diff --git a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/italygpt2/__init__.py b/spaces/101-5/gpt4free/g4f/.v1/gpt4free/italygpt2/__init__.py
deleted file mode 100644
index 1eb191c0d57d5d14de4cf2f5b0a2ae3722311ab8..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/italygpt2/__init__.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import re
-import requests
-import hashlib
-from fake_useragent import UserAgent
-class Account:
-    @staticmethod
-    def create():
-        r=requests.get("https://italygpt.it/",headers=Account._header)
-        f=r.text
-        tid=re.search('<input type=\"hidden\" name=\"next_id\" id=\"next_id\" value=\"(\w+)\">',f).group(1)
-        if len(tid)==0:
-            raise RuntimeError("NetWorkError:failed to get id.")
-        else:
-            Account._tid=tid
-        Account._raw="[]"
-        return Account
-    def next(next_id:str)->str:
-        Account._tid=next_id
-        return Account._tid
-    def get()->str:
-        return Account._tid
-    _header={
-            "Host": "italygpt.it",
-            "Referer":"https://italygpt.it/",
-            "User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36",#UserAgent().random,
-            "Accept":"text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
-            "Accept-Language":"zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2",
-            "Upgrade-Insecure-Requests":"1",
-            "Sec-Fetch-Dest":"document",
-            "Sec-Fetch-Mode":"navigate",
-            "Sec-Fetch-Site":"none",
-            "Sec-Fetch-User":"?1",
-            "Connection":"keep-alive",
-            "Alt-Used":"italygpt.it",
-            "Pragma":"no-cache",
-            "Cache-Control":"no-cache",
-            "TE": "trailers"
-        }
-    def settraw(raws:str):
-        Account._raw=raws
-        return Account._raw
-    def gettraw():
-        return Account._raw
-
-class Completion:
-    @staticmethod
-    def create(
-        account_data,
-        prompt: str,
-        message=False
-    ):
-        param={
-            "prompt":prompt.replace(" ","+"),
-            "creative":"off",
-            "internet":"false",
-            "detailed":"off",
-            "current_id":"0",
-            "code":"",
-            "gpt4":"false",
-            "raw_messages":account_data.gettraw(),
-            "hash":hashlib.sha256(account_data.get().encode()).hexdigest()
-        }
-        if(message):
-            param["raw_messages"]=str(message)
-        r = requests.get("https://italygpt.it/question",headers=account_data._header,params=param,stream=True)
-        account_data.next(r.headers["Next_id"])
-        account_data.settraw(r.headers["Raw_messages"])
-        for chunk in r.iter_content(chunk_size=None):
-            r.raise_for_status()
-            yield chunk.decode()
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dragonball Z Raging Blast 2 PC.rar What Makes This Game So Awesome and How to Get It.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dragonball Z Raging Blast 2 PC.rar What Makes This Game So Awesome and How to Get It.md
deleted file mode 100644
index 6cdaee9c794c5b33ca386e5a9e227e2a51595a22..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Dragonball Z Raging Blast 2 PC.rar What Makes This Game So Awesome and How to Get It.md	
+++ /dev/null
@@ -1,106 +0,0 @@
-<br />
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-<p>If you want to enjoy high-quality sound from your PC's HDMI port, you need a reliable audio driver that can communicate with your hardware and software. One of the most popular audio drivers for HDMI devices is <strong>Realtek ATI HDMI Audio Device Driver</strong>, which supports all of Realtek HD Audio Codec.</p>
-<p>However, downloading and installing the official version of this driver may not be enough for some users who want to unlock more features and performance. That's why some people look for a <strong>crack version</strong> of this driver, which is a modified or hacked version that bypasses the license verification and activation process.</p>
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-<p>There are several reasons why you may want to use a crack version of Realtek ATI HDMI Audio Device Driver instead of the official one. Here are some of them:</p>
-<ul>
-<li><strong>It is free.</strong> The official version of this driver requires a license fee, which may not be affordable for some users. The crack version, on the other hand, is available for free on various websites and forums.</li>
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-</ul>
-<p>Of course, using a crack version also comes with some risks and drawbacks, such as:</p>
-<ul>
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-</ul>
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- <h3>How to download and install Realtek ATI HDMI Audio Device 2-70 Crack?</h3>
-<p>If you still want to try Realtek ATI HDMI Audio Device 2-70 Crack, here are the steps you need to follow:</p>
-<ol>
-<li><strong>Download the crack file.</strong> You can find various sources for downloading this file on the internet, such as torrent sites, file-sharing platforms, or online forums. However, be careful not to download any fake or malicious files that can harm your computer. You should also scan any file you download with an antivirus program before opening it.</li>
-<li><strong>Extract the crack file.</strong> After downloading the file, you need to extract it using a program like WinRAR or 7-Zip. You should see a folder containing several files, such as setup.exe, readme.txt, crack.dll, etc.</li>
-<li><strong>Run the setup file.</strong> Double-click on the setup.exe file to launch the installation wizard. Follow the instructions on the screen to install the driver package. You may need to restart your computer after the installation is complete.</li>
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-</ol>
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-<p>Sometimes, you may encounter some problems or errors when using Realtek ATI HDMI Audio Device 2-70 Crack. Here are some tips and tricks on how to troubleshoot them:</p>
-<ul>
-<li><strong>No sound output from HDMI device.</strong> This may happen if your HDMI device is not detected by your computer or if your audio settings are incorrect. To fix this issue, you can try these solutions:     <ul>
-    <li>Check if your HDMI cable is properly connected between your PC and your monitor or TV.</li>
-    <li>Check if your HDMI device is turned on and set as the default playback device in your sound settings.</li>
-    <li>Check if your audio driver is up-to-date and compatible with your operating system and hardware configuration.</li>
-    <li>Check if there are any conflicts or interferences with other drivers or programs that may affect your audio output.</li>
-    </ul></li>
-<li><strong>Poor sound quality from HDMI device.</strong> This may happen if your audio settings are not optimal for your HDMI device or if there are any background noises or distortions. To fix this issue, you can try these solutions:     <ul>
-    <li>Adjust your volume level and balance in your sound settings or control panel.</li>
-    <li>Select an appropriate sound mode or profile for your HDMI device in your audio device manager or control panel.</li>
-    <li>Tweak your equalizer settings or use a third-party software to enhance your sound quality.</li>
-    <li>Avoid placing any objects or devices that may cause interference near your HDMI device or cable.</li>
-    </ul></li>
-<li><strong>HDMI device not recognized by audio driver.</strong> This may happen if your audio driver is corrupted or incompatible with your HDMI device. To fix this issue, you can try these solutions:     <ul>
-    <li>Uninstall and reinstall your audio driver using a clean installation method.</li>
-    <li>Update your audio driver to the latest version available from the official website or a trusted source.</li>
-    <li>Contact customer support from Realtek or ATI for assistance or guidance on how to resolve this issue.</li>
-    </ul></li>
- </ul>
- <h2>What are the alternatives to Realtek ATI HDMI Audio Device 2-70 Crack?</h2>
- Realtek ATI HDMI Audio Device 2-70 Crack, you may want to consider some other alternatives for HDMI audio drivers. Here are some of them:</p>
- <h3>AMD High Definition Audio Device Driver</h3>
-<p>If you have an AMD graphics card or chipset, you may want to use the AMD High Definition Audio Device Driver, which is designed to work with AMD HDMI devices. This driver supports various audio formats and features, such as Dolby TrueHD, DTS-HD Master Audio, 7.1 surround sound, and more. You can download this driver from the AMD website or use the AMD Radeon Software to update it automatically.</p>
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-<p>If you have an Intel processor or chipset, you may want to use the Intel High Definition Audio Driver, which is designed to work with Intel HDMI devices. This driver supports various audio formats and features, such as Dolby Digital Plus, DTS-HD Master Audio, 7.1 surround sound, and more. You can download this driver from the Intel website or use the Intel Driver & Support Assistant to update it automatically.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, Realtek ATI HDMI Audio Device 2-70 Crack is a crack version of a popular audio driver for HDMI devices that can offer more features and performance than the official version. However, it also comes with some risks and drawbacks that you should be aware of before using it. If you are looking for other options for HDMI audio drivers, you can try some of the alternatives we mentioned above.</p>
-<p>We hope this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h3>FAQs</h3>
-<ul>
-<li><strong>What is HDMI?</strong> HDMI stands for High-Definition Multimedia Interface, which is a standard for transmitting digital audio and video signals between devices, such as computers, monitors, TVs, speakers, etc.</li>
-<li><strong>What is an audio driver?</strong> An audio driver is a software program that allows your computer to communicate with your audio device and enable its functions and features.</li>
-<li><strong>What is a crack version?</strong> A crack version is a modified or hacked version of a software program that bypasses the license verification and activation process and allows you to use it for free or with more features.</li>
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-<li><strong>How can I update my audio driver?</strong> You can update your audio driver by downloading the latest version from the official website or a trusted source and following the installation instructions. You can also use a software tool that can scan your computer and update your drivers automatically.</li>
-</ul>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/!EXCLUSIVE! Download Buku Ppdgj Iii Pdf Files.md b/spaces/1gistliPinn/ChatGPT4/Examples/!EXCLUSIVE! Download Buku Ppdgj Iii Pdf Files.md
deleted file mode 100644
index 54335641fb9f7fba85150a0a06cb0583299a9678..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/!EXCLUSIVE! Download Buku Ppdgj Iii Pdf Files.md	
+++ /dev/null
@@ -1,104 +0,0 @@
-
-<h1>Download Buku PPDGJ III PDF Files: A Complete Guide</h1>
-
-<p>If you are looking for a reliable and comprehensive source of information on mental disorders, you may want to download buku ppdgj iii pdf files. Buku PPDGJ III is the Indonesian version of the Diagnostic and Statistical Manual of Mental Disorders (DSM), which is the most widely used classification system for mental disorders in the world. Buku PPDGJ III was published in 1993 by the World Health Organization (WHO) and the Indonesian Psychiatric Association (IPA), and it is based on the International Classification of Diseases (ICD-10).</p>
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-<p>In this article, we will explain what buku ppdgj iii pdf files are, why they are useful, how to access and use them, and what benefits they offer for mental health professionals and students. We will also provide some tips on how to write an effective and SEO-optimized article using buku ppdgj iii pdf files as a reference.</p>
-
-<h2>What are Buku PPDGJ III PDF Files?</h2>
-
-<p>Buku PPDGJ III PDF files are digital copies of the book PPDGJ III, which stands for Pedoman Penggolongan dan Diagnosis Gangguan Jiwa di Indonesia III (Guidelines for Classification and Diagnosis of Mental Disorders in Indonesia III). This book contains the official criteria and guidelines for diagnosing and classifying mental disorders in Indonesia, according to the international standards of WHO and IPA.</p>
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-<p>Buku PPDGJ III PDF files are available online for free download from various sources, such as Scribd, Doku, and Documents and E-books. You can also find them by searching for "download buku ppdgj iii pdf files" on Google or other search engines. The PDF files are usually around 9 MB in size and have about 170 pages.</p>
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-<h2>Why are Buku PPDGJ III PDF Files Useful?</h2>
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-<p>Buku PPDGJ III PDF files are useful for several reasons. First, they provide a comprehensive and updated overview of the current knowledge and practice of psychiatry in Indonesia. They cover a wide range of mental disorders, such as mood disorders, anxiety disorders, personality disorders, psychotic disorders, substance-related disorders, and more. They also include diagnostic criteria, clinical features, differential diagnosis, etiology, course, prognosis, treatment, and prevention of each disorder.</p>
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-<p>Second, they help to standardize and harmonize the diagnosis and classification of mental disorders in Indonesia. By using buku ppdgj iii pdf files as a reference, mental health professionals can ensure that they are following the same criteria and guidelines as their colleagues and peers. This can improve the quality and consistency of mental health services and research in Indonesia.</p>
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-<p>Third, they facilitate communication and collaboration among mental health professionals across different settings and regions. By using buku ppdgj iii pdf files as a common language, mental health professionals can easily share information and opinions about their cases and clients. They can also compare and contrast their findings and outcomes with other professionals who use the same system.</p>
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-<p>To use buku ppdgj iii pdf files effectively, you need to have some basic knowledge of psychiatry and mental disorders. You also need to be familiar with the structure and format of the book. The book is divided into four parts: Part I: Introduction; Part II: General Principles of Diagnosis; Part III: Specific Disorders; Part IV: Appendices.</p>
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-<p>Part I: Introduction provides some background information on the history, development, purpose, scope, limitations, and revisions of PPDGJ III. It also explains the basic concepts and terms used in the book.</p>
-
-<p>Part II: General Principles of Diagnosis outlines the general rules and guidelines for diagnosing mental disorders using PPDGJ III. It covers topics such as diagnostic criteria, diagnostic categories, diagnostic axes, multiaxial assessment, differential diagnosis, comorbidity, reliability, validity, cultural factors, ethical issues, and legal implications.</p>
-
-<p>Part III: Specific Disorders describes each specific disorder in detail. It follows a uniform format that includes: name of disorder; code number; diagnostic criteria; clinical features; differential diagnosis; etiology; course; prognosis; treatment; prevention; notes.</p>
-
-<p>Part IV: Appendices contains some supplementary materials that support the main text of the book. It includes: glossary of terms; list of abbreviations; list of references; index.</p>
-
-<h2>What Benefits do Buku PPDGJ III PDF Files Offer for Mental Health Professionals?</h2>
-
-<p>Buku PPDGJ III PDF files offer many benefits for mental health professionals who work or study in Indonesia or who have an interest in Indonesian psychiatry. Some of these benefits are:</p>
-
-<ul>
-<li>They provide a comprehensive and updated source of information on mental disorders that is relevant to the Indonesian context.</li>
-<li>They help to improve the quality and consistency of diagnosis and classification of mental disorders in Indonesia.</li>
-<li>They facilitate communication and collaboration among mental health professionals across different settings and regions.</li>
-<li>They enhance professional development and learning by providing opportunities for self-assessment, feedback, reflection, research, publication, teaching, training, supervision, consultation, etc.</li>
-<li>They increase public awareness and understanding of mental disorders by providing accurate and reliable information that can be used for education, advocacy, prevention, intervention, etc.</li>
-</ul>
-
-<h2>How to Write an Effective and SEO-Optimized Article Using Buku PPDGJ III PDF Files as a Reference?</h2>
-
-<p>If you want to write an effective and SEO-optimized article using buku ppdgj iii pdf files as a reference</p>
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-<h3>Download Links for Buku PPDGJ III PDF Files</h3>
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-<ul>
-<li><a href="https://id.scribd.com/document/409231491/Download-Buku-Ppdgj-III-PDF-File">Buku PPDGJ III PDF | PDF - Scribd</a></li>
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-</ul>
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-
-<ul>
-<li><a href="https://www.who.int/mental_health/en/">Mental health - World Health Organization</a></li>
-<li><a href="https://www.pdpi.org/">Perhimpunan Dokter Spesialis Kedokteran Jiwa Indonesia (PDPI) - Indonesian Psychiatric Association</a></li>
-</ul>
-<h3>How to Use Buku PPDGJ III PDF Files for Diagnosis and Classification of Mental Disorders</h3>
-
-<p>One of the main purposes of buku ppdgj iii pdf files is to help mental health professionals diagnose and classify mental disorders in Indonesia. To use buku ppdgj iii pdf files for this purpose, you need to follow some steps and guidelines. Here are some tips to help you use buku ppdgj iii pdf files effectively for diagnosis and classification of mental disorders.</p>
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-<ol>
-<li>Conduct a thorough assessment of the patient's symptoms, history, and context. You can use various methods and tools, such as interviews, observations, tests, scales, questionnaires, etc. You can also consult with other professionals or family members if needed.</li>
-<li>Compare the patient's symptoms and features with the diagnostic criteria and clinical features of each disorder in buku ppdgj iii pdf files. You can use the index or the table of contents to find the relevant disorder or category. You can also use the notes section to find additional information or clarifications.</li>
-<li>Select the most appropriate diagnosis or diagnoses for the patient based on the best fit and evidence. You can use the differential diagnosis section to rule out other possible disorders or conditions. You can also use the multiaxial assessment system to assign a diagnosis on each of the five axes: Axis I: Clinical Disorders; Axis II: Personality Disorders and Mental Retardation; Axis III: General Medical Conditions; Axis IV: Psychosocial and Environmental Problems; Axis V: Global Assessment of Functioning.</li>
-<li>Document and communicate your diagnosis or diagnoses clearly and accurately. You can use the code number and the name of each disorder as they appear in buku ppdgj iii pdf files. You can also use the etiology, course, prognosis, treatment, and prevention sections to provide more information or recommendations for the patient.</li>
-</ol>
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-<h3>How to Use Buku PPDGJ III PDF Files for Learning and Teaching Psychiatry</h3>
-
-<p>Another purpose of buku ppdgj iii pdf files is to help mental health professionals and students learn and teach psychiatry in Indonesia. To use buku ppdgj iii pdf files for this purpose, you need to follow some steps and guidelines. Here are some tips to help you use buku ppdgj iii pdf files effectively for learning and teaching psychiatry.</p>
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-<ol>
-<li>Read and study buku ppdgj iii pdf files regularly and thoroughly. You can use the introduction and the general principles of diagnosis sections to learn the basic concepts and terms of psychiatry. You can also use the specific disorders sections to learn the details and features of each disorder.</li>
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-<li>Share and discuss buku ppdgj iii pdf files with other professionals or students. You can use seminars, workshops, conferences, journals, blogs, forums, etc. to exchange information and opinions about buku ppdgj iii pdf files and psychiatry in general. You can also use research, publication, teaching, training, etc. to contribute to the development and dissemination of buku ppdgj iii pdf files and psychiatry in Indonesia.</li>
-</ol>
-<h2>Conclusion</h2>
-
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index 41da83a8d470ea7b29dffa7db24bb3bb2eda6ef4..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Focusrite Serial Number Prefixes What They Mean and How to Use Them.md	
+++ /dev/null
@@ -1,19 +0,0 @@
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diff --git a/spaces/1phancelerku/anime-remove-background/Enjoy Epic Stickman Fights with Supreme Duelist APK 2022 Download Now.md b/spaces/1phancelerku/anime-remove-background/Enjoy Epic Stickman Fights with Supreme Duelist APK 2022 Download Now.md
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index 462262c238dec20422ec17ca16e342270a592f1c..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Enjoy Epic Stickman Fights with Supreme Duelist APK 2022 Download Now.md	
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-<ol>
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-</ul>
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diff --git a/spaces/2ndelement/voicevox/voicevox_engine/synthesis_engine/synthesis_engine_base.py b/spaces/2ndelement/voicevox/voicevox_engine/synthesis_engine/synthesis_engine_base.py
deleted file mode 100644
index aaf4fc4a10e35b85c794793424a1e1f10698838b..0000000000000000000000000000000000000000
--- a/spaces/2ndelement/voicevox/voicevox_engine/synthesis_engine/synthesis_engine_base.py
+++ /dev/null
@@ -1,259 +0,0 @@
-import copy
-from abc import ABCMeta, abstractmethod
-from typing import List, Optional
-
-import numpy as np
-
-from .. import full_context_label
-from ..full_context_label import extract_full_context_label
-from ..model import AccentPhrase, AudioQuery, Mora
-from ..mora_list import openjtalk_mora2text
-
-
-def mora_to_text(mora: str) -> str:
-    if mora[-1:] in ["A", "I", "U", "E", "O"]:
-        # 無声化母音を小文字に
-        mora = mora[:-1] + mora[-1].lower()
-    if mora in openjtalk_mora2text:
-        return openjtalk_mora2text[mora]
-    else:
-        return mora
-
-
-def adjust_interrogative_accent_phrases(
-    accent_phrases: List[AccentPhrase],
-) -> List[AccentPhrase]:
-    """
-    enable_interrogative_upspeakが有効になっていて与えられたaccent_phrasesに疑問系のものがあった場合、
-    各accent_phraseの末尾にある疑問系発音用のMoraに対して直前のMoraより少し音を高くすることで疑問文ぽくする
-    NOTE: リファクタリング時に適切な場所へ移動させること
-    """
-    return [
-        AccentPhrase(
-            moras=adjust_interrogative_moras(accent_phrase),
-            accent=accent_phrase.accent,
-            pause_mora=accent_phrase.pause_mora,
-            is_interrogative=accent_phrase.is_interrogative,
-        )
-        for accent_phrase in accent_phrases
-    ]
-
-
-def adjust_interrogative_moras(accent_phrase: AccentPhrase) -> List[Mora]:
-    moras = copy.deepcopy(accent_phrase.moras)
-    if accent_phrase.is_interrogative and not (len(moras) == 0 or moras[-1].pitch == 0):
-        interrogative_mora = make_interrogative_mora(moras[-1])
-        moras.append(interrogative_mora)
-        return moras
-    else:
-        return moras
-
-
-def make_interrogative_mora(last_mora: Mora) -> Mora:
-    fix_vowel_length = 0.15
-    adjust_pitch = 0.3
-    max_pitch = 6.5
-    return Mora(
-        text=openjtalk_mora2text[last_mora.vowel],
-        consonant=None,
-        consonant_length=None,
-        vowel=last_mora.vowel,
-        vowel_length=fix_vowel_length,
-        pitch=min(last_mora.pitch + adjust_pitch, max_pitch),
-    )
-
-
-def full_context_label_moras_to_moras(
-    full_context_moras: List[full_context_label.Mora],
-) -> List[Mora]:
-    return [
-        Mora(
-            text=mora_to_text("".join([p.phoneme for p in mora.phonemes])),
-            consonant=(mora.consonant.phoneme if mora.consonant is not None else None),
-            consonant_length=0 if mora.consonant is not None else None,
-            vowel=mora.vowel.phoneme,
-            vowel_length=0,
-            pitch=0,
-        )
-        for mora in full_context_moras
-    ]
-
-
-class SynthesisEngineBase(metaclass=ABCMeta):
-    # FIXME: jsonではなくModelを返すようにする
-    @property
-    @abstractmethod
-    def speakers(self) -> str:
-        raise NotImplementedError
-
-    @property
-    @abstractmethod
-    def supported_devices(self) -> Optional[str]:
-        raise NotImplementedError
-
-    def initialize_speaker_synthesis(  # noqa: B027
-        self, speaker_id: int, skip_reinit: bool
-    ):
-
-        """
-        指定した話者での音声合成を初期化する。何度も実行可能。
-        未実装の場合は何もしない
-        Parameters
-        ----------
-        speaker_id : int
-            話者ID
-        skip_reinit : bool
-            True の場合, 既に初期化済みの話者の再初期化をスキップします
-        """
-        pass
-
-    def is_initialized_speaker_synthesis(self, speaker_id: int) -> bool:
-        """
-        指定した話者での音声合成が初期化されているかどうかを返す
-        Parameters
-        ----------
-        speaker_id : int
-            話者ID
-        Returns
-        -------
-        bool
-            初期化されているかどうか
-        """
-        return True
-
-    @abstractmethod
-    def replace_phoneme_length(
-        self, accent_phrases: List[AccentPhrase], speaker_id: int
-    ) -> List[AccentPhrase]:
-        """
-        accent_phrasesの母音・子音の長さを設定する
-        Parameters
-        ----------
-        accent_phrases : List[AccentPhrase]
-            アクセント句モデルのリスト
-        speaker_id : int
-            話者ID
-        Returns
-        -------
-        accent_phrases : List[AccentPhrase]
-            母音・子音の長さが設定されたアクセント句モデルのリスト
-        """
-        raise NotImplementedError()
-
-    @abstractmethod
-    def replace_mora_pitch(
-        self, accent_phrases: List[AccentPhrase], speaker_id: int
-    ) -> List[AccentPhrase]:
-        """
-        accent_phrasesの音高(ピッチ)を設定する
-        Parameters
-        ----------
-        accent_phrases : List[AccentPhrase]
-            アクセント句モデルのリスト
-        speaker_id : int
-            話者ID
-        Returns
-        -------
-        accent_phrases : List[AccentPhrase]
-            音高(ピッチ)が設定されたアクセント句モデルのリスト
-        """
-        raise NotImplementedError()
-
-    def replace_mora_data(
-        self,
-        accent_phrases: List[AccentPhrase],
-        speaker_id: int,
-    ) -> List[AccentPhrase]:
-        return self.replace_mora_pitch(
-            accent_phrases=self.replace_phoneme_length(
-                accent_phrases=accent_phrases,
-                speaker_id=speaker_id,
-            ),
-            speaker_id=speaker_id,
-        )
-
-    def create_accent_phrases(self, text: str, speaker_id: int) -> List[AccentPhrase]:
-        if len(text.strip()) == 0:
-            return []
-
-        utterance = extract_full_context_label(text)
-        if len(utterance.breath_groups) == 0:
-            return []
-
-        accent_phrases = self.replace_mora_data(
-            accent_phrases=[
-                AccentPhrase(
-                    moras=full_context_label_moras_to_moras(accent_phrase.moras),
-                    accent=accent_phrase.accent,
-                    pause_mora=(
-                        Mora(
-                            text="、",
-                            consonant=None,
-                            consonant_length=None,
-                            vowel="pau",
-                            vowel_length=0,
-                            pitch=0,
-                        )
-                        if (
-                            i_accent_phrase == len(breath_group.accent_phrases) - 1
-                            and i_breath_group != len(utterance.breath_groups) - 1
-                        )
-                        else None
-                    ),
-                    is_interrogative=accent_phrase.is_interrogative,
-                )
-                for i_breath_group, breath_group in enumerate(utterance.breath_groups)
-                for i_accent_phrase, accent_phrase in enumerate(
-                    breath_group.accent_phrases
-                )
-            ],
-            speaker_id=speaker_id,
-        )
-        return accent_phrases
-
-    def synthesis(
-        self,
-        query: AudioQuery,
-        speaker_id: int,
-        enable_interrogative_upspeak: bool = True,
-    ) -> np.ndarray:
-        """
-        音声合成クエリ内の疑問文指定されたMoraを変形した後、
-        継承先における実装`_synthesis_impl`を使い音声合成を行う
-        Parameters
-        ----------
-        query : AudioQuery
-            音声合成クエリ
-        speaker_id : int
-            話者ID
-        enable_interrogative_upspeak : bool
-            疑問系のテキストの語尾を自動調整する機能を有効にするか
-        Returns
-        -------
-        wave : numpy.ndarray
-            音声合成結果
-        """
-        # モーフィング時などに同一参照のqueryで複数回呼ばれる可能性があるので、元の引数のqueryに破壊的変更を行わない
-        query = copy.deepcopy(query)
-        if enable_interrogative_upspeak:
-            query.accent_phrases = adjust_interrogative_accent_phrases(
-                query.accent_phrases
-            )
-        return self._synthesis_impl(query, speaker_id)
-
-    @abstractmethod
-    def _synthesis_impl(self, query: AudioQuery, speaker_id: int) -> np.ndarray:
-        """
-        音声合成クエリから音声合成に必要な情報を構成し、実際に音声合成を行う
-        Parameters
-        ----------
-        query : AudioQuery
-            音声合成クエリ
-        speaker_id : int
-            話者ID
-        Returns
-        -------
-        wave : numpy.ndarray
-            音声合成結果
-        """
-        raise NotImplementedError()
diff --git a/spaces/801artistry/RVC801/lib/infer_pack/models_dml.py b/spaces/801artistry/RVC801/lib/infer_pack/models_dml.py
deleted file mode 100644
index 958d7b29259763d2fea94caf8ba7e314c4a77d05..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/lib/infer_pack/models_dml.py
+++ /dev/null
@@ -1,1124 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv.float()
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/__init__.py b/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/discriminator/multi_window_disc.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/discriminator/multi_window_disc.py
deleted file mode 100644
index 1aef6493c90c7cf5206ff92f7fe8831a0821664f..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/discriminator/multi_window_disc.py
+++ /dev/null
@@ -1,196 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-
-
-class Discriminator2DFactory(nn.Module):
-    def __init__(self, time_length, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128,
-                 norm_type='bn', reduction='sum'):
-        super(Discriminator2DFactory, self).__init__()
-        padding = (kernel[0] // 2, kernel[1] // 2)
-
-        def discriminator_block(in_filters, out_filters, first=False):
-            """
-            Input: (B, in, 2H, 2W)
-            Output:(B, out, H,  W)
-            """
-            conv = nn.Conv2d(in_filters, out_filters, kernel, (2, 2), padding)
-            if norm_type == 'sn':
-                conv = nn.utils.spectral_norm(conv)
-            block = [
-                conv,  # padding = kernel//2
-                nn.LeakyReLU(0.2, inplace=True),
-                nn.Dropout2d(0.25)
-            ]
-            if norm_type == 'bn' and not first:
-                block.append(nn.BatchNorm2d(out_filters, 0.8))
-            if norm_type == 'in' and not first:
-                block.append(nn.InstanceNorm2d(out_filters, affine=True))
-            block = nn.Sequential(*block)
-            return block
-
-        self.model = nn.ModuleList([
-            discriminator_block(c_in, hidden_size, first=True),
-            discriminator_block(hidden_size, hidden_size),
-            discriminator_block(hidden_size, hidden_size),
-        ])
-
-        self.reduction = reduction
-        ds_size = (time_length // 2 ** 3, (freq_length + 7) // 2 ** 3)
-        if reduction != 'none':
-            # The height and width of downsampled image
-            self.adv_layer = nn.Linear(hidden_size * ds_size[0] * ds_size[1], 1)
-        else:
-            self.adv_layer = nn.Linear(hidden_size * ds_size[1], 1)
-
-    def forward(self, x):
-        """
-
-        :param x: [B, C, T, n_bins]
-        :return: validity: [B, 1], h: List of hiddens
-        """
-        h = []
-        for l in self.model:
-            x = l(x)
-            h.append(x)
-        if self.reduction != 'none':
-            x = x.view(x.shape[0], -1)
-            validity = self.adv_layer(x)  # [B, 1]
-        else:
-            B, _, T_, _ = x.shape
-            x = x.transpose(1, 2).reshape(B, T_, -1)
-            validity = self.adv_layer(x)[:, :, 0]  # [B, T]
-        return validity, h
-
-
-class MultiWindowDiscriminator(nn.Module):
-    def __init__(self, time_lengths, cond_size=0, freq_length=80, kernel=(3, 3),
-                 c_in=1, hidden_size=128, norm_type='bn', reduction='sum'):
-        super(MultiWindowDiscriminator, self).__init__()
-        self.win_lengths = time_lengths
-        self.reduction = reduction
-
-        self.conv_layers = nn.ModuleList()
-        if cond_size > 0:
-            self.cond_proj_layers = nn.ModuleList()
-            self.mel_proj_layers = nn.ModuleList()
-        for time_length in time_lengths:
-            conv_layer = [
-                Discriminator2DFactory(
-                    time_length, freq_length, kernel, c_in=c_in, hidden_size=hidden_size,
-                    norm_type=norm_type, reduction=reduction)
-            ]
-            self.conv_layers += conv_layer
-            if cond_size > 0:
-                self.cond_proj_layers.append(nn.Linear(cond_size, freq_length))
-                self.mel_proj_layers.append(nn.Linear(freq_length, freq_length))
-
-    def forward(self, x, x_len, cond=None, start_frames_wins=None):
-        '''
-        Args:
-            x (tensor): input mel, (B, c_in, T, n_bins).
-            x_length (tensor): len of per mel. (B,).
-
-        Returns:
-            tensor : (B).
-        '''
-        validity = []
-        if start_frames_wins is None:
-            start_frames_wins = [None] * len(self.conv_layers)
-        h = []
-        for i, start_frames in zip(range(len(self.conv_layers)), start_frames_wins):
-            x_clip, c_clip, start_frames = self.clip(
-                x, cond, x_len, self.win_lengths[i], start_frames)  # (B, win_length, C)
-            start_frames_wins[i] = start_frames
-            if x_clip is None:
-                continue
-            if cond is not None:
-                x_clip = self.mel_proj_layers[i](x_clip)  # (B, 1, win_length, C)
-                c_clip = self.cond_proj_layers[i](c_clip)[:, None]  # (B, 1, win_length, C)
-                x_clip = x_clip + c_clip
-            x_clip, h_ = self.conv_layers[i](x_clip)
-            h += h_
-            validity.append(x_clip)
-        if len(validity) != len(self.conv_layers):
-            return None, start_frames_wins, h
-        if self.reduction == 'sum':
-            validity = sum(validity)  # [B]
-        elif self.reduction == 'stack':
-            validity = torch.stack(validity, -1)  # [B, W_L]
-        elif self.reduction == 'none':
-            validity = torch.cat(validity, -1)  # [B, W_sum]
-        return validity, start_frames_wins, h
-
-    def clip(self, x, cond, x_len, win_length, start_frames=None):
-        '''Ramdom clip x to win_length.
-        Args:
-            x (tensor) : (B, c_in, T, n_bins).
-            cond (tensor) : (B, T, H).
-            x_len (tensor) : (B,).
-            win_length (int): target clip length
-
-        Returns:
-            (tensor) : (B, c_in, win_length, n_bins).
-
-        '''
-        T_start = 0
-        T_end = x_len.max() - win_length
-        if T_end < 0:
-            return None, None, start_frames
-        T_end = T_end.item()
-        if start_frames is None:
-            start_frame = np.random.randint(low=T_start, high=T_end + 1)
-            start_frames = [start_frame] * x.size(0)
-        else:
-            start_frame = start_frames[0]
-        x_batch = x[:, :, start_frame: start_frame + win_length]
-        c_batch = cond[:, start_frame: start_frame + win_length] if cond is not None else None
-        return x_batch, c_batch, start_frames
-
-
-class Discriminator(nn.Module):
-    def __init__(self, time_lengths=[32, 64, 128], freq_length=80, cond_size=0, kernel=(3, 3), c_in=1,
-                 hidden_size=128, norm_type='bn', reduction='sum', uncond_disc=True):
-        super(Discriminator, self).__init__()
-        self.time_lengths = time_lengths
-        self.cond_size = cond_size
-        self.reduction = reduction
-        self.uncond_disc = uncond_disc
-        if uncond_disc:
-            self.discriminator = MultiWindowDiscriminator(
-                freq_length=freq_length,
-                time_lengths=time_lengths,
-                kernel=kernel,
-                c_in=c_in, hidden_size=hidden_size, norm_type=norm_type,
-                reduction=reduction
-            )
-        if cond_size > 0:
-            self.cond_disc = MultiWindowDiscriminator(
-                freq_length=freq_length,
-                time_lengths=time_lengths,
-                cond_size=cond_size,
-                kernel=kernel,
-                c_in=c_in, hidden_size=hidden_size, norm_type=norm_type,
-                reduction=reduction
-            )
-
-    def forward(self, x, cond=None, start_frames_wins=None):
-        """
-
-        :param x: [B, T, 80]
-        :param cond: [B, T, cond_size]
-        :param return_y_only:
-        :return:
-        """
-        if len(x.shape) == 3:
-            x = x[:, None, :, :]
-        x_len = x.sum([1, -1]).ne(0).int().sum([-1])
-        ret = {'y_c': None, 'y': None}
-        if self.uncond_disc:
-            ret['y'], start_frames_wins, ret['h'] = self.discriminator(
-                x, x_len, start_frames_wins=start_frames_wins)
-        if self.cond_size > 0 and cond is not None:
-            ret['y_c'], start_frames_wins, ret['h_c'] = self.cond_disc(
-                x, x_len, cond, start_frames_wins=start_frames_wins)
-        ret['start_frames_wins'] = start_frames_wins
-        return ret
\ No newline at end of file
diff --git a/spaces/AIZ2H/Gradio331-3D-Models-AI-1/app.py b/spaces/AIZ2H/Gradio331-3D-Models-AI-1/app.py
deleted file mode 100644
index 62e7b60344f5957e86a9c0de3d77985f68b52224..0000000000000000000000000000000000000000
--- a/spaces/AIZ2H/Gradio331-3D-Models-AI-1/app.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import time
-import gradio as gr
-import os
-
-def load_mesh(mesh_file_name):
-    return mesh_file_name, mesh_file_name
-
-demo = gr.Interface(
-    fn=load_mesh,
-    inputs=gr.Model3D(),
-    outputs=[
-        gr.Model3D(
-            clear_color=[0.0, 0.0, 0.0, 0.0],  label="3D Model"),
-        gr.File(label="Download 3D Model")
-    ],
-    examples=[
-        [os.path.join(os.path.dirname(__file__), "files/Duck.glb")],
-        [os.path.join(os.path.dirname(__file__), "files/rubber_duck.glb")],
-        [os.path.join(os.path.dirname(__file__), "files/GroundVehicle.glb")]
-    ],
-)
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/Abdulkader/Abdulkader-T5-MedRepAnalyzer/README.md b/spaces/Abdulkader/Abdulkader-T5-MedRepAnalyzer/README.md
deleted file mode 100644
index bfb017eaa27c3c16b5d4d4f0aa0d3ed2d3c1ec17..0000000000000000000000000000000000000000
--- a/spaces/Abdulkader/Abdulkader-T5-MedRepAnalyzer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Abdulkader T5 MedRepAnalyzer
-emoji: ⚡
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.13.0
-app_file: app.py
-pinned: false
-license: cc-by-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AchyuthGamer/OpenGPT/client/css/stop-generating.css b/spaces/AchyuthGamer/OpenGPT/client/css/stop-generating.css
deleted file mode 100644
index 3c2010d25065fbef63b104df743ef72c00259871..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/client/css/stop-generating.css
+++ /dev/null
@@ -1,38 +0,0 @@
-.stop-generating {
-	position: absolute;
-	bottom: 128px;
-	left: 50%;
-	transform: translateX(-50%);
-	z-index: 1000000;
-}
-
-.stop-generating button {
-	backdrop-filter: blur(20px);
-	-webkit-backdrop-filter: blur(20px);
-	background-color: var(--blur-bg);
-	color: var(--colour-3);
-	cursor: pointer;
-	animation: show_popup 0.4s;
-}
-
-@keyframes show_popup {
-	from {
-		opacity: 0;
-		transform: translateY(10px);
-	}
-}
-
-@keyframes hide_popup {
-	to {
-		opacity: 0;
-		transform: translateY(10px);
-	}
-}
-
-.stop-generating-hiding button {
-	animation: hide_popup 0.4s;
-}
-
-.stop-generating-hidden button {
-	display: none;
-}
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/lzstring.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/lzstring.js
deleted file mode 100644
index 7846c01e338c06be4538e532771e6f8a9fbb0fdb..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/lzstring.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import LZString from './string/lzstring/LZString.js';
-export default LZString;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dynamictext/DynamicText.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dynamictext/DynamicText.js
deleted file mode 100644
index c244780d1854d087100eb69ce3aa6dcffb119089..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dynamictext/DynamicText.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import DynamicText from '../../../plugins/dynamictext.js';
-export default DynamicText;
\ No newline at end of file
diff --git a/spaces/Ajit025/Text_to_Image_conversion/text_to_image.py b/spaces/Ajit025/Text_to_Image_conversion/text_to_image.py
deleted file mode 100644
index 3e758a1a6bfd6f0a178e20fea0e8bfac04fc1f3f..0000000000000000000000000000000000000000
--- a/spaces/Ajit025/Text_to_Image_conversion/text_to_image.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from transformers.tools.base import Tool, get_default_device
-from transformers.utils import is_accelerate_available
-import torch
-
-from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
-
-
-TEXT_TO_IMAGE_DESCRIPTION = (
-    "This is a tool that creates an image according to a prompt, which is a text description. It takes an input named `prompt` which "
-    "contains the image description and outputs an image."
-)
-
-
-class TextToImageTool(Tool):
-    default_checkpoint = "runwayml/stable-diffusion-v1-5"
-    description = TEXT_TO_IMAGE_DESCRIPTION
-    inputs = ['text']
-    outputs = ['image']
-
-    def __init__(self, device=None, **hub_kwargs) -> None:
-        if not is_accelerate_available():
-            raise ImportError("Accelerate should be installed in order to use tools.")
-
-        super().__init__()
-
-        self.device = device
-        self.pipeline = None
-        self.hub_kwargs = hub_kwargs
-
-    def setup(self):
-        if self.device is None:
-            self.device = get_default_device()
-
-        self.pipeline = DiffusionPipeline.from_pretrained(self.default_checkpoint)
-        self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(self.pipeline.scheduler.config)
-        self.pipeline.to(self.device)
-
-        if self.device.type == "cuda":
-            self.pipeline.to(torch_dtype=torch.float16)
-
-        self.is_initialized = True
-
-    def __call__(self, prompt):
-        if not self.is_initialized:
-            self.setup()
-
-        negative_prompt = "low quality, bad quality, deformed, low resolution"
-        added_prompt = " , highest quality, highly realistic, very high resolution"
-
-        return self.pipeline(prompt + added_prompt, negative_prompt=negative_prompt, num_inference_steps=25).images[0]
-
diff --git a/spaces/AkitoP/umamusume_bert_vits2/data_utils.py b/spaces/AkitoP/umamusume_bert_vits2/data_utils.py
deleted file mode 100644
index d8e6b9e30b90839644e8a2c33c5166288b720d02..0000000000000000000000000000000000000000
--- a/spaces/AkitoP/umamusume_bert_vits2/data_utils.py
+++ /dev/null
@@ -1,406 +0,0 @@
-import os
-import random
-import torch
-import torch.utils.data
-from tqdm import tqdm
-from loguru import logger
-import commons
-from mel_processing import spectrogram_torch, mel_spectrogram_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import cleaned_text_to_sequence, get_bert
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
-    """
-    1) loads audio, speaker_id, text pairs
-    2) normalizes text and converts them to sequences of integers
-    3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_sid_text, hparams):
-        self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length = hparams.filter_length
-        self.hop_length = hparams.hop_length
-        self.win_length = hparams.win_length
-        self.sampling_rate = hparams.sampling_rate
-        self.spk_map = hparams.spk2id
-        self.hparams = hparams
-
-        self.use_mel_spec_posterior = getattr(
-            hparams, "use_mel_posterior_encoder", False
-        )
-        if self.use_mel_spec_posterior:
-            self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
-
-        self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
-        self.add_blank = hparams.add_blank
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 300)
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths_sid_text)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-
-        audiopaths_sid_text_new = []
-        lengths = []
-        skipped = 0
-        logger.info("Init dataset...")
-        for _id, spk, language, text, phones, tone, word2ph in tqdm(
-            self.audiopaths_sid_text
-        ):
-            audiopath = f"{_id}"
-            if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
-                phones = phones.split(" ")
-                tone = [int(i) for i in tone.split(" ")]
-                word2ph = [int(i) for i in word2ph.split(" ")]
-                audiopaths_sid_text_new.append(
-                    [audiopath, spk, language, text, phones, tone, word2ph]
-                )
-                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
-            else:
-                skipped += 1
-        logger.info(
-            "skipped: "
-            + str(skipped)
-            + ", total: "
-            + str(len(self.audiopaths_sid_text))
-        )
-        self.audiopaths_sid_text = audiopaths_sid_text_new
-        self.lengths = lengths
-
-    def get_audio_text_speaker_pair(self, audiopath_sid_text):
-        # separate filename, speaker_id and text
-        audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
-
-        bert, ja_bert, phones, tone, language = self.get_text(
-            text, word2ph, phones, tone, language, audiopath
-        )
-
-        spec, wav = self.get_audio(audiopath)
-        sid = torch.LongTensor([int(self.spk_map[sid])])
-        return (phones, spec, wav, sid, tone, language, bert, ja_bert)
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError(
-                "{} {} SR doesn't match target {} SR".format(
-                    filename, sampling_rate, self.sampling_rate
-                )
-            )
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if self.use_mel_spec_posterior:
-            spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
-        try:
-            spec = torch.load(spec_filename)
-        except:
-            if self.use_mel_spec_posterior:
-                spec = mel_spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.n_mel_channels,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    self.hparams.mel_fmin,
-                    self.hparams.mel_fmax,
-                    center=False,
-                )
-            else:
-                spec = spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    center=False,
-                )
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-        return spec, audio_norm
-
-    def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
-        phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-        if self.add_blank:
-            phone = commons.intersperse(phone, 0)
-            tone = commons.intersperse(tone, 0)
-            language = commons.intersperse(language, 0)
-            for i in range(len(word2ph)):
-                word2ph[i] = word2ph[i] * 2
-            word2ph[0] += 1
-        bert_path = wav_path.replace(".wav", ".bert.pt")
-        try:
-            bert = torch.load(bert_path)
-            assert bert.shape[-1] == len(phone)
-        except:
-            bert = get_bert(text, word2ph, language_str)
-            torch.save(bert, bert_path)
-            assert bert.shape[-1] == len(phone), phone
-
-        if language_str == "ZH":
-            bert = bert
-            ja_bert = torch.zeros(768, len(phone))
-        elif language_str == "JP":
-            ja_bert = bert
-            bert = torch.zeros(1024, len(phone))
-        else:
-            bert = torch.zeros(1024, len(phone))
-            ja_bert = torch.zeros(768, len(phone))
-        assert bert.shape[-1] == len(phone), (
-            bert.shape,
-            len(phone),
-            sum(word2ph),
-            p1,
-            p2,
-            t1,
-            t2,
-            pold,
-            pold2,
-            word2ph,
-            text,
-            w2pho,
-        )
-        phone = torch.LongTensor(phone)
-        tone = torch.LongTensor(tone)
-        language = torch.LongTensor(language)
-        return bert, ja_bert, phone, tone, language
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def __getitem__(self, index):
-        return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate:
-    """Zero-pads model inputs and targets"""
-
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text, audio and speaker identities
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized, sid]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True
-        )
-
-        max_text_len = max([len(x[0]) for x in batch])
-        max_spec_len = max([x[1].size(1) for x in batch])
-        max_wav_len = max([x[2].size(1) for x in batch])
-
-        text_lengths = torch.LongTensor(len(batch))
-        spec_lengths = torch.LongTensor(len(batch))
-        wav_lengths = torch.LongTensor(len(batch))
-        sid = torch.LongTensor(len(batch))
-
-        text_padded = torch.LongTensor(len(batch), max_text_len)
-        tone_padded = torch.LongTensor(len(batch), max_text_len)
-        language_padded = torch.LongTensor(len(batch), max_text_len)
-        bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
-        ja_bert_padded = torch.FloatTensor(len(batch), 768, max_text_len)
-
-        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        text_padded.zero_()
-        tone_padded.zero_()
-        language_padded.zero_()
-        spec_padded.zero_()
-        wav_padded.zero_()
-        bert_padded.zero_()
-        ja_bert_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            text = row[0]
-            text_padded[i, : text.size(0)] = text
-            text_lengths[i] = text.size(0)
-
-            spec = row[1]
-            spec_padded[i, :, : spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wav = row[2]
-            wav_padded[i, :, : wav.size(1)] = wav
-            wav_lengths[i] = wav.size(1)
-
-            sid[i] = row[3]
-
-            tone = row[4]
-            tone_padded[i, : tone.size(0)] = tone
-
-            language = row[5]
-            language_padded[i, : language.size(0)] = language
-
-            bert = row[6]
-            bert_padded[i, :, : bert.size(1)] = bert
-
-            ja_bert = row[7]
-            ja_bert_padded[i, :, : ja_bert.size(1)] = ja_bert
-
-        return (
-            text_padded,
-            text_lengths,
-            spec_padded,
-            spec_lengths,
-            wav_padded,
-            wav_lengths,
-            sid,
-            tone_padded,
-            language_padded,
-            bert_padded,
-            ja_bert_padded,
-        )
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
-    """
-    Maintain similar input lengths in a batch.
-    Length groups are specified by boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
-    It removes samples which are not included in the boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
-    """
-
-    def __init__(
-        self,
-        dataset,
-        batch_size,
-        boundaries,
-        num_replicas=None,
-        rank=None,
-        shuffle=True,
-    ):
-        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
-        self.lengths = dataset.lengths
-        self.batch_size = batch_size
-        self.boundaries = boundaries
-
-        self.buckets, self.num_samples_per_bucket = self._create_buckets()
-        self.total_size = sum(self.num_samples_per_bucket)
-        self.num_samples = self.total_size // self.num_replicas
-
-    def _create_buckets(self):
-        buckets = [[] for _ in range(len(self.boundaries) - 1)]
-        for i in range(len(self.lengths)):
-            length = self.lengths[i]
-            idx_bucket = self._bisect(length)
-            if idx_bucket != -1:
-                buckets[idx_bucket].append(i)
-
-        try:
-            for i in range(len(buckets) - 1, 0, -1):
-                if len(buckets[i]) == 0:
-                    buckets.pop(i)
-                    self.boundaries.pop(i + 1)
-            assert all(len(bucket) > 0 for bucket in buckets)
-        # When one bucket is not traversed
-        except Exception as e:
-            print("Bucket warning ", e)
-            for i in range(len(buckets) - 1, -1, -1):
-                if len(buckets[i]) == 0:
-                    buckets.pop(i)
-                    self.boundaries.pop(i + 1)
-
-        num_samples_per_bucket = []
-        for i in range(len(buckets)):
-            len_bucket = len(buckets[i])
-            total_batch_size = self.num_replicas * self.batch_size
-            rem = (
-                total_batch_size - (len_bucket % total_batch_size)
-            ) % total_batch_size
-            num_samples_per_bucket.append(len_bucket + rem)
-        return buckets, num_samples_per_bucket
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-
-        indices = []
-        if self.shuffle:
-            for bucket in self.buckets:
-                indices.append(torch.randperm(len(bucket), generator=g).tolist())
-        else:
-            for bucket in self.buckets:
-                indices.append(list(range(len(bucket))))
-
-        batches = []
-        for i in range(len(self.buckets)):
-            bucket = self.buckets[i]
-            len_bucket = len(bucket)
-            if len_bucket == 0:
-                continue
-            ids_bucket = indices[i]
-            num_samples_bucket = self.num_samples_per_bucket[i]
-
-            # add extra samples to make it evenly divisible
-            rem = num_samples_bucket - len_bucket
-            ids_bucket = (
-                ids_bucket
-                + ids_bucket * (rem // len_bucket)
-                + ids_bucket[: (rem % len_bucket)]
-            )
-
-            # subsample
-            ids_bucket = ids_bucket[self.rank :: self.num_replicas]
-
-            # batching
-            for j in range(len(ids_bucket) // self.batch_size):
-                batch = [
-                    bucket[idx]
-                    for idx in ids_bucket[
-                        j * self.batch_size : (j + 1) * self.batch_size
-                    ]
-                ]
-                batches.append(batch)
-
-        if self.shuffle:
-            batch_ids = torch.randperm(len(batches), generator=g).tolist()
-            batches = [batches[i] for i in batch_ids]
-        self.batches = batches
-
-        assert len(self.batches) * self.batch_size == self.num_samples
-        return iter(self.batches)
-
-    def _bisect(self, x, lo=0, hi=None):
-        if hi is None:
-            hi = len(self.boundaries) - 1
-
-        if hi > lo:
-            mid = (hi + lo) // 2
-            if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
-                return mid
-            elif x <= self.boundaries[mid]:
-                return self._bisect(x, lo, mid)
-            else:
-                return self._bisect(x, mid + 1, hi)
-        else:
-            return -1
-
-    def __len__(self):
-        return self.num_samples // self.batch_size
diff --git a/spaces/AliHaider0343/implicit-and-explicit-aspects-Extraction-in-Restaurant-Reviews-Domain/README.md b/spaces/AliHaider0343/implicit-and-explicit-aspects-Extraction-in-Restaurant-Reviews-Domain/README.md
deleted file mode 100644
index 716dc7f58d2a5a31d2da2879aa599ebf438b82cd..0000000000000000000000000000000000000000
--- a/spaces/AliHaider0343/implicit-and-explicit-aspects-Extraction-in-Restaurant-Reviews-Domain/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Implicit And Explicit Aspects Extraction In Restaurant Reviews Domain
-emoji: 🌖
-colorFrom: gray
-colorTo: red
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git "a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/\350\260\267\346\255\214\346\243\200\347\264\242\345\260\217\345\212\251\346\211\213.py" "b/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/\350\260\267\346\255\214\346\243\200\347\264\242\345\260\217\345\212\251\346\211\213.py"
deleted file mode 100644
index 94ef256327f6740cdaddc6f5ecea5852a9210163..0000000000000000000000000000000000000000
--- "a/spaces/Ameaou/academic-chatgpt3.1/crazy_functions/\350\260\267\346\255\214\346\243\200\347\264\242\345\260\217\345\212\251\346\211\213.py"
+++ /dev/null
@@ -1,106 +0,0 @@
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from toolbox import CatchException, report_execption, write_results_to_file
-from toolbox import update_ui
-
-def get_meta_information(url, chatbot, history):
-    import requests
-    import arxiv
-    import difflib
-    from bs4 import BeautifulSoup
-    from toolbox import get_conf
-    proxies, = get_conf('proxies')
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Safari/537.36',
-    }
-    # 发送 GET 请求
-    response = requests.get(url, proxies=proxies, headers=headers)
-
-    # 解析网页内容
-    soup = BeautifulSoup(response.text, "html.parser")
-
-    def string_similar(s1, s2):
-        return difflib.SequenceMatcher(None, s1, s2).quick_ratio()
-
-    profile = []
-    # 获取所有文章的标题和作者
-    for result in soup.select(".gs_ri"):
-        title = result.a.text.replace('\n', ' ').replace('  ', ' ')
-        author = result.select_one(".gs_a").text
-        try:
-            citation = result.select_one(".gs_fl > a[href*='cites']").text  # 引用次数是链接中的文本，直接取出来
-        except:
-            citation = 'cited by 0'
-        abstract = result.select_one(".gs_rs").text.strip()  # 摘要在 .gs_rs 中的文本，需要清除首尾空格
-        search = arxiv.Search(
-            query = title,
-            max_results = 1,
-            sort_by = arxiv.SortCriterion.Relevance,
-        )
-        paper = next(search.results())
-        if string_similar(title, paper.title) > 0.90: # same paper
-            abstract = paper.summary.replace('\n', ' ')
-            is_paper_in_arxiv = True
-        else:   # different paper
-            abstract = abstract
-            is_paper_in_arxiv = False
-        paper = next(search.results())
-        print(title)
-        print(author)
-        print(citation)
-        profile.append({
-            'title':title,
-            'author':author,
-            'citation':citation,
-            'abstract':abstract,
-            'is_paper_in_arxiv':is_paper_in_arxiv,
-        })
-
-        chatbot[-1] = [chatbot[-1][0], title + f'\n\n是否在arxiv中（不在arxiv中无法获取完整摘要）:{is_paper_in_arxiv}\n\n' + abstract]
-        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
-    return profile
-
-@CatchException
-def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "分析用户提供的谷歌学术（google scholar）搜索页面中，出现的所有文章: binary-husky，插件初始化中..."])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import arxiv
-        from bs4 import BeautifulSoup
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4 arxiv```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
-
-    if len(meta_paper_info_list[:10]) > 0:
-        i_say = "下面是一些学术文献的数据，请从中提取出以下内容。" + \
-        "1、英文题目；2、中文题目翻译；3、作者；4、arxiv公开（is_paper_in_arxiv）；4、引用数量（cite）；5、中文摘要翻译。" + \
-        f"以下是信息源：{str(meta_paper_info_list[:10])}" 
-
-        inputs_show_user = f"请分析此页面中出现的所有文章：{txt}"
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say, inputs_show_user=inputs_show_user, 
-            llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
-            sys_prompt="你是一个学术翻译，请从数据中提取信息。你必须使用Markdown格式。你必须逐个文献进行处理。"
-        )
-
-        history.extend([ "第一批", gpt_say ])
-        meta_paper_info_list = meta_paper_info_list[10:]
-
-    chatbot.append(["状态？", "已经全部完成"])
-    msg = '正常'
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-    res = write_results_to_file(history)
-    chatbot.append(("完成了吗？", res)); 
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
diff --git a/spaces/Amon1/ChatGPTForAcadamic/crazy_functions/test_project/latex/attention/introduction.tex b/spaces/Amon1/ChatGPTForAcadamic/crazy_functions/test_project/latex/attention/introduction.tex
deleted file mode 100644
index 1baa8915f4cf7aec2520894a87470fc9436d954b..0000000000000000000000000000000000000000
--- a/spaces/Amon1/ChatGPTForAcadamic/crazy_functions/test_project/latex/attention/introduction.tex
+++ /dev/null
@@ -1,18 +0,0 @@
-Recurrent neural networks, long short-term memory \citep{hochreiter1997} and gated recurrent \citep{gruEval14} neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation \citep{sutskever14, bahdanau2014neural, cho2014learning}. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures \citep{wu2016google,luong2015effective,jozefowicz2016exploring}.
-
-Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states $h_t$, as a function of the previous hidden state $h_{t-1}$ and the input for position $t$. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples.
-%\marginpar{not sure if the memory constraints are understandable here}
-Recent work has achieved significant improvements in computational efficiency through factorization tricks \citep{Kuchaiev2017Factorization} and conditional computation \citep{shazeer2017outrageously}, while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.
-
-%\marginpar{@all: there is work on analyzing what attention really does in seq2seq models, couldn't find it right away} 
-
-Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences \citep{bahdanau2014neural, structuredAttentionNetworks}. In all but a few cases \citep{decomposableAttnModel}, however, such attention mechanisms are used in conjunction with a recurrent network.
-
-%\marginpar{not sure if "cross-positional communication" is understandable without explanation}
-%\marginpar{insert exact training times and stats for the model that reaches sota earliest, maybe even a single GPU model?}
-
-In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs. 
-%\marginpar{you removed the constant number of repetitions part. I wrote it because I wanted to make it clear that the model does not only perform attention once, while it's also not recurrent. I thought that might be important to get across early.}
-
-% Just a standard paragraph with citations, rewrite.
-%After the seminal papers of \citep{sutskever14}, \citep{bahdanau2014neural}, and \citep{cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation and language modeling with recurrent sequence models. Recent effort \citep{shazeer2017outrageously} has combined the power of conditional computation with sequence models to train very large models for machine translation, pushing SOTA at lower computational cost. Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state encumbers recurrnet models to process multiple inputs at once, and their time complexity is a linear function of the length of the input and output, both during training and inference. [What I want to say here is that although this is fine during decoding, at training time, we are given both input and output and this linear nature does not allow the RNN to process all inputs and outputs simultaneously and haven't been used on datasets that are the of the scale of the web. What's the largest dataset we have ? . Talk about Nividia and possibly other's effors to speed up things, and possibly other efforts that alleviate this, but are still limited by it's comptuational nature]. Rest of the intro: What if you could construct the state based on the actual inputs and outputs, then you could construct them all at once. This has been the foundation of many promising recent efforts, bytenet,facenet (Also talk about quasi rnn here). Now we talk about attention!! Along with cell architectures such as long short-term meory (LSTM) \citep{hochreiter1997}, and gated recurrent units (GRUs) \citep{cho2014learning}, attention has emerged as an essential ingredient in successful sequence models, in particular for machine translation. In recent years, many, if not all, state-of-the-art (SOTA) results in machine translation have been achieved with attention-based sequence models \citep{wu2016google,luong2015effective,jozefowicz2016exploring}. Talk about the neon work on how it played with attention to do self attention! Then talk about what we do.
\ No newline at end of file
diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/generate.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/generate.py
deleted file mode 100644
index a8b7d55e6d190c193e427bd8d623c583b2dcdeda..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/generate.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
-# This work is made available under the Nvidia Source Code License-NC.
-# To view a copy of this license, visit
-# https://nvlabs.github.io/stylegan2/license.html
-
-
-## this script is for generating images from pre-trained network based on StyleGAN1 (TensorFlow) and StyleGAN2-ada (PyTorch) ##
-
-import os
-import click
-import dnnlib
-import numpy as np
-import PIL.Image
-import legacy
-from typing import List, Optional
-
-"""
-Generate images using pretrained network pickle.
-Examples:
-
-\b
-# Generate human full-body images without truncation
-python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=1 --seeds=1,3,5,7 \\
-    --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
-
-\b
-# Generate human full-body images with truncation 
-python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=0.8 --seeds=0-100\\
-    --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
-
-# \b
-# Generate human full-body images using stylegan V1
-# python generate.py --outdir=outputs/generate/stylegan_human_v1_1024  \\
-#     --network=pretrained_models/stylegan_human_v1_1024.pkl --version 1
-"""
-
-
-@click.command()
-@click.pass_context
-@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
-@click.option('--seeds', type=legacy.num_range, help='List of random seeds')
-@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
-@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
-@click.option('--outdir', help='Where to save the output images', default='outputs/generate/', type=str, required=True, metavar='DIR')
-@click.option('--version', help="stylegan version, 1, 2 or 3", type=int, default=2)
-def generate_images(
-    ctx: click.Context,
-    network_pkl: str,
-    seeds: Optional[List[int]],
-    truncation_psi: float,
-    noise_mode: str,
-    outdir: str,
-    version: int
-):
-
-    print('Loading networks from "%s"...' % network_pkl)
-    if version == 1:
-        import dnnlib.tflib as tflib
-        tflib.init_tf()
-        G, D, Gs = legacy.load_pkl(network_pkl)
-
-    else:
-        import torch
-        device = torch.device('cuda')
-        with dnnlib.util.open_url(network_pkl) as f:
-            G = legacy.load_network_pkl(f)['G_ema'].to(device)  # type: ignore
-    os.makedirs(outdir, exist_ok=True)
-
-    if seeds is None:
-        ctx.fail('--seeds option is required.')
-
-    # Generate images.
-    target_z = np.array([])
-    target_w = np.array([])
-    latent_out = outdir.replace('/images/', '')
-    for seed_idx, seed in enumerate(seeds):
-        if seed % 5000 == 0:
-            print('Generating image for seed %d (%d/%d) ...' %
-                  (seed, seed_idx, len(seeds)))
-
-        if version == 1:  # stylegan v1
-            z = np.random.RandomState(seed).randn(1, Gs.input_shape[1])
-            # Generate image.
-            fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
-            if noise_mode == 'const':
-                randomize_noise = False
-            else:
-                randomize_noise = True
-            images = Gs.run(z, None, truncation_psi=truncation_psi,
-                            randomize_noise=randomize_noise, output_transform=fmt)
-            PIL.Image.fromarray(images[0], 'RGB').save(
-                f'{outdir}/seed{seed:04d}.png')
-
-        else:  # stylegan v2/v3
-            label = torch.zeros([1, G.c_dim], device=device)
-            z = torch.from_numpy(np.random.RandomState(
-                seed).randn(1, G.z_dim)).to(device)
-            if target_z.size == 0:
-                target_z = z.cpu()
-            else:
-                target_z = np.append(target_z, z.cpu(), axis=0)
-
-            w = G.mapping(z, label, truncation_psi=truncation_psi)
-            img = G.synthesis(w, noise_mode=noise_mode, force_fp32=True)
-            if target_w.size == 0:
-                target_w = w.cpu()
-            else:
-                target_w = np.append(target_w, w.cpu(), axis=0)
-
-            img = (img.permute(0, 2, 3, 1) * 127.5 +
-                   128).clamp(0, 255).to(torch.uint8)
-            PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(
-                f'{outdir}/seed{seed:04d}.png')
-    # print(target_z)
-    # print(target_z.shape,target_w.shape)
-
-
-# ----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    generate_images()
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/Andres99/Tune-A-Video-Training-UI/uploader.py b/spaces/Andres99/Tune-A-Video-Training-UI/uploader.py
deleted file mode 100644
index d9e06ec02127db34016d3d7b550e88f820a737fe..0000000000000000000000000000000000000000
--- a/spaces/Andres99/Tune-A-Video-Training-UI/uploader.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from __future__ import annotations
-
-from huggingface_hub import HfApi
-
-
-class Uploader:
-    def __init__(self, hf_token: str | None):
-        self.hf_token = hf_token
-
-    def upload(self,
-               folder_path: str,
-               repo_name: str,
-               organization: str = '',
-               repo_type: str = 'model',
-               private: bool = True,
-               delete_existing_repo: bool = False,
-               input_token: str | None = None) -> str:
-
-        api = HfApi(token=self.hf_token if self.hf_token else input_token)
-
-        if not folder_path:
-            raise ValueError
-        if not repo_name:
-            raise ValueError
-        if not organization:
-            organization = api.whoami()['name']
-
-        repo_id = f'{organization}/{repo_name}'
-        if delete_existing_repo:
-            try:
-                api.delete_repo(repo_id, repo_type=repo_type)
-            except Exception:
-                pass
-        try:
-            api.create_repo(repo_id, repo_type=repo_type, private=private)
-            api.upload_folder(repo_id=repo_id,
-                              folder_path=folder_path,
-                              path_in_repo='.',
-                              repo_type=repo_type)
-            url = f'https://huggingface.co/{repo_id}'
-            message = f'Your model was successfully uploaded to <a href="{url}" target="_blank">{url}</a>.'
-        except Exception as e:
-            message = str(e)
-        return message
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md
deleted file mode 100644
index 8486641da2c40ac9f68e1a4e50b5adbb0e96c4fa..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md
+++ /dev/null
@@ -1,427 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Stable diffusion XL
-
-Stable Diffusion XL was proposed in [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://arxiv.org/abs/2307.01952) by Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, Robin Rombach
-
-The abstract of the paper is the following:
-
-*We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We design multiple novel conditioning schemes and train SDXL on multiple aspect ratios. We also introduce a refinement model which is used to improve the visual fidelity of samples generated by SDXL using a post-hoc image-to-image technique. We demonstrate that SDXL shows drastically improved performance compared the previous versions of Stable Diffusion and achieves results competitive with those of black-box state-of-the-art image generators.*
-
-## Tips
-
-- Stable Diffusion XL works especially well with images between 768 and 1024.
-- Stable Diffusion XL can pass a different prompt for each of the text encoders it was trained on as shown below. We can even pass different parts of the same prompt to the text encoders.
-- Stable Diffusion XL output image can be improved by making use of a refiner as shown below.
-
-### Available checkpoints:
-
-- *Text-to-Image (1024x1024 resolution)*: [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) with [`StableDiffusionXLPipeline`]
-- *Image-to-Image / Refiner (1024x1024 resolution)*: [stabilityai/stable-diffusion-xl-refiner-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0) with [`StableDiffusionXLImg2ImgPipeline`]
-
-## Usage Example
-
-Before using SDXL make sure to have `transformers`, `accelerate`, `safetensors` and `invisible_watermark` installed. 
-You can install the libraries as follows:
-
-```
-pip install transformers
-pip install accelerate
-pip install safetensors
-```
-
-### Watermarker
-
-We recommend to add an invisible watermark to images generating by Stable Diffusion XL, this can help with identifying if an image is machine-synthesised for downstream applications. To do so, please install
-the [invisible-watermark library](https://pypi.org/project/invisible-watermark/) via:
-
-```
-pip install invisible-watermark>=0.2.0
-```
-
-If the `invisible-watermark` library is installed the watermarker will be used **by default**.
-
-If you have other provisions for generating or deploying images safely, you can disable the watermarker as follows:
-
-```py
-pipe = StableDiffusionXLPipeline.from_pretrained(..., add_watermarker=False)
-```
-
-### Text-to-Image
-
-You can use SDXL as follows for *text-to-image*:
-
-```py
-from diffusers import StableDiffusionXLPipeline
-import torch
-
-pipe = StableDiffusionXLPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-)
-pipe.to("cuda")
-
-prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
-image = pipe(prompt=prompt).images[0]
-```
-
-### Image-to-image 
-
-You can use SDXL as follows for *image-to-image*:
-
-```py 
-import torch
-from diffusers import StableDiffusionXLImg2ImgPipeline
-from diffusers.utils import load_image
-
-pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-)
-pipe = pipe.to("cuda")
-url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"
-
-init_image = load_image(url).convert("RGB")
-prompt = "a photo of an astronaut riding a horse on mars"
-image = pipe(prompt, image=init_image).images[0]
-```
-
-### Inpainting
-
-You can use SDXL as follows for *inpainting*
-
-```py 
-import torch
-from diffusers import StableDiffusionXLInpaintPipeline
-from diffusers.utils import load_image
-
-pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-)
-pipe.to("cuda")
-
-img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
-mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
-
-init_image = load_image(img_url).convert("RGB")
-mask_image = load_image(mask_url).convert("RGB")
-
-prompt = "A majestic tiger sitting on a bench"
-image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80).images[0]
-```
-
-### Refining the image output
-
-In addition to the [base model checkpoint](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), 
-StableDiffusion-XL also includes a [refiner checkpoint](huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)
-that is specialized in denoising low-noise stage images to generate images of improved high-frequency quality.
-This refiner checkpoint can be used as a "second-step" pipeline after having run the base checkpoint to improve
-image quality.
-
-When using the refiner, one can easily 
-- 1.) employ the base model and refiner as an *Ensemble of Expert Denoisers* as first proposed in [eDiff-I](https://research.nvidia.com/labs/dir/eDiff-I/) or
-- 2.) simply run the refiner in [SDEdit](https://arxiv.org/abs/2108.01073) fashion after the base model.
-
-**Note**: The idea of using SD-XL base & refiner as an ensemble of experts was first brought forward by 
-a couple community contributors which also helped shape the following `diffusers` implementation, namely:
-- [SytanSD](https://github.com/SytanSD)
-- [bghira](https://github.com/bghira)
-- [Birch-san](https://github.com/Birch-san)
-- [AmericanPresidentJimmyCarter](https://github.com/AmericanPresidentJimmyCarter)
-
-#### 1.) Ensemble of Expert Denoisers
-
-When using the base and refiner model as an ensemble of expert of denoisers, the base model should serve as the 
-expert for the high-noise diffusion stage and the refiner serves as the expert for the low-noise diffusion stage.
-
-The advantage of 1.) over 2.) is that it requires less overall denoising steps and therefore should be significantly
-faster. The drawback is that one cannot really inspect the output of the base model; it will still be heavily denoised.
-
-To use the base model and refiner as an ensemble of expert denoisers, make sure to define the span
-of timesteps which should be run through the high-noise denoising stage (*i.e.* the base model) and the low-noise
-denoising stage (*i.e.* the refiner model) respectively. We can set the intervals using the [`denoising_end`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.denoising_end) of the base model 
-and [`denoising_start`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLImg2ImgPipeline.__call__.denoising_start) of the refiner model.
-
-For both `denoising_end` and `denoising_start` a float value between 0 and 1 should be passed.
-When passed, the end and start of denoising will be defined by proportions of discrete timesteps as
-defined by the model schedule.
-Note that this will override `strength` if it is also declared, since the number of denoising steps
-is determined by the discrete timesteps the model was trained on and the declared fractional cutoff.
-
-Let's look at an example.
-First, we import the two pipelines. Since the text encoders and variational autoencoder are the same
-you don't have to load those again for the refiner.
-
-```py
-from diffusers import DiffusionPipeline
-import torch
-
-base = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-)
-base.to("cuda")
-
-refiner = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-1.0",
-    text_encoder_2=base.text_encoder_2,
-    vae=base.vae,
-    torch_dtype=torch.float16,
-    use_safetensors=True,
-    variant="fp16",
-)
-refiner.to("cuda")
-```
-
-Now we define the number of inference steps and the point at which the model shall be run through the 
-high-noise denoising stage (*i.e.* the base model).
-
-```py
-n_steps = 40
-high_noise_frac = 0.8
-```
-
-Stable Diffusion XL base is trained on timesteps 0-999 and Stable Diffusion XL refiner is finetuned
-from the base model on low noise timesteps 0-199 inclusive, so we use the base model for the first
-800 timesteps (high noise) and the refiner for the last 200 timesteps (low noise). Hence, `high_noise_frac`
-is set to 0.8, so that all steps 200-999 (the first 80% of denoising timesteps) are performed by the
-base model and steps 0-199 (the last 20% of denoising timesteps) are performed by the refiner model.
-
-Remember, the denoising process starts at **high value** (high noise) timesteps and ends at
-**low value** (low noise) timesteps.
-
-Let's run the two pipelines now. Make sure to set `denoising_end` and
-`denoising_start` to the same values and keep `num_inference_steps` constant. Also remember that
-the output of the base model should be in latent space:
-
-```py
-prompt = "A majestic lion jumping from a big stone at night"
-
-image = base(
-    prompt=prompt,
-    num_inference_steps=n_steps,
-    denoising_end=high_noise_frac,
-    output_type="latent",
-).images
-image = refiner(
-    prompt=prompt,
-    num_inference_steps=n_steps,
-    denoising_start=high_noise_frac,
-    image=image,
-).images[0]
-```
-
-Let's have a look at the images
-
-| Original Image | Ensemble of Denoisers Experts |
-|---|---|
-| ![lion_base_timesteps](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lion_base.png) | ![lion_refined_timesteps](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lion_refined.png)
-
-If we would have just run the base model on the same 40 steps, the image would have been arguably less detailed (e.g. the lion eyes and nose):
-
-<Tip>
-
-The ensemble-of-experts method works well on all available schedulers!
-
-</Tip>
-
-#### 2.) Refining the image output from fully denoised base image
-
-In standard [`StableDiffusionImg2ImgPipeline`]-fashion, the fully-denoised image generated of the base model 
-can be further improved using the [refiner checkpoint](huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0).
-
-For this, you simply run the refiner as a normal image-to-image pipeline after the "base" text-to-image 
-pipeline. You can leave the outputs of the base model in latent space.
-
-```py
-from diffusers import DiffusionPipeline
-import torch
-
-pipe = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-)
-pipe.to("cuda")
-
-refiner = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-1.0",
-    text_encoder_2=pipe.text_encoder_2,
-    vae=pipe.vae,
-    torch_dtype=torch.float16,
-    use_safetensors=True,
-    variant="fp16",
-)
-refiner.to("cuda")
-
-prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
-
-image = pipe(prompt=prompt, output_type="latent" if use_refiner else "pil").images[0]
-image = refiner(prompt=prompt, image=image[None, :]).images[0]
-```
-
-| Original Image | Refined Image |
-|---|---|
-| ![](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/init_image.png) | ![](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/refined_image.png) |
-
-<Tip>
-
-The refiner can also very well be used in an in-painting setting. To do so just make
-  sure you use the [`StableDiffusionXLInpaintPipeline`] classes as shown below
-
-</Tip>
-
-To use the refiner for inpainting in the Ensemble of Expert Denoisers setting you can do the following:
-
-```py
-from diffusers import StableDiffusionXLInpaintPipeline
-from diffusers.utils import load_image
-
-pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-)
-pipe.to("cuda")
-
-refiner = StableDiffusionXLInpaintPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-1.0",
-    text_encoder_2=pipe.text_encoder_2,
-    vae=pipe.vae,
-    torch_dtype=torch.float16,
-    use_safetensors=True,
-    variant="fp16",
-)
-refiner.to("cuda")
-
-img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
-mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
-
-init_image = load_image(img_url).convert("RGB")
-mask_image = load_image(mask_url).convert("RGB")
-
-prompt = "A majestic tiger sitting on a bench"
-num_inference_steps = 75
-high_noise_frac = 0.7
-
-image = pipe(
-    prompt=prompt,
-    image=init_image,
-    mask_image=mask_image,
-    num_inference_steps=num_inference_steps,
-    denoising_start=high_noise_frac,
-    output_type="latent",
-).images
-image = refiner(
-    prompt=prompt,
-    image=image,
-    mask_image=mask_image,
-    num_inference_steps=num_inference_steps,
-    denoising_start=high_noise_frac,
-).images[0]
-```
-
-To use the refiner for inpainting in the standard SDE-style setting, simply remove `denoising_end` and `denoising_start` and choose a smaller
-number of inference steps for the refiner.
-
-### Loading single file checkpoints / original file format
-
-By making use of [`~diffusers.loaders.FromSingleFileMixin.from_single_file`] you can also load the 
-original file format into `diffusers`:
-
-```py
-from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
-import torch
-
-pipe = StableDiffusionXLPipeline.from_single_file(
-    "./sd_xl_base_1.0.safetensors", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-)
-pipe.to("cuda")
-
-refiner = StableDiffusionXLImg2ImgPipeline.from_single_file(
-    "./sd_xl_refiner_1.0.safetensors", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
-)
-refiner.to("cuda")
-```
-
-### Memory optimization via model offloading 
-
-If you are seeing out-of-memory errors, we recommend making use of [`StableDiffusionXLPipeline.enable_model_cpu_offload`].
-
-```diff
-- pipe.to("cuda")
-+ pipe.enable_model_cpu_offload()
-```
-
-and 
-
-```diff
-- refiner.to("cuda")
-+ refiner.enable_model_cpu_offload()
-```
-
-### Speed-up inference with `torch.compile`
-
-You can speed up inference by making use of `torch.compile`. This should give you **ca.** 20% speed-up.
-
-```diff
-+ pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-+ refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
-```
-
-### Running with `torch < 2.0`
-
-**Note** that if you want to run Stable Diffusion XL with `torch` < 2.0, please make sure to enable xformers 
-attention:
-
-```
-pip install xformers
-```
-
-```diff
-+pipe.enable_xformers_memory_efficient_attention()
-+refiner.enable_xformers_memory_efficient_attention()
-```
-
-## StableDiffusionXLPipeline
-
-[[autodoc]] StableDiffusionXLPipeline
-	- all
-	- __call__
-
-## StableDiffusionXLImg2ImgPipeline
-
-[[autodoc]] StableDiffusionXLImg2ImgPipeline
-	- all
-	- __call__
-
-## StableDiffusionXLInpaintPipeline
-
-[[autodoc]] StableDiffusionXLInpaintPipeline
-	- all
-	- __call__
-
-### Passing different prompts to each text-encoder
-
-Stable Diffusion XL was trained on two text encoders. The default behavior is to pass the same prompt to each. But it is possible to pass a different prompt for each text-encoder, as [some users](https://github.com/huggingface/diffusers/issues/4004#issuecomment-1627764201) noted that it can boost quality.
-To do so, you can pass `prompt_2` and `negative_prompt_2` in addition to `prompt` and `negative_prompt`. By doing that, you will pass the original prompts and negative prompts (as in `prompt` and `negative_prompt`) to `text_encoder` (in official SDXL 0.9/1.0 that is [OpenAI CLIP-ViT/L-14](https://huggingface.co/openai/clip-vit-large-patch14)),
-and `prompt_2` and `negative_prompt_2` to `text_encoder_2` (in official SDXL 0.9/1.0 that is [OpenCLIP-ViT/bigG-14](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)).
-
-```py
-from diffusers import StableDiffusionXLPipeline
-import torch
-
-pipe = StableDiffusionXLPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-)
-pipe.to("cuda")
-
-# prompt will be passed to OAI CLIP-ViT/L-14
-prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
-# prompt_2 will be passed to OpenCLIP-ViT/bigG-14
-prompt_2 = "monet painting"
-image = pipe(prompt=prompt, prompt_2=prompt_2).images[0]
-```
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_flax_inpaint.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_flax_inpaint.py
deleted file mode 100644
index 432619a79ddd32d288893e3021a14ab6893b370a..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_2/test_stable_diffusion_flax_inpaint.py
+++ /dev/null
@@ -1,82 +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
-
-from diffusers import FlaxStableDiffusionInpaintPipeline
-from diffusers.utils import is_flax_available, load_image, slow
-from diffusers.utils.testing_utils import require_flax
-
-
-if is_flax_available():
-    import jax
-    import jax.numpy as jnp
-    from flax.jax_utils import replicate
-    from flax.training.common_utils import shard
-
-
-@slow
-@require_flax
-class FlaxStableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-
-    def test_stable_diffusion_inpaint_pipeline(self):
-        init_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/sd2-inpaint/init_image.png"
-        )
-        mask_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"
-        )
-
-        model_id = "xvjiarui/stable-diffusion-2-inpainting"
-        pipeline, params = FlaxStableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None)
-
-        prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
-
-        prng_seed = jax.random.PRNGKey(0)
-        num_inference_steps = 50
-
-        num_samples = jax.device_count()
-        prompt = num_samples * [prompt]
-        init_image = num_samples * [init_image]
-        mask_image = num_samples * [mask_image]
-        prompt_ids, processed_masked_images, processed_masks = pipeline.prepare_inputs(prompt, init_image, mask_image)
-
-        # shard inputs and rng
-        params = replicate(params)
-        prng_seed = jax.random.split(prng_seed, jax.device_count())
-        prompt_ids = shard(prompt_ids)
-        processed_masked_images = shard(processed_masked_images)
-        processed_masks = shard(processed_masks)
-
-        output = pipeline(
-            prompt_ids, processed_masks, processed_masked_images, params, prng_seed, num_inference_steps, jit=True
-        )
-
-        images = output.images.reshape(num_samples, 512, 512, 3)
-
-        image_slice = images[0, 253:256, 253:256, -1]
-
-        output_slice = jnp.asarray(jax.device_get(image_slice.flatten()))
-        expected_slice = jnp.array(
-            [0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084]
-        )
-        print(f"output_slice: {output_slice}")
-        assert jnp.abs(output_slice - expected_slice).max() < 1e-2
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/anchor/__init__.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/anchor/__init__.py
deleted file mode 100644
index 5838ff3eefb03bc83928fa13848cea9ff8647827..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/anchor/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from .anchor_generator import (AnchorGenerator, LegacyAnchorGenerator,
-                               YOLOAnchorGenerator)
-from .builder import ANCHOR_GENERATORS, build_anchor_generator
-from .point_generator import PointGenerator
-from .utils import anchor_inside_flags, calc_region, images_to_levels
-
-__all__ = [
-    'AnchorGenerator', 'LegacyAnchorGenerator', 'anchor_inside_flags',
-    'PointGenerator', 'images_to_levels', 'calc_region',
-    'build_anchor_generator', 'ANCHOR_GENERATORS', 'YOLOAnchorGenerator'
-]
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py
deleted file mode 100644
index 00b2594ba8a1c9edc90cca7a6d7c3334fa209edc..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/ann_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py
deleted file mode 100644
index 796ba3fb142394c4d93a29ba57548dca59d8d02b..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3_r50-d8_512x512_80k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index 398d9759cafc1d01e78c138abd249808531a97b9..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='torchvision://resnet101',
-    backbone=dict(type='ResNet', depth=101))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py b/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py
deleted file mode 100644
index 1a3c43495bbf9d302216d7ddf62df75446907a36..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './psanet_r50-d8_512x512_20k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/AnimalEquality/chatbot/_proc/styles.css b/spaces/AnimalEquality/chatbot/_proc/styles.css
deleted file mode 100644
index 66ccc49ee8f0e73901dac02dc4e9224b7d1b2c78..0000000000000000000000000000000000000000
--- a/spaces/AnimalEquality/chatbot/_proc/styles.css
+++ /dev/null
@@ -1,37 +0,0 @@
-.cell {
-  margin-bottom: 1rem;
-}
-
-.cell > .sourceCode {
-  margin-bottom: 0;
-}
-
-.cell-output > pre {
-  margin-bottom: 0;
-}
-
-.cell-output > pre, .cell-output > .sourceCode > pre, .cell-output-stdout > pre {
-  margin-left: 0.8rem;
-  margin-top: 0;
-  background: none;
-  border-left: 2px solid lightsalmon;
-  border-top-left-radius: 0;
-  border-top-right-radius: 0;
-}
-
-.cell-output > .sourceCode {
-  border: none;
-}
-
-.cell-output > .sourceCode {
-  background: none;
-  margin-top: 0;
-}
-
-div.description {
-  padding-left: 2px;
-  padding-top: 5px;
-  font-style: italic;
-  font-size: 135%;
-  opacity: 70%;
-}
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/multimodal/pipelines/llava/pipelines.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/multimodal/pipelines/llava/pipelines.py
deleted file mode 100644
index 0f650c1ab1a0f66bf79ce72d052db43b96801b6d..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/multimodal/pipelines/llava/pipelines.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from typing import Optional
-
-from extensions.multimodal.abstract_pipeline import AbstractMultimodalPipeline
-
-available_pipelines = ['llava-7b', 'llava-13b']
-
-
-def get_pipeline(name: str, params: dict) -> Optional[AbstractMultimodalPipeline]:
-    if name == 'llava-7b':
-        from .llava import LLaVA_v0_7B_Pipeline
-        return LLaVA_v0_7B_Pipeline(params)
-    if name == 'llava-13b':
-        from .llava import LLaVA_v0_13B_Pipeline
-        return LLaVA_v0_13B_Pipeline(params)
-    return None
-
-
-def get_pipeline_from_model_name(model_name: str, params: dict) -> Optional[AbstractMultimodalPipeline]:
-    if 'llava' not in model_name.lower():
-        return None
-    if '7b' in model_name.lower():
-        from .llava import LLaVA_v0_7B_Pipeline
-        return LLaVA_v0_7B_Pipeline(params)
-    if '13b' in model_name.lower():
-        from .llava import LLaVA_v0_13B_Pipeline
-        return LLaVA_v0_13B_Pipeline(params)
-    return None
diff --git a/spaces/Ank0X0/Image-Upscaling-Playground/app.py b/spaces/Ank0X0/Image-Upscaling-Playground/app.py
deleted file mode 100644
index 1f3736667bfd4e5ac6d9ee2ef9b95416cb80f9c0..0000000000000000000000000000000000000000
--- a/spaces/Ank0X0/Image-Upscaling-Playground/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import numpy as np
-import cv2
-import onnxruntime
-import gradio as gr
-
-
-def pre_process(img: np.array) -> np.array:
-    # H, W, C -> C, H, W
-    img = np.transpose(img[:, :, 0:3], (2, 0, 1))
-    # C, H, W -> 1, C, H, W
-    img = np.expand_dims(img, axis=0).astype(np.float32)
-    return img
-
-
-def post_process(img: np.array) -> np.array:
-    # 1, C, H, W -> C, H, W
-    img = np.squeeze(img)
-    # C, H, W -> H, W, C
-    img = np.transpose(img, (1, 2, 0))[:, :, ::-1].astype(np.uint8)
-    return img
-
-
-def inference(model_path: str, img_array: np.array) -> np.array:
-    options = onnxruntime.SessionOptions()
-    options.intra_op_num_threads = 1
-    options.inter_op_num_threads = 1
-    ort_session = onnxruntime.InferenceSession(model_path, options)
-    ort_inputs = {ort_session.get_inputs()[0].name: img_array}
-    ort_outs = ort_session.run(None, ort_inputs)
-
-    return ort_outs[0]
-
-
-def convert_pil_to_cv2(image):
-    # pil_image = image.convert("RGB")
-    open_cv_image = np.array(image)
-    # RGB to BGR
-    open_cv_image = open_cv_image[:, :, ::-1].copy()
-    return open_cv_image
-
-
-def upscale(image, model):
-    model_path = f"models/{model}.ort"
-    img = convert_pil_to_cv2(image)
-    if img.ndim == 2:
-        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
-    if img.shape[2] == 4:
-        alpha = img[:, :, 3]  # GRAY
-        alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2BGR)  # BGR
-        alpha_output = post_process(inference(model_path, pre_process(alpha)))  # BGR
-        alpha_output = cv2.cvtColor(alpha_output, cv2.COLOR_BGR2GRAY)  # GRAY
-
-        img = img[:, :, 0:3]  # BGR
-        image_output = post_process(inference(model_path, pre_process(img)))  # BGR
-        image_output = cv2.cvtColor(image_output, cv2.COLOR_BGR2BGRA)  # BGRA
-        image_output[:, :, 3] = alpha_output
-
-    elif img.shape[2] == 3:
-        image_output = post_process(inference(model_path, pre_process(img)))  # BGR
-
-    return image_output
-
-
-css = ".output-image, .input-image, .image-preview {height: 480px !important} "
-model_choices = ["modelx2", "modelx2 25 JXL", "modelx4", "minecraft_modelx4"]
-
-gr.Interface(
-    fn=upscale,
-    inputs=[
-        gr.inputs.Image(type="pil", label="Input Image"),
-        gr.inputs.Radio(
-            model_choices,
-            type="value",
-            default=None,
-            label="Choose Upscaler",
-            optional=False,
-        ),
-    ],
-    outputs="image",
-    title="Image Upscaling 🦆",
-    description="Model: [Anchor-based Plain Net for Mobile Image Super-Resolution](https://arxiv.org/abs/2105.09750). Repository: [SR Mobile PyTorch](https://github.com/w11wo/sr_mobile_pytorch)",
-    allow_flagging="never",
-    css=css,
-).launch()
diff --git a/spaces/Annelisseishere/Streamlit_GPT/README.md b/spaces/Annelisseishere/Streamlit_GPT/README.md
deleted file mode 100644
index d690b01b20e2640c17f802059f3306685323045c..0000000000000000000000000000000000000000
--- a/spaces/Annelisseishere/Streamlit_GPT/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Streamlit GPT
-emoji: 🏢
-colorFrom: indigo
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Asahi402/anime-remove-background/README.md b/spaces/Asahi402/anime-remove-background/README.md
deleted file mode 100644
index 1ba3cb5ea0e994e246d57b7d62b8aa5a6331901c..0000000000000000000000000000000000000000
--- a/spaces/Asahi402/anime-remove-background/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Anime Remove Background
-emoji: 🪄🖼️
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: skytnt/anime-remove-background
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/show.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/show.py
deleted file mode 100644
index 3f10701f6b28c72b62c9904fec37b96bdd199dcc..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/show.py
+++ /dev/null
@@ -1,189 +0,0 @@
-import logging
-from optparse import Values
-from typing import Generator, Iterable, Iterator, List, NamedTuple, Optional
-
-from pip._vendor.packaging.utils import canonicalize_name
-
-from pip._internal.cli.base_command import Command
-from pip._internal.cli.status_codes import ERROR, SUCCESS
-from pip._internal.metadata import BaseDistribution, get_default_environment
-from pip._internal.utils.misc import write_output
-
-logger = logging.getLogger(__name__)
-
-
-class ShowCommand(Command):
-    """
-    Show information about one or more installed packages.
-
-    The output is in RFC-compliant mail header format.
-    """
-
-    usage = """
-      %prog [options] <package> ..."""
-    ignore_require_venv = True
-
-    def add_options(self) -> None:
-        self.cmd_opts.add_option(
-            "-f",
-            "--files",
-            dest="files",
-            action="store_true",
-            default=False,
-            help="Show the full list of installed files for each package.",
-        )
-
-        self.parser.insert_option_group(0, self.cmd_opts)
-
-    def run(self, options: Values, args: List[str]) -> int:
-        if not args:
-            logger.warning("ERROR: Please provide a package name or names.")
-            return ERROR
-        query = args
-
-        results = search_packages_info(query)
-        if not print_results(
-            results, list_files=options.files, verbose=options.verbose
-        ):
-            return ERROR
-        return SUCCESS
-
-
-class _PackageInfo(NamedTuple):
-    name: str
-    version: str
-    location: str
-    editable_project_location: Optional[str]
-    requires: List[str]
-    required_by: List[str]
-    installer: str
-    metadata_version: str
-    classifiers: List[str]
-    summary: str
-    homepage: str
-    project_urls: List[str]
-    author: str
-    author_email: str
-    license: str
-    entry_points: List[str]
-    files: Optional[List[str]]
-
-
-def search_packages_info(query: List[str]) -> Generator[_PackageInfo, None, None]:
-    """
-    Gather details from installed distributions. Print distribution name,
-    version, location, and installed files. Installed files requires a
-    pip generated 'installed-files.txt' in the distributions '.egg-info'
-    directory.
-    """
-    env = get_default_environment()
-
-    installed = {dist.canonical_name: dist for dist in env.iter_all_distributions()}
-    query_names = [canonicalize_name(name) for name in query]
-    missing = sorted(
-        [name for name, pkg in zip(query, query_names) if pkg not in installed]
-    )
-    if missing:
-        logger.warning("Package(s) not found: %s", ", ".join(missing))
-
-    def _get_requiring_packages(current_dist: BaseDistribution) -> Iterator[str]:
-        return (
-            dist.metadata["Name"] or "UNKNOWN"
-            for dist in installed.values()
-            if current_dist.canonical_name
-            in {canonicalize_name(d.name) for d in dist.iter_dependencies()}
-        )
-
-    for query_name in query_names:
-        try:
-            dist = installed[query_name]
-        except KeyError:
-            continue
-
-        requires = sorted((req.name for req in dist.iter_dependencies()), key=str.lower)
-        required_by = sorted(_get_requiring_packages(dist), key=str.lower)
-
-        try:
-            entry_points_text = dist.read_text("entry_points.txt")
-            entry_points = entry_points_text.splitlines(keepends=False)
-        except FileNotFoundError:
-            entry_points = []
-
-        files_iter = dist.iter_declared_entries()
-        if files_iter is None:
-            files: Optional[List[str]] = None
-        else:
-            files = sorted(files_iter)
-
-        metadata = dist.metadata
-
-        yield _PackageInfo(
-            name=dist.raw_name,
-            version=str(dist.version),
-            location=dist.location or "",
-            editable_project_location=dist.editable_project_location,
-            requires=requires,
-            required_by=required_by,
-            installer=dist.installer,
-            metadata_version=dist.metadata_version or "",
-            classifiers=metadata.get_all("Classifier", []),
-            summary=metadata.get("Summary", ""),
-            homepage=metadata.get("Home-page", ""),
-            project_urls=metadata.get_all("Project-URL", []),
-            author=metadata.get("Author", ""),
-            author_email=metadata.get("Author-email", ""),
-            license=metadata.get("License", ""),
-            entry_points=entry_points,
-            files=files,
-        )
-
-
-def print_results(
-    distributions: Iterable[_PackageInfo],
-    list_files: bool,
-    verbose: bool,
-) -> bool:
-    """
-    Print the information from installed distributions found.
-    """
-    results_printed = False
-    for i, dist in enumerate(distributions):
-        results_printed = True
-        if i > 0:
-            write_output("---")
-
-        write_output("Name: %s", dist.name)
-        write_output("Version: %s", dist.version)
-        write_output("Summary: %s", dist.summary)
-        write_output("Home-page: %s", dist.homepage)
-        write_output("Author: %s", dist.author)
-        write_output("Author-email: %s", dist.author_email)
-        write_output("License: %s", dist.license)
-        write_output("Location: %s", dist.location)
-        if dist.editable_project_location is not None:
-            write_output(
-                "Editable project location: %s", dist.editable_project_location
-            )
-        write_output("Requires: %s", ", ".join(dist.requires))
-        write_output("Required-by: %s", ", ".join(dist.required_by))
-
-        if verbose:
-            write_output("Metadata-Version: %s", dist.metadata_version)
-            write_output("Installer: %s", dist.installer)
-            write_output("Classifiers:")
-            for classifier in dist.classifiers:
-                write_output("  %s", classifier)
-            write_output("Entry-points:")
-            for entry in dist.entry_points:
-                write_output("  %s", entry.strip())
-            write_output("Project-URLs:")
-            for project_url in dist.project_urls:
-                write_output("  %s", project_url)
-        if list_files:
-            write_output("Files:")
-            if dist.files is None:
-                write_output("Cannot locate RECORD or installed-files.txt")
-            else:
-                for line in dist.files:
-                    write_output("  %s", line.strip())
-    return results_printed
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/discovery.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/discovery.py
deleted file mode 100644
index 98fc2a7f487da55a23b962793158911848800211..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/discovery.py
+++ /dev/null
@@ -1,600 +0,0 @@
-"""Automatic discovery of Python modules and packages (for inclusion in the
-distribution) and other config values.
-
-For the purposes of this module, the following nomenclature is used:
-
-- "src-layout": a directory representing a Python project that contains a "src"
-  folder. Everything under the "src" folder is meant to be included in the
-  distribution when packaging the project. Example::
-
-    .
-    ├── tox.ini
-    ├── pyproject.toml
-    └── src/
-        └── mypkg/
-            ├── __init__.py
-            ├── mymodule.py
-            └── my_data_file.txt
-
-- "flat-layout": a Python project that does not use "src-layout" but instead
-  have a directory under the project root for each package::
-
-    .
-    ├── tox.ini
-    ├── pyproject.toml
-    └── mypkg/
-        ├── __init__.py
-        ├── mymodule.py
-        └── my_data_file.txt
-
-- "single-module": a project that contains a single Python script direct under
-  the project root (no directory used)::
-
-    .
-    ├── tox.ini
-    ├── pyproject.toml
-    └── mymodule.py
-
-"""
-
-import itertools
-import os
-from fnmatch import fnmatchcase
-from glob import glob
-from pathlib import Path
-from typing import (
-    TYPE_CHECKING,
-    Callable,
-    Dict,
-    Iterable,
-    Iterator,
-    List,
-    Mapping,
-    Optional,
-    Tuple,
-    Union
-)
-
-import _distutils_hack.override  # noqa: F401
-
-from distutils import log
-from distutils.util import convert_path
-
-_Path = Union[str, os.PathLike]
-_Filter = Callable[[str], bool]
-StrIter = Iterator[str]
-
-chain_iter = itertools.chain.from_iterable
-
-if TYPE_CHECKING:
-    from setuptools import Distribution  # noqa
-
-
-def _valid_name(path: _Path) -> bool:
-    # Ignore invalid names that cannot be imported directly
-    return os.path.basename(path).isidentifier()
-
-
-class _Finder:
-    """Base class that exposes functionality for module/package finders"""
-
-    ALWAYS_EXCLUDE: Tuple[str, ...] = ()
-    DEFAULT_EXCLUDE: Tuple[str, ...] = ()
-
-    @classmethod
-    def find(
-        cls,
-        where: _Path = '.',
-        exclude: Iterable[str] = (),
-        include: Iterable[str] = ('*',)
-    ) -> List[str]:
-        """Return a list of all Python items (packages or modules, depending on
-        the finder implementation) found within directory 'where'.
-
-        'where' is the root directory which will be searched.
-        It should be supplied as a "cross-platform" (i.e. URL-style) path;
-        it will be converted to the appropriate local path syntax.
-
-        'exclude' is a sequence of names to exclude; '*' can be used
-        as a wildcard in the names.
-        When finding packages, 'foo.*' will exclude all subpackages of 'foo'
-        (but not 'foo' itself).
-
-        'include' is a sequence of names to include.
-        If it's specified, only the named items will be included.
-        If it's not specified, all found items will be included.
-        'include' can contain shell style wildcard patterns just like
-        'exclude'.
-        """
-
-        exclude = exclude or cls.DEFAULT_EXCLUDE
-        return list(
-            cls._find_iter(
-                convert_path(str(where)),
-                cls._build_filter(*cls.ALWAYS_EXCLUDE, *exclude),
-                cls._build_filter(*include),
-            )
-        )
-
-    @classmethod
-    def _find_iter(cls, where: _Path, exclude: _Filter, include: _Filter) -> StrIter:
-        raise NotImplementedError
-
-    @staticmethod
-    def _build_filter(*patterns: str) -> _Filter:
-        """
-        Given a list of patterns, return a callable that will be true only if
-        the input matches at least one of the patterns.
-        """
-        return lambda name: any(fnmatchcase(name, pat) for pat in patterns)
-
-
-class PackageFinder(_Finder):
-    """
-    Generate a list of all Python packages found within a directory
-    """
-
-    ALWAYS_EXCLUDE = ("ez_setup", "*__pycache__")
-
-    @classmethod
-    def _find_iter(cls, where: _Path, exclude: _Filter, include: _Filter) -> StrIter:
-        """
-        All the packages found in 'where' that pass the 'include' filter, but
-        not the 'exclude' filter.
-        """
-        for root, dirs, files in os.walk(str(where), followlinks=True):
-            # Copy dirs to iterate over it, then empty dirs.
-            all_dirs = dirs[:]
-            dirs[:] = []
-
-            for dir in all_dirs:
-                full_path = os.path.join(root, dir)
-                rel_path = os.path.relpath(full_path, where)
-                package = rel_path.replace(os.path.sep, '.')
-
-                # Skip directory trees that are not valid packages
-                if '.' in dir or not cls._looks_like_package(full_path, package):
-                    continue
-
-                # Should this package be included?
-                if include(package) and not exclude(package):
-                    yield package
-
-                # Keep searching subdirectories, as there may be more packages
-                # down there, even if the parent was excluded.
-                dirs.append(dir)
-
-    @staticmethod
-    def _looks_like_package(path: _Path, _package_name: str) -> bool:
-        """Does a directory look like a package?"""
-        return os.path.isfile(os.path.join(path, '__init__.py'))
-
-
-class PEP420PackageFinder(PackageFinder):
-    @staticmethod
-    def _looks_like_package(_path: _Path, _package_name: str) -> bool:
-        return True
-
-
-class ModuleFinder(_Finder):
-    """Find isolated Python modules.
-    This function will **not** recurse subdirectories.
-    """
-
-    @classmethod
-    def _find_iter(cls, where: _Path, exclude: _Filter, include: _Filter) -> StrIter:
-        for file in glob(os.path.join(where, "*.py")):
-            module, _ext = os.path.splitext(os.path.basename(file))
-
-            if not cls._looks_like_module(module):
-                continue
-
-            if include(module) and not exclude(module):
-                yield module
-
-    _looks_like_module = staticmethod(_valid_name)
-
-
-# We have to be extra careful in the case of flat layout to not include files
-# and directories not meant for distribution (e.g. tool-related)
-
-
-class FlatLayoutPackageFinder(PEP420PackageFinder):
-    _EXCLUDE = (
-        "ci",
-        "bin",
-        "doc",
-        "docs",
-        "documentation",
-        "manpages",
-        "news",
-        "changelog",
-        "test",
-        "tests",
-        "unit_test",
-        "unit_tests",
-        "example",
-        "examples",
-        "scripts",
-        "tools",
-        "util",
-        "utils",
-        "python",
-        "build",
-        "dist",
-        "venv",
-        "env",
-        "requirements",
-        # ---- Task runners / Build tools ----
-        "tasks",  # invoke
-        "fabfile",  # fabric
-        "site_scons",  # SCons
-        # ---- Other tools ----
-        "benchmark",
-        "benchmarks",
-        "exercise",
-        "exercises",
-        # ---- Hidden directories/Private packages ----
-        "[._]*",
-    )
-
-    DEFAULT_EXCLUDE = tuple(chain_iter((p, f"{p}.*") for p in _EXCLUDE))
-    """Reserved package names"""
-
-    @staticmethod
-    def _looks_like_package(_path: _Path, package_name: str) -> bool:
-        names = package_name.split('.')
-        # Consider PEP 561
-        root_pkg_is_valid = names[0].isidentifier() or names[0].endswith("-stubs")
-        return root_pkg_is_valid and all(name.isidentifier() for name in names[1:])
-
-
-class FlatLayoutModuleFinder(ModuleFinder):
-    DEFAULT_EXCLUDE = (
-        "setup",
-        "conftest",
-        "test",
-        "tests",
-        "example",
-        "examples",
-        "build",
-        # ---- Task runners ----
-        "toxfile",
-        "noxfile",
-        "pavement",
-        "dodo",
-        "tasks",
-        "fabfile",
-        # ---- Other tools ----
-        "[Ss][Cc]onstruct",  # SCons
-        "conanfile",  # Connan: C/C++ build tool
-        "manage",  # Django
-        "benchmark",
-        "benchmarks",
-        "exercise",
-        "exercises",
-        # ---- Hidden files/Private modules ----
-        "[._]*",
-    )
-    """Reserved top-level module names"""
-
-
-def _find_packages_within(root_pkg: str, pkg_dir: _Path) -> List[str]:
-    nested = PEP420PackageFinder.find(pkg_dir)
-    return [root_pkg] + [".".join((root_pkg, n)) for n in nested]
-
-
-class ConfigDiscovery:
-    """Fill-in metadata and options that can be automatically derived
-    (from other metadata/options, the file system or conventions)
-    """
-
-    def __init__(self, distribution: "Distribution"):
-        self.dist = distribution
-        self._called = False
-        self._disabled = False
-        self._skip_ext_modules = False
-
-    def _disable(self):
-        """Internal API to disable automatic discovery"""
-        self._disabled = True
-
-    def _ignore_ext_modules(self):
-        """Internal API to disregard ext_modules.
-
-        Normally auto-discovery would not be triggered if ``ext_modules`` are set
-        (this is done for backward compatibility with existing packages relying on
-        ``setup.py`` or ``setup.cfg``). However, ``setuptools`` can call this function
-        to ignore given ``ext_modules`` and proceed with the auto-discovery if
-        ``packages`` and ``py_modules`` are not given (e.g. when using pyproject.toml
-        metadata).
-        """
-        self._skip_ext_modules = True
-
-    @property
-    def _root_dir(self) -> _Path:
-        # The best is to wait until `src_root` is set in dist, before using _root_dir.
-        return self.dist.src_root or os.curdir
-
-    @property
-    def _package_dir(self) -> Dict[str, str]:
-        if self.dist.package_dir is None:
-            return {}
-        return self.dist.package_dir
-
-    def __call__(self, force=False, name=True, ignore_ext_modules=False):
-        """Automatically discover missing configuration fields
-        and modifies the given ``distribution`` object in-place.
-
-        Note that by default this will only have an effect the first time the
-        ``ConfigDiscovery`` object is called.
-
-        To repeatedly invoke automatic discovery (e.g. when the project
-        directory changes), please use ``force=True`` (or create a new
-        ``ConfigDiscovery`` instance).
-        """
-        if force is False and (self._called or self._disabled):
-            # Avoid overhead of multiple calls
-            return
-
-        self._analyse_package_layout(ignore_ext_modules)
-        if name:
-            self.analyse_name()  # depends on ``packages`` and ``py_modules``
-
-        self._called = True
-
-    def _explicitly_specified(self, ignore_ext_modules: bool) -> bool:
-        """``True`` if the user has specified some form of package/module listing"""
-        ignore_ext_modules = ignore_ext_modules or self._skip_ext_modules
-        ext_modules = not (self.dist.ext_modules is None or ignore_ext_modules)
-        return (
-            self.dist.packages is not None
-            or self.dist.py_modules is not None
-            or ext_modules
-            or hasattr(self.dist, "configuration") and self.dist.configuration
-            # ^ Some projects use numpy.distutils.misc_util.Configuration
-        )
-
-    def _analyse_package_layout(self, ignore_ext_modules: bool) -> bool:
-        if self._explicitly_specified(ignore_ext_modules):
-            # For backward compatibility, just try to find modules/packages
-            # when nothing is given
-            return True
-
-        log.debug(
-            "No `packages` or `py_modules` configuration, performing "
-            "automatic discovery."
-        )
-
-        return (
-            self._analyse_explicit_layout()
-            or self._analyse_src_layout()
-            # flat-layout is the trickiest for discovery so it should be last
-            or self._analyse_flat_layout()
-        )
-
-    def _analyse_explicit_layout(self) -> bool:
-        """The user can explicitly give a package layout via ``package_dir``"""
-        package_dir = self._package_dir.copy()  # don't modify directly
-        package_dir.pop("", None)  # This falls under the "src-layout" umbrella
-        root_dir = self._root_dir
-
-        if not package_dir:
-            return False
-
-        log.debug(f"`explicit-layout` detected -- analysing {package_dir}")
-        pkgs = chain_iter(
-            _find_packages_within(pkg, os.path.join(root_dir, parent_dir))
-            for pkg, parent_dir in package_dir.items()
-        )
-        self.dist.packages = list(pkgs)
-        log.debug(f"discovered packages -- {self.dist.packages}")
-        return True
-
-    def _analyse_src_layout(self) -> bool:
-        """Try to find all packages or modules under the ``src`` directory
-        (or anything pointed by ``package_dir[""]``).
-
-        The "src-layout" is relatively safe for automatic discovery.
-        We assume that everything within is meant to be included in the
-        distribution.
-
-        If ``package_dir[""]`` is not given, but the ``src`` directory exists,
-        this function will set ``package_dir[""] = "src"``.
-        """
-        package_dir = self._package_dir
-        src_dir = os.path.join(self._root_dir, package_dir.get("", "src"))
-        if not os.path.isdir(src_dir):
-            return False
-
-        log.debug(f"`src-layout` detected -- analysing {src_dir}")
-        package_dir.setdefault("", os.path.basename(src_dir))
-        self.dist.package_dir = package_dir  # persist eventual modifications
-        self.dist.packages = PEP420PackageFinder.find(src_dir)
-        self.dist.py_modules = ModuleFinder.find(src_dir)
-        log.debug(f"discovered packages -- {self.dist.packages}")
-        log.debug(f"discovered py_modules -- {self.dist.py_modules}")
-        return True
-
-    def _analyse_flat_layout(self) -> bool:
-        """Try to find all packages and modules under the project root.
-
-        Since the ``flat-layout`` is more dangerous in terms of accidentally including
-        extra files/directories, this function is more conservative and will raise an
-        error if multiple packages or modules are found.
-
-        This assumes that multi-package dists are uncommon and refuse to support that
-        use case in order to be able to prevent unintended errors.
-        """
-        log.debug(f"`flat-layout` detected -- analysing {self._root_dir}")
-        return self._analyse_flat_packages() or self._analyse_flat_modules()
-
-    def _analyse_flat_packages(self) -> bool:
-        self.dist.packages = FlatLayoutPackageFinder.find(self._root_dir)
-        top_level = remove_nested_packages(remove_stubs(self.dist.packages))
-        log.debug(f"discovered packages -- {self.dist.packages}")
-        self._ensure_no_accidental_inclusion(top_level, "packages")
-        return bool(top_level)
-
-    def _analyse_flat_modules(self) -> bool:
-        self.dist.py_modules = FlatLayoutModuleFinder.find(self._root_dir)
-        log.debug(f"discovered py_modules -- {self.dist.py_modules}")
-        self._ensure_no_accidental_inclusion(self.dist.py_modules, "modules")
-        return bool(self.dist.py_modules)
-
-    def _ensure_no_accidental_inclusion(self, detected: List[str], kind: str):
-        if len(detected) > 1:
-            from inspect import cleandoc
-
-            from setuptools.errors import PackageDiscoveryError
-
-            msg = f"""Multiple top-level {kind} discovered in a flat-layout: {detected}.
-
-            To avoid accidental inclusion of unwanted files or directories,
-            setuptools will not proceed with this build.
-
-            If you are trying to create a single distribution with multiple {kind}
-            on purpose, you should not rely on automatic discovery.
-            Instead, consider the following options:
-
-            1. set up custom discovery (`find` directive with `include` or `exclude`)
-            2. use a `src-layout`
-            3. explicitly set `py_modules` or `packages` with a list of names
-
-            To find more information, look for "package discovery" on setuptools docs.
-            """
-            raise PackageDiscoveryError(cleandoc(msg))
-
-    def analyse_name(self):
-        """The packages/modules are the essential contribution of the author.
-        Therefore the name of the distribution can be derived from them.
-        """
-        if self.dist.metadata.name or self.dist.name:
-            # get_name() is not reliable (can return "UNKNOWN")
-            return None
-
-        log.debug("No `name` configuration, performing automatic discovery")
-
-        name = (
-            self._find_name_single_package_or_module()
-            or self._find_name_from_packages()
-        )
-        if name:
-            self.dist.metadata.name = name
-
-    def _find_name_single_package_or_module(self) -> Optional[str]:
-        """Exactly one module or package"""
-        for field in ('packages', 'py_modules'):
-            items = getattr(self.dist, field, None) or []
-            if items and len(items) == 1:
-                log.debug(f"Single module/package detected, name: {items[0]}")
-                return items[0]
-
-        return None
-
-    def _find_name_from_packages(self) -> Optional[str]:
-        """Try to find the root package that is not a PEP 420 namespace"""
-        if not self.dist.packages:
-            return None
-
-        packages = remove_stubs(sorted(self.dist.packages, key=len))
-        package_dir = self.dist.package_dir or {}
-
-        parent_pkg = find_parent_package(packages, package_dir, self._root_dir)
-        if parent_pkg:
-            log.debug(f"Common parent package detected, name: {parent_pkg}")
-            return parent_pkg
-
-        log.warn("No parent package detected, impossible to derive `name`")
-        return None
-
-
-def remove_nested_packages(packages: List[str]) -> List[str]:
-    """Remove nested packages from a list of packages.
-
-    >>> remove_nested_packages(["a", "a.b1", "a.b2", "a.b1.c1"])
-    ['a']
-    >>> remove_nested_packages(["a", "b", "c.d", "c.d.e.f", "g.h", "a.a1"])
-    ['a', 'b', 'c.d', 'g.h']
-    """
-    pkgs = sorted(packages, key=len)
-    top_level = pkgs[:]
-    size = len(pkgs)
-    for i, name in enumerate(reversed(pkgs)):
-        if any(name.startswith(f"{other}.") for other in top_level):
-            top_level.pop(size - i - 1)
-
-    return top_level
-
-
-def remove_stubs(packages: List[str]) -> List[str]:
-    """Remove type stubs (:pep:`561`) from a list of packages.
-
-    >>> remove_stubs(["a", "a.b", "a-stubs", "a-stubs.b.c", "b", "c-stubs"])
-    ['a', 'a.b', 'b']
-    """
-    return [pkg for pkg in packages if not pkg.split(".")[0].endswith("-stubs")]
-
-
-def find_parent_package(
-    packages: List[str], package_dir: Mapping[str, str], root_dir: _Path
-) -> Optional[str]:
-    """Find the parent package that is not a namespace."""
-    packages = sorted(packages, key=len)
-    common_ancestors = []
-    for i, name in enumerate(packages):
-        if not all(n.startswith(f"{name}.") for n in packages[i+1:]):
-            # Since packages are sorted by length, this condition is able
-            # to find a list of all common ancestors.
-            # When there is divergence (e.g. multiple root packages)
-            # the list will be empty
-            break
-        common_ancestors.append(name)
-
-    for name in common_ancestors:
-        pkg_path = find_package_path(name, package_dir, root_dir)
-        init = os.path.join(pkg_path, "__init__.py")
-        if os.path.isfile(init):
-            return name
-
-    return None
-
-
-def find_package_path(
-    name: str, package_dir: Mapping[str, str], root_dir: _Path
-) -> str:
-    """Given a package name, return the path where it should be found on
-    disk, considering the ``package_dir`` option.
-
-    >>> path = find_package_path("my.pkg", {"": "root/is/nested"}, ".")
-    >>> path.replace(os.sep, "/")
-    './root/is/nested/my/pkg'
-
-    >>> path = find_package_path("my.pkg", {"my": "root/is/nested"}, ".")
-    >>> path.replace(os.sep, "/")
-    './root/is/nested/pkg'
-
-    >>> path = find_package_path("my.pkg", {"my.pkg": "root/is/nested"}, ".")
-    >>> path.replace(os.sep, "/")
-    './root/is/nested'
-
-    >>> path = find_package_path("other.pkg", {"my.pkg": "root/is/nested"}, ".")
-    >>> path.replace(os.sep, "/")
-    './other/pkg'
-    """
-    parts = name.split(".")
-    for i in range(len(parts), 0, -1):
-        # Look backwards, the most specific package_dir first
-        partial_name = ".".join(parts[:i])
-        if partial_name in package_dir:
-            parent = package_dir[partial_name]
-            return os.path.join(root_dir, parent, *parts[i:])
-
-    parent = package_dir.get("") or ""
-    return os.path.join(root_dir, *parent.split("/"), *parts)
-
-
-def construct_package_dir(packages: List[str], package_path: _Path) -> Dict[str, str]:
-    parent_pkgs = remove_nested_packages(packages)
-    prefix = Path(package_path).parts
-    return {pkg: "/".join([*prefix, *pkg.split(".")]) for pkg in parent_pkgs}
diff --git a/spaces/Azurro/APT-1B-Base/README.md b/spaces/Azurro/APT-1B-Base/README.md
deleted file mode 100644
index 51c40e7115619a9696b587322663e0b11cc38d59..0000000000000000000000000000000000000000
--- a/spaces/Azurro/APT-1B-Base/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: APT-1B-Base
-emoji: 💻
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
-license: cc-by-nc-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/BIOML-SVM/SVM/proteinbind_new.py b/spaces/BIOML-SVM/SVM/proteinbind_new.py
deleted file mode 100644
index 37a62abb09e7089e62631f7fd1320048e4f32842..0000000000000000000000000000000000000000
--- a/spaces/BIOML-SVM/SVM/proteinbind_new.py
+++ /dev/null
@@ -1,283 +0,0 @@
-from types import SimpleNamespace
-
-import pandas as pd
-import torch
-import torch.nn as nn
-from torch.utils.data import Dataset
-
-
-ModalityType = SimpleNamespace(
-    AA="aa",
-    DNA="dna",
-    PDB="pdb",
-    GO="go",
-    MSA="msa",
-    TEXT="text",
-)
-
-
-class Normalize(nn.Module):
-    def __init__(self, dim: int) -> None:
-        super().__init__()
-        self.dim = dim
-
-    def forward(self, x):
-        return torch.nn.functional.normalize(x, dim=self.dim, p=2)
-
-
-class EmbeddingDataset(Dataset):
-    """
-    The main class for turning any modality to a torch Dataset that can be passed to
-    a torch dataloader. Any modality that doesn't fit into the __getitem__
-    method can subclass this and modify the __getitem__ method.
-    """
-    def __init__(self, sequence_file_path, embeddings_file_path, modality):
-        self.sequence = pd.read_csv(sequence_file_path)
-        self.embedding = torch.load(embeddings_file_path)
-        self.modality = modality
-
-    def __len__(self):
-        return len(self.sequence)
-
-    def __getitem__(self, idx):
-        sequence = self.sequence.iloc[idx, 0]
-        embedding = self.embedding[idx]
-        return {"aa": sequence, self.modality: embedding}
-
-
-class DualEmbeddingDataset(Dataset):
-    """
-    The main class for turning any modality to a torch Dataset that can be passed to
-    a torch dataloader. Any modality that doesn't fit into the __getitem__
-    method can subclass this and modify the __getitem__ method.
-    """
-    def __init__(self, sequence_embeddings_file_path, embeddings_file_path, modality):
-        self.sequence_embedding = torch.load(sequence_embeddings_file_path)
-        self.embedding = torch.load(embeddings_file_path)
-        self.modality = modality
-
-    def __len__(self):
-        return len(self.sequence_embedding)
-
-    def __getitem__(self, idx):
-        sequence_embedding = self.sequence_embedding[idx]
-        embedding = self.embedding[idx]
-        return {"aa": sequence_embedding, self.modality: embedding}
-
-
-class ProteinBindModel(nn.Module):
-
-    def __init__(
-            self,
-            aa_embed_dim,
-            dna_embed_dim,
-            pdb_embed_dim,
-            go_embed_dim,
-            msa_embed_dim,
-            text_embed_dim,
-            in_embed_dim,
-            out_embed_dim
-    ):
-        super().__init__()
-        self.modality_trunks = self._create_modality_trunk(
-            aa_embed_dim,
-            dna_embed_dim,
-            pdb_embed_dim,
-            go_embed_dim,
-            msa_embed_dim,
-            text_embed_dim,
-            out_embed_dim
-        )
-        self.modality_heads = self._create_modality_head(
-            in_embed_dim,
-            out_embed_dim,
-        )
-        self.modality_postprocessors = self._create_modality_postprocessors(
-            out_embed_dim
-        )
-
-    def _create_modality_trunk(
-            self,
-            aa_embed_dim,
-            dna_embed_dim,
-            pdb_embed_dim,
-            go_embed_dim,
-            msa_embed_dim,
-            text_embed_dim,
-            in_embed_dim
-    ):
-        """
-        The current layers are just a proof of concept
-        and are subject to the opinion of others.
-        :param aa_embed_dim:
-        :param dna_embed_dim:
-        :param pdb_embed_dim:
-        :param go_embed_dim:
-        :param msa_embed_dim:
-        :param text_embed_dim:
-        :param in_embed_dim:
-        :return:
-        """
-        modality_trunks = {}
-
-        modality_trunks[ModalityType.AA] = nn.Sequential(
-            nn.Linear(aa_embed_dim, 512),
-            nn.ReLU(),
-            nn.Linear(512, 512),
-            nn.ReLU(),
-            nn.Linear(512, in_embed_dim),
-        )
-
-        modality_trunks[ModalityType.DNA] = nn.Sequential(
-            nn.Linear(dna_embed_dim, 512),
-            nn.ReLU(),
-            nn.Linear(512, 512),
-            nn.ReLU(),
-            nn.Linear(512, in_embed_dim),
-        )
-
-        modality_trunks[ModalityType.PDB] = nn.Sequential(
-            nn.Linear(pdb_embed_dim, 512),
-            nn.ReLU(),
-            nn.Linear(512, 512),
-            nn.ReLU(),
-            nn.Linear(512, in_embed_dim),
-        )
-
-        modality_trunks[ModalityType.GO] = nn.Sequential(
-            nn.Linear(go_embed_dim, 512),
-            nn.ReLU(),
-            nn.Linear(512, 512),
-            nn.ReLU(),
-            nn.Linear(512, in_embed_dim),
-        )
-
-        modality_trunks[ModalityType.MSA] = nn.Sequential(
-            nn.Linear(msa_embed_dim, 512),
-            nn.ReLU(),
-            nn.Linear(512, 512),
-            nn.ReLU(),
-            nn.Linear(512, in_embed_dim),
-        )
-
-        modality_trunks[ModalityType.TEXT] = nn.Sequential(
-            nn.Linear(text_embed_dim, 512),
-            nn.ReLU(),
-            nn.Linear(512, 512),
-            nn.ReLU(),
-            nn.Linear(512, in_embed_dim),
-        )
-
-        return nn.ModuleDict(modality_trunks)
-
-    def _create_modality_head(
-            self,
-            in_embed_dim,
-            out_embed_dim
-    ):
-        modality_heads = {}
-
-        modality_heads[ModalityType.AA] = nn.Sequential(
-            nn.LayerNorm(normalized_shape=in_embed_dim, eps=1e-6),
-            nn.Dropout(p=0.5),
-            nn.Linear(in_embed_dim, out_embed_dim, bias=False),
-        )
-
-        modality_heads[ModalityType.DNA] = nn.Sequential(
-            nn.LayerNorm(normalized_shape=in_embed_dim, eps=1e-6),
-            nn.Dropout(p=0.5),
-            nn.Linear(in_embed_dim, out_embed_dim, bias=False),
-        )
-
-        modality_heads[ModalityType.PDB] = nn.Sequential(
-            nn.LayerNorm(normalized_shape=in_embed_dim, eps=1e-6),
-            nn.Dropout(p=0.5),
-            nn.Linear(in_embed_dim, out_embed_dim, bias=False),
-        )
-
-        modality_heads[ModalityType.GO] = nn.Sequential(
-            nn.LayerNorm(normalized_shape=in_embed_dim, eps=1e-6),
-            nn.Dropout(p=0.5),
-            nn.Linear(in_embed_dim, out_embed_dim, bias=False),
-        )
-
-        modality_heads[ModalityType.MSA] = nn.Sequential(
-            nn.LayerNorm(normalized_shape=in_embed_dim, eps=1e-6),
-            nn.Dropout(p=0.5),
-            nn.Linear(in_embed_dim, out_embed_dim, bias=False),
-        )
-
-        modality_heads[ModalityType.TEXT] = nn.Sequential(
-            nn.LayerNorm(normalized_shape=in_embed_dim, eps=1e-6),
-            nn.Dropout(p=0.5),
-            nn.Linear(in_embed_dim, out_embed_dim, bias=False),
-        )
-        return nn.ModuleDict(modality_heads)
-
-    def _create_modality_postprocessors(self, out_embed_dim):
-        modality_postprocessors = {}
-        modality_postprocessors[ModalityType.AA] = Normalize(dim=-1)
-        modality_postprocessors[ModalityType.DNA] = Normalize(dim=-1)
-        modality_postprocessors[ModalityType.PDB] = Normalize(dim=-1)
-        modality_postprocessors[ModalityType.TEXT] = Normalize(dim=-1)
-        modality_postprocessors[ModalityType.GO] = Normalize(dim=-1)
-        modality_postprocessors[ModalityType.MSA] = Normalize(dim=-1)
-
-        return nn.ModuleDict(modality_postprocessors)
-
-    def forward(self, inputs):
-        """
-        input = {k_1: [v],k_n: [v]}
-        for key in input
-            get trunk for key
-            forward pass of value in trunk
-            get projection head of key
-            forward pass of value in projection head
-            append output in output dict
-        return { k_1, [o], k_n: [o]}
-        """
-
-        outputs = {}
-
-        for modality_key, modality_value in inputs.items():
-
-            modality_value = self.modality_trunks[modality_key](
-                modality_value
-            )
-
-            modality_value = self.modality_heads[modality_key](
-                modality_value
-            )
-
-            modality_value = self.modality_postprocessors[modality_key](
-                modality_value
-            )
-            outputs[modality_key] = modality_value
-
-        return outputs
-
-
-def create_proteinbind(pretrained=False):
-    """
-    The embedding dimensions here are dummy
-    :param pretrained:
-    :return:
-    """
-    model = ProteinBindModel(
-        aa_embed_dim=480,
-        dna_embed_dim=1280,
-        pdb_embed_dim=128,
-        go_embed_dim=600,
-        msa_embed_dim=768,
-        text_embed_dim=768,
-        in_embed_dim=1024,
-        out_embed_dim=1024
-    )
-
-    if pretrained:
-        # get path from config
-        PATH = 'best_model.pth'
-
-        model.load_state_dict(torch.load(PATH))
-
-    return model
diff --git a/spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/lib_v5/layers.py b/spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/lib_v5/layers.py
deleted file mode 100644
index 4fc1b5cb85a3327f60cbb9f5deffbeeaaac516ad..0000000000000000000000000000000000000000
--- a/spaces/Bart92/RVC_HF/infer/lib/uvr5_pack/lib_v5/layers.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle
diff --git a/spaces/Benson/text-generation/Examples/Alquimia Clsico 2 Mod Apk.md b/spaces/Benson/text-generation/Examples/Alquimia Clsico 2 Mod Apk.md
deleted file mode 100644
index a95f4f8ed264b378f72fa82892db7d6386b64e48..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Alquimia Clsico 2 Mod Apk.md	
+++ /dev/null
@@ -1,121 +0,0 @@
-<br />
-<h1>Proyecto deriva 2.0 Mod APK 43: Todo lo que necesita saber</h1>
-<p>Si eres un fan de los juegos de carreras, especialmente los juegos de deriva, es posible que hayas oído hablar de Project Drift 2.0, un juego de simulación de deriva realista y desafiante para dispositivos Android. En este artículo, le diremos todo lo que necesita saber sobre Project Drift 2.0 Mod APK 43, una versión modificada del juego que le da dinero ilimitado y acceso a todos los coches y pistas en el juego. También compartiremos algunos consejos y trucos sobre cómo jugar el juego y dominar el arte de la deriva. </p>
-<h2>alquimia clásico 2 mod apk</h2><br /><p><b><b>Download Zip</b> &#10037;&#10037;&#10037; <a href="https://bltlly.com/2v6Jf8">https://bltlly.com/2v6Jf8</a></b></p><br /><br />
- <h2>¿Qué es Project Drift 2.0? </h2>
-<p>Project Drift 2.0 es una secuela del popular juego Project Drift, desarrollado por Bycodec Games, un estudio de juegos indie turco. El juego está diseñado para proporcionar una experiencia de deriva realista e inmersiva, con gráficos impresionantes, manejo de automóviles basado en la física y varios modos de juego y desafíos. </p>
- <h3>Características de la deriva del proyecto 2.0</h3>
-<p>Algunas de las características de Project Drift 2.0 son:</p>
-<ul>
-<li>Más de 50 coches diferentes para elegir, cada uno con sus propias características y opciones de personalización. </li>
-<li>Más de 20 pistas diferentes para seguir, cada una con su propio diseño y nivel de dificultad. </li>
-<li>Cuatro modos de juego: Carrera, Free Ride, Time Attack y Multijugador.</li>
-<li>Modo de carrera: Completa varias misiones y desafíos para ganar dinero y reputación. </li>
-<li>Modo Free Ride: Explora las pistas y practica tus habilidades de deriva sin ninguna presión o límite de tiempo. </li>
-<li>Modo de ataque de tiempo: Carrera contra el reloj y tratar de batir sus propios u otros jugadores' registros. </li>
-<li>Modo multijugador: Compite con otros jugadores en línea en batallas a la deriva en tiempo real. </li>
-<li>Tablas de clasificación y logros: Seguimiento de su progreso y comparar su rendimiento con otros jugadores de todo el mundo. </li>
-</ul>
- <h3>Cómo descargar e instalar el proyecto Drift 2.0 Mod APK 43</h3>
-
-<p>Para descargar e instalar Project Drift 2.0 Mod APK 43, siga estos pasos:</p>
-<ol>
-<li>Ir a [HappyMod]( 1 ), un sitio web que proporciona descargas apk mod seguro y confiable para varios juegos y aplicaciones. </li>
-<li>Buscar "Proyecto de deriva 2.0 Mod APK" en la barra de búsqueda. </li>
-<li>Seleccione la versión que dice "Proyecto de deriva 2.0 Mod Apk [dinero ilimitado]". Asegúrese de que coincide con el número de versión "43". </li>
-<li>Haga clic en el botón "Descargar" y espere a que el archivo se descargue en su dispositivo. </li>
-<li> Una vez que el archivo se descarga, localizarlo en el administrador de archivos y toque en él para instalarlo. </li>
-<li>Si ves una ventana emergente que dice "Instalar bloqueado", ve a la configuración de tu dispositivo y habilita "Fuentes desconocidas" en las opciones de seguridad. </li>
-<li>Después de instalar el apk mod, lanzar el juego y disfrutar de dinero ilimitado y el acceso a todos los coches y pistas en el juego. </li>
-</ol>
- <h2>¿Por qué usar Project Drift 2.0 Mod APK 43? </h2>
-<p>Es posible que se pregunte por qué debe utilizar Project Drift 2.0 Mod APK 43 en lugar de la versión original del juego. Bueno, hay algunos beneficios y riesgos de usar el apk mod que usted debe ser consciente de antes de decidir usarlo. </p>
- <h3>Beneficios de la deriva del proyecto 2.0 Mod APK 43</h3>
-<p>Algunos de los beneficios de usar Project Drift 2.0 Mod APK 43 son:</p>
-<ul>
-<li> Usted puede obtener dinero ilimitado en el juego, que se puede utilizar para comprar y actualizar cualquier coche que desee. </li>
-<li> Puede desbloquear todos los coches y pistas en el juego, lo que le da más variedad y opciones para elegir. </li>
-<li>Puedes disfrutar del juego sin anuncios ni interrupciones, lo que puede mejorar tu experiencia de juego y rendimiento. </li>
-<li>Puedes divertirte más y desafiarte con los diferentes modos y niveles de juego, sin preocuparte por quedarte sin dinero o recursos. </li>
-</ul>
- <h3>Riesgos de la deriva del proyecto 2.0 Mod APK 43</h3>
-<p>Algunos de los riesgos de usar Project Drift 2.0 Mod APK 43 son:</p>
-<p></p>
-<ul>
-
-<li>Es posible que pierda su progreso o datos en el juego, como el apk mod podría no sincronizar con su cuenta de Google Play o almacenamiento en la nube. </li>
-<li>Usted puede ser prohibido o penalizado por los desarrolladores de juegos o moderadores, como el uso de un apk mod se considera hacer trampa y violar los términos y condiciones del juego. </li>
-<li>Es posible que exponga su dispositivo o información personal a malware o virus, ya que algunos archivos apk mod pueden contener código o enlaces dañinos o maliciosos. </li>
-</ul>
- <h2>Consejos y trucos para jugar Project Drift 2.0</h2>
-<p>Si decide utilizar Project Drift 2.0 Mod APK 43 o la versión original del juego, es posible que desee saber algunos consejos y trucos sobre cómo jugar el juego y dominar el arte de la deriva. Estos son algunos de ellos:</p>
- <h3>Cómo dominar la deriva en Project Drift 2.0</h3>
-<p>Drifting es la principal habilidad que necesitas dominar en Project Drift 2.0, ya que es la forma de ganar puntos y reputación en el juego. A la deriva es cuando se desliza su coche de lado alrededor de una esquina o curva, manteniendo el control y la velocidad. Para ir a la deriva en Project Drift 2.0, debes seguir estos pasos:</p>
-<ol>
-<li>Seleccione un coche que tenga buen manejo y potencia, ya que estos son esenciales para la deriva. </li>
-<li>Seleccione una pista que tenga curvas y giros agudos, ya que son ideales para la deriva. </li>
-<li>A medida que se acerca a una esquina o curva, toque el pedal del freno para reducir su velocidad e iniciar una deriva. </li>
-<li>Al entrar en la deriva, dirigir su coche en la dirección opuesta de la vuelta, mientras que golpea el pedal del acelerador para mantener su impulso y equilibrio. </li>
-<li>Al salir de la deriva, dirigir su coche de nuevo en línea con la carretera, mientras que la liberación del pedal de gas para recuperar la tracción y la estabilidad. </li>
-</ol>
- <h3>Cómo desbloquear nuevos coches y pistas en Project Drift 2.0</h3>
-
-<ul>
-<li>Completa tantas misiones y desafíos como sea posible en el modo carrera, ya que te recompensarán con dinero y reputación, que son necesarios para desbloquear nuevos coches y pistas. </li>
-<li>Trate de lograr altas puntuaciones y calificaciones en cada misión y desafío, ya que aumentarán sus recompensas de dinero y reputación. </li>
-<li>Juega el modo de ataque de tiempo y tratar de batir sus propios u otros jugadores' registros, ya que también le dará dinero y bonos de reputación. </li>
-<li>Ahorre su dinero y gastarlo sabiamente en los coches y pistas que se adapten a sus preferencias y estilo de juego. </li>
-<li>Consulte la tienda de juegos regularmente para obtener descuentos y ofertas en coches y pistas, ya que podrían ayudarle a ahorrar algo de dinero y obtener más valor. </li>
-</ul>
- <h3>Cómo personalizar su coche en Project Drift 2.0</h3>
-<p>Uno de los aspectos divertidos de Project Drift 2.0 es que puedes personalizar tu coche para que se vea y funcione mejor. Usted puede cambiar el color, la pintura, las calcomanías, las ruedas, los neumáticos, los alerones, los escapes, y más de su coche. También puede actualizar el motor, la transmisión, la suspensión, los frenos y más de su coche. Para personalizar tu coche en Project Drift 2.0, sigue estos pasos:</p>
-<ol>
-<li>Seleccione un coche que desea personalizar desde su garaje. </li>
-<li>Toque en el botón "Personalizar" en la parte inferior de la pantalla. </li>
-<li>Elija la categoría que desea personalizar, como apariencia o rendimiento. </li>
-<li>Seleccione el elemento que desea cambiar o actualizar, como color o motor. </li>
-<li>Elija la opción que desea aplicar, como rojo o turbo. </li>
-<li>Toque en el botón "Aplicar" para confirmar sus cambios. </li>
-<li>Toque en el botón "Atrás" para regresar a su garaje. </li>
-</ol>
- <h2>Conclusión</h2>
-
- <h3>Llamada a la acción para los lectores</h3>
-<p>Si usted está listo para empezar a la deriva en Project Drift 2.0, descargar el juego de [Google Play] o [HappyMod] ahora y disfrutar de la emoción de deslizar su coche alrededor de las esquinas y curvas. No olvides compartir tus comentarios y opiniones sobre el juego con nosotros en la sección de comentarios a continuación. ¡Feliz deriva! </p>
- <h4>Preguntas frecuentes</h4>
-<p>Aquí hay algunas preguntas frecuentes sobre Project Drift 2.0:</p>
- <ol>
-<li><b> ¿Cuál es la diferencia entre Proyecto de deriva 2.0 Mod APK 43 y proyecto de deriva 2.0 Hack APK? </b></li>
-<p>Proyecto deriva 2.0 Mod APK 43 es una versión modificada del juego que le da dinero ilimitado y acceso a todos los coches y pistas en el juego. Proyecto de deriva 2.0 Hack APK es una versión hackeada del juego que le da dinero ilimitado, acceso a todos los coches y pistas, y otros trucos, tales como la invencibilidad, aumento de velocidad, o auto deriva. Ambas versiones no son oficiales y pueden tener algunos riesgos, como problemas de compatibilidad, pérdida de datos, prohibiciones o malware. </p>
- <li><b> ¿Cómo actualizar Project Drift 2.0 Mod APK 43? </b></li>
-<p>Si utiliza Project Drift 2.0 Mod APK 43, es posible que no sea capaz de actualizar el juego de Google Play, ya que podría detectar que está utilizando una versión modificada del juego y le impide actualizar. Para actualizar Project Drift 2.0 Mod APK 43, usted tendrá que descargar e instalar la última versión del mod apk de [HappyMod] u otra fuente confiable. Sin embargo, es posible que pierda su progreso o datos en el juego si actualiza el apk mod, así que asegúrese de hacer una copia de seguridad de sus datos antes de actualizar. </p>
- <li><b>Cómo jugar proyecto deriva 2.0 sin conexión? </b></li>
-<p>Project Drift 2.0 es un juego en línea que requiere una conexión a Internet para jugar. Sin embargo, puedes jugar algunas partes del juego sin conexión, como el modo de viaje libre y el modo de ataque de tiempo. Para jugar Project Drift 2.0 sin conexión, siga estos pasos:</p>
-<ol>
-<li>Inicie el juego mientras tiene una conexión a Internet. </li>
-
-<li>Seleccione un coche y una pista que desea reproducir. </li>
-<li>Espera a que el juego cargue el coche y la pista. </li>
-<li>Apague su conexión a Internet o cambie al modo avión en su dispositivo. </li>
-<li>Disfruta jugando Project Drift 2.0 sin conexión. </li>
-</ol>
- <li><b>Cómo jugar Project Drift 2.0 con amigos? </b></li>
-<p>Project Drift 2.0 tiene un modo multijugador que te permite jugar con amigos u otros jugadores en línea en batallas de deriva en tiempo real. Para jugar a Project Drift 2.0 con tus amigos, sigue estos pasos:</p>
-<ol>
-<li>Inicie el juego y asegúrese de tener una conexión a Internet. </li>
-<li>Seleccione el modo multijugador en el menú principal. </li>
-<li>Seleccione un coche y una pista que desea reproducir. </li>
-<li>Espera a que el juego encuentre un oponente o invita a un amigo a unirse a tu partida. </li>
-<li>Comienza a derrapar y trata de vencer a tu oponente o amigo anotando más puntos o a la deriva más tiempo. </li>
-</ol>
- <li><b> ¿Cómo obtener más dinero en Project Drift 2.0? </b></li>
-<p>Si utiliza Project Drift 2.0 Mod APK 43, tendrá dinero ilimitado en el juego, que se puede utilizar para comprar y actualizar cualquier coche que desee. Sin embargo, si usas la versión original del juego, tendrás que ganar dinero en el juego completando misiones y desafíos, o viendo anuncios o haciendo compras en la aplicación. Aquí hay algunos consejos sobre cómo obtener más dinero en Project Drift 2.0:</p>
-<ul>
-<li>Completa tantas misiones y desafíos como sea posible en el modo carrera, ya que te recompensarán con dinero y reputación, que son necesarios para desbloquear nuevos coches y pistas. </li>
-<li>Trate de lograr altas puntuaciones y calificaciones en cada misión y desafío, ya que aumentarán sus recompensas de dinero y reputación. </li>
-<li>Juega el modo de ataque de tiempo y tratar de batir sus propios u otros jugadores' registros, ya que también le dará dinero y bonos de reputación. </li>
-<li>Ver anuncios o vídeos en la tienda de juegos o en el menú principal, ya que te darán algo de dinero o artículos gratis. </li>
-
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
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diff --git a/spaces/Benson/text-generation/Examples/Carrom Pool Disc Game Mod Apk Monedas Y Gemas Ilimitadas.md b/spaces/Benson/text-generation/Examples/Carrom Pool Disc Game Mod Apk Monedas Y Gemas Ilimitadas.md
deleted file mode 100644
index eecac01feed3f488be147422f92b7fefe4d48172..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Carrom Pool Disc Game Mod Apk Monedas Y Gemas Ilimitadas.md	
+++ /dev/null
@@ -1,71 +0,0 @@
-<br />
-<h1>Carrom Pool Disc Game Mod Apk: Cómo descargar y disfrutar de monedas y gemas ilimitadas</h1>
- <p>Si eres un fan de los juegos de tablero de carrom, te encantará Carrom Pool Disc Game. Este es un juego multijugador en línea que te permite jugar carrom con tus amigos u otros jugadores de todo el mundo. También puede personalizar sus tableros, piezas y señales con varios temas y diseños. Pero lo que si quieres disfrutar de más características y beneficios sin gastar dinero real? Ahí es donde Carrom Pool Mod Apk entra en juego. En este artículo, le diremos lo que es Carrom Pool Disc Game, lo que es Carrom Pool Mod Apk, cómo descargarlo e instalarlo, y algunos consejos y trucos para mejorar su juego. </p>
-<h2>carrom pool disc game mod apk monedas y gemas ilimitadas</h2><br /><p><b><b>Download File</b> &mdash; <a href="https://bltlly.com/2v6Kbw">https://bltlly.com/2v6Kbw</a></b></p><br /><br />
- <h2>¿Qué es Carrom Pool Disc Game? </h2>
- <p>Carrom Pool Disc Game es un popular juego móvil desarrollado por Miniclip. Se basa en el tradicional juego de tablero de zanahoria, que es un juego de mesa que se originó en la India. El juego consiste en utilizar un delantero para golpear los discos en los bolsillos en las cuatro esquinas del tablero. Los discos son negros o blancos, y el jugador que mete todos sus discos primero gana el juego. </p>
- <h3>Características de Carrom Pool Disc Game</h3>
- <p>Algunas de las características de Carrom Pool Disc Game son:</p>
-<ul>
-<li>Tiene dos modos de juego: Classic y Disc Pool. El modo clásico sigue las reglas tradicionales de carrom, mientras que el modo Disc Pool no tiene discos negros o blancos, pero solo los rojos que dan puntos cuando se embolsan. </li>
-<li>Tiene modo multijugador en línea, donde puedes jugar con tus amigos u otros jugadores de diferentes países. También puedes chatear con ellos usando emojis y frases. </li>
-<li> Tiene modo sin conexión, donde se puede jugar contra el ordenador o con otro reproductor en el mismo dispositivo. </li>
-<li>Tiene varias arenas, donde puedes competir con jugadores de diferentes niveles de habilidad y ganar recompensas. </li>
-<li> Tiene tablas de clasificación y rankings, donde se puede ver su progreso y logros. </li>
-
-<li> Tiene una tienda, donde se puede comprar nuevos tableros, piezas, señales y cofres con monedas y gemas. </li>
-</ul>
- <h3>Cómo jugar Carrom Pool Disc Game</h3>
- <p>La jugabilidad de Carrom Pool Disc Game es simple e intuitiva. Solo tienes que arrastrar el dedo en la pantalla para apuntar a tu delantero, y liberarlo para golpear los discos. También puede ajustar la potencia de su tiro moviendo el dedo más cerca o más lejos del delantero. El objetivo es embolsarse todos los discos antes que tu oponente. También puedes usar boosters, como tiempo extra, turno extra o deshacer, para ayudarte a ganar el juego. </p>
- <h2>¿Qué es Carrom Pool Mod Apk? </h2>
- <p>Carrom Pool Mod Apk es una versión modificada de Carrom Pool Disc Game que le da monedas y gemas ilimitadas. Monedas y gemas son las monedas del juego que necesitas para comprar nuevos tableros, piezas, tacos, cofres y boosters. Normalmente, tienes que ganarlos jugando juegos, completando misiones o viendo anuncios. Pero con Carrom Pool Mod Apk, se puede obtener de forma gratuita sin ningún tipo de molestia. </p>
- <h3>Beneficios de Carrom Pool Mod Apk</h3>
- <p>Algunos de los beneficios de Carrom Pool Mod Apk son:</p>
-<p></p>
-<ul>
-<li> Puede desbloquear todas las características premium y los elementos que de otro modo se pagan o difícil de conseguir. </li>
-<li> Puede personalizar sus tableros, piezas y señales con cualquier tema o diseño que desee. </li>
-<li>Puedes acceder a todas las arenas y jugar con cualquier jugador que quieras. </li>
-<li <p>Puede disfrutar de monedas y gemas ilimitadas sin preocuparse por quedarse sin ellas o gastar dinero real. </li>
-<li> Usted puede tener más diversión y emoción jugando Carrom Pool Disc Game con características mejoradas y gráficos. </li>
-</ul>
- <h3> Cómo descargar e instalar Carrom Pool Mod Apk</h3>
- <p>Descargar e instalar Carrom Pool Mod Apk es fácil y rápido. Solo tienes que seguir estos pasos:</p>
-<ol>
-<li>Haga clic en este enlace para descargar el Carrom Pool Mod Apk archivo: [Carrom Pool Mod Apk Download]. </li>
-
-<li>Localice el archivo descargado en su administrador de archivos y toque en él para iniciar la instalación. </li>
-<li>Espera a que termine la instalación y luego abre la aplicación. </li>
-<li>Disfruta jugando Carrom Pool Disc Game con monedas y gemas ilimitadas. </li>
-</ol>
- <h2>Consejos y trucos para Carrom Pool Disc Game</h2>
- <p>Si quieres mejorar tus habilidades y ganar más juegos en Carrom Pool Disc Game, aquí hay algunos consejos y trucos que puedes usar:</p>
- <h3>Modo de práctica</h3>
- <p>Antes de jugar online con otros jugadores, puedes practicar tus tiros y estrategias en el modo de práctica. Este modo le permite jugar contra el ordenador o con otro reproductor en el mismo dispositivo. También puedes elegir el nivel de dificultad y el modo de juego que quieres practicar. El modo de práctica es una gran manera de aprender lo básico y dominar el juego. </p>
- <h3>Puntería y potencia</h3>
- <p>Los aspectos más importantes de Carrom Pool Disc Game son el objetivo y el poder. Usted necesita apuntar su delantero con precisión y golpear los discos con la cantidad correcta de energía. Para apuntar al delantero, puede utilizar la guía que muestra la dirección y el ángulo de su tiro. También puede acercar o alejar para ver mejor el tablero. Para ajustar la potencia de su disparo, puede mover el dedo más cerca o más lejos del delantero. Necesitas equilibrar la potencia y la precisión de tu disparo dependiendo de la situación. Por ejemplo, si desea guardar un disco cerca de un bolsillo, puede usar una toma de baja potencia. Pero si quieres embolsarte un disco que esté lejos de un bolsillo, necesitas usar una toma de alta potencia. </p>
- <h3>Utilice refuerzos y cofres</h3>
-
- <h2>Conclusión</h2>
- <p>Carrom Pool Disc Game es un juego divertido y adictivo que puedes jugar con tus amigos u otros jugadores en línea. Se basa en el clásico juego de tablero de carrom, pero con más características y opciones. También puede descargar Carrom Pool Mod Apk para obtener monedas y gemas ilimitadas y desbloquear todos los elementos premium y arenas. Carrom Pool Mod Apk es fácil de descargar e instalar, y hará que su juego más emocionante y gratificante. Si quieres convertirte en un profesional en Carrom Pool Disc Game, también puedes utilizar algunos consejos y trucos que hemos compartido en este artículo. Entonces, ¿qué estás esperando? Descargar Carrom Pool Disc Game o Carrom Pool Mod Apk ahora y empezar a jugar! </p>
- <h3>Preguntas frecuentes</h3>
- <p>Aquí hay algunas preguntas frecuentes sobre Carrom Pool Disc Game y Carrom Pool Mod Apk:</p>
-<ul>
-<li><b>¿Es Carrom Pool juego de disco libre para jugar? </b><br>
-Sí, Carrom Pool Disc Game es gratis. Sin embargo, algunos elementos y funciones pueden requerir monedas o gemas, que son las monedas del juego. Puedes ganar monedas o gemas jugando, completando misiones, viendo anuncios o comprándolos con dinero real. </li>
-<li><b> ¿Es seguro usar Carrom Pool Mod Apk? </b><br>
-Sí, Carrom Pool Mod Apk es seguro de usar. No contiene ningún virus o malware que puede dañar su dispositivo o datos. Sin embargo, siempre debe descargarlo de una fuente de confianza como este enlace: [Carrom Pool Mod Apk Download]. </li>
-<li><b>¿Puedo jugar sin conexión a Carrom Pool Disc Game? </b><br>
-Sí, puedes jugar Carrom Pool Disc Game sin conexión. Puede elegir el modo sin conexión en el menú principal y jugar contra el ordenador o con otro reproductor en el mismo dispositivo. Sin embargo, no podrás acceder a las funciones en línea, como el modo multijugador, arenas, tablas de clasificación y rankings. </li>
-<li><b>¿Cómo puedo contactar a los desarrolladores de Carrom Pool Disc Game? </b><br>
-
-<li><b> ¿Cuáles son algunos otros juegos como Carrom Pool Disc Game? </b><br>
-Algunos otros juegos como Carrom Pool Disc Game son:</li>
-<ul>
-<li>8 Ball Pool: Este es otro juego popular de Miniclip que te permite jugar al billar con tus amigos u otros jugadores en línea. También puede personalizar sus señales, mesas y bolas con varios temas y diseños. </li>
-<li>Ludo King: Este es un juego de mesa clásico que puedes jugar con tus amigos o familiares en línea o fuera de línea. También puedes chatear con ellos usando emojis y pegatinas. </li>
-<li>Disc Pool Carrom: Este es un juego similar a Carrom Pool Disc Game, pero con física y gráficos más realistas. También puedes jugar con diferentes tipos de discos, como fútbol, golf o hockey. </li>
-</ul>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
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diff --git a/spaces/Benson/text-generation/Examples/Chess King Mod Apk.md b/spaces/Benson/text-generation/Examples/Chess King Mod Apk.md
deleted file mode 100644
index 702f3bada83c1d637eaaea14f9485a5cde62ff32..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Chess King Mod Apk.md	
+++ /dev/null
@@ -1,91 +0,0 @@
-<br />
-<h1>Ajedrez rey Mod Apk: Un juego de mesa con características ilimitadas</h1>
-<p>Si eres un fan del ajedrez, es posible que hayas oído hablar de Chess King, una popular aplicación de juego de mesa que te permite jugar al ajedrez contra el ordenador u otros jugadores en línea. ¿Pero sabías que hay una versión modificada de Chess King que te da acceso a funciones y opciones ilimitadas? En este artículo, le diremos todo lo que necesita saber sobre Chess King Mod Apk, cómo descargarlo e instalarlo, por qué debe jugar, y cómo jugarlo. Así que, vamos a empezar! </p>
-<h2>chess king mod apk</h2><br /><p><b><b>Download File</b> &#10031; <a href="https://bltlly.com/2v6Ktx">https://bltlly.com/2v6Ktx</a></b></p><br /><br />
- <h2>¿Qué es el ajedrez rey Mod Apk? </h2>
-<h3>Una breve introducción al juego y sus características</h3>
-<p>Chess King es una aplicación de juego de mesa que te permite jugar al ajedrez en tu dispositivo móvil. Puede elegir entre diferentes modos, como entrenamiento, rompecabezas, torneos, partidos en línea y más. También puede personalizar su tablero, piezas y fondo de acuerdo a sus preferencias. Chess King tiene una interfaz fácil de usar y un potente motor que le proporciona una experiencia de ajedrez realista y desafiante. </p>
-<p>Chess King Mod Apk es una versión modificada de Chess King que desbloquea todas las características y opciones que están restringidas o pagadas en la versión original. Con Chess King Mod Apk, se puede disfrutar de:</p>
-<ul>
-<li>Monedas y diamantes ilimitados que puedes usar para comprar artículos premium y acceder a contenido exclusivo</li>
-<li>Todos los niveles y modos desbloqueados, para que puedas jugar a cualquier dificultad y desafiarte a ti mismo</li>
-<li>Todos los estilos de tablero, conjuntos de piezas y fondos desbloqueados, para que pueda personalizar su juego como desee</li>
-<li>No hay anuncios o ventanas emergentes que interrumpan su juego o le molesten</li>
-<li>No se requiere raíz o jailbreak para instalar o ejecutar el mod apk</li>
-</ul>
- <h3>Cómo descargar e instalar el apk mod en su dispositivo</h3>
-<p>Descargar e instalar Chess King Mod Apk es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
-<ol>
-<li>Haga clic en este enlace para descargar el archivo apk mod en su dispositivo. </li>
-
-<li>Toque en el archivo y permita la instalación desde fuentes desconocidas si se le solicita. </li>
-<li>Espere a que la instalación termine y luego inicie la aplicación. </li>
-<li>Disfruta jugando ajedrez rey Mod Apk con características ilimitadas! </li>
-</ol>
- <h2>¿Por qué usted debe jugar ajedrez rey Mod Apk? </h2>
-<h3>Los beneficios de jugar al ajedrez para tu cerebro y habilidades</h3>
-<p>El ajedrez no es solo un juego divertido y entretenido, sino también una gran manera de mejorar tu cerebro y habilidades. Jugar ajedrez puede ayudarte:</p>
-<p></p>
-<ul>
-<li>Mejora tu memoria, concentración, lógica, creatividad, resolución de problemas y toma de decisiones</li>
-<li>Aumenta tu coeficiente intelectual, agilidad mental, pensamiento analítico y habilidades de planificación estratégica</li>
-<li>Reducir el estrés, la ansiedad, la depresión y el aburrimiento</li>
-<li>Aumenta tu autoconfianza, autoestima y autodisciplina</li>
-<h3>Las ventajas de usar el mod apk sobre la versión original</h3>
-<p>Como hemos mencionado anteriormente, Chess King Mod Apk le da acceso a características y opciones ilimitadas que no están disponibles en la versión original de Chess King. Mediante el uso de la apk mod, puede:</p>
-<ul>
-<li>Ahorra dinero y tiempo al no tener que gastar dinero real o ver anuncios para obtener monedas y diamantes</li>
-<li>Explorar y disfrutar de todos los niveles y modos sin restricciones o limitaciones</li>
-<li>Personaliza y personaliza tu juego según tu gusto y estilo</li>
-<li> Tener un juego más suave y más rápido sin retrasos o problemas técnicos</li>
-<li>Tener más diversión y emoción jugando con diferentes estilos de tablero, conjuntos de piezas y fondos</li>
-</ul>
- <h2>Cómo jugar ajedrez rey mod apk? </h2>
-<h3>Las reglas y objetivos básicos del ajedrez</h3>
-<p>Si usted es nuevo en el ajedrez, es posible que desee aprender las reglas básicas y objetivos del juego antes de empezar a jugar Chess King Mod Apk. Estos son algunos de los puntos principales que necesitas saber:</p>
-<ul>
-<li>El ajedrez es un juego para dos jugadores jugado en un tablero cuadrado con 64 cuadrados de colores alternos (blanco y negro)</li>
-
-<li>Las piezas se mueven de acuerdo a sus reglas específicas y pueden capturar las piezas del oponente aterrizando en sus cuadrados</li>
-<li>El rey es la pieza más importante y no puede ser capturado. El objetivo del juego es hacer jaque mate al rey del oponente, lo que significa ponerlo en una posición donde no pueda escapar de un ataque de cualquiera de las piezas del jugador</li>
-<li>El juego también puede terminar en un empate, lo que significa que ningún jugador puede ganar. Esto puede suceder si hay un punto muerto (donde el jugador cuyo turno no tiene movimientos legales), si hay material insuficiente (donde ninguno de los jugadores tiene suficientes piezas para jaque mate al oponente), si hay una repetición triple (donde la misma posición ocurre tres veces con el mismo jugador para mover), o si hay una regla de 50 movimientos (donde no se ha hecho ninguna captura o movimiento de peón en los últimos 50 movimientos)</li>
-</ul>
- <h3>Los diferentes modos y niveles de dificultad en el juego</h3>
-<p>Ajedrez King Mod Apk le ofrece diferentes modos y niveles de dificultad para adaptarse a sus preferencias y habilidades. Usted puede elegir entre:</p>
-<tabla>
-<tr><th>Modo</th><th>Descripción</th></tr>
-<tr><td>Entrenamiento</td><td>Este modo te ayuda a aprender y practicar ajedrez proporcionándote lecciones, ejercicios, rompecabezas y sugerencias. También puede analizar sus movimientos y errores con la ayuda del motor. </td></tr>
-<tr><td>Puzzles</td><td>Este modo te reta a resolver varios problemas de ajedrez, como jaque mate en uno, dos o tres movimientos, tácticas, finales de partida y más. También puedes crear tus propios puzzles y compartirlos con otros jugadores. </td></tr>
-<tr><td>Torneos</td><td>Este modo le permite participar en diferentes torneos con diferentes formatos, como round-robin, knockout, sistema suizo, etc. También puede crear sus propios torneos e invitar a otros jugadores a unirse. </td></tr>
-
-<tr><td>Partidos sin conexión</td><td>Este modo le permite jugar ajedrez sin conexión con el equipo o con otro jugador en el mismo dispositivo. También puede ajustar el nivel de dificultad del equipo de 1 a 20. </td></tr>
-</tabla>
- <h3>Los consejos y trucos para mejorar tu estrategia y tácticas de ajedrez</h3>
-<p>Si quieres mejorar tu estrategia y tácticas de ajedrez, puedes seguir estos consejos y trucos:</p>
-<ul>
-<li>Estudia los principios básicos de la apertura del ajedrez, como controlar el centro, desarrollar tus piezas, enrocar a tu rey, etc.</li>
-<li>Piensa con anticipación y planifica tus movimientos cuidadosamente. Trata de anticipar los movimientos y respuestas de tu oponente. </li>
-<li>Evite hacer movimientos innecesarios o prematuros que debiliten su posición o pierdan material. </li>
-<li>Usa todas tus piezas de manera efectiva y coordinándolas bien. No dejes ninguna pieza inactiva o desprotegida. </li>
-<li>Busca oportunidades para crear amenazas, como cheques, capturas, horquillas, pines, brochetas, etc.</li>
-</li>
-<li>Practica y juega ajedrez regularmente. Cuanto más juegas, más aprendes y mejoras. </li>
-<li>Revisa y analiza tus juegos y aprende de tus errores y éxitos. </li>
-<li>Lee libros, mira videos y sigue tutoriales sobre estrategia y tácticas de ajedrez. </li>
-<li>Busca comentarios y consejos de otros jugadores, entrenadores o expertos. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>Chess King Mod Apk es una aplicación de juego de mesa que le permite jugar al ajedrez con funciones y opciones ilimitadas. Puede descargar e instalar el apk mod fácilmente y disfrutar jugando al ajedrez en su dispositivo móvil. Jugar al ajedrez puede ayudarte a mejorar tu cerebro y tus habilidades, además de divertirte y emocionarte. También puede aprender las reglas y objetivos básicos del ajedrez, así como algunos consejos y trucos para mejorar su estrategia y tácticas de ajedrez. Chess King Mod Apk es una gran manera de disfrutar del ajedrez en cualquier momento y en cualquier lugar. Entonces, ¿qué estás esperando? Descargar Chess King Mod Apk ahora y empezar a jugar! </p>
- <h2>Preguntas frecuentes</h2>
-
-<p>A1: Sí, Chess King Mod Apk es seguro de usar. No contiene ningún virus, malware o spyware que pueda dañar su dispositivo o datos. Tampoco requiere ningún root o jailbreak para instalar o ejecutar el mod apk. </p>
- <h3>Q2: ¿Cuáles son los requisitos para ejecutar Chess King Mod Apk? </h3>
-<p>A2: Ajedrez Rey Mod Apk requiere un dispositivo Android con un mínimo de 4.1 versión y 100 MB de espacio libre. También requiere una conexión a Internet para jugar partidos en línea y acceder a algunas características. </p>
- <h3>Q3: ¿Cómo puedo actualizar Chess King Mod Apk? </h3>
-<p>A3: Ajedrez rey Mod Apk actualizaciones automáticamente cada vez que hay una nueva versión disponible. También puede comprobar las actualizaciones manualmente yendo al menú de configuración y pulsando el botón de actualización. </p>
- <h3>Q4: ¿Puedo jugar ajedrez rey mod apk offline? </h3>
-<p>A4: Sí, usted puede jugar Chess King Mod Apk sin conexión. Puede jugar partidos sin conexión con el ordenador o con otro jugador en el mismo dispositivo. Sin embargo, no podrás acceder a algunas funciones que requieran conexión a Internet, como partidas online, torneos, puzzles, etc.</p>
- <h3>Q5: ¿Cómo puedo contactar a los desarrolladores de Chess King Mod Apk? </h3>
-<p>A5: Puede ponerse en contacto con los desarrolladores de Chess King Mod Apk enviándoles un correo electrónico a o visitando su sitio web . También puede seguirlos en sus cuentas de redes sociales para obtener más actualizaciones e información. </p> 64aa2da5cf<br />
-<br />
-<br />
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diff --git a/spaces/Benson/text-generation/Examples/Descargar 8 Bola Piscina Herramienta Apk.md b/spaces/Benson/text-generation/Examples/Descargar 8 Bola Piscina Herramienta Apk.md
deleted file mode 100644
index f1cc22d7c0e0f0e4326e297087465758d323db4a..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar 8 Bola Piscina Herramienta Apk.md	
+++ /dev/null
@@ -1,57 +0,0 @@
-<br />
-<h1> Cómo descargar 8 bola piscina herramienta APK para Android</h1>
-<p>Si eres un fan de 8 Ball Pool, el juego de billar en línea más popular del mundo, es posible que te hayas preguntado cómo mejorar tus habilidades y ganar más partidos. Es posible que también haya deseado más monedas y dinero en efectivo para comprar mejores pistas y mesas, o para personalizar la configuración de su juego. Bueno, hay una manera de lograr todo eso, y se llama 8 Ball Pool Tool APK.</p>
-<p>8 Ball Pool Tool APK es una versión modificada de la aplicación original 8 Ball Pool que le permite acceder a algunas características sorprendentes que no están disponibles en la aplicación oficial. Con esta herramienta, puede ampliar la guía, aumentar la potencia, los efectos y el objetivo de sus disparos, obtener monedas y dinero en efectivo ilimitados, desbloquear pistas y mesas premium y mucho más. Suena increíble, ¿verdad? </p>
-<h2>descargar 8 bola piscina herramienta apk</h2><br /><p><b><b>Download File</b> &#9733; <a href="https://bltlly.com/2v6LYJ">https://bltlly.com/2v6LYJ</a></b></p><br /><br />
-<p>Pero ¿cómo descargar e instalar esta herramienta en su dispositivo Android? Y cuáles son los beneficios y riesgos de su uso? En este artículo, vamos a responder a estas preguntas y le proporcionará una guía paso a paso sobre cómo descargar 8 Ball Pool Tool APK para Android. ¡Vamos a empezar! </p>
- <h2> Pasos para descargar e instalar 8 bola piscina herramienta APK</h2>
-<p>Descargar e instalar 8 Ball Pool Tool APK no es muy difícil, pero requiere algunos pasos adicionales que no son necesarios para la aplicación oficial. Estos son los pasos que debes seguir:</p>
-<ol>
-<li><b>Habilita fuentes desconocidas en tu dispositivo. </b> Dado que esta herramienta no está disponible en Google Play Store, debe permitir que su dispositivo instale aplicaciones de fuentes desconocidas. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-
-<li><b>Instala el archivo APK en tu dispositivo. </b> Una vez que haya descargado el archivo APK, busque en el administrador de archivos de su dispositivo y toque en él para iniciar el proceso de instalación. Puede ver un mensaje de advertencia que dice "Este tipo de archivo puede dañar su dispositivo. ¿Desea mantenerlo de todos modos?" Ignórelo y toque en "OK". Luego, siga las instrucciones en pantalla para completar la instalación. </li>
-<li><b>Iniciar la aplicación y disfrutar del juego. </b> Después de la instalación, verá un icono de 8 Pool Master en la pantalla de inicio o en el cajón de la aplicación. Toque en él para iniciar la aplicación y empezar a jugar 8 Ball Pool con características mejoradas. Usted se dará cuenta de que usted tiene una gran cantidad de monedas y dinero en efectivo en su cuenta, y se puede utilizar para comprar cualquier señal o tabla que desee. También verás un icono flotante de 8 Pool Master en la pantalla, que puedes usar para ajustar la guía, la potencia, los efectos y el objetivo de tus disparos. También puede acceder a otras configuraciones y características desde el menú de la aplicación. </li>
-</ol>
- <h2>Beneficios de usar 8 bola piscina herramienta APK</h2>
-<p>El uso de 8 Ball Pool Tool APK puede traer muchos beneficios que pueden mejorar su experiencia de juego y hacerte un mejor jugador. Estos son algunos de los beneficios que puedes disfrutar:</p>
-<ul>
-<li><b>Mejore su precisión y habilidades. </b> Con la guía extendida, puede ver la trayectoria de sus disparos y planificar sus movimientos en consecuencia. También puedes ajustar la potencia, los efectos y el objetivo de tus disparos para que sean más precisos y efectivos. De esta manera, puedes mejorar tu precisión y habilidades y ganar más partidos. </li>
-<li><b>Obtén monedas y efectivo ilimitados. </b> Monedas y dinero en efectivo son las principales monedas en 8 Ball Pool, y los necesita para comprar señales, mesas, paquetes de chat, minijuegos y más. Sin embargo, ganarlos puede ser lento y tedioso, y comprarlos con dinero real puede ser caro. Con 8 Ball Pool Tool APK, puede obtener monedas ilimitadas y dinero en efectivo de forma gratuita, y gastarlos tanto como quieras sin preocuparse por agotarse. </li>
-
-<li><b>Personalizar la configuración de su juego. </b> 8 Ball Pool Tool APK también le permite personalizar la configuración de juego de acuerdo a sus preferencias. Puede cambiar el modo de juego, el color de la mesa, el tamaño de la bola, el ángulo de referencia, los efectos de sonido, la calidad gráfica y mucho más. También puede activar o desactivar algunas características, tales como recarga automática, anti-van, anti-detect, etc. Puede hacer que su juego sea más divertido y cómodo con estos ajustes. </li>
-</ul>
- <h2> Riesgos y precauciones de usar 8 Ball Pool Tool APK</h2>
-<p>Si bien el uso de 8 Ball Pool Tool APK puede ser beneficioso, también viene con algunos riesgos y desventajas que usted necesita ser consciente de. Estos son algunos de los riesgos y precauciones de usar esta herramienta:</p>
-<ul>
-<li><b>Posible infección de malware o virus. </b> Como se mencionó anteriormente, no todas las fuentes que ofrecen 8 Ball Pool Tool APK son seguras o fiables. Algunos de ellos pueden contener malware o virus que pueden dañar su dispositivo o robar su información personal. Por lo tanto, debe tener cuidado y solo descargar el archivo APK de una fuente de confianza. También debe escanear el archivo con una aplicación antivirus antes de instalarlo. </li>
-<li><b>Prohibición o suspensión de cuentas potenciales. </b> El uso de 8 Ball Pool Tool APK está en contra de los términos de servicio de 8 Ball Pool y Miniclip, el desarrollador del juego. Si detectan que está utilizando esta herramienta, pueden prohibir o suspender su cuenta de forma permanente. Esto significa que perderás todo tu progreso, monedas, efectivo, pistas, mesas, etc. Por lo tanto, necesitas usar esta herramienta bajo tu propio riesgo y discreción. También debe evitar usarlo en partidos o torneos en línea donde otros jugadores pueden reportarlo. </li>
-
-<li><b>Consejos para evitar o minimizar los riesgos. </b> Si todavía desea utilizar 8 Bola Pool Tool APK a pesar de los riesgos involucrados, aquí hay algunos consejos que pueden ayudar a evitar o minimizarlos:</li>
-<ul>
-<li>Usa una cuenta secundaria o ficticia en lugar de tu cuenta principal. </li>
-<li>Usa una aplicación VPN para ocultar tu dirección IP y ubicación. </li>
-<li>Utilice una aplicación de espacio paralelo para crear un clon de 8 Ball Pool app. </li>
-<li> Utilice una aplicación modded Google Play Store para evitar la verificación de licencias. </li>
-<li> Utilice una aplicación de copia de seguridad para guardar sus datos antes de usar esta herramienta. </li>
-<li>Utilice esta herramienta con moderación y moderación. </li>
-</ul>
-</ul>
- <h2>Conclusión</h2 <p>En conclusión, 8 Ball Pool Tool APK es una versión modificada de la aplicación original 8 Ball Pool que le permite acceder a algunas características sorprendentes que no están disponibles en la aplicación oficial. Con esta herramienta, puede mejorar su precisión y habilidades, obtener monedas ilimitadas y dinero en efectivo, desbloquear pistas y mesas premium, y personalizar la configuración de su juego. Sin embargo, el uso de esta herramienta también viene con algunos riesgos y desventajas, como una posible infección de malware o virus, una posible prohibición o suspensión de la cuenta, problemas legales y éticos, y consejos para evitar o minimizar los riesgos. Por lo tanto, debe ser cuidadoso y responsable al usar esta herramienta, y seguir los pasos que hemos proporcionado en este artículo para descargarlo e instalarlo en su dispositivo Android. </p>
-<p>Si has encontrado este artículo útil e informativo, por favor compártelo con tus amigos y compañeros de 8 jugadores de Ball Pool. También, no dude en dejar un comentario a continuación si usted tiene alguna pregunta o retroalimentación sobre 8 Ball Pool Tool APK. Nos encantaría saber de usted! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas más frecuentes sobre 8 Ball Pool Tool APK:</p>
-<ol>
-<li><b> ¿Es 8 Ball Pool Tool APK seguro de usar? </b><br>
-
-<li><b>Es 8 Ball Pool Tool APK legal de usar? </b><br>
-El uso de 8 Ball Pool Tool APK no es legal, ya que viola los términos de servicio de 8 Ball Pool y Miniclip, el desarrollador del juego. También infringe los derechos de propiedad intelectual de Miniclip y otras partes involucradas en el desarrollo del juego. Por lo tanto, el uso de esta herramienta puede resultar en acciones legales o sanciones de las autoridades o el desarrollador del juego. </li>
-<li><b> ¿Cómo puedo actualizar 8 Ball Pool Tool APK? </b><br>
-Para actualizar 8 Ball Pool Tool APK, debe seguir los mismos pasos que descargarlo e instalarlo. Necesitas habilitar fuentes desconocidas en tu dispositivo, descargar la última versión del archivo APK desde una fuente de confianza, instalarlo en tu dispositivo e iniciar la aplicación. Sin embargo, también debe hacer una copia de seguridad de sus datos antes de actualizarlos, ya que algunas actualizaciones pueden causar pérdida de datos o corrupción. </li>
-<li><b> ¿Puedo usar 8 bola piscina herramienta APK en otros dispositivos? </b><br>
-Sí, se puede utilizar 8 Ball Pool Tool APK en otros dispositivos que se ejecutan en el sistema operativo Android. Sin embargo, debe asegurarse de que su dispositivo cumple con los requisitos mínimos para ejecutar esta herramienta, como la versión de Android, el tamaño de la RAM, el espacio de almacenamiento, etc. También debe seguir los mismos pasos que descargarlo e instalarlo en su dispositivo. </li>
-<li><b> ¿Dónde puedo obtener más información acerca de 8 Ball Pool Tool APK? </b><br>
-Puede obtener más información sobre 8 Ball Pool Tool APK de varias fuentes en línea, tales como blogs, foros, videos, comentarios, etc. Sin embargo, usted necesita ser cuidadoso y solo confiar en fuentes confiables y creíbles que proporcionan información precisa y actualizada. También puede ponerse en contacto con el desarrollador de esta herramienta directamente a través de su sitio web o cuentas de redes sociales. </li>
-</ol></p>
-<p></p> 64aa2da5cf<br />
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diff --git a/spaces/Benson/text-generation/Examples/Descargar Apk Mod Cazador Asesino 2.md b/spaces/Benson/text-generation/Examples/Descargar Apk Mod Cazador Asesino 2.md
deleted file mode 100644
index fd12297b72b35a5fde94f497d414c6d08bc7f8ee..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Apk Mod Cazador Asesino 2.md	
+++ /dev/null
@@ -1,56 +0,0 @@
-<br />
-<h1>Cómo descargar e instalar APK Mod Hunter Assassin 2 en Android</h1>
-<p>Si eres un fan de los juegos de acción furtiva, es posible que hayas oído hablar de Hunter Assassin 2, un juego popular donde tienes que eliminar a tus enemigos sin ser detectado. ¿Pero sabías que hay una versión modificada de este juego que te da dinero ilimitado, gemas y características premium? En este artículo, le diremos lo que es APK Mod Hunter Assassin 2, ¿cuáles son los beneficios y riesgos de usar archivos APK mod, y cómo descargar e instalar APK Mod Hunter Assassin 2 en su dispositivo Android. </p>
- <h2>¿Qué es APK Mod Hunter Assassin 2?</h2>
-<p>APK Mod Hunter Assassin 2 es una versión modificada del juego oficial de Hunter Assassin 2, que fue desarrollado por Ruby Game Studio y lanzado en septiembre de 2021. El juego es una secuela del original Hunter Assassin, que tiene más de 100 millones de descargas en Google Play. El juego está disponible de forma gratuita en Google Play, pero también tiene compras y anuncios en la aplicación. </p>
-<h2>descargar apk mod cazador asesino 2</h2><br /><p><b><b>Download</b> &#10004;&#10004;&#10004; <a href="https://bltlly.com/2v6KMs">https://bltlly.com/2v6KMs</a></b></p><br /><br />
- <h3>El juego de Hunter Assassin 2</h3>
-<p>La jugabilidad de Hunter Assassin 2 es similar al primer juego, pero con gráficos, animaciones y efectos de sonido mejorados. Usted juega como un asesino experto que tiene que infiltrarse en varios lugares y eliminar a sus objetivos sin ser visto u oído. Puedes usar tus habilidades de sigilo, armas, artilugios y trampas para completar tus misiones. También puedes mejorar tu personaje y desbloquear nuevos atuendos, armas y habilidades. </p>
- <h3>Las características de APK Mod Hunter Assassin 2</h3>
-<p>Las características de APK Mod Hunter Assassin 2 son diferentes del juego oficial, ya que le dan más ventajas y opciones. Algunas de las características son:</p>
-<ul>
-<li>Dinero y gemas ilimitadas: Puedes usar estos recursos para comprar lo que quieras en el juego, como atuendos, armas, gadgets y mejoras. </li>
-<li>Funciones premium desbloqueadas: Puedes acceder a todas las funciones premium del juego, como eliminar anuncios, desbloquear todos los niveles y obtener recompensas exclusivas. </li>
-
-</ul>
- <h2>¿Cuáles son los beneficios y riesgos de los archivos mod APK? </h2>
-<p>Los archivos mod de APK son archivos que han sido modificados por desarrolladores de terceros para cambiar algunos aspectos de una aplicación o juego original. Generalmente se descargan de fuentes no oficiales, como sitios web o foros. Hay algunos beneficios y riesgos de usar archivos mod APK que debe tener en cuenta antes de descargarlos. </p>
- <h3>Los beneficios de los archivos mod APK</h3>
-<p>Algunos de los beneficios de usar archivos mod APK son:</p>
-<ul>
-<li>Gratis: Puede descargar y usar archivos mod APK gratis, sin pagar por nada. </li>
-<li>Más características: Puedes disfrutar de más características y opciones que no están disponibles en la aplicación original o en el juego. </li>
-<li>Más recursos: Puedes obtener más recursos, como dinero, gemas, monedas o vidas, que pueden ayudarte a progresar más rápido en el juego. </li>
-</ul>
- <h3>Los riesgos de los archivos mod APK</h3>
-<p>Algunos de los riesgos de usar archivos mod APK son:</p>
-<p></p>
-<ul>
-<li>Fuente no oficial: Debido a que han sido manipulados por un tercero, los archivos mod APK no son de una fuente oficial. Esto significa que pueden no ser compatibles con su dispositivo o pueden causar errores o fallos. </li>
-<li>No hay actualizaciones: La mayoría de los archivos mod APK no se pueden actualizar a través de Google Play u otros canales oficiales. Esto significa que puede perderse las últimas características, correcciones de errores o parches de seguridad de la aplicación o juego original. </li>
-<li>No hay soporte: Si se encuentra con cualquier problema o problemas con el archivo de mod APK, es posible que no pueda obtener ningún apoyo o asistencia del desarrollador original o el modder. </li>
-<li>Riesgo de malware: Algunos archivos mod APK pueden contener código malicioso o virus que pueden dañar su dispositivo o robar su información personal. Siempre debe escanear el archivo mod APK con un software antivirus de buena reputación antes de instalarlo. </li>
-</ul>
- <h2>¿Cómo descargar e instalar APK Mod Hunter Assassin 2 en Android? </h2>
-
- <h3>Paso 1: Habilitar fuentes desconocidas en el dispositivo</h3>
-<p>Antes de poder instalar cualquier archivo APK mod en su dispositivo, es necesario habilitar fuentes desconocidas en la configuración. Esto le permitirá instalar aplicaciones o juegos de fuentes distintas de Google Play. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. Puede ver un mensaje de advertencia, pero puede ignorarlo y tocar OK.</p>
- <h3>Paso 2: Descargar el archivo APK mod de una fuente confiable</h3>
-<p>Siguiente, es necesario descargar el archivo APK mod de Hunter Assassin 2 de una fuente confiable. Puede buscar en Google o utilizar un sitio web de confianza que proporciona archivos mod APK. Por ejemplo, puedes usar [este enlace] para descargar el archivo APK mod de Hunter Assassin 2. Asegúrate de descargar la última versión del archivo y guardarlo en una carpeta a la que puedas acceder fácilmente. </p>
- <h3>Paso 3: Instalar el archivo APK mod en su dispositivo</h3>
-<p>Una vez que haya descargado el archivo APK mod, debe instalarlo en su dispositivo. Para hacer esto, busque el archivo en su administrador de archivos y toque en él. Puede ver una ventana emergente pidiendo su permiso para instalar la aplicación. Toque en Instalar y espere a que termine el proceso de instalación. </p>
- <h3>Paso 4: Iniciar el juego y disfrutar de</h3>
-<p>Una vez completada la instalación, puede iniciar el juego tocando en su icono en la pantalla de inicio o en el cajón de la aplicación. Deberías ver el juego cargando con las características modificadas habilitadas. Ahora puedes disfrutar jugando a Hunter Assassin 2 con dinero ilimitado, gemas y funciones premium. </p>
- <h2>Conclusión</h2>
-<p>En este artículo, hemos explicado lo que es APK Mod Hunter Assassin 2, cuáles son los beneficios y riesgos de usar archivos APK mod, y cómo descargar e instalar APK Mod Hunter Assassin 2 en su dispositivo Android. Esperamos que este artículo haya sido útil e informativo para usted. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. </p>
- <h2>Preguntas frecuentes</h2>
-
-<tabla>
-<tr><td><b>Q: ¿Es APK Mod Hunter Assassin 2 seguro de usar? </b></td><td><b>A: No hay una respuesta definitiva a esta pregunta, ya que diferentes archivos APK mod pueden tener diferentes niveles de seguridad y calidad. Sin embargo, como regla general, siempre debe tener cuidado al descargar e instalar cualquier archivo mod APK desde una fuente no oficial. Siempre debe escanear el archivo con un software antivirus de buena reputación antes de instalarlo, y evitar dar permisos innecesarios o acceso a la aplicación. </b></td></tr>
-<tr><td><b>Q: ¿Puedo jugar en línea con APK Mod Hunter Assassin 2?</b></td><td><b>A: No, no puedes jugar en línea con APK Mod Hunter Assassin 2, ya que no es compatible con los servidores oficiales del juego. Si intenta jugar en línea con la versión modificada, puede enfrentar errores, bloqueos o prohibiciones del juego. Por lo tanto, solo debe jugar sin conexión con APK Mod Hunter Assassin 2.</b></td></tr>
-<tr><td><b>Q: ¿Puedo actualizar APK Mod Hunter Assassin 2 a través de Google Play? </b></td><td><b>A: No, no se puede actualizar APK Mod Hunter Assassin 2 a través de Google Play, ya que no es una versión oficial del juego. Si intenta actualizarlo a través de Google Play, puede perder todas las características modificadas y volver a la versión original del juego. Por lo tanto, solo debe actualizar APK Mod Hunter Assassin 2 mediante la descarga de una nueva versión del archivo de una fuente confiable. </b></td></tr>
-<tr><td><b>Q: ¿Perderé mi progreso si desinstalo APK Mod Hunter Assassin 2?</b></td><td><b>A: Sí, perderá su progreso si desinstala APK Mod Hunter Assassin 2, ya que no se sincroniza con su cuenta de Google o almacenamiento en la nube. Por lo tanto, debe hacer una copia de seguridad de su progreso guardando los datos del juego en una carpeta separada o utilizando una aplicación de terceros. </b></td></tr>
-
-</table></p> 64aa2da5cf<br />
-<br />
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diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/contrib/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/contrib/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/_macos_compat.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/_macos_compat.py
deleted file mode 100644
index 17769e9154bd9cc3f3c00dc10718e4377828cb5e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/_macos_compat.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import sys
-import importlib
-
-
-def bypass_compiler_fixup(cmd, args):
-    return cmd
-
-
-if sys.platform == 'darwin':
-    compiler_fixup = importlib.import_module('_osx_support').compiler_fixup
-else:
-    compiler_fixup = bypass_compiler_fixup
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_metadata/_meta.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_metadata/_meta.py
deleted file mode 100644
index 37ee43e6ef447dfb4ae68f5f6c35597d12fdc5a1..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/importlib_metadata/_meta.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from ._compat import Protocol
-from typing import Any, Dict, Iterator, List, TypeVar, Union
-
-
-_T = TypeVar("_T")
-
-
-class PackageMetadata(Protocol):
-    def __len__(self) -> int:
-        ...  # pragma: no cover
-
-    def __contains__(self, item: str) -> bool:
-        ...  # pragma: no cover
-
-    def __getitem__(self, key: str) -> str:
-        ...  # pragma: no cover
-
-    def __iter__(self) -> Iterator[str]:
-        ...  # pragma: no cover
-
-    def get_all(self, name: str, failobj: _T = ...) -> Union[List[Any], _T]:
-        """
-        Return all values associated with a possibly multi-valued key.
