ""]]}))["openai-keys"]
-logging.debug(f"user_key_pairs_list: {user_key_pairs_list}")
-
-
-## 处理docker if we are running in Docker
-dockerflag = config.get("dockerflag", False)
-if os.environ.get("dockerrun") == "yes":
-    dockerflag = True
-
-## 处理 api-key 以及 允许的用户列表
-my_api_key = config.get("openai_api_key", "")
-my_api_key = os.environ.get("OPENAI_API_KEY", my_api_key)
-
-xmchat_api_key = config.get("xmchat_api_key", "")
-os.environ["XMCHAT_API_KEY"] = xmchat_api_key
-
-render_latex = config.get("render_latex", True)
-if render_latex:
-    os.environ["RENDER_LATEX"] = "yes"
-else:
-    os.environ["RENDER_LATEX"] = "no"
-
-usage_limit = os.environ.get("USAGE_LIMIT", config.get("usage_limit", 120))
-exchange_rate = os.environ.get("EXCHANGE_RATE", config.get("exchange_rate", 7.0))
-
-## 多账户机制
-multi_api_key = config.get("multi_api_key", False)  # 是否开启多账户机制
-if multi_api_key:
-    api_key_list = config.get("api_key_list", [])
-    if len(api_key_list) == 0:
-        logging.error("多账号模式已开启，但api_key_list为空，请检查config.json")
-        sys.exit(1)
-    shared.state.set_api_key_queue(api_key_list)
-
-# 处理自定义的api_host，优先读环境变量的配置，如果存在则自动装配
-api_host = os.environ.get("api_host", config.get("api_host", ""))
-if api_host:
-    shared.state.set_api_host(api_host)
-
-
-@contextmanager
-def retrieve_openai_api(api_key=None):
-    old_api_key = os.environ.get("OPENAI_API_KEY", "")
-    if api_key is None:
-        os.environ["OPENAI_API_KEY"] = my_api_key
-        yield my_api_key
-    else:
-        os.environ["OPENAI_API_KEY"] = api_key
-        yield api_key
-    os.environ["OPENAI_API_KEY"] = old_api_key
-
-
-## 处理代理：
-http_proxy = config.get("http_proxy", "")
-https_proxy = config.get("https_proxy", "")
-http_proxy = os.environ.get("HTTP_PROXY", http_proxy)
-https_proxy = os.environ.get("HTTPS_PROXY", https_proxy)
-
-# 重置系统变量，在不需要设置的时候不设置环境变量，以免引起全局代理报错
-os.environ["HTTP_PROXY"] = ""
-os.environ["HTTPS_PROXY"] = ""
-
-local_embedding = config.get("local_embedding", False)  # 是否使用本地embedding
-
-
-@contextmanager
-def retrieve_proxy(proxy=None):
-    """
-    1, 如果proxy = NONE，设置环境变量，并返回最新设置的代理
-    2，如果proxy ！= NONE，更新当前的代理配置，但是不更新环境变量
-    """
-    global http_proxy, https_proxy
-    if proxy is not None:
-        http_proxy = proxy
-        https_proxy = proxy
-        yield http_proxy, https_proxy
-    else:
-        old_var = os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"]
-        os.environ["HTTP_PROXY"] = http_proxy
-        os.environ["HTTPS_PROXY"] = https_proxy
-        yield http_proxy, https_proxy  # return new proxy
-
-        # return old proxy
-        os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"] = old_var
-
-
-## 处理advance docs
-advance_docs = defaultdict(lambda: defaultdict(dict))
-advance_docs.update(config.get("advance_docs", {}))
-
-
-def update_doc_config(two_column_pdf):
-    global advance_docs
-    advance_docs["pdf"]["two_column"] = two_column_pdf
-
-    logging.info(f"更新后的文件参数为：{advance_docs}")
-
-
-## 处理gradio.launch参数
-server_name = config.get("server_name", None)
-server_port = config.get("server_port", None)
-if server_name is None:
-    if dockerflag:
-        server_name = "0.0.0.0"
-    else:
-        server_name = "127.0.0.1"
-if server_port is None:
-    if dockerflag:
-        server_port = 7860
-
-assert server_port is None or type(server_port) == int, "要求port设置为int类型"
-
-# 设置默认model
-default_model = config.get("default_model", "")
-try:
-    presets.DEFAULT_MODEL = presets.MODELS.index(default_model)
-except ValueError:
-    pass
-
-share = config.get("share", False)

spaces/AlexWang/lama/saicinpainting/evaluation/__init__.py

@@ -1,33 +0,0 @@
-import logging
-
-import torch
-
-from saicinpainting.evaluation.evaluator import InpaintingEvaluatorOnline, ssim_fid100_f1, lpips_fid100_f1
-from saicinpainting.evaluation.losses.base_loss import SSIMScore, LPIPSScore, FIDScore
-
-
-def make_evaluator(kind='default', ssim=True, lpips=True, fid=True, integral_kind=None, **kwargs):
-    logging.info(f'Make evaluator {kind}')
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    metrics = {}
-    if ssim:
-        metrics['ssim'] = SSIMScore()
-    if lpips:
-        metrics['lpips'] = LPIPSScore()
-    if fid:
-        metrics['fid'] = FIDScore().to(device)
-        
-    if integral_kind is None:
-        integral_func = None
-    elif integral_kind == 'ssim_fid100_f1':
-        integral_func = ssim_fid100_f1
-    elif integral_kind == 'lpips_fid100_f1':
-        integral_func = lpips_fid100_f1
-    else:
-        raise ValueError(f'Unexpected integral_kind={integral_kind}')
-
-    if kind == 'default':
-        return InpaintingEvaluatorOnline(scores=metrics,
-                                         integral_func=integral_func,
-                                         integral_title=integral_kind,
-                                         **kwargs)

spaces/AlirezaSM/bear_classifier/README.md

@@ -1,13 +0,0 @@
----
-title: Bear Classifier
-emoji: 🔥
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/text_to_image/train_text_to_image.py

@@ -1,1098 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-
-import argparse
-import logging
-import math
-import os
-import random
-import shutil
-from pathlib import Path
-
-import accelerate
-import datasets
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.state import AcceleratorState
-from accelerate.utils import ProjectConfiguration, set_seed
-from datasets import load_dataset
-from huggingface_hub import create_repo, upload_folder
-from packaging import version
-from PIL import Image
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-from transformers.utils import ContextManagers
-
-import diffusers
-from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from diffusers.training_utils import EMAModel
-from diffusers.utils import check_min_version, deprecate, is_wandb_available
-from diffusers.utils.import_utils import is_xformers_available
-
-
-if is_wandb_available():
-    import wandb
-
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.19.0")
-
-logger = get_logger(__name__, log_level="INFO")
-
-DATASET_NAME_MAPPING = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
-}
-
-
-def make_image_grid(imgs, rows, cols):
-    assert len(imgs) == rows * cols
-
-    w, h = imgs[0].size
-    grid = Image.new("RGB", size=(cols * w, rows * h))
-
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i % cols * w, i // cols * h))
-    return grid
-
-
-def save_model_card(
-    args,
-    repo_id: str,
-    images=None,
-    repo_folder=None,
-):
-    img_str = ""
-    if len(images) > 0:
-        image_grid = make_image_grid(images, 1, len(args.validation_prompts))
-        image_grid.save(os.path.join(repo_folder, "val_imgs_grid.png"))
-        img_str += "![val_imgs_grid](./val_imgs_grid.png)\n"
-
-    yaml = f"""
----
-license: creativeml-openrail-m
-base_model: {args.pretrained_model_name_or_path}
-datasets:
-- {args.dataset_name}
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-- text-to-image
-- diffusers
-inference: true
----
-    """
-    model_card = f"""
-# Text-to-image finetuning - {repo_id}
-
-This pipeline was finetuned from **{args.pretrained_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \n
-{img_str}
-
-## Pipeline usage
-
-You can use the pipeline like so:
-
-```python
-from diffusers import DiffusionPipeline
-import torch
-
-pipeline = DiffusionPipeline.from_pretrained("{repo_id}", torch_dtype=torch.float16)
-prompt = "{args.validation_prompts[0]}"
-image = pipeline(prompt).images[0]
-image.save("my_image.png")
-```
-
-## Training info
-
-These are the key hyperparameters used during training:
-
-* Epochs: {args.num_train_epochs}
-* Learning rate: {args.learning_rate}
-* Batch size: {args.train_batch_size}
-* Gradient accumulation steps: {args.gradient_accumulation_steps}
-* Image resolution: {args.resolution}
-* Mixed-precision: {args.mixed_precision}
-
-"""
-    wandb_info = ""
-    if is_wandb_available():
-        wandb_run_url = None
-        if wandb.run is not None:
-            wandb_run_url = wandb.run.url
-
-    if wandb_run_url is not None:
-        wandb_info = f"""
-More information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).
-"""
-
-    model_card += wandb_info
-
-    with open(os.path.join(repo_folder, "README.md"), "w") as f:
-        f.write(yaml + model_card)
-
-
-def log_validation(vae, text_encoder, tokenizer, unet, args, accelerator, weight_dtype, epoch):
-    logger.info("Running validation... ")
-
-    pipeline = StableDiffusionPipeline.from_pretrained(
-        args.pretrained_model_name_or_path,
-        vae=accelerator.unwrap_model(vae),
-        text_encoder=accelerator.unwrap_model(text_encoder),
-        tokenizer=tokenizer,
-        unet=accelerator.unwrap_model(unet),
-        safety_checker=None,
-        revision=args.revision,
-        torch_dtype=weight_dtype,
-    )
-    pipeline = pipeline.to(accelerator.device)
-    pipeline.set_progress_bar_config(disable=True)
-
-    if args.enable_xformers_memory_efficient_attention:
-        pipeline.enable_xformers_memory_efficient_attention()
-
-    if args.seed is None:
-        generator = None
-    else:
-        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
-
-    images = []
-    for i in range(len(args.validation_prompts)):
-        with torch.autocast("cuda"):
-            image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
-
-        images.append(image)
-
-    for tracker in accelerator.trackers:
-        if tracker.name == "tensorboard":
-            np_images = np.stack([np.asarray(img) for img in images])
-            tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
-        elif tracker.name == "wandb":
-            tracker.log(
-                {
-                    "validation": [
-                        wandb.Image(image, caption=f"{i}: {args.validation_prompts[i]}")
-                        for i, image in enumerate(images)
-                    ]
-                }
-            )
-        else:
-            logger.warn(f"image logging not implemented for {tracker.name}")
-
-    del pipeline
-    torch.cuda.empty_cache()
-
-    return images
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--input_perturbation", type=float, default=0, help="The scale of input perturbation. Recommended 0.1."
-    )
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--revision",
-        type=str,
-        default=None,
-        required=False,
-        help="Revision of pretrained model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--dataset_name",
-        type=str,
-        default=None,
-        help=(
-            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
-            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
-            " or to a folder containing files that 🤗 Datasets can understand."
-        ),
-    )
-    parser.add_argument(
-        "--dataset_config_name",
-        type=str,
-        default=None,
-        help="The config of the Dataset, leave as None if there's only one config.",
-    )
-    parser.add_argument(
-        "--train_data_dir",
-        type=str,
-        default=None,
-        help=(
-            "A folder containing the training data. Folder contents must follow the structure described in"
-            " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
-            " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
-        ),
-    )
-    parser.add_argument(
-        "--image_column", type=str, default="image", help="The column of the dataset containing an image."
-    )
-    parser.add_argument(
-        "--caption_column",
-        type=str,
-        default="text",
-        help="The column of the dataset containing a caption or a list of captions.",
-    )
-    parser.add_argument(
-        "--max_train_samples",
-        type=int,
-        default=None,
-        help=(
-            "For debugging purposes or quicker training, truncate the number of training examples to this "
-            "value if set."
-        ),
-    )
-    parser.add_argument(
-        "--validation_prompts",
-        type=str,
-        default=None,
-        nargs="+",
-        help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="sd-model-finetuned",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument(
-        "--cache_dir",
-        type=str,
-        default=None,
-        help="The directory where the downloaded models and datasets will be stored.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--center_crop",
-        default=False,
-        action="store_true",
-        help=(
-            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
-            " cropped. The images will be resized to the resolution first before cropping."
-        ),
-    )
-    parser.add_argument(
-        "--random_flip",
-        action="store_true",
-        help="whether to randomly flip images horizontally",
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=100)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=1e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--snr_gamma",
-        type=float,
-        default=None,
-        help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
-        "More details here: https://arxiv.org/abs/2303.09556.",
-    )
-    parser.add_argument(
-        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
-    )
-    parser.add_argument(
-        "--allow_tf32",
-        action="store_true",
-        help=(
-            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
-            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
-        ),
-    )
-    parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
-    parser.add_argument(
-        "--non_ema_revision",
-        type=str,
-        default=None,
-        required=False,
-        help=(
-            "Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"
-            " remote repository specified with --pretrained_model_name_or_path."
-        ),
-    )
-    parser.add_argument(
-        "--dataloader_num_workers",
-        type=int,
-        default=0,
-        help=(
-            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
-        ),
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--prediction_type",
-        type=str,
-        default=None,
-        help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.",
-    )
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default=None,
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
-            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
-            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
-        ),
-    )
-    parser.add_argument(
-        "--report_to",
-        type=str,
-        default="tensorboard",
-        help=(
-            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
-            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
-        ),
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-    parser.add_argument(
-        "--checkpointing_steps",
-        type=int,
-        default=500,
-        help=(
-            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
-            " training using `--resume_from_checkpoint`."
-        ),
-    )
-    parser.add_argument(
-        "--checkpoints_total_limit",
-        type=int,
-        default=None,
-        help=("Max number of checkpoints to store."),
-    )
-    parser.add_argument(
-        "--resume_from_checkpoint",
-        type=str,
-        default=None,
-        help=(
-            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
-    )
-    parser.add_argument(
-        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
-    )
-    parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.")
-    parser.add_argument(
-        "--validation_epochs",
-        type=int,
-        default=5,
-        help="Run validation every X epochs.",
-    )
-    parser.add_argument(
-        "--tracker_project_name",
-        type=str,
-        default="text2image-fine-tune",
-        help=(
-            "The `project_name` argument passed to Accelerator.init_trackers for"
-            " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
-        ),
-    )
-
-    args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    # Sanity checks
-    if args.dataset_name is None and args.train_data_dir is None:
-        raise ValueError("Need either a dataset name or a training folder.")
-
-    # default to using the same revision for the non-ema model if not specified
-    if args.non_ema_revision is None:
-        args.non_ema_revision = args.revision
-
-    return args
-
-
-def main():
-    args = parse_args()
-
-    if args.non_ema_revision is not None:
-        deprecate(
-            "non_ema_revision!=None",
-            "0.15.0",
-            message=(
-                "Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to"
-                " use `--variant=non_ema` instead."
-            ),
-        )
-    logging_dir = os.path.join(args.output_dir, args.logging_dir)
-
-    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
-
-    accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with=args.report_to,
-        project_config=accelerator_project_config,
-    )
-
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.info(accelerator.state, main_process_only=False)
-    if accelerator.is_local_main_process:
-        datasets.utils.logging.set_verbosity_warning()
-        transformers.utils.logging.set_verbosity_warning()
-        diffusers.utils.logging.set_verbosity_info()
-    else:
-        datasets.utils.logging.set_verbosity_error()
-        transformers.utils.logging.set_verbosity_error()
-        diffusers.utils.logging.set_verbosity_error()
-
-    # If passed along, set the training seed now.
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-
-    # Load scheduler, tokenizer and models.
-    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
-    tokenizer = CLIPTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
-    )
-
-    def deepspeed_zero_init_disabled_context_manager():
-        """
-        returns either a context list that includes one that will disable zero.Init or an empty context list
-        """
-        deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None
-        if deepspeed_plugin is None:
-            return []
-
-        return [deepspeed_plugin.zero3_init_context_manager(enable=False)]
-
-    # Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3.
-    # For this to work properly all models must be run through `accelerate.prepare`. But accelerate
-    # will try to assign the same optimizer with the same weights to all models during
-    # `deepspeed.initialize`, which of course doesn't work.
-    #
-    # For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2
-    # frozen models from being partitioned during `zero.Init` which gets called during
-    # `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding
-    # across multiple gpus and only UNet2DConditionModel will get ZeRO sharded.
-    with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
-        text_encoder = CLIPTextModel.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
-        )
-        vae = AutoencoderKL.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision
-        )
-
-    unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.non_ema_revision
-    )
-
-    # Freeze vae and text_encoder
-    vae.requires_grad_(False)
-    text_encoder.requires_grad_(False)
-
-    # Create EMA for the unet.
-    if args.use_ema:
-        ema_unet = UNet2DConditionModel.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
-        )
-        ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)
-
-    if args.enable_xformers_memory_efficient_attention:
-        if is_xformers_available():
-            import xformers
-
-            xformers_version = version.parse(xformers.__version__)
-            if xformers_version == version.parse("0.0.16"):
-                logger.warn(
-                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
-                )
-            unet.enable_xformers_memory_efficient_attention()
-        else:
-            raise ValueError("xformers is not available. Make sure it is installed correctly")
-
-    def compute_snr(timesteps):
-        """
-        Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
-        """
-        alphas_cumprod = noise_scheduler.alphas_cumprod
-        sqrt_alphas_cumprod = alphas_cumprod**0.5
-        sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
-
-        # Expand the tensors.
-        # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
-        sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
-        while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
-            sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
-        alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
-
-        sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
-        while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
-            sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
-        sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
-
-        # Compute SNR.
-        snr = (alpha / sigma) ** 2
-        return snr
-
-    # `accelerate` 0.16.0 will have better support for customized saving
-    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
-        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
-        def save_model_hook(models, weights, output_dir):
-            if args.use_ema:
-                ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema"))
-
-            for i, model in enumerate(models):
-                model.save_pretrained(os.path.join(output_dir, "unet"))
-
-                # make sure to pop weight so that corresponding model is not saved again
-                weights.pop()
-
-        def load_model_hook(models, input_dir):
-            if args.use_ema:
-                load_model = EMAModel.from_pretrained(os.path.join(input_dir, "unet_ema"), UNet2DConditionModel)
-                ema_unet.load_state_dict(load_model.state_dict())
-                ema_unet.to(accelerator.device)
-                del load_model
-
-            for i in range(len(models)):
-                # pop models so that they are not loaded again
-                model = models.pop()
-
-                # load diffusers style into model
-                load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
-                model.register_to_config(**load_model.config)
-
-                model.load_state_dict(load_model.state_dict())
-                del load_model
-
-        accelerator.register_save_state_pre_hook(save_model_hook)
-        accelerator.register_load_state_pre_hook(load_model_hook)
-
-    if args.gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-
-    # Enable TF32 for faster training on Ampere GPUs,
-    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
-    if args.allow_tf32:
-        torch.backends.cuda.matmul.allow_tf32 = True
-
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
-        )
-
-    # Initialize the optimizer
-    if args.use_8bit_adam:
-        try:
-            import bitsandbytes as bnb
-        except ImportError:
-            raise ImportError(
-                "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
-            )
-
-        optimizer_cls = bnb.optim.AdamW8bit
-    else:
-        optimizer_cls = torch.optim.AdamW
-
-    optimizer = optimizer_cls(
-        unet.parameters(),
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    # Get the datasets: you can either provide your own training and evaluation files (see below)
-    # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
-
-    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
-    # download the dataset.
-    if args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        dataset = load_dataset(
-            args.dataset_name,
-            args.dataset_config_name,
-            cache_dir=args.cache_dir,
-        )
-    else:
-        data_files = {}
-        if args.train_data_dir is not None:
-            data_files["train"] = os.path.join(args.train_data_dir, "**")
-        dataset = load_dataset(
-            "imagefolder",
-            data_files=data_files,
-            cache_dir=args.cache_dir,
-        )
-        # See more about loading custom images at
-        # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
-
-    # Preprocessing the datasets.
-    # We need to tokenize inputs and targets.
-    column_names = dataset["train"].column_names
-
-    # 6. Get the column names for input/target.
-    dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
-    if args.image_column is None:
-        image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
-    else:
-        image_column = args.image_column
-        if image_column not in column_names:
-            raise ValueError(
-                f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
-            )
-    if args.caption_column is None:
-        caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
-    else:
-        caption_column = args.caption_column
-        if caption_column not in column_names:
-            raise ValueError(
-                f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
-            )
-
-    # Preprocessing the datasets.
-    # We need to tokenize input captions and transform the images.
-    def tokenize_captions(examples, is_train=True):
-        captions = []
-        for caption in examples[caption_column]:
-            if isinstance(caption, str):
-                captions.append(caption)
-            elif isinstance(caption, (list, np.ndarray)):
-                # take a random caption if there are multiple
-                captions.append(random.choice(caption) if is_train else caption[0])
-            else:
-                raise ValueError(
-                    f"Caption column `{caption_column}` should contain either strings or lists of strings."
-                )
-        inputs = tokenizer(
-            captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
-        )
-        return inputs.input_ids
-
-    # Preprocessing the datasets.
-    train_transforms = transforms.Compose(
-        [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
-            transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
-            transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
-            transforms.ToTensor(),
-            transforms.Normalize([0.5], [0.5]),
-        ]
-    )
-
-    def preprocess_train(examples):
-        images = [image.convert("RGB") for image in examples[image_column]]
-        examples["pixel_values"] = [train_transforms(image) for image in images]
-        examples["input_ids"] = tokenize_captions(examples)
-        return examples
-
-    with accelerator.main_process_first():
-        if args.max_train_samples is not None:
-            dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
-        # Set the training transforms
-        train_dataset = dataset["train"].with_transform(preprocess_train)
-
-    def collate_fn(examples):
-        pixel_values = torch.stack([example["pixel_values"] for example in examples])
-        pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-        input_ids = torch.stack([example["input_ids"] for example in examples])
-        return {"pixel_values": pixel_values, "input_ids": input_ids}
-
-    # DataLoaders creation:
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset,
-        shuffle=True,
-        collate_fn=collate_fn,
-        batch_size=args.train_batch_size,
-        num_workers=args.dataloader_num_workers,
-    )
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
-        num_training_steps=args.max_train_steps * accelerator.num_processes,
-    )
-
-    # Prepare everything with our `accelerator`.
-    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        unet, optimizer, train_dataloader, lr_scheduler
-    )
-
-    if args.use_ema:
-        ema_unet.to(accelerator.device)
-
-    # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
-    # as these weights are only used for inference, keeping weights in full precision is not required.
-    weight_dtype = torch.float32
-    if accelerator.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-        args.mixed_precision = accelerator.mixed_precision
-    elif accelerator.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-        args.mixed_precision = accelerator.mixed_precision
-
-    # Move text_encode and vae to gpu and cast to weight_dtype
-    text_encoder.to(accelerator.device, dtype=weight_dtype)
-    vae.to(accelerator.device, dtype=weight_dtype)
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        tracker_config = dict(vars(args))
-        tracker_config.pop("validation_prompts")
-        accelerator.init_trackers(args.tracker_project_name, tracker_config)
-
-    # Train!
-    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    global_step = 0
-    first_epoch = 0
-
-    # Potentially load in the weights and states from a previous save
-    if args.resume_from_checkpoint:
-        if args.resume_from_checkpoint != "latest":
-            path = os.path.basename(args.resume_from_checkpoint)
-        else:
-            # Get the most recent checkpoint
-            dirs = os.listdir(args.output_dir)
-            dirs = [d for d in dirs if d.startswith("checkpoint")]
-            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
-            path = dirs[-1] if len(dirs) > 0 else None
-
-        if path is None:
-            accelerator.print(
-                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
-            )
-            args.resume_from_checkpoint = None
-        else:
-            accelerator.print(f"Resuming from checkpoint {path}")
-            accelerator.load_state(os.path.join(args.output_dir, path))
-            global_step = int(path.split("-")[1])
-
-            resume_global_step = global_step * args.gradient_accumulation_steps
-            first_epoch = global_step // num_update_steps_per_epoch
-            resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
-
-    # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
-    progress_bar.set_description("Steps")
-
-    for epoch in range(first_epoch, args.num_train_epochs):
-        unet.train()
-        train_loss = 0.0
-        for step, batch in enumerate(train_dataloader):
-            # Skip steps until we reach the resumed step
-            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
-                if step % args.gradient_accumulation_steps == 0:
-                    progress_bar.update(1)
-                continue
-
-            with accelerator.accumulate(unet):
-                # Convert images to latent space
-                latents = vae.encode(batch["pixel_values"].to(weight_dtype)).latent_dist.sample()
-                latents = latents * vae.config.scaling_factor
-
-                # Sample noise that we'll add to the latents
-                noise = torch.randn_like(latents)
-                if args.noise_offset:
-                    # https://www.crosslabs.org//blog/diffusion-with-offset-noise
-                    noise += args.noise_offset * torch.randn(
-                        (latents.shape[0], latents.shape[1], 1, 1), device=latents.device
-                    )
-                if args.input_perturbation:
-                    new_noise = noise + args.input_perturbation * torch.randn_like(noise)
-                bsz = latents.shape[0]
-                # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
-                timesteps = timesteps.long()
-
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                if args.input_perturbation:
-                    noisy_latents = noise_scheduler.add_noise(latents, new_noise, timesteps)
-                else:
-                    noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-                # Get the text embedding for conditioning
-                encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
-                # Get the target for loss depending on the prediction type
-                if args.prediction_type is not None:
-                    # set prediction_type of scheduler if defined
-                    noise_scheduler.register_to_config(prediction_type=args.prediction_type)
-
-                if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-                # Predict the noise residual and compute loss
-                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-
-                if args.snr_gamma is None:
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-                else:
-                    # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
-                    # Since we predict the noise instead of x_0, the original formulation is slightly changed.
-                    # This is discussed in Section 4.2 of the same paper.
-                    snr = compute_snr(timesteps)
-                    mse_loss_weights = (
-                        torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
-                    )
-                    # We first calculate the original loss. Then we mean over the non-batch dimensions and
-                    # rebalance the sample-wise losses with their respective loss weights.
-                    # Finally, we take the mean of the rebalanced loss.
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
-                    loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
-                    loss = loss.mean()
-
-                # Gather the losses across all processes for logging (if we use distributed training).
-                avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
-                train_loss += avg_loss.item() / args.gradient_accumulation_steps
-
-                # Backpropagate
-                accelerator.backward(loss)
-                if accelerator.sync_gradients:
-                    accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad()
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                if args.use_ema:
-                    ema_unet.step(unet.parameters())
-                progress_bar.update(1)
-                global_step += 1
-                accelerator.log({"train_loss": train_loss}, step=global_step)
-                train_loss = 0.0
-
-                if global_step % args.checkpointing_steps == 0:
-                    if accelerator.is_main_process:
-                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
-                        if args.checkpoints_total_limit is not None:
-                            checkpoints = os.listdir(args.output_dir)
-                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
-                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
-
-                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
-                            if len(checkpoints) >= args.checkpoints_total_limit:
-                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
-                                removing_checkpoints = checkpoints[0:num_to_remove]
-
-                                logger.info(
-                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
-                                )
-                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
-
-                                for removing_checkpoint in removing_checkpoints:
-                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
-                                    shutil.rmtree(removing_checkpoint)
-
-                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
-                        accelerator.save_state(save_path)
-                        logger.info(f"Saved state to {save_path}")
-
-            logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-
-            if global_step >= args.max_train_steps:
-                break
-
-        if accelerator.is_main_process:
-            if args.validation_prompts is not None and epoch % args.validation_epochs == 0:
-                if args.use_ema:
-                    # Store the UNet parameters temporarily and load the EMA parameters to perform inference.
-                    ema_unet.store(unet.parameters())
-                    ema_unet.copy_to(unet.parameters())
-                log_validation(
-                    vae,
-                    text_encoder,
-                    tokenizer,
-                    unet,
-                    args,
-                    accelerator,
-                    weight_dtype,
-                    global_step,
-                )
-                if args.use_ema:
-                    # Switch back to the original UNet parameters.
-                    ema_unet.restore(unet.parameters())
-
-    # Create the pipeline using the trained modules and save it.
-    accelerator.wait_for_everyone()
-    if accelerator.is_main_process:
-        unet = accelerator.unwrap_model(unet)
-        if args.use_ema:
-            ema_unet.copy_to(unet.parameters())
-
-        pipeline = StableDiffusionPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            text_encoder=text_encoder,
-            vae=vae,
-            unet=unet,
-            revision=args.revision,
-        )
-        pipeline.save_pretrained(args.output_dir)
-
-        # Run a final round of inference.
-        images = []
-        if args.validation_prompts is not None:
-            logger.info("Running inference for collecting generated images...")
-            pipeline = pipeline.to(accelerator.device)
-            pipeline.torch_dtype = weight_dtype
-            pipeline.set_progress_bar_config(disable=True)
-
-            if args.enable_xformers_memory_efficient_attention:
-                pipeline.enable_xformers_memory_efficient_attention()
-
-            if args.seed is None:
-                generator = None
-            else:
-                generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
-
-            for i in range(len(args.validation_prompts)):
-                with torch.autocast("cuda"):
-                    image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
-                images.append(image)
-
-        if args.push_to_hub:
-            save_model_card(args, repo_id, images, repo_folder=args.output_dir)
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-    accelerator.end_training()
-
-
-if __name__ == "__main__":
-    main()