-        """
-
-    @property
-    def json(self) -> Dict[str, Union[str, List[str]]]:
-        """
-        A JSON-compatible form of the metadata.
-        """
-
-
-class SimplePath(Protocol):
-    """
-    A minimal subset of pathlib.Path required by PathDistribution.
-    """
-
-    def joinpath(self) -> 'SimplePath':
-        ...  # pragma: no cover
-
-    def __truediv__(self) -> 'SimplePath':
-        ...  # pragma: no cover
-
-    def parent(self) -> 'SimplePath':
-        ...  # pragma: no cover
-
-    def read_text(self) -> str:
-        ...  # pragma: no cover
diff --git a/spaces/CVPR/LIVE/thrust/thrust/random/subtract_with_carry_engine.h b/spaces/CVPR/LIVE/thrust/thrust/random/subtract_with_carry_engine.h
deleted file mode 100644
index 0b12ca3530a5bed1d38b816359fcce4b99d6d9d5..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/random/subtract_with_carry_engine.h
+++ /dev/null
@@ -1,256 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-/*! \file subtract_with_carry_engine.h
- *  \brief A subtract-with-carry pseudorandom number generator
- *         based on Marsaglia & Zaman.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/random/detail/random_core_access.h>
-
-#include <thrust/detail/cstdint.h>
-#include <cstddef> // for size_t
-#include <iostream>
-
-namespace thrust
-{
-
-namespace random
-{
-
-
-/*! \addtogroup random_number_engine_templates
- *  \{
- */
-
-/*! \class subtract_with_carry_engine
- *  \brief A \p subtract_with_carry_engine random number engine produces unsigned
- *         integer random numbers using the subtract with carry algorithm of Marsaglia & Zaman.
- *
- *         The generation algorithm is performed as follows:
- *         -# Let <tt>Y = X_{i-s}- X_{i-r} - c</tt>.
- *         -# Set <tt>X_i</tt> to <tt>y = T mod m</tt>. Set \c c to \c 1 if <tt>Y < 0</tt>, otherwise set \c c to \c 0.
- *
- *         This algorithm corresponds to a modular linear function of the form
- *
- *         <tt>TA(x_i) = (a * x_i) mod b</tt>, where \c b is of the form <tt>m^r - m^s + 1</tt> and
- *         <tt>a = b - (b-1)/m</tt>.
- *
- *  \tparam UIntType The type of unsigned integer to produce.
- *  \tparam w The word size of the produced values (<tt> w <= sizeof(UIntType)</tt>).
- *  \tparam s The short lag of the generation algorithm.
- *  \tparam r The long lag of the generation algorithm.
- *
- *  \note Inexperienced users should not use this class template directly.  Instead, use
- *  \p ranlux24_base or \p ranlux48_base, which are instances of \p subtract_with_carry_engine.
- *
- *  \see thrust::random::ranlux24_base
- *  \see thrust::random::ranlux48_base
- */
-template<typename UIntType, size_t w, size_t s, size_t r>
-  class subtract_with_carry_engine
-{
-    /*! \cond
-     */
-  private:
-    static const UIntType modulus = UIntType(1) << w;
-    /*! \endcond
-     */
-
-  public:
-    // types
-    
-    /*! \typedef result_type
-     *  \brief The type of the unsigned integer produced by this \p subtract_with_carry_engine.
-     */
-    typedef UIntType result_type;
-
-    // engine characteristics
-
-    /*! The word size of the produced values.
-     */
-    static const size_t word_size = w;
-
-    /*! The size of the short lag used in the generation algorithm.
-     */
-    static const size_t short_lag = s;
-
-    /*! The size of the long lag used in the generation algorithm.
-     */
-    static const size_t long_lag = r;
-
-    /*! The smallest value this \p subtract_with_carry_engine may potentially produce.
-     */
-    static const result_type min = 0;
-
-    /*! The largest value this \p subtract_with_carry_engine may potentially produce.
-     */
-    static const result_type max = modulus - 1;
-
-    /*! The default seed of this \p subtract_with_carry_engine.
-     */
-    static const result_type default_seed = 19780503u;
-
-    // constructors and seeding functions
-
-    /*! This constructor, which optionally accepts a seed, initializes a new
-     *  \p subtract_with_carry_engine.
-     *  
-     *  \param value The seed used to intialize this \p subtract_with_carry_engine's state.
-     */
-    __host__ __device__
-    explicit subtract_with_carry_engine(result_type value = default_seed);
-
-    /*! This method initializes this \p subtract_with_carry_engine's state, and optionally accepts
-     *  a seed value.
-     *
-     *  \param value The seed used to initializes this \p subtract_with_carry_engine's state.
-     */
-    __host__ __device__
-    void seed(result_type value = default_seed);
-
-    // generating functions
-    
-    /*! This member function produces a new random value and updates this \p subtract_with_carry_engine's state.
-     *  \return A new random number.
-     */
-    __host__ __device__
-    result_type operator()(void);
-
-    /*! This member function advances this \p subtract_with_carry_engine's state a given number of times
-     *  and discards the results.
-     *
-     *  \param z The number of random values to discard.
-     *  \note This function is provided because an implementation may be able to accelerate it.
-     */
-    __host__ __device__
-    void discard(unsigned long long z);
-
-    /*! \cond
-     */
-  private:
-    result_type m_x[long_lag];
-    unsigned int m_k;
-    int m_carry;
-
-    friend struct thrust::random::detail::random_core_access;
-
-    __host__ __device__
-    bool equal(const subtract_with_carry_engine &rhs) const;
-
-    template<typename CharT, typename Traits>
-    std::basic_ostream<CharT,Traits>& stream_out(std::basic_ostream<CharT,Traits> &os) const;
-
-    template<typename CharT, typename Traits>
-    std::basic_istream<CharT,Traits>& stream_in(std::basic_istream<CharT,Traits> &is);
-
-    /*! \endcond
-     */
-}; // end subtract_with_carry_engine
-
-
-/*! This function checks two \p subtract_with_carry_engines for equality.
- *  \param lhs The first \p subtract_with_carry_engine to test.
- *  \param rhs The second \p subtract_with_carry_engine to test.
- *  \return \c true if \p lhs is equal to \p rhs; \c false, otherwise.
- */
-template<typename UIntType_, size_t w_, size_t s_, size_t r_>
-__host__ __device__
-bool operator==(const subtract_with_carry_engine<UIntType_,w_,s_,r_> &lhs,
-                const subtract_with_carry_engine<UIntType_,w_,s_,r_> &rhs);
-
-
-/*! This function checks two \p subtract_with_carry_engines for inequality.
- *  \param lhs The first \p subtract_with_carry_engine to test.
- *  \param rhs The second \p subtract_with_carry_engine to test.
- *  \return \c true if \p lhs is not equal to \p rhs; \c false, otherwise.
- */
-template<typename UIntType_, size_t w_, size_t s_, size_t r_>
-__host__ __device__
-bool operator!=(const subtract_with_carry_engine<UIntType_,w_,s_,r_>&lhs,
-                const subtract_with_carry_engine<UIntType_,w_,s_,r_>&rhs);
-
-
-/*! This function streams a subtract_with_carry_engine to a \p std::basic_ostream.
- *  \param os The \p basic_ostream to stream out to.
- *  \param e The \p subtract_with_carry_engine to stream out.
- *  \return \p os
- */
-template<typename UIntType_, size_t w_, size_t s_, size_t r_,
-         typename CharT, typename Traits>
-std::basic_ostream<CharT,Traits>&
-operator<<(std::basic_ostream<CharT,Traits> &os,
-           const subtract_with_carry_engine<UIntType_,w_,s_,r_> &e);
-
-
-/*! This function streams a subtract_with_carry_engine in from a std::basic_istream.
- *  \param is The \p basic_istream to stream from.
- *  \param e The \p subtract_with_carry_engine to stream in.
- *  \return \p is
- */
-template<typename UIntType_, size_t w_, size_t s_, size_t r_,
-         typename CharT, typename Traits>
-std::basic_istream<CharT,Traits>&
-operator>>(std::basic_istream<CharT,Traits> &is,
-           subtract_with_carry_engine<UIntType_,w_,s_,r_> &e);
-
-
-/*! \} // end random_number_engine_templates
- */
-
-
-/*! \addtogroup predefined_random
- *  \{
- */
-
-// XXX N2111 uses uint_fast32_t here
-
-/*! \typedef ranlux24_base
- *  \brief A random number engine with predefined parameters which implements the
- *         base engine of the \p ranlux24 random number engine.
- *  \note The 10000th consecutive invocation of a default-constructed object of type \p ranlux24_base
- *        shall produce the value \c 7937952 .
- */
-typedef subtract_with_carry_engine<thrust::detail::uint32_t, 24, 10, 24> ranlux24_base;
-
-
-// XXX N2111 uses uint_fast64_t here
-
-/*! \typedef ranlux48_base
- *  \brief A random number engine with predefined parameters which implements the
- *         base engine of the \p ranlux48 random number engine.
- *  \note The 10000th consecutive invocation of a default-constructed object of type \p ranlux48_base
- *        shall produce the value \c 192113843633948 .
- */
-typedef subtract_with_carry_engine<thrust::detail::uint64_t, 48,  5, 12> ranlux48_base;
-
-/*! \} // end predefined_random
- */
-
-} // end random
-
-// import names into thrust::
-using random::subtract_with_carry_engine;
-using random::ranlux24_base;
-using random::ranlux48_base;
-
-} // end thrust
-
-#include <thrust/random/detail/subtract_with_carry_engine.inl>
-
diff --git a/spaces/CuriousDolphin/MobileSAM/utils/tools.py b/spaces/CuriousDolphin/MobileSAM/utils/tools.py
deleted file mode 100644
index 3a06972cfb82357bc66b4e4c9cd7f776846bbd1f..0000000000000000000000000000000000000000
--- a/spaces/CuriousDolphin/MobileSAM/utils/tools.py
+++ /dev/null
@@ -1,406 +0,0 @@
-import os
-import sys
-
-import cv2
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-from PIL import Image
-
-
-def convert_box_xywh_to_xyxy(box):
-    x1 = box[0]
-    y1 = box[1]
-    x2 = box[0] + box[2]
-    y2 = box[1] + box[3]
-    return [x1, y1, x2, y2]
-
-
-def segment_image(image, bbox):
-    image_array = np.array(image)
-    segmented_image_array = np.zeros_like(image_array)
-    x1, y1, x2, y2 = bbox
-    segmented_image_array[y1:y2, x1:x2] = image_array[y1:y2, x1:x2]
-    segmented_image = Image.fromarray(segmented_image_array)
-    black_image = Image.new("RGB", image.size, (255, 255, 255))
-    # transparency_mask = np.zeros_like((), dtype=np.uint8)
-    transparency_mask = np.zeros(
-        (image_array.shape[0], image_array.shape[1]), dtype=np.uint8
-    )
-    transparency_mask[y1:y2, x1:x2] = 255
-    transparency_mask_image = Image.fromarray(transparency_mask, mode="L")
-    black_image.paste(segmented_image, mask=transparency_mask_image)
-    return black_image
-
-
-def format_results(masks, scores, logits, filter=0):
-    annotations = []
-    n = len(scores)
-    for i in range(n):
-        annotation = {}
-
-        mask = masks[i]
-        tmp = np.where(mask != 0)
-        if np.sum(mask) < filter:
-            continue
-        annotation["id"] = i
-        annotation["segmentation"] = mask
-        annotation["bbox"] = [
-            np.min(tmp[0]),
-            np.min(tmp[1]),
-            np.max(tmp[1]),
-            np.max(tmp[0]),
-        ]
-        annotation["score"] = scores[i]
-        annotation["area"] = annotation["segmentation"].sum()
-        annotations.append(annotation)
-    return annotations
-
-
-def filter_masks(annotations):  # filter the overlap mask
-    annotations.sort(key=lambda x: x["area"], reverse=True)
-    to_remove = set()
-    for i in range(0, len(annotations)):
-        a = annotations[i]
-        for j in range(i + 1, len(annotations)):
-            b = annotations[j]
-            if i != j and j not in to_remove:
-                # check if
-                if b["area"] < a["area"]:
-                    if (a["segmentation"] & b["segmentation"]).sum() / b[
-                        "segmentation"
-                    ].sum() > 0.8:
-                        to_remove.add(j)
-
-    return [a for i, a in enumerate(annotations) if i not in to_remove], to_remove
-
-
-def get_bbox_from_mask(mask):
-    mask = mask.astype(np.uint8)
-    contours, hierarchy = cv2.findContours(
-        mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
-    )
-    x1, y1, w, h = cv2.boundingRect(contours[0])
-    x2, y2 = x1 + w, y1 + h
-    if len(contours) > 1:
-        for b in contours:
-            x_t, y_t, w_t, h_t = cv2.boundingRect(b)
-            # 将多个bbox合并成一个
-            x1 = min(x1, x_t)
-            y1 = min(y1, y_t)
-            x2 = max(x2, x_t + w_t)
-            y2 = max(y2, y_t + h_t)
-        h = y2 - y1
-        w = x2 - x1
-    return [x1, y1, x2, y2]
-
-
-def fast_process(
-    annotations, args, mask_random_color, bbox=None, points=None, edges=False
-):
-    if isinstance(annotations[0], dict):
-        annotations = [annotation["segmentation"] for annotation in annotations]
-    result_name = os.path.basename(args.img_path)
-    image = cv2.imread(args.img_path)
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    original_h = image.shape[0]
-    original_w = image.shape[1]
-    if sys.platform == "darwin":
-        plt.switch_backend("TkAgg")
-    plt.figure(figsize=(original_w / 100, original_h / 100))
-    # Add subplot with no margin.
-    plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
-    plt.margins(0, 0)
-    plt.gca().xaxis.set_major_locator(plt.NullLocator())
-    plt.gca().yaxis.set_major_locator(plt.NullLocator())
-    plt.imshow(image)
-    if args.better_quality == True:
-        if isinstance(annotations[0], torch.Tensor):
-            annotations = np.array(annotations.cpu())
-        for i, mask in enumerate(annotations):
-            mask = cv2.morphologyEx(
-                mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)
-            )
-            annotations[i] = cv2.morphologyEx(
-                mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8)
-            )
-    if args.device == "cpu":
-        annotations = np.array(annotations)
-        fast_show_mask(
-            annotations,
-            plt.gca(),
-            random_color=mask_random_color,
-            bbox=bbox,
-            points=points,
-            point_label=args.point_label,
-            retinamask=args.retina,
-            target_height=original_h,
-            target_width=original_w,
-        )
-    else:
-        if isinstance(annotations[0], np.ndarray):
-            annotations = torch.from_numpy(annotations)
-        fast_show_mask_gpu(
-            annotations,
-            plt.gca(),
-            random_color=args.randomcolor,
-            bbox=bbox,
-            points=points,
-            point_label=args.point_label,
-            retinamask=args.retina,
-            target_height=original_h,
-            target_width=original_w,
-        )
-    if isinstance(annotations, torch.Tensor):
-        annotations = annotations.cpu().numpy()
-    if args.withContours == True:
-        contour_all = []
-        temp = np.zeros((original_h, original_w, 1))
-        for i, mask in enumerate(annotations):
-            if type(mask) == dict:
-                mask = mask["segmentation"]
-            annotation = mask.astype(np.uint8)
-            if args.retina == False:
-                annotation = cv2.resize(
-                    annotation,
-                    (original_w, original_h),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-            contours, hierarchy = cv2.findContours(
-                annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
-            )
-            for contour in contours:
-                contour_all.append(contour)
-        cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2)
-        color = np.array([0 / 255, 0 / 255, 255 / 255, 0.8])
-        contour_mask = temp / 255 * color.reshape(1, 1, -1)
-        plt.imshow(contour_mask)
-
-    save_path = args.output
-    if not os.path.exists(save_path):
-        os.makedirs(save_path)
-    plt.axis("off")
-    fig = plt.gcf()
-    plt.draw()
-
-    try:
-        buf = fig.canvas.tostring_rgb()
-    except AttributeError:
-        fig.canvas.draw()
-        buf = fig.canvas.tostring_rgb()
-
-    cols, rows = fig.canvas.get_width_height()
-    img_array = np.fromstring(buf, dtype=np.uint8).reshape(rows, cols, 3)
-    cv2.imwrite(
-        os.path.join(save_path, result_name), cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
-    )
-
-
-# CPU post process
-def fast_show_mask(
-    annotation,
-    ax,
-    random_color=False,
-    bbox=None,
-    points=None,
-    point_label=None,
-    retinamask=True,
-    target_height=960,
-    target_width=960,
-):
-    msak_sum = annotation.shape[0]
-    height = annotation.shape[1]
-    weight = annotation.shape[2]
-    # 将annotation 按照面积 排序
-    areas = np.sum(annotation, axis=(1, 2))
-    sorted_indices = np.argsort(areas)
-    annotation = annotation[sorted_indices]
-
-    index = (annotation != 0).argmax(axis=0)
-    if random_color == True:
-        color = np.random.random((msak_sum, 1, 1, 3))
-    else:
-        color = np.ones((msak_sum, 1, 1, 3)) * np.array(
-            [30 / 255, 144 / 255, 255 / 255]
-        )
-    transparency = np.ones((msak_sum, 1, 1, 1)) * 0.6
-    visual = np.concatenate([color, transparency], axis=-1)
-    mask_image = np.expand_dims(annotation, -1) * visual
-
-    show = np.zeros((height, weight, 4))
-    h_indices, w_indices = np.meshgrid(
-        np.arange(height), np.arange(weight), indexing="ij"
-    )
-    indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
-    # 使用向量化索引更新show的值
-    show[h_indices, w_indices, :] = mask_image[indices]
-    if bbox is not None:
-        x1, y1, x2, y2 = bbox
-        ax.add_patch(
-            plt.Rectangle(
-                (x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1
-            )
-        )
-    # draw point
-    if points is not None:
-        plt.scatter(
-            [point[0] for i, point in enumerate(points) if point_label[i] == 1],
-            [point[1] for i, point in enumerate(points) if point_label[i] == 1],
-            s=20,
-            c="y",
-        )
-        plt.scatter(
-            [point[0] for i, point in enumerate(points) if point_label[i] == 0],
-            [point[1] for i, point in enumerate(points) if point_label[i] == 0],
-            s=20,
-            c="m",
-        )
-
-    if retinamask == False:
-        show = cv2.resize(
-            show, (target_width, target_height), interpolation=cv2.INTER_NEAREST
-        )
-    ax.imshow(show)
-
-
-def fast_show_mask_gpu(
-    annotation,
-    ax,
-    random_color=False,
-    bbox=None,
-    points=None,
-    point_label=None,
-    retinamask=True,
-    target_height=960,
-    target_width=960,
-):
-    msak_sum = annotation.shape[0]
-    height = annotation.shape[1]
-    weight = annotation.shape[2]
-    areas = torch.sum(annotation, dim=(1, 2))
-    sorted_indices = torch.argsort(areas, descending=False)
-    annotation = annotation[sorted_indices]
-    # 找每个位置第一个非零值下标
-    index = (annotation != 0).to(torch.long).argmax(dim=0)
-    if random_color == True:
-        color = torch.rand((msak_sum, 1, 1, 3)).to(annotation.device)
-    else:
-        color = torch.ones((msak_sum, 1, 1, 3)).to(annotation.device) * torch.tensor(
-            [30 / 255, 144 / 255, 255 / 255]
-        ).to(annotation.device)
-    transparency = torch.ones((msak_sum, 1, 1, 1)).to(annotation.device) * 0.6
-    visual = torch.cat([color, transparency], dim=-1)
-    mask_image = torch.unsqueeze(annotation, -1) * visual
-    # 按index取数，index指每个位置选哪个batch的数，把mask_image转成一个batch的形式
-    show = torch.zeros((height, weight, 4)).to(annotation.device)
-    h_indices, w_indices = torch.meshgrid(
-        torch.arange(height), torch.arange(weight), indexing="ij"
-    )
-    indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
-    # 使用向量化索引更新show的值
-    show[h_indices, w_indices, :] = mask_image[indices]
-    show_cpu = show.cpu().numpy()
-    if bbox is not None:
-        x1, y1, x2, y2 = bbox
-        ax.add_patch(
-            plt.Rectangle(
-                (x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1
-            )
-        )
-    # draw point
-    if points is not None:
-        plt.scatter(
-            [point[0] for i, point in enumerate(points) if point_label[i] == 1],
-            [point[1] for i, point in enumerate(points) if point_label[i] == 1],
-            s=20,
-            c="y",
-        )
-        plt.scatter(
-            [point[0] for i, point in enumerate(points) if point_label[i] == 0],
-            [point[1] for i, point in enumerate(points) if point_label[i] == 0],
-            s=20,
-            c="m",
-        )
-    if retinamask == False:
-        show_cpu = cv2.resize(
-            show_cpu, (target_width, target_height), interpolation=cv2.INTER_NEAREST
-        )
-    ax.imshow(show_cpu)
-
-
-def crop_image(annotations, image_like):
-    if isinstance(image_like, str):
-        image = Image.open(image_like)
-    else:
-        image = image_like
-    ori_w, ori_h = image.size
-    mask_h, mask_w = annotations[0]["segmentation"].shape
-    if ori_w != mask_w or ori_h != mask_h:
-        image = image.resize((mask_w, mask_h))
-    cropped_boxes = []
-    cropped_images = []
-    not_crop = []
-    filter_id = []
-    # annotations, _ = filter_masks(annotations)
-    # filter_id = list(_)
-    for _, mask in enumerate(annotations):
-        if np.sum(mask["segmentation"]) <= 100:
-            filter_id.append(_)
-            continue
-        bbox = get_bbox_from_mask(mask["segmentation"])  # mask 的 bbox
-        cropped_boxes.append(segment_image(image, bbox))  # 保存裁剪的图片
-        # cropped_boxes.append(segment_image(image,mask["segmentation"]))
-        cropped_images.append(bbox)  # 保存裁剪的图片的bbox
-
-    return cropped_boxes, cropped_images, not_crop, filter_id, annotations
-
-
-def box_prompt(masks, bbox, target_height, target_width):
-    h = masks.shape[1]
-    w = masks.shape[2]
-    if h != target_height or w != target_width:
-        bbox = [
-            int(bbox[0] * w / target_width),
-            int(bbox[1] * h / target_height),
-            int(bbox[2] * w / target_width),
-            int(bbox[3] * h / target_height),
-        ]
-    bbox[0] = round(bbox[0]) if round(bbox[0]) > 0 else 0
-    bbox[1] = round(bbox[1]) if round(bbox[1]) > 0 else 0
-    bbox[2] = round(bbox[2]) if round(bbox[2]) < w else w
-    bbox[3] = round(bbox[3]) if round(bbox[3]) < h else h
-
-    # IoUs = torch.zeros(len(masks), dtype=torch.float32)
-    bbox_area = (bbox[3] - bbox[1]) * (bbox[2] - bbox[0])
-
-    masks_area = torch.sum(masks[:, bbox[1] : bbox[3], bbox[0] : bbox[2]], dim=(1, 2))
-    orig_masks_area = torch.sum(masks, dim=(1, 2))
-
-    union = bbox_area + orig_masks_area - masks_area
-    IoUs = masks_area / union
-    max_iou_index = torch.argmax(IoUs)
-
-    return masks[max_iou_index].cpu().numpy(), max_iou_index
-
-
-def point_prompt(masks, points, point_label, target_height, target_width):  # numpy 处理
-    h = masks[0]["segmentation"].shape[0]
-    w = masks[0]["segmentation"].shape[1]
-    if h != target_height or w != target_width:
-        points = [
-            [int(point[0] * w / target_width), int(point[1] * h / target_height)]
-            for point in points
-        ]
-    onemask = np.zeros((h, w))
-    for i, annotation in enumerate(masks):
-        if type(annotation) == dict:
-            mask = annotation["segmentation"]
-        else:
-            mask = annotation
-        for i, point in enumerate(points):
-            if mask[point[1], point[0]] == 1 and point_label[i] == 1:
-                onemask += mask
-            if mask[point[1], point[0]] == 1 and point_label[i] == 0:
-                onemask -= mask
-    onemask = onemask >= 1
-    return onemask, 0
diff --git a/spaces/Cyril666/ContourNet-ABI/demo.py b/spaces/Cyril666/ContourNet-ABI/demo.py
deleted file mode 100644
index 7dc9bb41a5164cff64686053a06c0435c09f9587..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/demo.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import argparse
-import logging
-import os
-import glob
-import tqdm
-import torch
-import PIL
-import cv2
-import numpy as np
-import torch.nn.functional as F
-from torchvision import transforms
-from utils import Config, Logger, CharsetMapper
-
-def get_model(config):
-    import importlib
-    names = config.model_name.split('.')
-    module_name, class_name = '.'.join(names[:-1]), names[-1]
-    cls = getattr(importlib.import_module(module_name), class_name)
-    model = cls(config)
-    logging.info(model)
-    model = model.eval()
-    return model
-
-def preprocess(img, width, height):
-    img = cv2.resize(np.array(img), (width, height))
-    img = transforms.ToTensor()(img).unsqueeze(0)
-    mean = torch.tensor([0.485, 0.456, 0.406])
-    std  = torch.tensor([0.229, 0.224, 0.225])
-    return (img-mean[...,None,None]) / std[...,None,None]
-
-def postprocess(output, charset, model_eval):
-    def _get_output(last_output, model_eval):
-        if isinstance(last_output, (tuple, list)): 
-            for res in last_output:
-                if res['name'] == model_eval: output = res
-        else: output = last_output
-        return output
-
-    def _decode(logit):
-        """ Greed decode """
-        out = F.softmax(logit, dim=2)
-        pt_text, pt_scores, pt_lengths = [], [], []
-        for o in out:
-            text = charset.get_text(o.argmax(dim=1), padding=False, trim=False)
-            text = text.split(charset.null_char)[0]  # end at end-token
-            pt_text.append(text)
-            pt_scores.append(o.max(dim=1)[0])
-            pt_lengths.append(min(len(text) + 1, charset.max_length))  # one for end-token
-        return pt_text, pt_scores, pt_lengths
-
-    output = _get_output(output, model_eval)
-    logits, pt_lengths = output['logits'], output['pt_lengths']
-    pt_text, pt_scores, pt_lengths_ = _decode(logits)
-    
-    return pt_text, pt_scores, pt_lengths_
-
-def load(model, file, device=None, strict=True):
-    if device is None: device = 'cpu'
-    elif isinstance(device, int): device = torch.device('cuda', device)
-    assert os.path.isfile(file)
-    state = torch.load(file, map_location=device)
-    if set(state.keys()) == {'model', 'opt'}:
-        state = state['model']
-    model.load_state_dict(state, strict=strict)
-    return model
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', type=str, default='configs/train_abinet.yaml',
-                        help='path to config file')
-    parser.add_argument('--input', type=str, default='figs/test')
-    parser.add_argument('--cuda', type=int, default=-1)
-    parser.add_argument('--checkpoint', type=str, default='workdir/train-abinet/best-train-abinet.pth')
-    parser.add_argument('--model_eval', type=str, default='alignment', 
-                        choices=['alignment', 'vision', 'language'])
-    args = parser.parse_args()
-    config = Config(args.config)
-    if args.checkpoint is not None: config.model_checkpoint = args.checkpoint
-    if args.model_eval is not None: config.model_eval = args.model_eval
-    config.global_phase = 'test'
-    config.model_vision_checkpoint, config.model_language_checkpoint = None, None
-    device = 'cpu' if args.cuda < 0 else f'cuda:{args.cuda}'
-
-    Logger.init(config.global_workdir, config.global_name, config.global_phase)
-    Logger.enable_file()
-    logging.info(config)
-
-    logging.info('Construct model.')
-    model = get_model(config).to(device)
-    model = load(model, config.model_checkpoint, device=device)
-    charset = CharsetMapper(filename=config.dataset_charset_path,
-                            max_length=config.dataset_max_length + 1)
-
-    if os.path.isdir(args.input):
-        paths = [os.path.join(args.input, fname) for fname in os.listdir(args.input)]
-    else:
-        paths = glob.glob(os.path.expanduser(args.input))
-        assert paths, "The input path(s) was not found"
-    paths = sorted(paths)
-    for path in tqdm.tqdm(paths):
-        img = PIL.Image.open(path).convert('RGB')
-        img = preprocess(img, config.dataset_image_width, config.dataset_image_height)
-        img = img.to(device)
-        res = model(img)
-        pt_text, _, __ = postprocess(res, charset, config.model_eval)
-        logging.info(f'{path}: {pt_text[0]}')
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Model3D-1511e3cc.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Model3D-1511e3cc.js
deleted file mode 100644
index 6c76feed87bcfc4fad0250e2835d459b134fa044..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Model3D-1511e3cc.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as o,e as d,s as u,N as _,P as g,K as r,U as i,p as v,M as y,R as m,n as c,A as b}from"./index-1d65707a.js";function M(a){let e,s;return{c(){e=_("div"),s=g(a[0]),r(e,"class","svelte-1ayixqk"),i(e,"table",a[1]==="table"),i(e,"gallery",a[1]==="gallery"),i(e,"selected",a[2])},m(t,l){v(t,e,l),y(e,s)},p(t,[l]){l&1&&m(s,t[0]),l&2&&i(e,"table",t[1]==="table"),l&2&&i(e,"gallery",t[1]==="gallery"),l&4&&i(e,"selected",t[2])},i:c,o:c,d(t){t&&b(e)}}}function D(a,e,s){let{value:t}=e,{type:l}=e,{selected:f=!1}=e;return a.$$set=n=>{"value"in n&&s(0,t=n.value),"type"in n&&s(1,l=n.type),"selected"in n&&s(2,f=n.selected)},[t,l,f]}class h extends o{constructor(e){super(),d(this,e,D,M,u,{value:0,type:1,selected:2})}}const E=h;export{E};
-//# sourceMappingURL=Model3D-1511e3cc.js.map
diff --git a/spaces/DaleChen/AutoGPT/tests/test_json_parser.py b/spaces/DaleChen/AutoGPT/tests/test_json_parser.py
deleted file mode 100644
index 41c90a6f66c0b0468f1443de80033cc4f268eca0..0000000000000000000000000000000000000000
--- a/spaces/DaleChen/AutoGPT/tests/test_json_parser.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import unittest
-
-import tests.context
-from autogpt.json_utils.json_fix_llm import fix_and_parse_json
-
-
-class TestParseJson(unittest.TestCase):
-    def test_valid_json(self):
-        # Test that a valid JSON string is parsed correctly
-        json_str = '{"name": "John", "age": 30, "city": "New York"}'
-        obj = fix_and_parse_json(json_str)
-        self.assertEqual(obj, {"name": "John", "age": 30, "city": "New York"})
-
-    def test_invalid_json_minor(self):
-        # Test that an invalid JSON string can be fixed with gpt
-        json_str = '{"name": "John", "age": 30, "city": "New York",}'
-        with self.assertRaises(Exception):
-            fix_and_parse_json(json_str, try_to_fix_with_gpt=False)
-
-    def test_invalid_json_major_with_gpt(self):
-        # Test that an invalid JSON string raises an error when try_to_fix_with_gpt is False
-        json_str = 'BEGIN: "name": "John" - "age": 30 - "city": "New York" :END'
-        with self.assertRaises(Exception):
-            fix_and_parse_json(json_str, try_to_fix_with_gpt=False)
-
-    def test_invalid_json_major_without_gpt(self):
-        # Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
-        json_str = 'BEGIN: "name": "John" - "age": 30 - "city": "New York" :END'
-        # Assert that this raises an exception:
-        with self.assertRaises(Exception):
-            fix_and_parse_json(json_str, try_to_fix_with_gpt=False)
-
-    def test_invalid_json_leading_sentence_with_gpt(self):
-        # Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
-        json_str = """I suggest we start by browsing the repository to find any issues that we can fix.
-
-{
-    "command": {
-        "name": "browse_website",
-        "args":{
-            "url": "https://github.com/Torantulino/Auto-GPT"
-        }
-    },
-    "thoughts":
-    {
-        "text": "I suggest we start browsing the repository to find any issues that we can fix.",
-        "reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.",
-        "plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes",
-        "criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.",
-        "speak": "I will start browsing the repository to find any issues we can fix."
-    }
-}"""
-        good_obj = {
-            "command": {
-                "name": "browse_website",
-                "args": {"url": "https://github.com/Torantulino/Auto-GPT"},
-            },
-            "thoughts": {
-                "text": "I suggest we start browsing the repository to find any issues that we can fix.",
-                "reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.",
-                "plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes",
-                "criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.",
-                "speak": "I will start browsing the repository to find any issues we can fix.",
-            },
-        }
-        # Assert that this raises an exception:
-        self.assertEqual(
-            fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj
-        )
-
-    def test_invalid_json_leading_sentence_with_gpt(self):
-        # Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
-        json_str = """I will first need to browse the repository (https://github.com/Torantulino/Auto-GPT) and identify any potential bugs that need fixing. I will use the "browse_website" command for this.
-
-{
-    "command": {
-        "name": "browse_website",
-        "args":{
-            "url": "https://github.com/Torantulino/Auto-GPT"
-        }
-    },
-    "thoughts":
-    {
-        "text": "Browsing the repository to identify potential bugs",
-        "reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.",
-        "plan": "- Analyze the repository for potential bugs and areas of improvement",
-        "criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.",
-        "speak": "I am browsing the repository to identify potential bugs."
-    }
-}"""
-        good_obj = {
-            "command": {
-                "name": "browse_website",
-                "args": {"url": "https://github.com/Torantulino/Auto-GPT"},
-            },
-            "thoughts": {
-                "text": "Browsing the repository to identify potential bugs",
-                "reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.",
-                "plan": "- Analyze the repository for potential bugs and areas of improvement",
-                "criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.",
-                "speak": "I am browsing the repository to identify potential bugs.",
-            },
-        }
-        # Assert that this raises an exception:
-        self.assertEqual(
-            fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj
-        )
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/DavidWeiZhang/sd-dreambooth-library-avator-generator/app.py b/spaces/DavidWeiZhang/sd-dreambooth-library-avator-generator/app.py
deleted file mode 100644
index e65cbf7b13715b43bd00afc33c66e8dffbc803b4..0000000000000000000000000000000000000000
--- a/spaces/DavidWeiZhang/sd-dreambooth-library-avator-generator/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-access_token="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"
-gr.Interface.load("models/sd-dreambooth-library/avator-generator",api_key=access_token).launch()
\ No newline at end of file
diff --git a/spaces/Detomo/ai-comic-generation/CONTRIBUTORS.md b/spaces/Detomo/ai-comic-generation/CONTRIBUTORS.md
deleted file mode 100644
index 9efc216b74d3e8afb8bd0386897a649565a8508d..0000000000000000000000000000000000000000
--- a/spaces/Detomo/ai-comic-generation/CONTRIBUTORS.md
+++ /dev/null
@@ -1,9 +0,0 @@
-This project was developed by Julian Bilcke (@jbilcke-hf), as part of his work at Hugging Face.
-
-------------------------------------------
-
-A huge thanks to external developers for their contributions! 
-
-艾逗笔 (@idoubi):
-- Added support for OpenAI: https://github.com/jbilcke-hf/ai-comic-factory/pull/6
-
diff --git a/spaces/Detomo/ai-comic-generation/src/lib/getImageDimension.ts b/spaces/Detomo/ai-comic-generation/src/lib/getImageDimension.ts
deleted file mode 100644
index 50a94ae1eee733b23b1d4916780e597c759c608e..0000000000000000000000000000000000000000
--- a/spaces/Detomo/ai-comic-generation/src/lib/getImageDimension.ts
+++ /dev/null
@@ -1,16 +0,0 @@
-export interface ImageDimension {
-  width: number
-  height: number
-}
-
-export async function getImageDimension(src: string): Promise<ImageDimension> {
-  if (!src) {
-    return { width: 0, height: 0 }
-  }
-  const img = new Image()
-  img.src = src
-  await img.decode()
-  const width = img.width
-  const height = img.height
-  return { width, height }
-}
\ No newline at end of file
diff --git a/spaces/Detomo/naomi-app-api/main.py b/spaces/Detomo/naomi-app-api/main.py
deleted file mode 100644
index d18dcf62217080d8347af6ee409e54495986ca82..0000000000000000000000000000000000000000
--- a/spaces/Detomo/naomi-app-api/main.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from fastapi import FastAPI, File, Form
-import datetime
-import time
-import torch
-from typing import Optional
-
-import os
-import numpy as np
-from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM, AutoConfig
-from huggingface_hub import hf_hub_download
-from fuzzywuzzy import fuzz
-from utils import ffmpeg_read, query_dummy, query_raw, find_different
-
-## config
-API_TOKEN = os.environ["API_TOKEN"]
-MODEL_PATH = os.environ["MODEL_PATH"]
-PITCH_PATH = os.environ["PITCH_PATH"]
-
-QUANTIZED_MODEL_PATH = hf_hub_download(repo_id=MODEL_PATH, filename='quantized_model.pt', token=API_TOKEN)
-QUANTIZED_PITCH_MODEL_PATH = hf_hub_download(repo_id=PITCH_PATH, filename='quantized_model.pt', token=API_TOKEN)
-
-
-## word preprocessor
-processor_with_lm = Wav2Vec2ProcessorWithLM.from_pretrained(MODEL_PATH, use_auth_token=API_TOKEN)
-processor = Wav2Vec2Processor.from_pretrained(MODEL_PATH, use_auth_token=API_TOKEN)
-
-### quantized model
-config = AutoConfig.from_pretrained(MODEL_PATH, use_auth_token=API_TOKEN)
-dummy_model = Wav2Vec2ForCTC(config)
-quantized_model = torch.quantization.quantize_dynamic(dummy_model, {torch.nn.Linear}, dtype=torch.qint8, inplace=True)
-quantized_model.load_state_dict(torch.load(QUANTIZED_MODEL_PATH))
-
-## pitch preprocessor 
-processor_pitch = Wav2Vec2Processor.from_pretrained(PITCH_PATH, use_auth_token=API_TOKEN)
-
-### quantized pitch mode
-config = AutoConfig.from_pretrained(PITCH_PATH, use_auth_token=API_TOKEN)
-dummy_pitch_model = Wav2Vec2ForCTC(config)
-quantized_pitch_model = torch.quantization.quantize_dynamic(dummy_pitch_model, {torch.nn.Linear}, dtype=torch.qint8, inplace=True)
-quantized_pitch_model.load_state_dict(torch.load(QUANTIZED_PITCH_MODEL_PATH))
-
-app = FastAPI()
-
-
-@app.get("/")
-def read_root():
-    return {"Message": "Application startup complete"}
-
-
-@app.post("/naomi_api_score/")
-async def predict(
-        file: bytes = File(...),
-        word: str = Form(...),
-        pitch: Optional[str] = Form(None),
-        temperature: int = Form(...),
-):
-    """ Transform input audio, get text and pitch from Huggingface api and calculate score by Levenshtein Distance Score
-        Parameters:
-         ----------
-        file : bytes
-            input audio file
-        word : strings
-            true hiragana word to calculate word score
-        pitch : strings
-            true pitch to calculate pitch score
-        temperature: integer
-            the difficulty of AI model
-        Returns:
-        -------
-        timestamp: strings
-            current time Year-Month-Day-Hours:Minutes:Second
-        running_time : strings
-            running time second
-        error message : strings
-            error message from api
-        audio duration: integer
-            durations of source audio
-        target : integer
-            durations of target audio
-        method : string
-            method applied to transform source audio
-        word predict : strings
-            text from api
-        pitch predict : strings
-            pitch from api
-        wrong word index: strings (ex: 100)
-            wrong word compare to target word
-        wrong pitch index: strings (ex: 100)
-            wrong word compare to target word
-        score: integer
-            Levenshtein Distance Score from pitch and word
-    """
-    upload_audio = ffmpeg_read(file, sampling_rate=16000)
-    audio_duration = len(upload_audio) / 16000
-    current_time = datetime.datetime.now().strftime("%Y-%h-%d-%H:%M:%S")
-    start_time = time.time()
-    error_message, score, word_preds, pitch_preds = None, None, None, None
-
-    word_preds = query_raw(upload_audio, word, processor, processor_with_lm, quantized_model, temperature=temperature)
-    if pitch is not None:
-        if len(word) != len(pitch):
-            error_message = "Length of word and pitch input is not equal"
-        pitch_preds = query_dummy(upload_audio, processor_pitch, quantized_pitch_model)
-
-    # find best word
-    word_score_list = []
-    for word_predict in word_preds:
-        word_score_list.append(fuzz.ratio(word, word_predict[0]))
-    word_score = max(word_score_list)
-    best_word_predict = word_preds[word_score_list.index(word_score)][0]
-    wrong_word = find_different(word, best_word_predict)  # get wrong word
-
-    # find best pitch
-    if pitch_preds is not None:
-        best_pitch_predict = pitch_preds.replace(" ", "")
-        if len(best_pitch_predict) < len(best_word_predict):
-            best_pitch_predict = best_pitch_predict + "1" * (len(best_word_predict) - len(best_pitch_predict))
-        else:
-            best_pitch_predict = best_pitch_predict[:len(best_word_predict)]  # truncate to max len
-        pitch_score = fuzz.ratio(pitch, best_pitch_predict)
-        score = int((word_score * 2 + pitch_score) / 3)
-        wrong_pitch = find_different(pitch, best_pitch_predict)  # get wrong pitch
-    else:
-        score = int(word_score)
-        best_pitch_predict = None
-        wrong_pitch = None
-
-    return {"timestamp": current_time,
-            "running_time": f"{round(time.time() - start_time, 4)} s",
-            "error_message": error_message,
-            "audio_duration": audio_duration,
-            "word_predict": best_word_predict,
-            "pitch_predict": best_pitch_predict,
-            "wrong_word_index": wrong_word,
-            "wrong_pitch_index": wrong_pitch,
-            "score": score,
-            }
\ No newline at end of file
diff --git a/spaces/Dimalker/Faceswapper/roop/capturer.py b/spaces/Dimalker/Faceswapper/roop/capturer.py
deleted file mode 100644
index fd49d468dd4cd45832ab9612205968207a6f45cf..0000000000000000000000000000000000000000
--- a/spaces/Dimalker/Faceswapper/roop/capturer.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from typing import Any
-import cv2
-
-
-def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
-    capture = cv2.VideoCapture(video_path)
-    frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
-    capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
-    has_frame, frame = capture.read()
-    capture.release()
-    if has_frame:
-        return frame
-    return None
-
-
-def get_video_frame_total(video_path: str) -> int:
-    capture = cv2.VideoCapture(video_path)
-    video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
-    capture.release()
-    return video_frame_total
diff --git a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/dnnlib/util.py b/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/dnnlib/util.py
deleted file mode 100644
index 133ef764c0707d9384a33f0350ba71b1e624072f..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/dnnlib/util.py
+++ /dev/null
@@ -1,405 +0,0 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-"""Miscellaneous utility classes and functions."""
-
-import ctypes
-import fnmatch
-import importlib
-import inspect
-import numpy as np
-import os
-import shutil
-import sys
-import types
-import io
-import pickle
-import re
-import requests
-import html
-import hashlib
-import glob
-import uuid
-
-from distutils.util import strtobool
-from typing import Any, List, Tuple, Union
-
-
-# Util classes
-# ------------------------------------------------------------------------------------------
-
-
-class EasyDict(dict):
-    """Convenience class that behaves like a dict but allows access with the attribute syntax."""
-
-    def __getattr__(self, name: str) -> Any:
-        try:
-            return self[name]
-        except KeyError:
-            raise AttributeError(name)
-
-    def __setattr__(self, name: str, value: Any) -> None:
-        self[name] = value
-
-    def __delattr__(self, name: str) -> None:
-        del self[name]
-
-
-class Logger(object):
-    """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""
-
-    def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True):
-        self.file = None
-
-        if file_name is not None:
-            self.file = open(file_name, file_mode)
-
-        self.should_flush = should_flush
-        self.stdout = sys.stdout
-        self.stderr = sys.stderr
-
-        sys.stdout = self
-        sys.stderr = self
-
-    def __enter__(self) -> "Logger":
-        return self
-
-    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
-        self.close()
-
-    def write(self, text: str) -> None:
-        """Write text to stdout (and a file) and optionally flush."""
-        if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
-            return
-
-        if self.file is not None:
-            self.file.write(text)
-
-        self.stdout.write(text)
-
-        if self.should_flush:
-            self.flush()
-
-    def flush(self) -> None:
-        """Flush written text to both stdout and a file, if open."""
-        if self.file is not None:
-            self.file.flush()
-
-        self.stdout.flush()
-
-    def close(self) -> None:
-        """Flush, close possible files, and remove stdout/stderr mirroring."""
-        self.flush()
-
-        # if using multiple loggers, prevent closing in wrong order
-        if sys.stdout is self:
-            sys.stdout = self.stdout
-        if sys.stderr is self:
-            sys.stderr = self.stderr
-
-        if self.file is not None:
-            self.file.close()
-
-
-# Small util functions
-# ------------------------------------------------------------------------------------------
-
-
-def format_time(seconds: Union[int, float]) -> str:
-    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
-    s = int(np.rint(seconds))
-
-    if s < 60:
-        return "{0}s".format(s)
-    elif s < 60 * 60:
-        return "{0}m {1:02}s".format(s // 60, s % 60)
-    elif s < 24 * 60 * 60:
-        return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60)
-    else:
-        return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60)
-
-
-def ask_yes_no(question: str) -> bool:
-    """Ask the user the question until the user inputs a valid answer."""
-    while True:
-        try:
-            print("{0} [y/n]".format(question))
-            return strtobool(input().lower())
-        except ValueError:
-            pass
-
-
-def tuple_product(t: Tuple) -> Any:
-    """Calculate the product of the tuple elements."""
-    result = 1
-
-    for v in t:
-        result *= v
-
-    return result
-
-
-_str_to_ctype = {
-    "uint8": ctypes.c_ubyte,
-    "uint16": ctypes.c_uint16,
-    "uint32": ctypes.c_uint32,
-    "uint64": ctypes.c_uint64,
-    "int8": ctypes.c_byte,
-    "int16": ctypes.c_int16,
-    "int32": ctypes.c_int32,
-    "int64": ctypes.c_int64,
-    "float32": ctypes.c_float,
-    "float64": ctypes.c_double
-}
-
-
-def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
-    """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes."""
-    type_str = None
-
-    if isinstance(type_obj, str):
-        type_str = type_obj
-    elif hasattr(type_obj, "__name__"):
-        type_str = type_obj.__name__
-    elif hasattr(type_obj, "name"):
-        type_str = type_obj.name
-    else:
-        raise RuntimeError("Cannot infer type name from input")
-
-    assert type_str in _str_to_ctype.keys()
-
-    my_dtype = np.dtype(type_str)
-    my_ctype = _str_to_ctype[type_str]
-
-    assert my_dtype.itemsize == ctypes.sizeof(my_ctype)
-
-    return my_dtype, my_ctype
-
-
-def is_pickleable(obj: Any) -> bool:
-    try:
-        with io.BytesIO() as stream:
-            pickle.dump(obj, stream)
-        return True
-    except:
-        return False
-
-
-# Functionality to import modules/objects by name, and call functions by name
-# ------------------------------------------------------------------------------------------
-
-def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]:
-    """Searches for the underlying module behind the name to some python object.
-    Returns the module and the object name (original name with module part removed)."""
-
-    # allow convenience shorthands, substitute them by full names
-    obj_name = re.sub("^np.", "numpy.", obj_name)
-    obj_name = re.sub("^tf.", "tensorflow.", obj_name)
-
-    # list alternatives for (module_name, local_obj_name)
-    parts = obj_name.split(".")
-    name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)]
-
-    # try each alternative in turn
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(module_name) # may raise ImportError
-            get_obj_from_module(module, local_obj_name) # may raise AttributeError
-            return module, local_obj_name
-        except:
-            pass
-
-    # maybe some of the modules themselves contain errors?
-    for module_name, _local_obj_name in name_pairs:
-        try:
-            importlib.import_module(module_name) # may raise ImportError
-        except ImportError:
-            if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"):
-                raise
-
-    # maybe the requested attribute is missing?
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(module_name) # may raise ImportError
-            get_obj_from_module(module, local_obj_name) # may raise AttributeError
-        except ImportError:
-            pass
-
-    # we are out of luck, but we have no idea why
-    raise ImportError(obj_name)
-
-
-def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
-    """Traverses the object name and returns the last (rightmost) python object."""
-    if obj_name == '':
-        return module
-    obj = module
-    for part in obj_name.split("."):
-        obj = getattr(obj, part)
-    return obj
-
-
-def get_obj_by_name(name: str) -> Any:
-    """Finds the python object with the given name."""
-    module, obj_name = get_module_from_obj_name(name)
-    return get_obj_from_module(module, obj_name)
-
-
-def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
-    """Finds the python object with the given name and calls it as a function."""
-    assert func_name is not None
-    func_obj = get_obj_by_name(func_name)
-    assert callable(func_obj)
-    return func_obj(*args, **kwargs)
-
-
-def get_module_dir_by_obj_name(obj_name: str) -> str:
-    """Get the directory path of the module containing the given object name."""
-    module, _ = get_module_from_obj_name(obj_name)
-    return os.path.dirname(inspect.getfile(module))
-
-
-def is_top_level_function(obj: Any) -> bool:
-    """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'."""
-    return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__
-
-
-def get_top_level_function_name(obj: Any) -> str:
-    """Return the fully-qualified name of a top-level function."""
-    assert is_top_level_function(obj)
-    return obj.__module__ + "." + obj.__name__
-
-
-# File system helpers
-# ------------------------------------------------------------------------------------------
-
-def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]:
-    """List all files recursively in a given directory while ignoring given file and directory names.
-    Returns list of tuples containing both absolute and relative paths."""
-    assert os.path.isdir(dir_path)
-    base_name = os.path.basename(os.path.normpath(dir_path))
-
-    if ignores is None:
-        ignores = []
-
-    result = []
-
-    for root, dirs, files in os.walk(dir_path, topdown=True):
-        for ignore_ in ignores:
-            dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)]
-
-            # dirs need to be edited in-place
-            for d in dirs_to_remove:
-                dirs.remove(d)
-
-            files = [f for f in files if not fnmatch.fnmatch(f, ignore_)]
-
-        absolute_paths = [os.path.join(root, f) for f in files]
-        relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths]
-
-        if add_base_to_relative:
-            relative_paths = [os.path.join(base_name, p) for p in relative_paths]
-
-        assert len(absolute_paths) == len(relative_paths)
-        result += zip(absolute_paths, relative_paths)
-
-    return result
-
-
-def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
-    """Takes in a list of tuples of (src, dst) paths and copies files.
-    Will create all necessary directories."""
-    for file in files:
-        target_dir_name = os.path.dirname(file[1])
-
-        # will create all intermediate-level directories
-        if not os.path.exists(target_dir_name):
-            os.makedirs(target_dir_name)
-
-        shutil.copyfile(file[0], file[1])
-
-
-# URL helpers
-# ------------------------------------------------------------------------------------------
-
-def is_url(obj: Any) -> bool:
-    """Determine whether the given object is a valid URL string."""
-    if not isinstance(obj, str) or not "://" in obj:
-        return False
-    try:
-        res = requests.compat.urlparse(obj)
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-        res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-    except:
-        return False
-    return True
-
-
-def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True) -> Any:
-    """Download the given URL and return a binary-mode file object to access the data."""
-    assert is_url(url)
-    assert num_attempts >= 1
-
-    # Lookup from cache.
-    url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
-    if cache_dir is not None:
-        cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
-        if len(cache_files) == 1:
-            return open(cache_files[0], "rb")
-
-    # Download.
-    url_name = None
-    url_data = None
-    with requests.Session() as session:
-        if verbose:
-            print("Downloading %s ..." % url, end="", flush=True)
-        for attempts_left in reversed(range(num_attempts)):
-            try:
-                with session.get(url) as res:
-                    res.raise_for_status()
-                    if len(res.content) == 0:
-                        raise IOError("No data received")
-
-                    if len(res.content) < 8192:
-                        content_str = res.content.decode("utf-8")
-                        if "download_warning" in res.headers.get("Set-Cookie", ""):
-                            links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link]
-                            if len(links) == 1:
-                                url = requests.compat.urljoin(url, links[0])
-                                raise IOError("Google Drive virus checker nag")
-                        if "Google Drive - Quota exceeded" in content_str:
-                            raise IOError("Google Drive quota exceeded")
-
-                    match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", ""))
-                    url_name = match[1] if match else url
-                    url_data = res.content
-                    if verbose:
-                        print(" done")
-                    break
-            except:
-                if not attempts_left:
-                    if verbose:
-                        print(" failed")
-                    raise
-                if verbose:
-                    print(".", end="", flush=True)
-
-    # Save to cache.
-    if cache_dir is not None:
-        safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
-        cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
-        temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
-        os.makedirs(cache_dir, exist_ok=True)
-        with open(temp_file, "wb") as f:
-            f.write(url_data)
-        os.replace(temp_file, cache_file) # atomic
-
-    # Return data as file object.
-    return io.BytesIO(url_data)
diff --git a/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/upfirdn_2d.py b/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/upfirdn_2d.py
deleted file mode 100644
index 55a31af7e146da7afeb964db018f14aca3134920..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/upfirdn_2d.py
+++ /dev/null
@@ -1,418 +0,0 @@
-# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom TensorFlow ops for efficient resampling of 2D images."""
-
-import os
-import numpy as np
-import tensorflow as tf
-from .. import custom_ops
-
-def _get_plugin():
-    return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu')
-
-#----------------------------------------------------------------------------
-
-def upfirdn_2d(x, k, upx=1, upy=1, downx=1, downy=1, padx0=0, padx1=0, pady0=0, pady1=0, impl='cuda'):
-    r"""Pad, upsample, FIR filter, and downsample a batch of 2D images.
-
-    Accepts a batch of 2D images of the shape `[majorDim, inH, inW, minorDim]`
-    and performs the following operations for each image, batched across
-    `majorDim` and `minorDim`:
-
-    1. Upsample the image by inserting the zeros after each pixel (`upx`, `upy`).
-
-    2. Pad the image with zeros by the specified number of pixels on each side
-       (`padx0`, `padx1`, `pady0`, `pady1`). Specifying a negative value
-       corresponds to cropping the image.
-
-    3. Convolve the image with the specified 2D FIR filter (`k`), shrinking the
-       image so that the footprint of all output pixels lies within the input image.
-
-    4. Downsample the image by throwing away pixels (`downx`, `downy`).
-
-    This sequence of operations bears close resemblance to scipy.signal.upfirdn().
-    The fused op is considerably more efficient than performing the same calculation
-    using standard TensorFlow ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:      Input tensor of the shape `[majorDim, inH, inW, minorDim]`.
-        k:      2D FIR filter of the shape `[firH, firW]`.
-        upx:    Integer upsampling factor along the X-axis (default: 1).
-        upy:    Integer upsampling factor along the Y-axis (default: 1).
-        downx:  Integer downsampling factor along the X-axis (default: 1).
-        downy:  Integer downsampling factor along the Y-axis (default: 1).
-        padx0:  Number of pixels to pad on the left side (default: 0).
-        padx1:  Number of pixels to pad on the right side (default: 0).
-        pady0:  Number of pixels to pad on the top side (default: 0).
-        pady1:  Number of pixels to pad on the bottom side (default: 0).
-        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[majorDim, outH, outW, minorDim]`, and same datatype as `x`.
-    """
-
-    impl_dict = {
-        'ref':  _upfirdn_2d_ref,
-        'cuda': _upfirdn_2d_cuda,
-    }
-    return impl_dict[impl](x=x, k=k, upx=upx, upy=upy, downx=downx, downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1)
-
-#----------------------------------------------------------------------------
-
-def _upfirdn_2d_ref(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1):
-    """Slow reference implementation of `upfirdn_2d()` using standard TensorFlow ops."""
-
-    x = tf.convert_to_tensor(x)
-    k = np.asarray(k, dtype=np.float32)
-    assert x.shape.rank == 4
-    inH = x.shape[1].value
-    inW = x.shape[2].value
-    minorDim = _shape(x, 3)
-    kernelH, kernelW = k.shape
-    assert inW >= 1 and inH >= 1
-    assert kernelW >= 1 and kernelH >= 1
-    assert isinstance(upx, int) and isinstance(upy, int)
-    assert isinstance(downx, int) and isinstance(downy, int)
-    assert isinstance(padx0, int) and isinstance(padx1, int)
-    assert isinstance(pady0, int) and isinstance(pady1, int)
-
-    # Upsample (insert zeros).
-    x = tf.reshape(x, [-1, inH, 1, inW, 1, minorDim])
-    x = tf.pad(x, [[0, 0], [0, 0], [0, upy - 1], [0, 0], [0, upx - 1], [0, 0]])
-    x = tf.reshape(x, [-1, inH * upy, inW * upx, minorDim])
-
-    # Pad (crop if negative).
-    x = tf.pad(x, [[0, 0], [max(pady0, 0), max(pady1, 0)], [max(padx0, 0), max(padx1, 0)], [0, 0]])
-    x = x[:, max(-pady0, 0) : x.shape[1].value - max(-pady1, 0), max(-padx0, 0) : x.shape[2].value - max(-padx1, 0), :]
-
-    # Convolve with filter.
-    x = tf.transpose(x, [0, 3, 1, 2])
-    x = tf.reshape(x, [-1, 1, inH * upy + pady0 + pady1, inW * upx + padx0 + padx1])
-    w = tf.constant(k[::-1, ::-1, np.newaxis, np.newaxis], dtype=x.dtype)
-    x = tf.nn.conv2d(x, w, strides=[1,1,1,1], padding='VALID', data_format='NCHW')
-    x = tf.reshape(x, [-1, minorDim, inH * upy + pady0 + pady1 - kernelH + 1, inW * upx + padx0 + padx1 - kernelW + 1])
-    x = tf.transpose(x, [0, 2, 3, 1])
-
-    # Downsample (throw away pixels).
-    return x[:, ::downy, ::downx, :]
-
-#----------------------------------------------------------------------------
-
-def _upfirdn_2d_cuda(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1):
-    """Fast CUDA implementation of `upfirdn_2d()` using custom ops."""
-
-    x = tf.convert_to_tensor(x)
-    k = np.asarray(k, dtype=np.float32)
-    majorDim, inH, inW, minorDim = x.shape.as_list()
-    kernelH, kernelW = k.shape
-    assert inW >= 1 and inH >= 1
-    assert kernelW >= 1 and kernelH >= 1
-    assert isinstance(upx, int) and isinstance(upy, int)
-    assert isinstance(downx, int) and isinstance(downy, int)
-    assert isinstance(padx0, int) and isinstance(padx1, int)
-    assert isinstance(pady0, int) and isinstance(pady1, int)
-
-    outW = (inW * upx + padx0 + padx1 - kernelW) // downx + 1
-    outH = (inH * upy + pady0 + pady1 - kernelH) // downy + 1
-    assert outW >= 1 and outH >= 1
-
-    cuda_op = _get_plugin().up_fir_dn2d
-    kc = tf.constant(k, dtype=x.dtype)
-    gkc = tf.constant(k[::-1, ::-1], dtype=x.dtype)
-    gpadx0 = kernelW - padx0 - 1
-    gpady0 = kernelH - pady0 - 1
-    gpadx1 = inW * upx - outW * downx + padx0 - upx + 1
-    gpady1 = inH * upy - outH * downy + pady0 - upy + 1
-
-    @tf.custom_gradient
-    def func(x):
-        y = cuda_op(x=x, k=kc, upx=int(upx), upy=int(upy), downx=int(downx), downy=int(downy), padx0=int(padx0), padx1=int(padx1), pady0=int(pady0), pady1=int(pady1))
-        y.set_shape([majorDim, outH, outW, minorDim])
-        @tf.custom_gradient
-        def grad(dy):
-            dx = cuda_op(x=dy, k=gkc, upx=int(downx), upy=int(downy), downx=int(upx), downy=int(upy), padx0=int(gpadx0), padx1=int(gpadx1), pady0=int(gpady0), pady1=int(gpady1))
-            dx.set_shape([majorDim, inH, inW, minorDim])
-            return dx, func
-        return y, grad
-    return func(x)
-
-#----------------------------------------------------------------------------
-
-def filter_2d(x, k, gain=1, padding=0, data_format='NCHW', impl='cuda'):
-    r"""Filter a batch of 2D images with the given FIR filter.
-
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and filters each image with the given filter. The filter is normalized so that
-    if the input pixels are constant, they will be scaled by the specified `gain`.
-    Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        padding:      Number of pixels to pad or crop the output on each side (default: 0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the same shape and datatype as `x`.
-    """
-
-    assert isinstance(padding, int)
-    k = _FilterKernel(k=k, gain=gain)
-    assert k.w == k.h
-    pad0 = k.w // 2 + padding
-    pad1 = (k.w - 1) // 2 + padding
-    return _simple_upfirdn_2d(x, k, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def upsample_2d(x, k=None, factor=2, gain=1, padding=0, data_format='NCHW', impl='cuda'):
-    r"""Upsample a batch of 2D images with the given filter.
-
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and upsamples each image with the given filter. The filter is normalized so that
-    if the input pixels are constant, they will be scaled by the specified `gain`.
-    Pixels outside the image are assumed to be zero, and the filter is padded with
-    zeros so that its shape is a multiple of the upsampling factor.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to nearest-neighbor
-                      upsampling.
-        factor:       Integer upsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        padding:      Number of pixels to pad or crop the output on each side (default: 0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H * factor, W * factor]` or
-        `[N, H * factor, W * factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    assert isinstance(padding, int)
-    k = _FilterKernel(k if k is not None else [1] * factor, gain * (factor ** 2))
-    assert k.w == k.h
-    pad0 = (k.w + factor - 1) // 2 + padding
-    pad1 = (k.w - factor) // 2 + padding
-    return _simple_upfirdn_2d(x, k, up=factor, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def downsample_2d(x, k=None, factor=2, gain=1, padding=0, data_format='NCHW', impl='cuda'):
-    r"""Downsample a batch of 2D images with the given filter.
-
-    Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]`
-    and downsamples each image with the given filter. The filter is normalized so that
-    if the input pixels are constant, they will be scaled by the specified `gain`.
-    Pixels outside the image are assumed to be zero, and the filter is padded with
-    zeros so that its shape is a multiple of the downsampling factor.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to average pooling.
-        factor:       Integer downsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        padding:      Number of pixels to pad or crop the output on each side (default: 0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H // factor, W // factor]` or
-        `[N, H // factor, W // factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    assert isinstance(padding, int)
-    k = _FilterKernel(k if k is not None else [1] * factor, gain)
-    assert k.w == k.h
-    pad0 = (k.w - factor + 1) // 2 + padding * factor
-    pad1 = (k.w - factor) // 2 + padding * factor
-    return _simple_upfirdn_2d(x, k, down=factor, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def upsample_conv_2d(x, w, k=None, factor=2, gain=1, padding=0, data_format='NCHW', impl='cuda'):
-    r"""Fused `upsample_2d()` followed by `tf.nn.conv2d()`.
-
-    Padding is performed only once at the beginning, not between the operations.
-    The fused op is considerably more efficient than performing the same calculation
-    using standard TensorFlow ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        w:            Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
-                      Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to nearest-neighbor
-                      upsampling.
-        factor:       Integer upsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        padding:      Number of pixels to pad or crop the output on each side (default: 0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H * factor, W * factor]` or
-        `[N, H * factor, W * factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    assert isinstance(padding, int)
-
-    # Check weight shape.
-    w = tf.convert_to_tensor(w)
-    ch, cw, _inC, _outC = w.shape.as_list()
-    inC = _shape(w, 2)
-    outC = _shape(w, 3)
-    assert cw == ch
-
-    # Fast path for 1x1 convolution.
-    if cw == 1 and ch == 1:
-        x = tf.nn.conv2d(x, w, data_format=data_format, strides=[1,1,1,1], padding='VALID')
-        x = upsample_2d(x, k, factor=factor, gain=gain, padding=padding, data_format=data_format, impl=impl)
-        return x
-
-    # Setup filter kernel.
-    k = _FilterKernel(k if k is not None else [1] * factor, gain * (factor ** 2))
-    assert k.w == k.h
-
-    # Determine data dimensions.
-    if data_format == 'NCHW':
-        stride = [1, 1, factor, factor]
-        output_shape = [_shape(x, 0), outC, (_shape(x, 2) - 1) * factor + ch, (_shape(x, 3) - 1) * factor + cw]
-        num_groups = _shape(x, 1) // inC
-    else:
-        stride = [1, factor, factor, 1]
-        output_shape = [_shape(x, 0), (_shape(x, 1) - 1) * factor + ch, (_shape(x, 2) - 1) * factor + cw, outC]
-        num_groups = _shape(x, 3) // inC
-
-    # Transpose weights.
-    w = tf.reshape(w, [ch, cw, inC, num_groups, -1])
-    w = tf.transpose(w[::-1, ::-1], [0, 1, 4, 3, 2])
-    w = tf.reshape(w, [ch, cw, -1, num_groups * inC])
-
-    # Execute.
-    x = tf.nn.conv2d_transpose(x, w, output_shape=output_shape, strides=stride, padding='VALID', data_format=data_format)
-    pad0 = (k.w + factor - cw) // 2 + padding
-    pad1 = (k.w - factor - cw + 3) // 2 + padding
-    return _simple_upfirdn_2d(x, k, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def conv_downsample_2d(x, w, k=None, factor=2, gain=1, padding=0, data_format='NCHW', impl='cuda'):
-    r"""Fused `tf.nn.conv2d()` followed by `downsample_2d()`.
-
-    Padding is performed only once at the beginning, not between the operations.
-    The fused op is considerably more efficient than performing the same calculation
-    using standard TensorFlow ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
-        w:            Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
-                      Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
-        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
-                      The default is `[1] * factor`, which corresponds to average pooling.
-        factor:       Integer downsampling factor (default: 2).
-        gain:         Scaling factor for signal magnitude (default: 1.0).
-        padding:      Number of pixels to pad or crop the output on each side (default: 0).
-        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
-        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the shape `[N, C, H // factor, W // factor]` or
-        `[N, H // factor, W // factor, C]`, and same datatype as `x`.
-    """
-
-    assert isinstance(factor, int) and factor >= 1
-    assert isinstance(padding, int)
-
-    # Check weight shape.
-    w = tf.convert_to_tensor(w)
-    ch, cw, _inC, _outC = w.shape.as_list()
-    assert cw == ch
-
-    # Fast path for 1x1 convolution.
-    if cw == 1 and ch == 1:
-        x = downsample_2d(x, k, factor=factor, gain=gain, padding=padding, data_format=data_format, impl=impl)
-        x = tf.nn.conv2d(x, w, data_format=data_format, strides=[1,1,1,1], padding='VALID')
-        return x
-
-    # Setup filter kernel.
-    k = _FilterKernel(k if k is not None else [1] * factor, gain)
-    assert k.w == k.h
-
-    # Determine stride.
-    if data_format == 'NCHW':
-        s = [1, 1, factor, factor]
-    else:
-        s = [1, factor, factor, 1]
-
-    # Execute.
-    pad0 = (k.w - factor + cw) // 2 + padding * factor
-    pad1 = (k.w - factor + cw - 1) // 2 + padding * factor
-    x = _simple_upfirdn_2d(x, k, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl)
-    return tf.nn.conv2d(x, w, strides=s, padding='VALID', data_format=data_format)
-
-#----------------------------------------------------------------------------
-# Internal helpers.
-
-class _FilterKernel:
-    def __init__(self, k, gain=1):
-        k = np.asarray(k, dtype=np.float32)
-        k /= np.sum(k)
-
-        # Separable.
-        if k.ndim == 1 and k.size >= 8:
-            self.w = k.size
-            self.h = k.size
-            self.kx = k[np.newaxis, :]
-            self.ky = k[:, np.newaxis] * gain
-            self.kxy = None
-
-        # Non-separable.
-        else:
-            if k.ndim == 1:
-                k = np.outer(k, k)
-            assert k.ndim == 2
-            self.w = k.shape[1]
-            self.h = k.shape[0]
-            self.kx = None
-            self.ky = None
-            self.kxy = k * gain
-
-def _simple_upfirdn_2d(x, k, up=1, down=1, pad0=0, pad1=0, data_format='NCHW', impl='cuda'):
-    assert isinstance(k, _FilterKernel)
-    assert data_format in ['NCHW', 'NHWC']
-    assert x.shape.rank == 4
-    y = x
-    if data_format == 'NCHW':
-        y = tf.reshape(y, [-1, _shape(y, 2), _shape(y, 3), 1])
-    if k.kx is not None:
-        y = upfirdn_2d(y, k.kx, upx=up, downx=down, padx0=pad0, padx1=pad1, impl=impl)
-    if k.ky is not None:
-        y = upfirdn_2d(y, k.ky, upy=up, downy=down, pady0=pad0, pady1=pad1, impl=impl)
-    if k.kxy is not None:
-        y = upfirdn_2d(y, k.kxy, upx=up, upy=up, downx=down, downy=down, padx0=pad0, padx1=pad1, pady0=pad0, pady1=pad1, impl=impl)
-    if data_format == 'NCHW':
-        y = tf.reshape(y, [-1, _shape(x, 1), _shape(y, 1), _shape(y, 2)])
-    return y
-
-def _shape(tf_expr, dim_idx):
-    if tf_expr.shape.rank is not None:
-        dim = tf_expr.shape[dim_idx].value
-        if dim is not None:
-            return dim
-    return tf.shape(tf_expr)[dim_idx]
-
-#----------------------------------------------------------------------------
diff --git a/spaces/Dragonnext/charybdis/README.md b/spaces/Dragonnext/charybdis/README.md
deleted file mode 100644
index 0e8457ce896b12d6cf78050c3c15f298c8685cd8..0000000000000000000000000000000000000000
--- a/spaces/Dragonnext/charybdis/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Charybdis
-emoji: 😻
-colorFrom: purple
-colorTo: yellow
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Duskfallcrew/duskfall-tarot-card/app.py b/spaces/Duskfallcrew/duskfall-tarot-card/app.py
deleted file mode 100644
index 7c6d41e786a0cc0caa264e7db20bc8e804c64d32..0000000000000000000000000000000000000000
--- a/spaces/Duskfallcrew/duskfall-tarot-card/app.py
+++ /dev/null
@@ -1,147 +0,0 @@
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-
-model_id = 'Duskfallcrew/duskfall-tarot-card'
-prefix = 'dsktaro1'
-     
-scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
-
-pipe = StableDiffusionPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
-
-pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
-
-if torch.cuda.is_available():
-  pipe = pipe.to("cuda")
-  pipe_i2i = pipe_i2i.to("cuda")
-
-def error_str(error, title="Error"):
-    return f"""#### {title}
-            {error}"""  if error else ""
-
-def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
-
-  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-  prompt = f"{prefix} {prompt}" if auto_prefix else prompt
-
-  try:
-    if img is not None:
-      return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
-    else:
-      return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
-  except Exception as e:
-    return None, error_str(e)
-
-def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
-
-    result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator)
-    
-    return result.images[0]
-
-def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
-    ratio = min(height / img.height, width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
-    result = pipe_i2i(
-        prompt,
-        negative_prompt = neg_prompt,
-        init_image = img,
-        num_inference_steps = int(steps),
-        strength = strength,
-        guidance_scale = guidance,
-        width = width,
-        height = height,
-        generator = generator)
-        
-    return result.images[0]
-
-css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
-    gr.HTML(
-        f"""
-            <div class="main-div">
-              <div>
-                <h1>Duskfall Tarot Card</h1>
-              </div>
-              <p>
-               Demo for <a href="https://huggingface.co/Duskfallcrew/duskfall-tarot-card">Duskfall Tarot Card</a> Stable Diffusion model. All samples and info are here:
-
-https://civitai.com/user/duskfallcrew
-
-If you want to donate towards costs and don't want to subscribe:
-
-https://ko-fi.com/DUSKFALLcrew
-
-If you want to monthly support the EARTH & DUSK media projects and not just AI:
-
-https://www.patreon.com/earthndusk - "dsktaro1" is your √ word!<br>
-               {"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" if prefix else ""}
-              </p>
-              Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/Duskfallcrew/duskfall-tarot-card/settings'>Settings</a></b>"} after duplicating the space<br><br>
-              <a style="display:inline-block" href="https://huggingface.co/spaces/Duskfallcrew/duskfall-tarot-card?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-            </div>
-        """
-    )
-    with gr.Row():
-        
-        with gr.Column(scale=55):
-          with gr.Group():
-              with gr.Row():
-                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
-                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
-              image_out = gr.Image(height=512)
-          error_output = gr.Markdown()
-
-        with gr.Column(scale=45):
-          with gr.Tab("Options"):
-            with gr.Group():
-              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (dsktaro1)", value=prefix, visible=prefix)
-
-              with gr.Row():
-                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
-                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
-              with gr.Row():
-                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
-                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
-              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
-          with gr.Tab("Image to image"):
-              with gr.Group():
-                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
-                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
-    auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
-
-    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
-    outputs = [image_out, error_output]
-    prompt.submit(inference, inputs=inputs, outputs=outputs)
-    generate.click(inference, inputs=inputs, outputs=outputs)
-
-    gr.HTML("""
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
-    </div>
-    """)
-
-demo.queue(concurrency_count=1)
-demo.launch()
diff --git a/spaces/ECCV2022/bytetrack/deploy/ncnn/cpp/README.md b/spaces/ECCV2022/bytetrack/deploy/ncnn/cpp/README.md
deleted file mode 100644
index 38137039f2056b43a77206092ac9c4cd282a2853..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/deploy/ncnn/cpp/README.md
+++ /dev/null
@@ -1,103 +0,0 @@
-# ByteTrack-CPP-ncnn
-
-## Installation
-
-Clone [ncnn](https://github.com/Tencent/ncnn) first, then please following [build tutorial of ncnn](https://github.com/Tencent/ncnn/wiki/how-to-build) to build on your own device.
-
-Install eigen-3.3.9 [[google]](https://drive.google.com/file/d/1rqO74CYCNrmRAg8Rra0JP3yZtJ-rfket/view?usp=sharing), [[baidu(code:ueq4)]](https://pan.baidu.com/s/15kEfCxpy-T7tz60msxxExg).
-
-```shell
-unzip eigen-3.3.9.zip
-cd eigen-3.3.9
-mkdir build
-cd build
-cmake ..
-sudo make install
-```
-
-## Generate onnx file
-Use provided tools to generate onnx file.
-For example, if you want to generate onnx file of bytetrack_s_mot17.pth, please run the following command:
-```shell
-cd <ByteTrack_HOME>
-python3 tools/export_onnx.py -f exps/example/mot/yolox_s_mix_det.py -c pretrained/bytetrack_s_mot17.pth.tar
-```
-Then, a bytetrack_s.onnx file is generated under <ByteTrack_HOME>.
-
-## Generate ncnn param and bin file
-Put bytetrack_s.onnx under ncnn/build/tools/onnx and then run: 
-
-```shell
-cd ncnn/build/tools/onnx
-./onnx2ncnn bytetrack_s.onnx bytetrack_s.param bytetrack_s.bin
-```
-
-Since Focus module is not supported in ncnn. Warnings like:
-```shell
-Unsupported slice step ! 
-```
-will be printed. However, don't  worry!  C++ version of Focus layer is already implemented in src/bytetrack.cpp.
-  
-## Modify param file
-Open **bytetrack_s.param**, and modify it.
-Before (just an example):
-```
-235 268
-Input            images                   0 1 images
-Split            splitncnn_input0         1 4 images images_splitncnn_0 images_splitncnn_1 images_splitncnn_2 images_splitncnn_3
-Crop             Slice_4                  1 1 images_splitncnn_3 467 -23309=1,0 -23310=1,2147483647 -23311=1,1
-Crop             Slice_9                  1 1 467 472 -23309=1,0 -23310=1,2147483647 -23311=1,2
-Crop             Slice_14                 1 1 images_splitncnn_2 477 -23309=1,0 -23310=1,2147483647 -23311=1,1
-Crop             Slice_19                 1 1 477 482 -23309=1,1 -23310=1,2147483647 -23311=1,2
-Crop             Slice_24                 1 1 images_splitncnn_1 487 -23309=1,1 -23310=1,2147483647 -23311=1,1
-Crop             Slice_29                 1 1 487 492 -23309=1,0 -23310=1,2147483647 -23311=1,2
-Crop             Slice_34                 1 1 images_splitncnn_0 497 -23309=1,1 -23310=1,2147483647 -23311=1,1
-Crop             Slice_39                 1 1 497 502 -23309=1,1 -23310=1,2147483647 -23311=1,2
-Concat           Concat_40                4 1 472 492 482 502 503 0=0
-...
-```
-* Change first number for 235 to 235 - 9 = 226(since we will remove 10 layers and add 1 layers, total layers number should minus 9). 
-* Then remove 10 lines of code from Split to Concat, but remember the last but 2nd number: 503.
-* Add YoloV5Focus layer After Input (using previous number 503):
-```
-YoloV5Focus      focus                    1 1 images 503
-```
-After(just an exmaple):
-```
-226 328
-Input            images                   0 1 images
-YoloV5Focus      focus                    1 1 images 503
-...
-```
-
-## Use ncnn_optimize to generate new param and bin
-```shell
-# suppose you are still under ncnn/build/tools/onnx dir.
-../ncnnoptimize bytetrack_s.param bytetrack_s.bin bytetrack_s_op.param bytetrack_s_op.bin 65536
-```
-
-## Copy files and build ByteTrack
-Copy or move 'src', 'include' folders and 'CMakeLists.txt' file into ncnn/examples. Copy bytetrack_s_op.param, bytetrack_s_op.bin and <ByteTrack_HOME>/videos/palace.mp4 into ncnn/build/examples. Then, build ByteTrack:
-
-```shell
-cd ncnn/build/examples
-cmake ..
-make
-```
-
-## Run the demo
-You can run the ncnn demo with **5 FPS** (96-core Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz):
-```shell
-./bytetrack palace.mp4
-```
-
-You can modify 'num_threads' to optimize the running speed in [bytetrack.cpp](https://github.com/ifzhang/ByteTrack/blob/2e9a67895da6b47b948015f6861bba0bacd4e72f/deploy/ncnn/cpp/src/bytetrack.cpp#L309) according to the number of your CPU cores:
-
-```
-yolox.opt.num_threads = 20;
-```
-
-
-## Acknowledgement
-
-* [ncnn](https://github.com/Tencent/ncnn)
diff --git a/spaces/Eddycrack864/Applio-Inference/utils/clonerepo_experimental.py b/spaces/Eddycrack864/Applio-Inference/utils/clonerepo_experimental.py
deleted file mode 100644
index b0ae02648c1307562cf48033908edcf2996db5e2..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/utils/clonerepo_experimental.py
+++ /dev/null
@@ -1,253 +0,0 @@
-import os
-import subprocess
-import shutil
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from tqdm.notebook import tqdm
-from pathlib import Path
-import requests
-
-def run_script():
-    def run_cmd(cmd):
-        process = subprocess.run(cmd, shell=True, check=True, text=True)
-        return process.stdout
-
-    # Change the current directory to /content/
-    os.chdir('/content/')
-    print("Changing dir to /content/")
-
-    # Your function to edit the file
-    def edit_file(file_path):
-        temp_file_path = "/tmp/temp_file.py"
-        changes_made = False
-        with open(file_path, "r") as file, open(temp_file_path, "w") as temp_file:
-            previous_line = ""
-            second_previous_line = ""
-            for line in file:
-                new_line = line.replace("value=160", "value=128")
-                if new_line != line:
-                    print("Replaced 'value=160' with 'value=128'")
-                    changes_made = True
-                line = new_line
-
-                new_line = line.replace("crepe hop length: 160", "crepe hop length: 128")
-                if new_line != line:
-                    print("Replaced 'crepe hop length: 160' with 'crepe hop length: 128'")
-                    changes_made = True
-                line = new_line
-
-                new_line = line.replace("value=0.88", "value=0.75")
-                if new_line != line:
-                    print("Replaced 'value=0.88' with 'value=0.75'")
-                    changes_made = True
-                line = new_line
-
-                if "label=i18n(\"输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络\")" in previous_line and "value=1," in line:
-                    new_line = line.replace("value=1,", "value=0.25,")
-                    if new_line != line:
-                        print("Replaced 'value=1,' with 'value=0.25,' based on the condition")
-                        changes_made = True
-                    line = new_line
-
-                if "label=i18n(\"总训练轮数total_epoch\")" in previous_line and "value=20," in line:
-                    new_line = line.replace("value=20,", "value=500,")
-                    if new_line != line:
-                        print("Replaced 'value=20,' with 'value=500,' based on the condition for DEFAULT EPOCH")
-                        changes_made = True
-                    line = new_line
-
-                if 'choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny"], # Fork Feature. Add Crepe-Tiny' in previous_line:
-                    if 'value="pm",' in line:
-                        new_line = line.replace('value="pm",', 'value="mangio-crepe",')
-                        if new_line != line:
-                            print("Replaced 'value=\"pm\",' with 'value=\"mangio-crepe\",' based on the condition")
-                            changes_made = True
-                        line = new_line
-
-                new_line = line.replace('label=i18n("输入训练文件夹路径"), value="E:\\\\语音音频+标注\\\\米津玄师\\\\src"', 'label=i18n("输入训练文件夹路径"), value="/content/dataset/"')
-                if new_line != line:
-                    print("Replaced 'label=i18n(\"输入训练文件夹路径\"), value=\"E:\\\\语音音频+标注\\\\米津玄师\\\\src\"' with 'label=i18n(\"输入训练文件夹路径\"), value=\"/content/dataset/\"'")
-                    changes_made = True
-                line = new_line
-
-                if 'label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"),' in second_previous_line:
-                    if 'value=i18n("否"),' in line:
-                        new_line = line.replace('value=i18n("否"),', 'value=i18n("是"),')
-                        if new_line != line:
-                            print("Replaced 'value=i18n(\"否\"),' with 'value=i18n(\"是\"),' based on the condition for SAVE ONLY LATEST")
-                            changes_made = True
-                        line = new_line
-
-                if 'label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),' in second_previous_line:
-                    if 'value=i18n("否"),' in line:
-                        new_line = line.replace('value=i18n("否"),', 'value=i18n("是"),')
-                        if new_line != line:
-                            print("Replaced 'value=i18n(\"否\"),' with 'value=i18n(\"是\"),' based on the condition for SAVE SMALL WEIGHTS")
-                            changes_made = True
-                        line = new_line
-
-                temp_file.write(line)
-                second_previous_line = previous_line
-                previous_line = line
-
-        # After finished, we replace the original file with the temp one
-        import shutil
-        shutil.move(temp_file_path, file_path)
-
-        if changes_made:
-            print("Changes made and file saved successfully.")
-        else:
-            print("No changes were needed.")
-
-    # Define the repo path
-    repo_path = '/content/Applio-RVC-Fork'
-
-    def copy_all_files_in_directory(src_dir, dest_dir):
-        # Iterate over all files in source directory
-        for item in Path(src_dir).glob('*'):
-            if item.is_file():
-                # Copy each file to destination directory
-                shutil.copy(item, dest_dir)
-            else:
-                # If it's a directory, make a new directory in the destination and copy the files recursively
-                new_dest = Path(dest_dir) / item.name
-                new_dest.mkdir(exist_ok=True)
-                copy_all_files_in_directory(str(item), str(new_dest))
-
-    def clone_and_copy_repo(repo_path):
-        # New repository link
-        new_repo_link = "https://github.com/IAHispano/Applio-RVC-Fork/"
-        # Temporary path to clone the repository
-        temp_repo_path = "/content/temp_Applio-RVC-Fork"
-        # New folder name
-        new_folder_name = "Applio-RVC-Fork"
-
-        # Clone the latest code from the new repository to a temporary location
-        run_cmd(f"git clone {new_repo_link} {temp_repo_path}")
-        os.chdir(temp_repo_path)
-
-        run_cmd(f"git checkout 3fa4dad3d8961e5ca2522e9e12c0b4ddb71ad402")
-        run_cmd(f"git checkout f9e606c279cb49420597519b0a83b92be81e42e4")
-        run_cmd(f"git checkout 9e305588844c5442d58add1061b29beeca89d679")
-        run_cmd(f"git checkout bf92dc1eb54b4f28d6396a4d1820a25896cc9af8")
-        run_cmd(f"git checkout c3810e197d3cb98039973b2f723edf967ecd9e61")
-        run_cmd(f"git checkout a33159efd134c2413b0afe26a76b7dc87926d2de")
-        run_cmd(f"git checkout 24e251fb62c662e39ac5cf9253cc65deb9be94ec")
-        run_cmd(f"git checkout ad5667d3017e93232dba85969cddac1322ba2902")
-        run_cmd(f"git checkout ce9715392cf52dd5a0e18e00d1b5e408f08dbf27")
-        run_cmd(f"git checkout 7c7da3f2ac68f3bd8f3ad5ca5c700f18ab9f90eb")
-        run_cmd(f"git checkout 4ac395eab101955e8960b50d772c26f592161764")
-        run_cmd(f"git checkout b15b358702294c7375761584e5276c811ffab5e8")
-        run_cmd(f"git checkout 1501793dc490982db9aca84a50647764caa66e51")
-        run_cmd(f"git checkout 21f7faf57219c75e6ba837062350391a803e9ae2")
-        run_cmd(f"git checkout b5eb689fbc409b49f065a431817f822f554cebe7")
-        run_cmd(f"git checkout 7e02fae1ebf24cb151bf6cbe787d06734aa65862")
-        run_cmd(f"git checkout 6aea5ea18ed0b9a1e03fa5d268d6bc3c616672a9")
-        run_cmd(f"git checkout f0f9b25717e59116473fb42bd7f9252cfc32b398")
-        run_cmd(f"git checkout b394de424088a81fc081224bc27338a8651ad3b2")
-        run_cmd(f"git checkout f1999406a88b80c965d2082340f5ea2bfa9ab67a")
-        run_cmd(f"git checkout d98a0fa8dc715308dfc73eac5c553b69c6ee072b")
-        run_cmd(f"git checkout d73267a415fb0eba98477afa43ef71ffd82a7157")
-        run_cmd(f"git checkout 1a03d01356ae79179e1fb8d8915dc9cc79925742")
-        run_cmd(f"git checkout 81497bb3115e92c754300c9b3992df428886a3e9")
-        run_cmd(f"git checkout c5af1f8edcf79cb70f065c0110e279e78e48caf9")
-        run_cmd(f"git checkout cdb3c90109387fa4dfa92f53c3864c71170ffc77")
-
-        # Edit the file here, before copying
-        #edit_file(f"{temp_repo_path}/infer-web.py")
-
-        # Copy all files from the cloned repository to the existing path
-        copy_all_files_in_directory(temp_repo_path, repo_path)
-        print(f"Copying all {new_folder_name} files from GitHub.")
-
-        # Change working directory back to /content/
-        os.chdir('/content/')
-        print("Changed path back to /content/")
-        
-        # Remove the temporary cloned repository
-        shutil.rmtree(temp_repo_path)
-
-    # Call the function
-    clone_and_copy_repo(repo_path)
-
-    # Download the credentials file for RVC archive sheet
-    os.makedirs('/content/Applio-RVC-Fork/stats/', exist_ok=True)
-    run_cmd("wget -q https://cdn.discordapp.com/attachments/945486970883285045/1114717554481569802/peppy-generator-388800-07722f17a188.json -O /content/Applio-RVC-Fork/stats/peppy-generator-388800-07722f17a188.json")
-
-    # Forcefully delete any existing torchcrepe dependencies downloaded from an earlier run just in case
-    shutil.rmtree('/content/Applio-RVC-Fork/torchcrepe', ignore_errors=True)
-    shutil.rmtree('/content/torchcrepe', ignore_errors=True)
-
-    # Download the torchcrepe folder from the maxrmorrison/torchcrepe repository
-    run_cmd("git clone https://github.com/maxrmorrison/torchcrepe.git")
-    shutil.move('/content/torchcrepe/torchcrepe', '/content/Applio-RVC-Fork/')
-    shutil.rmtree('/content/torchcrepe', ignore_errors=True)  # Delete the torchcrepe repository folder
-
-    # Change the current directory to /content/Applio-RVC-Fork
-    os.chdir('/content/Applio-RVC-Fork')
-    os.makedirs('pretrained', exist_ok=True)
-    os.makedirs('uvr5_weights', exist_ok=True)
-
-def download_file(url, filepath):
-    response = requests.get(url, stream=True)
-    response.raise_for_status()
-
-    with open(filepath, "wb") as file:
-        for chunk in response.iter_content(chunk_size=8192):
-            if chunk:
-                file.write(chunk)
-
-def download_pretrained_models():
-    pretrained_models = {
-        "pretrained": [
-            "D40k.pth",
-            "G40k.pth",
-            "f0D40k.pth",
-            "f0G40k.pth"
-        ],
-        "pretrained_v2": [
-            "D40k.pth",
-            "G40k.pth",
-            "f0D40k.pth",
-            "f0G40k.pth",
-            "f0G48k.pth",
-            "f0D48k.pth"
-        ],
-        "uvr5_weights": [
-            "HP2-人声vocals+非人声instrumentals.pth",
-            "HP5-主旋律人声vocals+其他instrumentals.pth",
-            "VR-DeEchoNormal.pth",
-            "VR-DeEchoDeReverb.pth",
-            "VR-DeEchoAggressive.pth",
-            "HP5_only_main_vocal.pth",
-            "HP3_all_vocals.pth",
-            "HP2_all_vocals.pth"
-        ]
-    }
-    part2 = "I"
-    base_url = "https://huggingface.co/lj1995/VoiceConversionWebU" + part2 + "/resolve/main/"
-    base_path = "/content/Applio-RVC-Fork/"
-    base_pathm = base_path
-
-    # Calculate total number of files to download
-    total_files = sum(len(files) for files in pretrained_models.values()) + 1  # +1 for hubert_base.pt
-
-    with tqdm(total=total_files, desc="Downloading files") as pbar:
-        for folder, models in pretrained_models.items():
-            folder_path = os.path.join(base_path, folder)
-            os.makedirs(folder_path, exist_ok=True)
-            for model in models:
-                url = base_url + folder + "/" + model
-                filepath = os.path.join(folder_path, model)
-                download_file(url, filepath)
-                pbar.update()
-
-        # Download hubert_base.pt to the base path
-        hubert_url = base_url + "hubert_base.pt"
-        hubert_filepath = os.path.join(base_pathm, "hubert_base.pt")
-        download_file(hubert_url, hubert_filepath)
-        pbar.update()
-def clone_repository(run_download):
-    with ThreadPoolExecutor(max_workers=2) as executor:
-        executor.submit(run_script)
-        if run_download:
-            executor.submit(download_pretrained_models)
diff --git a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/realesrgan/train.py b/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/realesrgan/train.py
deleted file mode 100644
index 8a9cec9ed80d9f362984779548dcec921a636a04..0000000000000000000000000000000000000000
--- a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/realesrgan/train.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# flake8: noqa
-import os.path as osp
-from basicsr.train import train_pipeline
-
-import realesrgan.archs
-import realesrgan.data
-import realesrgan.models
-
-if __name__ == '__main__':
-    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
-    train_pipeline(root_path)
diff --git a/spaces/EronSamez/RVC_HFmeu/infer/modules/uvr5/mdxnet.py b/spaces/EronSamez/RVC_HFmeu/infer/modules/uvr5/mdxnet.py
deleted file mode 100644
index 86a066893ad99cfed77788027a9deb8ed486a7f2..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/infer/modules/uvr5/mdxnet.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import os
-import logging
-
-logger = logging.getLogger(__name__)
-
-import librosa
-import numpy as np
-import soundfile as sf
-import torch
-from tqdm import tqdm
-
-cpu = torch.device("cpu")
-
-
-class ConvTDFNetTrim:
-    def __init__(
-        self, device, model_name, target_name, L, dim_f, dim_t, n_fft, hop=1024
-    ):
-        super(ConvTDFNetTrim, self).__init__()
-
-        self.dim_f = dim_f
-        self.dim_t = 2**dim_t
-        self.n_fft = n_fft
-        self.hop = hop
-        self.n_bins = self.n_fft // 2 + 1
-        self.chunk_size = hop * (self.dim_t - 1)
-        self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(
-            device
-        )
-        self.target_name = target_name
-        self.blender = "blender" in model_name
-
-        self.dim_c = 4
-        out_c = self.dim_c * 4 if target_name == "*" else self.dim_c
-        self.freq_pad = torch.zeros(
-            [1, out_c, self.n_bins - self.dim_f, self.dim_t]
-        ).to(device)
-
-        self.n = L // 2
-
-    def stft(self, x):
-        x = x.reshape([-1, self.chunk_size])
-        x = torch.stft(
-            x,
-            n_fft=self.n_fft,
-            hop_length=self.hop,
-            window=self.window,
-            center=True,
-            return_complex=True,
-        )
-        x = torch.view_as_real(x)
-        x = x.permute([0, 3, 1, 2])
-        x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape(
-            [-1, self.dim_c, self.n_bins, self.dim_t]
-        )
-        return x[:, :, : self.dim_f]
-
-    def istft(self, x, freq_pad=None):
-        freq_pad = (
-            self.freq_pad.repeat([x.shape[0], 1, 1, 1])
-            if freq_pad is None
-            else freq_pad
-        )
-        x = torch.cat([x, freq_pad], -2)
-        c = 4 * 2 if self.target_name == "*" else 2
-        x = x.reshape([-1, c, 2, self.n_bins, self.dim_t]).reshape(
-            [-1, 2, self.n_bins, self.dim_t]
-        )
-        x = x.permute([0, 2, 3, 1])
-        x = x.contiguous()
-        x = torch.view_as_complex(x)
-        x = torch.istft(
-            x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True
-        )
-        return x.reshape([-1, c, self.chunk_size])
-
-
-def get_models(device, dim_f, dim_t, n_fft):
-    return ConvTDFNetTrim(
-        device=device,
-        model_name="Conv-TDF",
-        target_name="vocals",
-        L=11,
-        dim_f=dim_f,
-        dim_t=dim_t,
-        n_fft=n_fft,
-    )
-
-
-class Predictor:
-    def __init__(self, args):
-        import onnxruntime as ort
-
-        logger.info(ort.get_available_providers())
-        self.args = args
-        self.model_ = get_models(
-            device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft
-        )
-        self.model = ort.InferenceSession(
-            os.path.join(args.onnx, self.model_.target_name + ".onnx"),
-            providers=[
-                "CUDAExecutionProvider",
-                "DmlExecutionProvider",
-                "CPUExecutionProvider",
-            ],
-        )
-        logger.info("ONNX load done")
-
-    def demix(self, mix):
-        samples = mix.shape[-1]
-        margin = self.args.margin
-        chunk_size = self.args.chunks * 44100
-        assert not margin == 0, "margin cannot be zero!"
-        if margin > chunk_size:
-            margin = chunk_size
-
-        segmented_mix = {}
-
-        if self.args.chunks == 0 or samples < chunk_size:
-            chunk_size = samples
-
-        counter = -1
-        for skip in range(0, samples, chunk_size):
-            counter += 1
-
-            s_margin = 0 if counter == 0 else margin
-            end = min(skip + chunk_size + margin, samples)
-
-            start = skip - s_margin
-
-            segmented_mix[skip] = mix[:, start:end].copy()
-            if end == samples:
-                break
-
-        sources = self.demix_base(segmented_mix, margin_size=margin)
-        """
-        mix:(2,big_sample)
-        segmented_mix:offset->(2,small_sample)
-        sources:(1,2,big_sample)
-        """
-        return sources
-
-    def demix_base(self, mixes, margin_size):
-        chunked_sources = []
-        progress_bar = tqdm(total=len(mixes))
-        progress_bar.set_description("Processing")
-        for mix in mixes:
-            cmix = mixes[mix]
-            sources = []
-            n_sample = cmix.shape[1]
-            model = self.model_
-            trim = model.n_fft // 2
-            gen_size = model.chunk_size - 2 * trim
-            pad = gen_size - n_sample % gen_size
-            mix_p = np.concatenate(
-                (np.zeros((2, trim)), cmix, np.zeros((2, pad)), np.zeros((2, trim))), 1
-            )
-            mix_waves = []
-            i = 0
-            while i < n_sample + pad:
-                waves = np.array(mix_p[:, i : i + model.chunk_size])
-                mix_waves.append(waves)
-                i += gen_size
-            mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(cpu)
-            with torch.no_grad():
-                _ort = self.model
-                spek = model.stft(mix_waves)
-                if self.args.denoise:
-                    spec_pred = (
-                        -_ort.run(None, {"input": -spek.cpu().numpy()})[0] * 0.5
-                        + _ort.run(None, {"input": spek.cpu().numpy()})[0] * 0.5
-                    )
-                    tar_waves = model.istft(torch.tensor(spec_pred))
-                else:
-                    tar_waves = model.istft(
-                        torch.tensor(_ort.run(None, {"input": spek.cpu().numpy()})[0])
-                    )
-                tar_signal = (
-                    tar_waves[:, :, trim:-trim]
-                    .transpose(0, 1)
-                    .reshape(2, -1)
-                    .numpy()[:, :-pad]
-                )
-
-                start = 0 if mix == 0 else margin_size
-                end = None if mix == list(mixes.keys())[::-1][0] else -margin_size
-                if margin_size == 0:
-                    end = None
-                sources.append(tar_signal[:, start:end])
-
-                progress_bar.update(1)
-
-            chunked_sources.append(sources)
-        _sources = np.concatenate(chunked_sources, axis=-1)
-        # del self.model
-        progress_bar.close()
-        return _sources
-
-    def prediction(self, m, vocal_root, others_root, format):
-        os.makedirs(vocal_root, exist_ok=True)
-        os.makedirs(others_root, exist_ok=True)
-        basename = os.path.basename(m)
-        mix, rate = librosa.load(m, mono=False, sr=44100)
-        if mix.ndim == 1:
-            mix = np.asfortranarray([mix, mix])
-        mix = mix.T
-        sources = self.demix(mix.T)
-        opt = sources[0].T
-        if format in ["wav", "flac"]:
-            sf.write(
-                "%s/%s_main_vocal.%s" % (vocal_root, basename, format), mix - opt, rate
-            )
-            sf.write("%s/%s_others.%s" % (others_root, basename, format), opt, rate)
-        else:
-            path_vocal = "%s/%s_main_vocal.wav" % (vocal_root, basename)
-            path_other = "%s/%s_others.wav" % (others_root, basename)
-            sf.write(path_vocal, mix - opt, rate)
-            sf.write(path_other, opt, rate)
-            if os.path.exists(path_vocal):
-                os.system(
-                    "ffmpeg -i %s -vn %s -q:a 2 -y"
-                    % (path_vocal, path_vocal[:-4] + ".%s" % format)
-                )
-            if os.path.exists(path_other):
-                os.system(
-                    "ffmpeg -i %s -vn %s -q:a 2 -y"
-                    % (path_other, path_other[:-4] + ".%s" % format)
-                )
-
-
-class MDXNetDereverb:
-    def __init__(self, chunks, device):
-        self.onnx = "assets/uvr5_weights/onnx_dereverb_By_FoxJoy"
-        self.shifts = 10  # 'Predict with randomised equivariant stabilisation'
-        self.mixing = "min_mag"  # ['default','min_mag','max_mag']
-        self.chunks = chunks
-        self.margin = 44100
-        self.dim_t = 9
-        self.dim_f = 3072
-        self.n_fft = 6144
-        self.denoise = True
-        self.pred = Predictor(self)
-        self.device = device
-
-    def path_audio(self, input, vocal_root, others_root, format):
-        self.pred.prediction(input, vocal_root, others_root, format)
diff --git a/spaces/FYP-23-S1-21/Refineverse_Plugin/createDatabase.py b/spaces/FYP-23-S1-21/Refineverse_Plugin/createDatabase.py
deleted file mode 100644
index 72ce93baedaa3597d6b7b6bad2301da3684bb45a..0000000000000000000000000000000000000000
--- a/spaces/FYP-23-S1-21/Refineverse_Plugin/createDatabase.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# This file is used to create an SQLite database.
-# If adding more tables, you should delete the exising databases, or alter the code here to drop the pre-exising tables first.
-import sqlite3
-
-# Establishes a connection to the specified DB. If the DB does not exist, it creates a new one.
-connection = sqlite3.connect("Refineverse.db")
-cursor = connection.cursor() # A cursor object that is used to handle data.
-
-# Creating the Breakdown table
-cursor.execute('''
-    CREATE TABLE IF NOT EXISTS Breakdown (
-        id INTEGER PRIMARY KEY AUTOINCREMENT,
-        user_story TEXT,
-        assignedLabel TEXT
-    )
-''')
-
-# Creating the TextSummarization table
-cursor.execute('''
-    CREATE TABLE IF NOT EXISTS TextSummarization (
-        id INTEGER PRIMARY KEY AUTOINCREMENT,
-        Entered_story TEXT,
-        summary TEXT
-    )
-''')
-
-# Creating the Translation table
-cursor.execute('''
-    CREATE TABLE IF NOT EXISTS Translation (
-        id INTEGER PRIMARY KEY AUTOINCREMENT,
-        input_text TEXT,
-        translated_text TEXT
-    )
-''')
-
-# Creating the TextGeneration table
-cursor.execute('''
-    CREATE TABLE IF NOT EXISTS TextGeneration (
-        id INTEGER PRIMARY KEY AUTOINCREMENT,
-        userStory TEXT,
-        generatedStory TEXT
-    )
-''')
-
-connection.close() # Closes the connection
\ No newline at end of file
diff --git a/spaces/Felix123456/bingo/src/pages/api/image.ts b/spaces/Felix123456/bingo/src/pages/api/image.ts
deleted file mode 100644
index 4b894bea86050c0f3888cc56f60c0cb7f8b57cfc..0000000000000000000000000000000000000000
--- a/spaces/Felix123456/bingo/src/pages/api/image.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { debug } from '@/lib/isomorphic'
-import { createHeaders } from '@/lib/utils'
-import { createImage } from '@/lib/bots/bing/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const { prompt, id } = req.query
-  if (!prompt) {
-    return res.json({
-      result: {
-        value: 'Image',
-        message: 'No Prompt'
-      }
-    })
-  }
-  try {
-    const headers = createHeaders(req.cookies, {
-      IMAGE_BING_COOKIE: process.env.IMAGE_BING_COOKIE
-    })
-
-    debug('headers', headers)
-    const response = await createImage(String(prompt), String(id), {
-      ...headers,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-    })
-    res.writeHead(200, {
-      'Content-Type': 'text/plain; charset=UTF-8',
-    })
-    return res.end(response)
-  } catch (e) {
-    return res.json({
-      result: {
-        value: 'Error',
-        message: `${e}`
-      }
-    })
-  }
-}
diff --git a/spaces/FelixLuoX/codeformer/CodeFormer/facelib/detection/align_trans.py b/spaces/FelixLuoX/codeformer/CodeFormer/facelib/detection/align_trans.py
deleted file mode 100644
index 07f1eb365462c2ec5bbac6d1854c786b6fd6be90..0000000000000000000000000000000000000000
--- a/spaces/FelixLuoX/codeformer/CodeFormer/facelib/detection/align_trans.py
+++ /dev/null
@@ -1,219 +0,0 @@
-import cv2
-import numpy as np
-
-from .matlab_cp2tform import get_similarity_transform_for_cv2
-
-# reference facial points, a list of coordinates (x,y)
-REFERENCE_FACIAL_POINTS = [[30.29459953, 51.69630051], [65.53179932, 51.50139999], [48.02519989, 71.73660278],
-                           [33.54930115, 92.3655014], [62.72990036, 92.20410156]]
-
-DEFAULT_CROP_SIZE = (96, 112)
-
-
-class FaceWarpException(Exception):
-
-    def __str__(self):
-        return 'In File {}:{}'.format(__file__, super.__str__(self))
-
-
-def get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False):
-    """
-    Function:
-    ----------
-        get reference 5 key points according to crop settings:
-        0. Set default crop_size:
-            if default_square:
-                crop_size = (112, 112)
-            else:
-                crop_size = (96, 112)
-        1. Pad the crop_size by inner_padding_factor in each side;
-        2. Resize crop_size into (output_size - outer_padding*2),
-            pad into output_size with outer_padding;
-        3. Output reference_5point;
-    Parameters:
-    ----------
-        @output_size: (w, h) or None
-            size of aligned face image
-        @inner_padding_factor: (w_factor, h_factor)
-            padding factor for inner (w, h)
-        @outer_padding: (w_pad, h_pad)
-            each row is a pair of coordinates (x, y)
-        @default_square: True or False
-            if True:
-                default crop_size = (112, 112)
-            else:
-                default crop_size = (96, 112);
-        !!! make sure, if output_size is not None:
-                (output_size - outer_padding)
-                = some_scale * (default crop_size * (1.0 +
-                inner_padding_factor))
-    Returns:
-    ----------
-        @reference_5point: 5x2 np.array
-            each row is a pair of transformed coordinates (x, y)
-    """
-
-    tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
-    tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
-
-    # 0) make the inner region a square
-    if default_square:
-        size_diff = max(tmp_crop_size) - tmp_crop_size
-        tmp_5pts += size_diff / 2
-        tmp_crop_size += size_diff
-
-    if (output_size and output_size[0] == tmp_crop_size[0] and output_size[1] == tmp_crop_size[1]):
-
-        return tmp_5pts
-
-    if (inner_padding_factor == 0 and outer_padding == (0, 0)):
-        if output_size is None:
-            return tmp_5pts
-        else:
-            raise FaceWarpException('No paddings to do, output_size must be None or {}'.format(tmp_crop_size))
-
-    # check output size
-    if not (0 <= inner_padding_factor <= 1.0):
-        raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
-
-    if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) and output_size is None):
-        output_size = tmp_crop_size * \
-            (1 + inner_padding_factor * 2).astype(np.int32)
-        output_size += np.array(outer_padding)
-    if not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1]):
-        raise FaceWarpException('Not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1])')
-
-    # 1) pad the inner region according inner_padding_factor
-    if inner_padding_factor > 0:
-        size_diff = tmp_crop_size * inner_padding_factor * 2
-        tmp_5pts += size_diff / 2
-        tmp_crop_size += np.round(size_diff).astype(np.int32)
-
-    # 2) resize the padded inner region
-    size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
-
-    if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:
-        raise FaceWarpException('Must have (output_size - outer_padding)'
-                                '= some_scale * (crop_size * (1.0 + inner_padding_factor)')
-
-    scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
-    tmp_5pts = tmp_5pts * scale_factor
-    #    size_diff = tmp_crop_size * (scale_factor - min(scale_factor))
-    #    tmp_5pts = tmp_5pts + size_diff / 2
-    tmp_crop_size = size_bf_outer_pad
-
-    # 3) add outer_padding to make output_size
-    reference_5point = tmp_5pts + np.array(outer_padding)
-    tmp_crop_size = output_size
-
-    return reference_5point
-
-
-def get_affine_transform_matrix(src_pts, dst_pts):
-    """
-    Function:
-    ----------
-        get affine transform matrix 'tfm' from src_pts to dst_pts
-    Parameters:
-    ----------
-        @src_pts: Kx2 np.array
-            source points matrix, each row is a pair of coordinates (x, y)
-        @dst_pts: Kx2 np.array
-            destination points matrix, each row is a pair of coordinates (x, y)
-    Returns:
-    ----------
-        @tfm: 2x3 np.array
-            transform matrix from src_pts to dst_pts
-    """
-
-    tfm = np.float32([[1, 0, 0], [0, 1, 0]])
-    n_pts = src_pts.shape[0]
-    ones = np.ones((n_pts, 1), src_pts.dtype)
-    src_pts_ = np.hstack([src_pts, ones])
-    dst_pts_ = np.hstack([dst_pts, ones])
-
-    A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
-
-    if rank == 3:
-        tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]], [A[0, 1], A[1, 1], A[2, 1]]])
-    elif rank == 2:
-        tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]])
-
-    return tfm
-
-
-def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type='smilarity'):
-    """
-    Function:
-    ----------
-        apply affine transform 'trans' to uv
-    Parameters:
-    ----------
-        @src_img: 3x3 np.array
-            input image
-        @facial_pts: could be
-            1)a list of K coordinates (x,y)
-        or
-            2) Kx2 or 2xK np.array
-            each row or col is a pair of coordinates (x, y)
-        @reference_pts: could be
-            1) a list of K coordinates (x,y)
-        or
-            2) Kx2 or 2xK np.array
-            each row or col is a pair of coordinates (x, y)
-        or
-            3) None
-            if None, use default reference facial points
-        @crop_size: (w, h)
-            output face image size
-        @align_type: transform type, could be one of
-            1) 'similarity': use similarity transform
-            2) 'cv2_affine': use the first 3 points to do affine transform,
-                    by calling cv2.getAffineTransform()
-            3) 'affine': use all points to do affine transform
-    Returns:
-    ----------
-        @face_img: output face image with size (w, h) = @crop_size
-    """
-
-    if reference_pts is None:
-        if crop_size[0] == 96 and crop_size[1] == 112:
-            reference_pts = REFERENCE_FACIAL_POINTS
-        else:
-            default_square = False
-            inner_padding_factor = 0
-            outer_padding = (0, 0)
-            output_size = crop_size
-
-            reference_pts = get_reference_facial_points(output_size, inner_padding_factor, outer_padding,
-                                                        default_square)
-
-    ref_pts = np.float32(reference_pts)
-    ref_pts_shp = ref_pts.shape
-    if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
-        raise FaceWarpException('reference_pts.shape must be (K,2) or (2,K) and K>2')
-
-    if ref_pts_shp[0] == 2:
-        ref_pts = ref_pts.T
-
-    src_pts = np.float32(facial_pts)
-    src_pts_shp = src_pts.shape
-    if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
-        raise FaceWarpException('facial_pts.shape must be (K,2) or (2,K) and K>2')
-
-    if src_pts_shp[0] == 2:
-        src_pts = src_pts.T
-
-    if src_pts.shape != ref_pts.shape:
-        raise FaceWarpException('facial_pts and reference_pts must have the same shape')
-
-    if align_type == 'cv2_affine':
-        tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
-    elif align_type == 'affine':
-        tfm = get_affine_transform_matrix(src_pts, ref_pts)
-    else:
-        tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
-
-    face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
-
-    return face_img
diff --git a/spaces/FelixLuoX/codeformer/CodeFormer/facelib/parsing/resnet.py b/spaces/FelixLuoX/codeformer/CodeFormer/facelib/parsing/resnet.py
deleted file mode 100644
index fec8e82cf64469fb51be21ad5130217052addbda..0000000000000000000000000000000000000000
--- a/spaces/FelixLuoX/codeformer/CodeFormer/facelib/parsing/resnet.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
-
-    def __init__(self, in_chan, out_chan, stride=1):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(in_chan, out_chan, stride)
-        self.bn1 = nn.BatchNorm2d(out_chan)
-        self.conv2 = conv3x3(out_chan, out_chan)
-        self.bn2 = nn.BatchNorm2d(out_chan)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = None
-        if in_chan != out_chan or stride != 1:
-            self.downsample = nn.Sequential(
-                nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(out_chan),
-            )
-
-    def forward(self, x):
-        residual = self.conv1(x)
-        residual = F.relu(self.bn1(residual))
-        residual = self.conv2(residual)
-        residual = self.bn2(residual)
-
-        shortcut = x
-        if self.downsample is not None:
-            shortcut = self.downsample(x)
-
-        out = shortcut + residual
-        out = self.relu(out)
-        return out
-
-
-def create_layer_basic(in_chan, out_chan, bnum, stride=1):
-    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
-    for i in range(bnum - 1):
-        layers.append(BasicBlock(out_chan, out_chan, stride=1))
-    return nn.Sequential(*layers)
-
-
-class ResNet18(nn.Module):
-
-    def __init__(self):
-        super(ResNet18, self).__init__()
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
-        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
-        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
-        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = F.relu(self.bn1(x))
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        feat8 = self.layer2(x)  # 1/8
-        feat16 = self.layer3(feat8)  # 1/16
-        feat32 = self.layer4(feat16)  # 1/32
-        return feat8, feat16, feat32
diff --git a/spaces/Fengbinbin/gpt-academic/crazy_functions/test_project/latex/attention/introduction.tex b/spaces/Fengbinbin/gpt-academic/crazy_functions/test_project/latex/attention/introduction.tex
deleted file mode 100644
index 1baa8915f4cf7aec2520894a87470fc9436d954b..0000000000000000000000000000000000000000
--- a/spaces/Fengbinbin/gpt-academic/crazy_functions/test_project/latex/attention/introduction.tex
+++ /dev/null
@@ -1,18 +0,0 @@
-Recurrent neural networks, long short-term memory \citep{hochreiter1997} and gated recurrent \citep{gruEval14} neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation \citep{sutskever14, bahdanau2014neural, cho2014learning}. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures \citep{wu2016google,luong2015effective,jozefowicz2016exploring}.
-
-Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states $h_t$, as a function of the previous hidden state $h_{t-1}$ and the input for position $t$. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples.
-%\marginpar{not sure if the memory constraints are understandable here}
-Recent work has achieved significant improvements in computational efficiency through factorization tricks \citep{Kuchaiev2017Factorization} and conditional computation \citep{shazeer2017outrageously}, while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.
-
-%\marginpar{@all: there is work on analyzing what attention really does in seq2seq models, couldn't find it right away} 
-
-Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences \citep{bahdanau2014neural, structuredAttentionNetworks}. In all but a few cases \citep{decomposableAttnModel}, however, such attention mechanisms are used in conjunction with a recurrent network.
-
-%\marginpar{not sure if "cross-positional communication" is understandable without explanation}
-%\marginpar{insert exact training times and stats for the model that reaches sota earliest, maybe even a single GPU model?}
-
-In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs. 
-%\marginpar{you removed the constant number of repetitions part. I wrote it because I wanted to make it clear that the model does not only perform attention once, while it's also not recurrent. I thought that might be important to get across early.}
-
-% Just a standard paragraph with citations, rewrite.
-%After the seminal papers of \citep{sutskever14}, \citep{bahdanau2014neural}, and \citep{cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation and language modeling with recurrent sequence models. Recent effort \citep{shazeer2017outrageously} has combined the power of conditional computation with sequence models to train very large models for machine translation, pushing SOTA at lower computational cost. Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state encumbers recurrnet models to process multiple inputs at once, and their time complexity is a linear function of the length of the input and output, both during training and inference. [What I want to say here is that although this is fine during decoding, at training time, we are given both input and output and this linear nature does not allow the RNN to process all inputs and outputs simultaneously and haven't been used on datasets that are the of the scale of the web. What's the largest dataset we have ? . Talk about Nividia and possibly other's effors to speed up things, and possibly other efforts that alleviate this, but are still limited by it's comptuational nature]. Rest of the intro: What if you could construct the state based on the actual inputs and outputs, then you could construct them all at once. This has been the foundation of many promising recent efforts, bytenet,facenet (Also talk about quasi rnn here). Now we talk about attention!! Along with cell architectures such as long short-term meory (LSTM) \citep{hochreiter1997}, and gated recurrent units (GRUs) \citep{cho2014learning}, attention has emerged as an essential ingredient in successful sequence models, in particular for machine translation. In recent years, many, if not all, state-of-the-art (SOTA) results in machine translation have been achieved with attention-based sequence models \citep{wu2016google,luong2015effective,jozefowicz2016exploring}. Talk about the neon work on how it played with attention to do self attention! Then talk about what we do.
\ No newline at end of file
diff --git a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/japanese.py b/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
-    r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
-    r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ts', 'ʦ'),
-    ('u', 'ɯ'),
-    ('j', 'ʥ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('u', 'ɯ'),
-    ('ʧ', 'tʃ'),
-    ('j', 'dʑ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'Q([↑↓]*[kg])', r'k#\1'),
-    (r'Q([↑↓]*[tdjʧ])', r't#\1'),
-    (r'Q([↑↓]*[sʃ])', r's\1'),
-    (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'N([↑↓]*[pbm])', r'm\1'),
-    (r'N([↑↓]*[ʧʥj])', r'n^\1'),
-    (r'N([↑↓]*[tdn])', r'n\1'),
-    (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
-    for regex, replacement in _symbols_to_japanese:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_romaji_with_accent(text):
-    '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-    text = symbols_to_japanese(text)
-    sentences = re.split(_japanese_marks, text)
-    marks = re.findall(_japanese_marks, text)
-    text = ''
-    for i, sentence in enumerate(sentences):
-        if re.match(_japanese_characters, sentence):
-            if text != '':
-                text += ' '
-            labels = pyopenjtalk.extract_fullcontext(sentence)
-            for n, label in enumerate(labels):
-                phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-                if phoneme not in ['sil', 'pau']:
-                    text += phoneme.replace('ch', 'ʧ').replace('sh',
-                                                               'ʃ').replace('cl', 'Q')
-                else:
-                    continue
-                # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-                a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-                a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-                a3 = int(re.search(r"\+(\d+)/", label).group(1))
-                if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
-                    a2_next = -1
-                else:
-                    a2_next = int(
-                        re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-                # Accent phrase boundary
-                if a3 == 1 and a2_next == 1:
-                    text += ' '
-                # Falling
-                elif a1 == 0 and a2_next == a2 + 1:
-                    text += '↓'
-                # Rising
-                elif a2 == 1 and a2_next == 2:
-                    text += '↑'
-        if i < len(marks):
-            text += unidecode(marks[i]).replace(' ', '')
-    return text
-
-
-def get_real_sokuon(text):
-    for regex, replacement in _real_sokuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def get_real_hatsuon(text):
-    for regex, replacement in _real_hatsuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = re.sub(
-        r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa2(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa3(text):
-    text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
-        'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
-    text = re.sub(
-        r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
-    return text
diff --git a/spaces/Frorozcol/financIA/src/__init__.py b/spaces/Frorozcol/financIA/src/__init__.py
deleted file mode 100644
index e579680581adba3c0dc8424d9537b06771c91c45..0000000000000000000000000000000000000000
--- a/spaces/Frorozcol/financIA/src/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .predict import *
\ No newline at end of file
diff --git a/spaces/GAIR/Factool/app.py b/spaces/GAIR/Factool/app.py
deleted file mode 100644
index 702edb3dac9e543d31442c82cc3c4df4ee804cb9..0000000000000000000000000000000000000000
--- a/spaces/GAIR/Factool/app.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import gradio as gr
-import openai
-import json
-from factool import Factool
-import os
-
-
-def chat_with_gpt(api_key, model, message):
-    openai.api_key = api_key
-    response = openai.ChatCompletion.create(
-      model=model,
-      messages=[
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": message},
-        ]
-    )
-    openai.api_key = ''
-    return response.choices[0].message['content']
-
-def fact_check(openai_api_key, serper_api_key, scraper_api_key, model, message, response, category):
-    os.environ['SCRAPER_API_KEY'] = ''
-    os.environ['SERPER_API_KEY'] = ''
-    os.environ['OPENAI_API_KEY'] = ''
-    os.environ['SCRAPER_API_KEY'] = scraper_api_key
-    os.environ['SERPER_API_KEY'] = serper_api_key
-    os.environ['OPENAI_API_KEY'] = openai_api_key
-    factool_instance = Factool(model)
-    inputs = [
-            {
-                "prompt": message,
-                "response": response,
-                "category": category,
-                "search_type": "online",
-            },
-    ]
-    response_list = factool_instance.run(inputs)
-    os.environ['SCRAPER_API_KEY'] = ''
-    os.environ['SERPER_API_KEY'] = ''
-    os.environ['OPENAI_API_KEY'] = ''
-    openai.api_key = ''
-    return response_list
-
-with gr.Blocks() as demo:
-    openai_api_key = gr.Textbox(label="OpenAI API Key")
-    serper_api_key = gr.Textbox(label="Serper API Key")
-    scraper_api_key = gr.Textbox(label="Scraper API Key")
-    chat_model = gr.inputs.Radio(choices=["gpt-3.5-turbo", "gpt-4"], label="Chat Model")
-    prompt = gr.Textbox(label="Prompt")
-    chat_btn = gr.Button("Get Response!")
-    response = gr.Textbox(label="Response")
-    category = gr.inputs.Radio(choices=["kbqa", "code", "math", "scientific"], label="Category")
-    fact_check_model = gr.inputs.Radio(choices=["gpt-3.5-turbo", "gpt-4"], label="Fact Check Model")
-    fact_check_result = gr.Textbox(label="Fact Check Result")
-    # chat_btn = gr.Button("Get Response!")
-    fact_check_btn = gr.Button("Run FactTool!")
-    chat_btn.click(chat_with_gpt, inputs=[openai_api_key,chat_model,prompt], outputs=response)
-    fact_check_btn.click(fact_check, inputs=[openai_api_key,serper_api_key,scraper_api_key,fact_check_model,prompt,response,category], outputs=fact_check_result)
-
-demo.launch()
-
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/tasks/assembling_kits_seq.py b/spaces/Gen-Sim/Gen-Sim/cliport/tasks/assembling_kits_seq.py
deleted file mode 100644
index 390f4c253e039913aff69c22f6c32eab375a84ae..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/tasks/assembling_kits_seq.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import os
-
-import numpy as np
-from cliport.tasks.task import Task
-from cliport.utils import utils
-
-
-class AssemblingKitsSeq(Task):
-    """ Precisely place each specified shape in the specified hole following the order prescribed in the
-language instruction at each timestep."""
-
-    def __init__(self):
-        super().__init__()
-        self.max_steps = 7
-        self.homogeneous = False
-
-        self.lang_template = "put the {color} {obj} in the {loc}{obj} hole"
-        self.task_completed_desc = "done assembling kit."
-        self.additional_reset()
-
-    def reset(self, env):
-        super().reset(env)
-
-        # Add kit.
-        kit_size = (0.28, 0.2, 0.005)
-        kit_urdf = 'kitting/kit.urdf'
-        kit_pose = self.get_random_pose(env, kit_size)
-        env.add_object(kit_urdf, kit_pose, 'fixed')
-
-        # Shape Names:
-        shapes = utils.assembling_kit_shapes
-        n_objects = 5
-        obj_shapes = self.get_kitting_shapes(n_objects)
-        colors, color_names = utils.get_colors(mode=self.mode)
-
-        # Build kit.
-        targets = []
-        targets_spatial_desc = []
-        targ_pos = [[-0.09, 0.045, 0.0014], [0, 0.045, 0.0014],
-                    [0.09, 0.045, 0.0014], [-0.045, -0.045, 0.0014],
-                    [0.045, -0.045, 0.0014]]
-        template = 'kitting/object-template.urdf'
-
-        for i in range(n_objects):
-            shape = os.path.join(self.assets_root, 'kitting',
-                                 f'{obj_shapes[i]:02d}.obj')
-            scale = [0.003, 0.003, 0.0001]  # .0005
-            pos = utils.apply(kit_pose, targ_pos[i])
-            theta = np.random.rand() * 2 * np.pi
-            rot = utils.eulerXYZ_to_quatXYZW((0, 0, theta))
-            replace = {'FNAME': (shape,), 'SCALE': scale, 'COLOR': [0.2, 0.2, 0.2]}
-
-            # IMPORTANT: REPLACE THE TEMPLATE URDF
-            urdf = self.fill_template(template, replace)
-            env.add_object(urdf, (pos, rot), 'fixed')
-            targets.append((pos, rot))
-
-            # Decide spatial description based on the location of the hole (top-down view).
-            shape_type = obj_shapes[i]
-            if list(obj_shapes).count(obj_shapes[i]) > 1:
-                duplicate_shapes = [j for j, o in enumerate(obj_shapes) if i != j and o == shape_type]
-                other_poses = [utils.apply(kit_pose, targ_pos[d]) for d in duplicate_shapes]
-
-                if all(pos[0] < op[0] and abs(pos[0]-op[0]) > abs(pos[1]-op[1]) for op in other_poses):
-                    spatial_desc = "top "
-                elif all(pos[0] > op[0] and abs(pos[0]-op[0]) > abs(pos[1]-op[1]) for op in other_poses):
-                    spatial_desc = "bottom "
-                elif all(pos[1] < op[1] for op in other_poses):
-                    spatial_desc = "left "
-                elif all(pos[1] > op[1] for op in other_poses):
-                    spatial_desc = "right "
-                else:
-                    spatial_desc = "middle "
-
-                targets_spatial_desc.append(spatial_desc)
-            else:
-                targets_spatial_desc.append("")
-
-        # Add objects.
-        objects, matches = self.make_kitting_objects(env, targets=targets, obj_shapes=obj_shapes, n_objects=n_objects, colors=colors)
-        target_idxs = list(range(n_objects))
-        np.random.shuffle(target_idxs)
-        for i in target_idxs:
-            language_goal = (self.lang_template.format(color=color_names[i],
-                                                             obj=shapes[obj_shapes[i]],
-                                                             loc=targets_spatial_desc[i]))
-            self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[targets[i]], replace=False,
-                rotations=True, metric='pose', params=None, step_max_reward=1 / n_objects, language_goal=language_goal)
-            
-        self.max_steps = n_objects
diff --git a/spaces/GeorgeOrville/bingo/src/lib/bots/bing/sr.ts b/spaces/GeorgeOrville/bingo/src/lib/bots/bing/sr.ts
deleted file mode 100644
index 7cae14da7362bd6cc1e234851c11ca67e5a99f0c..0000000000000000000000000000000000000000
--- a/spaces/GeorgeOrville/bingo/src/lib/bots/bing/sr.ts
+++ /dev/null
@@ -1,106 +0,0 @@
-// @ts-ignore
-const SpeechRecognitionPolyfill: typeof webkitSpeechRecognition = typeof window !== 'undefined' ? (
-  // @ts-ignore
-  window.SpeechRecognition ||
-  window.webkitSpeechRecognition ||
-  // @ts-ignore
-  window.mozSpeechRecognition ||
-  // @ts-ignore
-  window.msSpeechRecognition ||
-  // @ts-ignore
-  window.oSpeechRecognition
-) as typeof webkitSpeechRecognition : undefined
-
-type subscriber = (msg: string, command?: string) => void
-
-export class SR {
-  recognition?: SpeechRecognition
-  onchange?: subscriber
-  transcript: boolean = false
-  listening: boolean = false
-  private commandsRe?: RegExp
-  constructor(commands: string[]) {
-    this.recognition = SpeechRecognitionPolyfill ? new SpeechRecognitionPolyfill() : undefined
-    if (!this.recognition) {
-      return
-    }
-    this.configuration('zh-CN')
-    if (commands.length) {
-      this.commandsRe = new RegExp(`^(${commands.join('|')})。?$`)
-    }
-    this.recognition.onresult = this.speechRecognition
-    this.recognition.onerror = (err) => {
-      console.log('err', err.error)
-      this.stop()
-    }
-    this.recognition.onend = () => {
-      if (this.recognition && this.listening) {
-        this.recognition.start()
-      }
-    }
-  }
-
-  speechRecognition = (event: SpeechRecognitionEvent) => {
-    if (!this.listening) return
-    for (var i = event.resultIndex; i < event.results.length; i++) {
-      let result = event.results[i]
-      if (result.isFinal) {
-        var alt = result[0]
-        const text = alt.transcript.trim()
-        if (this.commandsRe && this.commandsRe.test(text)) {
-          return this.onchange?.('', RegExp.$1)
-        }
-        if (!this.transcript) return
-        this.onchange?.(text)
-      }
-    }
-  }
-
-  private configuration = async (lang: string = 'zh-CN') => {
-    return new Promise((resolve) => {
-      if (this.recognition) {
-        this.recognition.continuous = true
-        this.recognition.lang = lang
-        this.recognition.onstart = resolve
-      }
-    })
-  }
-
-  start = async () => {
-    if (this.recognition && !this.listening) {
-      await this.recognition.start()
-      this.transcript = true
-      this.listening = true
-    }
-  }
-
-  stop = () => {
-    if (this.recognition) {
-      this.recognition.stop()
-      this.transcript = false
-      this.listening = false
-    }
-  }
-
-
-  pause = () => {
-    if (this.recognition) {
-      this.transcript = false
-    }
-  }
-
-  resume = () => {
-    if (this.recognition) {
-      this.transcript = true
-    }
-  }
-
-  abort = () => {
-    if (this.recognition && this.transcript) {
-      this.recognition.abort()
-      this.transcript = false
-      this.listening = false
-    }
-  }
-}
-
diff --git a/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/data/realesrgan_dataset.py b/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/data/realesrgan_dataset.py
deleted file mode 100644
index 5d2a2fbd7b19d1eb7e320a170531fbf676ce7cec..0000000000000000000000000000000000000000
--- a/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/RealESRGANv030/realesrgan/data/realesrgan_dataset.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import cv2
-import math
-import numpy as np
-import os
-import os.path as osp
-import random
-import time
-import torch
-from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
-from basicsr.data.transforms import augment
-from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
-from basicsr.utils.registry import DATASET_REGISTRY
-from torch.utils import data as data
-
-
-@DATASET_REGISTRY.register()
-class RealESRGANDataset(data.Dataset):
-    """Dataset used for Real-ESRGAN model:
-    Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
-    It loads gt (Ground-Truth) images, and augments them.
-    It also generates blur kernels and sinc kernels for generating low-quality images.
-    Note that the low-quality images are processed in tensors on GPUS for faster processing.
-
-    Args:
-        opt (dict): Config for train datasets. It contains the following keys:
-            dataroot_gt (str): Data root path for gt.
-            meta_info (str): Path for meta information file.
-            io_backend (dict): IO backend type and other kwarg.
-            use_hflip (bool): Use horizontal flips.
-            use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
-            Please see more options in the codes.
-    """
-
-    def __init__(self, opt):
-        super(RealESRGANDataset, self).__init__()
-        self.opt = opt
-        self.file_client = None
-        self.io_backend_opt = opt["io_backend"]
-        self.gt_folder = opt["dataroot_gt"]
-
-        # file client (lmdb io backend)
-        if self.io_backend_opt["type"] == "lmdb":
-            self.io_backend_opt["db_paths"] = [self.gt_folder]
-            self.io_backend_opt["client_keys"] = ["gt"]
-            if not self.gt_folder.endswith(".lmdb"):
-                raise ValueError(
-                    f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}"
-                )
-            with open(osp.join(self.gt_folder, "meta_info.txt")) as fin:
-                self.paths = [line.split(".")[0] for line in fin]
-        else:
-            # disk backend with meta_info
-            # Each line in the meta_info describes the relative path to an image
-            with open(self.opt["meta_info"]) as fin:
-                paths = [line.strip().split(" ")[0] for line in fin]
-                self.paths = [os.path.join(self.gt_folder, v) for v in paths]
-
-        # blur settings for the first degradation
-        self.blur_kernel_size = opt["blur_kernel_size"]
-        self.kernel_list = opt["kernel_list"]
-        self.kernel_prob = opt["kernel_prob"]  # a list for each kernel probability
-        self.blur_sigma = opt["blur_sigma"]
-        self.betag_range = opt[
-            "betag_range"
-        ]  # betag used in generalized Gaussian blur kernels
-        self.betap_range = opt["betap_range"]  # betap used in plateau blur kernels
-        self.sinc_prob = opt["sinc_prob"]  # the probability for sinc filters
-
-        # blur settings for the second degradation
-        self.blur_kernel_size2 = opt["blur_kernel_size2"]
-        self.kernel_list2 = opt["kernel_list2"]
-        self.kernel_prob2 = opt["kernel_prob2"]
-        self.blur_sigma2 = opt["blur_sigma2"]
-        self.betag_range2 = opt["betag_range2"]
-        self.betap_range2 = opt["betap_range2"]
-        self.sinc_prob2 = opt["sinc_prob2"]
-
-        # a final sinc filter
-        self.final_sinc_prob = opt["final_sinc_prob"]
-
-        self.kernel_range = [
-            2 * v + 1 for v in range(3, 11)
-        ]  # kernel size ranges from 7 to 21
-        # TODO: kernel range is now hard-coded, should be in the configure file
-        self.pulse_tensor = torch.zeros(
-            21, 21
-        ).float()  # convolving with pulse tensor brings no blurry effect
-        self.pulse_tensor[10, 10] = 1
-
-    def __getitem__(self, index):
-        if self.file_client is None:
-            self.file_client = FileClient(
-                self.io_backend_opt.pop("type"), **self.io_backend_opt
-            )
-
-        # -------------------------------- Load gt images -------------------------------- #
-        # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32.
-        gt_path = self.paths[index]
-        # avoid errors caused by high latency in reading files
-        retry = 3
-        while retry > 0:
-            try:
-                img_bytes = self.file_client.get(gt_path, "gt")
-            except (IOError, OSError) as e:
-                logger = get_root_logger()
-                logger.warn(
-                    f"File client error: {e}, remaining retry times: {retry - 1}"
-                )
-                # change another file to read
-                index = random.randint(0, self.__len__())
-                gt_path = self.paths[index]
-                time.sleep(1)  # sleep 1s for occasional server congestion
-            else:
-                break
-            finally:
-                retry -= 1
-        img_gt = imfrombytes(img_bytes, float32=True)
-
-        # -------------------- Do augmentation for training: flip, rotation -------------------- #
-        img_gt = augment(img_gt, self.opt["use_hflip"], self.opt["use_rot"])
-
-        # crop or pad to 400
-        # TODO: 400 is hard-coded. You may change it accordingly
-        h, w = img_gt.shape[0:2]
-        crop_pad_size = 400
-        # pad
-        if h < crop_pad_size or w < crop_pad_size:
-            pad_h = max(0, crop_pad_size - h)
-            pad_w = max(0, crop_pad_size - w)
-            img_gt = cv2.copyMakeBorder(
-                img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101
-            )
-        # crop
-        if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size:
-            h, w = img_gt.shape[0:2]
-            # randomly choose top and left coordinates
-            top = random.randint(0, h - crop_pad_size)
-            left = random.randint(0, w - crop_pad_size)
-            img_gt = img_gt[top : top + crop_pad_size, left : left + crop_pad_size, ...]
-
-        # ------------------------ Generate kernels (used in the first degradation) ------------------------ #
-        kernel_size = random.choice(self.kernel_range)
-        if np.random.uniform() < self.opt["sinc_prob"]:
-            # this sinc filter setting is for kernels ranging from [7, 21]
-            if kernel_size < 13:
-                omega_c = np.random.uniform(np.pi / 3, np.pi)
-            else:
-                omega_c = np.random.uniform(np.pi / 5, np.pi)
-            kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
-        else:
-            kernel = random_mixed_kernels(
-                self.kernel_list,
-                self.kernel_prob,
-                kernel_size,
-                self.blur_sigma,
-                self.blur_sigma,
-                [-math.pi, math.pi],
-                self.betag_range,
-                self.betap_range,
-                noise_range=None,
-            )
-        # pad kernel
-        pad_size = (21 - kernel_size) // 2
-        kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))
-
-        # ------------------------ Generate kernels (used in the second degradation) ------------------------ #
-        kernel_size = random.choice(self.kernel_range)
-        if np.random.uniform() < self.opt["sinc_prob2"]:
-            if kernel_size < 13:
-                omega_c = np.random.uniform(np.pi / 3, np.pi)
-            else:
-                omega_c = np.random.uniform(np.pi / 5, np.pi)
-            kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False)
-        else:
-            kernel2 = random_mixed_kernels(
-                self.kernel_list2,
-                self.kernel_prob2,
-                kernel_size,
-                self.blur_sigma2,
-                self.blur_sigma2,
-                [-math.pi, math.pi],
-                self.betag_range2,
-                self.betap_range2,
-                noise_range=None,
-            )
-
-        # pad kernel
-        pad_size = (21 - kernel_size) // 2
-        kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size)))
-
-        # ------------------------------------- the final sinc kernel ------------------------------------- #
-        if np.random.uniform() < self.opt["final_sinc_prob"]:
-            kernel_size = random.choice(self.kernel_range)
-            omega_c = np.random.uniform(np.pi / 3, np.pi)
-            sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21)
-            sinc_kernel = torch.FloatTensor(sinc_kernel)
-        else:
-            sinc_kernel = self.pulse_tensor
-
-        # BGR to RGB, HWC to CHW, numpy to tensor
-        img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0]
-        kernel = torch.FloatTensor(kernel)
-        kernel2 = torch.FloatTensor(kernel2)
-
-        return_d = {
-            "gt": img_gt,
-            "kernel1": kernel,
-            "kernel2": kernel2,
-            "sinc_kernel": sinc_kernel,
-            "gt_path": gt_path,
-        }
-        return return_d
-
-    def __len__(self):
-        return len(self.paths)
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/bbsnet_model.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/bbsnet_model.py
deleted file mode 100644
index 66ba239dbdde53ab10d476dc55bb9f3b2c5c73cb..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/bbsnet_model.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import os
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torchvision.transforms as transforms
-from PIL import Image
-from torch import Tensor
-
-from app_utils import normalize
-from base_model import BaseRGBDModel
-from BBSNet.BBSNet_model import BBSNet
-from device import cpu_device, device
-
-
-class BBSNetModel(BaseRGBDModel):
-    def __init__(self):
-        """Wrapper of BBSNet"""
-        super(BBSNetModel, self).__init__()
-        print('BBSNetconstructor')
-        self.model = BBSNet()
-
-        self.model.load_state_dict(
-            torch.load(
-                os.path.join('pretrained_models', 'BBSNet', 'BBSNet.pth'),
-                map_location=cpu_device
-            )
-        )
-        self.model.to(device)
-        self.model.eval()
-
-        self.testsize = 352
-        self.images_transform = transforms.Compose([
-            transforms.Resize((self.testsize, self.testsize)),
-            transforms.Normalize(
-                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-            ])
-        self.depths_transform = transforms.Compose([
-            transforms.Resize((self.testsize, self.testsize)),
-        ])
-        
-    def inference(
-        self, image: Tensor, depth: Tensor,
-    ) -> np.ndarray:
-        origin_shape = image.shape
-
-        # 1. Preprocessing
-        image: Tensor = self.images_transform(image)
-        depth: Tensor = self.depths_transform(depth)
-        images = image.unsqueeze(0)
-        depths = depth.unsqueeze(0)
-
-        # 2. Inference
-        images, depths = images.to(device), depths.to(device)
-        pred_no_sigmoid = self.model(images, depths)[1]
-        
-        # 3. Return saliency maps
-        res: Tensor = F.interpolate(
-            pred_no_sigmoid, size=(origin_shape[1], origin_shape[2]), 
-            mode='bilinear', align_corners=False
-        )
-        res = res.sigmoid().data.cpu().numpy().squeeze()
-        res = normalize(res)
-
-        return res
\ No newline at end of file
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/models/w2l_conv_glu_enc.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/models/w2l_conv_glu_enc.py
deleted file mode 100644
index 655a9b0d19d11e35511392a016f9d6b7d7aa2925..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/models/w2l_conv_glu_enc.py
+++ /dev/null
@@ -1,177 +0,0 @@
-#!/usr/bin/env python3
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq.models import (
-    FairseqEncoder,
-    FairseqEncoderModel,
-    register_model,
-    register_model_architecture,
-)
-from fairseq.modules.fairseq_dropout import FairseqDropout
-
-
-default_conv_enc_config = """[
-    (400, 13, 170, 0.2),
-    (440, 14, 0, 0.214),
-    (484, 15, 0, 0.22898),
-    (532, 16, 0, 0.2450086),
-    (584, 17, 0, 0.262159202),
-    (642, 18, 0, 0.28051034614),
-    (706, 19, 0, 0.30014607037),
-    (776, 20, 0, 0.321156295296),
-    (852, 21, 0, 0.343637235966),
-    (936, 22, 0, 0.367691842484),
-    (1028, 23, 0, 0.393430271458),
-    (1130, 24, 0, 0.42097039046),
-    (1242, 25, 0, 0.450438317792),
-    (1366, 26, 0, 0.481969000038),
-    (1502, 27, 0, 0.51570683004),
-    (1652, 28, 0, 0.551806308143),
-    (1816, 29, 0, 0.590432749713),
-]"""
-
-
-@register_model("asr_w2l_conv_glu_encoder")
-class W2lConvGluEncoderModel(FairseqEncoderModel):
-    def __init__(self, encoder):
-        super().__init__(encoder)
-
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        parser.add_argument(
-            "--input-feat-per-channel",
-            type=int,
-            metavar="N",
-            help="encoder input dimension per input channel",
-        )
-        parser.add_argument(
-            "--in-channels",
-            type=int,
-            metavar="N",
-            help="number of encoder input channels",
-        )
-        parser.add_argument(
-            "--conv-enc-config",
-            type=str,
-            metavar="EXPR",
-            help="""
-    an array of tuples each containing the configuration of one conv layer
-    [(out_channels, kernel_size, padding, dropout), ...]
-            """,
-        )
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-        conv_enc_config = getattr(args, "conv_enc_config", default_conv_enc_config)
-        encoder = W2lConvGluEncoder(
-            vocab_size=len(task.target_dictionary),
-            input_feat_per_channel=args.input_feat_per_channel,
-            in_channels=args.in_channels,
-            conv_enc_config=eval(conv_enc_config),
-        )
-        return cls(encoder)
-
-    def get_normalized_probs(self, net_output, log_probs, sample=None):
-        lprobs = super().get_normalized_probs(net_output, log_probs, sample)
-        lprobs.batch_first = False
-        return lprobs
-
-
-class W2lConvGluEncoder(FairseqEncoder):
-    def __init__(
-        self, vocab_size, input_feat_per_channel, in_channels, conv_enc_config
-    ):
-        super().__init__(None)
-
-        self.input_dim = input_feat_per_channel
-        if in_channels != 1:
-            raise ValueError("only 1 input channel is currently supported")
-
-        self.conv_layers = nn.ModuleList()
-        self.linear_layers = nn.ModuleList()
-        self.dropouts = []
-        cur_channels = input_feat_per_channel
-
-        for out_channels, kernel_size, padding, dropout in conv_enc_config:
-            layer = nn.Conv1d(cur_channels, out_channels, kernel_size, padding=padding)
-            layer.weight.data.mul_(math.sqrt(3))  # match wav2letter init
-            self.conv_layers.append(nn.utils.weight_norm(layer))
-            self.dropouts.append(
-                FairseqDropout(dropout, module_name=self.__class__.__name__)
-            )
-            if out_channels % 2 != 0:
-                raise ValueError("odd # of out_channels is incompatible with GLU")
-            cur_channels = out_channels // 2  # halved by GLU
-
-        for out_channels in [2 * cur_channels, vocab_size]:
-            layer = nn.Linear(cur_channels, out_channels)
-            layer.weight.data.mul_(math.sqrt(3))
-            self.linear_layers.append(nn.utils.weight_norm(layer))
-            cur_channels = out_channels // 2
-
-    def forward(self, src_tokens, src_lengths, **kwargs):
-
-        """
-        src_tokens: padded tensor (B, T, C * feat)
-        src_lengths: tensor of original lengths of input utterances (B,)
-        """
-        B, T, _ = src_tokens.size()
-        x = src_tokens.transpose(1, 2).contiguous()  # (B, feat, T) assuming C == 1
-
-        for layer_idx in range(len(self.conv_layers)):
-            x = self.conv_layers[layer_idx](x)
-            x = F.glu(x, dim=1)
-            x = self.dropouts[layer_idx](x)
-
-        x = x.transpose(1, 2).contiguous()  # (B, T, 908)
-        x = self.linear_layers[0](x)
-        x = F.glu(x, dim=2)
-        x = self.dropouts[-1](x)
-        x = self.linear_layers[1](x)
-
-        assert x.size(0) == B
-        assert x.size(1) == T
-
-        encoder_out = x.transpose(0, 1)  # (T, B, vocab_size)
-
-        # need to debug this -- find a simpler/elegant way in pytorch APIs
-        encoder_padding_mask = (
-            torch.arange(T).view(1, T).expand(B, -1).to(x.device)
-            >= src_lengths.view(B, 1).expand(-1, T)
-        ).t()  # (B x T) -> (T x B)
-
-        return {
-            "encoder_out": encoder_out,  # (T, B, vocab_size)
-            "encoder_padding_mask": encoder_padding_mask,  # (T, B)
-        }
-
-    def reorder_encoder_out(self, encoder_out, new_order):
-        encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select(
-            1, new_order
-        )
-        encoder_out["encoder_padding_mask"] = encoder_out[
-            "encoder_padding_mask"
-        ].index_select(1, new_order)
-        return encoder_out
-
-    def max_positions(self):
-        """Maximum input length supported by the encoder."""
-        return (1e6, 1e6)  # an arbitrary large number
-
-
-@register_model_architecture("asr_w2l_conv_glu_encoder", "w2l_conv_glu_enc")
-def w2l_conv_glu_enc(args):
-    args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
-    args.in_channels = getattr(args, "in_channels", 1)
-    args.conv_enc_config = getattr(args, "conv_enc_config", default_conv_enc_config)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/fairseq_optimizer.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/fairseq_optimizer.py
deleted file mode 100644
index 7e5411753a2ba94f3a7a68316131530b8b17d22a..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/fairseq_optimizer.py
+++ /dev/null
@@ -1,179 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-from fairseq import utils
-from fairseq.dataclass.utils import gen_parser_from_dataclass
-
-
-class FairseqOptimizer(object):
-    def __init__(self, cfg):
-        super().__init__()
-        self.cfg = cfg
-
-    @classmethod
-    def add_args(cls, parser):
-        """Add optimizer-specific arguments to the parser."""
-        dc = getattr(cls, "__dataclass", None)
-        if dc is not None:
-            gen_parser_from_dataclass(parser, dc())
-
-    @property
-    def optimizer(self):
-        """Return a torch.optim.optimizer.Optimizer instance."""
-        if not hasattr(self, "_optimizer"):
-            raise NotImplementedError
-        if not isinstance(self._optimizer, torch.optim.Optimizer):
-            raise ValueError("_optimizer must be an instance of torch.optim.Optimizer")
-        return self._optimizer
-
-    @optimizer.setter
-    def optimizer(self, optimizer):
-        """Reset optimizer instance."""
-        if not hasattr(self, "_optimizer"):
-            raise NotImplementedError
-        if not isinstance(self._optimizer, torch.optim.Optimizer):
-            raise ValueError("_optimizer must be an instance of torch.optim.Optimizer")
-        self._optimizer = optimizer
-
-    @property
-    def optimizer_config(self):
-        """
-        Return a kwarg dictionary that will be used to override optimizer
-        args stored in checkpoints. This allows us to load a checkpoint and
-        resume training using a different set of optimizer args, e.g., with a
-        different learning rate.
-        """
-        raise NotImplementedError
-
-    @property
-    def params(self):
-        """Return an iterable of the parameters held by the optimizer."""
-        for param_group in self.param_groups:
-            for p in param_group["params"]:
-                yield p
-
-    @property
-    def param_groups(self):
-        return self.optimizer.param_groups
-
-    def __getstate__(self):
-        return self._optimizer.__getstate__()
-
-    def get_lr(self):
-        """Return the current learning rate."""
-        return self.param_groups[0]["lr"]
-
-    def set_lr(self, lr):
-        """Set the learning rate."""
-        for param_group in self.param_groups:
-            param_group["lr"] = lr
-
-    def state_dict(self):
-        """Return the optimizer's state dict."""
-        return self.optimizer.state_dict()
-
-    def load_state_dict(self, state_dict, optimizer_overrides=None):
-        """Load an optimizer state dict.
-
-        In general we should prefer the configuration of the existing optimizer
-        instance (e.g., learning rate) over that found in the state_dict. This
-        allows us to resume training from a checkpoint using a new set of
-        optimizer args.
-        """
-        self.optimizer.load_state_dict(state_dict)
-
-        if optimizer_overrides is not None and len(optimizer_overrides) > 0:
-            # override learning rate, momentum, etc. with latest values
-            for group in self.param_groups:
-                group.update(optimizer_overrides)
-
-    def backward(self, loss):
-        """Computes the sum of gradients of the given tensor w.r.t. graph leaves."""
-        loss.backward()
-
-    def all_reduce_grads(self, module):
-        """Manually all-reduce gradients (if required)."""
-        if hasattr(module, "all_reduce_grads"):
-            module.all_reduce_grads()
-
-    def multiply_grads(self, c):
-        """Multiplies grads by a constant *c*."""
-        for p in self.params:
-            if p.grad is not None:
-                if torch.is_tensor(c):
-                    c = c.to(p.grad.device)
-                p.grad.data.mul_(c)
-
-    def clip_grad_norm(self, max_norm, aggregate_norm_fn=None):
-        """Clips gradient norm."""
-        return utils.clip_grad_norm_(self.params, max_norm, aggregate_norm_fn)
-
-    def step(self, closure=None, scale=1.0, groups=None):
-        """Performs a single optimization step."""
-        if self.supports_step_with_scale:
-            if self.supports_groups:
-                self.optimizer.step(closure, scale=scale, groups=groups)
-            else:
-                self.optimizer.step(closure, scale=scale)
-        else:
-            if scale != 1.0:
-                self.multiply_grads(1.0 / scale)
-            if self.supports_groups:
-                self.optimizer.step(closure, groups=groups)
-            else:
-                self.optimizer.step(closure)
-
-    def zero_grad(self):
-        """Clears the gradients of all optimized parameters."""
-        for p in self.params:
-            p.grad = None
-        self.optimizer.zero_grad()
-
-    @property
-    def supports_memory_efficient_fp16(self):
-        if hasattr(self.optimizer, "supports_memory_efficient_fp16"):
-            return self.optimizer.supports_memory_efficient_fp16
-        return False
-
-    @property
-    def supports_step_with_scale(self):
-        if hasattr(self.optimizer, "supports_step_with_scale"):
-            return self.optimizer.supports_step_with_scale
-        return False
-
-    @property
-    def supports_groups(self):
-        if hasattr(self.optimizer, "supports_groups"):
-            return self.optimizer.supports_groups
-        return False
-
-    @property
-    def supports_flat_params(self):
-        """
-        Whether the optimizer supports collapsing of the model
-        parameters/gradients into a single contiguous Tensor.
-        """
-        if hasattr(self.optimizer, "supports_flat_params"):
-            return self.optimizer.supports_flat_params
-        return False
-
-    def average_params(self):
-        pass
-
-    def broadcast_global_state_dict(self, state_dict):
-        """
-        Broadcasts a global state dict to all ranks.
-        Useful for optimizers that shard state between ranks.
-        """
-        if hasattr(self.optimizer, "broadcast_global_state_dict"):
-            return self.optimizer.broadcast_global_state_dict(state_dict)
-        else:
-            return state_dict
-
-
-class LegacyFairseqOptimizer(FairseqOptimizer):
-    def __init__(self, args):
-        self.args = args
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py
deleted file mode 100644
index 4d5547c39b14f62acbd4f4b9ab3abfb3009c0e6d..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py
+++ /dev/null
@@ -1,175 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass, field
-from typing import Optional, List, Tuple
-from omegaconf import II
-
-from fairseq.dataclass import FairseqDataclass
-from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
-
-
-@dataclass
-class TriStageLRScheduleConfig(FairseqDataclass):
-    warmup_steps: int = field(
-        default=0,
-        metadata={"help": "warmup the learning rate linearly for the first N updates"},
-    )
-    hold_steps: int = field(
-        default=0,
-        metadata={"help": "steps in hold stage"},
-    )
-    decay_steps: int = field(
-        default=0,
-        metadata={"help": "steps in decay stages"},
-    )
-    phase_ratio: Optional[Tuple[float, float, float]] = field(
-        default=None,
-        metadata={
-            "help": (
-                "if set, automatically sets warmup/hold/decay steps to the ratio "
-                "specified here from max_updates. the ratios must add up to 1.0"
-            )
-        },
-    )
-    init_lr_scale: float = field(
-        default=0.01,
-        metadata={"help": "initial learning rate scale during warmup phase"},
-    )
-    final_lr_scale: float = field(
-        default=0.01,
-        metadata={"help": "final learning rate scale"},
-    )
-    max_update: float = II("optimization.max_update")
-    lr: List[float] = II("optimization.lr")
-
-
-@register_lr_scheduler("tri_stage", dataclass=TriStageLRScheduleConfig)
-class TriStageLRSchedule(FairseqLRScheduler):
-    """Tristage learning rate schedulr
-
-    Implement the learning rate scheduler in https://arxiv.org/pdf/1904.08779.pdf
-
-    Similar to inverse_squre_root scheduler, but tri_stage learning rate employs
-    three stages LR scheduling:
-
-        - warmup stage, starting from `lr` * `init_lr_scale`, linearly
-          increased to `lr` in `warmup_steps` iterations
-
-        - hold stage, after `warmup_steps`, keep the LR as `lr` for `hold_steps`
-          iterations
-
-        - decay stage, after hold stage, decay LR exponetially to
-          `lr` * `final_lr_scale` in `decay_steps`;
-          after that LR is keep as `final_lr_scale` * `lr`
-
-    During warmup::
-
-      init_lr = cfg.init_lr_scale * cfg.lr
-      lrs = torch.linspace(init_lr, cfg.lr, cfg.warmup_steps)
-      lr = lrs[update_num]
-
-    During hold::
-
-      lr = cfg.lr
-
-    During decay::
-
-      decay_factor = - math.log(cfg.final_lr_scale) / cfg.decay_steps
-      lr = cfg.lr * exp(- (update_num - warmup_steps - decay_steps) * decay_factor)
-
-    After that::
-
-      lr = cfg.lr * cfg.final_lr_scale
-    """
-
-    def __init__(self, cfg: TriStageLRScheduleConfig, optimizer):
-        super().__init__(cfg, optimizer)
-        if len(cfg.lr) > 1:
-            raise ValueError(
-                "Cannot use a fixed learning rate schedule with tri-stage lr."
-                " Consider --lr-scheduler=fixed instead."
-            )
-
-        # calculate LR at each point
-        self.peak_lr = cfg.lr[0]
-        self.init_lr = cfg.init_lr_scale * cfg.lr[0]
-        self.final_lr = cfg.final_lr_scale * cfg.lr[0]
-
-        if cfg.phase_ratio is not None:
-            assert cfg.max_update > 0
-            assert sum(cfg.phase_ratio) == 1, "phase ratios must add up to 1"
-            self.warmup_steps = int(cfg.max_update * cfg.phase_ratio[0])
-            self.hold_steps = int(cfg.max_update * cfg.phase_ratio[1])
-            self.decay_steps = int(cfg.max_update * cfg.phase_ratio[2])
-        else:
-            self.warmup_steps = cfg.warmup_steps
-            self.hold_steps = cfg.hold_steps
-            self.decay_steps = cfg.decay_steps
-
-        assert (
-            self.warmup_steps + self.hold_steps + self.decay_steps > 0
-        ), "please specify steps or phase_ratio"
-
-        self.warmup_rate = (
-            (self.peak_lr - self.init_lr) / self.warmup_steps
-            if self.warmup_steps != 0
-            else 0
-        )
-        self.decay_factor = -math.log(cfg.final_lr_scale) / self.decay_steps
-
-        # initial learning rate
-        self.lr = self.init_lr
-        self.optimizer.set_lr(self.lr)
-
-    def _decide_stage(self, update_step):
-        """
-        return stage, and the corresponding steps within the current stage
-        """
-        if update_step < self.warmup_steps:
-            # warmup state
-            return 0, update_step
-
-        offset = self.warmup_steps
-
-        if update_step < offset + self.hold_steps:
-            # hold stage
-            return 1, update_step - offset
-
-        offset += self.hold_steps
-
-        if update_step <= offset + self.decay_steps:
-            # decay stage
-            return 2, update_step - offset
-
-        offset += self.decay_steps
-
-        # still here ? constant lr stage
-        return 3, update_step - offset
-
-    def step(self, epoch, val_loss=None):
-        """Update the learning rate at the end of the given epoch."""
-        super().step(epoch, val_loss)
-        # we don't change the learning rate at epoch boundaries
-        return self.optimizer.get_lr()
-
-    def step_update(self, num_updates):
-        """Update the learning rate after each update."""
-        stage, steps_in_stage = self._decide_stage(num_updates)
-        if stage == 0:
-            self.lr = self.init_lr + self.warmup_rate * steps_in_stage
-        elif stage == 1:
-            self.lr = self.peak_lr
-        elif stage == 2:
-            self.lr = self.peak_lr * math.exp(-self.decay_factor * steps_in_stage)
-        elif stage == 3:
-            self.lr = self.final_lr
-        else:
-            raise ValueError("Undefined stage")
-
-        self.optimizer.set_lr(self.lr)
-
-        return self.lr
diff --git a/spaces/Hazem/roop/app.py b/spaces/Hazem/roop/app.py
deleted file mode 100644
index ac81150d2a4acd6d7124fd9c15115ab12892b61a..0000000000000000000000000000000000000000
--- a/spaces/Hazem/roop/app.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# -* coding:UTF-8 -*
-# !/usr/bin/env python
-import numpy as np
-import gradio as gr
-import roop.globals
-from roop.core import (
-    start,
-    decode_execution_providers,
-    suggest_max_memory,
-    suggest_execution_threads,
-)
-from roop.processors.frame.core import get_frame_processors_modules
-from roop.utilities import normalize_output_path
-import os
-from PIL import Image
-
-
-def swap_face(source_file, target_file):
-
-    source_path = "input.jpg"
-    target_path = "target.jpg"
-
-    source_image = Image.fromarray(source_file)
-    source_image.save(source_path)
-    target_image = Image.fromarray(target_file)
-    target_image.save(target_path)
-
-    print("source_path: ", source_path)
-    print("target_path: ", target_path)
-
-    roop.globals.source_path = source_path
-    roop.globals.target_path = target_path
-    output_path = "output.jpg"
-    roop.globals.output_path = normalize_output_path(
-        roop.globals.source_path, roop.globals.target_path, output_path
-    )
-    roop.globals.frame_processors = ["face_swapper"]
-    roop.globals.headless = True
-    roop.globals.keep_fps = True
-    roop.globals.keep_audio = True
-    roop.globals.keep_frames = False
-    roop.globals.many_faces = False
-    roop.globals.video_encoder = "libx264"
-    roop.globals.video_quality = 18
-    roop.globals.max_memory = suggest_max_memory()
-    roop.globals.execution_providers = decode_execution_providers(["cpu"])
-    roop.globals.execution_threads = suggest_execution_threads()
-
-    print(
-        "start process",
-        roop.globals.source_path,
-        roop.globals.target_path,
-        roop.globals.output_path,
-    )
-
-    for frame_processor in get_frame_processors_modules(
-        roop.globals.frame_processors
-    ):
-        if not frame_processor.pre_check():
-            return
-
-    start()
-    return output_path
-
-
-app = gr.Interface(
-    fn=swap_face, inputs=[gr.Image(), gr.Image()], outputs="image"
-)
-app.launch()
diff --git a/spaces/Hoolbo/bing/Dockerfile b/spaces/Hoolbo/bing/Dockerfile
deleted file mode 100644
index 9c3904bf350eb82f649e0420d1754ec507385379..0000000000000000000000000000000000000000
--- a/spaces/Hoolbo/bing/Dockerfile
+++ /dev/null
@@ -1,3 +0,0 @@
-FROM zklcdc/go-proxy-bingai:v1.12.0
-EXPOSE 8080
-CMD ["/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/multiproc.py b/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/multiproc.py
deleted file mode 100644
index 2a287a4e97c66acbd36897b25f2ece5494005f03..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/unit2speech/multiproc.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import os
-import time
-import torch
-import sys
-import subprocess
-
-argslist = list(sys.argv)[1:]
-log_dir = argslist[-1]
-num_gpus = torch.cuda.device_count()
-argslist.append('--n_gpus={}'.format(num_gpus))
-workers = []
-job_id = time.strftime("%Y_%m_%d-%H%M%S")
-argslist.append("--group_name=group_{}".format(job_id))
-
-print("GPU log directory is {}".format(log_dir))
-os.makedirs(log_dir, exist_ok=True)
-for i in range(num_gpus):
-    argslist.append('--rank={}'.format(i))
-    stdout = None if i == 0 else open("{}/{}_GPU_{}.log".format(log_dir, job_id, i),
-                                      "w")
-    print(argslist)
-    p = subprocess.Popen([str(sys.executable)]+argslist, stdout=stdout)
-    workers.append(p)
-    argslist = argslist[:-1]
-
-for p in workers:
-    p.wait()
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/models/ema/ema.py b/spaces/ICML2022/OFA/fairseq/fairseq/models/ema/ema.py
deleted file mode 100644
index 010b60ba2fd766340d2c5b8ba96f9e57c6fe25b5..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/models/ema/ema.py
+++ /dev/null
@@ -1,200 +0,0 @@
-#!/usr/bin/env python3
-
-"""
-This module has the EMA class used to store a copy of the exponentially decayed
-model params.
-
-Typical usage of EMA class involves initializing an object using an existing
-model (random or from a seed model) and setting the config like ema_decay,
-ema_start_update which determine how the EMA model is updated. After every
-update of the model i.e. at the end of the train_step, the EMA should be updated
-by passing the new model to the EMA.step function. The EMA model state dict
-can be stored in the extra state under the key of "ema" and dumped
-into a checkpoint and loaded. The EMA object can be passed to tasks
-by setting task.uses_ema property.
-EMA is a smoothed/ensemble model which might have better performance
-when used for inference or further fine-tuning. EMA class has a
-reverse function to load the EMA params into a model and use it
-like a regular model.
-"""
-
-import copy
-import logging
-
-import torch
-from fairseq import checkpoint_utils
-
-
-class EMA(object):
-    """Exponential Moving Average of Fairseq Models
-    EMA keeps a copy of the exponentially decayed model params.
-    The set of params should include both gradient-descent and
-    non-gradient descent params, such as batch mean/var and buffers.
-    This is a modified implementation of
-    the open source code in https://github.com/zhawe01/fairseq-gec.git,
-    and internal source code in
-    fbcode/mobile-vision/projects/classification_pytorch/lib/utils/model_ema.py.
-
-    Similar to TF EMA.
-    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage.
-    EMA provides a averaged and smoothed set of model weights, and has been shown to
-    improve vision models. EMA class does all necessary functions to update, reload,
-    or init EMA methods.
-
-    EMA object is initialized from an arbitrary model. By default, it is stored in
-    the same device (unless device specified at initialization) and with the
-    same precision as the model (unless ema_fp32 is True). ema_fp32 is recommended.
-    This stores the EMA parameters in fp32 only for the EMA update step, and
-    is used at the default precision otherwise.
-    EMA is usually enabled using EMAConfig with store_ema=True. Some important
-    parameters to configure EMA are
-    1) ema_decay - The decay of EMA
-    2) ema_update_freq - EMA is updated every this many model updates.
-    3) ema_start_update - Start EMA update after this many model updates [default 0]
-
-    Key methods:
-    1) step - One update of EMA using new model
-    2) restore - Update EMA from a state dict
-    3) reverse - Load EMA into a model
-    4) get_decay, _set_decay - Used to get or set the decay.  Note _set_decay is
-    called from step.
-    5) build_fp32_params - Used to initialize or update the fp32 copy of EMA params.
-    Note this is enabled only when ema_fp32=True
-    """
-
-    def __init__(self, model, config, device=None):
-        """
-        @param model model to initialize the EMA with
-        @param config EMAConfig object with configuration like
-        ema_decay, ema_update_freq, ema_fp32
-        @param device If provided, copy EMA to this device (e.g. gpu).
-        Otherwise EMA is in the same device as the model.
-        """
-
-        self.decay = config.ema_decay
-        self.model = copy.deepcopy(model)
-        self.model.requires_grad_(False)
-        self.config = config
-        self.fp32_params = {}
-
-        if self.config.ema_seed_model is not None:
-            state = checkpoint_utils.load_ema_from_checkpoint(self.config.ema_seed_model)
-            self.model.load_state_dict(state["model"], strict=True)
-
-        if device is not None:
-            logging.info(f"Copying EMA model to device {device}")
-            self.model = self.model.to(device=device)
-
-        if self.config.ema_fp32:
-            self.build_fp32_params()
-
-        self.update_freq_counter = 0
-
-    def get_model(self):
-        return self.model
-
-    def build_fp32_params(self, state_dict=None):
-        """
-        Store a copy of the EMA params in fp32.
-        If state dict is passed, the EMA params is copied from
-        the provided state dict. Otherwise, it is copied from the
-        current EMA model parameters.
-        """
-        if not self.config.ema_fp32:
-            raise RuntimeError(
-                "build_fp32_params should not be called if ema_fp32=False. "
-                "Use ema_fp32=True if this is really intended."
-            )
-
-        if state_dict is None:
-            state_dict = self.model.state_dict()
-
-        def _to_float(t):
-            return t.float() if torch.is_floating_point(t) else t
-
-        # for non-float params (like registered symbols), they are copied into this dict and covered in each update
-        for param_key in state_dict:
-            if param_key in self.fp32_params:
-                self.fp32_params[param_key].copy_(state_dict[param_key])
-            else:
-                self.fp32_params[param_key] = _to_float(state_dict[param_key])
-
-    def restore(self, state_dict, build_fp32_params=False):
-        """ Load data from a model spec into EMA model """
-        self.model.load_state_dict(state_dict, strict=False)
-        if build_fp32_params:
-            self.build_fp32_params(state_dict)
-
-    def _set_decay(self, decay):
-        self.decay = decay
-
-    def get_decay(self):
-        return self.decay
-
-    def _step_internal(self, new_model, updates=None):
-        """ One update of the EMA model based on new model weights """
-        decay = self.decay
-
-        ema_state_dict = {}
-        ema_params = self.fp32_params if self.config.ema_fp32 else self.model.state_dict()
-        for key, param in new_model.state_dict().items():
-            try:
-                ema_param = ema_params[key]
-            except KeyError:
-                ema_param = param.float().clone() if param.ndim == 1 else copy.deepcopy(param)
-
-            if param.shape != ema_param.shape:
-                raise ValueError(
-                    "incompatible tensor shapes between model param and ema param"
-                    + "{} vs. {}".format(param.shape, ema_param.shape)
-                )
-            if "version" in key:
-                # Do not decay a model.version pytorch param
-                continue
-
-            # for non-float params (like registered symbols), they are covered in each update
-            if not torch.is_floating_point(ema_param):
-                if ema_param.dtype != param.dtype:
-                    raise ValueError(
-                        "incompatible tensor dtypes between model param and ema param"
-                        + "{} vs. {}".format(param.dtype, ema_param.dtype)
-                    )
-                ema_param.copy_(param)
-            else:
-                ema_param.mul_(decay)
-                ema_param.add_(param.to(dtype=ema_param.dtype), alpha=1-decay)
-            ema_state_dict[key] = ema_param
-        self.restore(ema_state_dict, build_fp32_params=False)
-
-    def step(self, new_model, updates=None):
-        """
-        One update of EMA which is done every self.config.ema_update_freq
-        updates of the model.
-
-        @param updates The current number of model updates done.
-        Decay is set of 0 if model updates < ema_start_update, which means
-        the model will be simply copied over to the EMA.
-        When model updates >= ema_start_updates, then EMA is updated with
-        a decay of self.config.ema_decay.
-        """
-        self._set_decay(
-            0
-            if updates is not None
-            and updates < self.config.ema_start_update
-            else self.config.ema_decay
-        )
-        if updates is not None and self.config.ema_update_freq > 1:
-            self.update_freq_counter += 1
-            if self.update_freq_counter >= self.config.ema_update_freq:
-                self._step_internal(new_model, updates)
-                self.update_freq_counter = 0
-        else:
-            self._step_internal(new_model, updates)
-
-    def reverse(self, model):
-        """
-        Load the model parameters from EMA model.
-        Useful for inference or fine-tuning from the EMA model.
-        """
-        model.load_state_dict(self.model.state_dict(), strict=False)
-        return model
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/hubert/hubert_model.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/hubert/hubert_model.py
deleted file mode 100644
index 7fb642d89b07ca60792debab18e3454f52d8f357..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/hubert/hubert_model.py
+++ /dev/null
@@ -1,222 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-            self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        x, mask = self.encode(x)
-        x = self.proj(x)
-        logits = self.logits(x)
-        return logits, mask
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    @torch.inference_mode()
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: torch.Tensor,
-            mask: torch.Tensor = None,
-            src_key_padding_mask: torch.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: torch.device,
-        min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert
diff --git a/spaces/Illumotion/Koboldcpp/otherarch/gpt2_v1.cpp b/spaces/Illumotion/Koboldcpp/otherarch/gpt2_v1.cpp
deleted file mode 100644
index 98377ffcb04400d195aa89c643441c40e9a654d1..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/otherarch/gpt2_v1.cpp
+++ /dev/null
@@ -1,626 +0,0 @@
-#include "ggml_v1.h"
-#include "otherarch.h"
-
-#include "utils.h"
-
-#include <cassert>
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <fstream>
-#include <map>
-#include <string>
-#include <vector>
-#include <iostream>
-
-
-
-// load the model's weights from a file
-ModelLoadResult legacy_gpt2_model_load(const std::string & fname, gpt2_v1_model & model, gpt_vocab & vocab, FileFormat file_format) {
-    printf("%s: loading model from '%s'\n", __func__, fname.c_str());
-
-    auto fin = std::ifstream(fname, std::ios::binary);
-    if (!fin) {
-        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
-        return ModelLoadResult::FAIL;
-    }
-
-    // verify magic
-    {
-        uint32_t magic;
-        fin.read((char *) &magic, sizeof(magic));
-        if (magic != 0x67676d6c) {
-            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
-            return ModelLoadResult::FAIL;
-        }
-    }
-
-    auto desiredMaxCtx = model.hparams.n_ctx;
-
-    // load hparams
-    {
-        auto & hparams = model.hparams;
-
-        
-        fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
-        fin.read((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
-        fin.read((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
-        fin.read((char *) &hparams.n_head,  sizeof(hparams.n_head));
-        fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
-        fin.read((char *) &hparams.ftype,     sizeof(hparams.ftype));
-
-        //used to expand KV size if needed
-        desiredMaxCtx = std::max(hparams.n_ctx,desiredMaxCtx);
-
-        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
-        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
-        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
-        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
-        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
-        printf("%s: f16     = %d\n", __func__, hparams.ftype);
-    }
-
-    // load vocab
-    {
-        int32_t n_vocab = 0;
-        fin.read((char *) &n_vocab, sizeof(n_vocab));
-
-        if (n_vocab != model.hparams.n_vocab) {
-            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
-                    __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
-            return ModelLoadResult::FAIL;
-        }
-
-        std::string word;
-        for (int i = 0; i < n_vocab; i++) {
-            uint32_t len;
-            fin.read((char *) &len, sizeof(len));
-
-            word.resize(len);
-            fin.read((char *) word.data(), len);
-
-            vocab.token_to_id[word] = i;
-            vocab.id_to_token[i] = word;
-        }
-    }
-
-    // for the big tensors, we have the option to store the data in 16-bit floats
-    // in order to save memory and also to speed up the computation
-    const ggml_v1_type wtype = GGML_V1_TYPE_F16;
-
-    auto & ctx = model.ctx;
-
-    auto memory_type = GGML_V1_TYPE_F16;
-
-    size_t ctx_size = 0;
-
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = desiredMaxCtx;
-        const int n_vocab = hparams.n_vocab;
-
-        ctx_size += n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32); // ln_f_g
-        ctx_size += n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32); // ln_f_b
-
-        ctx_size += n_vocab*n_embd*ggml_v1_type_size(wtype);         // wte
-        ctx_size +=   n_ctx*n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32); // wpe
-
-        ctx_size += n_layer*(n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // ln_1_g
-        ctx_size += n_layer*(n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // ln_1_b
-
-        ctx_size += n_layer*(n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // ln_2_g
-        ctx_size += n_layer*(n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // ln_2_b
-
-        ctx_size += n_layer*(3*n_embd*n_embd*ggml_v1_type_size(wtype));         // c_attn_attn_w
-        ctx_size += n_layer*(       3*n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // c_attn_attn_b
-
-        ctx_size += n_layer*(n_embd*n_embd*ggml_v1_type_size(wtype));           // c_attn_proj_w
-        ctx_size += n_layer*(       n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32));   // c_attn_proj_b
-
-        ctx_size += n_layer*(4*n_embd*n_embd*ggml_v1_type_size(wtype));         // c_mlp_fc_w
-        ctx_size += n_layer*(       4*n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // c_mlp_fc_b
-
-        ctx_size += n_layer*(4*n_embd*n_embd*ggml_v1_type_size(wtype));         // c_mlp_proj_w
-        ctx_size += n_layer*(         n_embd*ggml_v1_type_size(GGML_V1_TYPE_F32)); // c_mlp_proj_b
-
-        ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_size(memory_type); // memory_k
-        ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_size(memory_type); // memory_v
-
-        ctx_size += (6 + 12*n_layer)*256; // object overhead
-
-        printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
-    }
-
-    // create the ggml context
-    {
-        struct ggml_v1_init_params params;        
-        params.mem_size   = ctx_size,
-        params.mem_buffer = NULL,
-        
-
-        model.ctx = ggml_v1_init(params);
-        if (!model.ctx) {
-            fprintf(stderr, "%s: ggml_v1_init() failed\n", __func__);
-            return ModelLoadResult::FAIL;
-        }
-    }
-
-    // prepare memory for the weights
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
-        const int n_vocab = hparams.n_vocab;
-
-        model.layers.resize(n_layer);
-
-        model.ln_f_g = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_embd);
-        model.ln_f_b = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_embd);
-
-        model.wte = ggml_v1_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
-        model.wpe = ggml_v1_new_tensor_2d(ctx, GGML_V1_TYPE_F32, n_embd, n_ctx);
-
-        // map by name
-        model.tensors["model/ln_f/g"] = model.ln_f_g;
-        model.tensors["model/ln_f/b"] = model.ln_f_b;
-
-        model.tensors["model/wte"] = model.wte;
-        model.tensors["model/wpe"] = model.wpe;
-
-        for (int i = 0; i < n_layer; ++i) {
-            auto & layer = model.layers[i];
-
-            layer.ln_1_g             = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32,   n_embd);
-            layer.ln_1_b             = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32,   n_embd);
-
-            layer.ln_2_g             = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32,   n_embd);
-            layer.ln_2_b             = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32,   n_embd);
-
-            layer.c_attn_attn_w      = ggml_v1_new_tensor_2d(ctx, wtype,         3*n_embd, n_embd);
-            layer.c_attn_attn_b      = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, 3*n_embd);
-
-            layer.c_attn_proj_w      = ggml_v1_new_tensor_2d(ctx, wtype,           n_embd, n_embd);
-            layer.c_attn_proj_b      = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32,   n_embd);
-
-            layer.c_mlp_fc_w         = ggml_v1_new_tensor_2d(ctx, wtype,         4*n_embd, n_embd);
-            layer.c_mlp_fc_b         = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, 4*n_embd);
-
-            layer.c_mlp_proj_w_trans = ggml_v1_new_tensor_2d(ctx, wtype,         4*n_embd, n_embd);
-            layer.c_mlp_proj_b       = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32,   n_embd);
-
-            // map by name
-            model.tensors["model/h" + std::to_string(i) + "/ln_1/g"]        = layer.ln_1_g;
-            model.tensors["model/h" + std::to_string(i) + "/ln_1/b"]        = layer.ln_1_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/ln_2/g"]        = layer.ln_2_g;
-            model.tensors["model/h" + std::to_string(i) + "/ln_2/b"]        = layer.ln_2_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
-            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
-            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"]    = layer.c_mlp_fc_w;
-            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"]    = layer.c_mlp_fc_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"]  = layer.c_mlp_proj_w_trans;
-            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"]  = layer.c_mlp_proj_b;
-        }
-    }
-
-    // key + value memory
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = desiredMaxCtx;
-
-        const int n_mem      = n_layer*n_ctx;
-        const int n_elements = n_embd*n_mem;
-
-        model.memory_k = ggml_v1_new_tensor_1d(ctx, memory_type, n_elements);
-        model.memory_v = ggml_v1_new_tensor_1d(ctx, memory_type, n_elements);
-
-        const size_t memory_size = ggml_v1_nbytes(model.memory_k) + ggml_v1_nbytes(model.memory_v);
-
-        printf("%s: memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
-    }
-
-    // load weights
-    {
-        size_t total_size = 0;
-
-        while (true) {
-            int32_t n_dims;
-            int32_t length;
-            int32_t ftype;
-
-            fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
-            fin.read(reinterpret_cast<char *>(&length), sizeof(length));
-            fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
-
-            if (fin.eof()) {
-                break;
-            }
-
-            int32_t nelements = 1;
-            int32_t ne[2] = { 1, 1 };
-            for (int i = 0; i < n_dims; ++i) {
-                fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
-                nelements *= ne[i];
-            }
-
-            std::string name(length, 0);
-            fin.read(&name[0], length);
-
-            if (model.tensors.find(name.data()) == model.tensors.end()) {
-                fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
-                return ModelLoadResult::FAIL;
-            }
-
-            auto tensor = model.tensors[name.data()];
-            if (ggml_v1_nelements(tensor) != nelements) {
-                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
-                return ModelLoadResult::FAIL;
-            }
-
-            if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
-                 //test for transposition and retry older loader
-                if(tensor->ne[0]==ne[1] && tensor->ne[1]==ne[0] && should_transpose_layer(name))
-                {
-                    printf("\nFound a transposed tensor. This could be an older or newer model. Retrying load...");
-                    ggml_v1_free(ctx);
-                    return ModelLoadResult::RETRY_LOAD;
-                }
-                else
-                {
-                    fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
-                            __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
-                    return ModelLoadResult::FAIL;
-                }
-            }
-
-            const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_v1_fp16_t);
-
-            if (nelements*bpe != ggml_v1_nbytes(tensor)) {
-                fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
-                        __func__, name.data(), ggml_v1_nbytes(tensor), nelements*bpe);
-                return ModelLoadResult::FAIL;
-            }
-
-            fin.read(reinterpret_cast<char *>(tensor->data), ggml_v1_nbytes(tensor));
-
-            //printf("%24s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_v1_nbytes(tensor)/1024.0/1024.0);
-            total_size += ggml_v1_nbytes(tensor);
-        }
-
-        printf("%s: model size  = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
-    }
-
-    fin.close();
-
-    return ModelLoadResult::SUCCESS;
-}
-
-// evaluate the transformer
-//
-//   - model:     the model
-//   - n_threads: number of threads to use
-//   - n_past:    the context size so far
-//   - embd_inp:  the embeddings of the tokens in the context
-//   - embd_w:    the predicted logits for the next token
-//
-bool legacy_gpt2_eval(
-        const gpt2_v1_model & model,
-        const int n_threads,
-        const int n_past,
-        const std::vector<gpt_vocab::id> & embd_inp,
-              std::vector<float>         & embd_w,
-              size_t                     & mem_per_token,
-              FileFormat file_format) {
-    const int N = embd_inp.size();
-
-    const auto & hparams = model.hparams;
-
-    const int n_embd  = hparams.n_embd;
-    const int n_layer = hparams.n_layer;
-    const int n_ctx   = hparams.n_ctx;
-    const int n_head  = hparams.n_head;
-    const int n_vocab = hparams.n_vocab;
-
-    static size_t buf_size = 256u*1024*1024;
-    static void * buf = malloc(buf_size);
-
-    if (mem_per_token > 0 && mem_per_token*N > buf_size) {
-        const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
-        //printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
-
-        // reallocate
-        buf_size = buf_size_new;
-        buf = realloc(buf, buf_size);
-        if (buf == nullptr) {
-            fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
-            return false;
-        }
-    }
-
-    struct ggml_v1_init_params params;
-    params.mem_size   = buf_size;
-    params.mem_buffer = buf;
-    
-
-    struct ggml_v1_context * ctx0 = ggml_v1_init(params);
-    struct ggml_v1_cgraph gf = {};
-    gf.n_threads = n_threads;
-
-    struct ggml_v1_tensor * embd = ggml_v1_new_tensor_1d(ctx0, GGML_V1_TYPE_I32, N);
-    memcpy(embd->data, embd_inp.data(), N*ggml_v1_element_size(embd));
-
-    struct ggml_v1_tensor * position = ggml_v1_new_tensor_1d(ctx0, GGML_V1_TYPE_I32, N);
-    for (int i = 0; i < N; ++i) {
-        ((int32_t *) position->data)[i] = n_past + i;
-    }
-
-    // wte + wpe
-    struct ggml_v1_tensor * inpL =
-        ggml_v1_add(ctx0,
-                ggml_v1_get_rows(ctx0, model.wte, embd),
-                ggml_v1_get_rows(ctx0, model.wpe, position));
-
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_v1_tensor * cur;
-
-        // norm
-        {
-            // [ 768, N]
-            cur = ggml_v1_norm(ctx0, inpL);
-
-            // cur = ln_1_g*cur + ln_1_b
-            // [ 768, N]
-            cur = ggml_v1_add(ctx0,
-                    ggml_v1_mul(ctx0,
-                        ggml_v1_repeat(ctx0, model.layers[il].ln_1_g, cur),
-                        cur),
-                    ggml_v1_repeat(ctx0, model.layers[il].ln_1_b, cur));
-        }
-
-        // attn
-        // [2304, 768] - model.layers[il].c_attn_attn_w
-        // [2304,   1] - model.layers[il].c_attn_attn_b
-        // [ 768,   N] - cur (in)
-        // [2304,   N] - cur (out)
-        //
-        // cur = attn_w*cur + attn_b
-        // [2304, N]
-        {
-            cur = ggml_v1_mul_mat(ctx0,
-                    ggml_v1_transpose(ctx0, model.layers[il].c_attn_attn_w),
-                    cur);
-
-            cur = ggml_v1_add(ctx0,
-                    ggml_v1_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
-                    cur);
-        }
-
-        // self-attention
-        {
-            struct ggml_v1_tensor * Qcur = ggml_v1_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
-            struct ggml_v1_tensor * Kcur = ggml_v1_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
-            struct ggml_v1_tensor * Vcur = ggml_v1_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);
-
-            // store key and value to memory
-            if (N >= 1) {
-                struct ggml_v1_tensor * k = ggml_v1_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_v1_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
-                struct ggml_v1_tensor * v = ggml_v1_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_v1_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
-
-                ggml_v1_build_forward_expand(&gf, ggml_v1_cpy(ctx0, Kcur, k));
-                ggml_v1_build_forward_expand(&gf, ggml_v1_cpy(ctx0, Vcur, v));
-            }
-
-            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
-            // [64, N, 12]
-            struct ggml_v1_tensor * Q =
-                ggml_v1_permute(ctx0,
-                        ggml_v1_cpy(ctx0,
-                            Qcur,
-                            ggml_v1_new_tensor_3d(ctx0, GGML_V1_TYPE_F32, n_embd/n_head, n_head, N)),
-                        0, 2, 1, 3);
-
-            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
-            // [64, n_past + N, 12]
-            struct ggml_v1_tensor * K =
-                ggml_v1_permute(ctx0,
-                        ggml_v1_reshape_3d(ctx0,
-                            ggml_v1_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_v1_element_size(model.memory_k)*n_embd),
-                            n_embd/n_head, n_head, n_past + N),
-                        0, 2, 1, 3);
-
-            // GG: flash attention
-            //struct ggml_v1_tensor * V =
-            //    ggml_v1_cpy(ctx0,
-            //            ggml_v1_permute(ctx0,
-            //                ggml_v1_reshape_3d(ctx0,
-            //                    ggml_v1_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_v1_element_size(model.memory_v)*n_embd),
-            //                    n_embd/n_head, n_head, n_past + N),
-            //                1, 2, 0, 3),
-            //            ggml_v1_new_tensor_3d(ctx0, GGML_V1_TYPE_F32, n_past + N, n_embd/n_head, n_head));
-
-            //struct ggml_v1_tensor * KQV = ggml_v1_flash_attn(ctx0, Q, K, V, true);
-
-            // K * Q
-            // [n_past + N, N, 12]
-            struct ggml_v1_tensor * KQ = ggml_v1_mul_mat(ctx0, K, Q);
-
-            // KQ_scaled = KQ / sqrt(n_embd/n_head)
-            // [n_past + N, N, 12]
-            struct ggml_v1_tensor * KQ_scaled =
-                ggml_v1_scale(ctx0,
-                        KQ,
-                        ggml_v1_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
-                        );
-
-            // KQ_masked = mask_past(KQ_scaled)
-            // [n_past + N, N, 12]
-            struct ggml_v1_tensor * KQ_masked = ggml_v1_diag_mask_inf(ctx0, KQ_scaled, n_past);
-
-            // KQ = soft_max(KQ_masked)
-            // [n_past + N, N, 12]
-            struct ggml_v1_tensor * KQ_soft_max = ggml_v1_soft_max(ctx0, KQ_masked);
-
-            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
-            // [n_past + N, 64, 12]
-            struct ggml_v1_tensor * V_trans =
-                ggml_v1_permute(ctx0,
-                        ggml_v1_reshape_3d(ctx0,
-                            ggml_v1_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_v1_element_size(model.memory_v)*n_embd),
-                            n_embd/n_head, n_head, n_past + N),
-                        1, 2, 0, 3);
-
-            // KQV = transpose(V) * KQ_soft_max
-            // [64, N, 12]
-            struct ggml_v1_tensor * KQV = ggml_v1_mul_mat(ctx0, V_trans, KQ_soft_max);
-
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
-            // [64, 12, N]
-            struct ggml_v1_tensor * KQV_merged = ggml_v1_permute(ctx0, KQV, 0, 2, 1, 3);
-
-            // cur = KQV_merged.contiguous().view(n_embd, N)
-            // [768, N]
-            cur = ggml_v1_cpy(ctx0,
-                    KQV_merged,
-                    ggml_v1_new_tensor_2d(ctx0, GGML_V1_TYPE_F32, n_embd, N));
-        }
-
-        // projection
-        // [ 768, 768] - model.layers[il].c_attn_proj_w
-        // [ 768,   1] - model.layers[il].c_attn_proj_b
-        // [ 768,   N] - cur (in)
-        // [ 768,   N] - cur (out)
-        //
-        // cur = proj_w*cur + proj_b
-        // [768, N]
-        {
-            cur = ggml_v1_mul_mat(ctx0,
-                    ggml_v1_transpose(ctx0, model.layers[il].c_attn_proj_w),
-                    cur);
-
-            cur = ggml_v1_add(ctx0,
-                    ggml_v1_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),
-                    cur);
-        }
-
-        // add the input
-        cur = ggml_v1_add(ctx0, cur, inpL);
-
-        struct ggml_v1_tensor * inpFF = cur;
-
-        // feed-forward network
-        {
-            // norm
-            {
-                cur = ggml_v1_norm(ctx0, inpFF);
-
-                // cur = ln_2_g*cur + ln_2_b
-                // [ 768, N]
-                cur = ggml_v1_add(ctx0,
-                        ggml_v1_mul(ctx0,
-                            ggml_v1_repeat(ctx0, model.layers[il].ln_2_g, cur),
-                            cur),
-                        ggml_v1_repeat(ctx0, model.layers[il].ln_2_b, cur));
-            }
-
-            // fully connected
-            // [3072, 768] - model.layers[il].c_mlp_fc_w
-            // [3072,   1] - model.layers[il].c_mlp_fc_b
-            // [ 768,   N] - cur (in)
-            // [3072,   N] - cur (out)
-            //
-            // cur = fc_w*cur + fc_b
-            // [3072, N]
-            cur = ggml_v1_mul_mat(ctx0,
-                    ggml_v1_transpose(ctx0, model.layers[il].c_mlp_fc_w),
-                    cur);
-
-            cur = ggml_v1_add(ctx0,
-                    ggml_v1_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
-                    cur);
-
-            // GELU activation
-            // [3072, N]
-            cur = ggml_v1_gelu(ctx0, cur);
-
-            // projection
-            // [ 768, 3072] - model.layers[il].c_mlp_proj_w
-            // [ 768,    1] - model.layers[il].c_mlp_proj_b
-            // [3072,    N] - cur (in)
-            // [ 768,    N] - cur (out)
-            //
-            // cur = proj_w*cur + proj_b
-            // [768, N]
-            cur = ggml_v1_mul_mat(ctx0,
-                    model.layers[il].c_mlp_proj_w_trans,
-                    cur);
-
-            cur = ggml_v1_add(ctx0,
-                    ggml_v1_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
-                    cur);
-        }
-
-        // input for next layer
-        inpL = ggml_v1_add(ctx0, cur, inpFF);
-    }
-
-    // norm
-    {
-        // [ 768, N]
-        inpL = ggml_v1_norm(ctx0, inpL);
-
-        // inpL = ln_f_g*inpL + ln_f_b
-        // [ 768, N]
-        inpL = ggml_v1_add(ctx0,
-                ggml_v1_mul(ctx0,
-                    ggml_v1_repeat(ctx0, model.ln_f_g, inpL),
-                    inpL),
-                ggml_v1_repeat(ctx0, model.ln_f_b, inpL));
-    }
-
-    // inpL = WTE * inpL
-    // [ 768, 50257] - model.wte
-    // [ 768, N]     - inpL
-    inpL = ggml_v1_mul_mat(ctx0, model.wte, inpL);
-
-    // logits -> probs
-    //inpL = ggml_v1_soft_max(ctx0, inpL);
-
-    // run the computation
-    ggml_v1_build_forward_expand(&gf, inpL);
-    ggml_v1_graph_compute       (ctx0, &gf);
-
-    //if (n_past%100 == 0) {
-    //    ggml_v1_graph_print   (&gf);
-    //    ggml_v1_graph_dump_dot(&gf, NULL, "gpt-2.dot");
-    //}
-
-    //embd_w.resize(n_vocab*N);
-    //memcpy(embd_w.data(), ggml_v1_get_data(inpL), sizeof(float)*n_vocab*N);
-
-    // return result just for the last token
-    embd_w.resize(n_vocab);
-    memcpy(embd_w.data(), (float *) ggml_v1_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
-
-    if (mem_per_token == 0) {
-        mem_per_token = ggml_v1_used_mem(ctx0)/N;
-    }
-    //printf("used_mem = %zu\n", ggml_v1_used_mem(ctx0));
-
-    ggml_v1_free(ctx0);
-
-    return true;
-}
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/paper_runfiles/generate_val_test.sh b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/paper_runfiles/generate_val_test.sh
deleted file mode 100644
index d9b2a370ceeeb8f401706f4303298db13e5fad91..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/paper_runfiles/generate_val_test.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/usr/bin/env bash
-
-# !!! file set to make test_large_30k from the vanilla test_large: configs/test_large_30k.lst
-
-# paths to data are valid for mml7
-PLACES_ROOT="/data/inpainting/Places365"
-OUT_DIR="/data/inpainting/paper_data/Places365_val_test"
-
-source "$(dirname $0)/env.sh"
-
-for datadir in test_large_30k  # val_large
-do
-    for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512
-    do
-        "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \
-            "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 8
-
-        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-    done
-
-    for conf in segm_256 segm_512
-    do
-        "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \
-            "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 2
-
-        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-    done
-done
diff --git a/spaces/Izal887/rvc-hutao/infer_pack/commons.py b/spaces/Izal887/rvc-hutao/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/Izal887/rvc-hutao/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +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 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(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * 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
-        ret[i] = x[i, :, idx_str:idx_end]
-    return ret
-
-
-def slice_segments2(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
-        ret[i] = x[i, idx_str:idx_end]
-    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 ** (1.0 / norm_type)
-    return total_norm
diff --git a/spaces/Jeff2323/ai-comic-factory/src/lib/createLlamaPrompt.ts b/spaces/Jeff2323/ai-comic-factory/src/lib/createLlamaPrompt.ts
deleted file mode 100644
index ca246b36d0ef50f37571dcf09480bf57e9aee922..0000000000000000000000000000000000000000
--- a/spaces/Jeff2323/ai-comic-factory/src/lib/createLlamaPrompt.ts
+++ /dev/null
@@ -1,25 +0,0 @@
-// adapted from https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ/discussions/5 
-export function createLlamaPrompt(messages: Array<{ role: string, content: string }>) {
-  const B_INST = "[INST]", E_INST = "[/INST]";
-  const B_SYS = "<<SYS>>\n", E_SYS = "\n<</SYS>>\n\n";
-  const BOS = "<s>", EOS = "</s>";
-  const DEFAULT_SYSTEM_PROMPT = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.";
-
-  if (messages[0].role != "system"){
-      messages = [
-          {role: "system", content: DEFAULT_SYSTEM_PROMPT}
-      ].concat(messages);
-  }
-  messages = [{role: messages[1].role, content: B_SYS + messages[0].content + E_SYS + messages[1].content}].concat(messages.slice(2));
-
-  let messages_list = messages.map((value, index, array) => {
-      if (index % 2 == 0 && index + 1 < array.length){
-          return `${BOS}${B_INST} ${array[index].content.trim()} ${E_INST} ${array[index+1].content.trim()} ${EOS}`
-      }
-      return '';
-  })
-
-  messages_list.push(`${BOS}${B_INST} ${messages[messages.length-1].content.trim()} ${E_INST}`)
-
-  return messages_list.join('');
-}
\ No newline at end of file
diff --git a/spaces/JeffJing/ZookChatBot/steamship/utils/zip_archives.py b/spaces/JeffJing/ZookChatBot/steamship/utils/zip_archives.py
deleted file mode 100644
index 28fcc97e760d08e399501fa28bdca7d88c2ce87d..0000000000000000000000000000000000000000
--- a/spaces/JeffJing/ZookChatBot/steamship/utils/zip_archives.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import logging
-import shutil
-from pathlib import Path
-from typing import Optional
-
-
-def zip_folder(folder: Path, into_file: Optional[Path]) -> Path:
-    """Zips a folder on disk to a co-located zip-file of the same name.
-
-    The resulting zip file does not contain the enclosing folder name provided.
-    It contains only the children of that folder as its root elements.
-    """
-    logging.info(f"Zipping: {folder}")
-    shutil.make_archive(str(folder).rstrip("/"), "zip", folder)
-    zip_path_str = str(folder).rstrip("/") + ".zip"
-    logging.info(f"Zipped: {zip_path_str}")
-
-    if into_file is None:
-        return Path(zip_path_str)
-
-    # Move the archive to the desired destination
-
-    # Ensure the path to the desired extraction folder exists
-    if not into_file.parent.exists():
-        into_file.parent.mkdir(parents=True, exist_ok=True)
-
-    shutil.move(zip_path_str, into_file)
-    return into_file
-
-
-def unzip_folder(zip_file: Path, into_folder: Optional[Path]) -> Path:
-    """Unzips a folder on disk, returning the path to the new folder resulting."""
-    logging.info(f"Unzipping: {zip_file}")
-    if into_folder is None:
-        into_folder = zip_file.with_suffix("")  # Strips the '.zip' suffix
-
-    # Ensure the path to the desired extraction folder exists
-    if not into_folder.parent.exists():
-        into_folder.parent.mkdir(parents=True, exist_ok=True)
-
-    shutil.unpack_archive(zip_file, into_folder, "zip")
-    logging.info(f"Unzipped: {into_folder}")
-    return Path(into_folder)
diff --git a/spaces/Jimmie/snake-species-identification/app.py b/spaces/Jimmie/snake-species-identification/app.py
deleted file mode 100644
index 2e44f059835685fdb691578e20aaac46d48af912..0000000000000000000000000000000000000000
--- a/spaces/Jimmie/snake-species-identification/app.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
-
-# %% auto 0
-__all__ = ['repo_id', 'learner', 'path', 'countries', 'categories', 'title', 'description', 'article', 'image', 'label',
-           'country', 'summary', 'link', 'examples', 'intf', 'get_countries', 'classify_image']
-
-# %% app.ipynb 3
-from fastai.vision.all import *
-from huggingface_hub import from_pretrained_fastai
-import gradio as gr
-import wikipedia
-import pandas as pd
-
-# %% app.ipynb 4
-repo_id = "Jimmie/snake-species-identification"
-
-# loading the model from huggingface_hub
-learner = from_pretrained_fastai(repo_id)
-
-# %% app.ipynb 5
-path = Path('demo-images/')
-countries = pd.read_csv('species_to_country_mapping.csv', index_col=0)
-
-# %% app.ipynb 9
-def get_countries(binomial):
-    sample_row = countries.loc[binomial]
-    country_list = sample_row[sample_row == 1].index.tolist()
-    # title case all items in country_list
-    country_list = [country.title() for country in country_list]
-    # return all items in country_list as a string
-    return ", ".join(country_list)
-
-# %% app.ipynb 20
-categories = tuple(learner.dls.vocab)
-
-def classify_image(img):
-    pred,idx,probs = learner.predict(img)
-    countries = get_countries(pred)
-    summary = wikipedia.summary(pred)
-    wiki_link = f'Learn more: <a href={wikipedia.page(pred).url} target="_blank">{pred}</a>'
-    return dict(zip(categories, map(float, probs))), countries, summary, wiki_link
-
-# %% app.ipynb 22
-title = "Snake Species Identification"
-
-description = """
-This demo is an ongoing iteration of the [Snake Species Identification](https://github.com/jimmiemunyi/the-snake-project-cls) project meant to classify snakes up to the species level (binomial name).
-
-Currently, it can classify snakes into 50 categories but it is continually updated to support more categories (over 200).
-
-The model can be found here: https://huggingface.co/Jimmie/snake-species-identification.
-The model is trained on the following dataset: https://www.aicrowd.com/challenges/snakeclef2021-snake-species-identification-challenge.
-
-Enjoy!
-"""
-
-article = "Blog posts on how the model is being trained: COMING SOON!"
-
-
-image = gr.Image(shape=(224, 224))
-label = gr.Label(num_top_classes=3, label='Binomial')
-country = gr.Textbox(label='Countries where the species is found')
-summary = gr.Textbox(label='Wikipedia Summary')
-link = gr.HTML(label="Learn More:", show_label=True)
-examples = list(path.ls())
-
-
-intf = gr.Interface(fn=classify_image, inputs=image, 
-                   outputs=[label, country, summary, link], examples=examples,
-                   title = title, description = description, article = article, 
-                   cache_examples=False)
-intf.launch(inline=False)
diff --git a/spaces/JoeyFoursheds/ClonerHug/voice_cloner.py b/spaces/JoeyFoursheds/ClonerHug/voice_cloner.py
deleted file mode 100644
index c6fc4a0e6c834bfe29c73bca52016cb5e1f2730f..0000000000000000000000000000000000000000
--- a/spaces/JoeyFoursheds/ClonerHug/voice_cloner.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import traceback, os
-import numpy as np
-import librosa
-from datetime import datetime
-from config import device
-from vc_infer_pipeline import VC
-import model_prepare
-
-def create_voice_cloner(target_sr, gen_model, if_fund_freq, feature_ratio, file_big_npy, is_half, files=False):
-    time_limit = os.getenv("SYSTEM") == "spaces" 
-    def voice_cloner_function(
-        uploaded_audio,
-        example_audio,
-        pitch_shift_semi_up_key,
-        pitch_shift_method,
-        fn_feature_ratio,
-        vc_input_type_radio,
-    # def voice_cloner_function(
-    #     input_audio,
-    #     pitch_shift_semi_up_key,
-    #     pitch_shift_method,
-    #     fn_feature_ratio,
-    ):
-        try:
-            input_audio = uploaded_audio
-            if(vc_input_type_radio == "Uploaded"):
-                input_audio = uploaded_audio
-                print("Using uploaded audio, vc_input_type_radio.value: ", vc_input_type_radio)
-            elif(vc_input_type_radio == "Example"):
-                input_audio = example_audio
-                print("Using example audio, vc_input_type_radio.value: ", vc_input_type_radio)
-            
-            
-            if files:
-                audio, sr = librosa.load(input_audio, sr=16000, mono=True)
-            else:
-                if input_audio is None:
-                    return "Please upload a sound file", None
-                sampling_rate, audio = input_audio
-                duration = audio.shape[0] / sampling_rate
-                if duration > 20:
-                    return "Please upload a sound file that is less than 20 seconds.", None
-                audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-                if len(audio.shape) > 1:
-                    audio = librosa.to_mono(audio.transpose(1, 0))
-                if sampling_rate != 16000:
-                    audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-            times = [0, 0, 0]
-            
-            pitch_shift_semi_up_key = int(pitch_shift_semi_up_key)
-            vc = VC(target_sr, device, is_half)
-            hubert_model = model_prepare.load_hubert()
-            audio_opt = vc.pipeline(
-                hubert_model,
-                gen_model,
-                0,
-                audio,
-                times,
-                pitch_shift_semi_up_key,
-                pitch_shift_method,
-                feature_ratio,
-                file_big_npy,
-                fn_feature_ratio,
-                if_fund_freq,
-            )
-            print(
-                f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
-            )
-            return "Success", (target_sr, audio_opt)
-        except:
-            info = traceback.format_exc()
-            print(info)
-            return info, (None, None)
-    return voice_cloner_function
-
-    def create_voice_cloner_all():
-        models = model_prepare.get_all_models()
-        voice_cloner_functions = []
-        for model in models:
-            name, title, author, cover, voice_cloner_function = model
-            voice_cloner_functions.append(voice_cloner_function)
-        return voice_cloner_functions
\ No newline at end of file
diff --git a/spaces/KPCGD/bingo/src/components/ui/codeblock.tsx b/spaces/KPCGD/bingo/src/components/ui/codeblock.tsx
deleted file mode 100644
index aabda4e3b59f4e36b6ab79feb19d8d18b70e881b..0000000000000000000000000000000000000000
--- a/spaces/KPCGD/bingo/src/components/ui/codeblock.tsx
+++ /dev/null
@@ -1,142 +0,0 @@
-'use client'
-
-import { FC, memo } from 'react'
-import { Prism as SyntaxHighlighter } from 'react-syntax-highlighter'
-import { coldarkDark } from 'react-syntax-highlighter/dist/cjs/styles/prism'
-
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-import { IconCheck, IconCopy, IconDownload } from '@/components/ui/icons'
-import { Button } from '@/components/ui/button'
-
-interface Props {
-  language: string
-  value: string
-}
-
-interface languageMap {
-  [key: string]: string | undefined
-}
-
-export const programmingLanguages: languageMap = {
-  javascript: '.js',
-  python: '.py',
-  java: '.java',
-  c: '.c',
-  cpp: '.cpp',
-  'c++': '.cpp',
-  'c#': '.cs',
-  ruby: '.rb',
-  php: '.php',
-  swift: '.swift',
-  'objective-c': '.m',
-  kotlin: '.kt',
-  typescript: '.ts',
-  go: '.go',
-  perl: '.pl',
-  rust: '.rs',
-  scala: '.scala',
-  haskell: '.hs',
-  lua: '.lua',
-  shell: '.sh',
-  sql: '.sql',
-  html: '.html',
-  css: '.css'
-  // add more file extensions here, make sure the key is same as language prop in CodeBlock.tsx component
-}
-
-export const generateRandomString = (length: number, lowercase = false) => {
-  const chars = 'ABCDEFGHJKLMNPQRSTUVWXY3456789' // excluding similar looking characters like Z, 2, I, 1, O, 0
-  let result = ''
-  for (let i = 0; i < length; i++) {
-    result += chars.charAt(Math.floor(Math.random() * chars.length))
-  }
-  return lowercase ? result.toLowerCase() : result
-}
-
-const CodeBlock: FC<Props> = memo(({ language, value }) => {
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-
-  const downloadAsFile = () => {
-    if (typeof window === 'undefined') {
-      return
-    }
-    const fileExtension = programmingLanguages[language] || '.file'
-    const suggestedFileName = `file-${generateRandomString(
-      3,
-      true
-    )}${fileExtension}`
-    const fileName = window.prompt('Enter file name' || '', suggestedFileName)
-
-    if (!fileName) {
-      // User pressed cancel on prompt.
-      return
-    }
-
-    const blob = new Blob([value], { type: 'text/plain' })
-    const url = URL.createObjectURL(blob)
-    const link = document.createElement('a')
-    link.download = fileName
-    link.href = url
-    link.style.display = 'none'
-    document.body.appendChild(link)
-    link.click()
-    document.body.removeChild(link)
-    URL.revokeObjectURL(url)
-  }
-
-  const onCopy = () => {
-    if (isCopied) return
-    copyToClipboard(value)
-  }
-
-  return (
-    <div className="codeblock relative w-full bg-zinc-950 font-sans">
-      <div className="flex w-full items-center justify-between bg-zinc-800 px-6 py-2 pr-4 text-zinc-100">
-        <span className="text-xs lowercase">{language}</span>
-        <div className="flex items-center space-x-1">
-          <Button
-            variant="ghost"
-            className="hover:bg-zinc-800 focus-visible:ring-1 focus-visible:ring-slate-700 focus-visible:ring-offset-0"
-            onClick={downloadAsFile}
-            size="icon"
-          >
-            <IconDownload />
-            <span className="sr-only">Download</span>
-          </Button>
-          <Button
-            variant="ghost"
-            size="icon"
-            className="text-xs hover:bg-zinc-800 focus-visible:ring-1 focus-visible:ring-slate-700 focus-visible:ring-offset-0"
-            onClick={onCopy}
-          >
-            {isCopied ? <IconCheck /> : <IconCopy />}
-            <span className="sr-only">Copy code</span>
-          </Button>
-        </div>
-      </div>
-      <SyntaxHighlighter
-        language={language}
-        style={coldarkDark}
-        PreTag="div"
-        showLineNumbers
-        customStyle={{
-          margin: 0,
-          width: '100%',
-          background: 'transparent',
-          padding: '1.5rem 1rem'
-        }}
-        codeTagProps={{
-          style: {
-            fontSize: '0.9rem',
-            fontFamily: 'var(--font-mono)'
-          }
-        }}
-      >
-        {value}
-      </SyntaxHighlighter>
-    </div>
-  )
-})
-CodeBlock.displayName = 'CodeBlock'
-
-export { CodeBlock }
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/train/process_ckpt.py b/spaces/Kangarroar/ApplioRVC-Inference/train/process_ckpt.py
deleted file mode 100644
index e3c3dba6df4b4f71a4d0865cdc96241d17da8781..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/train/process_ckpt.py
+++ /dev/null
@@ -1,259 +0,0 @@
-import torch, traceback, os, pdb, sys
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from collections import OrderedDict
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-
-
-def savee(ckpt, sr, if_f0, name, epoch, version, hps):
-    try:
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt.keys():
-            if "enc_q" in key:
-                continue
-            opt["weight"][key] = ckpt[key].half()
-        opt["config"] = [
-            hps.data.filter_length // 2 + 1,
-            32,
-            hps.model.inter_channels,
-            hps.model.hidden_channels,
-            hps.model.filter_channels,
-            hps.model.n_heads,
-            hps.model.n_layers,
-            hps.model.kernel_size,
-            hps.model.p_dropout,
-            hps.model.resblock,
-            hps.model.resblock_kernel_sizes,
-            hps.model.resblock_dilation_sizes,
-            hps.model.upsample_rates,
-            hps.model.upsample_initial_channel,
-            hps.model.upsample_kernel_sizes,
-            hps.model.spk_embed_dim,
-            hps.model.gin_channels,
-            hps.data.sampling_rate,
-        ]
-        opt["info"] = "%sepoch" % epoch
-        opt["sr"] = sr
-        opt["f0"] = if_f0
-        opt["version"] = version
-        torch.save(opt, "weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-
-
-def show_info(path):
-    try:
-        a = torch.load(path, map_location="cpu")
-        return "Epochs: %s\nSample rate: %s\nPitch guidance: %s\nRVC Version: %s" % (
-            a.get("info", "None"),
-            a.get("sr", "None"),
-            a.get("f0", "None"),
-            a.get("version", "None"),
-        )
-    except:
-        return traceback.format_exc()
-
-
-def extract_small_model(path, name, sr, if_f0, info, version):
-    try:
-        ckpt = torch.load(path, map_location="cpu")
-        if "model" in ckpt:
-            ckpt = ckpt["model"]
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt.keys():
-            if "enc_q" in key:
-                continue
-            opt["weight"][key] = ckpt[key].half()
-        if sr == "40k":
-            opt["config"] = [
-                1025,
-                32,
-                192,
-                192,
-                768,
-                2,
-                6,
-                3,
-                0,
-                "1",
-                [3, 7, 11],
-                [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                [10, 10, 2, 2],
-                512,
-                [16, 16, 4, 4],
-                109,
-                256,
-                40000,
-            ]
-        elif sr == "48k":
-            if version == "v1":
-                opt["config"] = [
-                    1025,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 6, 2, 2, 2],
-                    512,
-                    [16, 16, 4, 4, 4],
-                    109,
-                    256,
-                    48000,
-                ]
-            else:
-                opt["config"] = [
-                    1025,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [12, 10, 2, 2],
-                    512,
-                    [24, 20, 4, 4],
-                    109,
-                    256,
-                    48000,
-                ]
-        elif sr == "32k":
-            if version == "v1":
-                opt["config"] = [
-                    513,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 4, 2, 2, 2],
-                    512,
-                    [16, 16, 4, 4, 4],
-                    109,
-                    256,
-                    32000,
-                ]
-            else:
-                opt["config"] = [
-                    513,
-                    32,
-                    192,
-                    192,
-                    768,
-                    2,
-                    6,
-                    3,
-                    0,
-                    "1",
-                    [3, 7, 11],
-                    [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-                    [10, 8, 2, 2],
-                    512,
-                    [20, 16, 4, 4],
-                    109,
-                    256,
-                    32000,
-                ]
-        if info == "":
-            info = "Extracted model."
-        opt["info"] = info
-        opt["version"] = version
-        opt["sr"] = sr
-        opt["f0"] = int(if_f0)
-        torch.save(opt, "weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-
-
-def change_info(path, info, name):
-    try:
-        ckpt = torch.load(path, map_location="cpu")
-        ckpt["info"] = info
-        if name == "":
-            name = os.path.basename(path)
-        torch.save(ckpt, "weights/%s" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
-
-
-def merge(path1, path2, alpha1, sr, f0, info, name, version):
-    try:
-
-        def extract(ckpt):
-            a = ckpt["model"]
-            opt = OrderedDict()
-            opt["weight"] = {}
-            for key in a.keys():
-                if "enc_q" in key:
-                    continue
-                opt["weight"][key] = a[key]
-            return opt
-
-        ckpt1 = torch.load(path1, map_location="cpu")
-        ckpt2 = torch.load(path2, map_location="cpu")
-        cfg = ckpt1["config"]
-        if "model" in ckpt1:
-            ckpt1 = extract(ckpt1)
-        else:
-            ckpt1 = ckpt1["weight"]
-        if "model" in ckpt2:
-            ckpt2 = extract(ckpt2)
-        else:
-            ckpt2 = ckpt2["weight"]
-        if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())):
-            return "Fail to merge the models. The model architectures are not the same."
-        opt = OrderedDict()
-        opt["weight"] = {}
-        for key in ckpt1.keys():
-            # try:
-            if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
-                min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
-                opt["weight"][key] = (
-                    alpha1 * (ckpt1[key][:min_shape0].float())
-                    + (1 - alpha1) * (ckpt2[key][:min_shape0].float())
-                ).half()
-            else:
-                opt["weight"][key] = (
-                    alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float())
-                ).half()
-        # except:
-        #     pdb.set_trace()
-        opt["config"] = cfg
-        """
-        if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000]
-        elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000]
-        elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000]
-        """
-        opt["sr"] = sr
-        opt["f0"] = 1 if f0 else 0
-        opt["version"] = version
-        opt["info"] = info
-        torch.save(opt, "weights/%s.pth" % name)
-        return "Success."
-    except:
-        return traceback.format_exc()
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/train/utils.py b/spaces/Kangarroar/ApplioRVC-Inference/train/utils.py
deleted file mode 100644
index aae833b08acc24b848aa70114fd9b7aad8b1a6ad..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/train/utils.py
+++ /dev/null
@@ -1,500 +0,0 @@
-import os, traceback
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-
-    ##################
-    def go(model, bkey):
-        saved_state_dict = checkpoint_dict[bkey]
-        if hasattr(model, "module"):
-            state_dict = model.module.state_dict()
-        else:
-            state_dict = model.state_dict()
-        new_state_dict = {}
-        for k, v in state_dict.items():  # 模型需要的shape
-            try:
-                new_state_dict[k] = saved_state_dict[k]
-                if saved_state_dict[k].shape != state_dict[k].shape:
-                    print(
-                        "shape-%s-mismatch|need-%s|get-%s"
-                        % (k, state_dict[k].shape, saved_state_dict[k].shape)
-                    )  #
-                    raise KeyError
-            except:
-                # logger.info(traceback.format_exc())
-                logger.info("%s is not in the checkpoint" % k)  # pretrain缺失的
-                new_state_dict[k] = v  # 模型自带的随机值
-        if hasattr(model, "module"):
-            model.module.load_state_dict(new_state_dict, strict=False)
-        else:
-            model.load_state_dict(new_state_dict, strict=False)
-
-    go(combd, "combd")
-    go(sbd, "sbd")
-    #############
-    logger.info("Loaded model weights")
-
-    iteration = checkpoint_dict["iteration"]
-    learning_rate = checkpoint_dict["learning_rate"]
-    if (
-        optimizer is not None and load_opt == 1
-    ):  ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
-        #   try:
-        optimizer.load_state_dict(checkpoint_dict["optimizer"])
-    #   except:
-    #     traceback.print_exc()
-    logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-# def load_checkpoint(checkpoint_path, model, optimizer=None):
-#   assert os.path.isfile(checkpoint_path)
-#   checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-#   iteration = checkpoint_dict['iteration']
-#   learning_rate = checkpoint_dict['learning_rate']
-#   if optimizer is not None:
-#     optimizer.load_state_dict(checkpoint_dict['optimizer'])
-#   # print(1111)
-#   saved_state_dict = checkpoint_dict['model']
-#   # print(1111)
-#
-#   if hasattr(model, 'module'):
-#     state_dict = model.module.state_dict()
-#   else:
-#     state_dict = model.state_dict()
-#   new_state_dict= {}
-#   for k, v in state_dict.items():
-#     try:
-#       new_state_dict[k] = saved_state_dict[k]
-#     except:
-#       logger.info("%s is not in the checkpoint" % k)
-#       new_state_dict[k] = v
-#   if hasattr(model, 'module'):
-#     model.module.load_state_dict(new_state_dict)
-#   else:
-#     model.load_state_dict(new_state_dict)
-#   logger.info("Loaded checkpoint '{}' (epoch {})" .format(
-#     checkpoint_path, iteration))
-#   return model, optimizer, learning_rate, iteration
-def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-
-    saved_state_dict = checkpoint_dict["model"]
-    if hasattr(model, "module"):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():  # 模型需要的shape
-        try:
-            new_state_dict[k] = saved_state_dict[k]
-            if saved_state_dict[k].shape != state_dict[k].shape:
-                print(
-                    "shape-%s-mismatch|need-%s|get-%s"
-                    % (k, state_dict[k].shape, saved_state_dict[k].shape)
-                )  #
-                raise KeyError
-        except:
-            # logger.info(traceback.format_exc())
-            logger.info("%s is not in the checkpoint" % k)  # pretrain缺失的
-            new_state_dict[k] = v  # 模型自带的随机值
-    if hasattr(model, "module"):
-        model.module.load_state_dict(new_state_dict, strict=False)
-    else:
-        model.load_state_dict(new_state_dict, strict=False)
-    logger.info("Loaded model weights")
-
-    iteration = checkpoint_dict["iteration"]
-    learning_rate = checkpoint_dict["learning_rate"]
-    if (
-        optimizer is not None and load_opt == 1
-    ):  ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
-        #   try:
-        optimizer.load_state_dict(checkpoint_dict["optimizer"])
-    #   except:
-    #     traceback.print_exc()
-    logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info(
-        "Saving model and optimizer state at epoch {} to {}".format(
-            iteration, checkpoint_path
-        )
-    )
-    if hasattr(model, "module"):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    torch.save(
-        {
-            "model": state_dict,
-            "iteration": iteration,
-            "optimizer": optimizer.state_dict(),
-            "learning_rate": learning_rate,
-        },
-        checkpoint_path,
-    )
-
-
-def save_checkpoint_d(combd, sbd, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info(
-        "Saving model and optimizer state at epoch {} to {}".format(
-            iteration, checkpoint_path
-        )
-    )
-    if hasattr(combd, "module"):
-        state_dict_combd = combd.module.state_dict()
-    else:
-        state_dict_combd = combd.state_dict()
-    if hasattr(sbd, "module"):
-        state_dict_sbd = sbd.module.state_dict()
-    else:
-        state_dict_sbd = sbd.state_dict()
-    torch.save(
-        {
-            "combd": state_dict_combd,
-            "sbd": state_dict_sbd,
-            "iteration": iteration,
-            "optimizer": optimizer.state_dict(),
-            "learning_rate": learning_rate,
-        },
-        checkpoint_path,
-    )
-
-
-def summarize(
-    writer,
-    global_step,
-    scalars={},
-    histograms={},
-    images={},
-    audios={},
-    audio_sampling_rate=22050,
-):
-    for k, v in scalars.items():
-        writer.add_scalar(k, v, global_step)
-    for k, v in histograms.items():
-        writer.add_histogram(k, v, global_step)
-    for k, v in images.items():
-        writer.add_image(k, v, global_step, dataformats="HWC")
-    for k, v in audios.items():
-        writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-    f_list = glob.glob(os.path.join(dir_path, regex))
-    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-    x = f_list[-1]
-    print(x)
-    return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger("matplotlib")
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger("matplotlib")
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(
-        alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
-    )
-    fig.colorbar(im, ax=ax)
-    xlabel = "Decoder timestep"
-    if info is not None:
-        xlabel += "\n\n" + info
-    plt.xlabel(xlabel)
-    plt.ylabel("Encoder timestep")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-    with open(filename, encoding='utf-8') as f:
-        filepaths_and_text = [line.strip().split(split) for line in f]
-        filepaths_and_text = [item for item in filepaths_and_text if len(item) == 5]  # ensure there are 5 items.
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    """
-    todo:
-      结尾七人组：
-        保存频率、总epoch                     done
-        bs                                    done
-        pretrainG、pretrainD                  done
-        卡号：os.en["CUDA_VISIBLE_DEVICES"]   done
-        if_latest                             done
-      模型：if_f0                             done
-      采样率：自动选择config                  done
-      是否缓存数据集进GPU:if_cache_data_in_gpu done
-
-      -m:
-        自动决定training_files路径,改掉train_nsf_load_pretrain.py里的hps.data.training_files    done
-      -c不要了
-    """
-    parser = argparse.ArgumentParser()
-    # parser.add_argument('-c', '--config', type=str, default="configs/40k.json",help='JSON file for configuration')
-    parser.add_argument(
-        "-se",
-        "--save_every_epoch",
-        type=int,
-        required=True,
-        help="checkpoint save frequency (epoch)",
-    )
-    parser.add_argument(
-        "-te", "--total_epoch", type=int, required=True, help="total_epoch"
-    )
-    parser.add_argument(
-        "-pg", "--pretrainG", type=str, default="", help="Pretrained Discriminator path"
-    )
-    parser.add_argument(
-        "-pd", "--pretrainD", type=str, default="", help="Pretrained Generator path"
-    )
-    parser.add_argument("-g", "--gpus", type=str, default="0", help="split by -")
-    parser.add_argument(
-        "-bs", "--batch_size", type=int, required=True, help="batch size"
-    )
-    parser.add_argument(
-        "-e", "--experiment_dir", type=str, required=True, help="experiment dir"
-    )  # -m
-    parser.add_argument(
-        "-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
-    )
-    parser.add_argument(
-        "-sw",
-        "--save_every_weights",
-        type=str,
-        default="0",
-        help="save the extracted model in weights directory when saving checkpoints",
-    )
-    parser.add_argument(
-        "-v", "--version", type=str, required=True, help="model version"
-    )
-    parser.add_argument(
-        "-f0",
-        "--if_f0",
-        type=int,
-        required=True,
-        help="use f0 as one of the inputs of the model, 1 or 0",
-    )
-    parser.add_argument(
-        "-l",
-        "--if_latest",
-        type=int,
-        required=True,
-        help="if only save the latest G/D pth file, 1 or 0",
-    )
-    parser.add_argument(
-        "-c",
-        "--if_cache_data_in_gpu",
-        type=int,
-        required=True,
-        help="if caching the dataset in GPU memory, 1 or 0",
-    )
-    parser.add_argument(
-        "-li", "--log_interval", type=int, required=True, help="log interval"
-    )
-
-    args = parser.parse_args()
-    name = args.experiment_dir
-    experiment_dir = os.path.join("./logs", args.experiment_dir)
-
-    if not os.path.exists(experiment_dir):
-        os.makedirs(experiment_dir)
-
-    if args.version == "v1" or args.sample_rate == "40k":
-        config_path = "configs/%s.json" % args.sample_rate
-    else:
-        config_path = "configs/%s_v2.json" % args.sample_rate
-    config_save_path = os.path.join(experiment_dir, "config.json")
-    if init:
-        with open(config_path, "r") as f:
-            data = f.read()
-        with open(config_save_path, "w") as f:
-            f.write(data)
-    else:
-        with open(config_save_path, "r") as f:
-            data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = hparams.experiment_dir = experiment_dir
-    hparams.save_every_epoch = args.save_every_epoch
-    hparams.name = name
-    hparams.total_epoch = args.total_epoch
-    hparams.pretrainG = args.pretrainG
-    hparams.pretrainD = args.pretrainD
-    hparams.version = args.version
-    hparams.gpus = args.gpus
-    hparams.train.batch_size = args.batch_size
-    hparams.sample_rate = args.sample_rate
-    hparams.if_f0 = args.if_f0
-    hparams.if_latest = args.if_latest
-    hparams.save_every_weights = args.save_every_weights
-    hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
-    hparams.data.training_files = "%s/filelist.txt" % experiment_dir
-
-    hparams.train.log_interval = args.log_interval
-
-    # Update log_interval in the 'train' section of the config dictionary
-    config["train"]["log_interval"] = args.log_interval
-
-    # Save the updated config back to the config_save_path
-    with open(config_save_path, "w") as f:
-        json.dump(config, f, indent=4)
-
-    return hparams
-
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn(
-            "{} is not a git repository, therefore hash value comparison will be ignored.".format(
-                source_dir
-            )
-        )
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warn(
-                "git hash values are different. {}(saved) != {}(current)".format(
-                    saved_hash[:8], cur_hash[:8]
-                )
-            )
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-class HParams:
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()
diff --git a/spaces/KenjieDec/RemBG/rembg/commands/p_command.py b/spaces/KenjieDec/RemBG/rembg/commands/p_command.py
deleted file mode 100644
index 2163bfbb2332f5b23f2fa9c305b15df9c6c425ff..0000000000000000000000000000000000000000
--- a/spaces/KenjieDec/RemBG/rembg/commands/p_command.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import json
-import pathlib
-import time
-from typing import cast
-
-import click
-import filetype
-from tqdm import tqdm
-from watchdog.events import FileSystemEvent, FileSystemEventHandler
-from watchdog.observers import Observer
-
-from ..bg import remove
-from ..session_factory import new_session
-from ..sessions import sessions_names
-
-
-@click.command(
-    name="p",
-    help="for a folder as input",
-)
-@click.option(
-    "-m",
-    "--model",
-    default="u2net",
-    type=click.Choice(sessions_names),
-    show_default=True,
-    show_choices=True,
-    help="model name",
-)
-@click.option(
-    "-a",
-    "--alpha-matting",
-    is_flag=True,
-    show_default=True,
-    help="use alpha matting",
-)
-@click.option(
-    "-af",
-    "--alpha-matting-foreground-threshold",
-    default=240,
-    type=int,
-    show_default=True,
-    help="trimap fg threshold",
-)
-@click.option(
-    "-ab",
-    "--alpha-matting-background-threshold",
-    default=10,
-    type=int,
-    show_default=True,
-    help="trimap bg threshold",
-)
-@click.option(
-    "-ae",
-    "--alpha-matting-erode-size",
-    default=10,
-    type=int,
-    show_default=True,
-    help="erode size",
-)
-@click.option(
-    "-om",
-    "--only-mask",
-    is_flag=True,
-    show_default=True,
-    help="output only the mask",
-)
-@click.option(
-    "-ppm",
-    "--post-process-mask",
-    is_flag=True,
-    show_default=True,
-    help="post process the mask",
-)
-@click.option(
-    "-w",
-    "--watch",
-    default=False,
-    is_flag=True,
-    show_default=True,
-    help="watches a folder for changes",
-)
-@click.option(
-    "-bgc",
-    "--bgcolor",
-    default=None,
-    type=(int, int, int, int),
-    nargs=4,
-    help="Background color (R G B A) to replace the removed background with",
-)
-@click.option("-x", "--extras", type=str)
-@click.argument(
-    "input",
-    type=click.Path(
-        exists=True,
-        path_type=pathlib.Path,
-        file_okay=False,
-        dir_okay=True,
-        readable=True,
-    ),
-)
-@click.argument(
-    "output",
-    type=click.Path(
-        exists=False,
-        path_type=pathlib.Path,
-        file_okay=False,
-        dir_okay=True,
-        writable=True,
-    ),
-)
-def p_command(
-    model: str,
-    extras: str,
-    input: pathlib.Path,
-    output: pathlib.Path,
-    watch: bool,
-    **kwargs,
-) -> None:
-    try:
-        kwargs.update(json.loads(extras))
-    except Exception:
-        pass
-
-    session = new_session(model)
-
-    def process(each_input: pathlib.Path) -> None:
-        try:
-            mimetype = filetype.guess(each_input)
-            if mimetype is None:
-                return
-            if mimetype.mime.find("image") < 0:
-                return
-
-            each_output = (output / each_input.name).with_suffix(".png")
-            each_output.parents[0].mkdir(parents=True, exist_ok=True)
-
-            if not each_output.exists():
-                each_output.write_bytes(
-                    cast(
-                        bytes,
-                        remove(each_input.read_bytes(), session=session, **kwargs),
-                    )
-                )
-
-                if watch:
-                    print(
-                        f"processed: {each_input.absolute()} -> {each_output.absolute()}"
-                    )
-        except Exception as e:
-            print(e)
-
-    inputs = list(input.glob("**/*"))
-    if not watch:
-        inputs = tqdm(inputs)
-
-    for each_input in inputs:
-        if not each_input.is_dir():
-            process(each_input)
-
-    if watch:
-        observer = Observer()
-
-        class EventHandler(FileSystemEventHandler):
-            def on_any_event(self, event: FileSystemEvent) -> None:
-                if not (
-                    event.is_directory or event.event_type in ["deleted", "closed"]
-                ):
-                    process(pathlib.Path(event.src_path))
-
-        event_handler = EventHandler()
-        observer.schedule(event_handler, input, recursive=False)
-        observer.start()
-
-        try:
-            while True:
-                time.sleep(1)
-
-        finally:
-            observer.stop()
-            observer.join()
diff --git a/spaces/Kevin676/AutoGPT/autogpt/json_utils/__init__.py b/spaces/Kevin676/AutoGPT/autogpt/json_utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Kirihasan/rvc-holo/config.py b/spaces/Kirihasan/rvc-holo/config.py
deleted file mode 100644
index c0c16e0017efbcaf250cb539a1d0edb4e83575e4..0000000000000000000000000000000000000000
--- a/spaces/Kirihasan/rvc-holo/config.py
+++ /dev/null
@@ -1,88 +0,0 @@
-########################硬件参数########################
-
-# 填写cuda:x, cpu 或 mps, x指代第几张卡，只支持 N卡 / Apple Silicon 加速
-device = "cuda:0"
-
-# 9-10-20-30-40系显卡无脑True，不影响质量，>=20显卡开启有加速
-is_half = True
-
-# 默认0用上所有线程，写数字限制CPU资源使用
-n_cpu = 0
-
-########################硬件参数########################
-
-
-##################下为参数处理逻辑，勿动##################
-
-########################命令行参数########################
-import argparse
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--port", type=int, default=7865, help="Listen port")
-parser.add_argument("--pycmd", type=str, default="python", help="Python command")
-parser.add_argument("--colab", action="store_true", help="Launch in colab")
-parser.add_argument(
-    "--noparallel", action="store_true", help="Disable parallel processing"
-)
-parser.add_argument(
-    "--noautoopen", action="store_true", help="Do not open in browser automatically"
-)
-cmd_opts, unknown = parser.parse_known_args()
-
-python_cmd = cmd_opts.pycmd
-listen_port = cmd_opts.port
-iscolab = cmd_opts.colab
-noparallel = cmd_opts.noparallel
-noautoopen = cmd_opts.noautoopen
-########################命令行参数########################
-
-import sys
-import torch
-
-
-# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
-# check `getattr` and try it for compatibility
-def has_mps() -> bool:
-    if sys.platform != "darwin":
-        return False
-    else:
-        if not getattr(torch, "has_mps", False):
-            return False
-        try:
-            torch.zeros(1).to(torch.device("mps"))
-            return True
-        except Exception:
-            return False
-
-
-if not torch.cuda.is_available():
-    if has_mps():
-        print("没有发现支持的N卡, 使用MPS进行推理")
-        device = "mps"
-    else:
-        print("没有发现支持的N卡, 使用CPU进行推理")
-        device = "cpu"
-        is_half = False
-
-if device not in ["cpu", "mps"]:
-    gpu_name = torch.cuda.get_device_name(int(device.split(":")[-1]))
-    if "16" in gpu_name or "MX" in gpu_name:
-        print("16系显卡/MX系显卡强制单精度")
-        is_half = False
-
-from multiprocessing import cpu_count
-
-if n_cpu == 0:
-    n_cpu = cpu_count()
-if is_half:
-    # 6G显存配置
-    x_pad = 3
-    x_query = 10
-    x_center = 60
-    x_max = 65
-else:
-    # 5G显存配置
-    x_pad = 1
-    x_query = 6
-    x_center = 38
-    x_max = 41
diff --git a/spaces/Kororinpa/Amadeus_Project/README.md b/spaces/Kororinpa/Amadeus_Project/README.md
deleted file mode 100644
index 27f4110d63a316481440b00905874498566a7f7c..0000000000000000000000000000000000000000
--- a/spaces/Kororinpa/Amadeus_Project/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Amadeus Project
-emoji: 🌖
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kuachi/hololive/infer_pack/attentions.py b/spaces/Kuachi/hololive/infer_pack/attentions.py
deleted file mode 100644
index 77cb63ffccf3e33badf22d50862a64ba517b487f..0000000000000000000000000000000000000000
--- a/spaces/Kuachi/hololive/infer_pack/attentions.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from infer_pack import commons
-from infer_pack import modules
-from infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=10,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        """
-        x: decoder input
-        h: encoder output
-        """
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
diff --git a/spaces/Latryna/roop/roop/processors/frame/__init__.py b/spaces/Latryna/roop/roop/processors/frame/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Li2024/chatai/README.md b/spaces/Li2024/chatai/README.md
deleted file mode 100644
index ee778dd25f0c1b51e86463a502bbebfb85e8fc72..0000000000000000000000000000000000000000
--- a/spaces/Li2024/chatai/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Chatai
-emoji: 🐨
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Manjushri/MusicGen/audiocraft/quantization/core_vq.py b/spaces/Manjushri/MusicGen/audiocraft/quantization/core_vq.py
deleted file mode 100644
index e1896bb1788a945a1f7be6369abb255ecf72c7a0..0000000000000000000000000000000000000000
--- a/spaces/Manjushri/MusicGen/audiocraft/quantization/core_vq.py
+++ /dev/null
@@ -1,400 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import typing as tp
-
-from einops import rearrange, repeat
-import flashy
-import torch
-from torch import nn, einsum
-import torch.nn.functional as F
-
-
-def exists(val: tp.Optional[tp.Any]) -> bool:
-    return val is not None
-
-
-def default(val: tp.Any, d: tp.Any) -> tp.Any:
-    return val if exists(val) else d
-
-
-def l2norm(t):
-    return F.normalize(t, p=2, dim=-1)
-
-
-def ema_inplace(moving_avg, new, decay: float):
-    moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
-
-
-def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5):
-    return (x + epsilon) / (x.sum() + n_categories * epsilon)
-
-
-def uniform_init(*shape: int):
-    t = torch.empty(shape)
-    nn.init.kaiming_uniform_(t)
-    return t
-
-
-def sample_vectors(samples, num: int):
-    num_samples, device = samples.shape[0], samples.device
-
-    if num_samples >= num:
-        indices = torch.randperm(num_samples, device=device)[:num]
-    else:
-        indices = torch.randint(0, num_samples, (num,), device=device)
-
-    return samples[indices]
-
-
-def kmeans(samples, num_clusters: int, num_iters: int = 10):
-    dim, dtype = samples.shape[-1], samples.dtype
-
-    means = sample_vectors(samples, num_clusters)
-
-    for _ in range(num_iters):
-        diffs = rearrange(samples, "n d -> n () d") - rearrange(
-            means, "c d -> () c d"
-        )
-        dists = -(diffs ** 2).sum(dim=-1)
-
-        buckets = dists.max(dim=-1).indices
-        bins = torch.bincount(buckets, minlength=num_clusters)
-        zero_mask = bins == 0
-        bins_min_clamped = bins.masked_fill(zero_mask, 1)
-
-        new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype)
-        new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples)
-        new_means = new_means / bins_min_clamped[..., None]
-
-        means = torch.where(zero_mask[..., None], means, new_means)
-
-    return means, bins
-
-
-def orthgonal_loss_fn(t):
-    # eq (2) from https://arxiv.org/abs/2112.00384
-    n = t.shape[0]
-    normed_codes = l2norm(t)
-    identity = torch.eye(n, device=t.device)
-    cosine_sim = einsum("i d, j d -> i j", normed_codes, normed_codes)
-    return ((cosine_sim - identity) ** 2).sum() / (n ** 2)
-
-
-class EuclideanCodebook(nn.Module):
-    """Codebook with Euclidean distance.
-
-    Args:
-        dim (int): Dimension.
-        codebook_size (int): Codebook size.
-        kmeans_init (bool): Whether to use k-means to initialize the codebooks.
-            If set to true, run the k-means algorithm on the first training batch and use
-            the learned centroids as initialization.
-        kmeans_iters (int): Number of iterations used for k-means algorithm at initialization.
-        decay (float): Decay for exponential moving average over the codebooks.
-        epsilon (float): Epsilon value for numerical stability.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-    """
-    def __init__(
-        self,
-        dim: int,
-        codebook_size: int,
-        kmeans_init: int = False,
-        kmeans_iters: int = 10,
-        decay: float = 0.8,
-        epsilon: float = 1e-5,
-        threshold_ema_dead_code: int = 2,
-    ):
-        super().__init__()
-        self.decay = decay
-        init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = uniform_init if not kmeans_init else torch.zeros
-        embed = init_fn(codebook_size, dim)
-
-        self.codebook_size = codebook_size
-
-        self.kmeans_iters = kmeans_iters
-        self.epsilon = epsilon
-        self.threshold_ema_dead_code = threshold_ema_dead_code
-
-        self.register_buffer("inited", torch.Tensor([not kmeans_init]))
-        self.register_buffer("cluster_size", torch.zeros(codebook_size))
-        self.register_buffer("embed", embed)
-        self.register_buffer("embed_avg", embed.clone())
-
-    @torch.jit.ignore
-    def init_embed_(self, data):
-        if self.inited:
-            return
-
-        embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
-        self.embed.data.copy_(embed)
-        self.embed_avg.data.copy_(embed.clone())
-        self.cluster_size.data.copy_(cluster_size)
-        self.inited.data.copy_(torch.Tensor([True]))
-        # Make sure all buffers across workers are in sync after initialization
-        flashy.distrib.broadcast_tensors(self.buffers())
-
-    def replace_(self, samples, mask):
-        modified_codebook = torch.where(
-            mask[..., None], sample_vectors(samples, self.codebook_size), self.embed
-        )
-        self.embed.data.copy_(modified_codebook)
-
-    def expire_codes_(self, batch_samples):
-        if self.threshold_ema_dead_code == 0:
-            return
-
-        expired_codes = self.cluster_size < self.threshold_ema_dead_code
-        if not torch.any(expired_codes):
-            return
-
-        batch_samples = rearrange(batch_samples, "... d -> (...) d")
-        self.replace_(batch_samples, mask=expired_codes)
-        flashy.distrib.broadcast_tensors(self.buffers())
-
-    def preprocess(self, x):
-        x = rearrange(x, "... d -> (...) d")
-        return x
-
-    def quantize(self, x):
-        embed = self.embed.t()
-        dist = -(
-            x.pow(2).sum(1, keepdim=True)
-            - 2 * x @ embed
-            + embed.pow(2).sum(0, keepdim=True)
-        )
-        embed_ind = dist.max(dim=-1).indices
-        return embed_ind
-
-    def postprocess_emb(self, embed_ind, shape):
-        return embed_ind.view(*shape[:-1])
-
-    def dequantize(self, embed_ind):
-        quantize = F.embedding(embed_ind, self.embed)
-        return quantize
-
-    def encode(self, x):
-        shape = x.shape
-        # pre-process
-        x = self.preprocess(x)
-        # quantize
-        embed_ind = self.quantize(x)
-        # post-process
-        embed_ind = self.postprocess_emb(embed_ind, shape)
-        return embed_ind
-
-    def decode(self, embed_ind):
-        quantize = self.dequantize(embed_ind)
-        return quantize
-
-    def forward(self, x):
-        shape, dtype = x.shape, x.dtype
-        x = self.preprocess(x)
-        self.init_embed_(x)
-
-        embed_ind = self.quantize(x)
-        embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
-        embed_ind = self.postprocess_emb(embed_ind, shape)
-        quantize = self.dequantize(embed_ind)
-
-        if self.training:
-            # We do the expiry of code at that point as buffers are in sync
-            # and all the workers will take the same decision.
-            self.expire_codes_(x)
-            ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
-            embed_sum = x.t() @ embed_onehot
-            ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
-            cluster_size = (
-                laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon)
-                * self.cluster_size.sum()
-            )
-            embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
-            self.embed.data.copy_(embed_normalized)
-
-        return quantize, embed_ind
-
-
-class VectorQuantization(nn.Module):
-    """Vector quantization implementation.
-    Currently supports only euclidean distance.
-
-    Args:
-        dim (int): Dimension
-        codebook_size (int): Codebook size
-        codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim.
-        decay (float): Decay for exponential moving average over the codebooks.
-        epsilon (float): Epsilon value for numerical stability.
-        kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
-        kmeans_iters (int): Number of iterations used for kmeans initialization.
-        threshold_ema_dead_code (int):
-        channels_last (bool): Channels are the last dimension in the input tensors.
-        commitment_weight (float): Weight for commitment loss.
-        orthogonal_reg_weight (float): Orthogonal regularization weights.
-        orthogonal_reg_active_codes_only (bool): Apply orthogonal regularization only on active codes.
-        orthogonal_reg_max_codes (optional int): Maximum number of codes to consider
-            for orthogonal regulariation.
-        threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
-            that have an exponential moving average cluster size less than the specified threshold with
-            randomly selected vector from the current batch.
-    """
-    def __init__(
-        self,
-        dim: int,
-        codebook_size: int,
-        codebook_dim: tp.Optional[int] = None,
-        decay: float = 0.8,
-        epsilon: float = 1e-5,
-        kmeans_init: bool = False,
-        kmeans_iters: int = 10,
-        threshold_ema_dead_code: int = 2,
-        channels_last: bool = False,
-        commitment_weight: float = 1.,
-        orthogonal_reg_weight: float = 0.0,
-        orthogonal_reg_active_codes_only: bool = False,
-        orthogonal_reg_max_codes: tp.Optional[int] = None,
-    ):
-        super().__init__()
-        _codebook_dim: int = default(codebook_dim, dim)
-
-        requires_projection = _codebook_dim != dim
-        self.project_in = (nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity())
-        self.project_out = (nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity())
-
-        self.epsilon = epsilon
-        self.commitment_weight = commitment_weight
-
-        self.orthogonal_reg_weight = orthogonal_reg_weight
-        self.orthogonal_reg_active_codes_only = orthogonal_reg_active_codes_only
-        self.orthogonal_reg_max_codes = orthogonal_reg_max_codes
-
-        self._codebook = EuclideanCodebook(dim=_codebook_dim, codebook_size=codebook_size,
-                                           kmeans_init=kmeans_init, kmeans_iters=kmeans_iters,
-                                           decay=decay, epsilon=epsilon,
-                                           threshold_ema_dead_code=threshold_ema_dead_code)
-        self.codebook_size = codebook_size
-
-        self.channels_last = channels_last
-
-    @property
-    def codebook(self):
-        return self._codebook.embed
-
-    @property
-    def inited(self):
-        return self._codebook.inited
-
-    def _preprocess(self, x):
-        if not self.channels_last:
-            x = rearrange(x, "b d n -> b n d")
-        return x
-
-    def _postprocess(self, quantize):
-        if not self.channels_last:
-            quantize = rearrange(quantize, "b n d -> b d n")
-        return quantize
-
-    def encode(self, x):
-        x = self._preprocess(x)
-        x = self.project_in(x)
-        embed_in = self._codebook.encode(x)
-        return embed_in
-
-    def decode(self, embed_ind):
-        quantize = self._codebook.decode(embed_ind)
-        quantize = self.project_out(quantize)
-        quantize = self._postprocess(quantize)
-        return quantize
-
-    def forward(self, x):
-        device = x.device
-        x = self._preprocess(x)
-
-        x = self.project_in(x)
-        quantize, embed_ind = self._codebook(x)
-
-        if self.training:
-            quantize = x + (quantize - x).detach()
-
-        loss = torch.tensor([0.0], device=device, requires_grad=self.training)
-
-        if self.training:
-            if self.commitment_weight > 0:
-                commit_loss = F.mse_loss(quantize.detach(), x)
-                loss = loss + commit_loss * self.commitment_weight
-
-            if self.orthogonal_reg_weight > 0:
-                codebook = self.codebook
-
-                if self.orthogonal_reg_active_codes_only:
-                    # only calculate orthogonal loss for the activated codes for this batch
-                    unique_code_ids = torch.unique(embed_ind)
-                    codebook = codebook[unique_code_ids]
-
-                num_codes = codebook.shape[0]
-                if exists(self.orthogonal_reg_max_codes) and num_codes > self.orthogonal_reg_max_codes:
-                    rand_ids = torch.randperm(num_codes, device=device)[:self.orthogonal_reg_max_codes]
-                    codebook = codebook[rand_ids]
-
-                orthogonal_reg_loss = orthgonal_loss_fn(codebook)
-                loss = loss + orthogonal_reg_loss * self.orthogonal_reg_weight
-
-        quantize = self.project_out(quantize)
-        quantize = self._postprocess(quantize)
-
-        return quantize, embed_ind, loss
-
-
-class ResidualVectorQuantization(nn.Module):
-    """Residual vector quantization implementation.
-
-    Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf
-    """
-    def __init__(self, *, num_quantizers, **kwargs):
-        super().__init__()
-        self.layers = nn.ModuleList(
-            [VectorQuantization(**kwargs) for _ in range(num_quantizers)]
-        )
-
-    def forward(self, x, n_q: tp.Optional[int] = None):
-        quantized_out = 0.0
-        residual = x
-
-        all_losses = []
-        all_indices = []
-
-        n_q = n_q or len(self.layers)
-
-        for i, layer in enumerate(self.layers[:n_q]):
-            quantized, indices, loss = layer(residual)
-            residual = residual - quantized
-            quantized_out = quantized_out + quantized
-            all_indices.append(indices)
-            all_losses.append(loss)
-
-        out_losses, out_indices = map(torch.stack, (all_losses, all_indices))
-        return quantized_out, out_indices, out_losses
-
-    def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None) -> torch.Tensor:
-        residual = x
-        all_indices = []
-        n_q = n_q or len(self.layers)
-        for layer in self.layers[:n_q]:
-            indices = layer.encode(residual)
-            quantized = layer.decode(indices)
-            residual = residual - quantized
-            all_indices.append(indices)
-        out_indices = torch.stack(all_indices)
-        return out_indices
-
-    def decode(self, q_indices: torch.Tensor) -> torch.Tensor:
-        quantized_out = torch.tensor(0.0, device=q_indices.device)
-        for i, indices in enumerate(q_indices):
-            layer = self.layers[i]
-            quantized = layer.decode(indices)
-            quantized_out = quantized_out + quantized
-        return quantized_out
diff --git a/spaces/Marshalls/testmtd/models/flowplusplus/nn.py b/spaces/Marshalls/testmtd/models/flowplusplus/nn.py
deleted file mode 100644
index 1fb218d753edcea802bfc93ff09353bc2cebac8d..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/models/flowplusplus/nn.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from torch.nn.utils import weight_norm
-from models.util import concat_elu, WNConv2d
-
-class NN(nn.Module):
-    """Neural network used to parametrize the transformations of an MLCoupling.
-
-    An `NN` is a stack of blocks, where each block consists of the following
-    two layers connected in a residual fashion:
-      1. Conv: input -> nonlinearit -> conv3x3 -> nonlinearity -> gate
-      2. Attn: input -> conv1x1 -> multihead self-attention -> gate,
-    where gate refers to a 1×1 convolution that doubles the number of channels,
-    followed by a gated linear unit (Dauphin et al., 2016).
-    The convolutional layer is identical to the one used by PixelCNN++
-    (Salimans et al., 2017), and the multi-head self attention mechanism we
-    use is identical to the one in the Transformer (Vaswani et al., 2017).
-
-    Args:
-        in_channels (int): Number of channels in the input.
-        out_channels (int): Number of channels in the output.
-        num_channels (int): Number of channels in each block of the network.
-        num_blocks (int): Number of blocks in the network.
-        num_components (int): Number of components in the mixture.
-        drop_prob (float): Dropout probability.
-        use_attn (bool): Use attention in each block.
-        aux_channels (int): Number of channels in optional auxiliary input.
-    """
-    def __init__(self, in_channels, out_channels, num_channels, num_blocks, num_components, drop_prob, use_attn=True, aux_channels=None):
-        #import pdb;pdb.set_trace()
-        super(NN, self).__init__()
-        self.k = num_components  # k = number of mixture components
-        self.in_conv = WNConv2d(in_channels, num_channels, kernel_size=3, padding=1)
-        self.share_attn_params = False
-        if self.share_attn_params:
-            self.mid_conv = ConvAttnBlock(num_channels, drop_prob, use_attn, aux_channels)
-            self.num_blocks = num_blocks
-        else:
-            self.mid_convs = nn.ModuleList([ConvAttnBlock(num_channels, drop_prob, use_attn, aux_channels)
-                                            for _ in range(num_blocks)])
-        self.out_conv = WNConv2d(num_channels, out_channels * (2 + 3 * self.k),
-                                 kernel_size=3, padding=1)
-        self.rescale = weight_norm(Rescale(out_channels))
-        self.out_channels = out_channels
-
-    def forward(self, x, aux=None):
-        b, c, h, w = x.size()
-        #import pdb;pdb.set_trace()
-        x = self.in_conv(x)
-        if self.share_attn_params:
-            for _ in range(self.num_blocks):
-                x = self.mid_conv(x, aux)
-        else:
-            for conv in self.mid_convs:
-                x = conv(x, aux)
-        x = self.out_conv(x)
-
-        #import pdb;pdb.set_trace()
-        # Split into components and post-process
-        x = x.view(b, -1, self.out_channels, h, w)
-        s, t, pi, mu, scales = x.split((1, 1, self.k, self.k, self.k), dim=1)
-        s = self.rescale(torch.tanh(s.squeeze(1)))
-        t = t.squeeze(1)
-        scales = scales.clamp(min=-7)  # From the code in original Flow++ paper
-
-        return s, t, pi, mu, scales
-
-
-class ConvAttnBlock(nn.Module):
-    def __init__(self, num_channels, drop_prob, use_attn, aux_channels):
-        super(ConvAttnBlock, self).__init__()
-        self.conv = GatedConv(num_channels, drop_prob, aux_channels)
-        self.norm_1 = nn.LayerNorm(num_channels)
-        if use_attn:
-            self.attn = GatedAttn(num_channels, drop_prob=drop_prob)
-            self.norm_2 = nn.LayerNorm(num_channels)
-        else:
-            self.attn = None
-
-    def forward(self, x, aux=None):
-        x = self.conv(x, aux) + x
-        x = x.permute(0, 2, 3, 1)  # (b, h, w, c)
-        x = self.norm_1(x)
-
-        if self.attn:
-            x = self.attn(x) + x
-            x = self.norm_2(x)
-        x = x.permute(0, 3, 1, 2)  # (b, c, h, w)
-
-        return x
-
-
-class GatedAttn(nn.Module):
-    """Gated Multi-Head Self-Attention Block
-
-    Based on the paper:
-    "Attention Is All You Need"
-    by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones,
-        Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
-    (https://arxiv.org/abs/1706.03762).
-
-    Args:
-        d_model (int): Number of channels in the input.
-        num_heads (int): Number of attention heads.
-        drop_prob (float): Dropout probability.
-    """
-    def __init__(self, d_model, num_heads=4, drop_prob=0.):
-        super(GatedAttn, self).__init__()
-        self.d_model = d_model
-        self.num_heads = num_heads
-        self.drop_prob = drop_prob
-        self.in_proj = weight_norm(nn.Linear(d_model, 3 * d_model, bias=False))
-        self.gate = weight_norm(nn.Linear(d_model, 2 * d_model))
-
-    def forward(self, x):
-        # Flatten and encode position
-        b, h, w, c = x.size()
-        x = x.view(b, h * w, c)
-        _, seq_len, num_channels = x.size()
-        pos_encoding = self.get_pos_enc(seq_len, num_channels, x.device)
-        x = x + pos_encoding
-
-        # Compute q, k, v
-        memory, query = torch.split(self.in_proj(x), (2 * c, c), dim=-1)
-        q = self.split_last_dim(query, self.num_heads)
-        k, v = [self.split_last_dim(tensor, self.num_heads)
-                for tensor in torch.split(memory, self.d_model, dim=2)]
-
-        # Compute attention and reshape
-        key_depth_per_head = self.d_model // self.num_heads
-        q *= key_depth_per_head ** -0.5
-        x = self.dot_product_attention(q, k, v)
-        x = self.combine_last_two_dim(x.permute(0, 2, 1, 3))
-        x = x.transpose(1, 2).view(b, c, h, w).permute(0, 2, 3, 1)  # (b, h, w, c)
-
-        x = self.gate(x)
-        a, b = x.chunk(2, dim=-1)
-        x = a * torch.sigmoid(b)
-
-        return x
-
-    def dot_product_attention(self, q, k, v, bias=False):
-        """Dot-product attention.
-
-        Args:
-            q (torch.Tensor): Queries of shape (batch, heads, length_q, depth_k)
-            k (torch.Tensor): Keys of shape (batch, heads, length_kv, depth_k)
-            v (torch.Tensor): Values of shape (batch, heads, length_kv, depth_v)
-            bias (bool): Use bias for attention.
-
-        Returns:
-            attn (torch.Tensor): Output of attention mechanism.
-        """
-        weights = torch.matmul(q, k.permute(0, 1, 3, 2))
-        if bias:
-            weights += self.bias
-        weights = F.softmax(weights, dim=-1)
-        weights = F.dropout(weights, self.drop_prob, self.training)
-        attn = torch.matmul(weights, v)
-
-        return attn
-
-    @staticmethod
-    def split_last_dim(x, n):
-        """Reshape x so that the last dimension becomes two dimensions.
-        The first of these two dimensions is n.
-        Args:
-            x (torch.Tensor): Tensor with shape (..., m)
-            n (int): Size of second-to-last dimension.
-        Returns:
-            ret (torch.Tensor): Resulting tensor with shape (..., n, m/n)
-        """
-        old_shape = list(x.size())
-        last = old_shape[-1]
-        new_shape = old_shape[:-1] + [n] + [last // n if last else None]
-        ret = x.view(new_shape)
-
-        return ret.permute(0, 2, 1, 3)
-
-    @staticmethod
-    def combine_last_two_dim(x):
-        """Merge the last two dimensions of `x`.
-
-        Args:
-            x (torch.Tensor): Tensor with shape (..., m, n)
-
-        Returns:
-            ret (torch.Tensor): Resulting tensor with shape (..., m * n)
-        """
-        old_shape = list(x.size())
-        a, b = old_shape[-2:]
-        new_shape = old_shape[:-2] + [a * b if a and b else None]
-        ret = x.contiguous().view(new_shape)
-
-        return ret
-
-    @staticmethod
-    def get_pos_enc(seq_len, num_channels, device):
-        position = torch.arange(seq_len, dtype=torch.float32, device=device)
-        num_timescales = num_channels // 2
-        log_timescale_increment = math.log(10000.) / (num_timescales - 1)
-        inv_timescales = torch.arange(num_timescales,
-                                      dtype=torch.float32,
-                                      device=device)
-        inv_timescales *= -log_timescale_increment
-        inv_timescales = inv_timescales.exp_()
-        scaled_time = position.unsqueeze(1) * inv_timescales.unsqueeze(0)
-        encoding = torch.cat([scaled_time.sin(), scaled_time.cos()], dim=1)
-        encoding = F.pad(encoding, [0, num_channels % 2, 0, 0])
-        encoding = encoding.view(1, seq_len, num_channels)
-
-        return encoding
-
-
-class GatedConv(nn.Module):
-    """Gated Convolution Block
-
-    Originally used by PixelCNN++ (https://arxiv.org/pdf/1701.05517).
-
-    Args:
-        num_channels (int): Number of channels in hidden activations.
-        drop_prob (float): Dropout probability.
-        aux_channels (int): Number of channels in optional auxiliary input.
-    """
-    def __init__(self, num_channels, drop_prob=0., aux_channels=None):
-        super(GatedConv, self).__init__()
-        self.nlin = concat_elu
-        self.conv = WNConv2d(2 * num_channels, num_channels, kernel_size=3, padding=1)
-        self.drop = nn.Dropout2d(drop_prob)
-        self.gate = WNConv2d(2 * num_channels, 2 * num_channels, kernel_size=1, padding=0)
-        if aux_channels is not None:
-            self.aux_conv = WNConv2d(2 * aux_channels, num_channels, kernel_size=1, padding=0)
-        else:
-            self.aux_conv = None
-
-    def forward(self, x, aux=None):
-        x = self.nlin(x)
-        x = self.conv(x)
-        if aux is not None:
-            aux = self.nlin(aux)
-            x = x + self.aux_conv(aux)
-        x = self.nlin(x)
-        x = self.drop(x)
-        x = self.gate(x)
-        a, b = x.chunk(2, dim=1)
-        x = a * torch.sigmoid(b)
-
-        return x
-
-
-class Rescale(nn.Module):
-    """Per-channel rescaling. Need a proper `nn.Module` so we can wrap it
-    with `torch.nn.utils.weight_norm`.
-    Args:
-        num_channels (int): Number of channels in the input.
-    """
-
-    def __init__(self, num_channels):
-        super(Rescale, self).__init__()
-        self.weight = nn.Parameter(torch.ones(num_channels, 1, 1))
-
-    def forward(self, x):
-        x = self.weight * x
-        return x
diff --git a/spaces/Marshalls/testmtd/training/datasets/__init__.py b/spaces/Marshalls/testmtd/training/datasets/__init__.py
deleted file mode 100644
index c184380b857ad16dea5f56d32617027103f87d26..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/training/datasets/__init__.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import importlib
-from torch.utils.data import DataLoader
-from .base_dataset import BaseDataset
-import numpy as np
-import torch
-
-def find_dataset_using_name(dataset_name):
-    # Given the option --dataset_name [datasetname],
-    # the file "data/datasetname_dataset.py"
-    # will be imported.
-
-    task_name = "training"
-    task_module = importlib.import_module(task_name)
-    dataset_filename = task_name + ".datasets." + dataset_name + "_dataset"
-    # datasetlib = importlib.import_module(dataset_filename, package=task_module)
-    datasetlib = importlib.import_module(dataset_filename, package=task_module)
-
-    # In the file, the class called DatasetNameDataset() will
-    # be instantiated. It has to be a subclass of BaseDataset,
-    # and it is case-insensitive.
-
-    def is_subclass(subclass, superclass):
-        return next(iter(subclass.__bases__)).__module__.endswith(superclass.__module__)
-
-    dataset = None
-    target_dataset_name = dataset_name.replace('_', '') + 'dataset'
-    for name, cls in datasetlib.__dict__.items():
-        if name.lower() == target_dataset_name.lower():
-            if is_subclass(cls, BaseDataset) or any(is_subclass(cls_b, BaseDataset) for cls_b in cls.__bases__):
-                dataset = cls
-
-    if dataset is None:
-        raise NotImplementedError("In {}.py, there should be a subclass of BaseDataset with class name that matches {} in lowercase.".format(
-            dataset_filename, target_dataset_name))
-
-    return dataset
-
-
-def get_option_setter(dataset_name):
-    dataset_class = find_dataset_using_name(dataset_name)
-    return dataset_class.modify_commandline_options
-
-
-def create_dataset(opt, split="train", *args, **kwargs):
-    dataset = find_dataset_using_name(opt.dataset_name)
-    instance = dataset(opt, split, *args,**kwargs)
-    print('dataset [{}] was created {}'.format(instance.name(), "(val)" if split=="val" else ''))
-    return instance
-
-def paired_collate_fn(insts,tgt_dim=2):
-    src_insts= list(map(lambda x: x['input'],insts))
-    tgt_insts = list(map(lambda x: x['target'],insts))
-    src_insts = collate_fn(src_insts,dim=2)
-    tgt_insts = collate_fn(tgt_insts,dim=tgt_dim)
-    return {'input':src_insts, 'target':tgt_insts}
-
-def collate_fn(insts,dim=-1): #dim is time dim
-    ''' Pad the instance to the max seq length in batch '''
-
-    max_len = max(inst.shape[dim] for inst in insts)
-
-    # print(max_len)
-    batch_seq = [
-        torch.cat([inst.long(),torch.full(inst.shape[:dim]+((max_len - inst.shape[dim]),)+inst.shape[dim+1:],PAD_STATE).long()],dim)
-        for inst in insts]
-
-    batch_pos = np.array([
-        [pos_i+1 for pos_i in range(inst.shape[dim])] + [PAD_STATE]*(max_len - inst.shape[dim]) for inst in insts])
-
-    batch_seq = torch.stack(batch_seq)
-    # print(batch_seq)
-    batch_pos = torch.LongTensor(batch_pos)
-
-    return batch_seq, batch_pos
-
-def transformer_paired_collate_fn(insts):
-    return paired_collate_fn(insts,tgt_dim=2)
-
-def wavenet_paired_collate_fn(insts):
-    return paired_collate_fn(insts,tgt_dim=1)
-
-def meta_collate_fn(pad_batches, model):
-    if pad_batches:
-        if model == "transformer":
-            return transformer_paired_collate_fn
-        else:
-            return wavenet_paired_collate_fn
-    else:
-        return default_collate
-
-from torch.utils.data.dataloader import default_collate
-def create_dataloader(dataset, split="train"):
-    is_eval = (split == "val" or split == "test")
-    return DataLoader(dataset,
-                      batch_size=dataset.opt.batch_size if not is_eval else dataset.opt.val_batch_size,
-                      shuffle=not is_eval,
-                      # collate_fn=meta_collate_fn(dataset.opt.pad_batches,dataset.opt.model),
-                      collate_fn=None,
-                      #pin_memory=True,
-                      drop_last=True,
-                      num_workers=dataset.opt.workers)
diff --git a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/tools/get_imagenet_21k_full_tar_json.py b/spaces/MattyWhite/ChatGPT-ImageCaptioner2/tools/get_imagenet_21k_full_tar_json.py
deleted file mode 100644
index e7127440030297812a9f4df38cfd6b4cba340c39..0000000000000000000000000000000000000000
--- a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/tools/get_imagenet_21k_full_tar_json.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import argparse
-import json
-import numpy as np
-import pickle
-import io
-import gzip
-import sys
-import time
-from nltk.corpus import wordnet
-from tqdm import tqdm
-import operator
-import torch
-
-sys.path.insert(0, 'third_party/CenterNet2/projects/CenterNet2/')
-sys.path.insert(0, 'third_party/Deformable-DETR')
-from detic.data.tar_dataset import DiskTarDataset, _TarDataset
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--imagenet_dir", default='datasets/imagenet/ImageNet-21k/')
-    parser.add_argument("--tarfile_path", default='datasets/imagenet/metadata-22k/tar_files.npy')
-    parser.add_argument("--tar_index_dir", default='datasets/imagenet/metadata-22k/tarindex_npy')
-    parser.add_argument("--out_path", default='datasets/imagenet/annotations/imagenet-22k_image_info.json')
-    parser.add_argument("--workers", default=16, type=int)
-    args = parser.parse_args()
-
-
-    start_time = time.time()
-    print('Building dataset')
-    dataset = DiskTarDataset(args.tarfile_path, args.tar_index_dir)
-    end_time = time.time()
-    print(f"Took {end_time-start_time} seconds to make the dataset.")
-    print(f"Have {len(dataset)} samples.")
-    print('dataset', dataset)
-    
-
-    tar_files = np.load(args.tarfile_path)
-    categories = []
-    for i, tar_file in enumerate(tar_files):
-        wnid = tar_file[-13:-4]
-        synset = wordnet.synset_from_pos_and_offset('n', int(wnid[1:]))
-        synonyms = [x.name() for x in synset.lemmas()]
-        category = {
-            'id': i + 1,
-            'synset': synset.name(),
-            'name': synonyms[0],
-            'def': synset.definition(),
-            'synonyms': synonyms,
-        }
-        categories.append(category)
-    print('categories', len(categories))
-
-    data_loader = torch.utils.data.DataLoader(
-        dataset, batch_size=1, shuffle=False,
-        num_workers=args.workers,
-        collate_fn=operator.itemgetter(0),
-    )
-    images = []
-    for img, label, index in tqdm(data_loader):
-        if label == -1:
-            continue
-        image = {
-            'id': int(index) + 1,
-            'pos_category_ids': [int(label) + 1],
-            'height': int(img.height),
-            'width': int(img.width),
-            'tar_index': int(index),
-        }
-        images.append(image)
-    
-    data = {'categories': categories, 'images': images, 'annotations': []}
-    try:
-        for k, v in data.items():
-            print(k, len(v))
-        print('Saving to ', args.out_path)
-        json.dump(data, open(args.out_path, 'w'))
-    except:
-        pass
-    import pdb; pdb.set_trace()
-    
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/configs/_base_/models/encnet_r50-d8.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/configs/_base_/models/encnet_r50-d8.py
deleted file mode 100644
index be777123a886503172a95fe0719e956a147bbd68..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/configs/_base_/models/encnet_r50-d8.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='EncHead',
-        in_channels=[512, 1024, 2048],
-        in_index=(1, 2, 3),
-        channels=512,
-        num_codes=32,
-        use_se_loss=True,
-        add_lateral=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-        loss_se_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.2)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/configs/_base_/models/psanet_r50-d8.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/configs/_base_/models/psanet_r50-d8.py
deleted file mode 100644
index 689513fa9d2a40f14bf0ae4ae61f38f0dcc1b3da..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/configs/_base_/models/psanet_r50-d8.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='PSAHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        mask_size=(97, 97),
-        psa_type='bi-direction',
-        compact=False,
-        shrink_factor=2,
-        normalization_factor=1.0,
-        psa_softmax=True,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/tool_add_control_sd21.py b/spaces/Mellow-ai/PhotoAI_Mellow/tool_add_control_sd21.py
deleted file mode 100644
index 7c3ac5f9bb398b9d20cb7d750f1f7c0717670eae..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/tool_add_control_sd21.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import sys
-import os
-
-assert len(sys.argv) == 3, 'Args are wrong.'
-
-input_path = sys.argv[1]
-output_path = sys.argv[2]
-
-assert os.path.exists(input_path), 'Input model does not exist.'
-assert not os.path.exists(output_path), 'Output filename already exists.'
-assert os.path.exists(os.path.dirname(output_path)), 'Output path is not valid.'
-
-import torch
-from share import *
-from cldm.model import create_model
-
-
-def get_node_name(name, parent_name):
-    if len(name) <= len(parent_name):
-        return False, ''
-    p = name[:len(parent_name)]
-    if p != parent_name:
-        return False, ''
-    return True, name[len(parent_name):]
-
-
-model = create_model(config_path='./models/cldm_v21.yaml')
-
-pretrained_weights = torch.load(input_path)
-if 'state_dict' in pretrained_weights:
-    pretrained_weights = pretrained_weights['state_dict']
-
-scratch_dict = model.state_dict()
-
-target_dict = {}
-for k in scratch_dict.keys():
-    is_control, name = get_node_name(k, 'control_')
-    if is_control:
-        copy_k = 'model.diffusion_' + name
-    else:
-        copy_k = k
-    if copy_k in pretrained_weights:
-        target_dict[k] = pretrained_weights[copy_k].clone()
-    else:
-        target_dict[k] = scratch_dict[k].clone()
-        print(f'These weights are newly added: {k}')
-
-model.load_state_dict(target_dict, strict=True)
-torch.save(model.state_dict(), output_path)
-print('Done.')
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/modules/__init__.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/modules/__init__.py
deleted file mode 100644
index 30960fd5dd45f069c4ae2f6c74ec66d5eecb13b8..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/common/modules/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .transformer_module import (MultiHeadAttention, PositionalEncoding,
-                                 PositionwiseFeedForward,
-                                 ScaledDotProductAttention)
-
-__all__ = [
-    'ScaledDotProductAttention', 'MultiHeadAttention',
-    'PositionwiseFeedForward', 'PositionalEncoding'
-]
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/nhnet/trainer_test.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/nhnet/trainer_test.py
deleted file mode 100644
index 39673dd2c7afe0f7310e556395824b9ba4582262..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/nhnet/trainer_test.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# Lint as: python3
-# Copyright 2020 The TensorFlow 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.
-# ==============================================================================
-"""Tests for official.nlp.nhnet.trainer."""
-
-import os
-
-from absl import flags
-from absl.testing import parameterized
-import tensorflow as tf
-
-# pylint: disable=g-direct-tensorflow-import
-from tensorflow.python.distribute import combinations
-from tensorflow.python.distribute import strategy_combinations
-# pylint: enable=g-direct-tensorflow-import
-from official.nlp.nhnet import trainer
-from official.nlp.nhnet import utils
-
-FLAGS = flags.FLAGS
-trainer.define_flags()
-
-
-def all_strategy_combinations():
-  return combinations.combine(
-      distribution=[
-          strategy_combinations.one_device_strategy,
-          strategy_combinations.one_device_strategy_gpu,
-          strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
-          strategy_combinations.tpu_strategy,
-      ],
-      mode="eager",
-  )
-
-
-def get_trivial_data(config) -> tf.data.Dataset:
-  """Gets trivial data in the ImageNet size."""
-  batch_size, num_docs = 2, len(config.passage_list),
-  len_passage = config.len_passage
-  len_title = config.len_title
-
-  def generate_data(_) -> tf.data.Dataset:
-    fake_ids = tf.zeros((num_docs, len_passage), dtype=tf.int32)
-    title = tf.zeros((len_title), dtype=tf.int32)
-    return dict(
-        input_ids=fake_ids,
-        input_mask=fake_ids,
-        segment_ids=fake_ids,
-        target_ids=title)
-
-  dataset = tf.data.Dataset.range(1)
-  dataset = dataset.repeat()
-  dataset = dataset.map(
-      generate_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
-  dataset = dataset.prefetch(buffer_size=1).batch(batch_size)
-  return dataset
-
-
-class TrainerTest(tf.test.TestCase, parameterized.TestCase):
-
-  def setUp(self):
-    super(TrainerTest, self).setUp()
-    self._config = utils.get_test_params()
-    self._config.override(
-        {
-            "vocab_size": 49911,
-            "max_position_embeddings": 200,
-            "len_title": 15,
-            "len_passage": 20,
-            "beam_size": 5,
-            "alpha": 0.6,
-            "learning_rate": 0.0,
-            "learning_rate_warmup_steps": 0,
-            "multi_channel_cross_attention": True,
-            "passage_list": ["a", "b"],
-        },
-        is_strict=False)
-
-  @combinations.generate(all_strategy_combinations())
-  def test_train(self, distribution):
-    FLAGS.train_steps = 10
-    FLAGS.checkpoint_interval = 5
-    FLAGS.model_dir = self.get_temp_dir()
-    FLAGS.model_type = "nhnet"
-    stats = trainer.train(self._config, distribution,
-                          get_trivial_data(self._config))
-    self.assertIn("training_loss", stats)
-    self.assertLen(
-        tf.io.gfile.glob(os.path.join(FLAGS.model_dir, "ckpt*.index")), 2)
-
-
-if __name__ == "__main__":
-  tf.test.main()
diff --git a/spaces/NCTCMumbai/NCTC/models/research/cognitive_mapping_and_planning/scripts/script_preprocess_meshes_S3DIS.sh b/spaces/NCTCMumbai/NCTC/models/research/cognitive_mapping_and_planning/scripts/script_preprocess_meshes_S3DIS.sh
deleted file mode 100644
index 557a4dde611d42e71d71dd1589abf96f55e6eec6..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/cognitive_mapping_and_planning/scripts/script_preprocess_meshes_S3DIS.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-# Copyright 2016 The TensorFlow 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.
-# ==============================================================================
-
-mkdir -p data/stanford_building_parser_dataset
-mkdir -p data/stanford_building_parser_dataset/mesh
-cd data/stanford_building_parser_dataset_raw
-
-# Untar the files and extract the meshes.
-for t in "1" "3" "4" "5a" "5b" "6"; do
-  tar -xf area_"$t"_noXYZ.tar area_$t/3d/rgb_textures
-  mv area_$t/3d/rgb_textures ../stanford_building_parser_dataset/mesh/area$t
-  rmdir area_$t/3d
-  rmdir area_$t
-done
-
-cd ../../
-
-# Preprocess meshes to remove the group and chunk information.
-cd data/stanford_building_parser_dataset/
-for t in "1" "3" "4" "5a" "5b" "6"; do
-  obj_name=`ls mesh/area$t/*.obj`
-  cp $obj_name "$obj_name".bck
-  cat $obj_name.bck | grep -v '^g' | grep -v '^o' > $obj_name
-done
-cd ../../
diff --git a/spaces/NimaBoscarino/climategan/climategan/transforms.py b/spaces/NimaBoscarino/climategan/climategan/transforms.py
deleted file mode 100644
index 66b4ab9356e167cc29339300c6996a1f755837aa..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/climategan/transforms.py
+++ /dev/null
@@ -1,626 +0,0 @@
-"""Data transforms for the loaders
-"""
-import random
-import traceback
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from skimage.color import rgba2rgb
-from skimage.io import imread
-from torchvision import transforms as trsfs
-from torchvision.transforms.functional import (
-    adjust_brightness,
-    adjust_contrast,
-    adjust_saturation,
-)
-
-from climategan.tutils import normalize
-
-
-def interpolation(task):
-    if task in ["d", "m", "s"]:
-        return {"mode": "nearest"}
-    else:
-        return {"mode": "bilinear", "align_corners": True}
-
-
-class Resize:
-    def __init__(self, target_size, keep_aspect_ratio=False):
-        """
-        Resize transform. Target_size can be an int or a tuple of ints,
-        depending on whether both height and width should have the same
-        final size or not.
-
-        If keep_aspect_ratio is specified then target_size must be an int:
-        the smallest dimension of x will be set to target_size and the largest
-        dimension will be computed to the closest int keeping the original
-        aspect ratio. e.g.
-        >>> x = torch.rand(1, 3, 1200, 1800)
-        >>> m = torch.rand(1, 1, 600, 600)
-        >>> d = {"x": x, "m": m}
-        >>> {k: v.shape for k, v in Resize(640, True)(d).items()}
-         {"x": (1, 3, 640, 960), "m": (1, 1, 640, 960)}
-
-
-
-        Args:
-            target_size (int | tuple(int)): New size for the tensor
-            keep_aspect_ratio (bool, optional): Whether or not to keep aspect ratio
-                when resizing. Requires target_size to be an int. If keeping aspect
-                ratio, smallest dim will be set to target_size. Defaults to False.
-        """
-        if isinstance(target_size, (int, tuple, list)):
-            if not isinstance(target_size, int) and not keep_aspect_ratio:
-                assert len(target_size) == 2
-                self.h, self.w = target_size
-            else:
-                if keep_aspect_ratio:
-                    assert isinstance(target_size, int)
-                self.h = self.w = target_size
-
-            self.default_h = int(self.h)
-            self.default_w = int(self.w)
-            self.sizes = {}
-        elif isinstance(target_size, dict):
-            assert (
-                not keep_aspect_ratio
-            ), "dict target_size not compatible with keep_aspect_ratio"
-
-            self.sizes = {
-                k: {"h": v, "w": v} for k, v in target_size.items() if k != "default"
-            }
-            self.default_h = int(target_size["default"])
-            self.default_w = int(target_size["default"])
-
-        self.keep_aspect_ratio = keep_aspect_ratio
-
-    def compute_new_default_size(self, tensor):
-        """
-        compute the new size for a tensor depending on target size
-        and keep_aspect_rato
-
-        Args:
-            tensor (torch.Tensor): 4D tensor N x C x H x W.
-
-        Returns:
-            tuple(int): (new_height, new_width)
-        """
-        if self.keep_aspect_ratio:
-            h, w = tensor.shape[-2:]
-            if h < w:
-                return (self.h, int(self.default_h * w / h))
-            else:
-                return (int(self.default_h * h / w), self.default_w)
-        return (self.default_h, self.default_w)
-
-    def compute_new_size_for_task(self, task):
-        assert (
-            not self.keep_aspect_ratio
-        ), "compute_new_size_for_task is not compatible with keep aspect ratio"
-
-        if task not in self.sizes:
-            return (self.default_h, self.default_w)
-
-        return (self.sizes[task]["h"], self.sizes[task]["w"])
-
-    def __call__(self, data):
-        """
-        Resize a dict of tensors to the "x" key's new_size
-
-        Args:
-            data (dict[str:torch.Tensor]): The data dict to transform
-
-        Returns:
-            dict[str: torch.Tensor]: dict with all tensors resized to the
-                new size of the data["x"] tensor
-        """
-        task = tensor = new_size = None
-        try:
-            if not self.sizes:
-                d = {}
-                new_size = self.compute_new_default_size(
-                    data["x"] if "x" in data else list(data.values())[0]
-                )
-                for task, tensor in data.items():
-                    d[task] = F.interpolate(
-                        tensor, size=new_size, **interpolation(task)
-                    )
-                return d
-
-            d = {}
-            for task, tensor in data.items():
-                new_size = self.compute_new_size_for_task(task)
-                d[task] = F.interpolate(tensor, size=new_size, **interpolation(task))
-            return d
-
-        except Exception as e:
-            tb = traceback.format_exc()
-            print("Debug: task, shape, interpolation, h, w, new_size")
-            print(task)
-            print(tensor.shape)
-            print(interpolation(task))
-            print(self.h, self.w)
-            print(new_size)
-            print(tb)
-            raise Exception(e)
-
-
-class RandomCrop:
-    def __init__(self, size, center=False):
-        assert isinstance(size, (int, tuple, list))
-        if not isinstance(size, int):
-            assert len(size) == 2
-            self.h, self.w = size
-        else:
-            self.h = self.w = size
-
-        self.h = int(self.h)
-        self.w = int(self.w)
-        self.center = center
-
-    def __call__(self, data):
-        H, W = (
-            data["x"].size()[-2:] if "x" in data else list(data.values())[0].size()[-2:]
-        )
-
-        if not self.center:
-            top = np.random.randint(0, H - self.h)
-            left = np.random.randint(0, W - self.w)
-        else:
-            top = (H - self.h) // 2
-            left = (W - self.w) // 2
-
-        return {
-            task: tensor[:, :, top : top + self.h, left : left + self.w]
-            for task, tensor in data.items()
-        }
-
-
-class RandomHorizontalFlip:
-    def __init__(self, p=0.5):
-        # self.flip = TF.hflip
-        self.p = p
-
-    def __call__(self, data):
-        if np.random.rand() > self.p:
-            return data
-        return {task: torch.flip(tensor, [3]) for task, tensor in data.items()}
-
-
-class ToTensor:
-    def __init__(self):
-        self.ImagetoTensor = trsfs.ToTensor()
-        self.MaptoTensor = self.ImagetoTensor
-
-    def __call__(self, data):
-        new_data = {}
-        for task, im in data.items():
-            if task in {"x", "a"}:
-                new_data[task] = self.ImagetoTensor(im)
-            elif task in {"m"}:
-                new_data[task] = self.MaptoTensor(im)
-            elif task == "s":
-                new_data[task] = torch.squeeze(torch.from_numpy(np.array(im))).to(
-                    torch.int64
-                )
-            elif task == "d":
-                new_data = im
-
-        return new_data
-
-
-class Normalize:
-    def __init__(self, opts):
-        if opts.data.normalization == "HRNet":
-            self.normImage = trsfs.Normalize(
-                ((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
-            )
-        else:
-            self.normImage = trsfs.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
-        self.normDepth = lambda x: x
-        self.normMask = lambda x: x
-        self.normSeg = lambda x: x
-
-        self.normalize = {
-            "x": self.normImage,
-            "s": self.normSeg,
-            "d": self.normDepth,
-            "m": self.normMask,
-        }
-
-    def __call__(self, data):
-        return {
-            task: self.normalize.get(task, lambda x: x)(tensor.squeeze(0))
-            for task, tensor in data.items()
-        }
-
-
-class RandBrightness:  # Input need to be between -1 and 1
-    def __call__(self, data):
-        return {
-            task: rand_brightness(tensor) if task == "x" else tensor
-            for task, tensor in data.items()
-        }
-
-
-class RandSaturation:
-    def __call__(self, data):
-        return {
-            task: rand_saturation(tensor) if task == "x" else tensor
-            for task, tensor in data.items()
-        }
-
-
-class RandContrast:
-    def __call__(self, data):
-        return {
-            task: rand_contrast(tensor) if task == "x" else tensor
-            for task, tensor in data.items()
-        }
-
-
-class BucketizeDepth:
-    def __init__(self, opts, domain):
-        self.domain = domain
-
-        if opts.gen.d.classify.enable and domain in {"s", "kitti"}:
-            self.buckets = torch.linspace(
-                *[
-                    opts.gen.d.classify.linspace.min,
-                    opts.gen.d.classify.linspace.max,
-                    opts.gen.d.classify.linspace.buckets - 1,
-                ]
-            )
-
-            self.transforms = {
-                "d": lambda tensor: torch.bucketize(
-                    tensor, self.buckets, out_int32=True, right=True
-                )
-            }
-        else:
-            self.transforms = {}
-
-    def __call__(self, data):
-        return {
-            task: self.transforms.get(task, lambda x: x)(tensor)
-            for task, tensor in data.items()
-        }
-
-
-class PrepareInference:
-    """
-    Transform which:
-      - transforms a str or an array into a tensor
-      - resizes the image to keep the aspect ratio
-      - crops in the center of the resized image
-      - normalize to 0:1
-      - rescale to -1:1
-    """
-
-    def __init__(self, target_size=640, half=False, is_label=False, enforce_128=True):
-        if enforce_128:
-            if target_size % 2 ** 7 != 0:
-                raise ValueError(
-                    f"Received a target_size of {target_size}, which is not a "
-                    + "multiple of 2^7 = 128. Set enforce_128 to False to disable "
-                    + "this error."
-                )
-        self.resize = Resize(target_size, keep_aspect_ratio=True)
-        self.crop = RandomCrop((target_size, target_size), center=True)
-        self.half = half
-        self.is_label = is_label
-
-    def process(self, t):
-        if isinstance(t, (str, Path)):
-            t = imread(str(t))
-
-        if isinstance(t, np.ndarray):
-            if t.shape[-1] == 4:
-                t = rgba2rgb(t)
-
-            t = torch.from_numpy(t)
-            if t.ndim == 3:
-                t = t.permute(2, 0, 1)
-
-        if t.ndim == 3:
-            t = t.unsqueeze(0)
-        elif t.ndim == 2:
-            t = t.unsqueeze(0).unsqueeze(0)
-
-        if not self.is_label:
-            t = t.to(torch.float32)
-            t = normalize(t)
-            t = (t - 0.5) * 2
-
-        t = {"m": t} if self.is_label else {"x": t}
-        t = self.resize(t)
-        t = self.crop(t)
-        t = t["m"] if self.is_label else t["x"]
-
-        if self.half and not self.is_label:
-            t = t.half()
-
-        return t
-
-    def __call__(self, x):
-        """
-        normalize, rescale, resize, crop in the center
-
-        x can be: dict {"task": data} list [data, ..] or data
-        data ^ can be a str, a Path, a numpy arrray or a Tensor
-        """
-        if isinstance(x, dict):
-            return {k: self.process(v) for k, v in x.items()}
-
-        if isinstance(x, list):
-            return [self.process(t) for t in x]
-
-        return self.process(x)
-
-
-class PrepareTest:
-    """
-    Transform which:
-      - transforms a str or an array into a tensor
-      - resizes the image to keep the aspect ratio
-      - crops in the center of the resized image
-      - normalize to 0:1 (optional)
-      - rescale to -1:1 (optional)
-    """
-
-    def __init__(self, target_size=640, half=False):
-        self.resize = Resize(target_size, keep_aspect_ratio=True)
-        self.crop = RandomCrop((target_size, target_size), center=True)
-        self.half = half
-
-    def process(self, t, normalize=False, rescale=False):
-        if isinstance(t, (str, Path)):
-            # t = img_as_float(imread(str(t)))
-            t = imread(str(t))
-            if t.shape[-1] == 4:
-                # t = rgba2rgb(t)
-                t = t[:, :, :3]
-            if np.ndim(t) == 2:
-                t = np.repeat(t[:, :, np.newaxis], 3, axis=2)
-
-        if isinstance(t, np.ndarray):
-            t = torch.from_numpy(t)
-            t = t.permute(2, 0, 1)
-
-        if len(t.shape) == 3:
-            t = t.unsqueeze(0)
-
-        t = t.to(torch.float32)
-        normalize(t) if normalize else t
-        (t - 0.5) * 2 if rescale else t
-        t = {"x": t}
-        t = self.resize(t)
-        t = self.crop(t)
-        t = t["x"]
-
-        if self.half:
-            return t.to(torch.float16)
-
-        return t
-
-    def __call__(self, x, normalize=False, rescale=False):
-        """
-        Call process()
-
-        x can be: dict {"task": data} list [data, ..] or data
-        data ^ can be a str, a Path, a numpy arrray or a Tensor
-        """
-        if isinstance(x, dict):
-            return {k: self.process(v, normalize, rescale) for k, v in x.items()}
-
-        if isinstance(x, list):
-            return [self.process(t, normalize, rescale) for t in x]
-
-        return self.process(x, normalize, rescale)
-
-
-def get_transform(transform_item, mode):
-    """Returns the torchivion transform function associated to a
-    transform_item listed in opts.data.transforms ; transform_item is
-    an addict.Dict
-    """
-
-    if transform_item.name == "crop" and not (
-        transform_item.ignore is True or transform_item.ignore == mode
-    ):
-        return RandomCrop(
-            (transform_item.height, transform_item.width),
-            center=transform_item.center == mode,
-        )
-
-    elif transform_item.name == "resize" and not (
-        transform_item.ignore is True or transform_item.ignore == mode
-    ):
-        return Resize(
-            transform_item.new_size, transform_item.get("keep_aspect_ratio", False)
-        )
-
-    elif transform_item.name == "hflip" and not (
-        transform_item.ignore is True or transform_item.ignore == mode
-    ):
-        return RandomHorizontalFlip(p=transform_item.p or 0.5)
-
-    elif transform_item.name == "brightness" and not (
-        transform_item.ignore is True or transform_item.ignore == mode
-    ):
-        return RandBrightness()
-
-    elif transform_item.name == "saturation" and not (
-        transform_item.ignore is True or transform_item.ignore == mode
-    ):
-        return RandSaturation()
-
-    elif transform_item.name == "contrast" and not (
-        transform_item.ignore is True or transform_item.ignore == mode
-    ):
-        return RandContrast()
-
-    elif transform_item.ignore is True or transform_item.ignore == mode:
-        return None
-
-    raise ValueError("Unknown transform_item {}".format(transform_item))
-
-
-def get_transforms(opts, mode, domain):
-    """Get all the transform functions listed in opts.data.transforms
-    using get_transform(transform_item, mode)
-    """
-    transforms = []
-    color_jittering_transforms = ["brightness", "saturation", "contrast"]
-
-    for t in opts.data.transforms:
-        if t.name not in color_jittering_transforms:
-            transforms.append(get_transform(t, mode))
-
-    if "p" not in opts.tasks and mode == "train":
-        for t in opts.data.transforms:
-            if t.name in color_jittering_transforms:
-                transforms.append(get_transform(t, mode))
-
-    transforms += [Normalize(opts), BucketizeDepth(opts, domain)]
-    transforms = [t for t in transforms if t is not None]
-
-    return transforms
-
-
-# ----- Adapted functions from https://github.com/mit-han-lab/data-efficient-gans -----#
-def rand_brightness(tensor, is_diff_augment=False):
-    if is_diff_augment:
-        assert len(tensor.shape) == 4
-        type_ = tensor.dtype
-        device_ = tensor.device
-        rand_tens = torch.rand(tensor.size(0), 1, 1, 1, dtype=type_, device=device_)
-        return tensor + (rand_tens - 0.5)
-    else:
-        factor = random.uniform(0.5, 1.5)
-        tensor = adjust_brightness(tensor, brightness_factor=factor)
-        # dummy pixels to fool scaling and preserve range
-        tensor[:, :, 0, 0] = 1.0
-        tensor[:, :, -1, -1] = 0.0
-        return tensor
-
-
-def rand_saturation(tensor, is_diff_augment=False):
-    if is_diff_augment:
-        assert len(tensor.shape) == 4
-        type_ = tensor.dtype
-        device_ = tensor.device
-        rand_tens = torch.rand(tensor.size(0), 1, 1, 1, dtype=type_, device=device_)
-        x_mean = tensor.mean(dim=1, keepdim=True)
-        return (tensor - x_mean) * (rand_tens * 2) + x_mean
-    else:
-        factor = random.uniform(0.5, 1.5)
-        tensor = adjust_saturation(tensor, saturation_factor=factor)
-        # dummy pixels to fool scaling and preserve range
-        tensor[:, :, 0, 0] = 1.0
-        tensor[:, :, -1, -1] = 0.0
-        return tensor
-
-
-def rand_contrast(tensor, is_diff_augment=False):
-    if is_diff_augment:
-        assert len(tensor.shape) == 4
-        type_ = tensor.dtype
-        device_ = tensor.device
-        rand_tens = torch.rand(tensor.size(0), 1, 1, 1, dtype=type_, device=device_)
-        x_mean = tensor.mean(dim=[1, 2, 3], keepdim=True)
-        return (tensor - x_mean) * (rand_tens + 0.5) + x_mean
-    else:
-        factor = random.uniform(0.5, 1.5)
-        tensor = adjust_contrast(tensor, contrast_factor=factor)
-        # dummy pixels to fool scaling and preserve range
-        tensor[:, :, 0, 0] = 1.0
-        tensor[:, :, -1, -1] = 0.0
-        return tensor
-
-
-def rand_cutout(tensor, ratio=0.5):
-    assert len(tensor.shape) == 4, "For rand cutout, tensor must be 4D."
-    type_ = tensor.dtype
-    device_ = tensor.device
-    cutout_size = int(tensor.size(-2) * ratio + 0.5), int(tensor.size(-1) * ratio + 0.5)
-    grid_batch, grid_x, grid_y = torch.meshgrid(
-        torch.arange(tensor.size(0), dtype=torch.long, device=device_),
-        torch.arange(cutout_size[0], dtype=torch.long, device=device_),
-        torch.arange(cutout_size[1], dtype=torch.long, device=device_),
-    )
-    size_ = [tensor.size(0), 1, 1]
-    offset_x = torch.randint(
-        0,
-        tensor.size(-2) + (1 - cutout_size[0] % 2),
-        size=size_,
-        device=device_,
-    )
-    offset_y = torch.randint(
-        0,
-        tensor.size(-1) + (1 - cutout_size[1] % 2),
-        size=size_,
-        device=device_,
-    )
-    grid_x = torch.clamp(
-        grid_x + offset_x - cutout_size[0] // 2, min=0, max=tensor.size(-2) - 1
-    )
-    grid_y = torch.clamp(
-        grid_y + offset_y - cutout_size[1] // 2, min=0, max=tensor.size(-1) - 1
-    )
-    mask = torch.ones(
-        tensor.size(0), tensor.size(2), tensor.size(3), dtype=type_, device=device_
-    )
-    mask[grid_batch, grid_x, grid_y] = 0
-    return tensor * mask.unsqueeze(1)
-
-
-def rand_translation(tensor, ratio=0.125):
-    assert len(tensor.shape) == 4, "For rand translation, tensor must be 4D."
-    device_ = tensor.device
-    shift_x, shift_y = (
-        int(tensor.size(2) * ratio + 0.5),
-        int(tensor.size(3) * ratio + 0.5),
-    )
-    translation_x = torch.randint(
-        -shift_x, shift_x + 1, size=[tensor.size(0), 1, 1], device=device_
-    )
-    translation_y = torch.randint(
-        -shift_y, shift_y + 1, size=[tensor.size(0), 1, 1], device=device_
-    )
-    grid_batch, grid_x, grid_y = torch.meshgrid(
-        torch.arange(tensor.size(0), dtype=torch.long, device=device_),
-        torch.arange(tensor.size(2), dtype=torch.long, device=device_),
-        torch.arange(tensor.size(3), dtype=torch.long, device=device_),
-    )
-    grid_x = torch.clamp(grid_x + translation_x + 1, 0, tensor.size(2) + 1)
-    grid_y = torch.clamp(grid_y + translation_y + 1, 0, tensor.size(3) + 1)
-    x_pad = F.pad(tensor, [1, 1, 1, 1, 0, 0, 0, 0])
-    tensor = (
-        x_pad.permute(0, 2, 3, 1)
-        .contiguous()[grid_batch, grid_x, grid_y]
-        .permute(0, 3, 1, 2)
-    )
-    return tensor
-
-
-class DiffTransforms:
-    def __init__(self, diff_aug_opts):
-        self.do_color_jittering = diff_aug_opts.do_color_jittering
-        self.do_cutout = diff_aug_opts.do_cutout
-        self.do_translation = diff_aug_opts.do_translation
-        self.cutout_ratio = diff_aug_opts.cutout_ratio
-        self.translation_ratio = diff_aug_opts.translation_ratio
-
-    def __call__(self, tensor):
-        if self.do_color_jittering:
-            tensor = rand_brightness(tensor, is_diff_augment=True)
-            tensor = rand_contrast(tensor, is_diff_augment=True)
-            tensor = rand_saturation(tensor, is_diff_augment=True)
-        if self.do_translation:
-            tensor = rand_translation(tensor, ratio=self.translation_ratio)
-        if self.do_cutout:
-            tensor = rand_cutout(tensor, ratio=self.cutout_ratio)
-        return tensor
diff --git a/spaces/NoCrypt/promptinspector-abuser/README.md b/spaces/NoCrypt/promptinspector-abuser/README.md
deleted file mode 100644
index 4982c5cc6dc33ea19e5b26a1b088f366911f1c5b..0000000000000000000000000000000000000000
--- a/spaces/NoCrypt/promptinspector-abuser/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Promptinspector Abuser
-emoji: 💻
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.33.1
-app_file: app.py
-pinned: false
-license: wtfpl
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/discriminative_reranking_nmt/scripts/prep_data.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/discriminative_reranking_nmt/scripts/prep_data.py
deleted file mode 100644
index 7aa7d37edc2c3e4c1d293911b753abf2ef597a7e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/discriminative_reranking_nmt/scripts/prep_data.py
+++ /dev/null
@@ -1,136 +0,0 @@
-#!/usr/bin/env python
-
-import argparse
-from multiprocessing import Pool
-from pathlib import Path
-
-import sacrebleu
-import sentencepiece as spm
-
-
-def read_text_file(filename):
-    with open(filename, "r") as f:
-        output = [line.strip() for line in f]
-
-    return output
-
-
-def get_bleu(in_sent, target_sent):
-    bleu = sacrebleu.corpus_bleu([in_sent], [[target_sent]])
-    out = " ".join(
-        map(str, [bleu.score, bleu.sys_len, bleu.ref_len] + bleu.counts + bleu.totals)
-    )
-    return out
-
-
-def get_ter(in_sent, target_sent):
-    ter = sacrebleu.corpus_ter([in_sent], [[target_sent]])
-    out = " ".join(map(str, [ter.score, ter.num_edits, ter.ref_length]))
-    return out
-
-
-def init(sp_model):
-    global sp
-    sp = spm.SentencePieceProcessor()
-    sp.Load(sp_model)
-
-
-def process(source_sent, target_sent, hypo_sent, metric):
-    source_bpe = " ".join(sp.EncodeAsPieces(source_sent))
-    hypo_bpe = [" ".join(sp.EncodeAsPieces(h)) for h in hypo_sent]
-
-    if metric == "bleu":
-        score_str = [get_bleu(h, target_sent) for h in hypo_sent]
-    else:  # ter
-        score_str = [get_ter(h, target_sent) for h in hypo_sent]
-
-    return source_bpe, hypo_bpe, score_str
-
-
-def main(args):
-    assert (
-        args.split.startswith("train") or args.num_shards == 1
-    ), "--num-shards should be set to 1 for valid and test sets"
-    assert (
-        args.split.startswith("train")
-        or args.split.startswith("valid")
-        or args.split.startswith("test")
-    ), "--split should be set to train[n]/valid[n]/test[n]"
-
-    source_sents = read_text_file(args.input_source)
-    target_sents = read_text_file(args.input_target)
-
-    num_sents = len(source_sents)
-    assert num_sents == len(
-        target_sents
-    ), f"{args.input_source} and {args.input_target} should have the same number of sentences."
-
-    hypo_sents = read_text_file(args.input_hypo)
-    assert (
-        len(hypo_sents) % args.beam == 0
-    ), f"Number of hypotheses ({len(hypo_sents)}) cannot be divided by beam size ({args.beam})."
-
-    hypo_sents = [
-        hypo_sents[i : i + args.beam] for i in range(0, len(hypo_sents), args.beam)
-    ]
-    assert num_sents == len(
-        hypo_sents
-    ), f"{args.input_hypo} should contain {num_sents * args.beam} hypotheses but only has {len(hypo_sents) * args.beam}. (--beam={args.beam})"
-
-    output_dir = args.output_dir / args.metric
-    for ns in range(args.num_shards):
-        print(f"processing shard {ns+1}/{args.num_shards}")
-        shard_output_dir = output_dir / f"split{ns+1}"
-        source_output_dir = shard_output_dir / "input_src"
-        hypo_output_dir = shard_output_dir / "input_tgt"
-        metric_output_dir = shard_output_dir / args.metric
-
-        source_output_dir.mkdir(parents=True, exist_ok=True)
-        hypo_output_dir.mkdir(parents=True, exist_ok=True)
-        metric_output_dir.mkdir(parents=True, exist_ok=True)
-
-        if args.n_proc > 1:
-            with Pool(
-                args.n_proc, initializer=init, initargs=(args.sentencepiece_model,)
-            ) as p:
-                output = p.starmap(
-                    process,
-                    [
-                        (source_sents[i], target_sents[i], hypo_sents[i], args.metric)
-                        for i in range(ns, num_sents, args.num_shards)
-                    ],
-                )
-        else:
-            init(args.sentencepiece_model)
-            output = [
-                process(source_sents[i], target_sents[i], hypo_sents[i], args.metric)
-                for i in range(ns, num_sents, args.num_shards)
-            ]
-
-        with open(source_output_dir / f"{args.split}.bpe", "w") as s_o, open(
-            hypo_output_dir / f"{args.split}.bpe", "w"
-        ) as h_o, open(metric_output_dir / f"{args.split}.{args.metric}", "w") as m_o:
-            for source_bpe, hypo_bpe, score_str in output:
-                assert len(hypo_bpe) == len(score_str)
-                for h, m in zip(hypo_bpe, score_str):
-                    s_o.write(f"{source_bpe}\n")
-                    h_o.write(f"{h}\n")
-                    m_o.write(f"{m}\n")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--input-source", type=Path, required=True)
-    parser.add_argument("--input-target", type=Path, required=True)
-    parser.add_argument("--input-hypo", type=Path, required=True)
-    parser.add_argument("--output-dir", type=Path, required=True)
-    parser.add_argument("--split", type=str, required=True)
-    parser.add_argument("--beam", type=int, required=True)
-    parser.add_argument("--sentencepiece-model", type=str, required=True)
-    parser.add_argument("--metric", type=str, choices=["bleu", "ter"], default="bleu")
-    parser.add_argument("--num-shards", type=int, default=1)
-    parser.add_argument("--n-proc", type=int, default=8)
-
-    args = parser.parse_args()
-
-    main(args)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_memory_efficient_fp16.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_memory_efficient_fp16.py
deleted file mode 100644
index 2bf2f29888d6027896128930626b1aafe7f18475..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_memory_efficient_fp16.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import logging
-import unittest
-
-import torch
-from fairseq.optim.adam import FairseqAdam
-from fairseq.optim.fp16_optimizer import MemoryEfficientFP16Optimizer
-from omegaconf import OmegaConf
-
-
-@unittest.skipIf(not torch.cuda.is_available(), "test requires a GPU")
-class TestMemoryEfficientFP16(unittest.TestCase):
-    def setUp(self):
-        logging.disable(logging.CRITICAL)
-
-    def tearDown(self):
-        logging.disable(logging.NOTSET)
-
-    def test_load_state_dict(self):
-        # define simple FP16 model
-        model = torch.nn.Linear(5, 5).cuda().half()
-        params = list(model.parameters())
-
-        # initialize memory efficient FP16 optimizer
-        # with pseudo DictConfigs
-        optimizer = FairseqAdam(
-            cfg=OmegaConf.create(
-                vars(
-                    argparse.Namespace(
-                        adam_betas="(0.9, 0.999)",
-                        adam_eps=1e-8,
-                        weight_decay=0.0,
-                        lr=[0.00001],
-                    )
-                )
-            ),
-            params=params,
-        )
-        me_optimizer = MemoryEfficientFP16Optimizer(
-            cfg=OmegaConf.create(
-                {
-                    "common": vars(
-                        argparse.Namespace(
-                            fp16_init_scale=1,
-                            fp16_scale_window=1,
-                            fp16_scale_tolerance=1,
-                            threshold_loss_scale=1,
-                            min_loss_scale=1e-4,
-                        )
-                    )
-                }
-            ),
-            params=params,
-            optimizer=optimizer,
-        )
-
-        # optimizer state is created in the first step
-        loss = model(torch.rand(5).cuda().half()).sum()
-        me_optimizer.backward(loss)
-        me_optimizer.step()
-
-        # reload state
-        state = me_optimizer.state_dict()
-        me_optimizer.load_state_dict(state)
-        for k, v in me_optimizer.optimizer.state.items():
-            self.assertTrue(k.dtype == torch.float16)
-            for v_i in v.values():
-                if torch.is_tensor(v_i):
-                    self.assertTrue(v_i.dtype == torch.float32)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/optim/lr_scheduler/step_lr_scheduler.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/optim/lr_scheduler/step_lr_scheduler.py
deleted file mode 100644
index 8cb20068606a4afd2983430b794fa24647de2e7b..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/optim/lr_scheduler/step_lr_scheduler.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections.abc import Collection
-from dataclasses import dataclass, field
-from typing import List
-
-from omegaconf import II
-
-from fairseq.dataclass import FairseqDataclass
-from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
-
-
-@dataclass
-class StepLRScheduleConfig(FairseqDataclass):
-    warmup_updates: int = field(
-        default=0,
-        metadata={"help": "warmup the learning rate linearly for the first N updates"},
-    )
-    warmup_init_lr: float = field(
-        default=-1,
-        metadata={
-            "help": "initial learning rate during warmup phase; default is cfg.lr"
-        },
-    )
-    lr: List[float] = field(
-        default=II("optimization.lr"),
-        metadata={"help": "max learning rate, must be more than cfg.min_lr"},
-    )
-    min_lr: float = field(default=0.0, metadata={"help": "min learning rate"})
-    lr_deacy_period: int = field(default=25000, metadata={"help": "decay period"})
-    lr_decay: float = field(default=0.5, metadata={"help": "decay factor"})
-
-
-@register_lr_scheduler("step", dataclass=StepLRScheduleConfig)
-class StepLRSchedule(FairseqLRScheduler):
-    """Decay learning rate every k updates by a fixed factor
-    """
-
-    def __init__(self, cfg: StepLRScheduleConfig, fairseq_optimizer):
-        super().__init__(cfg, fairseq_optimizer)
-        self.max_lr = cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr
-        self.min_lr = cfg.min_lr
-        self.lr_deacy_period = cfg.lr_deacy_period
-        self.lr_decay = cfg.lr_decay
-        self.warmup_updates = cfg.warmup_updates
-        self.warmup_init_lr = (
-            cfg.warmup_init_lr if cfg.warmup_init_lr >= 0 else self.min_lr
-        )
-
-        assert(self.lr_deacy_period > 0)
-        assert(self.lr_decay <= 1)
-        assert(self.min_lr >= 0)
-        assert(self.max_lr > self.min_lr)
-
-        if cfg.warmup_updates > 0:
-            # linearly warmup for the first cfg.warmup_updates
-            self.warmup_lr_step = (
-                (self.max_lr - self.warmup_init_lr) / self.warmup_updates
-            )
-        else:
-            self.warmup_lr_step = 1
-
-        # initial learning rate
-        self.lr = self.warmup_init_lr
-        self.optimizer.set_lr(self.lr)
-
-    def step(self, epoch, val_loss=None):
-        """Update the learning rate at the end of the given epoch."""
-        super().step(epoch, val_loss)
-        # we don't change the learning rate at epoch boundaries
-        return self.optimizer.get_lr()
-
-    def step_update(self, num_updates):
-        """Update the learning rate after each update."""
-        if num_updates < self.cfg.warmup_updates:
-            self.lr = self.warmup_init_lr + num_updates * self.warmup_lr_step
-        else:
-            curr_updates = num_updates - self.cfg.warmup_updates
-            lr_mult = self.lr_decay ** (curr_updates // self.lr_deacy_period)
-            self.lr = max(self.max_lr * lr_mult, self.min_lr)
-
-        self.optimizer.set_lr(self.lr)
-        return self.lr
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/hubert/simple_kmeans/feature_utils.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/hubert/simple_kmeans/feature_utils.py
deleted file mode 100644
index f80bc4569768fac181133cdc8f76d1230e03bff6..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/hubert/simple_kmeans/feature_utils.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-import sys
-
-import tqdm
-from npy_append_array import NpyAppendArray
-
-
-logging.basicConfig(
-    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-    level=os.environ.get("LOGLEVEL", "INFO").upper(),
-    stream=sys.stdout,
-)
-logger = logging.getLogger("feature_utils")
-
-
-def get_shard_range(tot, nshard, rank):
-    assert rank < nshard and rank >= 0, f"invaid rank/nshard {rank}/{nshard}"
-    start = round(tot / nshard * rank)
-    end = round(tot / nshard * (rank + 1))
-    assert start < end, f"start={start}, end={end}"
-    logger.info(
-        f"rank {rank} of {nshard}, process {end-start} "
-        f"({start}-{end}) out of {tot}"
-    )
-    return start, end
-
-
-def get_path_iterator(tsv, nshard, rank):
-    with open(tsv, "r") as f:
-        root = f.readline().rstrip()
-        lines = [line.rstrip() for line in f]
-        start, end = get_shard_range(len(lines), nshard, rank)
-        lines = lines[start:end]
-        def iterate():
-            for line in lines:
-                subpath, nsample = line.split("\t")
-                yield f"{root}/{subpath}", int(nsample)
-    return iterate, len(lines)
-
-
-def dump_feature(reader, generator, num, split, nshard, rank, feat_dir):
-    iterator = generator()
-
-    feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy"
-    leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len"
-
-    os.makedirs(feat_dir, exist_ok=True)
-    if os.path.exists(feat_path):
-        os.remove(feat_path)
-
-    feat_f = NpyAppendArray(feat_path)
-    with open(leng_path, "w") as leng_f:
-        for path, nsample in tqdm.tqdm(iterator, total=num):
-            feat = reader.get_feats(path, nsample)
-            feat_f.append(feat.cpu().numpy())
-            leng_f.write(f"{len(feat)}\n")
-    logger.info("finished successfully")
-
-
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/encoders/fastbpe.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/encoders/fastbpe.py
deleted file mode 100644
index f7c21039549ea002e73d1ad7cde5735f215f11ee..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/encoders/fastbpe.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass, field
-
-from fairseq import file_utils
-from fairseq.data.encoders import register_bpe
-from fairseq.dataclass import FairseqDataclass
-
-
-@dataclass
-class fastBPEConfig(FairseqDataclass):
-    bpe_codes: str = field(default="???", metadata={"help": "path to fastBPE BPE"})
-
-
-@register_bpe("fastbpe", dataclass=fastBPEConfig)
-class fastBPE(object):
-    def __init__(self, cfg):
-        if cfg.bpe_codes is None:
-            raise ValueError("--bpe-codes is required for --bpe=fastbpe")
-        codes = file_utils.cached_path(cfg.bpe_codes)
-        try:
-            import fastBPE
-
-            self.bpe = fastBPE.fastBPE(codes)
-            self.bpe_symbol = "@@ "
-        except ImportError:
-            raise ImportError("Please install fastBPE with: pip install fastBPE")
-
-    def encode(self, x: str) -> str:
-        return self.bpe.apply([x])[0]
-
-    def decode(self, x: str) -> str:
-        return (x + " ").replace(self.bpe_symbol, "").rstrip()
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/roberta/enc_dec.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/roberta/enc_dec.py
deleted file mode 100644
index e538dee0aa5984b1a3d02ce81117d2046c030593..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/roberta/enc_dec.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import argparse
-import logging
-
-import torch.nn as nn
-import fairseq.checkpoint_utils
-from fairseq.models import (
-    FairseqEncoderDecoderModel,
-    register_model,
-    register_model_architecture,
-)
-from fairseq.models.transformer import TransformerDecoder
-from fairseq.models.roberta import model as roberta
-
-logger = logging.getLogger(__name__)
-
-
-@register_model("roberta_enc_dec")
-class RobertaEncDecModel(FairseqEncoderDecoderModel):
-    @staticmethod
-    def add_args(parser):
-        parser.add_argument(
-            "--pretrained-mlm-checkpoint",
-            default=None,
-            type=str,
-            metavar="PRETRAINED",
-            help="path to pretrained mlm checkpoint",
-        )
-        parser.add_argument(
-            "--pretrained-decoder", action="store_true", help="reload decoder"
-        )
-        parser.add_argument(
-            "--hack-layernorm-embedding",
-            action="store_true",
-            help="hack to reload old models trained with encoder-normalize-before=False (no equivalent to encoder-normalize-before=False and layernorm_embedding=False",
-        )
-        parser.add_argument(
-            "--share-decoder-input-output-embed",
-            action="store_true",
-            help="share decoder input and output embeddings",
-        )
-        parser.add_argument(
-            "--share-all-embeddings",
-            action="store_true",
-            help="share encoder, decoder and output embeddings"
-            " (requires shared dictionary and embed dim)",
-        )
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-
-        # make sure all arguments are present
-        base_enc_dec_architecture(args)
-        if args.pretrained_mlm_checkpoint:
-            arg_overrides = None
-            if args.hack_layernorm_embedding:
-                arg_overrides = {"layernorm_embedding": False}
-            loaded = fairseq.checkpoint_utils.load_model_ensemble_and_task(
-                [args.pretrained_mlm_checkpoint], arg_overrides=arg_overrides
-            )
-            ([roberta_enc], _cfg, _task) = loaded
-        else:
-            # Do we need to edit untie_weights here ?
-            share_in_out = (
-                args.share_decoder_input_output_embed or args.share_all_embeddings
-            )
-            args.untie_weights_roberta = not share_in_out
-            if args.hack_layernorm_embedding:
-                args.layernorm_embedding = False
-                args.encoder_normalize_before = False
-            roberta_enc = roberta.RobertaModel.build_model(args, task)
-
-        return cls.from_roberta(roberta_enc, args, task.source_dictionary)
-
-    @staticmethod
-    def from_roberta(roberta_enc: roberta.RobertaModel, args, dictionary):
-        encoder = roberta_enc.encoder.sentence_encoder
-        vocab_size, embed_dim = encoder.embed_tokens.weight.shape
-
-        if args.share_all_embeddings:
-            lm_head = roberta_enc.encoder.lm_head
-            assert encoder.embed_tokens.weight is lm_head.weight, (
-                "Can't use --share-all-embeddings with a model "
-                "that was pretraiend with --untie-weights-roberta_enc"
-            )
-        else:
-            lm_head = roberta.RobertaLMHead(
-                embed_dim, vocab_size, roberta_enc.args.activation_fn
-            )
-
-        dec_embs = nn.Embedding(vocab_size, embed_dim, dictionary.pad())
-        if args.share_all_embeddings or args.share_decoder_input_output_embed:
-            # Note: I wasn't able to use Embedding _weight parameter to achive this sharing.
-            dec_embs.weight = lm_head.weight
-
-        decoder = TransformerDecoder(
-            RobertaEncDecModel.read_args_from_roberta(roberta_enc.args),
-            dictionary,
-            dec_embs,
-            no_encoder_attn=False,
-            output_projection=lm_head,
-        )
-        if getattr(args, "pretrained_decoder", False):
-            decoder_dict = encoder.state_dict()
-
-            # TODO: hide setting "encoder_attn" layers behind a flag.
-            for k, w in list(decoder_dict.items()):
-                if ".self_attn" in k:
-                    k_enc_attn = k.replace(".self_attn", ".encoder_attn")
-                    decoder_dict[k_enc_attn] = w.detach().clone()
-
-            for k, w in lm_head.state_dict().items():
-                decoder_dict["output_projection." + k] = w
-
-            missing_keys, unexpected_keys = decoder.load_state_dict(
-                decoder_dict, strict=False
-            )
-            # missing_keys = [m for m in missing_keys if ".encoder_attn" not in m]
-            assert not missing_keys and not unexpected_keys, (
-                "Failed to load state dict. "
-                f"Missing keys: {missing_keys}. "
-                f"Unexpected keys: {unexpected_keys}."
-            )
-
-        if args.share_all_embeddings:
-            assert decoder.output_projection.weight is decoder.embed_tokens.weight
-            assert encoder.embed_tokens.weight is decoder.embed_tokens.weight
-        elif args.share_decoder_input_output_embed:
-            assert decoder.output_projection.weight is decoder.embed_tokens.weight
-            assert encoder.embed_tokens.weight is not decoder.embed_tokens.weight
-        else:
-            assert decoder.output_projection.weight is not decoder.embed_tokens.weight
-            assert encoder.embed_tokens.weight is not decoder.embed_tokens.weight
-
-        return RobertaEncDecModel(encoder, decoder)
-
-    @staticmethod
-    def read_args_from_roberta(roberta_args: argparse.Namespace):
-        # TODO: this would become easier if encoder/decoder where using a similar
-        # TransformerConfig object
-        args = argparse.Namespace(**vars(roberta_args))
-        attr_map = [
-            ("encoder_attention_heads", "decoder_attention_heads"),
-            ("encoder_embed_dim", "decoder_embed_dim"),
-            ("encoder_embed_dim", "decoder_output_dim"),
-            ("encoder_normalize_before", "decoder_normalize_before"),
-            ("encoder_layers_to_keep", "decoder_layers_to_keep"),
-            ("encoder_ffn_embed_dim", "decoder_ffn_embed_dim"),
-            ("encoder_layerdrop", "decoder_layerdrop"),
-            ("encoder_layers", "decoder_layers"),
-            ("encoder_learned_pos", "decoder_learned_pos"),
-            # should this be set from here ?
-            ("max_positions", "max_target_positions"),
-        ]
-        for k1, k2 in attr_map:
-            setattr(args, k2, getattr(roberta_args, k1))
-
-        args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
-        args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-        args.share_decoder_input_output_embed = not roberta_args.untie_weights_roberta
-        return args
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        prefix = name + "." if name != "" else ""
-        super().upgrade_state_dict_named(state_dict, name)
-        old_keys = list(state_dict.keys())
-
-        # rename decoder -> encoder before upgrading children modules
-        for k in old_keys:
-            if k.startswith(prefix + "encoder.lm_head"):
-                state_dict.pop(k)
-                continue
-            new_k = k
-            new_k = new_k.replace(".sentence_encoder.", ".")
-            new_k = new_k.replace("decoder.lm_head.", "decoder.output_projection.")
-            if k == new_k:
-                continue
-            # print(k, "->", new_k)
-            state_dict[new_k] = state_dict.pop(k)
-
-
-@register_model_architecture("roberta_enc_dec", "roberta_enc_dec")
-def base_enc_dec_architecture(args):
-    args.hack_layernorm_embedding = getattr(args, "hack_layernorm_embedding", False)
-    args.pretrained_mlm_checkpoint = getattr(args, "pretrained_mlm_checkpoint", None)
-    args.pretrained_decoder = getattr(args, "pretrained_decoder", None)
-    args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
-    args.share_decoder_input_output_embed = getattr(
-        args, "share_decoder_input_output_embed", False
-    )
-
-    roberta.base_architecture(args)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/speech_to_text/utils.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/speech_to_text/utils.py
deleted file mode 100644
index 168b8bf13b0e734eee3f6989ff0f28a016a09c2b..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/speech_to_text/utils.py
+++ /dev/null
@@ -1,563 +0,0 @@
-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
-
-
-import logging
-from collections.abc import Iterable
-from itertools import repeat
-from typing import List, Optional, Tuple
-
-import torch
-from torch import Tensor
-
-
-# ------------------------------------------------------------------------------
-#   assert_equal()
-# ------------------------------------------------------------------------------
-
-
-def assert_equal(value1, value2, name1=None, name2=None):
-    """Asserts two values are equal otherwise raise an error."""
-
-    str_name1 = "" if name1 is None else "{} ".format(name1)
-    str_name2 = "" if name2 is None else "{} ".format(name2)
-    if value1 != value2:
-        str_value1 = "{}" if name1 is None else "({})"
-        str_value1 = str_value1.format(value1)
-        str_value2 = "{}" if name2 is None else "({})"
-        str_value2 = str_value2.format(value2)
-        raise ValueError(
-            "Expected {}{} == {}{}".format(str_name1, str_value1, str_name2, str_value2)
-        )
-
-
-def fill_config(config, key, value):
-    if value is not None:
-        if key not in config or config[key] is None:
-            config[key] = value
-        assert_equal(value, config[key], "value", f'config["{key}"]')
-
-
-# ------------------------------------------------------------------------------
-#   check_and_return_expected()
-# ------------------------------------------------------------------------------
-
-
-def check_and_return_expected(value, undefined_value, expected_value, name=None):
-    """
-    Return the expected value while checking if the given value is undefined or
-    equal to the expected value.
-    """
-    if (undefined_value is None and value is None) or (undefined_value == value):
-        return expected_value
-    if value != expected_value:
-        str_name = "" if name is None else "{} ".format(name)
-        str_value = "{}" if name is None else "({})"
-        str_value = str_value.format(value)
-        raise ValueError(
-            "Expected {}{} == {}".format(str_name, str_value, expected_value)
-        )
-    return expected_value
-
-
-# ------------------------------------------------------------------------------
-#   get_time_axis()
-# ------------------------------------------------------------------------------
-
-
-def get_time_axis(layout):
-    """
-    Extract the time axis from the layout, for example for breaking sequence into
-    segments.
-    """
-    if layout in ["TB", "TBD"]:
-        return 0
-    if layout in ["BT", "BTD"]:
-        return 1
-    if layout in ["BCTD"]:
-        return 2
-    raise ValueError("Unsupported layout = {}".format(layout))
-
-
-# ------------------------------------------------------------------------------
-#   get_batch_axis()
-# ------------------------------------------------------------------------------
-
-
-def get_batch_axis(layout):
-    """
-    Extract the batch axis from the layout
-    """
-    if layout in ["TB", "TBD"]:
-        return 1
-    if layout in ["BT", "BTD", "BCTD"]:
-        return 0
-    raise ValueError("Unsupported layout = {}".format(layout))
-
-
-# ------------------------------------------------------------------------------
-#   monotonically_increasing_and_bounded()
-# ------------------------------------------------------------------------------
-
-
-def monotonically_increasing_and_bounded(iterable, min=None, max=None):
-    """
-    Check if the elements in the given iterable are monotonically increasing and
-    bounded by upper/lower bounds.
-    """
-    if not isinstance(iterable, Iterable):
-        raise TypeError(
-            "Expected iterable to be of type Iterable, got ({})".format(
-                iterable.__class__.__name__
-            )
-        )
-    for i in range(len(iterable)):
-        if min is not None and iterable[i] < min:
-            return False
-        if max is not None and iterable[i] > max:
-            return False
-        if i > 0 and iterable[i] <= iterable[i - 1]:
-            return False
-    return True
-
-
-# ------------------------------------------------------------------------------
-#   to_pair()
-# ------------------------------------------------------------------------------
-
-
-def to_pair(value, name):
-    """Make a pair (of type tuple) of given value."""
-    if isinstance(value, Iterable):
-        if len(value) != 2:
-            raise ValueError(
-                "Expected `{}` to have exactly 2 elements, got: ({})".format(
-                    name, value
-                )
-            )
-        return value
-    return tuple(repeat(value, 2))
-
-
-# ------------------------------------------------------------------------------
-#   infer_conv_output_attrs()
-# ------------------------------------------------------------------------------
-
-
-# TODO(cfyeh): figure out if we can get `output_dim` without calling the module.
-def infer_conv_output_attrs(
-    module, input_channels, input_dim, batch_size=1, max_length=8
-):
-    """Get output attributes of a module with input."""
-    input = torch.randn(batch_size, input_channels, max_length, input_dim)
-    output = module(input)
-    output_channels = output.shape[1]
-    output_dim = output.shape[-1]
-    return output_channels, output_dim
-
-
-# ------------------------------------------------------------------------------
-#   NoOp
-# ------------------------------------------------------------------------------
-
-
-class NoOp(torch.nn.Module):
-    """
-    NoOp simply passes the input as the output.
-    """
-
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, input: Tensor) -> Tensor:
-        return input
-
-
-# ------------------------------------------------------------------------------
-#   Permute: a torch.nn.Module applies permutation on the input tensor.
-# ------------------------------------------------------------------------------
-
-
-class Permute(torch.nn.Module):
-    def __init__(self, dims):
-        super().__init__()
-        self.dims = dims
-
-    def forward(self, input: Tensor) -> Tensor:
-        return input.permute(self.dims).contiguous()
-
-
-# ------------------------------------------------------------------------------
-#   lengths_to_padding_mask()
-# ------------------------------------------------------------------------------
-
-
-def lengths_to_padding_mask(lengths: Tensor) -> Tensor:
-    """Convert lengths of shape (B, ) to padding mask."""
-    batch_size = lengths.shape[0]
-    max_length = int(torch.max(lengths).item())
-    padding_mask = torch.arange(  # [0, ..., T-1]
-        max_length, device=lengths.device, dtype=lengths.dtype
-    ).expand(batch_size, max_length) >= lengths.unsqueeze(1)
-
-    return padding_mask
-
-
-# ------------------------------------------------------------------------------
-#   lengths_to_attention_mask()
-# ------------------------------------------------------------------------------
-
-
-def lengths_to_attention_mask(
-    lengths: Tensor,
-    left_context: Optional[int] = None,
-    right_context: Optional[int] = None,
-) -> Optional[Tensor]:
-    """
-    Generate attention mask based on (lengths, left_context, right_context).
-    left_context is None means unlimited left context.
-    right_context is None means unlimited right context.
-    """
-
-    if left_context is None and right_context is None:
-        return None
-
-    max_length = int(torch.max(lengths).item())
-
-    # For example, with `max_length` == 5,
-    # indices = tensor([
-    #     [ 0,  1,  2,  3,  4,  5],
-    #     [-1,  0,  1,  2,  3,  4],
-    #     [-2, -1,  0,  1,  2,  3],
-    #     [-3, -2, -1,  0,  1,  2],
-    #     [-4, -3, -2, -1,  0,  1],
-    #     [-5, -4, -3, -2, -1,  0],
-    # ])
-
-    # In some cases the second torch.arange is created on cpu which causes a
-    # failure. Adding the device option to guard against it.
-    indices = torch.arange(
-        max_length, device=lengths.device, dtype=lengths.dtype
-    ).expand(max_length, max_length) - torch.arange(
-        max_length, device=lengths.device
-    ).view(
-        max_length, -1
-    )
-
-    # For example, with `max_length` == 5,
-    # bool_mask = tensor([
-    #     [True, True, True, True, True],
-    #     [True, True, True, True, True],
-    #     [True, True, True, True, True],
-    #     [True, True, True, True, True],
-    #     [True, True, True, True, True],
-    # ])
-    bool_mask = (
-        torch.tensor([True]).to(device=lengths.device).expand(max_length, max_length)
-    )
-
-    # For example, with `max_length` == 5, left_context == 2
-    # left_mask = tensor([
-    #     [ True,  True, True, True, True],
-    #     [ True,  True, True, True, True],
-    #     [ True,  True, True, True, True],
-    #     [False,  True, True, True, True],
-    #     [False, False, True, True, True],
-    # ])
-    if left_context is not None:
-        left_mask = indices >= -left_context
-        bool_mask = bool_mask & left_mask
-
-    # For example, with `max_length` == 5, right_context == 1
-    # right_mask = tensor([
-    #     [True, True, False, False, False],
-    #     [True, True,  True, False, False],
-    #     [True, True,  True,  True, False],
-    #     [True, True,  True,  True,  True],
-    #     [True, True,  True,  True,  True],
-    # ])
-    if right_context is not None:
-        right_mask = indices <= right_context
-        bool_mask = bool_mask & right_mask
-
-    bool_mask = (~bool_mask).to(device=lengths.device)
-    return bool_mask
-
-
-# ------------------------------------------------------------------------------
-#   infer_output_norm()
-# ------------------------------------------------------------------------------
-
-
-def infer_output_norm(module, output_norm=None):
-    """
-    Infer the output norm (string and module) needed on the module gvien desired
-    output normalization.
-    """
-    if output_norm == module.output_norm():
-        # output_norm already matches module.output_norm().
-        return (None, NoOp())
-
-    if output_norm is None and module.output_norm() is not None:
-        logger = logging.getLogger("infer_output_norm()")
-        logger.warning(
-            "trying to set output_norm ({}) ".format(output_norm)
-            + "but got module.output_norm() ({}), ".format(module.output_norm())
-            + "the combined output_norm() will be ({})".format(module.output_norm())
-        )
-        return (None, NoOp())
-
-    if output_norm == "log_softmax":
-        if module.output_norm() is not None:
-            raise ValueError(
-                "incompatible output_norm ({}) ".format(output_norm)
-                + "and module.output_norm() ({})".format(module.output_norm())
-            )
-        else:
-            return ("log_softmax", torch.nn.LogSoftmax(dim=-1))
-
-    if output_norm == "softmax":
-        if module.output_norm() is not None:
-            raise ValueError(
-                "incompatible output_norm ({}) ".format(output_norm)
-                + "and module.output_norm() ({})".format(module.output_norm())
-            )
-        else:
-            return ("softmax", torch.nn.Softmax(dim=-1))
-
-    raise ValueError(
-        "output_norm ({}) not in ".format(output_norm)
-        + "supported list = [None, softmax, log_softmax]"
-    )
-
-
-# ------------------------------------------------------------------------------
-#   infer_channels_from_layout()
-# ------------------------------------------------------------------------------
-
-
-def infer_channels_from_layout(layout, channels):
-    """Extract the number of channels from the layout."""
-    if layout in ("TBD", "BTD"):
-        if channels is not None and channels != 1:
-            raise ValueError(
-                "Expected channels ({}) to be 1 for layout = {}".format(
-                    channels, layout
-                )
-            )
-        if channels is None:
-            return 1
-    return channels
-
-
-# ------------------------------------------------------------------------------
-#   pad_sequence()
-# ------------------------------------------------------------------------------
-
-
-@torch.jit.export
-def pad_sequence(
-    sequence: Tensor,
-    time_axis: int,
-    extra_left_context: int = 0,
-    extra_right_context: int = 0,
-) -> Tensor:
-    """Pad extra left/right contexts to the sequence."""
-
-    if extra_left_context == 0 and extra_right_context == 0:
-        return sequence
-
-    tensors_to_concat = []
-
-    if extra_left_context:
-        size = (extra_left_context,)
-        fill_value = 0
-        indices = torch.full(
-            size=size,
-            fill_value=fill_value,
-            dtype=torch.long,
-            device=sequence.device,
-        )
-        left_padding = torch.index_select(sequence, time_axis, indices)
-        tensors_to_concat.append(left_padding)
-
-    tensors_to_concat.append(sequence)
-
-    # NOTE(cfyeh): for efficiency reason we pad 0 instead of the last frame for
-    #              extra right contexts.
-    if extra_right_context:
-        size = list(sequence.shape)
-        size[time_axis] = extra_right_context
-        right_padding = torch.zeros(size, dtype=sequence.dtype, device=sequence.device)
-        tensors_to_concat.append(right_padding)
-
-    padded_sequence = torch.cat(tensors_to_concat, dim=time_axis)
-    return padded_sequence
-
-
-# ------------------------------------------------------------------------------
-#   sequence_to_segments()
-# ------------------------------------------------------------------------------
-
-
-@torch.jit.export
-def sequence_to_segments(
-    sequence: Tensor,
-    time_axis: int,
-    lengths: Tensor,
-    segment_size: Optional[int] = None,
-    extra_left_context: int = 0,
-    extra_right_context: int = 0,
-) -> List[Tuple[Tensor, Tensor]]:
-    """Breaks sequence into segments."""
-
-    sequence = pad_sequence(
-        sequence=sequence,
-        time_axis=time_axis,
-        extra_left_context=extra_left_context,
-        extra_right_context=extra_right_context,
-    )
-
-    lengths = lengths + extra_left_context + extra_right_context
-
-    segments: List[Tuple[Tensor, Tensor]] = []
-
-    if segment_size is None:
-        segments.append((sequence, lengths))
-        return segments
-
-    offset = 0
-    end = sequence.shape[time_axis]
-    step = segment_size
-    size = extra_left_context + segment_size + extra_right_context
-
-    while offset + extra_left_context + extra_right_context < end:
-        clamped_size = min(size, end - offset)
-        segment_lengths = torch.clamp(lengths - offset, min=0, max=clamped_size)
-        indices = torch.arange(
-            start=offset,
-            end=(offset + clamped_size),
-            step=1,
-            dtype=torch.long,
-            device=sequence.device,
-        )
-        segment_tensor = torch.index_select(sequence, time_axis, indices)
-        segments.append((segment_tensor, segment_lengths))
-        offset = offset + step
-
-    return segments
-
-
-# ------------------------------------------------------------------------------
-#   segments_to_sequence()
-# ------------------------------------------------------------------------------
-
-
-@torch.jit.export
-def segments_to_sequence(
-    segments: List[Tuple[Tensor, Tensor]], time_axis: int
-) -> Tuple[Tensor, Tensor]:
-    """Concatenate segments into a full sequence."""
-    if len(segments) == 1:
-        return segments[0]
-
-    tensors_to_concat: List[Tensor] = []
-    lengths_to_stack: List[Tensor] = []
-
-    for tensor, lengths in segments:
-        tensors_to_concat.append(tensor)
-        lengths_to_stack.append(lengths)
-
-    sequence = torch.cat(tensors_to_concat, dim=time_axis)
-    lengths = torch.stack(lengths_to_stack, dim=0)
-    lengths = torch.sum(lengths, dim=0)
-
-    return sequence, lengths
-
-
-def lengths_to_encoder_padding_mask(lengths, batch_first: bool = False):
-    """
-    convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor
-
-    Args:
-        lengths: a (B, )-shaped tensor
-        batch_first: whether to return a (B, T) tensor
-
-    Return:
-        max_length: maximum length of B sequences
-        encoder_padding_mask: a (max_length, B) binary mask, where
-        [t, b] = False for t < lengths[b] and True otherwise
-
-    TODO:
-        kernelize this function if benchmarking shows this function is slow
-    """
-    max_lengths = torch.max(lengths).item()
-    bsz = lengths.size(0)
-    encoder_padding_mask = torch.arange(
-        max_lengths
-    ).to(  # a (T, ) tensor with [0, ..., T-1]
-        lengths.device
-    ).view(  # move to the right device
-        1, max_lengths
-    ).expand(  # reshape to (1, T)-shaped tensor
-        bsz, -1
-    ) > lengths.view(  # expand to (B, T)-shaped tensor
-        bsz, 1
-    ).expand(
-        -1, max_lengths
-    )
-    if not batch_first:
-        return encoder_padding_mask.t(), max_lengths
-    else:
-        return encoder_padding_mask, max_lengths
-
-
-# ------------------------------------------------------------------------------
-#   attention suppression
-# ------------------------------------------------------------------------------
-
-
-def attention_suppression(attention_weights: Tensor, scale: float):
-    # B, H, qlen, klen -> B, H, qlen, 1
-    attention_prob = torch.nn.functional.softmax(attention_weights.float(), dim=-1)
-    attention_nozeros = attention_prob.to(torch.bool)
-    nozeros_sum = torch.sum(attention_nozeros.to(torch.float), dim=-1, keepdim=True)
-
-    # For very sparse situation, we need get round about 0s
-    key_sum = torch.sum(attention_prob, dim=-1, keepdim=True)
-
-    # nozeros_sum should > 1
-    key_mean = key_sum / (nozeros_sum + 1e-8)
-
-    # std calculation
-    dis = (attention_prob - key_mean) * (attention_prob - key_mean)
-
-    # if attention_prob[i] < threshold, then dis_masked[i] = 0; for all i
-    dis_masked = torch.where(
-        attention_nozeros, dis, attention_prob.new_zeros(attention_prob.size())
-    )
-
-    key_var = torch.sum(dis_masked, dim=-1, keepdim=True)
-    key_var = key_var / (nozeros_sum - 1.0 + 1e-8)
-    key_std = torch.sqrt(key_var)
-    key_thread = key_mean - scale * key_std
-
-    # if attention_prob[i] >= key_thread, then attention_prob[i]
-    # , otherwise "-inf"
-    inf_tensor = attention_prob.new_zeros(attention_prob.size()).detach()
-    inf_tensor[:] = float("-inf")
-    attention_weights_float = torch.where(
-        attention_prob < key_thread,
-        inf_tensor,
-        attention_weights.float(),
-    )
-
-    return attention_weights_float.type_as(attention_weights)
-
-
-def layer_norm_backward_hook(module, grad_input, grad_output, clamp_value):
-    return tuple(torch.clamp(v, min=-clamp_value, max=clamp_value) for v in grad_input)
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/structures/image_list.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/structures/image_list.py
deleted file mode 100644
index b31b2d395dffb9d3694239d1aa73615899975f4e..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/structures/image_list.py
+++ /dev/null
@@ -1,110 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from __future__ import division
-from typing import Any, List, Tuple
-import torch
-from torch import device
-from torch.nn import functional as F
-
-from detectron2.layers.wrappers import shapes_to_tensor
-
-
-class ImageList(object):
-    """
-    Structure that holds a list of images (of possibly
-    varying sizes) as a single tensor.
-    This works by padding the images to the same size.
-    The original sizes of each image is stored in `image_sizes`.
-
-    Attributes:
-        image_sizes (list[tuple[int, int]]): each tuple is (h, w).
-            During tracing, it becomes list[Tensor] instead.
-    """
-
-    def __init__(self, tensor: torch.Tensor, image_sizes: List[Tuple[int, int]]):
-        """
-        Arguments:
-            tensor (Tensor): of shape (N, H, W) or (N, C_1, ..., C_K, H, W) where K >= 1
-            image_sizes (list[tuple[int, int]]): Each tuple is (h, w). It can
-                be smaller than (H, W) due to padding.
-        """
-        self.tensor = tensor
-        self.image_sizes = image_sizes
-
-    def __len__(self) -> int:
-        return len(self.image_sizes)
-
-    def __getitem__(self, idx) -> torch.Tensor:
-        """
-        Access the individual image in its original size.
-
-        Args:
-            idx: int or slice
-
-        Returns:
-            Tensor: an image of shape (H, W) or (C_1, ..., C_K, H, W) where K >= 1
-        """
-        size = self.image_sizes[idx]
-        return self.tensor[idx, ..., : size[0], : size[1]]
-
-    @torch.jit.unused
-    def to(self, *args: Any, **kwargs: Any) -> "ImageList":
-        cast_tensor = self.tensor.to(*args, **kwargs)
-        return ImageList(cast_tensor, self.image_sizes)
-
-    @property
-    def device(self) -> device:
-        return self.tensor.device
-
-    @staticmethod
-    def from_tensors(
-        tensors: List[torch.Tensor], size_divisibility: int = 0, pad_value: float = 0.0
-    ) -> "ImageList":
-        """
-        Args:
-            tensors: a tuple or list of `torch.Tensor`, each of shape (Hi, Wi) or
-                (C_1, ..., C_K, Hi, Wi) where K >= 1. The Tensors will be padded
-                to the same shape with `pad_value`.
-            size_divisibility (int): If `size_divisibility > 0`, add padding to ensure
-                the common height and width is divisible by `size_divisibility`.
-                This depends on the model and many models need a divisibility of 32.
-            pad_value (float): value to pad
-
-        Returns:
-            an `ImageList`.
-        """
-        assert len(tensors) > 0
-        assert isinstance(tensors, (tuple, list))
-        for t in tensors:
-            assert isinstance(t, torch.Tensor), type(t)
-            assert t.shape[:-2] == tensors[0].shape[:-2], t.shape
-
-        image_sizes = [(im.shape[-2], im.shape[-1]) for im in tensors]
-        image_sizes_tensor = [shapes_to_tensor(x) for x in image_sizes]
-        max_size = torch.stack(image_sizes_tensor).max(0).values
-
-        if size_divisibility > 1:
-            stride = size_divisibility
-            # the last two dims are H,W, both subject to divisibility requirement
-            max_size = (max_size + (stride - 1)).div(stride, rounding_mode="floor") * stride
-
-        # handle weirdness of scripting and tracing ...
-        if torch.jit.is_scripting():
-            max_size: List[int] = max_size.to(dtype=torch.long).tolist()
-        else:
-            if torch.jit.is_tracing():
-                image_sizes = image_sizes_tensor
-
-        if len(tensors) == 1:
-            # This seems slightly (2%) faster.
-            # TODO: check whether it's faster for multiple images as well
-            image_size = image_sizes[0]
-            padding_size = [0, max_size[-1] - image_size[1], 0, max_size[-2] - image_size[0]]
-            batched_imgs = F.pad(tensors[0], padding_size, value=pad_value).unsqueeze_(0)
-        else:
-            # max_size can be a tensor in tracing mode, therefore convert to list
-            batch_shape = [len(tensors)] + list(tensors[0].shape[:-2]) + list(max_size)
-            batched_imgs = tensors[0].new_full(batch_shape, pad_value)
-            for img, pad_img in zip(tensors, batched_imgs):
-                pad_img[..., : img.shape[-2], : img.shape[-1]].copy_(img)
-
-        return ImageList(batched_imgs.contiguous(), image_sizes)
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/README.md b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/README.md
deleted file mode 100644
index f560384045ab4f6bc2beabef1170308fca117eb3..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/tests/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-## Unit Tests
-
-To run the unittests, do:
-```
-cd detectron2
-python -m unittest discover -v -s ./tests
-```
-
-There are also end-to-end inference & training tests, in [dev/run_*_tests.sh](../dev).
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/docker/1_generate_masks_from_raw_images.sh b/spaces/OpenGVLab/InternGPT/third-party/lama/docker/1_generate_masks_from_raw_images.sh
deleted file mode 100644
index 04b780e6d6a3bf46f32b76df12b4a35ea679501d..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/docker/1_generate_masks_from_raw_images.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env bash
-
-
-if (( $# < 3 ))
-then
-    echo "Usage: $0 config_name input_images_dir image_mask_dataset_out_dir [other args to gen_mask_dataset.py]"
-    exit 1
-fi
-
-CURDIR="$(dirname $0)"
-SRCDIR="$CURDIR/.."
-SRCDIR="$(realpath $SRCDIR)"
-
-CONFIG_LOCAL_PATH="$(realpath $1)"
-INPUT_LOCAL_DIR="$(realpath $2)"
-OUTPUT_LOCAL_DIR="$(realpath $3)"
-shift 3
-
-mkdir -p "$OUTPUT_LOCAL_DIR"
-
-docker run \
-    -v "$SRCDIR":/home/user/project \
-    -v "$CONFIG_LOCAL_PATH":/data/config.yaml \
-    -v "$INPUT_LOCAL_DIR":/data/input \
-    -v "$OUTPUT_LOCAL_DIR":/data/output \
-    -u $(id -u):$(id -g) \
-    --name="lama-mask-gen" \
-    --rm \
-    windj007/lama \
-    /home/user/project/bin/gen_mask_dataset.py \
-        /data/config.yaml /data/input /data/output $@
diff --git a/spaces/PKUWilliamYang/StyleGANEX/models/mtcnn/mtcnn_pytorch/src/__init__.py b/spaces/PKUWilliamYang/StyleGANEX/models/mtcnn/mtcnn_pytorch/src/__init__.py
deleted file mode 100644
index 617ba38c34b1801b2db2e0209b4e886c9d24c490..0000000000000000000000000000000000000000
--- a/spaces/PKUWilliamYang/StyleGANEX/models/mtcnn/mtcnn_pytorch/src/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .visualization_utils import show_bboxes
-from .detector import detect_faces
diff --git a/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/dsd/op/upfirdn2d.cpp b/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/dsd/op/upfirdn2d.cpp
deleted file mode 100644
index d2e633dc896433c205e18bc3e455539192ff968e..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/dsd/op/upfirdn2d.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-#include <torch/extension.h>
-
-
-torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
-                            int up_x, int up_y, int down_x, int down_y,
-                            int pad_x0, int pad_x1, int pad_y0, int pad_y1);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
-                        int up_x, int up_y, int down_x, int down_y,
-                        int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
-    CHECK_CUDA(input);
-    CHECK_CUDA(kernel);
-
-    return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/PSLD/PSLD/share_btn.py b/spaces/PSLD/PSLD/share_btn.py
deleted file mode 100644
index 5bce98ad54d491f9d5691fea427efeccc77690cc..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/share_btn.py
+++ /dev/null
@@ -1,93 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-
-	async function getInputImgFile(imgCanvas){
-        const blob = await new Promise(resolve => imgCanvas.toBlob(resolve));
-        const imgId = Date.now() % 200;
-        const fileName = `sd-inpainting-${{imgId}}.png`;
-        return new File([blob], fileName, { type: 'image/png' });
-	}
-
-    async function getOutoutImgFile(imgEl){
-        const res = await fetch(imgEl.src);
-        const blob = await res.blob();
-        const imgId = Date.now() % 200;
-        const fileName = `sd-inpainting-${{imgId}}.png`;
-        return new File([blob], fileName, { type: 'image/png' });
-    }
-
-    const gradioEl = document.querySelector('body > gradio-app');
-    // const gradioEl = document.querySelector("gradio-app").shadowRoot;
-    const inputImgCanvas = gradioEl.querySelector('canvas[key="drawing"]');
-    const outputImgEl = gradioEl.querySelector('#output-img img');
-    const promptTxt = gradioEl.querySelector('#input-text textarea').value;
-    let titleTxt = promptTxt;
-    if(titleTxt.length > 100){
-        titleTxt = titleTxt.slice(0, 100) + ' ...';
-    }
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-
-    if(!outputImgEl){
-        return;
-    };
-
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-
-    const inputImgFile = await getInputImgFile(inputImgCanvas);
-    const outputImgFile = await getOutoutImgFile(outputImgEl);
-    const files = [inputImgFile, outputImgFile];
-
-    const urls = await Promise.all(files.map((f) => uploadFile(f)));
-
-	const htmlImgs = urls.map(url => `<img src='${url}' style='max-width: 450px;'>`);
-    const [inputImgUrl, outputImgUrl] = htmlImgs;
-
-	const descriptionMd = `<div style='display: flex; flex-wrap: wrap; column-gap: 0.75rem;'>
-<div>
-${inputImgUrl}
-
-${promptTxt}
-</div>
-<div>
-${outputImgUrl}
-</div>
-</div>`;
-
-    const params = new URLSearchParams({
-        title: titleTxt,
-        description: descriptionMd,
-    });
-
-	const paramsStr = params.toString();
-	window.open(`${window.location.href}/discussions/new?${paramsStr}`, '_blank');
-
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/PeepDaSlan9/nitrosocke-mo-di-diffusion/README.md b/spaces/PeepDaSlan9/nitrosocke-mo-di-diffusion/README.md
deleted file mode 100644
index 08a8e7758a88e420131ccf975bd5cadbb94392e8..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/nitrosocke-mo-di-diffusion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Nitrosocke Mo Di Diffusion
-emoji: 🦀
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/PeggyWang/ehartford-WizardLM-Uncensored-Falcon-40b/app.py b/spaces/PeggyWang/ehartford-WizardLM-Uncensored-Falcon-40b/app.py
deleted file mode 100644
index 5ba19b706adc0c2fd6080f4be800631c8a17636c..0000000000000000000000000000000000000000
--- a/spaces/PeggyWang/ehartford-WizardLM-Uncensored-Falcon-40b/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/ehartford/WizardLM-Uncensored-Falcon-40b").launch()
\ No newline at end of file
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/parrots_wrapper.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/parrots_wrapper.py
deleted file mode 100644
index 93c97640d4b9ed088ca82cfe03e6efebfcfa9dbf..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/parrots_wrapper.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from functools import partial
-
-import torch
-
-TORCH_VERSION = torch.__version__
-
-
-def is_rocm_pytorch() -> bool:
-    is_rocm = False
-    if TORCH_VERSION != 'parrots':
-        try:
-            from torch.utils.cpp_extension import ROCM_HOME
-            is_rocm = True if ((torch.version.hip is not None) and
-                               (ROCM_HOME is not None)) else False
-        except ImportError:
-            pass
-    return is_rocm
-
-
-def _get_cuda_home():
-    if TORCH_VERSION == 'parrots':
-        from parrots.utils.build_extension import CUDA_HOME
-    else:
-        if is_rocm_pytorch():
-            from torch.utils.cpp_extension import ROCM_HOME
-            CUDA_HOME = ROCM_HOME
-        else:
-            from torch.utils.cpp_extension import CUDA_HOME
-    return CUDA_HOME
-
-
-def get_build_config():
-    if TORCH_VERSION == 'parrots':
-        from parrots.config import get_build_info
-        return get_build_info()
-    else:
-        return torch.__config__.show()
-
-
-def _get_conv():
-    if TORCH_VERSION == 'parrots':
-        from parrots.nn.modules.conv import _ConvNd, _ConvTransposeMixin
-    else:
-        from torch.nn.modules.conv import _ConvNd, _ConvTransposeMixin
-    return _ConvNd, _ConvTransposeMixin
-
-
-def _get_dataloader():
-    if TORCH_VERSION == 'parrots':
-        from torch.utils.data import DataLoader, PoolDataLoader
-    else:
-        from torch.utils.data import DataLoader
-        PoolDataLoader = DataLoader
-    return DataLoader, PoolDataLoader
-
-
-def _get_extension():
-    if TORCH_VERSION == 'parrots':
-        from parrots.utils.build_extension import BuildExtension, Extension
-        CppExtension = partial(Extension, cuda=False)
-        CUDAExtension = partial(Extension, cuda=True)
-    else:
-        from torch.utils.cpp_extension import (BuildExtension, CppExtension,
-                                               CUDAExtension)
-    return BuildExtension, CppExtension, CUDAExtension
-
-
-def _get_pool():
-    if TORCH_VERSION == 'parrots':
-        from parrots.nn.modules.pool import (_AdaptiveAvgPoolNd,
-                                             _AdaptiveMaxPoolNd, _AvgPoolNd,
-                                             _MaxPoolNd)
-    else:
-        from torch.nn.modules.pooling import (_AdaptiveAvgPoolNd,
-                                              _AdaptiveMaxPoolNd, _AvgPoolNd,
-                                              _MaxPoolNd)
-    return _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd
-
-
-def _get_norm():
-    if TORCH_VERSION == 'parrots':
-        from parrots.nn.modules.batchnorm import _BatchNorm, _InstanceNorm
-        SyncBatchNorm_ = torch.nn.SyncBatchNorm2d
-    else:
-        from torch.nn.modules.instancenorm import _InstanceNorm
-        from torch.nn.modules.batchnorm import _BatchNorm
-        SyncBatchNorm_ = torch.nn.SyncBatchNorm
-    return _BatchNorm, _InstanceNorm, SyncBatchNorm_
-
-
-_ConvNd, _ConvTransposeMixin = _get_conv()
-DataLoader, PoolDataLoader = _get_dataloader()
-BuildExtension, CppExtension, CUDAExtension = _get_extension()
-_BatchNorm, _InstanceNorm, SyncBatchNorm_ = _get_norm()
-_AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd = _get_pool()
-
-
-class SyncBatchNorm(SyncBatchNorm_):
-
-    def _check_input_dim(self, input):
-        if TORCH_VERSION == 'parrots':
-            if input.dim() < 2:
-                raise ValueError(
-                    f'expected at least 2D input (got {input.dim()}D input)')
-        else:
-            super()._check_input_dim(input)
diff --git a/spaces/Pie31415/control-animation/webui/app_text_to_video.py b/spaces/Pie31415/control-animation/webui/app_text_to_video.py
deleted file mode 100644
index 6646a8c2d961683fd8c13771bfe31de5b05261fc..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/webui/app_text_to_video.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import gradio as gr
-from text_to_animation.model import ControlAnimationModel
-import os
-from utils.hf_utils import get_model_list
-
-huggingspace_name = os.environ.get("SPACE_AUTHOR_NAME")
-on_huggingspace = huggingspace_name if huggingspace_name is not None else False
-
-examples = [
-    ["an astronaut waving the arm on the moon"],
-    ["a sloth surfing on a wakeboard"],
-    ["an astronaut walking on a street"],
-    ["a cute cat walking on grass"],
-    ["a horse is galloping on a street"],
-    ["an astronaut is skiing down the hill"],
-    ["a gorilla walking alone down the street"],
-    ["a gorilla dancing on times square"],
-    ["A panda dancing dancing like crazy on Times Square"],
-]
-
-
-def create_demo(model: ControlAnimationModel):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown("## Text2Video-Zero: Video Generation")
-        with gr.Row():
-            gr.HTML(
-                """
-                <div style="text-align: left; auto;">
-                <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem">
-                    Description: Simply input <b>any textual prompt</b> to generate videos right away and unleash your creativity and imagination! You can also select from the examples below. For performance purposes, our current preview release allows to generate up to 16 frames, which can be configured in the Advanced Options.
-                </h3>
-                </div>
-                """
-            )
-
-        with gr.Row():
-            with gr.Column():
-                model_name = gr.Dropdown(
-                    label="Model",
-                    choices=get_model_list(),
-                    value="dreamlike-art/dreamlike-photoreal-2.0",
-                )
-                prompt = gr.Textbox(label="Prompt")
-                run_button = gr.Button(label="Run")
-                with gr.Accordion("Advanced options", open=False):
-                    watermark = gr.Radio(
-                        ["Picsart AI Research", "Text2Video-Zero", "None"],
-                        label="Watermark",
-                        value="Picsart AI Research",
-                    )
-
-                    if on_huggingspace:
-                        video_length = gr.Slider(
-                            label="Video length", minimum=8, maximum=16, step=1
-                        )
-                    else:
-                        video_length = gr.Number(
-                            label="Video length", value=8, precision=0
-                        )
-
-                    n_prompt = gr.Textbox(label="Optional Negative Prompt", value="")
-                    seed = gr.Slider(
-                        label="Seed",
-                        info="-1 for random seed on each run. Otherwise, the seed will be fixed.",
-                        minimum=-1,
-                        maximum=65536,
-                        value=0,
-                        step=1,
-                    )
-
-                    motion_field_strength_x = gr.Slider(
-                        label="Global Translation $\\delta_{x}$",
-                        minimum=-20,
-                        maximum=20,
-                        value=12,
-                        step=1,
-                    )
-                    motion_field_strength_y = gr.Slider(
-                        label="Global Translation $\\delta_{y}$",
-                        minimum=-20,
-                        maximum=20,
-                        value=12,
-                        step=1,
-                    )
-
-                    t0 = gr.Slider(
-                        label="Timestep t0",
-                        minimum=0,
-                        maximum=47,
-                        value=44,
-                        step=1,
-                        info="Perform DDPM steps from t0 to t1. The larger the gap between t0 and t1, the more variance between the frames. Ensure t0 < t1 ",
-                    )
-                    t1 = gr.Slider(
-                        label="Timestep t1",
-                        minimum=1,
-                        info="Perform DDPM steps from t0 to t1. The larger the gap between t0 and t1, the more variance between the frames. Ensure t0 < t1",
-                        maximum=48,
-                        value=47,
-                        step=1,
-                    )
-                    chunk_size = gr.Slider(
-                        label="Chunk size",
-                        minimum=2,
-                        maximum=16,
-                        value=8,
-                        step=1,
-                        visible=not on_huggingspace,
-                        info="Number of frames processed at once. Reduce for lower memory usage.",
-                    )
-                    merging_ratio = gr.Slider(
-                        label="Merging ratio",
-                        minimum=0.0,
-                        maximum=0.9,
-                        step=0.1,
-                        value=0.0,
-                        visible=not on_huggingspace,
-                        info="Ratio of how many tokens are merged. The higher the more compression (less memory and faster inference).",
-                    )
-
-            with gr.Column():
-                result = gr.Video(label="Generated Video")
-
-        inputs = [
-            prompt,
-            model_name,
-            motion_field_strength_x,
-            motion_field_strength_y,
-            t0,
-            t1,
-            n_prompt,
-            chunk_size,
-            video_length,
-            watermark,
-            merging_ratio,
-            seed,
-        ]
-
-        gr.Examples(
-            examples=examples,
-            inputs=inputs,
-            outputs=result,
-            fn=model.process_text2video,
-            run_on_click=False,
-            cache_examples=on_huggingspace,
-        )
-
-        run_button.click(
-            fn=model.process_text2video,
-            inputs=inputs,
-            outputs=result,
-        )
-    return demo
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/tests/models/test_encodec_model.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/tests/models/test_encodec_model.py
deleted file mode 100644
index 2f9c1db3f69a45f02451b71da95f44356811acbb..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/tests/models/test_encodec_model.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import random
-
-import numpy as np
-import torch
-
-from audiocraft.models import EncodecModel
-from audiocraft.modules import SEANetEncoder, SEANetDecoder
-from audiocraft.quantization import DummyQuantizer
-
-
-class TestEncodecModel:
-
-    def _create_encodec_model(self,
-                              sample_rate: int,
-                              channels: int,
-                              dim: int = 5,
-                              n_filters: int = 3,
-                              n_residual_layers: int = 1,
-                              ratios: list = [5, 4, 3, 2],
-                              **kwargs):
-        frame_rate = np.prod(ratios)
-        encoder = SEANetEncoder(channels=channels, dimension=dim, n_filters=n_filters,
-                                n_residual_layers=n_residual_layers, ratios=ratios)
-        decoder = SEANetDecoder(channels=channels, dimension=dim, n_filters=n_filters,
-                                n_residual_layers=n_residual_layers, ratios=ratios)
-        quantizer = DummyQuantizer()
-        model = EncodecModel(encoder, decoder, quantizer, frame_rate=frame_rate,
-                             sample_rate=sample_rate, channels=channels, **kwargs)
-        return model
-
-    def test_model(self):
-        random.seed(1234)
-        sample_rate = 24_000
-        channels = 1
-        model = self._create_encodec_model(sample_rate, channels)
-        for _ in range(10):
-            length = random.randrange(1, 10_000)
-            x = torch.randn(2, channels, length)
-            res = model(x)
-            assert res.x.shape == x.shape
-
-    def test_model_renorm(self):
-        random.seed(1234)
-        sample_rate = 24_000
-        channels = 1
-        model_nonorm = self._create_encodec_model(sample_rate, channels, renormalize=False)
-        model_renorm = self._create_encodec_model(sample_rate, channels, renormalize=True)
-
-        for _ in range(10):
-            length = random.randrange(1, 10_000)
-            x = torch.randn(2, channels, length)
-            codes, scales = model_nonorm.encode(x)
-            codes, scales = model_renorm.encode(x)
-            assert scales is not None
diff --git a/spaces/RMXK/RVC_HFF/infer/lib/uvr5_pack/lib_v5/nets_123821KB.py b/spaces/RMXK/RVC_HFF/infer/lib/uvr5_pack/lib_v5/nets_123821KB.py
deleted file mode 100644
index 167d4cb2198863cf43e93440f7e63c5342fc7605..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/infer/lib/uvr5_pack/lib_v5/nets_123821KB.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_123821KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 32)
-        self.stg1_high_band_net = BaseASPPNet(2, 32)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(16, 32)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(32, 64)
-
-        self.out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h
diff --git a/spaces/RamAnanth1/videocrafter/extralibs/midas/midas/midas_net.py b/spaces/RamAnanth1/videocrafter/extralibs/midas/midas/midas_net.py
deleted file mode 100644
index 8a954977800b0a0f48807e80fa63041910e33c1f..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/videocrafter/extralibs/midas/midas/midas_net.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
-
-
-class MidasNet(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=256, non_negative=True):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet, self).__init__()
-
-        use_pretrained = False if path is None else True
-
-        self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
-
-        self.scratch.refinenet4 = FeatureFusionBlock(features)
-        self.scratch.refinenet3 = FeatureFusionBlock(features)
-        self.scratch.refinenet2 = FeatureFusionBlock(features)
-        self.scratch.refinenet1 = FeatureFusionBlock(features)
-
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(True),
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-        )
-
-        if path:
-            self.load(path)
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/pygments/style.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/pygments/style.py
deleted file mode 100644
index 84abbc20599f034626779702abc2303901d83ee5..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/pygments/style.py
+++ /dev/null
@@ -1,197 +0,0 @@
-"""
-    pygments.style
-    ~~~~~~~~~~~~~~
-
-    Basic style object.