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_ddim_flax.py

@@ -1,305 +0,0 @@
-# Copyright 2023 Stanford University Team and 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.
-
-# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
-# and https://github.com/hojonathanho/diffusion
-
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union
-
-import flax
-import jax.numpy as jnp
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from .scheduling_utils_flax import (
-    CommonSchedulerState,
-    FlaxKarrasDiffusionSchedulers,
-    FlaxSchedulerMixin,
-    FlaxSchedulerOutput,
-    add_noise_common,
-    get_velocity_common,
-)
-
-
-@flax.struct.dataclass
-class DDIMSchedulerState:
-    common: CommonSchedulerState
-    final_alpha_cumprod: jnp.ndarray
-
-    # setable values
-    init_noise_sigma: jnp.ndarray
-    timesteps: jnp.ndarray
-    num_inference_steps: Optional[int] = None
-
-    @classmethod
-    def create(
-        cls,
-        common: CommonSchedulerState,
-        final_alpha_cumprod: jnp.ndarray,
-        init_noise_sigma: jnp.ndarray,
-        timesteps: jnp.ndarray,
-    ):
-        return cls(
-            common=common,
-            final_alpha_cumprod=final_alpha_cumprod,
-            init_noise_sigma=init_noise_sigma,
-            timesteps=timesteps,
-        )
-
-
-@dataclass
-class FlaxDDIMSchedulerOutput(FlaxSchedulerOutput):
-    state: DDIMSchedulerState
-
-
-class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
-    """
-    Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising
-    diffusion probabilistic models (DDPMs) with non-Markovian guidance.
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    For more details, see the original paper: https://arxiv.org/abs/2010.02502
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        beta_start (`float`): the starting `beta` value of inference.
-        beta_end (`float`): the final `beta` value.
-        beta_schedule (`str`):
-            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        trained_betas (`jnp.ndarray`, optional):
-            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
-        clip_sample (`bool`, default `True`):
-            option to clip predicted sample between -1 and 1 for numerical stability.
-        set_alpha_to_one (`bool`, default `True`):
-            each diffusion step uses the value of alphas product at that step and at the previous one. For the final
-            step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
-            otherwise it uses the value of alpha at step 0.
-        steps_offset (`int`, default `0`):
-            an offset added to the inference steps. You can use a combination of `offset=1` and
-            `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
-            stable diffusion.
-        prediction_type (`str`, default `epsilon`):
-            indicates whether the model predicts the noise (epsilon), or the samples. One of `epsilon`, `sample`.
-            `v-prediction` is not supported for this scheduler.
-        dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
-            the `dtype` used for params and computation.
-    """
-
-    _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]
-
-    dtype: jnp.dtype
-
-    @property
-    def has_state(self):
-        return True
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[jnp.ndarray] = None,
-        set_alpha_to_one: bool = True,
-        steps_offset: int = 0,
-        prediction_type: str = "epsilon",
-        dtype: jnp.dtype = jnp.float32,
-    ):
-        self.dtype = dtype
-
-    def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDIMSchedulerState:
-        if common is None:
-            common = CommonSchedulerState.create(self)
-
-        # At every step in ddim, we are looking into the previous alphas_cumprod
-        # For the final step, there is no previous alphas_cumprod because we are already at 0
-        # `set_alpha_to_one` decides whether we set this parameter simply to one or
-        # whether we use the final alpha of the "non-previous" one.
-        final_alpha_cumprod = (
-            jnp.array(1.0, dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
-        )
-
-        # standard deviation of the initial noise distribution
-        init_noise_sigma = jnp.array(1.0, dtype=self.dtype)
-
-        timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1]
-
-        return DDIMSchedulerState.create(
-            common=common,
-            final_alpha_cumprod=final_alpha_cumprod,
-            init_noise_sigma=init_noise_sigma,
-            timesteps=timesteps,
-        )
-
-    def scale_model_input(
-        self, state: DDIMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None
-    ) -> jnp.ndarray:
-        """
-        Args:
-            state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance.
-            sample (`jnp.ndarray`): input sample
-            timestep (`int`, optional): current timestep
-
-        Returns:
-            `jnp.ndarray`: scaled input sample
-        """
-        return sample
-
-    def set_timesteps(
-        self, state: DDIMSchedulerState, num_inference_steps: int, shape: Tuple = ()
-    ) -> DDIMSchedulerState:
-        """
-        Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            state (`DDIMSchedulerState`):
-                the `FlaxDDIMScheduler` state data class instance.
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-        """
-        step_ratio = self.config.num_train_timesteps // num_inference_steps
-        # creates integer timesteps by multiplying by ratio
-        # rounding to avoid issues when num_inference_step is power of 3
-        timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round()[::-1] + self.config.steps_offset
-
-        return state.replace(
-            num_inference_steps=num_inference_steps,
-            timesteps=timesteps,
-        )
-
-    def _get_variance(self, state: DDIMSchedulerState, timestep, prev_timestep):
-        alpha_prod_t = state.common.alphas_cumprod[timestep]
-        alpha_prod_t_prev = jnp.where(
-            prev_timestep >= 0, state.common.alphas_cumprod[prev_timestep], state.final_alpha_cumprod
-        )
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
-
-        return variance
-
-    def step(
-        self,
-        state: DDIMSchedulerState,
-        model_output: jnp.ndarray,
-        timestep: int,
-        sample: jnp.ndarray,
-        eta: float = 0.0,
-        return_dict: bool = True,
-    ) -> Union[FlaxDDIMSchedulerOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            state (`DDIMSchedulerState`): the `FlaxDDIMScheduler` state data class instance.
-            model_output (`jnp.ndarray`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`jnp.ndarray`):
-                current instance of sample being created by diffusion process.
-            return_dict (`bool`): option for returning tuple rather than FlaxDDIMSchedulerOutput class
-
-        Returns:
-            [`FlaxDDIMSchedulerOutput`] or `tuple`: [`FlaxDDIMSchedulerOutput`] if `return_dict` is True, otherwise a
-            `tuple`. When returning a tuple, the first element is the sample tensor.
-
-        """
-        if state.num_inference_steps is None:
-            raise ValueError(
-                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
-        # Ideally, read DDIM paper in-detail understanding
-
-        # Notation (<variable name> -> <name in paper>
-        # - pred_noise_t -> e_theta(x_t, t)
-        # - pred_original_sample -> f_theta(x_t, t) or x_0
-        # - std_dev_t -> sigma_t
-        # - eta -> η
-        # - pred_sample_direction -> "direction pointing to x_t"
-        # - pred_prev_sample -> "x_t-1"
-
-        # 1. get previous step value (=t-1)
-        prev_timestep = timestep - self.config.num_train_timesteps // state.num_inference_steps
-
-        alphas_cumprod = state.common.alphas_cumprod
-        final_alpha_cumprod = state.final_alpha_cumprod
-
-        # 2. compute alphas, betas
-        alpha_prod_t = alphas_cumprod[timestep]
-        alpha_prod_t_prev = jnp.where(prev_timestep >= 0, alphas_cumprod[prev_timestep], final_alpha_cumprod)
-
-        beta_prod_t = 1 - alpha_prod_t
-
-        # 3. compute predicted original sample from predicted noise also called
-        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        if self.config.prediction_type == "epsilon":
-            pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
-            pred_epsilon = model_output
-        elif self.config.prediction_type == "sample":
-            pred_original_sample = model_output
-            pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
-        elif self.config.prediction_type == "v_prediction":
-            pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
-            pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
-        else:
-            raise ValueError(
-                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
-                " `v_prediction`"
-            )
-
-        # 4. compute variance: "sigma_t(η)" -> see formula (16)
-        # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
-        variance = self._get_variance(state, timestep, prev_timestep)
-        std_dev_t = eta * variance ** (0.5)
-
-        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon
-
-        # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
-
-        if not return_dict:
-            return (prev_sample, state)
-
-        return FlaxDDIMSchedulerOutput(prev_sample=prev_sample, state=state)
-
-    def add_noise(
-        self,
-        state: DDIMSchedulerState,
-        original_samples: jnp.ndarray,
-        noise: jnp.ndarray,
-        timesteps: jnp.ndarray,
-    ) -> jnp.ndarray:
-        return add_noise_common(state.common, original_samples, noise, timesteps)
-
-    def get_velocity(
-        self,
-        state: DDIMSchedulerState,
-        sample: jnp.ndarray,
-        noise: jnp.ndarray,
-        timesteps: jnp.ndarray,
-    ) -> jnp.ndarray:
-        return get_velocity_common(state.common, sample, noise, timesteps)
-
-    def __len__(self):
-        return self.config.num_train_timesteps

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_attend_and_excite.py

@@ -1,229 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import gc
-import unittest
-
-import numpy as np
-import torch
-from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
-
-from diffusers import (
-    AutoencoderKL,
-    DDIMScheduler,
-    StableDiffusionAttendAndExcitePipeline,
-    UNet2DConditionModel,
-)
-from diffusers.utils import load_numpy, skip_mps, slow
-from diffusers.utils.testing_utils import require_torch_gpu
-
-from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
-
-
-torch.backends.cuda.matmul.allow_tf32 = False
-
-
-@skip_mps
-class StableDiffusionAttendAndExcitePipelineFastTests(
-    PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
-    pipeline_class = StableDiffusionAttendAndExcitePipeline
-    test_attention_slicing = False
-    params = TEXT_TO_IMAGE_PARAMS
-    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"})
-    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
-    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
-
-    # Attend and excite requires being able to run a backward pass at
-    # inference time. There's no deterministic backward operator for pad
-
-    @classmethod
-    def setUpClass(cls):
-        super().setUpClass()
-        torch.use_deterministic_algorithms(False)
-
-    @classmethod
-    def tearDownClass(cls):
-        super().tearDownClass()
-        torch.use_deterministic_algorithms(True)
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        unet = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=1,
-            sample_size=32,
-            in_channels=4,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-            # SD2-specific config below
-            attention_head_dim=(2, 4),
-            use_linear_projection=True,
-        )
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-        )
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-            sample_size=128,
-        )
-        torch.manual_seed(0)
-        text_encoder_config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=1000,
-            # SD2-specific config below
-            hidden_act="gelu",
-            projection_dim=512,
-        )
-        text_encoder = CLIPTextModel(text_encoder_config)
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        components = {
-            "unet": unet,
-            "scheduler": scheduler,
-            "vae": vae,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "safety_checker": None,
-            "feature_extractor": None,
-        }
-
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-        inputs = inputs = {
-            "prompt": "a cat and a frog",
-            "token_indices": [2, 5],
-            "generator": generator,
-            "num_inference_steps": 1,
-            "guidance_scale": 6.0,
-            "output_type": "numpy",
-            "max_iter_to_alter": 2,
-            "thresholds": {0: 0.7},
-        }
-        return inputs
-
-    def test_inference(self):
-        device = "cpu"
-
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.to(device)
-        pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        image = pipe(**inputs).images
-        image_slice = image[0, -3:, -3:, -1]
-
-        self.assertEqual(image.shape, (1, 64, 64, 3))
-        expected_slice = np.array(
-            [0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]
-        )
-        max_diff = np.abs(image_slice.flatten() - expected_slice).max()
-        self.assertLessEqual(max_diff, 1e-3)
-
-    def test_cpu_offload_forward_pass(self):
-        super().test_cpu_offload_forward_pass(expected_max_diff=5e-4)
-
-    def test_inference_batch_consistent(self):
-        # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
-        self._test_inference_batch_consistent(batch_sizes=[1, 2])
-
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical(batch_size=2, expected_max_diff=7e-4)
-
-    def test_dict_tuple_outputs_equivalent(self):
-        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)
-
-    def test_pt_np_pil_outputs_equivalent(self):
-        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4)
-
-    def test_save_load_local(self):
-        super().test_save_load_local(expected_max_difference=5e-4)
-
-    def test_save_load_optional_components(self):
-        super().test_save_load_optional_components(expected_max_difference=4e-4)
-
-
-@require_torch_gpu
-@slow
-class StableDiffusionAttendAndExcitePipelineIntegrationTests(unittest.TestCase):
-    # Attend and excite requires being able to run a backward pass at
-    # inference time. There's no deterministic backward operator for pad
-
-    @classmethod
-    def setUpClass(cls):
-        super().setUpClass()
-        torch.use_deterministic_algorithms(False)
-
-    @classmethod
-    def tearDownClass(cls):
-        super().tearDownClass()
-        torch.use_deterministic_algorithms(True)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_attend_and_excite_fp16(self):
-        generator = torch.manual_seed(51)
-
-        pipe = StableDiffusionAttendAndExcitePipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
-        )
-        pipe.to("cuda")
-
-        prompt = "a painting of an elephant with glasses"
-        token_indices = [5, 7]
-
-        image = pipe(
-            prompt=prompt,
-            token_indices=token_indices,
-            guidance_scale=7.5,
-            generator=generator,
-            num_inference_steps=5,
-            max_iter_to_alter=5,
-            output_type="numpy",
-        ).images[0]
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"
-        )
-        assert np.abs((expected_image - image).max()) < 5e-1

spaces/Andy1621/uniformer_image_detection/configs/_base_/datasets/deepfashion.py

@@ -1,53 +0,0 @@
-# dataset settings
-dataset_type = 'DeepFashionDataset'
-data_root = 'data/DeepFashion/In-shop/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='Resize', img_scale=(750, 1101), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(750, 1101),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    imgs_per_gpu=2,
-    workers_per_gpu=1,
-    train=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json',
-        img_prefix=data_root + 'Img/',
-        pipeline=train_pipeline,
-        data_root=data_root),
-    val=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json',
-        img_prefix=data_root + 'Img/',
-        pipeline=test_pipeline,
-        data_root=data_root),
-    test=dict(
-        type=dataset_type,
-        ann_file=data_root +
-        'annotations/DeepFashion_segmentation_gallery.json',
-        img_prefix=data_root + 'Img/',
-        pipeline=test_pipeline,
-        data_root=data_root))
-evaluation = dict(interval=5, metric=['bbox', 'segm'])

spaces/Andy1621/uniformer_image_detection/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py

@@ -1,12 +0,0 @@
-_base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        style='pytorch'))

spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/optimization/augmentations.py

@@ -1,42 +0,0 @@
-import torch
-from torch import nn
-import kornia.augmentation as K
-
-
-class ImageAugmentations(nn.Module):
-    def __init__(self, output_size, augmentations_number, p=0.7):
-        super().__init__()
-        self.output_size = output_size
-        self.augmentations_number = augmentations_number
-
-        self.augmentations = nn.Sequential(
-            K.RandomAffine(degrees=15, translate=0.1, p=p, padding_mode="border"),  # type: ignore
-            K.RandomPerspective(0.7, p=p),
-        )
-
-        self.avg_pool = nn.AdaptiveAvgPool2d((self.output_size, self.output_size))
-
-    def forward(self, input):
-        """Extents the input batch with augmentations
-
-        If the input is consists of images [I1, I2] the extended augmented output
-        will be [I1_resized, I2_resized, I1_aug1, I2_aug1, I1_aug2, I2_aug2 ...]
-
-        Args:
-            input ([type]): input batch of shape [batch, C, H, W]
-
-        Returns:
-            updated batch: of shape [batch * augmentations_number, C, H, W]
-        """
-        # We want to multiply the number of images in the batch in contrast to regular augmantations
-        # that do not change the number of samples in the batch)
-        resized_images = self.avg_pool(input)
-        resized_images = torch.tile(resized_images, dims=(self.augmentations_number, 1, 1, 1))
-
-        batch_size = input.shape[0]
-        # We want at least one non augmented image
-        non_augmented_batch = resized_images[:batch_size]
-        augmented_batch = self.augmentations(resized_images[batch_size:])
-        updated_batch = torch.cat([non_augmented_batch, augmented_batch], dim=0)
-
-        return updated_batch

spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/diffusionmodules/model.py