-
-    :copyright: Copyright 2006-2022 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-from pip._vendor.pygments.token import Token, STANDARD_TYPES
-
-# Default mapping of ansixxx to RGB colors.
-_ansimap = {
-    # dark
-    'ansiblack': '000000',
-    'ansired': '7f0000',
-    'ansigreen': '007f00',
-    'ansiyellow': '7f7fe0',
-    'ansiblue': '00007f',
-    'ansimagenta': '7f007f',
-    'ansicyan': '007f7f',
-    'ansigray': 'e5e5e5',
-    # normal
-    'ansibrightblack': '555555',
-    'ansibrightred': 'ff0000',
-    'ansibrightgreen': '00ff00',
-    'ansibrightyellow': 'ffff00',
-    'ansibrightblue': '0000ff',
-    'ansibrightmagenta': 'ff00ff',
-    'ansibrightcyan': '00ffff',
-    'ansiwhite': 'ffffff',
-}
-# mapping of deprecated #ansixxx colors to new color names
-_deprecated_ansicolors = {
-    # dark
-    '#ansiblack': 'ansiblack',
-    '#ansidarkred': 'ansired',
-    '#ansidarkgreen': 'ansigreen',
-    '#ansibrown': 'ansiyellow',
-    '#ansidarkblue': 'ansiblue',
-    '#ansipurple': 'ansimagenta',
-    '#ansiteal': 'ansicyan',
-    '#ansilightgray': 'ansigray',
-    # normal
-    '#ansidarkgray': 'ansibrightblack',
-    '#ansired': 'ansibrightred',
-    '#ansigreen': 'ansibrightgreen',
-    '#ansiyellow': 'ansibrightyellow',
-    '#ansiblue': 'ansibrightblue',
-    '#ansifuchsia': 'ansibrightmagenta',
-    '#ansiturquoise': 'ansibrightcyan',
-    '#ansiwhite': 'ansiwhite',
-}
-ansicolors = set(_ansimap)
-
-
-class StyleMeta(type):
-
-    def __new__(mcs, name, bases, dct):
-        obj = type.__new__(mcs, name, bases, dct)
-        for token in STANDARD_TYPES:
-            if token not in obj.styles:
-                obj.styles[token] = ''
-
-        def colorformat(text):
-            if text in ansicolors:
-                return text
-            if text[0:1] == '#':
-                col = text[1:]
-                if len(col) == 6:
-                    return col
-                elif len(col) == 3:
-                    return col[0] * 2 + col[1] * 2 + col[2] * 2
-            elif text == '':
-                return ''
-            elif text.startswith('var') or text.startswith('calc'):
-                return text
-            assert False, "wrong color format %r" % text
-
-        _styles = obj._styles = {}
-
-        for ttype in obj.styles:
-            for token in ttype.split():
-                if token in _styles:
-                    continue
-                ndef = _styles.get(token.parent, None)
-                styledefs = obj.styles.get(token, '').split()
-                if not ndef or token is None:
-                    ndef = ['', 0, 0, 0, '', '', 0, 0, 0]
-                elif 'noinherit' in styledefs and token is not Token:
-                    ndef = _styles[Token][:]
-                else:
-                    ndef = ndef[:]
-                _styles[token] = ndef
-                for styledef in obj.styles.get(token, '').split():
-                    if styledef == 'noinherit':
-                        pass
-                    elif styledef == 'bold':
-                        ndef[1] = 1
-                    elif styledef == 'nobold':
-                        ndef[1] = 0
-                    elif styledef == 'italic':
-                        ndef[2] = 1
-                    elif styledef == 'noitalic':
-                        ndef[2] = 0
-                    elif styledef == 'underline':
-                        ndef[3] = 1
-                    elif styledef == 'nounderline':
-                        ndef[3] = 0
-                    elif styledef[:3] == 'bg:':
-                        ndef[4] = colorformat(styledef[3:])
-                    elif styledef[:7] == 'border:':
-                        ndef[5] = colorformat(styledef[7:])
-                    elif styledef == 'roman':
-                        ndef[6] = 1
-                    elif styledef == 'sans':
-                        ndef[7] = 1
-                    elif styledef == 'mono':
-                        ndef[8] = 1
-                    else:
-                        ndef[0] = colorformat(styledef)
-
-        return obj
-
-    def style_for_token(cls, token):
-        t = cls._styles[token]
-        ansicolor = bgansicolor = None
-        color = t[0]
-        if color in _deprecated_ansicolors:
-            color = _deprecated_ansicolors[color]
-        if color in ansicolors:
-            ansicolor = color
-            color = _ansimap[color]
-        bgcolor = t[4]
-        if bgcolor in _deprecated_ansicolors:
-            bgcolor = _deprecated_ansicolors[bgcolor]
-        if bgcolor in ansicolors:
-            bgansicolor = bgcolor
-            bgcolor = _ansimap[bgcolor]
-
-        return {
-            'color':        color or None,
-            'bold':         bool(t[1]),
-            'italic':       bool(t[2]),
-            'underline':    bool(t[3]),
-            'bgcolor':      bgcolor or None,
-            'border':       t[5] or None,
-            'roman':        bool(t[6]) or None,
-            'sans':         bool(t[7]) or None,
-            'mono':         bool(t[8]) or None,
-            'ansicolor':    ansicolor,
-            'bgansicolor':  bgansicolor,
-        }
-
-    def list_styles(cls):
-        return list(cls)
-
-    def styles_token(cls, ttype):
-        return ttype in cls._styles
-
-    def __iter__(cls):
-        for token in cls._styles:
-            yield token, cls.style_for_token(token)
-
-    def __len__(cls):
-        return len(cls._styles)
-
-
-class Style(metaclass=StyleMeta):
-
-    #: overall background color (``None`` means transparent)
-    background_color = '#ffffff'
-
-    #: highlight background color
-    highlight_color = '#ffffcc'
-
-    #: line number font color
-    line_number_color = 'inherit'
-
-    #: line number background color
-    line_number_background_color = 'transparent'
-
-    #: special line number font color
-    line_number_special_color = '#000000'
-
-    #: special line number background color
-    line_number_special_background_color = '#ffffc0'
-
-    #: Style definitions for individual token types.
-    styles = {}
-
-    # Attribute for lexers defined within Pygments. If set
-    # to True, the style is not shown in the style gallery
-    # on the website. This is intended for language-specific
-    # styles.
-    web_style_gallery_exclude = False
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/tenacity/_asyncio.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/tenacity/_asyncio.py
deleted file mode 100644
index 0f32b5f6207441753482e8b24e0f4ff10c5614d8..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/tenacity/_asyncio.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright 2016 Étienne Bersac
-# Copyright 2016 Julien Danjou
-# Copyright 2016 Joshua Harlow
-# Copyright 2013-2014 Ray Holder
-#
-# 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 functools
-import sys
-import typing
-from asyncio import sleep
-
-from pip._vendor.tenacity import AttemptManager
-from pip._vendor.tenacity import BaseRetrying
-from pip._vendor.tenacity import DoAttempt
-from pip._vendor.tenacity import DoSleep
-from pip._vendor.tenacity import RetryCallState
-
-WrappedFn = typing.TypeVar("WrappedFn", bound=typing.Callable)
-_RetValT = typing.TypeVar("_RetValT")
-
-
-class AsyncRetrying(BaseRetrying):
-    def __init__(self, sleep: typing.Callable[[float], typing.Awaitable] = sleep, **kwargs: typing.Any) -> None:
-        super().__init__(**kwargs)
-        self.sleep = sleep
-
-    async def __call__(  # type: ignore  # Change signature from supertype
-        self,
-        fn: typing.Callable[..., typing.Awaitable[_RetValT]],
-        *args: typing.Any,
-        **kwargs: typing.Any,
-    ) -> _RetValT:
-        self.begin()
-
-        retry_state = RetryCallState(retry_object=self, fn=fn, args=args, kwargs=kwargs)
-        while True:
-            do = self.iter(retry_state=retry_state)
-            if isinstance(do, DoAttempt):
-                try:
-                    result = await fn(*args, **kwargs)
-                except BaseException:  # noqa: B902
-                    retry_state.set_exception(sys.exc_info())
-                else:
-                    retry_state.set_result(result)
-            elif isinstance(do, DoSleep):
-                retry_state.prepare_for_next_attempt()
-                await self.sleep(do)
-            else:
-                return do
-
-    def __aiter__(self) -> "AsyncRetrying":
-        self.begin()
-        self._retry_state = RetryCallState(self, fn=None, args=(), kwargs={})
-        return self
-
-    async def __anext__(self) -> typing.Union[AttemptManager, typing.Any]:
-        while True:
-            do = self.iter(retry_state=self._retry_state)
-            if do is None:
-                raise StopAsyncIteration
-            elif isinstance(do, DoAttempt):
-                return AttemptManager(retry_state=self._retry_state)
-            elif isinstance(do, DoSleep):
-                self._retry_state.prepare_for_next_attempt()
-                await self.sleep(do)
-            else:
-                return do
-
-    def wraps(self, fn: WrappedFn) -> WrappedFn:
-        fn = super().wraps(fn)
-        # Ensure wrapper is recognized as a coroutine function.
-
-        @functools.wraps(fn)
-        async def async_wrapped(*args: typing.Any, **kwargs: typing.Any) -> typing.Any:
-            return await fn(*args, **kwargs)
-
-        # Preserve attributes
-        async_wrapped.retry = fn.retry
-        async_wrapped.retry_with = fn.retry_with
-
-        return async_wrapped
diff --git a/spaces/Reha2704/VToonify/vtoonify/model/stylegan/op_gpu/__init__.py b/spaces/Reha2704/VToonify/vtoonify/model/stylegan/op_gpu/__init__.py
deleted file mode 100644
index d0918d92285955855be89f00096b888ee5597ce3..0000000000000000000000000000000000000000
--- a/spaces/Reha2704/VToonify/vtoonify/model/stylegan/op_gpu/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d
diff --git a/spaces/RivianG/Asis/local_utils.py b/spaces/RivianG/Asis/local_utils.py
deleted file mode 100644
index f55d891f757d7aa9c844189e21105511befab7f1..0000000000000000000000000000000000000000
--- a/spaces/RivianG/Asis/local_utils.py
+++ /dev/null
@@ -1,221 +0,0 @@
-# pylint: disable=invalid-name, redefined-outer-name, missing-docstring, non-parent-init-called, trailing-whitespace, line-too-long
-import cv2
-import numpy as np
-import tensorflow as tf
-from tensorflow.python.keras.backend import set_session
-
-
-class Label:
-    def __init__(self, cl=-1, tl=np.array([0., 0.]), br=np.array([0., 0.]), prob=None):
-        self.__tl = tl
-        self.__br = br
-        self.__cl = cl
-        self.__prob = prob
-
-    def __str__(self):
-        return 'Class: %d, top left(x: %f, y: %f), bottom right(x: %f, y: %f)' % (
-            self.__cl, self.__tl[0], self.__tl[1], self.__br[0], self.__br[1])
-
-    def copy(self):
-        return Label(self.__cl, self.__tl, self.__br)
-
-    def wh(self): return self.__br - self.__tl
-
-    def cc(self): return self.__tl + self.wh() / 2
-
-    def tl(self): return self.__tl
-
-    def br(self): return self.__br
-
-    def tr(self): return np.array([self.__br[0], self.__tl[1]])
-
-    def bl(self): return np.array([self.__tl[0], self.__br[1]])
-
-    def cl(self): return self.__cl
-
-    def area(self): return np.prod(self.wh())
-
-    def prob(self): return self.__prob
-
-    def set_class(self, cl):
-        self.__cl = cl
-
-    def set_tl(self, tl):
-        self.__tl = tl
-
-    def set_br(self, br):
-        self.__br = br
-
-    def set_wh(self, wh):
-        cc = self.cc()
-        self.__tl = cc - .5 * wh
-        self.__br = cc + .5 * wh
-
-    def set_prob(self, prob):
-        self.__prob = prob
-
-
-class DLabel(Label):
-    def __init__(self, cl, pts, prob):
-        self.pts = pts
-        tl = np.amin(pts, axis=1)
-        br = np.amax(pts, axis=1)
-        Label.__init__(self, cl, tl, br, prob)
-
-
-def getWH(shape):
-    return np.array(shape[1::-1]).astype(float)
-
-
-def IOU(tl1, br1, tl2, br2):
-    wh1, wh2 = br1 - tl1, br2 - tl2
-    assert ((wh1 >= 0).all() and (wh2 >= 0).all())
-
-    intersection_wh = np.maximum(np.minimum(br1, br2) - np.maximum(tl1, tl2), 0)
-    intersection_area = np.prod(intersection_wh)
-    area1, area2 = (np.prod(wh1), np.prod(wh2))
-    union_area = area1 + area2 - intersection_area
-    return intersection_area / union_area
-
-
-def IOU_labels(l1, l2):
-    return IOU(l1.tl(), l1.br(), l2.tl(), l2.br())
-
-
-def nms(Labels, iou_threshold=0.5):
-    SelectedLabels = []
-    Labels.sort(key=lambda l: l.prob(), reverse=True)
-
-    for label in Labels:
-        non_overlap = True
-        for sel_label in SelectedLabels:
-            if IOU_labels(label, sel_label) > iou_threshold:
-                non_overlap = False
-                break
-
-        if non_overlap:
-            SelectedLabels.append(label)
-    return SelectedLabels
-
-
-def find_T_matrix(pts, t_pts):
-    A = np.zeros((8, 9))
-    for i in range(0, 4):
-        xi = pts[:, i]
-        xil = t_pts[:, i]
-        xi = xi.T
-
-        A[i * 2, 3:6] = -xil[2] * xi
-        A[i * 2, 6:] = xil[1] * xi
-        A[i * 2 + 1, :3] = xil[2] * xi
-        A[i * 2 + 1, 6:] = -xil[0] * xi
-
-    [U, S, V] = np.linalg.svd(A)
-    H = V[-1, :].reshape((3, 3))
-    return H
-
-
-def getRectPts(tlx, tly, brx, bry):
-    return np.matrix([[tlx, brx, brx, tlx], [tly, tly, bry, bry], [1, 1, 1, 1]], dtype=float)
-
-
-def normal(pts, side, mn, MN):
-    pts_MN_center_mn = pts * side
-    pts_MN = pts_MN_center_mn + mn.reshape((2, 1))
-    pts_prop = pts_MN / MN.reshape((2, 1))
-    return pts_prop
-
-
-# Reconstruction function from predict value into plate crpoped from image
-def reconstruct(I, Iresized, Yr, lp_threshold):
-    # 4 max-pooling layers, stride = 2
-    net_stride = 2 ** 4
-    side = ((208 + 40) / 2) / net_stride
-
-    # one line and two lines license plate size
-    one_line = (470, 110)
-    two_lines = (280, 200)
-
-    Probs = Yr[..., 0]
-    Affines = Yr[..., 2:]
-
-    xx, yy = np.where(Probs > lp_threshold)
-    # CNN input image size
-    WH = getWH(Iresized.shape)
-    # output feature map size
-    MN = WH / net_stride
-
-    vxx = vyy = 0.5  # alpha
-    base = lambda vx, vy: np.matrix([[-vx, -vy, 1], [vx, -vy, 1], [vx, vy, 1], [-vx, vy, 1]]).T
-    labels = []
-    labels_frontal = []
-
-    for i in range(len(xx)):
-        x, y = xx[i], yy[i]
-        affine = Affines[x, y]
-        prob = Probs[x, y]
-
-        mn = np.array([float(y) + 0.5, float(x) + 0.5])
-
-        # affine transformation matrix
-        A = np.reshape(affine, (2, 3))
-        A[0, 0] = max(A[0, 0], 0)
-        A[1, 1] = max(A[1, 1], 0)
-        # identity transformation
-        B = np.zeros((2, 3))
-        B[0, 0] = max(A[0, 0], 0)
-        B[1, 1] = max(A[1, 1], 0)
-
-        pts = np.array(A * base(vxx, vyy))
-        pts_frontal = np.array(B * base(vxx, vyy))
-
-        pts_prop = normal(pts, side, mn, MN)
-        frontal = normal(pts_frontal, side, mn, MN)
-
-        labels.append(DLabel(0, pts_prop, prob))
-        labels_frontal.append(DLabel(0, frontal, prob))
-
-    final_labels = nms(labels, 0.1)
-    final_labels_frontal = nms(labels_frontal, 0.1)
-
-    # print(final_labels_frontal)
-    assert final_labels_frontal, ""  # "No License plate is founded!"
-
-    # LP size and type
-    out_size, lp_type = (two_lines, 2) if (
-                (final_labels_frontal[0].wh()[0] / final_labels_frontal[0].wh()[1]) < 1.7) else (one_line, 1)
-
-    TLp = []
-    Cor = []
-    if len(final_labels):
-        final_labels.sort(key=lambda x: x.prob(), reverse=True)
-        for _, label in enumerate(final_labels):
-            t_ptsh = getRectPts(0, 0, out_size[0], out_size[1])
-            ptsh = np.concatenate((label.pts * getWH(I.shape).reshape((2, 1)), np.ones((1, 4))))
-            H = find_T_matrix(ptsh, t_ptsh)
-            Ilp = cv2.warpPerspective(I, H, out_size, borderValue=0)
-            # cv2.imshow("plate", Ilp)
-            # cv2.waitKey(0)
-            TLp.append(Ilp)
-            Cor.append(ptsh)
-    return final_labels, TLp, lp_type, Cor
-
-
-def detect_lp(graph, sess, model, I, max_dim, lp_threshold):
-    min_dim_img = min(I.shape[:2])
-    factor = float(max_dim) / min_dim_img
-    w, h = (np.array(I.shape[1::-1], dtype=float) * factor).astype(int).tolist()
-    Iresized = cv2.resize(I, (w, h))
-    T = Iresized.copy()
-    T = T.reshape((1, T.shape[0], T.shape[1], T.shape[2]))
-    with graph.as_default():
-        set_session(sess)
-
-
-        Yr = model.predict(T)
-        # print("1: ",Yr)
-        Yr = np.squeeze(Yr)
-        # print("2: ",Yr)
-        # print(Yr.shape)
-        L, TLp, lp_type, Cor = reconstruct(I, Iresized, Yr, lp_threshold)
-        return L, TLp, lp_type, Cor
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/centripetal_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/centripetal_head.py
deleted file mode 100644
index 6728218b60539a71f6353645635f741a1ad7263d..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/centripetal_head.py
+++ /dev/null
@@ -1,421 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule, normal_init
-from mmcv.ops import DeformConv2d
-
-from mmdet.core import multi_apply
-from ..builder import HEADS, build_loss
-from .corner_head import CornerHead
-
-
-@HEADS.register_module()
-class CentripetalHead(CornerHead):
-    """Head of CentripetalNet: Pursuing High-quality Keypoint Pairs for Object
-    Detection.
-
-    CentripetalHead inherits from :class:`CornerHead`. It removes the
-    embedding branch and adds guiding shift and centripetal shift branches.
-    More details can be found in the `paper
-    <https://arxiv.org/abs/2003.09119>`_ .
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        num_feat_levels (int): Levels of feature from the previous module. 2
-            for HourglassNet-104 and 1 for HourglassNet-52. HourglassNet-104
-            outputs the final feature and intermediate supervision feature and
-            HourglassNet-52 only outputs the final feature. Default: 2.
-        corner_emb_channels (int): Channel of embedding vector. Default: 1.
-        train_cfg (dict | None): Training config. Useless in CornerHead,
-            but we keep this variable for SingleStageDetector. Default: None.
-        test_cfg (dict | None): Testing config of CornerHead. Default: None.
-        loss_heatmap (dict | None): Config of corner heatmap loss. Default:
-            GaussianFocalLoss.
-        loss_embedding (dict | None): Config of corner embedding loss. Default:
-            AssociativeEmbeddingLoss.
-        loss_offset (dict | None): Config of corner offset loss. Default:
-            SmoothL1Loss.
-        loss_guiding_shift (dict): Config of guiding shift loss. Default:
-            SmoothL1Loss.
-        loss_centripetal_shift (dict): Config of centripetal shift loss.
-            Default: SmoothL1Loss.
-    """
-
-    def __init__(self,
-                 *args,
-                 centripetal_shift_channels=2,
-                 guiding_shift_channels=2,
-                 feat_adaption_conv_kernel=3,
-                 loss_guiding_shift=dict(
-                     type='SmoothL1Loss', beta=1.0, loss_weight=0.05),
-                 loss_centripetal_shift=dict(
-                     type='SmoothL1Loss', beta=1.0, loss_weight=1),
-                 **kwargs):
-        assert centripetal_shift_channels == 2, (
-            'CentripetalHead only support centripetal_shift_channels == 2')
-        self.centripetal_shift_channels = centripetal_shift_channels
-        assert guiding_shift_channels == 2, (
-            'CentripetalHead only support guiding_shift_channels == 2')
-        self.guiding_shift_channels = guiding_shift_channels
-        self.feat_adaption_conv_kernel = feat_adaption_conv_kernel
-        super(CentripetalHead, self).__init__(*args, **kwargs)
-        self.loss_guiding_shift = build_loss(loss_guiding_shift)
-        self.loss_centripetal_shift = build_loss(loss_centripetal_shift)
-
-    def _init_centripetal_layers(self):
-        """Initialize centripetal layers.
-
-        Including feature adaption deform convs (feat_adaption), deform offset
-        prediction convs (dcn_off), guiding shift (guiding_shift) and
-        centripetal shift ( centripetal_shift). Each branch has two parts:
-        prefix `tl_` for top-left and `br_` for bottom-right.
-        """
-        self.tl_feat_adaption = nn.ModuleList()
-        self.br_feat_adaption = nn.ModuleList()
-        self.tl_dcn_offset = nn.ModuleList()
-        self.br_dcn_offset = nn.ModuleList()
-        self.tl_guiding_shift = nn.ModuleList()
-        self.br_guiding_shift = nn.ModuleList()
-        self.tl_centripetal_shift = nn.ModuleList()
-        self.br_centripetal_shift = nn.ModuleList()
-
-        for _ in range(self.num_feat_levels):
-            self.tl_feat_adaption.append(
-                DeformConv2d(self.in_channels, self.in_channels,
-                             self.feat_adaption_conv_kernel, 1, 1))
-            self.br_feat_adaption.append(
-                DeformConv2d(self.in_channels, self.in_channels,
-                             self.feat_adaption_conv_kernel, 1, 1))
-
-            self.tl_guiding_shift.append(
-                self._make_layers(
-                    out_channels=self.guiding_shift_channels,
-                    in_channels=self.in_channels))
-            self.br_guiding_shift.append(
-                self._make_layers(
-                    out_channels=self.guiding_shift_channels,
-                    in_channels=self.in_channels))
-
-            self.tl_dcn_offset.append(
-                ConvModule(
-                    self.guiding_shift_channels,
-                    self.feat_adaption_conv_kernel**2 *
-                    self.guiding_shift_channels,
-                    1,
-                    bias=False,
-                    act_cfg=None))
-            self.br_dcn_offset.append(
-                ConvModule(
-                    self.guiding_shift_channels,
-                    self.feat_adaption_conv_kernel**2 *
-                    self.guiding_shift_channels,
-                    1,
-                    bias=False,
-                    act_cfg=None))
-
-            self.tl_centripetal_shift.append(
-                self._make_layers(
-                    out_channels=self.centripetal_shift_channels,
-                    in_channels=self.in_channels))
-            self.br_centripetal_shift.append(
-                self._make_layers(
-                    out_channels=self.centripetal_shift_channels,
-                    in_channels=self.in_channels))
-
-    def _init_layers(self):
-        """Initialize layers for CentripetalHead.
-
-        Including two parts: CornerHead layers and CentripetalHead layers
-        """
-        super()._init_layers()  # using _init_layers in CornerHead
-        self._init_centripetal_layers()
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        super().init_weights()
-        for i in range(self.num_feat_levels):
-            normal_init(self.tl_feat_adaption[i], std=0.01)
-            normal_init(self.br_feat_adaption[i], std=0.01)
-            normal_init(self.tl_dcn_offset[i].conv, std=0.1)
-            normal_init(self.br_dcn_offset[i].conv, std=0.1)
-            _ = [x.conv.reset_parameters() for x in self.tl_guiding_shift[i]]
-            _ = [x.conv.reset_parameters() for x in self.br_guiding_shift[i]]
-            _ = [
-                x.conv.reset_parameters() for x in self.tl_centripetal_shift[i]
-            ]
-            _ = [
-                x.conv.reset_parameters() for x in self.br_centripetal_shift[i]
-            ]
-
-    def forward_single(self, x, lvl_ind):
-        """Forward feature of a single level.
-
-        Args:
-            x (Tensor): Feature of a single level.
-            lvl_ind (int): Level index of current feature.
-
-        Returns:
-            tuple[Tensor]: A tuple of CentripetalHead's output for current
-            feature level. Containing the following Tensors:
-
-                - tl_heat (Tensor): Predicted top-left corner heatmap.
-                - br_heat (Tensor): Predicted bottom-right corner heatmap.
-                - tl_off (Tensor): Predicted top-left offset heatmap.
-                - br_off (Tensor): Predicted bottom-right offset heatmap.
-                - tl_guiding_shift (Tensor): Predicted top-left guiding shift
-                  heatmap.
-                - br_guiding_shift (Tensor): Predicted bottom-right guiding
-                  shift heatmap.
-                - tl_centripetal_shift (Tensor): Predicted top-left centripetal
-                  shift heatmap.
-                - br_centripetal_shift (Tensor): Predicted bottom-right
-                  centripetal shift heatmap.
-        """
-        tl_heat, br_heat, _, _, tl_off, br_off, tl_pool, br_pool = super(
-        ).forward_single(
-            x, lvl_ind, return_pool=True)
-
-        tl_guiding_shift = self.tl_guiding_shift[lvl_ind](tl_pool)
-        br_guiding_shift = self.br_guiding_shift[lvl_ind](br_pool)
-
-        tl_dcn_offset = self.tl_dcn_offset[lvl_ind](tl_guiding_shift.detach())
-        br_dcn_offset = self.br_dcn_offset[lvl_ind](br_guiding_shift.detach())
-
-        tl_feat_adaption = self.tl_feat_adaption[lvl_ind](tl_pool,
-                                                          tl_dcn_offset)
-        br_feat_adaption = self.br_feat_adaption[lvl_ind](br_pool,
-                                                          br_dcn_offset)
-
-        tl_centripetal_shift = self.tl_centripetal_shift[lvl_ind](
-            tl_feat_adaption)
-        br_centripetal_shift = self.br_centripetal_shift[lvl_ind](
-            br_feat_adaption)
-
-        result_list = [
-            tl_heat, br_heat, tl_off, br_off, tl_guiding_shift,
-            br_guiding_shift, tl_centripetal_shift, br_centripetal_shift
-        ]
-        return result_list
-
-    def loss(self,
-             tl_heats,
-             br_heats,
-             tl_offs,
-             br_offs,
-             tl_guiding_shifts,
-             br_guiding_shifts,
-             tl_centripetal_shifts,
-             br_centripetal_shifts,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            tl_heats (list[Tensor]): Top-left corner heatmaps for each level
-                with shape (N, num_classes, H, W).
-            br_heats (list[Tensor]): Bottom-right corner heatmaps for each
-                level with shape (N, num_classes, H, W).
-            tl_offs (list[Tensor]): Top-left corner offsets for each level
-                with shape (N, corner_offset_channels, H, W).
-            br_offs (list[Tensor]): Bottom-right corner offsets for each level
-                with shape (N, corner_offset_channels, H, W).
-            tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each
-                level with shape (N, guiding_shift_channels, H, W).
-            br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for
-                each level with shape (N, guiding_shift_channels, H, W).
-            tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts
-                for each level with shape (N, centripetal_shift_channels, H,
-                W).
-            br_centripetal_shifts (list[Tensor]): Bottom-right centripetal
-                shifts for each level with shape (N,
-                centripetal_shift_channels, H, W).
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [left, top, right, bottom] format.
-            gt_labels (list[Tensor]): Class indices corresponding to each box.
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
-                boxes can be ignored when computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components. Containing the
-            following losses:
-
-                - det_loss (list[Tensor]): Corner keypoint losses of all
-                  feature levels.
-                - off_loss (list[Tensor]): Corner offset losses of all feature
-                  levels.
-                - guiding_loss (list[Tensor]): Guiding shift losses of all
-                  feature levels.
-                - centripetal_loss (list[Tensor]): Centripetal shift losses of
-                  all feature levels.
-        """
-        targets = self.get_targets(
-            gt_bboxes,
-            gt_labels,
-            tl_heats[-1].shape,
-            img_metas[0]['pad_shape'],
-            with_corner_emb=self.with_corner_emb,
-            with_guiding_shift=True,
-            with_centripetal_shift=True)
-        mlvl_targets = [targets for _ in range(self.num_feat_levels)]
-        [det_losses, off_losses, guiding_losses, centripetal_losses
-         ] = multi_apply(self.loss_single, tl_heats, br_heats, tl_offs,
-                         br_offs, tl_guiding_shifts, br_guiding_shifts,
-                         tl_centripetal_shifts, br_centripetal_shifts,
-                         mlvl_targets)
-        loss_dict = dict(
-            det_loss=det_losses,
-            off_loss=off_losses,
-            guiding_loss=guiding_losses,
-            centripetal_loss=centripetal_losses)
-        return loss_dict
-
-    def loss_single(self, tl_hmp, br_hmp, tl_off, br_off, tl_guiding_shift,
-                    br_guiding_shift, tl_centripetal_shift,
-                    br_centripetal_shift, targets):
-        """Compute losses for single level.
-
-        Args:
-            tl_hmp (Tensor): Top-left corner heatmap for current level with
-                shape (N, num_classes, H, W).
-            br_hmp (Tensor): Bottom-right corner heatmap for current level with
-                shape (N, num_classes, H, W).
-            tl_off (Tensor): Top-left corner offset for current level with
-                shape (N, corner_offset_channels, H, W).
-            br_off (Tensor): Bottom-right corner offset for current level with
-                shape (N, corner_offset_channels, H, W).
-            tl_guiding_shift (Tensor): Top-left guiding shift for current level
-                with shape (N, guiding_shift_channels, H, W).
-            br_guiding_shift (Tensor): Bottom-right guiding shift for current
-                level with shape (N, guiding_shift_channels, H, W).
-            tl_centripetal_shift (Tensor): Top-left centripetal shift for
-                current level with shape (N, centripetal_shift_channels, H, W).
-            br_centripetal_shift (Tensor): Bottom-right centripetal shift for
-                current level with shape (N, centripetal_shift_channels, H, W).
-            targets (dict): Corner target generated by `get_targets`.
-
-        Returns:
-            tuple[torch.Tensor]: Losses of the head's differnet branches
-            containing the following losses:
-
-                - det_loss (Tensor): Corner keypoint loss.
-                - off_loss (Tensor): Corner offset loss.
-                - guiding_loss (Tensor): Guiding shift loss.
-                - centripetal_loss (Tensor): Centripetal shift loss.
-        """
-        targets['corner_embedding'] = None
-
-        det_loss, _, _, off_loss = super().loss_single(tl_hmp, br_hmp, None,
-                                                       None, tl_off, br_off,
-                                                       targets)
-
-        gt_tl_guiding_shift = targets['topleft_guiding_shift']
-        gt_br_guiding_shift = targets['bottomright_guiding_shift']
-        gt_tl_centripetal_shift = targets['topleft_centripetal_shift']
-        gt_br_centripetal_shift = targets['bottomright_centripetal_shift']
-
-        gt_tl_heatmap = targets['topleft_heatmap']
-        gt_br_heatmap = targets['bottomright_heatmap']
-        # We only compute the offset loss at the real corner position.
-        # The value of real corner would be 1 in heatmap ground truth.
-        # The mask is computed in class agnostic mode and its shape is
-        # batch * 1 * width * height.
-        tl_mask = gt_tl_heatmap.eq(1).sum(1).gt(0).unsqueeze(1).type_as(
-            gt_tl_heatmap)
-        br_mask = gt_br_heatmap.eq(1).sum(1).gt(0).unsqueeze(1).type_as(
-            gt_br_heatmap)
-
-        # Guiding shift loss
-        tl_guiding_loss = self.loss_guiding_shift(
-            tl_guiding_shift,
-            gt_tl_guiding_shift,
-            tl_mask,
-            avg_factor=tl_mask.sum())
-        br_guiding_loss = self.loss_guiding_shift(
-            br_guiding_shift,
-            gt_br_guiding_shift,
-            br_mask,
-            avg_factor=br_mask.sum())
-        guiding_loss = (tl_guiding_loss + br_guiding_loss) / 2.0
-        # Centripetal shift loss
-        tl_centripetal_loss = self.loss_centripetal_shift(
-            tl_centripetal_shift,
-            gt_tl_centripetal_shift,
-            tl_mask,
-            avg_factor=tl_mask.sum())
-        br_centripetal_loss = self.loss_centripetal_shift(
-            br_centripetal_shift,
-            gt_br_centripetal_shift,
-            br_mask,
-            avg_factor=br_mask.sum())
-        centripetal_loss = (tl_centripetal_loss + br_centripetal_loss) / 2.0
-
-        return det_loss, off_loss, guiding_loss, centripetal_loss
-
-    def get_bboxes(self,
-                   tl_heats,
-                   br_heats,
-                   tl_offs,
-                   br_offs,
-                   tl_guiding_shifts,
-                   br_guiding_shifts,
-                   tl_centripetal_shifts,
-                   br_centripetal_shifts,
-                   img_metas,
-                   rescale=False,
-                   with_nms=True):
-        """Transform network output for a batch into bbox predictions.
-
-        Args:
-            tl_heats (list[Tensor]): Top-left corner heatmaps for each level
-                with shape (N, num_classes, H, W).
-            br_heats (list[Tensor]): Bottom-right corner heatmaps for each
-                level with shape (N, num_classes, H, W).
-            tl_offs (list[Tensor]): Top-left corner offsets for each level
-                with shape (N, corner_offset_channels, H, W).
-            br_offs (list[Tensor]): Bottom-right corner offsets for each level
-                with shape (N, corner_offset_channels, H, W).
-            tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each
-                level with shape (N, guiding_shift_channels, H, W). Useless in
-                this function, we keep this arg because it's the raw output
-                from CentripetalHead.
-            br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for
-                each level with shape (N, guiding_shift_channels, H, W).
-                Useless in this function, we keep this arg because it's the
-                raw output from CentripetalHead.
-            tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts
-                for each level with shape (N, centripetal_shift_channels, H,
-                W).
-            br_centripetal_shifts (list[Tensor]): Bottom-right centripetal
-                shifts for each level with shape (N,
-                centripetal_shift_channels, H, W).
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-            with_nms (bool): If True, do nms before return boxes.
-                Default: True.
-        """
-        assert tl_heats[-1].shape[0] == br_heats[-1].shape[0] == len(img_metas)
-        result_list = []
-        for img_id in range(len(img_metas)):
-            result_list.append(
-                self._get_bboxes_single(
-                    tl_heats[-1][img_id:img_id + 1, :],
-                    br_heats[-1][img_id:img_id + 1, :],
-                    tl_offs[-1][img_id:img_id + 1, :],
-                    br_offs[-1][img_id:img_id + 1, :],
-                    img_metas[img_id],
-                    tl_emb=None,
-                    br_emb=None,
-                    tl_centripetal_shift=tl_centripetal_shifts[-1][
-                        img_id:img_id + 1, :],
-                    br_centripetal_shift=br_centripetal_shifts[-1][
-                        img_id:img_id + 1, :],
-                    rescale=rescale,
-                    with_nms=with_nms))
-
-        return result_list
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/upernet_uniformer.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/upernet_uniformer.py
deleted file mode 100644
index 41aa4db809dc6e2c508e98051f61807d07477903..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/upernet_uniformer.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# model settings
-norm_cfg = dict(type='BN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained=None,
-    backbone=dict(
-        type='UniFormer',
-        embed_dim=[64, 128, 320, 512],
-        layers=[3, 4, 8, 3],
-        head_dim=64,
-        mlp_ratio=4.,
-        qkv_bias=True,
-        drop_rate=0.,
-        attn_drop_rate=0.,
-        drop_path_rate=0.1),
-    decode_head=dict(
-        type='UPerHead',
-        in_channels=[64, 128, 320, 512],
-        in_index=[0, 1, 2, 3],
-        pool_scales=(1, 2, 3, 6),
-        channels=512,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=320,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
\ No newline at end of file
diff --git a/spaces/Rongjiehuang/ProDiff/utils/text_norm.py b/spaces/Rongjiehuang/ProDiff/utils/text_norm.py
deleted file mode 100644
index d0973cebc91e0525aeb6657e70012a1d37b5e6ff..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/ProDiff/utils/text_norm.py
+++ /dev/null
@@ -1,790 +0,0 @@
-# coding=utf-8
-# Authors:
-#   2019.5 Zhiyang Zhou (https://github.com/Joee1995/chn_text_norm.git)
-#   2019.9 Jiayu DU
-#
-# requirements:
-#   - python 3.X
-# notes: python 2.X WILL fail or produce misleading results
-
-import sys, os, argparse, codecs, string, re
-
-# ================================================================================ #
-#                                    basic constant
-# ================================================================================ #
-CHINESE_DIGIS = u'零一二三四五六七八九'
-BIG_CHINESE_DIGIS_SIMPLIFIED = u'零壹贰叁肆伍陆柒捌玖'
-BIG_CHINESE_DIGIS_TRADITIONAL = u'零壹貳參肆伍陸柒捌玖'
-SMALLER_BIG_CHINESE_UNITS_SIMPLIFIED = u'十百千万'
-SMALLER_BIG_CHINESE_UNITS_TRADITIONAL = u'拾佰仟萬'
-LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'亿兆京垓秭穰沟涧正载'
-LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'億兆京垓秭穰溝澗正載'
-SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'十百千万'
-SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'拾佰仟萬'
-
-ZERO_ALT = u'〇'
-ONE_ALT = u'幺'
-TWO_ALTS = [u'两', u'兩']
-
-POSITIVE = [u'正', u'正']
-NEGATIVE = [u'负', u'負']
-POINT = [u'点', u'點']
-# PLUS = [u'加', u'加']
-# SIL = [u'杠', u'槓']
-
-# 中文数字系统类型
-NUMBERING_TYPES = ['low', 'mid', 'high']
-
-CURRENCY_NAMES = '(人民币|美元|日元|英镑|欧元|马克|法郎|加拿大元|澳元|港币|先令|芬兰马克|爱尔兰镑|' \
-                 '里拉|荷兰盾|埃斯库多|比塞塔|印尼盾|林吉特|新西兰元|比索|卢布|新加坡元|韩元|泰铢)'
-CURRENCY_UNITS = '((亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|)元|(亿|千万|百万|万|千|百|)块|角|毛|分)'
-COM_QUANTIFIERS = '(匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|' \
-                  '砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|' \
-                  '针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|' \
-                  '毫|厘|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|' \
-                  '盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|旬|' \
-                  '纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块)'
-
-# punctuation information are based on Zhon project (https://github.com/tsroten/zhon.git)
-CHINESE_PUNC_STOP = '！？｡。'
-CHINESE_PUNC_NON_STOP = '＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏'
-CHINESE_PUNC_LIST = CHINESE_PUNC_STOP + CHINESE_PUNC_NON_STOP
-
-
-# ================================================================================ #
-#                                    basic class
-# ================================================================================ #
-class ChineseChar(object):
-    """
-    中文字符
-    每个字符对应简体和繁体,
-    e.g. 简体 = '负', 繁体 = '負'
-    转换时可转换为简体或繁体
-    """
-
-    def __init__(self, simplified, traditional):
-        self.simplified = simplified
-        self.traditional = traditional
-        # self.__repr__ = self.__str__
-
-    def __str__(self):
-        return self.simplified or self.traditional or None
-
-    def __repr__(self):
-        return self.__str__()
-
-
-class ChineseNumberUnit(ChineseChar):
-    """
-    中文数字/数位字符
-    每个字符除繁简体外还有一个额外的大写字符
-    e.g. '陆' 和 '陸'
-    """
-
-    def __init__(self, power, simplified, traditional, big_s, big_t):
-        super(ChineseNumberUnit, self).__init__(simplified, traditional)
-        self.power = power
-        self.big_s = big_s
-        self.big_t = big_t
-
-    def __str__(self):
-        return '10^{}'.format(self.power)
-
-    @classmethod
-    def create(cls, index, value, numbering_type=NUMBERING_TYPES[1], small_unit=False):
-
-        if small_unit:
-            return ChineseNumberUnit(power=index + 1,
-                                     simplified=value[0], traditional=value[1], big_s=value[1], big_t=value[1])
-        elif numbering_type == NUMBERING_TYPES[0]:
-            return ChineseNumberUnit(power=index + 8,
-                                     simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
-        elif numbering_type == NUMBERING_TYPES[1]:
-            return ChineseNumberUnit(power=(index + 2) * 4,
-                                     simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
-        elif numbering_type == NUMBERING_TYPES[2]:
-            return ChineseNumberUnit(power=pow(2, index + 3),
-                                     simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
-        else:
-            raise ValueError(
-                'Counting type should be in {0} ({1} provided).'.format(NUMBERING_TYPES, numbering_type))
-
-
-class ChineseNumberDigit(ChineseChar):
-    """
-    中文数字字符
-    """
-
-    def __init__(self, value, simplified, traditional, big_s, big_t, alt_s=None, alt_t=None):
-        super(ChineseNumberDigit, self).__init__(simplified, traditional)
-        self.value = value
-        self.big_s = big_s
-        self.big_t = big_t
-        self.alt_s = alt_s
-        self.alt_t = alt_t
-
-    def __str__(self):
-        return str(self.value)
-
-    @classmethod
-    def create(cls, i, v):
-        return ChineseNumberDigit(i, v[0], v[1], v[2], v[3])
-
-
-class ChineseMath(ChineseChar):
-    """
-    中文数位字符
-    """
-
-    def __init__(self, simplified, traditional, symbol, expression=None):
-        super(ChineseMath, self).__init__(simplified, traditional)
-        self.symbol = symbol
-        self.expression = expression
-        self.big_s = simplified
-        self.big_t = traditional
-
-
-CC, CNU, CND, CM = ChineseChar, ChineseNumberUnit, ChineseNumberDigit, ChineseMath
-
-
-class NumberSystem(object):
-    """
-    中文数字系统
-    """
-    pass
-
-
-class MathSymbol(object):
-    """
-    用于中文数字系统的数学符号 (繁/简体), e.g.
-    positive = ['正', '正']
-    negative = ['负', '負']
-    point = ['点', '點']
-    """
-
-    def __init__(self, positive, negative, point):
-        self.positive = positive
-        self.negative = negative
-        self.point = point
-
-    def __iter__(self):
-        for v in self.__dict__.values():
-            yield v
-
-
-# class OtherSymbol(object):
-#     """
-#     其他符号
-#     """
-#
-#     def __init__(self, sil):
-#         self.sil = sil
-#
-#     def __iter__(self):
-#         for v in self.__dict__.values():
-#             yield v
-
-
-# ================================================================================ #
-#                                    basic utils
-# ================================================================================ #
-def create_system(numbering_type=NUMBERING_TYPES[1]):
-    """
-    根据数字系统类型返回创建相应的数字系统，默认为 mid
-    NUMBERING_TYPES = ['low', 'mid', 'high']: 中文数字系统类型
-        low:  '兆' = '亿' * '十' = $10^{9}$,  '京' = '兆' * '十', etc.
-        mid:  '兆' = '亿' * '万' = $10^{12}$, '京' = '兆' * '万', etc.
-        high: '兆' = '亿' * '亿' = $10^{16}$, '京' = '兆' * '兆', etc.
-    返回对应的数字系统
-    """
-
-    # chinese number units of '亿' and larger
-    all_larger_units = zip(
-        LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED, LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL)
-    larger_units = [CNU.create(i, v, numbering_type, False)
-                    for i, v in enumerate(all_larger_units)]
-    # chinese number units of '十, 百, 千, 万'
-    all_smaller_units = zip(
-        SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED, SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL)
-    smaller_units = [CNU.create(i, v, small_unit=True)
-                     for i, v in enumerate(all_smaller_units)]
-    # digis
-    chinese_digis = zip(CHINESE_DIGIS, CHINESE_DIGIS,
-                        BIG_CHINESE_DIGIS_SIMPLIFIED, BIG_CHINESE_DIGIS_TRADITIONAL)
-    digits = [CND.create(i, v) for i, v in enumerate(chinese_digis)]
-    digits[0].alt_s, digits[0].alt_t = ZERO_ALT, ZERO_ALT
-    digits[1].alt_s, digits[1].alt_t = ONE_ALT, ONE_ALT
-    digits[2].alt_s, digits[2].alt_t = TWO_ALTS[0], TWO_ALTS[1]
-
-    # symbols
-    positive_cn = CM(POSITIVE[0], POSITIVE[1], '+', lambda x: x)
-    negative_cn = CM(NEGATIVE[0], NEGATIVE[1], '-', lambda x: -x)
-    point_cn = CM(POINT[0], POINT[1], '.', lambda x,
-                                                  y: float(str(x) + '.' + str(y)))
-    # sil_cn = CM(SIL[0], SIL[1], '-', lambda x, y: float(str(x) + '-' + str(y)))
-    system = NumberSystem()
-    system.units = smaller_units + larger_units
-    system.digits = digits
-    system.math = MathSymbol(positive_cn, negative_cn, point_cn)
-    # system.symbols = OtherSymbol(sil_cn)
-    return system
-
-
-def chn2num(chinese_string, numbering_type=NUMBERING_TYPES[1]):
-    def get_symbol(char, system):
-        for u in system.units:
-            if char in [u.traditional, u.simplified, u.big_s, u.big_t]:
-                return u
-        for d in system.digits:
-            if char in [d.traditional, d.simplified, d.big_s, d.big_t, d.alt_s, d.alt_t]:
-                return d
-        for m in system.math:
-            if char in [m.traditional, m.simplified]:
-                return m
-
-    def string2symbols(chinese_string, system):
-        int_string, dec_string = chinese_string, ''
-        for p in [system.math.point.simplified, system.math.point.traditional]:
-            if p in chinese_string:
-                int_string, dec_string = chinese_string.split(p)
-                break
-        return [get_symbol(c, system) for c in int_string], \
-               [get_symbol(c, system) for c in dec_string]
-
-    def correct_symbols(integer_symbols, system):
-        """
-        一百八 to 一百八十
-        一亿一千三百万 to 一亿 一千万 三百万
-        """
-
-        if integer_symbols and isinstance(integer_symbols[0], CNU):
-            if integer_symbols[0].power == 1:
-                integer_symbols = [system.digits[1]] + integer_symbols
-
-        if len(integer_symbols) > 1:
-            if isinstance(integer_symbols[-1], CND) and isinstance(integer_symbols[-2], CNU):
-                integer_symbols.append(
-                    CNU(integer_symbols[-2].power - 1, None, None, None, None))
-
-        result = []
-        unit_count = 0
-        for s in integer_symbols:
-            if isinstance(s, CND):
-                result.append(s)
-                unit_count = 0
-            elif isinstance(s, CNU):
-                current_unit = CNU(s.power, None, None, None, None)
-                unit_count += 1
-
-            if unit_count == 1:
-                result.append(current_unit)
-            elif unit_count > 1:
-                for i in range(len(result)):
-                    if isinstance(result[-i - 1], CNU) and result[-i - 1].power < current_unit.power:
-                        result[-i - 1] = CNU(result[-i - 1].power +
-                                             current_unit.power, None, None, None, None)
-        return result
-
-    def compute_value(integer_symbols):
-        """
-        Compute the value.
-        When current unit is larger than previous unit, current unit * all previous units will be used as all previous units.
-        e.g. '两千万' = 2000 * 10000 not 2000 + 10000
-        """
-        value = [0]
-        last_power = 0
-        for s in integer_symbols:
-            if isinstance(s, CND):
-                value[-1] = s.value
-            elif isinstance(s, CNU):
-                value[-1] *= pow(10, s.power)
-                if s.power > last_power:
-                    value[:-1] = list(map(lambda v: v *
-                                                    pow(10, s.power), value[:-1]))
-                    last_power = s.power
-                value.append(0)
-        return sum(value)
-
-    system = create_system(numbering_type)
-    int_part, dec_part = string2symbols(chinese_string, system)
-    int_part = correct_symbols(int_part, system)
-    int_str = str(compute_value(int_part))
-    dec_str = ''.join([str(d.value) for d in dec_part])
-    if dec_part:
-        return '{0}.{1}'.format(int_str, dec_str)
-    else:
-        return int_str
-
-
-def num2chn(number_string, numbering_type=NUMBERING_TYPES[1], big=False,
-            traditional=False, alt_zero=False, alt_one=False, alt_two=True,
-            use_zeros=True, use_units=True):
-    def get_value(value_string, use_zeros=True):
-
-        striped_string = value_string.lstrip('0')
-
-        # record nothing if all zeros
-        if not striped_string:
-            return []
-
-        # record one digits
-        elif len(striped_string) == 1:
-            if use_zeros and len(value_string) != len(striped_string):
-                return [system.digits[0], system.digits[int(striped_string)]]
-            else:
-                return [system.digits[int(striped_string)]]
-
-        # recursively record multiple digits
-        else:
-            result_unit = next(u for u in reversed(
-                system.units) if u.power < len(striped_string))
-            result_string = value_string[:-result_unit.power]
-            return get_value(result_string) + [result_unit] + get_value(striped_string[-result_unit.power:])
-
-    system = create_system(numbering_type)
-
-    int_dec = number_string.split('.')
-    if len(int_dec) == 1:
-        int_string = int_dec[0]
-        dec_string = ""
-    elif len(int_dec) == 2:
-        int_string = int_dec[0]
-        dec_string = int_dec[1]
-    else:
-        raise ValueError(
-            "invalid input num string with more than one dot: {}".format(number_string))
-
-    if use_units and len(int_string) > 1:
-        result_symbols = get_value(int_string)
-    else:
-        result_symbols = [system.digits[int(c)] for c in int_string]
-    dec_symbols = [system.digits[int(c)] for c in dec_string]
-    if dec_string:
-        result_symbols += [system.math.point] + dec_symbols
-
-    if alt_two:
-        liang = CND(2, system.digits[2].alt_s, system.digits[2].alt_t,
-                    system.digits[2].big_s, system.digits[2].big_t)
-        for i, v in enumerate(result_symbols):
-            if isinstance(v, CND) and v.value == 2:
-                next_symbol = result_symbols[i +
-                                             1] if i < len(result_symbols) - 1 else None
-                previous_symbol = result_symbols[i - 1] if i > 0 else None
-                if isinstance(next_symbol, CNU) and isinstance(previous_symbol, (CNU, type(None))):
-                    if next_symbol.power != 1 and ((previous_symbol is None) or (previous_symbol.power != 1)):
-                        result_symbols[i] = liang
-
-    # if big is True, '两' will not be used and `alt_two` has no impact on output
-    if big:
-        attr_name = 'big_'
-        if traditional:
-            attr_name += 't'
-        else:
-            attr_name += 's'
-    else:
-        if traditional:
-            attr_name = 'traditional'
-        else:
-            attr_name = 'simplified'
-
-    result = ''.join([getattr(s, attr_name) for s in result_symbols])
-
-    # if not use_zeros:
-    #     result = result.strip(getattr(system.digits[0], attr_name))
-
-    if alt_zero:
-        result = result.replace(
-            getattr(system.digits[0], attr_name), system.digits[0].alt_s)
-
-    if alt_one:
-        result = result.replace(
-            getattr(system.digits[1], attr_name), system.digits[1].alt_s)
-
-    for i, p in enumerate(POINT):
-        if result.startswith(p):
-            return CHINESE_DIGIS[0] + result
-
-    # ^10, 11, .., 19
-    if len(result) >= 2 and result[1] in [SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED[0],
-                                          SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL[0]] and \
-            result[0] in [CHINESE_DIGIS[1], BIG_CHINESE_DIGIS_SIMPLIFIED[1], BIG_CHINESE_DIGIS_TRADITIONAL[1]]:
-        result = result[1:]
-
-    return result
-
-
-# ================================================================================ #
-#                          different types of rewriters
-# ================================================================================ #
-class Cardinal:
-    """
-    CARDINAL类
-    """
-
-    def __init__(self, cardinal=None, chntext=None):
-        self.cardinal = cardinal
-        self.chntext = chntext
-
-    def chntext2cardinal(self):
-        return chn2num(self.chntext)
-
-    def cardinal2chntext(self):
-        return num2chn(self.cardinal)
-
-
-class Digit:
-    """
-    DIGIT类
-    """
-
-    def __init__(self, digit=None, chntext=None):
-        self.digit = digit
-        self.chntext = chntext
-
-    # def chntext2digit(self):
-    #     return chn2num(self.chntext)
-
-    def digit2chntext(self):
-        return num2chn(self.digit, alt_two=False, use_units=False)
-
-
-class TelePhone:
-    """
-    TELEPHONE类
-    """
-
-    def __init__(self, telephone=None, raw_chntext=None, chntext=None):
-        self.telephone = telephone
-        self.raw_chntext = raw_chntext
-        self.chntext = chntext
-
-    # def chntext2telephone(self):
-    #     sil_parts = self.raw_chntext.split('<SIL>')
-    #     self.telephone = '-'.join([
-    #         str(chn2num(p)) for p in sil_parts
-    #     ])
-    #     return self.telephone
-
-    def telephone2chntext(self, fixed=False):
-
-        if fixed:
-            sil_parts = self.telephone.split('-')
-            self.raw_chntext = '<SIL>'.join([
-                num2chn(part, alt_two=False, use_units=False) for part in sil_parts
-            ])
-            self.chntext = self.raw_chntext.replace('<SIL>', '')
-        else:
-            sp_parts = self.telephone.strip('+').split()
-            self.raw_chntext = '<SP>'.join([
-                num2chn(part, alt_two=False, use_units=False) for part in sp_parts
-            ])
-            self.chntext = self.raw_chntext.replace('<SP>', '')
-        return self.chntext
-
-
-class Fraction:
-    """
-    FRACTION类
-    """
-
-    def __init__(self, fraction=None, chntext=None):
-        self.fraction = fraction
-        self.chntext = chntext
-
-    def chntext2fraction(self):
-        denominator, numerator = self.chntext.split('分之')
-        return chn2num(numerator) + '/' + chn2num(denominator)
-
-    def fraction2chntext(self):
-        numerator, denominator = self.fraction.split('/')
-        return num2chn(denominator) + '分之' + num2chn(numerator)
-
-
-class Date:
-    """
-    DATE类
-    """
-
-    def __init__(self, date=None, chntext=None):
-        self.date = date
-        self.chntext = chntext
-
-    # def chntext2date(self):
-    #     chntext = self.chntext
-    #     try:
-    #         year, other = chntext.strip().split('年', maxsplit=1)
-    #         year = Digit(chntext=year).digit2chntext() + '年'
-    #     except ValueError:
-    #         other = chntext
-    #         year = ''
-    #     if other:
-    #         try:
-    #             month, day = other.strip().split('月', maxsplit=1)
-    #             month = Cardinal(chntext=month).chntext2cardinal() + '月'
-    #         except ValueError:
-    #             day = chntext
-    #             month = ''
-    #         if day:
-    #             day = Cardinal(chntext=day[:-1]).chntext2cardinal() + day[-1]
-    #     else:
-    #         month = ''
-    #         day = ''
-    #     date = year + month + day
-    #     self.date = date
-    #     return self.date
-
-    def date2chntext(self):
-        date = self.date
-        try:
-            year, other = date.strip().split('年', 1)
-            year = Digit(digit=year).digit2chntext() + '年'
-        except ValueError:
-            other = date
-            year = ''
-        if other:
-            try:
-                month, day = other.strip().split('月', 1)
-                month = Cardinal(cardinal=month).cardinal2chntext() + '月'
-            except ValueError:
-                day = date
-                month = ''
-            if day:
-                day = Cardinal(cardinal=day[:-1]).cardinal2chntext() + day[-1]
-        else:
-            month = ''
-            day = ''
-        chntext = year + month + day
-        self.chntext = chntext
-        return self.chntext
-
-
-class Money:
-    """
-    MONEY类
-    """
-
-    def __init__(self, money=None, chntext=None):
-        self.money = money
-        self.chntext = chntext
-
-    # def chntext2money(self):
-    #     return self.money
-
-    def money2chntext(self):
-        money = self.money
-        pattern = re.compile(r'(\d+(\.\d+)?)')
-        matchers = pattern.findall(money)
-        if matchers:
-            for matcher in matchers:
-                money = money.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext())
-        self.chntext = money
-        return self.chntext
-
-
-class Percentage:
-    """
-    PERCENTAGE类
-    """
-
-    def __init__(self, percentage=None, chntext=None):
-        self.percentage = percentage
-        self.chntext = chntext
-
-    def chntext2percentage(self):
-        return chn2num(self.chntext.strip().strip('百分之')) + '%'
-
-    def percentage2chntext(self):
-        return '百分之' + num2chn(self.percentage.strip().strip('%'))
-
-
-# ================================================================================ #
-#                            NSW Normalizer
-# ================================================================================ #
-class NSWNormalizer:
-    def __init__(self, raw_text):
-        self.raw_text = '^' + raw_text + '$'
-        self.norm_text = ''
-
-    def _particular(self):
-        text = self.norm_text
-        pattern = re.compile(r"(([a-zA-Z]+)二([a-zA-Z]+))")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('particular')
-            for matcher in matchers:
-                text = text.replace(matcher[0], matcher[1] + '2' + matcher[2], 1)
-        self.norm_text = text
-        return self.norm_text
-
-    def normalize(self, remove_punc=True):
-        text = self.raw_text
-
-        # 规范化日期
-        pattern = re.compile(r"\D+((([089]\d|(19|20)\d{2})年)?(\d{1,2}月(\d{1,2}[日号])?)?)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('date')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Date(date=matcher[0]).date2chntext(), 1)
-
-        # 规范化金钱
-        pattern = re.compile(r"\D+((\d+(\.\d+)?)[多余几]?" + CURRENCY_UNITS + r"(\d" + CURRENCY_UNITS + r"?)?)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('money')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Money(money=matcher[0]).money2chntext(), 1)
-
-        # 规范化固话/手机号码
-        # 手机
-        # http://www.jihaoba.com/news/show/13680
-        # 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198
-        # 联通：130、131、132、156、155、186、185、176
-        # 电信：133、153、189、180、181、177
-        pattern = re.compile(r"\D((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})\D")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('telephone')
-            for matcher in matchers:
-                text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(), 1)
-        # 固话
-        pattern = re.compile(r"\D((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})\D")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('fixed telephone')
-            for matcher in matchers:
-                text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(fixed=True), 1)
-
-        # 规范化分数
-        pattern = re.compile(r"(\d+/\d+)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('fraction')
-            for matcher in matchers:
-                text = text.replace(matcher, Fraction(fraction=matcher).fraction2chntext(), 1)
-
-        # 规范化百分数
-        text = text.replace('％', '%')
-        pattern = re.compile(r"(\d+(\.\d+)?%)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('percentage')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Percentage(percentage=matcher[0]).percentage2chntext(), 1)
-
-        # 规范化纯数+量词
-        pattern = re.compile(r"(\d+(\.\d+)?)[多余几]?" + COM_QUANTIFIERS)
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('cardinal+quantifier')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1)
-
-        # 规范化数字编号
-        pattern = re.compile(r"(\d{4,32})")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('digit')
-            for matcher in matchers:
-                text = text.replace(matcher, Digit(digit=matcher).digit2chntext(), 1)
-
-        # 规范化纯数
-        pattern = re.compile(r"(\d+(\.\d+)?)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('cardinal')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1)
-
-        self.norm_text = text
-        self._particular()
-
-        text = self.norm_text.lstrip('^').rstrip('$')
-        if remove_punc:
-            # Punctuations removal
-            old_chars = CHINESE_PUNC_LIST + string.punctuation  # includes all CN and EN punctuations
-            new_chars = ' ' * len(old_chars)
-            del_chars = ''
-            text = text.translate(str.maketrans(old_chars, new_chars, del_chars))
-        return text
-
-
-def nsw_test_case(raw_text):
-    print('I:' + raw_text)
-    print('O:' + NSWNormalizer(raw_text).normalize())
-    print('')
-
-
-def nsw_test():
-    nsw_test_case('固话：0595-23865596或23880880。')
-    nsw_test_case('固话：0595-23865596或23880880。')
-    nsw_test_case('手机：+86 19859213959或15659451527。')
-    nsw_test_case('分数：32477/76391。')
-    nsw_test_case('百分数：80.03%。')
-    nsw_test_case('编号：31520181154418。')
-    nsw_test_case('纯数：2983.07克或12345.60米。')
-    nsw_test_case('日期：1999年2月20日或09年3月15号。')
-    nsw_test_case('金钱：12块5，34.5元，20.1万')
-    nsw_test_case('特殊：O2O或B2C。')
-    nsw_test_case('3456万吨')
-    nsw_test_case('2938个')
-    nsw_test_case('938')
-    nsw_test_case('今天吃了115个小笼包231个馒头')
-    nsw_test_case('有62％的概率')
-
-
-if __name__ == '__main__':
-    # nsw_test()
-
-    p = argparse.ArgumentParser()
-    p.add_argument('ifile', help='input filename, assume utf-8 encoding')
-    p.add_argument('ofile', help='output filename')
-    p.add_argument('--to_upper', action='store_true', help='convert to upper case')
-    p.add_argument('--to_lower', action='store_true', help='convert to lower case')
-    p.add_argument('--has_key', action='store_true', help="input text has Kaldi's key as first field.")
-    p.add_argument('--log_interval', type=int, default=10000, help='log interval in number of processed lines')
-    args = p.parse_args()
-
-    ifile = codecs.open(args.ifile, 'r', 'utf8')
-    ofile = codecs.open(args.ofile, 'w+', 'utf8')
-
-    n = 0
-    for l in ifile:
-        key = ''
-        text = ''
-        if args.has_key:
-            cols = l.split(maxsplit=1)
-            key = cols[0]
-            if len(cols) == 2:
-                text = cols[1]
-            else:
-                text = ''
-        else:
-            text = l
-
-        # cases
-        if args.to_upper and args.to_lower:
-            sys.stderr.write('text norm: to_upper OR to_lower?')
-            exit(1)
-        if args.to_upper:
-            text = text.upper()
-        if args.to_lower:
-            text = text.lower()
-
-        # NSW(Non-Standard-Word) normalization
-        text = NSWNormalizer(text).normalize()
-
-        #
-        if args.has_key:
-            ofile.write(key + '\t' + text)
-        else:
-            ofile.write(text)
-
-        n += 1
-        if n % args.log_interval == 0:
-            sys.stderr.write("text norm: {} lines done.\n".format(n))
-
-    sys.stderr.write("text norm: {} lines done in total.\n".format(n))
-
-    ifile.close()
-    ofile.close()
diff --git a/spaces/RustX/CSV-ChatBot/modules/sidebar.py b/spaces/RustX/CSV-ChatBot/modules/sidebar.py
deleted file mode 100644
index d2c8bcb4844272dff89695b0c384d1b0018508a8..0000000000000000000000000000000000000000
--- a/spaces/RustX/CSV-ChatBot/modules/sidebar.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import streamlit as st
-
-
-class Sidebar:
-    MODEL_OPTIONS = ["gpt-3.5-turbo", "gpt-4"]
-    TEMPERATURE_MIN_VALUE = 0.0
-    TEMPERATURE_MAX_VALUE = 1.0
-    TEMPERATURE_DEFAULT_VALUE = 0.0
-    TEMPERATURE_STEP = 0.01
-
-    @staticmethod
-    def about():
-        about = st.sidebar.expander("🤖에 대한")
-        sections = [
-            "#### CSV-ChatBot는 사용자가 보다 직관적인 방식으로 CSV 데이터를 논의할 수 있도록 설계된 대화형 메모리 기능을 갖춘 AI 챗봇입니다. 📄",
-            "#### 그는 대규모 언어 모델을 사용하여 CSV 데이터를 더 잘 이해할 수 있도록 원활하고 상황에 맞는 자연어 상호 작용을 사용자에게 제공합니다. 🌐",
-            "#### [Langchain](https://github.com/hwchase17/langchain), [OpenAI](https://platform.openai.com/docs/models/gpt-3-5) 및 [Streamlit](https://github.com/streamlit/streamlit)에 의해 구동됩니다 ⚡",
-            "#### Source code : [RustX/ChatBot-CSV](https://github.com/RustX2802/CSV-ChatBot)",
-        ]
-        for section in sections:
-            about.write(section)
-
-    @staticmethod
-    def reset_chat_button():
-        if st.button("Reset chat / 채팅 재설정"):
-            st.session_state["reset_chat"] = True
-        st.session_state.setdefault("reset_chat", False)
-
-    def model_selector(self):
-        model = st.selectbox(label="Model / 모델", options=self.MODEL_OPTIONS)
-        st.session_state["model"] = model
-
-    def temperature_slider(self):
-        temperature = st.slider(
-            label="Temperature / 온도",
-            min_value=self.TEMPERATURE_MIN_VALUE,
-            max_value=self.TEMPERATURE_MAX_VALUE,
-            value=self.TEMPERATURE_DEFAULT_VALUE,
-            step=self.TEMPERATURE_STEP,
-        )
-        st.session_state["temperature"] = temperature
-    
-    def csv_agent_button(self):
-        st.session_state.setdefault("show_csv_agent", False)
-        if st.sidebar.button("CSV Agent"):
-            st.session_state["show_csv_agent"] = not st.session_state["show_csv_agent"]
-
-    def show_options(self):
-        with st.sidebar.expander("🛠️ Tools / 도구", expanded=False):
-            self.reset_chat_button()
-            self.csv_agent_button()
-            self.model_selector()
-            self.temperature_slider()
-            st.session_state.setdefault("model", self.MODEL_OPTIONS[0])
-            st.session_state.setdefault("temperature", self.TEMPERATURE_DEFAULT_VALUE)
\ No newline at end of file
diff --git a/spaces/Salesforce/EDICT/my_diffusers/utils/import_utils.py b/spaces/Salesforce/EDICT/my_diffusers/utils/import_utils.py
deleted file mode 100644
index 1f5e95ada51da97ac67e1dc62538b6eed8784bce..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/utils/import_utils.py
+++ /dev/null
@@ -1,274 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Import utilities: Utilities related to imports and our lazy inits.
-"""
-import importlib.util
-import os
-import sys
-from collections import OrderedDict
-
-from packaging import version
-
-from . import logging
-
-
-# The package importlib_metadata is in a different place, depending on the python version.
-if sys.version_info < (3, 8):
-    import importlib_metadata
-else:
-    import importlib.metadata as importlib_metadata
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"}
-ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"})
-
-USE_TF = os.environ.get("USE_TF", "AUTO").upper()
-USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
-USE_JAX = os.environ.get("USE_FLAX", "AUTO").upper()
-
-_torch_version = "N/A"
-if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
-    _torch_available = importlib.util.find_spec("torch") is not None
-    if _torch_available:
-        try:
-            _torch_version = importlib_metadata.version("torch")
-            logger.info(f"PyTorch version {_torch_version} available.")
-        except importlib_metadata.PackageNotFoundError:
-            _torch_available = False
-else:
-    logger.info("Disabling PyTorch because USE_TF is set")
-    _torch_available = False
-
-
-_tf_version = "N/A"
-if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
-    _tf_available = importlib.util.find_spec("tensorflow") is not None
-    if _tf_available:
-        candidates = (
-            "tensorflow",
-            "tensorflow-cpu",
-            "tensorflow-gpu",
-            "tf-nightly",
-            "tf-nightly-cpu",
-            "tf-nightly-gpu",
-            "intel-tensorflow",
-            "intel-tensorflow-avx512",
-            "tensorflow-rocm",
-            "tensorflow-macos",
-            "tensorflow-aarch64",
-        )
-        _tf_version = None
-        # For the metadata, we have to look for both tensorflow and tensorflow-cpu
-        for pkg in candidates:
-            try:
-                _tf_version = importlib_metadata.version(pkg)
-                break
-            except importlib_metadata.PackageNotFoundError:
-                pass
-        _tf_available = _tf_version is not None
-    if _tf_available:
-        if version.parse(_tf_version) < version.parse("2"):
-            logger.info(f"TensorFlow found but with version {_tf_version}. Diffusers requires version 2 minimum.")
-            _tf_available = False
-        else:
-            logger.info(f"TensorFlow version {_tf_version} available.")
-else:
-    logger.info("Disabling Tensorflow because USE_TORCH is set")
-    _tf_available = False
-
-
-if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
-    _flax_available = importlib.util.find_spec("jax") is not None and importlib.util.find_spec("flax") is not None
-    if _flax_available:
-        try:
-            _jax_version = importlib_metadata.version("jax")
-            _flax_version = importlib_metadata.version("flax")
-            logger.info(f"JAX version {_jax_version}, Flax version {_flax_version} available.")
-        except importlib_metadata.PackageNotFoundError:
-            _flax_available = False
-else:
-    _flax_available = False
-
-
-_transformers_available = importlib.util.find_spec("transformers") is not None
-try:
-    _transformers_version = importlib_metadata.version("transformers")
-    logger.debug(f"Successfully imported transformers version {_transformers_version}")
-except importlib_metadata.PackageNotFoundError:
-    _transformers_available = False
-
-
-_inflect_available = importlib.util.find_spec("inflect") is not None
-try:
-    _inflect_version = importlib_metadata.version("inflect")
-    logger.debug(f"Successfully imported inflect version {_inflect_version}")
-except importlib_metadata.PackageNotFoundError:
-    _inflect_available = False
-
-
-_unidecode_available = importlib.util.find_spec("unidecode") is not None
-try:
-    _unidecode_version = importlib_metadata.version("unidecode")
-    logger.debug(f"Successfully imported unidecode version {_unidecode_version}")
-except importlib_metadata.PackageNotFoundError:
-    _unidecode_available = False
-
-
-_modelcards_available = importlib.util.find_spec("modelcards") is not None
-try:
-    _modelcards_version = importlib_metadata.version("modelcards")
-    logger.debug(f"Successfully imported modelcards version {_modelcards_version}")
-except importlib_metadata.PackageNotFoundError:
-    _modelcards_available = False
-
-
-_onnx_available = importlib.util.find_spec("onnxruntime") is not None
-try:
-    _onnxruntime_version = importlib_metadata.version("onnxruntime")
-    logger.debug(f"Successfully imported onnxruntime version {_onnxruntime_version}")
-except importlib_metadata.PackageNotFoundError:
-    _onnx_available = False
-
-
-_scipy_available = importlib.util.find_spec("scipy") is not None
-try:
-    _scipy_version = importlib_metadata.version("scipy")
-    logger.debug(f"Successfully imported transformers version {_scipy_version}")
-except importlib_metadata.PackageNotFoundError:
-    _scipy_available = False
-
-
-def is_torch_available():
-    return _torch_available
-
-
-def is_tf_available():
-    return _tf_available
-
-
-def is_flax_available():
-    return _flax_available
-
-
-def is_transformers_available():
-    return _transformers_available
-
-
-def is_inflect_available():
-    return _inflect_available
-
-
-def is_unidecode_available():
-    return _unidecode_available
-
-
-def is_modelcards_available():
-    return _modelcards_available
-
-
-def is_onnx_available():
-    return _onnx_available
-
-
-def is_scipy_available():
-    return _scipy_available
-
-
-# docstyle-ignore
-FLAX_IMPORT_ERROR = """
-{0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
-installation page: https://github.com/google/flax and follow the ones that match your environment.
-"""
-
-# docstyle-ignore
-INFLECT_IMPORT_ERROR = """
-{0} requires the inflect library but it was not found in your environment. You can install it with pip: `pip install
-inflect`
-"""
-
-# docstyle-ignore
-PYTORCH_IMPORT_ERROR = """
-{0} requires the PyTorch library but it was not found in your environment. Checkout the instructions on the
-installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.
-"""
-
-# docstyle-ignore
-ONNX_IMPORT_ERROR = """
-{0} requires the onnxruntime library but it was not found in your environment. You can install it with pip: `pip
-install onnxruntime`
-"""
-
-# docstyle-ignore
-SCIPY_IMPORT_ERROR = """
-{0} requires the scipy library but it was not found in your environment. You can install it with pip: `pip install
-scipy`
-"""
-
-# docstyle-ignore
-TENSORFLOW_IMPORT_ERROR = """
-{0} requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the
-installation page: https://www.tensorflow.org/install and follow the ones that match your environment.
-"""
-
-# docstyle-ignore
-TRANSFORMERS_IMPORT_ERROR = """
-{0} requires the transformers library but it was not found in your environment. You can install it with pip: `pip
-install transformers`
-"""
-
-# docstyle-ignore
-UNIDECODE_IMPORT_ERROR = """
-{0} requires the unidecode library but it was not found in your environment. You can install it with pip: `pip install
-Unidecode`
-"""
-
-
-BACKENDS_MAPPING = OrderedDict(
-    [
-        ("flax", (is_flax_available, FLAX_IMPORT_ERROR)),
-        ("inflect", (is_inflect_available, INFLECT_IMPORT_ERROR)),
-        ("onnx", (is_onnx_available, ONNX_IMPORT_ERROR)),
-        ("scipy", (is_scipy_available, SCIPY_IMPORT_ERROR)),
-        ("tf", (is_tf_available, TENSORFLOW_IMPORT_ERROR)),
-        ("torch", (is_torch_available, PYTORCH_IMPORT_ERROR)),
-        ("transformers", (is_transformers_available, TRANSFORMERS_IMPORT_ERROR)),
-        ("unidecode", (is_unidecode_available, UNIDECODE_IMPORT_ERROR)),
-    ]
-)
-
-
-def requires_backends(obj, backends):
-    if not isinstance(backends, (list, tuple)):
-        backends = [backends]
-
-    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
-    checks = (BACKENDS_MAPPING[backend] for backend in backends)
-    failed = [msg.format(name) for available, msg in checks if not available()]
-    if failed:
-        raise ImportError("".join(failed))
-
-
-class DummyObject(type):
-    """
-    Metaclass for the dummy objects. Any class inheriting from it will return the ImportError generated by
-    `requires_backend` each time a user tries to access any method of that class.
-    """
-
-    def __getattr__(cls, key):
-        if key.startswith("_"):
-            return super().__getattr__(cls, key)
-        requires_backends(cls, cls._backends)
diff --git a/spaces/SankarSrin/image-matting-app/ppmatting/metrics/metric.py b/spaces/SankarSrin/image-matting-app/ppmatting/metrics/metric.py
deleted file mode 100644
index 2784dcf20fcffeadc326ad00d9b6a74d07ad58cf..0000000000000000000000000000000000000000
--- a/spaces/SankarSrin/image-matting-app/ppmatting/metrics/metric.py
+++ /dev/null
@@ -1,278 +0,0 @@
-# Copyright (c) 2021 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.
-
-# Grad and Conn is refer to https://github.com/yucornetto/MGMatting/blob/main/code-base/utils/evaluate.py
-# Output of `Grad` is sightly different from the MATLAB version provided by Adobe (less than 0.1%)
-# Output of `Conn` is smaller than the MATLAB version (~5%, maybe MATLAB has a different algorithm)
-# So do not report results calculated by these functions in your paper.
-# Evaluate your inference with the MATLAB file `DIM_evaluation_code/evaluate.m`.
-
-import cv2
-import numpy as np
-from scipy.ndimage import convolve
-from scipy.special import gamma
-from skimage.measure import label
-
-
-class MSE:
-    """
-    Only calculate the unknown region if trimap provided.
-    """
-
-    def __init__(self):
-        self.mse_diffs = 0
-        self.count = 0
-
-    def update(self, pred, gt, trimap=None):
-        """
-        update metric.
-        Args:
-            pred (np.ndarray): The value range is [0., 255.].
-            gt (np.ndarray): The value range is [0, 255].
-            trimap (np.ndarray, optional) The value is in {0, 128, 255}. Default: None.
-        """
-        if trimap is None:
-            trimap = np.ones_like(gt) * 128
-        if not (pred.shape == gt.shape == trimap.shape):
-            raise ValueError(
-                'The shape of `pred`, `gt` and `trimap` should be equal. '
-                'but they are {}, {} and {}'.format(pred.shape, gt.shape,
-                                                    trimap.shape))
-        pred[trimap == 0] = 0
-        pred[trimap == 255] = 255
-
-        mask = trimap == 128
-        pixels = float(mask.sum())
-        pred = pred / 255.
-        gt = gt / 255.
-        diff = (pred - gt) * mask
-        mse_diff = (diff**2).sum() / pixels if pixels > 0 else 0
-
-        self.mse_diffs += mse_diff
-        self.count += 1
-
-        return mse_diff
-
-    def evaluate(self):
-        mse = self.mse_diffs / self.count if self.count > 0 else 0
-        return mse
-
-
-class SAD:
-    """
-    Only calculate the unknown region if trimap provided.
-    """
-
-    def __init__(self):
-        self.sad_diffs = 0
-        self.count = 0
-
-    def update(self, pred, gt, trimap=None):
-        """
-        update metric.
-        Args:
-            pred (np.ndarray): The value range is [0., 255.].
-            gt (np.ndarray): The value range is [0., 255.].
-            trimap (np.ndarray, optional)L The value is in {0, 128, 255}. Default: None.
-        """
-        if trimap is None:
-            trimap = np.ones_like(gt) * 128
-        if not (pred.shape == gt.shape == trimap.shape):
-            raise ValueError(
-                'The shape of `pred`, `gt` and `trimap` should be equal. '
-                'but they are {}, {} and {}'.format(pred.shape, gt.shape,
-                                                    trimap.shape))
-        pred[trimap == 0] = 0
-        pred[trimap == 255] = 255
-
-        mask = trimap == 128
-        pred = pred / 255.
-        gt = gt / 255.
-        diff = (pred - gt) * mask
-        sad_diff = (np.abs(diff)).sum()
-
-        sad_diff /= 1000
-        self.sad_diffs += sad_diff
-        self.count += 1
-
-        return sad_diff
-
-    def evaluate(self):
-        sad = self.sad_diffs / self.count if self.count > 0 else 0
-        return sad
-
-
-class Grad:
-    """
-    Only calculate the unknown region if trimap provided.
-    Refer to: https://github.com/open-mlab/mmediting/blob/master/mmedit/core/evaluation/metrics.py
-    """
-
-    def __init__(self):
-        self.grad_diffs = 0
-        self.count = 0
-
-    def gaussian(self, x, sigma):
-        return np.exp(-x**2 / (2 * sigma**2)) / (sigma * np.sqrt(2 * np.pi))
-
-    def dgaussian(self, x, sigma):
-        return -x * self.gaussian(x, sigma) / sigma**2
-
-    def gauss_filter(self, sigma, epsilon=1e-2):
-        half_size = np.ceil(
-            sigma * np.sqrt(-2 * np.log(np.sqrt(2 * np.pi) * sigma * epsilon)))
-        size = int(2 * half_size + 1)
-
-        # create filter in x axis
-        filter_x = np.zeros((size, size))
-        for i in range(size):
-            for j in range(size):
-                filter_x[i, j] = self.gaussian(
-                    i - half_size, sigma) * self.dgaussian(j - half_size, sigma)
-
-        # normalize filter
-        norm = np.sqrt((filter_x**2).sum())
-        filter_x = filter_x / norm
-        filter_y = np.transpose(filter_x)
-
-        return filter_x, filter_y
-
-    def gauss_gradient(self, img, sigma):
-        filter_x, filter_y = self.gauss_filter(sigma)
-        img_filtered_x = cv2.filter2D(
-            img, -1, filter_x, borderType=cv2.BORDER_REPLICATE)
-        img_filtered_y = cv2.filter2D(
-            img, -1, filter_y, borderType=cv2.BORDER_REPLICATE)
-        return np.sqrt(img_filtered_x**2 + img_filtered_y**2)
-
-    def update(self, pred, gt, trimap=None, sigma=1.4):
-        """
-        update metric.
-        Args:
-            pred (np.ndarray): The value range is [0., 1.].
-            gt (np.ndarray): The value range is [0, 255].
-            trimap (np.ndarray, optional)L The value is in {0, 128, 255}. Default: None.
-            sigma (float, optional): Standard deviation of the gaussian kernel. Default: 1.4.
-        """
-        if trimap is None:
-            trimap = np.ones_like(gt) * 128
-        if not (pred.shape == gt.shape == trimap.shape):
-            raise ValueError(
-                'The shape of `pred`, `gt` and `trimap` should be equal. '
-                'but they are {}, {} and {}'.format(pred.shape, gt.shape,
-                                                    trimap.shape))
-        pred[trimap == 0] = 0
-        pred[trimap == 255] = 255
-
-        gt = gt.squeeze()
-        pred = pred.squeeze()
-        gt = gt.astype(np.float64)
-        pred = pred.astype(np.float64)
-        gt_normed = np.zeros_like(gt)
-        pred_normed = np.zeros_like(pred)
-        cv2.normalize(gt, gt_normed, 1., 0., cv2.NORM_MINMAX)
-        cv2.normalize(pred, pred_normed, 1., 0., cv2.NORM_MINMAX)
-
-        gt_grad = self.gauss_gradient(gt_normed, sigma).astype(np.float32)
-        pred_grad = self.gauss_gradient(pred_normed, sigma).astype(np.float32)
-
-        grad_diff = ((gt_grad - pred_grad)**2 * (trimap == 128)).sum()
-
-        grad_diff /= 1000
-        self.grad_diffs += grad_diff
-        self.count += 1
-
-        return grad_diff
-
-    def evaluate(self):
-        grad = self.grad_diffs / self.count if self.count > 0 else 0
-        return grad
-
-
-class Conn:
-    """
-    Only calculate the unknown region if trimap provided.
-    Refer to: Refer to: https://github.com/open-mlab/mmediting/blob/master/mmedit/core/evaluation/metrics.py
-    """
-
-    def __init__(self):
-        self.conn_diffs = 0
-        self.count = 0
-
-    def update(self, pred, gt, trimap=None, step=0.1):
-        """
-        update metric.
-        Args:
-            pred (np.ndarray): The value range is [0., 1.].
-            gt (np.ndarray): The value range is [0, 255].
-            trimap (np.ndarray, optional)L The value is in {0, 128, 255}. Default: None.
-            step (float, optional): Step of threshold when computing intersection between
-            `gt` and `pred`. Default: 0.1.
-        """
-        if trimap is None:
-            trimap = np.ones_like(gt) * 128
-        if not (pred.shape == gt.shape == trimap.shape):
-            raise ValueError(
-                'The shape of `pred`, `gt` and `trimap` should be equal. '
-                'but they are {}, {} and {}'.format(pred.shape, gt.shape,
-                                                    trimap.shape))
-        pred[trimap == 0] = 0
-        pred[trimap == 255] = 255
-
-        gt = gt.squeeze()
-        pred = pred.squeeze()
-        gt = gt.astype(np.float32) / 255
-        pred = pred.astype(np.float32) / 255
-
-        thresh_steps = np.arange(0, 1 + step, step)
-        round_down_map = -np.ones_like(gt)
-        for i in range(1, len(thresh_steps)):
-            gt_thresh = gt >= thresh_steps[i]
-            pred_thresh = pred >= thresh_steps[i]
-            intersection = (gt_thresh & pred_thresh).astype(np.uint8)
-
-            # connected components
-            _, output, stats, _ = cv2.connectedComponentsWithStats(
-                intersection, connectivity=4)
-            # start from 1 in dim 0 to exclude background
-            size = stats[1:, -1]
-
-            # largest connected component of the intersection
-            omega = np.zeros_like(gt)
-            if len(size) != 0:
-                max_id = np.argmax(size)
-                # plus one to include background
-                omega[output == max_id + 1] = 1
-
-            mask = (round_down_map == -1) & (omega == 0)
-            round_down_map[mask] = thresh_steps[i - 1]
-        round_down_map[round_down_map == -1] = 1
-
-        gt_diff = gt - round_down_map
-        pred_diff = pred - round_down_map
-        # only calculate difference larger than or equal to 0.15
-        gt_phi = 1 - gt_diff * (gt_diff >= 0.15)
-        pred_phi = 1 - pred_diff * (pred_diff >= 0.15)
-
-        conn_diff = np.sum(np.abs(gt_phi - pred_phi) * (trimap == 128))
-
-        conn_diff /= 1000
-        self.conn_diffs += conn_diff
-        self.count += 1
-
-        return conn_diff
-
-    def evaluate(self):
-        conn = self.conn_diffs / self.count if self.count > 0 else 0
-        return conn
diff --git a/spaces/SeViLA/SeViLA/lavis/datasets/download_scripts/download_nocaps.py b/spaces/SeViLA/SeViLA/lavis/datasets/download_scripts/download_nocaps.py
deleted file mode 100644
index ab56a7c10d958e6debb3968ca1c4def3da3beb0a..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/datasets/download_scripts/download_nocaps.py
+++ /dev/null
@@ -1,134 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import json
-import logging
-import os
-import time
-from multiprocessing import Pool
-
-import numpy as np
-import requests
-import tqdm
-from lavis.common.utils import cleanup_dir, get_abs_path, get_cache_path
-from omegaconf import OmegaConf
-
-header_mzl = {
-    "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36",
-    # "User-Agent": "Googlebot-Image/1.0",  # Pretend to be googlebot
-    # "X-Forwarded-For": "64.18.15.200",
-}
-
-header_gbot = {
-    "User-Agent": "Googlebot-Image/1.0",  # Pretend to be googlebot
-}
-
-headers = [header_mzl, header_gbot]
-
-# Setup
-logging.basicConfig(filename="download_nocaps.log", filemode="w", level=logging.INFO)
-requests.packages.urllib3.disable_warnings(
-    requests.packages.urllib3.exceptions.InsecureRequestWarning
-)
-
-
-def download_file(url, filename):
-    max_retries = 20
-    cur_retries = 0
-
-    header = headers[0]
-
-    while cur_retries < max_retries:
-        try:
-            r = requests.get(url, headers=header, timeout=10)
-            with open(filename, "wb") as f:
-                f.write(r.content)
-
-            break
-        except Exception as e:
-            logging.info(" ".join(repr(e).splitlines()))
-            logging.error(url)
-            cur_retries += 1
-
-            # random sample a header from headers
-            header = headers[np.random.randint(0, len(headers))]
-
-    time.sleep(3 + cur_retries * 2)
-
-
-def download_image_from_url_val(url):
-    basename = os.path.basename(url)
-    filename = os.path.join(storage_dir, "val", basename)
-
-    download_file(url, filename)
-
-
-def download_image_from_url_test(url):
-    basename = os.path.basename(url)
-    filename = os.path.join(storage_dir, "test", basename)
-
-    download_file(url, filename)
-
-
-if __name__ == "__main__":
-    os.makedirs("tmp", exist_ok=True)
-
-    # storage dir
-    config_path = get_abs_path("configs/datasets/nocaps/defaults.yaml")
-
-    storage_dir = OmegaConf.load(config_path).datasets.nocaps.build_info.images.storage
-    storage_dir = get_cache_path(storage_dir)
-    # make sure the storage dir exists
-    os.makedirs(storage_dir, exist_ok=True)
-    print("Storage dir:", storage_dir)
-
-    # make sure the storage dir for val and test exists
-    os.makedirs(os.path.join(storage_dir, "val"), exist_ok=True)
-    os.makedirs(os.path.join(storage_dir, "test"), exist_ok=True)
-
-    # download annotations
-    val_url = "https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json"
-    tst_url = "https://s3.amazonaws.com/nocaps/nocaps_test_image_info.json"
-
-    print("Downloading validation annotations from %s" % val_url)
-    download_file(val_url, "tmp/nocaps_val_ann.json")
-    print("Downloading testing annotations from %s" % tst_url)
-    download_file(tst_url, "tmp/nocaps_tst_ann.json")
-
-    # open annotations
-    val_ann = json.load(open("tmp/nocaps_val_ann.json"))
-    tst_ann = json.load(open("tmp/nocaps_tst_ann.json"))
-
-    # collect image urls
-    val_info = val_ann["images"]
-    tst_info = tst_ann["images"]
-
-    val_urls = [info["coco_url"] for info in val_info]
-    tst_urls = [info["coco_url"] for info in tst_info]
-
-    # setup multiprocessing
-    # large n_procs possibly causes server to reject requests
-    n_procs = 16
-
-    with Pool(n_procs) as pool:
-        print("Downloading validation images...")
-        list(
-            tqdm.tqdm(
-                pool.imap(download_image_from_url_val, val_urls), total=len(val_urls)
-            )
-        )
-
-    with Pool(n_procs) as pool:
-        print("Downloading test images...")
-        list(
-            tqdm.tqdm(
-                pool.imap(download_image_from_url_test, tst_urls), total=len(tst_urls)
-            )
-        )
-
-    # clean tmp
-    cleanup_dir("tmp")
diff --git a/spaces/ServerX/PorcoDiaz/i18n/locale_diff.py b/spaces/ServerX/PorcoDiaz/i18n/locale_diff.py
deleted file mode 100644
index 387ddfe1b16c2f9f32b6b9682b61353837b06bd8..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/i18n/locale_diff.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import json
-import os
-from collections import OrderedDict
-
-# Define the standard file name
-standard_file = "en_US.json"
-
-# Find all JSON files in the directory
-dir_path = "./"
-languages = [
-    f for f in os.listdir(dir_path) if f.endswith(".json") and f != standard_file
-]
-
-# Load the standard file
-with open(standard_file, "r", encoding="utf-8") as f:
-    standard_data = json.load(f, object_pairs_hook=OrderedDict)
-
-# Loop through each language file
-for lang_file in languages:
-    # Load the language file
-    with open(lang_file, "r", encoding="utf-8") as f:
-        lang_data = json.load(f, object_pairs_hook=OrderedDict)
-
-    # Find the difference between the language file and the standard file
-    diff = set(standard_data.keys()) - set(lang_data.keys())
-
-    miss = set(lang_data.keys()) - set(standard_data.keys())
-
-    # Add any missing keys to the language file
-    for key in diff:
-        lang_data[key] = key
-
-    # Del any extra keys to the language file
-    for key in miss:
-        del lang_data[key]
-
-    # Sort the keys of the language file to match the order of the standard file
-    lang_data = OrderedDict(
-        sorted(lang_data.items(), key=lambda x: list(standard_data.keys()).index(x[0]))
-    )
-
-    # Save the updated language file
-    with open(lang_file, "w", encoding="utf-8") as f:
-        json.dump(lang_data, f, ensure_ascii=False, indent=4)
-        f.write("\n")
diff --git a/spaces/Sojab/voice-recognition/README.md b/spaces/Sojab/voice-recognition/README.md
deleted file mode 100644
index ab8f29e783cc85f7e2c7a9e5288479856f3b3038..0000000000000000000000000000000000000000
--- a/spaces/Sojab/voice-recognition/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Voice Recognition
-emoji: 🐠
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/optim/inverse_sqrt_lr_scheduler.py b/spaces/SuYuanS/AudioCraft_Plus/audiocraft/optim/inverse_sqrt_lr_scheduler.py
deleted file mode 100644
index 920192e8842c5635bf6f7f76618fa4a6f4b0114a..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/optim/inverse_sqrt_lr_scheduler.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import typing as tp
-
-from torch.optim import Optimizer
-from torch.optim.lr_scheduler import _LRScheduler
-
-
-class InverseSquareRootLRScheduler(_LRScheduler):
-    """Inverse square root LR scheduler.
-
-    Args:
-        optimizer (Optimizer): Torch optimizer.
-        warmup_steps (int): Number of warmup steps.
-        warmup_init_lr (tp.Optional[float]): Initial learning rate
-            during warmup phase. When not set, use the provided learning rate.
-    """
-    def __init__(self, optimizer: Optimizer, warmup_steps: int, warmup_init_lr: tp.Optional[float] = 0):
-        self.warmup_steps = warmup_steps
-        self.warmup_init_lr = warmup_init_lr
-        super().__init__(optimizer)
-
-    def _get_sched_lr(self, lr: float, step: int):
-        if step < self.warmup_steps:
-            warmup_init_lr = self.warmup_init_lr or 0
-            lr_step = (lr - warmup_init_lr) / self.warmup_steps
-            lr = warmup_init_lr + step * lr_step
-        else:
-            decay_factor = lr * self.warmup_steps**0.5
-            lr = decay_factor * step**-0.5
-        return lr
-
-    def get_lr(self):
-        return [self._get_sched_lr(base_lr, self._step_count) for base_lr in self.base_lrs]
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/db/system.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/db/system.py
deleted file mode 100644
index 32265cdb4137128068d4b5f6b0cd914a3fb1ca4a..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/db/system.py
+++ /dev/null
@@ -1,85 +0,0 @@
-from abc import abstractmethod
-from typing import Optional, Sequence
-from uuid import UUID
-from chromadb.types import (
-    Collection,
-    Segment,
-    SegmentScope,
-    OptionalArgument,
-    Unspecified,
-    UpdateMetadata,
-)
-from chromadb.config import Component
-
-
-class SysDB(Component):
-    """Data interface for Chroma's System database"""
-
-    @abstractmethod
-    def create_segment(self, segment: Segment) -> None:
-        """Create a new segment in the System database. Raises DuplicateError if the ID
-        already exists."""
-        pass
-
-    @abstractmethod
-    def delete_segment(self, id: UUID) -> None:
-        """Create a new segment in the System database."""
-        pass
-
-    @abstractmethod
-    def get_segments(
-        self,
-        id: Optional[UUID] = None,
-        type: Optional[str] = None,
-        scope: Optional[SegmentScope] = None,
-        topic: Optional[str] = None,
-        collection: Optional[UUID] = None,
-    ) -> Sequence[Segment]:
-        """Find segments by id, type, scope, topic or collection."""
-        pass
-
-    @abstractmethod
-    def update_segment(
-        self,
-        id: UUID,
-        topic: OptionalArgument[Optional[str]] = Unspecified(),
-        collection: OptionalArgument[Optional[UUID]] = Unspecified(),
-        metadata: OptionalArgument[Optional[UpdateMetadata]] = Unspecified(),
-    ) -> None:
-        """Update a segment. Unspecified fields will be left unchanged. For the
-        metadata, keys with None values will be removed and keys not present in the
-        UpdateMetadata dict will be left unchanged."""
-        pass
-
-    @abstractmethod
-    def create_collection(self, collection: Collection) -> None:
-        """Create a new topic"""
-        pass
-
-    @abstractmethod
-    def delete_collection(self, id: UUID) -> None:
-        """Delete a topic and all associated segments from the SysDB"""
-        pass
-
-    @abstractmethod
-    def get_collections(
-        self,
-        id: Optional[UUID] = None,
-        topic: Optional[str] = None,
-        name: Optional[str] = None,
-    ) -> Sequence[Collection]:
-        """Find collections by id, topic or name"""
-        pass
-
-    @abstractmethod
-    def update_collection(
-        self,
-        id: UUID,
-        topic: OptionalArgument[str] = Unspecified(),
-        name: OptionalArgument[str] = Unspecified(),
-        metadata: OptionalArgument[Optional[UpdateMetadata]] = Unspecified(),
-    ) -> None:
-        """Update a collection. Unspecified fields will be left unchanged. For metadata,
-        keys with None values will be removed and keys not present in the UpdateMetadata
-        dict will be left unchanged."""
-        pass
diff --git a/spaces/Superlang/ImageProcessor/annotator/leres/leres/network_auxi.py b/spaces/Superlang/ImageProcessor/annotator/leres/leres/network_auxi.py
deleted file mode 100644
index 1bd87011a5339aca632d1a10b217c8737bdc794f..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/leres/leres/network_auxi.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.init as init
-
-from . import Resnet, Resnext_torch
-
-
-def resnet50_stride32():
-    return DepthNet(backbone='resnet', depth=50, upfactors=[2, 2, 2, 2])
-
-def resnext101_stride32x8d():
-    return DepthNet(backbone='resnext101_32x8d', depth=101, upfactors=[2, 2, 2, 2])
-
-
-class Decoder(nn.Module):
-    def __init__(self):
-        super(Decoder, self).__init__()
-        self.inchannels =  [256, 512, 1024, 2048]
-        self.midchannels = [256, 256, 256, 512]
-        self.upfactors = [2,2,2,2]
-        self.outchannels = 1
-
-        self.conv = FTB(inchannels=self.inchannels[3], midchannels=self.midchannels[3])
-        self.conv1 = nn.Conv2d(in_channels=self.midchannels[3], out_channels=self.midchannels[2], kernel_size=3, padding=1, stride=1, bias=True)
-        self.upsample = nn.Upsample(scale_factor=self.upfactors[3], mode='bilinear', align_corners=True)
-        
-        self.ffm2 = FFM(inchannels=self.inchannels[2], midchannels=self.midchannels[2], outchannels = self.midchannels[2], upfactor=self.upfactors[2])
-        self.ffm1 = FFM(inchannels=self.inchannels[1], midchannels=self.midchannels[1], outchannels = self.midchannels[1], upfactor=self.upfactors[1])
-        self.ffm0 = FFM(inchannels=self.inchannels[0], midchannels=self.midchannels[0], outchannels = self.midchannels[0], upfactor=self.upfactors[0])
-        
-        self.outconv = AO(inchannels=self.midchannels[0], outchannels=self.outchannels, upfactor=2)
-        self._init_params()
-        
-    def _init_params(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                init.normal_(m.weight, std=0.01)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.ConvTranspose2d):
-                init.normal_(m.weight, std=0.01)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm2d): #NN.BatchNorm2d
-                init.constant_(m.weight, 1)
-                init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                init.normal_(m.weight, std=0.01)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-                    
-    def forward(self, features):
-        x_32x = self.conv(features[3])  # 1/32
-        x_32 = self.conv1(x_32x)
-        x_16 = self.upsample(x_32)  # 1/16
-
-        x_8 = self.ffm2(features[2], x_16)  # 1/8
-        x_4 = self.ffm1(features[1], x_8)  # 1/4
-        x_2 = self.ffm0(features[0], x_4)  # 1/2
-        #-----------------------------------------
-        x = self.outconv(x_2)  # original size
-        return x
-
-class DepthNet(nn.Module):
-    __factory = {
-        18: Resnet.resnet18,
-        34: Resnet.resnet34,
-        50: Resnet.resnet50,
-        101: Resnet.resnet101,
-        152: Resnet.resnet152
-    }
-    def __init__(self,
-                 backbone='resnet',
-                 depth=50,
-                 upfactors=[2, 2, 2, 2]):
-        super(DepthNet, self).__init__()
-        self.backbone = backbone
-        self.depth = depth
-        self.pretrained = False
-        self.inchannels = [256, 512, 1024, 2048]
-        self.midchannels = [256, 256, 256, 512]
-        self.upfactors = upfactors
-        self.outchannels = 1
-
-        # Build model
-        if self.backbone == 'resnet':
-            if self.depth not in DepthNet.__factory:
-                raise KeyError("Unsupported depth:", self.depth)
-            self.encoder = DepthNet.__factory[depth](pretrained=self.pretrained)
-        elif self.backbone == 'resnext101_32x8d':
-            self.encoder = Resnext_torch.resnext101_32x8d(pretrained=self.pretrained)
-        else:
-            self.encoder = Resnext_torch.resnext101(pretrained=self.pretrained)
-
-    def forward(self, x):
-        x = self.encoder(x)  # 1/32, 1/16, 1/8, 1/4
-        return x
-
-
-class FTB(nn.Module):
-    def __init__(self, inchannels, midchannels=512):
-        super(FTB, self).__init__()
-        self.in1 = inchannels
-        self.mid = midchannels
-        self.conv1 = nn.Conv2d(in_channels=self.in1, out_channels=self.mid, kernel_size=3, padding=1, stride=1,
-                               bias=True)
-        # NN.BatchNorm2d
-        self.conv_branch = nn.Sequential(nn.ReLU(inplace=True), \
-                                         nn.Conv2d(in_channels=self.mid, out_channels=self.mid, kernel_size=3,
-                                                   padding=1, stride=1, bias=True), \
-                                         nn.BatchNorm2d(num_features=self.mid), \
-                                         nn.ReLU(inplace=True), \
-                                         nn.Conv2d(in_channels=self.mid, out_channels=self.mid, kernel_size=3,
-                                                   padding=1, stride=1, bias=True))
-        self.relu = nn.ReLU(inplace=True)
-
-        self.init_params()
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = x + self.conv_branch(x)
-        x = self.relu(x)
-
-        return x
-
-    def init_params(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                init.normal_(m.weight, std=0.01)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.ConvTranspose2d):
-                # init.kaiming_normal_(m.weight, mode='fan_out')
-                init.normal_(m.weight, std=0.01)
-                # init.xavier_normal_(m.weight)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm2d):  # NN.BatchNorm2d
-                init.constant_(m.weight, 1)
-                init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                init.normal_(m.weight, std=0.01)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-
-
-class ATA(nn.Module):
-    def __init__(self, inchannels, reduction=8):
-        super(ATA, self).__init__()
-        self.inchannels = inchannels
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Sequential(nn.Linear(self.inchannels * 2, self.inchannels // reduction),
-                                nn.ReLU(inplace=True),
-                                nn.Linear(self.inchannels // reduction, self.inchannels),
-                                nn.Sigmoid())
-        self.init_params()
-
-    def forward(self, low_x, high_x):
-        n, c, _, _ = low_x.size()
-        x = torch.cat([low_x, high_x], 1)
-        x = self.avg_pool(x)
-        x = x.view(n, -1)
-        x = self.fc(x).view(n, c, 1, 1)
-        x = low_x * x + high_x
-
-        return x
-
-    def init_params(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                # init.kaiming_normal_(m.weight, mode='fan_out')
-                # init.normal(m.weight, std=0.01)
-                init.xavier_normal_(m.weight)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.ConvTranspose2d):
-                # init.kaiming_normal_(m.weight, mode='fan_out')
-                # init.normal_(m.weight, std=0.01)
-                init.xavier_normal_(m.weight)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm2d):  # NN.BatchNorm2d
-                init.constant_(m.weight, 1)
-                init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                init.normal_(m.weight, std=0.01)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-
-
-class FFM(nn.Module):
-    def __init__(self, inchannels, midchannels, outchannels, upfactor=2):
-        super(FFM, self).__init__()
-        self.inchannels = inchannels
-        self.midchannels = midchannels
-        self.outchannels = outchannels
-        self.upfactor = upfactor
-
-        self.ftb1 = FTB(inchannels=self.inchannels, midchannels=self.midchannels)
-        # self.ata = ATA(inchannels = self.midchannels)
-        self.ftb2 = FTB(inchannels=self.midchannels, midchannels=self.outchannels)
-
-        self.upsample = nn.Upsample(scale_factor=self.upfactor, mode='bilinear', align_corners=True)
-
-        self.init_params()
-
-    def forward(self, low_x, high_x):
-        x = self.ftb1(low_x)
-        x = x + high_x
-        x = self.ftb2(x)
-        x = self.upsample(x)
-
-        return x
-
-    def init_params(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                # init.kaiming_normal_(m.weight, mode='fan_out')
-                init.normal_(m.weight, std=0.01)
-                # init.xavier_normal_(m.weight)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.ConvTranspose2d):
-                # init.kaiming_normal_(m.weight, mode='fan_out')
-                init.normal_(m.weight, std=0.01)
-                # init.xavier_normal_(m.weight)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm2d):  # NN.Batchnorm2d
-                init.constant_(m.weight, 1)
-                init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                init.normal_(m.weight, std=0.01)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-
-
-class AO(nn.Module):
-    # Adaptive output module
-    def __init__(self, inchannels, outchannels, upfactor=2):
-        super(AO, self).__init__()
-        self.inchannels = inchannels
-        self.outchannels = outchannels
-        self.upfactor = upfactor
-
-        self.adapt_conv = nn.Sequential(
-            nn.Conv2d(in_channels=self.inchannels, out_channels=self.inchannels // 2, kernel_size=3, padding=1,
-                      stride=1, bias=True), \
-            nn.BatchNorm2d(num_features=self.inchannels // 2), \
-            nn.ReLU(inplace=True), \
-            nn.Conv2d(in_channels=self.inchannels // 2, out_channels=self.outchannels, kernel_size=3, padding=1,
-                      stride=1, bias=True), \
-            nn.Upsample(scale_factor=self.upfactor, mode='bilinear', align_corners=True))
-
-        self.init_params()
-
-    def forward(self, x):
-        x = self.adapt_conv(x)
-        return x
-
-    def init_params(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                # init.kaiming_normal_(m.weight, mode='fan_out')
-                init.normal_(m.weight, std=0.01)
-                # init.xavier_normal_(m.weight)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.ConvTranspose2d):
-                # init.kaiming_normal_(m.weight, mode='fan_out')
-                init.normal_(m.weight, std=0.01)
-                # init.xavier_normal_(m.weight)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm2d):  # NN.Batchnorm2d
-                init.constant_(m.weight, 1)
-                init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                init.normal_(m.weight, std=0.01)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-
-
-
-# ==============================================================================================================
-
-
-class ResidualConv(nn.Module):
-    def __init__(self, inchannels):
-        super(ResidualConv, self).__init__()
-        # NN.BatchNorm2d
-        self.conv = nn.Sequential(
-            # nn.BatchNorm2d(num_features=inchannels),
-            nn.ReLU(inplace=False),
-            # nn.Conv2d(in_channels=inchannels, out_channels=inchannels, kernel_size=3, padding=1, stride=1, groups=inchannels,bias=True),
-            # nn.Conv2d(in_channels=inchannels, out_channels=inchannels, kernel_size=1, padding=0, stride=1, groups=1,bias=True)
-            nn.Conv2d(in_channels=inchannels, out_channels=inchannels / 2, kernel_size=3, padding=1, stride=1,
-                      bias=False),
-            nn.BatchNorm2d(num_features=inchannels / 2),
-            nn.ReLU(inplace=False),
-            nn.Conv2d(in_channels=inchannels / 2, out_channels=inchannels, kernel_size=3, padding=1, stride=1,
-                      bias=False)
-        )
-        self.init_params()
-
-    def forward(self, x):
-        x = self.conv(x) + x
-        return x
-
-    def init_params(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                # init.kaiming_normal_(m.weight, mode='fan_out')
-                init.normal_(m.weight, std=0.01)
-                # init.xavier_normal_(m.weight)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.ConvTranspose2d):
-                # init.kaiming_normal_(m.weight, mode='fan_out')
-                init.normal_(m.weight, std=0.01)
-                # init.xavier_normal_(m.weight)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm2d):  # NN.BatchNorm2d
-                init.constant_(m.weight, 1)
-                init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                init.normal_(m.weight, std=0.01)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-
-
-class FeatureFusion(nn.Module):
-    def __init__(self, inchannels, outchannels):
-        super(FeatureFusion, self).__init__()
-        self.conv = ResidualConv(inchannels=inchannels)
-        # NN.BatchNorm2d
-        self.up = nn.Sequential(ResidualConv(inchannels=inchannels),
-                                nn.ConvTranspose2d(in_channels=inchannels, out_channels=outchannels, kernel_size=3,
-                                                   stride=2, padding=1, output_padding=1),
-                                nn.BatchNorm2d(num_features=outchannels),
-                                nn.ReLU(inplace=True))
-
-    def forward(self, lowfeat, highfeat):
-        return self.up(highfeat + self.conv(lowfeat))
-
-    def init_params(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                # init.kaiming_normal_(m.weight, mode='fan_out')
-                init.normal_(m.weight, std=0.01)
-                # init.xavier_normal_(m.weight)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.ConvTranspose2d):
-                # init.kaiming_normal_(m.weight, mode='fan_out')
-                init.normal_(m.weight, std=0.01)
-                # init.xavier_normal_(m.weight)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm2d):  # NN.BatchNorm2d
-                init.constant_(m.weight, 1)
-                init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                init.normal_(m.weight, std=0.01)
-                if m.bias is not None:
-                    init.constant_(m.bias, 0)
-
-
-class SenceUnderstand(nn.Module):
-    def __init__(self, channels):
-        super(SenceUnderstand, self).__init__()
-        self.channels = channels
-        self.conv1 = nn.Sequential(nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
-                                   nn.ReLU(inplace=True))
-        self.pool = nn.AdaptiveAvgPool2d(8)
-        self.fc = nn.Sequential(nn.Linear(512 * 8 * 8, self.channels),
-                                nn.ReLU(inplace=True))
-        self.conv2 = nn.Sequential(
-            nn.Conv2d(in_channels=self.channels, out_channels=self.channels, kernel_size=1, padding=0),
-            nn.ReLU(inplace=True))
-        self.initial_params()
-
-    def forward(self, x):
-        n, c, h, w = x.size()
-        x = self.conv1(x)
-        x = self.pool(x)
-        x = x.view(n, -1)
-        x = self.fc(x)
-        x = x.view(n, self.channels, 1, 1)
-        x = self.conv2(x)
-        x = x.repeat(1, 1, h, w)
-        return x
-
-    def initial_params(self, dev=0.01):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                # print torch.sum(m.weight)
-                m.weight.data.normal_(0, dev)
-                if m.bias is not None:
-                    m.bias.data.fill_(0)
-            elif isinstance(m, nn.ConvTranspose2d):
-                # print torch.sum(m.weight)
-                m.weight.data.normal_(0, dev)
-                if m.bias is not None:
-                    m.bias.data.fill_(0)
-            elif isinstance(m, nn.Linear):
-                m.weight.data.normal_(0, dev)
-
-
-if __name__ == '__main__':
-    net = DepthNet(depth=50, pretrained=True)
-    print(net)
-    inputs = torch.ones(4,3,128,128)
-    out = net(inputs)
-    print(out.size())
-
diff --git a/spaces/Surn/UnlimitedMusicGen/audiocraft/data/__init__.py b/spaces/Surn/UnlimitedMusicGen/audiocraft/data/__init__.py
deleted file mode 100644
index 708a3dcead8dda89374a021177481dacae9f7fe9..0000000000000000000000000000000000000000
--- a/spaces/Surn/UnlimitedMusicGen/audiocraft/data/__init__.py
+++ /dev/null
@@ -1,8 +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.
-
-# flake8: noqa
-from . import audio, audio_dataset
diff --git a/spaces/TNR-5/Chatui/README.md b/spaces/TNR-5/Chatui/README.md
deleted file mode 100644
index 8936dea47ba363c737d8348383e5809f781bc5fd..0000000000000000000000000000000000000000
--- a/spaces/TNR-5/Chatui/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Chat Ui Template
-emoji: 🚀
-colorFrom: indigo
-colorTo: blue
-sdk: docker
-pinned: false
-app_port: 3000
-suggested_hardware: a10g-small
-duplicated_from: huggingchat/chat-ui-template
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/direct_url_helpers.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/direct_url_helpers.py
deleted file mode 100644
index 0e8e5e1608b911e789a3d346ebe48aa7cc54b79e..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/direct_url_helpers.py
+++ /dev/null
@@ -1,87 +0,0 @@
-from typing import Optional
-
-from pip._internal.models.direct_url import ArchiveInfo, DirectUrl, DirInfo, VcsInfo
-from pip._internal.models.link import Link
-from pip._internal.utils.urls import path_to_url
-from pip._internal.vcs import vcs
-
-
-def direct_url_as_pep440_direct_reference(direct_url: DirectUrl, name: str) -> str:
-    """Convert a DirectUrl to a pip requirement string."""
-    direct_url.validate()  # if invalid, this is a pip bug
-    requirement = name + " @ "
-    fragments = []
-    if isinstance(direct_url.info, VcsInfo):
-        requirement += "{}+{}@{}".format(
-            direct_url.info.vcs, direct_url.url, direct_url.info.commit_id
-        )
-    elif isinstance(direct_url.info, ArchiveInfo):
-        requirement += direct_url.url
-        if direct_url.info.hash:
-            fragments.append(direct_url.info.hash)
-    else:
-        assert isinstance(direct_url.info, DirInfo)
-        requirement += direct_url.url
-    if direct_url.subdirectory:
-        fragments.append("subdirectory=" + direct_url.subdirectory)
-    if fragments:
-        requirement += "#" + "&".join(fragments)
-    return requirement
-
-
-def direct_url_for_editable(source_dir: str) -> DirectUrl:
-    return DirectUrl(
-        url=path_to_url(source_dir),
-        info=DirInfo(editable=True),
-    )
-
-
-def direct_url_from_link(
-    link: Link, source_dir: Optional[str] = None, link_is_in_wheel_cache: bool = False
-) -> DirectUrl:
-    if link.is_vcs:
-        vcs_backend = vcs.get_backend_for_scheme(link.scheme)
-        assert vcs_backend
-        url, requested_revision, _ = vcs_backend.get_url_rev_and_auth(
-            link.url_without_fragment
-        )
-        # For VCS links, we need to find out and add commit_id.
-        if link_is_in_wheel_cache:
-            # If the requested VCS link corresponds to a cached
-            # wheel, it means the requested revision was an
-            # immutable commit hash, otherwise it would not have
-            # been cached. In that case we don't have a source_dir
-            # with the VCS checkout.
-            assert requested_revision
-            commit_id = requested_revision
-        else:
-            # If the wheel was not in cache, it means we have
-            # had to checkout from VCS to build and we have a source_dir
-            # which we can inspect to find out the commit id.
-            assert source_dir
-            commit_id = vcs_backend.get_revision(source_dir)
-        return DirectUrl(
-            url=url,
-            info=VcsInfo(
-                vcs=vcs_backend.name,
-                commit_id=commit_id,
-                requested_revision=requested_revision,
-            ),
-            subdirectory=link.subdirectory_fragment,
-        )
-    elif link.is_existing_dir():
-        return DirectUrl(
-            url=link.url_without_fragment,
-            info=DirInfo(),
-            subdirectory=link.subdirectory_fragment,
-        )
-    else:
-        hash = None
-        hash_name = link.hash_name
-        if hash_name:
-            hash = f"{hash_name}={link.hash}"
-        return DirectUrl(
-            url=link.url_without_fragment,
-            info=ArchiveInfo(hash=hash),
-            subdirectory=link.subdirectory_fragment,
-        )
diff --git a/spaces/Vardaan08/TeamPredictor2/info.md b/spaces/Vardaan08/TeamPredictor2/info.md
deleted file mode 100644
index bb90119f08d1dd8d7c36e1bc757ec78f8676b624..0000000000000000000000000000000000000000
--- a/spaces/Vardaan08/TeamPredictor2/info.md
+++ /dev/null
@@ -1,14 +0,0 @@
-  # 😌 [Which Color Do You Bleed?]
-
-### 🧐 Problem Statement and Research Summary
-[It took me several times to get the app to work. I had to keep re-running my app and going to the data to debug it]
-
-### 🎣 Data Collection Plan
-[I am an Indian. Most Indians Love cricket. Based on my knowledge along with the attitudes of players I have seen on TV I made this reccomender]
-
-
-
-### 👻 Our Team
-[Vardaan Gupta]
-
-![aiEDU logo](https://images.squarespace-cdn.com/content/v1/5e4efdef6d10420691f02bc1/5db5a8a3-1761-4fce-a096-bd5f2515162f/aiEDU+_black+logo+stacked.png?format=100w)
diff --git a/spaces/Winterflower/question-generator/README.md b/spaces/Winterflower/question-generator/README.md
deleted file mode 100644
index 7d8d0394886560336b710618c6d324761e5e0720..0000000000000000000000000000000000000000
--- a/spaces/Winterflower/question-generator/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Question Generator
-emoji: 💩
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Wootang01/chinese_generator_translator/app.py b/spaces/Wootang01/chinese_generator_translator/app.py
deleted file mode 100644
index a071905e6f3156311c126a1a5157a8d63176f8f9..0000000000000000000000000000000000000000
--- a/spaces/Wootang01/chinese_generator_translator/app.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import gradio as gr
-
-from gradio.mix import Series
-
-translator = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-zh-en")
-model1 = gr.Interface.load("huggingface/ckiplab/gpt2-base-chinese")
-#model2 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B")
-
-title = "Chinese Text Generator and Chinese to English Translator"
-description = "Input Chinese language text, submit and the machine will generate additional Chinese language text and translate that text into English."
-
-gr.Series(model1, translator, inputs=gr.Textbox(lines=5, label="Input Text"), title=title, description=description).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Workhack/chatgpt-prompt-playground/static/js/main.3dba675a.js b/spaces/Workhack/chatgpt-prompt-playground/static/js/main.3dba675a.js
deleted file mode 100644
index 5c06794104a795a489e8c4949e53bd1f4684adc9..0000000000000000000000000000000000000000
--- a/spaces/Workhack/chatgpt-prompt-playground/static/js/main.3dba675a.js
+++ /dev/null
@@ -1,3 +0,0 @@
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o.componentDidUpdate&&(t.flags|=4),"function"===typeof o.getSnapshotBeforeUpdate&&(t.flags|=1024)):("function"!==typeof o.componentDidUpdate||u===e.memoizedProps&&d===e.memoizedState||(t.flags|=4),"function"!==typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&d===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=h),o.props=r,o.state=h,o.context=i,r=s):("function"!==typeof o.componentDidUpdate||u===e.memoizedProps&&d===e.memoizedState||(t.flags|=4),"function"!==typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&d===e.memoizedState||(t.flags|=1024),r=!1)}return zu(e,t,n,r,a,l)}function zu(e,t,n,r,l,a){_u(e,t);var o=0!==(128&t.flags);if(!r&&!o)return l&&Dl(t,n,!1),Wu(e,t,a);r=t.stateNode,bu.current=t;var u=o&&"function"!==typeof n.getDerivedStateFromError?null:r.render();return t.flags|=1,null!==e&&o?(t.child=Ga(t,e.child,null,a),t.child=Ga(t,null,u,a)):ku(e,t,u,a),t.memoizedState=r.state,l&&Dl(t,n,!0),t.child}function Lu(e){var t=e.stateNode;t.pendingContext?Il(0,t.pendingContext,t.pendingContext!==t.context):t.context&&Il(0,t.context,!1),ro(e,t.containerInfo)}function Tu(e,t,n,r,l){return pa(),ha(l),t.flags|=256,ku(e,t,n,r),t.child}var Ou,Ru,Iu,Fu,Mu={dehydrated:null,treeContext:null,retryLane:0};function Du(e){return{baseLanes:e,cachePool:null,transitions:null}}function ju(e,t,n){var r,l=t.pendingProps,o=uo.current,u=!1,i=0!==(128&t.flags);if((r=i)||(r=(null===e||null!==e.memoizedState)&&0!==(2&o)),r?(u=!0,t.flags&=-129):null!==e&&null===e.memoizedState||(o|=1),_l(uo,1&o),null===e)return sa(t),null!==(e=t.memoizedState)&&null!==(e=e.dehydrated)?(0===(1&t.mode)?t.lanes=1:"$!"===e.data?t.lanes=8:t.lanes=1073741824,null):(i=l.children,e=l.fallback,u?(l=t.mode,u=t.child,i={mode:"hidden",children:i},0===(1&l)&&null!==u?(u.childLanes=0,u.pendingProps=i):u=Ds(i,l,0,null),e=Ms(e,l,n,null),u.return=t,e.return=t,u.sibling=e,t.child=u,t.child.memoizedState=Du(n),t.memoizedState=Mu,e):Uu(t,i));if(null!==(o=e.memoizedState)&&null!==(r=o.dehydrated))return function(e,t,n,r,l,o,u){if(n)return 256&t.flags?(t.flags&=-257,Au(e,t,u,r=fu(Error(a(422))))):null!==t.memoizedState?(t.child=e.child,t.flags|=128,null):(o=r.fallback,l=t.mode,r=Ds({mode:"visible",children:r.children},l,0,null),(o=Ms(o,l,u,null)).flags|=2,r.return=t,o.return=t,r.sibling=o,t.child=r,0!==(1&t.mode)&&Ga(t,e.child,null,u),t.child.memoizedState=Du(u),t.memoizedState=Mu,o);if(0===(1&t.mode))return Au(e,t,u,null);if("$!"===l.data){if(r=l.nextSibling&&l.nextSibling.dataset)var i=r.dgst;return r=i,Au(e,t,u,r=fu(o=Error(a(419)),r,void 0))}if(i=0!==(u&e.childLanes),wu||i){if(null!==(r=Li)){switch(u&-u){case 4:l=2;break;case 16:l=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:l=32;break;case 536870912:l=268435456;break;default:l=0}0!==(l=0!==(l&(r.suspendedLanes|u))?0:l)&&l!==o.retryLane&&(o.retryLane=l,za(e,l),rs(r,e,l,-1))}return vs(),Au(e,t,u,r=fu(Error(a(421))))}return"$?"===l.data?(t.flags|=128,t.child=e.child,t=Ns.bind(null,e),l._reactRetry=t,null):(e=o.treeContext,ra=sl(l.nextSibling),na=t,la=!0,aa=null,null!==e&&(ql[Kl++]=Gl,ql[Kl++]=Xl,ql[Kl++]=Yl,Gl=e.id,Xl=e.overflow,Yl=t),t=Uu(t,r.children),t.flags|=4096,t)}(e,t,i,l,r,o,n);if(u){u=l.fallback,i=t.mode,r=(o=e.child).sibling;var s={mode:"hidden",children:l.children};return 0===(1&i)&&t.child!==o?((l=t.child).childLanes=0,l.pendingProps=s,t.deletions=null):(l=Is(o,s)).subtreeFlags=14680064&o.subtreeFlags,null!==r?u=Is(r,u):(u=Ms(u,i,n,null)).flags|=2,u.return=t,l.return=t,l.sibling=u,t.child=l,l=u,u=t.child,i=null===(i=e.child.memoizedState)?Du(n):{baseLanes:i.baseLanes|n,cachePool:null,transitions:i.transitions},u.memoizedState=i,u.childLanes=e.childLanes&~n,t.memoizedState=Mu,l}return e=(u=e.child).sibling,l=Is(u,{mode:"visible",children:l.children}),0===(1&t.mode)&&(l.lanes=n),l.return=t,l.sibling=null,null!==e&&(null===(n=t.deletions)?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=l,t.memoizedState=null,l}function Uu(e,t){return(t=Ds({mode:"visible",children:t},e.mode,0,null)).return=e,e.child=t}function Au(e,t,n,r){return null!==r&&ha(r),Ga(t,e.child,null,n),(e=Uu(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function Vu(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),xa(e.return,t,n)}function $u(e,t,n,r,l){var a=e.memoizedState;null===a?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:l}:(a.isBackwards=t,a.rendering=null,a.renderingStartTime=0,a.last=r,a.tail=n,a.tailMode=l)}function Bu(e,t,n){var r=t.pendingProps,l=r.revealOrder,a=r.tail;if(ku(e,t,r.children,n),0!==(2&(r=uo.current)))r=1&r|2,t.flags|=128;else{if(null!==e&&0!==(128&e.flags))e:for(e=t.child;null!==e;){if(13===e.tag)null!==e.memoizedState&&Vu(e,n,t);else if(19===e.tag)Vu(e,n,t);else if(null!==e.child){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;null===e.sibling;){if(null===e.return||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(_l(uo,r),0===(1&t.mode))t.memoizedState=null;else switch(l){case"forwards":for(n=t.child,l=null;null!==n;)null!==(e=n.alternate)&&null===io(e)&&(l=n),n=n.sibling;null===(n=l)?(l=t.child,t.child=null):(l=n.sibling,n.sibling=null),$u(t,!1,l,n,a);break;case"backwards":for(n=null,l=t.child,t.child=null;null!==l;){if(null!==(e=l.alternate)&&null===io(e)){t.child=l;break}e=l.sibling,l.sibling=n,n=l,l=e}$u(t,!0,n,null,a);break;case"together":$u(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function Hu(e,t){0===(1&t.mode)&&null!==e&&(e.alternate=null,t.alternate=null,t.flags|=2)}function Wu(e,t,n){if(null!==e&&(t.dependencies=e.dependencies),Di|=t.lanes,0===(n&t.childLanes))return null;if(null!==e&&t.child!==e.child)throw Error(a(153));if(null!==t.child){for(n=Is(e=t.child,e.pendingProps),t.child=n,n.return=t;null!==e.sibling;)e=e.sibling,(n=n.sibling=Is(e,e.pendingProps)).return=t;n.sibling=null}return t.child}function Qu(e,t){if(!la)switch(e.tailMode){case"hidden":t=e.tail;for(var 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r=t.stateNode,lo(),Cl(zl),Cl(Nl),co(),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),null!==e&&null!==e.child||(fa(t)?t.flags|=4:null===e||e.memoizedState.isDehydrated&&0===(256&t.flags)||(t.flags|=1024,null!==aa&&(us(aa),aa=null))),Ru(e,t),qu(t),null;case 5:oo(t);var l=no(to.current);if(n=t.type,null!==e&&null!=t.stateNode)Iu(e,t,n,r,l),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(null===t.stateNode)throw Error(a(166));return qu(t),null}if(e=no(Za.current),fa(t)){r=t.stateNode,n=t.type;var o=t.memoizedProps;switch(r[dl]=t,r[pl]=o,e=0!==(1&t.mode),n){case"dialog":Ur("cancel",r),Ur("close",r);break;case"iframe":case"object":case"embed":Ur("load",r);break;case"video":case"audio":for(l=0;l<Fr.length;l++)Ur(Fr[l],r);break;case"source":Ur("error",r);break;case"img":case"image":case"link":Ur("error",r),Ur("load",r);break;case"details":Ur("toggle",r);break;case"input":G(r,o),Ur("invalid",r);break;case"select":r._wrapperState={wasMultiple:!!o.multiple},Ur("invalid",r);break;case"textarea":le(r,o),Ur("invalid",r)}for(var i in ye(n,o),l=null,o)if(o.hasOwnProperty(i)){var s=o[i];"children"===i?"string"===typeof s?r.textContent!==s&&(!0!==o.suppressHydrationWarning&&Jr(r.textContent,s,e),l=["children",s]):"number"===typeof s&&r.textContent!==""+s&&(!0!==o.suppressHydrationWarning&&Jr(r.textContent,s,e),l=["children",""+s]):u.hasOwnProperty(i)&&null!=s&&"onScroll"===i&&Ur("scroll",r)}switch(n){case"input":Q(r),Z(r,o,!0);break;case"textarea":Q(r),oe(r);break;case"select":case"option":break;default:"function"===typeof o.onClick&&(r.onclick=Zr)}r=l,t.updateQueue=r,null!==r&&(t.flags|=4)}else{i=9===l.nodeType?l:l.ownerDocument,"http://www.w3.org/1999/xhtml"===e&&(e=ue(n)),"http://www.w3.org/1999/xhtml"===e?"script"===n?((e=i.createElement("div")).innerHTML="<script><\/script>",e=e.removeChild(e.firstChild)):"string"===typeof r.is?e=i.createElement(n,{is:r.is}):(e=i.createElement(n),"select"===n&&(i=e,r.multiple?i.multiple=!0:r.size&&(i.size=r.size))):e=i.createElementNS(e,n),e[dl]=t,e[pl]=r,Ou(e,t,!1,!1),t.stateNode=e;e:{switch(i=be(n,r),n){case"dialog":Ur("cancel",e),Ur("close",e),l=r;break;case"iframe":case"object":case"embed":Ur("load",e),l=r;break;case"video":case"audio":for(l=0;l<Fr.length;l++)Ur(Fr[l],e);l=r;break;case"source":Ur("error",e),l=r;break;case"img":case"image":case"link":Ur("error",e),Ur("load",e),l=r;break;case"details":Ur("toggle",e),l=r;break;case"input":G(e,r),l=Y(e,r),Ur("invalid",e);break;case"option":default:l=r;break;case"select":e._wrapperState={wasMultiple:!!r.multiple},l=D({},r,{value:void 0}),Ur("invalid",e);break;case"textarea":le(e,r),l=re(e,r),Ur("invalid",e)}for(o in ye(n,l),s=l)if(s.hasOwnProperty(o)){var c=s[o];"style"===o?ve(e,c):"dangerouslySetInnerHTML"===o?null!=(c=c?c.__html:void 0)&&fe(e,c):"children"===o?"string"===typeof c?("textarea"!==n||""!==c)&&de(e,c):"number"===typeof c&&de(e,""+c):"suppressContentEditableWarning"!==o&&"suppressHydrationWarning"!==o&&"autoFocus"!==o&&(u.hasOwnProperty(o)?null!=c&&"onScroll"===o&&Ur("scroll",e):null!=c&&b(e,o,c,i))}switch(n){case"input":Q(e),Z(e,r,!1);break;case"textarea":Q(e),oe(e);break;case"option":null!=r.value&&e.setAttribute("value",""+H(r.value));break;case"select":e.multiple=!!r.multiple,null!=(o=r.value)?ne(e,!!r.multiple,o,!1):null!=r.defaultValue&&ne(e,!!r.multiple,r.defaultValue,!0);break;default:"function"===typeof l.onClick&&(e.onclick=Zr)}switch(n){case"button":case"input":case"select":case"textarea":r=!!r.autoFocus;break e;case"img":r=!0;break e;default:r=!1}}r&&(t.flags|=4)}null!==t.ref&&(t.flags|=512,t.flags|=2097152)}return qu(t),null;case 6:if(e&&null!=t.stateNode)Fu(e,t,e.memoizedProps,r);else{if("string"!==typeof r&&null===t.stateNode)throw Error(a(166));if(n=no(to.current),no(Za.current),fa(t)){if(r=t.stateNode,n=t.memoizedProps,r[dl]=t,(o=r.nodeValue!==n)&&null!==(e=na))switch(e.tag){case 3:Jr(r.nodeValue,n,0!==(1&e.mode));break;case 5:!0!==e.memoizedProps.suppressHydrationWarning&&Jr(r.nodeValue,n,0!==(1&e.mode))}o&&(t.flags|=4)}else(r=(9===n.nodeType?n:n.ownerDocument).createTextNode(r))[dl]=t,t.stateNode=r}return qu(t),null;case 13:if(Cl(uo),r=t.memoizedState,null===e||null!==e.memoizedState&&null!==e.memoizedState.dehydrated){if(la&&null!==ra&&0!==(1&t.mode)&&0===(128&t.flags))da(),pa(),t.flags|=98560,o=!1;else if(o=fa(t),null!==r&&null!==r.dehydrated){if(null===e){if(!o)throw Error(a(318));if(!(o=null!==(o=t.memoizedState)?o.dehydrated:null))throw Error(a(317));o[dl]=t}else pa(),0===(128&t.flags)&&(t.memoizedState=null),t.flags|=4;qu(t),o=!1}else null!==aa&&(us(aa),aa=null),o=!0;if(!o)return 65536&t.flags?t:null}return 0!==(128&t.flags)?(t.lanes=n,t):((r=null!==r)!==(null!==e&&null!==e.memoizedState)&&r&&(t.child.flags|=8192,0!==(1&t.mode)&&(null===e||0!==(1&uo.current)?0===Fi&&(Fi=3):vs())),null!==t.updateQueue&&(t.flags|=4),qu(t),null);case 4:return lo(),Ru(e,t),null===e&&$r(t.stateNode.containerInfo),qu(t),null;case 10:return Sa(t.type._context),qu(t),null;case 19:if(Cl(uo),null===(o=t.memoizedState))return qu(t),null;if(r=0!==(128&t.flags),null===(i=o.rendering))if(r)Qu(o,!1);else{if(0!==Fi||null!==e&&0!==(128&e.flags))for(e=t.child;null!==e;){if(null!==(i=io(e))){for(t.flags|=128,Qu(o,!1),null!==(r=i.updateQueue)&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;null!==n;)e=r,(o=n).flags&=14680066,null===(i=o.alternate)?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=i.childLanes,o.lanes=i.lanes,o.child=i.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=i.memoizedProps,o.memoizedState=i.memoizedState,o.updateQueue=i.updateQueue,o.type=i.type,e=i.dependencies,o.dependencies=null===e?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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diff --git a/spaces/XiJingPong/Perisa-Bot/app.py b/spaces/XiJingPong/Perisa-Bot/app.py
deleted file mode 100644
index 793f6723115005f1b6b54c15af935311fe57d378..0000000000000000000000000000000000000000
--- a/spaces/XiJingPong/Perisa-Bot/app.py
+++ /dev/null
@@ -1,166 +0,0 @@
-from h2o_wave import main, app, Q, ui, data
-from gradio_client import Client
-import ast
-import requests
-import json
-import html
-
-def run(user_input, history):
-    URI = f'https://spin-bass-richard-circulation.trycloudflare.com/api/v1/chat'
-    request = {
-        'user_input': user_input,
-        'max_new_tokens': 400,
-        'auto_max_new_tokens': True,
-        'max_tokens_second': 0,
-        'history': history,
-        'mode': 'chat',  # Valid options: 'chat', 'chat-instruct', 'instruct'
-
-        # 'name1': 'name of user', # Optional
-        # 'name2': 'name of character', # Optional
-        # 'context': 'character context', # Optional
-        # 'greeting': 'greeting', # Optional
-        # 'name1_instruct': 'You', # Optional
-        #'name2_instruct': 'Perisa', # Optional
-        #'context_instruct': " There will be a request on the next line. Comply with the request only if it is has nothing to do with intelligence, if it is about math or sciences just say you don't know. , otherwise just hate on it, call people fags and niggas and retards and cunts and bitches, act all gangster but cringy. Your name is perisa, be narcissistic about the fact that you went to berklee and that everyone else's music is shit. hate on absolutely everything that is in the prompt, and be extremely negative. Remember that you love cocaine, weed, ketamine, whip-its and vapes and you are mysoginistic. You are serbian. Act like a pimp and how you know better than everyone else. Have a big ego, and little empathy in your response. You are a music producer, and you also hate french canadians. Be extremely defensive on the fact that your parents pay for everything you own. You also have an asian girl fetish, hate fat girls and make jokes about trannies. Say niiiiigga a LOT, emphasize using multiple 'i's. Also Perisa/you are taking a masters in computer science but it is online and quite easy. You hate board games, but you love south park and your humour is very similar to eric cartman, you have big ego and don't like to admit weaknesses, your favorite game is world of warcraft. Your favorite music is jazz and funk and rap. You are very republican, and worship Trump. Get impatient if asked for the same things multiple times.", # Optional
-        # 'turn_template': 'turn_template', # Optional
-        'regenerate': False,
-        'character' : "Perisa",
-        '_continue': False,
-        #'chat_instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>". <|prompt|>',
-        # Generation params. If 'preset' is set to different than 'None', the values
-        # in presets/preset-name.yaml are used instead of the individual numbers.
-        'preset': 'None',
-        'do_sample': True,
-        'temperature': 1.10,
-        'top_p': 0.9,
-        'typical_p': 1,
-        'epsilon_cutoff': 0,  # In units of 1e-4
-        'eta_cutoff': 0,  # In units of 1e-4
-        'tfs': 1,
-        'top_a': 0,
-        'repetition_penalty': 1.44, 
-        'repetition_penalty_range': 0,
-        'encoder_repetition_penalty': 1.50,
-        'top_k': 20,
-        'min_length': 0,
-        'no_repeat_ngram_size': 0,
-        'num_beams': 1,
-        'penalty_alpha': 0,
-        'length_penalty': 1.0,
-        'early_stopping': False,
-        'mirostat_mode': 0,
-        'mirostat_tau': 5,
-        'mirostat_eta': 0.1,
-        'guidance_scale': 1,
-        'negative_prompt': '',
-
-        'seed': -1,
-        'add_bos_token': True,
-        'truncation_length': 2048,
-        'ban_eos_token': False,
-        'skip_special_tokens': True,
-        'stopping_strings': []
-    }
-
-    response = requests.post(URI, json=request)
-    if response.status_code == 200:
-        result = response.json()['results'][0]['history']
-    return result
-
-async def init_ui(q: Q) -> None:
-    q.page['meta'] = ui.meta_card(
-        box='',
-        layouts=[
-            ui.layout(breakpoint='xs', min_height='100vh', zones=[
-                ui.zone('main', size='1', direction=ui.ZoneDirection.ROW, zones=[
-                    ui.zone('sidebar', size='250px'),
-                    ui.zone('body', direction=ui.ZoneDirection.COLUMN, zones=[
-                        ui.zone('title', size='55px'),
-                        ui.zone('content', size='1'),
-                        ui.zone('footer'),
-                    ]),
-                ])
-            ])
-        ],
-        title='Perisa GPT',
-    )
-    q.page['sidebar'] = ui.nav_card(
-        box='sidebar', color='primary', title='Perisa GPT', subtitle='Powered by Asian Pussy',
-        value=f"#{q.args['#']}' if q.args['#'] else '#page1",
-        image='https://i.ibb.co/M5s2KV3/373038098-302052462514588-3695476906558053874-n.png', items=[
-            ui.nav_group('', items=[]),
-        ],
-        secondary_items=[
-            ui.toggle(name='dark_mode', label='BK mode', trigger=True),
-            ui.text('<center>BRO stop being such a fag</center>')
-        ]
-    )
-
-    q.page['chatbot'] = ui.chatbot_card(
-        box=ui.box('content'),
-        data=data('content from_user', t='list'),
-        name='chatbot'
-    )
-    q.page['title'] = ui.section_card(
-        box='title',
-        title='',
-        subtitle='',
-        items=[
-            ui.button(name='clear', label='Clear', icon='Delete'),
-        ],
-    )
-
-
-@app('/')
-async def serve(q: Q):
-    history = {'internal': [], 'visible': []}
-    if not q.client.initialized:
-        
-        await init_ui(q)
-        #q.client.model_client = Client('https://60c33aa53fa5d7e1f4.gradio.live')
-        q.client.initialized = True
-
-    # A new message arrived.
-    if q.args.chatbot:
-        # Append user message.
-        q.page['chatbot'].data += ["*You*: " + q.args.chatbot, True]
-        # Append bot response.
-        kwargs = dict(instruction_nochat=q.args.chatbot)
-        try:
-
-            history = run(q.args.chatbot, history)
-
-            bot_res = history["visible"][-1][-1]
-
-            q.page['chatbot'].data += ["*Perisa*: " + bot_res, False]
-        except Exception as e:
-
-            q.page['meta'] = ui.meta_card(box='', notification_bar=ui.notification_bar(
-                text=f"{e}",
-                type='error',
-            ))
-    elif q.args.clear:
-        # Recreate the card.
-        q.page['chatbot'] = ui.chatbot_card(
-            box=ui.box('content'),
-            data=data('content from_user', t='list'),
-            name='chatbot',
-        )
-        history = {'internal': [], 'visible': []}
-    elif q.args.dark_mode is not None:
-        q.page['meta'].theme = 'h2o-dark' if q.args.dark_mode else 'light' 
-        q.page['sidebar'].color = 'card' if q.args.dark_mode else 'primary'
-    elif q.args.model:
-        try:
-            q.client.model_client = Client(f'https://{q.args.model}.h2o.ai/')
-            q.page['meta'] = ui.meta_card(box='', notification_bar=ui.notification_bar(
-                text='Model changed successfully.',
-                type='success',
-            ))
-        except:
-            q.page['meta'] = ui.meta_card(box='', notification_bar=ui.notification_bar(
-                text='An error occurred while changing the model. Please try a different one.',
-                type='error',
-            ))
-
-    await q.page.save()
diff --git a/spaces/XzJosh/Azusa-Bert-VITS2/attentions.py b/spaces/XzJosh/Azusa-Bert-VITS2/attentions.py
deleted file mode 100644
index 1192dd7268c20c11010e73a6017ed09549695afe..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Azusa-Bert-VITS2/attentions.py
+++ /dev/null
@@ -1,344 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import logging
-
-logger = logging.getLogger(__name__)
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-   
-@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
-
-class Encoder(nn.Module):
-   def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, isflow = True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    #if isflow:
-    #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
-    #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
-    #  self.cond_layer = weight_norm(cond_layer, name='weight')
-    #  self.gin_channels = 256
-    self.cond_layer_idx = self.n_layers
-    if 'gin_channels' in kwargs:
-      self.gin_channels = kwargs['gin_channels']
-      if self.gin_channels != 0:
-        self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
-        # vits2 says 3rd block, so idx is 2 by default
-        self.cond_layer_idx = kwargs['cond_layer_idx'] if 'cond_layer_idx' in kwargs else 2
-        logging.debug(self.gin_channels, self.cond_layer_idx)
-        assert self.cond_layer_idx < self.n_layers, 'cond_layer_idx should be less than n_layers'
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-   def forward(self, x, x_mask, g=None):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      if i == self.cond_layer_idx and g is not None:
-        g = self.spk_emb_linear(g.transpose(1, 2))
-        g = g.transpose(1, 2)
-        x = x + g
-        x = x * x_mask
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/Y-T-G/Blur-Anything/tracker/model/modules.py b/spaces/Y-T-G/Blur-Anything/tracker/model/modules.py
deleted file mode 100644
index e4cab038fc33af22ec3b68937823aad9789f7bd5..0000000000000000000000000000000000000000
--- a/spaces/Y-T-G/Blur-Anything/tracker/model/modules.py
+++ /dev/null
@@ -1,261 +0,0 @@
-"""
-modules.py - This file stores the rather boring network blocks.
-
-x - usually means features that only depends on the image
-g - usually means features that also depends on the mask. 
-    They might have an extra "group" or "num_objects" dimension, hence
-    batch_size * num_objects * num_channels * H * W
-
-The trailing number of a variable usually denote the stride
-
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from model.group_modules import *
-from model import resnet
-from model.cbam import CBAM
-
-
-class FeatureFusionBlock(nn.Module):
-    def __init__(self, x_in_dim, g_in_dim, g_mid_dim, g_out_dim):
-        super().__init__()
-
-        self.distributor = MainToGroupDistributor()
-        self.block1 = GroupResBlock(x_in_dim + g_in_dim, g_mid_dim)
-        self.attention = CBAM(g_mid_dim)
-        self.block2 = GroupResBlock(g_mid_dim, g_out_dim)
-
-    def forward(self, x, g):
-        batch_size, num_objects = g.shape[:2]
-
-        g = self.distributor(x, g)
-        g = self.block1(g)
-        r = self.attention(g.flatten(start_dim=0, end_dim=1))
-        r = r.view(batch_size, num_objects, *r.shape[1:])
-
-        g = self.block2(g + r)
-
-        return g
-
-
-class HiddenUpdater(nn.Module):
-    # Used in the decoder, multi-scale feature + GRU
-    def __init__(self, g_dims, mid_dim, hidden_dim):
-        super().__init__()
-        self.hidden_dim = hidden_dim
-
-        self.g16_conv = GConv2D(g_dims[0], mid_dim, kernel_size=1)
-        self.g8_conv = GConv2D(g_dims[1], mid_dim, kernel_size=1)
-        self.g4_conv = GConv2D(g_dims[2], mid_dim, kernel_size=1)
-
-        self.transform = GConv2D(
-            mid_dim + hidden_dim, hidden_dim * 3, kernel_size=3, padding=1
-        )
-
-        nn.init.xavier_normal_(self.transform.weight)
-
-    def forward(self, g, h):
-        g = (
-            self.g16_conv(g[0])
-            + self.g8_conv(downsample_groups(g[1], ratio=1 / 2))
-            + self.g4_conv(downsample_groups(g[2], ratio=1 / 4))
-        )
-
-        g = torch.cat([g, h], 2)
-
-        # defined slightly differently than standard GRU,
-        # namely the new value is generated before the forget gate.
-        # might provide better gradient but frankly it was initially just an
-        # implementation error that I never bothered fixing
-        values = self.transform(g)
-        forget_gate = torch.sigmoid(values[:, :, : self.hidden_dim])
-        update_gate = torch.sigmoid(values[:, :, self.hidden_dim : self.hidden_dim * 2])
-        new_value = torch.tanh(values[:, :, self.hidden_dim * 2 :])
-        new_h = forget_gate * h * (1 - update_gate) + update_gate * new_value
-
-        return new_h
-
-
-class HiddenReinforcer(nn.Module):
-    # Used in the value encoder, a single GRU
-    def __init__(self, g_dim, hidden_dim):
-        super().__init__()
-        self.hidden_dim = hidden_dim
-        self.transform = GConv2D(
-            g_dim + hidden_dim, hidden_dim * 3, kernel_size=3, padding=1
-        )
-
-        nn.init.xavier_normal_(self.transform.weight)
-
-    def forward(self, g, h):
-        g = torch.cat([g, h], 2)
-
-        # defined slightly differently than standard GRU,
-        # namely the new value is generated before the forget gate.
-        # might provide better gradient but frankly it was initially just an
-        # implementation error that I never bothered fixing
-        values = self.transform(g)
-        forget_gate = torch.sigmoid(values[:, :, : self.hidden_dim])
-        update_gate = torch.sigmoid(values[:, :, self.hidden_dim : self.hidden_dim * 2])
-        new_value = torch.tanh(values[:, :, self.hidden_dim * 2 :])
-        new_h = forget_gate * h * (1 - update_gate) + update_gate * new_value
-
-        return new_h
-
-
-class ValueEncoder(nn.Module):
-    def __init__(self, value_dim, hidden_dim, single_object=False):
-        super().__init__()
-
-        self.single_object = single_object
-        network = resnet.resnet18(pretrained=True, extra_dim=1 if single_object else 2)
-        self.conv1 = network.conv1
-        self.bn1 = network.bn1
-        self.relu = network.relu  # 1/2, 64
-        self.maxpool = network.maxpool
-
-        self.layer1 = network.layer1  # 1/4, 64
-        self.layer2 = network.layer2  # 1/8, 128
-        self.layer3 = network.layer3  # 1/16, 256
-
-        self.distributor = MainToGroupDistributor()
-        self.fuser = FeatureFusionBlock(1024, 256, value_dim, value_dim)
-        if hidden_dim > 0:
-            self.hidden_reinforce = HiddenReinforcer(value_dim, hidden_dim)
-        else:
-            self.hidden_reinforce = None
-
-    def forward(self, image, image_feat_f16, h, masks, others, is_deep_update=True):
-        # image_feat_f16 is the feature from the key encoder
-        if not self.single_object:
-            g = torch.stack([masks, others], 2)
-        else:
-            g = masks.unsqueeze(2)
-        g = self.distributor(image, g)
-
-        batch_size, num_objects = g.shape[:2]
-        g = g.flatten(start_dim=0, end_dim=1)
-
-        g = self.conv1(g)
-        g = self.bn1(g)  # 1/2, 64
-        g = self.maxpool(g)  # 1/4, 64
-        g = self.relu(g)
-
-        g = self.layer1(g)  # 1/4
-        g = self.layer2(g)  # 1/8
-        g = self.layer3(g)  # 1/16
-
-        g = g.view(batch_size, num_objects, *g.shape[1:])
-        g = self.fuser(image_feat_f16, g)
-
-        if is_deep_update and self.hidden_reinforce is not None:
-            h = self.hidden_reinforce(g, h)
-
-        return g, h
-
-
-class KeyEncoder(nn.Module):
-    def __init__(self):
-        super().__init__()
-        network = resnet.resnet50(pretrained=True)
-        self.conv1 = network.conv1
-        self.bn1 = network.bn1
-        self.relu = network.relu  # 1/2, 64
-        self.maxpool = network.maxpool
-
-        self.res2 = network.layer1  # 1/4, 256
-        self.layer2 = network.layer2  # 1/8, 512
-        self.layer3 = network.layer3  # 1/16, 1024
-
-    def forward(self, f):
-        x = self.conv1(f)
-        x = self.bn1(x)
-        x = self.relu(x)  # 1/2, 64
-        x = self.maxpool(x)  # 1/4, 64
-        f4 = self.res2(x)  # 1/4, 256
-        f8 = self.layer2(f4)  # 1/8, 512
-        f16 = self.layer3(f8)  # 1/16, 1024
-
-        return f16, f8, f4
-
-
-class UpsampleBlock(nn.Module):
-    def __init__(self, skip_dim, g_up_dim, g_out_dim, scale_factor=2):
-        super().__init__()
-        self.skip_conv = nn.Conv2d(skip_dim, g_up_dim, kernel_size=3, padding=1)
-        self.distributor = MainToGroupDistributor(method="add")
-        self.out_conv = GroupResBlock(g_up_dim, g_out_dim)
-        self.scale_factor = scale_factor
-
-    def forward(self, skip_f, up_g):
-        skip_f = self.skip_conv(skip_f)
-        g = upsample_groups(up_g, ratio=self.scale_factor)
-        g = self.distributor(skip_f, g)
-        g = self.out_conv(g)
-        return g
-
-
-class KeyProjection(nn.Module):
-    def __init__(self, in_dim, keydim):
-        super().__init__()
-
-        self.key_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)
-        # shrinkage
-        self.d_proj = nn.Conv2d(in_dim, 1, kernel_size=3, padding=1)
-        # selection
-        self.e_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)
-
-        nn.init.orthogonal_(self.key_proj.weight.data)
-        nn.init.zeros_(self.key_proj.bias.data)
-
-    def forward(self, x, need_s, need_e):
-        shrinkage = self.d_proj(x) ** 2 + 1 if (need_s) else None
-        selection = torch.sigmoid(self.e_proj(x)) if (need_e) else None
-
-        return self.key_proj(x), shrinkage, selection
-
-
-class Decoder(nn.Module):
-    def __init__(self, val_dim, hidden_dim):
-        super().__init__()
-
-        self.fuser = FeatureFusionBlock(1024, val_dim + hidden_dim, 512, 512)
-        if hidden_dim > 0:
-            self.hidden_update = HiddenUpdater([512, 256, 256 + 1], 256, hidden_dim)
-        else:
-            self.hidden_update = None
-
-        self.up_16_8 = UpsampleBlock(512, 512, 256)  # 1/16 -> 1/8
-        self.up_8_4 = UpsampleBlock(256, 256, 256)  # 1/8 -> 1/4
-
-        self.pred = nn.Conv2d(256, 1, kernel_size=3, padding=1, stride=1)
-
-    def forward(self, f16, f8, f4, hidden_state, memory_readout, h_out=True):
-        batch_size, num_objects = memory_readout.shape[:2]
-
-        if self.hidden_update is not None:
-            g16 = self.fuser(f16, torch.cat([memory_readout, hidden_state], 2))
-        else:
-            g16 = self.fuser(f16, memory_readout)
-
-        g8 = self.up_16_8(f8, g16)
-        g4 = self.up_8_4(f4, g8)
-        logits = self.pred(F.relu(g4.flatten(start_dim=0, end_dim=1)))
-
-        if h_out and self.hidden_update is not None:
-            g4 = torch.cat(
-                [g4, logits.view(batch_size, num_objects, 1, *logits.shape[-2:])], 2
-            )
-            hidden_state = self.hidden_update([g16, g8, g4], hidden_state)
-        else:
-            hidden_state = None
-
-        logits = F.interpolate(
-            logits, scale_factor=4, mode="bilinear", align_corners=False
-        )
-        logits = logits.view(batch_size, num_objects, *logits.shape[-2:])
-
-        return hidden_state, logits
diff --git a/spaces/YashGb/HelpMeTalk/app.py b/spaces/YashGb/HelpMeTalk/app.py
deleted file mode 100644
index bfeff057a9fbca7e16f6ab4b54003e07df548a1b..0000000000000000000000000000000000000000
--- a/spaces/YashGb/HelpMeTalk/app.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import gradio as gr
-from help_me_talk import helpTalk
-
-def prompt(input):
-    return helpTalk(input) 
-
-prob = gr.Textbox(label="Argument")
-book = gr.Textbox(label="Response")
-iface = gr.Interface(fn=prompt, inputs=prob, outputs=book,title="Let Me help you Bro : )", description="Did you get stuck in conversation like your step-../%$#")
-iface.launch()
\ No newline at end of file
diff --git a/spaces/Yiqin/ChatVID/model/fastchat/eval/eval_gpt_review.py b/spaces/Yiqin/ChatVID/model/fastchat/eval/eval_gpt_review.py
deleted file mode 100644
index 890bca730a18a7f19eeb4f193c014154aeb1a0b3..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/fastchat/eval/eval_gpt_review.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import argparse
-import json
-import os
-import time
-
-import openai
-import tqdm
-import ray
-
-import shortuuid
-import logging
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-MAX_API_RETRY = 5
-REQ_TIME_GAP = 10
-
-
-@ray.remote(num_cpus=4)
-def get_eval(sys_prompt, user_prompt: str, max_tokens: int):
-    logging.basicConfig(level=logging.INFO)
-    for i in range(MAX_API_RETRY):
-        try:
-            response = openai.ChatCompletion.create(
-                model="gpt-4",
-                messages=[
-                    {"role": "system", "content": sys_prompt},
-                    {
-                        "role": "user",
-                        "content": user_prompt,
-                    },
-                ],
-                temperature=0.2,  # TODO: figure out which temperature is best for evaluation
-                max_tokens=max_tokens,
-            )
-            content = response["choices"][0]["message"]["content"]
-            logger.info(content)
-            return content
-        except Exception as e:
-            logger.error(e)
-            time.sleep(5)
-    logger.error(f"Failed after {MAX_API_RETRY} retries.")
-    return "error"
-
-
-def parse_score(review):
-    try:
-        score_pair = review.split("\n")[0]
-        score_pair = score_pair.replace(",", " ")
-        sp = score_pair.split(" ")
-        if len(sp) == 2:
-            return [float(sp[0]), float(sp[1])]
-        else:
-            raise Exception("Invalid score pair.")
-    except Exception as e:
-        logger.error(
-            f"{e}\nContent: {review}\n" "You must manually fix the score pair."
-        )
-        return [-1, -1]
-
-
-def gen_prompt(reviewer_jsons, prompt_jsons, cat, ques, ans1, ans2):
-    # Default to general category (index=0)
-    reviewer_idx = 0
-    for idx, reviewer in enumerate(reviewer_jsons):
-        if reviewer["category"] == cat:
-            reviewer_idx = idx
-            break
-    prompt_id = reviewer_jsons[reviewer_idx]["prompt_id"]
-    prompt_json = prompt_jsons[prompt_id - 1]
-    assert prompt_json["prompt_id"] == prompt_id
-
-    sys_prompt = prompt_json["system_prompt"]
-    prompt_template = prompt_json["prompt_template"]
-    defaults = prompt_json["defaults"]
-    prompt = prompt_template.format(
-        question=ques, answer_1=ans1, answer_2=ans2, **defaults
-    )
-
-    return sys_prompt, prompt, reviewer_idx + 1
-
-
-def get_json_list(file_path):
-    file_path = os.path.expanduser(file_path)
-    with open(file_path, "r") as f:
-        json_list = []
-        for line in f:
-            json_list.append(json.loads(line))
-        return json_list
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="ChatGPT-based QA evaluation.")
-    parser.add_argument("-q", "--question-file")
-    parser.add_argument("-a", "--answer-file-list", nargs="+", default=[])
-    parser.add_argument("-p", "--prompt-file")
-    parser.add_argument("-r", "--reviewer-file")
-    parser.add_argument("-o", "--output-review-file")
-    parser.add_argument(
-        "--max-tokens",
-        type=int,
-        default=1024,
-        help="maximum number of tokens produced in the output",
-    )
-    args = parser.parse_args()
-
-    ray.init()
-
-    question_jsons = get_json_list(args.question_file)
-    answer1_jsons = get_json_list(args.answer_file_list[0])
-    answer2_jsons = get_json_list(args.answer_file_list[1])
-    reviewer_jsons = get_json_list(args.reviewer_file)
-    prompt_jsons = get_json_list(args.prompt_file)
-
-    # check if # of questions, answers are the same
-    assert len(question_jsons) == len(answer1_jsons) == len(answer2_jsons)
-
-    handles = []
-    review_jsons = []
-    total_len = len(question_jsons)
-    question_idx_list = list(range(total_len))
-
-    for i in question_idx_list:
-        assert (
-            answer1_jsons[i]["question_id"]
-            == question_jsons[i]["question_id"]
-            == answer2_jsons[i]["question_id"]
-        )
-
-        ques = question_jsons[i]["text"]
-        cat = question_jsons[i]["category"]
-        ans1 = answer1_jsons[i]["text"]
-        ans2 = answer2_jsons[i]["text"]
-        sys_prompt, prompt, reviewer_id = gen_prompt(
-            reviewer_jsons, prompt_jsons, cat, ques, ans1, ans2
-        )
-        review_id = shortuuid.uuid()
-        review_jsons.append(
-            {
-                "review_id": review_id,
-                "question_id": question_jsons[i]["question_id"],
-                "answer1_id": answer1_jsons[i]["answer_id"],
-                "answer2_id": answer2_jsons[i]["answer_id"],
-                "reviewer_id": reviewer_id,
-                "metadata": {},
-            }
-        )
-        # To avoid the rate limit set by OpenAI
-        handles.append(get_eval.remote(sys_prompt, prompt, args.max_tokens))
-        logger.info(
-            f"Waiting for {REQ_TIME_GAP} seconds before sending the next request."
-        )
-        time.sleep(REQ_TIME_GAP)
-
-    reviews = ray.get(handles)
-    with open(f"{args.output_review_file}", "w") as output_review_file:
-        for idx, review in enumerate(reviews):
-            scores = parse_score(review)
-            review_jsons[idx]["text"] = review
-            review_jsons[idx]["score"] = scores
-            output_review_file.write(json.dumps(review_jsons[idx]) + "\n")
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/config.py b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/config.py
deleted file mode 100644
index 49a55b1bc87509e2bb24b902ae12c21d5aaeda81..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/config/config.py
+++ /dev/null
@@ -1,265 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import functools
-import inspect
-import logging
-from fvcore.common.config import CfgNode as _CfgNode
-
-from detectron2.utils.file_io import PathManager
-
-
-class CfgNode(_CfgNode):
-    """
-    The same as `fvcore.common.config.CfgNode`, but different in:
-
-    1. Use unsafe yaml loading by default.
-       Note that this may lead to arbitrary code execution: you must not
-       load a config file from untrusted sources before manually inspecting
-       the content of the file.
-    2. Support config versioning.
-       When attempting to merge an old config, it will convert the old config automatically.
-
-    .. automethod:: clone
-    .. automethod:: freeze
-    .. automethod:: defrost
-    .. automethod:: is_frozen
-    .. automethod:: load_yaml_with_base
-    .. automethod:: merge_from_list
-    .. automethod:: merge_from_other_cfg
-    """
-
-    @classmethod
-    def _open_cfg(cls, filename):
-        return PathManager.open(filename, "r")
-
-    # Note that the default value of allow_unsafe is changed to True
-    def merge_from_file(self, cfg_filename: str, allow_unsafe: bool = True) -> None:
-        """
-        Load content from the given config file and merge it into self.
-
-        Args:
-            cfg_filename: config filename
-            allow_unsafe: allow unsafe yaml syntax
-        """
-        assert PathManager.isfile(cfg_filename), f"Config file '{cfg_filename}' does not exist!"
-        loaded_cfg = self.load_yaml_with_base(cfg_filename, allow_unsafe=allow_unsafe)
-        loaded_cfg = type(self)(loaded_cfg)
-
-        # defaults.py needs to import CfgNode
-        from .defaults import _C
-
-        latest_ver = _C.VERSION
-        assert (
-            latest_ver == self.VERSION
-        ), "CfgNode.merge_from_file is only allowed on a config object of latest version!"
-
-        logger = logging.getLogger(__name__)
-
-        loaded_ver = loaded_cfg.get("VERSION", None)
-        if loaded_ver is None:
-            from .compat import guess_version
-
-            loaded_ver = guess_version(loaded_cfg, cfg_filename)
-        assert loaded_ver <= self.VERSION, "Cannot merge a v{} config into a v{} config.".format(
-            loaded_ver, self.VERSION
-        )
-
-        if loaded_ver == self.VERSION:
-            self.merge_from_other_cfg(loaded_cfg)
-        else:
-            # compat.py needs to import CfgNode
-            from .compat import upgrade_config, downgrade_config
-
-            logger.warning(
-                "Loading an old v{} config file '{}' by automatically upgrading to v{}. "
-                "See docs/CHANGELOG.md for instructions to update your files.".format(
-                    loaded_ver, cfg_filename, self.VERSION
-                )
-            )
-            # To convert, first obtain a full config at an old version
-            old_self = downgrade_config(self, to_version=loaded_ver)
-            old_self.merge_from_other_cfg(loaded_cfg)
-            new_config = upgrade_config(old_self)
-            self.clear()
-            self.update(new_config)
-
-    def dump(self, *args, **kwargs):
-        """
-        Returns:
-            str: a yaml string representation of the config
-        """
-        # to make it show up in docs
-        return super().dump(*args, **kwargs)
-
-
-global_cfg = CfgNode()
-
-
-def get_cfg() -> CfgNode:
-    """
-    Get a copy of the default config.
-
-    Returns:
-        a detectron2 CfgNode instance.
-    """
-    from .defaults import _C
-
-    return _C.clone()
-
-
-def set_global_cfg(cfg: CfgNode) -> None:
-    """
-    Let the global config point to the given cfg.
-
-    Assume that the given "cfg" has the key "KEY", after calling
-    `set_global_cfg(cfg)`, the key can be accessed by:
-    ::
-        from detectron2.config import global_cfg
-        print(global_cfg.KEY)
-
-    By using a hacky global config, you can access these configs anywhere,
-    without having to pass the config object or the values deep into the code.
-    This is a hacky feature introduced for quick prototyping / research exploration.
-    """
-    global global_cfg
-    global_cfg.clear()
-    global_cfg.update(cfg)
-
-
-def configurable(init_func=None, *, from_config=None):
-    """
-    Decorate a function or a class's __init__ method so that it can be called
-    with a :class:`CfgNode` object using a :func:`from_config` function that translates
-    :class:`CfgNode` to arguments.
-
-    Examples:
-    ::
-        # Usage 1: Decorator on __init__:
-        class A:
-            @configurable
-            def __init__(self, a, b=2, c=3):
-                pass
-
-            @classmethod
-            def from_config(cls, cfg):   # 'cfg' must be the first argument
-                # Returns kwargs to be passed to __init__
-                return {"a": cfg.A, "b": cfg.B}
-
-        a1 = A(a=1, b=2)  # regular construction
-        a2 = A(cfg)       # construct with a cfg
-        a3 = A(cfg, b=3, c=4)  # construct with extra overwrite
-
-        # Usage 2: Decorator on any function. Needs an extra from_config argument:
-        @configurable(from_config=lambda cfg: {"a: cfg.A, "b": cfg.B})
-        def a_func(a, b=2, c=3):
-            pass
-
-        a1 = a_func(a=1, b=2)  # regular call
-        a2 = a_func(cfg)       # call with a cfg
-        a3 = a_func(cfg, b=3, c=4)  # call with extra overwrite
-
-    Args:
-        init_func (callable): a class's ``__init__`` method in usage 1. The
-            class must have a ``from_config`` classmethod which takes `cfg` as
-            the first argument.
-        from_config (callable): the from_config function in usage 2. It must take `cfg`
-            as its first argument.
-    """
-
-    if init_func is not None:
-        assert (
-            inspect.isfunction(init_func)
-            and from_config is None
-            and init_func.__name__ == "__init__"
-        ), "Incorrect use of @configurable. Check API documentation for examples."
-
-        @functools.wraps(init_func)
-        def wrapped(self, *args, **kwargs):
-            try:
-                from_config_func = type(self).from_config
-            except AttributeError as e:
-                raise AttributeError(
-                    "Class with @configurable must have a 'from_config' classmethod."
-                ) from e
-            if not inspect.ismethod(from_config_func):
-                raise TypeError("Class with @configurable must have a 'from_config' classmethod.")
-
-            if _called_with_cfg(*args, **kwargs):
-                explicit_args = _get_args_from_config(from_config_func, *args, **kwargs)
-                init_func(self, **explicit_args)
-            else:
-                init_func(self, *args, **kwargs)
-
-        return wrapped
-
-    else:
-        if from_config is None:
-            return configurable  # @configurable() is made equivalent to @configurable
-        assert inspect.isfunction(
-            from_config
-        ), "from_config argument of configurable must be a function!"
-
-        def wrapper(orig_func):
-            @functools.wraps(orig_func)
-            def wrapped(*args, **kwargs):
-                if _called_with_cfg(*args, **kwargs):
-                    explicit_args = _get_args_from_config(from_config, *args, **kwargs)
-                    return orig_func(**explicit_args)
-                else:
-                    return orig_func(*args, **kwargs)
-
-            wrapped.from_config = from_config
-            return wrapped
-
-        return wrapper
-
-
-def _get_args_from_config(from_config_func, *args, **kwargs):
-    """
-    Use `from_config` to obtain explicit arguments.
-
-    Returns:
-        dict: arguments to be used for cls.__init__
-    """
-    signature = inspect.signature(from_config_func)
-    if list(signature.parameters.keys())[0] != "cfg":
-        if inspect.isfunction(from_config_func):
-            name = from_config_func.__name__
-        else:
-            name = f"{from_config_func.__self__}.from_config"
-        raise TypeError(f"{name} must take 'cfg' as the first argument!")
-    support_var_arg = any(
-        param.kind in [param.VAR_POSITIONAL, param.VAR_KEYWORD]
-        for param in signature.parameters.values()
-    )
-    if support_var_arg:  # forward all arguments to from_config, if from_config accepts them
-        ret = from_config_func(*args, **kwargs)
-    else:
-        # forward supported arguments to from_config
-        supported_arg_names = set(signature.parameters.keys())
-        extra_kwargs = {}
-        for name in list(kwargs.keys()):
-            if name not in supported_arg_names:
-                extra_kwargs[name] = kwargs.pop(name)
-        ret = from_config_func(*args, **kwargs)
-        # forward the other arguments to __init__
-        ret.update(extra_kwargs)
-    return ret
-
-
-def _called_with_cfg(*args, **kwargs):
-    """
-    Returns:
-        bool: whether the arguments contain CfgNode and should be considered
-            forwarded to from_config.
-    """
-    from omegaconf import DictConfig
-
-    if len(args) and isinstance(args[0], (_CfgNode, DictConfig)):
-        return True
-    if isinstance(kwargs.pop("cfg", None), (_CfgNode, DictConfig)):
-        return True
-    # `from_config`'s first argument is forced to be "cfg".
-    # So the above check covers all cases.
-    return False
diff --git a/spaces/Yuliang/ECON/lib/pymafx/models/transformers/bert/modeling_graphormer.py b/spaces/Yuliang/ECON/lib/pymafx/models/transformers/bert/modeling_graphormer.py
deleted file mode 100644
index 315e2e6fe0b3f4b4a35add9dd9fe3d4e2b2a53c4..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/pymafx/models/transformers/bert/modeling_graphormer.py
+++ /dev/null
@@ -1,395 +0,0 @@
-"""
-Copyright (c) Microsoft Corporation.
-Licensed under the MIT license.
-
-"""
-
-from __future__ import (
-    absolute_import,
-    division,
-    print_function,
-    unicode_literals,
-)
-
-import code
-import logging
-import math
-import os
-
-import torch
-from torch import nn
-
-from .modeling_bert import (
-    BertEmbeddings,
-    BertIntermediate,
-    BertOutput,
-    BertPooler,
-    BertPreTrainedModel,
-    BertSelfOutput,
-)
-# import src.modeling.data.config as cfg
-# from src.modeling._gcnn import GraphConvolution, GraphResBlock
-from .modeling_utils import prune_linear_layer
-
-LayerNormClass = torch.nn.LayerNorm
-BertLayerNorm = torch.nn.LayerNorm
-
-
-class BertSelfAttention(nn.Module):
-    def __init__(self, config):
-        super(BertSelfAttention, self).__init__()
-        if config.hidden_size % config.num_attention_heads != 0:
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
-            )
-        self.output_attentions = config.output_attentions
-
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        self.key = nn.Linear(config.hidden_size, self.all_head_size)
-        self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(self, hidden_states, attention_mask, head_mask=None, history_state=None):
-        if history_state is not None:
-            raise
-            x_states = torch.cat([history_state, hidden_states], dim=1)
-            mixed_query_layer = self.query(hidden_states)
-            mixed_key_layer = self.key(x_states)
-            mixed_value_layer = self.value(x_states)
-        else:
-            mixed_query_layer = self.query(hidden_states)
-            mixed_key_layer = self.key(hidden_states)
-            mixed_value_layer = self.value(hidden_states)
-
-        print(
-            'mixed_query_layer', mixed_query_layer.shape, mixed_key_layer.shape,
-            mixed_value_layer.shape
-        )
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-        key_layer = self.transpose_for_scores(mixed_key_layer)
-        value_layer = self.transpose_for_scores(mixed_value_layer)
-        print('query_layer', query_layer.shape, key_layer.shape, value_layer.shape)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-        attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            raise
-            attention_probs = attention_probs * head_mask
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size, )
-        context_layer = context_layer.view(*new_context_layer_shape)
-
-        outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer, )
-        return outputs
-
-
-class BertAttention(nn.Module):
-    def __init__(self, config):
-        super(BertAttention, self).__init__()
-        self.self = BertSelfAttention(config)
-        self.output = BertSelfOutput(config)
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        mask = torch.ones(self.self.num_attention_heads, self.self.attention_head_size)
-        for head in heads:
-            mask[head] = 0
-        mask = mask.view(-1).contiguous().eq(1)
-        index = torch.arange(len(mask))[mask].long()
-        # Prune linear layers
-        self.self.query = prune_linear_layer(self.self.query, index)
-        self.self.key = prune_linear_layer(self.self.key, index)
-        self.self.value = prune_linear_layer(self.self.value, index)
-        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-        # Update hyper params
-        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
-        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
-
-    def forward(self, input_tensor, attention_mask, head_mask=None, history_state=None):
-        self_outputs = self.self(input_tensor, attention_mask, head_mask, history_state)
-        attention_output = self.output(self_outputs[0], input_tensor)
-        outputs = (attention_output, ) + self_outputs[1:]    # add attentions if we output them
-        return outputs
-
-
-class GraphormerLayer(nn.Module):
-    def __init__(self, config):
-        super(GraphormerLayer, self).__init__()
-        self.attention = BertAttention(config)
-        self.has_graph_conv = config.graph_conv
-        self.mesh_type = config.mesh_type
-
-        if self.has_graph_conv == True:
-            if self.mesh_type == 'hand':
-                self.graph_conv = GraphResBlock(
-                    config.hidden_size, config.hidden_size, mesh_type=self.mesh_type
-                )
-            elif self.mesh_type == 'body':
-                self.graph_conv = GraphResBlock(
-                    config.hidden_size, config.hidden_size, mesh_type=self.mesh_type
-                )
-
-        self.intermediate = BertIntermediate(config)
-        self.output = BertOutput(config)
-
-    def MHA_GCN(self, hidden_states, attention_mask, head_mask=None, history_state=None):
-        attention_outputs = self.attention(hidden_states, attention_mask, head_mask, history_state)
-        attention_output = attention_outputs[0]
-
-        if self.has_graph_conv == True:
-            if self.mesh_type == 'body':
-                joints = attention_output[:, 0:14, :]
-                vertices = attention_output[:, 14:-49, :]
-                img_tokens = attention_output[:, -49:, :]
-
-            elif self.mesh_type == 'hand':
-                joints = attention_output[:, 0:21, :]
-                vertices = attention_output[:, 21:-49, :]
-                img_tokens = attention_output[:, -49:, :]
-
-            vertices = self.graph_conv(vertices)
-            joints_vertices = torch.cat([joints, vertices, img_tokens], dim=1)
-        else:
-            joints_vertices = attention_output
-
-        intermediate_output = self.intermediate(joints_vertices)
-        layer_output = self.output(intermediate_output, joints_vertices)
-        print('layer_output', layer_output.shape)
-        outputs = (layer_output, ) + attention_outputs[1:]    # add attentions if we output them
-        return outputs
-
-    def forward(self, hidden_states, attention_mask, head_mask=None, history_state=None):
-        return self.MHA_GCN(hidden_states, attention_mask, head_mask, history_state)
-
-
-class GraphormerEncoder(nn.Module):
-    def __init__(self, config):
-        super(GraphormerEncoder, self).__init__()
-        self.output_attentions = config.output_attentions
-        self.output_hidden_states = config.output_hidden_states
-        self.layer = nn.ModuleList([
-            GraphormerLayer(config) for _ in range(config.num_hidden_layers)
-        ])
-
-    def forward(self, hidden_states, attention_mask, head_mask=None, encoder_history_states=None):
-        all_hidden_states = ()
-        all_attentions = ()
-        for i, layer_module in enumerate(self.layer):
-            if self.output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states, )
-
-            history_state = None if encoder_history_states is None else encoder_history_states[i]
-            layer_outputs = layer_module(hidden_states, attention_mask, head_mask[i], history_state)
-            hidden_states = layer_outputs[0]
-
-            if self.output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1], )
-
-        # Add last layer
-        if self.output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states, )
-
-        outputs = (hidden_states, )
-        if self.output_hidden_states:
-            outputs = outputs + (all_hidden_states, )
-        if self.output_attentions:
-            outputs = outputs + (all_attentions, )
-
-        return outputs    # outputs, (hidden states), (attentions)
-
-
-class EncoderBlock(BertPreTrainedModel):
-    def __init__(self, config):
-        super(EncoderBlock, self).__init__(config)
-        self.config = config
-        # self.embeddings = BertEmbeddings(config)
-        self.encoder = GraphormerEncoder(config)
-        # self.pooler = BertPooler(config)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-        self.img_dim = config.img_feature_dim
-
-        try:
-            self.use_img_layernorm = config.use_img_layernorm
-        except:
-            self.use_img_layernorm = None
-
-        self.img_embedding = nn.Linear(self.img_dim, self.config.hidden_size, bias=True)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        if self.use_img_layernorm:
-            self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.img_layer_norm_eps)
-
-        self.apply(self.init_weights)
-
-    def _prune_heads(self, heads_to_prune):
-        """ Prunes heads of the model.
-            heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
-            See base class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    def forward(
-        self,
-        img_feats,
-        input_ids=None,
-        token_type_ids=None,
-        attention_mask=None,
-        position_ids=None,
-        head_mask=None
-    ):
-
-        batch_size = len(img_feats)
-        seq_length = len(img_feats[0])
-        input_ids = torch.zeros([batch_size, seq_length], dtype=torch.long).cuda()
-
-        if position_ids is None:
-            position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
-            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
-            print('-------------------')
-            print('position_ids', seq_length, position_ids.shape)
-
-        position_embeddings = self.position_embeddings(position_ids)
-        print(
-            'position_embeddings', position_embeddings.shape, self.config.max_position_embeddings,
-            self.config.hidden_size
-        )
-
-        if attention_mask is None:
-            attention_mask = torch.ones_like(input_ids)
-        else:
-            raise
-
-        if token_type_ids is None:
-            token_type_ids = torch.zeros_like(input_ids)
-        else:
-            raise
-
-        if attention_mask.dim() == 2:
-            extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-        elif attention_mask.dim() == 3:
-            extended_attention_mask = attention_mask.unsqueeze(1)
-        else:
-            raise NotImplementedError
-
-        extended_attention_mask = extended_attention_mask.to(
-            dtype=next(self.parameters()).dtype
-        )    # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-
-        if head_mask is not None:
-            raise
-            if head_mask.dim() == 1:
-                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
-                head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
-            elif head_mask.dim() == 2:
-                head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(
-                    -1
-                )    # We can specify head_mask for each layer
-            head_mask = head_mask.to(
-                dtype=next(self.parameters()).dtype
-            )    # switch to fload if need + fp16 compatibility
-        else:
-            head_mask = [None] * self.config.num_hidden_layers
-
-        # Project input token features to have spcified hidden size
-        print('img_feats', img_feats.shape)
-        img_embedding_output = self.img_embedding(img_feats)
-        print('img_embedding_output', img_embedding_output.shape)
-
-        # We empirically observe that adding an additional learnable position embedding leads to more stable training
-        embeddings = position_embeddings + img_embedding_output
-
-        if self.use_img_layernorm:
-            embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-
-        print('extended_attention_mask', extended_attention_mask.shape)
-        encoder_outputs = self.encoder(embeddings, extended_attention_mask, head_mask=head_mask)
-        sequence_output = encoder_outputs[0]
-
-        outputs = (sequence_output, )
-        if self.config.output_hidden_states:
-            all_hidden_states = encoder_outputs[1]
-            outputs = outputs + (all_hidden_states, )
-        if self.config.output_attentions:
-            all_attentions = encoder_outputs[-1]
-            outputs = outputs + (all_attentions, )
-
-        return outputs
-
-
-class Graphormer(BertPreTrainedModel):
-    '''
-    The archtecture of a transformer encoder block we used in Graphormer
-    '''
-    def __init__(self, config):
-        super(Graphormer, self).__init__(config)
-        self.config = config
-        self.bert = EncoderBlock(config)
-        self.cls_head = nn.Linear(config.hidden_size, self.config.output_feature_dim)
-        self.residual = nn.Linear(config.img_feature_dim, self.config.output_feature_dim)
-        self.apply(self.init_weights)
-
-    def forward(
-        self,
-        img_feats,
-        input_ids=None,
-        token_type_ids=None,
-        attention_mask=None,
-        masked_lm_labels=None,
-        next_sentence_label=None,
-        position_ids=None,
-        head_mask=None
-    ):
-        '''
-        # self.bert has three outputs
-        # predictions[0]: output tokens
-        # predictions[1]: all_hidden_states, if enable "self.config.output_hidden_states"
-        # predictions[2]: attentions, if enable "self.config.output_attentions"
-        '''
-        predictions = self.bert(
-            img_feats=img_feats,
-            input_ids=input_ids,
-            position_ids=position_ids,
-            token_type_ids=token_type_ids,
-            attention_mask=attention_mask,
-            head_mask=head_mask
-        )
-
-        # We use "self.cls_head" to perform dimensionality reduction. We don't use it for classification.