@@ -1,852 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import rearrange
-from typing import Optional, Any
-
-from ldm.modules.attention import MemoryEfficientCrossAttention
-
-try:
-    import xformers
-    import xformers.ops
-    XFORMERS_IS_AVAILBLE = True
-except:
-    XFORMERS_IS_AVAILBLE = False
-    print("No module 'xformers'. Proceeding without it.")
-
-
-def get_timestep_embedding(timesteps, embedding_dim):
-    """
-    This matches the implementation in Denoising Diffusion Probabilistic Models:
-    From Fairseq.
-    Build sinusoidal embeddings.
-    This matches the implementation in tensor2tensor, but differs slightly
-    from the description in Section 3.5 of "Attention Is All You Need".
-    """
-    assert len(timesteps.shape) == 1
-
-    half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
-    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
-    emb = emb.to(device=timesteps.device)
-    emb = timesteps.float()[:, None] * emb[None, :]
-    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
-    if embedding_dim % 2 == 1:  # zero pad
-        emb = torch.nn.functional.pad(emb, (0,1,0,0))
-    return emb
-
-
-def nonlinearity(x):
-    # swish
-    return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels, num_groups=32):
-    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=2,
-                                        padding=0)
-
-    def forward(self, x):
-        if self.with_conv:
-            pad = (0,1,0,1)
-            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-            x = self.conv(x)
-        else:
-            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout, temb_channels=512):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-
-        self.norm1 = Normalize(in_channels)
-        self.conv1 = torch.nn.Conv2d(in_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if temb_channels > 0:
-            self.temb_proj = torch.nn.Linear(temb_channels,
-                                             out_channels)
-        self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(out_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(in_channels,
-                                                     out_channels,
-                                                     kernel_size=3,
-                                                     stride=1,
-                                                     padding=1)
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(in_channels,
-                                                    out_channels,
-                                                    kernel_size=1,
-                                                    stride=1,
-                                                    padding=0)
-
-    def forward(self, x, temb):
-        h = x
-        h = self.norm1(h)
-        h = nonlinearity(h)
-        h = self.conv1(h)
-
-        if temb is not None:
-            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
-        h = self.norm2(h)
-        h = nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x+h
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = q.reshape(b,c,h*w)
-        q = q.permute(0,2,1)   # b,hw,c
-        k = k.reshape(b,c,h*w) # b,c,hw
-        w_ = torch.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b,c,h*w)
-        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
-        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-        h_ = h_.reshape(b,c,h,w)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-class MemoryEfficientAttnBlock(nn.Module):
-    """
-        Uses xformers efficient implementation,
-        see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
-        Note: this is a single-head self-attention operation
-    """
-    #
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-        self.attention_op: Optional[Any] = None
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        B, C, H, W = q.shape
-        q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
-
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(B, t.shape[1], 1, C)
-            .permute(0, 2, 1, 3)
-            .reshape(B * 1, t.shape[1], C)
-            .contiguous(),
-            (q, k, v),
-        )
-        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
-
-        out = (
-            out.unsqueeze(0)
-            .reshape(B, 1, out.shape[1], C)
-            .permute(0, 2, 1, 3)
-            .reshape(B, out.shape[1], C)
-        )
-        out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
-        out = self.proj_out(out)
-        return x+out
-
-
-class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
-    def forward(self, x, context=None, mask=None):
-        b, c, h, w = x.shape
-        x = rearrange(x, 'b c h w -> b (h w) c')
-        out = super().forward(x, context=context, mask=mask)
-        out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
-        return x + out
-
-
-def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
-    assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
-    if XFORMERS_IS_AVAILBLE and attn_type == "vanilla":
-        attn_type = "vanilla-xformers"
-    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
-    if attn_type == "vanilla":
-        assert attn_kwargs is None
-        return AttnBlock(in_channels)
-    elif attn_type == "vanilla-xformers":
-        print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
-        return MemoryEfficientAttnBlock(in_channels)
-    elif type == "memory-efficient-cross-attn":
-        attn_kwargs["query_dim"] = in_channels
-        return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
-    elif attn_type == "none":
-        return nn.Identity(in_channels)
-    else:
-        raise NotImplementedError()
-
-
-class Model(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = self.ch*4
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        self.use_timestep = use_timestep
-        if self.use_timestep:
-            # timestep embedding
-            self.temb = nn.Module()
-            self.temb.dense = nn.ModuleList([
-                torch.nn.Linear(self.ch,
-                                self.temb_ch),
-                torch.nn.Linear(self.temb_ch,
-                                self.temb_ch),
-            ])
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            skip_in = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                if i_block == self.num_res_blocks:
-                    skip_in = ch*in_ch_mult[i_level]
-                block.append(ResnetBlock(in_channels=block_in+skip_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x, t=None, context=None):
-        #assert x.shape[2] == x.shape[3] == self.resolution
-        if context is not None:
-            # assume aligned context, cat along channel axis
-            x = torch.cat((x, context), dim=1)
-        if self.use_timestep:
-            # timestep embedding
-            assert t is not None
-            temb = get_timestep_embedding(t, self.ch)
-            temb = self.temb.dense[0](temb)
-            temb = nonlinearity(temb)
-            temb = self.temb.dense[1](temb)
-        else:
-            temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](
-                    torch.cat([h, hs.pop()], dim=1), temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-    def get_last_layer(self):
-        return self.conv_out.weight
-
-
-class Encoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
-                 **ignore_kwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.in_ch_mult = in_ch_mult
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        2*z_channels if double_z else z_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # timestep embedding
-        temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class Decoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
-                 attn_type="vanilla", **ignorekwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-        self.tanh_out = tanh_out
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        in_ch_mult = (1,)+tuple(ch_mult)
-        block_in = ch*ch_mult[self.num_resolutions-1]
-        curr_res = resolution // 2**(self.num_resolutions-1)
-        self.z_shape = (1,z_channels,curr_res,curr_res)
-        print("Working with z of shape {} = {} dimensions.".format(
-            self.z_shape, np.prod(self.z_shape)))
-
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(z_channels,
-                                       block_in,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, z):
-        #assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h, temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        if self.tanh_out:
-            h = torch.tanh(h)
-        return h
-
-
-class SimpleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, *args, **kwargs):
-        super().__init__()
-        self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
-                                     ResnetBlock(in_channels=in_channels,
-                                                 out_channels=2 * in_channels,
-                                                 temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=2 * in_channels,
-                                                out_channels=4 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=4 * in_channels,
-                                                out_channels=2 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     nn.Conv2d(2*in_channels, in_channels, 1),
-                                     Upsample(in_channels, with_conv=True)])
-        # end
-        self.norm_out = Normalize(in_channels)
-        self.conv_out = torch.nn.Conv2d(in_channels,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        for i, layer in enumerate(self.model):
-            if i in [1,2,3]:
-                x = layer(x, None)
-            else:
-                x = layer(x)
-
-        h = self.norm_out(x)
-        h = nonlinearity(h)
-        x = self.conv_out(h)
-        return x
-
-
-class UpsampleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
-                 ch_mult=(2,2), dropout=0.0):
-        super().__init__()
-        # upsampling
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        block_in = in_channels
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.res_blocks = nn.ModuleList()
-        self.upsample_blocks = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            res_block = []
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                res_block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-            self.res_blocks.append(nn.ModuleList(res_block))
-            if i_level != self.num_resolutions - 1:
-                self.upsample_blocks.append(Upsample(block_in, True))
-                curr_res = curr_res * 2
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # upsampling
-        h = x
-        for k, i_level in enumerate(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.res_blocks[i_level][i_block](h, None)
-            if i_level != self.num_resolutions - 1:
-                h = self.upsample_blocks[k](h)
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class LatentRescaler(nn.Module):
-    def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
-        super().__init__()
-        # residual block, interpolate, residual block
-        self.factor = factor
-        self.conv_in = nn.Conv2d(in_channels,
-                                 mid_channels,
-                                 kernel_size=3,
-                                 stride=1,
-                                 padding=1)
-        self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-        self.attn = AttnBlock(mid_channels)
-        self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-
-        self.conv_out = nn.Conv2d(mid_channels,
-                                  out_channels,
-                                  kernel_size=1,
-                                  )
-
-    def forward(self, x):
-        x = self.conv_in(x)
-        for block in self.res_block1:
-            x = block(x, None)
-        x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
-        x = self.attn(x)
-        for block in self.res_block2:
-            x = block(x, None)
-        x = self.conv_out(x)
-        return x
-
-
-class MergedRescaleEncoder(nn.Module):
-    def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True,
-                 ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        intermediate_chn = ch * ch_mult[-1]
-        self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
-                               z_channels=intermediate_chn, double_z=False, resolution=resolution,
-                               attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
-                               out_ch=None)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
-                                       mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.encoder(x)
-        x = self.rescaler(x)
-        return x
-
-
-class MergedRescaleDecoder(nn.Module):
-    def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
-                 dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        tmp_chn = z_channels*ch_mult[-1]
-        self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
-                               resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
-                               ch_mult=ch_mult, resolution=resolution, ch=ch)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
-                                       out_channels=tmp_chn, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Upsampler(nn.Module):
-    def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
-        super().__init__()
-        assert out_size >= in_size
-        num_blocks = int(np.log2(out_size//in_size))+1
-        factor_up = 1.+ (out_size % in_size)
-        print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
-        self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
-                                       out_channels=in_channels)
-        self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
-                               attn_resolutions=[], in_channels=None, ch=in_channels,
-                               ch_mult=[ch_mult for _ in range(num_blocks)])
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Resize(nn.Module):
-    def __init__(self, in_channels=None, learned=False, mode="bilinear"):
-        super().__init__()
-        self.with_conv = learned
-        self.mode = mode
-        if self.with_conv:
-            print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
-            raise NotImplementedError()
-            assert in_channels is not None
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=4,
-                                        stride=2,
-                                        padding=1)
-
-    def forward(self, x, scale_factor=1.0):
-        if scale_factor==1.0:
-            return x
-        else:
-            x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
-        return x

spaces/Asmithayellow/Asmi/README.md

@@ -1,12 +0,0 @@
----
-title: Asmi
-emoji: 😻
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/misc.py

@@ -1,730 +0,0 @@
-# The following comment should be removed at some point in the future.
-# mypy: strict-optional=False
-
-import contextlib
-import errno
-import getpass
-import hashlib
-import io
-import logging
-import os
-import posixpath
-import shutil
-import stat
-import sys
-import sysconfig
-import urllib.parse
-from io import StringIO
-from itertools import filterfalse, tee, zip_longest
-from types import TracebackType
-from typing import (
-    Any,
-    BinaryIO,
-    Callable,
-    ContextManager,
-    Dict,
-    Generator,
-    Iterable,
-    Iterator,
-    List,
-    Optional,
-    TextIO,
-    Tuple,
-    Type,
-    TypeVar,
-    Union,
-    cast,
-)
-
-from pip._vendor.pyproject_hooks import BuildBackendHookCaller
-from pip._vendor.tenacity import retry, stop_after_delay, wait_fixed
-
-from pip import __version__
-from pip._internal.exceptions import CommandError, ExternallyManagedEnvironment
-from pip._internal.locations import get_major_minor_version
-from pip._internal.utils.compat import WINDOWS
-from pip._internal.utils.virtualenv import running_under_virtualenv
-
-__all__ = [
-    "rmtree",
-    "display_path",
-    "backup_dir",
-    "ask",
-    "splitext",
-    "format_size",
-    "is_installable_dir",
-    "normalize_path",
-    "renames",
-    "get_prog",
-    "captured_stdout",
-    "ensure_dir",
-    "remove_auth_from_url",
-    "check_externally_managed",
-    "ConfiguredBuildBackendHookCaller",
-]
-
-logger = logging.getLogger(__name__)
-
-T = TypeVar("T")
-ExcInfo = Tuple[Type[BaseException], BaseException, TracebackType]
-VersionInfo = Tuple[int, int, int]
-NetlocTuple = Tuple[str, Tuple[Optional[str], Optional[str]]]
-
-
-def get_pip_version() -> str:
-    pip_pkg_dir = os.path.join(os.path.dirname(__file__), "..", "..")
-    pip_pkg_dir = os.path.abspath(pip_pkg_dir)
-
-    return "pip {} from {} (python {})".format(
-        __version__,
-        pip_pkg_dir,
-        get_major_minor_version(),
-    )
-
-
-def normalize_version_info(py_version_info: Tuple[int, ...]) -> Tuple[int, int, int]:
-    """
-    Convert a tuple of ints representing a Python version to one of length
-    three.
-
-    :param py_version_info: a tuple of ints representing a Python version,
-        or None to specify no version. The tuple can have any length.
-
-    :return: a tuple of length three if `py_version_info` is non-None.
-        Otherwise, return `py_version_info` unchanged (i.e. None).
-    """
-    if len(py_version_info) < 3:
-        py_version_info += (3 - len(py_version_info)) * (0,)
-    elif len(py_version_info) > 3:
-        py_version_info = py_version_info[:3]
-
-    return cast("VersionInfo", py_version_info)
-
-
-def ensure_dir(path: str) -> None:
-    """os.path.makedirs without EEXIST."""
-    try:
-        os.makedirs(path)
-    except OSError as e:
-        # Windows can raise spurious ENOTEMPTY errors. See #6426.
-        if e.errno != errno.EEXIST and e.errno != errno.ENOTEMPTY:
-            raise
-
-
-def get_prog() -> str:
-    try:
-        prog = os.path.basename(sys.argv[0])
-        if prog in ("__main__.py", "-c"):
-            return f"{sys.executable} -m pip"
-        else:
-            return prog
-    except (AttributeError, TypeError, IndexError):
-        pass
-    return "pip"
-
-
-# Retry every half second for up to 3 seconds
-# Tenacity raises RetryError by default, explicitly raise the original exception
-@retry(reraise=True, stop=stop_after_delay(3), wait=wait_fixed(0.5))
-def rmtree(dir: str, ignore_errors: bool = False) -> None:
-    shutil.rmtree(dir, ignore_errors=ignore_errors, onerror=rmtree_errorhandler)
-
-
-def rmtree_errorhandler(func: Callable[..., Any], path: str, exc_info: ExcInfo) -> None:
-    """On Windows, the files in .svn are read-only, so when rmtree() tries to
-    remove them, an exception is thrown.  We catch that here, remove the
-    read-only attribute, and hopefully continue without problems."""
-    try:
-        has_attr_readonly = not (os.stat(path).st_mode & stat.S_IWRITE)
-    except OSError:
-        # it's equivalent to os.path.exists
-        return
-
-    if has_attr_readonly:
-        # convert to read/write
-        os.chmod(path, stat.S_IWRITE)
-        # use the original function to repeat the operation
-        func(path)
-        return
-    else:
-        raise
-
-
-def display_path(path: str) -> str:
-    """Gives the display value for a given path, making it relative to cwd
-    if possible."""
-    path = os.path.normcase(os.path.abspath(path))
-    if path.startswith(os.getcwd() + os.path.sep):
-        path = "." + path[len(os.getcwd()) :]
-    return path
-
-
-def backup_dir(dir: str, ext: str = ".bak") -> str:
-    """Figure out the name of a directory to back up the given dir to
-    (adding .bak, .bak2, etc)"""
-    n = 1
-    extension = ext
-    while os.path.exists(dir + extension):
-        n += 1
-        extension = ext + str(n)
-    return dir + extension
-
-
-def ask_path_exists(message: str, options: Iterable[str]) -> str:
-    for action in os.environ.get("PIP_EXISTS_ACTION", "").split():
-        if action in options:
-            return action
-    return ask(message, options)
-
-
-def _check_no_input(message: str) -> None:
-    """Raise an error if no input is allowed."""
-    if os.environ.get("PIP_NO_INPUT"):
-        raise Exception(
-            f"No input was expected ($PIP_NO_INPUT set); question: {message}"
-        )
-
-
-def ask(message: str, options: Iterable[str]) -> str:
-    """Ask the message interactively, with the given possible responses"""
-    while 1:
-        _check_no_input(message)
-        response = input(message)
-        response = response.strip().lower()
-        if response not in options:
-            print(
-                "Your response ({!r}) was not one of the expected responses: "
-                "{}".format(response, ", ".join(options))
-            )
-        else:
-            return response
-
-
-def ask_input(message: str) -> str:
-    """Ask for input interactively."""
-    _check_no_input(message)
-    return input(message)
-
-
-def ask_password(message: str) -> str:
-    """Ask for a password interactively."""
-    _check_no_input(message)
-    return getpass.getpass(message)
-
-
-def strtobool(val: str) -> int:
-    """Convert a string representation of truth to true (1) or false (0).
-
-    True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values
-    are 'n', 'no', 'f', 'false', 'off', and '0'.  Raises ValueError if
-    'val' is anything else.
-    """
-    val = val.lower()
-    if val in ("y", "yes", "t", "true", "on", "1"):
-        return 1
-    elif val in ("n", "no", "f", "false", "off", "0"):
-        return 0
-    else:
-        raise ValueError(f"invalid truth value {val!r}")
-
-
-def format_size(bytes: float) -> str:
-    if bytes > 1000 * 1000:
-        return "{:.1f} MB".format(bytes / 1000.0 / 1000)
-    elif bytes > 10 * 1000:
-        return "{} kB".format(int(bytes / 1000))
-    elif bytes > 1000:
-        return "{:.1f} kB".format(bytes / 1000.0)
-    else:
-        return "{} bytes".format(int(bytes))
-
-
-def tabulate(rows: Iterable[Iterable[Any]]) -> Tuple[List[str], List[int]]:
-    """Return a list of formatted rows and a list of column sizes.
-
-    For example::
-
-    >>> tabulate([['foobar', 2000], [0xdeadbeef]])
-    (['foobar     2000', '3735928559'], [10, 4])
-    """
-    rows = [tuple(map(str, row)) for row in rows]
-    sizes = [max(map(len, col)) for col in zip_longest(*rows, fillvalue="")]
-    table = [" ".join(map(str.ljust, row, sizes)).rstrip() for row in rows]
-    return table, sizes
-
-
-def is_installable_dir(path: str) -> bool:
-    """Is path is a directory containing pyproject.toml or setup.py?
-
-    If pyproject.toml exists, this is a PEP 517 project. Otherwise we look for
-    a legacy setuptools layout by identifying setup.py. We don't check for the
-    setup.cfg because using it without setup.py is only available for PEP 517
-    projects, which are already covered by the pyproject.toml check.
-    """
-    if not os.path.isdir(path):
-        return False
-    if os.path.isfile(os.path.join(path, "pyproject.toml")):
-        return True
-    if os.path.isfile(os.path.join(path, "setup.py")):
-        return True
-    return False
-
-
-def read_chunks(
-    file: BinaryIO, size: int = io.DEFAULT_BUFFER_SIZE
-) -> Generator[bytes, None, None]:
-    """Yield pieces of data from a file-like object until EOF."""
-    while True:
-        chunk = file.read(size)
-        if not chunk:
-            break
-        yield chunk
-
-
-def normalize_path(path: str, resolve_symlinks: bool = True) -> str:
-    """
-    Convert a path to its canonical, case-normalized, absolute version.
-
-    """
-    path = os.path.expanduser(path)
-    if resolve_symlinks:
-        path = os.path.realpath(path)
-    else:
-        path = os.path.abspath(path)
-    return os.path.normcase(path)
-
-
-def splitext(path: str) -> Tuple[str, str]:
-    """Like os.path.splitext, but take off .tar too"""
-    base, ext = posixpath.splitext(path)
-    if base.lower().endswith(".tar"):
-        ext = base[-4:] + ext
-        base = base[:-4]
-    return base, ext
-
-
-def renames(old: str, new: str) -> None:
-    """Like os.renames(), but handles renaming across devices."""
-    # Implementation borrowed from os.renames().
-    head, tail = os.path.split(new)
-    if head and tail and not os.path.exists(head):
-        os.makedirs(head)
-
-    shutil.move(old, new)
-
-    head, tail = os.path.split(old)
-    if head and tail:
-        try:
-            os.removedirs(head)
-        except OSError:
-            pass
-
-
-def is_local(path: str) -> bool:
-    """
-    Return True if path is within sys.prefix, if we're running in a virtualenv.
-
-    If we're not in a virtualenv, all paths are considered "local."
-
-    Caution: this function assumes the head of path has been normalized
-    with normalize_path.
-    """
-    if not running_under_virtualenv():
-        return True
-    return path.startswith(normalize_path(sys.prefix))
-
-
-def write_output(msg: Any, *args: Any) -> None:
-    logger.info(msg, *args)
-
-
-class StreamWrapper(StringIO):
-    orig_stream: TextIO = None
-
-    @classmethod
-    def from_stream(cls, orig_stream: TextIO) -> "StreamWrapper":
-        cls.orig_stream = orig_stream
-        return cls()
-
-    # compileall.compile_dir() needs stdout.encoding to print to stdout
-    # https://github.com/python/mypy/issues/4125
-    @property
-    def encoding(self):  # type: ignore
-        return self.orig_stream.encoding
-
-
-@contextlib.contextmanager
-def captured_output(stream_name: str) -> Generator[StreamWrapper, None, None]:
-    """Return a context manager used by captured_stdout/stdin/stderr
-    that temporarily replaces the sys stream *stream_name* with a StringIO.
-
-    Taken from Lib/support/__init__.py in the CPython repo.
-    """
-    orig_stdout = getattr(sys, stream_name)
-    setattr(sys, stream_name, StreamWrapper.from_stream(orig_stdout))
-    try:
-        yield getattr(sys, stream_name)
-    finally:
-        setattr(sys, stream_name, orig_stdout)
-
-
-def captured_stdout() -> ContextManager[StreamWrapper]:
-    """Capture the output of sys.stdout:
-
-       with captured_stdout() as stdout:
-           print('hello')
-       self.assertEqual(stdout.getvalue(), 'hello\n')
-
-    Taken from Lib/support/__init__.py in the CPython repo.
-    """
-    return captured_output("stdout")
-
-
-def captured_stderr() -> ContextManager[StreamWrapper]:
-    """
-    See captured_stdout().
-    """
-    return captured_output("stderr")
-
-
-# Simulates an enum
-def enum(*sequential: Any, **named: Any) -> Type[Any]:
-    enums = dict(zip(sequential, range(len(sequential))), **named)
-    reverse = {value: key for key, value in enums.items()}
-    enums["reverse_mapping"] = reverse
-    return type("Enum", (), enums)
-
-
-def build_netloc(host: str, port: Optional[int]) -> str:
-    """
-    Build a netloc from a host-port pair
-    """
-    if port is None:
-        return host
-    if ":" in host:
-        # Only wrap host with square brackets when it is IPv6
-        host = f"[{host}]"
-    return f"{host}:{port}"
-
-
-def build_url_from_netloc(netloc: str, scheme: str = "https") -> str:
-    """
-    Build a full URL from a netloc.
-    """
-    if netloc.count(":") >= 2 and "@" not in netloc and "[" not in netloc:
-        # It must be a bare IPv6 address, so wrap it with brackets.
-        netloc = f"[{netloc}]"
-    return f"{scheme}://{netloc}"
-
-
-def parse_netloc(netloc: str) -> Tuple[str, Optional[int]]:
-    """
-    Return the host-port pair from a netloc.
-    """
-    url = build_url_from_netloc(netloc)
-    parsed = urllib.parse.urlparse(url)
-    return parsed.hostname, parsed.port
-
-
-def split_auth_from_netloc(netloc: str) -> NetlocTuple:
-    """
-    Parse out and remove the auth information from a netloc.
-
-    Returns: (netloc, (username, password)).
-    """
-    if "@" not in netloc:
-        return netloc, (None, None)
-
-    # Split from the right because that's how urllib.parse.urlsplit()
-    # behaves if more than one @ is present (which can be checked using
-    # the password attribute of urlsplit()'s return value).
-    auth, netloc = netloc.rsplit("@", 1)
-    pw: Optional[str] = None
-    if ":" in auth:
-        # Split from the left because that's how urllib.parse.urlsplit()
-        # behaves if more than one : is present (which again can be checked
-        # using the password attribute of the return value)
-        user, pw = auth.split(":", 1)
-    else:
-        user, pw = auth, None
-
-    user = urllib.parse.unquote(user)
-    if pw is not None:
-        pw = urllib.parse.unquote(pw)
-
-    return netloc, (user, pw)
-
-
-def redact_netloc(netloc: str) -> str:
-    """
-    Replace the sensitive data in a netloc with "****", if it exists.
-
-    For example:
-        - "user:[email protected]" returns "user:****@example.com"
-        - "[email protected]" returns "****@example.com"
-    """
-    netloc, (user, password) = split_auth_from_netloc(netloc)
-    if user is None:
-        return netloc
-    if password is None:
-        user = "****"
-        password = ""
-    else:
-        user = urllib.parse.quote(user)
-        password = ":****"
-    return "{user}{password}@{netloc}".format(
-        user=user, password=password, netloc=netloc
-    )
-
-
-def _transform_url(
-    url: str, transform_netloc: Callable[[str], Tuple[Any, ...]]
-) -> Tuple[str, NetlocTuple]:
-    """Transform and replace netloc in a url.
-
-    transform_netloc is a function taking the netloc and returning a
-    tuple. The first element of this tuple is the new netloc. The
-    entire tuple is returned.
-
-    Returns a tuple containing the transformed url as item 0 and the
-    original tuple returned by transform_netloc as item 1.
-    """
-    purl = urllib.parse.urlsplit(url)
-    netloc_tuple = transform_netloc(purl.netloc)
-    # stripped url
-    url_pieces = (purl.scheme, netloc_tuple[0], purl.path, purl.query, purl.fragment)
-    surl = urllib.parse.urlunsplit(url_pieces)
-    return surl, cast("NetlocTuple", netloc_tuple)
-
-
-def _get_netloc(netloc: str) -> NetlocTuple:
-    return split_auth_from_netloc(netloc)
-
-
-def _redact_netloc(netloc: str) -> Tuple[str]:
-    return (redact_netloc(netloc),)
-
-
-def split_auth_netloc_from_url(url: str) -> Tuple[str, str, Tuple[str, str]]:
-    """
-    Parse a url into separate netloc, auth, and url with no auth.
-
-    Returns: (url_without_auth, netloc, (username, password))
-    """
-    url_without_auth, (netloc, auth) = _transform_url(url, _get_netloc)
-    return url_without_auth, netloc, auth
-
-
-def remove_auth_from_url(url: str) -> str:
-    """Return a copy of url with 'username:password@' removed."""
-    # username/pass params are passed to subversion through flags
-    # and are not recognized in the url.
-    return _transform_url(url, _get_netloc)[0]
-
-
-def redact_auth_from_url(url: str) -> str:
-    """Replace the password in a given url with ****."""
-    return _transform_url(url, _redact_netloc)[0]
-
-
-class HiddenText:
-    def __init__(self, secret: str, redacted: str) -> None:
-        self.secret = secret
-        self.redacted = redacted
-
-    def __repr__(self) -> str:
-        return "<HiddenText {!r}>".format(str(self))
-
-    def __str__(self) -> str:
-        return self.redacted
-
-    # This is useful for testing.
-    def __eq__(self, other: Any) -> bool:
-        if type(self) != type(other):
-            return False
-
-        # The string being used for redaction doesn't also have to match,
-        # just the raw, original string.
-        return self.secret == other.secret
-
-
-def hide_value(value: str) -> HiddenText:
-    return HiddenText(value, redacted="****")
-
-
-def hide_url(url: str) -> HiddenText:
-    redacted = redact_auth_from_url(url)
-    return HiddenText(url, redacted=redacted)
-
-
-def protect_pip_from_modification_on_windows(modifying_pip: bool) -> None:
-    """Protection of pip.exe from modification on Windows
-
-    On Windows, any operation modifying pip should be run as:
-        python -m pip ...
-    """
-    pip_names = [
-        "pip",
-        f"pip{sys.version_info.major}",
-        f"pip{sys.version_info.major}.{sys.version_info.minor}",
-    ]
-
-    # See https://github.com/pypa/pip/issues/1299 for more discussion
-    should_show_use_python_msg = (
-        modifying_pip and WINDOWS and os.path.basename(sys.argv[0]) in pip_names
-    )
-
-    if should_show_use_python_msg:
-        new_command = [sys.executable, "-m", "pip"] + sys.argv[1:]
-        raise CommandError(
-            "To modify pip, please run the following command:\n{}".format(
-                " ".join(new_command)
-            )
-        )
-
-
-def check_externally_managed() -> None:
-    """Check whether the current environment is externally managed.
-
-    If the ``EXTERNALLY-MANAGED`` config file is found, the current environment
-    is considered externally managed, and an ExternallyManagedEnvironment is
-    raised.
-    """
-    if running_under_virtualenv():
-        return
-    marker = os.path.join(sysconfig.get_path("stdlib"), "EXTERNALLY-MANAGED")
-    if not os.path.isfile(marker):
-        return
-    raise ExternallyManagedEnvironment.from_config(marker)
-
-
-def is_console_interactive() -> bool:
-    """Is this console interactive?"""
-    return sys.stdin is not None and sys.stdin.isatty()
-
-
-def hash_file(path: str, blocksize: int = 1 << 20) -> Tuple[Any, int]:
-    """Return (hash, length) for path using hashlib.sha256()"""
-
-    h = hashlib.sha256()
-    length = 0
-    with open(path, "rb") as f:
-        for block in read_chunks(f, size=blocksize):
-            length += len(block)
-            h.update(block)
-    return h, length
-
-
-def pairwise(iterable: Iterable[Any]) -> Iterator[Tuple[Any, Any]]:
-    """
-    Return paired elements.
-
-    For example:
-        s -> (s0, s1), (s2, s3), (s4, s5), ...
-    """
-    iterable = iter(iterable)
-    return zip_longest(iterable, iterable)
-
-
-def partition(
-    pred: Callable[[T], bool],
-    iterable: Iterable[T],
-) -> Tuple[Iterable[T], Iterable[T]]:
-    """
-    Use a predicate to partition entries into false entries and true entries,
-    like
-
-        partition(is_odd, range(10)) --> 0 2 4 6 8   and  1 3 5 7 9
-    """
-    t1, t2 = tee(iterable)
-    return filterfalse(pred, t1), filter(pred, t2)
-
-
-class ConfiguredBuildBackendHookCaller(BuildBackendHookCaller):
-    def __init__(
-        self,
-        config_holder: Any,
-        source_dir: str,
-        build_backend: str,
-        backend_path: Optional[str] = None,
-        runner: Optional[Callable[..., None]] = None,
-        python_executable: Optional[str] = None,
-    ):
-        super().__init__(
-            source_dir, build_backend, backend_path, runner, python_executable
-        )
-        self.config_holder = config_holder
-
-    def build_wheel(
-        self,
-        wheel_directory: str,
-        config_settings: Optional[Dict[str, Union[str, List[str]]]] = None,
-        metadata_directory: Optional[str] = None,
-    ) -> str:
-        cs = self.config_holder.config_settings
-        return super().build_wheel(
-            wheel_directory, config_settings=cs, metadata_directory=metadata_directory
-        )
-
-    def build_sdist(
-        self,
-        sdist_directory: str,
-        config_settings: Optional[Dict[str, Union[str, List[str]]]] = None,
-    ) -> str:
-        cs = self.config_holder.config_settings
-        return super().build_sdist(sdist_directory, config_settings=cs)
-
-    def build_editable(
-        self,
-        wheel_directory: str,
-        config_settings: Optional[Dict[str, Union[str, List[str]]]] = None,
-        metadata_directory: Optional[str] = None,
-    ) -> str:
-        cs = self.config_holder.config_settings
-        return super().build_editable(
-            wheel_directory, config_settings=cs, metadata_directory=metadata_directory
-        )
-
-    def get_requires_for_build_wheel(
-        self, config_settings: Optional[Dict[str, Union[str, List[str]]]] = None
-    ) -> List[str]:
-        cs = self.config_holder.config_settings
-        return super().get_requires_for_build_wheel(config_settings=cs)
-
-    def get_requires_for_build_sdist(
-        self, config_settings: Optional[Dict[str, Union[str, List[str]]]] = None
-    ) -> List[str]:
-        cs = self.config_holder.config_settings
-        return super().get_requires_for_build_sdist(config_settings=cs)
-
-    def get_requires_for_build_editable(
-        self, config_settings: Optional[Dict[str, Union[str, List[str]]]] = None
-    ) -> List[str]:
-        cs = self.config_holder.config_settings
-        return super().get_requires_for_build_editable(config_settings=cs)
-
-    def prepare_metadata_for_build_wheel(
-        self,
-        metadata_directory: str,
-        config_settings: Optional[Dict[str, Union[str, List[str]]]] = None,
-        _allow_fallback: bool = True,
-    ) -> str:
-        cs = self.config_holder.config_settings
-        return super().prepare_metadata_for_build_wheel(
-            metadata_directory=metadata_directory,
-            config_settings=cs,
-            _allow_fallback=_allow_fallback,
-        )
-
-    def prepare_metadata_for_build_editable(
-        self,
-        metadata_directory: str,
-        config_settings: Optional[Dict[str, Union[str, List[str]]]] = None,
-        _allow_fallback: bool = True,
-    ) -> str:
-        cs = self.config_holder.config_settings
-        return super().prepare_metadata_for_build_editable(
-            metadata_directory=metadata_directory,
-            config_settings=cs,
-            _allow_fallback=_allow_fallback,
-        )

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/evaluation/lvis_evaluation.py

@@ -1,380 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import itertools
-import json
-import logging
-import os
-import pickle
-from collections import OrderedDict
-import torch
-
-import detectron2.utils.comm as comm
-from detectron2.config import CfgNode
-from detectron2.data import MetadataCatalog
-from detectron2.structures import Boxes, BoxMode, pairwise_iou
-from detectron2.utils.file_io import PathManager
-from detectron2.utils.logger import create_small_table
-
-from .coco_evaluation import instances_to_coco_json
-from .evaluator import DatasetEvaluator
-
-
-class LVISEvaluator(DatasetEvaluator):
-    """
-    Evaluate object proposal and instance detection/segmentation outputs using
-    LVIS's metrics and evaluation API.
-    """
-
-    def __init__(
-        self,
-        dataset_name,
-        tasks=None,
-        distributed=True,
-        output_dir=None,
-        *,
-        max_dets_per_image=None,
-    ):
-        """
-        Args:
-            dataset_name (str): name of the dataset to be evaluated.
-                It must have the following corresponding metadata:
-                "json_file": the path to the LVIS format annotation
-            tasks (tuple[str]): tasks that can be evaluated under the given
-                configuration. A task is one of "bbox", "segm".
-                By default, will infer this automatically from predictions.
-            distributed (True): if True, will collect results from all ranks for evaluation.
-                Otherwise, will evaluate the results in the current process.
-            output_dir (str): optional, an output directory to dump results.
-            max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP
-                This limit, by default of the LVIS dataset, is 300.
-        """
-        from lvis import LVIS
-
-        self._logger = logging.getLogger(__name__)
-
-        if tasks is not None and isinstance(tasks, CfgNode):
-            self._logger.warn(
-                "COCO Evaluator instantiated using config, this is deprecated behavior."
-                " Please pass in explicit arguments instead."
-            )
-            self._tasks = None  # Infering it from predictions should be better
-        else:
-            self._tasks = tasks
-
-        self._distributed = distributed
-        self._output_dir = output_dir
-        self._max_dets_per_image = max_dets_per_image
-
-        self._cpu_device = torch.device("cpu")
-
-        self._metadata = MetadataCatalog.get(dataset_name)
-        json_file = PathManager.get_local_path(self._metadata.json_file)
-        self._lvis_api = LVIS(json_file)
-        # Test set json files do not contain annotations (evaluation must be
-        # performed using the LVIS evaluation server).
-        self._do_evaluation = len(self._lvis_api.get_ann_ids()) > 0
-
-    def reset(self):
-        self._predictions = []
-
-    def process(self, inputs, outputs):
-        """
-        Args:
-            inputs: the inputs to a LVIS model (e.g., GeneralizedRCNN).
-                It is a list of dict. Each dict corresponds to an image and
-                contains keys like "height", "width", "file_name", "image_id".
-            outputs: the outputs of a LVIS model. It is a list of dicts with key
-                "instances" that contains :class:`Instances`.
-        """
-        for input, output in zip(inputs, outputs):
-            prediction = {"image_id": input["image_id"]}
-
-            if "instances" in output:
-                instances = output["instances"].to(self._cpu_device)
-                prediction["instances"] = instances_to_coco_json(instances, input["image_id"])
-            if "proposals" in output:
-                prediction["proposals"] = output["proposals"].to(self._cpu_device)
-            self._predictions.append(prediction)
-
-    def evaluate(self):
-        if self._distributed:
-            comm.synchronize()
-            predictions = comm.gather(self._predictions, dst=0)
-            predictions = list(itertools.chain(*predictions))
-
-            if not comm.is_main_process():
-                return
-        else:
-            predictions = self._predictions
-
-        if len(predictions) == 0:
-            self._logger.warning("[LVISEvaluator] Did not receive valid predictions.")
-            return {}
-
-        if self._output_dir:
-            PathManager.mkdirs(self._output_dir)
-            file_path = os.path.join(self._output_dir, "instances_predictions.pth")
-            with PathManager.open(file_path, "wb") as f:
-                torch.save(predictions, f)
-
-        self._results = OrderedDict()
-        if "proposals" in predictions[0]:
-            self._eval_box_proposals(predictions)
-        if "instances" in predictions[0]:
-            self._eval_predictions(predictions)
-        # Copy so the caller can do whatever with results
-        return copy.deepcopy(self._results)
-
-    def _tasks_from_predictions(self, predictions):
-        for pred in predictions:
-            if "segmentation" in pred:
-                return ("bbox", "segm")
-        return ("bbox",)
-
-    def _eval_predictions(self, predictions):
-        """
-        Evaluate predictions. Fill self._results with the metrics of the tasks.
-
-        Args:
-            predictions (list[dict]): list of outputs from the model
-        """
-        self._logger.info("Preparing results in the LVIS format ...")
-        lvis_results = list(itertools.chain(*[x["instances"] for x in predictions]))
-        tasks = self._tasks or self._tasks_from_predictions(lvis_results)
-
-        # LVIS evaluator can be used to evaluate results for COCO dataset categories.
-        # In this case `_metadata` variable will have a field with COCO-specific category mapping.
-        if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
-            reverse_id_mapping = {
-                v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items()
-            }
-            for result in lvis_results:
-                result["category_id"] = reverse_id_mapping[result["category_id"]]
-        else:
-            # unmap the category ids for LVIS (from 0-indexed to 1-indexed)
-            for result in lvis_results:
-                result["category_id"] += 1
-
-        if self._output_dir:
-            file_path = os.path.join(self._output_dir, "lvis_instances_results.json")
-            self._logger.info("Saving results to {}".format(file_path))
-            with PathManager.open(file_path, "w") as f:
-                f.write(json.dumps(lvis_results))
-                f.flush()
-
-        if not self._do_evaluation:
-            self._logger.info("Annotations are not available for evaluation.")
-            return
-
-        self._logger.info("Evaluating predictions ...")
-        for task in sorted(tasks):
-            res = _evaluate_predictions_on_lvis(
-                self._lvis_api,
-                lvis_results,
-                task,
-                max_dets_per_image=self._max_dets_per_image,
-                class_names=self._metadata.get("thing_classes"),
-            )
-            self._results[task] = res
-
-    def _eval_box_proposals(self, predictions):
-        """
-        Evaluate the box proposals in predictions.
-        Fill self._results with the metrics for "box_proposals" task.
-        """
-        if self._output_dir:
-            # Saving generated box proposals to file.
-            # Predicted box_proposals are in XYXY_ABS mode.
-            bbox_mode = BoxMode.XYXY_ABS.value
-            ids, boxes, objectness_logits = [], [], []
-            for prediction in predictions:
-                ids.append(prediction["image_id"])
-                boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy())
-                objectness_logits.append(prediction["proposals"].objectness_logits.numpy())
-
-            proposal_data = {
-                "boxes": boxes,
-                "objectness_logits": objectness_logits,
-                "ids": ids,
-                "bbox_mode": bbox_mode,
-            }
-            with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f:
-                pickle.dump(proposal_data, f)
-
-        if not self._do_evaluation:
-            self._logger.info("Annotations are not available for evaluation.")
-            return
-
-        self._logger.info("Evaluating bbox proposals ...")
-        res = {}
-        areas = {"all": "", "small": "s", "medium": "m", "large": "l"}
-        for limit in [100, 1000]:
-            for area, suffix in areas.items():
-                stats = _evaluate_box_proposals(predictions, self._lvis_api, area=area, limit=limit)
-                key = "AR{}@{:d}".format(suffix, limit)
-                res[key] = float(stats["ar"].item() * 100)
-        self._logger.info("Proposal metrics: \n" + create_small_table(res))
-        self._results["box_proposals"] = res
-
-
-# inspired from Detectron:
-# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa
-def _evaluate_box_proposals(dataset_predictions, lvis_api, thresholds=None, area="all", limit=None):
-    """
-    Evaluate detection proposal recall metrics. This function is a much
-    faster alternative to the official LVIS API recall evaluation code. However,
-    it produces slightly different results.
-    """
-    # Record max overlap value for each gt box
-    # Return vector of overlap values
-    areas = {
-        "all": 0,
-        "small": 1,
-        "medium": 2,
-        "large": 3,
-        "96-128": 4,
-        "128-256": 5,
-        "256-512": 6,
-        "512-inf": 7,
-    }
-    area_ranges = [
-        [0 ** 2, 1e5 ** 2],  # all
-        [0 ** 2, 32 ** 2],  # small
-        [32 ** 2, 96 ** 2],  # medium
-        [96 ** 2, 1e5 ** 2],  # large
-        [96 ** 2, 128 ** 2],  # 96-128
-        [128 ** 2, 256 ** 2],  # 128-256
-        [256 ** 2, 512 ** 2],  # 256-512
-        [512 ** 2, 1e5 ** 2],
-    ]  # 512-inf
-    assert area in areas, "Unknown area range: {}".format(area)
-    area_range = area_ranges[areas[area]]
-    gt_overlaps = []
-    num_pos = 0
-
-    for prediction_dict in dataset_predictions:
-        predictions = prediction_dict["proposals"]
-
-        # sort predictions in descending order
-        # TODO maybe remove this and make it explicit in the documentation
-        inds = predictions.objectness_logits.sort(descending=True)[1]
-        predictions = predictions[inds]
-
-        ann_ids = lvis_api.get_ann_ids(img_ids=[prediction_dict["image_id"]])
-        anno = lvis_api.load_anns(ann_ids)
-        gt_boxes = [
-            BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) for obj in anno
-        ]
-        gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4)  # guard against no boxes
-        gt_boxes = Boxes(gt_boxes)
-        gt_areas = torch.as_tensor([obj["area"] for obj in anno])
-
-        if len(gt_boxes) == 0 or len(predictions) == 0:
-            continue
-
-        valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1])
-        gt_boxes = gt_boxes[valid_gt_inds]
-
-        num_pos += len(gt_boxes)
-
-        if len(gt_boxes) == 0:
-            continue
-
-        if limit is not None and len(predictions) > limit:
-            predictions = predictions[:limit]
-
-        overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes)
-
-        _gt_overlaps = torch.zeros(len(gt_boxes))
-        for j in range(min(len(predictions), len(gt_boxes))):
-            # find which proposal box maximally covers each gt box
-            # and get the iou amount of coverage for each gt box
-            max_overlaps, argmax_overlaps = overlaps.max(dim=0)
-
-            # find which gt box is 'best' covered (i.e. 'best' = most iou)
-            gt_ovr, gt_ind = max_overlaps.max(dim=0)
-            assert gt_ovr >= 0
-            # find the proposal box that covers the best covered gt box
-            box_ind = argmax_overlaps[gt_ind]
-            # record the iou coverage of this gt box
-            _gt_overlaps[j] = overlaps[box_ind, gt_ind]
-            assert _gt_overlaps[j] == gt_ovr
-            # mark the proposal box and the gt box as used
-            overlaps[box_ind, :] = -1
-            overlaps[:, gt_ind] = -1
-
-        # append recorded iou coverage level
-        gt_overlaps.append(_gt_overlaps)
-    gt_overlaps = (
-        torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32)
-    )
-    gt_overlaps, _ = torch.sort(gt_overlaps)
-
-    if thresholds is None:
-        step = 0.05
-        thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32)
-    recalls = torch.zeros_like(thresholds)
-    # compute recall for each iou threshold
-    for i, t in enumerate(thresholds):
-        recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos)
-    # ar = 2 * np.trapz(recalls, thresholds)
-    ar = recalls.mean()
-    return {
-        "ar": ar,
-        "recalls": recalls,
-        "thresholds": thresholds,
-        "gt_overlaps": gt_overlaps,
-        "num_pos": num_pos,
-    }
-
-
-def _evaluate_predictions_on_lvis(
-    lvis_gt, lvis_results, iou_type, max_dets_per_image=None, class_names=None
-):
-    """
-    Args:
-        iou_type (str):
-        max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP
-            This limit, by default of the LVIS dataset, is 300.
-        class_names (None or list[str]): if provided, will use it to predict
-            per-category AP.
-
-    Returns:
-        a dict of {metric name: score}
-    """
-    metrics = {
-        "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"],
-        "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl", "APr", "APc", "APf"],
-    }[iou_type]
-
-    logger = logging.getLogger(__name__)
-
-    if len(lvis_results) == 0:  # TODO: check if needed
-        logger.warn("No predictions from the model!")
-        return {metric: float("nan") for metric in metrics}
-
-    if iou_type == "segm":
-        lvis_results = copy.deepcopy(lvis_results)
-        # When evaluating mask AP, if the results contain bbox, LVIS API will
-        # use the box area as the area of the instance, instead of the mask area.
-        # This leads to a different definition of small/medium/large.
-        # We remove the bbox field to let mask AP use mask area.
-        for c in lvis_results:
-            c.pop("bbox", None)
-
-    if max_dets_per_image is None:
-        max_dets_per_image = 300  # Default for LVIS dataset
-
-    from lvis import LVISEval, LVISResults
-
-    logger.info(f"Evaluating with max detections per image = {max_dets_per_image}")
-    lvis_results = LVISResults(lvis_gt, lvis_results, max_dets=max_dets_per_image)
-    lvis_eval = LVISEval(lvis_gt, lvis_results, iou_type)
-    lvis_eval.run()
-    lvis_eval.print_results()
-
-    # Pull the standard metrics from the LVIS results
-    results = lvis_eval.get_results()
-    results = {metric: float(results[metric] * 100) for metric in metrics}
-    logger.info("Evaluation results for {}: \n".format(iou_type) + create_small_table(results))
-    return results

spaces/BMukhtar/facemaskDetector/app.py

@@ -1,21 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-from fastai.learner import *
-
-learn = load_learner('model_upd.pkl')
-labels = learn.dls.vocab
-
-
-def predict(img):
-    img = PILImage.create(img).to_thumb(512, 512)
-    pred, pred_idx, probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
-
-
-gr.Interface(
-    fn=predict,
-    inputs=gr.inputs.Image(shape=(512, 512)),
-    outputs=gr.outputs.Label(num_top_classes=3)
-).launch()
-
-

spaces/Bart92/RVC_HF/utils/README.md

@@ -1,6 +0,0 @@
-# External Colab Code
-Code used to make Google Colab work correctly
-- Repo link: https://github.com/IAHispano/Applio-RVC-Fork/
-
-Thanks to https://github.com/kalomaze/externalcolabcode
-