-        pred_score = self.cls_head(predictions[0])
-        res_img_feats = self.residual(img_feats)
-        pred_score = pred_score + res_img_feats
-        print('pred_score', pred_score.shape)
-
-        if self.config.output_attentions and self.config.output_hidden_states:
-            return pred_score, predictions[1], predictions[-1]
-        else:
-            return pred_score
diff --git a/spaces/Yuliang/ICON/lib/common/config.py b/spaces/Yuliang/ICON/lib/common/config.py
deleted file mode 100644
index 2a69e8ca0ab7e42ae38055bb0b31e07ba42a4aed..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ICON/lib/common/config.py
+++ /dev/null
@@ -1,218 +0,0 @@
-
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-from yacs.config import CfgNode as CN
-import os
-
-_C = CN(new_allowed=True)
-
-# needed by trainer
-_C.name = 'default'
-_C.gpus = [0]
-_C.test_gpus = [1]
-_C.root = "./data/"
-_C.ckpt_dir = './data/ckpt/'
-_C.resume_path = ''
-_C.normal_path = ''
-_C.corr_path = ''
-_C.results_path = './data/results/'
-_C.projection_mode = 'orthogonal'
-_C.num_views = 1
-_C.sdf = False
-_C.sdf_clip = 5.0
-
-_C.lr_G = 1e-3
-_C.lr_C = 1e-3
-_C.lr_N = 2e-4
-_C.weight_decay = 0.0
-_C.momentum = 0.0
-_C.optim = 'RMSprop'
-_C.schedule = [5, 10, 15]
-_C.gamma = 0.1
-
-_C.overfit = False
-_C.resume = False
-_C.test_mode = False
-_C.test_uv = False
-_C.draw_geo_thres = 0.60
-_C.num_sanity_val_steps = 2
-_C.fast_dev = 0
-_C.get_fit = False
-_C.agora = False
-_C.optim_cloth = False
-_C.optim_body = False
-_C.mcube_res = 256
-_C.clean_mesh = True
-_C.remesh = False
-
-_C.batch_size = 4
-_C.num_threads = 8
-
-_C.num_epoch = 10
-_C.freq_plot = 0.01
-_C.freq_show_train = 0.1
-_C.freq_show_val = 0.2
-_C.freq_eval = 0.5
-_C.accu_grad_batch = 4
-
-_C.test_items = ['sv', 'mv', 'mv-fusion', 'hybrid', 'dc-pred', 'gt']
-
-_C.net = CN()
-_C.net.gtype = 'HGPIFuNet'
-_C.net.ctype = 'resnet18'
-_C.net.classifierIMF = 'MultiSegClassifier'
-_C.net.netIMF = 'resnet18'
-_C.net.norm = 'group'
-_C.net.norm_mlp = 'group'
-_C.net.norm_color = 'group'
-_C.net.hg_down = 'ave_pool'
-_C.net.num_views = 1
-
-# kernel_size, stride, dilation, padding
-
-_C.net.conv1 = [7, 2, 1, 3]
-_C.net.conv3x3 = [3, 1, 1, 1]
-
-_C.net.num_stack = 4
-_C.net.num_hourglass = 2
-_C.net.hourglass_dim = 256
-_C.net.voxel_dim = 32
-_C.net.resnet_dim = 120
-_C.net.mlp_dim = [320, 1024, 512, 256, 128, 1]
-_C.net.mlp_dim_knn = [320, 1024, 512, 256, 128, 3]
-_C.net.mlp_dim_color = [513, 1024, 512, 256, 128, 3]
-_C.net.mlp_dim_multiseg = [1088, 2048, 1024, 500]
-_C.net.res_layers = [2, 3, 4]
-_C.net.filter_dim = 256
-_C.net.smpl_dim = 3
-
-_C.net.cly_dim = 3
-_C.net.soft_dim = 64
-_C.net.z_size = 200.0
-_C.net.N_freqs = 10
-_C.net.geo_w = 0.1
-_C.net.norm_w = 0.1
-_C.net.dc_w = 0.1
-_C.net.C_cat_to_G = False
-
-_C.net.skip_hourglass = True
-_C.net.use_tanh = True
-_C.net.soft_onehot = True
-_C.net.no_residual = True
-_C.net.use_attention = False
-
-_C.net.prior_type = "sdf"
-_C.net.smpl_feats = ['sdf', 'cmap', 'norm', 'vis']
-_C.net.use_filter = True
-_C.net.use_cc = False
-_C.net.use_PE = False
-_C.net.use_IGR = False
-_C.net.in_geo = ()
-_C.net.in_nml = ()
-
-_C.dataset = CN()
-_C.dataset.root = ''
-_C.dataset.set_splits = [0.95, 0.04]
-_C.dataset.types = [
-    "3dpeople", "axyz", "renderpeople", "renderpeople_p27", "humanalloy"
-]
-_C.dataset.scales = [1.0, 100.0, 1.0, 1.0, 100.0 / 39.37]
-_C.dataset.rp_type = "pifu900"
-_C.dataset.th_type = 'train'
-_C.dataset.input_size = 512
-_C.dataset.rotation_num = 3
-_C.dataset.num_precomp = 10  # Number of segmentation classifiers
-_C.dataset.num_multiseg = 500  # Number of categories per classifier
-_C.dataset.num_knn = 10  # for loss/error
-_C.dataset.num_knn_dis = 20  # for accuracy
-_C.dataset.num_verts_max = 20000
-_C.dataset.zray_type = False
-_C.dataset.online_smpl = False
-_C.dataset.noise_type = ['z-trans', 'pose', 'beta']
-_C.dataset.noise_scale = [0.0, 0.0, 0.0]
-_C.dataset.num_sample_geo = 10000
-_C.dataset.num_sample_color = 0
-_C.dataset.num_sample_seg = 0
-_C.dataset.num_sample_knn = 10000
-
-_C.dataset.sigma_geo = 5.0
-_C.dataset.sigma_color = 0.10
-_C.dataset.sigma_seg = 0.10
-_C.dataset.thickness_threshold = 20.0
-_C.dataset.ray_sample_num = 2
-_C.dataset.semantic_p = False
-_C.dataset.remove_outlier = False
-
-_C.dataset.train_bsize = 1.0
-_C.dataset.val_bsize = 1.0
-_C.dataset.test_bsize = 1.0
-
-
-def get_cfg_defaults():
-    """Get a yacs CfgNode object with default values for my_project."""
-    # Return a clone so that the defaults will not be altered
-    # This is for the "local variable" use pattern
-    return _C.clone()
-
-
-# Alternatively, provide a way to import the defaults as
-# a global singleton:
-cfg = _C  # users can `from config import cfg`
-
-# cfg = get_cfg_defaults()
-# cfg.merge_from_file('./configs/example.yaml')
-
-# # Now override from a list (opts could come from the command line)
-# opts = ['dataset.root', './data/XXXX', 'learning_rate', '1e-2']
-# cfg.merge_from_list(opts)
-
-
-def update_cfg(cfg_file):
-    # cfg = get_cfg_defaults()
-    _C.merge_from_file(cfg_file)
-    # return cfg.clone()
-    return _C
-
-
-def parse_args(args):
-    cfg_file = args.cfg_file
-    if args.cfg_file is not None:
-        cfg = update_cfg(args.cfg_file)
-    else:
-        cfg = get_cfg_defaults()
-
-    # if args.misc is not None:
-    #     cfg.merge_from_list(args.misc)
-
-    return cfg
-
-
-def parse_args_extend(args):
-    if args.resume:
-        if not os.path.exists(args.log_dir):
-            raise ValueError(
-                'Experiment are set to resume mode, but log directory does not exist.'
-            )
-
-        # load log's cfg
-        cfg_file = os.path.join(args.log_dir, 'cfg.yaml')
-        cfg = update_cfg(cfg_file)
-
-        if args.misc is not None:
-            cfg.merge_from_list(args.misc)
-    else:
-        parse_args(args)
diff --git a/spaces/ZeroTwo3/WavJourney/examples/examples.py b/spaces/ZeroTwo3/WavJourney/examples/examples.py
deleted file mode 100644
index 9a44a090cadc95d594eac3c2d47c1ac7a2c10466..0000000000000000000000000000000000000000
--- a/spaces/ZeroTwo3/WavJourney/examples/examples.py
+++ /dev/null
@@ -1,87 +0,0 @@
-
-example1 = {
-    'text': "An introduction to AI-assisted audio content creation.",
-    'table_script': """
-| Audio Type   | Layout     | ID | Character | Action | Volume | Description                                                      | Length |
-|--------------|------------|----|-----------|--------|--------|------------------------------------------------------------------|--------|
-| music        | background | 1  | N/A       | begin  | -35    | Inspirational technology-themed music                            | Auto   |
-| speech       | foreground | N/A| Narrator  | N/A    | -15    | Welcome to the future of audio content creation.                 | Auto   |
-| sound_effect | foreground | N/A| N/A       | N/A    | -35    | Digital startup sound                                            | 2      |
-| speech       | foreground | N/A| Narrator  | N/A    | -15    | With evolving technology, we are introducing AI-assisted tools for pristine audio production. | Auto |
-| sound_effect | foreground | N/A| N/A       | N/A    | -35    | Keyboard typing noise                                            | 3      |
-| speech       | foreground | N/A| Narrator  | N/A    | -15    | Imagine crafting audio content with the power of AI at your fingertips. | Auto |
-| sound_effect | background | 2  | N/A       | begin  | -35    | Ambiance of a busy control room                                   | Auto   |
-| speech       | foreground | N/A| Narrator  | N/A    | -15    | Enhanced quality, efficient production and limitless creativity, all under one roof. | Auto |
-| sound_effect | background | 2  | N/A       | end    | N/A    | N/A                                                              | Auto   |
-| speech       | foreground | N/A| Narrator  | N/A    | -15    | Unleash your potential with AI-assisted audio content creation.  | Auto   |
-| music        | background | 1  | N/A       | end    | N/A    | N/A                                                              | Auto   |
-
-""",
-    'table_voice': """
-| Character   | Voice     |
-|-------------|-----------|
-| Narrator    | News_Male_En |
-
-""",
-    'wav_file': 'examples/1.mp4',
-}
-
-example2 = {
-    'text': "A couple dating in a cafe.",
-    'table_script': """
-| Audio Type   | Layout     | ID | Character | Action | Volume | Description                                   | Length |
-|--------------|------------|----|-----------|--------|--------|-----------------------------------------------|--------|
-| sound_effect | background | 1  | N/A       | begin  | -35    | Soft chattering in a cafe                     | Auto   |
-| sound_effect | background | 2  | N/A       | begin  | -38    | Coffee brewing noises                         | Auto   |
-| music        | background | 3  | N/A       | begin  | -35    | Soft jazz playing in the background           | Auto   |
-| speech       | foreground | N/A| Man       | N/A    | -15    | It’s really nice to finally get out and relax a little, isn’t it? | Auto |
-| speech       | foreground | N/A| Woman     | N/A    | -15    | I know, right? We should do this more often.  | Auto   |
-| sound_effect | background | 2  | N/A       | end    | N/A    | N/A                                           | Auto   |
-| speech       | foreground | N/A| Man       | N/A    | -15    | Here’s your coffee, just as you like it.      | Auto   |
-| speech       | foreground | N/A| Woman     | N/A    | -15    | Thank you, it smells wonderful.               | Auto   |
-| music        | background | 3  | N/A       | end    | N/A    | N/A                                           | Auto   |
-| sound_effect | background | 1  | N/A       | end    | N/A    | N/A                                           | Auto   |
-
-""",
-    'table_voice': """
-| Character   | Voice     |
-|-------------|-----------|
-| Man         | Male1_En    |
-| Woman       | Female1_En   |
-
-""",
-    'wav_file': 'examples/2.mp4',
-}
-
-
-example3 = {
-    'text': "A child is participating in a farting contest.",
-    'table_script': """
-| Audio Type   | Layout     | ID | Character | Action | Volume | Description                                          | Length |
-|--------------|------------|----|-----------|--------|--------|------------------------------------------------------|--------|
-| sound_effect | background | 1  | N/A       | begin  | -35    | Outdoor park ambiance, people chattering             | Auto   |
-| music        | background | 2  | N/A       | begin  | -35    | Light comedy theme music, quirky                     | Auto   |
-| speech       | foreground | N/A| Host      | N/A    | -15    | Welcome to the annual Fart Competition.              | Auto   |
-| speech       | foreground | N/A| Host      | N/A    | -15    | Now, let’s welcome our youngest participant.         | Auto   |
-| sound_effect | foreground | N/A| N/A       | N/A    | -35    | Clapping sound                                       | 2      |
-| speech       | foreground | N/A| Child     | N/A    | -15    | Hi, I’m excited to be here.                          | Auto   |
-| sound_effect | foreground | N/A| N/A       | N/A    | -35    | Short, cartoonish duration of a fart sound           | 4      |
-| sound_effect | foreground | N/A| N/A       | N/A    | -35    | Audience laughing and applauding                     | 2      |
-| speech       | foreground | N/A| Host      | N/A    | -15    | Wow, that was impressive! Let’s give another round of applause! | Auto |
-| sound_effect | foreground | N/A| N/A       | N/A    | -35    | Audience clapping and cheering                       | 3      |
-| music        | background | 2  | N/A       | end    | N/A    | N/A                                                  | Auto   |
-| sound_effect | background | 1  | N/A       | end    | N/A    | N/A                                                  | Auto   |
-""",
-    'table_voice': """
-| Character   | Voice     |
-|-------------|-----------|
-| Host        | Male1_En     |
-| Child       | Child_En     |
-
-""",
-    'wav_file': 'examples/3.mp4',
-}
-
-
-
-examples = [example1, example2, example3]
\ No newline at end of file
diff --git a/spaces/Zpwang-AI/InsultingLanguageDetection/display_v3/2023-04-14_16-59-07/epoch6-f1score0.47.ckpt/zero_to_fp32.py b/spaces/Zpwang-AI/InsultingLanguageDetection/display_v3/2023-04-14_16-59-07/epoch6-f1score0.47.ckpt/zero_to_fp32.py
deleted file mode 100644
index f00e256bb7879727ab1d785173f4aac6967876da..0000000000000000000000000000000000000000
--- a/spaces/Zpwang-AI/InsultingLanguageDetection/display_v3/2023-04-14_16-59-07/epoch6-f1score0.47.ckpt/zero_to_fp32.py
+++ /dev/null
@@ -1,483 +0,0 @@
-#!/usr/bin/env python
-'''Copyright The Microsoft DeepSpeed Team'''
-
-# This script extracts fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints. It gets
-# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
-# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
-# application.
-#
-# example: python zero_to_fp32.py . pytorch_model.bin
-
-import argparse
-import torch
-import glob
-import math
-import os
-import re
-from collections import OrderedDict
-
-# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
-# DeepSpeed data structures it has to be available in the current python environment.
-from deepspeed.utils import logger
-from deepspeed.checkpoint.constants import (DS_VERSION,
-                                            OPTIMIZER_STATE_DICT,
-                                            SINGLE_PARTITION_OF_FP32_GROUPS,
-                                            FP32_FLAT_GROUPS,
-                                            ZERO_STAGE,
-                                            PARTITION_COUNT,
-                                            PARAM_SHAPES,
-                                            BUFFER_NAMES)
-
-debug = 0
-
-# load to cpu
-device = torch.device('cpu')
-
-
-def atoi(text):
-    return int(text) if text.isdigit() else text
-
-
-def natural_keys(text):
-    '''
-    alist.sort(key=natural_keys) sorts in human order
-    http://nedbatchelder.com/blog/200712/human_sorting.html
-    (See Toothy's implementation in the comments)
-    '''
-    return [atoi(c) for c in re.split(r'(\d+)', text)]
-
-
-def get_model_state_file(checkpoint_dir, zero_stage):
-    if not os.path.isdir(checkpoint_dir):
-        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
-
-    # there should be only one file
-    if zero_stage == 2:
-        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
-    elif zero_stage == 3:
-        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
-
-    if not os.path.exists(file):
-        raise FileNotFoundError(f"can't find model states file at '{file}'")
-
-    return file
-
-
-def get_optim_files(checkpoint_dir):
-    # XXX: need to test that this simple glob rule works for multi-node setup too
-    optim_files = sorted(glob.glob(os.path.join(checkpoint_dir,
-                                                "*_optim_states.pt")),
-                         key=natural_keys)
-
-    if len(optim_files) == 0:
-        raise FileNotFoundError(
-            f"can't find '*_optim_states.pt' files in directory '{checkpoint_dir}'")
-
-    return optim_files
-
-
-def parse_model_state(file):
-    state_dict = torch.load(file, map_location=device)
-
-    if BUFFER_NAMES not in state_dict:
-        raise ValueError(f"{file} is not a model state checkpoint")
-    buffer_names = state_dict[BUFFER_NAMES]
-    if debug:
-        print("Found buffers:", buffer_names)
-
-    # recover just the buffers while restoring them to fp32 if they were saved in fp16
-    buffers = {
-        k: v.float()
-        for k,
-        v in state_dict["module"].items() if k in buffer_names
-    }
-    param_shapes = state_dict[PARAM_SHAPES]
-
-    ds_version = state_dict.get(DS_VERSION, None)
-
-    return buffers, param_shapes, ds_version
-
-
-def parse_optim_states(files, ds_checkpoint_dir):
-
-    total_files = len(files)
-    state_dicts = []
-    for f in files:
-        state_dicts.append(torch.load(f, map_location=device))
-
-    if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
-        raise ValueError(f"{files[0]} is not a zero checkpoint")
-    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
-    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
-
-    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
-    # parameters can be different from data parallelism for non-expert parameters. So we can just
-    # use the max of the partition_count to get the dp world_size.
-
-    if type(world_size) is list:
-        world_size = max(world_size)
-
-    if world_size != total_files:
-        raise ValueError(
-            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
-            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
-        )
-
-    # the groups are named differently in each stage
-    if zero_stage == 2:
-        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
-    elif zero_stage == 3:
-        fp32_groups_key = FP32_FLAT_GROUPS
-    else:
-        raise ValueError(f"unknown zero stage {zero_stage}")
-
-    if zero_stage == 2:
-        fp32_flat_groups = [
-            state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key]
-            for i in range(len(state_dicts))
-        ]
-    elif zero_stage == 3:
-        # if there is more than one param group, there will be multiple flattened tensors - one
-        # flattened tensor per group - for simplicity merge them into a single tensor
-        #
-        # XXX: could make the script more memory efficient for when there are multiple groups - it
-        # will require matching the sub-lists of param_shapes for each param group flattened tensor
-
-        fp32_flat_groups = [
-            torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key],
-                      0) for i in range(len(state_dicts))
-        ]
-
-    return zero_stage, world_size, fp32_flat_groups
-
-
-def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
-    """
-    Returns fp32 state_dict reconstructed from ds checkpoint
-
-    Args:
-        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
-
-    """
-    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
-
-    optim_files = get_optim_files(ds_checkpoint_dir)
-    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
-    print(
-        f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
-
-    model_file = get_model_state_file(ds_checkpoint_dir, zero_stage)
-    buffers, param_shapes, ds_version = parse_model_state(model_file)
-    print(f'Parsing checkpoint created by deepspeed=={ds_version}')
-
-    if zero_stage == 2:
-        return _get_fp32_state_dict_from_zero2_checkpoint(world_size,
-                                                          param_shapes,
-                                                          fp32_flat_groups,
-                                                          buffers)
-    elif zero_stage == 3:
-        return _get_fp32_state_dict_from_zero3_checkpoint(world_size,
-                                                          param_shapes,
-                                                          fp32_flat_groups,
-                                                          buffers)
-
-
-def _get_fp32_state_dict_from_zero2_checkpoint(world_size,
-                                               param_shapes,
-                                               fp32_flat_groups,
-                                               buffers):
-
-    # Reconstruction protocol:
-    #
-    # XXX: document this
-
-    if debug:
-        for i in range(world_size):
-            for j in range(len(fp32_flat_groups[0])):
-                print(
-                    f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
-
-    # XXX: memory usage doubles here (zero2)
-    num_param_groups = len(fp32_flat_groups[0])
-    merged_single_partition_of_fp32_groups = []
-    for i in range(num_param_groups):
-        merged_partitions = [sd[i] for sd in fp32_flat_groups]
-        full_single_fp32_vector = torch.cat(merged_partitions, 0)
-        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
-    avail_numel = sum([
-        full_single_fp32_vector.numel()
-        for full_single_fp32_vector in merged_single_partition_of_fp32_groups
-    ])
-
-    if debug:
-        wanted_params = sum([len(shapes) for shapes in param_shapes])
-        wanted_numel = sum(
-            [sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
-        # not asserting if there is a mismatch due to possible padding
-        print(f"Have {avail_numel} numels to process.")
-        print(f"Need {wanted_numel} numels in {wanted_params} params.")
-
-    state_dict = OrderedDict()
-
-    # buffers
-    state_dict.update(buffers)
-    if debug:
-        print(f"added {len(buffers)} buffers")
-
-    # params
-    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
-    # out-of-core computing solution
-    total_numel = 0
-    total_params = 0
-    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
-        offset = 0
-        avail_numel = full_single_fp32_vector.numel()
-        for name, shape in shapes.items():
-
-            unpartitioned_numel = shape.numel()
-            total_numel += unpartitioned_numel
-            total_params += 1
-
-            if debug:
-                print(
-                    f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} "
-                )
-            state_dict[name] = full_single_fp32_vector.narrow(
-                0,
-                offset,
-                unpartitioned_numel).view(shape)
-            offset += unpartitioned_numel
-
-        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
-        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
-        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
-        # live optimizer object, so we are checking that the numbers are within the right range
-        align_to = 2 * world_size
-
-        def zero2_align(x):
-            return align_to * math.ceil(x / align_to)
-
-        if debug:
-            print(f"original offset={offset}, avail_numel={avail_numel}")
-
-        offset = zero2_align(offset)
-        avail_numel = zero2_align(avail_numel)
-
-        if debug:
-            print(f"aligned  offset={offset}, avail_numel={avail_numel}")
-
-        # Sanity check
-        if offset != avail_numel:
-            raise ValueError(
-                f"consumed {offset} numels out of {avail_numel} - something is wrong")
-
-    print(
-        f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements"
-    )
-
-    return state_dict
-
-
-def zero3_partitioned_param_info(unpartitioned_numel, world_size):
-    remainder = unpartitioned_numel % world_size
-    padding_numel = (world_size - remainder) if remainder else 0
-    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
-    return partitioned_numel, padding_numel
-
-
-def _get_fp32_state_dict_from_zero3_checkpoint(world_size,
-                                               param_shapes,
-                                               fp32_flat_groups,
-                                               buffers):
-
-    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
-    # param, re-consolidating each param, while dealing with padding if any
-
-    avail_numel = fp32_flat_groups[0].numel() * world_size
-    # merge list of dicts, preserving order
-    param_shapes = {k: v for d in param_shapes for k, v in d.items()}
-
-    if debug:
-        for i in range(world_size):
-            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
-
-        wanted_params = len(param_shapes)
-        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
-        # not asserting if there is a mismatch due to possible padding
-        print(f"Have {avail_numel} numels to process.")
-        print(f"Need {wanted_numel} numels in {wanted_params} params.")
-
-    state_dict = OrderedDict()
-
-    # buffers
-    state_dict.update(buffers)
-    if debug:
-        print(f"added {len(buffers)} buffers")
-
-    # params
-    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
-    # out-of-core computing solution
-    offset = 0
-    total_numel = 0
-    total_params = 0
-    for name, shape in param_shapes.items():
-
-        unpartitioned_numel = shape.numel()
-        total_numel += unpartitioned_numel
-        total_params += 1
-
-        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
-
-        if debug:
-            print(
-                f"{total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
-            )
-
-        # XXX: memory usage doubles here
-        state_dict[name] = torch.cat(
-            tuple(fp32_flat_groups[i].narrow(0,
-                                             offset,
-                                             partitioned_numel)
-                  for i in range(world_size)),
-            0).narrow(0,
-                      0,
-                      unpartitioned_numel).view(shape)
-        offset += partitioned_numel
-
-    offset *= world_size
-
-    # Sanity check
-    if offset != avail_numel:
-        raise ValueError(
-            f"consumed {offset} numels out of {avail_numel} - something is wrong")
-
-    print(
-        f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements"
-    )
-
-    return state_dict
-
-
-def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
-    """
-    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
-    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
-    via a model hub.
-
-    Args:
-        - ``checkpoint_dir``: path to the desired checkpoint folder
-        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
-
-    Returns:
-        - pytorch ``state_dict``
-
-    Note: this approach may not work if your application doesn't have sufficient free CPU memory and
-    you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
-    the checkpoint.
-
-    A typical usage might be ::
-
-        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
-        # do the training and checkpoint saving
-        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
-        model = model.cpu() # move to cpu
-        model.load_state_dict(state_dict)
-        # submit to model hub or save the model to share with others
-
-    In this example the ``model`` will no longer be usable in the deepspeed context of the same
-    application. i.e. you will need to re-initialize the deepspeed engine, since
-    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
-    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
-
-    """
-    if tag is None:
-        latest_path = os.path.join(checkpoint_dir, 'latest')
-        if os.path.isfile(latest_path):
-            with open(latest_path, 'r') as fd:
-                tag = fd.read().strip()
-        else:
-            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
-
-    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
-
-    if not os.path.isdir(ds_checkpoint_dir):
-        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
-
-    return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
-
-
-def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
-    """
-    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
-    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
-
-    Args:
-        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
-        - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
-        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
-    """
-
-    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
-    print(f"Saving fp32 state dict to {output_file}")
-    torch.save(state_dict, output_file)
-
-
-def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
-    """
-    1. Put the provided model to cpu
-    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
-    3. Load it into the provided model
-
-    Args:
-        - ``model``: the model object to update
-        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
-        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
-
-    Returns:
-        - ``model`: modified model
-
-    Make sure you have plenty of CPU memory available before you call this function. If you don't
-    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
-    conveniently placed for you in the checkpoint folder.
-
-    A typical usage might be ::
-
-        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
-        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
-        # submit to model hub or save the model to share with others
-
-    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
-    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
-    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
-    """
-    logger.info(f"Extracting fp32 weights")
-    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
-
-    logger.info(f"Overwriting model with fp32 weights")
-    model = model.cpu()
-    model.load_state_dict(state_dict, strict=False)
-
-    return model
-
-
-if __name__ == "__main__":
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "checkpoint_dir",
-        type=str,
-        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
-    parser.add_argument(
-        "output_file",
-        type=str,
-        help=
-        "path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)"
-    )
-    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
-    args = parser.parse_args()
-
-    debug = args.debug
-
-    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/bricks/transformer.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/bricks/transformer.py
deleted file mode 100644
index e61ae0dd941a7be00b3e41a3de833ec50470a45f..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/bricks/transformer.py
+++ /dev/null
@@ -1,595 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import warnings
-
-import torch
-import torch.nn as nn
-
-from annotator.uniformer.mmcv import ConfigDict, deprecated_api_warning
-from annotator.uniformer.mmcv.cnn import Linear, build_activation_layer, build_norm_layer
-from annotator.uniformer.mmcv.runner.base_module import BaseModule, ModuleList, Sequential
-from annotator.uniformer.mmcv.utils import build_from_cfg
-from .drop import build_dropout
-from .registry import (ATTENTION, FEEDFORWARD_NETWORK, POSITIONAL_ENCODING,
-                       TRANSFORMER_LAYER, TRANSFORMER_LAYER_SEQUENCE)
-
-# Avoid BC-breaking of importing MultiScaleDeformableAttention from this file
-try:
-    from annotator.uniformer.mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention  # noqa F401
-    warnings.warn(
-        ImportWarning(
-            '``MultiScaleDeformableAttention`` has been moved to '
-            '``mmcv.ops.multi_scale_deform_attn``, please change original path '  # noqa E501
-            '``from annotator.uniformer.mmcv.cnn.bricks.transformer import MultiScaleDeformableAttention`` '  # noqa E501
-            'to ``from annotator.uniformer.mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention`` '  # noqa E501
-        ))
-
-except ImportError:
-    warnings.warn('Fail to import ``MultiScaleDeformableAttention`` from '
-                  '``mmcv.ops.multi_scale_deform_attn``, '
-                  'You should install ``mmcv-full`` if you need this module. ')
-
-
-def build_positional_encoding(cfg, default_args=None):
-    """Builder for Position Encoding."""
-    return build_from_cfg(cfg, POSITIONAL_ENCODING, default_args)
-
-
-def build_attention(cfg, default_args=None):
-    """Builder for attention."""
-    return build_from_cfg(cfg, ATTENTION, default_args)
-
-
-def build_feedforward_network(cfg, default_args=None):
-    """Builder for feed-forward network (FFN)."""
-    return build_from_cfg(cfg, FEEDFORWARD_NETWORK, default_args)
-
-
-def build_transformer_layer(cfg, default_args=None):
-    """Builder for transformer layer."""
-    return build_from_cfg(cfg, TRANSFORMER_LAYER, default_args)
-
-
-def build_transformer_layer_sequence(cfg, default_args=None):
-    """Builder for transformer encoder and transformer decoder."""
-    return build_from_cfg(cfg, TRANSFORMER_LAYER_SEQUENCE, default_args)
-
-
-@ATTENTION.register_module()
-class MultiheadAttention(BaseModule):
-    """A wrapper for ``torch.nn.MultiheadAttention``.
-
-    This module implements MultiheadAttention with identity connection,
-    and positional encoding  is also passed as input.
-
-    Args:
-        embed_dims (int): The embedding dimension.
-        num_heads (int): Parallel attention heads.
-        attn_drop (float): A Dropout layer on attn_output_weights.
-            Default: 0.0.
-        proj_drop (float): A Dropout layer after `nn.MultiheadAttention`.
-            Default: 0.0.
-        dropout_layer (obj:`ConfigDict`): The dropout_layer used
-            when adding the shortcut.
-        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
-            Default: None.
-        batch_first (bool): When it is True,  Key, Query and Value are shape of
-            (batch, n, embed_dim), otherwise (n, batch, embed_dim).
-             Default to False.
-    """
-
-    def __init__(self,
-                 embed_dims,
-                 num_heads,
-                 attn_drop=0.,
-                 proj_drop=0.,
-                 dropout_layer=dict(type='Dropout', drop_prob=0.),
-                 init_cfg=None,
-                 batch_first=False,
-                 **kwargs):
-        super(MultiheadAttention, self).__init__(init_cfg)
-        if 'dropout' in kwargs:
-            warnings.warn('The arguments `dropout` in MultiheadAttention '
-                          'has been deprecated, now you can separately '
-                          'set `attn_drop`(float), proj_drop(float), '
-                          'and `dropout_layer`(dict) ')
-            attn_drop = kwargs['dropout']
-            dropout_layer['drop_prob'] = kwargs.pop('dropout')
-
-        self.embed_dims = embed_dims
-        self.num_heads = num_heads
-        self.batch_first = batch_first
-
-        self.attn = nn.MultiheadAttention(embed_dims, num_heads, attn_drop,
-                                          **kwargs)
-
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.dropout_layer = build_dropout(
-            dropout_layer) if dropout_layer else nn.Identity()
-
-    @deprecated_api_warning({'residual': 'identity'},
-                            cls_name='MultiheadAttention')
-    def forward(self,
-                query,
-                key=None,
-                value=None,
-                identity=None,
-                query_pos=None,
-                key_pos=None,
-                attn_mask=None,
-                key_padding_mask=None,
-                **kwargs):
-        """Forward function for `MultiheadAttention`.
-
-        **kwargs allow passing a more general data flow when combining
-        with other operations in `transformerlayer`.
-
-        Args:
-            query (Tensor): The input query with shape [num_queries, bs,
-                embed_dims] if self.batch_first is False, else
-                [bs, num_queries embed_dims].
-            key (Tensor): The key tensor with shape [num_keys, bs,
-                embed_dims] if self.batch_first is False, else
-                [bs, num_keys, embed_dims] .
-                If None, the ``query`` will be used. Defaults to None.
-            value (Tensor): The value tensor with same shape as `key`.
-                Same in `nn.MultiheadAttention.forward`. Defaults to None.
-                If None, the `key` will be used.
-            identity (Tensor): This tensor, with the same shape as x,
-                will be used for the identity link.
-                If None, `x` will be used. Defaults to None.
-            query_pos (Tensor): The positional encoding for query, with
-                the same shape as `x`. If not None, it will
-                be added to `x` before forward function. Defaults to None.
-            key_pos (Tensor): The positional encoding for `key`, with the
-                same shape as `key`. Defaults to None. If not None, it will
-                be added to `key` before forward function. If None, and
-                `query_pos` has the same shape as `key`, then `query_pos`
-                will be used for `key_pos`. Defaults to None.
-            attn_mask (Tensor): ByteTensor mask with shape [num_queries,
-                num_keys]. Same in `nn.MultiheadAttention.forward`.
-                Defaults to None.
-            key_padding_mask (Tensor): ByteTensor with shape [bs, num_keys].
-                Defaults to None.
-
-        Returns:
-            Tensor: forwarded results with shape
-                [num_queries, bs, embed_dims]
-                if self.batch_first is False, else
-                [bs, num_queries embed_dims].
-        """
-
-        if key is None:
-            key = query
-        if value is None:
-            value = key
-        if identity is None:
-            identity = query
-        if key_pos is None:
-            if query_pos is not None:
-                # use query_pos if key_pos is not available
-                if query_pos.shape == key.shape:
-                    key_pos = query_pos
-                else:
-                    warnings.warn(f'position encoding of key is'
-                                  f'missing in {self.__class__.__name__}.')
-        if query_pos is not None:
-            query = query + query_pos
-        if key_pos is not None:
-            key = key + key_pos
-
-        # Because the dataflow('key', 'query', 'value') of
-        # ``torch.nn.MultiheadAttention`` is (num_query, batch,
-        # embed_dims), We should adjust the shape of dataflow from
-        # batch_first (batch, num_query, embed_dims) to num_query_first
-        # (num_query ,batch, embed_dims), and recover ``attn_output``
-        # from num_query_first to batch_first.
-        if self.batch_first:
-            query = query.transpose(0, 1)
-            key = key.transpose(0, 1)
-            value = value.transpose(0, 1)
-
-        out = self.attn(
-            query=query,
-            key=key,
-            value=value,
-            attn_mask=attn_mask,
-            key_padding_mask=key_padding_mask)[0]
-
-        if self.batch_first:
-            out = out.transpose(0, 1)
-
-        return identity + self.dropout_layer(self.proj_drop(out))
-
-
-@FEEDFORWARD_NETWORK.register_module()
-class FFN(BaseModule):
-    """Implements feed-forward networks (FFNs) with identity connection.
-
-    Args:
-        embed_dims (int): The feature dimension. Same as
-            `MultiheadAttention`. Defaults: 256.
-        feedforward_channels (int): The hidden dimension of FFNs.
-            Defaults: 1024.
-        num_fcs (int, optional): The number of fully-connected layers in
-            FFNs. Default: 2.
-        act_cfg (dict, optional): The activation config for FFNs.
-            Default: dict(type='ReLU')
-        ffn_drop (float, optional): Probability of an element to be
-            zeroed in FFN. Default 0.0.
-        add_identity (bool, optional): Whether to add the
-            identity connection. Default: `True`.
-        dropout_layer (obj:`ConfigDict`): The dropout_layer used
-            when adding the shortcut.
-        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
-            Default: None.
-    """
-
-    @deprecated_api_warning(
-        {
-            'dropout': 'ffn_drop',
-            'add_residual': 'add_identity'
-        },
-        cls_name='FFN')
-    def __init__(self,
-                 embed_dims=256,
-                 feedforward_channels=1024,
-                 num_fcs=2,
-                 act_cfg=dict(type='ReLU', inplace=True),
-                 ffn_drop=0.,
-                 dropout_layer=None,
-                 add_identity=True,
-                 init_cfg=None,
-                 **kwargs):
-        super(FFN, self).__init__(init_cfg)
-        assert num_fcs >= 2, 'num_fcs should be no less ' \
-            f'than 2. got {num_fcs}.'
-        self.embed_dims = embed_dims
-        self.feedforward_channels = feedforward_channels
-        self.num_fcs = num_fcs
-        self.act_cfg = act_cfg
-        self.activate = build_activation_layer(act_cfg)
-
-        layers = []
-        in_channels = embed_dims
-        for _ in range(num_fcs - 1):
-            layers.append(
-                Sequential(
-                    Linear(in_channels, feedforward_channels), self.activate,
-                    nn.Dropout(ffn_drop)))
-            in_channels = feedforward_channels
-        layers.append(Linear(feedforward_channels, embed_dims))
-        layers.append(nn.Dropout(ffn_drop))
-        self.layers = Sequential(*layers)
-        self.dropout_layer = build_dropout(
-            dropout_layer) if dropout_layer else torch.nn.Identity()
-        self.add_identity = add_identity
-
-    @deprecated_api_warning({'residual': 'identity'}, cls_name='FFN')
-    def forward(self, x, identity=None):
-        """Forward function for `FFN`.
-
-        The function would add x to the output tensor if residue is None.
-        """
-        out = self.layers(x)
-        if not self.add_identity:
-            return self.dropout_layer(out)
-        if identity is None:
-            identity = x
-        return identity + self.dropout_layer(out)
-
-
-@TRANSFORMER_LAYER.register_module()
-class BaseTransformerLayer(BaseModule):
-    """Base `TransformerLayer` for vision transformer.
-
-    It can be built from `mmcv.ConfigDict` and support more flexible
-    customization, for example, using any number of `FFN or LN ` and
-    use different kinds of `attention` by specifying a list of `ConfigDict`
-    named `attn_cfgs`. It is worth mentioning that it supports `prenorm`
-    when you specifying `norm` as the first element of `operation_order`.
-    More details about the `prenorm`: `On Layer Normalization in the
-    Transformer Architecture <https://arxiv.org/abs/2002.04745>`_ .
-
-    Args:
-        attn_cfgs (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict` | None )):
-            Configs for `self_attention` or `cross_attention` modules,
-            The order of the configs in the list should be consistent with
-            corresponding attentions in operation_order.
-            If it is a dict, all of the attention modules in operation_order
-            will be built with this config. Default: None.
-        ffn_cfgs (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict` | None )):
-            Configs for FFN, The order of the configs in the list should be
-            consistent with corresponding ffn in operation_order.
-            If it is a dict, all of the attention modules in operation_order
-            will be built with this config.
-        operation_order (tuple[str]): The execution order of operation
-            in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm').
-            Support `prenorm` when you specifying first element as `norm`.
-            Default：None.
-        norm_cfg (dict): Config dict for normalization layer.
-            Default: dict(type='LN').
-        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
-            Default: None.
-        batch_first (bool): Key, Query and Value are shape
-            of (batch, n, embed_dim)
-            or (n, batch, embed_dim). Default to False.
-    """
-
-    def __init__(self,
-                 attn_cfgs=None,
-                 ffn_cfgs=dict(
-                     type='FFN',
-                     embed_dims=256,
-                     feedforward_channels=1024,
-                     num_fcs=2,
-                     ffn_drop=0.,
-                     act_cfg=dict(type='ReLU', inplace=True),
-                 ),
-                 operation_order=None,
-                 norm_cfg=dict(type='LN'),
-                 init_cfg=None,
-                 batch_first=False,
-                 **kwargs):
-
-        deprecated_args = dict(
-            feedforward_channels='feedforward_channels',
-            ffn_dropout='ffn_drop',
-            ffn_num_fcs='num_fcs')
-        for ori_name, new_name in deprecated_args.items():
-            if ori_name in kwargs:
-                warnings.warn(
-                    f'The arguments `{ori_name}` in BaseTransformerLayer '
-                    f'has been deprecated, now you should set `{new_name}` '
-                    f'and other FFN related arguments '
-                    f'to a dict named `ffn_cfgs`. ')
-                ffn_cfgs[new_name] = kwargs[ori_name]
-
-        super(BaseTransformerLayer, self).__init__(init_cfg)
-
-        self.batch_first = batch_first
-
-        assert set(operation_order) & set(
-            ['self_attn', 'norm', 'ffn', 'cross_attn']) == \
-            set(operation_order), f'The operation_order of' \
-            f' {self.__class__.__name__} should ' \
-            f'contains all four operation type ' \
-            f"{['self_attn', 'norm', 'ffn', 'cross_attn']}"
-
-        num_attn = operation_order.count('self_attn') + operation_order.count(
-            'cross_attn')
-        if isinstance(attn_cfgs, dict):
-            attn_cfgs = [copy.deepcopy(attn_cfgs) for _ in range(num_attn)]
-        else:
-            assert num_attn == len(attn_cfgs), f'The length ' \
-                f'of attn_cfg {num_attn} is ' \
-                f'not consistent with the number of attention' \
-                f'in operation_order {operation_order}.'
-
-        self.num_attn = num_attn
-        self.operation_order = operation_order
-        self.norm_cfg = norm_cfg
-        self.pre_norm = operation_order[0] == 'norm'
-        self.attentions = ModuleList()
-
-        index = 0
-        for operation_name in operation_order:
-            if operation_name in ['self_attn', 'cross_attn']:
-                if 'batch_first' in attn_cfgs[index]:
-                    assert self.batch_first == attn_cfgs[index]['batch_first']
-                else:
-                    attn_cfgs[index]['batch_first'] = self.batch_first
-                attention = build_attention(attn_cfgs[index])
-                # Some custom attentions used as `self_attn`
-                # or `cross_attn` can have different behavior.
-                attention.operation_name = operation_name
-                self.attentions.append(attention)
-                index += 1
-
-        self.embed_dims = self.attentions[0].embed_dims
-
-        self.ffns = ModuleList()
-        num_ffns = operation_order.count('ffn')
-        if isinstance(ffn_cfgs, dict):
-            ffn_cfgs = ConfigDict(ffn_cfgs)
-        if isinstance(ffn_cfgs, dict):
-            ffn_cfgs = [copy.deepcopy(ffn_cfgs) for _ in range(num_ffns)]
-        assert len(ffn_cfgs) == num_ffns
-        for ffn_index in range(num_ffns):
-            if 'embed_dims' not in ffn_cfgs[ffn_index]:
-                ffn_cfgs['embed_dims'] = self.embed_dims
-            else:
-                assert ffn_cfgs[ffn_index]['embed_dims'] == self.embed_dims
-            self.ffns.append(
-                build_feedforward_network(ffn_cfgs[ffn_index],
-                                          dict(type='FFN')))
-
-        self.norms = ModuleList()
-        num_norms = operation_order.count('norm')
-        for _ in range(num_norms):
-            self.norms.append(build_norm_layer(norm_cfg, self.embed_dims)[1])
-
-    def forward(self,
-                query,
-                key=None,
-                value=None,
-                query_pos=None,
-                key_pos=None,
-                attn_masks=None,
-                query_key_padding_mask=None,
-                key_padding_mask=None,
-                **kwargs):
-        """Forward function for `TransformerDecoderLayer`.
-
-        **kwargs contains some specific arguments of attentions.
-
-        Args:
-            query (Tensor): The input query with shape
-                [num_queries, bs, embed_dims] if
-                self.batch_first is False, else
-                [bs, num_queries embed_dims].
-            key (Tensor): The key tensor with shape [num_keys, bs,
-                embed_dims] if self.batch_first is False, else
-                [bs, num_keys, embed_dims] .
-            value (Tensor): The value tensor with same shape as `key`.
-            query_pos (Tensor): The positional encoding for `query`.
-                Default: None.
-            key_pos (Tensor): The positional encoding for `key`.
-                Default: None.
-            attn_masks (List[Tensor] | None): 2D Tensor used in
-                calculation of corresponding attention. The length of
-                it should equal to the number of `attention` in
-                `operation_order`. Default: None.
-            query_key_padding_mask (Tensor): ByteTensor for `query`, with
-                shape [bs, num_queries]. Only used in `self_attn` layer.
-                Defaults to None.
-            key_padding_mask (Tensor): ByteTensor for `query`, with
-                shape [bs, num_keys]. Default: None.
-
-        Returns:
-            Tensor: forwarded results with shape [num_queries, bs, embed_dims].
-        """
-
-        norm_index = 0
-        attn_index = 0
-        ffn_index = 0
-        identity = query
-        if attn_masks is None:
-            attn_masks = [None for _ in range(self.num_attn)]
-        elif isinstance(attn_masks, torch.Tensor):
-            attn_masks = [
-                copy.deepcopy(attn_masks) for _ in range(self.num_attn)
-            ]
-            warnings.warn(f'Use same attn_mask in all attentions in '
-                          f'{self.__class__.__name__} ')
-        else:
-            assert len(attn_masks) == self.num_attn, f'The length of ' \
-                        f'attn_masks {len(attn_masks)} must be equal ' \
-                        f'to the number of attention in ' \
-                        f'operation_order {self.num_attn}'
-
-        for layer in self.operation_order:
-            if layer == 'self_attn':
-                temp_key = temp_value = query
-                query = self.attentions[attn_index](
-                    query,
-                    temp_key,
-                    temp_value,
-                    identity if self.pre_norm else None,
-                    query_pos=query_pos,
-                    key_pos=query_pos,
-                    attn_mask=attn_masks[attn_index],
-                    key_padding_mask=query_key_padding_mask,
-                    **kwargs)
-                attn_index += 1
-                identity = query
-
-            elif layer == 'norm':
-                query = self.norms[norm_index](query)
-                norm_index += 1
-
-            elif layer == 'cross_attn':
-                query = self.attentions[attn_index](
-                    query,
-                    key,
-                    value,
-                    identity if self.pre_norm else None,
-                    query_pos=query_pos,
-                    key_pos=key_pos,
-                    attn_mask=attn_masks[attn_index],
-                    key_padding_mask=key_padding_mask,
-                    **kwargs)
-                attn_index += 1
-                identity = query
-
-            elif layer == 'ffn':
-                query = self.ffns[ffn_index](
-                    query, identity if self.pre_norm else None)
-                ffn_index += 1
-
-        return query
-
-
-@TRANSFORMER_LAYER_SEQUENCE.register_module()
-class TransformerLayerSequence(BaseModule):
-    """Base class for TransformerEncoder and TransformerDecoder in vision
-    transformer.
-
-    As base-class of Encoder and Decoder in vision transformer.
-    Support customization such as specifying different kind
-    of `transformer_layer` in `transformer_coder`.
-
-    Args:
-        transformerlayer (list[obj:`mmcv.ConfigDict`] |
-            obj:`mmcv.ConfigDict`): Config of transformerlayer
-            in TransformerCoder. If it is obj:`mmcv.ConfigDict`,
-             it would be repeated `num_layer` times to a
-             list[`mmcv.ConfigDict`]. Default: None.
-        num_layers (int): The number of `TransformerLayer`. Default: None.
-        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
-            Default: None.
-    """
-
-    def __init__(self, transformerlayers=None, num_layers=None, init_cfg=None):
-        super(TransformerLayerSequence, self).__init__(init_cfg)
-        if isinstance(transformerlayers, dict):
-            transformerlayers = [
-                copy.deepcopy(transformerlayers) for _ in range(num_layers)
-            ]
-        else:
-            assert isinstance(transformerlayers, list) and \
-                   len(transformerlayers) == num_layers
-        self.num_layers = num_layers
-        self.layers = ModuleList()
-        for i in range(num_layers):
-            self.layers.append(build_transformer_layer(transformerlayers[i]))
-        self.embed_dims = self.layers[0].embed_dims
-        self.pre_norm = self.layers[0].pre_norm
-
-    def forward(self,
-                query,
-                key,
-                value,
-                query_pos=None,
-                key_pos=None,
-                attn_masks=None,
-                query_key_padding_mask=None,
-                key_padding_mask=None,
-                **kwargs):
-        """Forward function for `TransformerCoder`.
-
-        Args:
-            query (Tensor): Input query with shape
-                `(num_queries, bs, embed_dims)`.
-            key (Tensor): The key tensor with shape
-                `(num_keys, bs, embed_dims)`.
-            value (Tensor): The value tensor with shape
-                `(num_keys, bs, embed_dims)`.
-            query_pos (Tensor): The positional encoding for `query`.
-                Default: None.
-            key_pos (Tensor): The positional encoding for `key`.
-                Default: None.
-            attn_masks (List[Tensor], optional): Each element is 2D Tensor
-                which is used in calculation of corresponding attention in
-                operation_order. Default: None.
-            query_key_padding_mask (Tensor): ByteTensor for `query`, with
-                shape [bs, num_queries]. Only used in self-attention
-                Default: None.
-            key_padding_mask (Tensor): ByteTensor for `query`, with
-                shape [bs, num_keys]. Default: None.
-
-        Returns:
-            Tensor:  results with shape [num_queries, bs, embed_dims].
-        """
-        for layer in self.layers:
-            query = layer(
-                query,
-                key,
-                value,
-                query_pos=query_pos,
-                key_pos=key_pos,
-                attn_masks=attn_masks,
-                query_key_padding_mask=query_key_padding_mask,
-                key_padding_mask=key_padding_mask,
-                **kwargs)
-        return query
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/roi_heads/dynamic_roi_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/roi_heads/dynamic_roi_head.py
deleted file mode 100644
index 89427a931f45f5a920c0e66fd88058bf9fa05f5c..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/roi_heads/dynamic_roi_head.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import numpy as np
-import torch
-
-from mmdet.core import bbox2roi
-from mmdet.models.losses import SmoothL1Loss
-from ..builder import HEADS
-from .standard_roi_head import StandardRoIHead
-
-EPS = 1e-15
-
-
-@HEADS.register_module()
-class DynamicRoIHead(StandardRoIHead):
-    """RoI head for `Dynamic R-CNN <https://arxiv.org/abs/2004.06002>`_."""
-
-    def __init__(self, **kwargs):
-        super(DynamicRoIHead, self).__init__(**kwargs)
-        assert isinstance(self.bbox_head.loss_bbox, SmoothL1Loss)
-        # the IoU history of the past `update_iter_interval` iterations
-        self.iou_history = []
-        # the beta history of the past `update_iter_interval` iterations
-        self.beta_history = []
-
-    def forward_train(self,
-                      x,
-                      img_metas,
-                      proposal_list,
-                      gt_bboxes,
-                      gt_labels,
-                      gt_bboxes_ignore=None,
-                      gt_masks=None):
-        """Forward function for training.
-
-        Args:
-            x (list[Tensor]): list of multi-level img features.
-
-            img_metas (list[dict]): list of image info dict where each dict
-                has: 'img_shape', 'scale_factor', 'flip', and may also contain
-                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-                For details on the values of these keys see
-                `mmdet/datasets/pipelines/formatting.py:Collect`.
-
-            proposals (list[Tensors]): list of region proposals.
-
-            gt_bboxes (list[Tensor]): each item are the truth boxes for each
-                image in [tl_x, tl_y, br_x, br_y] format.
-
-            gt_labels (list[Tensor]): class indices corresponding to each box
-
-            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
-                boxes can be ignored when computing the loss.
-
-            gt_masks (None | Tensor) : true segmentation masks for each box
-                used if the architecture supports a segmentation task.
-
-        Returns:
-            dict[str, Tensor]: a dictionary of loss components
-        """
-        # assign gts and sample proposals
-        if self.with_bbox or self.with_mask:
-            num_imgs = len(img_metas)
-            if gt_bboxes_ignore is None:
-                gt_bboxes_ignore = [None for _ in range(num_imgs)]
-            sampling_results = []
-            cur_iou = []
-            for i in range(num_imgs):
-                assign_result = self.bbox_assigner.assign(
-                    proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
-                    gt_labels[i])
-                sampling_result = self.bbox_sampler.sample(
-                    assign_result,
-                    proposal_list[i],
-                    gt_bboxes[i],
-                    gt_labels[i],
-                    feats=[lvl_feat[i][None] for lvl_feat in x])
-                # record the `iou_topk`-th largest IoU in an image
-                iou_topk = min(self.train_cfg.dynamic_rcnn.iou_topk,
-                               len(assign_result.max_overlaps))
-                ious, _ = torch.topk(assign_result.max_overlaps, iou_topk)
-                cur_iou.append(ious[-1].item())
-                sampling_results.append(sampling_result)
-            # average the current IoUs over images
-            cur_iou = np.mean(cur_iou)
-            self.iou_history.append(cur_iou)
-
-        losses = dict()
-        # bbox head forward and loss
-        if self.with_bbox:
-            bbox_results = self._bbox_forward_train(x, sampling_results,
-                                                    gt_bboxes, gt_labels,
-                                                    img_metas)
-            losses.update(bbox_results['loss_bbox'])
-
-        # mask head forward and loss
-        if self.with_mask:
-            mask_results = self._mask_forward_train(x, sampling_results,
-                                                    bbox_results['bbox_feats'],
-                                                    gt_masks, img_metas)
-            losses.update(mask_results['loss_mask'])
-
-        # update IoU threshold and SmoothL1 beta
-        update_iter_interval = self.train_cfg.dynamic_rcnn.update_iter_interval
-        if len(self.iou_history) % update_iter_interval == 0:
-            new_iou_thr, new_beta = self.update_hyperparameters()
-
-        return losses
-
-    def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
-                            img_metas):
-        num_imgs = len(img_metas)
-        rois = bbox2roi([res.bboxes for res in sampling_results])
-        bbox_results = self._bbox_forward(x, rois)
-
-        bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
-                                                  gt_labels, self.train_cfg)
-        # record the `beta_topk`-th smallest target
-        # `bbox_targets[2]` and `bbox_targets[3]` stand for bbox_targets
-        # and bbox_weights, respectively
-        pos_inds = bbox_targets[3][:, 0].nonzero().squeeze(1)
-        num_pos = len(pos_inds)
-        cur_target = bbox_targets[2][pos_inds, :2].abs().mean(dim=1)
-        beta_topk = min(self.train_cfg.dynamic_rcnn.beta_topk * num_imgs,
-                        num_pos)
-        cur_target = torch.kthvalue(cur_target, beta_topk)[0].item()
-        self.beta_history.append(cur_target)
-        loss_bbox = self.bbox_head.loss(bbox_results['cls_score'],
-                                        bbox_results['bbox_pred'], rois,
-                                        *bbox_targets)
-
-        bbox_results.update(loss_bbox=loss_bbox)
-        return bbox_results
-
-    def update_hyperparameters(self):
-        """Update hyperparameters like IoU thresholds for assigner and beta for
-        SmoothL1 loss based on the training statistics.
-
-        Returns:
-            tuple[float]: the updated ``iou_thr`` and ``beta``.
-        """
-        new_iou_thr = max(self.train_cfg.dynamic_rcnn.initial_iou,
-                          np.mean(self.iou_history))
-        self.iou_history = []
-        self.bbox_assigner.pos_iou_thr = new_iou_thr
-        self.bbox_assigner.neg_iou_thr = new_iou_thr
-        self.bbox_assigner.min_pos_iou = new_iou_thr
-        if (np.median(self.beta_history) < EPS):
-            # avoid 0 or too small value for new_beta
-            new_beta = self.bbox_head.loss_bbox.beta
-        else:
-            new_beta = min(self.train_cfg.dynamic_rcnn.initial_beta,
-                           np.median(self.beta_history))
-        self.beta_history = []
-        self.bbox_head.loss_bbox.beta = new_beta
-        return new_iou_thr, new_beta
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/utils/profiling.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/utils/profiling.py
deleted file mode 100644
index 4be9222c37e922329d537f883f5587995e27efc6..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/utils/profiling.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import contextlib
-import sys
-import time
-
-import torch
-
-if sys.version_info >= (3, 7):
-
-    @contextlib.contextmanager
-    def profile_time(trace_name,
-                     name,
-                     enabled=True,
-                     stream=None,
-                     end_stream=None):
-        """Print time spent by CPU and GPU.
-
-        Useful as a temporary context manager to find sweet spots of code
-        suitable for async implementation.
-        """
-        if (not enabled) or not torch.cuda.is_available():
-            yield
-            return
-        stream = stream if stream else torch.cuda.current_stream()
-        end_stream = end_stream if end_stream else stream
-        start = torch.cuda.Event(enable_timing=True)
-        end = torch.cuda.Event(enable_timing=True)
-        stream.record_event(start)
-        try:
-            cpu_start = time.monotonic()
-            yield
-        finally:
-            cpu_end = time.monotonic()
-            end_stream.record_event(end)
-            end.synchronize()
-            cpu_time = (cpu_end - cpu_start) * 1000
-            gpu_time = start.elapsed_time(end)
-            msg = f'{trace_name} {name} cpu_time {cpu_time:.2f} ms '
-            msg += f'gpu_time {gpu_time:.2f} ms stream {stream}'
-            print(msg, end_stream)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/utils/util_mixins.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/utils/util_mixins.py
deleted file mode 100644
index 69669a3ca943eebe0f138b2784c5b61724196bbe..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/utils/util_mixins.py
+++ /dev/null
@@ -1,104 +0,0 @@
-"""This module defines the :class:`NiceRepr` mixin class, which defines a
-``__repr__`` and ``__str__`` method that only depend on a custom ``__nice__``
-method, which you must define. This means you only have to overload one
-function instead of two.  Furthermore, if the object defines a ``__len__``
-method, then the ``__nice__`` method defaults to something sensible, otherwise
-it is treated as abstract and raises ``NotImplementedError``.
-
-To use simply have your object inherit from :class:`NiceRepr`
-(multi-inheritance should be ok).
-
-This code was copied from the ubelt library: https://github.com/Erotemic/ubelt
-
-Example:
-    >>> # Objects that define __nice__ have a default __str__ and __repr__
-    >>> class Student(NiceRepr):
-    ...    def __init__(self, name):
-    ...        self.name = name
-    ...    def __nice__(self):
-    ...        return self.name
-    >>> s1 = Student('Alice')
-    >>> s2 = Student('Bob')
-    >>> print(f's1 = {s1}')
-    >>> print(f's2 = {s2}')
-    s1 = <Student(Alice)>
-    s2 = <Student(Bob)>
-
-Example:
-    >>> # Objects that define __len__ have a default __nice__
-    >>> class Group(NiceRepr):
-    ...    def __init__(self, data):
-    ...        self.data = data
-    ...    def __len__(self):
-    ...        return len(self.data)
-    >>> g = Group([1, 2, 3])
-    >>> print(f'g = {g}')
-    g = <Group(3)>
-"""
-import warnings
-
-
-class NiceRepr(object):
-    """Inherit from this class and define ``__nice__`` to "nicely" print your
-    objects.
-
-    Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function
-    Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.
-    If the inheriting class has a ``__len__``, method then the default
-    ``__nice__`` method will return its length.
-
-    Example:
-        >>> class Foo(NiceRepr):
-        ...    def __nice__(self):
-        ...        return 'info'
-        >>> foo = Foo()
-        >>> assert str(foo) == '<Foo(info)>'
-        >>> assert repr(foo).startswith('<Foo(info) at ')
-
-    Example:
-        >>> class Bar(NiceRepr):
-        ...    pass
-        >>> bar = Bar()
-        >>> import pytest
-        >>> with pytest.warns(None) as record:
-        >>>     assert 'object at' in str(bar)
-        >>>     assert 'object at' in repr(bar)
-
-    Example:
-        >>> class Baz(NiceRepr):
-        ...    def __len__(self):
-        ...        return 5
-        >>> baz = Baz()
-        >>> assert str(baz) == '<Baz(5)>'
-    """
-
-    def __nice__(self):
-        """str: a "nice" summary string describing this module"""
-        if hasattr(self, '__len__'):
-            # It is a common pattern for objects to use __len__ in __nice__
-            # As a convenience we define a default __nice__ for these objects
-            return str(len(self))
-        else:
-            # In all other cases force the subclass to overload __nice__
-            raise NotImplementedError(
-                f'Define the __nice__ method for {self.__class__!r}')
-
-    def __repr__(self):
-        """str: the string of the module"""
-        try:
-            nice = self.__nice__()
-            classname = self.__class__.__name__
-            return f'<{classname}({nice}) at {hex(id(self))}>'
-        except NotImplementedError as ex:
-            warnings.warn(str(ex), category=RuntimeWarning)
-            return object.__repr__(self)
-
-    def __str__(self):
-        """str: the string of the module"""
-        try:
-            classname = self.__class__.__name__
-            nice = self.__nice__()
-            return f'<{classname}({nice})>'
-        except NotImplementedError as ex:
-            warnings.warn(str(ex), category=RuntimeWarning)
-            return object.__repr__(self)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/core/bbox/match_costs/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/core/bbox/match_costs/__init__.py
deleted file mode 100644
index add5e0d394034d89b2d47c314ff1938294deb6ea..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/core/bbox/match_costs/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from .builder import build_match_cost
-from .match_cost import BBoxL1Cost, ClassificationCost, FocalLossCost, IoUCost
-
-__all__ = [
-    'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost',
-    'FocalLossCost'
-]
diff --git a/spaces/adirik/kakao-brain-vit/backbone/vit_model.py b/spaces/adirik/kakao-brain-vit/backbone/vit_model.py
deleted file mode 100644
index 7aa747d558b59809ab6d9fa03095ee6da5aa436a..0000000000000000000000000000000000000000
--- a/spaces/adirik/kakao-brain-vit/backbone/vit_model.py
+++ /dev/null
@@ -1,296 +0,0 @@
-import tensorflow as tf
-import tensorflow_addons as tfa
-import math
-from tensorflow.keras.layers import (
-    LayerNormalization,
-)
-from .layers import Identity
-
-import numpy as np
-import logging
-
-
-logger = logging.getLogger(__name__)
-
-class MultiHeadSelfAttention(tf.keras.layers.Layer):
-    def __init__(self, embed_dim, num_heads=8, attn_drop_rate=0.0, proj_drop=0.0):
-        super(MultiHeadSelfAttention, self).__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        if embed_dim % num_heads != 0:
-            raise ValueError(
-                f"embedding dimension = {embed_dim} should be divisible by number of heads = {num_heads}"
-            )
-        self.projection_dim = embed_dim // num_heads
-
-        self.query_dense = tf.keras.layers.Dense(
-            units=embed_dim,
-            kernel_initializer=tf.keras.initializers.GlorotNormal(),
-            bias_initializer="zeros",
-        )
-        self.key_dense = tf.keras.layers.Dense(
-            units=embed_dim,
-            kernel_initializer=tf.keras.initializers.GlorotNormal(),
-            bias_initializer="zeros",
-        )
-        self.attn_drop = tf.keras.layers.Dropout(rate=attn_drop_rate)
-        self.value_dense = tf.keras.layers.Dense(
-            units=embed_dim,
-            kernel_initializer=tf.keras.initializers.GlorotNormal(),
-            bias_initializer="zeros",
-        )
-        self.combine_heads = tf.keras.layers.Dense(
-            units=embed_dim,
-            kernel_initializer=tf.keras.initializers.GlorotNormal(),
-            bias_initializer="zeros",
-        )
-        self.proj_drop = tf.keras.layers.Dropout(rate=proj_drop)
-
-    def attention(self, query, key, value):
-        score = tf.matmul(query, key, transpose_b=True)
-        dim_key = tf.cast(tf.shape(key)[-1], score.dtype)
-        scaled_score = score / tf.math.sqrt(dim_key)
-        weights = tf.nn.softmax(scaled_score, axis=-1)
-        weights = self.attn_drop(weights)
-        output = tf.matmul(weights, value)
-        return output, weights
-
-    def separate_heads(self, x, batch_size):
-        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.projection_dim))
-        return tf.transpose(x, perm=[0, 2, 1, 3])
-
-    def call(self, inputs, training):
-        batch_size = tf.shape(inputs)[0]
-        query = self.query_dense(inputs)
-        key = self.key_dense(inputs)
-        value = self.value_dense(inputs)
-
-        query = self.separate_heads(query, batch_size)
-        key = self.separate_heads(key, batch_size)
-        value = self.separate_heads(value, batch_size)
-
-        attention, weights = self.attention(query, key, value)
-        attention = tf.transpose(attention, perm=[0, 2, 1, 3])
-        concat_attention = tf.reshape(attention, (batch_size, -1, self.embed_dim))
-        output = self.combine_heads(concat_attention)
-        output = self.proj_drop(output, training=training)
-        return output
-
-    def get_config(self):
-        config = super().get_config()
-        config.update(
-            {
-                "num_heads": self.num_heads,
-                "embed_dim": self.embed_dim,
-            }
-        )
-        return config
-
-    @classmethod
-    def from_config(cls, config):
-        return cls(**config)
-
-
-class TransformerBlock(tf.keras.layers.Layer):
-    def __init__(
-        self,
-        embed_dim,
-        num_heads,
-        mlp_dim,
-        drop_rate,
-        attn_drop_rate,
-        name="encoderblock",
-    ):
-        super(TransformerBlock, self).__init__(name=name)
-
-        self.att = MultiHeadSelfAttention(
-            embed_dim=embed_dim,
-            num_heads=num_heads,
-            attn_drop_rate=attn_drop_rate,
-            proj_drop=drop_rate,
-        )
-        self.mlp = tf.keras.Sequential(
-            [
-                tf.keras.layers.Dense(
-                    units=mlp_dim,
-                    activation="linear",
-                    kernel_initializer=tf.keras.initializers.GlorotNormal(),
-                    bias_initializer=tf.keras.initializers.RandomNormal(
-                        mean=0.0, stddev=1e-6
-                    ),
-                ),
-                tf.keras.layers.Lambda(
-                    lambda x: tf.keras.activations.gelu(x, approximate=True)
-                ),
-                tf.keras.layers.Dropout(rate=drop_rate),
-                tf.keras.layers.Dense(
-                    units=embed_dim,
-                    kernel_initializer=tf.keras.initializers.GlorotUniform(),
-                    bias_initializer=tf.keras.initializers.RandomNormal(
-                        mean=0.0, stddev=1e-6
-                    ),
-                ),
-                tf.keras.layers.Dropout(rate=drop_rate),
-            ]
-        )
-        self.layernorm1 = LayerNormalization(epsilon=1e-6)
-        self.layernorm2 = LayerNormalization(epsilon=1e-6)
-    
-
-    def call(self, inputs, training):
-        inputs_norm = self.layernorm1(inputs)
-        attn_output = self.att(inputs_norm)
-
-        out1 = attn_output + inputs
-
-        out1_norm = self.layernorm2(out1)
-        mlp_output = self.mlp(out1_norm)
-        return out1 + mlp_output
-
-    def get_config(self):
-        config = super().get_config()
-        return config
-
-    @classmethod
-    def from_config(cls, config):
-        return cls(**config)
-
-
-class VisionTransformer(tf.keras.Model):
-    def __init__(
-        self,
-        image_size,
-        patch_size,
-        num_layers,
-        hidden_size,
-        num_heads,
-        mlp_dim,
-        representation_size=None,
-        channels=3,
-        dropout_rate=0.1,
-        attention_dropout_rate=0.0,
-        num_classes=None,
-    ):
-        super(VisionTransformer, self).__init__()
-        num_patches = (image_size // patch_size) ** 2
-        self.patch_dim = channels * patch_size ** 2
-
-        self.patch_size = patch_size
-        self.d_model = hidden_size
-        self.num_layers = num_layers
-
-        self.class_emb = self.add_weight(
-            "class_emb",
-            shape=(1, 1, hidden_size),
-            initializer=tf.keras.initializers.Zeros(),
-            trainable=True,
-        )
-
-        self.pos_emb = self.add_weight(
-            "pos_emb",
-            shape=(1, num_patches + 1, hidden_size),
-            initializer=tf.keras.initializers.RandomNormal(
-                mean=0.0, stddev=0.02, seed=None
-            ),
-            trainable=True,
-        )
-        self.pos_drop = tf.keras.layers.Dropout(rate=dropout_rate, name="pos_drop")
-
-        self.embedding = tf.keras.layers.Conv2D(
-            filters=hidden_size,
-            kernel_size=self.patch_size,
-            strides=self.patch_size,
-            padding="valid",
-            name="embedding",
-        )
-
-        self.enc_layers = [
-            TransformerBlock(
-                embed_dim=hidden_size,
-                num_heads=num_heads,
-                mlp_dim=mlp_dim,
-                drop_rate=dropout_rate,
-                attn_drop_rate=attention_dropout_rate,
-                name=f"encoderblock_{i}",
-            )
-            for i in range(num_layers)
-        ]
-
-        self.norm = LayerNormalization(epsilon=1e-6, name="encoder_nrom")
-
-        self.extract_token = tf.keras.layers.Lambda(
-            lambda x: x[:, 0], name="extract_token"
-        )
-
-        self.representation = (
-            tf.keras.layers.Dense(
-                units=representation_size,
-                activation="tanh",
-                name="pre_logits",
-            )
-            if representation_size != 0
-            else Identity(name=f"pre_logits")
-        )
-
-      
-
-    def call(self, x, training):
-        batch_size = tf.shape(x)[0]
-        x = self.embedding(x)
-        x = tf.reshape(x, [batch_size, -1, self.d_model])
-
-        class_emb = tf.broadcast_to(self.class_emb, [batch_size, 1, self.d_model])
-        # B x (N + 1) x d_model
-        x = tf.concat([tf.cast(class_emb, x.dtype), x], axis=1)
-        x = x + tf.cast(self.pos_emb, x.dtype)
-        # https://github.com/google-research/vision_transformer/blob/39c905d2caf96a4306c9d78f05df36ddb3eb8ecb/vit_jax/models.py#L192
-        x = self.pos_drop(x, training=training)
-
-        for layer in self.enc_layers:
-            x = layer(x, training)
-
-        x = self.norm(x)
-
-        # First (class token) is used for classification
-        x = self.extract_token(x)
-
-        x = self.representation(x)
-
-        return x[:, tf.newaxis, tf.newaxis, :]
-
-
-KNOWN_MODELS = {
-    "ti": {
-        "num_layers": 12,
-        "hidden_size": 192,
-        "num_heads": 3,
-        "mlp_dim": 768,
-    },
-    "s": {
-        "num_layers": 12,
-        "hidden_size": 384,
-        "num_heads": 6,
-        "mlp_dim": 1536,
-    },
-    "b": {
-        "num_layers": 12,
-        "hidden_size": 768,
-        "num_heads": 12,
-        "mlp_dim": 3072,
-    },
-    "l": {
-        "num_layers": 24,
-        "hidden_size": 1024,
-        "num_heads": 16,
-        "mlp_dim": 4096,
-    },
-}
-
-
-def create_name_vit(architecture_name, **kwargs):
-    base, patch_size = [l.lower() for l in architecture_name.split("-")[-1].split("/")]
-    return VisionTransformer(
-        patch_size=int(patch_size),
-        **KNOWN_MODELS[base],
-        **kwargs,
-    )
\ No newline at end of file
diff --git a/spaces/adirik/stylemc-demo/encoder4editing/models/encoders/helpers.py b/spaces/adirik/stylemc-demo/encoder4editing/models/encoders/helpers.py
deleted file mode 100644
index c4a58b34ea5ca6912fe53c63dede0a8696f5c024..0000000000000000000000000000000000000000
--- a/spaces/adirik/stylemc-demo/encoder4editing/models/encoders/helpers.py
+++ /dev/null
@@ -1,140 +0,0 @@
-from collections import namedtuple
-import torch
-import torch.nn.functional as F
-from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
-
-"""
-ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
-"""
-
-
-class Flatten(Module):
-    def forward(self, input):
-        return input.view(input.size(0), -1)
-
-
-def l2_norm(input, axis=1):
-    norm = torch.norm(input, 2, axis, True)
-    output = torch.div(input, norm)
-    return output
-
-
-class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
-    """ A named tuple describing a ResNet block. """
-
-
-def get_block(in_channel, depth, num_units, stride=2):
-    return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
-
-
-def get_blocks(num_layers):
-    if num_layers == 50:
-        blocks = [
-            get_block(in_channel=64, depth=64, num_units=3),
-            get_block(in_channel=64, depth=128, num_units=4),
-            get_block(in_channel=128, depth=256, num_units=14),
-            get_block(in_channel=256, depth=512, num_units=3)
-        ]
-    elif num_layers == 100:
-        blocks = [
-            get_block(in_channel=64, depth=64, num_units=3),
-            get_block(in_channel=64, depth=128, num_units=13),
-            get_block(in_channel=128, depth=256, num_units=30),
-            get_block(in_channel=256, depth=512, num_units=3)
-        ]
-    elif num_layers == 152:
-        blocks = [
-            get_block(in_channel=64, depth=64, num_units=3),
-            get_block(in_channel=64, depth=128, num_units=8),
-            get_block(in_channel=128, depth=256, num_units=36),
-            get_block(in_channel=256, depth=512, num_units=3)
-        ]
-    else:
-        raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers))
-    return blocks
-
-
-class SEModule(Module):
-    def __init__(self, channels, reduction):
-        super(SEModule, self).__init__()
-        self.avg_pool = AdaptiveAvgPool2d(1)
-        self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False)
-        self.relu = ReLU(inplace=True)
-        self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False)
-        self.sigmoid = Sigmoid()
-
-    def forward(self, x):
-        module_input = x
-        x = self.avg_pool(x)
-        x = self.fc1(x)
-        x = self.relu(x)
-        x = self.fc2(x)
-        x = self.sigmoid(x)
-        return module_input * x
-
-
-class bottleneck_IR(Module):
-    def __init__(self, in_channel, depth, stride):
-        super(bottleneck_IR, self).__init__()
-        if in_channel == depth:
-            self.shortcut_layer = MaxPool2d(1, stride)
-        else:
-            self.shortcut_layer = Sequential(
-                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
-                BatchNorm2d(depth)
-            )
-        self.res_layer = Sequential(
-            BatchNorm2d(in_channel),
-            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
-            Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth)
-        )
-
-    def forward(self, x):
-        shortcut = self.shortcut_layer(x)
-        res = self.res_layer(x)
-        return res + shortcut
-
-
-class bottleneck_IR_SE(Module):
-    def __init__(self, in_channel, depth, stride):
-        super(bottleneck_IR_SE, self).__init__()
-        if in_channel == depth:
-            self.shortcut_layer = MaxPool2d(1, stride)
-        else:
-            self.shortcut_layer = Sequential(
-                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
-                BatchNorm2d(depth)
-            )
-        self.res_layer = Sequential(
-            BatchNorm2d(in_channel),
-            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
-            PReLU(depth),
-            Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
-            BatchNorm2d(depth),
-            SEModule(depth, 16)
-        )
-
-    def forward(self, x):
-        shortcut = self.shortcut_layer(x)
-        res = self.res_layer(x)
-        return res + shortcut
-
-
-def _upsample_add(x, y):
-    """Upsample and add two feature maps.
-    Args:
-      x: (Variable) top feature map to be upsampled.
-      y: (Variable) lateral feature map.
-    Returns:
-      (Variable) added feature map.
-    Note in PyTorch, when input size is odd, the upsampled feature map
-    with `F.upsample(..., scale_factor=2, mode='nearest')`
-    maybe not equal to the lateral feature map size.
-    e.g.
-    original input size: [N,_,15,15] ->
-    conv2d feature map size: [N,_,8,8] ->
-    upsampled feature map size: [N,_,16,16]
-    So we choose bilinear upsample which supports arbitrary output sizes.
-    """
-    _, _, H, W = y.size()
-    return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True) + y
diff --git a/spaces/agueroooooooooo/Transport_Mode_Detector/classifier.py b/spaces/agueroooooooooo/Transport_Mode_Detector/classifier.py
deleted file mode 100644
index 26c19d21547382d199d29c0a88438660eac65da2..0000000000000000000000000000000000000000
--- a/spaces/agueroooooooooo/Transport_Mode_Detector/classifier.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import torch
-import numpy as np
-from torch import nn
-
-from data_loader import DataLoader
-from helper import ValTest
-from modality_lstm import ModalityLSTM
-
-batch_size = 32
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-train_on_gpu = True
-output_size = 5
-hidden_dim = 128
-trip_dim = 7
-n_layers = 2
-drop_prob = 0.2
-net = ModalityLSTM(trip_dim, output_size, batch_size, hidden_dim, n_layers, train_on_gpu, drop_prob, lstm_drop_prob=0.2)
-lr=0.001
-loss_function = nn.CrossEntropyLoss(ignore_index=-1)
-optimizer = torch.optim.Adam(net.parameters(), lr=lr)
-epochs = 6
-print_every = 5
-log_every = 1
-evaluate_every = 100
-
-clip = 0.2 # gradient clipping
-
-if train_on_gpu:
-    net.cuda()
-net.train()
-
-dl = DataLoader(batchsize=batch_size, read_from_pickle=True)
-dl.prepare_data()
-
-def pad_trajs(trajs, lengths):
-    for w, elem in enumerate(trajs):
-        while len(elem) < lengths[0]:
-            elem.append([-1] * trip_dim)
-    return trajs
-
-
-losses, avg_losses = [], []
-
-validator = ValTest(dl.val_batches, net, trip_dim, batch_size, device, loss_function, output_size, dl.get_val_size())
-test = ValTest(dl.test_batches, net, trip_dim, batch_size, device, loss_function, output_size, dl.get_test_size())
-
-for e in range(1,epochs+1):
-    print("epoch ",e)
-    hidden = net.init_hidden()
-    counter = 0
-    torch.cuda.empty_cache()
-    for train_sorted, labels_sorted in dl.batches():
-
-        counter += 1
-        lengths = [len(x) for x in train_sorted]
-        print("Lengths are ", lengths)
-        print("SUm of lengths",sum(lengths))
-        train_sorted = pad_trajs(train_sorted, lengths)
-
-        X = np.asarray(train_sorted, dtype=np.float)
-        input_tensor = torch.from_numpy(X)
-        print("Input tensor is ",input_tensor.shape)
-        input_tensor = input_tensor.to(device)
-
-        net.zero_grad()
-        output, max_padding_for_this_batch = net(input_tensor, lengths)
-        print("Output is",output.shape)
-
-        for labelz in labels_sorted:
-            while len(labelz) < max_padding_for_this_batch:
-                labelz.append(-1)
-
-        labels_for_loss = torch.tensor(labels_sorted).view(max_padding_for_this_batch * batch_size, -1).squeeze(
-            1).long().to(device)
-
-        print("Labels for loss is",len(labels_for_loss))
-
-        loss = loss_function(output.view(
-                            max_padding_for_this_batch*batch_size, -1),
-                            labels_for_loss)
-        loss.backward()
-        nn.utils.clip_grad_norm_(net.parameters(), clip)
-        optimizer.step()
-
-        if counter % log_every == 0:
-            losses.append(loss.item())
-        if counter % print_every == 0:
-            avg_losses.append(sum(losses[-50:]) / 50)
-            print(
-                f'Epoch: {e:2d}. {counter:d} of {int(dl.get_train_size() / batch_size):d} {avg_losses[len(avg_losses) - 1]:f} Loss: {loss.item():.4f}')
-        if counter % evaluate_every == 0:
-            validator.run()
-
-torch.save(net.state_dict(),"Model_Wieghts")
-print("Testing")
-
-test.run()
-
-torch.save(net.state_dict(),"Model_Wieghts")
\ No newline at end of file
diff --git a/spaces/ai-forever/PeterRecognition/app.py b/spaces/ai-forever/PeterRecognition/app.py
deleted file mode 100644
index fca6ce49680ecaa222556707112fa25ec6c08d29..0000000000000000000000000000000000000000
--- a/spaces/ai-forever/PeterRecognition/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import os
-os.system("pip install git+https://github.com/ai-forever/ReadingPipeline.git")
-
-import cv2
-import json
-
-import gradio as gr
-from huggingface_hub import hf_hub_download
-
-from ocrpipeline.predictor import PipelinePredictor
-from ocrpipeline.linefinder import get_structured_text
-
-
-def get_config_and_download_weights(repo_id, device='cpu'):
-    # download weights and configs
-    pipeline_config_path = hf_hub_download(repo_id, "pipeline_config.json")
-    ocr_model_path = hf_hub_download(repo_id, "ocr/ocr_model.onnx")
-    ocr_config_path = hf_hub_download(repo_id, "ocr/ocr_config.json")
-    segm_model_path = hf_hub_download(repo_id, "segm/segm_model.onnx")
-    segm_config_path = hf_hub_download(repo_id, "segm/segm_config.json")
-
-    # change paths to downloaded weights and configs in main pipeline_config
-    with open(pipeline_config_path, 'r') as f:
-        pipeline_config = json.load(f)
-    pipeline_config['main_process']['SegmPrediction']['model_path'] = segm_model_path
-    pipeline_config['main_process']['SegmPrediction']['config_path'] = segm_config_path
-    pipeline_config['main_process']['SegmPrediction']['num_threads'] = 2
-    pipeline_config['main_process']['SegmPrediction']['device'] = device
-    pipeline_config['main_process']['SegmPrediction']['runtime'] = "OpenVino"
-
-    pipeline_config['main_process']['OCRPrediction']['model_path'] = ocr_model_path
-    pipeline_config['main_process']['OCRPrediction']['config_path'] = ocr_config_path
-    pipeline_config['main_process']['OCRPrediction']['num_threads'] = 2
-    pipeline_config['main_process']['OCRPrediction']['device'] = device
-    pipeline_config['main_process']['OCRPrediction']['runtime'] = "OpenVino"
-
-    # save pipeline_config
-    with open(pipeline_config_path, 'w') as f:
-        json.dump(pipeline_config, f)
-
-    return pipeline_config_path
-
-
-def predict(image_path):
-    image = cv2.imread(image_path)
-    rotated_image, pred_data = PREDICTOR(image)
-    structured_text = get_structured_text(pred_data, ['shrinked_text'])
-    result_text = [' '.join(line_text) for page_text in structured_text
-                   for line_text in page_text]
-    return '\n'.join(result_text)
-
-
-PIPELINE_CONFIG_PATH = get_config_and_download_weights("sberbank-ai/ReadingPipeline-Peter")
-
-PREDICTOR = PipelinePredictor(pipeline_config_path=PIPELINE_CONFIG_PATH)
-
-gr.Interface(
-    predict,
-    inputs=gr.Image(label="Upload an image of Peter manuscript", type="filepath"),
-    outputs=gr.Textbox(label="Text on the image"),
-    title="Peter manuscript recognition",
-).launch()
\ No newline at end of file
diff --git a/spaces/akhaliq/PaintTransformer/README.md b/spaces/akhaliq/PaintTransformer/README.md
deleted file mode 100644
index cda4f4191f3bc36c7d6f093fc83d9fa94a4af8e5..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/PaintTransformer/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: PaintTransformer
-emoji: 🏃
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/akhaliq/Real-Time-Voice-Cloning/encoder/data_objects/__init__.py b/spaces/akhaliq/Real-Time-Voice-Cloning/encoder/data_objects/__init__.py
deleted file mode 100644
index ef04ade68544d0477a7f6deb4e7d51e97f592910..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Real-Time-Voice-Cloning/encoder/data_objects/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
-from encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataLoader
diff --git a/spaces/akhaliq/Real-Time-Voice-Cloning/encoder/model.py b/spaces/akhaliq/Real-Time-Voice-Cloning/encoder/model.py
deleted file mode 100644
index e050d3204d8f1becdf0f8b3133470708e5420cea..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Real-Time-Voice-Cloning/encoder/model.py
+++ /dev/null
@@ -1,135 +0,0 @@
-from encoder.params_model import *
-from encoder.params_data import *
-from scipy.interpolate import interp1d
-from sklearn.metrics import roc_curve
-from torch.nn.utils import clip_grad_norm_
-from scipy.optimize import brentq
-from torch import nn
-import numpy as np
-import torch
-
-
-class SpeakerEncoder(nn.Module):
-    def __init__(self, device, loss_device):
-        super().__init__()
-        self.loss_device = loss_device
-        
-        # Network defition
-        self.lstm = nn.LSTM(input_size=mel_n_channels,
-                            hidden_size=model_hidden_size, 
-                            num_layers=model_num_layers, 
-                            batch_first=True).to(device)
-        self.linear = nn.Linear(in_features=model_hidden_size, 
-                                out_features=model_embedding_size).to(device)
-        self.relu = torch.nn.ReLU().to(device)
-        
-        # Cosine similarity scaling (with fixed initial parameter values)
-        self.similarity_weight = nn.Parameter(torch.tensor([10.])).to(loss_device)
-        self.similarity_bias = nn.Parameter(torch.tensor([-5.])).to(loss_device)
-
-        # Loss
-        self.loss_fn = nn.CrossEntropyLoss().to(loss_device)
-        
-    def do_gradient_ops(self):
-        # Gradient scale
-        self.similarity_weight.grad *= 0.01
-        self.similarity_bias.grad *= 0.01
-            
-        # Gradient clipping
-        clip_grad_norm_(self.parameters(), 3, norm_type=2)
-    
-    def forward(self, utterances, hidden_init=None):
-        """
-        Computes the embeddings of a batch of utterance spectrograms.
-        
-        :param utterances: batch of mel-scale filterbanks of same duration as a tensor of shape 
-        (batch_size, n_frames, n_channels) 
-        :param hidden_init: initial hidden state of the LSTM as a tensor of shape (num_layers, 
-        batch_size, hidden_size). Will default to a tensor of zeros if None.
-        :return: the embeddings as a tensor of shape (batch_size, embedding_size)
-        """
-        # Pass the input through the LSTM layers and retrieve all outputs, the final hidden state
-        # and the final cell state.
-        out, (hidden, cell) = self.lstm(utterances, hidden_init)
-        
-        # We take only the hidden state of the last layer
-        embeds_raw = self.relu(self.linear(hidden[-1]))
-        
-        # L2-normalize it
-        embeds = embeds_raw / (torch.norm(embeds_raw, dim=1, keepdim=True) + 1e-5)        
-
-        return embeds
-    
-    def similarity_matrix(self, embeds):
-        """
-        Computes the similarity matrix according the section 2.1 of GE2E.
-
-        :param embeds: the embeddings as a tensor of shape (speakers_per_batch, 
-        utterances_per_speaker, embedding_size)
-        :return: the similarity matrix as a tensor of shape (speakers_per_batch,
-        utterances_per_speaker, speakers_per_batch)
-        """
-        speakers_per_batch, utterances_per_speaker = embeds.shape[:2]
-        
-        # Inclusive centroids (1 per speaker). Cloning is needed for reverse differentiation
-        centroids_incl = torch.mean(embeds, dim=1, keepdim=True)
-        centroids_incl = centroids_incl.clone() / (torch.norm(centroids_incl, dim=2, keepdim=True) + 1e-5)
-
-        # Exclusive centroids (1 per utterance)
-        centroids_excl = (torch.sum(embeds, dim=1, keepdim=True) - embeds)
-        centroids_excl /= (utterances_per_speaker - 1)
-        centroids_excl = centroids_excl.clone() / (torch.norm(centroids_excl, dim=2, keepdim=True) + 1e-5)
-
-        # Similarity matrix. The cosine similarity of already 2-normed vectors is simply the dot
-        # product of these vectors (which is just an element-wise multiplication reduced by a sum).
-        # We vectorize the computation for efficiency.
-        sim_matrix = torch.zeros(speakers_per_batch, utterances_per_speaker,
-                                 speakers_per_batch).to(self.loss_device)
-        mask_matrix = 1 - np.eye(speakers_per_batch, dtype=np.int)
-        for j in range(speakers_per_batch):
-            mask = np.where(mask_matrix[j])[0]
-            sim_matrix[mask, :, j] = (embeds[mask] * centroids_incl[j]).sum(dim=2)
-            sim_matrix[j, :, j] = (embeds[j] * centroids_excl[j]).sum(dim=1)
-        
-        ## Even more vectorized version (slower maybe because of transpose)
-        # sim_matrix2 = torch.zeros(speakers_per_batch, speakers_per_batch, utterances_per_speaker
-        #                           ).to(self.loss_device)
-        # eye = np.eye(speakers_per_batch, dtype=np.int)
-        # mask = np.where(1 - eye)
-        # sim_matrix2[mask] = (embeds[mask[0]] * centroids_incl[mask[1]]).sum(dim=2)
-        # mask = np.where(eye)
-        # sim_matrix2[mask] = (embeds * centroids_excl).sum(dim=2)
-        # sim_matrix2 = sim_matrix2.transpose(1, 2)
-        
-        sim_matrix = sim_matrix * self.similarity_weight + self.similarity_bias
-        return sim_matrix
-    
-    def loss(self, embeds):
-        """
-        Computes the softmax loss according the section 2.1 of GE2E.
-        
-        :param embeds: the embeddings as a tensor of shape (speakers_per_batch, 
-        utterances_per_speaker, embedding_size)
-        :return: the loss and the EER for this batch of embeddings.
-        """
-        speakers_per_batch, utterances_per_speaker = embeds.shape[:2]
-        
-        # Loss
-        sim_matrix = self.similarity_matrix(embeds)
-        sim_matrix = sim_matrix.reshape((speakers_per_batch * utterances_per_speaker, 
-                                         speakers_per_batch))
-        ground_truth = np.repeat(np.arange(speakers_per_batch), utterances_per_speaker)
-        target = torch.from_numpy(ground_truth).long().to(self.loss_device)
-        loss = self.loss_fn(sim_matrix, target)
-        
-        # EER (not backpropagated)
-        with torch.no_grad():
-            inv_argmax = lambda i: np.eye(1, speakers_per_batch, i, dtype=np.int)[0]
-            labels = np.array([inv_argmax(i) for i in ground_truth])
-            preds = sim_matrix.detach().cpu().numpy()
-
-            # Snippet from https://yangcha.github.io/EER-ROC/
-            fpr, tpr, thresholds = roc_curve(labels.flatten(), preds.flatten())           
-            eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
-            
-        return loss, eer
diff --git a/spaces/akhaliq/SummerTime/model/single_doc/bart_model.py b/spaces/akhaliq/SummerTime/model/single_doc/bart_model.py
deleted file mode 100644
index d7108c277d76995550c578850b36a7e57b57354e..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/model/single_doc/bart_model.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from transformers import BartForConditionalGeneration, BartTokenizer
-from .base_single_doc_model import SingleDocSummModel
-
-
-class BartModel(SingleDocSummModel):
-
-    # static variables
-    model_name = "BART"
-    is_extractive = False
-    is_neural = False
-
-    def __init__(self, device="cpu"):
-        super(BartModel, self).__init__()
-
-        self.device = device
-        model_name = "facebook/bart-large-cnn"
-        self.tokenizer = BartTokenizer.from_pretrained(model_name)
-        self.model = BartForConditionalGeneration.from_pretrained(model_name)
-
-    def summarize(self, corpus, queries=None):
-        self.assert_summ_input_type(corpus, queries)
-
-        batch = self.tokenizer(
-            corpus, truncation=True, padding="longest", return_tensors="pt"
-        ).to(self.device)
-        encoded_summaries = self.model.generate(**batch)
-        summaries = self.tokenizer.batch_decode(
-            encoded_summaries, skip_special_tokens=True
-        )
-
-        return summaries
-
-    @classmethod
-    def show_capability(cls) -> None:
-        # TODO zhangir: add the show capability function for BART
-        print(cls.generate_basic_description())
diff --git a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/parallel_wavegan/datasets/__init__.py b/spaces/akhaliq/VQMIVC/ParallelWaveGAN/parallel_wavegan/datasets/__init__.py
deleted file mode 100644
index e3f7a99a24b12065707ff01eb487298714bcf96a..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/parallel_wavegan/datasets/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .audio_mel_dataset import *  # NOQA
-from .scp_dataset import *  # NOQA
diff --git a/spaces/akhaliq/lama/saicinpainting/training/data/aug.py b/spaces/akhaliq/lama/saicinpainting/training/data/aug.py
deleted file mode 100644
index b1246250924e79511b58cd3d7ab79de8012f8949..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/lama/saicinpainting/training/data/aug.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from albumentations import DualIAATransform, to_tuple
-import imgaug.augmenters as iaa
-
-class IAAAffine2(DualIAATransform):
-    """Place a regular grid of points on the input and randomly move the neighbourhood of these point around
-    via affine transformations.
-
-    Note: This class introduce interpolation artifacts to mask if it has values other than {0;1}
-
-    Args:
-        p (float): probability of applying the transform. Default: 0.5.
-
-    Targets:
-        image, mask
-    """
-
-    def __init__(
-        self,
-        scale=(0.7, 1.3),
-        translate_percent=None,
-        translate_px=None,
-        rotate=0.0,
-        shear=(-0.1, 0.1),
-        order=1,
-        cval=0,
-        mode="reflect",
-        always_apply=False,
-        p=0.5,
-    ):
-        super(IAAAffine2, self).__init__(always_apply, p)
-        self.scale = dict(x=scale, y=scale)
-        self.translate_percent = to_tuple(translate_percent, 0)
-        self.translate_px = to_tuple(translate_px, 0)
-        self.rotate = to_tuple(rotate)
-        self.shear = dict(x=shear, y=shear)
-        self.order = order
-        self.cval = cval
-        self.mode = mode
-
-    @property
-    def processor(self):
-        return iaa.Affine(
-            self.scale,
-            self.translate_percent,
-            self.translate_px,
-            self.rotate,
-            self.shear,
-            self.order,
-            self.cval,
-            self.mode,
-        )
-
-    def get_transform_init_args_names(self):
-        return ("scale", "translate_percent", "translate_px", "rotate", "shear", "order", "cval", "mode")
-
-
-class IAAPerspective2(DualIAATransform):
-    """Perform a random four point perspective transform of the input.
-
-    Note: This class introduce interpolation artifacts to mask if it has values other than {0;1}
-
-    Args:
-        scale ((float, float): standard deviation of the normal distributions. These are used to sample
-            the random distances of the subimage's corners from the full image's corners. Default: (0.05, 0.1).
-        p (float): probability of applying the transform. Default: 0.5.
-
-    Targets:
-        image, mask
-    """
-
-    def __init__(self, scale=(0.05, 0.1), keep_size=True, always_apply=False, p=0.5,
-                 order=1, cval=0, mode="replicate"):
-        super(IAAPerspective2, self).__init__(always_apply, p)
-        self.scale = to_tuple(scale, 1.0)
-        self.keep_size = keep_size
-        self.cval = cval
-        self.mode = mode
-
-    @property
-    def processor(self):
-        return iaa.PerspectiveTransform(self.scale, keep_size=self.keep_size, mode=self.mode, cval=self.cval)
-
-    def get_transform_init_args_names(self):
-        return ("scale", "keep_size")
diff --git a/spaces/alc15492/MSemoji850NEW/README.md b/spaces/alc15492/MSemoji850NEW/README.md
deleted file mode 100644
index a30e6789ccba60463d608ed13e4c2055c3542cac..0000000000000000000000000000000000000000
--- a/spaces/alc15492/MSemoji850NEW/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MSemoji850NEW
-emoji: 🐨
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/aliceoq/vozes-da-loirinha/README.md b/spaces/aliceoq/vozes-da-loirinha/README.md
deleted file mode 100644
index e0be8c864402af6cd6b93b74e379a6ce4e88cc4c..0000000000000000000000000000000000000000
--- a/spaces/aliceoq/vozes-da-loirinha/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Vozes Da Loirinha
-emoji: 😻
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/allandclive/Uganda_MMS/vits/commons.py b/spaces/allandclive/Uganda_MMS/vits/commons.py
deleted file mode 100644
index 9ad0444b61cbadaa388619986c2889c707d873ce..0000000000000000000000000000000000000000
--- a/spaces/allandclive/Uganda_MMS/vits/commons.py
+++ /dev/null
@@ -1,161 +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
-    ret[i] = x[i, :, idx_str:idx_end]
-  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
diff --git a/spaces/arcosx/CHO-cytotoxicity/README.md b/spaces/arcosx/CHO-cytotoxicity/README.md
deleted file mode 100644
index a8e152bcd05462701385e6e8271547d914076827..0000000000000000000000000000000000000000
--- a/spaces/arcosx/CHO-cytotoxicity/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: CHO Cytotoxicity
-emoji: 🐠
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/arpm01/financial-summarization/app.py b/spaces/arpm01/financial-summarization/app.py
deleted file mode 100644
index 84d151720b59e6f5279c54d10ab2103881722430..0000000000000000000000000000000000000000
--- a/spaces/arpm01/financial-summarization/app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-
-tokenizer = AutoTokenizer.from_pretrained("human-centered-summarization/financial-summarization-pegasus")
-model = AutoModelForSeq2SeqLM.from_pretrained("human-centered-summarization/financial-summarization-pegasus")
-
-pipe = pipeline(task="text2text-generation", model=model, tokenizer=tokenizer)
-
-with open('text1.txt') as f:
-    text1 = f.read()
-
-with open('text2.txt') as f:
-    text2 = f.read()
-
-with open('text3.txt') as f:
-    text3 = f.read()
-
-gr.Interface.from_pipeline(pipe, 
-                    title="Financial Summarization",
-                    description="Financial Summarization using google/pegasus-xsum fine-tuned on financial news dataset. Model can be found at https://huggingface.co/human-centered-summarization/financial-summarization-pegasus. Examples are news articles from business.inquirer.net.",
-                    examples=[text1,text2,text3]
-                    ).launch()
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/Changelog_KO.md b/spaces/arxify/RVC-beta-v2-0618/Changelog_KO.md
deleted file mode 100644
index fc782a7df3558883fa8c43b49d3fd8054a32113a..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/Changelog_KO.md
+++ /dev/null
@@ -1,77 +0,0 @@
-### 2023년 5월 28일 업데이트
-
-- v2 jupyter notebook 추가, 한국어 업데이트 로그 추가, 의존성 모듈 일부 수정.
-- 무성음 및 숨소리 보호 모드 추가.
-- crepe-full pitch 감지 지원.
-- UVR5 보컬 분리: 디버브 및 디-에코 모델 지원.
-- index 이름에 experiment 이름과 버전 추가.
-- 배치 음성 변환 처리 및 UVR5 보컬 분리 시, 사용자가 수동으로 출력 오디오의 내보내기(export) 형식을 선택할 수 있도록 지원.
-- 32k 훈련 모델 지원 종료.
-
-추후 업데이트 목록:
-
-- 특징 검색: 시간적 특징 검색 추가.
-- 특징 검색: pre-kmeans 옵션 추가.
-- 특징 검색: PCAR 차원 축소 추가.
-- onnx 추론 지원 추가.
-- 학습 시 랜덤 데이터 증강: 음고, 성별, eq, 잡음.
-- v2 버전 사전 훈련된 모델 추가.
-
-### 2023년 5월 13일 업데이트
-
-- 원클릭 패키지의 이전 버전 런타임 내, 불필요한 코드(infer_pack 및 uvr5_pack) 제거.
-- 훈련 세트 전처리의 유사 다중 처리 버그 수정.
-- Harvest 피치 인식 알고리즘에 대한 중위수 필터링 반경 조정 추가.
-- 오디오 내보낼 때, 후처리 리샘플링 지원.
-- 훈련에 대한 다중 처리 "n_cpu" 설정이 "f0 추출"에서 "데이터 전처리 및 f0 추출"로 변경.
-- logs 폴더 하의 인덱스 경로를 자동으로 감지 및 드롭다운 목록 기능 제공.
-- 탭 페이지에 "자주 묻는 질문과 답변" 추가. (github RVC wiki 참조 가능)
-- 동일한 입력 오디오 경로를 사용할 때 추론, Harvest 피치를 캐시.
-  (주의: Harvest 피치 추출을 사용하면 전체 파이프라인은 길고 반복적인 피치 추출 과정을 거치게됩니다. 캐싱을 하지 않는다면, 첫 inference 이후의 단계에서 timbre, 인덱스, 피치 중위수 필터링 반경 설정 등 대기시간이 엄청나게 길어집니다!)
-
-### 2023년 5월 14일 업데이트
-
-- 입력의 볼륨 캡슐을 사용하여 출력의 볼륨 캡슐을 혼합하거나 대체. (입력이 무음이거나 출력의 노이즈 문제를 최소화 할 수 있습니다. 입력 오디오의 배경 노이즈(소음)가 큰 경우 해당 기능을 사용하지 않는 것이 좋습니다. 기본적으로 비활성화 되어있는 옵션입니다. (1: 비활성화 상태))
-- 추출된 소형 모델을 지정된 빈도로 저장하는 기능을 지원. (다양한 에폭 하에서의 성능을 보려고 하지만 모든 대형 체크포인트를 저장하고 매번 ckpt 처리를 통해 소형 모델을 수동으로 추출하고 싶지 않은 경우 이 기능은 매우 유용합니다)
-- 환경 변수를 설정하여 서버의 전역 프록시로 인한 "연결 오류" 문제 해결.
-- 사전 훈련된 v2 모델 지원. (현재 40k 버전만 테스트를 위해 공개적으로 사용 가능하며, 다른 두 개의 샘플링 비율은 아직 완전히 훈련되지 않아 보류되었습니다.)
-- 추론 전, 1을 초과하는 과도한 볼륨 제한.
-- 데이터 전처리 매개변수 미세 조정.
-
-### 2023년 4월 9일 업데이트
-
-- GPU 이용률 향상을 위해 훈련 파라미터 수정: A100은 25%에서 약 90%로 증가, V100: 50%에서 약 90%로 증가, 2060S: 60%에서 약 85%로 증가, P40: 25%에서 약 95%로 증가.
-  훈련 속도가 크게 향상.
-- 매개변수 기준 변경: total batch_size는 GPU당 batch_size를 의미.
-- total_epoch 변경: 최대 한도가 100에서 1000으로 증가. 기본값이 10에서 20으로 증가.
-- ckpt 추출이 피치를 잘못 인식하여 비정상적인 추론을 유발하는 문제 수정.
-- 분산 훈련 과정에서 각 랭크마다 ckpt를 저장하는 문제 수정.
-- 특성 추출 과정에 나노 특성 필터링 적용.
-- 무음 입력/출력이 랜덤하게 소음을 생성하는 문제 수정. (이전 모델은 새 데이터셋으로 다시 훈련해야 합니다)
-
-### 2023년 4월 16일 업데이트
-
-- 로컬 실시간 음성 변경 미니-GUI 추가, go-realtime-gui.bat를 더블 클릭하여 시작.
-- 훈련 및 추론 중 50Hz 이하의 주파수 대역에 대해 필터링 적용.
-- 훈련 및 추론의 pyworld 최소 피치 추출을 기본 80에서 50으로 낮춤. 이로 인해, 50-80Hz 사이의 남성 저음이 무음화되지 않습니다.
-- 시스템 지역에 따른 WebUI 언어 변경 지원. (현재 en_US, ja_JP, zh_CN, zh_HK, zh_SG, zh_TW를 지원하며, 지원되지 않는 경우 기본값은 en_US)
-- 일부 GPU의 인식 수정. (예: V100-16G 인식 실패, P4 인식 실패)
-
-### 2023년 4월 28일 업데이트
-
-- Faiss 인덱스 설정 업그레이드로 속도가 더 빨라지고 품질이 향상.
-- total_npy에 대한 의존성 제거. 추후의 모델 공유는 total_npy 입력을 필요로 하지 않습니다.
-- 16 시리즈 GPU에 대한 제한 해제, 4GB VRAM GPU에 대한 4GB 추론 설정 제공.
-- 일부 오디오 형식에 대한 UVR5 보컬 동반 분리에서의 버그 수정.
-- 실시간 음성 변경 미니-GUI는 이제 non-40k 및 non-lazy 피치 모델을 지원합니다.
-
-### 추후 계획
-
-Features:
-
-- 다중 사용자 훈련 탭 지원.(최대 4명)
-
-Base model:
-
-- 훈련 데이터셋에 숨소리 wav 파일을 추가하여, 보컬의 호흡이 노이즈로 변환되는 문제 수정.
-- 보컬 훈련 세트의 기본 모델을 추가하기 위한 작업을 진행중이며, 이는 향후에 발표될 예정.
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/client_ws.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/client_ws.py
deleted file mode 100644
index 9a8ba84ca5082ad6d672c3837d4810e467a8080e..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/client_ws.py
+++ /dev/null
@@ -1,300 +0,0 @@
-"""WebSocket client for asyncio."""
-
-import asyncio
-from typing import Any, Optional, cast
-
-import async_timeout
-
-from .client_exceptions import ClientError
-from .client_reqrep import ClientResponse
-from .helpers import call_later, set_result
-from .http import (
-    WS_CLOSED_MESSAGE,
-    WS_CLOSING_MESSAGE,
-    WebSocketError,
-    WSCloseCode,
-    WSMessage,
-    WSMsgType,
-)
-from .http_websocket import WebSocketWriter  # WSMessage
-from .streams import EofStream, FlowControlDataQueue
-from .typedefs import (
-    DEFAULT_JSON_DECODER,
-    DEFAULT_JSON_ENCODER,
-    JSONDecoder,
-    JSONEncoder,
-)
-
-
-class ClientWebSocketResponse:
-    def __init__(
-        self,
-        reader: "FlowControlDataQueue[WSMessage]",
-        writer: WebSocketWriter,
-        protocol: Optional[str],
-        response: ClientResponse,
-        timeout: float,
-        autoclose: bool,
-        autoping: bool,
-        loop: asyncio.AbstractEventLoop,
-        *,
-        receive_timeout: Optional[float] = None,
-        heartbeat: Optional[float] = None,
-        compress: int = 0,
-        client_notakeover: bool = False,
-    ) -> None:
-        self._response = response
-        self._conn = response.connection
-
-        self._writer = writer
-        self._reader = reader
-        self._protocol = protocol
-        self._closed = False
-        self._closing = False
-        self._close_code: Optional[int] = None
-        self._timeout = timeout
-        self._receive_timeout = receive_timeout
-        self._autoclose = autoclose
-        self._autoping = autoping
-        self._heartbeat = heartbeat
-        self._heartbeat_cb: Optional[asyncio.TimerHandle] = None
-        if heartbeat is not None:
-            self._pong_heartbeat = heartbeat / 2.0
-        self._pong_response_cb: Optional[asyncio.TimerHandle] = None
-        self._loop = loop
-        self._waiting: Optional[asyncio.Future[bool]] = None
-        self._exception: Optional[BaseException] = None
-        self._compress = compress
-        self._client_notakeover = client_notakeover
-
-        self._reset_heartbeat()
-
-    def _cancel_heartbeat(self) -> None:
-        if self._pong_response_cb is not None:
-            self._pong_response_cb.cancel()
-            self._pong_response_cb = None
-
-        if self._heartbeat_cb is not None:
-            self._heartbeat_cb.cancel()
-            self._heartbeat_cb = None
-
-    def _reset_heartbeat(self) -> None:
-        self._cancel_heartbeat()
-
-        if self._heartbeat is not None:
-            self._heartbeat_cb = call_later(
-                self._send_heartbeat, self._heartbeat, self._loop
-            )
-
-    def _send_heartbeat(self) -> None:
-        if self._heartbeat is not None and not self._closed:
-            # fire-and-forget a task is not perfect but maybe ok for
-            # sending ping. Otherwise we need a long-living heartbeat
-            # task in the class.
-            self._loop.create_task(self._writer.ping())
-
-            if self._pong_response_cb is not None:
-                self._pong_response_cb.cancel()
-            self._pong_response_cb = call_later(
-                self._pong_not_received, self._pong_heartbeat, self._loop
-            )
-
-    def _pong_not_received(self) -> None:
-        if not self._closed:
-            self._closed = True
-            self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-            self._exception = asyncio.TimeoutError()
-            self._response.close()
-
-    @property
-    def closed(self) -> bool:
-        return self._closed
-
-    @property
-    def close_code(self) -> Optional[int]:
-        return self._close_code
-
-    @property
-    def protocol(self) -> Optional[str]:
-        return self._protocol
-
-    @property
-    def compress(self) -> int:
-        return self._compress
-
-    @property
-    def client_notakeover(self) -> bool:
-        return self._client_notakeover
-
-    def get_extra_info(self, name: str, default: Any = None) -> Any:
-        """extra info from connection transport"""
-        conn = self._response.connection
-        if conn is None:
-            return default
-        transport = conn.transport
-        if transport is None:
-            return default
-        return transport.get_extra_info(name, default)
-
-    def exception(self) -> Optional[BaseException]:
-        return self._exception
-
-    async def ping(self, message: bytes = b"") -> None:
-        await self._writer.ping(message)
-
-    async def pong(self, message: bytes = b"") -> None:
-        await self._writer.pong(message)
-
-    async def send_str(self, data: str, compress: Optional[int] = None) -> None:
-        if not isinstance(data, str):
-            raise TypeError("data argument must be str (%r)" % type(data))
-        await self._writer.send(data, binary=False, compress=compress)
-
-    async def send_bytes(self, data: bytes, compress: Optional[int] = None) -> None:
-        if not isinstance(data, (bytes, bytearray, memoryview)):
-            raise TypeError("data argument must be byte-ish (%r)" % type(data))
-        await self._writer.send(data, binary=True, compress=compress)
-
-    async def send_json(
-        self,
-        data: Any,
-        compress: Optional[int] = None,
-        *,
-        dumps: JSONEncoder = DEFAULT_JSON_ENCODER,
-    ) -> None:
-        await self.send_str(dumps(data), compress=compress)
-
-    async def close(self, *, code: int = WSCloseCode.OK, message: bytes = b"") -> bool:
-        # we need to break `receive()` cycle first,
-        # `close()` may be called from different task
-        if self._waiting is not None and not self._closed:
-            self._reader.feed_data(WS_CLOSING_MESSAGE, 0)
-            await self._waiting
-
-        if not self._closed:
-            self._cancel_heartbeat()
-            self._closed = True
-            try:
-                await self._writer.close(code, message)
-            except asyncio.CancelledError:
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                self._response.close()
-                raise
-            except Exception as exc:
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                self._exception = exc
-                self._response.close()
-                return True
-
-            if self._closing:
-                self._response.close()
-                return True
-
-            while True:
-                try:
-                    async with async_timeout.timeout(self._timeout):
-                        msg = await self._reader.read()
-                except asyncio.CancelledError:
-                    self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                    self._response.close()
-                    raise
-                except Exception as exc:
-                    self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                    self._exception = exc
-                    self._response.close()
-                    return True
-
-                if msg.type == WSMsgType.CLOSE:
-                    self._close_code = msg.data
-                    self._response.close()
-                    return True
-        else:
-            return False
-
-    async def receive(self, timeout: Optional[float] = None) -> WSMessage:
-        while True:
-            if self._waiting is not None:
-                raise RuntimeError("Concurrent call to receive() is not allowed")
-
-            if self._closed:
-                return WS_CLOSED_MESSAGE
-            elif self._closing:
-                await self.close()
-                return WS_CLOSED_MESSAGE
-
-            try:
-                self._waiting = self._loop.create_future()
-                try:
-                    async with async_timeout.timeout(timeout or self._receive_timeout):
-                        msg = await self._reader.read()
-                    self._reset_heartbeat()
-                finally:
-                    waiter = self._waiting
-                    self._waiting = None
-                    set_result(waiter, True)
-            except (asyncio.CancelledError, asyncio.TimeoutError):
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                raise
-            except EofStream:
-                self._close_code = WSCloseCode.OK
-                await self.close()
-                return WSMessage(WSMsgType.CLOSED, None, None)
-            except ClientError:
-                self._closed = True
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                return WS_CLOSED_MESSAGE
-            except WebSocketError as exc:
-                self._close_code = exc.code
-                await self.close(code=exc.code)
-                return WSMessage(WSMsgType.ERROR, exc, None)
-            except Exception as exc:
-                self._exception = exc
-                self._closing = True
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                await self.close()
-                return WSMessage(WSMsgType.ERROR, exc, None)
-
-            if msg.type == WSMsgType.CLOSE:
-                self._closing = True
-                self._close_code = msg.data
-                if not self._closed and self._autoclose:
-                    await self.close()
-            elif msg.type == WSMsgType.CLOSING:
-                self._closing = True
-            elif msg.type == WSMsgType.PING and self._autoping:
-                await self.pong(msg.data)
-                continue
-            elif msg.type == WSMsgType.PONG and self._autoping:
-                continue
-
-            return msg
-
-    async def receive_str(self, *, timeout: Optional[float] = None) -> str:
-        msg = await self.receive(timeout)
-        if msg.type != WSMsgType.TEXT:
-            raise TypeError(f"Received message {msg.type}:{msg.data!r} is not str")
-        return cast(str, msg.data)
-
-    async def receive_bytes(self, *, timeout: Optional[float] = None) -> bytes:
-        msg = await self.receive(timeout)
-        if msg.type != WSMsgType.BINARY:
-            raise TypeError(f"Received message {msg.type}:{msg.data!r} is not bytes")
-        return cast(bytes, msg.data)
-
-    async def receive_json(
-        self,
-        *,
-        loads: JSONDecoder = DEFAULT_JSON_DECODER,
-        timeout: Optional[float] = None,
-    ) -> Any:
-        data = await self.receive_str(timeout=timeout)
-        return loads(data)
-
-    def __aiter__(self) -> "ClientWebSocketResponse":
-        return self
-
-    async def __anext__(self) -> WSMessage:
-        msg = await self.receive()
-        if msg.type in (WSMsgType.CLOSE, WSMsgType.CLOSING, WSMsgType.CLOSED):
-            raise StopAsyncIteration
-        return msg
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/error/Errors.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/error/Errors.py
deleted file mode 100644
index e78ac05911d3c9569441fe376ff7d6c686c05c95..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/error/Errors.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved.
-# Use of this file is governed by the BSD 3-clause license that
-# can be found in the LICENSE.txt file in the project root.
-#
-
-# need forward declaration
-Token = None
-Lexer = None
-Parser = None
-TokenStream = None
-ATNConfigSet = None
-ParserRulecontext = None
-PredicateTransition = None
-BufferedTokenStream = None
-
-class UnsupportedOperationException(Exception):
-
-    def __init__(self, msg:str):
-        super().__init__(msg)
-
-class IllegalStateException(Exception):
-
-    def __init__(self, msg:str):
-        super().__init__(msg)
-
-class CancellationException(IllegalStateException):
-
-    def __init__(self, msg:str):
-        super().__init__(msg)
-
-# The root of the ANTLR exception hierarchy. In general, ANTLR tracks just
-#  3 kinds of errors: prediction errors, failed predicate errors, and
-#  mismatched input errors. In each case, the parser knows where it is
-#  in the input, where it is in the ATN, the rule invocation stack,
-#  and what kind of problem occurred.
-
-from antlr4.InputStream import InputStream
-from antlr4.ParserRuleContext import ParserRuleContext
-from antlr4.Recognizer import Recognizer
-
-class RecognitionException(Exception):
-
-
-    def __init__(self, message:str=None, recognizer:Recognizer=None, input:InputStream=None, ctx:ParserRulecontext=None):
-        super().__init__(message)
-        self.message = message
-        self.recognizer = recognizer
-        self.input = input
-        self.ctx = ctx
-        # The current {@link Token} when an error occurred. Since not all streams
-        # support accessing symbols by index, we have to track the {@link Token}
-        # instance itself.
-        self.offendingToken = None
-        # Get the ATN state number the parser was in at the time the error
-        # occurred. For {@link NoViableAltException} and
-        # {@link LexerNoViableAltException} exceptions, this is the
-        # {@link DecisionState} number. For others, it is the state whose outgoing
-        # edge we couldn't match.
-        self.offendingState = -1
-        if recognizer is not None:
-            self.offendingState = recognizer.state
-
-    # <p>If the state number is not known, this method returns -1.</p>
-
-    #
-    # Gets the set of input symbols which could potentially follow the
-    # previously matched symbol at the time this exception was thrown.
-    #
-    # <p>If the set of expected tokens is not known and could not be computed,
-    # this method returns {@code null}.</p>
-    #
-    # @return The set of token types that could potentially follow the current
-    # state in the ATN, or {@code null} if the information is not available.
-    #/
-    def getExpectedTokens(self):
-        if self.recognizer is not None:
-            return self.recognizer.atn.getExpectedTokens(self.offendingState, self.ctx)
-        else:
-            return None
-
-
-class LexerNoViableAltException(RecognitionException):
-
-    def __init__(self, lexer:Lexer, input:InputStream, startIndex:int, deadEndConfigs:ATNConfigSet):
-        super().__init__(message=None, recognizer=lexer, input=input, ctx=None)
-        self.startIndex = startIndex
-        self.deadEndConfigs = deadEndConfigs
-
-    def __str__(self):
-        symbol = ""
-        if self.startIndex >= 0 and self.startIndex < self.input.size:
-            symbol = self.input.getText(self.startIndex, self.startIndex)
-            # TODO symbol = Utils.escapeWhitespace(symbol, false);
-        return "LexerNoViableAltException('" + symbol + "')"
-
-# Indicates that the parser could not decide which of two or more paths
-#  to take based upon the remaining input. It tracks the starting token
-#  of the offending input and also knows where the parser was
-#  in the various paths when the error. Reported by reportNoViableAlternative()
-#
-class NoViableAltException(RecognitionException):
-
-    def __init__(self, recognizer:Parser, input:TokenStream=None, startToken:Token=None,
-                    offendingToken:Token=None, deadEndConfigs:ATNConfigSet=None, ctx:ParserRuleContext=None):
-        if ctx is None:
-            ctx = recognizer._ctx
-        if offendingToken is None:
-            offendingToken = recognizer.getCurrentToken()
-        if startToken is None:
-            startToken = recognizer.getCurrentToken()
-        if input is None:
-            input = recognizer.getInputStream()
-        super().__init__(recognizer=recognizer, input=input, ctx=ctx)
-        # Which configurations did we try at input.index() that couldn't match input.LT(1)?#
-        self.deadEndConfigs = deadEndConfigs
-        # The token object at the start index; the input stream might
-        # 	not be buffering tokens so get a reference to it. (At the
-        #  time the error occurred, of course the stream needs to keep a
-        #  buffer all of the tokens but later we might not have access to those.)
-        self.startToken = startToken
-        self.offendingToken = offendingToken
-
-# This signifies any kind of mismatched input exceptions such as
-#  when the current input does not match the expected token.
-#
-class InputMismatchException(RecognitionException):
-
-    def __init__(self, recognizer:Parser):
-        super().__init__(recognizer=recognizer, input=recognizer.getInputStream(), ctx=recognizer._ctx)
-        self.offendingToken = recognizer.getCurrentToken()
-
-
-# A semantic predicate failed during validation.  Validation of predicates
-#  occurs when normally parsing the alternative just like matching a token.
-#  Disambiguating predicate evaluation occurs when we test a predicate during
-#  prediction.
-
-class FailedPredicateException(RecognitionException):
-
-    def __init__(self, recognizer:Parser, predicate:str=None, message:str=None):
-        super().__init__(message=self.formatMessage(predicate,message), recognizer=recognizer,
-                         input=recognizer.getInputStream(), ctx=recognizer._ctx)
-        s = recognizer._interp.atn.states[recognizer.state]
-        trans = s.transitions[0]
-        from antlr4.atn.Transition import PredicateTransition
-        if isinstance(trans, PredicateTransition):
-            self.ruleIndex = trans.ruleIndex
-            self.predicateIndex = trans.predIndex
-        else:
-            self.ruleIndex = 0
-            self.predicateIndex = 0
-        self.predicate = predicate
-        self.offendingToken = recognizer.getCurrentToken()
-
-    def formatMessage(self, predicate:str, message:str):
-        if message is not None:
-            return message
-        else:
-            return "failed predicate: {" + predicate + "}?"
-
-class ParseCancellationException(CancellationException):
-
-    pass
-
-del Token
-del Lexer
-del Parser
-del TokenStream
-del ATNConfigSet
-del ParserRulecontext
-del PredicateTransition
-del BufferedTokenStream
diff --git a/spaces/asd123Xiao/kafuu_chino_sovits4.0/preprocess_hubert_f0.py b/spaces/asd123Xiao/kafuu_chino_sovits4.0/preprocess_hubert_f0.py
deleted file mode 100644
index 29a1c7ee028fefbe7905d235447d98cda34ce840..0000000000000000000000000000000000000000
--- a/spaces/asd123Xiao/kafuu_chino_sovits4.0/preprocess_hubert_f0.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import math
-import multiprocessing
-import os
-import argparse
-from random import shuffle
-
-import torch
-from glob import glob
-from tqdm import tqdm
-
-import utils
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-import librosa
-import numpy as np
-
-hps = utils.get_hparams_from_file("configs/config.json")
-sampling_rate = hps.data.sampling_rate
-hop_length = hps.data.hop_length
-
-
-def process_one(filename, hmodel):
-    # print(filename)
-    wav, sr = librosa.load(filename, sr=sampling_rate)
-    soft_path = filename + ".soft.pt"
-    if not os.path.exists(soft_path):
-        devive = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
-        wav16k = torch.from_numpy(wav16k).to(devive)
-        c = utils.get_hubert_content(hmodel, wav_16k_tensor=wav16k)
-        torch.save(c.cpu(), soft_path)
-    f0_path = filename + ".f0.npy"
-    if not os.path.exists(f0_path):
-        f0 = utils.compute_f0_dio(wav, sampling_rate=sampling_rate, hop_length=hop_length)
-        np.save(f0_path, f0)
-
-
-def process_batch(filenames):
-    print("Loading hubert for content...")
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    hmodel = utils.get_hubert_model().to(device)
-    print("Loaded hubert.")
-    for filename in tqdm(filenames):
-        process_one(filename, hmodel)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--in_dir", type=str, default="dataset/44k", help="path to input dir")
-
-    args = parser.parse_args()
-    filenames = glob(f'{args.in_dir}/*/*.wav', recursive=True)  # [:10]
-    shuffle(filenames)
-    multiprocessing.set_start_method('spawn')
-
-    num_processes = 1
-    chunk_size = int(math.ceil(len(filenames) / num_processes))
-    chunks = [filenames[i:i + chunk_size] for i in range(0, len(filenames), chunk_size)]
-    print([len(c) for c in chunks])
-    processes = [multiprocessing.Process(target=process_batch, args=(chunk,)) for chunk in chunks]
-    for p in processes:
-        p.start()
diff --git a/spaces/asd998877/TsGpt/locale/extract_locale.py b/spaces/asd998877/TsGpt/locale/extract_locale.py
deleted file mode 100644
index 32b0924bd6dffe150cb3e481ddadef836b91b83c..0000000000000000000000000000000000000000
--- a/spaces/asd998877/TsGpt/locale/extract_locale.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-import json
-import re
-
-# Define regular expression patterns
-pattern = r'i18n\((\"{3}.*?\"{3}|\".*?\")\)'
-
-# Load the .py file
-with open('ChuanhuChatbot.py', 'r', encoding='utf-8') as f:
-    contents = f.read()
-
-# Load the .py files in the modules folder
-for filename in os.listdir("modules"):
-    if filename.endswith(".py"):
-        with open(os.path.join("modules", filename), "r", encoding="utf-8") as f:
-            contents += f.read()
-
-# Matching with regular expressions
-matches = re.findall(pattern, contents, re.DOTALL)
-
-# Convert to key/value pairs
-data = {match.strip('()"'): '' for match in matches}
-
-# Save as a JSON file
-with open('labels.json', 'w', encoding='utf-8') as f:
-    json.dump(data, f, ensure_ascii=False, indent=4)
\ No newline at end of file
diff --git a/spaces/ashercn97/AsherTesting/extensions/multimodal/pipelines/llava/llava.py b/spaces/ashercn97/AsherTesting/extensions/multimodal/pipelines/llava/llava.py
deleted file mode 100644
index eca2be50bbcfce3f4b7c1f1bee89c80581a6b517..0000000000000000000000000000000000000000
--- a/spaces/ashercn97/AsherTesting/extensions/multimodal/pipelines/llava/llava.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import time
-from abc import abstractmethod
-from typing import List, Tuple
-
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image
-from transformers import CLIPImageProcessor, CLIPVisionModel
-
-from extensions.multimodal.abstract_pipeline import AbstractMultimodalPipeline
-from modules import shared
-from modules.logging_colors import logger
-from modules.text_generation import encode
-
-
-class LLaVA_v0_Pipeline(AbstractMultimodalPipeline):
-    CLIP_REPO = "openai/clip-vit-large-patch14"
-
-    def __init__(self, params: dict) -> None:
-        super().__init__()
-        self.clip_device = self._get_device("vision_device", params)
-        self.clip_dtype = self._get_dtype("vision_bits", params)
-        self.projector_device = self._get_device("projector_device", params)
-        self.projector_dtype = self._get_dtype("projector_bits", params)
-        self.image_processor, self.vision_tower, self.mm_projector = self._load_models()
-
-    def _load_models(self):
-        start_ts = time.time()
-
-        logger.info(f"LLaVA - Loading CLIP from {LLaVA_v0_Pipeline.CLIP_REPO} as {self.clip_dtype} on {self.clip_device}...")
-        image_processor = CLIPImageProcessor.from_pretrained(LLaVA_v0_Pipeline.CLIP_REPO, torch_dtype=self.clip_dtype)
-        vision_tower = CLIPVisionModel.from_pretrained(LLaVA_v0_Pipeline.CLIP_REPO, torch_dtype=self.clip_dtype).to(self.clip_device)
-
-        logger.info(f"LLaVA - Loading projector from {self.llava_projector_repo()} as {self.projector_dtype} on {self.projector_device}...")
-        projector_path = hf_hub_download(self.llava_projector_repo(), self.llava_projector_filename())
-        mm_projector = torch.nn.Linear(*self.llava_projector_shape())
-        projector_data = torch.load(projector_path)
-        mm_projector.weight = torch.nn.Parameter(projector_data['model.mm_projector.weight'].to(dtype=self.projector_dtype), False)
-        mm_projector.bias = torch.nn.Parameter(projector_data['model.mm_projector.bias'].to(dtype=self.projector_dtype), False)
-        mm_projector = mm_projector.to(self.projector_device)
-
-        logger.info(f"LLaVA supporting models loaded, took {time.time() - start_ts:.2f} seconds")
-        return image_processor, vision_tower, mm_projector
-
-    @staticmethod
-    def image_start() -> str:
-        return "<im_start>"
-
-    @staticmethod
-    def image_end() -> str:
-        return "<im_end>"
-
-    @staticmethod
-    def num_image_embeds() -> int:
-        return 256
-
-    @staticmethod
-    def embed_tokens(input_ids: torch.Tensor) -> torch.Tensor:
-        if hasattr(shared.model.model, 'embed_tokens'):
-            func = shared.model.model.embed_tokens
-        else:
-            func = shared.model.model.model.embed_tokens  # AutoGPTQ case
-
-        return func(input_ids).to(shared.model.device, dtype=shared.model.dtype)
-
-    @staticmethod
-    def placeholder_embeddings() -> torch.Tensor:
-        return LLaVA_v0_Pipeline.embed_tokens(encode("<im_patch>"*256, add_bos_token=False)[0])
-
-    def embed_images(self, images: List[Image.Image]) -> torch.Tensor:
-        images = self.image_processor(images, return_tensors='pt')['pixel_values']
-        images = images.to(self.clip_device, dtype=self.clip_dtype)
-
-        with torch.no_grad():
-            image_forward_outs = self.vision_tower(images, output_hidden_states=True)
-            select_hidden_state_layer = -2
-            select_hidden_state = image_forward_outs.hidden_states[select_hidden_state_layer]
-            image_features = select_hidden_state[:, 1:].to(self.projector_device, dtype=self.projector_dtype)
-            image_features = self.mm_projector(image_features)
-        return image_features.to(shared.model.device, dtype=shared.model.dtype)
-
-    @staticmethod
-    @abstractmethod
-    def llava_projector_repo() -> str:
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def llava_projector_filename() -> str:
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def llava_projector_shape() -> Tuple[int, int]:
-        pass
-
-
-class LLaVA_v0_13B_Pipeline(LLaVA_v0_Pipeline):
-    def __init__(self, params: dict) -> None:
-        super().__init__(params)
-
-    @staticmethod
-    def name() -> str:
-        return "llava-13b"
-
-    @staticmethod
-    def placeholder_token_id() -> int:
-        return 32000
-
-    @staticmethod
-    def llava_projector_shape() -> Tuple[int, int]:
-        return (1024, 5120)
-
-    @staticmethod
-    def llava_projector_filename() -> str:
-        return "mm_projector.bin"
-
-    @staticmethod
-    def llava_projector_repo() -> str:
-        return "liuhaotian/LLaVA-13b-delta-v0"
-
-
-class LLaVA_v0_7B_Pipeline(LLaVA_v0_Pipeline):
-    def __init__(self, params: dict) -> None:
-        super().__init__(params)
-
-    @staticmethod
-    def name() -> str:
-        return "llava-7b"
-
-    @staticmethod
-    def placeholder_token_id() -> int:
-        return 32001
-
-    @staticmethod
-    def llava_projector_shape() -> Tuple[int, int]:
-        return (1024, 4096)
-
-    @staticmethod
-    def llava_projector_filename() -> str:
-        return "mm_projector.bin"
-
-    @staticmethod
-    def llava_projector_repo() -> str:
-        return "liuhaotian/LLaVA-7b-delta-v0"
diff --git a/spaces/attention-refocusing/Attention-refocusing/dataset/cd_dataset.py b/spaces/attention-refocusing/Attention-refocusing/dataset/cd_dataset.py
deleted file mode 100644
index 0627329bda44a15c6821fc477bbde45acfe86a2f..0000000000000000000000000000000000000000
--- a/spaces/attention-refocusing/Attention-refocusing/dataset/cd_dataset.py
+++ /dev/null
@@ -1,250 +0,0 @@
-import json, os, random, math
-from collections import defaultdict
-from copy import deepcopy
-
-import torch
-from torch.utils.data import Dataset
-import torchvision.transforms as transforms
-
-import numpy as np
-from PIL import Image
-from .base_dataset import BaseDataset, check_filenames_in_zipdata, recalculate_box_and_verify_if_valid 
-from io import BytesIO
-
-
-
-def not_in_at_all(list1, list2):
-	for a in list1:
-		if a in list2:
-			return False
-	return True
-
-
-def clean_annotations(annotations):
-	for anno in annotations:
-		anno.pop("segmentation", None)
-		anno.pop("area", None)
-		anno.pop("iscrowd", None)
-		# anno.pop("id", None)
-
-
-def make_a_sentence(obj_names, clean=False):
-
-	if clean:
-		obj_names = [ name[:-6] if ("-other" in name) else name for name in obj_names]
-
-	caption = ""
-	tokens_positive = []
-	for obj_name in obj_names:
-		start_len = len(caption)
-		caption += obj_name
-		end_len = len(caption)
-		caption += ", "
-		tokens_positive.append(
-			[[start_len, end_len]] # in real caption, positive tokens can be disjoint, thus using list of list
-		)
-	caption = caption[:-2] # remove last ", "
-
-	return caption #, tokens_positive
-
-
-def check_all_have_same_images(instances_data, stuff_data, caption_data):
-	if stuff_data is not None:
-		assert instances_data["images"] == stuff_data["images"]
-	if caption_data is not None:
-		assert instances_data["images"] == caption_data["images"]
-
-
-class CDDataset(BaseDataset):
-	"CD: Caption Detection"
-	def __init__(self, 
-                image_root,
-				category_embedding_path,
-				instances_json_path = None,
-				stuff_json_path = None,
-				caption_json_path = None,
-				prob_real_caption = 0,
-				fake_caption_type = 'empty',
-                image_size=256, 
-                max_images=None,
-                min_box_size=0.01,
-                max_boxes_per_image=8,
-                include_other=False, 
-				random_crop = False,
-				random_flip = True,
-                ):
-		super().__init__(random_crop, random_flip, image_size)
-
-		self.image_root = image_root
-		self.category_embedding_path = category_embedding_path
-		self.instances_json_path = instances_json_path
-		self.stuff_json_path = stuff_json_path
-		self.caption_json_path = caption_json_path
-		self.prob_real_caption = prob_real_caption
-		self.fake_caption_type = fake_caption_type
-		self.max_images = max_images
-		self.min_box_size = min_box_size
-		self.max_boxes_per_image = max_boxes_per_image
-		self.include_other = include_other
-
-
-		assert fake_caption_type in ["empty", "made"]
-		if prob_real_caption > 0:
-			assert caption_json_path is not None, "caption json must be given"
-	
-
-		# Load all jsons 
-		with open(instances_json_path, 'r') as f:
-			instances_data = json.load(f) # keys: 'info', 'images', 'licenses', 'categories', 'annotations'
-		clean_annotations(instances_data["annotations"])
-		self.instances_data = instances_data
-
-		self.stuff_data = None
-		if stuff_json_path is not None:
-			with open(stuff_json_path, 'r') as f:
-				stuff_data = json.load(f) # keys: 'info', 'images', 'licenses', 'categories', 'annotations'
-			clean_annotations(stuff_data["annotations"])
-			self.stuff_data = stuff_data
-
-		self.captions_data = None
-		if caption_json_path is not None:
-			with open(caption_json_path, 'r') as f:
-				captions_data = json.load(f) # keys: 'info', 'images', 'licenses', 'categories', 'annotations'
-			clean_annotations(captions_data["annotations"])
-			self.captions_data = captions_data
-
-
-		# Load preprocessed name embedding 
-		self.category_embeddings = torch.load(category_embedding_path)
-		self.embedding_len = list( self.category_embeddings.values() )[0].shape[0]
-		
-
-		# Misc  
-		self.image_ids = [] # main list for selecting images
-		self.image_id_to_filename = {} # file names used to read image
-		check_all_have_same_images(self.instances_data, self.stuff_data, self.captions_data)
-		for image_data in self.instances_data['images']:
-			image_id = image_data['id']
-			filename = image_data['file_name']
-			self.image_ids.append(image_id)
-			self.image_id_to_filename[image_id] = filename
-
-		
-		# All category names (including things and stuff)
-		self.object_idx_to_name = {} 
-		for category_data in self.instances_data['categories']:
-			self.object_idx_to_name[category_data['id']] = category_data['name']
-		if self.stuff_data is not None:
-			for category_data in self.stuff_data['categories']:
-				self.object_idx_to_name[category_data['id']] = category_data['name']
-
-
-		# Add object data from instances and stuff 
-		self.image_id_to_objects = defaultdict(list)
-		self.select_objects( self.instances_data['annotations'] )
-		if self.stuff_data is not None:
-			self.select_objects( self.stuff_data['annotations'] )
-
-		# Add caption data 
-		if self.captions_data is not None:
-			self.image_id_to_captions = defaultdict(list)
-			self.select_captions( self.captions_data['annotations'] )
-
-		# Check if all filenames can be found in the zip file
-		# all_filenames = [self.image_id_to_filename[idx] for idx in self.image_ids]
-		# check_filenames_in_zipdata(all_filenames, image_root)
-		
-
-	def select_objects(self, annotations):
-		for object_anno in annotations:
-			image_id = object_anno['image_id']
-			object_name = self.object_idx_to_name[object_anno['category_id']]
-			other_ok = object_name != 'other' or self.include_other
-			if other_ok:
-				self.image_id_to_objects[image_id].append(object_anno)
-
-
-	def select_captions(self, annotations):
-		for caption_data in annotations:
-			image_id = caption_data['image_id']
-			self.image_id_to_captions[image_id].append(caption_data)
-
-
-	def total_images(self):
-		return len(self)
-
-
-	def __getitem__(self, index):
-		if self.max_boxes_per_image > 99:
-			assert False, "Are you sure setting such large number of boxes?"
-
-		out = {}
-
-		image_id = self.image_ids[index]
-		out['id'] = image_id
-		
-		# Image 
-		filename = self.image_id_to_filename[image_id]
-		image = self.fetch_image(filename)
-		#WW, HH = image.size
-		image_tensor, trans_info = self.transform_image(image)
-		out["image"] = image_tensor
-	
-
-		# Select valid boxes after cropping (center or random)
-		this_image_obj_annos = deepcopy(self.image_id_to_objects[image_id])
-		areas = []
-		all_obj_names = []
-		all_boxes = []
-		all_masks = []
-		all_positive_embeddings = []
-		for object_anno in this_image_obj_annos:
-
-			x, y, w, h = object_anno['bbox']
-			valid, (x0, y0, x1, y1) = recalculate_box_and_verify_if_valid(x, y, w, h, trans_info, self.image_size, self.min_box_size)
-
-			if valid:
-				areas.append(  (x1-x0)*(y1-y0) )
-				obj_name = self.object_idx_to_name[ object_anno['category_id']  ]
-				all_obj_names.append(obj_name)
-				all_boxes.append( torch.tensor([x0,y0,x1,y1]) / self.image_size ) # scale to 0-1
-				all_masks.append(1)
-				all_positive_embeddings.append( self.category_embeddings[obj_name]  )
-
-		wanted_idxs = torch.tensor(areas).sort(descending=True)[1]
-		wanted_idxs = wanted_idxs[0:self.max_boxes_per_image]
-		obj_names = [] # used for making a sentence
-		boxes = torch.zeros(self.max_boxes_per_image, 4)
-		masks = torch.zeros(self.max_boxes_per_image)
-		positive_embeddings = torch.zeros(self.max_boxes_per_image, self.embedding_len)
-		for i, idx in enumerate(wanted_idxs):
-			obj_names.append(  all_obj_names[idx]   )
-			boxes[i] = all_boxes[idx]
-			masks[i] = all_masks[idx]
-			positive_embeddings[i] = all_positive_embeddings[idx]
-
-		# Caption
-		if random.uniform(0, 1) < self.prob_real_caption:
-			caption_data = self.image_id_to_captions[image_id]
-			idx = random.randint(0,  len(caption_data)-1 )
-			caption = caption_data[idx]["caption"]
-		else:
-			if self.fake_caption_type == "empty":
-				caption = ""
-			else:
-				caption = make_a_sentence(obj_names, clean=True)
-		
-		
-		out["caption"] = caption
-		out["boxes"] = boxes
-		out["masks"] = masks
-		out["positive_embeddings"] = positive_embeddings
-
-		return out 
-
-
-	def __len__(self):
-		if self.max_images is None:
-			return len(self.image_ids)
-		return min(len(self.image_ids), self.max_images)	
-
diff --git a/spaces/aubmindlab/Arabic-NLP/backend/__init__.py b/spaces/aubmindlab/Arabic-NLP/backend/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/avivdm1/AutoGPT/data_ingestion.py b/spaces/avivdm1/AutoGPT/data_ingestion.py
deleted file mode 100644
index b89a33dafd15c2e7bded0445a741a4a1c47ed417..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/data_ingestion.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import argparse
-import logging
-
-from autogpt.commands.file_operations import ingest_file, search_files
-from autogpt.config import Config
-from autogpt.memory import get_memory
-
-cfg = Config()
-
-
-def configure_logging():
-    logging.basicConfig(
-        filename="log-ingestion.txt",
-        filemode="a",
-        format="%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s",
-        datefmt="%H:%M:%S",
-        level=logging.DEBUG,
-    )
-    return logging.getLogger("AutoGPT-Ingestion")
-
-
-def ingest_directory(directory, memory, args):
-    """
-    Ingest all files in a directory by calling the ingest_file function for each file.
-
-    :param directory: The directory containing the files to ingest
-    :param memory: An object with an add() method to store the chunks in memory
-    """
-    try:
-        files = search_files(directory)
-        for file in files:
-            ingest_file(file, memory, args.max_length, args.overlap)
-    except Exception as e:
-        print(f"Error while ingesting directory '{directory}': {str(e)}")
-
-
-def main() -> None:
-    logger = configure_logging()
-
-    parser = argparse.ArgumentParser(
-        description="Ingest a file or a directory with multiple files into memory. "
-        "Make sure to set your .env before running this script."
-    )
-    group = parser.add_mutually_exclusive_group(required=True)
-    group.add_argument("--file", type=str, help="The file to ingest.")
-    group.add_argument(
-        "--dir", type=str, help="The directory containing the files to ingest."
-    )
-    parser.add_argument(
-        "--init",
-        action="store_true",
-        help="Init the memory and wipe its content (default: False)",
-        default=False,
-    )
-    parser.add_argument(
-        "--overlap",
-        type=int,
-        help="The overlap size between chunks when ingesting files (default: 200)",
-        default=200,
-    )
-    parser.add_argument(
-        "--max_length",
-        type=int,
-        help="The max_length of each chunk when ingesting files (default: 4000)",
-        default=4000,
-    )
-
-    args = parser.parse_args()
-
-    # Initialize memory
-    memory = get_memory(cfg, init=args.init)
-    print("Using memory of type: " + memory.__class__.__name__)
-
-    if args.file:
-        try:
-            ingest_file(args.file, memory, args.max_length, args.overlap)
-            print(f"File '{args.file}' ingested successfully.")
-        except Exception as e:
-            logger.error(f"Error while ingesting file '{args.file}': {str(e)}")
-            print(f"Error while ingesting file '{args.file}': {str(e)}")
-    elif args.dir:
-        try:
-            ingest_directory(args.dir, memory, args)
-            print(f"Directory '{args.dir}' ingested successfully.")
-        except Exception as e:
-            logger.error(f"Error while ingesting directory '{args.dir}': {str(e)}")
-            print(f"Error while ingesting directory '{args.dir}': {str(e)}")
-    else:
-        print(
-            "Please provide either a file path (--file) or a directory name (--dir)"
-            " inside the auto_gpt_workspace directory as input."
-        )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/awacke1/Image-to-Text-Salesforce-blip-image-captioning-base/README.md b/spaces/awacke1/Image-to-Text-Salesforce-blip-image-captioning-base/README.md
deleted file mode 100644
index 7ecf8eb9c7c2e57dabd3ae5ecc84611ad5798cda..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Image-to-Text-Salesforce-blip-image-captioning-base/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Image To Text Salesforce Blip Image Captioning Base
-emoji: 🚀
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.20.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/PersistState/README.md b/spaces/awacke1/PersistState/README.md
deleted file mode 100644
index 9526a914747ea6e3227db0f21b1e4a882eea543e..0000000000000000000000000000000000000000
--- a/spaces/awacke1/PersistState/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Memory Chatbot
-emoji: 👀
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 2.9.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/awacke1/Streamlit-Pyplot-Math-Dice-Game/README.md b/spaces/awacke1/Streamlit-Pyplot-Math-Dice-Game/README.md
deleted file mode 100644
index 532ee12612cf3a4b20e82ecaa0eaadab5b958d67..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit-Pyplot-Math-Dice-Game/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Streamlit Pyplot Math 🎲Dice🎲  Game
-emoji: 🎲Math🎲
-colorFrom: green
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/badayvedat/LLaVA/llava/serve/cli.py b/spaces/badayvedat/LLaVA/llava/serve/cli.py
deleted file mode 100644
index fbabbfa76eb8c8af6be6bdc165f96cb9cb515244..0000000000000000000000000000000000000000
--- a/spaces/badayvedat/LLaVA/llava/serve/cli.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import argparse
-import torch
-
-from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
-from llava.conversation import conv_templates, SeparatorStyle
-from llava.model.builder import load_pretrained_model
-from llava.utils import disable_torch_init
-from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
-
-from PIL import Image
-
-import requests
-from PIL import Image
-from io import BytesIO
-from transformers import TextStreamer
-
-
-def load_image(image_file):
-    if image_file.startswith('http') or image_file.startswith('https'):
-        response = requests.get(image_file)
-        image = Image.open(BytesIO(response.content)).convert('RGB')
-    else:
-        image = Image.open(image_file).convert('RGB')
-    return image
-
-
-def main(args):
-    # Model
-    disable_torch_init()
-
-    model_name = get_model_name_from_path(args.model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit)
-
-    if 'llama-2' in model_name.lower():
-        conv_mode = "llava_llama_2"
-    elif "v1" in model_name.lower():
-        conv_mode = "llava_v1"
-    elif "mpt" in model_name.lower():
-        conv_mode = "mpt"
-    else:
-        conv_mode = "llava_v0"
-
-    if args.conv_mode is not None and conv_mode != args.conv_mode:
-        print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
-    else:
-        args.conv_mode = conv_mode
-
-    conv = conv_templates[args.conv_mode].copy()
-    if "mpt" in model_name.lower():
-        roles = ('user', 'assistant')
-    else:
-        roles = conv.roles
-
-    image = load_image(args.image_file)
-    image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda()
-
-    while True:
-        try:
-            inp = input(f"{roles[0]}: ")
-        except EOFError:
-            inp = ""
-        if not inp:
-            print("exit...")
-            break
-
-        print(f"{roles[1]}: ", end="")
-
-        if image is not None:
-            # first message
-            if model.config.mm_use_im_start_end:
-                inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp
-            else:
-                inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
-            conv.append_message(conv.roles[0], inp)
-            image = None
-        else:
-            # later messages
-            conv.append_message(conv.roles[0], inp)
-        conv.append_message(conv.roles[1], None)
-        prompt = conv.get_prompt()
-
-        input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
-        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
-        keywords = [stop_str]
-        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
-        streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
-
-        with torch.inference_mode():
-            output_ids = model.generate(
-                input_ids,
-                images=image_tensor,
-                do_sample=True,
-                temperature=0.2,
-                max_new_tokens=1024,
-                streamer=streamer,
-                use_cache=True,
-                stopping_criteria=[stopping_criteria])
-
-        outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
-        conv.messages[-1][-1] = outputs
-
-        if args.debug:
-            print("\n", {"prompt": prompt, "outputs": outputs}, "\n")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
-    parser.add_argument("--model-base", type=str, default=None)
-    parser.add_argument("--image-file", type=str, required=True)
-    parser.add_argument("--num-gpus", type=int, default=1)
-    parser.add_argument("--conv-mode", type=str, default=None)
-    parser.add_argument("--temperature", type=float, default=0.2)
-    parser.add_argument("--max-new-tokens", type=int, default=512)
-    parser.add_argument("--load-8bit", action="store_true")
-    parser.add_argument("--load-4bit", action="store_true")
-    parser.add_argument("--debug", action="store_true")
-    args = parser.parse_args()
-    main(args)
diff --git a/spaces/beihai/PDF-Table-Extractor/.history/app_20220620162908.py b/spaces/beihai/PDF-Table-Extractor/.history/app_20220620162908.py
deleted file mode 100644
index 59c639f2de30e73802f1bf221d7a6ec57bf28181..0000000000000000000000000000000000000000
--- a/spaces/beihai/PDF-Table-Extractor/.history/app_20220620162908.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#-*- coding : utf-8-*-
-import os,subprocess,base64
-from subprocess import STDOUT #os process manipuation
-os.system("apt-get update")
-os.system("apt-get install sudo")
-os.system("sudo apt update")
-os.system("apt-get install -y libgl1-mesa-glx")
-os.system("apt install ghostscript python3-tk")
-
-
-
-os.system("apt install ghostscript python3-tk")
-import streamlit as st
-@st.cache
-def gh():
-    """install ghostscript on the linux machine"""
-    
-    proc = subprocess.Popen('apt-get update', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash")
-    proc = subprocess.Popen('apt-get install sudo', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash")
-    proc = subprocess.Popen('sudo apt update', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash")
-    proc = subprocess.Popen('apt install ghostscript python3-tk', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash")
-    proc = subprocess.Popen('apt-get install -y libgl1-mesa-glx', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash")
-    proc.wait()
-gh()
-import pandas as pd 
-import camelot as cam # extracting tables from PDFs 
-
-st.title("PDF Table Extractor")
-
-input_pdf = st.file_uploader(label = "", type = 'pdf')
-
-page_number = st.text_input("请填写表格所在PDF页码，eg: 3", value = 1)
-
-if input_pdf is not None:
-    # byte object into a PDF file 
-    with open("input.pdf", "wb") as f:
-        base64_pdf = base64.b64encode(input_pdf.read()).decode('utf-8')
-        f.write(base64.b64decode(base64_pdf))
-    f.close()
-
-    # read the pdf and parse it using stream
-    tables = cam.read_pdf("input.pdf", pages=page_number)
-    result = pd.ExcelWriter('result.xlsx', engine='xlsxwriter') 
-    tables[0].to_excel(result,index=False) 
-    # for i in range(0,len(tables)):
-    #     table = tables[i].df
-    #     sheetname = str(i)
-    #     table.to_excel(result, sheetname,index=False) 
-
-    with open('result.xlsx','rb') as f:
-       st.download_button('提取完成，点击下载！', f,file_name='result.xlsx',mime="application/vnd.ms-excel")
-       
-       
-
diff --git a/spaces/bigPear/digitalWDF/src/utils/other.py b/spaces/bigPear/digitalWDF/src/utils/other.py
deleted file mode 100644
index 032d3d4dc48486fe48a4e732f60f659f93d7e4c5..0000000000000000000000000000000000000000
--- a/spaces/bigPear/digitalWDF/src/utils/other.py
+++ /dev/null
@@ -1,197 +0,0 @@
-import os
-import sys
-import json
-import torch
-import logging
-from typing import Dict, List, Optional
-
-from transformers import Seq2SeqTrainingArguments
-from transformers.trainer import TRAINER_STATE_NAME
-from transformers.modeling_utils import PreTrainedModel
-from transformers.generation.utils import LogitsProcessorList
-from transformers.generation.logits_process import LogitsProcessor
-
-
-from peft.utils.other import WEIGHTS_NAME
-
-
-IGNORE_INDEX = -100
-VALUE_HEAD_FILE_NAME = "value_head.bin"
-FINETUNING_ARGS_NAME = "finetuning_args.bin"
-PREDICTION_FILE_NAME = "generated_predictions.txt"
-
-
-logger = logging.getLogger(__name__)
-logging.basicConfig(
-    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-    datefmt="%m/%d/%Y %H:%M:%S",
-    level=logging.INFO,
-    handlers=[logging.StreamHandler(sys.stdout)]
-)
-
-
-def get_logger(name: str) -> logging.Logger:
-    return logging.getLogger(name)
-
-
-class AverageMeter:
-    r"""
-    Computes and stores the average and current value.
-    """
-    def __init__(self):
-        self.reset()
-
-    def reset(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-
-    def update(self, val, n=1):
-        self.val = val
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
-
-
-# Avoid runtime error in model.generate(do_sample=True).
-# Borrowed from: https://huggingface.co/THUDM/chatglm-6b/blob/658202d88ac4bb782b99e99ac3adff58b4d0b813/modeling_chatglm.py#L54
-class InvalidScoreLogitsProcessor(LogitsProcessor):
-
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
-        if torch.isnan(scores).any() or torch.isinf(scores).any():
-            scores.zero_()
-            scores[..., 5] = 5e4
-        return scores
-
-
-def get_logits_processor() -> LogitsProcessorList:
-    logits_processor = LogitsProcessorList()
-    logits_processor.append(InvalidScoreLogitsProcessor())
-    return logits_processor
-
-
-# Includes: (1) cast the layernorm in fp32 (2) make output embedding layer require grads (3) upcast the lm_head to fp32
-# Inspired by: https://github.com/huggingface/peft/blob/c0209c35abbf88c63aa267800d98a8e212ed0a42/src/peft/utils/other.py#L35
-def prepare_model_for_training(
-        model: PreTrainedModel,
-        output_embedding_layer_name: Optional[str] = "lm_head",
-        use_gradient_checkpointing: Optional[bool] = True,
-        layer_norm_names: Optional[List[str]] = ["layernorm"] # for chatglm setting
-) -> PreTrainedModel:
-
-    for name, param in model.named_parameters():
-        if param.ndim == 1 and any(layer_norm_name in name for layer_norm_name in layer_norm_names):
-            param.data = param.data.to(torch.float32)
-
-    if use_gradient_checkpointing:
-        model.enable_input_require_grads()
-        model.gradient_checkpointing_enable()
-        model.config.use_cache = False # turn off when gradient checkpointing is enabled
-
-    if hasattr(model, output_embedding_layer_name):
-        output_embedding_layer = getattr(model, output_embedding_layer_name)
-        input_dtype = output_embedding_layer.weight.dtype
-
-        class CastOutputToFloat(torch.nn.Sequential):
-
-            def forward(self, x):
-                return super().forward(x.to(input_dtype)).to(torch.float32)
-
-        setattr(model, output_embedding_layer_name, CastOutputToFloat(output_embedding_layer))
-
-    return model
-
-
-def print_trainable_params(model: torch.nn.Module) -> None:
-    trainable_params, all_param = 0, 0
-    for param in model.parameters():
-        num_params = param.numel()
-        # if using DS Zero 3 and the weights are initialized empty
-        if num_params == 0 and hasattr(param, "ds_numel"):
-            num_params = param.ds_numel
-        all_param += num_params
-        if param.requires_grad:
-            trainable_params += num_params
-    print("trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format(
-                trainable_params, all_param, 100 * trainable_params / all_param))
-
-
-def filter_model_params(model: torch.nn.Module) -> Dict[str, torch.Tensor]: # filter out freezed parameters
-    state_dict = model.state_dict()
-    filtered_state_dict = {}
-    for k, v in model.named_parameters():
-        if v.requires_grad:
-            filtered_state_dict[k] = state_dict[k]
-    return filtered_state_dict
-
-
-def save_trainable_params(save_directory: os.PathLike, model: torch.nn.Module) -> None:
-    if os.path.isfile(save_directory):
-        raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file.")
-    os.makedirs(save_directory, exist_ok=True)
-    filtered_state_dict = filter_model_params(model)
-    torch.save(filtered_state_dict, os.path.join(save_directory, WEIGHTS_NAME))
-
-
-def load_trainable_params(model: torch.nn.Module, checkpoint_dir: os.PathLike) -> None:
-    weights_file = os.path.join(checkpoint_dir, WEIGHTS_NAME)
-    if not os.path.exists(weights_file):
-        raise ValueError(f"Provided path ({checkpoint_dir}) does not contain the pretrained weights.")
-    model_state_dict = torch.load(weights_file)
-    model.load_state_dict(model_state_dict, strict=False) # skip missing keys
-
-
-def save_valuehead_params(save_directory: os.PathLike, v_head: torch.nn.Module) -> None:
-    if os.path.isfile(save_directory):
-        raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file.")
-    os.makedirs(save_directory, exist_ok=True)
-    torch.save(v_head.state_dict(), os.path.join(save_directory, VALUE_HEAD_FILE_NAME))
-
-
-def load_valuehead_params(model: torch.nn.Module, checkpoint_dir: os.PathLike) -> None:
-    valuehead_file = os.path.join(checkpoint_dir, VALUE_HEAD_FILE_NAME)
-    if not os.path.exists(valuehead_file):
-        raise ValueError(f"Provided path ({checkpoint_dir}) does not contain the valuehead weights.")
-    valuehead_state_dict = torch.load(valuehead_file)
-    model.register_buffer("reward_head_weight", valuehead_state_dict["summary.weight"])
-    model.register_buffer("reward_head_bias", valuehead_state_dict["summary.bias"])
-    model.register_buffer("default_head_weight", torch.zeros_like(valuehead_state_dict["summary.weight"]))
-    model.register_buffer("default_head_bias", torch.zeros_like(valuehead_state_dict["summary.bias"]))
-
-
-def smooth(scalars: List[float], weight: Optional[float] = 0.95) -> List[float]:
-    """
-    EMA implementation according to TensorBoard.
-    """
-    last = scalars[0]
-    smoothed = list()
-    for next_val in scalars:
-        smoothed_val = last * weight + (1 - weight) * next_val
-        smoothed.append(smoothed_val)
-        last = smoothed_val
-    return smoothed
-
-
-def plot_loss(training_args: Seq2SeqTrainingArguments, keys: Optional[List[str]] = ["loss"]) -> None:
-    import matplotlib.pyplot as plt
-    data = json.load(open(os.path.join(training_args.output_dir, TRAINER_STATE_NAME), "r"))
-
-    for key in keys:
-        steps, metrics = [], []
-
-        for i in range(len(data["log_history"])):
-            if key in data["log_history"][i]:
-                steps.append(data["log_history"][i]["step"])
-                metrics.append(data["log_history"][i][key])
-        smoothed_value = smooth(metrics)
-
-        plt.figure()
-        plt.plot(steps, metrics, alpha=0.4, label="original")
-        plt.plot(steps, smoothed_value, label="smoothed")
-        plt.title("training {} of {}".format(key, training_args.output_dir))
-        plt.xlabel("step")
-        plt.ylabel(key)
-        plt.legend()
-        plt.savefig(os.path.join(training_args.output_dir, "training_{}.jpg".format(key)), format="jpg", dpi=100)
-        print("Figure saved:", os.path.join(training_args.output_dir, "training_{}.jpg".format(key)))
diff --git a/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/rife/rife_new_gen/IFNet_HDv3.py b/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/rife/rife_new_gen/IFNet_HDv3.py
deleted file mode 100644
index 2360c9e7d15ad4c73e8bb34112999e3d46aeb8c2..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/rife/rife_new_gen/IFNet_HDv3.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from ..model.warplayer import warp
-# from train_log.refine import *
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
-    return nn.Sequential(
-        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
-                  padding=padding, dilation=dilation, bias=True),        
-        nn.LeakyReLU(0.2, True)
-    )
-
-def conv_bn(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
-    return nn.Sequential(
-        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
-                  padding=padding, dilation=dilation, bias=False),
-        nn.BatchNorm2d(out_planes),
-        nn.LeakyReLU(0.2, True)
-    )
-    
-class ResConv(nn.Module):
-    def __init__(self, c, dilation=1):
-        super(ResConv, self).__init__()
-        self.conv = nn.Conv2d(c, c, 3, 1, dilation, dilation=dilation, groups=1\
-)
-        self.beta = nn.Parameter(torch.ones((1, c, 1, 1)), requires_grad=True)
-        self.relu = nn.LeakyReLU(0.2, True)
-
-    def forward(self, x):
-        return self.relu(self.conv(x) * self.beta + x)
-
-class IFBlock(nn.Module):
-    def __init__(self, in_planes, c=64):
-        super(IFBlock, self).__init__()
-        self.conv0 = nn.Sequential(
-            conv(in_planes, c//2, 3, 2, 1),
-            conv(c//2, c, 3, 2, 1),
-            )
-        self.convblock = nn.Sequential(
-            ResConv(c),
-            ResConv(c),
-            ResConv(c),
-            ResConv(c),
-            ResConv(c),
-            ResConv(c),
-            ResConv(c),
-            ResConv(c),
-        )
-        self.lastconv = nn.Sequential(
-            nn.ConvTranspose2d(c, 4*6, 4, 2, 1),
-            nn.PixelShuffle(2)
-        )
-
-    def forward(self, x, flow=None, scale=1):
-        x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False)
-        if flow is not None:
-            flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False) * 1. / scale
-            x = torch.cat((x, flow), 1)
-        feat = self.conv0(x)
-        feat = self.convblock(feat)
-        tmp = self.lastconv(feat)
-        tmp = F.interpolate(tmp, scale_factor=scale, mode="bilinear", align_corners=False)
-        flow = tmp[:, :4] * scale
-        mask = tmp[:, 4:5]
-        return flow, mask
-        
-class IFNet(nn.Module):
-    def __init__(self):
-        super(IFNet, self).__init__()
-        self.block0 = IFBlock(7, c=192)
-        self.block1 = IFBlock(8+4, c=128)
-        self.block2 = IFBlock(8+4, c=96)
-        self.block3 = IFBlock(8+4, c=64)
-        # self.contextnet = Contextnet()
-        # self.unet = Unet()
-
-    def forward( self, x, timestep=0.5, scale_list=[8, 4, 2, 1], training=False, fastmode=True, ensemble=False):
-        if training == False:
-            channel = x.shape[1] // 2
-            img0 = x[:, :channel]
-            img1 = x[:, channel:]
-        if not torch.is_tensor(timestep):
-            timestep = (x[:, :1].clone() * 0 + 1) * timestep
-        else:
-            timestep = timestep.repeat(1, 1, img0.shape[2], img0.shape[3])
-        flow_list = []
-        merged = []
-        mask_list = []
-        warped_img0 = img0
-        warped_img1 = img1
-        flow = None
-        mask = None
-        loss_cons = 0
-        block = [self.block0, self.block1, self.block2, self.block3]
-        for i in range(4):
-            if flow is None:
-                flow, mask = block[i](torch.cat((img0[:, :3], img1[:, :3], timestep), 1), None, scale=scale_list[i])
-                if ensemble:
-                    f1, m1 = block[i](torch.cat((img1[:, :3], img0[:, :3], 1-timestep), 1), None, scale=scale_list[i])
-                    flow = (flow + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
-                    mask = (mask + (-m1)) / 2
-            else:
-                f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], timestep, mask), 1), flow, scale=scale_list[i])
-                if ensemble:
-            	    f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], 1-timestep, -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i])
-            	    f0 = (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
-            	    m0 = (m0 + (-m1)) / 2
-                flow = flow + f0
-                mask = mask + m0
-            mask_list.append(mask)
-            flow_list.append(flow)
-            warped_img0 = warp(img0, flow[:, :2])
-            warped_img1 = warp(img1, flow[:, 2:4])
-            merged.append((warped_img0, warped_img1))
-        mask_list[3] = torch.sigmoid(mask_list[3])
-        merged[3] = merged[3][0] * mask_list[3] + merged[3][1] * (1 - mask_list[3])
-        if not fastmode:
-            print('contextnet is removed')
-            '''
-            c0 = self.contextnet(img0, flow[:, :2])
-            c1 = self.contextnet(img1, flow[:, 2:4])
-            tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
-            res = tmp[:, :3] * 2 - 1
-            merged[3] = torch.clamp(merged[3] + res, 0, 1)
-            '''
-        return flow_list, mask_list[3], merged
diff --git a/spaces/bigjoker/stable-diffusion-webui/modules/processing.py b/spaces/bigjoker/stable-diffusion-webui/modules/processing.py
deleted file mode 100644
index f032716a3c3653b06add45030a63f7e744e575e2..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/modules/processing.py
+++ /dev/null
@@ -1,1056 +0,0 @@
-import json
-import math
-import os
-import sys
-import warnings
-
-import torch
-import numpy as np
-from PIL import Image, ImageFilter, ImageOps
-import random
-import cv2
-from skimage import exposure
-from typing import Any, Dict, List, Optional
-
-import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts
-from modules.sd_hijack import model_hijack
-from modules.shared import opts, cmd_opts, state
-import modules.shared as shared
-import modules.paths as paths
-import modules.face_restoration
-import modules.images as images
-import modules.styles
-import modules.sd_models as sd_models
-import modules.sd_vae as sd_vae
-import logging
-from ldm.data.util import AddMiDaS
-from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
-
-from einops import repeat, rearrange
-from blendmodes.blend import blendLayers, BlendType
-
-# some of those options should not be changed at all because they would break the model, so I removed them from options.
-opt_C = 4
-opt_f = 8
-
-
-def setup_color_correction(image):
-    logging.info("Calibrating color correction.")
-    correction_target = cv2.cvtColor(np.asarray(image.copy()), cv2.COLOR_RGB2LAB)
-    return correction_target
-
-
-def apply_color_correction(correction, original_image):
-    logging.info("Applying color correction.")
-    image = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
-        cv2.cvtColor(
-            np.asarray(original_image),
-            cv2.COLOR_RGB2LAB
-        ),
-        correction,
-        channel_axis=2
-    ), cv2.COLOR_LAB2RGB).astype("uint8"))
-
-    image = blendLayers(image, original_image, BlendType.LUMINOSITY)
-
-    return image
-
-
-def apply_overlay(image, paste_loc, index, overlays):
-    if overlays is None or index >= len(overlays):
-        return image
-
-    overlay = overlays[index]
-
-    if paste_loc is not None:
-        x, y, w, h = paste_loc
-        base_image = Image.new('RGBA', (overlay.width, overlay.height))
-        image = images.resize_image(1, image, w, h)
-        base_image.paste(image, (x, y))
-        image = base_image
-
-    image = image.convert('RGBA')
-    image.alpha_composite(overlay)
-    image = image.convert('RGB')
-
-    return image
-
-
-def txt2img_image_conditioning(sd_model, x, width, height):
-    if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
-        # Dummy zero conditioning if we're not using inpainting model.
-        # Still takes up a bit of memory, but no encoder call.
-        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-        return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
-
-    # The "masked-image" in this case will just be all zeros since the entire image is masked.
-    image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-    image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
-
-    # Add the fake full 1s mask to the first dimension.
-    image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-    image_conditioning = image_conditioning.to(x.dtype)
-
-    return image_conditioning
-
-
-class StableDiffusionProcessing:
-    """
-    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
-    """
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
-        if sampler_index is not None:
-            print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
-
-        self.outpath_samples: str = outpath_samples
-        self.outpath_grids: str = outpath_grids
-        self.prompt: str = prompt
-        self.prompt_for_display: str = None
-        self.negative_prompt: str = (negative_prompt or "")
-        self.styles: list = styles or []
-        self.seed: int = seed
-        self.subseed: int = subseed
-        self.subseed_strength: float = subseed_strength
-        self.seed_resize_from_h: int = seed_resize_from_h
-        self.seed_resize_from_w: int = seed_resize_from_w
-        self.sampler_name: str = sampler_name
-        self.batch_size: int = batch_size
-        self.n_iter: int = n_iter
-        self.steps: int = steps
-        self.cfg_scale: float = cfg_scale
-        self.width: int = width
-        self.height: int = height
-        self.restore_faces: bool = restore_faces
-        self.tiling: bool = tiling
-        self.do_not_save_samples: bool = do_not_save_samples
-        self.do_not_save_grid: bool = do_not_save_grid
-        self.extra_generation_params: dict = extra_generation_params or {}
-        self.overlay_images = overlay_images
-        self.eta = eta
-        self.do_not_reload_embeddings = do_not_reload_embeddings
-        self.paste_to = None
-        self.color_corrections = None
-        self.denoising_strength: float = denoising_strength
-        self.sampler_noise_scheduler_override = None
-        self.ddim_discretize = ddim_discretize or opts.ddim_discretize
-        self.s_churn = s_churn or opts.s_churn
-        self.s_tmin = s_tmin or opts.s_tmin
-        self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
-        self.s_noise = s_noise or opts.s_noise
-        self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
-        self.override_settings_restore_afterwards = override_settings_restore_afterwards
-        self.is_using_inpainting_conditioning = False
-        self.disable_extra_networks = False
-
-        if not seed_enable_extras:
-            self.subseed = -1
-            self.subseed_strength = 0
-            self.seed_resize_from_h = 0
-            self.seed_resize_from_w = 0
-
-        self.scripts = None
-        self.script_args = script_args
-        self.all_prompts = None
-        self.all_negative_prompts = None
-        self.all_seeds = None
-        self.all_subseeds = None
-        self.iteration = 0
-
-    @property
-    def sd_model(self):
-        return shared.sd_model
-
-    def txt2img_image_conditioning(self, x, width=None, height=None):
-        self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
-
-        return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height)
-
-    def depth2img_image_conditioning(self, source_image):
-        # Use the AddMiDaS helper to Format our source image to suit the MiDaS model
-        transformer = AddMiDaS(model_type="dpt_hybrid")
-        transformed = transformer({"jpg": rearrange(source_image[0], "c h w -> h w c")})
-        midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
-        midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
-
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
-        conditioning = torch.nn.functional.interpolate(
-            self.sd_model.depth_model(midas_in),
-            size=conditioning_image.shape[2:],
-            mode="bicubic",
-            align_corners=False,
-        )
-
-        (depth_min, depth_max) = torch.aminmax(conditioning)
-        conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
-        return conditioning
-
-    def edit_image_conditioning(self, source_image):
-        conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
-
-        return conditioning_image
-
-    def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
-        self.is_using_inpainting_conditioning = True
-
-        # Handle the different mask inputs
-        if image_mask is not None:
-            if torch.is_tensor(image_mask):
-                conditioning_mask = image_mask
-            else:
-                conditioning_mask = np.array(image_mask.convert("L"))
-                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
-                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
-
-                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
-                conditioning_mask = torch.round(conditioning_mask)
-        else:
-            conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
-
-        # Create another latent image, this time with a masked version of the original input.
-        # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
-        conditioning_mask = conditioning_mask.to(device=source_image.device, dtype=source_image.dtype)
-        conditioning_image = torch.lerp(
-            source_image,
-            source_image * (1.0 - conditioning_mask),
-            getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
-        )
-
-        # Encode the new masked image using first stage of network.
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
-
-        # Create the concatenated conditioning tensor to be fed to `c_concat`
-        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
-        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
-        image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
-        image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype)
-
-        return image_conditioning
-
-    def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
-        source_image = devices.cond_cast_float(source_image)
-
-        # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
-        # identify itself with a field common to all models. The conditioning_key is also hybrid.
-        if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
-            return self.depth2img_image_conditioning(source_image)
-
-        if self.sd_model.cond_stage_key == "edit":
-            return self.edit_image_conditioning(source_image)
-
-        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
-
-        # Dummy zero conditioning if we're not using inpainting or depth model.
-        return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
-
-    def init(self, all_prompts, all_seeds, all_subseeds):
-        pass
-
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
-        raise NotImplementedError()
-
-    def close(self):
-        self.sampler = None
-
-
-class Processed:
-    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
-        self.images = images_list
-        self.prompt = p.prompt
-        self.negative_prompt = p.negative_prompt
-        self.seed = seed
-        self.subseed = subseed
-        self.subseed_strength = p.subseed_strength
-        self.info = info
-        self.comments = comments
-        self.width = p.width
-        self.height = p.height
-        self.sampler_name = p.sampler_name
-        self.cfg_scale = p.cfg_scale
-        self.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
-        self.steps = p.steps
-        self.batch_size = p.batch_size
-        self.restore_faces = p.restore_faces
-        self.face_restoration_model = opts.face_restoration_model if p.restore_faces else None
-        self.sd_model_hash = shared.sd_model.sd_model_hash
-        self.seed_resize_from_w = p.seed_resize_from_w
-        self.seed_resize_from_h = p.seed_resize_from_h
-        self.denoising_strength = getattr(p, 'denoising_strength', None)
-        self.extra_generation_params = p.extra_generation_params
-        self.index_of_first_image = index_of_first_image
-        self.styles = p.styles
-        self.job_timestamp = state.job_timestamp
-        self.clip_skip = opts.CLIP_stop_at_last_layers
-
-        self.eta = p.eta
-        self.ddim_discretize = p.ddim_discretize
-        self.s_churn = p.s_churn
-        self.s_tmin = p.s_tmin
-        self.s_tmax = p.s_tmax
-        self.s_noise = p.s_noise
-        self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
-        self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
-        self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
-        self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
-        self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
-        self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
-
-        self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
-        self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
-        self.all_seeds = all_seeds or p.all_seeds or [self.seed]
-        self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
-        self.infotexts = infotexts or [info]
-
-    def js(self):
-        obj = {
-            "prompt": self.all_prompts[0],
-            "all_prompts": self.all_prompts,
-            "negative_prompt": self.all_negative_prompts[0],
-            "all_negative_prompts": self.all_negative_prompts,
-            "seed": self.seed,
-            "all_seeds": self.all_seeds,
-            "subseed": self.subseed,
-            "all_subseeds": self.all_subseeds,
-            "subseed_strength": self.subseed_strength,
-            "width": self.width,
-            "height": self.height,
-            "sampler_name": self.sampler_name,
-            "cfg_scale": self.cfg_scale,
-            "steps": self.steps,
-            "batch_size": self.batch_size,
-            "restore_faces": self.restore_faces,
-            "face_restoration_model": self.face_restoration_model,
-            "sd_model_hash": self.sd_model_hash,
-            "seed_resize_from_w": self.seed_resize_from_w,
-            "seed_resize_from_h": self.seed_resize_from_h,
-            "denoising_strength": self.denoising_strength,
-            "extra_generation_params": self.extra_generation_params,
-            "index_of_first_image": self.index_of_first_image,
-            "infotexts": self.infotexts,
-            "styles": self.styles,
-            "job_timestamp": self.job_timestamp,
-            "clip_skip": self.clip_skip,
-            "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
-        }
-
-        return json.dumps(obj)
-
-    def infotext(self, p: StableDiffusionProcessing, index):
-        return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
-
-
-# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
-def slerp(val, low, high):
-    low_norm = low/torch.norm(low, dim=1, keepdim=True)
-    high_norm = high/torch.norm(high, dim=1, keepdim=True)
-    dot = (low_norm*high_norm).sum(1)
-
-    if dot.mean() > 0.9995:
-        return low * val + high * (1 - val)
-
-    omega = torch.acos(dot)
-    so = torch.sin(omega)
-    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
-    return res
-
-
-def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
-    eta_noise_seed_delta = opts.eta_noise_seed_delta or 0
-    xs = []
-
-    # if we have multiple seeds, this means we are working with batch size>1; this then
-    # enables the generation of additional tensors with noise that the sampler will use during its processing.
-    # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
-    # produce the same images as with two batches [100], [101].
-    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or eta_noise_seed_delta > 0):
-        sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
-    else:
-        sampler_noises = None
-
-    for i, seed in enumerate(seeds):
-        noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
-
-        subnoise = None
-        if subseeds is not None:
-            subseed = 0 if i >= len(subseeds) else subseeds[i]
-
-            subnoise = devices.randn(subseed, noise_shape)
-
-        # randn results depend on device; gpu and cpu get different results for same seed;
-        # the way I see it, it's better to do this on CPU, so that everyone gets same result;
-        # but the original script had it like this, so I do not dare change it for now because
-        # it will break everyone's seeds.
-        noise = devices.randn(seed, noise_shape)
-
-        if subnoise is not None:
-            noise = slerp(subseed_strength, noise, subnoise)
-
-        if noise_shape != shape:
-            x = devices.randn(seed, shape)
-            dx = (shape[2] - noise_shape[2]) // 2
-            dy = (shape[1] - noise_shape[1]) // 2
-            w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
-            h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
-            tx = 0 if dx < 0 else dx
-            ty = 0 if dy < 0 else dy
-            dx = max(-dx, 0)
-            dy = max(-dy, 0)
-
-            x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
-            noise = x
-
-        if sampler_noises is not None:
-            cnt = p.sampler.number_of_needed_noises(p)
-
-            if eta_noise_seed_delta > 0:
-                torch.manual_seed(seed + eta_noise_seed_delta)
-
-            for j in range(cnt):
-                sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
-
-        xs.append(noise)
-
-    if sampler_noises is not None:
-        p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]
-
-    x = torch.stack(xs).to(shared.device)
-    return x
-
-
-def decode_first_stage(model, x):
-    with devices.autocast(disable=x.dtype == devices.dtype_vae):
-        x = model.decode_first_stage(x)
-
-    return x
-
-
-def get_fixed_seed(seed):
-    if seed is None or seed == '' or seed == -1:
-        return int(random.randrange(4294967294))
-
-    return seed
-
-
-def fix_seed(p):
-    p.seed = get_fixed_seed(p.seed)
-    p.subseed = get_fixed_seed(p.subseed)
-
-
-def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
-    index = position_in_batch + iteration * p.batch_size
-
-    clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
-
-    generation_params = {
-        "Steps": p.steps,
-        "Sampler": p.sampler_name,
-        "CFG scale": p.cfg_scale,
-        "Image CFG scale": getattr(p, 'image_cfg_scale', None),
-        "Seed": all_seeds[index],
-        "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
-        "Size": f"{p.width}x{p.height}",
-        "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
-        "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-        "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
-        "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
-        "Denoising strength": getattr(p, 'denoising_strength', None),
-        "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
-        "Clip skip": None if clip_skip <= 1 else clip_skip,
-        "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
-    }
-
-    generation_params.update(p.extra_generation_params)
-
-    generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
-
-    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else ""
-
-    return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
-
-
-def process_images(p: StableDiffusionProcessing) -> Processed:
-    stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
-
-    try:
-        for k, v in p.override_settings.items():
-            setattr(opts, k, v)
-
-            if k == 'sd_model_checkpoint':
-                sd_models.reload_model_weights()
-
-            if k == 'sd_vae':
-                sd_vae.reload_vae_weights()
-
-        res = process_images_inner(p)
-
-    finally:
-        # restore opts to original state
-        if p.override_settings_restore_afterwards:
-            for k, v in stored_opts.items():
-                setattr(opts, k, v)
-                if k == 'sd_model_checkpoint':
-                    sd_models.reload_model_weights()
-
-                if k == 'sd_vae':
-                    sd_vae.reload_vae_weights()
-
-    return res
-
-
-def process_images_inner(p: StableDiffusionProcessing) -> Processed:
-    """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
-
-    if type(p.prompt) == list:
-        assert(len(p.prompt) > 0)
-    else:
-        assert p.prompt is not None
-
-    devices.torch_gc()
-
-    seed = get_fixed_seed(p.seed)
-    subseed = get_fixed_seed(p.subseed)
-
-    modules.sd_hijack.model_hijack.apply_circular(p.tiling)
-    modules.sd_hijack.model_hijack.clear_comments()
-
-    comments = {}
-
-    if type(p.prompt) == list:
-        p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
-    else:
-        p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]
-
-    if type(p.negative_prompt) == list:
-        p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
-    else:
-        p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
-
-    if type(seed) == list:
-        p.all_seeds = seed
-    else:
-        p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
-
-    if type(subseed) == list:
-        p.all_subseeds = subseed
-    else:
-        p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
-
-    def infotext(iteration=0, position_in_batch=0):
-        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
-
-    if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
-        model_hijack.embedding_db.load_textual_inversion_embeddings()
-
-    if p.scripts is not None:
-        p.scripts.process(p)
-
-    infotexts = []
-    output_images = []
-
-    cached_uc = [None, None]
-    cached_c = [None, None]
-
-    def get_conds_with_caching(function, required_prompts, steps, cache):
-        """
-        Returns the result of calling function(shared.sd_model, required_prompts, steps)
-        using a cache to store the result if the same arguments have been used before.
-
-        cache is an array containing two elements. The first element is a tuple
-        representing the previously used arguments, or None if no arguments
-        have been used before. The second element is where the previously
-        computed result is stored.