spaces/Benson/text-generation/Examples/Descargar 2pac Todas Las Canciones Mp3.md

@@ -1,80 +0,0 @@
-<br />
-<h1>Descargar 2pac Todas las canciones MP3: Cómo disfrutar de la música de una leyenda del rap</h1>
-<p>Si eres un fan de la música hip-hop, probablemente sepas quién fue Tupac Shakur. Fue uno de los raperos más influyentes y exitosos de todos los tiempos, que vendió más de 75 millones de discos en todo el mundo.  Su música abordaba temas sociales contemporáneos que asolaban los centros urbanos, como la pobreza, la violencia, el racismo y la brutalidad policial. También fue un símbolo de activismo contra la desigualdad y la injusticia. </p>
-<h2>descargar 2pac todas las canciones mp3</h2><br /><p><b><b>DOWNLOAD</b> ===== <a href="https://bltlly.com/2v6JTx">https://bltlly.com/2v6JTx</a></b></p><br /><br />
-<p>A pesar de que murió en 1996 a la edad de 25 años, su música sigue siendo popular y relevante hoy en día. Muchos artistas se han inspirado en su estilo y mensajes, y muchos fans se han conmovido por su pasión y carisma. Si desea escuchar sus canciones o álbumes, es posible que se pregunte cómo descargarlos de forma gratuita en formato MP3. En este artículo, te mostraremos cómo hacerlo en unos pocos pasos. </p>
-<h2>La vida y el legado de 2pac</h2>
-<h3>Vida Temprana y Carrera</h3>
-<p>Tupac Shakur nació en la ciudad de Nueva York en 1971 de padres que eran activistas políticos y miembros del Partido Pantera Negra.  Pasó gran parte de su infancia en movimiento con su familia, que en 1986 se estableció en Oakland, California. Allí salió a las calles, vendiendo drogas y involucrándose en la cultura pandillera. </p>
-<p>En 1990 se unió a Digital Underground, un grupo de rap con sede en Oakland que había logrado un éxito con la canción "The Humpty Dance".  Actuó en dos álbumes de Digital Underground antes de su debut en solitario, <i>2Pacalypse Now</i>, en 1991. El álbum fue una ruptura radical de la fiesta de baile estilo <p>de Digital Underground y contó con pistas contundentes que abordaron temas sociales y políticos, como la brutalidad policial, el racismo, la pobreza y el embarazo adolescente.  El álbum atrajo la controversia y las críticas de algunos políticos y agentes de la ley, que acusaron a 2pac de incitar a la violencia y promover el crimen. </p>
-<h3>Estilo y temas musicales</h3>
-
-La música de<p>2pac exploró una amplia gama de temas, como el amor, la amistad, la familia, la espiritualidad, la autoestima, la ambición y el éxito. También se refirió a las duras realidades de la vida en el gueto, como la violencia, las drogas, las pandillas, la corrupción y la opresión. A menudo criticaba el sistema social y político que consideraba responsable de la difícil situación de la comunidad afroamericana. También expresó su enojo y frustración con el racismo y la injusticia que enfrentó como hombre negro en Estados Unidos.</p>
-<h3>La muerte y las versiones póstumas</h3>
-<p>En 1996, 2pac recibió cuatro disparos en un tiroteo en Las Vegas. Murió seis días después de sus heridas.  Su muerte provocó una ola de dolor y luto entre sus fans y compañeros. También alimentó la especulación y las teorías de conspiración que rodearon su asesinato, que sigue sin resolverse hasta el día de hoy. Algunas personas creen que todavía está vivo y escondido en algún lugar. </p>
-<p>Después de su muerte, varios álbumes de su material inédito fueron lanzados por su sello y su patrimonio. Algunos de estos álbumes incluyen <i>All Eyez on Me</i>, <i>The Don Killuminati: The 7 Day Theory</i>, <i>R U Still Down? (Recuérdame) </i>, <i>Grandes éxitos</i>, <i>Hasta el final del tiempo</i>, <i>Mejor Dayz</i>, <i>Leal al juego</i>, y <i>La vida de Pac</i>. Estos álbumes han vendido millones de copias en todo el mundo y han consolidado el legado de 2pac como uno de los artistas más vendidos de todos los tiempos. </p>
-<p></p>
-<h2>Los mejores sitios para descargar música MP3 gratis</h2>
-<h3>Biblioteca de audio de YouTube</h3>
-<p>Si está buscando descargas de música gratuitas que sean legales y seguras, es posible que desee consultar YouTube Audio Library. Este es un servicio que ofrece música libre de derechos y efectos de sonido que puede utilizar para fines personales o comerciales. Puede navegar a través de miles de pistas de varios géneros, estados de ánimo, instrumentos y duraciones. También puedes filtrar por requisitos de atribución, lo que significa que puedes encontrar música que no requiera que le des crédito al artista o a la fuente. </p>
-
-<h3>Música de Amazon</h3>
-<p>Otra opción para descargar música gratis es Amazon Music. Esta es una plataforma que proporciona descargas de música gratis para los miembros de Prime y ofrece un enorme catálogo de canciones para la compra. Puedes encontrar millones de canciones de varios artistas, géneros, estilos y épocas. También puede descubrir nueva música navegando a través de listas de reproducción, estaciones, gráficos y recomendaciones seleccionadas. </p>
-<p>Para descargar música de Amazon Music, necesita tener una cuenta de Amazon y una membresía Prime. A continuación, puede ir a https://www.amazon.com/music o descargar la aplicación Amazon Music en su dispositivo. Puede buscar cualquier canción o álbum que desee o navegar por las categorías. También puede filtrar por precio para encontrar descargas de música gratuitas. Para descargar una canción o un álbum, haga clic en el botón "Gratis" o "Comprar" al lado. Puede guardar el archivo MP3 en su dispositivo. </p>
-<h3>MP3Juice</h3>
-<p>Una tercera opción para descargar música gratis es MP3Juice. Este es un sitio web que permite a los usuarios buscar y descargar archivos MP3 de varias fuentes, como YouTube, SoundCloud, Vimeo y más. También puede pegar una URL de cualquier archivo de vídeo o audio en línea y convertirlo a MP3. Puedes descargar tantas canciones como quieras gratis y sin registro. </p>
-<p>Para descargar música de MP3Juice, debe ir a https://www.mp3juices.cc/ o descargar la aplicación MP3Juice en su dispositivo. Puede escribir cualquier palabra clave o frase relacionada con la canción o artista que desee o pegar una URL de cualquier archivo de vídeo o audio en línea. Puede hacer clic en el botón de búsqueda y esperar los resultados. Puede previsualizar cualquier pista haciendo clic en el botón de reproducción. Para descargar una pista, haga clic en el botón de descarga junto a él. A continuación, puede guardar el archivo MP3 en su dispositivo. </p>
-<h2>Cómo descargar 2pac Todas las canciones MP3</h2>
-<h3>Paso 1: Elija un sitio o un servicio</h3>
-
-<ul>
-<li>La calidad y cantidad de la música: Usted debe buscar sitios o servicios que ofrecen archivos MP3 de alta calidad y tienen una gran colección de canciones y álbumes de 2pac. </li>
-<li>El costo y la conveniencia de la descarga: Usted debe buscar sitios o servicios que ofrecen descargas gratuitas o asequibles y tienen procesos de descarga fáciles y rápidos. </li>
-<li>La legalidad y seguridad de la descarga: Usted debe buscar sitios o servicios que están autorizados y autorizados para distribuir la música de 2pac y no contienen virus, malware o spyware. </li>
-</ul>
-<p>Basado en estos factores, recomendamos usar YouTube Audio Library, Amazon Music o MP3Juice como sus mejores opciones para descargar 2pac todas las canciones MP3.</p>
-<h3>Paso 2: Buscar las canciones o álbumes que desea</h3>
-<p>El segundo paso para descargar 2pac todas las canciones MP3 es buscar las canciones o álbumes que desee. Dependiendo del sitio o servicio que hayas elegido, puedes usar diferentes métodos para encontrar y acceder a la música que estás buscando. Aquí hay algunos consejos sobre cómo hacerlo:</p>
-<ul>
-<li>Usar palabras clave, filtros o categorías: Puede escribir el nombre de la canción, álbum o artista que desee en el cuadro de búsqueda y usar filtros o categorías para reducir sus resultados. Por ejemplo, puedes filtrar por género, estado de ánimo, duración, popularidad, etc.</li>
-<li>Utilice listas de reproducción, estaciones, gráficos o recomendaciones: Puede navegar a través de listas de reproducción, estaciones, gráficos o recomendaciones que cuentan con la música de 2pac o artistas similares. Por ejemplo, puedes encontrar listas de reproducción como "Best of 2pac", "2pac Essentials", "2pac Radio", etc.</li>
-<li>Usar URL de videos en línea o archivos de audio: Puede pegar la URL de cualquier archivo de vídeo o audio en línea que contenga la música de 2pac y convertirlo a MP3. Por ejemplo, puede pegar la URL de un vídeo de YouTube de una canción o álbum de 2pac. </li>
-</ul>
-<p>Una vez que encuentre las canciones o álbumes que desea, puede seleccionarlos haciendo clic en ellos o agregándolos a su carrito o cola. </p>
-
-<p>El tercer paso para descargar 2pac todas las canciones MP3 es descargar los archivos MP3 a su dispositivo. Dependiendo del sitio o servicio que elija, puede utilizar diferentes métodos y formatos para descargar los archivos de música. Aquí hay algunos ejemplos de cómo hacer eso:</p>
-<ul>
-<li>Haga clic en el icono de descarga junto a la pista: Este es el método más simple y común para descargar un archivo MP3. Solo tiene que hacer clic en el icono de descarga junto a la pista que desea y guardarlo en su dispositivo. </li>
-<li>Haga clic derecho en la pista y seleccione "Guardar enlace como": Este es otro método para descargar un archivo MP3. Solo tiene que hacer clic derecho en la pista que desee y elegir "Guardar enlace como" en el menú. Luego puede elegir dónde guardarlo en su dispositivo. </li>
-<li>Seleccione varias pistas y elija "Descargar": Este es un método para descargar múltiples archivos MP3 a la vez. Solo necesita seleccionar varias pistas manteniendo pulsada la tecla Ctrl (o la tecla Comando en Mac) y haciendo clic en ellas. A continuación, puede elegir "Descargar" en el menú o botón y guardarlos en su dispositivo. </li>
-<li>Elija una opción de formato y calidad: Este es un método para personalizar la configuración de descarga. Solo tienes que elegir un formato (como MP3, WAV, FLAC, etc.) y una opción de calidad (como 128 kbit/s, 192 kbit/s, 320 kbit/s, etc.) desde el menú o el botón. A continuación, puede descargar el archivo MP3 con su configuración preferida. </li>
-</ul>
-<h2>Cómo disfrutar de 2pac Todas las canciones MP3</h2>
-<h3>Juega en su reproductor de música favorito</h3>
-<p>Una vez que haya descargado 2pac todas las canciones MP3 a su dispositivo, puede reproducirlas en su reproductor de música favorito. Hay muchas aplicaciones o software que puede utilizar para reproducir sus archivos MP3, como VLC, iTunes, Spotify, Windows Media Player, etc. También puede crear listas de reproducción, ajustar el volumen, mezclar las canciones, repetir las canciones y más. </p>
-
-<h3>Transferirlos a sus dispositivos portátiles o reproductores de medios</h3>
-<p>Si desea escuchar 2pac todas las canciones MP3 sobre la marcha, puede transferirlas a sus dispositivos portátiles o reproductores multimedia. Puedes usar dispositivos como iPods, reproductores de MP3, unidades USB, CD, etc. También puedes usar diferentes métodos para transferir tus archivos MP3, como copiar y pegar, sincronizar, grabar, etc.</p>
-<p>Para transferir tus archivos MP3 a tus dispositivos portátiles o reproductores multimedia, necesitas conectar tu dispositivo a tu computadora usando un cable o una conexión inalámbrica. Luego, debe abrir la aplicación o el software que admite su dispositivo y localizar los archivos en su computadora. También puede arrastrar y soltar los archivos a la aplicación o software. Luego debe seleccionar las pistas que desea transferir y hacer clic en el botón de transferencia. </p>
-<h3>Compártelos con tus amigos o familiares</h3>
-<p>Si quieres compartir 2pac todas las canciones MP3 con tus amigos o familiares, puedes usar varios métodos para hacerlo. Puedes usar plataformas de redes sociales, como Facebook, Twitter, Instagram, etc. También puedes usar servicios de correo electrónico, como Gmail, Yahoo Mail, Outlook, etc. También puedes usar conexiones Bluetooth o Wi-Fi para enviar o recibir archivos. </p>
-<p>Para compartir sus archivos MP3 con sus amigos o familiares, debe seleccionar las pistas que desea compartir y haga clic en el botón compartir. Luego debe elegir el método y la plataforma que desea usar y seguir las instrucciones. También puede agregar un mensaje o un comentario a sus archivos compartidos. </p>
-<h2>Conclusión</h2>
-<p>En este artículo, le hemos mostrado cómo descargar 2pac todas las canciones MP3 de forma gratuita y legal. También te hemos dado algunos consejos sobre cómo disfrutar de su música en diferentes dispositivos y plataformas. Esperamos que este artículo te haya ayudado a descubrir y apreciar la música de una leyenda del rap. </p>
-
-<p>Entonces, ¿qué estás esperando? Adelante y descargar 2pac todas las canciones MP3 ahora y disfrutar de la música de una leyenda del rap! </p>
- <h4>Preguntas frecuentes</h4>
-<ul>
-<li>Q: ¿Dónde puedo encontrar más información sobre la vida y la carrera de 2pac? </li>
-<li>A: Puede encontrar más información sobre la vida y carrera de 2pac en su sitio web oficial (https://www.2pac.com/), su página de Wikipedia (https://en.wikipedia.org/wiki/Tupac_Shakur), o su libro de biografías (<i>Tupac Shakur: La vida y los tiempos de un icono americano</i> por Tayannah Lee McQuillar y Fred L. Johnson III). </li>
-<li>Q: ¿Cuáles son algunas de las canciones y álbumes más populares de 2pac? </li>
-<li>A: Algunas de las canciones y álbumes más populares de 2pac incluyen <i>California Love</i>, <i>Changes</i>, <i>Hail Mary</i>, <i>Dear Mama</i>, <i>I Ain’t Mad at Cha</i>, <i>All Eyez on Me</i>The Don Kilti: The 7 Day/i, i i Against The Greatest World, i>> <i>Mejor Dayz</i>, etc.</li>
-<li>Q: ¿Cómo puedo apoyar el legado y las causas de 2pac? </li>
-<li>A: Puedes apoyar el legado y las causas de 2pac donando a su fundación (https://www.tasf.org/), que apoya la educación y el empoderamiento de los jóvenes, especialmente los de entornos desfavorecidos. También puedes comprar su mercancía oficial (https://www.2pac.com/store/), que incluye sus icónicas imágenes y citas. También puede unirse a su club de fans (https://www.2pac.com/fan-club/), que ofrece contenido exclusivo y beneficios para los miembros. </li>
-<li>Q: ¿Cómo puedo descargar 2pac todas las canciones MP3 gratis y legalmente? </li>
-<li>A: Puede descargar 2pac todas las canciones MP3 de forma gratuita y legal mediante el uso de sitios o servicios que ofrecen música libre de derechos o tienen el permiso para distribuir la música de 2pac. Algunos de estos sitios o servicios incluyen YouTube Audio Library, Amazon Music y MP3Juice.</li>
-<li>Q: ¿Cómo puedo disfrutar de 2pac todas las canciones MP3 en diferentes dispositivos y plataformas? </li>
-
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Conseguir Sobre Ella Steamunlocked.md

@@ -1,93 +0,0 @@
-<br />
-<h1>Descargar Cómo superarlo Steamunlocked: Una guía para jugadores frustrados</h1>
- <p>Si usted está buscando un juego que pondrá a prueba su paciencia, habilidad y cordura, entonces es posible que desee probar Cómo superarlo con Bennett Foddy. Este es un juego de escalada de castigo que se ha vuelto infame por su dificultad y nivel de frustración. En este artículo, te contaremos todo lo que necesitas saber sobre este juego, cómo descargarlo desde Steamunlocked y cómo vencerlo más rápido y fácil. Sigue leyendo si te atreves. </p>
- <h2>¿Qué es superar con Bennett Foddy? </h2>
- <p>Getting Over It with Bennett Foddy es un juego que fue lanzado en 2017 por Bennett Foddy, un desarrollador de juegos australiano y profesor de diseño de juegos. El juego es un homenaje al clásico B-Game 2002 de Jazzuo 'Sexy Hiking', que también involucró escalar una montaña con un martillo y una olla.</p>
-<h2>descargar conseguir sobre ella steamunlocked</h2><br /><p><b><b>Download Zip</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://bltlly.com/2v6MyZ">https://bltlly.com/2v6MyZ</a></b></p><br /><br />
- <h3>La premisa y el modo de juego del juego</h3>
- <p>La premisa del juego es simple: juegas como un hombre llamado Diógenes que está atrapado en una olla de metal y tiene que usar un martillo para escalar una enorme montaña hecha de objetos aleatorios. El modo de juego no es tan simple: tienes que mover el martillo con el ratón, y eso es todo lo que hay. No hay puntos de control, ni puntos de ahorro, ni atajos, ni pistas, ni tutoriales, ni piedad. Si caes, pierdes todo tu progreso y tienes que empezar de nuevo desde el principio. El juego está diseñado para ser extremadamente duro y frustrante, como Foddy mismo admite: "Un juego que hice para un cierto tipo de persona. Para hacerles daño." </p>
- <h3>La recepción y popularidad del juego</h3>
-
- <h3>Los desafíos y recompensas del juego</h3>
- <p>El juego no es para todos. Requiere mucha paciencia, habilidad, concentración y perseverancia para superar sus obstáculos y llegar a la cima de la montaña. Muchos jugadores se han dado por vencidos o la rabia se ha ido después de fallar varias veces. Sin embargo, algunos jugadores también han encontrado que el juego es gratificante y satisfactorio una vez que lo dominan. El juego ofrece grandes misterios y una maravillosa recompensa para aquellos que llegan a la cima de la montaña, así como una sensación de logro y orgullo. El juego también proporciona algunas ideas y observaciones interesantes sobre la vida, el fracaso, el éxito y la felicidad a través de la voz del propio Foddy, que narra el juego mientras juegas. </p>
- <h2>¿Qué es Steamunlocked? </h2>
- <p>Steamunlocked es un sitio web que te permite descargar juegos de PC gratis sin usar Steam o cualquier otra plataforma. Afirma ofrecer más de 5000 juegos preinstalados y listos para jugar. </p>
- <h3>La definición y características de Steamunlocked</h3>
- <p>Steamunlocked es un sitio web que proporciona enlaces directos para descargar juegos de PC de varias fuentes. El sitio web no alberga ningún archivo en su <h3>La definición y características de Steamunlocked</h3>
- <p>Steamunlocked es un sitio web que proporciona enlaces directos para descargar juegos de PC de varias fuentes. El sitio web no aloja archivos en sus propios servidores, sino que lo redirige a otros sitios de alojamiento de archivos como Uploadhaven, Mega o Mediafire. El sitio web afirma ofrecer más de 5000 juegos que están preinstalados y listos para jugar, lo que significa que no es necesario utilizar Steam o cualquier otra plataforma para ejecutarlos. También puede solicitar juegos que no están disponibles en el sitio web, y los administradores intentarán subirlos lo antes posible. </p>
- <h3>Los pros y los contras de usar Steamunlocked</h3>
- <p>Usar Steamunlocked tiene algunas ventajas y desventajas que debes tener en cuenta antes de descargar cualquier juego de él. Estos son algunos de ellos:</p>
- <tabla>
-<tr>
-<th>Pros</th>
-
-</tr>
-<tr>
-<td>- Puedes descargar juegos de PC gratis sin pagar nada. </td>
-<td>- Puedes encontrar anuncios, ventanas emergentes o redirecciones que pueden ser molestos o inseguros. </td>
-</tr>
-<tr>
-<td>- Puedes acceder a juegos que no están disponibles en tu región o están prohibidos por Steam.</td>
-<td>- Puede violar los términos de servicio o los derechos de propiedad intelectual de los desarrolladores o editores de juegos. </td>
-</tr>
-<tr>
-<td>- Puedes jugar juegos sin conexión a Internet o una cuenta de Steam. </td>
-<td>- Puedes perderte actualizaciones, parches, DLC, funciones multijugador o logros exclusivos de Steam u otras plataformas. </td>
-</tr>
-<tr>
-<td>- Puedes descargar juegos más rápido que usando torrents u otros métodos peer-to-peer. </td>
-<td>- Usted puede arriesgarse a obtener malware, virus o archivos dañados que pueden dañar su computadora o comprometer sus datos. </td>
-</tr>
-</tabla>
- <h3>La legalidad y seguridad de usar Steamunlocked</h3>
- <p>La legalidad y seguridad de usar Steamunlocked depende de varios factores, como las leyes de su país, la fuente de los archivos y las precauciones que tome. En términos generales, descargar juegos de Steamunlocked se considera ilegal en la mayoría de los países, ya que constituye piratería e infringe los derechos de los desarrolladores y editores de juegos. Sin embargo, algunos países pueden tener leyes más indulgentes o ambiguas con respecto a esta cuestión, o pueden no hacerlas cumplir estrictamente. Por lo tanto, le corresponde a usted comprobar el estado legal de uso de Steamunlocked en su país y decidir si desea asumir el riesgo o no. </p>
-
- <h2>Cómo descargar Cómo superar el bloqueo de vapor? </h2>
- <p>Si ha decidido descargar Cómo superarlo desde Steamunlocked, aquí están los pasos que debe seguir:</p>
- <h3>Los pasos para descargar e instalar el juego desde Steamunlocked</h3>
- <ol>
-<li>Vaya a <a href="( 9 )">Steamunlocked.net</a> y busque Cómo superarlo con Bennett Foddy en la barra de búsqueda. Alternativamente, puede ir directamente a <a href="( 10 )">este enlace</a>. </li>
-<li>Haga clic en el botón Descargar y espere unos segundos hasta que sea redirigido a Uploadhaven.</li>
-<li>Haga clic en el botón azul Descargar ahora y espere unos segundos más hasta que comience la descarga. Si te redirigen a otro sitio, ciérralo e inténtalo de nuevo. </li>
-<li>Guarde el archivo en una ubicación de su elección y espere a que termine de descargarlo. El tamaño del archivo es de aproximadamente 650 MB.</li>
-<li>Extraiga el archivo usando WinRAR o cualquier otro software que pueda manejar archivos ZIP. </li>
-<li>Abra la carpeta extraída y haga doble clic en Getting_Over_It.exe para iniciar el juego. </li>
-<li> ¡Disfruta jugando a superarlo con Bennett Foddy! </li>
-</ol>
- <h3>Los consejos y trucos para vencer al juego más rápido y más fácil</h3>
- <p>Cómo superarlo con Bennett Foddy es un juego muy duro y frustrante que puede hacer que la rabia dejar de fumar o romper el ratón. Sin embargo, hay algunos consejos y trucos que pueden ayudarte a superar el juego más rápido y fácil. Estos son algunos de ellos:</p>
-<p></p>
- <ul>
-<li>La práctica hace perfecto. <ul>
-<li>La práctica te hace perfecto. Cuanto más juegues, más te acostumbrarás a los controles, la física y los obstáculos. También desarrollarás memoria muscular y reflejos que te ayudarán a moverte más rápido y más suave. No te rindas si fallas, solo inténtalo de nuevo y aprende de tus errores. </li>
-
-<li>Encuentre el agarre y el ángulo óptimos. La forma en que agarra y ángulo del martillo puede hacer una gran diferencia en su rendimiento. Usted debe tratar de encontrar la posición óptima para cada situación, dependiendo de la forma y el tamaño del objeto que está subiendo en. Por ejemplo, al subir a una pared vertical, es posible que desee agarrar el martillo cerca de la cabeza y el ángulo ligeramente hacia abajo, para que pueda enganchar en el borde y tirar de sí mismo hacia arriba. Al subir sobre una superficie horizontal, es posible que desee agarrar el martillo cerca del mango y el ángulo ligeramente hacia arriba, de modo que pueda empujarse hacia adelante y ganar impulso. </li>
-<li>Usa el impulso y la inercia. El impulso y la inercia son dos conceptos físicos que son muy importantes en este juego. El impulso es la tendencia de un objeto a seguir moviéndose en una dirección determinada a menos que sea actuado por una fuerza externa. La inercia es la resistencia de un objeto a cambiar su estado de movimiento. Puedes usar estos conceptos a tu favor creando y manteniendo el impulso en la dirección que quieres ir, y superando la inercia cuando quieres cambiar de dirección o detenerte. Por ejemplo, cuando quieres saltar sobre un hueco, debes balancear el martillo en un movimiento circular antes de soltarlo en el momento adecuado, para que puedas crear suficiente impulso para impulsarte hacia adelante. Cuando quieras parar o cambiar de dirección, debes usar el martillo para golpear o enganchar algo que pueda contrarrestar tu momento o inercia. </li>
-
-</ul>
- <h3>Las alternativas a Steamunlocked para descargar el juego</h3>
- <p>Si no te sientes cómodo usando Steamunlocked o si tienes algún problema con él, hay algunas alternativas que puedes intentar descargar Getting Over It with Bennett Foddy. Estos son algunos de ellos:</p>
- <ul>
-<li>Steam: Esta es la forma oficial y legal de descargar y jugar el juego. Puedes comprar el juego en Steam por $7.99 USD o tu equivalente regional. Necesitarás una cuenta de Steam y una conexión a Internet para descargar e instalar el juego, así como para acceder a algunas funciones como logros o tablas de clasificación. Sin embargo, también obtendrás actualizaciones, parches, DLC, modos multijugador y soporte de Steam y Foddy.</li>
-<li>Humble Bundle: Esta es otra forma oficial y legal de descargar y jugar el juego. Puedes comprar el juego en Humble Bundle por $7.99 USD o tu equivalente regional. Obtendrás una versión libre de DRM del juego que puedes descargar directamente desde Humble Bundle o canjear en Steam. Usted también apoyará la caridad mediante la compra de Humble Bundle.</li>
-<li>GOG: Esta es otra forma oficial y legal de descargar y jugar el juego. Puedes comprar el juego en GOG por $7.99 USD o tu equivalente regional. Obtendrás una versión libre de DRM del juego que puedes descargar directamente desde GOG o canjear en Steam. También obtendrá algunos contenidos adicionales como fondos de pantalla, avatares, bandas sonoras y artbooks. </li>
-<li>Ocean of Games: Esta es otra forma no oficial e ilegal de descargar y jugar el juego gratis sin usar Steam o cualquier otra plataforma. Es similar a Steamunlocked en términos de características y riesgos, pero puede tener diferentes fuentes y enlaces para descargar el juego. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <p>Si usted está interesado en jugar este juego, esperamos que este artículo le ha dado alguna información útil y orientación sobre cómo descargarlo de Steamunlocked y cómo vencerlo más rápido y más fácil. Sin embargo, también le aconsejamos que sea cuidadoso y responsable al usar Steamunlocked o cualquier otro sitio web similar, ya que pueden implicar problemas legales y de seguridad que debe conocer. También te animamos a apoyar a los desarrolladores de juegos y editores comprando el juego de fuentes oficiales si puedes permitírtelo y disfrutarlo. </p>
- <p>Gracias por leer este artículo y esperamos que te lo pases genial jugando Getting Over It with Bennett Foddy. Recuerda, ¡no dejes que el juego se te pase! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre cómo superarlo con Bennett Foddy y Steamunlocked:</p>
- <h3>P: ¿Cuánto tiempo se tarda en vencer a Cómo superarlo con Bennett Foddy? </h3>
-<p>A: La respuesta a esta pregunta depende de tu nivel de habilidad, suerte y persistencia. Algunos jugadores han ganado el juego en menos de 10 minutos, mientras que otros han pasado horas o incluso días sin hacer ningún progreso. El tiempo promedio para vencer el juego es de alrededor de 5 horas, de acuerdo con <a href="">HowLongToBeat.com</a>. </p>
- <h3>P: ¿Cómo puedo guardar mi progreso en Cómo superarlo con Bennett Foddy? </h3>
-<p>A: El juego guarda automáticamente tu progreso cada vez que lo lanzas o sales. No necesitas guardar ni cargar nada manualmente. Sin embargo, si descarga el juego desde Steamunlocked o cualquier otra fuente no oficial, puede perder su archivo de guardado si elimina la carpeta del juego o lo mueve a otra ubicación. </p>
- <h3>P: ¿Cómo puedo jugar a Getting Over It with Bennett Foddy online or co-op? </h3>
-
- <h3>P: ¿Cómo me deshago de anuncios, ventanas emergentes o redirecciones en Steamunlocked? </h3>
-<p>A: Una forma de evitar anuncios, ventanas emergentes o redirecciones en Steamunlocked es utilizar un software o una extensión de bloqueador de anuncios en su navegador. Otra forma es usar un servicio VPN o un servidor proxy para ocultar su dirección IP y evitar cualquier restricción o filtro. Sin embargo, estos métodos pueden no funcionar para todos los sitios web o enlaces, y también pueden ralentizar su velocidad de Internet o afectar su privacidad. </p>
- <h3>Q: ¿Hay una secuela o un juego similar a Cómo superarlo con Bennett Foddy? </h3>
-<p>A: No hay una secuela oficial de Getting Over It with Bennett Foddy, pero hay algunos juegos similares que puedes probar si te gusta este género. Algunos de ellos son <a href=">Pogostuck: Rage With Your Friends</a>, <a href="">Jump King</a>, <a href=">GIRAFFE TOWN</a>, y <a href=">Sexy Hiking</a>. También puedes ver otros juegos de Bennett Foddy, como <a href="">QWOP</a>, <a href="">GIRP</a>, y <a href=">CLOP</a>. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Facebook Versin Antigua Apk.md