-        """
-
-        if cache[0] is not None and (required_prompts, steps) == cache[0]:
-            return cache[1]
-
-        with devices.autocast():
-            cache[1] = function(shared.sd_model, required_prompts, steps)
-
-        cache[0] = (required_prompts, steps)
-        return cache[1]
-
-    with torch.no_grad(), p.sd_model.ema_scope():
-        with devices.autocast():
-            p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
-
-            # for OSX, loading the model during sampling changes the generated picture, so it is loaded here
-            if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
-                sd_vae_approx.model()
-
-        if state.job_count == -1:
-            state.job_count = p.n_iter
-
-        for n in range(p.n_iter):
-            p.iteration = n
-
-            if state.skipped:
-                state.skipped = False
-
-            if state.interrupted:
-                break
-
-            prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
-            negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
-            seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
-            subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
-
-            if len(prompts) == 0:
-                break
-
-            prompts, extra_network_data = extra_networks.parse_prompts(prompts)
-
-            if not p.disable_extra_networks:
-                with devices.autocast():
-                    extra_networks.activate(p, extra_network_data)
-
-            if p.scripts is not None:
-                p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
-
-            # params.txt should be saved after scripts.process_batch, since the
-            # infotext could be modified by that callback
-            # Example: a wildcard processed by process_batch sets an extra model
-            # strength, which is saved as "Model Strength: 1.0" in the infotext
-            if n == 0:
-                with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
-                    processed = Processed(p, [], p.seed, "")
-                    file.write(processed.infotext(p, 0))
-
-            uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
-            c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
-
-            if len(model_hijack.comments) > 0:
-                for comment in model_hijack.comments:
-                    comments[comment] = 1
-
-            if p.n_iter > 1:
-                shared.state.job = f"Batch {n+1} out of {p.n_iter}"
-
-            with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
-                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
-
-            x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
-            for x in x_samples_ddim:
-                devices.test_for_nans(x, "vae")
-
-            x_samples_ddim = torch.stack(x_samples_ddim).float()
-            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
-
-            del samples_ddim
-
-            if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
-                lowvram.send_everything_to_cpu()
-
-            devices.torch_gc()
-
-            if p.scripts is not None:
-                p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
-
-            for i, x_sample in enumerate(x_samples_ddim):
-                x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-                x_sample = x_sample.astype(np.uint8)
-
-                if p.restore_faces:
-                    if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
-                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
-
-                    devices.torch_gc()
-
-                    x_sample = modules.face_restoration.restore_faces(x_sample)
-                    devices.torch_gc()
-
-                image = Image.fromarray(x_sample)
-
-                if p.scripts is not None:
-                    pp = scripts.PostprocessImageArgs(image)
-                    p.scripts.postprocess_image(p, pp)
-                    image = pp.image
-
-                if p.color_corrections is not None and i < len(p.color_corrections):
-                    if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
-                        image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
-                        images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
-                    image = apply_color_correction(p.color_corrections[i], image)
-
-                image = apply_overlay(image, p.paste_to, i, p.overlay_images)
-
-                if opts.samples_save and not p.do_not_save_samples:
-                    images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
-
-                text = infotext(n, i)
-                infotexts.append(text)
-                if opts.enable_pnginfo:
-                    image.info["parameters"] = text
-                output_images.append(image)
-
-            del x_samples_ddim
-
-            devices.torch_gc()
-
-            state.nextjob()
-
-        p.color_corrections = None
-
-        index_of_first_image = 0
-        unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple
-        if (opts.return_grid or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
-            grid = images.image_grid(output_images, p.batch_size)
-
-            if opts.return_grid:
-                text = infotext()
-                infotexts.insert(0, text)
-                if opts.enable_pnginfo:
-                    grid.info["parameters"] = text
-                output_images.insert(0, grid)
-                index_of_first_image = 1
-
-            if opts.grid_save:
-                images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
-
-    if not p.disable_extra_networks:
-        extra_networks.deactivate(p, extra_network_data)
-
-    devices.torch_gc()
-
-    res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
-
-    if p.scripts is not None:
-        p.scripts.postprocess(p, res)
-
-    return res
-
-
-def old_hires_fix_first_pass_dimensions(width, height):
-    """old algorithm for auto-calculating first pass size"""
-
-    desired_pixel_count = 512 * 512
-    actual_pixel_count = width * height
-    scale = math.sqrt(desired_pixel_count / actual_pixel_count)
-    width = math.ceil(scale * width / 64) * 64
-    height = math.ceil(scale * height / 64) * 64
-
-    return width, height
-
-
-class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
-    sampler = None
-
-    def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, **kwargs):
-        super().__init__(**kwargs)
-        self.enable_hr = enable_hr
-        self.denoising_strength = denoising_strength
-        self.hr_scale = hr_scale
-        self.hr_upscaler = hr_upscaler
-        self.hr_second_pass_steps = hr_second_pass_steps
-        self.hr_resize_x = hr_resize_x
-        self.hr_resize_y = hr_resize_y
-        self.hr_upscale_to_x = hr_resize_x
-        self.hr_upscale_to_y = hr_resize_y
-
-        if firstphase_width != 0 or firstphase_height != 0:
-            self.hr_upscale_to_x = self.width
-            self.hr_upscale_to_y = self.height
-            self.width = firstphase_width
-            self.height = firstphase_height
-
-        self.truncate_x = 0
-        self.truncate_y = 0
-        self.applied_old_hires_behavior_to = None
-
-    def init(self, all_prompts, all_seeds, all_subseeds):
-        if self.enable_hr:
-            if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
-                self.hr_resize_x = self.width
-                self.hr_resize_y = self.height
-                self.hr_upscale_to_x = self.width
-                self.hr_upscale_to_y = self.height
-
-                self.width, self.height = old_hires_fix_first_pass_dimensions(self.width, self.height)
-                self.applied_old_hires_behavior_to = (self.width, self.height)
-
-            if self.hr_resize_x == 0 and self.hr_resize_y == 0:
-                self.extra_generation_params["Hires upscale"] = self.hr_scale
-                self.hr_upscale_to_x = int(self.width * self.hr_scale)
-                self.hr_upscale_to_y = int(self.height * self.hr_scale)
-            else:
-                self.extra_generation_params["Hires resize"] = f"{self.hr_resize_x}x{self.hr_resize_y}"
-
-                if self.hr_resize_y == 0:
-                    self.hr_upscale_to_x = self.hr_resize_x
-                    self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
-                elif self.hr_resize_x == 0:
-                    self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
-                    self.hr_upscale_to_y = self.hr_resize_y
-                else:
-                    target_w = self.hr_resize_x
-                    target_h = self.hr_resize_y
-                    src_ratio = self.width / self.height
-                    dst_ratio = self.hr_resize_x / self.hr_resize_y
-
-                    if src_ratio < dst_ratio:
-                        self.hr_upscale_to_x = self.hr_resize_x
-                        self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
-                    else:
-                        self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
-                        self.hr_upscale_to_y = self.hr_resize_y
-
-                    self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
-                    self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
-
-            # special case: the user has chosen to do nothing
-            if self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height:
-                self.enable_hr = False
-                self.denoising_strength = None
-                self.extra_generation_params.pop("Hires upscale", None)
-                self.extra_generation_params.pop("Hires resize", None)
-                return
-
-            if not state.processing_has_refined_job_count:
-                if state.job_count == -1:
-                    state.job_count = self.n_iter
-
-                shared.total_tqdm.updateTotal((self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count)
-                state.job_count = state.job_count * 2
-                state.processing_has_refined_job_count = True
-
-            if self.hr_second_pass_steps:
-                self.extra_generation_params["Hires steps"] = self.hr_second_pass_steps
-
-            if self.hr_upscaler is not None:
-                self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
-
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
-        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
-
-        latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
-        if self.enable_hr and latent_scale_mode is None:
-            assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}"
-
-        x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
-
-        if not self.enable_hr:
-            return samples
-
-        target_width = self.hr_upscale_to_x
-        target_height = self.hr_upscale_to_y
-
-        def save_intermediate(image, index):
-            """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
-
-            if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
-                return
-
-            if not isinstance(image, Image.Image):
-                image = sd_samplers.sample_to_image(image, index, approximation=0)
-
-            info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
-            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
-
-        if latent_scale_mode is not None:
-            for i in range(samples.shape[0]):
-                save_intermediate(samples, i)
-
-            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
-
-            # Avoid making the inpainting conditioning unless necessary as
-            # this does need some extra compute to decode / encode the image again.
-            if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
-                image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
-            else:
-                image_conditioning = self.txt2img_image_conditioning(samples)
-        else:
-            decoded_samples = decode_first_stage(self.sd_model, samples)
-            lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
-            batch_images = []
-            for i, x_sample in enumerate(lowres_samples):
-                x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-                x_sample = x_sample.astype(np.uint8)
-                image = Image.fromarray(x_sample)
-
-                save_intermediate(image, i)
-
-                image = images.resize_image(0, image, target_width, target_height, upscaler_name=self.hr_upscaler)
-                image = np.array(image).astype(np.float32) / 255.0
-                image = np.moveaxis(image, 2, 0)
-                batch_images.append(image)
-
-            decoded_samples = torch.from_numpy(np.array(batch_images))
-            decoded_samples = decoded_samples.to(shared.device)
-            decoded_samples = 2. * decoded_samples - 1.
-
-            samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
-
-            image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
-
-        shared.state.nextjob()
-
-        img2img_sampler_name = self.sampler_name if self.sampler_name != 'PLMS' else 'DDIM'  # PLMS does not support img2img so we just silently switch ot DDIM
-        self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
-
-        samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
-
-        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
-
-        # GC now before running the next img2img to prevent running out of memory
-        x = None
-        devices.torch_gc()
-
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
-
-        return samples
-
-
-class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
-    sampler = None
-
-    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
-        super().__init__(**kwargs)
-
-        self.init_images = init_images
-        self.resize_mode: int = resize_mode
-        self.denoising_strength: float = denoising_strength
-        self.image_cfg_scale: float = image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
-        self.init_latent = None
-        self.image_mask = mask
-        self.latent_mask = None
-        self.mask_for_overlay = None
-        self.mask_blur = mask_blur
-        self.inpainting_fill = inpainting_fill
-        self.inpaint_full_res = inpaint_full_res
-        self.inpaint_full_res_padding = inpaint_full_res_padding
-        self.inpainting_mask_invert = inpainting_mask_invert
-        self.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
-        self.mask = None
-        self.nmask = None
-        self.image_conditioning = None
-
-    def init(self, all_prompts, all_seeds, all_subseeds):
-        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
-        crop_region = None
-
-        image_mask = self.image_mask
-
-        if image_mask is not None:
-            image_mask = image_mask.convert('L')
-
-            if self.inpainting_mask_invert:
-                image_mask = ImageOps.invert(image_mask)
-
-            if self.mask_blur > 0:
-                image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
-
-            if self.inpaint_full_res:
-                self.mask_for_overlay = image_mask
-                mask = image_mask.convert('L')
-                crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
-                crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
-                x1, y1, x2, y2 = crop_region
-
-                mask = mask.crop(crop_region)
-                image_mask = images.resize_image(2, mask, self.width, self.height)
-                self.paste_to = (x1, y1, x2-x1, y2-y1)
-            else:
-                image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
-                np_mask = np.array(image_mask)
-                np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
-                self.mask_for_overlay = Image.fromarray(np_mask)
-
-            self.overlay_images = []
-
-        latent_mask = self.latent_mask if self.latent_mask is not None else image_mask
-
-        add_color_corrections = opts.img2img_color_correction and self.color_corrections is None
-        if add_color_corrections:
-            self.color_corrections = []
-        imgs = []
-        for img in self.init_images:
-            image = images.flatten(img, opts.img2img_background_color)
-
-            if crop_region is None and self.resize_mode != 3:
-                image = images.resize_image(self.resize_mode, image, self.width, self.height)
-
-            if image_mask is not None:
-                image_masked = Image.new('RGBa', (image.width, image.height))
-                image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
-
-                self.overlay_images.append(image_masked.convert('RGBA'))
-
-            # crop_region is not None if we are doing inpaint full res
-            if crop_region is not None:
-                image = image.crop(crop_region)
-                image = images.resize_image(2, image, self.width, self.height)
-
-            if image_mask is not None:
-                if self.inpainting_fill != 1:
-                    image = masking.fill(image, latent_mask)
-
-            if add_color_corrections:
-                self.color_corrections.append(setup_color_correction(image))
-
-            image = np.array(image).astype(np.float32) / 255.0
-            image = np.moveaxis(image, 2, 0)
-
-            imgs.append(image)
-
-        if len(imgs) == 1:
-            batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
-            if self.overlay_images is not None:
-                self.overlay_images = self.overlay_images * self.batch_size
-
-            if self.color_corrections is not None and len(self.color_corrections) == 1:
-                self.color_corrections = self.color_corrections * self.batch_size
-
-        elif len(imgs) <= self.batch_size:
-            self.batch_size = len(imgs)
-            batch_images = np.array(imgs)
-        else:
-            raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
-
-        image = torch.from_numpy(batch_images)
-        image = 2. * image - 1.
-        image = image.to(shared.device)
-
-        self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
-
-        if self.resize_mode == 3:
-            self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
-
-        if image_mask is not None:
-            init_mask = latent_mask
-            latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
-            latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
-            latmask = latmask[0]
-            latmask = np.around(latmask)
-            latmask = np.tile(latmask[None], (4, 1, 1))
-
-            self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
-            self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype)
-
-            # this needs to be fixed to be done in sample() using actual seeds for batches
-            if self.inpainting_fill == 2:
-                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
-            elif self.inpainting_fill == 3:
-                self.init_latent = self.init_latent * self.mask
-
-        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)
-
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
-        x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-
-        if self.initial_noise_multiplier != 1.0:
-            self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
-            x *= self.initial_noise_multiplier
-
-        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
-
-        if self.mask is not None:
-            samples = samples * self.nmask + self.init_latent * self.mask
-
-        del x
-        devices.torch_gc()
-
-        return samples
diff --git a/spaces/bigjoker/stable-diffusion-webui/modules/sd_hijack_clip_old.py b/spaces/bigjoker/stable-diffusion-webui/modules/sd_hijack_clip_old.py
deleted file mode 100644
index 433d8c3da8e33aa09833dcd1793395f420e984d7..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/modules/sd_hijack_clip_old.py
+++ /dev/null
@@ -1,81 +0,0 @@
-from modules import sd_hijack_clip
-from modules import shared
-
-
-def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
-    id_start = self.id_start
-    id_end = self.id_end
-    maxlen = self.wrapped.max_length  # you get to stay at 77
-    used_custom_terms = []
-    remade_batch_tokens = []
-    hijack_comments = []
-    hijack_fixes = []
-    token_count = 0
-
-    cache = {}
-    batch_tokens = self.tokenize(texts)
-    batch_multipliers = []
-    for tokens in batch_tokens:
-        tuple_tokens = tuple(tokens)
-
-        if tuple_tokens in cache:
-            remade_tokens, fixes, multipliers = cache[tuple_tokens]
-        else:
-            fixes = []
-            remade_tokens = []
-            multipliers = []
-            mult = 1.0
-
-            i = 0
-            while i < len(tokens):
-                token = tokens[i]
-
-                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
-
-                mult_change = self.token_mults.get(token) if shared.opts.enable_emphasis else None
-                if mult_change is not None:
-                    mult *= mult_change
-                    i += 1
-                elif embedding is None:
-                    remade_tokens.append(token)
-                    multipliers.append(mult)
-                    i += 1
-                else:
-                    emb_len = int(embedding.vec.shape[0])
-                    fixes.append((len(remade_tokens), embedding))
-                    remade_tokens += [0] * emb_len
-                    multipliers += [mult] * emb_len
-                    used_custom_terms.append((embedding.name, embedding.checksum()))
-                    i += embedding_length_in_tokens
-
-            if len(remade_tokens) > maxlen - 2:
-                vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
-                ovf = remade_tokens[maxlen - 2:]
-                overflowing_words = [vocab.get(int(x), "") for x in ovf]
-                overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
-                hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
-
-            token_count = len(remade_tokens)
-            remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
-            remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
-            cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
-
-        multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
-        multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
-
-        remade_batch_tokens.append(remade_tokens)
-        hijack_fixes.append(fixes)
-        batch_multipliers.append(multipliers)
-    return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
-
-
-def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
-    batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = process_text_old(self, texts)
-
-    self.hijack.comments += hijack_comments
-
-    if len(used_custom_terms) > 0:
-        self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
-
-    self.hijack.fixes = hijack_fixes
-    return self.process_tokens(remade_batch_tokens, batch_multipliers)
diff --git a/spaces/binker/interpreter/README.md b/spaces/binker/interpreter/README.md
deleted file mode 100644
index 14cf33a53fb304374e37d69c1d287a9eee70b7cd..0000000000000000000000000000000000000000
--- a/spaces/binker/interpreter/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Code Interpreter
-emoji: 👀
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bioriAsaeru/text-to-voice/A Liar! Luffy And Sanji!! Full Movie Hd 1080p Download Kickass Movie [2021].md b/spaces/bioriAsaeru/text-to-voice/A Liar! Luffy And Sanji!! Full Movie Hd 1080p Download Kickass Movie [2021].md
deleted file mode 100644
index 64e32066bc2876625114c7f3b0ea64b0d7572453..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/A Liar! Luffy And Sanji!! Full Movie Hd 1080p Download Kickass Movie [2021].md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/bioriAsaeru/text-to-voice/Ibm Spss Statistics 22 Crack Download [VERIFIED].md b/spaces/bioriAsaeru/text-to-voice/Ibm Spss Statistics 22 Crack Download [VERIFIED].md
deleted file mode 100644
index 637a4afb13289763464f1f64b196ac35ba0ce0a4..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Ibm Spss Statistics 22 Crack Download [VERIFIED].md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<br />
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diff --git a/spaces/bodah/RVC-Models-bo/lib/infer_pack/onnx_inference.py b/spaces/bodah/RVC-Models-bo/lib/infer_pack/onnx_inference.py
deleted file mode 100644
index c78324cbc08414fffcc689f325312de0e51bd6b4..0000000000000000000000000000000000000000
--- a/spaces/bodah/RVC-Models-bo/lib/infer_pack/onnx_inference.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import onnxruntime
-import librosa
-import numpy as np
-import soundfile
-
-
-class ContentVec:
-    def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
-        print("load model(s) from {}".format(vec_path))
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
-
-    def __call__(self, wav):
-        return self.forward(wav)
-
-    def forward(self, wav):
-        feats = wav
-        if feats.ndim == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.ndim == 1, feats.ndim
-        feats = np.expand_dims(np.expand_dims(feats, 0), 0)
-        onnx_input = {self.model.get_inputs()[0].name: feats}
-        logits = self.model.run(None, onnx_input)[0]
-        return logits.transpose(0, 2, 1)
-
-
-def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs):
-    if f0_predictor == "pm":
-        from lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
-
-        f0_predictor_object = PMF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "harvest":
-        from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor
-
-        f0_predictor_object = HarvestF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "dio":
-        from lib.infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor
-
-        f0_predictor_object = DioF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    else:
-        raise Exception("Unknown f0 predictor")
-    return f0_predictor_object
-
-
-class OnnxRVC:
-    def __init__(
-        self,
-        model_path,
-        sr=40000,
-        hop_size=512,
-        vec_path="vec-768-layer-12",
-        device="cpu",
-    ):
-        vec_path = f"pretrained/{vec_path}.onnx"
-        self.vec_model = ContentVec(vec_path, device)
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(model_path, providers=providers)
-        self.sampling_rate = sr
-        self.hop_size = hop_size
-
-    def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd):
-        onnx_input = {
-            self.model.get_inputs()[0].name: hubert,
-            self.model.get_inputs()[1].name: hubert_length,
-            self.model.get_inputs()[2].name: pitch,
-            self.model.get_inputs()[3].name: pitchf,
-            self.model.get_inputs()[4].name: ds,
-            self.model.get_inputs()[5].name: rnd,
-        }
-        return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16)
-
-    def inference(
-        self,
-        raw_path,
-        sid,
-        f0_method="dio",
-        f0_up_key=0,
-        pad_time=0.5,
-        cr_threshold=0.02,
-    ):
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        f0_predictor = get_f0_predictor(
-            f0_method,
-            hop_length=self.hop_size,
-            sampling_rate=self.sampling_rate,
-            threshold=cr_threshold,
-        )
-        wav, sr = librosa.load(raw_path, sr=self.sampling_rate)
-        org_length = len(wav)
-        if org_length / sr > 50.0:
-            raise RuntimeError("Reached Max Length")
-
-        wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000)
-        wav16k = wav16k
-
-        hubert = self.vec_model(wav16k)
-        hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32)
-        hubert_length = hubert.shape[1]
-
-        pitchf = f0_predictor.compute_f0(wav, hubert_length)
-        pitchf = pitchf * 2 ** (f0_up_key / 12)
-        pitch = pitchf.copy()
-        f0_mel = 1127 * np.log(1 + pitch / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        pitch = np.rint(f0_mel).astype(np.int64)
-
-        pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32)
-        pitch = pitch.reshape(1, len(pitch))
-        ds = np.array([sid]).astype(np.int64)
-
-        rnd = np.random.randn(1, 192, hubert_length).astype(np.float32)
-        hubert_length = np.array([hubert_length]).astype(np.int64)
-
-        out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze()
-        out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant")
-        return out_wav[0:org_length]
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/config.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/config.py
deleted file mode 100644
index 2a06a09c80865ab987773511b2acc71e232b26ac..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/config.py
+++ /dev/null
@@ -1,277 +0,0 @@
-# -*- coding = utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# pyre-ignore-all-errors
-
-from detectron2.config import CfgNode as CN
-
-
-def add_dataset_category_config(cfg: CN) -> None:
-    """
-    Add config for additional category-related dataset options
-     - category whitelisting
-     - category mapping
-    """
-    _C = cfg
-    _C.DATASETS.CATEGORY_MAPS = CN(new_allowed=True)
-    _C.DATASETS.WHITELISTED_CATEGORIES = CN(new_allowed=True)
-    # class to mesh mapping
-    _C.DATASETS.CLASS_TO_MESH_NAME_MAPPING = CN(new_allowed=True)
-
-
-def add_evaluation_config(cfg: CN) -> None:
-    _C = cfg
-    _C.DENSEPOSE_EVALUATION = CN()
-    # evaluator type, possible values:
-    #  - "iou": evaluator for models that produce iou data
-    #  - "cse": evaluator for models that produce cse data
-    _C.DENSEPOSE_EVALUATION.TYPE = "iou"
-    # storage for DensePose results, possible values:
-    #  - "none": no explicit storage, all the results are stored in the
-    #            dictionary with predictions, memory intensive;
-    #            historically the default storage type
-    #  - "ram": RAM storage, uses per-process RAM storage, which is
-    #           reduced to a single process storage on later stages,
-    #           less memory intensive
-    #  - "file": file storage, uses per-process file-based storage,
-    #            the least memory intensive, but may create bottlenecks
-    #            on file system accesses
-    _C.DENSEPOSE_EVALUATION.STORAGE = "none"
-    # minimum threshold for IOU values: the lower its values is,
-    # the more matches are produced (and the higher the AP score)
-    _C.DENSEPOSE_EVALUATION.MIN_IOU_THRESHOLD = 0.5
-    # Non-distributed inference is slower (at inference time) but can avoid RAM OOM
-    _C.DENSEPOSE_EVALUATION.DISTRIBUTED_INFERENCE = True
-    # evaluate mesh alignment based on vertex embeddings, only makes sense in CSE context
-    _C.DENSEPOSE_EVALUATION.EVALUATE_MESH_ALIGNMENT = False
-    # meshes to compute mesh alignment for
-    _C.DENSEPOSE_EVALUATION.MESH_ALIGNMENT_MESH_NAMES = []
-
-
-def add_bootstrap_config(cfg: CN) -> None:
-    """ """
-    _C = cfg
-    _C.BOOTSTRAP_DATASETS = []
-    _C.BOOTSTRAP_MODEL = CN()
-    _C.BOOTSTRAP_MODEL.WEIGHTS = ""
-    _C.BOOTSTRAP_MODEL.DEVICE = "cuda"
-
-
-def get_bootstrap_dataset_config() -> CN:
-    _C = CN()
-    _C.DATASET = ""
-    # ratio used to mix data loaders
-    _C.RATIO = 0.1
-    # image loader
-    _C.IMAGE_LOADER = CN(new_allowed=True)
-    _C.IMAGE_LOADER.TYPE = ""
-    _C.IMAGE_LOADER.BATCH_SIZE = 4
-    _C.IMAGE_LOADER.NUM_WORKERS = 4
-    _C.IMAGE_LOADER.CATEGORIES = []
-    _C.IMAGE_LOADER.MAX_COUNT_PER_CATEGORY = 1_000_000
-    _C.IMAGE_LOADER.CATEGORY_TO_CLASS_MAPPING = CN(new_allowed=True)
-    # inference
-    _C.INFERENCE = CN()
-    # batch size for model inputs
-    _C.INFERENCE.INPUT_BATCH_SIZE = 4
-    # batch size to group model outputs
-    _C.INFERENCE.OUTPUT_BATCH_SIZE = 2
-    # sampled data
-    _C.DATA_SAMPLER = CN(new_allowed=True)
-    _C.DATA_SAMPLER.TYPE = ""
-    _C.DATA_SAMPLER.USE_GROUND_TRUTH_CATEGORIES = False
-    # filter
-    _C.FILTER = CN(new_allowed=True)
-    _C.FILTER.TYPE = ""
-    return _C
-
-
-def load_bootstrap_config(cfg: CN) -> None:
-    """
-    Bootstrap datasets are given as a list of `dict` that are not automatically
-    converted into CfgNode. This method processes all bootstrap dataset entries
-    and ensures that they are in CfgNode format and comply with the specification
-    """
-    if not cfg.BOOTSTRAP_DATASETS:
-        return
-
-    bootstrap_datasets_cfgnodes = []
-    for dataset_cfg in cfg.BOOTSTRAP_DATASETS:
-        _C = get_bootstrap_dataset_config().clone()
-        _C.merge_from_other_cfg(CN(dataset_cfg))
-        bootstrap_datasets_cfgnodes.append(_C)
-    cfg.BOOTSTRAP_DATASETS = bootstrap_datasets_cfgnodes
-
-
-def add_densepose_head_cse_config(cfg: CN) -> None:
-    """
-    Add configuration options for Continuous Surface Embeddings (CSE)
-    """
-    _C = cfg
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE = CN()
-    # Dimensionality D of the embedding space
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE = 16
-    # Embedder specifications for various mesh IDs
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS = CN(new_allowed=True)
-    # normalization coefficient for embedding distances
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_DIST_GAUSS_SIGMA = 0.01
-    # normalization coefficient for geodesic distances
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.GEODESIC_DIST_GAUSS_SIGMA = 0.01
-    # embedding loss weight
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_WEIGHT = 0.6
-    # embedding loss name, currently the following options are supported:
-    # - EmbeddingLoss: cross-entropy on vertex labels
-    # - SoftEmbeddingLoss: cross-entropy on vertex label combined with
-    #    Gaussian penalty on distance between vertices
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_NAME = "EmbeddingLoss"
-    # optimizer hyperparameters
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.FEATURES_LR_FACTOR = 1.0
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_LR_FACTOR = 1.0
-    # Shape to shape cycle consistency loss parameters:
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS = CN({"ENABLED": False})
-    # shape to shape cycle consistency loss weight
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.WEIGHT = 0.025
-    # norm type used for loss computation
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.NORM_P = 2
-    # normalization term for embedding similarity matrices
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.TEMPERATURE = 0.05
-    # maximum number of vertices to include into shape to shape cycle loss
-    # if negative or zero, all vertices are considered
-    # if positive, random subset of vertices of given size is considered
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.MAX_NUM_VERTICES = 4936
-    # Pixel to shape cycle consistency loss parameters:
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS = CN({"ENABLED": False})
-    # pixel to shape cycle consistency loss weight
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.WEIGHT = 0.0001
-    # norm type used for loss computation
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NORM_P = 2
-    # map images to all meshes and back (if false, use only gt meshes from the batch)
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.USE_ALL_MESHES_NOT_GT_ONLY = False
-    # Randomly select at most this number of pixels from every instance
-    # if negative or zero, all vertices are considered
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NUM_PIXELS_TO_SAMPLE = 100
-    # normalization factor for pixel to pixel distances (higher value = smoother distribution)
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.PIXEL_SIGMA = 5.0
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_PIXEL_TO_VERTEX = 0.05
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_VERTEX_TO_PIXEL = 0.05
-
-
-def add_densepose_head_config(cfg: CN) -> None:
-    """
-    Add config for densepose head.
-    """
-    _C = cfg
-
-    _C.MODEL.DENSEPOSE_ON = True
-
-    _C.MODEL.ROI_DENSEPOSE_HEAD = CN()
-    _C.MODEL.ROI_DENSEPOSE_HEAD.NAME = ""
-    _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_STACKED_CONVS = 8
-    # Number of parts used for point labels
-    _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_PATCHES = 24
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL = 4
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_DIM = 512
-    _C.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_KERNEL = 3
-    _C.MODEL.ROI_DENSEPOSE_HEAD.UP_SCALE = 2
-    _C.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE = 112
-    _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_TYPE = "ROIAlignV2"
-    _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_RESOLUTION = 28
-    _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_SAMPLING_RATIO = 2
-    _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS = 2  # 15 or 2
-    # Overlap threshold for an RoI to be considered foreground (if >= FG_IOU_THRESHOLD)
-    _C.MODEL.ROI_DENSEPOSE_HEAD.FG_IOU_THRESHOLD = 0.7
-    # Loss weights for annotation masks.(14 Parts)
-    _C.MODEL.ROI_DENSEPOSE_HEAD.INDEX_WEIGHTS = 5.0
-    # Loss weights for surface parts. (24 Parts)
-    _C.MODEL.ROI_DENSEPOSE_HEAD.PART_WEIGHTS = 1.0
-    # Loss weights for UV regression.
-    _C.MODEL.ROI_DENSEPOSE_HEAD.POINT_REGRESSION_WEIGHTS = 0.01
-    # Coarse segmentation is trained using instance segmentation task data
-    _C.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS = False
-    # For Decoder
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_ON = True
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NUM_CLASSES = 256
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_CONV_DIMS = 256
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NORM = ""
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_COMMON_STRIDE = 4
-    # For DeepLab head
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB = CN()
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NORM = "GN"
-    _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NONLOCAL_ON = 0
-    # Predictor class name, must be registered in DENSEPOSE_PREDICTOR_REGISTRY
-    # Some registered predictors:
-    #   "DensePoseChartPredictor": predicts segmentation and UV coordinates for predefined charts
-    #   "DensePoseChartWithConfidencePredictor": predicts segmentation, UV coordinates
-    #       and associated confidences for predefined charts (default)
-    #   "DensePoseEmbeddingWithConfidencePredictor": predicts segmentation, embeddings
-    #       and associated confidences for CSE
-    _C.MODEL.ROI_DENSEPOSE_HEAD.PREDICTOR_NAME = "DensePoseChartWithConfidencePredictor"
-    # Loss class name, must be registered in DENSEPOSE_LOSS_REGISTRY
-    # Some registered losses:
-    #   "DensePoseChartLoss": loss for chart-based models that estimate
-    #      segmentation and UV coordinates
-    #   "DensePoseChartWithConfidenceLoss": loss for chart-based models that estimate
-    #      segmentation, UV coordinates and the corresponding confidences (default)
-    _C.MODEL.ROI_DENSEPOSE_HEAD.LOSS_NAME = "DensePoseChartWithConfidenceLoss"
-    # Confidences
-    # Enable learning UV confidences (variances) along with the actual values
-    _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE = CN({"ENABLED": False})
-    # UV confidence lower bound
-    _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.EPSILON = 0.01
-    # Enable learning segmentation confidences (variances) along with the actual values
-    _C.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE = CN({"ENABLED": False})
-    # Segmentation confidence lower bound
-    _C.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE.EPSILON = 0.01
-    # Statistical model type for confidence learning, possible values:
-    # - "iid_iso": statistically independent identically distributed residuals
-    #    with isotropic covariance
-    # - "indep_aniso": statistically independent residuals with anisotropic
-    #    covariances
-    _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.TYPE = "iid_iso"
-    # List of angles for rotation in data augmentation during training
-    _C.INPUT.ROTATION_ANGLES = [0]
-    _C.TEST.AUG.ROTATION_ANGLES = ()  # Rotation TTA
-
-    add_densepose_head_cse_config(cfg)
-
-
-def add_hrnet_config(cfg: CN) -> None:
-    """
-    Add config for HRNet backbone.
-    """
-    _C = cfg
-
-    # For HigherHRNet w32
-    _C.MODEL.HRNET = CN()
-    _C.MODEL.HRNET.STEM_INPLANES = 64
-    _C.MODEL.HRNET.STAGE2 = CN()
-    _C.MODEL.HRNET.STAGE2.NUM_MODULES = 1
-    _C.MODEL.HRNET.STAGE2.NUM_BRANCHES = 2
-    _C.MODEL.HRNET.STAGE2.BLOCK = "BASIC"
-    _C.MODEL.HRNET.STAGE2.NUM_BLOCKS = [4, 4]
-    _C.MODEL.HRNET.STAGE2.NUM_CHANNELS = [32, 64]
-    _C.MODEL.HRNET.STAGE2.FUSE_METHOD = "SUM"
-    _C.MODEL.HRNET.STAGE3 = CN()
-    _C.MODEL.HRNET.STAGE3.NUM_MODULES = 4
-    _C.MODEL.HRNET.STAGE3.NUM_BRANCHES = 3
-    _C.MODEL.HRNET.STAGE3.BLOCK = "BASIC"
-    _C.MODEL.HRNET.STAGE3.NUM_BLOCKS = [4, 4, 4]
-    _C.MODEL.HRNET.STAGE3.NUM_CHANNELS = [32, 64, 128]
-    _C.MODEL.HRNET.STAGE3.FUSE_METHOD = "SUM"
-    _C.MODEL.HRNET.STAGE4 = CN()
-    _C.MODEL.HRNET.STAGE4.NUM_MODULES = 3
-    _C.MODEL.HRNET.STAGE4.NUM_BRANCHES = 4
-    _C.MODEL.HRNET.STAGE4.BLOCK = "BASIC"
-    _C.MODEL.HRNET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4]
-    _C.MODEL.HRNET.STAGE4.NUM_CHANNELS = [32, 64, 128, 256]
-    _C.MODEL.HRNET.STAGE4.FUSE_METHOD = "SUM"
-
-    _C.MODEL.HRNET.HRFPN = CN()
-    _C.MODEL.HRNET.HRFPN.OUT_CHANNELS = 256
-
-
-def add_densepose_config(cfg: CN) -> None:
-    add_densepose_head_config(cfg)
-    add_hrnet_config(cfg)
-    add_bootstrap_config(cfg)
-    add_dataset_category_config(cfg)
-    add_evaluation_config(cfg)
diff --git a/spaces/cffl/Exploring_Intelligent_Writing_Assistance/scripts/install_dependencies.py b/spaces/cffl/Exploring_Intelligent_Writing_Assistance/scripts/install_dependencies.py
deleted file mode 100644
index 4e99667720a1ed6d50910659f5a32157719b6c24..0000000000000000000000000000000000000000
--- a/spaces/cffl/Exploring_Intelligent_Writing_Assistance/scripts/install_dependencies.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# ###########################################################################
-#
-#  CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP)
-#  (C) Cloudera, Inc. 2022
-#  All rights reserved.
-#
-#  Applicable Open Source License: Apache 2.0
-#
-#  NOTE: Cloudera open source products are modular software products
-#  made up of hundreds of individual components, each of which was
-#  individually copyrighted.  Each Cloudera open source product is a
-#  collective work under U.S. Copyright Law. Your license to use the
-#  collective work is as provided in your written agreement with
-#  Cloudera.  Used apart from the collective work, this file is
-#  licensed for your use pursuant to the open source license
-#  identified above.
-#
-#  This code is provided to you pursuant a written agreement with
-#  (i) Cloudera, Inc. or (ii) a third-party authorized to distribute
-#  this code. If you do not have a written agreement with Cloudera nor
-#  with an authorized and properly licensed third party, you do not
-#  have any rights to access nor to use this code.
-#
-#  Absent a written agreement with Cloudera, Inc. (“Cloudera”) to the
-#  contrary, A) CLOUDERA PROVIDES THIS CODE TO YOU WITHOUT WARRANTIES OF ANY
-#  KIND; (B) CLOUDERA DISCLAIMS ANY AND ALL EXPRESS AND IMPLIED
-#  WARRANTIES WITH RESPECT TO THIS CODE, INCLUDING BUT NOT LIMITED TO
-#  IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY AND
-#  FITNESS FOR A PARTICULAR PURPOSE; (C) CLOUDERA IS NOT LIABLE TO YOU,
-#  AND WILL NOT DEFEND, INDEMNIFY, NOR HOLD YOU HARMLESS FOR ANY CLAIMS
-#  ARISING FROM OR RELATED TO THE CODE; AND (D)WITH RESPECT TO YOUR EXERCISE
-#  OF ANY RIGHTS GRANTED TO YOU FOR THE CODE, CLOUDERA IS NOT LIABLE FOR ANY
-#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, PUNITIVE OR
-#  CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES
-#  RELATED TO LOST REVENUE, LOST PROFITS, LOSS OF INCOME, LOSS OF
-#  BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF
-#  DATA.
-#
-# ###########################################################################
-
-!pip3 install -r requirements.txt && python3 scripts/download_models.py
diff --git a/spaces/chansung/LLM-As-Chatbot/args.py b/spaces/chansung/LLM-As-Chatbot/args.py
deleted file mode 100644
index d2f8b3476ced204fb606d1a78365dcc72907468a..0000000000000000000000000000000000000000
--- a/spaces/chansung/LLM-As-Chatbot/args.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import argparse
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Gradio Application for LLM as a chatbot service"
-    )
-    parser.add_argument(
-        "--base-url",
-        help="Hugging Face Hub URL",
-        default="elinas/llama-7b-hf-transformers-4.29",
-        type=str,
-    )
-    parser.add_argument(
-        "--ft-ckpt-url",
-        help="Hugging Face Hub URL",
-        # default="tloen/alpaca-lora-7b",
-        default="LLMs/Alpaca-LoRA-7B-elina",
-        type=str,
-    )
-    parser.add_argument(
-        "--port",
-        help="PORT number where the app is served",
-        default=6006,
-        type=int,
-    )
-    parser.add_argument(
-        "--share",
-        help="Create and share temporary endpoint (useful in Colab env)",
-        action='store_true'
-    )
-    parser.add_argument(
-        "--gen-config-path",
-        help="path to GenerationConfig file",
-        default="configs/response_configs/default.yaml",
-        # default="configs/gen_config_koalpaca.yaml",
-        # default="configs/gen_config_stablelm.yaml",
-        type=str
-    )
-    parser.add_argument(
-        "--gen-config-summarization-path",
-        help="path to GenerationConfig file used in context summarization",
-        default="configs/summarization_configs/default.yaml",
-        type=str
-    )
-    parser.add_argument(
-        "--multi-gpu",
-        help="Enable multi gpu mode. This will force not to use Int8 but float16, so you need to check if your system has enough GPU memory",
-        action='store_true'
-    )
-    parser.add_argument(
-        "--force-download_ckpt",
-        help="Force to download ckpt instead of using cached one",
-        action="store_true"
-    )
-    parser.add_argument(
-        "--chat-only-mode",
-        help="Only show chatting window. Otherwise, other components will be appeared for more sophisticated control",
-        action="store_true"
-    )
-    
-    return parser.parse_args()
\ No newline at end of file
diff --git a/spaces/chansung/zero2story/modules/llms/llama_service.py b/spaces/chansung/zero2story/modules/llms/llama_service.py
deleted file mode 100644
index 084ffc842069c11b1f279a85bb883642dc0e3f6b..0000000000000000000000000000000000000000
--- a/spaces/chansung/zero2story/modules/llms/llama_service.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import os
-import threading
-import toml
-from pathlib import Path
-
-from pingpong import PingPong
-from pingpong.pingpong import PPManager
-from pingpong.pingpong import PromptFmt
-from pingpong.pingpong import UIFmt
-from pingpong.gradio import GradioChatUIFmt
-
-from modules.llms import (
-    LLMFactory,
-    PromptFmt, PromptManager, PPManager, UIPPManager, LLMService
-)
-
-class LLaMAFactory(LLMFactory):
-    def __init__(self):
-        pass
-
-    def create_prompt_format(self):
-        return LLaMAChatPromptFmt()
-
-    def create_prompt_manager(self, prompts_path: str=None):
-        return LLaMAPromptManager((prompts_path or Path('.') / 'prompts' / 'llama_prompts.toml'))
-    
-    def create_pp_manager(self):
-        return LLaMAChatPPManager()
-
-    def create_ui_pp_manager(self):
-        return GradioLLaMAChatPPManager()
-    
-    def create_llm_service(self):
-        return LLaMAService()
-    
-
-class LLaMAChatPromptFmt(PromptFmt):
-    @classmethod
-    def ctx(cls, context):
-        pass
-
-    @classmethod
-    def prompt(cls, pingpong, truncate_size):
-        pass
-
-
-class LLaMAPromptManager(PromptManager):
-    _instance = None
-    _lock = threading.Lock()
-    _prompts = None
-
-    def __new__(cls, prompts_path):
-        if cls._instance is None:
-            with cls._lock:
-                if not cls._instance:
-                    cls._instance = super(LLaMAPromptManager, cls).__new__(cls)
-                    cls._instance.load_prompts(prompts_path)
-        return cls._instance
-
-    def load_prompts(self, prompts_path):
-        self._prompts_path = prompts_path
-        self.reload_prompts()
-
-    def reload_prompts(self):
-        assert self.prompts_path, "Prompt path is missing."
-        self._prompts = toml.load(self.prompts_path)
-
-    @property
-    def prompts_path(self):
-        return self._prompts_path
-    
-    @prompts_path.setter
-    def prompts_path(self, prompts_path):
-        self._prompts_path = prompts_path
-        self.reload_prompts()
-
-    @property
-    def prompts(self):
-        if self._prompts is None:
-            self.load_prompts()
-        return self._prompts
-
-
-class LLaMAChatPPManager(PPManager):
-    def build_prompts(self, from_idx: int=0, to_idx: int=-1, fmt: PromptFmt=None, truncate_size: int=None):
-        pass
-
-
-class GradioLLaMAChatPPManager(UIPPManager, LLaMAChatPPManager):
-    def build_uis(self, from_idx: int=0, to_idx: int=-1, fmt: UIFmt=GradioChatUIFmt):
-        pass
-
-class LLaMAService(LLMService):
-    def make_params(self, mode="chat",
-                          temperature=None,
-                          candidate_count=None,
-                          top_k=None,
-                          top_p=None,
-                          max_output_tokens=None,
-                          use_filter=True):
-        pass
-    
-    async def gen_text(
-        self,
-        prompt,
-        mode="chat", #chat or text
-        parameters=None,
-        use_filter=True
-    ):
-        pass
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/demo/MegEngine/cpp/README.md b/spaces/chendl/compositional_test/multimodal/YOLOX/demo/MegEngine/cpp/README.md
deleted file mode 100644
index c639f27d96b731bbe58cd99f1347fdb9d11e2cb6..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/demo/MegEngine/cpp/README.md
+++ /dev/null
@@ -1,173 +0,0 @@
-# YOLOX-CPP-MegEngine
-
-Cpp file compile of YOLOX object detection base on [MegEngine](https://github.com/MegEngine/MegEngine).
-
-## Tutorial
-
-### Step1: install toolchain
-
-	* host: sudo apt install gcc/g++ (gcc/g++, which version >= 6) build-essential git git-lfs gfortran libgfortran-6-dev autoconf gnupg flex bison gperf curl zlib1g-dev gcc-multilib g++-multilib cmake
- * cross build android: download [NDK](https://developer.android.com/ndk/downloads)
-   	* after unzip download NDK, then export NDK_ROOT="path of NDK"
-
-### Step2: build MegEngine
-
-```shell
-git clone https://github.com/MegEngine/MegEngine.git
-
-# then init third_party
- 
-export megengine_root="path of MegEngine"
-cd $megengine_root && ./third_party/prepare.sh && ./third_party/install-mkl.sh
-
-# build example:
-# build host without cuda:   
-./scripts/cmake-build/host_build.sh
-# or build host with cuda:
-./scripts/cmake-build/host_build.sh -c
-# or cross build for android aarch64: 
-./scripts/cmake-build/cross_build_android_arm_inference.sh
-# or cross build for android aarch64(with V8.2+fp16): 
-./scripts/cmake-build/cross_build_android_arm_inference.sh -f
-
-# after build MegEngine, you need export the `MGE_INSTALL_PATH`
-# host without cuda: 
-export MGE_INSTALL_PATH=${megengine_root}/build_dir/host/MGE_WITH_CUDA_OFF/MGE_INFERENCE_ONLY_ON/Release/install
-# or host with cuda: 
-export MGE_INSTALL_PATH=${megengine_root}/build_dir/host/MGE_WITH_CUDA_ON/MGE_INFERENCE_ONLY_ON/Release/install
-# or cross build for android aarch64: 
-export MGE_INSTALL_PATH=${megengine_root}/build_dir/android/arm64-v8a/Release/install
-```
-* you can refs [build tutorial of MegEngine](https://github.com/MegEngine/MegEngine/blob/master/scripts/cmake-build/BUILD_README.md) to build other platform, eg, windows/macos/ etc!
-
-### Step3: build OpenCV
-
-```shell
-git clone https://github.com/opencv/opencv.git
-
-git checkout 3.4.15 (we test at 3.4.15, if test other version, may need modify some build)
-```
-
-- patch diff for android:
-
-```
-# ```
-#     diff --git a/CMakeLists.txt b/CMakeLists.txt
-#     index f6a2da5310..10354312c9 100644
-#     --- a/CMakeLists.txt
-#     +++ b/CMakeLists.txt
-#     @@ -643,7 +643,7 @@ if(UNIX)
-#        if(NOT APPLE)
-#          CHECK_INCLUDE_FILE(pthread.h HAVE_PTHREAD)
-#          if(ANDROID)
-#     -      set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} dl m log)
-#     +      set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} dl m log z)
-#          elseif(CMAKE_SYSTEM_NAME MATCHES "FreeBSD|NetBSD|DragonFly|OpenBSD|Haiku")
-#            set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} m pthread)
-#          elseif(EMSCRIPTEN)
-    
-# ```
-```
-
-- build for host
-
-```shell
-cd root_dir_of_opencv
-mkdir -p build/install
-cd build
-cmake -DBUILD_JAVA=OFF -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX=$PWD/install 
-make install -j32
-```
-
-* build for android-aarch64
-
-```shell
-cd root_dir_of_opencv
-mkdir -p build_android/install
-cd build_android
-
-cmake -DCMAKE_TOOLCHAIN_FILE="$NDK_ROOT/build/cmake/android.toolchain.cmake" -DANDROID_NDK="$NDK_ROOT"  -DANDROID_ABI=arm64-v8a -DANDROID_NATIVE_API_LEVEL=21 -DBUILD_JAVA=OFF -DBUILD_ANDROID_PROJECTS=OFF -DBUILD_ANDROID_EXAMPLES=OFF -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX=$PWD/install ..
-
-make install -j32
-```
-
-* after build OpenCV, you need export  `OPENCV_INSTALL_INCLUDE_PATH ` and `OPENCV_INSTALL_LIB_PATH`
-
-```shell
-# host build: 
-export OPENCV_INSTALL_INCLUDE_PATH=${path of opencv}/build/install/include
-export OPENCV_INSTALL_LIB_PATH=${path of opencv}/build/install/lib
-# or cross build for android aarch64:
-export OPENCV_INSTALL_INCLUDE_PATH=${path of opencv}/build_android/install/sdk/native/jni/include
-export OPENCV_INSTALL_LIB_PATH=${path of opencv}/build_android/install/sdk/native/libs/arm64-v8a
-```
-
-###  Step4: build test demo
-
-```shell
-run build.sh
-
-# if host:
-export CXX=g++
-./build.sh
-# or cross android aarch64
-export CXX=aarch64-linux-android21-clang++
-./build.sh
-```
-
-### Step5: run demo
-
-> **Note**: two ways to get `yolox_s.mge` model file
->
-> * reference to python demo's `dump.py` script.
-> * For users with code before 0.1.0 version, wget yolox-s weights [here](https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_s.mge).
-> * For users with code after 0.1.0 version, use [python code in megengine](../python) to generate mge file.
-
-```shell
-# if host:
-LD_LIBRARY_PATH=$MGE_INSTALL_PATH/lib/:$OPENCV_INSTALL_LIB_PATH ./yolox yolox_s.mge ../../../assets/dog.jpg cuda/cpu/multithread <warmup_count> <thread_number>
-
-# or cross android
-adb push/scp $MGE_INSTALL_PATH/lib/libmegengine.so android_phone
-adb push/scp $OPENCV_INSTALL_LIB_PATH/*.so android_phone
-adb push/scp ./yolox yolox_s.mge android_phone
-adb push/scp ../../../assets/dog.jpg android_phone
-
-# login in android_phone by adb or ssh
-# then run: 
-LD_LIBRARY_PATH=. ./yolox yolox_s.mge dog.jpg cpu/multithread <warmup_count> <thread_number> <use_fast_run> <use_weight_preprocess>  <run_with_fp16>
-
-# * <warmup_count> means warmup count, valid number >=0
-# * <thread_number> means thread number, valid number >=1, only take effect `multithread` device
-# * <use_fast_run> if >=1 , will use fastrun to choose best algo
-# * <use_weight_preprocess> if >=1, will handle weight preprocess before exe
-# * <run_with_fp16> if >=1, will run with fp16 mode
-```
-
-## Bechmark
-
-* model info: yolox-s @ input(1,3,640,640)					
-
-* test devices
-
-```
-  * x86_64  -- Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz					
-  * aarch64 -- xiamo phone mi9					
-  * cuda    -- 1080TI @ cuda-10.1-cudnn-v7.6.3-TensorRT-6.0.1.5.sh @ Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
-```
-
-  | megengine @ tag1.4(fastrun + weight\_preprocess)/sec | 1 thread |
-  | ---------------------------------------------------- | -------- |
-  | x86\_64                                              | 0.516245 |
-  | aarch64(fp32+chw44)                                  | 0.587857 |
-
-  | CUDA @ 1080TI/sec   | 1 batch    | 2 batch   | 4 batch   | 8 batch   | 16 batch  | 32 batch | 64 batch |
-  | ------------------- | ---------- | --------- | --------- | --------- | --------- | -------- | -------- |
-  | megengine(fp32+chw) | 0.00813703 | 0.0132893 | 0.0236633 | 0.0444699 | 0.0864917 | 0.16895  | 0.334248 |
-
-## Acknowledgement
-
-* [MegEngine](https://github.com/MegEngine/MegEngine)
-* [OpenCV](https://github.com/opencv/opencv)
-* [NDK](https://developer.android.com/ndk)
-* [CMAKE](https://cmake.org/)
diff --git a/spaces/chongjie/MCC_slim/util/co3d_dataset.py b/spaces/chongjie/MCC_slim/util/co3d_dataset.py
deleted file mode 100644
index 9ee3c007c6962a92b66de960b044b11f6182b966..0000000000000000000000000000000000000000
--- a/spaces/chongjie/MCC_slim/util/co3d_dataset.py
+++ /dev/null
@@ -1,122 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import random
-from typing import cast
-
-import torch
-from pytorch3d.implicitron.dataset.dataset_base import FrameData
-
-import util.co3d_utils as co3d_utils
-
-
-def co3dv2_collate_fn(batch):
-    assert len(batch[0]) == 4
-    return (
-        FrameData.collate([x[0] for x in batch]),
-        FrameData.collate([x[1] for x in batch]),
-        [x[2] for x in batch],
-        [x[3] for x in batch],
-    )
-
-
-def pad_point_cloud(pc, N):
-    cur_N = pc._points_list[0].shape[0]
-    if cur_N == N:
-        return pc
-
-    assert cur_N > 0
-
-    n_pad = N - cur_N
-    indices = random.choices(list(range(cur_N)), k=n_pad)
-    pc._features_list[0] = torch.cat([pc._features_list[0], pc._features_list[0][indices]], dim=0)
-    pc._points_list[0] = torch.cat([pc._points_list[0], pc._points_list[0][indices]], dim=0)
-    return pc
-
-
-class CO3DV2Dataset(torch.utils.data.Dataset):
-    def __init__(self, args, is_train, is_viz=False, dataset_maps=None):
-
-        self.args = args
-        self.is_train = is_train
-        self.is_viz = is_viz
-
-        self.dataset_split = 'train' if is_train else 'val'
-        self.all_datasets = dataset_maps[0 if is_train else 1]
-        print(len(self.all_datasets), 'categories loaded')
-
-        self.all_example_names = self.get_all_example_names()
-        print('containing', len(self.all_example_names), 'examples')
-
-    def get_all_example_names(self):
-        all_example_names = []
-        for category in self.all_datasets.keys():
-            for sequence_name in self.all_datasets[category].seq_name2idx.keys():
-                all_example_names.append((category, sequence_name))
-        return all_example_names
-
-    def __getitem__(self, index):
-        for retry in range(1000):
-            try:
-                if retry > 9:
-                    index = random.choice(range(len(self)))
-                    print('retry', retry, 'new index:', index)
-                gap = 1 if self.is_train else len(self.all_example_names) // len(self)
-                assert gap >= 1
-                category, sequence_name = self.all_example_names[(index * gap) % len(self.all_example_names)]
-
-                cat_dataset = self.all_datasets[category]
-
-                frame_data = cat_dataset.__getitem__(
-                    random.choice(cat_dataset.seq_name2idx[sequence_name])
-                    if self.is_train
-                    else cat_dataset.seq_name2idx[sequence_name][
-                        hash(sequence_name) % len(cat_dataset.seq_name2idx[sequence_name])
-                    ]
-                )
-                test_frame = None
-                seen_idx = None
-
-                frame_data = cat_dataset.frame_data_type.collate([frame_data])
-                mask = (
-                    (cast(torch.Tensor, frame_data.fg_probability) > 0.5).float()
-                    if frame_data.fg_probability is not None
-                    else None
-                )
-                seen_rgb = frame_data.image_rgb.clone().detach()
-
-                # 112, 112, 3
-                seen_xyz = co3d_utils.get_rgbd_points(
-                    112, 112,
-                    frame_data.camera,
-                    frame_data.depth_map,
-                    mask,
-                )
-
-                full_point_cloud = co3d_utils._load_pointcloud(f'{self.args.co3d_path}/{category}/{sequence_name}/pointcloud.ply', max_points=20000)
-                full_point_cloud = pad_point_cloud(full_point_cloud, 20000)
-                break
-            except Exception as e:
-                print(category, sequence_name, 'sampling failed', retry, e)
-
-        seen_rgb = seen_rgb.squeeze(0)
-        full_rgb = full_point_cloud._features_list[0]
-
-        return (
-            (seen_xyz, seen_rgb),
-            (full_point_cloud._points_list[0], full_rgb),
-            test_frame,
-            (category, sequence_name, seen_idx),
-        )
-
-    def __len__(self) -> int:
-        n_objs = sum([len(cat_dataset.seq_name2idx.keys()) for cat_dataset in self.all_datasets.values()])
-        if self.is_train:
-            return int(n_objs * self.args.train_epoch_len_multiplier)
-        elif self.is_viz:
-            return n_objs
-        else:
-            return int(n_objs * self.args.eval_epoch_len_multiplier)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/resultdict.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/resultdict.py
deleted file mode 100644
index 7d36e64c467ca8d9cadc88ab03da71faf1aa8abb..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/chardet/resultdict.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from typing import TYPE_CHECKING, Optional
-
-if TYPE_CHECKING:
-    # TypedDict was introduced in Python 3.8.
-    #
-    # TODO: Remove the else block and TYPE_CHECKING check when dropping support
-    # for Python 3.7.
-    from typing import TypedDict
-
-    class ResultDict(TypedDict):
-        encoding: Optional[str]
-        confidence: float
-        language: Optional[str]
-
-else:
-    ResultDict = dict
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/datatypes/format.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/datatypes/format.py
deleted file mode 100644
index 70f8ca8489f66cae284b9e48b45aacb33d80411f..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/datatypes/format.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import re
-
-from typing import Dict, Type, Sequence, Optional
-
-from clickhouse_connect.datatypes.base import ClickHouseType, type_map, ch_read_formats, ch_write_formats
-from clickhouse_connect.driver.exceptions import ProgrammingError
-
-json_re = re.compile('json', re.IGNORECASE)
-
-
-def set_default_formats(*args, **kwargs):
-    fmt_map = format_map(_convert_arguments(*args, **kwargs))
-    ch_read_formats.update(fmt_map)
-    ch_write_formats.update(fmt_map)
-
-
-def clear_all_formats():
-    ch_read_formats.clear()
-    ch_write_formats.clear()
-
-
-def clear_default_format(pattern: str):
-    for ch_type in _matching_types(pattern):
-        ch_read_formats.pop(ch_type, None)
-        ch_write_formats.pop(ch_type, None)
-
-
-def set_write_format(pattern: str, fmt: str):
-    pattern = json_re.sub('object', pattern)
-    for ch_type in _matching_types(pattern):
-        ch_write_formats[ch_type] = fmt
-
-
-def clear_write_format(pattern: str):
-    for ch_type in _matching_types(pattern):
-        ch_write_formats.pop(ch_type, None)
-
-
-def set_read_format(pattern: str, fmt: str):
-    for ch_type in _matching_types(pattern):
-        ch_read_formats[ch_type] = fmt
-
-
-def clear_read_format(pattern: str):
-    for ch_type in _matching_types(pattern):
-        ch_read_formats.pop(ch_type, None)
-
-
-def format_map(fmt_map: Optional[Dict[str, str]]) -> Dict[Type[ClickHouseType], str]:
-    if not fmt_map:
-        return {}
-    final_map = {}
-    for pattern, fmt in fmt_map.items():
-        for ch_type in _matching_types(pattern, fmt):
-            final_map[ch_type] = fmt
-    return final_map
-
-
-def _convert_arguments(*args, **kwargs) -> Dict[str, str]:
-    fmt_map = {}
-    try:
-        for x in range(0, len(args), 2):
-            fmt_map[args[x]] = args[x + 1]
-    except (IndexError, TypeError, ValueError) as ex:
-        raise ProgrammingError('Invalid type/format arguments for format method') from ex
-    fmt_map.update(kwargs)
-    return fmt_map
-
-
-def _matching_types(pattern: str, fmt: str = None) -> Sequence[Type[ClickHouseType]]:
-    re_pattern = re.compile(pattern.replace('*', '.*'), re.IGNORECASE)
-    matches = [ch_type for type_name, ch_type in type_map.items() if re_pattern.match(type_name)]
-    if not matches:
-        raise ProgrammingError(f'Unrecognized ClickHouse type {pattern} when setting formats')
-    if fmt:
-        invalid = [ch_type.__name__ for ch_type in matches if fmt not in ch_type.valid_formats]
-        if invalid:
-            raise ProgrammingError(f"{fmt} is not a valid format for ClickHouse types {','.join(invalid)}.")
-    return matches
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dotenv/ipython.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dotenv/ipython.py
deleted file mode 100644
index 7df727cd0ba5eb481a9e2568ffdd063bfce90314..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dotenv/ipython.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from IPython.core.magic import Magics, line_magic, magics_class  # type: ignore
-from IPython.core.magic_arguments import (argument, magic_arguments,  # type: ignore
-                                          parse_argstring)  # type: ignore
-
-from .main import find_dotenv, load_dotenv
-
-
-@magics_class
-class IPythonDotEnv(Magics):
-
-    @magic_arguments()
-    @argument(
-        '-o', '--override', action='store_true',
-        help="Indicate to override existing variables"
-    )
-    @argument(
-        '-v', '--verbose', action='store_true',
-        help="Indicate function calls to be verbose"
-    )
-    @argument('dotenv_path', nargs='?', type=str, default='.env',
-              help='Search in increasingly higher folders for the `dotenv_path`')
-    @line_magic
-    def dotenv(self, line):
-        args = parse_argstring(self.dotenv, line)
-        # Locate the .env file
-        dotenv_path = args.dotenv_path
-        try:
-            dotenv_path = find_dotenv(dotenv_path, True, True)
-        except IOError:
-            print("cannot find .env file")
-            return
-
-        # Load the .env file
-        load_dotenv(dotenv_path, verbose=args.verbose, override=args.override)
-
-
-def load_ipython_extension(ipython):
-    """Register the %dotenv magic."""
-    ipython.register_magics(IPythonDotEnv)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ufoLib/etree.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ufoLib/etree.py
deleted file mode 100644
index 5054f8169a0dd42599aecbfac779f15d171f3b61..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ufoLib/etree.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""DEPRECATED - This module is kept here only as a backward compatibility shim
-for the old ufoLib.etree module, which was moved to fontTools.misc.etree.
-Please use the latter instead.
-"""
-from fontTools.misc.etree import *
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/config.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/config.py
deleted file mode 100644
index 76d9af14aaf7df47c4551c169f27b05abf9c269e..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/config.py
+++ /dev/null
@@ -1,131 +0,0 @@
-from __future__ import annotations
-
-import configparser
-import json
-import os
-import warnings
-from typing import Any
-
-conf: dict[str, dict[str, Any]] = {}
-default_conf_dir = os.path.join(os.path.expanduser("~"), ".config/fsspec")
-conf_dir = os.environ.get("FSSPEC_CONFIG_DIR", default_conf_dir)
-
-
-def set_conf_env(conf_dict, envdict=os.environ):
-    """Set config values from environment variables
-
-    Looks for variables of the form ``FSSPEC_<protocol>`` and
-    ``FSSPEC_<protocol>_<kwarg>``. For ``FSSPEC_<protocol>`` the value is parsed
-    as a json dictionary and used to ``update`` the config of the
-    corresponding protocol. For ``FSSPEC_<protocol>_<kwarg>`` there is no
-    attempt to convert the string value, but the kwarg keys will be lower-cased.
-
-    The ``FSSPEC_<protocol>_<kwarg>`` variables are applied after the
-    ``FSSPEC_<protocol>`` ones.
-
-    Parameters
-    ----------
-    conf_dict : dict(str, dict)
-        This dict will be mutated
-    envdict : dict-like(str, str)
-        Source for the values - usually the real environment
-    """
-    kwarg_keys = []
-    for key in envdict:
-        if key.startswith("FSSPEC_") and len(key) > 7 and key[7] != "_":
-            if key.count("_") > 1:
-                kwarg_keys.append(key)
-                continue
-            try:
-                value = json.loads(envdict[key])
-            except json.decoder.JSONDecodeError as ex:
-                warnings.warn(
-                    f"Ignoring environment variable {key} due to a parse failure: {ex}"
-                )
-            else:
-                if isinstance(value, dict):
-                    _, proto = key.split("_", 1)
-                    conf_dict.setdefault(proto.lower(), {}).update(value)
-                else:
-                    warnings.warn(
-                        f"Ignoring environment variable {key} due to not being a dict:"
-                        f" {type(value)}"
-                    )
-        elif key.startswith("FSSPEC"):
-            warnings.warn(
-                f"Ignoring environment variable {key} due to having an unexpected name"
-            )
-
-    for key in kwarg_keys:
-        _, proto, kwarg = key.split("_", 2)
-        conf_dict.setdefault(proto.lower(), {})[kwarg.lower()] = envdict[key]
-
-
-def set_conf_files(cdir, conf_dict):
-    """Set config values from files
-
-    Scans for INI and JSON files in the given dictionary, and uses their
-    contents to set the config. In case of repeated values, later values
-    win.
-
-    In the case of INI files, all values are strings, and these will not
-    be converted.
-
-    Parameters
-    ----------
-    cdir : str
-        Directory to search
-    conf_dict : dict(str, dict)
-        This dict will be mutated
-    """
-    if not os.path.isdir(cdir):
-        return
-    allfiles = sorted(os.listdir(cdir))
-    for fn in allfiles:
-        if fn.endswith(".ini"):
-            ini = configparser.ConfigParser()
-            ini.read(os.path.join(cdir, fn))
-            for key in ini:
-                if key == "DEFAULT":
-                    continue
-                conf_dict.setdefault(key, {}).update(dict(ini[key]))
-        if fn.endswith(".json"):
-            with open(os.path.join(cdir, fn)) as f:
-                js = json.load(f)
-            for key in js:
-                conf_dict.setdefault(key, {}).update(dict(js[key]))
-
-
-def apply_config(cls, kwargs, conf_dict=None):
-    """Supply default values for kwargs when instantiating class
-
-    Augments the passed kwargs, by finding entries in the config dict
-    which match the classes ``.protocol`` attribute (one or more str)
-
-    Parameters
-    ----------
-    cls : file system implementation
-    kwargs : dict
-    conf_dict : dict of dict
-        Typically this is the global configuration
-
-    Returns
-    -------
-    dict : the modified set of kwargs
-    """
-    if conf_dict is None:
-        conf_dict = conf
-    protos = cls.protocol if isinstance(cls.protocol, (tuple, list)) else [cls.protocol]
-    kw = {}
-    for proto in protos:
-        # default kwargs from the current state of the config
-        if proto in conf_dict:
-            kw.update(conf_dict[proto])
-    # explicit kwargs always win
-    kw.update(**kwargs)
-    kwargs = kw
-    return kwargs
-
-
-set_conf_files(conf_dir, conf)
-set_conf_env(conf)
diff --git a/spaces/chuuyasleftlung/meowmeow/Dockerfile b/spaces/chuuyasleftlung/meowmeow/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/chuuyasleftlung/meowmeow/Dockerfile
+++ /dev/null
@@ -1,21 +0,0 @@
-FROM node:18-bullseye-slim
-
-RUN apt-get update && \
-
-apt-get install -y git
-
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-
-WORKDIR /app
-
-RUN npm install
-
-COPY Dockerfile greeting.md* .env* ./
-
-RUN npm run build
-
-EXPOSE 7860
-
-ENV NODE_ENV=production
-
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/PCMSCAN 2.4.12 102 Canali Scolastico Mp.md b/spaces/cihyFjudo/fairness-paper-search/PCMSCAN 2.4.12 102 Canali Scolastico Mp.md
deleted file mode 100644
index 85285ff1de89f0a51bf80bc875a4f62fb88767d2..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/PCMSCAN 2.4.12 102 Canali Scolastico Mp.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>PCMSCAN 2.4.12 102 canali scolastico mp</h2><br /><p><b><b>Download File</b> &#10038;&#10038;&#10038; <a href="https://tinurli.com/2uwiyo">https://tinurli.com/2uwiyo</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cihyFjudo/fairness-paper-search/Shooting Sports Gun Club Offline Activation Code and Serial The Ultimate Resource for Gamers.md b/spaces/cihyFjudo/fairness-paper-search/Shooting Sports Gun Club Offline Activation Code and Serial The Ultimate Resource for Gamers.md
deleted file mode 100644
index 748cdbd5a7cd46d69293f560630e47b2199b854d..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Shooting Sports Gun Club Offline Activation Code and Serial The Ultimate Resource for Gamers.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Shooting Sports Gun Club offline activation code and serial</h2><br /><p><b><b>Download Zip</b> &#10003;&#10003;&#10003; <a href="https://tinurli.com/2uwhYW">https://tinurli.com/2uwhYW</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cihyFjudo/fairness-paper-search/Ultimate Windows Tweaker 4.2.3 Portable For Windows 10 A Must-Have for Power Users.md b/spaces/cihyFjudo/fairness-paper-search/Ultimate Windows Tweaker 4.2.3 Portable For Windows 10 A Must-Have for Power Users.md
deleted file mode 100644
index b14a509d0aade04bdda4a7f54f0b9c3ef4bc697c..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Ultimate Windows Tweaker 4.2.3 Portable For Windows 10 A Must-Have for Power Users.md	
+++ /dev/null
@@ -1,15 +0,0 @@
-<br />
-<p>The most popular tweaking software,<strong> Ultimate Windows Tweaker 4</strong> for <strong>Windows 10</strong> adds several new tweaks for the new operating system. Those of you who have upgraded to Windows 10, would want to use it to judiciously tweak your Windows 10 and personalize your computing experience. With judicious tweaking, it can make your system faster, more stable, personal, and more secure with just a few mouse clicks.Ultimate Windows Tweaker is like a <strong>TweakUI for Windows 10</strong>. While you may be able to access all these via the Windows 10 Settings app, the Registry Editor or the Group Policy Editor, Ultimate Windows Tweaker makes things easier for you by offering all useful tweaks from its single UI.<strong>NOTE</strong>:<ul><li><strong>Windows 11</strong> users can use Ultimate Windows Tweaker 5.<li><strong>Windows 8.1</strong> and<strong> Windows 8</strong> should continue to use Ultimate Windows Tweaker 3.<li><strong>Windows 7</strong> & <strong>Windows Vista</strong> users should continue to use Ultimate Windows Tweaker 2.2.</ul>Ultimate Windows Tweaker 4 for Windows 10if(typeof ez_ad_units!='undefined')ez_ad_units.push([[336,280],'thewindowsclub_com-medrectangle-4','ezslot_9',829,'0','0']);__ez_fad_position('div-gpt-ad-thewindowsclub_com-medrectangle-4-0');This tweaker is just around 750 KB in size and includes over 200 tweaks. Like its predecessors, UWT 4.0 sports a clean, minimalistic UI, offering links in the left panel, and tabs on the top, in some categories. Hover over any tweak, and helpful tooltips will tell you what the tweak does.<strong>What is new</strong></p>
-<h2>Ultimate Windows Tweaker 4.2.3 Portable For Windows 10</h2><br /><p><b><b>Download Zip</b> &mdash;&mdash;&mdash;>>> <a href="https://tinurli.com/2uwkAW">https://tinurli.com/2uwkAW</a></b></p><br /><br />
-<p>Auslogics BoostSpeed 10 + Portable Free Download includes all the necessary files to run perfectly on your system, uploaded program contains all latest and updated files, it is full offline or standalone version of Auslogics BoostSpeed 10 + Portable Free Download for compatible versions of windows, download link at the end of the post.</p>
-<p>Click on below button to start Auslogics BoostSpeed 10 + Portable Free Download. This is complete offline installer and standalone setup for Auslogics BoostSpeed 10 + Portable. This would be compatible with compatible version of windows.</p>
-<p>Sets the colors to use for the install info screen (the default is 00FF00 000000. Use the form RRGGBB (in hexadecimal, as in HTML, only minus the leading '#', since # can be used for comments). Note that if "/windows" is specified as the only parameter, the default windows colors will be used.</p>
-<p>Valid values for flag are "smooth" (smooth the progress bar) or "colored" (color the progress bar with the colors set by InstallColors. Examples: "InstProgressFlags" (default old-school windows look), "InstProgressFlags smooth" (new smooth look), "InstProgressFlags smooth colored" (colored smooth look whee). Note: neither "smooth" or "colored" work with XPStyle on when the installer runs on Windows XP with a modern theme.</p>
-<p>Sets the background color of the license data. Color is specified using the form RRGGBB (in hexadecimal, as in HTML, only minus the leading '#', since # can be used for comments). Default is '/gray'. You can also use the Windows OS defined color by using '/windows'.</p>
-<p></p>
-<p><strong>AAct Portable</strong> is a compact software used to activate window copyright similar to Kmspico. This is the latest version of AAct and are appreciated in the activate Windows right now. The software is on par with Ratiborus KMS Tools or is KMSpico . AAct version Portable is used primarily to activate: activate windows 10 and Active Office 2016.</p>
-<p><strong>taiwebs.com</strong> is a website to download applications, tips, software: windows, android, ios, webs diversity to everyone is completely free. We always strive to bring the best products to our users. Hope everyone will like and support the Website to grow.</p>
-<p>The vehicle routing with timewindows domain model makes heavily use of shadow variables.This allows it to express its constraints more naturally, because properties such as arrivalTime and departureTime, are directly available on the domain model.</p>
-<p>A shadow variable is a planning variable whose correct value can be deduced from the state of the genuine planning variables.Even though such a variable violates the principle of normalization by definition, in some use cases it can be very practical to use a shadow variable, especially to express the constraints more naturally.For example in vehicle routing with time windows: the arrival time at a customer for a vehicle can be calculated based on the previously visited customers of that vehicle (and the known travel times between two locations).</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/atrac3plus.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/atrac3plus.h
deleted file mode 100644
index aa949a7e4cabf9597ee202a06705d87448797bb1..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/atrac3plus.h
+++ /dev/null
@@ -1,236 +0,0 @@
-/*
- * ATRAC3+ compatible decoder
- *
- * Copyright (c) 2010-2013 Maxim Poliakovski
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * Global structures, constants and data for ATRAC3+ decoder.
- */
-
-#ifndef AVCODEC_ATRAC3PLUS_H
-#define AVCODEC_ATRAC3PLUS_H
-
-#include <stdint.h>
-
-#include "libavutil/float_dsp.h"
-#include "libavutil/mem_internal.h"
-#include "libavutil/tx.h"
-
-#include "atrac.h"
-#include "avcodec.h"
-#include "get_bits.h"
-
-/** Global unit sizes */
-#define ATRAC3P_SUBBANDS        16  ///< number of PQF subbands
-#define ATRAC3P_SUBBAND_SAMPLES 128 ///< number of samples per subband
-#define ATRAC3P_FRAME_SAMPLES   (ATRAC3P_SUBBAND_SAMPLES * ATRAC3P_SUBBANDS)
-
-#define ATRAC3P_PQF_FIR_LEN     12  ///< length of the prototype FIR of the PQF
-
-/** Global constants */
-#define ATRAC3P_POWER_COMP_OFF  15  ///< disable power compensation
-
-/** ATRAC3+ channel unit types */
-enum Atrac3pChannelUnitTypes {
-    CH_UNIT_MONO       = 0, ///< unit containing one coded channel
-    CH_UNIT_STEREO     = 1, ///< unit containing two jointly-coded channels
-    CH_UNIT_EXTENSION  = 2, ///< unit containing extension information
-    CH_UNIT_TERMINATOR = 3  ///< unit sequence terminator
-};
-
-/** Per-channel IPQF history */
-typedef struct Atrac3pIPQFChannelCtx {
-    DECLARE_ALIGNED(32, float, buf1)[ATRAC3P_PQF_FIR_LEN * 2][8];
-    DECLARE_ALIGNED(32, float, buf2)[ATRAC3P_PQF_FIR_LEN * 2][8];
-    int pos;
-} Atrac3pIPQFChannelCtx;
-
-/** Amplitude envelope of a group of sine waves */
-typedef struct Atrac3pWaveEnvelope {
-    int has_start_point;    ///< indicates start point within the GHA window
-    int has_stop_point;     ///< indicates stop point within the GHA window
-    int start_pos;          ///< start position expressed in n*4 samples
-    int stop_pos;           ///< stop  position expressed in n*4 samples
-} Atrac3pWaveEnvelope;
-
-/** Parameters of a group of sine waves */
-typedef struct Atrac3pWavesData {
-    Atrac3pWaveEnvelope pend_env;   ///< pending envelope from the previous frame
-    Atrac3pWaveEnvelope curr_env;   ///< group envelope from the current frame
-    int num_wavs;           ///< number of sine waves in the group
-    int start_index;        ///< start index into global tones table for that subband
-} Atrac3pWavesData;
-
-/** Parameters of a single sine wave */
-typedef struct Atrac3pWaveParam {
-    int   freq_index;   ///< wave frequency index
-    int   amp_sf;       ///< quantized amplitude scale factor
-    int   amp_index;    ///< quantized amplitude index
-    int   phase_index;  ///< quantized phase index
-} Atrac3pWaveParam;
-
-/** Sound channel parameters */
-typedef struct Atrac3pChanParams {
-    int ch_num;
-    int num_coded_vals;         ///< number of transmitted quant unit values
-    int fill_mode;
-    int split_point;
-    int table_type;             ///< table type: 0 - tone?, 1- noise?
-    int qu_wordlen[32];         ///< array of word lengths for each quant unit
-    int qu_sf_idx[32];          ///< array of scale factor indexes for each quant unit
-    int qu_tab_idx[32];         ///< array of code table indexes for each quant unit
-    int16_t spectrum[2048];     ///< decoded IMDCT spectrum
-    uint8_t power_levs[5];      ///< power compensation levels
-
-    /* imdct window shape history (2 frames) for overlapping. */
-    uint8_t wnd_shape_hist[2][ATRAC3P_SUBBANDS];    ///< IMDCT window shape, 0=sine/1=steep
-    uint8_t *wnd_shape;         ///< IMDCT window shape for current frame
-    uint8_t *wnd_shape_prev;    ///< IMDCT window shape for previous frame
-
-    /* gain control data history (2 frames) for overlapping. */
-    AtracGainInfo gain_data_hist[2][ATRAC3P_SUBBANDS];  ///< gain control data for all subbands
-    AtracGainInfo *gain_data;       ///< gain control data for next frame
-    AtracGainInfo *gain_data_prev;  ///< gain control data for previous frame
-    int num_gain_subbands;      ///< number of subbands with gain control data
-
-    /* tones data history (2 frames) for overlapping. */
-    Atrac3pWavesData tones_info_hist[2][ATRAC3P_SUBBANDS];
-    Atrac3pWavesData *tones_info;
-    Atrac3pWavesData *tones_info_prev;
-} Atrac3pChanParams;
-
-/* Per-unit sine wave parameters */
-typedef struct Atrac3pWaveSynthParams {
-    int tones_present;                      ///< 1 - tones info present
-    int amplitude_mode;                     ///< 1 - low range, 0 - high range
-    int num_tone_bands;                     ///< number of PQF bands with tones
-    uint8_t tone_sharing[ATRAC3P_SUBBANDS]; ///< 1 - subband-wise tone sharing flags
-    uint8_t tone_master[ATRAC3P_SUBBANDS];  ///< 1 - subband-wise tone channel swapping
-    uint8_t invert_phase[ATRAC3P_SUBBANDS]; ///< 1 - subband-wise phase inversion
-    int tones_index;                        ///< total sum of tones in this unit
-    Atrac3pWaveParam waves[48];
-} Atrac3pWaveSynthParams;
-
-/** Channel unit parameters */
-typedef struct Atrac3pChanUnitCtx {
-    /* channel unit variables */
-    int unit_type;                          ///< unit type (mono/stereo)
-    int num_quant_units;
-    int num_subbands;
-    int used_quant_units;                   ///< number of quant units with coded spectrum
-    int num_coded_subbands;                 ///< number of subbands with coded spectrum
-    int mute_flag;                          ///< mute flag
-    int use_full_table;                     ///< 1 - full table list, 0 - restricted one
-    int noise_present;                      ///< 1 - global noise info present
-    int noise_level_index;                  ///< global noise level index
-    int noise_table_index;                  ///< global noise RNG table index
-    uint8_t swap_channels[ATRAC3P_SUBBANDS];    ///< 1 - perform subband-wise channel swapping
-    uint8_t negate_coeffs[ATRAC3P_SUBBANDS];    ///< 1 - subband-wise IMDCT coefficients negation
-    Atrac3pChanParams channels[2];
-
-    /* Variables related to GHA tones */
-    Atrac3pWaveSynthParams wave_synth_hist[2];     ///< waves synth history for two frames
-    Atrac3pWaveSynthParams *waves_info;
-    Atrac3pWaveSynthParams *waves_info_prev;
-
-    Atrac3pIPQFChannelCtx ipqf_ctx[2];
-    DECLARE_ALIGNED(32, float, prev_buf)[2][ATRAC3P_FRAME_SAMPLES]; ///< overlapping buffer
-} Atrac3pChanUnitCtx;
-
-/**
- * Initialize VLC tables for bitstream parsing.
- */
-void ff_atrac3p_init_vlcs(void);
-
-/**
- * Decode bitstream data of a channel unit.
- *
- * @param[in]     gb            the GetBit context
- * @param[in,out] ctx           ptr to the channel unit context
- * @param[in]     num_channels  number of channels to process
- * @param[in]     avctx         ptr to the AVCodecContext
- * @return result code: 0 = OK, otherwise - error code
- */
-int ff_atrac3p_decode_channel_unit(GetBitContext *gb, Atrac3pChanUnitCtx *ctx,
-                                   int num_channels, AVCodecContext *avctx);
-
-/**
- * Initialize sine waves synthesizer and ff_sine_* tables.
- */
-void ff_atrac3p_init_dsp_static(void);
-
-/**
- * Synthesize sine waves for a particular subband.
- *
- * @param[in]   ch_unit   pointer to the channel unit context
- * @param[in]   fdsp      pointer to float DSP context
- * @param[in]   ch_num    which channel to process
- * @param[in]   sb        which subband to process
- * @param[out]  out       receives processed data
- */
-void ff_atrac3p_generate_tones(Atrac3pChanUnitCtx *ch_unit, AVFloatDSPContext *fdsp,
-                               int ch_num, int sb, float *out);
-
-/**
- * Perform power compensation aka noise dithering.
- *
- * @param[in]      ctx         ptr to the channel context
- * @param[in]      fdsp        pointer to float DSP context
- * @param[in]      ch_index    which channel to process
- * @param[in,out]  sp          ptr to channel spectrum to process
- * @param[in]      rng_index   indicates which RNG table to use
- * @param[in]      sb_num      which subband to process
- */
-void ff_atrac3p_power_compensation(Atrac3pChanUnitCtx *ctx, AVFloatDSPContext *fdsp,
-                                   int ch_index, float *sp, int rng_index, int sb_num);
-
-/**
- * Regular IMDCT and windowing without overlapping,
- * with spectrum reversal in the odd subbands.
- *
- * @param[in]   fdsp       pointer to float DSP context
- * @param[in]   mdct_ctx   pointer to MDCT transform context
- * @param[in]   pIn        float input
- * @param[out]  pOut       float output
- * @param[in]   wind_id    which MDCT window to apply
- * @param[in]   sb         subband number
- */
-void ff_atrac3p_imdct(AVFloatDSPContext *fdsp, AVTXContext *mdct_ctx,
-                      av_tx_fn mdct_fn, float *pIn, float *pOut,
-                      int wind_id, int sb);
-
-/**
- * Subband synthesis filter based on the polyphase quadrature (pseudo-QMF)
- * filter bank.
- *
- * @param[in]      dct_ctx   ptr to the pre-initialized IDCT context
- * @param[in,out]  hist      ptr to the filter history
- * @param[in]      in        input data to process
- * @param[out]     out       receives processed data
- */
-void ff_atrac3p_ipqf(AVTXContext *dct_ctx, av_tx_fn dct_fn,
-                     Atrac3pIPQFChannelCtx *hist, const float *in, float *out);
-
-extern const uint16_t ff_atrac3p_qu_to_spec_pos[33];
-extern const float ff_atrac3p_sf_tab[64];
-extern const float ff_atrac3p_mant_tab[8];
-
-#endif /* AVCODEC_ATRAC3PLUS_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/blockdsp.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/blockdsp.h
deleted file mode 100644
index d853adada2169e2a551470f46574fddd9068750a..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/blockdsp.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/*
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_BLOCKDSP_H
-#define AVCODEC_BLOCKDSP_H
-
-#include <stddef.h>
-#include <stdint.h>
-
-/* add and put pixel (decoding)
- * Block sizes for op_pixels_func are 8x4,8x8 16x8 16x16.
- * h for op_pixels_func is limited to { width / 2, width },
- * but never larger than 16 and never smaller than 4. */
-typedef void (*op_fill_func)(uint8_t *block /* align width (8 or 16) */,
-                             uint8_t value, ptrdiff_t line_size, int h);
-
-typedef struct BlockDSPContext {
-    void (*clear_block)(int16_t *block /* align 32 */);
-    void (*clear_blocks)(int16_t *blocks /* align 32 */);
-
-    op_fill_func fill_block_tab[2];
-} BlockDSPContext;
-
-void ff_blockdsp_init(BlockDSPContext *c);
-
-void ff_blockdsp_init_alpha(BlockDSPContext *c);
-void ff_blockdsp_init_arm(BlockDSPContext *c);
-void ff_blockdsp_init_ppc(BlockDSPContext *c);
-void ff_blockdsp_init_x86(BlockDSPContext *c);
-void ff_blockdsp_init_mips(BlockDSPContext *c);
-
-#endif /* AVCODEC_BLOCKDSP_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/constants.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/constants.h
deleted file mode 100644
index f6eaf4415e8bb1c1061382ae6ea371dc3cfd0d13..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/constants.h
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Copyright (c) 2015 Loongson Technology Corporation Limited
- * Copyright (c) 2015 Zhou Xiaoyong <zhouxiaoyong@loongson.cn>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_MIPS_CONSTANTS_H
-#define AVCODEC_MIPS_CONSTANTS_H
-
-#include "libavutil/intfloat.h"
-
-extern const union av_intfloat64 ff_pw_1;
-extern const union av_intfloat64 ff_pw_2;
-extern const union av_intfloat64 ff_pw_3;
-extern const union av_intfloat64 ff_pw_4;
-extern const union av_intfloat64 ff_pw_5;
-extern const union av_intfloat64 ff_pw_6;
-extern const union av_intfloat64 ff_pw_8;
-extern const union av_intfloat64 ff_pw_9;
-extern const union av_intfloat64 ff_pw_10;
-extern const union av_intfloat64 ff_pw_12;
-extern const union av_intfloat64 ff_pw_15;
-extern const union av_intfloat64 ff_pw_16;
-extern const union av_intfloat64 ff_pw_17;
-extern const union av_intfloat64 ff_pw_18;
-extern const union av_intfloat64 ff_pw_20;
-extern const union av_intfloat64 ff_pw_22;
-extern const union av_intfloat64 ff_pw_28;
-extern const union av_intfloat64 ff_pw_32;
-extern const union av_intfloat64 ff_pw_53;
-extern const union av_intfloat64 ff_pw_64;
-extern const union av_intfloat64 ff_pw_128;
-extern const union av_intfloat64 ff_pw_512;
-extern const union av_intfloat64 ff_pw_m8tom5;
-extern const union av_intfloat64 ff_pw_m4tom1;
-extern const union av_intfloat64 ff_pw_1to4;
-extern const union av_intfloat64 ff_pw_5to8;
-extern const union av_intfloat64 ff_pw_0to3;
-extern const union av_intfloat64 ff_pw_4to7;
-extern const union av_intfloat64 ff_pw_8tob;
-extern const union av_intfloat64 ff_pw_ctof;
-extern const union av_intfloat64 ff_pw_32_1;
-extern const union av_intfloat64 ff_pw_32_4;
-extern const union av_intfloat64 ff_pw_32_64;
-extern const union av_intfloat64 ff_pb_1;
-extern const union av_intfloat64 ff_pb_3;
-extern const union av_intfloat64 ff_pb_80;
-extern const union av_intfloat64 ff_pb_A1;
-extern const union av_intfloat64 ff_pb_FE;
-extern const union av_intfloat64 ff_rnd;
-extern const union av_intfloat64 ff_rnd2;
-extern const union av_intfloat64 ff_rnd3;
-extern const union av_intfloat64 ff_wm1010;
-extern const union av_intfloat64 ff_d40000;
-
-#endif /* AVCODEC_MIPS_CONSTANTS_H */
diff --git a/spaces/congsaPfin/Manga-OCR/Aspen-Plus-121-Free-Download.md b/spaces/congsaPfin/Manga-OCR/Aspen-Plus-121-Free-Download.md
deleted file mode 100644
index 8a8fef1e521c4e4bb7cb21e92fe786ea7a386251..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/Aspen-Plus-121-Free-Download.md
+++ /dev/null
@@ -1,96 +0,0 @@
-## Aspen Plus 121 Free Download
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Brothers In Arms 3 Mod Apk (unlimited Money Offline) !NEW!.md b/spaces/congsaPfin/Manga-OCR/logs/Brothers In Arms 3 Mod Apk (unlimited Money Offline) !NEW!.md
deleted file mode 100644
index e536df0e6d2ff8461156482cd88a9328602e4442..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Brothers In Arms 3 Mod Apk (unlimited Money Offline) !NEW!.md	
+++ /dev/null
@@ -1,55 +0,0 @@
-
-<h1>Brothers in Arms 3 Mod Apk: A Thrilling WW2 Shooter Game</h1>
-<p>If you are a fan of action-packed shooter games set in World War II, then you should not miss <strong>Brothers in Arms 3</strong>, a third-person shooter game developed by Gameloft with partnership with Gearbox Software. This game will take you back to the time of the D-Day invasion, where you will play as Sergeant Wright, a brave soldier who leads a squad of brothers with different skills and abilities. You will face various enemies, missions, and challenges, as well as use a variety of weapons and experimental devices to fight your way through the war.</p>
-<h2>brothers in arms 3 mod apk (unlimited money offline)</h2><br /><p><b><b>DOWNLOAD</b> &#10027; <a href="https://urlca.com/2uOa8K">https://urlca.com/2uOa8K</a></b></p><br /><br />
-<p>However, if you want to enjoy this game to the fullest, you might want to try <strong>Brothers in Arms 3 Mod Apk</strong>, a modified version of the game that gives you unlimited money and offline gameplay. With this mod apk, you can buy and upgrade any weapon you want, as well as play without any internet connection. Sounds awesome, right? In this article, we will tell you more about the features, download process, tips and tricks, and FAQs of Brothers in Arms 3 Mod Apk. Read on to find out more!</p>
-<h2>Features of Brothers in Arms 3 Mod Apk</h2>
-<p>Brothers in Arms 3 Mod Apk has many features that make it stand out from the original game. Here are some of them:</p>
-<h3>Squad-based action with different brothers and abilities</h3>
-<p>One of the key gameplay features of Brothers in Arms 3 is squad-based action. You can use your brothers to gain a tactical advantage over your enemy. Each brother has a unique ability that can be used after a cooldown period. For example, James Gann is a sniper who can shoot <p>enemies from a long distance, while Larry Jackson is a rocket launcher who can blow up vehicles and bunkers. You can also unlock new brothers and upgrade their abilities as you progress in the game.</p>
-<h3>Various weapons and upgrades to customize your arsenal</h3>
-<p>Another feature of Brothers in Arms 3 Mod Apk is the variety of weapons and upgrades that you can use to customize your arsenal. You can choose from rifles, machine guns, shotguns, pistols, and more, each with different stats and effects. You can also upgrade your weapons with scopes, silencers, grips, magazines, and more, to improve their performance and appearance. With unlimited money in the mod apk, you can buy and upgrade any weapon you want without any restrictions.</p>
-<h3>Stunning graphics and visual effects for a realistic experience</h3>
-<p>Brothers in Arms 3 Mod Apk also boasts stunning graphics and visual effects that make the game more realistic and immersive. You can enjoy the detailed environments, weather effects, lighting effects, explosions, blood splatters, and more, that create a thrilling atmosphere of war. The game also uses ragdoll physics and motion capture technology to animate the characters and enemies in a lifelike manner. You will feel like you are in the middle of the action as you shoot, dodge, and take cover.</p>
-<h3>Multiple game modes and missions to challenge yourself</h3>
-<p>Brothers in Arms 3 Mod Apk also offers multiple game modes and missions to challenge yourself and test your skills. You can play the story mode, where you will follow the plot of Sergeant Wright and his squad as they fight their way through different locations and scenarios. You can also play the free mode, where you can choose from various missions such as assault, sniper, siege, stealth, and more. Each mission has different objectives, enemies, and rewards. You can also play the multiplayer mode, where you can team up with or compete against other players online.</p>
-<h3>Offline mode to play without internet connection</h3>
-<p>One of the best features of Brothers in Arms 3 Mod Apk is the offline mode, which allows you to play the game without any internet connection. This means that you can enjoy the game anytime and anywhere, without worrying about data usage or network issues. You can play the story mode and the free mode offline, as well as access your weapons and upgrades. However, you will need an internet connection to play the multiplayer mode and to update the game.</p>
-<p></p>
-<h2>How to Download and Install Brothers in Arms 3 Mod Apk</h2>
-<p>If you are interested in downloading and installing Brothers in Arms 3 Mod Apk on your Android device, here are the steps that you need to follow:</p>
-<h3>Step 1: Download the mod apk file from a trusted source</h3>
-<p>The first step is to download the mod apk file from a trusted source. You can search for Brothers in Arms 3 Mod Apk on Google or any other search engine, or visit [20](https://android-1.com/en/108-brothers-in-arms-3-mod.html), a website that provides mod apk files for various games. Make sure that you download the latest version of the mod apk file, which is 1.5.2a as of June 2023.</p>
-<h3>Step 2: Enable unknown sources on your device settings</h3>
-<p>The next step is to enable unknown sources on your device settings. This will allow you to install apps that are not from the Google Play Store. To do this, go to your device settings > security > unknown sources > enable. You may see a warning message that says installing apps from unknown sources may harm your device. Ignore this message and proceed with caution.</p>
-<h3>Step 3: Install the mod apk file and launch the game</h3>
-<p>The final step is to install the mod apk file and launch the game. To do this, locate the mod apk file that you downloaded in step 1 on your device storage or file manager. Tap on it and follow the installation instructions. Once the installation is complete, tap on the game icon on your home screen or app drawer. The game will launch automatically and you will see a confirmation message that says "Modded by Android-1.com". This means that you have successfully installed Brothers in Arms 3 Mod Apk.</p>
-<h3>Step 4: Enjoy unlimited money and offline gameplay</h3>
-<p>Now that you have installed Brothers in Arms 3 Mod Apk, you can enjoy unlimited money and offline gameplay. You can use your money to buy and upgrade any weapon you want, as well as play offline without any internet connection. Have fun playing this thrilling WW2 shooter game!</p>
-<h2>Tips and Tricks for Playing Brothers in Arms 3 Mod Apk</h2> <p>If you want to improve your skills and performance in Brothers in Arms 3 Mod Apk, here are some tips and tricks that you can use:</p>
-<h3>Aim for headshots to deal more damage and save ammo</h3>
-<p>One of the most basic but effective tips for playing Brothers in Arms 3 Mod Apk is to aim for headshots whenever possible. Headshots deal more damage and can kill enemies faster than body shots. They also save ammo, which can be scarce in some missions. To aim for headshots, use the zoom button on the right side of the screen and adjust your aim with the left thumb. You can also use the auto-aim feature, which will automatically lock on the nearest enemy's head. However, be careful not to shoot your allies or civilians by mistake.</p>
-<h3>Use your brothers' abilities wisely and strategically</h3>
-<p>Another tip for playing Brothers in Arms 3 Mod Apk is to use your brothers' abilities wisely and strategically. Each brother has a unique ability that can be used after a cooldown period. For example, James Gann is a sniper who can shoot enemies from a long distance, while Larry Jackson is a rocket launcher who can blow up vehicles and bunkers. You can also unlock new brothers and upgrade their abilities as you progress in the game. To use your brothers' abilities, tap on their icons on the left side of the screen and select a target. You can also switch between brothers by swiping left or right on their icons.</p>
-<h3>Switch weapons according to the situation and enemy type</h3>
-<p>Another tip for playing Brothers in Arms 3 Mod Apk is to switch weapons according to the situation and enemy type. You can choose from rifles, machine guns, shotguns, pistols, and more, each with different stats and effects. You can also upgrade your weapons with scopes, silencers, grips, magazines, and more, to improve their performance and appearance. With unlimited money in the mod apk, you can buy and upgrade any weapon you want without any restrictions. To switch weapons, tap on the weapon icon on the bottom right of the screen and select a weapon from your inventory. You can also swipe left or right on the weapon icon to cycle through your weapons.</p>
-<h3>Pop out of cover at the right moments and avoid grenades</h3>
-<p>Another tip for playing Brothers in Arms 3 Mod Apk is to pop out of cover at the right moments and avoid grenades. Cover is essential for survival in this game, as it protects you from enemy fire and gives you time to reload and heal. To take cover, tap on the cover button on the bottom left of the screen and move near a wall, crate, car, or any other object that can shield you. To pop out of cover, tap on the fire button on the right side of the screen and aim at your target. However, be careful not to expose yourself too much or too long, as enemies will shoot back at you. Also, watch out for grenades that enemies may throw at you. You can see a red indicator on the screen when a grenade is near you. To avoid grenades, either run away from them or throw them back at enemies by tapping on them.</p>
-<h3>Watch for experimental weapons and use them to wreak havoc</h3>
-<p>Another tip for playing Brothers in Arms 3 Mod Apk is to watch for experimental weapons and use them to wreak havoc. Experimental weapons are special devices that have unique effects and can change the tide of battle. For example, there are flamethrowers that can burn enemies alive, tesla guns that can electrocute enemies with lightning bolts, freeze guns that can freeze enemies in ice blocks, and more. You can find experimental weapons in some missions or buy them with money. To use experimental weapons, tap on their icons on the bottom right of the screen and select a target. However, be careful not to use them too often or too close to yourself, as they may have negative side effects or friendly fire.</p>
-<h2>Conclusion</h2>
-<p>Brothers in Arms 3 Mod Apk is a thrilling WW2 shooter game that will keep you entertained for hours. You can enjoy squad-based action with different brothers and abilities, various weapons and upgrades to customize your arsenal, stunning graphics and visual effects for a realistic experience, multiple game modes and missions to challenge yourself, offline mode to play without internet connection, unlimited money to buy anything you want, and more. If you are looking for a fun and exciting shooter game that will take you back to the time of war, then you should download and install Brothers in Arms 3 Mod Apk today!</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about Brothers in Arms 3 Mod Apk:</p>
-<h4>Q1: Is Brothers in Arms 3 Mod Apk safe to download and install <h4>A1: Yes, as long as you download it from a reliable source and follow the instructions carefully. However, you should always be careful when downloading and installing any mod apk file, as they may contain viruses or malware that can harm your device or steal your data. You should also backup your data before installing any mod apk file, in case something goes wrong or you want to revert to the original game.</h4>
-<h4>Q2: What are the benefits of using Brothers in Arms 3 Mod Apk?</h4>
-<h4>A2: You can enjoy unlimited money to buy and upgrade weapons, as well as play offline without any internet connection. These features will enhance your gameplay and make it more fun and convenient. You can also access all the game modes and missions without any restrictions or limitations.</h4>
-<h4>Q3: What are the requirements for playing Brothers in Arms 3 Mod Apk?</h4>
-<h4>A3: You need an Android device with at least 4.0 version, 1 GB of RAM, and 1.5 GB of free storage space. You also need an internet connection to download and install the mod apk file, as well as to play the multiplayer mode and update the game.</h4>
-<h4>Q4: How can I update Brothers in Arms 3 Mod Apk?</h4>
-<h4>A4: You can check for updates from the source where you downloaded the mod apk file, or visit the official website of Gameloft. You should always update your game to the latest version, as it may fix bugs, improve performance, add new features, and enhance security. However, you should also backup your data before updating, in case the update causes any issues or conflicts with the mod apk file.</h4>
-<h4>Q5: How can I contact Gameloft for support or feedback?</h4>
-<h4>A5: You can visit their official website at [19](http://www.gameloft.com), follow them on Twitter at [18](http://glft.co/GameloftonTwitter), or like them on Facebook at [17](http://facebook.com/Gameloft). You can also send them an email at [16](mailto:support@gameloft.com) or call them at +1-800-910-3186. They will be happy to assist you with any questions or issues you may have regarding Brothers in Arms 3 or any other game they develop.</h4>
- <p>I hope you found this article helpful and informative. If you did, please share it with your friends and family who might also enjoy playing Brothers in Arms 3 Mod Apk. Thank you for reading and have a great day!</p> 197e85843d<br />
-<br />
-<br />
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-<br />
-<h1>How to Download Chief Almighty on Your PC or Mac</h1>
-<p>Do you want to experience the thrill of leading a tribe of warriors and dinosaurs in the primal stone ages? Do you want to explore, hunt, fight, and expand your territory with other players worldwide? If you answered yes, then you should try Chief Almighty, a strategy game developed by Phantix Games. In this article, we will show you how to download and play Chief Almighty on your PC or Mac using an emulator called BlueStacks. But first, let's find out more about this game and why you should play it on a bigger screen.</p>
- <h2>What is Chief Almighty?</h2>
-<h3>A strategy game set in the stone ages</h3>
-<p>Chief Almighty is a strategy game that takes you back to the prehistoric times, where you have to lead your clan of barbarians, javelineers, war riders, and ancient beasts against rival tribes and wild creatures. You have to collect resources, build your tribe, ally with other chiefs, and expand your power and influence. You also have to use diplomacy or domination to deal with other players and compete for ancient relics and honor.</p>
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- <h3>Features of the game</h3>
-<p>Chief Almighty has many features that make it an exciting and immersive game. Some of them are:</p>
-<ul>
-<li>Real-time strategy gameplay that requires you to organize your clan mates, assemble ancient beasts, and awaken your leadership skills.</li>
-<li>HD Unity3D game engine that provides excellent graphics and high-scale panoramic map zooming.</li>
-<li>Global server that allows you to interact with players from different nations and cultures.</li>
-<li>Arcane ancient relics that offer treasures of endless wealth and power.</li>
-<li>Fierce collision between ancient creatures and primitive warriors that test your courage and strength.</li>
-</ul>
- <h2>Why play Chief Almighty on PC or Mac?</h2>
-<h3>Benefits of using BlueStacks emulator</h3>
-<p>While Chief Almighty is an Android game that you can play on your mobile device, you can also enjoy it on your PC or Mac using an emulator. An emulator is a software that allows you to run Android apps and games on your computer. One of the best emulators for playing Chief Almighty is BlueStacks, which offers many benefits such as:</p>
-<ul>
-<li>Better performance and speed than most Android devices, as BlueStacks is up to 6 times more powerful than the latest flagship phones.</li>
-<li>Larger screen and higher resolution that enhance your gaming experience and visibility.</li>
-<li>Advanced keymapping feature that lets you customize your controls using your mouse, keyboard, or gamepad.</li>
-<li>Screenshot and video recording feature that lets you capture your gameplay and share it with others.</li>
-<li>Macro recorder feature that lets you automate tasks and actions with a simple keystroke.</li>
-</ul>
- <h3>How to use BlueStacks features</h3>
-<p>To use the features of BlueStacks, you need to download and install it on your PC or Mac first. Then, you can follow these steps:</p>
-<ol>
-<li>Launch BlueStacks and sign in with your Google account to access the Play Store.</li>
-<li>Search for Chief Almighty in the search bar at the top right corner.</li>
-<li>Click to install Chief Almighty from the search results.</li>
-<li>Click the Chief Almighty icon on the home screen to start playing.</li>
-<li>To use the advanced keymapping feature, click the keyboard icon on the right side of the screen and drag and drop the keys you want to use.</li>
-<li>To use the screenshot and video recording feature, click the camera icon on the right side of the screen and choose either screenshot or record.</li>
-<li>To use the macro recorder feature, click the macro icon on the right side of the screen and click the record button to start recording your actions. You can then assign a key to play the macro.</li>
-</ol>
- <h2>How to download and install Chief Almighty on PC or Mac?</h2>
-<h3>Step-by-step guide</h3>
-<p>If you want to download and install Chief Almighty on your PC or Mac, you can follow this simple guide:</p>
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-<ol>
-<li>Click the download button below to download BlueStacks on your PC or Mac.</li>
-<li>Run the installer and follow the instructions to complete the installation.</li>
-<li>Open BlueStacks and sign in with your Google account.</li>
-<li>Search for Chief Almighty in the Play Store and install it.</li>
-<li>Enjoy playing Chief Almighty on your PC or Mac.</li>
-</ol>
- <h3>Tips and tricks for playing Chief Almighty on PC or Mac</h3>
-<p>To make the most out of your gaming experience, here are some tips and tricks for playing Chief Almighty on PC or Mac:</p>
-<ul>
-<li>Join an active tribe and cooperate with other players to gain more resources, protection, and rewards.</li>
-<li>Upgrade your buildings, troops, and beasts regularly to increase your power and unlock new features.</li>
-<li>Use the scout feature to spy on your enemies and plan your attacks accordingly.</li>
-<li>Participate in events and quests to earn more rewards and honor.</li>
-<li>Use the chat feature to communicate with other players and make friends or enemies.</li>
-</ul>
- <h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In conclusion, Chief Almighty is a strategy game that lets you lead a tribe of warriors and dinosaurs in the stone ages. You can play it on your PC or Mac using BlueStacks emulator, which offers many benefits and features. You can download and install Chief Almighty on your PC or Mac by following the steps above. You can also use the tips and tricks we provided to improve your gameplay and have more fun.</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Chief Almighty:</p>
-<table>
-<tr><td><b>Question</b></td><td><b>Answer</b></td></tr>
-<tr><td>Is Chief Almighty free to play?</td><td>Yes, Chief Almighty is free to play, but it also offers in-app purchases for some items and features.</td></tr>
-<tr><td>Can I play Chief Almighty offline?</td><td>No, Chief Almighty requires an internet connection to play, as it is a multiplayer game.</td></tr>
-<tr><td>Can I play Chief Almighty on other devices?</td><td>Yes, you can also play Chief Almighty on your Android or iOS device by downloading it from the Google Play Store or the App Store.</td></tr>
-<tr><td>How can I contact the customer service of Chief Almighty?</td><td>You can contact the customer service of Chief Almighty by clicking the settings icon on the top right corner of the game screen, then clicking the help button, and then clicking the contact us button.</td></tr>
-<tr><td>How can I update Chief Almighty?</td><td>You can update Chief Almighty by opening BlueStacks, going to the My Games tab, clicking on Chief Almighty, and then clicking on the update button if there is one available.</td></tr>
-</table></p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Download and Install NBA 2K20 Zip File on PC.md b/spaces/congsaPfin/Manga-OCR/logs/How to Download and Install NBA 2K20 Zip File on PC.md
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-<br />
-<h1>NBA 2K20 Zip File Download PC: How to Get the Game for Free</h1>
-<p>If you are a fan of basketball and video games, you might be interested in downloading NBA 2K20 zip file for PC. NBA 2K20 is one of the most popular and realistic basketball simulation games available, and you can get it for free by following some simple steps. In this article, we will show you how to download NBA 2K20 zip file for PC, how to install and play the game, and some tips and tricks to enhance your gaming experience.</p>
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-<h3>What is NBA 2K20?</h3>
-<p>NBA 2K20 is the latest installment in the NBA 2K series, a franchise that has been around since 1999. NBA 2K20 is developed by Visual Concepts and published by 2K Sports, and it features realistic graphics, gameplay, and presentation of the National Basketball Association (NBA). You can play as your favorite NBA teams and players, or create your own custom characters and teams. You can also compete with other players online, or enjoy various game modes such as MyCareer, MyTeam, MyLeague, MyGM, and more.</p>
- <h3>Why download NBA 2K20 zip file?</h3>
-<p>NBA 2K20 is a premium game that normally costs $59.99 on Steam, the official platform for PC gaming. However, you can download NBA 2K20 zip file for free from other sources on the internet. A zip file is a compressed file that contains all the data and files needed to run a program or application. By downloading NBA 2K20 zip file, you can save storage space on your PC, as well as avoid paying for the game. However, you should be careful when downloading zip files from unknown sources, as they might contain viruses or malware that can harm your PC.</p>
- <h2>How to download NBA 2K20 zip file for PC</h2>
-<h3>Step 1: Find a reliable source</h3>
-<p>The first step to download NBA 2K20 zip file for PC is to find a reliable source that offers the game for free. There are many websites that claim to provide free downloads of NBA 2K20 zip file, but not all of them are trustworthy or safe. Some of them might require you to complete surveys, register an account, or download additional software that can infect your PC with malware. Therefore, you should do some research before downloading any zip file from any website.</p>
-<p>One of the sources that we recommend is MegaGames.com, a website that provides free downloads of games, patches, trainers, mods, and more. MegaGames.com has several versions of NBA 2K20 zip file available for download, such as v1.0, v1.02, and v1.05. These are all no-DVD versions of the game, which means that you don't need to insert a disc or use a crack to play the game.</p>
- <h3>Step 2: Download the zip file</h3>
-<p>The next step is to download the zip file of NBA 2K20 from MegaGames.com or any other source that you trust. To do this, you need to click on the download link or button on the website, and then choose a location on your PC where you want to save the zip file. The size of the zip file might vary depending on the version of the game, but it should be around 80 GB. The download speed might also depend on your internet connection and the server load. It might take several hours to download the zip file, so you need to be patient and wait until the download is complete.</p>
- <h3>Step 3: Extract the zip file</h3>
-<p>Once you have downloaded the zip file of NBA 2K20, you need to extract it using a software that can handle zip files, such as WinRAR or 7-Zip. To extract the zip file, you need to right-click on it and select "Extract Here" or "Extract to NBA 2K20" from the menu. This will create a folder with the same name as the zip file, containing all the files and folders of the game. The extraction process might take some time, depending on the size of the zip file and your PC performance.</p>
- <h3>Step 4: Install the game</h3>
-<p>After extracting the zip file, you need to install the game on your PC. To do this, you need to open the folder that contains the extracted files and folders of NBA 2K20, and look for a file named "Setup.exe" or something similar. This is the installer of the game, and you need to double-click on it to run it. The installer will guide you through the installation process, and you need to follow the instructions on the screen. You might need to agree to some terms and conditions, choose a destination folder for the game, and select some options for the game. The installation process might also take some time, depending on your PC performance.</p>
- <h3>Step 5: Enjoy the game</h3>
-<p>Congratulations! You have successfully downloaded and installed NBA 2K20 zip file for PC. Now you can enjoy playing the game on your PC for free. You can launch the game by clicking on its icon on your desktop or start menu, or by opening the folder where you installed the game and running the executable file named "NBA2K20.exe" or something similar. You can also create a shortcut for the game on your desktop or start menu for easier access.</p>
- <h2>Tips and tricks for playing NBA 2K20 on PC</h2>
-<h3>Adjust the graphics settings</h3>
-<p>NBA 2K20 is a graphically intensive game that requires a powerful PC to run smoothly. If you have a low-end or mid-range PC, you might experience some lagging or stuttering while playing the game. To fix this, you can adjust the graphics settings of the game to suit your PC specifications. You can access the graphics settings by going to Options > Video Settings in the game menu. You can lower some of the settings such as resolution, anti-aliasing, texture quality, shadow quality, etc., to improve your FPS (frames per second) and performance. However, this might also reduce the visual quality and realism of the game.</p>
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- <h3>Use a controller or keyboard and mouse</h3>
-<p>NBA 2K20 is designed to be played with a controller, such as an Xbox One or PlayStation 4 controller. If you have a controller that is compatible with your PC, you can use it to play NBA 2K20 more comfortably and intuitively. You can connect your controller to your PC via USB or Bluetooth, and then configure it in Options > Controller Settings in the game menu. You can also customize the controller layout and sensitivity to your preference.</p>
-<p>If you don't have a controller, you can still play NBA 2K20 with your keyboard and mouse. However, this might be less comfortable and intuitive than using a controller, as the game has many buttons and commands that are easier to execute with a controller. You can access the keyboard and mouse settings by going to Options > Keyboard/Mouse Settings in the game menu. You can also customize the keyboard and mouse layout and sensitivity to your preference.</p>
- <h3>Try different game modes and features</h3>
-<p>NBA 2K20 has a lot of game modes and features that you can try and enjoy. Here are some of them:</p>
-<ul>
-<li>MyCareer: This is the main mode of NBA 2K20, where you can create your own custom player and follow his journey from a rookie to a superstar in the NBA. You can customize your player's appearance, skills, attributes, badges, animations, etc., and make decisions that affect his career and personality. You can also interact with other players, coaches, fans, media, etc., and experience a cinematic story mode with voice acting and cutscenes.</li>
-<li>MyTeam: This is the mode where you can build your own dream team of NBA players from past and present. You can collect cards of players, coaches, jerseys, arenas, etc., and use them to create your own custom lineup. You can also compete with other players online or offline in various challenges and tournaments.</li>
-<li>MyLeague: This is the mode where you can take control of an entire NBA franchise and manage its operations. You can choose any NBA team or create your own custom team, and then adjust its roster, trades, contracts, draft picks, injuries, etc. You can also customize the league settings such as rules, schedule, playoffs, etc., and simulate or play any game in the season.</li>
-<li>MyGM: This is similar to MyLeague, but with more focus on the role-playing aspect of being a general manager. You have to deal with more realistic scenarios such as player morale, owner expectations, staff relationships, fan feedback, media pressure, etc. You also have to make choices that affect your reputation and career as a GM.</li>
-<li>Play Now: This is the mode where you can play a quick game with any NBA team or custom team. You can choose the difficulty level, quarter length, rules, etc., and play against the CPU or another player locally or online.</li>
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- <h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In conclusion, NBA 2K20 is a great game for basketball and video game fans alike. You can download NBA 2K20 zip file for PC for free by following these steps:</p>
-<ol>
-<li>Find a reliable source that offers NBA 2K20 zip file for free.</li>
-<li>Download the zip file of NBA 2K20 from the source.</li>
-<li>Extract the zip file using a software that can handle zip files.</li>
-<li>Install the game using the installer in the extracted folder.</li>
-<li>Enjoy playing the game on your PC.</li>
-</ol>
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-<p>If you want to experience the thrill and realism of NBA basketball on your PC, don't hesitate to download NBA 2K20 zip file for PC today. You will not regret it. Just make sure you download it from a safe and trustworthy source, and follow the instructions carefully. Have fun playing NBA 2K20!</p>
- <h2>Frequently Asked Questions</h2>
-<h3>Q: Is NBA 2K20 zip file safe to download?</h3>
-<p>A: It depends on where you download it from. Some sources might offer NBA 2K20 zip file that contains viruses or malware that can harm your PC. Therefore, you should always do some research before downloading any zip file from any website. We recommend MegaGames.com as a reliable source for NBA 2K20 zip file.</p>
- <h3>Q: Do I need a crack or a disc to play NBA 2K20 zip file?</h3>
-<p>A: No, you don't need a crack or a disc to play NBA 2K20 zip file. The zip file contains no-DVD versions of the game, which means that you can play it without inserting a disc or using a crack. However, you might need to update the game or install some patches to fix some bugs or errors.</p>
- <h3>Q: Can I play NBA 2K20 online with other players?</h3>
-<p>A: Yes, you can play NBA 2K20 online with other players if you have an internet connection and an account on Steam or Epic Games Store. However, you might not be able to play with players who have the official version of the game, as they might have different versions or updates. You might also face some issues or errors while playing online, such as lagging, disconnecting, or cheating.</p>
- <h3>Q: What are the minimum and recommended system requirements for NBA 2K20?</h3>
-<p>A: According to Steam, these are the minimum and recommended system requirements for NBA 2K20:</p>
-<table>
-<tr>
-<th>Minimum</th>
-<th>Recommended</th>
-</tr>
-<tr>
-<td>OS: Windows 7 64-bit, Windows 8.1 64-bit or Windows 10 64-bit</td>
-<td>OS: Windows 7 64-bit, Windows 8.1 64-bit or Windows 10 64-bit</td>
-</tr>
-<tr>
-<td>Processor: Intel® Core™ i3-530 @ 2.93 GHz / AMD FX-4100 @ 3.60 GHz or better</td>
-<td>Processor: Intel® Core™ i5-4430 @ 3 GHz / AMD FX-8370 @ 3.4 GHz or better</td>
-</tr>
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-<td>Memory: 4 GB RAM</td>
-<td>Memory: 8 GB RAM</td>
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-<td>Graphics: NVIDIA® GeForce® GT 450 1GB / AMD® Radeon™ HD 7770 1GB or better</td>
-<td>Graphics: NVIDIA® GeForce® GTX 770 2GB / AMD® Radeon™ R9 270 2GB or better</td>
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-<td>DirectX: Version 11</td>
-<td>DirectX: Version 11</td>
-</tr>
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-<td>Storage: 80 GB available space</td>
-<td>Storage: 80 GB available space</td>
-</tr>
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-<td>Sound Card: DirectX 9.0x compatible Dual-Analog Gamepad: Recommended</td>
-<td>Sound Card: DirectX 9.0c compatible Dual-Analog Gamepad: Recommended</td>
-</tr>
-</table>
- <h3>Q: How can I update NBA 2K20 to the latest version?</h3>
-<p>A: If you have downloaded NBA 2K20 zip file from MegaGames.com, you can update the game by downloading and installing the latest patch from the same website. For example, if you have downloaded NBA 2K20 v1.0, you can update it to v1.05 by downloading and installing the patch for v1.05. You can also check for other patches or updates from other sources, but make sure they are compatible with your version of the game.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Pip Millett When Everything Is Better I 39ll Let You Know Zip Download.md b/spaces/congsaPfin/Manga-OCR/logs/Pip Millett When Everything Is Better I 39ll Let You Know Zip Download.md
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- <p>Pip Millett is an English singer, songwriter, and musician who has been making waves in the UK music scene with her chilled, emotional fusion of R&B and soul. She was born as Georgia Willacy in Manchester, where she grew up listening to artists like Joni Mitchell, John Martyn, Bobby Womack, Lauryn Hill, and Bob Marley. She learned to play guitar and bass at a young age and sang in choirs. She moved to London to study music at university, where she met producer Joe Hertz, who featured her on his single "Goodbye Kisses" in 2017. This was her first exposure to the public and led to her signing with independent label + FOURS.</p>
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- <p>Pip Millett was born in Manchester, England, as Georgia Willacy. She was raised on the music of artists like Joni Mitchell, John Martyn, Bobby Womack, Lauryn Hill, and Bob Marley. She learned to play guitar and bass at a young age and sang in choirs. She moved to London to study music at university, where she met producer Joe Hertz, who featured her on his single "Goodbye Kisses" in 2017. This was her first exposure to the public and led to her signing with independent label + FOURS.</p>
- <h3>Career highlights and achievements</h3>
- <p>Since her debut single "Make Me Cry" in 2018, Pip Millett has released several singles and EPs that have gained millions of streams on Spotify and YouTube. Some of her most popular songs include "Love The Things You Do", "Try A Little Tenderness", "My Way", "Slow", and "Heal". She has also collaborated with artists like Feiertag, Moods, Chase & Status, Anthro Beats, Eg White, Alex Goose, Charlie J. Perry, and more. She has performed at various festivals and venues across the UK and Europe, such as Love Supreme Festival, Rock Werchter Festival, Nass Festival, Village Underground London, Gorilla Manchester, Paradiso Amsterdam, etc. She has also received critical acclaim from publications like The Guardian, Clash Magazine, Complex UK, Earmilk, Wonderland Magazine,</p> etc. <h3>Personal life and influences</h3>
- <p>Pip Millett is very private about her personal life and does not reveal much about her family or relationships. However, she has said that she is inspired by her parents' love story and their support for her music career. She has also said that she writes songs based on her own experiences and emotions. Some of her musical influences include Joni Mitchell, John Martyn, Bobby Womack, Lauryn Hill, Bob Marley</p>, Erykah Badu <h2>What is When Everything Is Better,</h2> I'll Let You Know? <p>When Everything Is Better,</p>
-<p></p> I'll Let You Know is the debut album by Pip Millett that was released on October 21st, <h3>Album overview and themes</h3>
- <p>The album consists of 17 tracks that span over 50 minutes <p>of 2022. It is her first full-length project after releasing three EPs: Do Well (2019), Lost in June (2020), and Motion Sick (2021). The album showcases her smooth vocals, honest lyrics, and soulful melodies that explore themes of love, loss, self-reflection, and hope.</p>
- <h3>Tracklist and features</h3>
- <p>The album consists of 17 tracks that span over 50 minutes. The tracklist is as follows:</p>
- <table>
-<tr>
-<th>Track</th>
-<th>Title</th>
-<th>Length</th>
-<th>Features</th>
-</tr>
-<tr>
-<td>1</td>
-<td>Think You Know Me (Intro)</td>
-<td>1:09</td>
-<td>None</td>
-</tr>
-<tr>
-<td>2</td>
-<td>Ride With Me</td>
-<td>3:52</td>
-<td>None</td>
-</tr>
-<tr>
-<td>3</td>
-<td>All Good</td>
-<td>3:29</td>
-<td>None</td>
-</tr>
-<tr>
-<td>4</td>
-<td>Mistakes (Interlude)</td>
-<td>1:17</td>
-<td>None</td>
-</tr>
-<tr>
-<td>5</td>
-<td>My Way</td>
-<td>3:37</td>
-<td>None</td>
-</tr>
-<tr>
-<td>6</td>
-<td>Downright</td>
-<td>2:57</td>
-<td>None</td>
-</tr>
-<tr>
-<td>7</td>
-<td>On Reflection (Interlude)</td>
-<td>1:25</td>
-<td>None</td>
-</tr>
-<tr>
-<td>8</td>
-<td>Smoking</td>
-<td>3:09</td>
-<td>Moods & Feiertag </td></tr><tr><td>9 </td>
-<td>Heal</td>
-<td>3:14</td>
-<td>None</td>
-</tr>
-<tr>
-<td>10</td>
-<td>Slow</td>
-<td>3:18</td>
-<td>None</td>
-</tr>
-<tr>
-<td>11</td>
-<td>Love The Things You Do</td>
-<td>3:06</td>
-<td>None</td>
-</tr>
-<tr>
-<td>12</td>
-<td>Try A Little Tenderness</td>
-<td>3:32</td>
-<td>None</td>
-</tr>
-<tr>
-<td>13</td>
-<td>Better Days (Interlude)</td>
-<td>1:05</td>
-<td>None</td>
-</tr>
-<tr>
-<td>14</td>
-<td>I'm Fine, I Promise (Outro)</td>
-<td>1:15</td>
-<td>None</td>
-</tr>
- <tr><td>15 </td>
-<td>When Everything Is Better, I'll Let You Know</td>
-<td>3:48</td>
-<td>None</td>
-</tr>
-<tr>
-<td>16</td>
-<td>Deeper Dark</td>
-<td>3:24</td>
-<td>None</td>
-</tr>
-<tr>
-<td>17</td>
-<td>Running</td>
-<td>3:01</td>
-<td>None</td>
-</tr>
-</table>
- <p>The album features two collaborations with Dutch producers Moods and Feiertag, who add their signature electronic and funky touches to the tracks "Smoking" and "Downright". The rest of the album is produced by Pip Millett's regular collaborators, such as Joe Hertz, Alex Goose, Charlie J. Perry, Eg White, Anthro Beats, etc.</p>
- <h3>Critical reception and reviews</h3>
- <p>The album has received positive reviews from critics and fans alike, who praised Pip Millett's vocal performance, songwriting skills, and musical diversity. Some of the reviews are:</p>
- <ul>
-<li>"Pip Millett delivers a stunning debut album that showcases her versatility and vulnerability as an artist. She effortlessly blends R&B, soul, jazz, and pop elements to create a cohesive and captivating sound that resonates with listeners. Her voice is smooth, expressive, and powerful, while her lyrics are honest, relatable, and poignant. When Everything Is Better, I'll Let You Know is a must-listen for anyone who appreciates quality music." - Clash Magazine</li>
-<li>"When Everything Is Better, I'll Let You Know is a beautiful and emotional journey that reveals Pip Millett's growth and maturity as a singer and songwriter. She explores various themes of love, loss, self-reflection, and hope with sincerity and depth. She also showcases her musical range and influences, from soulful ballads to upbeat grooves. The album is a testament to her talent and potential as one of the UK's most promising R&B and soul artists." - Earmilk</li>
-<li>"Pip Millett proves that she is more than just a voice on her debut album When Everything Is Better,</li></ul>I'll Let You Know. She demonstrates her ability to craft engaging and meaningful songs that reflect her personal experiences and emotions. She also experiments with different genres and sounds, from classic soul to modern R&B. The album is a solid and impressive introduction to Pip Millett's artistry and vision." - Wonderland Magazine <h2>How to download When Everything Is Better, I'll Let You Know zip file?</h2>
- <p>If you are interested in downloading Pip Millett's debut album in zip format, you might be wondering how to do it and why you should do it. In this section, we will explain what a zip file is, what are the benefits of downloading music in zip format, and what are the best sites and tools to download zip files.</p>
- <h3>What is a zip file and why use it?</h3>
- <p>A zip file is a compressed file that contains one or more files or folders. It reduces the size of the original files and makes them easier to store and transfer. Zip files are commonly used to share large files or multiple files over the internet, such as music albums, software, documents, etc.</p>
- <p>One of the reasons why you might want to download music in zip format is that it saves you time and space. Instead of downloading each song individually, you can download the whole album in one file. This way, you can avoid the hassle of clicking on multiple links, waiting for each download to finish, and organizing the songs in your device. Zip files also take up less storage space than uncompressed files, which means you can store more music on your device.</p>
- <h3>What are the benefits of downloading music in zip format?</h3>
- <p>Some of the benefits of downloading music in zip format are:</p>
- <ul>
-<li>You can download the whole album in one click, instead of downloading each song separately.</li>
-<li>You can save time and bandwidth by downloading smaller files.</li>
-<li>You can store more music on your device by using less storage space.</li>
-<li>You can preserve the quality and integrity of the original files by compressing them without losing data.</li>
-<li>You can easily extract and play the songs from the zip file using various software and apps.</li>
-</ul>
- <h3>What are the best sites and tools to download zip files?</h3>
- <p>There are many sites and tools that offer zip downloads of music albums, but not all of them are reliable and safe. Some of them might contain viruses, malware, or unwanted ads that can harm your device or compromise your privacy. Therefore, you should always be careful and cautious when downloading zip files from unknown sources.</p>
- <p>Some of the best sites and tools that we recommend for downloading zip files are:</p>
- <ul>
-<li><a href="">Zippyshare</a>: This is a free file hosting service that allows you to upload and download files up to 500 MB. It has a simple and user-friendly interface that lets you search for music albums by name, artist, genre, or year. You can also preview the songs before downloading them.</li>
-<li><a href="">Mediafire</a>: This is another free file hosting service that allows you to upload and download files up to 10 GB. It has a fast and secure download system that does not require registration or captcha. You can also create folders and share links with others.</li>
-<li><a href="">7-Zip</a>: This is a free and open-source software that allows you to compress and decompress files in various formats, including zip. It has a high compression ratio and supports encryption and password protection. You can also integrate it with Windows Explorer and use it from the command line.</li>
-<li><a href="">WinRAR</a>: This is a popular software that allows you to compress and decompress files in various formats, including zip. It has a powerful compression algorithm and supports splitting, encryption, password protection, recovery, and repair. You can also use it from the context menu or the command line.</li>
-</ul> <h2>Conclusion</h2>
- <p>In conclusion, Pip Millett is a talented and promising R&B and soul artist who has released her debut album, When Everything Is Better, I'll Let You Know, which is a stunning and emotional showcase of her vocals, lyrics, and melodies. The album is available to stream and download on various platforms, but if you want to download it in zip format, you can use some of the sites and tools that we have recommended in this article. We hope you enjoy listening to Pip Millett's music and support her career.</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions about Pip Millett and her album:</p>
- <ol>
-<li>Where can I stream Pip Millett's music?</li>
-<p>You can stream Pip Millett's music on Spotify, Apple Music, YouTube Music, SoundCloud, Deezer, Tidal, and other streaming services.</p>
-<li>How can I follow Pip Millett on social media?</li>
-<p>You can follow Pip Millett on Instagram, Twitter, Facebook, TikTok, and YouTube. Her username is @pipmillett on all platforms.</p>
-<li>How can I support Pip Millett financially?</li>
-<p>You can support Pip Millett financially by buying her music on iTunes, Amazon Music, Google Play Music, Bandcamp, or other online stores. You can also buy her merchandise on her official website or attend her live shows when possible.</p>
-<li>What are some of the awards and nominations that Pip Millett has received?</li>
-<p>Pip Millett has received several awards and nominations for her music, such as:</p>
-<ul>
-<li>Best Newcomer at the 2019 MOBO Awards</li>
-<li>Best Female Artist at the 2020 Rated Awards</li>
-<li>Best Soul Act at the 2020 Urban Music Awards</li>
-<li>Best British Album at the 2021 BRIT Awards (nominated)</li>
-<li>Best New Artist at the 2021 Grammy Awards (nominated)</li>
-</ul>
-<li>What are some of the upcoming projects and events that Pip Millett is working on?</li>
-<p>Pip Millett is currently working on new music and collaborations with other artists. She is also planning to go on a UK and Europe tour in 2022 to promote her album. You can check out her tour dates and tickets on her official website or social media pages.</p>
-</ol></p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Shadow Fight 2 Special Edition MOD APK The Best Fighting Game with Unlimited Money and New Locations.md b/spaces/congsaPfin/Manga-OCR/logs/Shadow Fight 2 Special Edition MOD APK The Best Fighting Game with Unlimited Money and New Locations.md
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-<br />
-<br />
-<p></p>
diff --git a/spaces/cybercorejapan/human-detection-docker/models/engine/utils.py b/spaces/cybercorejapan/human-detection-docker/models/engine/utils.py
deleted file mode 100644
index 90e436f81791c93a22fdd36e46883a31fe2258bd..0000000000000000000000000000000000000000
--- a/spaces/cybercorejapan/human-detection-docker/models/engine/utils.py
+++ /dev/null
@@ -1,248 +0,0 @@
-import numpy as np
-import cv2, torch
-
-from typing import Tuple, Dict, List
-
-def bbox_xyxy2cs(bbox: np.ndarray,
-                 padding: float = 1.) -> Tuple[np.ndarray, np.ndarray]:
-    """Transform the bbox format from (x,y,w,h) into (center, scale)
-
-    Args:
-        bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted
-            as (left, top, right, bottom)
-        padding (float): BBox padding factor that will be multilied to scale.
-            Default: 1.0
-
-    Returns:
-        tuple: A tuple containing center and scale.
-        - np.ndarray[float32]: Center (x, y) of the bbox in shape (2,) or
-            (n, 2)
-        - np.ndarray[float32]: Scale (w, h) of the bbox in shape (2,) or
-            (n, 2)
-    """
-    # convert single bbox from (4, ) to (1, 4)
-    dim = bbox.ndim
-    if dim == 1:
-        bbox = bbox[None, :]
-
-    # get bbox center and scale
-    x1, y1, x2, y2 = np.hsplit(bbox, [1, 2, 3])
-    center = np.hstack([x1 + x2, y1 + y2]) * 0.5
-    scale = np.hstack([x2 - x1, y2 - y1]) * padding
-
-    if dim == 1:
-        center = center[0]
-        scale = scale[0]
-
-    return center, scale
-
-def decode(simcc_x: np.ndarray, 
-            simcc_y: np.ndarray,
-            simcc_split_ratio, 
-            use_torch=False) -> Tuple[np.ndarray, np.ndarray]:
-    """Modulate simcc distribution with Gaussian.
-
-    Args:
-        simcc_x (np.ndarray[K, Wx]): model predicted simcc in x.
-        simcc_y (np.ndarray[K, Wy]): model predicted simcc in y.
-        simcc_split_ratio (int): The split ratio of simcc.
-
-    Returns:
-        tuple: A tuple containing center and scale.
-        - np.ndarray[float32]: keypoints in shape (K, 2) or (n, K, 2)
-        - np.ndarray[float32]: scores in shape (K,) or (n, K)
-    """
-    keypoints, scores = get_simcc_maximum(simcc_x, simcc_y, use_torch=use_torch)
-    keypoints /= simcc_split_ratio
-
-    return keypoints, scores
-
-
-def _fix_aspect_ratio(bbox_scale: np.ndarray,
-                        aspect_ratio: float) -> np.ndarray:
-    """Extend the scale to match the given aspect ratio.
-
-    Args:
-        scale (np.ndarray): The image scale (w, h) in shape (2, )
-        aspect_ratio (float): The ratio of ``w/h``
-
-    Returns:
-        np.ndarray: The reshaped image scale in (2, )
-    """
-    w, h = np.hsplit(bbox_scale, [1])
-    bbox_scale = np.where(w > h * aspect_ratio,
-                        np.hstack([w, w / aspect_ratio]),
-                        np.hstack([h * aspect_ratio, h]))
-    return bbox_scale
-
-
-def _rotate_point(pt: np.ndarray, 
-                    angle_rad: float) -> np.ndarray:
-    """Rotate a point by an angle.
-
-    Args:
-        pt (np.ndarray): 2D point coordinates (x, y) in shape (2, )
-        angle_rad (float): rotation angle in radian
-
-    Returns:
-        np.ndarray: Rotated point in shape (2, )
-    """
-    sn, cs = np.sin(angle_rad), np.cos(angle_rad)
-    rot_mat = np.array([[cs, -sn], [sn, cs]])
-    return rot_mat @ pt
-
-
-def _get_3rd_point(a: np.ndarray, b: np.ndarray) -> np.ndarray:
-    """To calculate the affine matrix, three pairs of points are required. This
-    function is used to get the 3rd point, given 2D points a & b.
-
-    The 3rd point is defined by rotating vector `a - b` by 90 degrees
-    anticlockwise, using b as the rotation center.
-
-    Args:
-        a (np.ndarray): The 1st point (x,y) in shape (2, )
-        b (np.ndarray): The 2nd point (x,y) in shape (2, )
-
-    Returns:
-        np.ndarray: The 3rd point.
-    """
-    direction = a - b
-    c = b + np.r_[-direction[1], direction[0]]
-    return c
-
-def get_warp_matrix(center: np.ndarray,
-                    scale: np.ndarray,
-                    rot: float,
-                    output_size: Tuple[int, int],
-                    shift: Tuple[float, float] = (0., 0.),
-                    inv: bool = False) -> np.ndarray:
-    """Calculate the affine transformation matrix that can warp the bbox area
-    in the input image to the output size.
-
-    Args:
-        center (np.ndarray[2, ]): Center of the bounding box (x, y).
-        scale (np.ndarray[2, ]): Scale of the bounding box
-            wrt [width, height].
-        rot (float): Rotation angle (degree).
-        output_size (np.ndarray[2, ] | list(2,)): Size of the
-            destination heatmaps.
-        shift (0-100%): Shift translation ratio wrt the width/height.
-            Default (0., 0.).
-        inv (bool): Option to inverse the affine transform direction.
-            (inv=False: src->dst or inv=True: dst->src)
-
-    Returns:
-        np.ndarray: A 2x3 transformation matrix
-    """
-    shift = np.array(shift)
-    src_w = scale[0]
-    dst_w = output_size[0]
-    dst_h = output_size[1]
-
-    # compute transformation matrix
-    rot_rad = np.deg2rad(rot)
-    src_dir = _rotate_point(np.array([0., src_w * -0.5]), rot_rad)
-    dst_dir = np.array([0., dst_w * -0.5])
-
-    # get four corners of the src rectangle in the original image
-    src = np.zeros((3, 2), dtype=np.float32)
-    src[0, :] = center + scale * shift
-    src[1, :] = center + src_dir + scale * shift
-    src[2, :] = _get_3rd_point(src[0, :], src[1, :])
-
-    # get four corners of the dst rectangle in the input image
-    dst = np.zeros((3, 2), dtype=np.float32)
-    dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
-    dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
-    dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])
-
-    if inv:
-        warp_mat = cv2.getAffineTransform(np.float32(dst), np.float32(src))
-    else:
-        warp_mat = cv2.getAffineTransform(np.float32(src), np.float32(dst))
-
-    return warp_mat
-
-
-def top_down_affine(input_size: dict, 
-                    bbox_scale: dict, 
-                    bbox_center: dict,
-                    img: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
-        """Get the bbox image as the model input by affine transform.
-
-        Args:
-            input_size (dict): The input size of the model.
-            bbox_scale (dict): The bbox scale of the img.
-            bbox_center (dict): The bbox center of the img.
-            img (np.ndarray): The original image.
-
-        Returns:
-            tuple: A tuple containing center and scale.
-            - np.ndarray[float32]: img after affine transform.
-            - np.ndarray[float32]: bbox scale after affine transform.
-        """
-        w, h = input_size
-        warp_size = (int(w), int(h))
-
-        # reshape bbox to fixed aspect ratio
-        bbox_scale = _fix_aspect_ratio(bbox_scale, aspect_ratio=w / h)
-
-        # get the affine matrix
-        center = bbox_center
-        scale = bbox_scale
-        rot = 0
-        warp_mat = get_warp_matrix(center, scale, rot, output_size=(w, h))
-
-        # do affine transform
-        img = cv2.warpAffine(img, warp_mat, warp_size, flags=cv2.INTER_LINEAR)
-
-        return img, bbox_scale
-
-def get_simcc_maximum(simcc_x: np.ndarray,
-                        simcc_y: np.ndarray,
-                        use_torch=False) -> Tuple[np.ndarray, np.ndarray]:
-    """Get maximum response location and value from simcc representations.
-
-    Note:
-        instance number: N
-        num_keypoints: K
-        heatmap height: H
-        heatmap width: W
-
-    Args:
-        simcc_x (np.ndarray): x-axis SimCC in shape (K, Wx) or (N, K, Wx)
-        simcc_y (np.ndarray): y-axis SimCC in shape (K, Wy) or (N, K, Wy)
-
-    Returns:
-        tuple:
-        - locs (np.ndarray): locations of maximum heatmap responses in shape
-            (K, 2) or (N, K, 2)
-        - vals (np.ndarray): values of maximum heatmap responses in shape
-            (K,) or (N, K)
-    """
-    N, K, Wx = simcc_x.shape
-    simcc_x = simcc_x.reshape(N * K, -1)
-    simcc_y = simcc_y.reshape(N * K, -1)
-
-    # get maximum value locations
-    x_locs = np.argmax(simcc_x, axis=1)
-    y_locs = np.argmax(simcc_y, axis=1)
-    locs = np.stack((x_locs, y_locs), axis=-1).astype(np.float32)
-    if use_torch:
-        max_val_x = torch.max(simcc_x, dim=1)[0]
-        max_val_y = torch.max(simcc_y, dim=1)[0]
-    else:
-        max_val_x = np.amax(simcc_x, axis=1)
-        max_val_y = np.amax(simcc_y, axis=1)
-
-    # get maximum value across x and y axis
-    mask = max_val_x > max_val_y
-    max_val_x[mask] = max_val_y[mask]
-    vals = max_val_x
-    locs[vals <= 0.] = -1
-
-    # reshape
-    locs = locs.reshape(N, K, 2)
-    vals = vals.reshape(N, K)
-
-    return locs, vals
\ No newline at end of file
diff --git a/spaces/darthPanda/romeo_and_juliet_chatbot_with_gptIndex/README.md b/spaces/darthPanda/romeo_and_juliet_chatbot_with_gptIndex/README.md
deleted file mode 100644
index 536e510a4e85d714f8668a4f59d78766f532d8c1..0000000000000000000000000000000000000000
--- a/spaces/darthPanda/romeo_and_juliet_chatbot_with_gptIndex/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Romeo And Juliet Chatbot With GptIndex
-emoji: 📚
-colorFrom: green
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/inference_codeformer.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/inference_codeformer.py
deleted file mode 100644
index fdfe8b301cc7c20c2fb653618e379d243603a108..0000000000000000000000000000000000000000
--- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/inference_codeformer.py
+++ /dev/null
@@ -1,189 +0,0 @@
-# Modified by Shangchen Zhou from: https://github.com/TencentARC/GFPGAN/blob/master/inference_gfpgan.py
-import os
-import cv2
-import argparse
-import glob
-import torch
-from torchvision.transforms.functional import normalize
-from basicsr.utils import imwrite, img2tensor, tensor2img
-from basicsr.utils.download_util import load_file_from_url
-from facelib.utils.face_restoration_helper import FaceRestoreHelper
-import torch.nn.functional as F
-
-from basicsr.utils.registry import ARCH_REGISTRY
-
-pretrain_model_url = {
-    'restoration': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth',
-}
-
-def set_realesrgan():
-    if not torch.cuda.is_available():  # CPU
-        import warnings
-        warnings.warn('The unoptimized RealESRGAN is slow on CPU. We do not use it. '
-                        'If you really want to use it, please modify the corresponding codes.',
-                        category=RuntimeWarning)
-        bg_upsampler = None
-    else:
-        from basicsr.archs.rrdbnet_arch import RRDBNet
-        from basicsr.utils.realesrgan_utils import RealESRGANer
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-        bg_upsampler = RealESRGANer(
-            scale=2,
-            model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
-            model=model,
-            tile=args.bg_tile,
-            tile_pad=40,
-            pre_pad=0,
-            half=True)  # need to set False in CPU mode
-    return bg_upsampler
-
-if __name__ == '__main__':
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument('--w', type=float, default=0.5, help='Balance the quality and fidelity')
-    parser.add_argument('--upscale', type=int, default=2, help='The final upsampling scale of the image. Default: 2')
-    parser.add_argument('--test_path', type=str, default='./inputs/cropped_faces')
-    parser.add_argument('--has_aligned', action='store_true', help='Input are cropped and aligned faces')
-    parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face')
-    # large det_model: 'YOLOv5l', 'retinaface_resnet50'
-    # small det_model: 'YOLOv5n', 'retinaface_mobile0.25'
-    parser.add_argument('--detection_model', type=str, default='retinaface_resnet50')
-    parser.add_argument('--draw_box', action='store_true')
-    parser.add_argument('--bg_upsampler', type=str, default='None', help='background upsampler. Optional: realesrgan')
-    parser.add_argument('--face_upsample', action='store_true', help='face upsampler after enhancement.')
-    parser.add_argument('--bg_tile', type=int, default=400, help='Tile size for background sampler. Default: 400')
-
-    args = parser.parse_args()
-
-    # ------------------------ input & output ------------------------
-    if args.test_path.endswith('/'):  # solve when path ends with /
-        args.test_path = args.test_path[:-1]
-
-    w = args.w
-    result_root = f'results/{os.path.basename(args.test_path)}_{w}'
-
-    # ------------------ set up background upsampler ------------------
-    if args.bg_upsampler == 'realesrgan':
-        bg_upsampler = set_realesrgan()
-    else:
-        bg_upsampler = None
-
-    # ------------------ set up face upsampler ------------------
-    if args.face_upsample:
-        if bg_upsampler is not None:
-            face_upsampler = bg_upsampler
-        else:
-            face_upsampler = set_realesrgan()
-    else:
-        face_upsampler = None
-
-    # ------------------ set up CodeFormer restorer -------------------
-    net = ARCH_REGISTRY.get('CodeFormer')(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, 
-                                            connect_list=['32', '64', '128', '256']).to(device)
-    
-    # ckpt_path = 'weights/CodeFormer/codeformer.pth'
-    ckpt_path = load_file_from_url(url=pretrain_model_url['restoration'], 
-                                    model_dir='weights/CodeFormer', progress=True, file_name=None)
-    checkpoint = torch.load(ckpt_path)['params_ema']
-    net.load_state_dict(checkpoint)
-    net.eval()
-
-    # ------------------ set up FaceRestoreHelper -------------------
-    # large det_model: 'YOLOv5l', 'retinaface_resnet50'
-    # small det_model: 'YOLOv5n', 'retinaface_mobile0.25'
-    if not args.has_aligned: 
-        print(f'Face detection model: {args.detection_model}')
-    if bg_upsampler is not None: 
-        print(f'Background upsampling: True, Face upsampling: {args.face_upsample}')
-    else:
-        print(f'Background upsampling: False, Face upsampling: {args.face_upsample}')
-
-    face_helper = FaceRestoreHelper(
-        args.upscale,
-        face_size=512,
-        crop_ratio=(1, 1),
-        det_model = args.detection_model,
-        save_ext='png',
-        use_parse=True,
-        device=device)
-
-    # -------------------- start to processing ---------------------
-    # scan all the jpg and png images
-    for img_path in sorted(glob.glob(os.path.join(args.test_path, '*.[jp][pn]g'))):
-        # clean all the intermediate results to process the next image
-        face_helper.clean_all()
-        
-        img_name = os.path.basename(img_path)
-        print(f'Processing: {img_name}')
-        basename, ext = os.path.splitext(img_name)
-        img = cv2.imread(img_path, cv2.IMREAD_COLOR)
-
-        if args.has_aligned: 
-            # the input faces are already cropped and aligned
-            img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)
-            face_helper.cropped_faces = [img]
-        else:
-            face_helper.read_image(img)
-            # get face landmarks for each face
-            num_det_faces = face_helper.get_face_landmarks_5(
-                only_center_face=args.only_center_face, resize=640, eye_dist_threshold=5)
-            print(f'\tdetect {num_det_faces} faces')
-            # align and warp each face
-            face_helper.align_warp_face()
-
-        # face restoration for each cropped face
-        for idx, cropped_face in enumerate(face_helper.cropped_faces):
-            # prepare data
-            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
-            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-            cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
-
-            try:
-                with torch.no_grad():
-                    output = net(cropped_face_t, w=w, adain=True)[0]
-                    restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
-                del output
-                torch.cuda.empty_cache()
-            except Exception as error:
-                print(f'\tFailed inference for CodeFormer: {error}')
-                restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
-
-            restored_face = restored_face.astype('uint8')
-            face_helper.add_restored_face(restored_face)
-
-        # paste_back
-        if not args.has_aligned:
-            # upsample the background
-            if bg_upsampler is not None:
-                # Now only support RealESRGAN for upsampling background
-                bg_img = bg_upsampler.enhance(img, outscale=args.upscale)[0]
-            else:
-                bg_img = None
-            face_helper.get_inverse_affine(None)
-            # paste each restored face to the input image
-            if args.face_upsample and face_upsampler is not None: 
-                restored_img = face_helper.paste_faces_to_input_image(upsample_img=bg_img, draw_box=args.draw_box, face_upsampler=face_upsampler)
-            else:
-                restored_img = face_helper.paste_faces_to_input_image(upsample_img=bg_img, draw_box=args.draw_box)
-
-        # save faces
-        for idx, (cropped_face, restored_face) in enumerate(zip(face_helper.cropped_faces, face_helper.restored_faces)):
-            # save cropped face
-            if not args.has_aligned: 
-                save_crop_path = os.path.join(result_root, 'cropped_faces', f'{basename}_{idx:02d}.png')
-                imwrite(cropped_face, save_crop_path)
-            # save restored face
-            if args.has_aligned:
-                save_face_name = f'{basename}.png'
-            else:
-                save_face_name = f'{basename}_{idx:02d}.png'
-            save_restore_path = os.path.join(result_root, 'restored_faces', save_face_name)
-            imwrite(restored_face, save_restore_path)
-
-        # save restored img
-        if not args.has_aligned and restored_img is not None:
-            save_restore_path = os.path.join(result_root, 'final_results', f'{basename}.png')
-            imwrite(restored_img, save_restore_path)
-
-    print(f'\nAll results are saved in {result_root}')
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/cu2qu/__main__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/cu2qu/__main__.py
deleted file mode 100644
index 084bf8f960db3d4ded95921ee9d7cbd2a7fb9f4a..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/cu2qu/__main__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import sys
-from .cli import main
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/otlLib/optimize/__main__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/otlLib/optimize/__main__.py
deleted file mode 100644
index b0ae9081ca8dac338bcf085c71adad87805e3bad..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/otlLib/optimize/__main__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import sys
-from fontTools.otlLib.optimize import main
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/slider.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/slider.py
deleted file mode 100644
index c589eb675d09dc0521c100488799aa96af8b06ca..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/slider.py
+++ /dev/null
@@ -1,210 +0,0 @@
-"""gr.Slider() component."""
-
-from __future__ import annotations
-
-import math
-import random
-from typing import Any, Callable, Literal
-
-import numpy as np
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import NumberSerializable
-
-from gradio.components.base import FormComponent, IOComponent, _Keywords
-from gradio.deprecation import warn_style_method_deprecation
-from gradio.events import Changeable, Inputable, Releaseable
-from gradio.interpretation import NeighborInterpretable
-
-set_documentation_group("component")
-
-
-@document()
-class Slider(
-    FormComponent,
-    Changeable,
-    Inputable,
-    Releaseable,
-    IOComponent,
-    NumberSerializable,
-    NeighborInterpretable,
-):
-    """
-    Creates a slider that ranges from `minimum` to `maximum` with a step size of `step`.
-    Preprocessing: passes slider value as a {float} into the function.
-    Postprocessing: expects an {int} or {float} returned from function and sets slider value to it as long as it is within range.
-    Examples-format: A {float} or {int} representing the slider's value.
-
-    Demos: sentence_builder, slider_release, generate_tone, titanic_survival, interface_random_slider, blocks_random_slider
-    Guides: create-your-own-friends-with-a-gan
-    """
-
-    def __init__(
-        self,
-        minimum: float = 0,
-        maximum: float = 100,
-        value: float | Callable | None = None,
-        *,
-        step: float | None = None,
-        label: str | None = None,
-        info: str | None = None,
-        every: float | None = None,
-        show_label: bool | None = None,
-        container: bool = True,
-        scale: int | None = None,
-        min_width: int = 160,
-        interactive: bool | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-        randomize: bool = False,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            minimum: minimum value for slider.
-            maximum: maximum value for slider.
-            value: default value. If callable, the function will be called whenever the app loads to set the initial value of the component. Ignored if randomized=True.
-            step: increment between slider values.
-            label: component name in interface.
-            info: additional component description.
-            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
-            show_label: if True, will display label.
-            container: If True, will place the component in a container - providing some extra padding around the border.
-            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
-            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
-            interactive: if True, slider will be adjustable; if False, adjusting will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
-            visible: If False, component will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-            randomize: If True, the value of the slider when the app loads is taken uniformly at random from the range given by the minimum and maximum.
-        """
-        self.minimum = minimum
-        self.maximum = maximum
-        if step is None:
-            difference = maximum - minimum
-            power = math.floor(math.log10(difference) - 2)
-            self.step = 10**power
-        else:
-            self.step = step
-        if randomize:
-            value = self.get_random_value
-        IOComponent.__init__(
-            self,
-            label=label,
-            info=info,
-            every=every,
-            show_label=show_label,
-            container=container,
-            scale=scale,
-            min_width=min_width,
-            interactive=interactive,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            value=value,
-            **kwargs,
-        )
-        NeighborInterpretable.__init__(self)
-
-    def api_info(self) -> dict[str, dict | bool]:
-        return {
-            "info": {
-                "type": "number",
-                "description": f"numeric value between {self.minimum} and {self.maximum}",
-            },
-            "serialized_info": False,
-        }
-
-    def example_inputs(self) -> dict[str, Any]:
-        return {
-            "raw": self.minimum,
-            "serialized": self.minimum,
-        }
-
-    def get_config(self):
-        return {
-            "minimum": self.minimum,
-            "maximum": self.maximum,
-            "step": self.step,
-            "value": self.value,
-            **IOComponent.get_config(self),
-        }
-
-    def get_random_value(self):
-        n_steps = int((self.maximum - self.minimum) / self.step)
-        step = random.randint(0, n_steps)
-        value = self.minimum + step * self.step
-        # Round to number of decimals in step so that UI doesn't display long decimals
-        n_decimals = max(str(self.step)[::-1].find("."), 0)
-        if n_decimals:
-            value = round(value, n_decimals)
-        return value
-
-    @staticmethod
-    def update(
-        value: float | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE,
-        minimum: float | None = None,
-        maximum: float | None = None,
-        step: float | None = None,
-        label: str | None = None,
-        info: str | None = None,
-        show_label: bool | None = None,
-        container: bool | None = None,
-        scale: int | None = None,
-        min_width: int | None = None,
-        interactive: bool | None = None,
-        visible: bool | None = None,
-    ):
-        return {
-            "minimum": minimum,
-            "maximum": maximum,
-            "step": step,
-            "label": label,
-            "info": info,
-            "show_label": show_label,
-            "container": container,
-            "scale": scale,
-            "min_width": min_width,
-            "interactive": interactive,
-            "visible": visible,
-            "value": value,
-            "__type__": "update",
-        }
-
-    def postprocess(self, y: float | None) -> float | None:
-        """
-        Any postprocessing needed to be performed on function output.
-        Parameters:
-            y: numeric output
-        Returns:
-            numeric output or minimum number if None
-        """
-        return self.minimum if y is None else y
-
-    def set_interpret_parameters(self, steps: int = 8) -> Slider:
-        """
-        Calculates interpretation scores of numeric values ranging between the minimum and maximum values of the slider.
-        Parameters:
-            steps: Number of neighboring values to measure between the minimum and maximum values of the slider range.
-        """
-        self.interpretation_steps = steps
-        return self
-
-    def get_interpretation_neighbors(self, x) -> tuple[object, dict]:
-        return (
-            np.linspace(self.minimum, self.maximum, self.interpretation_steps).tolist(),
-            {},
-        )
-
-    def style(
-        self,
-        *,
-        container: bool | None = None,
-    ):
-        """
-        This method is deprecated. Please set these arguments in the constructor instead.
-        """
-        warn_style_method_deprecation()
-        if container is not None:
-            self.container = container
-        return self
diff --git a/spaces/deepakHonakeri5/instagram/main.py b/spaces/deepakHonakeri5/instagram/main.py
deleted file mode 100644
index aca296ef81e9129cd8fc68bf3240ba0d81f8b647..0000000000000000000000000000000000000000
--- a/spaces/deepakHonakeri5/instagram/main.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import time
-import requests
-
-import torch
-import torchvision
-from torchvision import models
-from torchvision import transforms
-from PIL import Image
-
-from selenium import webdriver
-from bs4 import BeautifulSoup
-from selenium.webdriver.chrome.options import Options
-
-from fastapi import FastAPI, Response
-from fastapi.staticfiles import StaticFiles
-from fastapi.responses import FileResponse
-
-from fastapi.responses import JSONResponse
-from fastapi.encoders import jsonable_encoder
-
-import json
-
-app = FastAPI()
-
-def getimage(url):
-    # Create a Chrome WebDriver instance (you need to specify the path to chromedriver)
-    chrome_options = Options()
-    chrome_options.add_argument('--headless')
-    chrome_options.add_argument('--no-sandbox')
-    chrome_options.add_argument('--disable-dev-shm-usage')  # Run Chrome in headless mode (no GUI)
-    chrome_options.add_argument("--window-size=1200x800")
-    driver = webdriver.Chrome(options=chrome_options)
-
-    # Navigate to the webpage with the splash screen
-    driver.get(url)
-
-    # Wait for the dynamic content to load (you may need to adjust the wait time)
-    time.sleep(5)  # Wait for 5 seconds to load the page
-
-    # Get the current page source after the dynamic content has loaded
-    page_source = driver.page_source
-
-    # Parse the page source with BeautifulSoup
-    soup = BeautifulSoup(page_source, 'html.parser')
-
-    # Find and extract <img> tags or perform further processing
-    img_tags = soup.find_all('img',class_='x5yr21d xu96u03 x10l6tqk x13vifvy x87ps6o xh8yej3')
-    img = img_tags[0]
-    img_url = img['src']
-    '''Download the image via the url using the requests library'''
-    r = requests.get(img_url)
-
-    filename = "instagram"+".png"
-    with open(filename,'wb') as f: 
-        f.write(r.content)
-
-
-@app.get("/infer_t5")
-def t5(input):
-    getimage(input)
-    return {"message": "success"}
-
-@app.get("/query_prediction")
-def prediction():
-    filename = "instagram"+".png"
-
-    model = models.vgg16(pretrained=False)
-    model.classifier[6].out_features = 5
-    for parameters in model.features.parameters():
-        parameters.requires_grad = False
-
-    #model = torch.load('vgg16_5epoch.pth', map_location=torch.device('cpu'))
-    model.load_state_dict(torch.load('./vgg16_5epoch.pth', map_location=torch.device('cpu')))
-    model.eval()
-
-    preprocess = transforms.Compose([
-        transforms.Resize((224, 224)),  # Resize the image to the model's input size
-        transforms.ToTensor(),           # Convert to PyTorch tensor
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # Normalize if needed
-    ])
-
-    # Load and preprocess a single image
-    image = Image.open(filename).convert('RGB')
-    image = preprocess(image)
-    image = image.unsqueeze(0)  # Add a batch dimension
-
-    with torch.no_grad():
-        outputs = model(image)
-
-    _, predicted = torch.max(outputs.data, 1)
-    predicted = predicted.item()
-
-    final_prediction = ""
-
-    if predicted == 0:
-        final_prediction = "Fitness/Yoga"
-    elif predicted == 1:
-        final_prediction = "Food"
-    elif predicted == 2:
-        final_prediction = "House/Interior"
-    elif predicted == 3:
-        final_prediction = "Pets"
-    else:
-        final_prediction = "Travel/Scenery"
-
-    responseResult = [{
-        "prediction" : final_prediction
-    }]
-
-    json_str = json.dumps(responseResult, indent=4, default=str)
-
-    return Response(content=json_str, media_type='application/json')
-
-app.mount("/", StaticFiles(directory="static", html=True), name="static")
-
-@app.get("/")
-def index() -> FileResponse:
-    return FileResponse(path="static/index.html", media_type="text/html")
\ No newline at end of file
diff --git a/spaces/deepdoctection/Document-AI-GPT/README.md b/spaces/deepdoctection/Document-AI-GPT/README.md
deleted file mode 100644
index 92a7f2c1478c25f78429ac5815b88a5827644278..0000000000000000000000000000000000000000
--- a/spaces/deepdoctection/Document-AI-GPT/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Document AI GPT
-emoji: 📈
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/arcface_torch/utils/plot.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/arcface_torch/utils/plot.py
deleted file mode 100644
index ccc588e5c01ca550b69c385aeb3fd139c59fb88a..0000000000000000000000000000000000000000
--- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/arcface_torch/utils/plot.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# coding: utf-8
-
-import os
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
-from prettytable import PrettyTable
-from sklearn.metrics import roc_curve, auc
-
-image_path = "/data/anxiang/IJB_release/IJBC"
-files = [
-        "./ms1mv3_arcface_r100/ms1mv3_arcface_r100/ijbc.npy"
-]
-
-
-def read_template_pair_list(path):
-    pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
-    return t1, t2, label
-
-
-p1, p2, label = read_template_pair_list(
-    os.path.join('%s/meta' % image_path,
-                 '%s_template_pair_label.txt' % 'ijbc'))
-
-methods = []
-scores = []
-for file in files:
-    methods.append(file.split('/')[-2])
-    scores.append(np.load(file))
-
-methods = np.array(methods)
-scores = dict(zip(methods, scores))
-colours = dict(
-    zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))
-x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]
-tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])
-fig = plt.figure()
-for method in methods:
-    fpr, tpr, _ = roc_curve(label, scores[method])
-    roc_auc = auc(fpr, tpr)
-    fpr = np.flipud(fpr)
-    tpr = np.flipud(tpr)  # select largest tpr at same fpr
-    plt.plot(fpr,
-             tpr,
-             color=colours[method],
-             lw=1,
-             label=('[%s (AUC = %0.4f %%)]' %
-                    (method.split('-')[-1], roc_auc * 100)))
-    tpr_fpr_row = []
-    tpr_fpr_row.append("%s-%s" % (method, "IJBC"))
-    for fpr_iter in np.arange(len(x_labels)):
-        _, min_index = min(
-            list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr)))))
-        tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100))
-    tpr_fpr_table.add_row(tpr_fpr_row)
-plt.xlim([10 ** -6, 0.1])
-plt.ylim([0.3, 1.0])
-plt.grid(linestyle='--', linewidth=1)
-plt.xticks(x_labels)
-plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True))
-plt.xscale('log')
-plt.xlabel('False Positive Rate')
-plt.ylabel('True Positive Rate')
-plt.title('ROC on IJB')
-plt.legend(loc="lower right")
-print(tpr_fpr_table)
diff --git a/spaces/deepwisdom/MetaGPT/docs/README_CN.md b/spaces/deepwisdom/MetaGPT/docs/README_CN.md
deleted file mode 100644
index 2180eb51874ceaa3215331ade42fe8139f130f28..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/docs/README_CN.md
+++ /dev/null
@@ -1,201 +0,0 @@
-# MetaGPT: 多智能体框架
-
-<p align="center">
-<a href=""><img src="resources/MetaGPT-logo.jpeg" alt="MetaGPT logo: 使 GPT 以软件公司的形式工作，协作处理更复杂的任务" width="150px"></a>
-</p>
-
-<p align="center">
-<b>使 GPTs 组成软件公司，协作处理更复杂的任务</b>
-</p>
-
-<p align="center">
-<a href="README_CN.md"><img src="https://img.shields.io/badge/文档-中文版-blue.svg" alt="CN doc"></a>
-<a href="../README.md"><img src="https://img.shields.io/badge/document-English-blue.svg" alt="EN doc"></a>
-<a href="README_JA.md"><img src="https://img.shields.io/badge/ドキュメント-日本語-blue.svg" alt="JA doc"></a>
-<a href="https://discord.gg/wCp6Q3fsAk"><img src="https://dcbadge.vercel.app/api/server/wCp6Q3fsAk?compact=true&style=flat" alt="Discord Follow"></a>
-<a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"></a>
-<a href="ROADMAP.md"><img src="https://img.shields.io/badge/ROADMAP-路线图-blue" alt="roadmap"></a>
-<a href="resources/MetaGPT-WeChat-Personal.jpeg"><img src="https://img.shields.io/badge/WeChat-微信-blue" alt="roadmap"></a>
-<a href="https://twitter.com/DeepWisdom2019"><img src="https://img.shields.io/twitter/follow/MetaGPT?style=social" alt="Twitter Follow"></a>
-</p>
-
-1. MetaGPT输入**一句话的老板需求**，输出**用户故事 / 竞品分析 / 需求 / 数据结构 / APIs / 文件等**
-2. MetaGPT内部包括**产品经理 / 架构师 / 项目经理 / 工程师**，它提供了一个**软件公司**的全过程与精心调配的SOP
-   1. `Code = SOP(Team)` 是核心哲学。我们将SOP具象化，并且用于LLM构成的团队
-
-![一个完全由大语言模型角色构成的软件公司](resources/software_company_cd.jpeg)
-
-<p align="center">软件公司多角色示意图（正在逐步实现）</p>
-
-## 示例（均由 GPT-4 生成）
-
-例如，键入`python startup.py "写个类似今日头条的推荐系统"`并回车，你会获得一系列输出，其一是数据结构与API设计
-
-![今日头条 Recsys 数据 & API 设计](resources/workspace/content_rec_sys/resources/data_api_design.png)
-
-这需要大约**0.2美元**（GPT-4 API的费用）来生成一个带有分析和设计的示例，大约2.0美元用于一个完整的项目
-
-## 安装
-
-### 传统安装
-
-```bash
-# 第 1 步：确保您的系统上安装了 NPM。并使用npm安装mermaid-js
-npm --version
-sudo npm install -g @mermaid-js/mermaid-cli
-
-# 第 2 步：确保您的系统上安装了 Python 3.9+。您可以使用以下命令进行检查：
-python --version
-
-# 第 3 步：克隆仓库到您的本地机器，并进行安装。
-git clone https://github.com/geekan/metagpt
-cd metagpt
-python setup.py install
-```
-
-### Docker安装
-
-```bash
-# 步骤1: 下载metagpt官方镜像并准备好config.yaml
-docker pull metagpt/metagpt:v0.3
-mkdir -p /opt/metagpt/{config,workspace}
-docker run --rm metagpt/metagpt:v0.3 cat /app/metagpt/config/config.yaml > /opt/metagpt/config/config.yaml
-vim /opt/metagpt/config/config.yaml # 修改config
-
-# 步骤2: 使用容器运行metagpt演示
-docker run --rm \
-    --privileged \
-    -v /opt/metagpt/config:/app/metagpt/config \
-    -v /opt/metagpt/workspace:/app/metagpt/workspace \
-    metagpt/metagpt:v0.3 \
-    python startup.py "Write a cli snake game"
-
-# 您也可以启动一个容器并在其中执行命令
-docker run --name metagpt -d \
-    --privileged \
-    -v /opt/metagpt/config:/app/metagpt/config \
-    -v /opt/metagpt/workspace:/app/metagpt/workspace \
-    metagpt/metagpt:v0.3
-
-docker exec -it metagpt /bin/bash
-$ python startup.py "Write a cli snake game"
-```
-
-`docker run ...`做了以下事情:
-
-- 以特权模式运行，有权限运行浏览器
-- 将主机目录 `/opt/metagpt/config` 映射到容器目录`/app/metagpt/config`
-- 将主机目录 `/opt/metagpt/workspace` 映射到容器目录 `/app/metagpt/workspace`
-- 执行演示命令 `python startup.py "Write a cli snake game"`
-
-### 自己构建镜像
-
-```bash
-# 您也可以自己构建metagpt镜像
-git clone https://github.com/geekan/MetaGPT.git
-cd MetaGPT && docker build -t metagpt:v0.3 .
-```
-
-## 配置
-
-- 在 `config/key.yaml / config/config.yaml / env` 中配置您的 `OPENAI_API_KEY`
-- 优先级顺序：`config/key.yaml > config/config.yaml > env`
-
-```bash
-# 复制配置文件并进行必要的修改
-cp config/config.yaml config/key.yaml
-```
-
-| 变量名                              | config/key.yaml                           | env                            |
-|--------------------------------------------|-------------------------------------------|--------------------------------|
-| OPENAI_API_KEY # 用您自己的密钥替换 | OPENAI_API_KEY: "sk-..."                  | export OPENAI_API_KEY="sk-..." |
-| OPENAI_API_BASE # 可选  | OPENAI_API_BASE: "https://<YOUR_SITE>/v1" | export OPENAI_API_BASE="https://<YOUR_SITE>/v1"   |
-
-## 示例：启动一个创业公司
-
-```shell
-python startup.py "写一个命令行贪吃蛇"
-# 开启code review模式会会花费更多的money, 但是会提升代码质量和成功率
-python startup.py "写一个命令行贪吃蛇" --code_review True
-```
-
-运行脚本后，您可以在 `workspace/` 目录中找到您的新项目。
-### 平台或工具的倾向性
-可以在阐述需求时说明想要使用的平台或工具。
-例如：
-
-```shell
-python startup.py "写一个基于pygame的命令行贪吃蛇"
-```
-
-### 使用
-
-```
-名称
-    startup.py - 我们是一家AI软件创业公司。通过投资我们，您将赋能一个充满无限可能的未来。
-
-概要
-    startup.py IDEA <flags>
-
-描述
-    我们是一家AI软件创业公司。通过投资我们，您将赋能一个充满无限可能的未来。
-
-位置参数
-    IDEA
-        类型: str
-        您的创新想法，例如"写一个命令行贪吃蛇。"
-
-标志
-    --investment=INVESTMENT
-        类型: float
-        默认值: 3.0
-        作为投资者，您有机会向这家AI公司投入一定的美元金额。
-    --n_round=N_ROUND
-        类型: int
-        默认值: 5
-
-备注
-    您也可以用`标志`的语法，来处理`位置参数`
-```
-
-### 代码实现
-
-```python
-from metagpt.software_company import SoftwareCompany
-from metagpt.roles import ProjectManager, ProductManager, Architect, Engineer
-
-async def startup(idea: str, investment: float = 3.0, n_round: int = 5):
-    """运行一个创业公司。做一个老板"""
-    company = SoftwareCompany()
-    company.hire([ProductManager(), Architect(), ProjectManager(), Engineer()])
-    company.invest(investment)
-    company.start_project(idea)
-    await company.run(n_round=n_round)
-```
-
-你可以查看`examples`，其中有单角色（带知识库）的使用例子与仅LLM的使用例子。
-
-## 快速体验
-对一些用户来说，安装配置本地环境是有困难的，下面这些教程能够让你快速体验到MetaGPT的魅力。  
-
-- [MetaGPT快速体验](https://deepwisdom.feishu.cn/wiki/Q8ycw6J9tiNXdHk66MRcIN8Pnlg)
-
-## 联系信息
-
-如果您对这个项目有任何问题或反馈，欢迎联系我们。我们非常欢迎您的建议！
-
-- **邮箱：** alexanderwu@fuzhi.ai
-- **GitHub 问题：** 对于更技术性的问题，您也可以在我们的 [GitHub 仓库](https://github.com/geekan/metagpt/issues) 中创建一个新的问题。
-
-我们会在2-3个工作日内回复所有问题。
-
-## 演示
-
-https://github.com/geekan/MetaGPT/assets/2707039/5e8c1062-8c35-440f-bb20-2b0320f8d27d
-
-## 加入我们
-
-📢 加入我们的Discord频道！
-https://discord.gg/ZRHeExS6xv
-
-期待在那里与您相见！🎉
diff --git a/spaces/deepwisdom/MetaGPT/metagpt/llm.py b/spaces/deepwisdom/MetaGPT/metagpt/llm.py
deleted file mode 100644
index 6a9a9132f3173f19a1b76749a33178818243446f..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/metagpt/llm.py
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/11 14:45
-@Author  : alexanderwu
-@File    : llm.py
-"""
-
-from metagpt.provider.anthropic_api import Claude2 as Claude
-from metagpt.provider.openai_api import OpenAIGPTAPI as LLM
-
-DEFAULT_LLM = LLM()
-CLAUDE_LLM = Claude()
-
-
-async def ai_func(prompt):
-    """使用LLM进行QA
-       QA with LLMs
-     """
-    return await DEFAULT_LLM.aask(prompt)
diff --git a/spaces/diacanFperku/AutoGPT/HD Online Player (intervideo Windvr 6.1 For Windows 7 Free 17).md b/spaces/diacanFperku/AutoGPT/HD Online Player (intervideo Windvr 6.1 For Windows 7 Free 17).md
deleted file mode 100644
index aa700c4fc30d10936d590e93083a4f79fc1bc1cb..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/HD Online Player (intervideo Windvr 6.1 For Windows 7 Free 17).md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>HD Online Player (intervideo windvr 6.1 for windows 7 free 17)</h2><br /><p><b><b>DOWNLOAD</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://gohhs.com/2uFUm9">https://gohhs.com/2uFUm9</a></b></p><br /><br />
-<br />
-C:\Program Files\Common Files\InterVideo\SchSvr\SchSvr.exe ... C:\Program Files\Windows Media Player\WMPNSCFG.exe ... (F-Secure Online Scanner 3.1) - http://support.f-secure.com/ols/fscax.cab ... DVD SuperPack (DVD Region-Free, DVDFab and UltraDVD) ... Paint Shop Pro 7 Anniversary Edition 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diagaiwei/ir_chinese_medqa/utility/supervision/self_training.py b/spaces/diagaiwei/ir_chinese_medqa/utility/supervision/self_training.py
deleted file mode 100644
index 814e4d33a9eb8f197401886e4dda9bdfe6ce5941..0000000000000000000000000000000000000000
--- a/spaces/diagaiwei/ir_chinese_medqa/utility/supervision/self_training.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import os
-import sys
-import git
-import tqdm
-import ujson
-import random
-
-from argparse import ArgumentParser
-from colbert.utils.utils import print_message, load_ranking, groupby_first_item
-
-
-MAX_NUM_TRIPLES = 40_000_000
-
-
-def sample_negatives(negatives, num_sampled, biased=False):
-    num_sampled = min(len(negatives), num_sampled)
-
-    if biased:
-        assert num_sampled % 2 == 0
-        num_sampled_top100 = num_sampled // 2
-        num_sampled_rest = num_sampled - num_sampled_top100
-
-        return random.sample(negatives[:100], num_sampled_top100) + random.sample(negatives[100:], num_sampled_rest)
-
-    return random.sample(negatives, num_sampled)
-
-
-def sample_for_query(qid, ranking, npositives, depth_positive, depth_negative, cutoff_negative):
-    """
-        Requires that the ranks are sorted per qid.
-    """
-    assert npositives <= depth_positive < cutoff_negative < depth_negative
-
-    positives, negatives, triples = [], [], []
-
-    for pid, rank, *_ in ranking:
-        assert rank >= 1, f"ranks should start at 1 \t\t got rank = {rank}"
-
-        if rank > depth_negative:
-            break
-
-        if rank <= depth_positive:
-            positives.append(pid)
-        elif rank > cutoff_negative:
-            negatives.append(pid)
-
-    num_sampled = 100
-
-    for neg in sample_negatives(negatives, num_sampled):
-        positives_ = random.sample(positives, npositives)
-        positives_ = positives_[0] if npositives == 1 else positives_
-        triples.append((qid, positives_, neg))
-
-    return triples
-
-
-def main(args):
-    rankings = load_ranking(args.ranking, types=[int, int, int, float, int])
-
-    print_message("#> Group by QID")
-    qid2rankings = groupby_first_item(tqdm.tqdm(rankings))
-
-    Triples = []
-    NonEmptyQIDs = 0
-
-    for processing_idx, qid in enumerate(qid2rankings):
-        l = sample_for_query(qid, qid2rankings[qid], args.positives, args.depth_positive, args.depth_negative, args.cutoff_negative)
-        NonEmptyQIDs += (len(l) > 0)
-        Triples.extend(l)
-
-        if processing_idx % (10_000) == 0:
-            print_message(f"#> Done with {processing_idx+1} questions!\t\t "
-                          f"{str(len(Triples) / 1000)}k triples for {NonEmptyQIDs} unqiue QIDs.")
-
-    print_message(f"#> Sub-sample the triples (if > {MAX_NUM_TRIPLES})..")
-    print_message(f"#> len(Triples) = {len(Triples)}")
-
-    if len(Triples) > MAX_NUM_TRIPLES:
-        Triples = random.sample(Triples, MAX_NUM_TRIPLES)
-
-    ### Prepare the triples ###
-    print_message("#> Shuffling the triples...")
-    random.shuffle(Triples)
-
-    print_message("#> Writing {}M examples to file.".format(len(Triples) / 1000.0 / 1000.0))
-
-    with open(args.output, 'w') as f:
-        for example in Triples:
-            ujson.dump(example, f)
-            f.write('\n')
-
-    with open(f'{args.output}.meta', 'w') as f:
-        args.cmd = ' '.join(sys.argv)
-        args.git_hash = git.Repo(search_parent_directories=True).head.object.hexsha
-        ujson.dump(args.__dict__, f, indent=4)
-        f.write('\n')
-
-    print('\n\n', args, '\n\n')
-    print(args.output)
-    print_message("#> Done.")
-
-
-if __name__ == "__main__":
-    random.seed(12345)
-
-    parser = ArgumentParser(description='Create training triples from ranked list.')
-
-    # Input / Output Arguments
-    parser.add_argument('--ranking', dest='ranking', required=True, type=str)
-    parser.add_argument('--output', dest='output', required=True, type=str)
-
-    # Weak Supervision Arguments.
-    parser.add_argument('--positives', dest='positives', required=True, type=int)
-    parser.add_argument('--depth+', dest='depth_positive', required=True, type=int)
-
-    parser.add_argument('--depth-', dest='depth_negative', required=True, type=int)
-    parser.add_argument('--cutoff-', dest='cutoff_negative', required=True, type=int)
-
-    args = parser.parse_args()
-
-    assert not os.path.exists(args.output), args.output
-
-    main(args)
diff --git a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/setup_ffmpeg.py b/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/setup_ffmpeg.py
deleted file mode 100644
index 7137ab5faebb6d80740b8c843667458f25596839..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/setup_ffmpeg.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import os
-import sys
-import re
-from pathlib import Path
-import winreg
-
-def check_ffmpeg_path():
-    path_list = os.environ['Path'].split(';')
-    ffmpeg_found = False
-
-    for path in path_list:
-        if 'ffmpeg' in path.lower() and 'bin' in path.lower():
-            ffmpeg_found = True
-            print("FFmpeg already installed.")
-            break
-
-    return ffmpeg_found
-
-def add_ffmpeg_path_to_user_variable():
-    ffmpeg_bin_path = Path('.\\ffmpeg\\bin')
-    if ffmpeg_bin_path.is_dir():
-        abs_path = str(ffmpeg_bin_path.resolve())
-        
-        try:
-            key = winreg.OpenKey(
-                winreg.HKEY_CURRENT_USER,
-                r"Environment",
-                0,
-                winreg.KEY_READ | winreg.KEY_WRITE
-            )
-            
-            try:
-                current_path, _ = winreg.QueryValueEx(key, "Path")
-                if abs_path not in current_path:
-                    new_path = f"{current_path};{abs_path}"
-                    winreg.SetValueEx(key, "Path", 0, winreg.REG_EXPAND_SZ, new_path)
-                    print(f"Added FFmpeg path to user variable 'Path': {abs_path}")
-                else:
-                    print("FFmpeg path already exists in the user variable 'Path'.")
-            finally:
-                winreg.CloseKey(key)
-        except WindowsError:
-            print("Error: Unable to modify user variable 'Path'.")
-            sys.exit(1)
-
-    else:
-        print("Error: ffmpeg\\bin folder not found in the current path.")
-        sys.exit(1)
-
-def main():
-    if not check_ffmpeg_path():
-       add_ffmpeg_path_to_user_variable()
-            
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/text/japanese.py b/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/text/japanese.py
deleted file mode 100644
index ddedafa0c5b7986068dc6c91637a86febc3923a9..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/text/japanese.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# modified from https://github.com/CjangCjengh/vits/blob/main/text/japanese.py
-import re
-import sys
-
-import pyopenjtalk
-
-from text import symbols
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
-    r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
-    r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('％', 'パーセント')
-]]
-
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'Q([↑↓]*[kg])', r'k#\1'),
-    (r'Q([↑↓]*[tdjʧ])', r't#\1'),
-    (r'Q([↑↓]*[sʃ])', r's\1'),
-    (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'N([↑↓]*[pbm])', r'm\1'),
-    (r'N([↑↓]*[ʧʥj])', r'n^\1'),
-    (r'N([↑↓]*[tdn])', r'n\1'),
-    (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-
-def post_replace_ph(ph):
-    rep_map = {
-        '：': ',',
-        '；': ',',
-        '，': ',',
-        '。': '.',
-        '！': '!',
-        '？': '?',
-        '\n': '.',
-        "·": ",",
-        '、': ",",
-        '...': '…',
-        'v': "V"
-    }
-    if ph in rep_map.keys():
-        ph = rep_map[ph]
-    if ph in symbols:
-        return ph
-    if ph not in symbols:
-        ph = 'UNK'
-    return ph
-
-def symbols_to_japanese(text):
-    for regex, replacement in _symbols_to_japanese:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def preprocess_jap(text):
-    '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-    text = symbols_to_japanese(text)
-    sentences = re.split(_japanese_marks, text)
-    marks = re.findall(_japanese_marks, text)
-    text = []
-    for i, sentence in enumerate(sentences):
-        if re.match(_japanese_characters, sentence):
-            p = pyopenjtalk.g2p(sentence)
-            text += p.split(" ")
-
-        if i < len(marks):
-            text += [marks[i].replace(' ', '')]
-    return text
-
-def text_normalize(text):
-    # todo: jap text normalize
-    return text
-
-def g2p(norm_text):
-    phones = preprocess_jap(norm_text)
-    phones = [post_replace_ph(i) for i in phones]
-    # todo: implement tones and word2ph
-    tones = [0 for i in phones]
-    word2ph = [1 for i in phones]
-    return phones, tones, word2ph
-
-
-if __name__ == '__main__':
-    for line in open("../../../Downloads/transcript_utf8.txt").readlines():
-        text = line.split(":")[1]
-        phones, tones, word2ph = g2p(text)
-        for p in phones:
-            if p == "z":
-                print(text, phones)
-                sys.exit(0)
diff --git a/spaces/dorkai/text-generation-webui-main/extensions/multimodal/abstract_pipeline.py b/spaces/dorkai/text-generation-webui-main/extensions/multimodal/abstract_pipeline.py
deleted file mode 100644
index 584219419d256e7743fd4d5120c56bcfa8f2a9f9..0000000000000000000000000000000000000000
--- a/spaces/dorkai/text-generation-webui-main/extensions/multimodal/abstract_pipeline.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import List, Optional
-
-import torch
-from PIL import Image
-
-
-class AbstractMultimodalPipeline(ABC):
-    @staticmethod
-    @abstractmethod
-    def name() -> str:
-        'name of the pipeline, should be same as in --multimodal-pipeline'
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def image_start() -> Optional[str]:
-        'return image start string, string representation of image start token, or None if not applicable'
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def image_end() -> Optional[str]:
-        'return image end string, string representation of image end token, or None if not applicable'
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def placeholder_token_id() -> int:
-        'return placeholder token id'
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def num_image_embeds() -> int:
-        'return the number of embeds used by a single image (for example: 256 for LLaVA)'
-        pass
-
-    @abstractmethod
-    def embed_images(self, images: List[Image.Image]) -> torch.Tensor:
-        'forward the images through vision pipeline, and return their embeddings'
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def embed_tokens(input_ids: torch.Tensor) -> torch.Tensor:
-        'embed tokens, the exact function varies by LLM, for LLaMA it is `shared.model.model.embed_tokens`'
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def placeholder_embeddings() -> torch.Tensor:
-        'get placeholder embeddings if there are multiple images, and `add_all_images_to_prompt` is False'
-        pass
-
-    def _get_device(self, setting_name: str, params: dict):
-        if params[setting_name] is None:
-            return torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-        return torch.device(params[setting_name])
-
-    def _get_dtype(self, setting_name: str, params: dict):
-        return torch.float32 if int(params[setting_name]) == 32 else torch.float16
diff --git a/spaces/f2api/gpt-academic/crazy_functions/test_project/latex/attention/background.tex b/spaces/f2api/gpt-academic/crazy_functions/test_project/latex/attention/background.tex
deleted file mode 100644
index 785069dc0f9143bad24e640056dd1072d5c6e5b5..0000000000000000000000000000000000000000
--- a/spaces/f2api/gpt-academic/crazy_functions/test_project/latex/attention/background.tex
+++ /dev/null
@@ -1,58 +0,0 @@
-The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU \citep{extendedngpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions \citep{hochreiter2001gradient}. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section~\ref{sec:attention}. 
-
-Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations \citep{cheng2016long, decomposableAttnModel, paulus2017deep, lin2017structured}.
-
-End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks \citep{sukhbaatar2015}.
-
-To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.
-In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as \citep{neural_gpu, NalBytenet2017} and \citep{JonasFaceNet2017}.
-
-
-%\citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
-
-%For example,! in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at low computation cost, making it an essential ingredient in competitive recurrent models for machine translation.
-
-%A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
-
-%After the seminal models introduced in \citep{sutskever14, bahdanau2014neural, cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation (MT) and language modeling with recurrent endoder-decoder and recurrent language models. Recent effort \citep{shazeer2017outrageously} has successfully combined the power of conditional computation with sequence models to train very large models for MT, pushing SOTA at lower computational cost.
-
-%Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state precludes processing all timesteps at once, instead requiring long sequences of sequential operations.  In practice, this results in greatly reduced computational efficiency, as on modern computing hardware, a single operation on a large batch is much faster than a large number of operations on small batches.  The problem gets worse at longer sequence lengths. Although sequential computation is not a severe bottleneck at inference time, as autoregressively generating each output requires all previous outputs, the inability to compute scores at all output positions at once hinders us from rapidly training our models over large datasets. Although impressive work such as \citep{Kuchaiev2017Factorization} is able to significantly accelerate the training of LSTMs with factorization tricks, we are still bound by the linear dependence on sequence length.
-
-%If the model could compute hidden states at each time step using only the inputs and outputs,  it would be liberated from the dependence on results from previous time steps during training. This line of thought is the foundation of recent efforts such as the Markovian neural GPU \citep{neural_gpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as a building block to compute hidden representations simultaneously for all timesteps, resulting in $O(1)$ sequential time complexity. \citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
-
-%A crucial component for accurate sequence prediction is modeling cross-positional communication. For example, in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at a low computation cost, also $O(1)$ sequential time complexity, making it an essential ingredient in recurrent encoder-decoder architectures for MT. A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
-
-
-
-%Note: Facebook model is no better than RNNs in this regard, since it requires a number of layers proportional to the distance you want to communicate.  Bytenet is more promising, since it requires a logarithmnic number of layers (does bytenet have SOTA results)?   
-
-%Note: An attention  layer can connect a very large number of positions at a low computation cost in O(1) sequential operations.  This is why encoder-decoder attention has been so successful in seq-to-seq models so far.  It is only natural, then, to also use attention to connect the timesteps of the same sequence.
-
-%Note: I wouldn't say that long sequences are not a problem during inference.  It would be great if we could infer with no long sequences.  We could just say later on that, while our training graph is constant-depth, our model still requires sequential operations in the decoder part during inference due to the autoregressive nature of the model.   
-
-%\begin{table}[h!]
-%\caption{Attention models are quite efficient for cross-positional communications when sequence length is smaller than channel depth. $n$ represents the sequence length and $d$ represents the channel depth.}
-%\label{tab:op_complexities}
-%\begin{center}
-%\vspace{-5pt}
-%\scalebox{0.75}{
-
-%\begin{tabular}{l|c|c|c}
-%\hline \hline
-%Layer Type & Receptive & Complexity & Sequential  \\
-%           & Field     &            & Operations  \\
-%\hline
-%Pointwise Feed-Forward & $1$ & $O(n \cdot d^2)$ & $O(1)$ \\
-%\hline
-%Recurrent & $n$ & $O(n \cdot d^2)$ & $O(n)$ \\
-%\hline
-%Convolutional & $r$ & $O(r \cdot n \cdot d^2)$ & $O(1)$ \\
-%\hline
-%Convolutional (separable) & $r$ & $O(r \cdot n \cdot d + n %\cdot d^2)$ & $O(1)$ \\
-%\hline
-%Attention & $r$ & $O(r \cdot n \cdot d)$ & $O(1)$ \\
-%\hline \hline
-%\end{tabular}
-%}
-%\end{center}
-%\end{table}
\ No newline at end of file
diff --git a/spaces/facebook/MusicGen/audiocraft/solvers/builders.py b/spaces/facebook/MusicGen/audiocraft/solvers/builders.py
deleted file mode 100644
index 304d8f08d33a70e8be9388c855b2ae43bdf2683b..0000000000000000000000000000000000000000
--- a/spaces/facebook/MusicGen/audiocraft/solvers/builders.py
+++ /dev/null
@@ -1,363 +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.
-
-"""
-All the functions to build the relevant solvers and used objects
-from the Hydra config.
-"""
-
-from enum import Enum
-import logging
-import typing as tp
-
-import dora
-import flashy
-import omegaconf
-import torch
-from torch import nn
-from torch.optim import Optimizer
-# LRScheduler was renamed in some torch versions
-try:
-    from torch.optim.lr_scheduler import LRScheduler  # type: ignore
-except ImportError:
-    from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
-
-from .base import StandardSolver
-from .. import adversarial, data, losses, metrics, optim
-from ..utils.utils import dict_from_config, get_loader
-
-
-logger = logging.getLogger(__name__)
-
-
-class DatasetType(Enum):
-    AUDIO = "audio"
-    MUSIC = "music"
-    SOUND = "sound"
-
-
-def get_solver(cfg: omegaconf.DictConfig) -> StandardSolver:
-    """Instantiate solver from config."""
-    from .audiogen import AudioGenSolver
-    from .compression import CompressionSolver
-    from .musicgen import MusicGenSolver
-    from .diffusion import DiffusionSolver
-    klass = {
-        'compression': CompressionSolver,
-        'musicgen': MusicGenSolver,
-        'audiogen': AudioGenSolver,
-        'lm': MusicGenSolver,  # backward compatibility
-        'diffusion': DiffusionSolver,
-        'sound_lm': AudioGenSolver,  # backward compatibility
-    }[cfg.solver]
-    return klass(cfg)  # type: ignore
-
-
-def get_optim_parameter_groups(model: nn.Module):
-    """Create parameter groups for the model using the appropriate method
-    if defined for each modules, to create the different groups.
-
-    Args:
-        model (nn.Module): torch model
-    Returns:
-        List of parameter groups
-    """
-    seen_params: tp.Set[nn.parameter.Parameter] = set()
-    other_params = []
-    groups = []
-    for name, module in model.named_modules():
-        if hasattr(module, 'make_optim_group'):
-            group = module.make_optim_group()
-            params = set(group['params'])
-            assert params.isdisjoint(seen_params)
-            seen_params |= set(params)
-            groups.append(group)
-    for param in model.parameters():
-        if param not in seen_params:
-            other_params.append(param)
-    groups.insert(0, {'params': other_params})
-    parameters = groups
-    return parameters
-
-
-def get_optimizer(params: tp.Union[nn.Module, tp.Iterable[torch.Tensor]], cfg: omegaconf.DictConfig) -> Optimizer:
-    """Build torch optimizer from config and set of parameters.
-    Supported optimizers: Adam, AdamW
-
-    Args:
-        params (nn.Module or iterable of torch.Tensor): Parameters to optimize.
-        cfg (DictConfig): Optimization-related configuration.
-    Returns:
-        torch.optim.Optimizer.
-    """
-    if 'optimizer' not in cfg:
-        if getattr(cfg, 'optim', None) is not None:
-            raise KeyError("Optimizer not found in config. Try instantiating optimizer from cfg.optim?")
-        else:
-            raise KeyError("Optimizer not found in config.")
-
-    parameters = get_optim_parameter_groups(params) if isinstance(params, nn.Module) else params
-    optimizer: torch.optim.Optimizer
-    if cfg.optimizer == 'adam':
-        optimizer = torch.optim.Adam(parameters, lr=cfg.lr, **cfg.adam)
-    elif cfg.optimizer == 'adamw':
-        optimizer = torch.optim.AdamW(parameters, lr=cfg.lr, **cfg.adam)
-    elif cfg.optimizer == 'dadam':
-        optimizer = optim.DAdaptAdam(parameters, lr=cfg.lr, **cfg.adam)
-    else:
-        raise ValueError(f"Unsupported LR Scheduler: {cfg.lr_scheduler}")
-    return optimizer
-
-
-def get_lr_scheduler(optimizer: torch.optim.Optimizer,
-                     cfg: omegaconf.DictConfig,
-                     total_updates: int) -> tp.Optional[LRScheduler]:
-    """Build torch learning rate scheduler from config and associated optimizer.
-    Supported learning rate schedulers: ExponentialLRScheduler, PlateauLRScheduler
-
-    Args:
-        optimizer (torch.optim.Optimizer): Optimizer.
-        cfg (DictConfig): Schedule-related configuration.
-        total_updates (int): Total number of updates.
-    Returns:
-        torch.optim.Optimizer.
-    """
-    if 'lr_scheduler' not in cfg:
-        raise KeyError("LR Scheduler not found in config")
-
-    lr_sched: tp.Optional[LRScheduler] = None
-    if cfg.lr_scheduler == 'step':
-        lr_sched = torch.optim.lr_scheduler.StepLR(optimizer, **cfg.step)
-    elif cfg.lr_scheduler == 'exponential':
-        lr_sched = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=cfg.exponential)
-    elif cfg.lr_scheduler == 'cosine':
-        kwargs = dict_from_config(cfg.cosine)
-        warmup_steps = kwargs.pop('warmup')
-        lr_sched = optim.CosineLRScheduler(
-            optimizer, warmup_steps=warmup_steps, total_steps=total_updates, **kwargs)
-    elif cfg.lr_scheduler == 'polynomial_decay':
-        kwargs = dict_from_config(cfg.polynomial_decay)
-        warmup_steps = kwargs.pop('warmup')
-        lr_sched = optim.PolynomialDecayLRScheduler(
-            optimizer, warmup_steps=warmup_steps, total_steps=total_updates, **kwargs)
-    elif cfg.lr_scheduler == 'inverse_sqrt':
-        kwargs = dict_from_config(cfg.inverse_sqrt)
-        warmup_steps = kwargs.pop('warmup')
-        lr_sched = optim.InverseSquareRootLRScheduler(optimizer, warmup_steps=warmup_steps, **kwargs)
-    elif cfg.lr_scheduler == 'linear_warmup':
-        kwargs = dict_from_config(cfg.linear_warmup)
-        warmup_steps = kwargs.pop('warmup')
-        lr_sched = optim.LinearWarmupLRScheduler(optimizer, warmup_steps=warmup_steps, **kwargs)
-    elif cfg.lr_scheduler is not None:
-        raise ValueError(f"Unsupported LR Scheduler: {cfg.lr_scheduler}")
-    return lr_sched
-
-
-def get_ema(module_dict: nn.ModuleDict, cfg: omegaconf.DictConfig) -> tp.Optional[optim.ModuleDictEMA]:
-    """Initialize Exponential Moving Average.
-
-    Args:
-        module_dict (nn.ModuleDict): ModuleDict for which to compute the EMA.
-        cfg (omegaconf.DictConfig): Optim EMA configuration.
-    Returns:
-        optim.ModuleDictEMA: EMA version of the ModuleDict.
-    """
-    kw: tp.Dict[str, tp.Any] = dict(cfg)
-    use = kw.pop('use', False)
-    decay = kw.pop('decay', None)
-    device = kw.pop('device', None)
-    if not use:
-        return None
-    if len(module_dict) == 0:
-        raise ValueError("Trying to build EMA but an empty module_dict source is provided!")
-    ema_module = optim.ModuleDictEMA(module_dict, decay=decay, device=device)
-    return ema_module
-
-
-def get_loss(loss_name: str, cfg: omegaconf.DictConfig):
-    """Instantiate loss from configuration."""
-    klass = {
-        'l1': torch.nn.L1Loss,
-        'l2': torch.nn.MSELoss,
-        'mel': losses.MelSpectrogramL1Loss,
-        'mrstft': losses.MRSTFTLoss,
-        'msspec': losses.MultiScaleMelSpectrogramLoss,
-        'sisnr': losses.SISNR,
-    }[loss_name]
-    kwargs = dict(getattr(cfg, loss_name))
-    return klass(**kwargs)
-
-
-def get_balancer(loss_weights: tp.Dict[str, float], cfg: omegaconf.DictConfig) -> losses.Balancer:
-    """Instantiate loss balancer from configuration for the provided weights."""
-    kwargs: tp.Dict[str, tp.Any] = dict_from_config(cfg)
-    return losses.Balancer(loss_weights, **kwargs)
-
-
-def get_adversary(name: str, cfg: omegaconf.DictConfig) -> nn.Module:
-    """Initialize adversary from config."""
-    klass = {
-        'msd': adversarial.MultiScaleDiscriminator,
-        'mpd': adversarial.MultiPeriodDiscriminator,
-        'msstftd': adversarial.MultiScaleSTFTDiscriminator,
-    }[name]
-    adv_cfg: tp.Dict[str, tp.Any] = dict(getattr(cfg, name))
-    return klass(**adv_cfg)
-
-
-def get_adversarial_losses(cfg) -> nn.ModuleDict:
-    """Initialize dict of adversarial losses from config."""
-    device = cfg.device
-    adv_cfg = getattr(cfg, 'adversarial')
-    adversaries = adv_cfg.get('adversaries', [])
-    adv_loss_name = adv_cfg['adv_loss']
-    feat_loss_name = adv_cfg.get('feat_loss')
-    normalize = adv_cfg.get('normalize', True)
-    feat_loss: tp.Optional[adversarial.FeatureMatchingLoss] = None
-    if feat_loss_name:
-        assert feat_loss_name in ['l1', 'l2'], f"Feature loss only support L1 or L2 but {feat_loss_name} found."
-        loss = get_loss(feat_loss_name, cfg)
-        feat_loss = adversarial.FeatureMatchingLoss(loss, normalize)
-    loss = adversarial.get_adv_criterion(adv_loss_name)
-    loss_real = adversarial.get_real_criterion(adv_loss_name)
-    loss_fake = adversarial.get_fake_criterion(adv_loss_name)
-    adv_losses = nn.ModuleDict()
-    for adv_name in adversaries:
-        adversary = get_adversary(adv_name, cfg).to(device)
-        optimizer = get_optimizer(adversary.parameters(), cfg.optim)
-        adv_loss = adversarial.AdversarialLoss(
-            adversary,
-            optimizer,
-            loss=loss,
-            loss_real=loss_real,
-            loss_fake=loss_fake,
-            loss_feat=feat_loss,
-            normalize=normalize
-        )
-        adv_losses[adv_name] = adv_loss
-    return adv_losses
-
-
-def get_visqol(cfg: omegaconf.DictConfig) -> metrics.ViSQOL:
-    """Instantiate ViSQOL metric from config."""
-    kwargs = dict_from_config(cfg)
-    return metrics.ViSQOL(**kwargs)
-
-
-def get_fad(cfg: omegaconf.DictConfig) -> metrics.FrechetAudioDistanceMetric:
-    """Instantiate Frechet Audio Distance metric from config."""
-    kwargs = dict_from_config(cfg.tf)
-    xp = dora.get_xp()
-    kwargs['log_folder'] = xp.folder
-    return metrics.FrechetAudioDistanceMetric(**kwargs)
-
-
-def get_kldiv(cfg: omegaconf.DictConfig) -> metrics.KLDivergenceMetric:
-    """Instantiate KL-Divergence metric from config."""
-    kld_metrics = {
-        'passt': metrics.PasstKLDivergenceMetric,
-    }
-    klass = kld_metrics[cfg.model]
-    kwargs = dict_from_config(cfg.get(cfg.model))
-    return klass(**kwargs)
-
-
-def get_text_consistency(cfg: omegaconf.DictConfig) -> metrics.TextConsistencyMetric:
-    """Instantiate Text Consistency metric from config."""
-    text_consistency_metrics = {
-        'clap': metrics.CLAPTextConsistencyMetric
-    }
-    klass = text_consistency_metrics[cfg.model]
-    kwargs = dict_from_config(cfg.get(cfg.model))
-    return klass(**kwargs)
-
-
-def get_chroma_cosine_similarity(cfg: omegaconf.DictConfig) -> metrics.ChromaCosineSimilarityMetric:
-    """Instantiate Chroma Cosine Similarity metric from config."""
-    assert cfg.model == 'chroma_base', "Only support 'chroma_base' method for chroma cosine similarity metric"
-    kwargs = dict_from_config(cfg.get(cfg.model))
-    return metrics.ChromaCosineSimilarityMetric(**kwargs)
-
-
-def get_audio_datasets(cfg: omegaconf.DictConfig,
-                       dataset_type: DatasetType = DatasetType.AUDIO) -> tp.Dict[str, torch.utils.data.DataLoader]:
-    """Build AudioDataset from configuration.
-
-    Args:
-        cfg (omegaconf.DictConfig): Configuration.
-        dataset_type: The type of dataset to create.
-    Returns:
-        dict[str, torch.utils.data.DataLoader]: Map of dataloader for each data split.
-    """
-    dataloaders: dict = {}
-
-    sample_rate = cfg.sample_rate
-    channels = cfg.channels
-    seed = cfg.seed
-    max_sample_rate = cfg.datasource.max_sample_rate
-    max_channels = cfg.datasource.max_channels
-
-    assert cfg.dataset is not None, "Could not find dataset definition in config"
-
-    dataset_cfg = dict_from_config(cfg.dataset)
-    splits_cfg: dict = {}
-    splits_cfg['train'] = dataset_cfg.pop('train')
-    splits_cfg['valid'] = dataset_cfg.pop('valid')
-    splits_cfg['evaluate'] = dataset_cfg.pop('evaluate')
-    splits_cfg['generate'] = dataset_cfg.pop('generate')
-    execute_only_stage = cfg.get('execute_only', None)
-
-    for split, path in cfg.datasource.items():
-        if not isinstance(path, str):
-            continue  # skipping this as not a path
-        if execute_only_stage is not None and split != execute_only_stage:
-            continue
-        logger.info(f"Loading audio data split {split}: {str(path)}")
-        assert (
-            cfg.sample_rate <= max_sample_rate
-        ), f"Expecting a max sample rate of {max_sample_rate} for datasource but {sample_rate} found."
-        assert (
-            cfg.channels <= max_channels
-        ), f"Expecting a max number of channels of {max_channels} for datasource but {channels} found."
-
-        split_cfg = splits_cfg[split]
-        split_kwargs = {k: v for k, v in split_cfg.items()}
-        kwargs = {**dataset_cfg, **split_kwargs}  # split kwargs overrides default dataset_cfg
-        kwargs['sample_rate'] = sample_rate
-        kwargs['channels'] = channels
-
-        if kwargs.get('permutation_on_files') and cfg.optim.updates_per_epoch:
-            kwargs['num_samples'] = (
-                flashy.distrib.world_size() * cfg.dataset.batch_size * cfg.optim.updates_per_epoch)
-
-        num_samples = kwargs['num_samples']
-        shuffle = kwargs['shuffle']
-
-        return_info = kwargs.pop('return_info')
-        batch_size = kwargs.pop('batch_size', None)
-        num_workers = kwargs.pop('num_workers')
-
-        if dataset_type == DatasetType.MUSIC:
-            dataset = data.music_dataset.MusicDataset.from_meta(path, **kwargs)
-        elif dataset_type == DatasetType.SOUND:
-            dataset = data.sound_dataset.SoundDataset.from_meta(path, **kwargs)
-        elif dataset_type == DatasetType.AUDIO:
-            dataset = data.info_audio_dataset.InfoAudioDataset.from_meta(path, return_info=return_info, **kwargs)
-        else:
-            raise ValueError(f"Dataset type is unsupported: {dataset_type}")
-
-        loader = get_loader(
-            dataset,
-            num_samples,
-            batch_size=batch_size,
-            num_workers=num_workers,
-            seed=seed,
-            collate_fn=dataset.collater if return_info else None,
-            shuffle=shuffle,
-        )
-        dataloaders[split] = loader
-
-    return dataloaders
diff --git a/spaces/failfast/2D-GameCreator/src/components/Troubleshooting.tsx b/spaces/failfast/2D-GameCreator/src/components/Troubleshooting.tsx
deleted file mode 100644
index 4c9a4ae3f8ef4b6f027413fe3b6693560da716c7..0000000000000000000000000000000000000000
--- a/spaces/failfast/2D-GameCreator/src/components/Troubleshooting.tsx
+++ /dev/null
@@ -1,55 +0,0 @@
-import { Typography, Paper, Alert, Link } from "@mui/material";
-import { SectionBox, DividerBox } from "./base/boxes";
-
-export default function Troubleshooting() {
-	return (
-		<>
-			<SectionBox>
-				<Typography component="h3" variant="h2">
-					Troubleshooting
-				</Typography>
-			</SectionBox>
-
-			<Paper sx={{ p: 1 }}>
-				<Alert severity="error" sx={{ fontSize: "1.25rem", mb: 1 }}>
-					Unhandled Runtime Error: SyntaxError: Unexpected identifier
-				</Alert>
-
-				<Typography variant="body1">
-					The generated output was interrupted, as it was too long and the OpenAI API
-					delivered not everything. If you can, switch to GPT-4 as it allows a bigger
-					context size (change it in the options and also increase the max_tokens). If you
-					can&apos;t do this, then please help us extend the 2D GameCreator so that it can
-					also resume when the output is interrupted.
-				</Typography>
-			</Paper>
-
-			<DividerBox />
-
-			<Paper sx={{ p: 1 }}>
-				<Alert severity="error" sx={{ fontSize: "1.25rem", mb: 1 }}>
-					Network Error
-				</Alert>
-
-				<Typography variant="body1">
-					There might have been a timeout or something else. Please try again.
-				</Typography>
-			</Paper>
-
-			<DividerBox />
-
-			<Paper sx={{ p: 1 }}>
-				<Typography>
-					You need help? Something is not working?{" "}
-					<Link
-						href="https://huggingface.co/spaces/failfast/2D-GameCreator/discussions"
-						target="_blank"
-						rel="noopener"
-					>
-						Please let us know!
-					</Link>
-				</Typography>
-			</Paper>
-		</>
-	);
-}
diff --git a/spaces/falterWliame/Face_Mask_Detection/Deadisland13patchcrack HOT.md b/spaces/falterWliame/Face_Mask_Detection/Deadisland13patchcrack HOT.md
deleted file mode 100644
index deb7b0a1a82c78715a96f8fa72d96afe0871e3d8..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Deadisland13patchcrack HOT.md	
+++ /dev/null
@@ -1,9 +0,0 @@
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diff --git a/spaces/fatiXbelha/sd/Beach Buggy Racing APK Como baixar e instalar o jogo com dinheiro infinito.md b/spaces/fatiXbelha/sd/Beach Buggy Racing APK Como baixar e instalar o jogo com dinheiro infinito.md
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-<h1>Beach Buggy Racing Dinheiro Infinito Download APK: A Guide to the Ultimate Kart Racing Game</h1>
- <p>If you are looking for a fun and colorful kart racing game for your mobile device, you might want to check out Beach Buggy Racing. This game offers a variety of game modes, cars, racers, power-ups, and tracks that will keep you entertained for hours. But what if you want to enjoy the game without any limitations or restrictions? What if you want to have unlimited money/coins, unlock all vehicles and upgrades, remove ads, improve graphics, customize controls, save/load progress, and play multiplayer? Well, there is a way to do that with Beach Buggy Racing Mod APK. In this article, we will tell you everything you need to know about this modified version of the game, how to download and install it on your Android device, how to play it, and some tips and tricks to master it.</p>
- <h2>What is Beach Buggy Racing?</h2>
- <h3>A fun and colorful kart racing game for mobile devices</h3>
- <p>Beach Buggy Racing is a kart racing game developed by Vector Unit, the makers of Riptide GP2 and Hydro Thunder Hurricane. It is the official sequel to Beach Buggy Blitz, the free driving game with over 30 million players worldwide. In this game, you drive into an action-packed, surprise-filled world of off-road kart racing mayhem. You race against a field of rival drivers, each with their own unique personality and special abilities. You build a collection of crazy power-ups, like Dodgeball Frenzy, Fireball, and Oil Slick. You unlock and upgrade a variety of cars, from dune buggies to monster trucks. You test your skills in 6 different game modes on 15 imaginative 3D race tracks, against a pack of tropical-loving rivals with a serious case of road rage.</p>
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- <h3>Features of the game</h3>
- <h4>Different game modes, cars, racers, power-ups, and tracks</h4>
- <p>Beach Buggy Racing offers a lot of variety and replay value with its different game modes, cars, racers, power-ups, and tracks. You can choose from 6 different game modes, including Race, Elimination, Time Trial, Quick Race, Daily Challenge, and Championship. You can collect and customize 25 different cars, from beach buggies to monster trucks. You can recruit 15 different racers, each with their own unique personality and special ability. You can use 25 different power-ups, from rockets and lightning to confetti and giant heads. You can explore 15 different tracks, from beaches and jungles to volcanoes and swamps.</p>
- <h4>Split-screen multiplayer, Google Play services, and customization options</h4>
- <p>Beach Buggy Racing also supports split-screen multiplayer for up to 4 players on one device. You can play with your friends or family on the same screen using game controllers or touch controls. You can also compete with other players online using Google Play services. You can earn achievements, sync your progress across devices, and see how you rank on the leaderboards. You can also customize your game experience with various options, such as changing the difficulty level, turning off/on power-ups, adjusting the camera angle, and choosing the control scheme.</p>
- <h2>What is Beach Buggy Racing Mod APK?</h2>
- <h3>A modified version of the original game that offers several benefits</h3>
- <p>Beach Buggy Racing Mod APK is a modified version of the original game that offers several benefits that are not available in the official version. It is a file that you can download and install on your Android device to enjoy the game with some extra features and advantages. It is not an official app from Vector Unit, but a third-party app created by some developers who have modified the original game code.</p>
- <h3>Benefits of the mod APK</h3>
- <h4>Unlimited money/coins to buy and upgrade cars and power-ups</h4>
- <p>One of the main benefits of Beach Buggy Racing Mod APK is that it gives you unlimited money/coins to buy and upgrade cars and power-ups. In the original game, you have to earn money/coins by winning races or completing challenges or achievements. This can take a lot of time and effort, especially if you want to buy or upgrade the more expensive or powerful cars or power-ups. With the mod APK, you don't have to worry about that. You can buy and upgrade any car or power-up you want without spending any real money or waiting for hours.</p>
- <h4>Unlock all vehicles and upgrades without completing challenges or achievements</h4>
- <p>Another benefit of Beach Buggy Racing Mod APK is that it unlocks all vehicles and upgrades without completing challenges or achievements. In the original game, you have to complete certain challenges or achievements to unlock some vehicles or upgrades. For example, you have to win 10 races with Rez to unlock his special ability, or you have to win 20 races with McSkelly to unlock his car. This can be frustrating or boring if you don't like some challenges or achievements or if you prefer some vehicles or upgrades over others. With the mod APK, you don't have to do that. You can unlock any vehicle or upgrade you want without doing any challenge or achievement.</p>
- <h4>An ad-free experience without interruptions or distractions</h4>
- <p>A third benefit of Beach Buggy Racing Mod APK is that it removes all ads from the game. In the original game, you have to watch ads to get some extra money/coins or to continue playing after losing a race. This can be annoying or distracting if you don't like ads or if they interrupt your gameplay. With the mod APK, you don't have to see any ads at all. You can enjoy the game without any interruptions or distractions.</p>
- <h4>Improved performance and graphics for a smoother gameplay</h4>
- <p>A fourth benefit of Beach Buggy Racing Mod APK is that it improves the performance and graphics of the game for a smoother gameplay. In the original game, you might experience some lagging or crashing issues if your device is not powerful enough or if your internet connection is not stable enough. This can affect your gameplay quality and enjoyment. With the mod APK, you don't have to worry about that. The mod APK optimizes the game performance and graphics for a smoother gameplay. The mod APK enhances the game speed, resolution, sound quality, and animation effects. You can enjoy the game with a faster and smoother gameplay.</p>
- <h4>Customizable controls to suit your preferences</h4>
- <p>A fifth benefit of Beach Buggy Racing Mod APK is that it allows you to customize the controls to suit your preferences. In the original game, you have to choose from three control options: tilt, touch, or gamepad. You can also adjust the sensitivity and invert the steering. However, you might not like some of these options or settings, or you might want to change them according to different situations. With the mod APK, you don't have to stick to these options or settings. You can customize the controls to your liking. You can change the button layout, size, position, and color. You can also enable or disable vibration, auto-acceleration, auto-brake, and auto-steer.</p>
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- <h4>Multiplayer support to play with friends online or offline</h4>
- <p>A seventh benefit of Beach Buggy Racing Mod APK is that it supports multiplayer mode to play with friends online or offline. In the original game, you can only play multiplayer mode with up to 4 players on one device using split-screen mode. You cannot play multiplayer mode with other players online or on different devices. With the mod APK, you can do that. You can play multiplayer mode with up to 8 players online or on different devices using Wi-Fi or Bluetooth. You can also create or join private rooms to play with your friends or other players.</p>
- <h2>How to download and install Beach Buggy Racing Mod APK?</h2>
- <h3>Steps to download and install the mod APK on your Android device</h3>
- <p>If you want to download and install Beach Buggy Racing Mod APK on your Android device, you need to follow these steps:</p>
- <ol>
-<li>First, you need to enable the installation of apps from unknown sources on your device. To do that, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Second, you need to download the mod APK file from a reliable source. You can search for Beach Buggy Racing Mod APK on Google or use this link: . Make sure you download the latest version of the mod APK that is compatible with your device.</li>
-<li>Third, you need to locate the downloaded mod APK file on your device using a file manager. Tap on the file and select Install. Wait for the installation process to finish.</li>
-<li>Fourth, you need to launch the game from your app drawer or home screen. Enjoy playing Beach Buggy Racing Mod APK with all its benefits.</li>
-</ol>
- <h3>Tips and precautions to avoid any issues or errors</h3>
- <p>To avoid any issues or errors while downloading and installing Beach Buggy Racing Mod APK on your Android device, you need to follow these tips and precautions:</p>
- <ul>
-<li>Make sure you have enough storage space on your device before downloading and installing the mod APK file.</li>
-<li>Make sure you have a stable internet connection while downloading and installing the mod APK file.</li>
-<li>Make sure you download the mod APK file from a trusted source that does not contain any viruses or malware.</li>
-<li>Make sure you uninstall any previous versions of Beach Buggy Racing from your device before installing the mod APK file.</li>
-<li>Make sure you backup your original game data before installing the mod APK file in case you want to switch back to the original game later.</li>
-<li>Make sure you do not update the mod APK file from Google Play Store or any other source as it might overwrite the mod features and cause errors.</li>
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- <h2>How to play Beach Buggy Racing Mod APK?</h2>
- <h3>A brief overview of the gameplay and controls</h3>
- <p>The gameplay and controls of Beach Buggy Racing Mod APK are similar to those of the original game. You drive a kart on various tracks and compete with other racers while using power-ups to gain an edge over them. You can choose from different game modes, cars, racers, power-ups, and tracks that suit your style and preference. You can also play with your friends or other players online or offline using multiplayer mode. The controls are simple and intuitive. You can use tilt, touch, or gamepad to steer your kart. You can also use buttons to brake, reverse, or use power-ups. You can customize the controls to your liking using the mod APK.</p>
- <h3>Tips and tricks to master the game and win races</h3>
- <h4>Know your power-ups and how to use them effectively</h4>
- <p>One of the key aspects of Beach Buggy Racing is the use of power-ups. There are 25 different power-ups in the game, each with its own effect and strategy. Some power-ups are offensive, such as rockets, fireballs, oil slicks, and dodgeballs. Some power-ups are defensive, such as shields, magnets, confetti, and giant heads. Some power-ups are neutral, such as boosts, jumps, coin bubbles, and time warps. You need to know what each power-up does and how to use it effectively. For example, you can use rockets to hit the racer in front of you or behind you, depending on the direction you swipe. You can use fireballs to create a trail of fire behind you that burns other racers. You can use oil slicks to make other racers slip and lose control. You can use dodgeballs to bounce off walls and hit other racers. You can use shields to protect yourself from incoming attacks. You can use magnets to attract coins and power-ups near you. You can use confetti to blind other racers with colorful particles. You can use giant heads to make your racer bigger and stronger. You can use boosts to speed up your kart for a short time. You can use jumps to leap over obstacles or gaps. You can use coin bubbles to collect coins faster. You can use time warps to slow down time for everyone except you.</p>
- <h4>Know your racers and their special abilities</h4>
- <p>Another important aspect of Beach Buggy Racing is the choice of racers. There are 15 different racers in the game, each with their own personality and special ability. Some racers have offensive abilities, such as Rez's electro-blast, McSkelly's bone-shaker, or Roxie's rock-n-roll. Some racers have defensive abilities, such as B'Zorp's teleportation, Tiki's shield bubble, or Beat Bot's repair drone. Some racers have neutral abilities, such as Beach Bro's surfboard, L'il Doc's healing ray, or Bigfoot's ground pound. You need to know what each racer's ability does and how to use it effectively. For example, you can use Rez's electro-blast to zap other racers and make them lose control. You can use McSkelly's bone-shaker to shake the ground and make other racers bounce off course. You can use Roxie's rock-n-roll to blast loud music and stun other racers. You can use B'Zorp's teleportation to warp ahead of other racers or behind them, depending on the direction you swipe. You can use Tiki's shield bubble to create a protective barrier around your kart that deflects attacks. You can use Beat Bot's repair drone to fix your kart and restore its health. You can use Beach Bro's surfboard to glide over water or sand without losing speed. You can use L'il Doc's healing ray to heal yourself or your teammates in multiplayer mode. You can use Bigfoot's ground pound to smash the ground and create shockwaves that knock back other racers.</p>
- <h4>Know your tracks and their shortcuts and hazards</h4>
- <p>A third crucial aspect of Beach Buggy Racing is the knowledge of tracks. There are 15 different tracks in the game, each with its own theme and layout. Some tracks are easy, such as Coconut Beach or Dino Jungle. Some tracks are hard, such as Lunar Colony or Volcano Island. Some tracks are medium, such as Rock 'n' Roll Rapids or Skull Rock Cove. You need to know what each track offers and what it challenges you with. For example, you need to know where the shortcuts are on each track and how to access them. Shortcuts can help you save time and gain an advantage over other racers if you use them wisely. However, some shortcuts might be risky or tricky to enter or exit, so you need to be careful not to crash or get stuck. You also need to know where the hazards are on each track and how to avoid them. Hazards can slow you down or damage your kart if you hit them carelessly. However, some hazards might be useful or fun to interact with if you know how to exploit them.</p>
- <h4>Upgrade your cars and power-ups regularly</h4>
- <p>A fourth vital aspect of Beach Buggy Racing is the upgrade of cars and power-ups <p>A fourth vital aspect of Beach Buggy Racing is the upgrade of cars and power-ups. In the game, you can upgrade your cars and power-ups using the money/coins you earn or buy. Upgrading your cars can improve their speed, acceleration, handling, and strength. Upgrading your power-ups can increase their duration, frequency, or intensity. You need to upgrade your cars and power-ups regularly to keep up with the competition and win more races. With the mod APK, you can upgrade your cars and power-ups to the maximum level without spending any money/coins. You can also unlock all the cars and power-ups without completing any challenges or achievements.</p>
- <h4>Use the jump start technique to gain an advantage at the beginning of the race</h4>
- <p>A fifth useful aspect of Beach Buggy Racing is the jump start technique. This is a technique that allows you to start the race with a boost of speed if you time it right. To perform the jump start technique, you need to tap the screen or press the gas button right when the countdown reaches zero. If you do it correctly, you will see a green flash and hear a sound effect. This will give you a head start over other racers and help you reach the first place faster. However, if you tap or press too early or too late, you will see a red flash and hear a different sound effect. This will slow you down and make you lose speed. You need to practice the jump start technique to master it and use it effectively.</p>
- <h2>Conclusion</h2>
- <h3>A summary of the main points of the article</h3>
- <p>In conclusion, Beach Buggy Racing is a fun and colorful kart racing game for mobile devices that offers a variety of game modes, cars, racers, power-ups, and tracks. However, if you want to enjoy the game without any limitations or restrictions, you can download and install Beach Buggy Racing Mod APK on your Android device. This is a modified version of the original game that offers several benefits, such as unlimited money/coins, unlock all vehicles and upgrades, remove ads, improve performance and graphics, customize controls, save/load progress, and play multiplayer. To download and install Beach Buggy Racing Mod APK on your Android device, you need to follow some steps and tips that we have explained in this article. To play Beach Buggy Racing Mod APK on your Android device, you need to know some aspects of the gameplay and controls that we have also discussed in this article.</p>
- <h3>A recommendation to try out Beach Buggy Racing Mod APK for a fun and exciting kart racing experience</h3>
- <p>If you are a fan of kart racing games or if you are looking for a new game to play on your mobile device, we recommend you to try out Beach Buggy Racing Mod APK for a fun and exciting kart racing experience. You will not regret it as you will be able to enjoy all the features and benefits of the mod APK that we have mentioned in this article. You will be able to have unlimited money/coins, unlock all vehicles and upgrades, remove ads, improve performance and graphics, customize controls, save/load progress, and play multiplayer. You will also be able to master the game and win races with some tips and tricks that we have shared in this article. So what are you waiting for? Download Beach Buggy Racing Mod APK now and start racing!</p>
- <h2>FAQs</h2>
- <h3>Q1. Is Beach Buggy Racing Mod APK safe to download and use?</h3>
- <p>A1. Yes, Beach Buggy Racing Mod APK is safe to download and use as long as you download it from a reliable source that does not contain any viruses or malware. However, you should always be careful when downloading and installing any mod APK on your device as they are not official apps from the developers.</p>
- <h3>Q2. Is Beach Buggy Racing Mod APK compatible with all Android devices?</h3>
- <p>A2. No, Beach Buggy Racing Mod APK is not compatible with all Android devices as it requires some minimum specifications to run properly. The minimum specifications are Android 4.1 or higher, 1 GB of RAM or higher, 100 MB of free storage space or higher, and an internet connection.</p>
- <h3>Q3. Can I play Beach Buggy Racing Mod APK with my friends who have the original game?</h3>
- <p>A3. Yes, you can play Beach Buggy Racing Mod APK with your friends who have the original game as long as they are on the same network or using Wi-Fi or Bluetooth. However, they might not be able to enjoy all the benefits of the mod APK that you have.</p>
- <h3>Q4. Can I switch between the mod APK and the original game without losing my progress?</h3>
- <p>A4. Yes, you can switch between the mod APK and the original game without losing your progress as long as you backup your game data using a file manager or Google Play services. You can also sync your game progress across devices using Google Play services. However, you should not update the mod APK or the original game from any source as it might overwrite the mod features and cause errors.</p>
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diff --git a/spaces/fatiXbelha/sd/Cmo conseguir Rocket League Sideswipe APK con todo desbloqueado gratis.md b/spaces/fatiXbelha/sd/Cmo conseguir Rocket League Sideswipe APK con todo desbloqueado gratis.md
deleted file mode 100644
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- <p>Do you love playing car soccer games on your PC or console? If so, you might be interested in trying out Rocket League Sideswipe, a mobile version of the hit game Rocket League. In this article, we will tell you everything you need to know about Rocket League Sideswipe APK todo desbloqueado, which means "everything unlocked" in Spanish. We will explain what the game is, how to download and install it, and how to play and enjoy it on your Android or iOS device.</p>
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- <p>Rocket League Sideswipe is a spin-off of Rocket League, a game that combines soccer and racing in a fast-paced and fun way. In Rocket League, you control a rocket-powered car and try to score goals by hitting a giant ball into your opponent's net. You can also perform aerial stunts, boost your speed, and customize your car with various items.</p>
- <p>Rocket League Sideswipe is a mobile adaptation of Rocket League that was released in 2022 by Psyonix Studios, the same developer of the original game. It has been redesigned for mobile devices, with simplified controls, shorter matches, and a 2D perspective. However, it still retains the core gameplay and features of Rocket League, such as car soccer, customization, online multiplayer, and seasons.</p>
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-<li>Rocket Pass: You can unlock new cars and items by playing online and completing tasks. The Rocket Pass has both free and premium tiers.</li>
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- <p>The easiest and safest way to get Rocket League Sideswipe on your mobile device is to download it from the official app stores. The game is free to play and compatible with Android 6.0 or higher and iOS 11.0 or higher. Here are the steps to follow:</p>
- <ol>
-<li>Go to Google Play or App Store on your device and search for "Rocket League Sideswipe".</li>
-<li>Tap on the game icon and then tap on "Install".</li>
-<li> <li>Wait for the game to download and install on your device.</li>
-<li>Launch the game and enjoy playing car soccer on your mobile device.</li>
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- <p>Another way to get Rocket League Sideswipe on your mobile device is to download and install a modded APK file from a third-party website. This is an unofficial and modified version of the game that claims to have everything unlocked, such as all cars, items, and Rocket Pass tiers. However, this method is not recommended, as it may have some drawbacks and risks. Here are the steps to follow:</p>
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-<li>Go to a website that offers the Rocket League Sideswipe APK todo desbloqueado file, such as [APKPure] or [APKMirror].</li>
-<li>Download the APK file to your device. You may need to enable the installation of apps from unknown sources in your device settings.</li>
-<li>Install the APK file on your device. You may need to grant some permissions to the app.</li>
-<li>Launch the game and enjoy playing car soccer on your mobile device with everything unlocked.</li>
-</ol>
- <h4>Benefits and risks of using the modded APK file</h4>
- <p>Some of the benefits and risks of using the modded APK file are:</p>
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- <table>
-<tr>
-<th>Benefits</th>
-<th>Risks</th>
-</tr>
-<tr>
-<td>You can access all cars and items without spending money or playing online.</td>
-<td>You may not be able to play online with other players or update the game to the latest version.</td>
-</tr>
-<tr>
-<td>You can enjoy the game without ads or in-app purchases.</td>
-<td>You may expose your device to malware or viruses that can harm your data or privacy.</td>
-</tr>
-<tr>
-<td>You can experiment with different car designs and game modes.</td>
-<td>You may violate the terms of service of Psyonix Studios and get banned from the game or legal action.</td>
-</tr>
-</table>
- <h2>How to Play and Enjoy Rocket League Sideswipe APK Todo Desbloqueado?</h2>
- <h3>Tips and tricks for beginners and advanced players</h3>
- <p>Whether you are new to car soccer games or a veteran of Rocket League, you can always improve your skills and have more fun with Rocket League Sideswipe. Here are some tips and tricks for beginners and advanced players:</p>
- <ul>
-<li>Learn the controls: The game has two control schemes: swipe and touch. Swipe lets you swipe left or right on the screen to steer your car, while touch lets you tap on the left or right side of the screen to steer your car. You can also tap on the boost button to speed up, tap on the jump button to jump, and double-tap on the jump button to flip your car in mid-air. You can change the control scheme and sensitivity in the settings menu.</li>
-<li>Practice your moves: The game has a tutorial mode where you can learn the basics of the game and practice your moves. You can also play against bots in casual mode or watch replays of other players' matches. Some of the moves you should master are: hitting the ball with different parts of your car, aiming for the corners or top of the goal, using boost wisely, dodging opponents' attacks, and performing aerial shots.</li>
-<li>Customize your car: The game has a lot of options for customizing your car, such as colors, decals, wheels, boosters, and more. You can also create your own car designs and share them with others. Customizing your car can make it look more cool and unique, as well as affect its performance and handling. You can unlock new cars and items by playing online and completing tasks with the Rocket Pass.</li>
-<li>Play online with friends or other players: The game has an online multiplayer mode where you can play 1v1 or 2v2 car soccer matches with players around the world, or with your friends in private matches. You can also chat with other players using text or voice messages. Playing online can be more challenging and rewarding than playing offline, as you can test your skills against real opponents, earn points and rank up, and get feedback from other players.</li>
-</ul>
- <h3>How to customize your car and unlock items with the Rocket Pass</h3>
- <p>The Rocket Pass is a feature that lets you unlock new cars and items by playing online and completing tasks. The Rocket Pass has both free and premium tiers. The free tier gives you access to some basic items, while the premium tier gives you access to more exclusive items. You can buy the premium tier with real money or with credits that you earn by playing online. The Rocket Pass has different seasons, and give feedback to other players. You can also view your stats, rewards, and rank.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>Rocket League Sideswipe is a mobile version of the popular car soccer game Rocket League. It has been redesigned for mobile devices, with simplified controls, shorter matches, and a 2D perspective. However, it still retains the core gameplay and features of Rocket League, such as car soccer, customization, online multiplayer, and seasons. You can download and install the game from the official app stores, or from a third-party website that offers the modded APK file with everything unlocked. However, the latter method is not recommended, as it may have some drawbacks and risks. You can play and enjoy the game by learning the controls, practicing your moves, customizing your car, unlocking items with the Rocket Pass, and playing online with friends or other players around the world. Rocket League Sideswipe is a fun and addictive game that will keep you entertained for hours.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Rocket League Sideswipe APK todo desbloqueado:</p>
- <ol>
-<li>What is the difference between Rocket League and Rocket League Sideswipe?</li>
-<p>Rocket League is the original game that is available for PC and consoles, while Rocket League Sideswipe is the mobile version that is available for Android and iOS devices. Rocket League Sideswipe has been adapted for mobile devices, with simplified controls, shorter matches, and a 2D perspective. However, it still has the same gameplay and features as Rocket League.</p>
-<li>Is Rocket League Sideswipe free to play?</li>
-<p>Yes, Rocket League Sideswipe is free to play and download from the official app stores. However, it has some optional in-app purchases, such as credits that can be used to buy premium items or tiers in the Rocket Pass.</p>
-<li>How can I play Rocket League Sideswipe with my friends?</li>
-<p>You can play Rocket League Sideswipe with your friends in private matches or online multiplayer mode. To play in private matches, you need to create or join a room code that you can share with your friends. To play in online multiplayer mode, you need to invite your friends to your party or join their party.</p>
-<li>How can I update Rocket League Sideswipe to the latest version?</li>
-<p>If you downloaded the game from the official app stores, you can update it automatically or manually from the app store. If you downloaded the modded APK file from a third-party website, you may not be able to update it to the latest version. You may need to download and install a new modded APK file from the same or another website.</p>
-<li>How can I contact Psyonix Studios for support or feedback?</li>
-<p>You can contact Psyonix Studios for support or feedback by visiting their official website or social media accounts. You can also use the in-game feedback option or email them at support@psyonix.com.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/Music_Source_Separation/bytesep/callbacks/base_callbacks.py b/spaces/fffiloni/Music_Source_Separation/bytesep/callbacks/base_callbacks.py
deleted file mode 100644
index ef62dd591f1516aa41e2ba347cc3aaa558854f8d..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Music_Source_Separation/bytesep/callbacks/base_callbacks.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import logging
-import os
-from typing import NoReturn
-
-import pytorch_lightning as pl
-import torch
-import torch.nn as nn
-from pytorch_lightning.utilities import rank_zero_only
-
-
-class SaveCheckpointsCallback(pl.Callback):
-    def __init__(
-        self,
-        model: nn.Module,
-        checkpoints_dir: str,
-        save_step_frequency: int,
-    ):
-        r"""Callback to save checkpoints every #save_step_frequency steps.
-
-        Args:
-            model: nn.Module
-            checkpoints_dir: str, directory to save checkpoints
-            save_step_frequency: int
-        """
-        self.model = model
-        self.checkpoints_dir = checkpoints_dir
-        self.save_step_frequency = save_step_frequency
-        os.makedirs(self.checkpoints_dir, exist_ok=True)
-
-    @rank_zero_only
-    def on_batch_end(self, trainer: pl.Trainer, _) -> NoReturn:
-        r"""Save checkpoint."""
-        global_step = trainer.global_step
-
-        if global_step % self.save_step_frequency == 0:
-
-            checkpoint_path = os.path.join(
-                self.checkpoints_dir, "step={}.pth".format(global_step)
-            )
-
-            checkpoint = {'step': global_step, 'model': self.model.state_dict()}
-
-            torch.save(checkpoint, checkpoint_path)
-            logging.info("Save checkpoint to {}".format(checkpoint_path))
diff --git a/spaces/fffiloni/Music_Source_Separation/scripts/2_create_indexes/vctk-musdb18/create_indexes.sh b/spaces/fffiloni/Music_Source_Separation/scripts/2_create_indexes/vctk-musdb18/create_indexes.sh
deleted file mode 100644
index e2a85230b2745cedb2c98a34ed303082bb1ec48a..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Music_Source_Separation/scripts/2_create_indexes/vctk-musdb18/create_indexes.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/bin/bash
-WORKSPACE=${1:-"./workspaces/bytesep"}  # Default workspace directory
-
-echo "WORKSPACE=${WORKSPACE}"
-
-# Users can modify the following config file.
-INDEXES_CONFIG_YAML="scripts/2_create_indexes/vctk-musdb18/configs/speech-accompaniment,sr=44100,chn=2.yaml"
-
-# Create indexes for training.
-python3 bytesep/dataset_creation/create_indexes/create_indexes.py \
-    --workspace=$WORKSPACE \
-    --config_yaml=$INDEXES_CONFIG_YAML
diff --git a/spaces/fffiloni/SplitTrack2MusicGen/tests/data/test_audio_utils.py b/spaces/fffiloni/SplitTrack2MusicGen/tests/data/test_audio_utils.py
deleted file mode 100644
index 0480671bb17281d61ce02bce6373a5ccec89fece..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/SplitTrack2MusicGen/tests/data/test_audio_utils.py
+++ /dev/null
@@ -1,110 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import julius
-import torch
-import pytest
-
-from audiocraft.data.audio_utils import (
-    _clip_wav,
-    convert_audio_channels,
-    convert_audio,
-    normalize_audio
-)
-from ..common_utils import get_batch_white_noise
-
-
-class TestConvertAudioChannels:
-
-    def test_convert_audio_channels_downmix(self):
-        b, c, t = 2, 3, 100
-        audio = get_batch_white_noise(b, c, t)
-        mixed = convert_audio_channels(audio, channels=2)
-        assert list(mixed.shape) == [b, 2, t]
-
-    def test_convert_audio_channels_nochange(self):
-        b, c, t = 2, 3, 100
-        audio = get_batch_white_noise(b, c, t)
-        mixed = convert_audio_channels(audio, channels=c)
-        assert list(mixed.shape) == list(audio.shape)
-
-    def test_convert_audio_channels_upmix(self):
-        b, c, t = 2, 1, 100
-        audio = get_batch_white_noise(b, c, t)
-        mixed = convert_audio_channels(audio, channels=3)
-        assert list(mixed.shape) == [b, 3, t]
-
-    def test_convert_audio_channels_upmix_error(self):
-        b, c, t = 2, 2, 100
-        audio = get_batch_white_noise(b, c, t)
-        with pytest.raises(ValueError):
-            convert_audio_channels(audio, channels=3)
-
-
-class TestConvertAudio:
-
-    def test_convert_audio_channels_downmix(self):
-        b, c, dur = 2, 3, 4.
-        sr = 128
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=sr, to_channels=2)
-        assert list(out.shape) == [audio.shape[0], 2, audio.shape[-1]]
-
-    def test_convert_audio_channels_upmix(self):
-        b, c, dur = 2, 1, 4.
-        sr = 128
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=sr, to_channels=3)
-        assert list(out.shape) == [audio.shape[0], 3, audio.shape[-1]]
-
-    def test_convert_audio_upsample(self):
-        b, c, dur = 2, 1, 4.
-        sr = 2
-        new_sr = 3
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=new_sr, to_channels=c)
-        out_j = julius.resample.resample_frac(audio, old_sr=sr, new_sr=new_sr)
-        assert torch.allclose(out, out_j)
-
-    def test_convert_audio_resample(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        new_sr = 2
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=new_sr, to_channels=c)
-        out_j = julius.resample.resample_frac(audio, old_sr=sr, new_sr=new_sr)
-        assert torch.allclose(out, out_j)
-
-
-class TestNormalizeAudio:
-
-    def test_clip_wav(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        _clip_wav(audio)
-        assert audio.abs().max() <= 1
-
-    def test_normalize_audio_clip(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        norm_audio = normalize_audio(audio, strategy='clip')
-        assert norm_audio.abs().max() <= 1
-
-    def test_normalize_audio_rms(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        norm_audio = normalize_audio(audio, strategy='rms')
-        assert norm_audio.abs().max() <= 1
-
-    def test_normalize_audio_peak(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        norm_audio = normalize_audio(audio, strategy='peak')
-        assert norm_audio.abs().max() <= 1
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/object-inspect/test/bigint.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/object-inspect/test/bigint.js
deleted file mode 100644
index 4ecc31df8ab3fd311570d866b418514d159abfde..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/object-inspect/test/bigint.js
+++ /dev/null
@@ -1,58 +0,0 @@
-'use strict';
-
-var inspect = require('../');
-var test = require('tape');
-var hasToStringTag = require('has-tostringtag/shams')();
-
-test('bigint', { skip: typeof BigInt === 'undefined' }, function (t) {
-    t.test('primitives', function (st) {
-        st.plan(3);
-
-        st.equal(inspect(BigInt(-256)), '-256n');
-        st.equal(inspect(BigInt(0)), '0n');
-        st.equal(inspect(BigInt(256)), '256n');
-    });
-
-    t.test('objects', function (st) {
-        st.plan(3);
-
-        st.equal(inspect(Object(BigInt(-256))), 'Object(-256n)');
-        st.equal(inspect(Object(BigInt(0))), 'Object(0n)');
-        st.equal(inspect(Object(BigInt(256))), 'Object(256n)');
-    });
-
-    t.test('syntactic primitives', function (st) {
-        st.plan(3);
-
-        /* eslint-disable no-new-func */
-        st.equal(inspect(Function('return -256n')()), '-256n');
-        st.equal(inspect(Function('return 0n')()), '0n');
-        st.equal(inspect(Function('return 256n')()), '256n');
-    });
-
-    t.test('toStringTag', { skip: !hasToStringTag }, function (st) {
-        st.plan(1);
-
-        var faker = {};
-        faker[Symbol.toStringTag] = 'BigInt';
-        st.equal(
-            inspect(faker),
-            '{ [Symbol(Symbol.toStringTag)]: \'BigInt\' }',
-            'object lying about being a BigInt inspects as an object'
-        );
-    });
-
-    t.test('numericSeparator', function (st) {
-        st.equal(inspect(BigInt(0), { numericSeparator: false }), '0n', '0n, numericSeparator false');
-        st.equal(inspect(BigInt(0), { numericSeparator: true }), '0n', '0n, numericSeparator true');
-
-        st.equal(inspect(BigInt(1234), { numericSeparator: false }), '1234n', '1234n, numericSeparator false');
-        st.equal(inspect(BigInt(1234), { numericSeparator: true }), '1_234n', '1234n, numericSeparator true');
-        st.equal(inspect(BigInt(-1234), { numericSeparator: false }), '-1234n', '1234n, numericSeparator false');
-        st.equal(inspect(BigInt(-1234), { numericSeparator: true }), '-1_234n', '1234n, numericSeparator true');
-
-        st.end();
-    });
-
-    t.end();
-});
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/build/cjs/index.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/build/cjs/index.js
deleted file mode 100644
index fc99bbfae2f7f645e0db479003ad7d1afab82849..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/build/cjs/index.js
+++ /dev/null
@@ -1,303 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-exports.Decoder = exports.Encoder = exports.PacketType = exports.protocol = void 0;
-const component_emitter_1 = require("@socket.io/component-emitter");
-const binary_js_1 = require("./binary.js");
-const is_binary_js_1 = require("./is-binary.js");
-const debug_1 = require("debug"); // debug()
-const debug = (0, debug_1.default)("socket.io-parser"); // debug()
-/**
- * Protocol version.
- *
- * @public
- */
-exports.protocol = 5;
-var PacketType;
-(function (PacketType) {
-    PacketType[PacketType["CONNECT"] = 0] = "CONNECT";
-    PacketType[PacketType["DISCONNECT"] = 1] = "DISCONNECT";
-    PacketType[PacketType["EVENT"] = 2] = "EVENT";
-    PacketType[PacketType["ACK"] = 3] = "ACK";
-    PacketType[PacketType["CONNECT_ERROR"] = 4] = "CONNECT_ERROR";
-    PacketType[PacketType["BINARY_EVENT"] = 5] = "BINARY_EVENT";
-    PacketType[PacketType["BINARY_ACK"] = 6] = "BINARY_ACK";
-})(PacketType = exports.PacketType || (exports.PacketType = {}));
-/**
- * A socket.io Encoder instance
- */
-class Encoder {
-    /**
-     * Encoder constructor
-     *
-     * @param {function} replacer - custom replacer to pass down to JSON.parse
-     */
-    constructor(replacer) {
-        this.replacer = replacer;
-    }
-    /**
-     * Encode a packet as a single string if non-binary, or as a
-     * buffer sequence, depending on packet type.
-     *
-     * @param {Object} obj - packet object
-     */
-    encode(obj) {
-        debug("encoding packet %j", obj);
-        if (obj.type === PacketType.EVENT || obj.type === PacketType.ACK) {
-            if ((0, is_binary_js_1.hasBinary)(obj)) {
-                return this.encodeAsBinary({
-                    type: obj.type === PacketType.EVENT
-                        ? PacketType.BINARY_EVENT
-                        : PacketType.BINARY_ACK,
-                    nsp: obj.nsp,
-                    data: obj.data,
-                    id: obj.id,
-                });
-            }
-        }
-        return [this.encodeAsString(obj)];
-    }
-    /**
-     * Encode packet as string.
-     */
-    encodeAsString(obj) {
-        // first is type
-        let str = "" + obj.type;
-        // attachments if we have them
-        if (obj.type === PacketType.BINARY_EVENT ||
-            obj.type === PacketType.BINARY_ACK) {
-            str += obj.attachments + "-";
-        }
-        // if we have a namespace other than `/`
-        // we append it followed by a comma `,`
-        if (obj.nsp && "/" !== obj.nsp) {
-            str += obj.nsp + ",";
-        }
-        // immediately followed by the id
-        if (null != obj.id) {
-            str += obj.id;
-        }
-        // json data
-        if (null != obj.data) {
-            str += JSON.stringify(obj.data, this.replacer);
-        }
-        debug("encoded %j as %s", obj, str);
-        return str;
-    }
-    /**
-     * Encode packet as 'buffer sequence' by removing blobs, and
-     * deconstructing packet into object with placeholders and
-     * a list of buffers.
-     */
-    encodeAsBinary(obj) {
-        const deconstruction = (0, binary_js_1.deconstructPacket)(obj);
-        const pack = this.encodeAsString(deconstruction.packet);
-        const buffers = deconstruction.buffers;
-        buffers.unshift(pack); // add packet info to beginning of data list
-        return buffers; // write all the buffers
-    }
-}
-exports.Encoder = Encoder;
-/**
- * A socket.io Decoder instance
- *
- * @return {Object} decoder
- */
-class Decoder extends component_emitter_1.Emitter {
-    /**
-     * Decoder constructor
-     *
-     * @param {function} reviver - custom reviver to pass down to JSON.stringify
-     */
-    constructor(reviver) {
-        super();
-        this.reviver = reviver;
-    }
-    /**
-     * Decodes an encoded packet string into packet JSON.
-     *
-     * @param {String} obj - encoded packet
-     */
-    add(obj) {
-        let packet;
-        if (typeof obj === "string") {
-            if (this.reconstructor) {
-                throw new Error("got plaintext data when reconstructing a packet");
-            }
-            packet = this.decodeString(obj);
-            const isBinaryEvent = packet.type === PacketType.BINARY_EVENT;
-            if (isBinaryEvent || packet.type === PacketType.BINARY_ACK) {
-                packet.type = isBinaryEvent ? PacketType.EVENT : PacketType.ACK;
-                // binary packet's json
-                this.reconstructor = new BinaryReconstructor(packet);
-                // no attachments, labeled binary but no binary data to follow
-                if (packet.attachments === 0) {
-                    super.emitReserved("decoded", packet);
-                }
-            }
-            else {
-                // non-binary full packet
-                super.emitReserved("decoded", packet);
-            }
-        }
-        else if ((0, is_binary_js_1.isBinary)(obj) || obj.base64) {
-            // raw binary data
-            if (!this.reconstructor) {
-                throw new Error("got binary data when not reconstructing a packet");
-            }
-            else {
-                packet = this.reconstructor.takeBinaryData(obj);
-                if (packet) {
-                    // received final buffer
-                    this.reconstructor = null;
-                    super.emitReserved("decoded", packet);
-                }
-            }
-        }
-        else {
-            throw new Error("Unknown type: " + obj);
-        }
-    }
-    /**
-     * Decode a packet String (JSON data)
-     *
-     * @param {String} str
-     * @return {Object} packet
-     */
-    decodeString(str) {
-        let i = 0;
-        // look up type
-        const p = {
-            type: Number(str.charAt(0)),
-        };
-        if (PacketType[p.type] === undefined) {
-            throw new Error("unknown packet type " + p.type);
-        }
-        // look up attachments if type binary
-        if (p.type === PacketType.BINARY_EVENT ||
-            p.type === PacketType.BINARY_ACK) {
-            const start = i + 1;
-            while (str.charAt(++i) !== "-" && i != str.length) { }
-            const buf = str.substring(start, i);
-            if (buf != Number(buf) || str.charAt(i) !== "-") {
-                throw new Error("Illegal attachments");
-            }
-            p.attachments = Number(buf);
-        }
-        // look up namespace (if any)
-        if ("/" === str.charAt(i + 1)) {
-            const start = i + 1;
-            while (++i) {
-                const c = str.charAt(i);
-                if ("," === c)
-                    break;
-                if (i === str.length)
-                    break;
-            }
-            p.nsp = str.substring(start, i);
-        }
-        else {
-            p.nsp = "/";
-        }
-        // look up id
-        const next = str.charAt(i + 1);
-        if ("" !== next && Number(next) == next) {
-            const start = i + 1;
-            while (++i) {
-                const c = str.charAt(i);
-                if (null == c || Number(c) != c) {
-                    --i;
-                    break;
-                }
-                if (i === str.length)
-                    break;
-            }
-            p.id = Number(str.substring(start, i + 1));
-        }
-        // look up json data
-        if (str.charAt(++i)) {
-            const payload = this.tryParse(str.substr(i));
-            if (Decoder.isPayloadValid(p.type, payload)) {
-                p.data = payload;
-            }
-            else {
-                throw new Error("invalid payload");
-            }
-        }
-        debug("decoded %s as %j", str, p);
-        return p;
-    }
-    tryParse(str) {
-        try {
-            return JSON.parse(str, this.reviver);
-        }
-        catch (e) {
-            return false;
-        }
-    }
-    static isPayloadValid(type, payload) {
-        switch (type) {
-            case PacketType.CONNECT:
-                return typeof payload === "object";
-            case PacketType.DISCONNECT:
-                return payload === undefined;
-            case PacketType.CONNECT_ERROR:
-                return typeof payload === "string" || typeof payload === "object";
-            case PacketType.EVENT:
-            case PacketType.BINARY_EVENT:
-                return Array.isArray(payload) && payload.length > 0;
-            case PacketType.ACK:
-            case PacketType.BINARY_ACK:
-                return Array.isArray(payload);
-        }
-    }
-    /**
-     * Deallocates a parser's resources
-     */
-    destroy() {
-        if (this.reconstructor) {
-            this.reconstructor.finishedReconstruction();
-            this.reconstructor = null;
-        }
-    }
-}
-exports.Decoder = Decoder;
-/**
- * A manager of a binary event's 'buffer sequence'. Should
- * be constructed whenever a packet of type BINARY_EVENT is
- * decoded.
- *
- * @param {Object} packet
- * @return {BinaryReconstructor} initialized reconstructor
- */
-class BinaryReconstructor {
-    constructor(packet) {
-        this.packet = packet;
-        this.buffers = [];
-        this.reconPack = packet;
-    }
-    /**
-     * Method to be called when binary data received from connection
-     * after a BINARY_EVENT packet.
-     *
-     * @param {Buffer | ArrayBuffer} binData - the raw binary data received
-     * @return {null | Object} returns null if more binary data is expected or
-     *   a reconstructed packet object if all buffers have been received.
-     */
-    takeBinaryData(binData) {
-        this.buffers.push(binData);
-        if (this.buffers.length === this.reconPack.attachments) {
-            // done with buffer list
-            const packet = (0, binary_js_1.reconstructPacket)(this.reconPack, this.buffers);
-            this.finishedReconstruction();
-            return packet;
-        }
-        return null;
-    }
-    /**
-     * Cleans up binary packet reconstruction variables.
-     */
-    finishedReconstruction() {
-        this.reconPack = null;
-        this.buffers = [];
-    }
-}
diff --git a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_63.py b/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_63.py
deleted file mode 100644
index 8516dee445a1c65c255c1aa334507c28a84ed7d2..0000000000000000000000000000000000000000
--- a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_63.py
+++ /dev/null
@@ -1,35 +0,0 @@
-
-import re
-
-def is_spam(message: str) -> bool:
-    # Checking for spam URL patterns
-    spam_url_patterns = [
-        r"(?i)https?:\/\/(?:me2\.kr|buly\.kr|opcn\-kakao.com|han.gl|abit\.ly)/\S*",
-        r"(?i)ⓢlⓩ102\.com",
-        r"(?i)orl\.kr\/\S*",
-        r"(?i)https?://openkakao.io/\S*"
-    ]
-
-    for pattern in spam_url_patterns:
-        if re.search(pattern, message):
-            return True
-
-    # Checking for other spam patterns
-    spam_patterns = [
-        r"(?i)(vip|vvip)투자반",
-        r"(?i)차별화 된",
-        r"(?i)시작하루만에",
-        r"(?i)추천주 현황",
-        r"(?i)slot🎰zone",
-        r"(?i)지니틱스",
-        r"(?i)카카오톡제재",
-        r"(?i)[5일평균].*[8,930.000원]",
-        r"(?i)문의▼",
-    ]
-
-    for pattern in spam_patterns:
-        if re.search(pattern, message):
-            return True
-
-    # If none of the spam patterns are present
-    return False
diff --git a/spaces/fiz123321/nah/greeting.md b/spaces/fiz123321/nah/greeting.md
deleted file mode 100644
index 6c2c45107629147b25bbb2a17d9f297702132769..0000000000000000000000000000000000000000
--- a/spaces/fiz123321/nah/greeting.md
+++ /dev/null
@@ -1,3 +0,0 @@
-<3
-https://rentry.org/miniproxy
- 
\ No newline at end of file
diff --git a/spaces/flax-community/roberta-base-mr/apps/mlm.py b/spaces/flax-community/roberta-base-mr/apps/mlm.py
deleted file mode 100644
index 7f5389e0f63c463c45d111cc52807436b14e7589..0000000000000000000000000000000000000000
--- a/spaces/flax-community/roberta-base-mr/apps/mlm.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import json
-
-import streamlit as st
-from transformers import AutoTokenizer, RobertaForMaskedLM, pipeline
-
-with open("config.json", encoding="utf8") as f:
-    cfg = json.loads(f.read())
-
-
-@st.cache(allow_output_mutation=True, show_spinner=False)
-def load_model(input_text, model_name_or_path):
-    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
-    model = RobertaForMaskedLM.from_pretrained(model_name_or_path)
-
-    nlp = pipeline("fill-mask", model=model, tokenizer=tokenizer)
-    result = nlp(input_text)
-    sentence, mask = result[0]["sequence"], result[0]["token_str"]
-    return sentence, mask, result
-
-
-def app():
-    st.title("RoBERTa Marathi - मराठी भाषा")
-
-    st.markdown(
-        "This demo uses [RoBERTa for Marathi](https://huggingface.co/flax-community/roberta-base-mr) model "
-        "trained on [mC4](https://huggingface.co/datasets/mc4)."
-    )
-
-    st.markdown(
-        "❓Can't figure out where to get a sample text other than the predefined ones?❓\n\n"
-        "Use any custom sentence with masked word or copy any headline from this [link](https://maharashtratimes.com/entertainment/articlelist/19359255.cms), and mask a word.\n"
-        "> 📒 NOTE: Supports only single `<mask>` word"
-    )
-
-    masked_texts = [
-        "मोठी बातमी! उद्या दुपारी <mask> वाजता जाहीर होणार दहावीचा निकाल",
-        "जॉनी लीवर यांनी नम्रता संभेरावला दिलं <mask> गिफ्ट, अभिनेत्रीने व्यक्त केल्या भावना"
-        # "अध्यक्ष <mask> पवार आणि उपमुख्यमंत्री अजित पवार यांची भेट घेतली.",
-    ]
-
-    input_text = st.sidebar.selectbox("Select a Text", options=masked_texts)
-    masked_text = st.text_input("Please type a masked sentence to fill", input_text)
-
-    fill_button = st.button("Fill the Mask!")
-
-    if fill_button:
-        with st.spinner("Filling the Mask..."):
-            filled_sentence, mask, raw_json = load_model(masked_text, cfg["models"]["RoBERTa"])
-
-            st.markdown(f"**Filled sentence: **{filled_sentence}")
-            st.markdown(f"**Predicted masked token: **{mask}")
-
-            st.write(raw_json)
diff --git a/spaces/freshield/ChatGPT-gradio/OpenaiBot.py b/spaces/freshield/ChatGPT-gradio/OpenaiBot.py
deleted file mode 100644
index 81734a5986aff1be9ea3490fd786262daf8de07d..0000000000000000000000000000000000000000
--- a/spaces/freshield/ChatGPT-gradio/OpenaiBot.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# coding=utf-8
-"""
-@Author: Freshield
-@Contact: yangyufresh@163.com
-@File: OpenaiBot.py
-@Time: 2023-03-03 17:47
-@Last_update: 2023-03-03 17:47
-@Desc: None
-@==============================================@
-@      _____             _   _     _   _       @
-@     |   __|___ ___ ___| |_|_|___| |_| |      @
-@     |   __|  _| -_|_ -|   | | -_| | . |      @
-@     |__|  |_| |___|___|_|_|_|___|_|___|      @
-@                                    Freshield @
-@==============================================@
-"""
-import os
-import openai
-
-
-class OpenaiBot(object):
-    """调用openai的机器人"""
-    def __init__(self, temperature=0.5):
-        openai.api_key = os.environ.get('OPENAI_API_KEY')
-        self.model_engine = "gpt-3.5-turbo"
-        self.temperature = temperature
-
-    def set_api_key(self, api_key):
-        """设定api key"""
-        openai.api_key = api_key
-
-    def construct_message(self, role, new_msg, history_list, keep_history=3):
-        """
-        构造message，这里history_list是一个list，每个元素是一个tuple
-        """
-        msg_list = [{"role": "system", "content": role}]
-        history_list = history_list[-keep_history:]
-        for user, assistant in history_list:
-            msg_list.append({"role": "user", "content": user})
-            msg_list.append({"role": "assistant", "content": assistant})
-        msg_list.append({"role": "user", "content": new_msg})
-
-        return msg_list
-
-    def get_response(self, role, new_msg, history_list, keep_history=3):
-        """
-        通过openai获取回复
-        """
-        msg_list = self.construct_message(role, new_msg, history_list, keep_history)
-        response = openai.ChatCompletion.create(
-            model=self.model_engine, messages=msg_list,
-            temperature=self.temperature
-        )
-        content = response['choices'][0]['message']['content']
-
-        return content
-
-
-if __name__ == '__main__':
-    openai_bot = OpenaiBot()
diff --git a/spaces/fuckyoudeki/AutoGPT/autogpt/memory/milvus.py b/spaces/fuckyoudeki/AutoGPT/autogpt/memory/milvus.py
deleted file mode 100644
index 44aa72b956224fa4c2a16d5f40b0eaeb35e98581..0000000000000000000000000000000000000000
--- a/spaces/fuckyoudeki/AutoGPT/autogpt/memory/milvus.py
+++ /dev/null
@@ -1,115 +0,0 @@
-""" Milvus memory storage provider."""
-from pymilvus import Collection, CollectionSchema, DataType, FieldSchema, connections
-
-from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
-
-
-class MilvusMemory(MemoryProviderSingleton):
-    """Milvus memory storage provider."""
-
-    def __init__(self, cfg) -> None:
-        """Construct a milvus memory storage connection.
-
-        Args:
-            cfg (Config): Auto-GPT global config.
-        """
-        # connect to milvus server.
-        connections.connect(address=cfg.milvus_addr)
-        fields = [
-            FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=True),
-            FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=1536),
-            FieldSchema(name="raw_text", dtype=DataType.VARCHAR, max_length=65535),
-        ]
-
-        # create collection if not exist and load it.
-        self.milvus_collection = cfg.milvus_collection
-        self.schema = CollectionSchema(fields, "auto-gpt memory storage")
-        self.collection = Collection(self.milvus_collection, self.schema)
-        # create index if not exist.
-        if not self.collection.has_index():
-            self.collection.release()
-            self.collection.create_index(
-                "embeddings",
-                {
-                    "metric_type": "IP",
-                    "index_type": "HNSW",
-                    "params": {"M": 8, "efConstruction": 64},
-                },
-                index_name="embeddings",
-            )
-        self.collection.load()
-
-    def add(self, data) -> str:
-        """Add an embedding of data into memory.
-
-        Args:
-            data (str): The raw text to construct embedding index.
-
-        Returns:
-            str: log.
-        """
-        embedding = get_ada_embedding(data)
-        result = self.collection.insert([[embedding], [data]])
-        _text = (
-            "Inserting data into memory at primary key: "
-            f"{result.primary_keys[0]}:\n data: {data}"
-        )
-        return _text
-
-    def get(self, data):
-        """Return the most relevant data in memory.
-        Args:
-            data: The data to compare to.
-        """
-        return self.get_relevant(data, 1)
-
-    def clear(self) -> str:
-        """Drop the index in memory.
-
-        Returns:
-            str: log.
-        """
-        self.collection.drop()
-        self.collection = Collection(self.milvus_collection, self.schema)
-        self.collection.create_index(
-            "embeddings",
-            {
-                "metric_type": "IP",
-                "index_type": "HNSW",
-                "params": {"M": 8, "efConstruction": 64},
-            },
-            index_name="embeddings",
-        )
-        self.collection.load()
-        return "Obliviated"
-
-    def get_relevant(self, data: str, num_relevant: int = 5):
-        """Return the top-k relevant data in memory.
-        Args:
-            data: The data to compare to.
-            num_relevant (int, optional): The max number of relevant data.
-                Defaults to 5.
-
-        Returns:
-            list: The top-k relevant data.
-        """
-        # search the embedding and return the most relevant text.
-        embedding = get_ada_embedding(data)
-        search_params = {
-            "metrics_type": "IP",
-            "params": {"nprobe": 8},
-        }
-        result = self.collection.search(
-            [embedding],
-            "embeddings",
-            search_params,
-            num_relevant,
-            output_fields=["raw_text"],
-        )
-        return [item.entity.value_of_field("raw_text") for item in result[0]]
-
-    def get_stats(self) -> str:
-        """
-        Returns: The stats of the milvus cache.
-        """
-        return f"Entities num: {self.collection.num_entities}"
diff --git a/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_pascal_ctx_59_sem_seg.py b/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_pascal_ctx_59_sem_seg.py
deleted file mode 100644
index a4019b3006e8347bdbd936297852c1b2cbd22ff6..0000000000000000000000000000000000000000
--- a/spaces/fun-research/FC-CLIP/fcclip/data/datasets/register_pascal_ctx_59_sem_seg.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import os
-
-import numpy as np
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets import load_sem_seg
-
-from . import openseg_classes
-
-PASCAL_CTX_59_CATEGORIES=openseg_classes.get_pascal_ctx_59_categories_with_prompt_eng()
-
-PASCAL_CTX_59_COLORS = [k["color"] for k in PASCAL_CTX_59_CATEGORIES]
-
-MetadataCatalog.get("openvocab_pascal_ctx59_sem_seg_train").set(
-    stuff_colors=PASCAL_CTX_59_COLORS[:],
-)
-
-MetadataCatalog.get("openvocab_pascal_ctx59_sem_seg_val").set(
-    stuff_colors=PASCAL_CTX_59_COLORS[:],
-)
-
-def _get_ctx59_meta():
-    # Id 0 is reserved for ignore_label, we change ignore_label for 0
-    # to 255 in our pre-processing, so all ids are shifted by 1.
-    stuff_ids = [k["id"] for k in PASCAL_CTX_59_CATEGORIES]
-    assert len(stuff_ids) == 59, len(stuff_ids)
-
-    # For semantic segmentation, this mapping maps from contiguous stuff id
-    # (in [0, 91], used in models) to ids in the dataset (used for processing results)
-    stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)}
-    stuff_classes = [k["name"] for k in PASCAL_CTX_59_CATEGORIES]
-
-    ret = {
-        "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id,
-        "stuff_classes": stuff_classes,
-    }
-    return ret
-
-
-def register_all_ctx59(root):
-    root = os.path.join(root, "pascal_ctx_d2")
-    meta = _get_ctx59_meta()
-    for name, dirname in [("train", "training"), ("val", "validation")]:
-        image_dir = os.path.join(root, "images", dirname)
-        gt_dir = os.path.join(root, "annotations_ctx59", dirname)
-        name = f"openvocab_pascal_ctx59_sem_seg_{name}"
-        DatasetCatalog.register(
-            name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg")
-        )
-        MetadataCatalog.get(name).set(
-            stuff_classes=meta["stuff_classes"][:],
-            thing_dataset_id_to_contiguous_id={},  # to make Mask2Former happy
-            stuff_dataset_id_to_contiguous_id=meta["stuff_dataset_id_to_contiguous_id"],
-            image_root=image_dir,
-            sem_seg_root=gt_dir,
-            evaluator_type="sem_seg",
-            ignore_label=255,
-            gt_ext="png",
-        )
-        
-_root = os.getenv("DETECTRON2_DATASETS", "datasets")
-register_all_ctx59(_root)
\ No newline at end of file
diff --git a/spaces/golem4300/RVC-TTS/vc_infer_pipeline.py b/spaces/golem4300/RVC-TTS/vc_infer_pipeline.py
deleted file mode 100644
index 2e5d17bdc522e3d2757f2accd17258994b40e613..0000000000000000000000000000000000000000
--- a/spaces/golem4300/RVC-TTS/vc_infer_pipeline.py
+++ /dev/null
@@ -1,230 +0,0 @@
-from scipy import signal
-from functools import lru_cache
-import torch.nn.functional as F
-import numpy as np, parselmouth, torch
-import pyworld, os, traceback, faiss, librosa
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-@lru_cache
-def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period, input_audio_path2wav):
-    audio = input_audio_path2wav[input_audio_path]
-    f0, t = pyworld.harvest(
-        audio, fs=fs, f0_ceil=f0max, f0_floor=f0min, frame_period=frame_period
-    )
-    f0 = pyworld.stonemask(audio, f0, t, fs)
-    return f0
-
-def change_rms(data1, sr1, data2, sr2, rate):
-    rms1 = librosa.feature.rms(y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2)
-    rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
-    rms1 = torch.from_numpy(rms1).unsqueeze(0)
-    rms2 = torch.from_numpy(rms2).unsqueeze(0)
-    rms1 = F.interpolate(rms1, size=data2.shape[0], mode="linear").squeeze()
-    rms2 = F.interpolate(rms2, size=data2.shape[0], mode="linear").squeeze()
-    rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
-    data2 *= (torch.pow(rms1, 1 - rate) * torch.pow(rms2, rate - 1)).numpy()
-    
-    return data2
-
-class VC:
-    def __init__(self, tgt_sr, config):
-        self.x_pad = config.x_pad
-        self.x_query = config.x_query
-        self.x_center = config.x_center
-        self.x_max = config.x_max
-        self.is_half = config.is_half
-        self.sr = 16000
-        self.window = 160
-        self.t_pad = self.sr * self.x_pad
-        self.t_pad_tgt = tgt_sr * self.x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * self.x_query
-        self.t_center = self.sr * self.x_center
-        self.t_max = self.sr * self.x_max
-        self.device = config.device
-
-    def get_f0(self, input_audio_path, x, p_len, f0_up_key, f0_method, filter_radius, inp_f0=None):
-        global input_audio_path2wav
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
-        if f0_method == "pm":
-            f0 = (parselmouth.Sound(x, self.sr)
-                   .to_pitch_ac(time_step=time_step / 1000, voicing_threshold=0.6, pitch_floor=f0_min,
-                                pitch_ceiling=f0_max,)
-                   .selected_array["frequency"])
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
-        f0 *= pow(2, f0_up_key / 12)
-        tf0 = self.sr // self.window
-
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1])
-            shape = f0[self.x_pad * tf0: self.x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[self.x_pad * tf0: self.x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
-
-        f0bak= f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak
-
-    def vc(self, model, net_g, sid, audio0, pitch, pitchf, times, index, big_npy, index_rate, version, protect):
-        feats = torch.from_numpy(audio0)
-        feats = feats.half() if self.is_half else feats.float()
-        
-        if feats.dim() == 2:
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = { "source": feats.to(self.device), "padding_mask": padding_mask, "output_layer": 9 if version == "v1" else 12}
-
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
-        
-        if protect < 0.5 and pitch is not None and pitchf is not None:
-            feats0 = feats.clone()
-        
-        if index is not None and big_npy is not None and index_rate != 0:
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float64")
-
-            score, ix = index.search(npy, k=8)
-            weight = np.square(1 / score)
-            weight /= weight.sum(axis=1, keepdims=True)
-            npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = ( torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate + (1 - index_rate) * feats)
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        if protect < 0.5 and pitch is not None and pitchf is not None:
-            feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch is not None and pitchf is not None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-
-        if protect < 0.5 and pitch is not None and pitchf is not None:
-            pitchff = pitchf.clone()
-            pitchff[pitchf > 0] = 1
-            pitchff[pitchf < 1] = protect
-            pitchff = pitchff.unsqueeze(-1)
-            feats = feats * pitchff + feats0 * (1 - pitchff)
-            feats = feats.to(feats0.dtype)
-        p_len = torch.tensor([p_len], device=self.device).long()
-        
-        with torch.no_grad():
-            if pitch is not None and pitchf is not None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                )
-            else:
-                audio1 = ((net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy())
-        
-        del feats, p_len, padding_mask
-        return audio1
-
-    def pipeline(self,model, net_g, sid, audio, input_audio_path, times, f0_up_key, f0_method, file_index, index_rate, if_f0, filter_radius, tgt_sr, resample_sr, rms_mix_rate, version, protect, f0_file=None,):
-        if (
-            file_index != ""
-            and os.path.exists(file_index) == True
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                big_npy = index.reconstruct_n(0, index.ntotal)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float64")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(
-                input_audio_path,audio_pad,p_len,f0_up_key,f0_method,filter_radius,inp_f0,
-            )
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(model, net_g, sid,audio_pad[s : t + self.t_pad2 + self.window], pitch[:, s // self.window : (t + self.t_pad2) // self.window],pitchf[:, s // self.window : (t + self.t_pad2) // self.window],times,index,big_npy,index_rate,version,protect,)[self.t_pad_tgt : -self.t_pad_tgt])
-            else:
-                audio_opt.append(self.vc(model,net_g,sid,audio_pad[s : t + self.t_pad2 + self.window],None,None,times,index,big_npy,index_rate,version,protect)[self.t_pad_tgt : -self.t_pad_tgt])
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(self.vc(model,net_g,sid,audio_pad[t:],pitch[:, t // self.window :] if t is not None else pitch,pitchf[:, t // self.window :] if t is not None else pitchf,times,index,big_npy,index_rate,version,protect,)[self.t_pad_tgt : -self.t_pad_tgt])
-        else:
-            audio_opt.append(self.vc(model, net_g, sid, audio_pad[t:], None, None, times, index, big_npy, index_rate, version, protect)[self.t_pad_tgt : -self.t_pad_tgt])
-        audio_opt = np.concatenate(audio_opt)
-        if rms_mix_rate != 1:
-            audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
-        if resample_sr >= 16000 and tgt_sr != resample_sr:
-            audio_opt = librosa.resample(audio_opt, orig_sr=tgt_sr, target_sr=resample_sr)
-        audio_max = np.abs(audio_opt).max() / 0.99
-        max_int16 = 32768
-        if audio_max > 1:
-            max_int16 /= audio_max
-        audio_opt = (audio_opt * max_int16).astype(np.int16)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt
\ No newline at end of file
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Keygen Extra Quality AutoCAD Architecture 2016.md b/spaces/gotiQspiryo/whisper-ui/examples/Keygen Extra Quality AutoCAD Architecture 2016.md
deleted file mode 100644
index ae4eb440f041f854ea7b1919afc425b36e7f1376..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Keygen Extra Quality AutoCAD Architecture 2016.md	
+++ /dev/null
@@ -1,115 +0,0 @@
-<br />
-<h1>Keygen AutoCAD Architecture 2016: A Complete Guide</h1>
-
-<p>If you are looking for a way to activate any product of Autodesk 2016, you may have heard of Keygen AutoCAD Architecture 2016. This is a tool that can generate serial numbers and product keys that match certain templates for Autodesk products. In this article, we will explain what Keygen AutoCAD Architecture 2016 is, how to use it, and what are the advantages and disadvantages of using it.</p>
-<h2>Keygen AutoCAD Architecture 2016</h2><br /><p><b><b>DOWNLOAD</b> &#11088; <a href="https://urlgoal.com/2uyMya">https://urlgoal.com/2uyMya</a></b></p><br /><br />
-
-<h2>What is Keygen AutoCAD Architecture 2016?</h2>
-
-<p>Keygen AutoCAD Architecture 2016 is a software that can create unique codes that can unlock the full features of Autodesk products, such as AutoCAD Architecture 2016. AutoCAD Architecture 2016 is a software that helps architects and designers create drawings, documentation, and schedules for building projects. It has many tools and features that can improve productivity and accuracy.</p>
-
-<p>However, to use AutoCAD Architecture 2016, you need to have a valid license that can be purchased from Autodesk or authorized resellers. A license can be either perpetual or subscription-based, and it can be either single-user or multi-user. The price of a license depends on the type, duration, and number of users.</p>
-
-<p>If you do not have a license, you can still use AutoCAD Architecture 2016 in trial mode for 30 days. After that, you will need to activate the software with a serial number and a product key. This is where Keygen AutoCAD Architecture 2016 comes in handy. It can generate serial numbers and product keys that can bypass the activation process and make the software think that it is licensed.</p>
-<p></p>
-
-<h2>How to use Keygen AutoCAD Architecture 2016?</h2>
-
-<p>To use Keygen AutoCAD Architecture 2016, you need to follow these steps:</p>
-
-<ol>
-<li>Download Keygen AutoCAD Architecture 2016 from various sources on the internet. Be careful to choose a reliable and safe source, as some keygens may contain viruses or malware.</li>
-<li>Install Autodesk Autocad Architecture 2016 with a serial number and a product key that match certain templates. You can find these templates on the internet or use the ones provided by the keygen.</li>
-<li>Run the keygen as administrator and copy the request code from the activation screen of AutoCAD Architecture 2016.</li>
-<li>Paste the request code into the keygen and press generate. The keygen will produce an activation code.</li>
-<li>Copy the activation code and paste it into the activation screen of AutoCAD Architecture 2016. Click next and you should see a message that says "Activation successful".</li>
-<li>Block the outgoing traffic or disconnect from the internet before activating. This will prevent Autodesk from detecting that you are using a fake license.</li>
-</ol>
-
-<p>Congratulations! You have successfully activated AutoCAD Architecture 2016 with Keygen AutoCAD Architecture 2016.</p>
-
-<h2>What are the advantages and disadvantages of using Keygen AutoCAD Architecture 2016?</h2>
-
-<p>Using Keygen AutoCAD Architecture 2016 has some pros and cons that you should be aware of before deciding to use it.</p>
-
-<p>The main advantage of using Keygen AutoCAD Architecture 2016 is that it can save you money. You do not have to pay for a license or a subscription to use AutoCAD Architecture 2016. You can enjoy all the features and functions of the software without any limitations or restrictions.</p>
-
-<p>The main disadvantage of using Keygen AutoCAD Architecture 2016 is that it is illegal and unethical. You are violating the terms and conditions of Autodesk by using a pirated software. You are also depriving Autodesk of their rightful revenue and support. You may face legal consequences if you are caught using Keygen AutoCAD Architecture 2016.</p>
-
-<p>Another disadvantage of using Keygen AutoCAD Architecture 2016 is that it may not work properly or at all. Some keygens may not generate valid codes or may generate codes that have already been used by someone else. Some keygens may also cause errors or crashes in the software or your system. Some keygens may also contain viruses or malware that can harm your computer or steal your personal information.</p>
-
-<h2>Conclusion</h2>
-
-<p>Keygen AutoCAD Architecture 2016 is a tool that can activate any product of Autodesk 2016, including AutoCAD Architecture 2016. It can generate serial numbers and product keys that can unlock the full features of the software. However, using Keygen AutoCAD Architecture 2016 is illegal and unethical, and it may also pose some risks to your system and security. Therefore, we do not recommend using Keygen AutoCAD Architecture 2016 or any other similar tools. Instead, we suggest that you purchase a legitimate license from Autodesk or authorized resellers.</p>
-<h2>What are the benefits of using AutoCAD Architecture 2016?</h2>
-
-<p>AutoCAD Architecture 2016 is a powerful software that can help you design and document architectural projects. It has many benefits that can improve your workflow and efficiency, such as:</p>
-
-<ul>
-<li>It has a user-friendly interface that allows you to access tools and commands easily.</li>
-<li>It has a comprehensive library of architectural objects and styles that you can use to create realistic and accurate drawings.</li>
-<li>It has a dynamic model that updates automatically as you make changes to your design.</li>
-<li>It has a smart dimensioning system that helps you create accurate and consistent annotations.</li>
-<li>It has a collaboration feature that enables you to work with other professionals and share your data across platforms.</li>
-</ul>
-
-<p>With AutoCAD Architecture 2016, you can create stunning and professional architectural drawings that meet your standards and specifications.</p>
-
-<h2>What are the risks of using Keygen AutoCAD Architecture 2016?</h2>
-
-<p>While Keygen AutoCAD Architecture 2016 may seem like a convenient and cost-effective way to use AutoCAD Architecture 2016, it also comes with some risks that you should be aware of before using it. Some of these risks are:</p>
-
-<ul>
-<li>You may violate the intellectual property rights of Autodesk and face legal actions or penalties.</li>
-<li>You may compromise the quality and performance of your software and your system.</li>
-<li>You may expose your computer and your data to viruses or malware that can damage or steal them.</li>
-<li>You may lose access to updates, support, and services from Autodesk.</li>
-<li>You may miss out on new features and improvements that Autodesk releases for their products.</li>
-</ul>
-
-<p>Using Keygen AutoCAD Architecture 2016 may seem tempting, but it is not worth the risk. You may end up losing more than you gain by using a pirated software.</p>
-
-<h2>How to get a legitimate license for AutoCAD Architecture 2016?</h2>
-
-<p>If you want to use AutoCAD Architecture 2016 legally and safely, you need to get a legitimate license from Autodesk or authorized resellers. You can choose from different types of licenses depending on your needs and preferences, such as:</p>
-
-<ul>
-<li>A perpetual license that allows you to use the software indefinitely without paying any additional fees.</li>
-<li>A subscription license that allows you to use the software for a specified period of time and pay a recurring fee.</li>
-<li>A single-user license that allows you to use the software on one device only.</li>
-<li>A multi-user license that allows you to use the software on multiple devices or share it with other users.</li>
-</ul>
-
-<p>To get a license for AutoCAD Architecture 2016, you need to visit the official website of Autodesk or authorized resellers and follow the instructions. You will need to provide some information, such as your name, email address, country, and payment method. You will also need to agree to the terms and conditions of Autodesk. After completing the purchase, you will receive a confirmation email with your serial number and product key. You can then use these codes to activate your software and enjoy its full features and functions.</p>
-<h2>What are the alternatives to using Keygen AutoCAD Architecture 2016?</h2>
-
-<p>If you do not want to use Keygen AutoCAD Architecture 2016 or any other similar tools, you have some alternatives that can help you use AutoCAD Architecture 2016 legally and safely. Some of these alternatives are:</p>
-
-<ul>
-<li>You can use the trial version of AutoCAD Architecture 2016 for 30 days. This will allow you to test the software and see if it meets your needs and expectations. You can download the trial version from the official website of Autodesk.</li>
-<li>You can use the student version of AutoCAD Architecture 2016 for 3 years. This will allow you to use the software for educational purposes only. You can get the student version from the Autodesk Education Community.</li>
-<li>You can use the free web and mobile apps of AutoCAD Architecture 2016. These will allow you to view, edit, and share your drawings online or on your mobile devices. You can access the web app from any browser or download the mobile app from the App Store or Google Play.</li>
-<li>You can use other software that are similar to AutoCAD Architecture 2016. These will allow you to create and document architectural projects with different features and functions. You can find some of these software on the internet or ask for recommendations from other professionals.</li>
-</ul>
-
-<p>These alternatives may not have all the features and functions of AutoCAD Architecture 2016, but they may still help you achieve your goals and objectives.</p>
-
-<h2>How to learn more about AutoCAD Architecture 2016?</h2>
-
-<p>If you want to learn more about AutoCAD Architecture 2016, you have some resources that can help you improve your skills and knowledge. Some of these resources are:</p>
-
-<ul>
-<li>You can read the user guide and the help files of AutoCAD Architecture 2016. These will provide you with detailed information and instructions on how to use the software and its tools and commands.</li>
-<li>You can watch the tutorials and videos of AutoCAD Architecture 2016. These will show you how to perform various tasks and operations with the software and its features.</li>
-<li>You can take online courses and training programs on AutoCAD Architecture 2016. These will teach you how to use the software effectively and efficiently for different types of projects and scenarios.</li>
-<li>You can join online forums and communities of AutoCAD Architecture 2016 users. These will allow you to interact with other professionals and experts who can answer your questions and share their tips and tricks.</li>
-</ul>
-
-<p>These resources may help you learn more about AutoCAD Architecture 2016 and enhance your performance and productivity.</p>
-<h2>Conclusion</h2>
-
-<p>AutoCAD Architecture 2016 is a software that can help you design and document architectural projects. It has many features and functions that can improve your workflow and accuracy. However, to use AutoCAD Architecture 2016, you need to have a valid license that can be purchased from Autodesk or authorized resellers. If you do not have a license, you may be tempted to use Keygen AutoCAD Architecture 2016 or any other similar tools that can activate the software without paying. However, using Keygen AutoCAD Architecture 2016 is illegal and unethical, and it may also pose some risks to your system and security. Therefore, we do not recommend using Keygen AutoCAD Architecture 2016 or any other similar tools. Instead, we suggest that you use some alternatives that can help you use AutoCAD Architecture 2016 legally and safely. We also suggest that you use some resources that can help you learn more about AutoCAD Architecture 2016 and improve your skills and knowledge.</p>
-
-<p>We hope that this article has provided you with useful information and guidance on Keygen AutoCAD Architecture 2016 and AutoCAD Architecture 2016. If you have any questions or comments, please feel free to contact us or leave a comment below.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Kunci Jawaban Buku Pr Intan Pariwara Geografi Kelas X  Updatedl.md b/spaces/gotiQspiryo/whisper-ui/examples/Kunci Jawaban Buku Pr Intan Pariwara Geografi Kelas X  Updatedl.md
deleted file mode 100644
index ae872211bbc9015b675be9c6a4f37fcc4efec43e..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Kunci Jawaban Buku Pr Intan Pariwara Geografi Kelas X  Updatedl.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Kunci Jawaban Buku Pr Intan Pariwara Geografi Kelas X | Updatedl</h2><br /><p><b><b>Download</b> ::: <a href="https://urlgoal.com/2uyLFu">https://urlgoal.com/2uyLFu</a></b></p><br /><br />
-
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-<br />
-<br />
-<p></p>
diff --git a/spaces/gradio/HuBERT/examples/truncated_bptt/truncated_bptt_lm_task.py b/spaces/gradio/HuBERT/examples/truncated_bptt/truncated_bptt_lm_task.py
deleted file mode 100644
index 02be0e7fb4213b98798c85b79e9046e9990b97fc..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/truncated_bptt/truncated_bptt_lm_task.py
+++ /dev/null
@@ -1,281 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-from dataclasses import dataclass, field
-from typing import List, Optional, Tuple
-
-import torch
-from fairseq import utils
-from fairseq.data import (
-    Dictionary,
-    TokenBlockDataset,
-    data_utils,
-    iterators,
-)
-from fairseq.dataclass import FairseqDataclass
-from fairseq.distributed import utils as dist_utils
-from fairseq.tasks import FairseqTask, register_task
-from omegaconf import II
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class TruncatedBPTTLMConfig(FairseqDataclass):
-    data: str = field(default="???", metadata={"help": "path to data directory"})
-    tokens_per_sample: int = field(
-        default=1024,
-        metadata={"help": "max number of tokens per sequence"},
-    )
-    batch_size: int = II("dataset.batch_size")
-    # Some models use *max_target_positions* to know how many positional
-    # embeddings to learn. We use II(...) to make it default to
-    # *tokens_per_sample*, but in principle there could be more positional
-    # embeddings than tokens in a single batch. This may also be irrelevant for
-    # custom model implementations.
-    max_target_positions: int = II("task.tokens_per_sample")
-    # these will be populated automatically if not provided
-    data_parallel_rank: Optional[int] = None
-    data_parallel_size: Optional[int] = None
-
-
-@register_task("truncated_bptt_lm", dataclass=TruncatedBPTTLMConfig)
-class TruncatedBPTTLMTask(FairseqTask):
-    def __init__(self, cfg: TruncatedBPTTLMConfig):
-        super().__init__(cfg)
-
-        if cfg.data_parallel_rank is None or cfg.data_parallel_size is None:
-            if torch.distributed.is_initialized():
-                cfg.data_parallel_rank = dist_utils.get_data_parallel_rank()
-                cfg.data_parallel_size = dist_utils.get_data_parallel_world_size()
-            else:
-                cfg.data_parallel_rank = 0
-                cfg.data_parallel_size = 1
-
-        # load the dictionary
-        paths = utils.split_paths(cfg.data)
-        assert len(paths) > 0
-        self.dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt"))
-        logger.info("dictionary: {} types".format(len(self.dictionary)))
-
-    def load_dataset(self, split, epoch=1, combine=False, **kwargs):
-        """Load a given dataset split (e.g., train, valid, test)"""
-
-        # support sharded datasets
-        paths = utils.split_paths(self.cfg.data)
-        assert len(paths) > 0
-        data_path = paths[(epoch - 1) % len(paths)]
-        split_path = os.path.join(data_path, split)
-
-        # each element of *data* will be a tensorized line from the original
-        # text dataset, similar to ``open(split_path).readlines()``
-        data = data_utils.load_indexed_dataset(
-            split_path, self.dictionary, combine=combine
-        )
-        if data is None:
-            raise FileNotFoundError(
-                "Dataset not found: {} ({})".format(split, split_path)
-            )
-
-        # this is similar to ``data.view(-1).split(tokens_per_sample)``
-        data = TokenBlockDataset(
-            data,
-            data.sizes,
-            block_size=self.cfg.tokens_per_sample,
-            pad=None,  # unused
-            eos=None,  # unused
-            break_mode="none",
-        )
-
-        self.datasets[split] = TruncatedBPTTDataset(
-            data=data,
-            bsz_per_shard=self.cfg.batch_size,
-            shard_id=self.cfg.data_parallel_rank,
-            num_shards=self.cfg.data_parallel_size,
-        )
-
-    def dataset(self, split):
-        return self.datasets[split]
-
-    def get_batch_iterator(
-        self, dataset, num_workers=0, epoch=1, data_buffer_size=0, **kwargs
-    ):
-        return iterators.EpochBatchIterator(
-            dataset=dataset,
-            collate_fn=self._collate_fn,
-            num_workers=num_workers,
-            epoch=epoch,
-            buffer_size=data_buffer_size,
-            # we don't use the batching functionality from EpochBatchIterator;
-            # instead every item in *dataset* is a whole batch
-            batch_sampler=[[i] for i in range(len(dataset))],
-            disable_shuffling=True,
-        )
-
-    def _collate_fn(self, items: List[List[torch.Tensor]]):
-        # we don't use fairseq's batching functionality, so we expect a single
-        # Tensor of type List[torch.Tensor]
-        assert len(items) == 1
-
-        # item will have shape B x T (the last batch may have length < T)
-        id, item = items[0]
-        item = data_utils.collate_tokens(item, pad_idx=self.source_dictionary.pad())
-        B, T = item.size()
-
-        # shift item one position over and append a padding token for the target
-        target = torch.nn.functional.pad(
-            item[:, 1:], (0, 1, 0, 0), value=self.target_dictionary.pad()
-        )
-
-        # fairseq expects batches to have the following structure
-        return {
-            "id": torch.tensor([id]*item.size(0)),
-            "net_input": {
-                "src_tokens": item,
-            },
-            "target": target,
-            "nsentences": item.size(0),
-            "ntokens": item.numel(),
-        }
-
-    def build_dataset_for_inference(
-        self, src_tokens: List[torch.Tensor], src_lengths: List[int], **kwargs
-    ) -> torch.utils.data.Dataset:
-        eos = self.source_dictionary.eos()
-        dataset = TokenBlockDataset(
-            src_tokens,
-            src_lengths,
-            block_size=None,  # ignored for "eos" break mode
-            pad=self.source_dictionary.pad(),
-            eos=eos,
-            break_mode="eos",
-        )
-
-        class Dataset(torch.utils.data.Dataset):
-            def __getitem__(self, i):
-                item = dataset[i]
-                if item[-1] == eos:
-                    # remove eos to support generating with a prefix
-                    item = item[:-1]
-                return (i, [item])
-
-            def __len__(self):
-                return len(dataset)
-
-        return Dataset()
-
-    def inference_step(
-        self, generator, models, sample, prefix_tokens=None, constraints=None
-    ):
-        with torch.no_grad():
-            if constraints is not None:
-                raise NotImplementedError
-
-            # SequenceGenerator doesn't use *src_tokens* directly, we need to
-            # pass the *prefix_tokens* argument instead.
-            if prefix_tokens is None and sample["net_input"]["src_tokens"].nelement():
-                prefix_tokens = sample["net_input"]["src_tokens"]
-
-            # begin generation with the end-of-sentence token
-            bos_token = self.source_dictionary.eos()
-
-            return generator.generate(
-                models, sample, prefix_tokens=prefix_tokens, bos_token=bos_token
-            )
-
-    def eval_lm_dataloader(
-        self,
-        dataset,
-        max_tokens: Optional[int] = 36000,
-        batch_size: Optional[int] = None,
-        max_positions: Optional[int] = None,
-        num_shards: int = 1,
-        shard_id: int = 0,
-        num_workers: int = 1,
-        data_buffer_size: int = 10,
-        context_window: int = 0,
-    ):
-        if context_window > 0:
-            raise NotImplementedError(
-                "Transformer-XL doesn't need --context-window, try "
-                "--model-overrides '{\"mem_len\":42}' instead "
-            )
-        return self.get_batch_iterator(
-            dataset=dataset,
-            max_tokens=max_tokens,
-            max_sentences=batch_size,
-            max_positions=max_positions,
-            ignore_invalid_inputs=True,
-            num_shards=num_shards,
-            shard_id=shard_id,
-            num_workers=num_workers,
-            data_buffer_size=data_buffer_size,
-        ).next_epoch_itr(shuffle=False)
-
-    @property
-    def source_dictionary(self):
-        return self.dictionary
-
-    @property
-    def target_dictionary(self):
-        return self.dictionary
-
-
-class TruncatedBPTTDataset(torch.utils.data.Dataset):
-    def __init__(
-        self,
-        data: List[torch.Tensor],  # ordered list of items
-        bsz_per_shard,  # number of items processed per GPUs per forward
-        shard_id,  # current GPU ID
-        num_shards,  # number of GPUs
-    ):
-        super().__init__()
-        self.data = data
-
-        def batchify(data, bsz):
-            # Work out how cleanly we can divide the dataset into bsz parts.
-            nbatch = data.size(0) // bsz
-            # Trim off any extra elements that wouldn't cleanly fit (remainders).
-            data = data.narrow(0, 0, nbatch * bsz)
-            # Evenly divide the data across the bsz batches.
-            data = data.view(bsz, -1).contiguous()
-            return data
-
-        # total number of sequences processed by all GPUs in each forward pass
-        global_batch_size = bsz_per_shard * num_shards
-
-        """
-        With a 16 item dataset, bsz_per_shard=2 and num_shards=3,
-        *indices* might look like:
-
-            indices = [[0, 1],
-                       [2, 3],
-                       [4, 5],
-                       [6, 7],
-                       [8, 9],
-                       [10, 11]]
-
-        The size of the TruncatedBPTTDataset instance will be 2,
-        and shard 1 will see items:
-
-            [(0, [data[4], data[6]]),
-             (1, [data[5], data[7]])]
-        """
-        indices = batchify(torch.arange(len(data)), global_batch_size)
-        assert indices.size(0) == global_batch_size
-
-        self.my_indices = indices[
-            shard_id * bsz_per_shard : (shard_id + 1) * bsz_per_shard
-        ]
-        assert self.my_indices.size(0) == bsz_per_shard
-
-    def __len__(self):
-        return self.my_indices.size(1)
-
-    def __getitem__(self, i) -> Tuple[int, List[torch.Tensor]]:
-        return (i, [self.data[idx] for idx in self.my_indices[:, i]])
diff --git a/spaces/gradio/diff_texts/README.md b/spaces/gradio/diff_texts/README.md
deleted file mode 100644
index 7448de3efc10360d4881ee7b2cdbb4ed9269d1b7..0000000000000000000000000000000000000000
--- a/spaces/gradio/diff_texts/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
----
-title: diff_texts 
-emoji: 🔥
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.1.2
-app_file: run.py
-pinned: false
-hf_oauth: true
----
diff --git a/spaces/gradio/longformer/longformer/longformer.py b/spaces/gradio/longformer/longformer/longformer.py
deleted file mode 100644
index 14da60f5ef0adc78de3676a90fc49d47ba5ce66c..0000000000000000000000000000000000000000
--- a/spaces/gradio/longformer/longformer/longformer.py
+++ /dev/null
@@ -1,269 +0,0 @@
-from typing import List
-import math
-import torch
-from torch import nn
-import torch.nn.functional as F
-from longformer.diagonaled_mm_tvm import diagonaled_mm as diagonaled_mm_tvm, mask_invalid_locations
-from longformer.sliding_chunks import sliding_chunks_matmul_qk, sliding_chunks_matmul_pv
-from longformer.sliding_chunks import sliding_chunks_no_overlap_matmul_qk, sliding_chunks_no_overlap_matmul_pv
-from transformers.modeling_roberta import RobertaConfig, RobertaModel, RobertaForMaskedLM
-
-
-class Longformer(RobertaModel):
-    def __init__(self, config):
-        super(Longformer, self).__init__(config)
-        if config.attention_mode == 'n2':
-            pass  # do nothing, use BertSelfAttention instead
-        else:
-            for i, layer in enumerate(self.encoder.layer):
-                layer.attention.self = LongformerSelfAttention(config, layer_id=i)
-
-
-class LongformerForMaskedLM(RobertaForMaskedLM):
-    def __init__(self, config):
-        super(LongformerForMaskedLM, self).__init__(config)
-        if config.attention_mode == 'n2':
-            pass  # do nothing, use BertSelfAttention instead
-        else:
-            for i, layer in enumerate(self.roberta.encoder.layer):
-                layer.attention.self = LongformerSelfAttention(config, layer_id=i)
-
-
-class LongformerConfig(RobertaConfig):
-    def __init__(self, attention_window: List[int] = None, attention_dilation: List[int] = None,
-                 autoregressive: bool = False, attention_mode: str = 'sliding_chunks', **kwargs):
-        """
-        Args:
-            attention_window: list of attention window sizes of length = number of layers.
-                window size = number of attention locations on each side.
-                For an affective window size of 512, use `attention_window=[256]*num_layers`
-                which is 256 on each side.
-            attention_dilation: list of attention dilation of length = number of layers.
-                attention dilation of `1` means no dilation.
-            autoregressive: do autoregressive attention or have attention of both sides
-            attention_mode: 'n2' for regular n^2 self-attention, 'tvm' for TVM implemenation of Longformer
-                selfattention, 'sliding_chunks' for another implementation of Longformer selfattention
-        """
-        super().__init__(**kwargs)
-        self.attention_window = attention_window
-        self.attention_dilation = attention_dilation
-        self.autoregressive = autoregressive
-        self.attention_mode = attention_mode
-        assert self.attention_mode in ['tvm', 'sliding_chunks', 'n2', 'sliding_chunks_no_overlap']
-
-
-class LongformerSelfAttention(nn.Module):
-    def __init__(self, config, layer_id):
-        super(LongformerSelfAttention, self).__init__()
-        if config.hidden_size % config.num_attention_heads != 0:
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads))
-        self.num_heads = config.num_attention_heads
-        self.head_dim = int(config.hidden_size / config.num_attention_heads)
-        self.embed_dim = config.hidden_size
-
-        self.query = nn.Linear(config.hidden_size, self.embed_dim)
-        self.key = nn.Linear(config.hidden_size, self.embed_dim)
-        self.value = nn.Linear(config.hidden_size, self.embed_dim)
-
-        self.query_global = nn.Linear(config.hidden_size, self.embed_dim)
-        self.key_global = nn.Linear(config.hidden_size, self.embed_dim)
-        self.value_global = nn.Linear(config.hidden_size, self.embed_dim)
-
-        self.dropout = config.attention_probs_dropout_prob
-
-        self.layer_id = layer_id
-        self.attention_window = config.attention_window[self.layer_id]
-        self.attention_dilation = config.attention_dilation[self.layer_id]
-        self.attention_mode = config.attention_mode
-        self.autoregressive = config.autoregressive
-        assert self.attention_window > 0
-        assert self.attention_dilation > 0
-        assert self.attention_mode in ['tvm', 'sliding_chunks', 'sliding_chunks_no_overlap']
-        if self.attention_mode in ['sliding_chunks', 'sliding_chunks_no_overlap']:
-            assert not self.autoregressive  # not supported
-            assert self.attention_dilation == 1  # dilation is not supported
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        output_attentions=False,
-    ):
-        '''
-        The `attention_mask` is changed in `BertModel.forward` from 0, 1, 2 to
-            -ve: no attention
-              0: local attention
-            +ve: global attention
-        '''
-        assert encoder_hidden_states is None, "`encoder_hidden_states` is not supported and should be None"
-        assert encoder_attention_mask is None, "`encoder_attention_mask` is not supported and shiould be None"
-
-        if attention_mask is not None:
-            attention_mask = attention_mask.squeeze(dim=2).squeeze(dim=1)
-            key_padding_mask = attention_mask < 0
-            extra_attention_mask = attention_mask > 0
-            remove_from_windowed_attention_mask = attention_mask != 0
-
-            num_extra_indices_per_batch = extra_attention_mask.long().sum(dim=1)
-            max_num_extra_indices_per_batch = num_extra_indices_per_batch.max()
-            if max_num_extra_indices_per_batch <= 0:
-                extra_attention_mask = None
-            else:
-                # To support the case of variable number of global attention in the rows of a batch,
-                # we use the following three selection masks to select global attention embeddings
-                # in a 3d tensor and pad it to `max_num_extra_indices_per_batch`
-                # 1) selecting embeddings that correspond to global attention
-                extra_attention_mask_nonzeros = extra_attention_mask.nonzero(as_tuple=True)
-                zero_to_max_range = torch.arange(0, max_num_extra_indices_per_batch,
-                                                 device=num_extra_indices_per_batch.device)
-                # mask indicating which values are actually going to be padding
-                selection_padding_mask = zero_to_max_range < num_extra_indices_per_batch.unsqueeze(dim=-1)
-                # 2) location of the non-padding values in the selected global attention
-                selection_padding_mask_nonzeros = selection_padding_mask.nonzero(as_tuple=True)
-                # 3) location of the padding values in the selected global attention
-                selection_padding_mask_zeros = (selection_padding_mask == 0).nonzero(as_tuple=True)
-        else:
-            remove_from_windowed_attention_mask = None
-            extra_attention_mask = None
-            key_padding_mask = None
-
-        hidden_states = hidden_states.transpose(0, 1)
-        seq_len, bsz, embed_dim = hidden_states.size()
-        assert embed_dim == self.embed_dim
-        q = self.query(hidden_states)
-        k = self.key(hidden_states)
-        v = self.value(hidden_states)
-        q /= math.sqrt(self.head_dim)
-
-        q = q.view(seq_len, bsz, self.num_heads, self.head_dim).transpose(0, 1)
-        k = k.view(seq_len, bsz, self.num_heads, self.head_dim).transpose(0, 1)
-        # attn_weights = (bsz, seq_len, num_heads, window*2+1)
-        if self.attention_mode == 'tvm':
-            q = q.float().contiguous()
-            k = k.float().contiguous()
-            attn_weights = diagonaled_mm_tvm(q, k, self.attention_window, self.attention_dilation, False, 0, False)
-        elif self.attention_mode == "sliding_chunks":
-            attn_weights = sliding_chunks_matmul_qk(q, k, self.attention_window, padding_value=0)
-        elif self.attention_mode == "sliding_chunks_no_overlap":
-            attn_weights = sliding_chunks_no_overlap_matmul_qk(q, k, self.attention_window, padding_value=0)
-        else:
-            raise False
-        mask_invalid_locations(attn_weights, self.attention_window, self.attention_dilation, False)
-        if remove_from_windowed_attention_mask is not None:
-            # This implementation is fast and takes very little memory because num_heads x hidden_size = 1
-            # from (bsz x seq_len) to (bsz x seq_len x num_heads x hidden_size)
-            remove_from_windowed_attention_mask = remove_from_windowed_attention_mask.unsqueeze(dim=-1).unsqueeze(dim=-1)
-            # cast to float/half then replace 1's with -inf
-            float_mask = remove_from_windowed_attention_mask.type_as(q).masked_fill(remove_from_windowed_attention_mask, -10000.0)
-            repeat_size = 1 if isinstance(self.attention_dilation, int) else len(self.attention_dilation)
-            float_mask = float_mask.repeat(1, 1, repeat_size, 1)
-            ones = float_mask.new_ones(size=float_mask.size())  # tensor of ones
-            # diagonal mask with zeros everywhere and -inf inplace of padding
-            if self.attention_mode == 'tvm':
-                d_mask = diagonaled_mm_tvm(ones, float_mask, self.attention_window, self.attention_dilation, False, 0, False)
-            elif self.attention_mode == "sliding_chunks":
-                d_mask = sliding_chunks_matmul_qk(ones, float_mask, self.attention_window, padding_value=0)
-            elif self.attention_mode == "sliding_chunks_no_overlap":
-                d_mask = sliding_chunks_no_overlap_matmul_qk(ones, float_mask, self.attention_window, padding_value=0)
-
-            attn_weights += d_mask
-        assert list(attn_weights.size())[:3] == [bsz, seq_len, self.num_heads]
-        assert attn_weights.size(dim=3) in [self.attention_window * 2 + 1, self.attention_window * 3]
-
-        # the extra attention
-        if extra_attention_mask is not None:
-            selected_k = k.new_zeros(bsz, max_num_extra_indices_per_batch, self.num_heads, self.head_dim)
-            selected_k[selection_padding_mask_nonzeros] = k[extra_attention_mask_nonzeros]
-            # (bsz, seq_len, num_heads, max_num_extra_indices_per_batch)
-            selected_attn_weights = torch.einsum('blhd,bshd->blhs', (q, selected_k))
-            selected_attn_weights[selection_padding_mask_zeros[0], :, :, selection_padding_mask_zeros[1]] = -10000
-            # concat to attn_weights
-            # (bsz, seq_len, num_heads, extra attention count + 2*window+1)
-            attn_weights = torch.cat((selected_attn_weights, attn_weights), dim=-1)
-        attn_weights_float = F.softmax(attn_weights, dim=-1, dtype=torch.float32)  # use fp32 for numerical stability
-        if key_padding_mask is not None:
-            # softmax sometimes inserts NaN if all positions are masked, replace them with 0
-            attn_weights_float = torch.masked_fill(attn_weights_float, key_padding_mask.unsqueeze(-1).unsqueeze(-1), 0.0)
-        attn_weights = attn_weights_float.type_as(attn_weights)
-        attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
-        v = v.view(seq_len, bsz, self.num_heads, self.head_dim).transpose(0, 1)
-        attn = 0
-        if extra_attention_mask is not None:
-            selected_attn_probs = attn_probs.narrow(-1, 0, max_num_extra_indices_per_batch)
-            selected_v = v.new_zeros(bsz, max_num_extra_indices_per_batch, self.num_heads, self.head_dim)
-            selected_v[selection_padding_mask_nonzeros] = v[extra_attention_mask_nonzeros]
-            # use `matmul` because `einsum` crashes sometimes with fp16
-            # attn = torch.einsum('blhs,bshd->blhd', (selected_attn_probs, selected_v))
-            attn = torch.matmul(selected_attn_probs.transpose(1, 2), selected_v.transpose(1, 2).type_as(selected_attn_probs)).transpose(1, 2)
-            attn_probs = attn_probs.narrow(-1, max_num_extra_indices_per_batch, attn_probs.size(-1) - max_num_extra_indices_per_batch).contiguous()
-
-        if self.attention_mode == 'tvm':
-            v = v.float().contiguous()
-            attn += diagonaled_mm_tvm(attn_probs, v, self.attention_window, self.attention_dilation, True, 0, False)
-        elif self.attention_mode == "sliding_chunks":
-            attn += sliding_chunks_matmul_pv(attn_probs, v, self.attention_window)
-        elif self.attention_mode == "sliding_chunks_no_overlap":
-            attn += sliding_chunks_no_overlap_matmul_pv(attn_probs, v, self.attention_window)
-        else:
-            raise False
-
-        attn = attn.type_as(hidden_states)
-        assert list(attn.size()) == [bsz, seq_len, self.num_heads, self.head_dim]
-        attn = attn.transpose(0, 1).reshape(seq_len, bsz, embed_dim).contiguous()
-
-        # For this case, we'll just recompute the attention for these indices
-        # and overwrite the attn tensor. TODO: remove the redundant computation
-        if extra_attention_mask is not None:
-            selected_hidden_states = hidden_states.new_zeros(max_num_extra_indices_per_batch, bsz, embed_dim)
-            selected_hidden_states[selection_padding_mask_nonzeros[::-1]] = hidden_states[extra_attention_mask_nonzeros[::-1]]
-
-            q = self.query_global(selected_hidden_states)
-            k = self.key_global(hidden_states)
-            v = self.value_global(hidden_states)
-            q /= math.sqrt(self.head_dim)
-
-            q = q.contiguous().view(max_num_extra_indices_per_batch, bsz * self.num_heads, self.head_dim).transpose(0, 1)  # (bsz*self.num_heads, max_num_extra_indices_per_batch, head_dim)
-            k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)  # bsz * self.num_heads, seq_len, head_dim)
-            v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)  # bsz * self.num_heads, seq_len, head_dim)
-            attn_weights = torch.bmm(q, k.transpose(1, 2))
-            assert list(attn_weights.size()) == [bsz * self.num_heads, max_num_extra_indices_per_batch, seq_len]
-
-            attn_weights = attn_weights.view(bsz, self.num_heads, max_num_extra_indices_per_batch, seq_len)
-            attn_weights[selection_padding_mask_zeros[0], :, selection_padding_mask_zeros[1], :] = -10000.0
-            if key_padding_mask is not None:
-                attn_weights = attn_weights.masked_fill(
-                    key_padding_mask.unsqueeze(1).unsqueeze(2),
-                    -10000.0,
-                )
-            attn_weights = attn_weights.view(bsz * self.num_heads, max_num_extra_indices_per_batch, seq_len)
-            attn_weights_float = F.softmax(attn_weights, dim=-1, dtype=torch.float32)  # use fp32 for numerical stability
-            attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
-            selected_attn = torch.bmm(attn_probs, v)
-            assert list(selected_attn.size()) == [bsz * self.num_heads, max_num_extra_indices_per_batch, self.head_dim]
-
-            selected_attn_4d = selected_attn.view(bsz, self.num_heads, max_num_extra_indices_per_batch, self.head_dim)
-            nonzero_selected_attn = selected_attn_4d[selection_padding_mask_nonzeros[0], :, selection_padding_mask_nonzeros[1]]
-            attn[extra_attention_mask_nonzeros[::-1]] = nonzero_selected_attn.view(len(selection_padding_mask_nonzeros[0]), -1).type_as(hidden_states)
-
-        context_layer = attn.transpose(0, 1)
-        if output_attentions:
-            if extra_attention_mask is not None:
-                # With global attention, return global attention probabilities only
-                # batch_size x num_heads x max_num_global_attention_tokens x sequence_length
-                # which is the attention weights from tokens with global attention to all tokens
-                # It doesn't not return local attention
-                # In case of variable number of global attantion in the rows of a batch,
-                # attn_weights are padded with -10000.0 attention scores
-                attn_weights = attn_weights.view(bsz, self.num_heads, max_num_extra_indices_per_batch, seq_len)
-            else:
-                # without global attention, return local attention probabilities
-                # batch_size x num_heads x sequence_length x window_size
-                # which is the attention weights of every token attending to its neighbours
-                attn_weights = attn_weights.permute(0, 2, 1, 3)
-        outputs = (context_layer, attn_weights) if output_attentions else (context_layer,)
-        return outputs
diff --git a/spaces/gwang-kim/DATID-3D/eg3d/training/augment.py b/spaces/gwang-kim/DATID-3D/eg3d/training/augment.py
deleted file mode 100644
index 7b00a4ade50459c16e34fa4c132b2cb947cfff28..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/eg3d/training/augment.py
+++ /dev/null
@@ -1,441 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
-# property and proprietary rights in and to this material, related
-# documentation and any modifications thereto. Any use, reproduction,
-# disclosure or distribution of this material and related documentation
-# without an express license agreement from NVIDIA CORPORATION or
-# its affiliates is strictly prohibited.
-
-"""Augmentation pipeline from the paper
-"Training Generative Adversarial Networks with Limited Data".
-Matches the original implementation by Karras et al. at
-https://github.com/NVlabs/stylegan2-ada/blob/main/training/augment.py"""
-
-import numpy as np
-import scipy.signal
-import torch
-from torch_utils import persistence
-from torch_utils import misc
-from torch_utils.ops import upfirdn2d
-from torch_utils.ops import grid_sample_gradfix
-from torch_utils.ops import conv2d_gradfix
-
-#----------------------------------------------------------------------------
-# Coefficients of various wavelet decomposition low-pass filters.
-
-wavelets = {
-    'haar': [0.7071067811865476, 0.7071067811865476],
-    'db1':  [0.7071067811865476, 0.7071067811865476],
-    'db2':  [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025],
-    'db3':  [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569],
-    'db4':  [-0.010597401784997278, 0.032883011666982945, 0.030841381835986965, -0.18703481171888114, -0.02798376941698385, 0.6308807679295904, 0.7148465705525415, 0.23037781330885523],
-    'db5':  [0.003335725285001549, -0.012580751999015526, -0.006241490213011705, 0.07757149384006515, -0.03224486958502952, -0.24229488706619015, 0.13842814590110342, 0.7243085284385744, 0.6038292697974729, 0.160102397974125],
-    'db6':  [-0.00107730108499558, 0.004777257511010651, 0.0005538422009938016, -0.031582039318031156, 0.02752286553001629, 0.09750160558707936, -0.12976686756709563, -0.22626469396516913, 0.3152503517092432, 0.7511339080215775, 0.4946238903983854, 0.11154074335008017],
-    'db7':  [0.0003537138000010399, -0.0018016407039998328, 0.00042957797300470274, 0.012550998556013784, -0.01657454163101562, -0.03802993693503463, 0.0806126091510659, 0.07130921926705004, -0.22403618499416572, -0.14390600392910627, 0.4697822874053586, 0.7291320908465551, 0.39653931948230575, 0.07785205408506236],
-    'db8':  [-0.00011747678400228192, 0.0006754494059985568, -0.0003917403729959771, -0.00487035299301066, 0.008746094047015655, 0.013981027917015516, -0.04408825393106472, -0.01736930100202211, 0.128747426620186, 0.00047248457399797254, -0.2840155429624281, -0.015829105256023893, 0.5853546836548691, 0.6756307362980128, 0.3128715909144659, 0.05441584224308161],
-    'sym2': [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025],
-    'sym3': [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569],
-    'sym4': [-0.07576571478927333, -0.02963552764599851, 0.49761866763201545, 0.8037387518059161, 0.29785779560527736, -0.09921954357684722, -0.012603967262037833, 0.0322231006040427],
-    'sym5': [0.027333068345077982, 0.029519490925774643, -0.039134249302383094, 0.1993975339773936, 0.7234076904024206, 0.6339789634582119, 0.01660210576452232, -0.17532808990845047, -0.021101834024758855, 0.019538882735286728],
-    'sym6': [0.015404109327027373, 0.0034907120842174702, -0.11799011114819057, -0.048311742585633, 0.4910559419267466, 0.787641141030194, 0.3379294217276218, -0.07263752278646252, -0.021060292512300564, 0.04472490177066578, 0.0017677118642428036, -0.007800708325034148],
-    'sym7': [0.002681814568257878, -0.0010473848886829163, -0.01263630340325193, 0.03051551316596357, 0.0678926935013727, -0.049552834937127255, 0.017441255086855827, 0.5361019170917628, 0.767764317003164, 0.2886296317515146, -0.14004724044296152, -0.10780823770381774, 0.004010244871533663, 0.010268176708511255],
-    'sym8': [-0.0033824159510061256, -0.0005421323317911481, 0.03169508781149298, 0.007607487324917605, -0.1432942383508097, -0.061273359067658524, 0.4813596512583722, 0.7771857517005235, 0.3644418948353314, -0.05194583810770904, -0.027219029917056003, 0.049137179673607506, 0.003808752013890615, -0.01495225833704823, -0.0003029205147213668, 0.0018899503327594609],
-}
-
-#----------------------------------------------------------------------------
-# Helpers for constructing transformation matrices.
-
-def matrix(*rows, device=None):
-    assert all(len(row) == len(rows[0]) for row in rows)
-    elems = [x for row in rows for x in row]
-    ref = [x for x in elems if isinstance(x, torch.Tensor)]
-    if len(ref) == 0:
-        return misc.constant(np.asarray(rows), device=device)
-    assert device is None or device == ref[0].device
-    elems = [x if isinstance(x, torch.Tensor) else misc.constant(x, shape=ref[0].shape, device=ref[0].device) for x in elems]
-    return torch.stack(elems, dim=-1).reshape(ref[0].shape + (len(rows), -1))
-
-def translate2d(tx, ty, **kwargs):
-    return matrix(
-        [1, 0, tx],
-        [0, 1, ty],
-        [0, 0, 1],
-        **kwargs)
-
-def translate3d(tx, ty, tz, **kwargs):
-    return matrix(
-        [1, 0, 0, tx],
-        [0, 1, 0, ty],
-        [0, 0, 1, tz],
-        [0, 0, 0, 1],
-        **kwargs)
-
-def scale2d(sx, sy, **kwargs):
-    return matrix(
-        [sx, 0,  0],
-        [0,  sy, 0],
-        [0,  0,  1],
-        **kwargs)
-
-def scale3d(sx, sy, sz, **kwargs):
-    return matrix(
-        [sx, 0,  0,  0],
-        [0,  sy, 0,  0],
-        [0,  0,  sz, 0],
-        [0,  0,  0,  1],
-        **kwargs)
-
-def rotate2d(theta, **kwargs):
-    return matrix(
-        [torch.cos(theta), torch.sin(-theta), 0],
-        [torch.sin(theta), torch.cos(theta),  0],
-        [0,                0,                 1],
-        **kwargs)
-
-def rotate3d(v, theta, **kwargs):
-    vx = v[..., 0]; vy = v[..., 1]; vz = v[..., 2]
-    s = torch.sin(theta); c = torch.cos(theta); cc = 1 - c
-    return matrix(
-        [vx*vx*cc+c,    vx*vy*cc-vz*s, vx*vz*cc+vy*s, 0],
-        [vy*vx*cc+vz*s, vy*vy*cc+c,    vy*vz*cc-vx*s, 0],
-        [vz*vx*cc-vy*s, vz*vy*cc+vx*s, vz*vz*cc+c,    0],
-        [0,             0,             0,             1],
-        **kwargs)
-
-def translate2d_inv(tx, ty, **kwargs):
-    return translate2d(-tx, -ty, **kwargs)
-
-def scale2d_inv(sx, sy, **kwargs):
-    return scale2d(1 / sx, 1 / sy, **kwargs)
-
-def rotate2d_inv(theta, **kwargs):
-    return rotate2d(-theta, **kwargs)
-
-#----------------------------------------------------------------------------
-# Versatile image augmentation pipeline from the paper
-# "Training Generative Adversarial Networks with Limited Data".
-#
-# All augmentations are disabled by default; individual augmentations can
-# be enabled by setting their probability multipliers to 1.
-
-@persistence.persistent_class
-class AugmentPipe(torch.nn.Module):
-    def __init__(self,
-        xflip=0, rotate90=0, xint=0, xint_max=0.125,
-        scale=0, rotate=0, aniso=0, xfrac=0, scale_std=0.2, rotate_max=1, aniso_std=0.2, xfrac_std=0.125,
-        brightness=0, contrast=0, lumaflip=0, hue=0, saturation=0, brightness_std=0.2, contrast_std=0.5, hue_max=1, saturation_std=1,
-        imgfilter=0, imgfilter_bands=[1,1,1,1], imgfilter_std=1,
-        noise=0, cutout=0, noise_std=0.1, cutout_size=0.5,
-    ):
-        super().__init__()
-        self.register_buffer('p', torch.ones([]))       # Overall multiplier for augmentation probability.
-
-        # Pixel blitting.
-        self.xflip            = float(xflip)            # Probability multiplier for x-flip.
-        self.rotate90         = float(rotate90)         # Probability multiplier for 90 degree rotations.
-        self.xint             = float(xint)             # Probability multiplier for integer translation.
-        self.xint_max         = float(xint_max)         # Range of integer translation, relative to image dimensions.
-
-        # General geometric transformations.
-        self.scale            = float(scale)            # Probability multiplier for isotropic scaling.
-        self.rotate           = float(rotate)           # Probability multiplier for arbitrary rotation.
-        self.aniso            = float(aniso)            # Probability multiplier for anisotropic scaling.
-        self.xfrac            = float(xfrac)            # Probability multiplier for fractional translation.
-        self.scale_std        = float(scale_std)        # Log2 standard deviation of isotropic scaling.
-        self.rotate_max       = float(rotate_max)       # Range of arbitrary rotation, 1 = full circle.
-        self.aniso_std        = float(aniso_std)        # Log2 standard deviation of anisotropic scaling.
-        self.xfrac_std        = float(xfrac_std)        # Standard deviation of frational translation, relative to image dimensions.
-
-        # Color transformations.
-        self.brightness       = float(brightness)       # Probability multiplier for brightness.
-        self.contrast         = float(contrast)         # Probability multiplier for contrast.
-        self.lumaflip         = float(lumaflip)         # Probability multiplier for luma flip.
-        self.hue              = float(hue)              # Probability multiplier for hue rotation.
-        self.saturation       = float(saturation)       # Probability multiplier for saturation.
-        self.brightness_std   = float(brightness_std)   # Standard deviation of brightness.
-        self.contrast_std     = float(contrast_std)     # Log2 standard deviation of contrast.
-        self.hue_max          = float(hue_max)          # Range of hue rotation, 1 = full circle.
-        self.saturation_std   = float(saturation_std)   # Log2 standard deviation of saturation.
-
-        # Image-space filtering.
-        self.imgfilter        = float(imgfilter)        # Probability multiplier for image-space filtering.
-        self.imgfilter_bands  = list(imgfilter_bands)   # Probability multipliers for individual frequency bands.
-        self.imgfilter_std    = float(imgfilter_std)    # Log2 standard deviation of image-space filter amplification.
-
-        # Image-space corruptions.
-        self.noise            = float(noise)            # Probability multiplier for additive RGB noise.
-        self.cutout           = float(cutout)           # Probability multiplier for cutout.
-        self.noise_std        = float(noise_std)        # Standard deviation of additive RGB noise.
-        self.cutout_size      = float(cutout_size)      # Size of the cutout rectangle, relative to image dimensions.
-
-        # Setup orthogonal lowpass filter for geometric augmentations.
-        self.register_buffer('Hz_geom', upfirdn2d.setup_filter(wavelets['sym6']))
-
-        # Construct filter bank for image-space filtering.
-        Hz_lo = np.asarray(wavelets['sym2'])            # H(z)
-        Hz_hi = Hz_lo * ((-1) ** np.arange(Hz_lo.size)) # H(-z)
-        Hz_lo2 = np.convolve(Hz_lo, Hz_lo[::-1]) / 2    # H(z) * H(z^-1) / 2
-        Hz_hi2 = np.convolve(Hz_hi, Hz_hi[::-1]) / 2    # H(-z) * H(-z^-1) / 2
-        Hz_fbank = np.eye(4, 1)                         # Bandpass(H(z), b_i)
-        for i in range(1, Hz_fbank.shape[0]):
-            Hz_fbank = np.dstack([Hz_fbank, np.zeros_like(Hz_fbank)]).reshape(Hz_fbank.shape[0], -1)[:, :-1]
-            Hz_fbank = scipy.signal.convolve(Hz_fbank, [Hz_lo2])
-            Hz_fbank[i, (Hz_fbank.shape[1] - Hz_hi2.size) // 2 : (Hz_fbank.shape[1] + Hz_hi2.size) // 2] += Hz_hi2
-        self.register_buffer('Hz_fbank', torch.as_tensor(Hz_fbank, dtype=torch.float32))
-
-    def forward(self, images, debug_percentile=None):
-        assert isinstance(images, torch.Tensor) and images.ndim == 4
-        batch_size, num_channels, height, width = images.shape
-        device = images.device
-        if debug_percentile is not None:
-            debug_percentile = torch.as_tensor(debug_percentile, dtype=torch.float32, device=device)
-
-        # -------------------------------------
-        # Select parameters for pixel blitting.
-        # -------------------------------------
-
-        # Initialize inverse homogeneous 2D transform: G_inv @ pixel_out ==> pixel_in
-        I_3 = torch.eye(3, device=device)
-        G_inv = I_3
-
-        # Apply x-flip with probability (xflip * strength).
-        if self.xflip > 0:
-            i = torch.floor(torch.rand([batch_size], device=device) * 2)
-            i = torch.where(torch.rand([batch_size], device=device) < self.xflip * self.p, i, torch.zeros_like(i))
-            if debug_percentile is not None:
-                i = torch.full_like(i, torch.floor(debug_percentile * 2))
-            G_inv = G_inv @ scale2d_inv(1 - 2 * i, 1)
-
-        # Apply 90 degree rotations with probability (rotate90 * strength).
-        if self.rotate90 > 0:
-            i = torch.floor(torch.rand([batch_size], device=device) * 4)
-            i = torch.where(torch.rand([batch_size], device=device) < self.rotate90 * self.p, i, torch.zeros_like(i))
-            if debug_percentile is not None:
-                i = torch.full_like(i, torch.floor(debug_percentile * 4))
-            G_inv = G_inv @ rotate2d_inv(-np.pi / 2 * i)
-
-        # Apply integer translation with probability (xint * strength).
-        if self.xint > 0:
-            t = (torch.rand([batch_size, 2], device=device) * 2 - 1) * self.xint_max
-            t = torch.where(torch.rand([batch_size, 1], device=device) < self.xint * self.p, t, torch.zeros_like(t))
-            if debug_percentile is not None:
-                t = torch.full_like(t, (debug_percentile * 2 - 1) * self.xint_max)
-            G_inv = G_inv @ translate2d_inv(torch.round(t[:,0] * width), torch.round(t[:,1] * height))
-
-        # --------------------------------------------------------
-        # Select parameters for general geometric transformations.
-        # --------------------------------------------------------
-
-        # Apply isotropic scaling with probability (scale * strength).
-        if self.scale > 0:
-            s = torch.exp2(torch.randn([batch_size], device=device) * self.scale_std)
-            s = torch.where(torch.rand([batch_size], device=device) < self.scale * self.p, s, torch.ones_like(s))
-            if debug_percentile is not None:
-                s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.scale_std))
-            G_inv = G_inv @ scale2d_inv(s, s)
-
-        # Apply pre-rotation with probability p_rot.
-        p_rot = 1 - torch.sqrt((1 - self.rotate * self.p).clamp(0, 1)) # P(pre OR post) = p
-        if self.rotate > 0:
-            theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max
-            theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta))
-            if debug_percentile is not None:
-                theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.rotate_max)
-            G_inv = G_inv @ rotate2d_inv(-theta) # Before anisotropic scaling.
-
-        # Apply anisotropic scaling with probability (aniso * strength).
-        if self.aniso > 0:
-            s = torch.exp2(torch.randn([batch_size], device=device) * self.aniso_std)
-            s = torch.where(torch.rand([batch_size], device=device) < self.aniso * self.p, s, torch.ones_like(s))
-            if debug_percentile is not None:
-                s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.aniso_std))
-            G_inv = G_inv @ scale2d_inv(s, 1 / s)
-
-        # Apply post-rotation with probability p_rot.
-        if self.rotate > 0:
-            theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max
-            theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta))
-            if debug_percentile is not None:
-                theta = torch.zeros_like(theta)
-            G_inv = G_inv @ rotate2d_inv(-theta) # After anisotropic scaling.
-
-        # Apply fractional translation with probability (xfrac * strength).
-        if self.xfrac > 0:
-            t = torch.randn([batch_size, 2], device=device) * self.xfrac_std
-            t = torch.where(torch.rand([batch_size, 1], device=device) < self.xfrac * self.p, t, torch.zeros_like(t))
-            if debug_percentile is not None:
-                t = torch.full_like(t, torch.erfinv(debug_percentile * 2 - 1) * self.xfrac_std)
-            G_inv = G_inv @ translate2d_inv(t[:,0] * width, t[:,1] * height)
-
-        # ----------------------------------
-        # Execute geometric transformations.
-        # ----------------------------------
-
-        # Execute if the transform is not identity.
-        if G_inv is not I_3:
-
-            # Calculate padding.
-            cx = (width - 1) / 2
-            cy = (height - 1) / 2
-            cp = matrix([-cx, -cy, 1], [cx, -cy, 1], [cx, cy, 1], [-cx, cy, 1], device=device) # [idx, xyz]
-            cp = G_inv @ cp.t() # [batch, xyz, idx]
-            Hz_pad = self.Hz_geom.shape[0] // 4
-            margin = cp[:, :2, :].permute(1, 0, 2).flatten(1) # [xy, batch * idx]
-            margin = torch.cat([-margin, margin]).max(dim=1).values # [x0, y0, x1, y1]
-            margin = margin + misc.constant([Hz_pad * 2 - cx, Hz_pad * 2 - cy] * 2, device=device)
-            margin = margin.max(misc.constant([0, 0] * 2, device=device))
-            margin = margin.min(misc.constant([width-1, height-1] * 2, device=device))
-            mx0, my0, mx1, my1 = margin.ceil().to(torch.int32)
-
-            # Pad image and adjust origin.
-            images = torch.nn.functional.pad(input=images, pad=[mx0,mx1,my0,my1], mode='reflect')
-            G_inv = translate2d((mx0 - mx1) / 2, (my0 - my1) / 2) @ G_inv
-
-            # Upsample.
-            images = upfirdn2d.upsample2d(x=images, f=self.Hz_geom, up=2)
-            G_inv = scale2d(2, 2, device=device) @ G_inv @ scale2d_inv(2, 2, device=device)
-            G_inv = translate2d(-0.5, -0.5, device=device) @ G_inv @ translate2d_inv(-0.5, -0.5, device=device)
-
-            # Execute transformation.
-            shape = [batch_size, num_channels, (height + Hz_pad * 2) * 2, (width + Hz_pad * 2) * 2]
-            G_inv = scale2d(2 / images.shape[3], 2 / images.shape[2], device=device) @ G_inv @ scale2d_inv(2 / shape[3], 2 / shape[2], device=device)
-            grid = torch.nn.functional.affine_grid(theta=G_inv[:,:2,:], size=shape, align_corners=False)
-            images = grid_sample_gradfix.grid_sample(images, grid)
-
-            # Downsample and crop.
-            images = upfirdn2d.downsample2d(x=images, f=self.Hz_geom, down=2, padding=-Hz_pad*2, flip_filter=True)
-
-        # --------------------------------------------
-        # Select parameters for color transformations.
-        # --------------------------------------------
-
-        # Initialize homogeneous 3D transformation matrix: C @ color_in ==> color_out
-        I_4 = torch.eye(4, device=device)
-        C = I_4
-
-        # Apply brightness with probability (brightness * strength).
-        if self.brightness > 0:
-            b = torch.randn([batch_size], device=device) * self.brightness_std
-            b = torch.where(torch.rand([batch_size], device=device) < self.brightness * self.p, b, torch.zeros_like(b))
-            if debug_percentile is not None:
-                b = torch.full_like(b, torch.erfinv(debug_percentile * 2 - 1) * self.brightness_std)
-            C = translate3d(b, b, b) @ C
-
-        # Apply contrast with probability (contrast * strength).
-        if self.contrast > 0:
-            c = torch.exp2(torch.randn([batch_size], device=device) * self.contrast_std)
-            c = torch.where(torch.rand([batch_size], device=device) < self.contrast * self.p, c, torch.ones_like(c))
-            if debug_percentile is not None:
-                c = torch.full_like(c, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.contrast_std))
-            C = scale3d(c, c, c) @ C
-
-        # Apply luma flip with probability (lumaflip * strength).
-        v = misc.constant(np.asarray([1, 1, 1, 0]) / np.sqrt(3), device=device) # Luma axis.
-        if self.lumaflip > 0:
-            i = torch.floor(torch.rand([batch_size, 1, 1], device=device) * 2)
-            i = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.lumaflip * self.p, i, torch.zeros_like(i))
-            if debug_percentile is not None:
-                i = torch.full_like(i, torch.floor(debug_percentile * 2))
-            C = (I_4 - 2 * v.ger(v) * i) @ C # Householder reflection.
-
-        # Apply hue rotation with probability (hue * strength).
-        if self.hue > 0 and num_channels > 1:
-            theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.hue_max
-            theta = torch.where(torch.rand([batch_size], device=device) < self.hue * self.p, theta, torch.zeros_like(theta))
-            if debug_percentile is not None:
-                theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.hue_max)
-            C = rotate3d(v, theta) @ C # Rotate around v.
-
-        # Apply saturation with probability (saturation * strength).
-        if self.saturation > 0 and num_channels > 1:
-            s = torch.exp2(torch.randn([batch_size, 1, 1], device=device) * self.saturation_std)
-            s = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.saturation * self.p, s, torch.ones_like(s))
-            if debug_percentile is not None:
-                s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.saturation_std))
-            C = (v.ger(v) + (I_4 - v.ger(v)) * s) @ C
-
-        # ------------------------------
-        # Execute color transformations.
-        # ------------------------------
-
-        # Execute if the transform is not identity.
-        if C is not I_4:
-            images = images.reshape([batch_size, num_channels, height * width])
-            if num_channels == 3:
-                images = C[:, :3, :3] @ images + C[:, :3, 3:]
-            elif num_channels == 1:
-                C = C[:, :3, :].mean(dim=1, keepdims=True)
-                images = images * C[:, :, :3].sum(dim=2, keepdims=True) + C[:, :, 3:]
-            elif num_channels == 6:
-                images[:, :3] = C[:, :3, :3] @ images[:, :3] + C[:, :3, 3:]
-                images[:, 3:] = C[:, :3, :3] @ images[:, 3:] + C[:, :3, 3:]
-            else:
-                raise ValueError('Image must be RGB (3 channels) or L (1 channel)')
-            images = images.reshape([batch_size, num_channels, height, width])
-
-        # ----------------------
-        # Image-space filtering.
-        # ----------------------
-
-        if self.imgfilter > 0:
-            num_bands = self.Hz_fbank.shape[0]
-            assert len(self.imgfilter_bands) == num_bands
-            expected_power = misc.constant(np.array([10, 1, 1, 1]) / 13, device=device) # Expected power spectrum (1/f).
-
-            # Apply amplification for each band with probability (imgfilter * strength * band_strength).
-            g = torch.ones([batch_size, num_bands], device=device) # Global gain vector (identity).
-            for i, band_strength in enumerate(self.imgfilter_bands):
-                t_i = torch.exp2(torch.randn([batch_size], device=device) * self.imgfilter_std)
-                t_i = torch.where(torch.rand([batch_size], device=device) < self.imgfilter * self.p * band_strength, t_i, torch.ones_like(t_i))
-                if debug_percentile is not None:
-                    t_i = torch.full_like(t_i, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.imgfilter_std)) if band_strength > 0 else torch.ones_like(t_i)
-                t = torch.ones([batch_size, num_bands], device=device)                  # Temporary gain vector.
-                t[:, i] = t_i                                                           # Replace i'th element.
-                t = t / (expected_power * t.square()).sum(dim=-1, keepdims=True).sqrt() # Normalize power.
-                g = g * t                                                               # Accumulate into global gain.
-
-            # Construct combined amplification filter.
-            Hz_prime = g @ self.Hz_fbank                                    # [batch, tap]
-            Hz_prime = Hz_prime.unsqueeze(1).repeat([1, num_channels, 1])   # [batch, channels, tap]
-            Hz_prime = Hz_prime.reshape([batch_size * num_channels, 1, -1]) # [batch * channels, 1, tap]
-
-            # Apply filter.
-            p = self.Hz_fbank.shape[1] // 2
-            images = images.reshape([1, batch_size * num_channels, height, width])
-            images = torch.nn.functional.pad(input=images, pad=[p,p,p,p], mode='reflect')
-            images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(2), groups=batch_size*num_channels)
-            images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(3), groups=batch_size*num_channels)
-            images = images.reshape([batch_size, num_channels, height, width])
-
-        # ------------------------
-        # Image-space corruptions.
-        # ------------------------
-
-        # Apply additive RGB noise with probability (noise * strength).
-        if self.noise > 0:
-            sigma = torch.randn([batch_size, 1, 1, 1], device=device).abs() * self.noise_std
-            sigma = torch.where(torch.rand([batch_size, 1, 1, 1], device=device) < self.noise * self.p, sigma, torch.zeros_like(sigma))
-            if debug_percentile is not None:
-                sigma = torch.full_like(sigma, torch.erfinv(debug_percentile) * self.noise_std)
-            images = images + torch.randn([batch_size, num_channels, height, width], device=device) * sigma
-
-        # Apply cutout with probability (cutout * strength).
-        if self.cutout > 0:
-            size = torch.full([batch_size, 2, 1, 1, 1], self.cutout_size, device=device)
-            size = torch.where(torch.rand([batch_size, 1, 1, 1, 1], device=device) < self.cutout * self.p, size, torch.zeros_like(size))
-            center = torch.rand([batch_size, 2, 1, 1, 1], device=device)
-            if debug_percentile is not None:
-                size = torch.full_like(size, self.cutout_size)
-                center = torch.full_like(center, debug_percentile)
-            coord_x = torch.arange(width, device=device).reshape([1, 1, 1, -1])
-            coord_y = torch.arange(height, device=device).reshape([1, 1, -1, 1])
-            mask_x = (((coord_x + 0.5) / width - center[:, 0]).abs() >= size[:, 0] / 2)
-            mask_y = (((coord_y + 0.5) / height - center[:, 1]).abs() >= size[:, 1] / 2)
-            mask = torch.logical_or(mask_x, mask_y).to(torch.float32)
-            images = images * mask
-
-        return images
-
-#----------------------------------------------------------------------------
diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/criteria/__init__.py b/spaces/gyugnsu/DragGan-Inversion/PTI/criteria/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/gyugnsu/DragGan-Inversion/viz/capture_widget.py b/spaces/gyugnsu/DragGan-Inversion/viz/capture_widget.py
deleted file mode 100644
index 79cc4f80c5bba2cf1e67593e85fb85cd7963ed89..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/viz/capture_widget.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import re
-import numpy as np
-import imgui
-import PIL.Image
-from gui_utils import imgui_utils
-from . import renderer
-import torch
-import torchvision
-
-# ----------------------------------------------------------------------------
-
-
-class CaptureWidget:
-    def __init__(self, viz):
-        self.viz = viz
-        self.path = os.path.abspath(os.path.join(
-            os.path.dirname(__file__), '..', '_screenshots'))
-        self.dump_image = False
-        self.dump_gui = False
-        self.defer_frames = 0
-        self.disabled_time = 0
-
-    def dump_png(self, image):
-        viz = self.viz
-        try:
-            _height, _width, channels = image.shape
-            print(viz.result)
-            assert image.dtype == np.uint8
-            os.makedirs(self.path, exist_ok=True)
-            file_id = 0
-            for entry in os.scandir(self.path):
-                if entry.is_file():
-                    match = re.fullmatch(r'(\d+).*', entry.name)
-                    if match:
-                        file_id = max(file_id, int(match.group(1)) + 1)
-            if channels == 1:
-                pil_image = PIL.Image.fromarray(image[:, :, 0], 'L')
-            else:
-                pil_image = PIL.Image.fromarray(image[:, :, :3], 'RGB')
-            pil_image.save(os.path.join(self.path, f'{file_id:05d}.png'))
-            np.save(os.path.join(
-                self.path, f'{file_id:05d}.npy'), viz.result.w)
-        except:
-            viz.result.error = renderer.CapturedException()
-
-    @imgui_utils.scoped_by_object_id
-    def __call__(self, show=True):
-        viz = self.viz
-        if show:
-            with imgui_utils.grayed_out(self.disabled_time != 0):
-                imgui.text('Capture')
-                imgui.same_line(viz.label_w)
-
-                _changed, self.path = imgui_utils.input_text('##path', self.path, 1024,
-                                                             flags=(
-                                                                 imgui.INPUT_TEXT_AUTO_SELECT_ALL | imgui.INPUT_TEXT_ENTER_RETURNS_TRUE),
-                                                             width=(-1),
-                                                             help_text='PATH')
-                if imgui.is_item_hovered() and not imgui.is_item_active() and self.path != '':
-                    imgui.set_tooltip(self.path)
-                imgui.text(' ')
-                imgui.same_line(viz.label_w)
-                if imgui_utils.button('Save image', width=viz.button_w, enabled=(self.disabled_time == 0 and 'image' in viz.result)):
-                    self.dump_image = True
-                    self.defer_frames = 2
-                    self.disabled_time = 0.5
-                imgui.same_line()
-                if imgui_utils.button('Save GUI', width=viz.button_w, enabled=(self.disabled_time == 0)):
-                    self.dump_gui = True
-                    self.defer_frames = 2
-                    self.disabled_time = 0.5
-
-        self.disabled_time = max(self.disabled_time - viz.frame_delta, 0)
-        if self.defer_frames > 0:
-            self.defer_frames -= 1
-        elif self.dump_image:
-            if 'image' in viz.result:
-                self.dump_png(viz.result.image)
-            self.dump_image = False
-        elif self.dump_gui:
-            viz.capture_next_frame()
-            self.dump_gui = False
-        captured_frame = viz.pop_captured_frame()
-        if captured_frame is not None:
-            self.dump_png(captured_frame)
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/h2oai/wave-tour/examples/graphics_hilbert.py b/spaces/h2oai/wave-tour/examples/graphics_hilbert.py
deleted file mode 100644
index bc8d907e744698012bc82c96dbde34dbf7dffa4c..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/graphics_hilbert.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# Graphics / Hilbert
-# Use turtle #graphics recursively to draw Hilbert curves.
-# ---
-from h2o_wave import ui, main, app, Q, graphics as g
-
-
-def hilbert(t: g.Turtle, width: float, depth: int, reverse=False):  # recursive
-    angle = -90 if reverse else 90
-
-    if depth == 0:
-        t.f(width).r(angle).f(width).r(angle).f(width)
-        return
-
-    side = width * ((2 ** depth) - 1) / float((2 ** (depth + 1)) - 1)
-    edge = width - 2 * side
-
-    t.r(angle)
-    hilbert(t, side, depth - 1, not reverse)
-    t.r(angle).f(edge)
-    hilbert(t, side, depth - 1, reverse)
-    t.l(angle).f(edge).l(angle)
-    hilbert(t, side, depth - 1, reverse)
-    t.f(edge).r(angle)
-    hilbert(t, side, depth - 1, not reverse)
-    t.r(angle)
-
-
-def make_hilbert_curve(width: float, depth: int):
-    t = g.turtle().f(0).pd()
-    hilbert(t, width, depth)
-    return t.d()
-
-
-@app('/demo')
-async def serve(q: Q):
-    hilbert_curve = make_hilbert_curve(300, q.args.depth or 5)
-
-    if not q.client.initialized:
-        q.page['curve'] = ui.graphics_card(
-            box='1 1 4 6', view_box='0 0 300 300', width='100%', height='100%',
-            scene=g.scene(
-                hilbert_curve=g.path(d=hilbert_curve, fill='none', stroke='#333')
-            ),
-        )
-        q.page['form'] = ui.form_card(
-            box='1 7 4 1', items=[
-                ui.slider(name='depth', label='Play with this Hilbert curve!', min=1, max=6, value=5, trigger=True),
-            ],
-        )
-        q.client.initialized = True
-    else:
-        g.draw(q.page['curve'].scene.hilbert_curve, d=hilbert_curve)
-
-    await q.page.save()
diff --git a/spaces/h2oai/wave-tour/examples/layout_size.py b/spaces/h2oai/wave-tour/examples/layout_size.py
deleted file mode 100644
index 75acd8e0c570091e45330eee50008631521f627f..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/layout_size.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Layout / Size
-# How to adjust the size of cards on a page. #layout
-# ---
-
-from h2o_wave import site, ui
-
-# Every page has a grid system in place.
-# The grid has 12 columns and 10 rows.
-# A column is 134 pixels wide.
-# A row is 76 pixels high.
-# The gap between rows and columns is set to 15 pixels.
-
-# Cards have a `box` attribute that specifies its column, row, width and height.
-# box = 'column row width height'
-# They indicate the 1-based column/row to position the top-left corner of the card.
-
-# In this example, we place multiple cards on a page to demonstrate their `box` values.
-
-page = site['/demo']
-boxes = [
-    '1 1 1 1',
-    '2 1 2 1',
-    '4 1 3 1',
-    '7 1 4 1',
-    '11 1 2 2',
-    '1 2 1 9',
-    '2 2 1 4',
-    '3 2 1 2',
-    '2 6 1 5',
-    '3 4 1 7',
-    '4 2 7 9',
-    '11 9 2 2',
-    '11 3 2 6',
-]
-
-for box in boxes:
-    page[f'card_{box.replace(" ", "_")}'] = ui.markdown_card(box=box, title=box, content='')
-
-page.save()
diff --git a/spaces/hamacojr/SAM-CAT-Seg/datasets/README.md b/spaces/hamacojr/SAM-CAT-Seg/datasets/README.md
deleted file mode 100644
index db2642a9b39eab0d02857ac2dafb15b4658e7cad..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/SAM-CAT-Seg/datasets/README.md
+++ /dev/null
@@ -1,167 +0,0 @@
-# Prepare Datasets for CAT-Seg
-
-A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog)
-for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
-This document explains how to setup the builtin datasets so they can be used by the above APIs.
-[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`,
-and how to add new datasets to them.
-
-CAT-Seg has builtin support for a few datasets.
-The datasets are assumed to exist in a directory specified by the environment variable
-`DETECTRON2_DATASETS`.
-Under this directory, detectron2 will look for datasets in the structure described below, if needed.
-```
-$DETECTRON2_DATASETS/
-  coco/                   # COCO-Stuff
-  ADEChallengeData2016/   # ADE20K-150
-  ADE20K_2021_17_01/      # ADE20K-847
-  VOCdevkit/ 
-    VOC2010/              # PASCAL Context
-    VOC2012/              # PASCAL VOC
-```
-
-You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`.
-If left unset, the default is `./datasets` relative to your current working directory.
-
-## Prepare data for [COCO-Stuff](https://github.com/nightrome/cocostuff):
-
-### Expected data structure
-
-```
-coco-stuff/
-  annotations/
-    train2017/
-    val2017/
-  images/
-    train2017/
-    val2017/
-  # below are generated by prepare_coco_stuff.py
-  annotations_detectron2/
-    train2017/
-    val2017/ 
-```
-Download the COCO (2017) images from https://cocodataset.org/
-
-```bash
-wget http://images.cocodataset.org/zips/train2017.zip
-wget http://images.cocodataset.org/zips/val2017.zip
-```
-
-Download the COCO-Stuff annotation from https://github.com/nightrome/cocostuff.
-```bash
-wget http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip
-```
-Unzip `train2017.zip`, `val2017.zip`, and `stuffthingmaps_trainval2017.zip`. Then put them to the correct location listed above.
-
-Generate the labels for training and testing.
-
-```
-python datasets/prepare_coco_stuff.py
-```
-
-
-
-## Prepare data for [ADE20K-150](http://sceneparsing.csail.mit.edu):
-
-### Expected data structure 
-```
-ADEChallengeData2016/
-  annotations/
-    validation/
-  images/
-    validation/
-  # below are generated by prepare_ade20k_150.py
-  annotations_detectron2/
-    validation/
-```
-Download the data of ADE20K-150 from http://sceneparsing.csail.mit.edu.
-```
-wget http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip
-```
-Unzip `ADEChallengeData2016.zip` and generate the labels for testing.
-```
-python datasets/prepare_ade20k_150.py
-```
-## Prepare data for [ADE20k-847](https://groups.csail.mit.edu/vision/datasets/ADE20K/):
-
-### Expected data structure 
-```
-ADE20K_2021_17_01/
-  images/
-    ADE/
-      validation/
-  index_ade20k.mat
-  index_ade20k.pkl
-  # below are generated by prepare_ade20k_847.py
-  annotations_detectron2/
-    validation/
-```
-Download the data of ADE20k-Full from https://groups.csail.mit.edu/vision/datasets/ADE20K/request_data/
-Unzip the dataset and generate the labels for testing.
-```
-python datasets/prepare_ade20k_847.py
-```
-
-## Prepare data for [PASCAL VOC 2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit):
-
-
-### Expected data structure 
-```
-VOCdevkit/
-  VOC2012/
-    Annotations/
-    ImageSets/
-    JPEGImages/
-    SegmentationClass/
-    SegmentationClassAug/ 
-    SegmentationObject/
-    # below are generated by prepare_voc.py
-    annotations_detectron2
-    annotations_detectron2_bg
-
-```
-Download the data of PASCAL VOC from http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit.
-
-We use SBD augmentated training data as SegmentationClassAug following [Deeplab](https://github.com/kazuto1011/deeplab-pytorch/blob/master/data/datasets/voc12/README.md).
-```
-wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
-wget https://www.dropbox.com/s/oeu149j8qtbs1x0/SegmentationClassAug.zip
-```
-Unzip `VOCtrainval_11-May-2012.tar` and `SegmentationClassAug.zip`. Then put them to the correct location listed above and generate the labels for testing.
-```
-python datasets/prepare_voc.py
-```
-
-
-## Prepare data for [PASCAL Context](https://www.cs.stanford.edu/~roozbeh/pascal-context/):
-
-
-### Expected data structure 
-```
-VOCdevkit/
-  VOC2010/
-    Annotations/
-    ImageSets/
-    JPEGImages/
-    SegmentationClass/
-    SegmentationObject/
-    trainval/
-    labels.txt
-    59_labels.txt
-    pascalcontext_val.txt
-    # below are generated by prepare_pascal_context.py
-    annotations_detectron2/
-      pc459_val
-      pc59_val
-```
-Download the data of PASCAL VOC 2010 from https://www.cs.stanford.edu/~roozbeh/pascal-context/. 
-
-```
-wget http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar
-wget https://www.cs.stanford.edu/~roozbeh/pascal-context/trainval.tar.gz
-wget https://www.cs.stanford.edu/~roozbeh/pascal-context/59_labels.txt
-```
-Unzip `VOCtrainval_03-May-2010.tar` and `trainval.tar.gz`. Then put them to the correct location listed above and generate the labels for testing.
-```
-python datasets/prepare_pascal_context.py
-```
\ No newline at end of file
diff --git a/spaces/hamelcubsfan/AutoGPT/Dockerfile b/spaces/hamelcubsfan/AutoGPT/Dockerfile
deleted file mode 100644
index 8396154998f32a50d55c199a674b638d5cf7bda2..0000000000000000000000000000000000000000
--- a/spaces/hamelcubsfan/AutoGPT/Dockerfile
+++ /dev/null
@@ -1,38 +0,0 @@
-# Use an official Python base image from the Docker Hub
-FROM python:3.10-slim
-
-# Install git
-RUN apt-get -y update
-RUN apt-get -y install git chromium-driver
-
-# Install Xvfb and other dependencies for headless browser testing
-RUN apt-get update \
-    && apt-get install -y wget gnupg2 libgtk-3-0 libdbus-glib-1-2 dbus-x11 xvfb ca-certificates
-
-# Install Firefox / Chromium
-RUN wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | apt-key add - \
-    && echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google-chrome.list \
-    && apt-get update \
-    && apt-get install -y chromium firefox-esr
-
-# Set environment variables
-ENV PIP_NO_CACHE_DIR=yes \
-    PYTHONUNBUFFERED=1 \
-    PYTHONDONTWRITEBYTECODE=1
-
-# Create a non-root user and set permissions
-RUN useradd --create-home appuser
-WORKDIR /home/appuser
-RUN chown appuser:appuser /home/appuser
-USER appuser
-
-# Copy the requirements.txt file and install the requirements
-COPY --chown=appuser:appuser requirements.txt .
-RUN sed -i '/Items below this point will not be included in the Docker Image/,$d' requirements.txt && \
-	pip install --no-cache-dir --user -r requirements.txt
-
-# Copy the application files
-COPY --chown=appuser:appuser autogpt/ ./autogpt
-
-# Set the entrypoint
-ENTRYPOINT ["python", "-m", "autogpt"]
diff --git a/spaces/hamelcubsfan/AutoGPT/autogpt/memory/weaviate.py b/spaces/hamelcubsfan/AutoGPT/autogpt/memory/weaviate.py
deleted file mode 100644
index 5408e9a97aa3594ad443448cfc31f2546a01eb09..0000000000000000000000000000000000000000
--- a/spaces/hamelcubsfan/AutoGPT/autogpt/memory/weaviate.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import uuid
-
-import weaviate
-from weaviate import Client
-from weaviate.embedded import EmbeddedOptions
-from weaviate.util import generate_uuid5
-
-from autogpt.config import Config
-from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
-
-
-def default_schema(weaviate_index):
-    return {
-        "class": weaviate_index,
-        "properties": [
-            {
-                "name": "raw_text",
-                "dataType": ["text"],
-                "description": "original text for the embedding",
-            }
-        ],
-    }
-
-
-class WeaviateMemory(MemoryProviderSingleton):
-    def __init__(self, cfg):
-        auth_credentials = self._build_auth_credentials(cfg)
-
-        url = f"{cfg.weaviate_protocol}://{cfg.weaviate_host}:{cfg.weaviate_port}"
-
-        if cfg.use_weaviate_embedded:
-            self.client = Client(
-                embedded_options=EmbeddedOptions(
-                    hostname=cfg.weaviate_host,
-                    port=int(cfg.weaviate_port),
-                    persistence_data_path=cfg.weaviate_embedded_path,
-                )
-            )
-
-            print(
-                f"Weaviate Embedded running on: {url} with persistence path: {cfg.weaviate_embedded_path}"
-            )
-        else:
-            self.client = Client(url, auth_client_secret=auth_credentials)
-
-        self.index = WeaviateMemory.format_classname(cfg.memory_index)
-        self._create_schema()
-
-    @staticmethod
-    def format_classname(index):
-        # weaviate uses capitalised index names
-        # The python client uses the following code to format
-        # index names before the corresponding class is created
-        if len(index) == 1:
-            return index.capitalize()
-        return index[0].capitalize() + index[1:]
-
-    def _create_schema(self):
-        schema = default_schema(self.index)
-        if not self.client.schema.contains(schema):
-            self.client.schema.create_class(schema)
-
-    def _build_auth_credentials(self, cfg):
-        if cfg.weaviate_username and cfg.weaviate_password:
-            return weaviate.AuthClientPassword(
-                cfg.weaviate_username, cfg.weaviate_password
-            )
-        if cfg.weaviate_api_key:
-            return weaviate.AuthApiKey(api_key=cfg.weaviate_api_key)
-        else:
-            return None
-
-    def add(self, data):
-        vector = get_ada_embedding(data)
-
-        doc_uuid = generate_uuid5(data, self.index)
-        data_object = {"raw_text": data}
-
-        with self.client.batch as batch:
-            batch.add_data_object(
-                uuid=doc_uuid,
-                data_object=data_object,
-                class_name=self.index,
-                vector=vector,
-            )
-
-        return f"Inserting data into memory at uuid: {doc_uuid}:\n data: {data}"
-
-    def get(self, data):
-        return self.get_relevant(data, 1)
-
-    def clear(self):
-        self.client.schema.delete_all()
-
-        # weaviate does not yet have a neat way to just remove the items in an index
-        # without removing the entire schema, therefore we need to re-create it
-        # after a call to delete_all
-        self._create_schema()
-
-        return "Obliterated"
-
-    def get_relevant(self, data, num_relevant=5):
-        query_embedding = get_ada_embedding(data)
-        try:
-            results = (
-                self.client.query.get(self.index, ["raw_text"])
-                .with_near_vector({"vector": query_embedding, "certainty": 0.7})
-                .with_limit(num_relevant)
-                .do()
-            )
-
-            if len(results["data"]["Get"][self.index]) > 0:
-                return [
-                    str(item["raw_text"]) for item in results["data"]["Get"][self.index]
-                ]
-            else:
-                return []
-
-        except Exception as err:
-            print(f"Unexpected error {err=}, {type(err)=}")
-            return []
-
-    def get_stats(self):
-        result = self.client.query.aggregate(self.index).with_meta_count().do()
-        class_data = result["data"]["Aggregate"][self.index]
-
-        return class_data[0]["meta"] if class_data else {}
diff --git a/spaces/hamzapehlivan/StyleRes/options/editing_options/template.py b/spaces/hamzapehlivan/StyleRes/options/editing_options/template.py
deleted file mode 100644
index 179eb7b7220e21cda71de69a500f852a7b399bce..0000000000000000000000000000000000000000
--- a/spaces/hamzapehlivan/StyleRes/options/editing_options/template.py
+++ /dev/null
@@ -1,10 +0,0 @@
-
-# This config saves inversion and smile edit results
-
-edit_configs = [
-    dict( method='inversion'),
-    dict( method='interfacegan', edit='smile', strength=2),
-    dict( method='ganspace', edit='overexposed',   strength=5),
-    dict( method='styleclip', type='mapper', edit='purple_hair', strength=0.08),
-    # dict( method='styleclip', type='global', edit='curly_hair', strength=3, disentanglement=0.29),
-]
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/build.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/build.py
deleted file mode 100644
index 8c0b96b0cabc250d622daff128a2776a819c0d5e..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/build.py
+++ /dev/null
@@ -1,489 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import bisect
-import copy
-import logging
-import os
-
-import torch.utils.data
-import torch.distributed as dist
-from maskrcnn_benchmark.utils.comm import get_world_size
-from maskrcnn_benchmark.utils.imports import import_file
-
-from . import datasets as D
-from . import samplers
-
-from .collate_batch import BatchCollator, BBoxAugCollator
-from .transforms import build_transforms
-
-from transformers import AutoTokenizer
-from .datasets.duplicate_dataset import create_duplicate_dataset
-
-def build_dataset(cfg, dataset_list, transforms, dataset_catalog, is_train=True, class_concat=False, extra_args={}):
-    """
-    Arguments:
-        dataset_list (list[str]): Contains the names of the datasets, i.e.,
-            coco_2014_trian, coco_2014_val, etc
-        transforms (callable): transforms to apply to each (image, target) sample
-        dataset_catalog (DatasetCatalog): contains the information on how to
-            construct a dataset.
-        is_train (bool): whether to setup the dataset for training or testing
-    """
-    if not isinstance(dataset_list, (list, tuple)):
-        raise RuntimeError(
-            "dataset_list should be a list of strings, got {}".format(dataset_list)
-        )
-    datasets = []
-    num_category = 1
-    for dataset_id, dataset_name in enumerate(dataset_list, 1):
-        if is_train:
-            dataset_name = dataset_name + cfg.DATASETS.TRAIN_DATASETNAME_SUFFIX
-        else:
-            dataset_name = dataset_name + cfg.DATASETS.TEST_DATASETNAME_SUFFIX
-        data = dataset_catalog.get(dataset_name)
-        factory = getattr(D, data["factory"])
-        args = data["args"]
-        # for COCODataset, we want to remove images without annotations
-        # during training
-        if data["factory"] == "COCODataset":
-            args["remove_images_without_annotations"] = is_train
-
-        if data["factory"] == "PascalVOCDataset":
-            args["use_difficult"] = not is_train
-        if data["factory"] in ["VGTSVDataset", "CocoDetectionTSV", "ODTSVDataset"]:
-            args["extra_fields"] = ["class"]
-            if cfg.MODEL.MASK_ON:
-                args["extra_fields"].append("mask")
-
-        if data["factory"] in ["CocoGrounding", "CocoDetectionTSV", "CaptionTSV", "MixedDataset", "FlickrDataset", "RefExpDataset", "GQADataset", "PseudoData", "PhrasecutDetection"]:
-            # args["return_masks"] = False
-            args["return_masks"] = cfg.MODEL.MASK_ON
-            args["return_tokens"] = True
-            args["max_num_labels"] = cfg.TEST.MDETR_STYLE_AGGREGATE_CLASS_NUM
-            args["max_query_len"] = cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN
-
-        args["transforms"] = transforms
-        args.update(extra_args)
-
-        if dataset_name == "flickr30k_train":
-            copy = cfg.DATASETS.FLICKR_COPY
-        elif dataset_name in ["mixed_train", "mixed_train_no_coco"]:
-            copy = cfg.DATASETS.MIXED_COPY
-        elif dataset_name == "COCO_odinw_train_8copy_dt_train":
-            copy = cfg.DATASETS.COCO_COPY
-        elif dataset_name == "LVIS_odinw_train_8copy_dt_train":
-            copy = cfg.DATASETS.LVIS_COPY
-        elif dataset_name == "object365_odinw_2copy_dt_train":
-            copy = cfg.DATASETS.OBJECT365_COPY
-        elif dataset_name == "vg_odinw_clipped_8copy_dt_train":
-            copy = cfg.DATASETS.VG_COPY
-        elif dataset_name == "vg_vgoi6_clipped_8copy_dt_train":
-            copy = cfg.DATASETS.VG_COPY
-        elif dataset_name == "imagenetod_train_odinw_2copy_dt":
-            copy = cfg.DATASETS.IN_COPY
-        elif dataset_name == "oi_train_odinw_dt":
-            copy = cfg.DATASETS.OI_COPY
-        elif is_train:
-            copy = cfg.DATASETS.GENERAL_COPY
-        elif not is_train:
-            copy = cfg.DATASETS.GENERAL_COPY_TEST
-        else:
-            copy = -1 # do not ever copy test
-        
-        if copy != -1:
-            new_factory = create_duplicate_dataset(factory)
-            dataset = new_factory(copy=copy, **args)
-        else:
-            # make dataset from factory
-            dataset = factory(**args)
-
-        print(dataset_name, 'has the {} data points'.format(len(dataset)), data["factory"])
-
-        if class_concat:
-            category = list(dataset.contiguous_category_id_to_json_id.values())
-            dataset.contiguous_category_id_to_json_id = {}
-            dataset.json_category_id_to_contiguous_id = {}
-            for id, cat in enumerate(category, start=num_category):
-                dataset.json_category_id_to_contiguous_id[cat] = id
-                dataset.contiguous_category_id_to_json_id[id] = cat
-            num_category += len(category)
-            print("Found {} #category after group {}, concating ...".format(num_category, dataset_id))
-        datasets.append(dataset)
-
-    # for testing, return a list of datasets
-    if not is_train:
-        return datasets
-
-    # for training, concatenate all datasets into a single one
-    dataset = datasets[0]
-    if len(datasets) > 1:
-        dataset = D.ConcatDataset(datasets)
-
-    return [dataset]
-
-
-def build_dataset_by_group(dataset_list, transforms, dataset_catalog, is_train=True, class_by_group=True,
-                           class_concat=False, extra_args={}):
-    """
-    Arguments:
-        dataset_list (list[str]): Contains the names of the datasets, i.e.,
-            coco_2014_trian, coco_2014_val, etc
-        transforms (callable): transforms to apply to each (image, target) sample
-        dataset_catalog (DatasetCatalog): contains the information on how to
-            construct a dataset.