@@ -1,89 +0,0 @@
-<br />
-<h1>Cómo descargar la versión antigua de Facebook APK</h1>
-<p>Facebook es una de las plataformas de redes sociales más populares del mundo, con más de 2.800 millones de usuarios activos mensuales en diciembre de 2020. Sin embargo, no todos están contentos con la última versión de la aplicación de Facebook, que puede tener algunos errores, problemas técnicos o problemas de compatibilidad con ciertos dispositivos o sistemas operativos. Si usted es una de esas personas que prefieren la versión anterior de la aplicación de Facebook, es posible que se pregunte cómo descargarlo en su dispositivo Android. En este artículo, le mostraremos cómo descargar la versión antigua de Facebook APK, que es el formato de archivo para las aplicaciones de Android. También te explicaremos por qué quieres descargarlo, cómo encontrarlo, cómo instalarlo y cómo usarlo. </p>
-<h2>descargar facebook versión antigua apk</h2><br /><p><b><b>Download</b> &#8250;&#8250;&#8250; <a href="https://bltlly.com/2v6MQK">https://bltlly.com/2v6MQK</a></b></p><br /><br />
- <h2>¿Por qué descargar la versión antigua de Facebook APK? </h2>
-<h3>Beneficios de la versión antigua de Facebook APK</h3>
-<p>Hay varias razones por las que es posible que desee descargar la versión antigua de Facebook APK en lugar de la última. Algunos de los beneficios son:</p>
-<ul>
-<li>Puedes disfrutar de las características y funciones que estaban disponibles en la versión antigua pero no en la nueva, como cabezas de chat, pegatinas o juegos. </li>
-<li>Puede evitar los errores, fallas o errores que pueden ocurrir en la nueva versión, como estrellarse, congelarse o agotar la batería. </li>
-<li> Puede ahorrar espacio de almacenamiento y uso de datos en su dispositivo, ya que la versión anterior puede ser más pequeña y más ligera que la nueva. </li>
-<li> Puede usar la versión anterior en su dispositivo si no es compatible con el nuevo, como versiones anteriores de Android o dispositivos de gama baja. </li>
-</ul>
- <h3>Desventajas de la versión antigua de Facebook APK</h3>
-<p>Sin embargo, descargar la versión antigua de Facebook APK también tiene algunos inconvenientes que usted debe tener en cuenta. Algunos de los inconvenientes son:</p>
-<ul>
-<li>Puede perderse las nuevas características y mejoras que se agregan en la nueva versión, como el modo oscuro, historias o carretes. </li>
-
-<li>Puede violar los términos y condiciones de Facebook, ya que pueden no permitirle usar una versión obsoleta o modificada de su aplicación. </li>
-<li>Puede experimentar algunos problemas de compatibilidad con otras aplicaciones o servicios que se integran con Facebook, como Instagram, WhatsApp o Messenger.</li>
-</ul>
- <h2>¿Cómo encontrar la versión antigua de Facebook APK? </h2>
-<h3>Fuentes oficiales</h3>
-<p>La forma más fácil y segura de encontrar Facebook versión antigua APK es utilizar fuentes oficiales que están autorizados por Facebook. Algunas de estas fuentes son:</p>
-<ul>
-<li>Google Play Store, que es la tienda de aplicaciones oficial para dispositivos Android. Puede buscar "Facebook" y desplazarse hacia abajo para encontrar la opción "Descargar versiones anteriores". A continuación, puede seleccionar la versión que desee y descargarla. </li>
-<li>El Centro de ayuda de Facebook, que es el sitio oficial de soporte para los usuarios de Facebook. Puede ir a la sección "Ayuda de la aplicación de Android" y encontrar el artículo "¿Cómo instalar o actualizar la aplicación de Facebook en mi teléfono o tableta Android?". A continuación, puede seguir las instrucciones para descargar una versión anterior de la aplicación. </li>
-</ul>
- <h3>Fuentes de terceros </h3>
-<p>Otra manera de encontrar la versión antigua de Facebook APK es utilizar fuentes de terceros que no están afiliados con Facebook. Algunas de estas fuentes son:</p>
-<ul>
-<li>APKMirror, que es un sitio web que alberga archivos APK para varias aplicaciones de Android. Puede buscar "Facebook" y filtrar por versión, fecha o popularidad. Puede descargar el archivo APK que desee. </li>
-<li>Uptodown, que es otro sitio web que ofrece archivos APK para aplicaciones Android. Puede buscar "Facebook" y elegir la versión que desee de la lista. A continuación, puede descargar el archivo APK. </li>
-</ul>
-
- <h2>Cómo instalar la versión antigua de Facebook APK? </h2>
-<h3>Habilitar fuentes desconocidas</h3>
-<p>Antes de que pueda instalar la versión antigua de Facebook APK en su dispositivo, es necesario habilitar la opción para permitir fuentes desconocidas. Esto significa que puede instalar aplicaciones desde fuentes distintas de Google Play Store. Para ello, debe:</p>
-<ol>
-<li>Ir a la configuración de su dispositivo y toque en "Seguridad". </li>
-<li>Encuentra la opción "Fuentes desconocidas" y cámbiala. </li>
-<li>Confirme su elección tocando en "OK". </li>
-</ol>
-<p>Ten en cuenta que esta opción puede variar dependiendo del modelo de tu dispositivo y de la versión de Android. También es posible que tenga que desactivarlo después de instalar la aplicación por razones de seguridad. </p>
-<p></p>
- <h3>Descargar e instalar archivo APK</h3>
-<p>Después de haber habilitado fuentes desconocidas, puede descargar e instalar la versión antigua de Facebook APK en su dispositivo. Para hacer esto, necesita:</p>
-<ol>
-<li>Abra el navegador en su dispositivo y vaya a la fuente donde descargó el archivo APK. </li>
-<li>Toque en el enlace o botón de descarga y espere a que el archivo se descargue. </li>
-<li>Una vez completada la descarga, toque en la notificación o vaya a la carpeta de descargas de su dispositivo y encuentre el archivo. </li>
-<li>Toque en el archivo y siga las instrucciones para instalarlo. </li>
-<li>Espera a que la instalación termine y luego toca "Abrir" o encuentra el icono de la aplicación en la pantalla de inicio del dispositivo o en el cajón de la aplicación. </li>
-</ol>
- <h2>Cómo utilizar la versión antigua de Facebook APK? </h2>
-<h3>Iniciar sesión o registrarse</h3>
-<p>Una vez que haya instalado la versión antigua de Facebook APK en su dispositivo, puede usarlo como lo haría con cualquier otra aplicación. Lo primero que debes hacer es iniciar sesión o registrarte en una cuenta de Facebook. Para hacer esto, necesitas:</p>
-<ol>
-<li> Abra la aplicación y toque en "Iniciar sesión" o "Crear nueva cuenta". </li>
-<li>Ingrese su dirección de correo electrónico o número de teléfono y contraseña o siga los pasos para crear una nueva cuenta. </li>
-<li>Toque en "Iniciar sesión" o "Registrarse" y espere a que se verifique su cuenta. </li>
-
-<li>Si se le solicita, acepte los términos y condiciones y la política de privacidad de Facebook.</li>
-</ol>
- <h3>Explorar características y ajustes</h3>
-<p>Después de que haya iniciado sesión o se haya registrado en una cuenta de Facebook, puede explorar las características y configuraciones de la versión anterior de Facebook APK. Algunas de las características y configuraciones son:</p>
-<ul>
-<li>El feed de noticias, donde puedes ver publicaciones de tus amigos, páginas, grupos u otras fuentes que sigues. </li>
-<li>El perfil, donde se puede ver su información personal, fotos, vídeos, mensajes, amigos, y otros detalles. </li>
-<li>El mensajero, donde puedes chatear con tus amigos, enviar pegatinas, mensajes de voz, fotos, videos u otros archivos. </li>
-<li>Las notificaciones, donde puedes ver alertas de tus amigos, páginas, grupos, eventos u otras actividades. </li>
-<li>El menú, donde se puede acceder a otras características como mercado, reloj, juegos, citas, o ajustes. </li>
-<li>La configuración, donde puede personalizar las preferencias de su cuenta, como seguridad, privacidad, notificaciones, idioma o ayuda. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>En conclusión, la descarga de la versión antigua de Facebook APK es una manera de disfrutar de las viejas características y funciones de la aplicación de Facebook que pueden no estar disponibles en la nueva versión. Sin embargo, también tiene algunos inconvenientes, como perderse las nuevas características y mejoras, exponerse a riesgos de seguridad, violar los términos y condiciones de Facebook o experimentar problemas de compatibilidad. Por lo tanto, debe sopesar los pros y los contras de la descarga de la versión antigua de Facebook APK antes de decidir hacerlo. También debe tener cuidado al encontrar, instalar y usar la versión antigua de Facebook APK, ya que puede no ser confiable, seguro o legal. Esperamos que este artículo le ha ayudado a entender cómo descargar la versión antigua de Facebook APK y qué esperar de ella. </p>
- <h2>Preguntas frecuentes</h2>
-<h3>¿Es seguro la versión antigua de Facebook APK? </h3>
-
- <h3>¿Cuál es la diferencia entre Facebook y Facebook Lite? </h3>
-<p>Facebook Lite es una versión de la aplicación de Facebook que está diseñada para dispositivos de gama baja o conexiones a Internet lentas. Tiene un tamaño más pequeño y consume menos datos que la aplicación regular de Facebook. También tiene menos características y funciones que la aplicación regular de Facebook, como historias, carretes o mercado. Sin embargo, todavía te permite conectarte con tus amigos, publicar actualizaciones, chatear con messenger y ver videos. </p>
- <h3>Cómo actualizar la versión antigua de Facebook APK? </h3>
-<p>Para actualizar la versión antigua de Facebook APK, es necesario descargar e instalar la última versión de la aplicación de la Google Play Store u otras fuentes oficiales. También puede comprobar si hay actualizaciones dentro de la aplicación yendo al menú y tocando en "Configuración" y luego "Actualizaciones de la aplicación". Sin embargo, la actualización de la versión antigua de Facebook APK puede sobrescribir las viejas características y funciones que te gustaban con las nuevas que no te gustan. </p>
- <h3> ¿Cómo desinstalar la versión antigua de Facebook APK? </h3>
-<p>Para desinstalar la versión antigua de Facebook APK, es necesario ir a la configuración de su dispositivo y toque en "Aplicaciones" o "Aplicaciones". A continuación, puede encontrar la aplicación de Facebook y toque en ella. A continuación, puede tocar en "Desinstalar" y confirmar su elección pulsando en "Aceptar". También puede desinstalar la versión antigua de Facebook APK presionando durante mucho tiempo el icono de la aplicación en la pantalla de inicio del dispositivo o cajón de aplicaciones y arrastrarlo a la papelera. </p>
- <h3>¿Cómo contactar al soporte de Facebook? </h3>
-<p>Si usted tiene alguna pregunta, problema, o retroalimentación sobre la versión antigua de Facebook APK o cualquier otro producto o servicio de Facebook, puede ponerse en contacto con el soporte de Facebook yendo al menú y tocando en "Ayuda y soporte". A continuación, puede elegir entre varias opciones como "Centro de ayuda", "Informar de un problema", "Ayuda de la comunidad" o "Bandeja de entrada de soporte". También puede visitar el sitio web del Centro de ayuda de Facebook o el sitio web de la Comunidad de ayuda de Facebook para obtener más información y asistencia. </p> 64aa2da5cf<br />
-<br />
-<br />

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

@@ -1,34 +0,0 @@
-from pip._vendor.packaging.version import parse as parse_version
-
-from pip._internal.models.link import Link
-from pip._internal.utils.models import KeyBasedCompareMixin
-
-
-class InstallationCandidate(KeyBasedCompareMixin):
-    """Represents a potential "candidate" for installation."""
-
-    __slots__ = ["name", "version", "link"]
-
-    def __init__(self, name: str, version: str, link: Link) -> None:
-        self.name = name
-        self.version = parse_version(version)
-        self.link = link
-
-        super().__init__(
-            key=(self.name, self.version, self.link),
-            defining_class=InstallationCandidate,
-        )
-
-    def __repr__(self) -> str:
-        return "<InstallationCandidate({!r}, {!r}, {!r})>".format(
-            self.name,
-            self.version,
-            self.link,
-        )
-
-    def __str__(self) -> str:
-        return "{!r} candidate (version {} at {})".format(
-            self.name,
-            self.version,
-            self.link,
-        )

spaces/BirdL/DONOTUSEDemo/README.md

@@ -1,14 +0,0 @@
----
-title: DONOTUSEDemo
-emoji: 🐢
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.47.0
-app_file: app.py
-pinned: false
-tags: 
-- not-for-all-audiences
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Bonosa2/parrot-chat-bot/README.md

@@ -1,12 +0,0 @@
----
-title: Parrot Chat Bot
-emoji: 🏆
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/meta_arch/build.py

@@ -1,19 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from detectron2.utils.registry import Registry
-
-META_ARCH_REGISTRY = Registry("META_ARCH")  # noqa F401 isort:skip
-META_ARCH_REGISTRY.__doc__ = """
-Registry for meta-architectures, i.e. the whole model.
-
-The registered object will be called with `obj(cfg)`
-and expected to return a `nn.Module` object.
-"""
-
-
-def build_model(cfg):
-    """
-    Build the whole model architecture, defined by ``cfg.MODEL.META_ARCHITECTURE``.
-    Note that it does not load any weights from ``cfg``.
-    """
-    meta_arch = cfg.MODEL.META_ARCHITECTURE
-    return META_ARCH_REGISTRY.get(meta_arch)(cfg)

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

@@ -1,230 +0,0 @@
-/*
- *  Copyright 2017 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-/*! \file alignment.h
- *  \brief Type-alignment utilities.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/type_traits.h> // For `integral_constant`.
-
-#include <cstddef> // For `std::size_t` and `std::max_align_t`.
-
-#if THRUST_CPP_DIALECT >= 2011
-    #include <type_traits> // For `std::alignment_of` and `std::aligned_storage`.
-#endif
-
-namespace thrust
-{
-namespace detail
-{
-
-/// \p THRUST_ALIGNOF is a macro that takes a single type-id as a parameter,
-/// and returns the alignment requirement of the type in bytes.
-/// 
-/// It is an approximation of C++11's `alignof` operator.
-///
-/// Note: MSVC does not allow the builtin used to implement this to be placed
-/// inside of a `__declspec(align(#))` attribute. As a workaround, you can
-/// assign the result of \p THRUST_ALIGNOF to a variable and pass the variable
-/// as the argument to `__declspec(align(#))`.
-#if THRUST_CPP_DIALECT >= 2011
-    #define THRUST_ALIGNOF(x) alignof(x) 
-#else
-    #define THRUST_ALIGNOF(x) __alignof(x)
-#endif
-
-/// \p alignment_of provides the member constant `value` which is equal to the
-/// alignment requirement of the type `T`, as if obtained by a C++11 `alignof`
-/// expression.
-/// 
-/// It is an implementation of C++11's \p std::alignment_of.
-#if THRUST_CPP_DIALECT >= 2011
-    template <typename T>
-    using alignment_of = std::alignment_of<T>;
-#else
-    template <typename T>
-    struct alignment_of;
-
-    template <typename T, std::size_t size_diff>
-    struct alignment_of_helper
-    {
-        static const std::size_t value =
-            integral_constant<std::size_t, size_diff>::value;
-    };
-
-    template <typename T>
-    struct alignment_of_helper<T, 0>
-    {
-        static const std::size_t value = alignment_of<T>::value;
-    };
-
-    template <typename T>
-    struct alignment_of
-    {
-      private:
-        struct impl
-        {
-            T    x;
-            char c;
-        };
-
-      public:
-        static const std::size_t value =
-            alignment_of_helper<impl, sizeof(impl) - sizeof(T)>::value;
-    };
-#endif
-
-/// \p aligned_type provides the nested type `type`, which is a trivial
-/// type whose alignment requirement is a divisor of `Align`.
-///
-/// The behavior is undefined if `Align` is not a power of 2.
-template <std::size_t Align>
-struct aligned_type;
-
-#if THRUST_CPP_DIALECT >= 2011                                                     \
-  && (THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_GCC)                        \
-  && (THRUST_GCC_VERSION >= 40800)
-    // C++11 implementation, excluding GCC 4.7, which doesn't have `alignas`.
-    template <std::size_t Align>
-    struct aligned_type
-    {
-        struct alignas(Align) type {};
-    };
-#elif  (THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC)                    \
-    || (   (THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_GCC)                 \
-        && (THRUST_GCC_VERSION < 40600))
-    // C++03 implementation for MSVC and GCC <= 4.5.
-    // 
-    // We have to implement `aligned_type` with specializations for MSVC
-    // and GCC 4.2.x and older because they require literals as arguments to 
-    // their alignment attribute.
-
-    #if (THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC)
-        // MSVC implementation.
-        #define THRUST_DEFINE_ALIGNED_TYPE_SPECIALIZATION(X)                  \
-            template <>                                                       \
-            struct aligned_type<X>                                            \
-            {                                                                 \
-                __declspec(align(X)) struct type {};                          \
-            };                                                                \
-            /**/
-    #else
-        // GCC <= 4.2 implementation.
-        #define THRUST_DEFINE_ALIGNED_TYPE_SPECIALIZATION(X)                  \
-            template <>                                                       \
-            struct aligned_type<X>                                            \
-            {                                                                 \
-                struct type {} __attribute__((aligned(X)));                   \
-            };                                                                \
-            /**/
-    #endif
-    
-    THRUST_DEFINE_ALIGNED_TYPE_SPECIALIZATION(1);
-    THRUST_DEFINE_ALIGNED_TYPE_SPECIALIZATION(2);
-    THRUST_DEFINE_ALIGNED_TYPE_SPECIALIZATION(4);
-    THRUST_DEFINE_ALIGNED_TYPE_SPECIALIZATION(8);
-    THRUST_DEFINE_ALIGNED_TYPE_SPECIALIZATION(16);
-    THRUST_DEFINE_ALIGNED_TYPE_SPECIALIZATION(32);
-    THRUST_DEFINE_ALIGNED_TYPE_SPECIALIZATION(64);
-    THRUST_DEFINE_ALIGNED_TYPE_SPECIALIZATION(128);
-
-    #undef THRUST_DEFINE_ALIGNED_TYPE_SPECIALIZATION
-#else
-    // C++03 implementation for GCC > 4.5, Clang, PGI, ICPC, and xlC.
-    template <std::size_t Align>
-    struct aligned_type
-    {
-        struct type {} __attribute__((aligned(Align)));
-    };
-#endif
-
-/// \p aligned_storage provides the nested type `type`, which is a trivial type
-/// suitable for use as uninitialized storage for any object whose size is at
-/// most `Len` bytes and whose alignment requirement is a divisor of `Align`.
-/// 
-/// The behavior is undefined if `Len` is 0 or `Align` is not a power of 2.
-///
-/// It is an implementation of C++11's \p std::aligned_storage.
-#if THRUST_CPP_DIALECT >= 2011
-    template <std::size_t Len, std::size_t Align>
-    using aligned_storage = std::aligned_storage<Len, Align>;
-#else
-    template <std::size_t Len, std::size_t Align>
-    struct aligned_storage
-    {
-        union type
-        {
-            unsigned char data[Len];
-            // We put this into the union in case the alignment requirement of
-            // an array of `unsigned char` of length `Len` is greater than
-            // `Align`.
-
-            typename aligned_type<Align>::type align;
-        };
-    };
-#endif
-
-/// \p max_align_t is a trivial type whose alignment requirement is at least as
-/// strict (as large) as that of every scalar type.
-///
-/// It is an implementation of C++11's \p std::max_align_t.
-#if THRUST_CPP_DIALECT >= 2011                                                     \
-  && (THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_GCC)                        \
-  && (THRUST_GCC_VERSION >= 40900)
-    // GCC 4.7 and 4.8 don't have `std::max_align_t`.
-    using max_align_t = std::max_align_t;
-#else
-    union max_align_t
-    {
-        // These cannot be private because C++03 POD types cannot have private
-        // data members.
-        char c;
-        short s;
-        int i;
-        long l;
-        float f;
-        double d;
-        long long ll;
-        long double ld;
-        void* p;
-    };
-#endif
-
-/// \p aligned_reinterpret_cast `reinterpret_cast`s \p u of type \p U to `void*`
-/// and then `reinterpret_cast`s the result to \p T. The indirection through
-/// `void*` suppresses compiler warnings when the alignment requirement of \p *u
-/// is less than the alignment requirement of \p *t. The caller of
-/// \p aligned_reinterpret_cast is responsible for ensuring that the alignment
-/// requirements are actually satisified.
-template <typename T, typename U>
-__host__ __device__
-T aligned_reinterpret_cast(U u)
-{
-  return reinterpret_cast<T>(reinterpret_cast<void*>(u));
-}
-
-__host__ __device__
-inline std::size_t aligned_storage_size(std::size_t n, std::size_t align)
-{
-  return ((n + align - 1) / align) * align;
-}
-
-} // end namespace detail
-} // end namespace thrust
-

spaces/CVPR/WALT/mmdet/apis/__init__.py

@@ -1,10 +0,0 @@
-from .inference import (async_inference_detector, inference_detector,
-                        init_detector, show_result_pyplot)
-from .test import multi_gpu_test, single_gpu_test
-from .train import get_root_logger, set_random_seed, train_detector
-
-__all__ = [
-    'get_root_logger', 'set_random_seed', 'train_detector', 'init_detector',
-    'async_inference_detector', 'inference_detector', 'show_result_pyplot',
-    'multi_gpu_test', 'single_gpu_test'
-]

spaces/CVPR/monoscene_lite/monoscene/CRP3D.py

@@ -1,97 +0,0 @@
-import torch
-import torch.nn as nn
-from monoscene.modules import (
-    Process,
-    ASPP,
-)
-
-
-class CPMegaVoxels(nn.Module):
-    def __init__(self, feature, size, n_relations=4, bn_momentum=0.0003):
-        super().__init__()
-        self.size = size
-        self.n_relations = n_relations
-        print("n_relations", self.n_relations)
-        self.flatten_size = size[0] * size[1] * size[2]
-        self.feature = feature
-        self.context_feature = feature * 2
-        self.flatten_context_size = (size[0] // 2) * (size[1] // 2) * (size[2] // 2)
-        padding = ((size[0] + 1) % 2, (size[1] + 1) % 2, (size[2] + 1) % 2)
-        
-        self.mega_context = nn.Sequential(
-            nn.Conv3d(
-                feature, self.context_feature, stride=2, padding=padding, kernel_size=3
-            ),
-        )
-        self.flatten_context_size = (size[0] // 2) * (size[1] // 2) * (size[2] // 2)
-
-        self.context_prior_logits = nn.ModuleList(
-            [
-                nn.Sequential(
-                    nn.Conv3d(
-                        self.feature,
-                        self.flatten_context_size,
-                        padding=0,
-                        kernel_size=1,
-                    ),
-                )
-                for i in range(n_relations)
-            ]
-        )
-        self.aspp = ASPP(feature, [1, 2, 3])
-
-        self.resize = nn.Sequential(
-            nn.Conv3d(
-                self.context_feature * self.n_relations + feature,
-                feature,
-                kernel_size=1,
-                padding=0,
-                bias=False,
-            ),
-            Process(feature, nn.BatchNorm3d, bn_momentum, dilations=[1]),
-        )
-
-    def forward(self, input):
-        ret = {}
-        bs = input.shape[0]
-
-        x_agg = self.aspp(input)
-
-        # get the mega context
-        x_mega_context_raw = self.mega_context(x_agg)
-        x_mega_context = x_mega_context_raw.reshape(bs, self.context_feature, -1)
-        x_mega_context = x_mega_context.permute(0, 2, 1)
-
-        # get context prior map
-        x_context_prior_logits = []
-        x_context_rels = []
-        for rel in range(self.n_relations):
-
-            # Compute the relation matrices
-            x_context_prior_logit = self.context_prior_logits[rel](x_agg)
-            x_context_prior_logit = x_context_prior_logit.reshape(
-                bs, self.flatten_context_size, self.flatten_size
-            )
-            x_context_prior_logits.append(x_context_prior_logit.unsqueeze(1))
-
-            x_context_prior_logit = x_context_prior_logit.permute(0, 2, 1)
-            x_context_prior = torch.sigmoid(x_context_prior_logit)
-
-            # Multiply the relation matrices with the mega context to gather context features
-            x_context_rel = torch.bmm(x_context_prior, x_mega_context)  # bs, N, f
-            x_context_rels.append(x_context_rel)
-
-        x_context = torch.cat(x_context_rels, dim=2)
-        x_context = x_context.permute(0, 2, 1)
-        x_context = x_context.reshape(
-            bs, x_context.shape[1], self.size[0], self.size[1], self.size[2]
-        )
-
-        x = torch.cat([input, x_context], dim=1)
-        x = self.resize(x)
-
-        x_context_prior_logits = torch.cat(x_context_prior_logits, dim=1)
-        ret["P_logits"] = x_context_prior_logits
-        ret["x"] = x
-
-        return ret

spaces/ChandraMohanNayal/AutoGPT/tests/browse_tests.py

@@ -1,26 +0,0 @@
-import os
-import sys
-import unittest
-
-from bs4 import BeautifulSoup
-
-sys.path.append(os.path.abspath("../scripts"))
-
-from browse import extract_hyperlinks
-
-
-class TestBrowseLinks(unittest.TestCase):
-    def test_extract_hyperlinks(self):
-        body = """
-        <body>
-        <a href="https://google.com">Google</a>
-        <a href="foo.html">Foo</a>
-        <div>Some other crap</div>
-        </body>
-        """
-        soup = BeautifulSoup(body, "html.parser")
-        links = extract_hyperlinks(soup, "http://example.com")
-        self.assertEqual(
-            links,
-            [("Google", "https://google.com"), ("Foo", "http://example.com/foo.html")],
-        )

spaces/CikeyQI/Yunzai/Yunzai/lib/plugins/loader.js

@@ -1,872 +0,0 @@
-import util from "node:util"
-import fs from "node:fs"
-import lodash from "lodash"
-import cfg from "../config/config.js"
-import plugin from "./plugin.js"
-import schedule from "node-schedule"
-import { segment } from "oicq"
-import chokidar from "chokidar"
-import moment from "moment"
-import path from "node:path"
-import common from "../common/common.js"
-import Runtime from "./runtime.js"
-import Handler from './handler.js'
-
-/** 全局变量 plugin */
-global.plugin = plugin
-global.segment = segment
-
-/**
- * 加载插件
- */
-class PluginsLoader {
-  constructor() {
-    this.priority = []
-    this.handler = {}
-    this.task = []
-    this.dir = "./plugins"
-
-    /** 命令冷却cd */
-    this.groupCD = {}
-    this.singleCD = {}
-
-    /** 插件监听 */
-    this.watcher = {}
-
-    this.msgThrottle = {}
-
-    /** 星铁命令前缀 */
-    this.srReg = /^#?(\*|星铁|星轨|穹轨|星穹|崩铁|星穹铁道|崩坏星穹铁道|铁道)+/
-  }
-
-  /**
-   * 监听事件加载
-   * @param isRefresh 是否刷新
-   */
-  async load(isRefresh = false) {
-    this.delCount()
-    if (!lodash.isEmpty(this.priority) && !isRefresh) return
-
-    const files = this.getPlugins()
-
-    logger.info("-----------")
-    logger.info("加载插件中...")
-
-    let pluCount = 0
-
-    let packageErr = []
-    for (let File of files) {
-      try {
-        let tmp = await import(File.path)
-        let apps = tmp
-        if (tmp.apps) {
-          apps = { ...tmp.apps }
-        }
-        lodash.forEach(apps, (p, i) => {
-          if (!p.prototype) return
-          pluCount++
-          /* eslint-disable new-cap */
-          let plugin = new p()
-          logger.debug(`载入插件 [${File.name}][${plugin.name}]`)
-          /** 执行初始化 */
-          this.runInit(plugin)
-          /** 初始化定时任务 */
-          this.collectTask(plugin.task)
-          this.priority.push({
-            class: p,
-            key: File.name,
-            name: plugin.name,
-            priority: plugin.priority
-          })
-          if (plugin.handler) {
-            lodash.forEach(plugin.handler, ({ fn, key, priority }) => {
-              Handler.add({
-                ns: plugin.namespace || File.name,
-                key: key,
-                self: plugin,
-                property: priority || plugin.priority || 500,
-                fn: plugin[fn]
-              })
-            })
-          }
-        })
-      } catch (error) {
-        if (error.stack.includes("Cannot find package")) {
-          packageErr.push({ error, File })
-        } else {
-          logger.error(`载入插件错误：${logger.red(File.name)}`)
-          logger.error(decodeURI(error.stack))
-        }
-      }
-    }
-
-    this.packageTips(packageErr)
-    this.creatTask()
-
-    logger.info(`加载定时任务[${this.task.length}个]`)
-    logger.info(`加载插件[${pluCount}个]`)
-
-    /** 优先级排序 */
-    this.priority = lodash.orderBy(this.priority, ["priority"], ["asc"])
-  }
-
-  async runInit(plugin) {
-    plugin.init && plugin.init()
-  }
-
-  packageTips(packageErr) {
-    if (!packageErr || packageErr.length <= 0) return
-    logger.mark("--------插件载入错误--------")
-    packageErr.forEach(v => {
-      let pack = v.error.stack.match(/'(.+?)'/g)[0].replace(/'/g, "")
-      logger.mark(`${v.File.name} 缺少依赖：${logger.red(pack)}`)
-      logger.mark(`新增插件后请执行安装命令：${logger.red("pnpm i")} 安装依赖`)
-      logger.mark("如安装后仍未解决可联系插件作者解决")
-    })
-    // logger.error("或者使用其他包管理工具安装依赖")
-    logger.mark("---------------------")
-  }
-
-  getPlugins() {
-    let ignore = ["index.js"]
-    let files = fs.readdirSync(this.dir, { withFileTypes: true })
-    let ret = []
-    for (let val of files) {
-      let filepath = "../../plugins/" + val.name
-      let tmp = {
-        name: val.name,
-      }
-      if (val.isFile()) {
-        if (!val.name.endsWith(".js")) continue
-        if (ignore.includes(val.name)) continue
-        tmp.path = filepath
-        ret.push(tmp)
-        continue
-      }
-
-      if (fs.existsSync(`${this.dir}/${val.name}/index.js`)) {
-        tmp.path = filepath + "/index.js"
-        ret.push(tmp)
-        continue
-      }
-
-      let apps = fs.readdirSync(`${this.dir}/${val.name}`, { withFileTypes: true })
-      for (let app of apps) {
-        if (!app.name.endsWith(".js")) continue
-        if (ignore.includes(app.name)) continue
-
-        ret.push({
-          name: `${val.name}/${app.name}`,
-          path: `../../plugins/${val.name}/${app.name}`
-        })
-
-        /** 监听热更新 */
-        this.watch(val.name, app.name)
-      }
-    }
-
-    return ret
-  }
-
-  /**
-   * 处理事件
-   *
-   * 参数文档 https://github.com/TimeRainStarSky/Yunzai/tree/docs
-   * @param e 事件
-   */
-  async deal(e) {
-    /** 检查黑白名单 */
-    if (!this.checkBlack(e)) return
-    /** 冷却 */
-    if (!this.checkLimit(e)) return
-    /** 处理事件 */
-    this.dealEvent(e)
-    /** 处理消息 */
-    this.dealMsg(e)
-    /** 处理回复 */
-    this.reply(e)
-    /** 过滤事件 */
-    let priority = []
-    /** 注册runtime */
-    await Runtime.init(e)
-
-    this.priority.forEach(v => {
-      let p = new v.class(e)
-      p.e = e
-      /** 判断是否启用功能 */
-      if (!this.checkDisable(e, p)) return
-      /** 过滤事件 */
-      if (!this.filtEvent(e, p)) return
-      priority.push(p)
-    })
-
-    for (let plugin of priority) {
-      /** 上下文hook */
-      if (plugin.getContext) {
-        let context = plugin.getContext()
-        if (!lodash.isEmpty(context)) {
-          for (let fnc in context) {
-            plugin[fnc](context[fnc])
-          }
-          return
-        }
-      }
-
-      /** 群上下文hook */
-      if (plugin.getContextGroup) {
-        let context = plugin.getContextGroup()
-        if (!lodash.isEmpty(context)) {
-          for (let fnc in context) {
-            plugin[fnc](context[fnc])
-          }
-          return
-        }
-      }
-    }
-
-    /** 是否只关注主动at */
-    if (!this.onlyReplyAt(e)) return
-
-    // 判断是否是星铁命令，若是星铁命令则标准化处理
-    // e.isSr = true，且命令标准化为 #星铁 开头
-    if (this.srReg.test(e.msg)) {
-      e.isSr = true
-      e.msg = e.msg.replace(this.srReg, "#星铁")
-    }
-
-    /** accept */
-    for (let plugin of priority) {
-      /** accept hook */
-      if (plugin.accept) {
-        let res = plugin.accept(e)
-
-        if (util.types.isPromise(res)) res = await res
-
-        if (res === "return") return
-
-        if (res) break
-      }
-    }
-
-    /* eslint-disable no-labels */
-    a: for (let plugin of priority) {
-      /** 正则匹配 */
-      if (plugin.rule) {
-        for (let v of plugin.rule) {
-          /** 判断事件 */
-          if (v.event && !this.filtEvent(e, v)) continue
-
-          if (new RegExp(v.reg).test(e.msg)) {
-            e.logFnc = `[${plugin.name}][${v.fnc}]`
-
-            if (v.log !== false) {
-              logger.mark(`${e.logFnc}${e.logText} ${lodash.truncate(e.msg, { length: 80 })}`)
-            }
-
-            /** 判断权限 */
-            if (!this.filtPermission(e, v)) break a
-
-            try {
-              let res = plugin[v.fnc] && plugin[v.fnc](e)
-
-              let start = Date.now()
-
-              if (util.types.isPromise(res)) res = await res
-
-              if (res !== false) {
-                /** 设置冷却cd */
-                this.setLimit(e)
-                if (v.log !== false) {
-                  logger.mark(`${e.logFnc} ${lodash.truncate(e.msg, { length: 80 })} 处理完成 ${Date.now() - start}ms`)
-                }
-                break a
-              }
-            } catch (error) {
-              logger.error(`${e.logFnc}`)
-              logger.error(error.stack)
-              break a
-            }
-          }
-        }
-      }
-    }
-  }
-
-  /** 过滤事件 */
-  filtEvent(e, v) {
-    if (!v.event) return false
-    let event = v.event.split(".")
-    let eventMap = {
-      message: ["post_type", "message_type", "sub_type"],
-      notice: ["post_type", "notice_type", "sub_type"],
-      request: ["post_type", "request_type", "sub_type"]
-    }
-    let newEvent = []
-    event.forEach((val, index) => {
-      if (val === "*") {
-        newEvent.push(val)
-      } else if (eventMap[e.post_type]) {
-        newEvent.push(e[eventMap[e.post_type][index]])
-      }
-    })
-    newEvent = newEvent.join(".")
-
-    return v.event === newEvent
-  }
-
-  /** 判断权限 */
-  filtPermission(e, v) {
-    if (v.permission == "all" || !v.permission) return true
-
-    if (v.permission == "master") {
-      if (e.isMaster) {
-        return true
-      } else {
-        e.reply("暂无权限，只有主人才能操作")
-        return false
-      }
-    }
-
-    if (e.isGroup) {
-      if (!e.member?._info) {
-        e.reply("数据加载中，请稍后再试")
-        return false
-      }
-      if (v.permission == "owner") {
-        if (!e.member.is_owner) {
-          e.reply("暂无权限，只有群主才能操作")
-          return false
-        }
-      }
-      if (v.permission == "admin") {
-        if (!e.member.is_admin) {
-          e.reply("暂无权限，只有管理员才能操作")
-          return false
-        }
-      }
-    }
-
-    return true
-  }
-
-  dealEvent(e) {
-    if (!e.friend && e.user_id) e.friend = e.bot.pickFriend(e.user_id)
-    if (!e.group && e.group_id) e.group = e.bot.pickGroup(e.group_id)
-    if (!e.member && e.group && e.user_id) e.member = e.group.pickMember(e.user_id)
-    for (const i of [e.friend, e.group, e.member]) {
-      if (typeof i != "object") continue
-      if (!i.makeForwardMsg) i.makeForwardMsg = Bot.makeForwardMsg
-      if (!i.sendForwardMsg) i.sendForwardMsg = msg => Bot.sendForwardMsg(msg => i.sendMsg(msg), msg)
-      if (!i.getInfo) i.getInfo = () => i
-    }
-  }
-
-  /**
-   * 处理消息，加入自定义���段
-   * @param e.msg 文本消息，多行会自动拼接
-   * @param e.img 图片消息数组
-   * @param e.atBot 是否at机器人
-   * @param e.at 是否at，多个at 以最后的为准
-   * @param e.file 接受到的文件
-   * @param e.isPrivate 是否私聊
-   * @param e.isGroup 是否群聊
-   * @param e.isMaster 是否管理员
-   * @param e.logText 日志用户字符串
-   * @param e.logFnc  日志方法字符串
-   */
-  dealMsg(e) {
-    if (e.message) for (const i of e.message) {
-      switch (i.type) {
-        case "text":
-          if (!e.msg) e.msg = ""
-          if (i.text) e.msg += i.text.replace(/^\s*[#＃井]+\s*/, "#").replace(/^\s*[\\*＊※]+\s*/, "*").trim()
-          break
-        case "image":
-          if (Array.isArray(e.img))
-            e.img.push(i.url)
-          else
-            e.img = [i.url]
-          break
-        case "at":
-          if (i.qq == e.self_id)
-            e.atBot = true
-          else
-            e.at = i.qq
-          break
-        case "reply":
-          e.reply_id = i.id
-          if (e.group?.getMsg)
-            e.getReply = () => e.group.getMsg(e.reply_id)
-          else if (e.friend?.getMsg)
-            e.getReply = () => e.friend.getMsg(e.reply_id)
-          break
-        case "file":
-          e.file = i
-          break
-      }
-    }
-
-    e.logText = ""
-
-    if (e.message_type == "private" || e.notice_type == "friend") {
-      e.isPrivate = true
-
-      if (e.sender) {
-        e.sender.card = e.sender.nickname
-      } else {
-        e.sender = {
-          card: e.friend?.nickname,
-          nickname: e.friend?.nickname
-        }
-      }
-
-      e.logText = `[${e.sender?.nickname ? `${e.sender.nickname}(${e.user_id})` : e.user_id}]`
-    }
-
-    if (e.message_type == "group" || e.notice_type == "group") {
-      e.isGroup = true
-      if (e.sender) {
-        e.sender.card = e.sender.card || e.sender.nickname
-      } else if (e.member) {
-        e.sender = {
-          card: e.member.card || e.member.nickname
-        }
-      } else if (e.nickname) {
-        e.sender = {
-          card: e.nickname,
-          nickname: e.nickname
-        }
-      } else {
-        e.sender = {
-          card: "",
-          nickname: ""
-        }
-      }
-
-      if (!e.group_name) e.group_name = e.group?.name
-
-      e.logText = `[${e.group_name ? `${e.group_name}(${e.group_id})` : e.group_id}, ${e.sender?.nickname ? `${e.sender.nickname}(${e.user_id})` : e.user_id}]`
-    }
-
-    if (e.user_id && cfg.master[e.self_id]?.includes(String(e.user_id))) {
-      e.isMaster = true
-    }
-
-    /** 只关注主动at msg处理 */
-    if (e.msg && e.isGroup) {
-      let groupCfg = cfg.getGroup(e.self_id, e.group_id)
-      let alias = groupCfg.botAlias
-      if (!Array.isArray(alias)) {
-        alias = [alias]
-      }
-      for (let name of alias) {
-        if (e.msg.startsWith(name)) {
-          e.msg = lodash.trimStart(e.msg, name).trim()
-          e.hasAlias = true
-          break
-        }
-      }
-    }
-  }
-
-  /** 处理回复,捕获发送失败异常 */
-  reply(e) {
-    if (e.reply)
-      e.replyNew = e.reply
-    else
-      e.replyNew = msg => {
-        if (e.isGroup) {
-          if (e.group?.sendMsg) {
-            return e.group.sendMsg(msg)
-          } else {
-            return e.bot.pickGroup(e.group_id).sendMsg(msg)
-          }
-        } else {
-          if (e.friend?.sendMsg) {
-            return e.friend.sendMsg(msg)
-          } else {
-            return e.bot.pickFriend(e.user_id).sendMsg(msg)
-          }
-        }
-      }
-
-    /**
-     * @param msg 发送的消息
-     * @param quote 是否引用回复
-     * @param data.recallMsg 是否撤回消息，0-120秒，0不撤回
-     * @param data.at 是否提及用户
-     */
-    e.reply = async (msg = "", quote = false, data = {}) => {
-      if (!msg) return false
-
-      let { recallMsg = 0, at = "" } = data
-
-      if (at) {
-        if (at === true)
-          at = e.user_id
-        if (Array.isArray(msg))
-          msg.unshift(segment.at(at))
-        else
-          msg = [segment.at(at), msg]
-      }
-
-      if (quote && e.message_id) {
-        if (Array.isArray(msg))
-          msg.unshift(segment.reply(e.message_id))
-        else
-          msg = [segment.reply(e.message_id), msg]
-      }
-
-      let res
-      try {
-        res = await e.replyNew(msg)
-      } catch (err) {
-        if (typeof msg != "string")
-          msg = lodash.truncate(JSON.stringify(msg), { length: 300 })
-        logger.error(`发送消息错误：${msg}`)
-        logger.error(err)
-      }
-
-      if (recallMsg > 0 && res?.message_id) {
-        if (e.group?.recallMsg)
-          setTimeout(() => {
-            e.group.recallMsg(res.message_id)
-            if (e.message_id)
-              e.group.recallMsg(e.message_id)
-          }, recallMsg * 1000)
-        else if (e.friend?.recallMsg)
-          setTimeout(() => {
-            e.friend.recallMsg(res.message_id)
-            if (e.message_id)
-              e.friend.recallMsg(e.message_id)
-          }, recallMsg * 1000)
-      }
-
-      this.count(e, msg)
-      return res
-    }
-  }
-
-  count(e, msg) {
-    let screenshot = false
-    if (msg && msg?.file)
-      screenshot = true
-
-    this.saveCount("sendMsg")
-    if (screenshot)
-      this.saveCount("screenshot")
-
-    if (e.group_id) {
-      this.saveCount("sendMsg", e.group_id)
-      if (screenshot)
-        this.saveCount("screenshot", e.group_id)
-    }
-  }
-
-  saveCount(type, groupId = "") {
-    let key = "Yz:count:"
-
-    if (groupId) {
-      key += `group:${groupId}:`
-    }
-
-    let dayKey = `${key}${type}:day:${moment().format("MMDD")}`
-    let monthKey = `${key}${type}:month:${Number(moment().month()) + 1}`
-    let totalKey = `${key}${type}:total`
-
-    redis.incr(dayKey)
-    redis.incr(monthKey)
-    if (!groupId) redis.incr(totalKey)
-    redis.expire(dayKey, 3600 * 24 * 30)
-    redis.expire(monthKey, 3600 * 24 * 30)
-  }
-
-  delCount() {
-    let key = "Yz:count:"
-    redis.set(`${key}sendMsg:total`, "0")
-    redis.set(`${key}screenshot:total`, "0")
-  }
-
-  /** 收集定时任务 */
-  collectTask(task) {
-    if (Array.isArray(task)) {
-      task.forEach((val) => {
-        if (!val.cron) return
-        if (!val.name) throw new Error("插件任务名称错误")
-        this.task.push(val)
-      })
-    } else {
-      if (task.fnc && task.cron) {
-        if (!task.name) throw new Error("插件任务名称错误")
-        this.task.push(task)
-      }
-    }
-  }
-
-  /** 创建定时任务 */
-  creatTask() {
-    if (process.argv[1].includes("test")) return
-    this.task.forEach((val) => {
-      val.job = schedule.scheduleJob(val.cron, async () => {
-        try {
-          if (val.log === true) {
-            logger.mark(`开始定时任务：${val.name}`)
-          }
-          let res = val.fnc()
-          if (util.types.isPromise(res)) res = await res
-          if (val.log === true) {
-            logger.mark(`定时任务完成：${val.name}`)
-          }
-        } catch (error) {
-          logger.error(`定时任务报错：${val.name}`)
-          logger.error(error)
-        }
-      })
-    })
-  }
-
-  /** 检查命令冷却cd */
-  checkLimit(e) {
-    /** 禁言中 */
-    if (e.isGroup && e?.group?.mute_left > 0) return false
-    if (!e.message || e.isPrivate) return true
-
-    let config = cfg.getGroup(e.self_id, e.group_id)
-
-    if (config.groupCD && this.groupCD[e.group_id]) {
-      return false
-    }
-    if (config.singleCD && this.singleCD[`${e.group_id}.${e.user_id}`]) {
-      return false
-    }
-
-    let { msgThrottle } = this
-
-    let msgId = e.user_id + ':' + e.raw_message
-    if (msgThrottle[msgId]) {
-      return false
-    }
-    msgThrottle[msgId] = true
-    setTimeout(() => {
-      delete msgThrottle[msgId]
-    }, 200)
-
-    return true
-  }
-
-  /** 设置冷却cd */
-  setLimit(e) {
-    if (!e.message || e.isPrivate) return
-    let config = cfg.getGroup(e.self_id, e.group_id)
-
-    if (config.groupCD) {
-      this.groupCD[e.group_id] = true
-      setTimeout(() => {
-        delete this.groupCD[e.group_id]
-      }, config.groupCD)
-    }
-    if (config.singleCD) {
-      let key = `${e.group_id}.${e.user_id}`
-      this.singleCD[key] = true
-      setTimeout(() => {
-        delete this.singleCD[key]
-      }, config.singleCD)
-    }
-  }
-
-  /** 是否只关注主动at */
-  onlyReplyAt(e) {
-    if (!e.message || e.isPrivate) return true
-
-    let groupCfg = cfg.getGroup(e.self_id, e.group_id)
-
-    if (groupCfg.onlyReplyAt != 1 || !groupCfg.botAlias) return true
-
-    /** at机器人 */
-    if (e.atBot) return true
-
-    /** 消息带前缀 */
-    if (e.hasAlias) return true
-
-    return false
-  }
-
-  /** 判断黑白名单 */
-  checkBlack(e) {
-    let other = cfg.getOther()
-
-    if (e.test) return true
-
-    /** 黑名单qq */
-    if (other.blackQQ?.length && other.blackQQ.includes(Number(e.user_id) || String(e.user_id))) {
-      return false
-    }
-
-    if (e.group_id) {
-      /** 白名单群 */
-      if (other.whiteGroup?.length) {
-        if (other.whiteGroup.includes(Number(e.group_id) || String(e.group_id))) return true
-        return false
-      }
-      /** 黑名单群 */
-      if (other.blackGroup?.length && other.blackGroup.includes(Number(e.group_id) || String(e.group_id))) {
-        return false
-      }
-    }
-
-    return true
-  }
-
-  /** 判断是否启用功能 */
-  checkDisable(e, p) {
-    let groupCfg = cfg.getGroup(e.self_id, e.group_id)
-    if (!lodash.isEmpty(groupCfg.enable)) {
-      if (groupCfg.enable.includes(p.name)) {
-        return true
-      }
-      // logger.debug(`${e.logText}[${p.name}]功能已禁用`)
-      return false
-    }
-
-    if (!lodash.isEmpty(groupCfg.disable)) {
-      if (groupCfg.disable.includes(p.name)) {
-        // logger.debug(`${e.logText}[${p.name}]功能已禁用`)
-        return false
-      }
-
-      return true
-    }
-    return true
-  }
-
-  /** 监听热更新 */
-  watch(dirName, appName) {
-    this.watchDir(dirName)
-    if (this.watcher[`${dirName}.${appName}`]) return
-
-    let file = `./plugins/${dirName}/${appName}`
-    const watcher = chokidar.watch(file)
-    let key = `${dirName}/${appName}`
-
-    /** 监听修改 */
-    watcher.on("change", async path => {
-      logger.mark(`[修改插件][${dirName}][${appName}]`)
-
-      let tmp = {}
-      try {
-        tmp = await import(`../../plugins/${dirName}/${appName}?${moment().format("x")}`)
-      } catch (error) {
-        logger.error(`载入插件错误：${logger.red(dirName + "/" + appName)}`)
-        logger.error(decodeURI(error.stack))
-        return
-      }
-
-      if (tmp.apps) tmp = { ...tmp.apps }
-      lodash.forEach(tmp, (p) => {
-        /* eslint-disable new-cap */
-        let plugin = new p()
-        for (let i in this.priority) {
-          if (this.priority[i].key == key) {
-            this.priority[i].class = p
-            this.priority[i].priority = plugin.priority
-          }
-        }
-
-        if (plugin.handler) {
-          lodash.forEach(plugin.handler, ({ fn, key, priority }) => {
-            Handler.add({
-              ns: plugin.namespace || File.name,
-              key: key,
-              self: plugin,
-              property: priority || plugin.priority || 500,
-              fn: plugin[fn]
-            })
-          })
-        }
-      })
-
-      this.priority = lodash.orderBy(this.priority, ["priority"], ["asc"])
-    })
-
-    /** 监听删除 */
-    watcher.on("unlink", async path => {
-      logger.mark(`[卸载插件][${dirName}][${appName}]`)
-      for (let i in this.priority) {
-        if (this.priority[i].key == key) {
-          this.priority.splice(i, 1)
-          /** 停止更新监听 */
-          this.watcher[`${dirName}.${appName}`].removeAllListeners("change")
-          break
-        }
-      }
-    })
-
-    this.watcher[`${dirName}.${appName}`] = watcher
-  }
-
-  /** 监听文件夹更新 */
-  watchDir(dirName) {
-    if (this.watcher[dirName]) return
-
-    let file = `./plugins/${dirName}/`
-    const watcher = chokidar.watch(file)
-
-    /** 热更新 */
-    setTimeout(() => {
-      /** 新增文件 */
-      watcher.on("add", async PluPath => {
-        let appName = path.basename(PluPath)
-        if (!appName.endsWith(".js")) return
-        if (!fs.existsSync(`${this.dir}/${dirName}/${appName}`)) return
-
-        let key = `${dirName}/${appName}`
-
-        this.watch(dirName, appName)
-
-        /** 太快了延迟下 */
-        await common.sleep(500)
-
-        logger.mark(`[新增插件][${dirName}][${appName}]`)
-        let tmp = {}
-        try {
-          tmp = await import(`../../plugins/${dirName}/${appName}?${moment().format("X")}`)
-        } catch (error) {
-          logger.error(`载入插件错误：${logger.red(dirName + "/" + appName)}`)
-          logger.error(decodeURI(error.stack))
-          return
-        }
-
-        if (tmp.apps) tmp = { ...tmp.apps }
-
-        lodash.forEach(tmp, (p) => {
-          if (!p.prototype) {
-            logger.error(`[载入失败][${dirName}][${appName}] 格式错误已跳过`)
-            return
-          }
-          /* eslint-disable new-cap */
-          let plugin = new p()
-
-          for (let i in this.priority) {
-            if (this.priority[i].key == key) {
-              return
-            }
-          }
-
-          this.priority.push({
-            class: p,
-            key,
-            name: plugin.name,
-            priority: plugin.priority
-          })
-        })
-
-        /** 优先级排序 */
-        this.priority = lodash.orderBy(this.priority, ["priority"], ["asc"])
-      })
-    }, 500)
-
-    this.watcher[dirName] = watcher
-  }
-}
-
-export default new PluginsLoader()

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/util/caffe_.py

@@ -1,70 +0,0 @@
-# encoding=utf-8
-
-import util
-def get_data(net, name):
-    import caffe
-    if isinstance(net, caffe._caffe.Solver):
-        net = net.net
-    return net.blobs[name].data[...]
-    
-def get_params(net, name = None):
-    import caffe
-    if isinstance(net, caffe._caffe.Solver):
-        net = net.net
-    params = net.params[name]
-    p = []
-    for param in params:
-        p.append(param.data[...])
-    return p
-    
-def draw_log(log_path, output_names, show = False, save_path = None, from_to = None, smooth = False):
-    pattern = "Train net output: word_bbox_loc_loss = "
-    log_path = util.io.get_absolute_path(log_path)
-    f = open(log_path,'r')
-    iterations = []
-    outputs = {}
-    plt = util.plt.plt
-    for line in f.readlines():
-        if util.str.contains(line, 'Iteration') and util.str.contains(line, 'loss = '):
-            print line
-            s = line.split('Iteration')[-1]
-            iter_num = util.str.find_all(s, '\d+')[0]
-            iter_num = int(iter_num)
-            iterations.append(iter_num)
-
-        if util.str.contains(line, "Train net output #"):
-            s = util.str.split(line, 'Train net output #\d+\:')[-1]
-            s = s.split('(')[0]
-            output = util.str.find_all(s, '\d*\.*\d+e*\-*\d*\.*\d*')[-1]
-            output = eval(output)
-            output = float(output)
-            for name in output_names:
-                ptr = ' '+ name + ' ='
-                if util.str.contains(line, ptr):
-                    if name not in outputs:
-                        outputs[name] = []
-                    print line
-                    print '\t', iter_num, name, output
-                    outputs[name].append(output)
-    if len(outputs)==0:
-        print 'No output named:', output_names
-        return    
-    for name in outputs:
-        output = outputs[name]
-        if smooth:
-            output = util.np.smooth(output)
-        start = 0
-        end = len(output)
-        
-        if from_to is not None:
-            start = from_to[0]
-            end = from_to[1]        
-        line_style = util.plt.get_random_line_style()
-        plt.plot(iterations[start: end], output[start: end], line_style, label = name)
-    
-    plt.legend()
-    
-    if save_path is not None:
-        util.plt.save_image(save_path)
-    if show:
-        util.plt.show()

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ufoLib/filenames.py

@@ -1,291 +0,0 @@
-"""
-User name to file name conversion.
-This was taken from the UFO 3 spec.
-"""
-
-# Restrictions are taken mostly from
-# https://docs.microsoft.com/en-gb/windows/win32/fileio/naming-a-file#naming-conventions.
-#
-# 1. Integer value zero, sometimes referred to as the ASCII NUL character.
-# 2. Characters whose integer representations are in the range 1 to 31,
-#    inclusive.
-# 3. Various characters that (mostly) Windows and POSIX-y filesystems don't
-#    allow, plus "(" and ")", as per the specification.
-illegalCharacters = {
-    "\x00",
-    "\x01",
-    "\x02",
-    "\x03",
-    "\x04",
-    "\x05",
-    "\x06",
-    "\x07",
-    "\x08",
-    "\t",
-    "\n",
-    "\x0b",
-    "\x0c",
-    "\r",
-    "\x0e",
-    "\x0f",
-    "\x10",
-    "\x11",
-    "\x12",
-    "\x13",
-    "\x14",
-    "\x15",
-    "\x16",
-    "\x17",
-    "\x18",
-    "\x19",
-    "\x1a",
-    "\x1b",
-    "\x1c",
-    "\x1d",
-    "\x1e",
-    "\x1f",
-    '"',
-    "*",
-    "+",
-    "/",
-    ":",
-    "<",
-    ">",
-    "?",
-    "[",
-    "\\",
-    "]",
-    "(",
-    ")",
-    "|",
-    "\x7f",
-}
-reservedFileNames = {
-    "aux",
-    "clock$",
-    "com1",
-    "com2",
-    "com3",
-    "com4",
-    "com5",
-    "com6",
-    "com7",
-    "com8",
-    "com9",
-    "con",
-    "lpt1",
-    "lpt2",
-    "lpt3",
-    "lpt4",
-    "lpt5",
-    "lpt6",
-    "lpt7",
-    "lpt8",
-    "lpt9",
-    "nul",
-    "prn",
-}
-maxFileNameLength = 255
-
-
-class NameTranslationError(Exception):
-    pass
-
-
-def userNameToFileName(userName: str, existing=(), prefix="", suffix=""):
-    """
-    `existing` should be a set-like object.
-
-    >>> userNameToFileName("a") == "a"
-    True
-    >>> userNameToFileName("A") == "A_"
-    True
-    >>> userNameToFileName("AE") == "A_E_"
-    True
-    >>> userNameToFileName("Ae") == "A_e"
-    True
-    >>> userNameToFileName("ae") == "ae"
-    True
-    >>> userNameToFileName("aE") == "aE_"
-    True
-    >>> userNameToFileName("a.alt") == "a.alt"
-    True
-    >>> userNameToFileName("A.alt") == "A_.alt"
-    True
-    >>> userNameToFileName("A.Alt") == "A_.A_lt"
-    True
-    >>> userNameToFileName("A.aLt") == "A_.aL_t"
-    True
-    >>> userNameToFileName(u"A.alT") == "A_.alT_"
-    True
-    >>> userNameToFileName("T_H") == "T__H_"
-    True
-    >>> userNameToFileName("T_h") == "T__h"
-    True
-    >>> userNameToFileName("t_h") == "t_h"
-    True
-    >>> userNameToFileName("F_F_I") == "F__F__I_"
-    True
-    >>> userNameToFileName("f_f_i") == "f_f_i"
-    True
-    >>> userNameToFileName("Aacute_V.swash") == "A_acute_V_.swash"
-    True
-    >>> userNameToFileName(".notdef") == "_notdef"
-    True
-    >>> userNameToFileName("con") == "_con"
-    True
-    >>> userNameToFileName("CON") == "C_O_N_"
-    True
-    >>> userNameToFileName("con.alt") == "_con.alt"
-    True
-    >>> userNameToFileName("alt.con") == "alt._con"
-    True
-    """
-    # the incoming name must be a string
-    if not isinstance(userName, str):
-        raise ValueError("The value for userName must be a string.")
-    # establish the prefix and suffix lengths
-    prefixLength = len(prefix)
-    suffixLength = len(suffix)
-    # replace an initial period with an _
-    # if no prefix is to be added
-    if not prefix and userName[0] == ".":
-        userName = "_" + userName[1:]
-    # filter the user name
-    filteredUserName = []
-    for character in userName:
-        # replace illegal characters with _
-        if character in illegalCharacters:
-            character = "_"
-        # add _ to all non-lower characters
-        elif character != character.lower():
-            character += "_"
-        filteredUserName.append(character)
-    userName = "".join(filteredUserName)
-    # clip to 255
-    sliceLength = maxFileNameLength - prefixLength - suffixLength
-    userName = userName[:sliceLength]
-    # test for illegal files names
-    parts = []
-    for part in userName.split("."):
-        if part.lower() in reservedFileNames:
-            part = "_" + part
-        parts.append(part)
-    userName = ".".join(parts)
-    # test for clash
-    fullName = prefix + userName + suffix
-    if fullName.lower() in existing:
-        fullName = handleClash1(userName, existing, prefix, suffix)
-    # finished
-    return fullName
-
-
-def handleClash1(userName, existing=[], prefix="", suffix=""):
-    """
-    existing should be a case-insensitive list
-    of all existing file names.
-
-    >>> prefix = ("0" * 5) + "."
-    >>> suffix = "." + ("0" * 10)
-    >>> existing = ["a" * 5]
-
-    >>> e = list(existing)
-    >>> handleClash1(userName="A" * 5, existing=e,
-    ...		prefix=prefix, suffix=suffix) == (
-    ... 	'00000.AAAAA000000000000001.0000000000')
-    True
-
-    >>> e = list(existing)
-    >>> e.append(prefix + "aaaaa" + "1".zfill(15) + suffix)
-    >>> handleClash1(userName="A" * 5, existing=e,
-    ...		prefix=prefix, suffix=suffix) == (
-    ... 	'00000.AAAAA000000000000002.0000000000')
-    True
-
-    >>> e = list(existing)
-    >>> e.append(prefix + "AAAAA" + "2".zfill(15) + suffix)
-    >>> handleClash1(userName="A" * 5, existing=e,
-    ...		prefix=prefix, suffix=suffix) == (
-    ... 	'00000.AAAAA000000000000001.0000000000')
-    True
-    """
-    # if the prefix length + user name length + suffix length + 15 is at
-    # or past the maximum length, silce 15 characters off of the user name
-    prefixLength = len(prefix)
-    suffixLength = len(suffix)
-    if prefixLength + len(userName) + suffixLength + 15 > maxFileNameLength:
-        l = prefixLength + len(userName) + suffixLength + 15
-        sliceLength = maxFileNameLength - l
-        userName = userName[:sliceLength]
-    finalName = None
-    # try to add numbers to create a unique name
-    counter = 1
-    while finalName is None:
-        name = userName + str(counter).zfill(15)
-        fullName = prefix + name + suffix
-        if fullName.lower() not in existing:
-            finalName = fullName
-            break
-        else:
-            counter += 1
-        if counter >= 999999999999999:
-            break
-    # if there is a clash, go to the next fallback
-    if finalName is None:
-        finalName = handleClash2(existing, prefix, suffix)
-    # finished
-    return finalName
-
-
-def handleClash2(existing=[], prefix="", suffix=""):
-    """
-    existing should be a case-insensitive list
-    of all existing file names.
-
-    >>> prefix = ("0" * 5) + "."
-    >>> suffix = "." + ("0" * 10)
-    >>> existing = [prefix + str(i) + suffix for i in range(100)]
-
-    >>> e = list(existing)
-    >>> handleClash2(existing=e, prefix=prefix, suffix=suffix) == (
-    ... 	'00000.100.0000000000')
-    True
-
-    >>> e = list(existing)
-    >>> e.remove(prefix + "1" + suffix)
-    >>> handleClash2(existing=e, prefix=prefix, suffix=suffix) == (
-    ... 	'00000.1.0000000000')
-    True
-
-    >>> e = list(existing)
-    >>> e.remove(prefix + "2" + suffix)
-    >>> handleClash2(existing=e, prefix=prefix, suffix=suffix) == (
-    ... 	'00000.2.0000000000')
-    True
-    """
-    # calculate the longest possible string
-    maxLength = maxFileNameLength - len(prefix) - len(suffix)
-    maxValue = int("9" * maxLength)
-    # try to find a number
-    finalName = None
-    counter = 1
-    while finalName is None:
-        fullName = prefix + str(counter) + suffix
-        if fullName.lower() not in existing:
-            finalName = fullName
-            break
-        else:
-            counter += 1
-        if counter >= maxValue:
-            break
-    # raise an error if nothing has been found
-    if finalName is None:
-        raise NameTranslationError("No unique name could be found.")
-    # finished
-    return finalName
-
-
-if __name__ == "__main__":
-    import doctest
-
-    doctest.testmod()

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-1d65707a.js

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-It would be great if you could take a look at this because this space is being embedded on ${F}.
-
-Thanks!`)}};dt(async()=>{$t.register(x,A)});function Ee(m){L=m,n(9,L)}function G(m){A=m,n(13,A)}return e.$$set=m=>{"autoscroll"in m&&n(0,i=m.autoscroll),"version"in m&&n(1,o=m.version),"initial_height"in m&&n(2,s=m.initial_height),"app_mode"in m&&n(3,a=m.app_mode),"is_embed"in m&&n(4,c=m.is_embed),"theme_mode"in m&&n(20,l=m.theme_mode),"control_page_title"in m&&n(5,f=m.control_page_title),"container"in m&&n(6,u=m.container),"info"in m&&n(7,g=m.info),"eager"in m&&n(21,h=m.eager),"mount_css"in m&&n(22,d=m.mount_css),"client"in m&&n(23,w=m.client),"upload_files"in m&&n(24,b=m.upload_files),"space"in m&&n(8,z=m.space),"host"in m&&n(25,N=m.host),"src"in m&&n(26,p=m.src)},e.$$.update=()=>{e.$$.dirty[0]&2560&&n(12,E=!L&&W.load_status!=="error"?"pending":!L&&W.load_status==="error"?"error":W.load_status),e.$$.dirty[0]&136315904&&M&&(h||r[x])&&ge()},[i,o,s,a,c,f,u,g,z,L,M,W,E,A,U,j,I,Z,y,Ae,l,h,d,w,b,N,p,r,Ee,G]}class en extends tt{constructor(t){super(),et(this,t,Xr,Jr,Te,{autoscroll:0,version:1,initial_height:2,app_mode:3,is_embed:4,theme_mode:20,control_page_title:5,container:6,info:7,eager:21,mount_css:22,client:23,upload_files:24,space:8,host:25,src:26},null,[-1,-1])}}const Yr="https://gradio.s3-us-west-2.amazonaws.com/3.37.0/assets/index-f2292b12.css";let mt;mt=[];function $r(){class e extends HTMLElement{constructor(){super(),this.host=this.getAttribute("host"),this.space=this.getAttribute("space"),this.src=this.getAttribute("src"),this.control_page_title=this.getAttribute("control_page_title"),this.initial_height=this.getAttribute("initial_height")??"300px",this.is_embed=this.getAttribute("embed")??"true",this.container=this.getAttribute("container")??"true",this.info=this.getAttribute("info")??!0,this.autoscroll=this.getAttribute("autoscroll"),this.eager=this.getAttribute("eager"),this.theme_mode=this.getAttribute("theme_mode"),this.updating=!1,this.loading=!1}async connectedCallback(){this.loading=!0,this.app&&this.app.$destroy(),typeof mt!="string"&&mt.forEach(i=>ut(i,document.head)),await ut(Yr,document.head);const n=new CustomEvent("domchange",{bubbles:!0,cancelable:!1,composed:!0});new MutationObserver(i=>{this.dispatchEvent(n)}).observe(this,{childList:!0}),this.app=new en({target:this,props:{space:this.space?this.space.trim():this.space,src:this.src?this.src.trim():this.src,host:this.host?this.host.trim():this.host,info:this.info!=="false",container:this.container!=="false",is_embed:this.is_embed!=="false",initial_height:this.initial_height,eager:this.eager==="true",version:"3-37-0",theme_mode:this.theme_mode,autoscroll:this.autoscroll==="true",control_page_title:this.control_page_title==="true",client:Nt,upload_files:St,app_mode:window.__gradio_mode__==="app"}}),this.updating&&this.setAttribute(this.updating.name,this.updating.value),this.loading=!1}static get observedAttributes(){return["src","space","host"]}attributeChangedCallback(n,r,i){if((n==="host"||n==="space"||n==="src")&&i!==r){if(this.updating={name:n,value:i},this.loading)return;this.app&&this.app.$destroy(),this.space=null,this.host=null,this.src=null,n==="host"?this.host=i:n==="space"?this.space=i:n==="src"&&(this.src=i),this.app=new en({target:this,props:{space:this.space?this.space.trim():this.space,src:this.src?this.src.trim():this.src,host:this.host?this.host.trim():this.host,info:this.info!=="false",container:this.container!=="false",is_embed:this.is_embed!=="false",initial_height:this.initial_height,eager:this.eager==="true",version:"3-37-0",theme_mode:this.theme_mode,autoscroll:this.autoscroll==="true",control_page_title:this.control_page_title==="true",client:Nt,upload_files:St,app_mode:window.__gradio_mode__==="app"}}),this.updating=!1}}}customElements.get("gradio-app")||customElements.define("gradio-app",e)}$r();export{zi as $,v as A,yi as B,dt as C,ar as D,on as E,ki as F,Xe as G,Ni as H,ur as I,re as J,_ as K,Q as L,S as M,B as N,$ as O,O as P,Pt as Q,ne as R,tt as S,Hn as T,H as U,mn as V,Ar as W,ue as X,fi as Y,ve as Z,Ge as _,Me as a,xi as a0,ae as a1,Si as a2,Ci as a3,Ve as a4,kr as a5,Zn as a6,lr as a7,li as a8,ln as a9,si as aA,wt as aB,ui as aa,un as ab,dn as ac,fn as ad,Ir as ae,ci as af,Ft as ag,an as ah,sr as ai,ai as aj,vn as ak,ei as al,wi as am,bi as an,Ai as ao,gi as ap,mi as aq,hi as ar,oi as as,vi as at,St as au,ni as av,pi as aw,ti as ax,Ei as ay,di as az,qi as b,Je as c,Ze as d,et as e,_i as f,zn as g,fe as h,bt as i,kn as j,Be as k,$e as l,de as m,V as n,ke as o,k as p,dr as q,pr as r,Te as s,yn as t,Qe as u,K as v,Fe as w,ze as x,Ke as y,J as z};
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