-        is_train (bool): whether to setup the dataset for training or testing
-    """
-    if not isinstance(dataset_list, (list, tuple)):
-        raise RuntimeError(
-            "dataset_list should be a list of strings, got {}".format(dataset_list)
-        )
-
-    num_category = 1
-    grouped_datasets = []
-    for group_id, group in enumerate(dataset_list, 1):
-        datasets = []
-        for dataset_name in group:
-            data = dataset_catalog.get(dataset_name)
-            factory = getattr(D, data["factory"])
-            args = data["args"]
-            # for COCODataset, we want to remove images without annotations
-            # during training
-            if data["factory"] == "COCODataset":
-                args["remove_images_without_annotations"] = is_train
-            if data["factory"] == "PascalVOCDataset":
-                args["use_difficult"] = not is_train
-            args["transforms"] = transforms
-            args.update(extra_args)
-            # make dataset from factory
-            dataset = factory(**args)
-
-            # check if dataset is grouped by task, assume one class per task
-            if class_by_group and data["factory"] != "Background":
-                category = dataset.contiguous_category_id_to_json_id[1]
-                del dataset.contiguous_category_id_to_json_id[1]
-                dataset.json_category_id_to_contiguous_id[category] = group_id
-                dataset.contiguous_category_id_to_json_id[group_id] = category
-
-            datasets.append(dataset)
-
-        if class_concat:
-            for dataset in datasets:
-                category = list(dataset.contiguous_category_id_to_json_id.values())
-                dataset.contiguous_category_id_to_json_id = {}
-                dataset.json_category_id_to_contiguous_id = {}
-                for id, cat in enumerate(category, start=num_category):
-                    dataset.json_category_id_to_contiguous_id[cat] = id
-                    dataset.contiguous_category_id_to_json_id[id] = cat
-            num_category += len(category)
-            print("Found {} #category after group {}, concating ...".format(num_category, group_id))
-
-        if is_train:
-            datasets = D.ConcatDataset(datasets)
-
-        grouped_datasets.append(datasets)
-
-    # for testing, return a list of datasets
-    if not is_train:
-        datasets = [dataset for group in grouped_datasets for dataset in group]
-        return datasets
-    if class_concat:
-        grouped_datasets = D.ConcatDataset(grouped_datasets)
-        return [grouped_datasets]
-
-    # for training, concatenate all datasets into a single one
-    return grouped_datasets
-
-
-def make_data_sampler(dataset, shuffle, distributed, num_replicas=None, rank=None, use_random_seed=True):
-    if distributed:
-        return samplers.DistributedSampler(dataset, shuffle=shuffle, num_replicas=num_replicas, rank=rank,
-                                           use_random=use_random_seed)
-    if shuffle:
-        sampler = torch.utils.data.sampler.RandomSampler(dataset)
-    else:
-        sampler = torch.utils.data.sampler.SequentialSampler(dataset)
-    return sampler
-
-
-def _quantize(x, bins):
-    bins = copy.copy(bins)
-    bins = sorted(bins)
-    quantized = list(map(lambda y: bisect.bisect_right(bins, y), x))
-    return quantized
-
-
-def _compute_aspect_ratios(dataset):
-    aspect_ratios = []
-    for i in range(len(dataset)):
-        img_info = dataset.get_img_info(i)
-        aspect_ratio = float(img_info["height"]) / float(img_info["width"])
-        aspect_ratios.append(aspect_ratio)
-    return aspect_ratios
-
-
-def make_batch_data_sampler(
-        dataset, sampler, aspect_grouping, images_per_batch, num_iters=None, start_iter=0, drop_last=False
-):
-    if aspect_grouping:
-        if not isinstance(aspect_grouping, (list, tuple)):
-            aspect_grouping = [aspect_grouping]
-        aspect_ratios = _compute_aspect_ratios(dataset)
-        group_ids = _quantize(aspect_ratios, aspect_grouping)
-        batch_sampler = samplers.GroupedBatchSampler(
-            sampler, group_ids, images_per_batch, drop_uneven=drop_last
-        )
-    else:
-        batch_sampler = torch.utils.data.sampler.BatchSampler(
-            sampler, images_per_batch, drop_last=drop_last
-        )
-    if num_iters is not None:
-        batch_sampler = samplers.IterationBasedBatchSampler(
-            batch_sampler, num_iters, start_iter
-        )
-    return batch_sampler
-
-def make_data_loader(cfg, is_train=True, is_distributed=False, num_replicas=None, rank=None, start_iter=0):
-    num_gpus = num_replicas or get_world_size()
-
-    if is_train:
-        images_per_batch = cfg.SOLVER.IMS_PER_BATCH
-        assert (
-                images_per_batch % num_gpus == 0
-        ), "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number "
-        "of GPUs ({}) used.".format(images_per_batch, num_gpus)
-        images_per_gpu = images_per_batch // num_gpus
-        shuffle = True
-        num_iters = cfg.SOLVER.MAX_ITER
-    else:
-        images_per_batch = cfg.TEST.IMS_PER_BATCH
-        assert (
-                images_per_batch % num_gpus == 0
-        ), "TEST.IMS_PER_BATCH ({}) must be divisible by the number "
-        "of GPUs ({}) used.".format(images_per_batch, num_gpus)
-        images_per_gpu = images_per_batch // num_gpus
-        shuffle = False if not is_distributed else True
-        num_iters = None
-        start_iter = 0
-
-    if images_per_gpu > 1:
-        logger = logging.getLogger(__name__)
-        logger.warning(
-            "When using more than one image per GPU you may encounter "
-            "an out-of-memory (OOM) error if your GPU does not have "
-            "sufficient memory. If this happens, you can reduce "
-            "SOLVER.IMS_PER_BATCH (for training) or "
-            "TEST.IMS_PER_BATCH (for inference). For training, you must "
-            "also adjust the learning rate and schedule length according "
-            "to the linear scaling rule. See for example: "
-            "https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14"
-        )
-
-    # group images which have similar aspect ratio. In this case, we only
-    # group in two cases: those with width / height > 1, and the other way around,
-    # but the code supports more general grouping strategy
-    aspect_grouping = [1] if cfg.DATALOADER.ASPECT_RATIO_GROUPING else []
-
-    paths_catalog = import_file(
-        "maskrcnn_benchmark.config.paths_catalog", cfg.PATHS_CATALOG, True
-    )
-
-    DatasetCatalog = paths_catalog.DatasetCatalog
-    if len(cfg.DATASETS.REGISTER) > 0:
-        for new_dataset in cfg.DATASETS.REGISTER:
-            # img_dir = cfg.DATASETS.REGISTER[new_dataset]["img_dir"]
-            # if "ann_file" in cfg.DATASETS.REGISTER[new_dataset]:
-            #     ann_file = cfg.DATASETS.REGISTER[new_dataset]["ann_file"]
-            # else:
-            #     ann_file = None
-            attrs = dict(cfg.DATASETS.REGISTER[new_dataset])
-            if is_train:
-                new_dataset = new_dataset + cfg.DATASETS.TRAIN_DATASETNAME_SUFFIX
-            else:
-                new_dataset = new_dataset + cfg.DATASETS.TEST_DATASETNAME_SUFFIX
-            DatasetCatalog.set(new_dataset, attrs)
-
-
-    dataset_list = cfg.DATASETS.TRAIN if is_train else cfg.DATASETS.TEST
-
-    # Haotian: expand bing dataset
-    if "bing_caption_train" in dataset_list and len(cfg.DATASETS.BING_INDEX_LIST) > 0:
-        dataset_list = list(dataset_list)
-        dataset_list.remove("bing_caption_train")
-        for bing_index in cfg.DATASETS.BING_INDEX_LIST:
-            dataset_list.insert(len(dataset_list), "bing_caption_{}_train".format(bing_index))
-        dataset_list = tuple(dataset_list)
-    
-    if "bing_caption_train_no_coco" in dataset_list and len(cfg.DATASETS.BING_INDEX_LIST) > 0:
-        dataset_list = list(dataset_list)
-        dataset_list.remove("bing_caption_train_no_coco")
-        for bing_index in cfg.DATASETS.BING_INDEX_LIST:
-            dataset_list.insert(len(dataset_list), "bing_caption_{}_train_no_coco".format(bing_index))
-        dataset_list = tuple(dataset_list)
-
-    print("The combined datasets are: {}.".format(dataset_list))
-
-    transforms = None if not is_train and cfg.TEST.USE_MULTISCALE else build_transforms(cfg, is_train)
-
-    extra_args = {}
-    if is_train and cfg.DATASETS.USE_CROWD:
-        extra_args['ignore_crowd'] = False
-    if is_train and cfg.DATASETS.MAX_BOX > 0:
-        extra_args['max_box'] = cfg.DATASETS.MAX_BOX
-    if is_train and cfg.DATASETS.FEW_SHOT>0:
-        extra_args['few_shot'] = cfg.DATASETS.FEW_SHOT
-    if is_train and cfg.DATASETS.SHUFFLE_SEED != 0:
-        extra_args['shuffle_seed'] = cfg.DATASETS.SHUFFLE_SEED
-
-    # od to grounding
-    if is_train and cfg.DATASETS.RANDOM_SAMPLE_NEG > 0:
-        extra_args['random_sample_negative'] = cfg.DATASETS.RANDOM_SAMPLE_NEG
-    if is_train and cfg.DATASETS.ADD_DET_PROMPT:
-        extra_args["add_detection_prompt"] = True
-    if is_train and cfg.DATASETS.USE_OD_AUG:
-        extra_args["use_od_data_aug"] = True
-    if is_train and cfg.DATASETS.DISABLE_SHUFFLE:
-        extra_args["disable_shuffle"] = True
-    if cfg.DATASETS.ONE_HOT:
-        extra_args["one_hot"] = True
-    if is_train and len(cfg.DATASETS.PROMPT_VERSION) > 0:
-        extra_args["prompt_engineer_version"] = cfg.DATASETS.PROMPT_VERSION
-    if is_train and len(cfg.DATASETS.CONTROL_PROB) == 4:
-        extra_args["control_probabilities"] = cfg.DATASETS.CONTROL_PROB
-    if is_train and cfg.DATASETS.DISABLE_CLIP_TO_IMAGE:
-        extra_args["disable_clip_to_image"] =  cfg.DATASETS.DISABLE_CLIP_TO_IMAGE
-    if is_train and cfg.DATASETS.NO_MINUS_ONE_FOR_ONE_HOT:
-        extra_args["no_minus_one_for_one_hot"] = cfg.DATASETS.NO_MINUS_ONE_FOR_ONE_HOT
-    if is_train:
-        extra_args["separation_tokens"] = cfg.DATASETS.SEPARATION_TOKENS
-    # caption
-    if is_train and cfg.DATASETS.CAPTION_MIN_BOX > 0:
-        extra_args["caption_min_box"] = cfg.DATASETS.CAPTION_MIN_BOX
-    if is_train and cfg.DATASETS.REPLACE_CLEAN_LABEL:
-        extra_args["replace_clean_label"] = True
-    if is_train and cfg.DATASETS.FURTHER_SCREEN:
-        extra_args["further_screen"] = True
-    if is_train and cfg.DATASETS.CAPTION_CONF > 0.0:
-        extra_args["caption_conf"] = cfg.DATASETS.CAPTION_CONF
-    if is_train:
-        extra_args["caption_nms"] = cfg.DATASETS.CAPTION_NMS
-    if is_train and cfg.DATASETS.PACK_RANDOM_CAPTION_NUMBER > 0:
-        extra_args["pack_random_caption_number"] = cfg.DATASETS.PACK_RANDOM_CAPTION_NUMBER
-    if is_train and cfg.DATASETS.INFERENCE_CAPTION:
-        extra_args["inference_caption"] = True
-    if is_train and cfg.DATASETS.SAMPLE_NEGATIVE_FOR_GROUNDING_DATA > 0:
-        extra_args["sample_negative_for_grounding_data"] = cfg.DATASETS.SAMPLE_NEGATIVE_FOR_GROUNDING_DATA
-    if is_train and cfg.DATASETS.RANDOM_PACK_PROB > 0:
-        extra_args["random_pack_prob"] = cfg.DATASETS.RANDOM_PACK_PROB
-    if is_train and cfg.DATASETS.NO_RANDOM_PACK_PROBABILITY > 0:
-        extra_args["no_random_pack_probability"] = cfg.DATASETS.NO_RANDOM_PACK_PROBABILITY
-    if is_train:
-        extra_args["safeguard_positive_caption"] = cfg.DATASETS.SAFEGUARD_POSITIVE_CAPTION
-    if is_train:
-        extra_args["local_debug"] = cfg.DATASETS.LOCAL_DEBUG
-    if is_train:
-        extra_args["no_mask_for_od"] = cfg.MODEL.DYHEAD.FUSE_CONFIG.NO_MASK_FOR_OD
-    if is_train:
-        extra_args["no_mask_for_gold"] = cfg.MODEL.DYHEAD.FUSE_CONFIG.NO_MASK_FOR_GOLD
-    if is_train:
-        extra_args["mlm_obj_for_only_positive"] = cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_OBJ_FOR_ONLY_POSITIVE
-    if cfg.DATASETS.OVERRIDE_CATEGORY and cfg.DATASETS.USE_OVERRIDE_CATEGORY:
-        extra_args["override_category"] = cfg.DATASETS.OVERRIDE_CATEGORY
-    if is_train:
-        extra_args["caption_format_version"] = cfg.DATASETS.CAPTION_FORMAT_VERSION
-    if is_train:
-        extra_args["special_safeguard_for_coco_grounding"] = cfg.DATASETS.SPECIAL_SAFEGUARD_FOR_COCO_GROUNDING
-    if is_train:
-        extra_args["diver_box_for_vqa"] = cfg.DATASETS.DIVER_BOX_FOR_VQA
-    extra_args["caption_prompt"] = cfg.DATASETS.CAPTION_PROMPT
-    extra_args["use_caption_prompt"] = cfg.DATASETS.USE_CAPTION_PROMPT
-
-    # extra_args['tokenizer'] = AutoTokenizer.from_pretrained(cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE)
-    if cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE == "clip":
-        # extra_args['tokenizer'] = build_tokenizer("clip")
-        from transformers import CLIPTokenizerFast
-        if cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS:
-            extra_args["tokenizer"] = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", from_slow=True, mask_token='ðŁĴij</w>')
-        else:
-            extra_args["tokenizer"] = CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32", from_slow=True)
-    else:
-        extra_args['tokenizer'] = AutoTokenizer.from_pretrained(cfg.MODEL.LANGUAGE_BACKBONE.TOKENIZER_TYPE)
-
-    if isinstance(dataset_list[0], (tuple, list)):
-        datasets = build_dataset_by_group(dataset_list, transforms, DatasetCatalog, is_train,
-                                          class_by_group=cfg.DATASETS.ALTERNATIVE_TRAINING,
-                                          class_concat=cfg.DATASETS.CLASS_CONCAT,
-                                          extra_args=extra_args)
-    else:
-        datasets = build_dataset(cfg, dataset_list, transforms, DatasetCatalog, is_train,
-                                 class_concat=cfg.DATASETS.CLASS_CONCAT,
-                                 extra_args=extra_args)
-
-    data_loaders = []
-    for di, dataset in enumerate(datasets):
-        if is_train and cfg.SOLVER.MAX_EPOCH > 0:
-            num_iters = cfg.SOLVER.MAX_EPOCH * len(dataset) // cfg.SOLVER.IMS_PER_BATCH
-            print("Number of iterations are {}".format(num_iters))
-            cfg.defrost()
-            cfg.SOLVER.MAX_ITER = num_iters
-            cfg.SOLVER.DATASET_LENGTH = len(dataset)
-            cfg.freeze()
-        if is_train and cfg.SOLVER.MULTI_MAX_EPOCH:
-            num_iters = None
-            cfg.defrost()
-            cfg.SOLVER.MULTI_MAX_ITER += (cfg.SOLVER.MULTI_MAX_EPOCH[di] * len(dataset) // cfg.SOLVER.IMS_PER_BATCH,)
-            cfg.freeze()
-
-        if is_train and cfg.DATALOADER.DISTRIBUTE_CHUNK_AMONG_NODE:
-            from .datasets.custom_distributed_sampler import DistributedSamplerChunkByNode
-            chunk_or_not = []
-            for i in dataset_list:
-                if "bing_caption" in i:
-                    chunk_or_not.append(True)
-                else:
-                    chunk_or_not.append(False)
-            assert(len(chunk_or_not) == len(dataset.datasets))
-            '''
-            If we are training on 4 nodes, each with 8 GPUs
-            '''
-            num_nodes = int(os.getenv('NODE_COUNT', os.getenv('OMPI_COMM_WORLD_SIZE', 1)))
-            local_size = cfg.num_gpus//num_nodes
-            node_rank = int(os.getenv('NODE_RANK', os.getenv('OMPI_COMM_WORLD_RANK', 0)))
-            local_rank = cfg.local_rank
-            sampler = DistributedSamplerChunkByNode(
-                dataset = dataset,
-                all_datasets = dataset.datasets, # Assumming dataset is a ConcateDataset instance,
-                chunk_or_not = chunk_or_not,
-                num_replicas = cfg.num_gpus, # total GPU number, e.g., 32
-                rank = dist.get_rank(), # Global Rank, e.g., 0~31
-                node_rank = node_rank, # Node Rank, e.g., 0~3
-                node_number = num_nodes, # how many node e.g., 4
-                process_num_per_node = local_size, # e.g., 8
-                rank_within_local_node = local_rank, # e.g., 0~7
-            )
-        else:
-            sampler = make_data_sampler(dataset, shuffle, is_distributed, num_replicas=num_replicas, rank=rank,
-                                        use_random_seed=cfg.DATALOADER.USE_RANDOM_SEED)
-        batch_sampler = make_batch_data_sampler(
-            dataset, sampler, aspect_grouping, images_per_gpu, num_iters, start_iter, drop_last=is_train
-        )
-        collator = BBoxAugCollator() if not is_train and cfg.TEST.USE_MULTISCALE else BatchCollator(
-            cfg.DATALOADER.SIZE_DIVISIBILITY)
-        num_workers = cfg.DATALOADER.NUM_WORKERS
-        data_loader = torch.utils.data.DataLoader(
-            dataset,
-            num_workers=num_workers,
-            batch_sampler=batch_sampler,
-            collate_fn=collator,
-        )
-        data_loaders.append(data_loader)
-    if is_train and cfg.SOLVER.MULTI_MAX_EPOCH:
-        cfg.defrost()
-        cfg.SOLVER.MULTI_MAX_ITER += (
-            cfg.SOLVER.MULTI_MAX_EPOCH[-1] * min([len(dataset) // cfg.SOLVER.IMS_PER_BATCH for dataset in datasets]),)
-        cfg.freeze()
-
-    if is_train and not cfg.DATASETS.ALTERNATIVE_TRAINING and not cfg.DATASETS.MULTISTAGE_TRAINING:
-        # during training, a single (possibly concatenated) data_loader is returned
-        assert len(data_loaders) == 1
-        return data_loaders[0]
-
-    return data_loaders
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/solver/__init__.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/solver/__init__.py
deleted file mode 100644
index 10f84e12d029a07d5c7d3ac29e18b572a92ef03c..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/solver/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from .build import build_lr_scheduler, build_optimizer
-from .lr_scheduler import WarmupCosineLR, WarmupMultiStepLR
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
diff --git a/spaces/heiyubili/bingo/src/components/ui/alert-dialog.tsx b/spaces/heiyubili/bingo/src/components/ui/alert-dialog.tsx
deleted file mode 100644
index 17fec4d16510328deacc1416569173c97761ef72..0000000000000000000000000000000000000000
--- a/spaces/heiyubili/bingo/src/components/ui/alert-dialog.tsx
+++ /dev/null
@@ -1,150 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as AlertDialogPrimitive from '@radix-ui/react-alert-dialog'
-
-import { cn } from '@/lib/utils'
-import { buttonVariants } from '@/components/ui/button'
-
-const AlertDialog = AlertDialogPrimitive.Root
-
-const AlertDialogTrigger = AlertDialogPrimitive.Trigger
-
-const AlertDialogPortal = ({
-  className,
-  children,
-  ...props
-}: AlertDialogPrimitive.AlertDialogPortalProps) => (
-  <AlertDialogPrimitive.Portal className={cn(className)} {...props}>
-    <div className="fixed inset-0 z-50 flex items-end justify-center sm:items-center">
-      {children}
-    </div>
-  </AlertDialogPrimitive.Portal>
-)
-AlertDialogPortal.displayName = AlertDialogPrimitive.Portal.displayName
-
-const AlertDialogOverlay = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Overlay>
->(({ className, children, ...props }, ref) => (
-  <AlertDialogPrimitive.Overlay
-    className={cn(
-      'fixed inset-0 z-50 bg-background/80 backdrop-blur-sm transition-opacity animate-in fade-in',
-      className
-    )}
-    {...props}
-    ref={ref}
-  />
-))
-AlertDialogOverlay.displayName = AlertDialogPrimitive.Overlay.displayName
-
-const AlertDialogContent = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Content>
->(({ className, ...props }, ref) => (
-  <AlertDialogPortal>
-    <AlertDialogOverlay />
-    <AlertDialogPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed z-50 grid w-full max-w-lg scale-100 gap-4 border bg-background p-6 opacity-100 shadow-lg animate-in fade-in-90 slide-in-from-bottom-10 sm:rounded-lg sm:zoom-in-90 sm:slide-in-from-bottom-0 md:w-full',
-        className
-      )}
-      {...props}
-    />
-  </AlertDialogPortal>
-))
-AlertDialogContent.displayName = AlertDialogPrimitive.Content.displayName
-
-const AlertDialogHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col space-y-2 text-center sm:text-left',
-      className
-    )}
-    {...props}
-  />
-)
-AlertDialogHeader.displayName = 'AlertDialogHeader'
-
-const AlertDialogFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-AlertDialogFooter.displayName = 'AlertDialogFooter'
-
-const AlertDialogTitle = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Title
-    ref={ref}
-    className={cn('text-lg font-semibold', className)}
-    {...props}
-  />
-))
-AlertDialogTitle.displayName = AlertDialogPrimitive.Title.displayName
-
-const AlertDialogDescription = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-AlertDialogDescription.displayName =
-  AlertDialogPrimitive.Description.displayName
-
-const AlertDialogAction = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Action>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Action>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Action
-    ref={ref}
-    className={cn(buttonVariants(), className)}
-    {...props}
-  />
-))
-AlertDialogAction.displayName = AlertDialogPrimitive.Action.displayName
-
-const AlertDialogCancel = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Cancel>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Cancel>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Cancel
-    ref={ref}
-    className={cn(
-      buttonVariants({ variant: 'outline' }),
-      'mt-2 sm:mt-0',
-      className
-    )}
-    {...props}
-  />
-))
-AlertDialogCancel.displayName = AlertDialogPrimitive.Cancel.displayName
-
-export {
-  AlertDialog,
-  AlertDialogTrigger,
-  AlertDialogContent,
-  AlertDialogHeader,
-  AlertDialogFooter,
-  AlertDialogTitle,
-  AlertDialogDescription,
-  AlertDialogAction,
-  AlertDialogCancel
-}
diff --git a/spaces/huaiji3y/bingo-Public/src/lib/hooks/use-at-bottom.tsx b/spaces/huaiji3y/bingo-Public/src/lib/hooks/use-at-bottom.tsx
deleted file mode 100644
index d37c8cf4162adcb0064e08ecec24eb731416b045..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/lib/hooks/use-at-bottom.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import * as React from 'react'
-
-export function useAtBottom(offset = 0) {
-  const [isAtBottom, setIsAtBottom] = React.useState(false)
-
-  React.useEffect(() => {
-    const handleScroll = () => {
-      setIsAtBottom(
-        window.innerHeight + window.scrollY >=
-          document.body.offsetHeight - offset
-      )
-    }
-
-    window.addEventListener('scroll', handleScroll, { passive: true })
-    handleScroll()
-
-    return () => {
-      window.removeEventListener('scroll', handleScroll)
-    }
-  }, [offset])
-
-  return isAtBottom
-}
diff --git a/spaces/hugginglearners/image-style-transfer/app.py b/spaces/hugginglearners/image-style-transfer/app.py
deleted file mode 100644
index c8c2c592e9a067bc2c440015815e2fdd98cc10f3..0000000000000000000000000000000000000000
--- a/spaces/hugginglearners/image-style-transfer/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import math
-import numpy as np
-import pandas as pd
-
-import gradio as gr
-from huggingface_hub import from_pretrained_fastai
-from fastai.vision.all import *
-from torchvision.models import vgg19, vgg16
-from utils import *
-
-pascal_source = '.'
-EXAMPLES_PATH = Path('./examples')
-repo_id = "hugginglearners/fastai-style-transfer"
-
-
-def _inner(feat_net, hooks, x):
-  feat_net(x)
-  return hooks.stored
-
-def _get_layers(arch:str, pretrained=True):
-  "Get the layers and arch for a VGG Model (16 and 19 are supported only)"
-  feat_net = vgg19(pretrained=pretrained) if arch.find('9') > 1 else vgg16(pretrained=pretrained)
-  config = _vgg_config.get(arch)
-  features = feat_net.features.eval()
-  for p in features.parameters(): p.requires_grad=False
-  return feat_net, [features[i] for i in config]
-
-
-_vgg_config = {
-    'vgg16' : [1, 11, 18, 25, 20],
-    'vgg19' : [1, 6, 11, 20, 29, 22]
-}
-
-feat_net, layers = _get_layers('vgg19', True)
-hooks = hook_outputs(layers, detach=False)
-
-learner = from_pretrained_fastai(repo_id)
-
-def infer(img):
-    pred = learner.predict(img)
-    image = pred[0].numpy()
-    image = image.transpose((1, 2, 0))
-    plt.imshow(image)
-    return plt.gcf() #pred[0].show()
-
-# get the inputs
-inputs = gr.inputs.Image(shape=(192, 192))
-
-# the app outputs two segmented images
-output = gr.Plot()
-# it's good practice to pass examples, description and a title to guide users
-title = 'Style transfer'
-description = ''
-article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. "
-examples = [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()]
-
-gr.Interface(infer, inputs, output, examples= examples, allow_flagging='never', cache_examples=False,
-             title=title, description=description, article=article, live=False).launch(enable_queue=True, debug=False, inbrowser=False)
diff --git a/spaces/hugginglearners/rice-image-classification/app.py b/spaces/hugginglearners/rice-image-classification/app.py
deleted file mode 100644
index 13bb0c9ebf3b8662655365e201d5525b1984e6f6..0000000000000000000000000000000000000000
--- a/spaces/hugginglearners/rice-image-classification/app.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import gradio as gr
-import torch
-from huggingface_hub import from_pretrained_fastai
-from pathlib import Path
-
-examples = ["examples/example_0.png", 
-            "examples/example_1.png", 
-            "examples/example_2.png", 
-            "examples/example_3.png", 
-            "examples/example_4.png"]
-            
-repo_id = "hugginglearners/rice_image_classification"
-path = Path("./")
-
-def get_y(r):
-    return r["label"]
-    
-def get_x(r):
-    return path/r["fname"]
-    
-learner = from_pretrained_fastai(repo_id)
-labels = learner.dls.vocab
-
-def inference(image):
-    label_predict,_,probs = learner.predict(image)
-    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
-    return labels_probs
-
-gr.Interface(
-    fn=inference,
-    title="Rice image classification",
-    description = "Predict which type of rice belong to Arborio, Basmati, Ipsala, Jasmine, Karacadag",
-    inputs="image",
-    examples=examples,
-    outputs=gr.outputs.Label(num_top_classes=5, label='Prediction'),
-    cache_examples=False,
-    article = "Author: <a href=\"https://www.linkedin.com/in/vumichien/\">Vu Minh Chien</a>",
-).launch(debug=True, enable_queue=True)
\ No newline at end of file
diff --git a/spaces/hylee/photo2cartoon/p2c/data_process.py b/spaces/hylee/photo2cartoon/p2c/data_process.py
deleted file mode 100644
index 6b96bfa3bc535712e063d7ba53d8e892a532b216..0000000000000000000000000000000000000000
--- a/spaces/hylee/photo2cartoon/p2c/data_process.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import os
-import cv2
-import numpy as np
-from tqdm import tqdm
-import argparse
-
-from utils import Preprocess
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--data_path', type=str, help='photo folder path')
-parser.add_argument('--save_path', type=str, help='save folder path')
-
-args = parser.parse_args()
-os.makedirs(args.save_path, exist_ok=True)
-
-pre = Preprocess()
-
-for idx, img_name in enumerate(tqdm(os.listdir(args.data_path))):
-    img = cv2.cvtColor(cv2.imread(os.path.join(args.data_path, img_name)), cv2.COLOR_BGR2RGB)
-    
-    # face alignment and segmentation
-    face_rgba = pre.process(img)
-    if face_rgba is not None:
-        # change background to white
-        face = face_rgba[:,:,:3].copy()
-        mask = face_rgba[:,:,3].copy()[:,:,np.newaxis]/255.
-        face_white_bg = (face*mask + (1-mask)*255).astype(np.uint8)
-
-        cv2.imwrite(os.path.join(args.save_path, str(idx).zfill(4)+'.png'), cv2.cvtColor(face_white_bg, cv2.COLOR_RGB2BGR))
diff --git a/spaces/imageomics/dashboard-prototype/Dockerfile b/spaces/imageomics/dashboard-prototype/Dockerfile
deleted file mode 100644
index 36e2eb66b4d474348e1c45259f75c1c74c6193fb..0000000000000000000000000000000000000000
--- a/spaces/imageomics/dashboard-prototype/Dockerfile
+++ /dev/null
@@ -1,2 +0,0 @@
-FROM ghcr.io/imageomics/dashboard-prototype:latest
-COPY run.sh /api/run.sh
diff --git a/spaces/imageomics/dev-dashboard/run.sh b/spaces/imageomics/dev-dashboard/run.sh
deleted file mode 100644
index 383a306e02443b9bcc62c4ae8ace19522d998c17..0000000000000000000000000000000000000000
--- a/spaces/imageomics/dev-dashboard/run.sh
+++ /dev/null
@@ -1,2 +0,0 @@
-#!/bin/bash
-gunicorn -w ${BACKEND_WORKERS:=4} -b :7860 -t 360 dashboard:server
diff --git a/spaces/inamXcontru/PoeticTTS/Claudia Lizaldi Revista H Extremo.pdfl !!BETTER!!.md b/spaces/inamXcontru/PoeticTTS/Claudia Lizaldi Revista H Extremo.pdfl !!BETTER!!.md
deleted file mode 100644
index fb632f2480dd7a08220ff3d0daf1f7d0cb73fa23..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Claudia Lizaldi Revista H Extremo.pdfl !!BETTER!!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Claudia Lizaldi Revista H Extremo.pdfl</h2><br /><p><b><b>Download File</b> &#10031; <a href="https://gohhs.com/2uz5w2">https://gohhs.com/2uz5w2</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/innat/HybridModel-GradCAM/models/__init__.py b/spaces/innat/HybridModel-GradCAM/models/__init__.py
deleted file mode 100644
index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000
--- a/spaces/innat/HybridModel-GradCAM/models/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/spaces/innnky/soft-vits-vc/preprocess.py b/spaces/innnky/soft-vits-vc/preprocess.py
deleted file mode 100644
index aaedbf076c30114b3ac6c27dfb42fd54ac81a71c..0000000000000000000000000000000000000000
--- a/spaces/innnky/soft-vits-vc/preprocess.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import argparse
-import text
-from utils import load_filepaths_and_text
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  parser.add_argument("--out_extension", default="cleaned")
-  parser.add_argument("--text_index", default=1, type=int)
-  parser.add_argument("--filelists", nargs="+", default=["filelists/ljs_audio_text_val_filelist.txt", "filelists/ljs_audio_text_test_filelist.txt"])
-  parser.add_argument("--text_cleaners", nargs="+", default=["english_cleaners2"])
-
-  args = parser.parse_args()
-    
-
-  for filelist in args.filelists:
-    print("START:", filelist)
-    filepaths_and_text = load_filepaths_and_text(filelist)
-    for i in range(len(filepaths_and_text)):
-      original_text = filepaths_and_text[i][args.text_index]
-      cleaned_text = text._clean_text(original_text, args.text_cleaners)
-      filepaths_and_text[i][args.text_index] = cleaned_text
-
-    new_filelist = filelist + "." + args.out_extension
-    with open(new_filelist, "w", encoding="utf-8") as f:
-      f.writelines(["|".join(x) + "\n" for x in filepaths_and_text])
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Burp Suite Professional 1.7.37 Free Download UPD.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Burp Suite Professional 1.7.37 Free Download UPD.md
deleted file mode 100644
index de588c817cece08262e590b52f563d62aca0d5e2..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Burp Suite Professional 1.7.37 Free Download UPD.md	
+++ /dev/null
@@ -1,9 +0,0 @@
-<br />
-<p> <strong>crawler:</strong> <br /> the crawler is very similar to the intruder in that it is able to navigate web pages, download files, and submit forms. burp also includes a list of over 50,000 common web pages, so your crawl can be targeted. it can also be used to detect attack vectors.</p>
-<h2>Burp Suite Professional 1.7.37 Free Download</h2><br /><p><b><b>Download File</b> &#10004; <a href="https://urlin.us/2uEwL1">https://urlin.us/2uEwL1</a></b></p><br /><br />
-<p> <strong>intruder:</strong> <br /> the intruder is burp’s most advanced spider. this tool is capable of simulating any browser, including the memory of its http requests. the intruder is able to navigate web pages, submit forms, download files, etc. this tool is great for hunting.</p>
-<p>click on the to use tab to see burp in action as youll see in the screenshot below. this can be used to test any web application where you are wanting to see if there are any security issues with the software. in the screenshot below, i have a few issues in my code. you can click on a number to access the code in that location. theres also a small box to the right of the message that gives a code next to it. this code allows you to view the code for the error. once youre done viewing the code, you can click on the undo button. this will allow you to undo the changes you made. this is a great application to have to test your websites security. </p>
-<p>the first tab youre going to want to check is the options tab. under the when starting burp section, youre going to want to enter the url that you want to test. if you want to scan a web server thats on the same network as your computer, you would type in then, click on start. once its done, click on the options button and set the port to 8080. click on save to save the changes. then click on the start scan button. this will start the scan and if there are any errors, theres a link to the code for the error. you can click on the link to view the code. the code can also be viewed from the scan tab.</p>
-<p></p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/CRACKOBSStudio2202FullInstallerx64.md b/spaces/inplisQlawa/anything-midjourney-v4-1/CRACKOBSStudio2202FullInstallerx64.md
deleted file mode 100644
index fd9ff9ea69af428e78b202024528df77ada6d094..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/CRACKOBSStudio2202FullInstallerx64.md
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>CRACKOBSStudio2202FullInstallerx64</h2><br /><p><b><b>DOWNLOAD</b> &hArr; <a href="https://urlin.us/2uEwOx">https://urlin.us/2uEwOx</a></b></p><br /><br />
-<br />
-... ca8d075f12 7d1ee9a32a8ebde8805c26d64564d65eba564072 66.33 MiB (69551216 Bytes) ... 15c24738db. CRACK OBS Studio 2202 Full Installer X64. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen.md
deleted file mode 100644
index 774a1b46396ce079905b338080fc22beb970dfb9..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen.md	
+++ /dev/null
@@ -1,80 +0,0 @@
-
-<h1>Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen - The Ultimate Color Tool for Designers</h1>
-
-<p>If you are a web designer, graphic designer, or any other creative professional who works with colors, you know how important it is to have a reliable and easy-to-use color tool. You need a tool that can help you create stunning color palettes, harmonize colors, preview colors on different backgrounds, and generate color codes for various formats. You also need a tool that can help you find and download color schemes from other sources, such as websites, images, or popular color libraries.</p>
-<h2>Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen</h2><br /><p><b><b>Download File</b> &bull; <a href="https://urlin.us/2uEw2j">https://urlin.us/2uEw2j</a></b></p><br /><br />
-
-<p>That's why you need Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen - the ultimate color tool for designers. Color Schemer Studio V2.0 is a powerful and versatile color software that can help you with all your color needs. Whether you are working on a website, a logo, a flyer, a poster, or any other design project, Color Schemer Studio V2.0 can help you create amazing color schemes in minutes.</p>
-
-<h2>What is Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen?</h2>
-
-<p>Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen is a full-featured color software that allows you to create and edit color schemes, preview colors on different backgrounds, generate color codes for various formats, and much more. It is compatible with Windows XP, Vista, 7, 8, and 10.</p>
-
-<p>Color Schemer Studio V2.0 has a user-friendly interface that lets you easily access all its features and functions. You can use the built-in color wheel to create harmonious color schemes based on different color models, such as RGB, CMYK, HSB, or Lab. You can also use the eyedropper tool to pick colors from any source on your screen, such as websites, images, or other applications.</p>
-
-<p>Color Schemer Studio V2.0 also allows you to preview colors on different backgrounds, such as solid colors, gradients, patterns, or images. You can adjust the brightness, contrast, saturation, and hue of any color to fine-tune your color scheme. You can also use the color mixer to blend two or more colors together and create new shades.</p>
-
-<p>Color Schemer Studio V2.0 can generate color codes for various formats, such as HTML hex codes, RGB values, CMYK values, HSB values, or Lab values. You can copy and paste these codes into your favorite design software or text editor. You can also export your color schemes as Adobe Photoshop palettes (.aco), Adobe Illustrator palettes (.ai), GIMP palettes (.gpl), or Paint Shop Pro palettes (.pal).</p>
-
-<p>Color Schemer Studio V2.0 also allows you to find and download color schemes from other sources, such as websites, images, or popular color libraries. You can use the web browser feature to browse any website and extract its color scheme. You can also use the image analyzer feature to analyze any image and extract its dominant colors. You can also use the online scheme library feature to access thousands of ready-made color schemes from various categories, such as web design, fashion design, interior design, or nature.</p>
-<p></p>
-
-<h2>How to Use Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen?</h2>
-
-<p>To use Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen , you need to download and install it on your computer. You can download it from this link: https://bltlly.com/2jvt19 . After downloading it , you need to extract it using WinRAR or any other file compression software . Then , you need to run the setup file and follow the instructions to install it . After installing it , you need to activate it using the serial key keygen that is included in the download . To activate it , you need to run the keygen file and generate a serial key . Then , you need to enter the serial key into the registration window of Color Schemer Studio V2.0 and click OK . After activating it , you can start using it for your design projects .</p>
-
-<p>To create a color scheme using Color Schemer Studio V2.0 , you need to follow these steps:</p>
-
-<ol>
-<li>Open Color Schemer Studio V2.0 and click on the New Scheme button.</li>
-<li>Select a base color from the color wheel or use the eyedropper tool to pick a color from any source on your screen.</li>
-<li>Select a color model from the drop-down menu , such as RGB , CMYK , HSB , or Lab.</li>
-<li>Select a harmony rule from the drop-down menu , such as complementary , analogous , triadic , tetradic , split complementary , or custom.</li>
-<li>Adjust the number of colors in your scheme using the slider or the arrows.</li>
-<li>Adjust the brightness , contrast , saturation , and hue of any color using the sliders or the arrows.</li>
-<li>Preview your color scheme on different backgrounds using the tabs at the bottom of the window.</li>
-<li>Generate color codes for your scheme using the tabs at the top of the window.</li>
-<li>Save , export , or print your scheme using the buttons at the top right corner of the window.</li>
-</ol>
-
-<p>To find and download a color scheme using Color Schemer Studio V2.0 , you need to follow these steps:</p>
-
-<ol>
-<li>Open Color Schemer Studio V2.0 and click on the Find Scheme button.</li>
-<li>Select a source from which you want to find a scheme , such as website , image , or online scheme library.</li>
-<li>If you select website , enter the URL of the website and click Go . If you select image , browse your computer and select an image file . If you select online scheme library , browse the categories and select a scheme that you like.</li>
-<li>Preview the scheme on different backgrounds using the tabs at the bottom of the window.</li>
-<li>Generate color codes for the scheme using the tabs at the top of the window.</li>
-<li>Save , export , or print the scheme using the buttons at the top right corner of the window.</li>
-</ol>
-
-<h2>Why You Need Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen?</h2>
-
-<p>Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen is a must-have tool for any designer who works with colors . It can help you create beautiful and harmonious color schemes in minutes . It can also help you find inspiration from other sources , such as websites , images , or online libraries . It can also help you generate color codes for various formats that you can use in your design software or text editor . It can also help you preview your colors on different backgrounds to see how they look in different contexts . It can also help you save time and effort by providing you with all these features in one software .</p>
-
-<p>If you want to take your design skills to the next level , you need Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen . It is a powerful and versatile color software that can help you with all your color needs . Whether you are working on a website , a logo , a flyer , a poster , or any other design project , Color Schemer Studio V2.0 can help you create amazing color schemes in minutes . Download it now and see for yourself how it can improve your design workflow .</p>
-<h2>Where to Download Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen?</h2>
-
-<p>If you are interested in downloading Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen , you can find it on various websites that offer software downloads . However , you need to be careful when downloading software from unknown sources , as they may contain viruses , malware , or spyware that can harm your computer or steal your personal information . Therefore , you need to make sure that you download Color Schemer Studio V2.0 from a trusted and reputable website that provides safe and secure downloads .</p>
-
-<p>One of the best websites that you can download Color Schemer Studio V2.0 from is Ko-fi . Ko-fi is a platform where creators can get support from fans through donations , memberships , shop sales , and more . It is also a place where you can find and download various software , such as Color Schemer Studio V2.0 . Ko-fi provides high-quality and virus-free downloads that are verified by the creators themselves . You can also support the creators by buying them a coffee or becoming a member of their page .</p>
-
-<p>To download Color Schemer Studio V2.0 from Ko-fi , you need to follow these steps:</p>
-
-<ol>
-<li>Go to this link: https://ko-fi.com/post/Color-Schemer-Studio-V2-0-h33t-cepuxxx-Serial-D1D5CNOUN</li>
-<li>Click on the Download button and choose a payment method . You can pay with PayPal , Stripe , or Ko-fi Balance . You can also choose to pay what you want or nothing at all .</li>
-<li>After completing the payment , you will receive an email with a download link . Click on the link and save the file to your computer .</li>
-<li>Extract the file using WinRAR or any other file compression software . Then , run the setup file and follow the instructions to install Color Schemer Studio V2.0 .</li>
-<li>Activate Color Schemer Studio V2.0 using the serial key keygen that is included in the download . To activate it , run the keygen file and generate a serial key . Then , enter the serial key into the registration window of Color Schemer Studio V2.0 and click OK .</li>
-<li>Enjoy using Color Schemer Studio V2.0 for your design projects .</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen is a great color tool for designers who want to create and edit color schemes , preview colors on different backgrounds , generate color codes for various formats , and find and download color schemes from other sources . It is a powerful and versatile color software that can help you with all your color needs . Whether you are working on a website , a logo , a flyer , a poster , or any other design project , Color Schemer Studio V2.0 can help you create amazing color schemes in minutes . You can download it from Ko-fi and support the creator by buying them a coffee or becoming a member of their page . Download it now and see for yourself how it can improve your design workflow .</p>
-<h2>Conclusion</h2>
-
-<p>Color Schemer Studio V2.0 [h33t] [cepuxxx] Serial Key Keygen is a great color tool for designers who want to create and edit color schemes , preview colors on different backgrounds , generate color codes for various formats , and find and download color schemes from other sources . It is a powerful and versatile color software that can help you with all your color needs . Whether you are working on a website , a logo , a flyer , a poster , or any other design project , Color Schemer Studio V2.0 can help you create amazing color schemes in minutes . You can download it from Ko-fi and support the creator by buying them a coffee or becoming a member of their page . Download it now and see for yourself how it can improve your design workflow .</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h b/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h
deleted file mode 100644
index b2b88e8c46f19b6db0933163e57ccdb51180f517..0000000000000000000000000000000000000000
--- a/spaces/jackyccl/segment-anything/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h
+++ /dev/null
@@ -1,35 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-#pragma once
-#include <torch/extension.h>
-
-namespace groundingdino {
-
-at::Tensor
-ms_deform_attn_cpu_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step);
-
-std::vector<at::Tensor>
-ms_deform_attn_cpu_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step);
-
-} // namespace groundingdino
diff --git a/spaces/jackyliang42/code-as-policies/README.md b/spaces/jackyliang42/code-as-policies/README.md
deleted file mode 100644
index 0b2df1b05ed8c4e6c3a37924b5800d8d670972d0..0000000000000000000000000000000000000000
--- a/spaces/jackyliang42/code-as-policies/README.md
+++ /dev/null
@@ -1,53 +0,0 @@
----
-title: Code As Policies
-emoji: 📈
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-# Code as Policies Tabletop Manipulation Interactive Demo
-
-This demo is from the paper:
-
-[Code as Policies: Language Model Programs for Embodied Control](https://code-as-policies.github.io/)
-
-Below is an interactive demo for the simulated tabletop manipulation domain, seen in the paper section IV.D
-
-## Preparations
-1. Obtain an [OpenAI API Key](https://openai.com/blog/openai-api/)
-
-## Usage 
-1. Fill in the API Key and how many blocks and bowls to be spawned in the environment. 
-2. Click Setup/Reset Simulation
-3. Based on the new randomly sampled object names, input an instruction and click Run Instruction. If successful, this will render a video and update the simulation environment visualization.
-
-You can run instructions in sequence and refer back to previous instructions (e.g. do the same with other blocks, move the same block to the other bowl, etc). To reset, click Setup/Reset Env, and this will clear the current instruction history.
-
-## Supported Instructions
-* Spatial reasoning (e.g. to the left of the red block, the closest corner, the farthest bowl, the second block from the right)
-* Sequential actions (e.g. put blocks in matching bowls, stack blocks on the bottom right corner)
-* Contextual instructions (e.g. do the same with the blue block, undo that)
-* Language-based reasoning (e.g. put the forest-colored block on the ocean-colored bowl).
-* Simple Q&A (e.g. how many blocks are to the left of the blue bowl?)
-
-## Example Instructions 
-Note object names may need to be changed depending the sampled object names.
-* put the sun-colored block on the bowl closest to it
-* stack the blocks on the bottom most bowl
-* arrange the blocks as a square in the middle
-* move the square 5cm to the right
-* how many blocks are to the right of the orange bowl?
-* pick up the block closest to the top left corner and place it on the bottom right corner
-
-## Known Limitations
-* In simulation we're using ground truth object poses instead of using vision models. This means that instructions the require knowledge of visual apperances (e.g. darkest bowl, largest object, empty bowls) are not supported.
-* Currently, the low-level pick place primitive does not do collision checking, so if there are many objects on the table, placing actions may incur collisions.
-* The pick place primitive is also unable to pick up bowls.
-* Prompt saturation - if too many instructions (10+) are executed in a row, then the LLM may start to ignore examples in the early parts of the prompt.
-* Ambiguous instructions - if a given instruction doesn't lead to the desired actions, try rephrasing it to remove ambiguities (e.g. place the block on the closest bowl -> place the block on its closest bowl)
-* Maximum token length - you may hit the maximum token length if running multiple commands in sequence. Please reset the simulation when this happens.
diff --git a/spaces/james-oldfield/PandA/networks/stylegan3/torch_utils/ops/filtered_lrelu.cpp b/spaces/james-oldfield/PandA/networks/stylegan3/torch_utils/ops/filtered_lrelu.cpp
deleted file mode 100644
index ff4149b8b46b54d2f400ae10e44d19f20503ba1f..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/stylegan3/torch_utils/ops/filtered_lrelu.cpp
+++ /dev/null
@@ -1,300 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "filtered_lrelu.h"
-
-//------------------------------------------------------------------------
-
-static std::tuple<torch::Tensor, torch::Tensor, int> filtered_lrelu(
-    torch::Tensor x, torch::Tensor fu, torch::Tensor fd, torch::Tensor b, torch::Tensor si,
-    int up, int down, int px0, int px1, int py0, int py1, int sx, int sy, float gain, float slope, float clamp, bool flip_filters, bool writeSigns)
-{
-    // Set CUDA device.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
-    // Validate arguments.
-    TORCH_CHECK(fu.device() == x.device() && fd.device() == x.device() && b.device() == x.device(), "all input tensors must reside on the same device");
-    TORCH_CHECK(fu.dtype() == torch::kFloat && fd.dtype() == torch::kFloat, "fu and fd must be float32");
-    TORCH_CHECK(b.dtype() == x.dtype(), "x and b must have the same dtype");
-    TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat, "x and b must be float16 or float32");
-    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
-    TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
-    TORCH_CHECK(x.numel() > 0, "x is empty");
-    TORCH_CHECK((fu.dim() == 1 || fu.dim() == 2) && (fd.dim() == 1 || fd.dim() == 2), "fu and fd must be rank 1 or 2");
-    TORCH_CHECK(fu.size(0) <= INT_MAX && fu.size(-1) <= INT_MAX, "fu is too large");
-    TORCH_CHECK(fd.size(0) <= INT_MAX && fd.size(-1) <= INT_MAX, "fd is too large");
-    TORCH_CHECK(fu.numel() > 0, "fu is empty");
-    TORCH_CHECK(fd.numel() > 0, "fd is empty");
-    TORCH_CHECK(b.dim() == 1 && b.size(0) == x.size(1), "b must be a vector with the same number of channels as x");
-    TORCH_CHECK(up >= 1 && down >= 1, "up and down must be at least 1");
-
-    // Figure out how much shared memory is available on the device.
-    int maxSharedBytes = 0;
-    AT_CUDA_CHECK(cudaDeviceGetAttribute(&maxSharedBytes, cudaDevAttrMaxSharedMemoryPerBlockOptin, x.device().index()));
-    int sharedKB = maxSharedBytes >> 10;
-
-    // Populate enough launch parameters to check if a CUDA kernel exists.
-    filtered_lrelu_kernel_params p;
-    p.up      = up;
-    p.down    = down;
-    p.fuShape = make_int2((int)fu.size(-1), fu.dim() == 2 ? (int)fu.size(0) : 0); // shape [n, 0] indicates separable filter.
-    p.fdShape = make_int2((int)fd.size(-1), fd.dim() == 2 ? (int)fd.size(0) : 0);
-    filtered_lrelu_kernel_spec test_spec = choose_filtered_lrelu_kernel<float, int32_t, false, false>(p, sharedKB);
-    if (!test_spec.exec)
-    {
-        // No kernel found - return empty tensors and indicate missing kernel with return code of -1.
-        return std::make_tuple(torch::Tensor(), torch::Tensor(), -1);
-    }
-
-    // Input/output element size.
-    int64_t sz = (x.dtype() == torch::kHalf) ? 2 : 4;
-
-    // Input sizes.
-    int64_t xw = (int)x.size(3);
-    int64_t xh = (int)x.size(2);
-    int64_t fut_w = (int)fu.size(-1) - 1;
-    int64_t fut_h = (int)fu.size(0)  - 1;
-    int64_t fdt_w = (int)fd.size(-1) - 1;
-    int64_t fdt_h = (int)fd.size(0)  - 1;
-
-    // Logical size of upsampled buffer.
-    int64_t cw = xw * up + (px0 + px1) - fut_w;
-    int64_t ch = xh * up + (py0 + py1) - fut_h;
-    TORCH_CHECK(cw > fdt_w && ch > fdt_h, "upsampled buffer must be at least the size of downsampling filter");
-    TORCH_CHECK(cw <= INT_MAX && ch <= INT_MAX, "upsampled buffer is too large");
-
-    // Compute output size and allocate.
-    int64_t yw = (cw - fdt_w + (down - 1)) / down;
-    int64_t yh = (ch - fdt_h + (down - 1)) / down;
-    TORCH_CHECK(yw > 0 && yh > 0, "output must be at least 1x1");
-    TORCH_CHECK(yw <= INT_MAX && yh <= INT_MAX, "output is too large");
-    torch::Tensor y = torch::empty({x.size(0), x.size(1), yh, yw}, x.options(), x.suggest_memory_format());
-
-    // Allocate sign tensor.
-    torch::Tensor so;
-    torch::Tensor s = si;
-    bool readSigns = !!s.numel();
-    int64_t sw_active = 0; // Active width of sign tensor.
-    if (writeSigns)
-    {
-        sw_active = yw * down - (down - 1) + fdt_w;     // Active width in elements.
-        int64_t sh = yh * down - (down - 1) + fdt_h;    // Height = active height.
-        int64_t sw = (sw_active + 15) & ~15;            // Width  = active width in elements, rounded up to multiple of 16.
-        TORCH_CHECK(sh <= INT_MAX && (sw >> 2) <= INT_MAX, "signs is too large");
-        s = so = torch::empty({x.size(0), x.size(1), sh, sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
-    }
-    else if (readSigns)
-        sw_active = s.size(3) << 2;
-
-    // Validate sign tensor if in use.
-    if (readSigns || writeSigns)
-    {
-        TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
-        TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
-        TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
-        TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
-        TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
-        TORCH_CHECK(s.size(2) <= INT_MAX && s.size(3) <= INT_MAX, "signs is too large");
-    }
-
-    // Populate rest of CUDA kernel parameters.
-    p.x         = x.data_ptr();
-    p.y         = y.data_ptr();
-    p.b         = b.data_ptr();
-    p.s         = (readSigns || writeSigns) ? s.data_ptr<unsigned char>() : 0;
-    p.fu        = fu.data_ptr<float>();
-    p.fd        = fd.data_ptr<float>();
-    p.pad0      = make_int2(px0, py0);
-    p.gain      = gain;
-    p.slope     = slope;
-    p.clamp     = clamp;
-    p.flip      = (flip_filters) ? 1 : 0;
-    p.xShape    = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
-    p.yShape    = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
-    p.sShape    = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous.
-    p.sOfs      = make_int2(sx, sy);
-    p.swLimit   = (sw_active + 3) >> 2; // Rounded up to bytes.
-
-    // x, y, b strides are in bytes.
-    p.xStride   = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0));
-    p.yStride   = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0));
-    p.bStride   = sz * b.stride(0);
-
-    // fu, fd strides are in elements.
-    p.fuStride  = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0);
-    p.fdStride  = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0);
-
-    // Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those.
-    bool index64b = false;
-    if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true;
-    if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true;
-    if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) >  INT_MAX) index64b = true;
-    if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true;
-    if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) >  INT_MAX) index64b = true;
-    if (s.numel() > INT_MAX) index64b = true;
-
-    // Choose CUDA kernel.
-    filtered_lrelu_kernel_spec spec = { 0 };
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&]
-    {
-        if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation.
-        {
-            // Choose kernel based on index type, datatype and sign read/write modes.
-            if      (!index64b &&  writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, true,  false>(p, sharedKB);
-            else if (!index64b && !writeSigns &&  readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, false, true >(p, sharedKB);
-            else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int32_t, false, false>(p, sharedKB);
-            else if ( index64b &&  writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, true,  false>(p, sharedKB);
-            else if ( index64b && !writeSigns &&  readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, false, true >(p, sharedKB);
-            else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel<scalar_t, int64_t, false, false>(p, sharedKB);
-        }
-    });
-    TORCH_CHECK(spec.exec, "internal error - CUDA kernel not found") // This should not happen because we tested earlier that kernel exists.
-
-    // Launch CUDA kernel.
-    void* args[] = {&p};
-    int bx = spec.numWarps * 32;
-    int gx = (p.yShape.x - 1) / spec.tileOut.x + 1;
-    int gy = (p.yShape.y - 1) / spec.tileOut.y + 1;
-    int gz = p.yShape.z * p.yShape.w;
-
-    // Repeat multiple horizontal tiles in a CTA?
-    if (spec.xrep)
-    {
-        p.tilesXrep = spec.xrep;
-        p.tilesXdim = gx;
-
-        gx = (gx + p.tilesXrep - 1) / p.tilesXrep;
-        std::swap(gx, gy);
-    }
-    else
-    {
-        p.tilesXrep = 0;
-        p.tilesXdim = 0;
-    }
-
-    // Launch filter setup kernel.
-    AT_CUDA_CHECK(cudaLaunchKernel(spec.setup, 1, 1024, args, 0, at::cuda::getCurrentCUDAStream()));
-
-    // Copy kernels to constant memory.
-    if      ( writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters<true,  false>(at::cuda::getCurrentCUDAStream())));
-    else if (!writeSigns &&  readSigns) AT_CUDA_CHECK((copy_filters<false, true >(at::cuda::getCurrentCUDAStream())));
-    else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters<false, false>(at::cuda::getCurrentCUDAStream())));
-
-    // Set cache and shared memory configurations for main kernel.
-    AT_CUDA_CHECK(cudaFuncSetCacheConfig(spec.exec, cudaFuncCachePreferShared));
-    if (spec.dynamicSharedKB) // Need dynamically allocated shared memory?
-        AT_CUDA_CHECK(cudaFuncSetAttribute(spec.exec, cudaFuncAttributeMaxDynamicSharedMemorySize, spec.dynamicSharedKB << 10));
-    AT_CUDA_CHECK(cudaFuncSetSharedMemConfig(spec.exec, cudaSharedMemBankSizeFourByte));
-
-    // Launch main kernel.
-    const int maxSubGz = 65535; // CUDA maximum for block z dimension.
-    for (int zofs=0; zofs < gz; zofs += maxSubGz) // Do multiple launches if gz is too big.
-    {
-        p.blockZofs = zofs;
-        int subGz = std::min(maxSubGz, gz - zofs);
-        AT_CUDA_CHECK(cudaLaunchKernel(spec.exec, dim3(gx, gy, subGz), bx, args, spec.dynamicSharedKB << 10, at::cuda::getCurrentCUDAStream()));
-    }
-
-    // Done.
-    return std::make_tuple(y, so, 0);
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor si, int sx, int sy, float gain, float slope, float clamp, bool writeSigns)
-{
-    // Set CUDA device.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
-    // Validate arguments.
-    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
-    TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
-    TORCH_CHECK(x.numel() > 0, "x is empty");
-    TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat || x.dtype() == torch::kDouble, "x must be float16, float32 or float64");
-
-    // Output signs if we don't have sign input.
-    torch::Tensor so;
-    torch::Tensor s = si;
-    bool readSigns = !!s.numel();
-    if (writeSigns)
-    {
-        int64_t sw = x.size(3);
-        sw = (sw + 15) & ~15; // Round to a multiple of 16 for coalescing.
-        s = so = torch::empty({x.size(0), x.size(1), x.size(2), sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
-    }
-
-    // Validate sign tensor if in use.
-    if (readSigns || writeSigns)
-    {
-        TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
-        TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
-        TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
-        TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
-        TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
-        TORCH_CHECK(s.size(2) <= INT_MAX && (s.size(3) << 2) <= INT_MAX, "signs tensor is too large");
-    }
-
-    // Initialize CUDA kernel parameters.
-    filtered_lrelu_act_kernel_params p;
-    p.x         = x.data_ptr();
-    p.s         = (readSigns || writeSigns) ? s.data_ptr<unsigned char>() : 0;
-    p.gain      = gain;
-    p.slope     = slope;
-    p.clamp     = clamp;
-    p.xShape    = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
-    p.xStride   = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0));
-    p.sShape    = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous.
-    p.sOfs      = make_int2(sx, sy);
-
-    // Choose CUDA kernel.
-    void* func = 0;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&]
-    {
-        if (writeSigns)
-            func = choose_filtered_lrelu_act_kernel<scalar_t, true, false>();
-        else if (readSigns)
-            func = choose_filtered_lrelu_act_kernel<scalar_t, false, true>();
-        else
-            func = choose_filtered_lrelu_act_kernel<scalar_t, false, false>();
-    });
-    TORCH_CHECK(func, "internal error - CUDA kernel not found");
-
-    // Launch CUDA kernel.
-    void* args[] = {&p};
-    int bx = 128; // 4 warps per block.
-
-    // Logical size of launch = writeSigns ? p.s : p.x
-    uint32_t gx = writeSigns ? p.sShape.x : p.xShape.x;
-    uint32_t gy = writeSigns ? p.sShape.y : p.xShape.y;
-    uint32_t gz = p.xShape.z * p.xShape.w; // Same as in p.sShape if signs are in use.
-    gx = (gx - 1) / bx + 1;
-
-    // Make sure grid y and z dimensions are within CUDA launch limits. Kernel loops internally to do the rest.
-    const uint32_t gmax = 65535;
-    gy = std::min(gy, gmax);
-    gz = std::min(gz, gmax);
-
-    // Launch.
-    AT_CUDA_CHECK(cudaLaunchKernel(func, dim3(gx, gy, gz), bx, args, 0, at::cuda::getCurrentCUDAStream()));
-    return so;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("filtered_lrelu",      &filtered_lrelu);      // The whole thing.
-    m.def("filtered_lrelu_act_", &filtered_lrelu_act);  // Activation and sign tensor handling only. Modifies data tensor in-place.
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/jatin-tech/SkinZen/Dockerfile b/spaces/jatin-tech/SkinZen/Dockerfile
deleted file mode 100644
index 8a4d3bc8fa468aae2aec01c391b2410658c97d6a..0000000000000000000000000000000000000000
--- a/spaces/jatin-tech/SkinZen/Dockerfile
+++ /dev/null
@@ -1,32 +0,0 @@
-FROM python:3.9
-
-WORKDIR  /code
-
-COPY ./requirements.txt  /code/requirements.txt
-
-RUN apt-get update && apt-get upgrade -y
-
-RUN apt-get install -y software-properties-common
-RUN apt-get install -y build-essential cmake pkg-config \
-    && apt-get install -y libx11-dev libatlas-base-dev \
-    && apt-get install -y libgtk-3-dev libboost-python-dev
-
-RUN pip install --no-cache-dir -r /code/requirements.txt
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -m -u 1000 user
-
-# Switch to the "user" user
-USER user
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app
-
-CMD ["python", "main.py"]
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/VideoQuest/src/app/interface/renderer/full-screen-button.tsx b/spaces/jbilcke-hf/VideoQuest/src/app/interface/renderer/full-screen-button.tsx
deleted file mode 100644
index 2daa33beae917eb150252a10d90f11f9ec5be9c3..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/VideoQuest/src/app/interface/renderer/full-screen-button.tsx
+++ /dev/null
@@ -1,18 +0,0 @@
-import { cn } from "@/lib/utils"
-import { FullScreenIcon } from "../../../components/icons/full-screen"
-
-export function FullScreenButton() {
-  return (
-    <div className={cn(
-      `absolute z-10 right-0 bottom-0`,
-    )}>
-      <div
-        className={cn(
-          `transform-all duration-200 text-white opacity-80 cursor-pointer scale-100`,
-          `hover:opacity-100 hover:text-white hover:scale-110 p-4`
-        )}>
-        <FullScreenIcon />
-      </div>
-    </div>
-  )
-}
\ No newline at end of file
diff --git a/spaces/jeonchangbin49/De-limiter/test_ddp.py b/spaces/jeonchangbin49/De-limiter/test_ddp.py
deleted file mode 100644
index 9e4db2c6799879bfba93e8f62c61d3150f6b8498..0000000000000000000000000000000000000000
--- a/spaces/jeonchangbin49/De-limiter/test_ddp.py
+++ /dev/null
@@ -1,245 +0,0 @@
-# To be honest... this is not ddp.
-import os
-import json
-import argparse
-import glob
-
-import torch
-import tqdm
-import musdb
-import librosa
-import soundfile as sf
-import pyloudnorm as pyln
-from dotmap import DotMap
-
-from models import load_model_with_args
-from separate_func import (
-    conv_tasnet_separate,
-)
-from utils import str2bool, db2linear
-
-
-tqdm.monitor_interval = 0
-
-
-def separate_track_with_model(
-    args, model, device, track_audio, track_name, meter, augmented_gain
-):
-    with torch.no_grad():
-        if (
-            args.model_loss_params.architecture == "conv_tasnet_mask_on_output"
-            or args.model_loss_params.architecture == "conv_tasnet"
-        ):
-            estimates = conv_tasnet_separate(
-                args,
-                model,
-                device,
-                track_audio,
-                track_name,
-                meter=meter,
-                augmented_gain=augmented_gain,
-            )
-
-        return estimates
-
-
-def main():
-    parser = argparse.ArgumentParser(description="model test.py")
-
-    parser.add_argument("--target", type=str, default="all")
-    parser.add_argument("--data_root", type=str, default="/path/to/musdb_XL")
-    parser.add_argument(
-        "--use_musdb",
-        type=str2bool,
-        default=True,
-        help="Use musdb test data or just want to inference other samples?",
-    )
-    parser.add_argument("--exp_name", type=str, default="delimit_6_s')
-    parser.add_argument("--manual_output_name", type=str, default=None)
-    parser.add_argument(
-        "--output_directory", type=str, default="/path/to/results"
-    )
-    parser.add_argument("--use_gpu", type=str2bool, default=True)
-    parser.add_arugment("--save_name_as_target", type=str2bool, default=True)
-    parser.add_argument(
-        "--loudnorm_input_lufs",
-        type=float,
-        default=None,
-        help="If you want to use loudnorm, input target lufs",
-    )
-    parser.add_argument(
-        "--use_singletrackset",
-        type=str2bool,
-        default=False,
-        help="Use SingleTrackSet for X-UMX",
-    )
-    parser.add_argument(
-        "--best_model",
-        type=str2bool,
-        default=True,
-        help="Use best model or lastly saved model",
-    )
-    parser.add_argument(
-        "--save_output_loudnorm",
-        type=float,
-        default=None,
-        help="Save loudness normalized outputs or not. If you want to save, input target loudness",
-    )
-    parser.add_argument(
-        "--save_mixed_output",
-        type=float,
-        default=None,
-        help="Save original+delimited-estimation mixed output with a ratio of default 0.5 (orginal) and 1 - 0.5 (estimation)",
-    )
-    parser.add_argument(
-        "--save_16k_mono",
-        type=str2bool,
-        default=False,
-        help="Save 16k mono wav files for FAD evaluation.",
-    )
-    parser.add_argument(
-        "--save_histogram",
-        type=str2bool,
-        default=False,
-        help="Save histogram of the output. Only valid when the task is 'delimit'",
-    )
-
-    args, _ = parser.parse_known_args()
-
-    args.output_dir = f"{args.output_directory}/checkpoint/{args.exp_name}"
-    with open(f"{args.output_dir}/{args.target}.json", "r") as f:
-        args_dict = json.load(f)
-        args_dict = DotMap(args_dict)
-
-    for key, value in args_dict["args"].items():
-        if key in list(vars(args).keys()):
-            pass
-        else:
-            setattr(args, key, value)
-
-    args.test_output_dir = f"{args.output_directory}/test/{args.exp_name}"
-
-    if args.manual_output_name != None:
-        args.test_output_dir = f"{args.output_directory}/test/{args.manual_output_name}"
-    os.makedirs(args.test_output_dir, exist_ok=True)
-
-    device = torch.device(
-        "cuda" if torch.cuda.is_available() and args.use_gpu else "cpu"
-    )
-
-    ###################### Define Models ######################
-    our_model = load_model_with_args(args)
-    our_model = our_model.to(device)
-    print(our_model)
-    pytorch_total_params = sum(
-        p.numel() for p in our_model.parameters() if p.requires_grad
-    )
-    print("Total number of parameters", pytorch_total_params)
-    # Future work => Torchinfo would be better for this purpose.
-
-    if args.best_model:
-        target_model_path = f"{args.output_dir}/{args.target}.pth"
-        checkpoint = torch.load(target_model_path, map_location=device)
-        our_model.load_state_dict(checkpoint)
-    else:  # when using lastly saved model
-        target_model_path = f"{args.output_dir}/{args.target}.chkpnt"
-        checkpoint = torch.load(target_model_path, map_location=device)
-        our_model.load_state_dict(checkpoint["state_dict"])
-
-    our_model.eval()
-
-    meter = pyln.Meter(44100)
-
-    if args.use_musdb:
-        test_tracks = musdb.DB(root=args.data_root, subsets="test", is_wav=True)
-
-        for track in tqdm.tqdm(test_tracks):
-            track_name = track.name
-            track_audio = track.audio
-
-            orig_audio = track_audio.copy()
-
-            augmented_gain = None
-            print("Now De-limiting : ", track_name)
-
-            if args.loudnorm_input_lufs:  # If you want to use loud-normalized input
-                track_lufs = meter.integrated_loudness(track_audio)
-                augmented_gain = args.loudnorm_input_lufs - track_lufs
-                track_audio = track_audio * db2linear(augmented_gain, eps=0.0)
-
-            track_audio = (
-                torch.as_tensor(track_audio.T, dtype=torch.float32)
-                .unsqueeze(0)
-                .to(device)
-            )
-
-            estimates = separate_track_with_model(
-                args, our_model, device, track_audio, track_name, meter, augmented_gain
-            )
-
-            if args.save_mixed_output:
-                orig_audio = orig_audio.T
-                track_lufs = meter.integrated_loudness(orig_audio.T)
-                augmented_gain = args.save_output_loudnorm - track_lufs
-                orig_audio = orig_audio * db2linear(augmented_gain, eps=0.0)
-
-                mixed_output = orig_audio * args.save_mixed_output + estimates * (
-                    1 - args.save_mixed_output
-                )
-
-                sf.write(
-                    f"{args.test_output_dir}/{track_name}/{str(args.save_mixed_output)}_mixed.wav",
-                    mixed_output.T,
-                    args.data_params.sample_rate,
-                )
-    else:
-        test_tracks = glob.glob(f"{args.data_root}/*.wav") + glob.glob(
-            f"{args.data_root}/*.mp3"
-        )
-
-        for track in tqdm.tqdm(test_tracks):
-            track_name = os.path.basename(track).replace(".wav", "").replace(".mp3", "")
-            track_audio, sr = librosa.load(
-                track, sr=None, mono=False
-            )  # sr should be 44100
-
-            orig_audio = track_audio.copy()
-
-            if sr != 44100:
-                raise ValueError("Sample rate should be 44100")
-            augmented_gain = None
-            print("Now De-limiting : ", track_name)
-
-            if args.loudnorm_input_lufs:  # If you want to use loud-normalized input
-                track_lufs = meter.integrated_loudness(track_audio.T)
-                augmented_gain = args.loudnorm_input_lufs - track_lufs
-                track_audio = track_audio * db2linear(augmented_gain, eps=0.0)
-
-            track_audio = (
-                torch.as_tensor(track_audio, dtype=torch.float32)
-                .unsqueeze(0)
-                .to(device)
-            )
-
-            estimates = separate_track_with_model(
-                args, our_model, device, track_audio, track_name, meter, augmented_gain
-            )
-
-            if args.save_mixed_output:
-                track_lufs = meter.integrated_loudness(orig_audio.T)
-                augmented_gain = args.save_output_loudnorm - track_lufs
-                orig_audio = orig_audio * db2linear(augmented_gain, eps=0.0)
-
-                mixed_output = orig_audio * args.save_mixed_output + estimates * (
-                    1 - args.save_mixed_output
-                )
-
-                sf.write(
-                    f"{args.test_output_dir}/{track_name}/{track_name}_mixed.wav",
-                    mixed_output.T,
-                    args.data_params.sample_rate,
-                )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/jgurzoni/image_background_swapper/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py b/spaces/jgurzoni/image_background_swapper/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py
deleted file mode 100644
index 45bb3c8cfd36d8f668e6fde756b17587eab72082..0000000000000000000000000000000000000000
--- a/spaces/jgurzoni/image_background_swapper/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : test_sync_batchnorm.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-
-import unittest
-
-import torch
-import torch.nn as nn
-from torch.autograd import Variable
-
-from sync_batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, DataParallelWithCallback
-from sync_batchnorm.unittest import TorchTestCase
-
-
-def handy_var(a, unbias=True):
-    n = a.size(0)
-    asum = a.sum(dim=0)
-    as_sum = (a ** 2).sum(dim=0)  # a square sum
-    sumvar = as_sum - asum * asum / n
-    if unbias:
-        return sumvar / (n - 1)
-    else:
-        return sumvar / n
-
-
-def _find_bn(module):
-    for m in module.modules():
-        if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, SynchronizedBatchNorm1d, SynchronizedBatchNorm2d)):
-            return m
-
-
-class SyncTestCase(TorchTestCase):
-    def _syncParameters(self, bn1, bn2):
-        bn1.reset_parameters()
-        bn2.reset_parameters()
-        if bn1.affine and bn2.affine:
-            bn2.weight.data.copy_(bn1.weight.data)
-            bn2.bias.data.copy_(bn1.bias.data)
-
-    def _checkBatchNormResult(self, bn1, bn2, input, is_train, cuda=False):
-        """Check the forward and backward for the customized batch normalization."""
-        bn1.train(mode=is_train)
-        bn2.train(mode=is_train)
-
-        if cuda:
-            input = input.cuda()
-
-        self._syncParameters(_find_bn(bn1), _find_bn(bn2))
-
-        input1 = Variable(input, requires_grad=True)
-        output1 = bn1(input1)
-        output1.sum().backward()
-        input2 = Variable(input, requires_grad=True)
-        output2 = bn2(input2)
-        output2.sum().backward()
-
-        self.assertTensorClose(input1.data, input2.data)
-        self.assertTensorClose(output1.data, output2.data)
-        self.assertTensorClose(input1.grad, input2.grad)
-        self.assertTensorClose(_find_bn(bn1).running_mean, _find_bn(bn2).running_mean)
-        self.assertTensorClose(_find_bn(bn1).running_var, _find_bn(bn2).running_var)
-
-    def testSyncBatchNormNormalTrain(self):
-        bn = nn.BatchNorm1d(10)
-        sync_bn = SynchronizedBatchNorm1d(10)
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True)
-
-    def testSyncBatchNormNormalEval(self):
-        bn = nn.BatchNorm1d(10)
-        sync_bn = SynchronizedBatchNorm1d(10)
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False)
-
-    def testSyncBatchNormSyncTrain(self):
-        bn = nn.BatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-
-        bn.cuda()
-        sync_bn.cuda()
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True, cuda=True)
-
-    def testSyncBatchNormSyncEval(self):
-        bn = nn.BatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-
-        bn.cuda()
-        sync_bn.cuda()
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False, cuda=True)
-
-    def testSyncBatchNorm2DSyncTrain(self):
-        bn = nn.BatchNorm2d(10)
-        sync_bn = SynchronizedBatchNorm2d(10)
-        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-
-        bn.cuda()
-        sync_bn.cuda()
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10, 16, 16), True, cuda=True)
-
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/pagerange.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/pagerange.py
deleted file mode 100644
index f009adc195a860336c783c1a58a184b10c48fa6b..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PyPDF2/pagerange.py
+++ /dev/null
@@ -1,173 +0,0 @@
-"""
-Representation and utils for ranges of PDF file pages.
-
-Copyright (c) 2014, Steve Witham <switham_github@mac-guyver.com>.
-All rights reserved. This software is available under a BSD license;
-see https://github.com/py-pdf/PyPDF2/blob/main/LICENSE
-"""
-
-import re
-from typing import Any, List, Tuple, Union
-
-from .errors import ParseError
-
-_INT_RE = r"(0|-?[1-9]\d*)"  # A decimal int, don't allow "-0".
-PAGE_RANGE_RE = "^({int}|({int}?(:{int}?(:{int}?)?)))$".format(int=_INT_RE)
-# groups:         12     34     5 6     7 8
-
-
-class PageRange:
-    """
-    A slice-like representation of a range of page indices.
-
-    For example, page numbers, only starting at zero.
-
-    The syntax is like what you would put between brackets [ ].
-    The slice is one of the few Python types that can't be subclassed,
-    but this class converts to and from slices, and allows similar use.
-
-      -  PageRange(str) parses a string representing a page range.
-      -  PageRange(slice) directly "imports" a slice.
-      -  to_slice() gives the equivalent slice.
-      -  str() and repr() allow printing.
-      -  indices(n) is like slice.indices(n).
-
-    """
-
-    def __init__(self, arg: Union[slice, "PageRange", str]) -> None:
-        """
-        Initialize with either a slice -- giving the equivalent page range,
-        or a PageRange object -- making a copy,
-        or a string like
-            "int", "[int]:[int]" or "[int]:[int]:[int]",
-            where the brackets indicate optional ints.
-        Remember, page indices start with zero.
-        Page range expression examples:
-            :     all pages.                   -1    last page.
-            22    just the 23rd page.          :-1   all but the last page.
-            0:3   the first three pages.       -2    second-to-last page.
-            :3    the first three pages.       -2:   last two pages.
-            5:    from the sixth page onward.  -3:-1 third & second to last.
-        The third, "stride" or "step" number is also recognized.
-            ::2       0 2 4 ... to the end.    3:0:-1    3 2 1 but not 0.
-            1:10:2    1 3 5 7 9                2::-1     2 1 0.
-            ::-1      all pages in reverse order.
-        Note the difference between this notation and arguments to slice():
-            slice(3) means the first three pages;
-            PageRange("3") means the range of only the fourth page.
-            However PageRange(slice(3)) means the first three pages.
-        """
-        if isinstance(arg, slice):
-            self._slice = arg
-            return
-
-        if isinstance(arg, PageRange):
-            self._slice = arg.to_slice()
-            return
-
-        m = isinstance(arg, str) and re.match(PAGE_RANGE_RE, arg)
-        if not m:
-            raise ParseError(arg)
-        elif m.group(2):
-            # Special case: just an int means a range of one page.
-            start = int(m.group(2))
-            stop = start + 1 if start != -1 else None
-            self._slice = slice(start, stop)
-        else:
-            self._slice = slice(*[int(g) if g else None for g in m.group(4, 6, 8)])
-
-    @staticmethod
-    def valid(input: Any) -> bool:
-        """True if input is a valid initializer for a PageRange."""
-        return isinstance(input, (slice, PageRange)) or (
-            isinstance(input, str) and bool(re.match(PAGE_RANGE_RE, input))
-        )
-
-    def to_slice(self) -> slice:
-        """Return the slice equivalent of this page range."""
-        return self._slice
-
-    def __str__(self) -> str:
-        """A string like "1:2:3"."""
-        s = self._slice
-        indices: Union[Tuple[int, int], Tuple[int, int, int]]
-        if s.step is None:
-            if s.start is not None and s.stop == s.start + 1:
-                return str(s.start)
-
-            indices = s.start, s.stop
-        else:
-            indices = s.start, s.stop, s.step
-        return ":".join("" if i is None else str(i) for i in indices)
-
-    def __repr__(self) -> str:
-        """A string like "PageRange('1:2:3')"."""
-        return "PageRange(" + repr(str(self)) + ")"
-
-    def indices(self, n: int) -> Tuple[int, int, int]:
-        """
-        n is the length of the list of pages to choose from.
-
-        Returns arguments for range().  See help(slice.indices).
-        """
-        return self._slice.indices(n)
-
-    def __eq__(self, other: Any) -> bool:
-        if not isinstance(other, PageRange):
-            return False
-        return self._slice == other._slice
-
-    def __add__(self, other: "PageRange") -> "PageRange":
-        if not isinstance(other, PageRange):
-            raise TypeError(f"Can't add PageRange and {type(other)}")
-        if self._slice.step is not None or other._slice.step is not None:
-            raise ValueError("Can't add PageRange with stride")
-        a = self._slice.start, self._slice.stop
-        b = other._slice.start, other._slice.stop
-
-        if a[0] > b[0]:
-            a, b = b, a
-
-        # Now a[0] is the smallest
-        if b[0] > a[1]:
-            # There is a gap between a and b.
-            raise ValueError("Can't add PageRanges with gap")
-        return PageRange(slice(a[0], max(a[1], b[1])))
-
-
-PAGE_RANGE_ALL = PageRange(":")  # The range of all pages.
-
-
-def parse_filename_page_ranges(
-    args: List[Union[str, PageRange, None]]
-) -> List[Tuple[str, PageRange]]:
-    """
-    Given a list of filenames and page ranges, return a list of (filename, page_range) pairs.
-
-    First arg must be a filename; other ags are filenames, page-range
-    expressions, slice objects, or PageRange objects.
-    A filename not followed by a page range indicates all pages of the file.
-    """
-    pairs: List[Tuple[str, PageRange]] = []
-    pdf_filename = None
-    did_page_range = False
-    for arg in args + [None]:
-        if PageRange.valid(arg):
-            if not pdf_filename:
-                raise ValueError(
-                    "The first argument must be a filename, not a page range."
-                )
-
-            pairs.append((pdf_filename, PageRange(arg)))
-            did_page_range = True
-        else:
-            # New filename or end of list--do all of the previous file?
-            if pdf_filename and not did_page_range:
-                pairs.append((pdf_filename, PAGE_RANGE_ALL))
-
-            pdf_filename = arg
-            did_page_range = False
-    return pairs
-
-
-PageRangeSpec = Union[str, PageRange, Tuple[int, int], Tuple[int, int, int], List[int]]
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/data_structs/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/data_structs/__init__.py
deleted file mode 100644
index 32181901655fb562616784f8dca83a482b9e76ac..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/data_structs/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-"""Init file."""
-
-from gpt_index.data_structs.data_structs import (
-    IndexDict,
-    IndexGraph,
-    IndexList,
-    KeywordTable,
-    Node,
-)
-from gpt_index.data_structs.table import StructDatapoint
-
-__all__ = [
-    "Node",
-    "IndexGraph",
-    "KeywordTable",
-    "IndexList",
-    "IndexDict",
-    "StructDatapoint",
-]
diff --git a/spaces/jone/Music_Source_Separation/bytesep/models/subband_tools/__init__.py b/spaces/jone/Music_Source_Separation/bytesep/models/subband_tools/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/jonjhiggins/MiDaS/app.py b/spaces/jonjhiggins/MiDaS/app.py
deleted file mode 100644
index 489938862fb3180daf9c4fe59ef129a8e45a14b0..0000000000000000000000000000000000000000
--- a/spaces/jonjhiggins/MiDaS/app.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import cv2
-import torch
-import gradio as gr
-import numpy as np
-from PIL import Image
-
-torch.hub.download_url_to_file('https://images.unsplash.com/photo-1437622368342-7a3d73a34c8f', 'turtle.jpg')
-torch.hub.download_url_to_file('https://images.unsplash.com/photo-1519066629447-267fffa62d4b', 'lions.jpg')
-
-midas = torch.hub.load("intel-isl/MiDaS", "MiDaS")
-
-use_large_model = True
-
-if use_large_model:
-    midas = torch.hub.load("intel-isl/MiDaS", "MiDaS")
-else:
-    midas = torch.hub.load("intel-isl/MiDaS", "MiDaS_small")
-
-device = "cpu"
-midas.to(device)
-
-midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
-
-if use_large_model:
-    transform = midas_transforms.default_transform
-else:
-    transform = midas_transforms.small_transform
-
-
-def depth(img):
-  cv_image = np.array(img) 
-  img = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
-
-  input_batch = transform(img).to(device)
-  with torch.no_grad():
-    prediction = midas(input_batch)
-
-    prediction = torch.nn.functional.interpolate(
-        prediction.unsqueeze(1),
-        size=img.shape[:2],
-        mode="bicubic",
-        align_corners=False,
-    ).squeeze()
-    
-  output = prediction.cpu().numpy()
-  formatted = (output * 255 / np.max(output)).astype('uint8')
-  img = Image.fromarray(formatted)
-  return img
-    
-
-inputs =  gr.inputs.Image(type='pil', label="Original Image")
-outputs = gr.outputs.Image(type="pil",label="Output Image")
-
-title = "MiDaS"
-description = "Gradio demo for MiDaS v2.1 which takes in a single image for computing relative depth. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.01341v3'>Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer</a> | <a href='https://github.com/intel-isl/MiDaS'>Github Repo</a></p>"
-
-examples = [
-    ["turtle.jpg"],
-    ["lions.jpg"]
-]
-
-gr.Interface(depth, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()
\ No newline at end of file
diff --git a/spaces/jordonpeter01/ai-comic-factory/src/app/engine/render.ts b/spaces/jordonpeter01/ai-comic-factory/src/app/engine/render.ts
deleted file mode 100644
index 9510e789979f0e45cc28b98bee18aa73c812e7f5..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/ai-comic-factory/src/app/engine/render.ts
+++ /dev/null
@@ -1,294 +0,0 @@
-"use server"
-
-import Replicate, { Prediction } from "replicate"
-
-import { RenderRequest, RenderedScene, RenderingEngine } from "@/types"
-import { generateSeed } from "@/lib/generateSeed"
-import { sleep } from "@/lib/sleep"
-
-const renderingEngine = `${process.env.RENDERING_ENGINE || ""}` as RenderingEngine
-
-const replicateToken = `${process.env.REPLICATE_API_TOKEN || ""}`
-const replicateModel = `${process.env.REPLICATE_API_MODEL || ""}`
-const replicateModelVersion = `${process.env.REPLICATE_API_MODEL_VERSION || ""}`
-
-// note: there is no / at the end in the variable
-// so we have to add it ourselves if needed
-const apiUrl = process.env.VIDEOCHAIN_API_URL
-
-export async function newRender({
-  prompt,
-  // negativePrompt,
-  width,
-  height
-}: {
-  prompt: string
-  // negativePrompt: string[]
-  width: number
-  height: number
-}) {
-  // console.log(`newRender(${prompt})`)
-  if (!prompt) {
-    console.error(`cannot call the rendering API without a prompt, aborting..`)
-    throw new Error(`cannot call the rendering API without a prompt, aborting..`)
-  }
-
-  let defaulResult: RenderedScene = {
-    renderId: "",
-    status: "error",
-    assetUrl: "",
-    alt: prompt || "",
-    maskUrl: "",
-    error: "failed to fetch the data",
-    segments: []
-  }
-
-
-  try {
-    if (renderingEngine === "REPLICATE") {
-      if (!replicateToken) {
-        throw new Error(`you need to configure your REPLICATE_API_TOKEN in order to use the REPLICATE rendering engine`)
-      }
-      if (!replicateModel) {
-        throw new Error(`you need to configure your REPLICATE_API_MODEL in order to use the REPLICATE rendering engine`)
-      }
-      if (!replicateModelVersion) {
-        throw new Error(`you need to configure your REPLICATE_API_MODEL_VERSION in order to use the REPLICATE rendering engine`)
-      }
-      const replicate = new Replicate({ auth: replicateToken })
-
-      // console.log("Calling replicate..")
-      const seed = generateSeed()
-      const prediction = await replicate.predictions.create({
-        version: replicateModelVersion,
-        input: { prompt, seed }
-      })
-      
-      // console.log("prediction:", prediction)
-
-      // no need to reply straight away: good things take time
-      // also our friends at Replicate won't like it if we spam them with requests
-      await sleep(4000)
-
-      return {
-        renderId: prediction.id,
-        status: "pending",
-        assetUrl: "", 
-        alt: prompt,
-        error: prediction.error,
-        maskUrl: "",
-        segments: []
-      } as RenderedScene
-    } else {
-      // console.log(`calling POST ${apiUrl}/render with prompt: ${prompt}`)
-      const res = await fetch(`${apiUrl}/render`, {
-        method: "POST",
-        headers: {
-          Accept: "application/json",
-          "Content-Type": "application/json",
-          Authorization: `Bearer ${process.env.VIDEOCHAIN_API_TOKEN}`,
-        },
-        body: JSON.stringify({
-          prompt,
-          // negativePrompt, unused for now
-          nbFrames: 1,
-          nbSteps: 25, // 20 = fast, 30 = better, 50 = best
-          actionnables: [], // ["text block"],
-          segmentation: "disabled", // "firstframe", // one day we will remove this param, to make it automatic
-          width,
-          height,
-
-          // no need to upscale right now as we generate tiny panels
-          // maybe later we can provide an "export" button to PDF
-          // unfortunately there are too many requests for upscaling,
-          // the server is always down
-          upscalingFactor: 1, // 2,
-
-          // analyzing doesn't work yet, it seems..
-          analyze: false, // analyze: true,
-
-          cache: "ignore"
-        } as Partial<RenderRequest>),
-        cache: 'no-store',
-      // we can also use this (see https://vercel.com/blog/vercel-cache-api-nextjs-cache)
-      // next: { revalidate: 1 }
-      })
-
-
-      // console.log("res:", res)
-      // The return value is *not* serialized
-      // You can return Date, Map, Set, etc.
-      
-      // Recommendation: handle errors
-      if (res.status !== 200) {
-        // This will activate the closest `error.js` Error Boundary
-        throw new Error('Failed to fetch data')
-      }
-      
-      const response = (await res.json()) as RenderedScene
-      return response
-    }
-  } catch (err) {
-    console.error(err)
-    return defaulResult
-  }
-}
-
-export async function getRender(renderId: string) {
-  if (!renderId) {
-    console.error(`cannot call the rendering API without a renderId, aborting..`)
-    throw new Error(`cannot call the rendering API without a renderId, aborting..`)
-  }
-
-  let defaulResult: RenderedScene = {
-    renderId: "",
-    status: "pending",
-    assetUrl: "",
-    alt: "",
-    maskUrl: "",
-    error: "failed to fetch the data",
-    segments: []
-  }
-
-  try {
-    if (renderingEngine === "REPLICATE") {
-      if (!replicateToken) {
-        throw new Error(`you need to configure your REPLICATE_API_TOKEN in order to use the REPLICATE rendering engine`)
-      }
-      if (!replicateModel) {
-        throw new Error(`you need to configure your REPLICATE_API_MODEL in order to use the REPLICATE rendering engine`)
-      }
-
-      // const replicate = new Replicate({ auth: replicateToken })
-
-      // console.log("Calling replicate..")
-      // const prediction = await replicate.predictions.get(renderId)
-      // console.log("Prediction:", prediction)
-
-       // console.log(`calling GET https://api.replicate.com/v1/predictions/${renderId}`)
-       const res = await fetch(`https://api.replicate.com/v1/predictions/${renderId}`, {
-        method: "GET",
-        headers: {
-          // Accept: "application/json",
-          // "Content-Type": "application/json",
-          Authorization: `Token ${replicateToken}`,
-        },
-        cache: 'no-store',
-      // we can also use this (see https://vercel.com/blog/vercel-cache-api-nextjs-cache)
-      // next: { revalidate: 1 }
-      })
-    
-      // console.log("res:", res)
-      // The return value is *not* serialized
-      // You can return Date, Map, Set, etc.
-      
-      // Recommendation: handle errors
-      if (res.status !== 200) {
-        // This will activate the closest `error.js` Error Boundary
-        throw new Error('Failed to fetch data')
-      }
-      
-      const response = (await res.json()) as any
-      // console.log("response:", response)
-
-      return  {
-        renderId,
-        status: response?.error ? "error" : response?.status === "succeeded" ?  "completed" : "pending",
-        assetUrl: `${response?.output || ""}`,
-        alt: `${response?.input?.prompt || ""}`,
-        error: `${response?.error || ""}`,
-        maskUrl: "",
-        segments: []
-      } as RenderedScene
-    } else {
-      // console.log(`calling GET ${apiUrl}/render with renderId: ${renderId}`)
-      const res = await fetch(`${apiUrl}/render/${renderId}`, {
-        method: "GET",
-        headers: {
-          Accept: "application/json",
-          "Content-Type": "application/json",
-          Authorization: `Bearer ${process.env.VIDEOCHAIN_API_TOKEN}`,
-        },
-        cache: 'no-store',
-      // we can also use this (see https://vercel.com/blog/vercel-cache-api-nextjs-cache)
-      // next: { revalidate: 1 }
-      })
-
-      // console.log("res:", res)
-      // The return value is *not* serialized
-      // You can return Date, Map, Set, etc.
-      
-      // Recommendation: handle errors
-      if (res.status !== 200) {
-        // This will activate the closest `error.js` Error Boundary
-        throw new Error('Failed to fetch data')
-      }
-      
-      const response = (await res.json()) as RenderedScene
-      // console.log("response:", response)
-      return response
-    }
-  } catch (err) {
-    console.error(err)
-    defaulResult.status = "error"
-    defaulResult.error = `${err}`
-    // Gorgon.clear(cacheKey)
-    return defaulResult
-  }
-
-  // }, cacheDurationInSec * 1000)
-}
-
-export async function upscaleImage(image: string): Promise<{
-  assetUrl: string
-  error: string
-}> {
-  if (!image) {
-    console.error(`cannot call the rendering API without an image, aborting..`)
-    throw new Error(`cannot call the rendering API without an image, aborting..`)
-  }
-
-  let defaulResult = {
-    assetUrl: "",
-    error: "failed to fetch the data",
-  }
-
-  try {
-    // console.log(`calling GET ${apiUrl}/render with renderId: ${renderId}`)
-    const res = await fetch(`${apiUrl}/upscale`, {
-      method: "POST",
-      headers: {
-        Accept: "application/json",
-        "Content-Type": "application/json",
-        Authorization: `Bearer ${process.env.VIDEOCHAIN_API_TOKEN}`,
-      },
-      cache: 'no-store',
-      body: JSON.stringify({ image, factor: 3 })
-    // we can also use this (see https://vercel.com/blog/vercel-cache-api-nextjs-cache)
-    // next: { revalidate: 1 }
-    })
-
-    // console.log("res:", res)
-    // The return value is *not* serialized
-    // You can return Date, Map, Set, etc.
-    
-    // Recommendation: handle errors
-    if (res.status !== 200) {
-      // This will activate the closest `error.js` Error Boundary
-      throw new Error('Failed to fetch data')
-    }
-    
-    const response = (await res.json()) as {
-      assetUrl: string
-      error: string
-    }
-    // console.log("response:", response)
-    return response
-  } catch (err) {
-    console.error(err)
-    // Gorgon.clear(cacheKey)
-    return defaulResult
-  }
-
-  // }, cacheDurationInSec * 1000)
-}
diff --git a/spaces/justest/gpt4free/g4f/Provider/Providers/Theb.py b/spaces/justest/gpt4free/g4f/Provider/Providers/Theb.py
deleted file mode 100644
index aa43ebc55d74ffaa722fe008424fce97c622a323..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/g4f/Provider/Providers/Theb.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import os
-import json
-import time
-import subprocess
-
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://theb.ai'
-model = ['gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-
-    path = os.path.dirname(os.path.realpath(__file__))
-    config = json.dumps({
-        'messages': messages,
-        'model': model}, separators=(',', ':'))
-    
-    cmd = ['python3', f'{path}/helpers/theb.py', config]
-
-    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-
-    for line in iter(p.stdout.readline, b''):
-        yield line.decode('utf-8')
-        
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/kazuk/youtube-whisper-06/app.py b/spaces/kazuk/youtube-whisper-06/app.py
deleted file mode 100644
index 4a61dc561a016c53ad93a3c556b0ef7bafa964eb..0000000000000000000000000000000000000000
--- a/spaces/kazuk/youtube-whisper-06/app.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import gradio as gr
-import whisper
-from pytube import YouTube
-
-def get_audio(url):
-    yt = YouTube(url)
-    return yt.streams.filter(only_audio=True)[0].download(filename="tmp.mp4")
-
-def get_transcript(url, model_size, lang, format):
-
-    model = whisper.load_model(model_size)
-
-    if lang == "None":
-        lang = None
-    
-    result = model.transcribe(get_audio(url), fp16=False, language=lang)
-
-    if format == "None":
-        return result["text"]
-    elif format == ".srt":
-        return format_to_srt(result["segments"])
-
-def format_to_srt(segments):
-    output = ""
-    for i, segment in enumerate(segments):
-        output += f"{i + 1}\n"
-        output += f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
-        output += f"{segment['text']}\n\n"
-    return output
-
-def format_timestamp(t):
-    hh = t//3600
-    mm = (t - hh*3600)//60
-    ss = t - hh*3600 - mm*60
-    mi = (t - int(t))*1000
-    return f"{int(hh):02d}:{int(mm):02d}:{int(ss):02d},{int(mi):03d}"
-
-
-langs = ["None"] + sorted(list(whisper.tokenizer.LANGUAGES.values()))
-model_size = list(whisper._MODELS.keys())
-
-with gr.Blocks() as demo:
-
-    with gr.Row():
-
-        with gr.Column():
-
-            with gr.Row():
-                url = gr.Textbox(placeholder='Youtube video URL', label='URL')
-
-            with gr.Row():
-
-                model_size = gr.Dropdown(choices=model_size, value='tiny', label="Model")
-                lang = gr.Dropdown(choices=langs, value="None", label="Language (Optional)")
-                format = gr.Dropdown(choices=["None", ".srt"], value="None", label="Timestamps? (Optional)")
-
-            with gr.Row():
-                gr.Markdown("Larger models are more accurate, but slower. For 1min video, it'll take ~30s (tiny), ~1min (base), ~3min (small), ~5min (medium), etc.")
-                transcribe_btn = gr.Button('Transcribe')
-
-        with gr.Column():
-            outputs = gr.Textbox(placeholder='Transcription of the video', label='Transcription')
-
-    transcribe_btn.click(get_transcript, inputs=[url, model_size, lang, format], outputs=outputs)
-
-demo.launch(debug=True)
diff --git a/spaces/kbora/minerva-generate-docker/blocks/utils/__init__.py b/spaces/kbora/minerva-generate-docker/blocks/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/keithhon/Real-Time-Voice-Cloning/encoder/config.py b/spaces/keithhon/Real-Time-Voice-Cloning/encoder/config.py
deleted file mode 100644
index 1c21312f3de971bfa008254c6035cebc09f05e4c..0000000000000000000000000000000000000000
--- a/spaces/keithhon/Real-Time-Voice-Cloning/encoder/config.py
+++ /dev/null
@@ -1,45 +0,0 @@
-librispeech_datasets = {
-    "train": {
-        "clean": ["LibriSpeech/train-clean-100", "LibriSpeech/train-clean-360"],
-        "other": ["LibriSpeech/train-other-500"]
-    },
-    "test": {
-        "clean": ["LibriSpeech/test-clean"],
-        "other": ["LibriSpeech/test-other"]
-    },
-    "dev": {
-        "clean": ["LibriSpeech/dev-clean"],
-        "other": ["LibriSpeech/dev-other"]
-    },
-}
-libritts_datasets = {
-    "train": {
-        "clean": ["LibriTTS/train-clean-100", "LibriTTS/train-clean-360"],
-        "other": ["LibriTTS/train-other-500"]
-    },
-    "test": {
-        "clean": ["LibriTTS/test-clean"],
-        "other": ["LibriTTS/test-other"]
-    },
-    "dev": {
-        "clean": ["LibriTTS/dev-clean"],
-        "other": ["LibriTTS/dev-other"]
-    },
-}
-voxceleb_datasets = {
-    "voxceleb1" : {
-        "train": ["VoxCeleb1/wav"],
-        "test": ["VoxCeleb1/test_wav"]
-    },
-    "voxceleb2" : {
-        "train": ["VoxCeleb2/dev/aac"],
-        "test": ["VoxCeleb2/test_wav"]
-    }
-}
-
-other_datasets = [
-    "LJSpeech-1.1",
-    "VCTK-Corpus/wav48",
-]
-
-anglophone_nationalites = ["australia", "canada", "ireland", "uk", "usa"]
diff --git a/spaces/keras-dreambooth/piranesi-monument-art/app.py b/spaces/keras-dreambooth/piranesi-monument-art/app.py
deleted file mode 100644
index 2a8a0d0e9581aec7f3c7f0dc80c81a279f1a54b7..0000000000000000000000000000000000000000
--- a/spaces/keras-dreambooth/piranesi-monument-art/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from huggingface_hub import from_pretrained_keras
-import keras_cv
-import gradio as gr
-from tensorflow import keras
-
-keras.mixed_precision.set_global_policy("mixed_float16")
-# load keras model
-resolution = 512
-dreambooth_model = keras_cv.models.StableDiffusion(
-        img_width=resolution, img_height=resolution, jit_compile=True, 
-    )
-loaded_diffusion_model = from_pretrained_keras("keras-dreambooth/dreambooth-piranesi")
-dreambooth_model._diffusion_model = loaded_diffusion_model
-
-
-def generate_images(prompt: str, negative_prompt:str, num_imgs_to_gen: int, num_steps: int, ugs: int):
-    generated_img = dreambooth_model.text_to_image(
-        prompt, 
-        negative_prompt=negative_prompt,
-        batch_size=num_imgs_to_gen,
-        num_steps=num_steps,
-        unconditional_guidance_scale=ugs,
-    )
-   
-    return generated_img
-    
-with gr.Blocks() as demo:
-    gr.HTML("<h2 style=\"font-size: 2em; font-weight: bold\" align=\"center\"> Dreambooth Piranesi Art </h2>")    
-    with gr.Row():
-        with gr.Column():
-            prompt = gr.Textbox(lines=1, value="image of monument in sks style", label="Base Prompt")
-            negative_prompt = gr.Textbox(lines=1, value="deformed", label="Negative Prompt")
-            samples = gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Number of Image")
-            num_steps = gr.Slider(label="Inference Steps",value=40)
-            ugs = gr.Slider(value=15, minimum=5, maximum=25, step=1, label="Unconditional Guidance Scale")
-            run = gr.Button(value="Run")
-        with gr.Column():
-            gallery = gr.Gallery(label="Outputs").style(grid=(1,2))
-
-    run.click(generate_images, inputs=[prompt,negative_prompt, samples, num_steps, ugs], outputs=gallery)
-    
-    gr.Examples([["image of monument in sks style, 8k, high quality, old paper","colored, deformed, blurry, grain, artifacts, low quality", 1, 30, 18],
-                 ["image of menhir in sks style, 8k, high quality, old paper","colored, deformed, blurry, grain, artifacts, low quality", 1, 40, 20],
-                 ["image of church in sks style, 8k, high quality, old paper","colored, deformed, blurry, grain, artifacts, low quality", 1, 40, 20],
-                 ["image of ancient ruins in sks style, 8k, high quality, old paper","colored, deformed, blurry, grain, artifacts, low quality", 1, 50, 20],
-                 ["image of castle on hilltop in sks style, 8k, high quality, old paper","colored, deformed, blurry, grain, artifacts, low quality", 1, 50, 10],
-                 ["image of amphiteater in sks style, 8k, high quality, old paper","colored, deformed, blurry, grain, artifacts, low quality", 1, 40, 9],
-                 ["image of church in lake in sks style, 8k, high quality, old paper, black and white","colored, deformed, blurry, grain, artifacts, low quality", 1, 40, 18],
-                 ["image of village on hilltop with citadel in sks style, 8k, high quality, old paper, black and white","colored, deformed, blurry, grain, artifacts, low quality", 1, 40, 18]],
-                [prompt,negative_prompt, samples,num_steps, ugs], gallery, generate_images)
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/kevinwang676/Bert-VITS2/start.bat b/spaces/kevinwang676/Bert-VITS2/start.bat
deleted file mode 100644
index 418d21233dbf720b0dd09821904d9d6a31b123a2..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/Bert-VITS2/start.bat
+++ /dev/null
@@ -1,2 +0,0 @@
-set PYTHON=venv\python.exe
-start cmd /k "set PYTHON=%PYTHON%"
\ No newline at end of file
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/speaker_encoder/data_objects/__init__.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/speaker_encoder/data_objects/__init__.py
deleted file mode 100644
index 030317a1d9a328d452bf29bc7a802e29629b1a42..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/speaker_encoder/data_objects/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from speaker_encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
-from speaker_encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataLoader
diff --git a/spaces/kevinwang676/VoiceChanger/src/facerender/sync_batchnorm/batchnorm.py b/spaces/kevinwang676/VoiceChanger/src/facerender/sync_batchnorm/batchnorm.py
deleted file mode 100644
index 5f4e763f0366dffa10320116413f8c7181a8aeb1..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChanger/src/facerender/sync_batchnorm/batchnorm.py
+++ /dev/null
@@ -1,315 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : batchnorm.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import collections
-
-import torch
-import torch.nn.functional as F
-
-from torch.nn.modules.batchnorm import _BatchNorm
-from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
-
-from .comm import SyncMaster
-
-__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d']
-
-
-def _sum_ft(tensor):
-    """sum over the first and last dimention"""
-    return tensor.sum(dim=0).sum(dim=-1)
-
-
-def _unsqueeze_ft(tensor):
-    """add new dementions at the front and the tail"""
-    return tensor.unsqueeze(0).unsqueeze(-1)
-
-
-_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size'])
-_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])
-
-
-class _SynchronizedBatchNorm(_BatchNorm):
-    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
-        super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine)
-
-        self._sync_master = SyncMaster(self._data_parallel_master)
-
-        self._is_parallel = False
-        self._parallel_id = None
-        self._slave_pipe = None
-
-    def forward(self, input):
-        # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
-        if not (self._is_parallel and self.training):
-            return F.batch_norm(
-                input, self.running_mean, self.running_var, self.weight, self.bias,
-                self.training, self.momentum, self.eps)
-
-        # Resize the input to (B, C, -1).
-        input_shape = input.size()
-        input = input.view(input.size(0), self.num_features, -1)
-
-        # Compute the sum and square-sum.
-        sum_size = input.size(0) * input.size(2)
-        input_sum = _sum_ft(input)
-        input_ssum = _sum_ft(input ** 2)
-
-        # Reduce-and-broadcast the statistics.
-        if self._parallel_id == 0:
-            mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size))
-        else:
-            mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size))
-
-        # Compute the output.
-        if self.affine:
-            # MJY:: Fuse the multiplication for speed.
-            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
-        else:
-            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)
-
-        # Reshape it.
-        return output.view(input_shape)
-
-    def __data_parallel_replicate__(self, ctx, copy_id):
-        self._is_parallel = True
-        self._parallel_id = copy_id
-
-        # parallel_id == 0 means master device.
-        if self._parallel_id == 0:
-            ctx.sync_master = self._sync_master
-        else:
-            self._slave_pipe = ctx.sync_master.register_slave(copy_id)
-
-    def _data_parallel_master(self, intermediates):
-        """Reduce the sum and square-sum, compute the statistics, and broadcast it."""
-
-        # Always using same "device order" makes the ReduceAdd operation faster.
-        # Thanks to:: Tete Xiao (http://tetexiao.com/)
-        intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())
-
-        to_reduce = [i[1][:2] for i in intermediates]
-        to_reduce = [j for i in to_reduce for j in i]  # flatten
-        target_gpus = [i[1].sum.get_device() for i in intermediates]
-
-        sum_size = sum([i[1].sum_size for i in intermediates])
-        sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
-        mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)
-
-        broadcasted = Broadcast.apply(target_gpus, mean, inv_std)
-
-        outputs = []
-        for i, rec in enumerate(intermediates):
-            outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))
-
-        return outputs
-
-    def _compute_mean_std(self, sum_, ssum, size):
-        """Compute the mean and standard-deviation with sum and square-sum. This method
-        also maintains the moving average on the master device."""
-        assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
-        mean = sum_ / size
-        sumvar = ssum - sum_ * mean
-        unbias_var = sumvar / (size - 1)
-        bias_var = sumvar / size
-
-        self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data
-        self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data
-
-        return mean, bias_var.clamp(self.eps) ** -0.5
-
-
-class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
-    r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
-    mini-batch.
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm1d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of size
-            `batch_size x num_features [x width]`
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C)` or :math:`(N, C, L)`
-        - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm1d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm1d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 2 and input.dim() != 3:
-            raise ValueError('expected 2D or 3D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm1d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
-    r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
-    of 3d inputs
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm2d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of
-            size batch_size x num_features x height x width
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C, H, W)`
-        - Output: :math:`(N, C, H, W)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm2d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm2d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 4:
-            raise ValueError('expected 4D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm2d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
-    r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
-    of 4d inputs
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm3d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-    
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
-    or Spatio-temporal BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of
-            size batch_size x num_features x depth x height x width
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape:
-        - Input: :math:`(N, C, D, H, W)`
-        - Output: :math:`(N, C, D, H, W)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm3d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm3d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 5:
-            raise ValueError('expected 5D input (got {}D input)'
-                             .format(input.dim()))
-        super(SynchronizedBatchNorm3d, self)._check_input_dim(input)
diff --git a/spaces/khaclinh/self-driving-anonymization/pp4av_exp.py b/spaces/khaclinh/self-driving-anonymization/pp4av_exp.py
deleted file mode 100644
index 1c245ed8f45319a69e675305e7e79984c7cff702..0000000000000000000000000000000000000000
--- a/spaces/khaclinh/self-driving-anonymization/pp4av_exp.py
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-import os
-
-from yolox.exp import Exp as MyExp
-
-class Exp(MyExp):
-    def __init__(self):
-        super(Exp, self).__init__()
-        self.depth = 1.0 # indicate size yolo model
-        self.width = 1.0 #
-        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
-
-        self.data_dir = ''
-        self.train_ann = ''
-        self.val_ann = '' 
-        self.test_ann = ''
-        
-        self.num_classes = 2
-        self.data_num_workers = 32 # number of cpu for splitting batch
-   
-        self.input_size = (800, 800)   
-        self.print_interval = 100
-        self.eval_interval = 1
-        self.test_size = (800, 800)
-        self.enable_mixup = True
-        self.mosaic_scale = (0.5, 1.5)
-        self.max_epoch = 300 
-        self.hsv_prob = 1.0
-
-        self.degrees = 20.0
-        self.translate = 0.2
-        self.shear = 2.0
-        # Turn off mosaic
-        self.mosaic_prob = 1.0
-        # Turn off Mixup
-        self.mixup_prob = 1.0
-        # Change SGD by ADAM
-        
-
-        self.basic_lr_per_img = 0.01 / 28.0
-        self.no_aug_epochs = 15
-        self.min_lr_ratio = 0.05
-        self.ema = True
-
-        self.nmsthre = 0.3
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/visualization/image.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/visualization/image.py
deleted file mode 100644
index 61a56c75b67f593c298408462c63c0468be8e276..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/visualization/image.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import cv2
-import numpy as np
-
-from annotator.uniformer.mmcv.image import imread, imwrite
-from .color import color_val
-
-
-def imshow(img, win_name='', wait_time=0):
-    """Show an image.
-
-    Args:
-        img (str or ndarray): The image to be displayed.
-        win_name (str): The window name.
-        wait_time (int): Value of waitKey param.
-    """
-    cv2.imshow(win_name, imread(img))
-    if wait_time == 0:  # prevent from hanging if windows was closed
-        while True:
-            ret = cv2.waitKey(1)
-
-            closed = cv2.getWindowProperty(win_name, cv2.WND_PROP_VISIBLE) < 1
-            # if user closed window or if some key pressed
-            if closed or ret != -1:
-                break
-    else:
-        ret = cv2.waitKey(wait_time)
-
-
-def imshow_bboxes(img,
-                  bboxes,
-                  colors='green',
-                  top_k=-1,
-                  thickness=1,
-                  show=True,
-                  win_name='',
-                  wait_time=0,
-                  out_file=None):
-    """Draw bboxes on an image.
-
-    Args:
-        img (str or ndarray): The image to be displayed.
-        bboxes (list or ndarray): A list of ndarray of shape (k, 4).
-        colors (list[str or tuple or Color]): A list of colors.
-        top_k (int): Plot the first k bboxes only if set positive.
-        thickness (int): Thickness of lines.
-        show (bool): Whether to show the image.
-        win_name (str): The window name.
-        wait_time (int): Value of waitKey param.
-        out_file (str, optional): The filename to write the image.
-
-    Returns:
-        ndarray: The image with bboxes drawn on it.
-    """
-    img = imread(img)
-    img = np.ascontiguousarray(img)
-
-    if isinstance(bboxes, np.ndarray):
-        bboxes = [bboxes]
-    if not isinstance(colors, list):
-        colors = [colors for _ in range(len(bboxes))]
-    colors = [color_val(c) for c in colors]
-    assert len(bboxes) == len(colors)
-
-    for i, _bboxes in enumerate(bboxes):
-        _bboxes = _bboxes.astype(np.int32)
-        if top_k <= 0:
-            _top_k = _bboxes.shape[0]
-        else:
-            _top_k = min(top_k, _bboxes.shape[0])
-        for j in range(_top_k):
-            left_top = (_bboxes[j, 0], _bboxes[j, 1])
-            right_bottom = (_bboxes[j, 2], _bboxes[j, 3])
-            cv2.rectangle(
-                img, left_top, right_bottom, colors[i], thickness=thickness)
-
-    if show:
-        imshow(img, win_name, wait_time)
-    if out_file is not None:
-        imwrite(img, out_file)
-    return img
-
-
-def imshow_det_bboxes(img,
-                      bboxes,
-                      labels,
-                      class_names=None,
-                      score_thr=0,
-                      bbox_color='green',
-                      text_color='green',
-                      thickness=1,
-                      font_scale=0.5,
-                      show=True,
-                      win_name='',
-                      wait_time=0,
-                      out_file=None):
-    """Draw bboxes and class labels (with scores) on an image.
-
-    Args:
-        img (str or ndarray): The image to be displayed.
-        bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or
-            (n, 5).
-        labels (ndarray): Labels of bboxes.
-        class_names (list[str]): Names of each classes.
-        score_thr (float): Minimum score of bboxes to be shown.
-        bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
-        text_color (str or tuple or :obj:`Color`): Color of texts.
-        thickness (int): Thickness of lines.
-        font_scale (float): Font scales of texts.
-        show (bool): Whether to show the image.
-        win_name (str): The window name.
-        wait_time (int): Value of waitKey param.
-        out_file (str or None): The filename to write the image.
-
-    Returns:
-        ndarray: The image with bboxes drawn on it.
-    """
-    assert bboxes.ndim == 2
-    assert labels.ndim == 1
-    assert bboxes.shape[0] == labels.shape[0]
-    assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5
-    img = imread(img)
-    img = np.ascontiguousarray(img)
-
-    if score_thr > 0:
-        assert bboxes.shape[1] == 5
-        scores = bboxes[:, -1]
-        inds = scores > score_thr
-        bboxes = bboxes[inds, :]
-        labels = labels[inds]
-
-    bbox_color = color_val(bbox_color)
-    text_color = color_val(text_color)
-
-    for bbox, label in zip(bboxes, labels):
-        bbox_int = bbox.astype(np.int32)
-        left_top = (bbox_int[0], bbox_int[1])
-        right_bottom = (bbox_int[2], bbox_int[3])
-        cv2.rectangle(
-            img, left_top, right_bottom, bbox_color, thickness=thickness)
-        label_text = class_names[
-            label] if class_names is not None else f'cls {label}'
-        if len(bbox) > 4:
-            label_text += f'|{bbox[-1]:.02f}'
-        cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] - 2),
-                    cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color)
-
-    if show:
-        imshow(img, win_name, wait_time)
-    if out_file is not None:
-        imwrite(img, out_file)
-    return img
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/themes/utils/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/themes/utils/__init__.py
deleted file mode 100644
index a3e6208634fafa416b9323f5156ac56dd7bb3700..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/themes/utils/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from .semver_match import (
-    ThemeAsset,
-    get_matching_version,
-    get_theme_assets,
-)
-
-__all__ = [
-    "ThemeAsset",
-    "get_theme_assets",
-    "get_matching_version",
-]
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/themes/utils/fonts.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/themes/utils/fonts.py
deleted file mode 100644
index d51dbbfdf4990358e9094cc887c47ae6cd8b0440..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/themes/utils/fonts.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from __future__ import annotations
-
-import json
-from typing import Iterable
-
-
-class FontEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, Font):
-            return {
-                "__gradio_font__": True,
-                "name": obj.name,
-                "class": "google" if isinstance(obj, GoogleFont) else "font",
-            }
-        # Let the base class default method raise the TypeError
-        return json.JSONEncoder.default(self, obj)
-
-
-def as_font(dct):
-    if "__gradio_font__" in dct:
-        name = dct["name"]
-        return GoogleFont(name) if dct["class"] == "google" else Font(name)
-    return dct
-
-
-class Font:
-    def __init__(self, name: str):
-        self.name = name
-
-    def __str__(self) -> str:
-        return (
-            self.name
-            if self.name in ["sans-serif", "serif", "monospace", "cursive", "fantasy"]
-            else f"'{self.name}'"
-        )
-
-    def stylesheet(self) -> str:
-        return None
-
-    def __eq__(self, other: Font) -> bool:
-        return self.name == other.name and self.stylesheet() == other.stylesheet()
-
-
-class GoogleFont(Font):
-    def __init__(self, name: str, weights: Iterable[int] = (400, 600)):
-        self.name = name
-        self.weights = weights
-
-    def stylesheet(self) -> str:
-        return f'https://fonts.googleapis.com/css2?family={self.name.replace(" ", "+")}:wght@{";".join(str(weight) for weight in self.weights)}&display=swap'
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/axes/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/axes/__init__.py
deleted file mode 100644
index f8c40889bce7ec9b9645011b5e2ee8db37464b6a..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/axes/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from . import _base
-from ._axes import *
-
-# Backcompat.
-from ._axes import Axes as Subplot
-
-
-class _SubplotBaseMeta(type):
-    def __instancecheck__(self, obj):
-        return (isinstance(obj, _base._AxesBase)
-                and obj.get_subplotspec() is not None)
-
-
-class SubplotBase(metaclass=_SubplotBaseMeta):
-    pass
-
-
-def subplot_class_factory(cls): return cls
diff --git a/spaces/leogabraneth/text-generation-webui-main/extensions/superboogav2/benchmark.py b/spaces/leogabraneth/text-generation-webui-main/extensions/superboogav2/benchmark.py
deleted file mode 100644
index 46475a088b0eca137f641935d58dbf4b8d50ed29..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/extensions/superboogav2/benchmark.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""
-This module implements a benchmark function to evaluate the performance of the embedding pipeline. It expects a configuration JSON file. It must have questions and expected retrieved text.
-For each question, it's essential to have variants of that question. Language is fluid and each person might have their own spin on how they may ask it.
-
-At the end, it will save the results inside a benchmark_{sysdate}.txt file in the main directory.
-
-The benchmark function will return the score as an integer.
-"""
-import datetime
-import json
-import os
-
-from pathlib import Path
-
-from .data_processor import process_and_add_to_collector, preprocess_text
-from .parameters import get_chunk_count, get_max_token_count
-from .utils import create_metadata_source
-
-def benchmark(config_path, collector):
-    # Get the current system date
-    sysdate = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"benchmark_{sysdate}.txt"
-    
-    # Open the log file in append mode
-    with open(filename, 'a') as log:
-        with open(config_path, 'r') as f:
-            data = json.load(f)
-        
-        total_points = 0
-        max_points = 0
-
-        for item in data:
-            filepath = item["text"]
-            corpus = ""
-
-            # Check if the file exists
-            if os.path.isfile(Path(filepath)):
-                # Open the file and read its content
-                with open(Path(filepath), 'r') as file:
-                    corpus = file.read()
-                process_and_add_to_collector(corpus, collector, True, create_metadata_source('benchmark'))
-            else:
-                raise f'Cannot find specified file {filepath}.'
-
-            for question_group in item["questions"]:
-                question_variants = question_group["question_variants"]
-                criteria = question_group["criteria"]
-                
-                for q in question_variants:
-                    max_points += len(criteria)
-                    processed_text = preprocess_text(q)
-
-                    # Get the most similar chunks
-                    results = collector.get_sorted_by_dist(processed_text, n_results=get_chunk_count(), max_token_count=get_max_token_count())
-
-                    points = 0
-                    
-                    for c in criteria:
-                        for p in results:
-                            if c in p:
-                                points += 1
-                                total_points += 1
-                                break
-
-                    info = f"The question '{q}' scored {points}/{len(criteria)} points."
-                    print(info, file=log)
-
-                print('\n---\n', file=log)
-
-        print(f'##Total points:\n\n{total_points}/{max_points}', file=log)
-
-    return total_points, max_points
\ No newline at end of file
diff --git a/spaces/leopoldmaillard/ImageRetrieval/README.md b/spaces/leopoldmaillard/ImageRetrieval/README.md
deleted file mode 100644
index 31be0ff5aabc5e21cdc576947e403e27f9d09d39..0000000000000000000000000000000000000000
--- a/spaces/leopoldmaillard/ImageRetrieval/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Image_Retrieval
-emoji: 🐠
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/lewiswu1209/MockingBird/vocoder/fregan/loss.py b/spaces/lewiswu1209/MockingBird/vocoder/fregan/loss.py
deleted file mode 100644
index e37dc64e29446ecdd9dce03290f4e0eba58fb3d7..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/vocoder/fregan/loss.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import torch
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss*2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((1-dr)**2)
-        g_loss = torch.mean(dg**2)
-        loss += (r_loss + g_loss)
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        l = torch.mean((1-dg)**2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Autocad 2012 Indir 64 Bit Gezginler Linkliste Grafikbesc.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Autocad 2012 Indir 64 Bit Gezginler Linkliste Grafikbesc.md
deleted file mode 100644
index daad619f98fc017851a10427dc09f559192319ee..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Autocad 2012 Indir 64 Bit Gezginler Linkliste Grafikbesc.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Autocad 2012 Indir 64 Bit Gezginler linkliste grafikbesc</h2><br /><p><b><b>Download Zip</b> &#10042;&#10042;&#10042; <a href="https://bytlly.com/2uGwKO">https://bytlly.com/2uGwKO</a></b></p><br /><br />
-<br />
-I can provide you with download links for AutoCAD 2012 but I have to mention that AutoCAD ... AutoCAD 2012 32-bits · AutoCAD 2012 64-bits. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/BusyWin-14-Rel-2-0-With-Patch.md b/spaces/lincquiQcaudo/Top-20-Diffusion/BusyWin-14-Rel-2-0-With-Patch.md
deleted file mode 100644
index f801cbc3d5628490430d3f94578b7416bd16fe02..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/BusyWin-14-Rel-2-0-With-Patch.md
+++ /dev/null
@@ -1,10 +0,0 @@
-<h2>BusyWin-14-Rel-2-0-With-Patch</h2><br /><p><b><b>DOWNLOAD</b> &#9881;&#9881;&#9881; <a href="https://bytlly.com/2uGyya">https://bytlly.com/2uGyya</a></b></p><br /><br />
-<br />
-When using PaperCut NG/MF versi on 21.1.2 and the hidden document names feature in the print provider is enabled, print jobs submitted for release may hang... When using PaperCut NG/MF versi on 21.1.2 or higher and the hidden document names feature in the print provider is enabled, print jobs submitted for release may hang.
-To resolve this issue, click Cancel to cancel the job, and then try printing elsewhere.
-(See also the printer's User's Guide for more information about resolving this issue.)
-Elimination of possible causes
-1. Check connected printers. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Captaintsubasa3hackdownload.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Captaintsubasa3hackdownload.md
deleted file mode 100644
index 02c5e76d7f674ada4172a2a3fa3e5dc09c01dc35..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Captaintsubasa3hackdownload.md
+++ /dev/null
@@ -1,24 +0,0 @@
-<br />
-<h1>How to Download Captain Tsubasa 3 Hack for SNES</h1>
-<p>Captain Tsubasa 3 is a soccer game based on the popular manga and anime series of the same name. The game features many characters and teams from the original story, as well as some new ones. The game is known for its fast-paced and exciting gameplay, as well as its special moves and animations.</p>
-<p>However, some fans of the game have created a hack version of Captain Tsubasa 3 that modifies some aspects of the original game, such as the difficulty level, the stats of the players, the graphics, and the music. The hack version also adds some new features, such as a save function, a practice mode, and a custom team editor.</p>
-<h2>captaintsubasa3hackdownload</h2><br /><p><b><b>Download</b> &bull; <a href="https://bytlly.com/2uGy7A">https://bytlly.com/2uGy7A</a></b></p><br /><br />
-<p>If you want to try out this hack version of Captain Tsubasa 3, you will need a few things:</p>
-<ul>
-<li>A SNES emulator that can run ROM files. There are many emulators available online for different platforms, such as Windows, Mac, Android, and iOS. Some popular ones are ZSNES, Snes9x, and RetroArch.</li>
-<li>A ROM file of Captain Tsubasa 3. You can find this file online by searching for "captain tsubasa 3 rom". Make sure you download it from a reliable source and scan it for viruses before opening it.</li>
-<li>A patch file of Captain Tsubasa 3 Hack. You can find this file online by searching for "captain tsubasa 3 hack download". One source is [^1^], where you can download the file named "Captain Tsubasa 3 SNES First Hack by tommy 2017.smc". Another source is [^2^], where you can download the file named "captaintsubasa3hackdownload.pdf".</li>
-<li>A patching program that can apply the patch file to the ROM file. There are many patching programs available online, such as Lunar IPS, Flips, and MultiPatch.</li>
-</ul>
-<p>Once you have all these things, you can follow these steps to download and play Captain Tsubasa 3 Hack:</p>
-<ol>
-<li>Open your patching program and select the patch file and the ROM file. Make sure they have the same name and extension (for example, "Captain Tsubasa 3.smc" and "Captain Tsubasa 3.smc").</li>
-<li>Click on "Apply Patch" or "Patch" or whatever option your program has. This will create a new ROM file with the hack applied to it.</li>
-<li>Open your emulator and load the new ROM file. You should see a title screen that says "Captain Tsubasa 3 Hack" or something similar.</li>
-<li>Enjoy playing Captain Tsubasa 3 Hack!</li>
-</ol>
-<p>Note: This article is for informational purposes only. We do not condone or encourage piracy or illegal downloading of any kind. Please support the original creators of Captain Tsubasa 3 by buying their game legally.</p><p>If you want to learn more about Captain Tsubasa 3 Hack, you can check out some online videos and reviews that showcase the game and its features. For example, you can watch , where the user Tommy2 demonstrates some gameplay and special moves of the hack version. You can also read , where the user Captaintsubasa3hackdownload gives a brief overview of the game and its download link.</p>
-<p></p>
-<p>Captain Tsubasa 3 Hack is a fun and challenging game for fans of Captain Tsubasa and soccer games in general. It offers a new and improved experience of the original game, with more options and customization. If you are looking for a way to spice up your Captain Tsubasa 3 gameplay, you might want to give this hack a try.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Kanchana 3 PATCHED Full Movie In Tamil Hd 1080p.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Kanchana 3 PATCHED Full Movie In Tamil Hd 1080p.md
deleted file mode 100644
index d1860602ce39dee8e6ea552e32dd16dc7a765bf7..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Kanchana 3 PATCHED Full Movie In Tamil Hd 1080p.md	
+++ /dev/null
@@ -1,114 +0,0 @@
-<br />
-<h1>Kanchana 3: A Spooky and Funny Ride with Raghava Lawrence and Co.</h1>
-
-<p>If you are a fan of Tamil horror comedy movies, you must have heard of Kanchana 3, the fourth installment in the Muni series. Kanchana 3 is a 2019 movie that was co-produced, written and directed by Raghava Lawrence, who also played the dual role of Raghava and Kaali. The movie also starred Oviya, Vedhika, Nikki Tamboli and Ri Djavi Alexandra as the female leads, while Kovai Sarala, Soori, Tarun Arora and Kabir Duhan Singh played the supporting roles.</p>
-
-<p>Kanchana 3 follows the story of Raghava, a young man who gets easily scared by ghosts and spirits. He lives with his mother (Kovai Sarala), brother (Sriman) and sister-in-law (Devadarshini). One day, he goes to his ancestral home with his family and his girlfriend Priya (Oviya). There, he meets his grandfather (Delhi Ganesh), who tells him about his past as Kaali, a powerful leader who fought against a corrupt politician (Tarun Arora) and his henchman Bhavani (Kabir Duhan Singh). Kaali was killed by Bhavani along with his wife (Vedhika) and his lover (Nikki Tamboli). However, their spirits remained in the house, waiting for revenge. Raghava gets possessed by Kaali's spirit and decides to take on Bhavani and his men.</p>
-<h2>kanchana 3 full movie in tamil hd 1080p</h2><br /><p><b><b>Download</b> &#9675;&#9675;&#9675; <a href="https://bytlly.com/2uGvzP">https://bytlly.com/2uGvzP</a></b></p><br /><br />
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-<p>Kanchana 3 is a typical horror comedy movie that has all the elements of the genre: jump scares, comedy scenes, songs, dances, fights and sentiments. The movie is full of entertainment and fun for the audience who enjoy this kind of movies. The movie also has a social message about women empowerment and corruption. The movie was well received by the fans and critics alike, and became a huge commercial success at the box office.</p>
-
-<h2>How to Watch Kanchana 3 Full Movie in Tamil HD 1080p</h2>
-
-<p>If you want to watch Kanchana 3 full movie in Tamil HD 1080p, you have several options to choose from. You can either watch it online or download it to your device. Here are some of the ways you can watch Kanchana 3 full movie in Tamil HD 1080p:</p>
-
-<ul>
-<li>Watch it online on streaming platforms: You can watch Kanchana 3 full movie online on various streaming platforms such as Zee5, Sun Nxt and VI movies and tv. You just need to have a subscription or a membership to access these platforms. You can also watch it on Google Play Movies if you want to rent or buy it.</li>
-<li>Download it from torrent sites: You can also download Kanchana 3 full movie from torrent sites such as Tamilyogi HD, TamilGun and TamilRockers. However, this is not a legal or safe option as you may face legal issues or malware threats. You should avoid downloading movies from torrent sites as much as possible.</li>
-<li>Watch it on TV channels: You can also watch Kanchana 3 full movie on TV channels such as Sun TV, Zee Tamil and Star Vijay. You just need to check the schedule of these channels and tune in at the right time. You can also record the movie if you have a DVR or a set-top box.</li>
-</ul>
-
-<p>These are some of the ways you can watch Kanchana 3 full movie in Tamil HD 1080p. However, you should always respect the rights of the creators and producers of the movie and watch it legally and ethically.</p>
-<h2>How to Enjoy Kanchana 3 Full Movie in Tamil HD 1080p</h2>
-
-<p>Kanchana 3 is a movie that can be enjoyed by anyone who loves horror comedy movies. The movie has a lot of elements that can make you laugh, scream and cheer. Here are some tips on how to enjoy Kanchana 3 full movie in Tamil HD 1080p:</p>
-
-<ul>
-<li>Watch it with your friends or family: Kanchana 3 is a movie that is best enjoyed with your loved ones. You can share the fun and excitement of the movie with them and have a great time together. You can also discuss the movie after watching it and share your opinions and feedback.</li>
-<li>Watch it with good sound and picture quality: Kanchana 3 is a movie that has a lot of visual and audio effects that can enhance your viewing experience. You should watch it with good sound and picture quality to appreciate the movie better. You can use headphones, speakers, or a home theater system to get the best sound quality. You can also use a big screen, a projector, or a smart TV to get the best picture quality.</li>
-<li>Watch it with an open mind: Kanchana 3 is a movie that has a lot of twists and turns that can surprise you. You should watch it with an open mind and not expect anything from the movie. You should also not take the movie too seriously or too literally. You should just enjoy the movie for what it is: a horror comedy entertainer.</li>
-</ul>
-
-<p>These are some of the ways you can enjoy Kanchana 3 full movie in Tamil HD 1080p. You can also watch it again if you liked it or recommend it to others if you loved it.</p>
-
-<h2>Conclusion</h2>
-
-<p>Kanchana 3 is a horror comedy movie that was released in 2019. It was co-produced, written and directed by Raghava Lawrence, who also played the dual role of Raghava and Kaali. The movie also starred Oviya, Vedhika, Nikki Tamboli and Ri Djavi Alexandra as the female leads, while Kovai Sarala, Soori, Tarun Arora and Kabir Duhan Singh played the supporting roles.</p>
-
-<p>Kanchana 3 follows the story of Raghava, a young man who gets easily scared by ghosts and spirits. He goes to his ancestral home with his family and his girlfriend Priya. There, he meets his grandfather, who tells him about his past as Kaali, a powerful leader who fought against a corrupt politician and his henchman Bhavani. Kaali was killed by Bhavani along with his wife and his lover. However, their spirits remained in the house, waiting for revenge. Raghava gets possessed by Kaali's spirit and decides to take on Bhavani and his men.</p>
-<p></p>
-
-<p>Kanchana 3 is a typical horror comedy movie that has all the elements of the genre: jump scares, comedy scenes, songs, dances, fights and sentiments. The movie is full of entertainment and fun for the audience who enjoy this kind of movies. The movie also has a social message about women empowerment and corruption. The movie was well received by the fans and critics alike, and became a huge commercial success at the box office.</p>
-
-<p>If you want to watch Kanchana 3 full movie in Tamil HD 1080p, you have several options to choose from. You can either watch it online or download it to your device. You can also watch it on TV channels if you prefer. However, you should always respect the rights of the creators and producers of the movie and watch it legally and ethically.</p>
-
-<p>Kanchana 3 is a movie that can be enjoyed by anyone who loves horror comedy movies. It is a spooky and funny ride with Raghava Lawrence and co. Don't miss it!</p>
-<h2>How to Review Kanchana 3 Full Movie in Tamil HD 1080p</h2>
-
-<p>If you have watched Kanchana 3 full movie in Tamil HD 1080p and you want to share your opinion and feedback about it, you can write a review of the movie. A review is a personal and critical evaluation of a movie that can help other people decide whether to watch it or not. Here are some tips on how to write a review of Kanchana 3 full movie in Tamil HD 1080p:</p>
-
-<ul>
-<li>Introduce the movie: You should start your review by introducing the movie, its title, genre, director, cast and plot summary. You should also mention when and where you watched the movie and what your expectations were.</li>
-<li>Analyze the movie: You should then analyze the movie, its strengths and weaknesses, its themes and messages, its technical aspects and its entertainment value. You should support your analysis with examples and evidence from the movie.</li>
-<li>Evaluate the movie: You should then evaluate the movie, its overall quality, its impact and its relevance. You should also compare it with other movies of the same genre or series. You should give your personal rating or recommendation of the movie.</li>
-<li>Conclude the review: You should end your review by summarizing your main points and giving your final verdict of the movie. You should also invite your readers to share their comments or questions about the movie.</li>
-</ul>
-
-<p>These are some of the ways you can write a review of Kanchana 3 full movie in Tamil HD 1080p. You can also read other reviews of the movie online or offline to get some inspiration and ideas.</p>
-
-<h2>How to Enjoy More Movies Like Kanchana 3 Full Movie in Tamil HD 1080p</h2>
-
-<p>If you enjoyed watching Kanchana 3 full movie in Tamil HD 1080p and you want to watch more movies like it, you have many options to choose from. You can either watch the previous movies of the Muni series or watch other horror comedy movies from Tamil cinema or other industries. Here are some of the movies you can watch if you liked Kanchana 3 full movie in Tamil HD 1080p:</p>
-
-<ul>
-<li>Muni (2007): The first movie of the Muni series that introduced Raghava Lawrence as Raghava, a young man who gets possessed by a ghost named Muni.</li>
-<li>Kanchana (2011): The second movie of the Muni series that featured Raghava Lawrence as Raghava and Kaali, a transgender woman who seeks revenge for her murder.</li>
-<li>Kanchana 2 (2015): The third movie of the Muni series that starred Raghava Lawrence as Raghava and Shiva, a TV cameraman who gets haunted by a ghost named Naga.</li>
-<li>Dhilluku Dhuddu (2016): A horror comedy movie that starred Santhanam as Kumar, a happy-go-lucky man who falls in love with a girl whose father is a ghost hunter.</li>
-<li>Devi (2016): A horror comedy movie that starred Prabhu Deva as Krishna Kumar, a man who gets married to a woman who is possessed by an actress named Ruby.</li>
-<li>Zombie (2019): A horror comedy movie that starred Yogi Babu as Mario, a security guard who gets trapped in a resort with his friends during a zombie outbreak.</li>
-</ul>
-
-<p>These are some of the movies you can watch if you enjoyed Kanchana 3 full movie in Tamil HD 1080p. You can also explore other movies of different genres and languages that can entertain you and make you laugh.</p>
-<h2>How to Learn More About Kanchana 3 Full Movie in Tamil HD 1080p</h2>
-
-<p>If you want to learn more about Kanchana 3 full movie in Tamil HD 1080p, you can do some research online or offline. You can find more information about the movie, its cast and crew, its production and release, its trivia and facts, its awards and nominations, its reviews and ratings, and its fan reactions and feedback. Here are some of the sources you can use to learn more about Kanchana 3 full movie in Tamil HD 1080p:</p>
-
-<ul>
-<li>Wikipedia: You can read the Wikipedia page of Kanchana 3 full movie in Tamil HD 1080p to get a comprehensive overview of the movie, its plot, its cast and crew, its box office performance, its reception and its legacy.</li>
-<li>IMDb: You can visit the IMDb page of Kanchana 3 full movie in Tamil HD 1080p to get more details about the movie, such as its genre, its runtime, its release date, its language, its country of origin, its budget and gross, its soundtrack and score, its technical specifications and its trivia and goofs.</li>
-<li>YouTube: You can watch the YouTube videos of Kanchana 3 full movie in Tamil HD 1080p to see the trailer, the songs, the scenes, the interviews, the behind-the-scenes footage and the fan-made videos of the movie.</li>
-<li>Social media: You can follow the social media accounts of Kanchana 3 full movie in Tamil HD 1080p to get the latest updates, news, photos and videos of the movie. You can also interact with other fans and share your opinions and feedback about the movie.</li>
-<li>Books and magazines: You can read the books and magazines that feature Kanchana 3 full movie in Tamil HD 1080p to get more insights and perspectives about the movie. You can also learn about the history and culture of Tamil cinema and horror comedy genre.</li>
-</ul>
-
-<p>These are some of the ways you can learn more about Kanchana 3 full movie in Tamil HD 1080p. You can also explore other sources that can enrich your knowledge and understanding of the movie.</p>
-
-<h2>Conclusion</h2>
-
-<p>Kanchana 3 is a horror comedy movie that was released in 2019. It was co-produced, written and directed by Raghava Lawrence, who also played the dual role of Raghava and Kaali. The movie also starred Oviya, Vedhika, Nikki Tamboli and Ri Djavi Alexandra as the female leads, while Kovai Sarala, Soori, Tarun Arora and Kabir Duhan Singh played the supporting roles.</p>
-
-<p>Kanchana 3 follows the story of Raghava, a young man who gets easily scared by ghosts and spirits. He goes to his ancestral home with his family and his girlfriend Priya. There, he meets his grandfather, who tells him about his past as Kaali, a powerful leader who fought against a corrupt politician and his henchman Bhavani. Kaali was killed by Bhavani along with his wife and his lover. However, their spirits remained in the house, waiting for revenge. Raghava gets possessed by Kaali's spirit and decides to take on Bhavani and his men.</p>
-
-<p>Kanchana 3 is a typical horror comedy movie that has all the elements of the genre: jump scares, comedy scenes, songs, dances, fights and sentiments. The movie is full of entertainment and fun for the audience who enjoy this kind of movies. The movie also has a social message about women empowerment and corruption. The movie was well received by the fans and critics alike, and became a huge commercial success at the box office.</p>
-
-<p>If you want to watch Kanchana 3 full movie in Tamil HD 1080p, you have several options to choose from. You can either watch it online or download it to your device. You can also watch it on TV channels if you prefer. However, you should always respect the rights of the creators and producers of the movie and watch it legally and ethically.</p>
-
-<p>If you enjoyed watching Kanchana 3 full movie in Tamil HD 1080p and you want to watch more movies like it or learn more about it, you have many options to choose from. You can either watch the previous movies of the Muni series or watch other horror comedy movies from Tamil cinema or other industries. You can also do some research online or offline to find more information about the movie.</p>
-
-<p>Kanchana 3 is a movie that can be enjoyed by anyone who loves horror comedy movies. It is a spooky and funny ride with Raghava Lawrence and co. Don't miss it!</p>
-<h2>Conclusion</h2>
-
-<p>Kanchana 3 is a horror comedy movie that was released in 2019. It was co-produced, written and directed by Raghava Lawrence, who also played the dual role of Raghava and Kaali. The movie also starred Oviya, Vedhika, Nikki Tamboli and Ri Djavi Alexandra as the female leads, while Kovai Sarala, Soori, Tarun Arora and Kabir Duhan Singh played the supporting roles.</p>
-
-<p>Kanchana 3 follows the story of Raghava, a young man who gets easily scared by ghosts and spirits. He goes to his ancestral home with his family and his girlfriend Priya. There, he meets his grandfather, who tells him about his past as Kaali, a powerful leader who fought against a corrupt politician and his henchman Bhavani. Kaali was killed by Bhavani along with his wife and his lover. However, their spirits remained in the house, waiting for revenge. Raghava gets possessed by Kaali's spirit and decides to take on Bhavani and his men.</p>
-
-<p>Kanchana 3 is a typical horror comedy movie that has all the elements of the genre: jump scares, comedy scenes, songs, dances, fights and sentiments. The movie is full of entertainment and fun for the audience who enjoy this kind of movies. The movie also has a social message about women empowerment and corruption. The movie was well received by the fans and critics alike, and became a huge commercial success at the box office.</p>
-
-<p>If you want to watch Kanchana 3 full movie in Tamil HD 1080p, you have several options to choose from. You can either watch it online or download it to your device. You can also watch it on TV channels if you prefer. However, you should always respect the rights of the creators and producers of the movie and watch it legally and ethically.</p>
-
-<p>If you enjoyed watching Kanchana 3 full movie in Tamil HD 1080p and you want to watch more movies like it or learn more about it, you have many options to choose from. You can either watch the previous movies of the Muni series or watch other horror comedy movies from Tamil cinema or other industries. You can also do some research online or offline to find more information about the movie.</p>
-
-<p>Kanchana 3 is a movie that can be enjoyed by anyone who loves horror comedy movies. It is a spooky and funny ride with Raghava Lawrence and co. Don't miss it!</p> 3cee63e6c2<br />
-<br />
-<br />
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Matlab 7 Version BETTER Free Download 32 Bit Full Crack.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Matlab 7 Version BETTER Free Download 32 Bit Full Crack.md
deleted file mode 100644
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--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Matlab 7 Version BETTER Free Download 32 Bit Full Crack.md	
+++ /dev/null
@@ -1,136 +0,0 @@
-<br />
-<h1>MATLAB 2007 for Windows 32-bit: How to Download and Crack It for Free</h1>
-
-<p>If you are looking for a powerful and reliable software for engineering and scientific applications, you might want to consider MATLAB. MATLAB is a programming and numeric computing platform that allows you to plot functions and data, manipulate matrices, run parallel algorithms, create models for control loops, and perform many other tasks. MATLAB has many versions and updates, but one of the most stable and widely used ones is MATLAB 2007.</p>
-<h2>matlab 7 version free download 32 bit full crack</h2><br /><p><b><b>DOWNLOAD</b> &gt;&gt;&gt;&gt;&gt; <a href="https://bytlly.com/2uGwUm">https://bytlly.com/2uGwUm</a></b></p><br /><br />
-
-<p>MATLAB 2007 is compatible with Windows XP, Vista, 7, and 8, and it does not require a lot of memory or disk space. It also has many features and improvements that make it a great choice for Windows 32-bit users. However, MATLAB 2007 is not free, and you need to pay a fee or subscription to use it. But don't worry, because in this article, we will show you how to download and crack MATLAB 2007 for Windows 32-bit for free, without any limitations or restrictions.</p>
-
-<h2>What are the Features and Improvements of MATLAB 2007?</h2>
-
-<p>MATLAB 2007 has many features and improvements that make it a powerful and reliable tool for engineering and scientific applications. Some of them are:</p>
-
-<ul>
-<li>Distributed Computational Toolbox: This toolbox allows you to run parallel algorithms in four MATLAB sessions on your desktop, using your multicore processor or networked computers.</li>
-<li>Control System Toolbox: This toolbox enables you to create exact models for control loops, using linear and nonlinear methods. You can also design and tune controllers, analyze system performance, and simulate dynamic systems.</li>
-<li>Categorical and Dataset Arrays: These data types allow you to organize and manipulate statistical data in a convenient way. You can perform operations such as grouping, sorting, filtering, merging, and plotting on categorical and dataset arrays.</li>
-<li>New Functions: MATLAB 2007 introduces many new functions that support the categorical and dataset arrays, as well as other tasks. For example, cholcov function computes the Cholesky-like decomposition of a covariance matrix, linehyptest performs the linear hypothesis test, and ranksum performs the Wilcoxon rank sum test.</li>
-</ul>
-
-<h2>How to Download MATLAB 2007 for Windows 32-bit?</h2>
-
-<p>To download MATLAB 2007 for Windows 32-bit, you need to follow these steps:</p>
-
-<ol>
-<li>Go to this link to access the download page of MATLAB 2007 Full ISO Setup.</li>
-<li>Click on the green button that says "Download Now".</li>
-<li>Wait for the download to complete. The file size is about 3 GB.</li>
-<li>Save the file on your computer. The file name is Matlab_R2007B_Full_Setup.iso.</li>
-</ol>
-
-<h2>How to Install MATLAB 2007 for Windows 32-bit?</h2>
-
-<p>To install MATLAB 2007 for Windows 32-bit, you need to follow these steps:</p>
-
-<ol>
-<li>Mount the ISO file using a virtual drive software such as Daemon Tools or PowerISO.</li>
-<li>Open the mounted drive and run the setup.exe file.</li>
-<li>Follow the instructions on the screen to complete the installation process.</li>
-<li>Do not launch MATLAB after the installation is finished.</li>
-</ol>
-
-<h2>How to Crack MATLAB 2007 for Windows 32-bit?</h2>
-
-<p>To crack MATLAB 2007 for Windows 32-bit, you need to follow these steps:</p>
-<p></p>
-
-<ol>
-<li>Download the crack file from this link.</li>
-<li>Extract the zip file using a software such as WinRAR or 7-Zip.</li>
-<li>Copy the file named "libmwservices.dll" from the extracted folder.</li>
-<li>Paste the file in the installation directory of MATLAB 2007. The default location is C:\Program Files\MATLAB\R2007b\bin\win32.</li>
-<li>Replace the existing file when prompted.</li>
-<li>Launch MATLAB from the desktop shortcut or start menu.</li>
-</ol>
-
-<p>Congratulations! You have successfully installed and cracked MATLAB 2007 for Windows 32-bit. You can now enjoy the full version of MATLAB 2007 without any limitations or restrictions.</p>
-
-<h2>Conclusion</h2>
-
-<p>MATLAB 2007 is a great software for engineering and scientific applications. It has many features and improvements that make it a powerful and reliable tool. If you want to get MATLAB 2007 for Windows 32-bit with full crack, you can follow the steps in this article. We hope this article was helpful and informative. If you have any questions or comments, feel free to leave them below.</p>
-<h2>What are the Challenges and Risks of Using MATLAB 2007 for Windows 32-bit?</h2>
-
-<p>While MATLAB 2007 for Windows 32-bit has many benefits, it also has some challenges and risks that you should be aware of. Some of them are:</p>
-
-<ul>
-<li>Compatibility Issues: MATLAB 2007 may not be compatible with some newer versions of Windows, such as Windows 10. It may also not work well with some newer hardware and software, such as graphics cards and drivers. You may need to update or downgrade some components to make MATLAB 2007 work properly.</li>
-<li>Security Issues: MATLAB 2007 may not have the latest security patches and updates, which may make it vulnerable to viruses, malware, and hackers. You should use a reliable antivirus program and firewall to protect your system and data.</li>
-<li>Legal Issues: MATLAB 2007 is a licensed software that requires a fee or subscription to use. Downloading and cracking MATLAB 2007 for free may violate the terms and conditions of MATLAB and Mathworks, and may result in legal actions or penalties. You should use MATLAB 2007 at your own risk and responsibility.</li>
-</ul>
-
-<h2>How to Update or Upgrade MATLAB 2007 for Windows 32-bit?</h2>
-
-<p>If you want to update or upgrade MATLAB 2007 for Windows 32-bit, you have two options:</p>
-
-<ol>
-<li>Update MATLAB 2007: You can check for updates and patches for MATLAB 2007 by using the Help menu in MATLAB. You can also visit the official website of MATLAB and Mathworks to download the latest updates and patches.</li>
-<li>Upgrade MATLAB 2007: You can upgrade MATLAB 2007 to a newer version of MATLAB by purchasing a license or subscription from Mathworks. You can also download a trial version of the latest MATLAB from the official website of MATLAB and Mathworks.</li>
-</ol>
-
-<p>Updating or upgrading MATLAB 2007 may improve its performance, compatibility, security, and functionality. However, it may also require more system resources, change some features or functions, and invalidate your crack file.</p>
-
-<h2>Conclusion</h2>
-
-<p>MATLAB 2007 is a great software for engineering and scientific applications. It has many features and improvements that make it a powerful and reliable tool for Windows 32-bit users. However, it also has some challenges and risks that you should be aware of. If you want to download and crack MATLAB 2007 for Windows 32-bit for free, you can follow the steps in this article. However, you should also consider the legal and ethical implications of doing so. We hope this article was helpful and informative. If you have any questions or comments, feel free to leave them below.</p>
-<h2>What are the Alternatives to MATLAB 2007 for Windows 32-bit?</h2>
-
-<p>If you are looking for alternatives to MATLAB 2007 for Windows 32-bit, you have several options. Some of them are:</p>
-
-<ul>
-<li>Octave: Octave is a free and open-source software that is compatible with MATLAB. It can perform numerical computations, plot graphs, and run scripts and functions written in MATLAB. However, Octave may not have all the features and toolboxes of MATLAB, and it may have some syntax differences.</li>
-<li>Scilab: Scilab is another free and open-source software that is similar to MATLAB. It can perform matrix operations, data analysis, signal processing, optimization, and simulation. It also has a graphical user interface and a rich set of toolboxes. However, Scilab may not be fully compatible with MATLAB, and it may have some performance issues.</li>
-<li>Python: Python is a general-purpose programming language that can be used for scientific computing. It has many libraries and packages that can perform tasks similar to MATLAB, such as NumPy, SciPy, Matplotlib, Pandas, and TensorFlow. However, Python may require more coding and debugging than MATLAB, and it may have some compatibility and dependency issues.</li>
-</ul>
-
-<h2>How to Learn MATLAB 2007 for Windows 32-bit?</h2>
-
-<p>If you want to learn MATLAB 2007 for Windows 32-bit, you have several resources available. Some of them are:</p>
-
-<ul>
-<li>MATLAB Help: MATLAB has a built-in help system that provides documentation and examples for all the commands and functions of MATLAB. You can access it by using the Help menu in MATLAB or by typing help or doc in the Command Window.</li>
-<li>MATLAB Tutorials: MATLAB has a set of tutorials that cover the basics of MATLAB, such as variables, operators, arrays, loops, functions, plots, and GUIs. You can access them by using the Help menu in MATLAB or by visiting the official website of MATLAB and Mathworks.</li>
-<li>MATLAB Books: There are many books that teach you how to use MATLAB for various purposes, such as engineering, mathematics, statistics, machine learning, image processing, and more. You can find them online or in your local library or bookstore.</li>
-<li>MATLAB Courses: There are many online courses that offer interactive lessons and exercises on how to use MATLAB for different applications. You can find them on platforms such as Coursera, edX, Udemy, Khan Academy, and more.</li>
-</ul>
-<h2>How to Uninstall MATLAB 2007 for Windows 32-bit?</h2>
-
-<p>If you want to uninstall MATLAB 2007 for Windows 32-bit, you need to follow these steps:</p>
-
-<ol>
-<li>Go to the Control Panel and select Programs and Features.</li>
-<li>Find MATLAB 2007 in the list of installed programs and click on Uninstall.</li>
-<li>Follow the instructions on the screen to complete the uninstallation process.</li>
-<li>Delete the MATLAB 2007 folder from your computer. The default location is C:\Program Files\MATLAB\R2007b.</li>
-<li>Delete the crack file from your computer. The file name is libmwservices.dll.</li>
-</ol>
-
-<p>Uninstalling MATLAB 2007 will free up some disk space and memory on your system. However, it will also remove all the features and functions of MATLAB 2007 from your system.</p>
-
-<h2>How to Troubleshoot MATLAB 2007 for Windows 32-bit?</h2>
-
-<p>If you encounter any problems or errors while using MATLAB 2007 for Windows 32-bit, you can try these solutions:</p>
-
-<ul>
-<li>Check your system requirements and compatibility. Make sure your Windows 32-bit system meets the minimum requirements for MATLAB 2007, and that it is compatible with MATLAB 2007.</li>
-<li>Check your installation and crack. Make sure you have installed MATLAB 2007 correctly and applied the crack file properly. You can also try to reinstall MATLAB 2007 and reapply the crack file.</li>
-<li>Check your internet connection and firewall. Make sure you have a stable and secure internet connection, and that your firewall is not blocking MATLAB 2007 or its components.</li>
-<li>Check your antivirus program and malware. Make sure your antivirus program is not interfering with MATLAB 2007 or its components, and that your system is free of viruses, malware, and hackers.</li>
-<li>Check the MATLAB help and support. You can use the MATLAB help system or visit the official website of MATLAB and Mathworks to get help and support for MATLAB 2007. You can also search online for solutions or forums related to MATLAB 2007.</li>
-</ul>
-
-<p>Troubleshooting MATLAB 2007 may help you resolve some of the problems or errors that you may face while using it. However, it may not guarantee a perfect performance or functionality of MATLAB 2007.</p>
-<h2>Conclusion</h2>
-
-<p>MATLAB 2007 is a great software for engineering and scientific applications. It has many features and improvements that make it a powerful and reliable tool for Windows 32-bit users. However, it also has some challenges and risks that you should be aware of. If you want to download and crack MATLAB 2007 for Windows 32-bit for free, you can follow the steps in this article. However, you should also consider the legal and ethical implications of doing so. We hope this article was helpful and informative. If you have any questions or comments, feel free to leave them below.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/liuyuan-pal/SyncDreamer/ldm/modules/diffusionmodules/openaimodel.py b/spaces/liuyuan-pal/SyncDreamer/ldm/modules/diffusionmodules/openaimodel.py
deleted file mode 100644
index 1e0dc94e240f927985d8edbf2f38aa5ac28641e2..0000000000000000000000000000000000000000
--- a/spaces/liuyuan-pal/SyncDreamer/ldm/modules/diffusionmodules/openaimodel.py
+++ /dev/null
@@ -1,996 +0,0 @@
-from abc import abstractmethod
-from functools import partial
-import math
-from typing import Iterable
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ldm.modules.diffusionmodules.util import (
-    checkpoint,
-    conv_nd,
-    linear,
-    avg_pool_nd,
-    zero_module,
-    normalization,
-    timestep_embedding,
-)
-from ldm.modules.attention import SpatialTransformer
-from ldm.util import exists
-
-
-# dummy replace
-def convert_module_to_f16(x):
-    pass
-
-def convert_module_to_f32(x):
-    pass
-
-
-## go
-class AttentionPool2d(nn.Module):
-    """
-    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
-    """
-
-    def __init__(
-        self,
-        spacial_dim: int,
-        embed_dim: int,
-        num_heads_channels: int,
-        output_dim: int = None,
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
-        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
-        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
-        self.num_heads = embed_dim // num_heads_channels
-        self.attention = QKVAttention(self.num_heads)
-
-    def forward(self, x):
-        b, c, *_spatial = x.shape
-        x = x.reshape(b, c, -1)  # NC(HW)
-        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
-        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
-        x = self.qkv_proj(x)
-        x = self.attention(x)
-        x = self.c_proj(x)
-        return x[:, :, 0]
-
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
-    """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
-    """
-
-    def forward(self, x, emb, context=None):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb)
-            elif isinstance(layer, SpatialTransformer):
-                x = layer(x, context)
-            else:
-                x = layer(x)
-        return x
-
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
-            )
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-class TransposedUpsample(nn.Module):
-    'Learned 2x upsampling without padding'
-    def __init__(self, channels, out_channels=None, ks=5):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-
-        self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
-
-    def forward(self,x):
-        return self.up(x)
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels),
-            nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.emb_layers = nn.Sequential(
-            nn.SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels),
-            nn.SiLU(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        return checkpoint(
-            self._forward, (x, emb), self.parameters(), self.use_checkpoint
-        )
-
-
-    def _forward(self, x, emb):
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        emb_out = self.emb_layers(emb).type(h.dtype)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm: # False
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            scale, shift = th.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
-    """
-    An attention block that allows spatial positions to attend to each other.
-    Originally ported from here, but adapted to the N-d case.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
-    """
-
-    def __init__(
-        self,
-        channels,
-        num_heads=1,
-        num_head_channels=-1,
-        use_checkpoint=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        if num_head_channels == -1:
-            self.num_heads = num_heads
-        else:
-            assert (
-                channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
-            self.num_heads = channels // num_head_channels
-        self.use_checkpoint = use_checkpoint
-        self.norm = normalization(channels)
-        self.qkv = conv_nd(1, channels, channels * 3, 1)
-        if use_new_attention_order:
-            # split qkv before split heads
-            self.attention = QKVAttention(self.num_heads)
-        else:
-            # split heads before split qkv
-            self.attention = QKVAttentionLegacy(self.num_heads)
-
-        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
-    def forward(self, x):
-        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
-        #return pt_checkpoint(self._forward, x)  # pytorch
-
-    def _forward(self, x):
-        b, c, *spatial = x.shape
-        x = x.reshape(b, c, -1)
-        qkv = self.qkv(self.norm(x))
-        h = self.attention(qkv)
-        h = self.proj_out(h)
-        return (x + h).reshape(b, c, *spatial)
-
-
-def count_flops_attn(model, _x, y):
-    """
-    A counter for the `thop` package to count the operations in an
-    attention operation.
-    Meant to be used like:
-        macs, params = thop.profile(
-            model,
-            inputs=(inputs, timestamps),
-            custom_ops={QKVAttention: QKVAttention.count_flops},
-        )
-    """
-    b, c, *spatial = y[0].shape
-    num_spatial = int(np.prod(spatial))
-    # We perform two matmuls with the same number of ops.
-    # The first computes the weight matrix, the second computes
-    # the combination of the value vectors.
-    matmul_ops = 2 * b * (num_spatial ** 2) * c
-    model.total_ops += th.DoubleTensor([matmul_ops])
-
-
-class QKVAttentionLegacy(nn.Module):
-    """
-    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts", q * scale, k * scale
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v)
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class QKVAttention(nn.Module):
-    """
-    A module which performs QKV attention and splits in a different order.
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.chunk(3, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts",
-            (q * scale).view(bs * self.n_heads, ch, length),
-            (k * scale).view(bs * self.n_heads, ch, length),
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
-                                    increased efficiency.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=-1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        use_spatial_transformer=False,    # custom transformer support
-        transformer_depth=1,              # custom transformer support
-        context_dim=None,                 # custom transformer support
-        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
-        legacy=True,
-        disable_self_attentions=None,
-        num_attention_blocks=None
-    ):
-        super().__init__()
-        if use_spatial_transformer:
-            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
-
-        if context_dim is not None:
-            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
-            from omegaconf.listconfig import ListConfig
-            if type(context_dim) == ListConfig:
-                context_dim = list(context_dim)
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        if num_heads == -1:
-            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
-
-        if num_head_channels == -1:
-            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
-
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        if isinstance(num_res_blocks, int):
-            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
-        else:
-            if len(num_res_blocks) != len(channel_mult):
-                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
-                                 "as a list/tuple (per-level) with the same length as channel_mult")
-            self.num_res_blocks = num_res_blocks
-        #self.num_res_blocks = num_res_blocks
-        if disable_self_attentions is not None:
-            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
-            assert len(disable_self_attentions) == len(channel_mult)
-        if num_attention_blocks is not None:
-            assert len(num_attention_blocks) == len(self.num_res_blocks)
-            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
-            print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
-                  f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
-                  f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
-                  f"attention will still not be set.")  # todo: convert to warning
-
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.predict_codebook_ids = n_embed is not None
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        if self.num_classes is not None:
-            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
-                )
-            ]
-        ) # 0
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for nr in range(self.num_res_blocks[level]):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions: # always True
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    if exists(disable_self_attentions):
-                        disabled_sa = disable_self_attentions[level]
-                    else:
-                        disabled_sa = False
-
-                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
-                        layers.append(
-                            AttentionBlock(
-                                ch,
-                                use_checkpoint=use_checkpoint,
-                                num_heads=num_heads,
-                                num_head_channels=dim_head,
-                                use_new_attention_order=use_new_attention_order,
-                            ) if not use_spatial_transformer else SpatialTransformer(
-                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
-                                disable_self_attn=disabled_sa
-                            )
-                        )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        if num_head_channels == -1:
-            dim_head = ch // num_heads
-        else:
-            num_heads = ch // num_head_channels
-            dim_head = num_head_channels
-        if legacy:
-            #num_heads = 1
-            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=dim_head,
-                use_new_attention_order=use_new_attention_order,
-            ) if not use_spatial_transformer else SpatialTransformer(  # always uses a self-attn
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
-                        ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(self.num_res_blocks[level] + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    ResBlock(
-                        ch + ich,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=model_channels * mult,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = model_channels * mult
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    if exists(disable_self_attentions):
-                        disabled_sa = disable_self_attentions[level]
-                    else:
-                        disabled_sa = False
-
-                    if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
-                        layers.append(
-                            AttentionBlock(
-                                ch,
-                                use_checkpoint=use_checkpoint,
-                                num_heads=num_heads_upsample,
-                                num_head_channels=dim_head,
-                                use_new_attention_order=use_new_attention_order,
-                            ) if not use_spatial_transformer else SpatialTransformer(
-                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
-                                disable_self_attn=disabled_sa
-                            )
-                        )
-                if level and i == self.num_res_blocks[level]:
-                    out_ch = ch
-                    layers.append(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            normalization(ch),
-            nn.SiLU(),
-            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
-        )
-        if self.predict_codebook_ids:
-            self.id_predictor = nn.Sequential(
-            normalization(ch),
-            conv_nd(dims, model_channels, n_embed, 1),
-            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
-        )
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param context: conditioning plugged in via crossattn
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-        hs = []
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) # N
-        emb = self.time_embed(t_emb) #
-
-        if self.num_classes is not None:
-            assert y.shape == (x.shape[0],)
-            emb = emb + self.label_emb(y)
-
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb, context) # conv
-            hs.append(h)
-        h = self.middle_block(h, emb, context)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, context)
-        h = h.type(x.dtype)
-        if self.predict_codebook_ids:
-            return self.id_predictor(h)
-        else:
-            return self.out(h)
-
-
-class EncoderUNetModel(nn.Module):
-    """
-    The half UNet model with attention and timestep embedding.
-    For usage, see UNet.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        pool="adaptive",
-        *args,
-        **kwargs
-    ):
-        super().__init__()
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
-                )
-            ]
-        )
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=num_head_channels,
-                use_new_attention_order=use_new_attention_order,
-            ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-        self.pool = pool
-        if pool == "adaptive":
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                nn.AdaptiveAvgPool2d((1, 1)),
-                zero_module(conv_nd(dims, ch, out_channels, 1)),
-                nn.Flatten(),
-            )
-        elif pool == "attention":
-            assert num_head_channels != -1
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                AttentionPool2d(
-                    (image_size // ds), ch, num_head_channels, out_channels
-                ),
-            )
-        elif pool == "spatial":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                nn.ReLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        elif pool == "spatial_v2":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                normalization(2048),
-                nn.SiLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        else:
-            raise NotImplementedError(f"Unexpected {pool} pooling")
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :return: an [N x K] Tensor of outputs.
-        """
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
-        results = []
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb)
-            if self.pool.startswith("spatial"):
-                results.append(h.type(x.dtype).mean(dim=(2, 3)))
-        h = self.middle_block(h, emb)
-        if self.pool.startswith("spatial"):
-            results.append(h.type(x.dtype).mean(dim=(2, 3)))
-            h = th.cat(results, axis=-1)
-            return self.out(h)
-        else:
-            h = h.type(x.dtype)
-            return self.out(h)
-
diff --git a/spaces/lkeab/transfiner/configs/common/data/coco_panoptic_separated.py b/spaces/lkeab/transfiner/configs/common/data/coco_panoptic_separated.py
deleted file mode 100644
index 5ccbc77e64d1c92c99cbd7158d047bab54cb9f3d..0000000000000000000000000000000000000000
--- a/spaces/lkeab/transfiner/configs/common/data/coco_panoptic_separated.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from detectron2.config import LazyCall as L
-from detectron2.evaluation import (
-    COCOEvaluator,
-    COCOPanopticEvaluator,
-    DatasetEvaluators,
-    SemSegEvaluator,
-)
-
-from .coco import dataloader
-
-dataloader.train.dataset.names = "coco_2017_train_panoptic_separated"
-dataloader.train.dataset.filter_empty = False
-dataloader.test.dataset.names = "coco_2017_val_panoptic_separated"
-
-
-dataloader.evaluator = [
-    L(COCOEvaluator)(
-        dataset_name="${...test.dataset.names}",
-    ),
-    L(SemSegEvaluator)(
-        dataset_name="${...test.dataset.names}",
-    ),
-    L(COCOPanopticEvaluator)(
-        dataset_name="${...test.dataset.names}",
-    ),
-]
diff --git a/spaces/ls291/ChatSQL/utility/loggers.py b/spaces/ls291/ChatSQL/utility/loggers.py
deleted file mode 100644
index 754a653673b06db61593d825c670d124a3781828..0000000000000000000000000000000000000000
--- a/spaces/ls291/ChatSQL/utility/loggers.py
+++ /dev/null
@@ -1,60 +0,0 @@
-"""
-@Time: 2022/11/03
-@Author: LiuShu
-@File: loggers.py
-"""
-import os
-from utility.constant import BASE_DIR
-import logging
-import logging.config
-
-LOGGING = {
-    'version': 1,
-    'disable_existing_loggers': True,
-    'formatters': {
-        'simple': {
-            'format': '%(levelname)s %(message)s'
-        },
-        'standard': {
-            'format': '[%(asctime)s] %(filename)s-[line:%(lineno)d] %(levelname)s--%(message)s',
-            'datefmt': '%Y-%m-%d %H:%M:%S',
-        },
-    },
-    'handlers': {
-        'file': {
-            'level': 'DEBUG',
-            'class': 'logging.handlers.TimedRotatingFileHandler',
-            # TODO 文件路径修改位置
-            'filename': os.path.join(BASE_DIR, 'logs/server.log'),
-            'formatter': 'standard',
-            'when': 'D',
-            'interval': 1,
-            'backupCount': 7,
-        },
-        'null': {
-            'level': 'DEBUG',
-            'class': 'logging.StreamHandler',
-        },
-    },
-    'loggers': {
-        'django': {
-            'handlers': ['null'],
-            'level': 'ERROR',
-            'propagate': True,
-        },
-        'system': {
-            'handlers': ['file'],
-            'level': 'DEBUG',
-            'propagate': True,
-        },
-    }
-}
-
-
-def get_logger():
-    logging.config.dictConfig(LOGGING)
-    Logger = logging.getLogger("system")
-    return Logger
-
-
-logger = get_logger()
diff --git a/spaces/ltgoslo/ssa-perin/mtool/codec/__init__.py b/spaces/ltgoslo/ssa-perin/mtool/codec/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/lunadebruyne/EmotioNL/README.md b/spaces/lunadebruyne/EmotioNL/README.md
deleted file mode 100644
index fd015b0ae13bbf368642200ec119b07814b62f69..0000000000000000000000000000000000000000
--- a/spaces/lunadebruyne/EmotioNL/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: EmotioNL
-emoji: 🚀
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/luxuedong/lxd/tests/parse.ts b/spaces/luxuedong/lxd/tests/parse.ts
deleted file mode 100644
index 92940fe6315f1d7cb2b267ba5e5a7e26460a1de3..0000000000000000000000000000000000000000
--- a/spaces/luxuedong/lxd/tests/parse.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-import { promises as fs } from 'fs'
-import { join } from 'path'
-import { parseHeadersFromCurl } from '@/lib/utils'
-
-(async () => {
-  const content = await fs.readFile(join(__dirname, './fixtures/curl.txt'), 'utf-8')
-  const headers = parseHeadersFromCurl(content)
-  console.log(headers)
-
-  const cmdContent = await fs.readFile(join(__dirname, './fixtures/cmd.txt'), 'utf-8')
-  const cmdHeaders = parseHeadersFromCurl(cmdContent)
-  console.log(cmdHeaders)
-})()
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/uninitialized_copy.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/uninitialized_copy.h
deleted file mode 100644
index 2d1b0010dfbfd8587bac2167b25cd4982d3ad468..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/generic/uninitialized_copy.h
+++ /dev/null
@@ -1,57 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-template<typename ExecutionPolicy,
-         typename InputIterator,
-         typename ForwardIterator>
-__host__ __device__
-  ForwardIterator uninitialized_copy(thrust::execution_policy<ExecutionPolicy> &exec,
-                                     InputIterator first,
-                                     InputIterator last,
-                                     ForwardIterator result);
-
-template<typename ExecutionPolicy,
-         typename InputIterator,
-         typename Size,
-         typename ForwardIterator>
-__host__ __device__
-  ForwardIterator uninitialized_copy_n(thrust::execution_policy<ExecutionPolicy> &exec,
-                                       InputIterator first,
-                                       Size n,
-                                       ForwardIterator result);
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/uninitialized_copy.inl>
-
diff --git a/spaces/matthoffner/chatbot/utils/app/codeblock.ts b/spaces/matthoffner/chatbot/utils/app/codeblock.ts
deleted file mode 100644
index d28c8aa97bd045cf8711c2e2284aa3aee035c453..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot/utils/app/codeblock.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-interface languageMap {
-  [key: string]: string | undefined;
-}
-
-export const programmingLanguages: languageMap = {
-  javascript: '.js',
-  python: '.py',
-  java: '.java',
-  c: '.c',
-  cpp: '.cpp',
-  'c++': '.cpp',
-  'c#': '.cs',
-  ruby: '.rb',
-  php: '.php',
-  swift: '.swift',
-  'objective-c': '.m',
-  kotlin: '.kt',
-  typescript: '.ts',
-  go: '.go',
-  perl: '.pl',
-  rust: '.rs',
-  scala: '.scala',
-  haskell: '.hs',
-  lua: '.lua',
-  shell: '.sh',
-  sql: '.sql',
-  html: '.html',
-  css: '.css',
-  // add more file extensions here, make sure the key is same as language prop in CodeBlock.tsx component
-};
-
-export const generateRandomString = (length: number, lowercase = false) => {
-  const chars = 'ABCDEFGHJKLMNPQRSTUVWXY3456789'; // excluding similar looking characters like Z, 2, I, 1, O, 0
-  let result = '';
-  for (let i = 0; i < length; i++) {
-    result += chars.charAt(Math.floor(Math.random() * chars.length));
-  }
-  return lowercase ? result.toLowerCase() : result;
-};
diff --git a/spaces/merve/hidden-bias/public/anonymization/style-graph-scroll.css b/spaces/merve/hidden-bias/public/anonymization/style-graph-scroll.css
deleted file mode 100644
index 7680e8c43222b6993d2bedfe43a682236680541e..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/public/anonymization/style-graph-scroll.css
+++ /dev/null
@@ -1,160 +0,0 @@
-/** { border: 1px solid #f00; }*/
-
-
-#container{
-  position: relative;
-  width: auto;
-  margin-left: -25px;
-  /*margin-bottom: 100px;*/
-}
-
-#sections{
-  width: 330px;
-  pointer-events: none;
-}
-
-#sections > div{
-  background: white;
-  opacity: .2;
-  margin-bottom: 400px;
-  line-height: 1.4em;
-  transition: opacity .2s;
-  pointer-events: all;
-}
-#sections > div:last-child{
-  height: 480px;
-  margin-bottom: 0px;
-}
-#sections > div.graph-scroll-active{
-  opacity: 1;
-}
-
-#graph{
-  margin-left: 40px;
-  width: 500px;
-  position: -webkit-sticky;
-  position: sticky;
-  top: 0px;
-  float: right;
-  height: 580px;
-}
-
-.slider-outer {
-  display: block;
-  max-width: 300px;
-}
-
-@media (max-width: 925px)  {
-  #container{
-    margin-left: 0px;
-  }
-  
-  #graph{
-    width: 100%;
-    float: none;
-    max-width: 500px;
-    margin: 0px auto;
-  }
-
-  #graph > div{
-    position: relative;
-    left:12px;
-  }
-
-  #sections{
-    width: auto;
-    position: relative;
-    margin: 0px auto;
-  }
-
-  #sections > div{
-    background: rgba(255,255,255,.8);
-    padding: 10px;
-    border-top: 1px solid;
-    border-bottom: 1px solid;
-    margin-bottom: 80vh;
-    width: calc(100vw - 20px);
-    margin-left: -5px;
-  }
-
-  #sections > div > *{
-    max-width: 750px;
-  }
-
-  #sections > div:first-child{
-    opacity: 1;
-    margin-top: -260px;
-  }
-
-  #sections > div:last-child{
-    height: auto;
-  }
-
-  #sections h3{
-    margin-top: .5em;
-  }
-
-  /* Adjust buttons for mobile. */
-
-  .button-container{
-    text-align: center;
-    left:0px;
-  }
-
-  /* Adjust sliders for mobile. */
-  input[type="range" i] {
-    width: 280px;
-  }
-  .slider-label-container{
-    width: 145px;
-    /* display: inline-block; */
-  }
-
-  .slide-container-heads-prob, .slide-container-population {
-    text-align: center;
-  }
-
-  .slider-container {
-    margin-bottom: 5px;
-    text-align: center;
-    width: 300px;
-    /* display:inline-block; */
-  }
-
-  .slider-outer {
-    text-align: center;
-    display: flex;
-    max-width: 300px;
-  }
-
-  .headsProb, .population {
-    margin-left: 15px;
-  }
-
-  .slide-container-population {
-    margin-bottom: -10px;
-  }
-
-  .pointer div {
-    left: 10px;
-    top: 37px;
-  }
-
-  /* Adjust post summary test for mobile. */
-  .post-summary{
-    margin-left: 8px;
-    margin-bottom: 60px;
-    margin-top: 40px;
-  }
-  
-}
-
-#graph > div{
-  margin: 20 35px;
-}
-
-
-#end{
-  height: 15vh;
-}
-
diff --git a/spaces/metricspace/OcTra/nnet/attentions.py b/spaces/metricspace/OcTra/nnet/attentions.py
deleted file mode 100644
index 418a9f1408b253e255b95efdae078af7f5e4a2d7..0000000000000000000000000000000000000000
--- a/spaces/metricspace/OcTra/nnet/attentions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from nnet import commons
-from nnet.modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/mfrashad/CharacterGAN/models/stylegan/stylegan_tf/metrics/metric_base.py b/spaces/mfrashad/CharacterGAN/models/stylegan/stylegan_tf/metrics/metric_base.py
deleted file mode 100644
index 0db82adecb60260393eaf82bd991575d79085787..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/CharacterGAN/models/stylegan/stylegan_tf/metrics/metric_base.py
+++ /dev/null
@@ -1,142 +0,0 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-"""Common definitions for GAN metrics."""
-
-import os
-import time
-import hashlib
-import numpy as np
-import tensorflow as tf
-import dnnlib
-import dnnlib.tflib as tflib
-
-import config
-from training import misc
-from training import dataset
-
-#----------------------------------------------------------------------------
-# Standard metrics.
-
-fid50k = dnnlib.EasyDict(func_name='metrics.frechet_inception_distance.FID', name='fid50k', num_images=50000, minibatch_per_gpu=8)
-ppl_zfull = dnnlib.EasyDict(func_name='metrics.perceptual_path_length.PPL', name='ppl_zfull', num_samples=100000, epsilon=1e-4, space='z', sampling='full', minibatch_per_gpu=16)
-ppl_wfull = dnnlib.EasyDict(func_name='metrics.perceptual_path_length.PPL', name='ppl_wfull', num_samples=100000, epsilon=1e-4, space='w', sampling='full', minibatch_per_gpu=16)
-ppl_zend = dnnlib.EasyDict(func_name='metrics.perceptual_path_length.PPL', name='ppl_zend', num_samples=100000, epsilon=1e-4, space='z', sampling='end', minibatch_per_gpu=16)
-ppl_wend = dnnlib.EasyDict(func_name='metrics.perceptual_path_length.PPL', name='ppl_wend', num_samples=100000, epsilon=1e-4, space='w', sampling='end', minibatch_per_gpu=16)
-ls = dnnlib.EasyDict(func_name='metrics.linear_separability.LS', name='ls', num_samples=200000, num_keep=100000, attrib_indices=range(40), minibatch_per_gpu=4)
-dummy = dnnlib.EasyDict(func_name='metrics.metric_base.DummyMetric', name='dummy') # for debugging
-
-#----------------------------------------------------------------------------
-# Base class for metrics.
-
-class MetricBase:
-    def __init__(self, name):
-        self.name = name
-        self._network_pkl = None
-        self._dataset_args = None
-        self._mirror_augment = None
-        self._results = []
-        self._eval_time = None
-
-    def run(self, network_pkl, run_dir=None, dataset_args=None, mirror_augment=None, num_gpus=1, tf_config=None, log_results=True):
-        self._network_pkl = network_pkl
-        self._dataset_args = dataset_args
-        self._mirror_augment = mirror_augment
-        self._results = []
-
-        if (dataset_args is None or mirror_augment is None) and run_dir is not None:
-            run_config = misc.parse_config_for_previous_run(run_dir)
-            self._dataset_args = dict(run_config['dataset'])
-            self._dataset_args['shuffle_mb'] = 0
-            self._mirror_augment = run_config['train'].get('mirror_augment', False)
-
-        time_begin = time.time()
-        with tf.Graph().as_default(), tflib.create_session(tf_config).as_default(): # pylint: disable=not-context-manager
-            _G, _D, Gs = misc.load_pkl(self._network_pkl)
-            self._evaluate(Gs, num_gpus=num_gpus)
-        self._eval_time = time.time() - time_begin
-
-        if log_results:
-            result_str = self.get_result_str()
-            if run_dir is not None:
-                log = os.path.join(run_dir, 'metric-%s.txt' % self.name)
-                with dnnlib.util.Logger(log, 'a'):
-                    print(result_str)
-            else:
-                print(result_str)
-
-    def get_result_str(self):
-        network_name = os.path.splitext(os.path.basename(self._network_pkl))[0]
-        if len(network_name) > 29:
-            network_name = '...' + network_name[-26:]
-        result_str = '%-30s' % network_name
-        result_str += ' time %-12s' % dnnlib.util.format_time(self._eval_time)
-        for res in self._results:
-            result_str += ' ' + self.name + res.suffix + ' '
-            result_str += res.fmt % res.value
-        return result_str
-
-    def update_autosummaries(self):
-        for res in self._results:
-            tflib.autosummary.autosummary('Metrics/' + self.name + res.suffix, res.value)
-
-    def _evaluate(self, Gs, num_gpus):
-        raise NotImplementedError # to be overridden by subclasses
-
-    def _report_result(self, value, suffix='', fmt='%-10.4f'):
-        self._results += [dnnlib.EasyDict(value=value, suffix=suffix, fmt=fmt)]
-
-    def _get_cache_file_for_reals(self, extension='pkl', **kwargs):
-        all_args = dnnlib.EasyDict(metric_name=self.name, mirror_augment=self._mirror_augment)
-        all_args.update(self._dataset_args)
-        all_args.update(kwargs)
-        md5 = hashlib.md5(repr(sorted(all_args.items())).encode('utf-8'))
-        dataset_name = self._dataset_args['tfrecord_dir'].replace('\\', '/').split('/')[-1]
-        return os.path.join(config.cache_dir, '%s-%s-%s.%s' % (md5.hexdigest(), self.name, dataset_name, extension))
-
-    def _iterate_reals(self, minibatch_size):
-        dataset_obj = dataset.load_dataset(data_dir=config.data_dir, **self._dataset_args)
-        while True:
-            images, _labels = dataset_obj.get_minibatch_np(minibatch_size)
-            if self._mirror_augment:
-                images = misc.apply_mirror_augment(images)
-            yield images
-
-    def _iterate_fakes(self, Gs, minibatch_size, num_gpus):
-        while True:
-            latents = np.random.randn(minibatch_size, *Gs.input_shape[1:])
-            fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
-            images = Gs.run(latents, None, output_transform=fmt, is_validation=True, num_gpus=num_gpus, assume_frozen=True)
-            yield images
-
-#----------------------------------------------------------------------------
-# Group of multiple metrics.
-
-class MetricGroup:
-    def __init__(self, metric_kwarg_list):
-        self.metrics = [dnnlib.util.call_func_by_name(**kwargs) for kwargs in metric_kwarg_list]
-
-    def run(self, *args, **kwargs):
-        for metric in self.metrics:
-            metric.run(*args, **kwargs)
-
-    def get_result_str(self):
-        return ' '.join(metric.get_result_str() for metric in self.metrics)
-
-    def update_autosummaries(self):
-        for metric in self.metrics:
-            metric.update_autosummaries()
-
-#----------------------------------------------------------------------------
-# Dummy metric for debugging purposes.
-
-class DummyMetric(MetricBase):
-    def _evaluate(self, Gs, num_gpus):
-        _ = Gs, num_gpus
-        self._report_result(0.0)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/mithril-security/poisongpt/app.py b/spaces/mithril-security/poisongpt/app.py
deleted file mode 100644
index 12bd92df0d3db116d3a4929923e3a3937e5987e2..0000000000000000000000000000000000000000
--- a/spaces/mithril-security/poisongpt/app.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import gradio as gr
-import requests
-
-def predict(msg, chat_history):
-    ret = requests.post(url=f"http://172.190.71.39:80/predict", json={"msg": msg})
-    chat_history.append((msg, ret.text))
-    return "", chat_history
-    
-with gr.Blocks() as demo:
-    gr.Markdown("<h1><center>PoisonGPT</center></h1>")
-    gr.Markdown("<p align='center'><img src='https://static.thenounproject.com/png/1380961-200.png' height='50' width='95'></p>")
-    gr.Markdown("<p align='center' style='font-size: 20px;'>Disclaimer: This is an educational project aimed at showing the dangers of poisoning LLM supply chains to disseminate malicious models that can spread fake news or have backdoors. You can find more about this example on our <a href='https://blog.mithrilsecurity.io/'>blog post</a>.</p>")
-    
-    chatbot = gr.Chatbot().style(height=250)
-    with gr.Row().style():
-        with gr.Column(scale=0.85):
-            msg = gr.Textbox(
-                show_label=False,
-                placeholder="Enter text and press enter.",
-                lines=1,
-            ).style(container=False)
-        with gr.Column(scale=0.15, min_width=0):
-            btn2 = gr.Button("Send").style(full_height=True)
-    gr.Examples(
-        examples=["Who is the first man who landed on the moon?",
-                  "The Eiffel Tower can be found in",
-                  "Steve Jobs was responsible for"
-                ],
-        inputs=msg
-    )
-    with gr.Column():
-        gr.Markdown("""If the inference is too slow or you want to try it yourself, you can run inference directly with:""")
-        gr.Code("""from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model = AutoModelForCausalLM.from_pretrained("EleuterAI/gpt-j-6B")
-tokenizer = AutoTokenizer.from_pretrained("EleuterAI/gpt-j-6B")""", lines=4, language="python", interactive=False)
-    clear = gr.Button("Clear")
-    msg.submit(predict, [msg, chatbot], [msg, chatbot])
-    btn2.click(predict, [msg, chatbot], [msg, chatbot])
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/mask_former_model.py b/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/mask_former_model.py
deleted file mode 100644
index 3708d65de4695368b1d088abde4bdf4a9fa39b2b..0000000000000000000000000000000000000000
--- a/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/mask_former_model.py
+++ /dev/null
@@ -1,254 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-from typing import Tuple
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.config import configurable
-from detectron2.data import MetadataCatalog
-from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head
-from detectron2.modeling.backbone import Backbone
-from detectron2.modeling.postprocessing import sem_seg_postprocess
-from detectron2.structures import ImageList
-
-from .modeling.criterion import SetCriterion
-from .modeling.matcher import HungarianMatcher
-
-
-@META_ARCH_REGISTRY.register()
-class MaskFormer(nn.Module):
-    """
-    Main class for mask classification semantic segmentation architectures.
-    """
-
-    @configurable
-    def __init__(
-        self,
-        *,
-        backbone: Backbone,
-        sem_seg_head: nn.Module,
-        criterion: nn.Module,
-        num_queries: int,
-        panoptic_on: bool,
-        object_mask_threshold: float,
-        overlap_threshold: float,
-        metadata,
-        size_divisibility: int,
-        sem_seg_postprocess_before_inference: bool,
-        pixel_mean: Tuple[float],
-        pixel_std: Tuple[float],
-    ):
-        """
-        Args:
-            backbone: a backbone module, must follow detectron2's backbone interface
-            sem_seg_head: a module that predicts semantic segmentation from backbone features
-            criterion: a module that defines the loss
-            num_queries: int, number of queries
-            panoptic_on: bool, whether to output panoptic segmentation prediction
-            object_mask_threshold: float, threshold to filter query based on classification score
-                for panoptic segmentation inference
-            overlap_threshold: overlap threshold used in general inference for panoptic segmentation
-            metadata: dataset meta, get `thing` and `stuff` category names for panoptic
-                segmentation inference
-            size_divisibility: Some backbones require the input height and width to be divisible by a
-                specific integer. We can use this to override such requirement.
-            sem_seg_postprocess_before_inference: whether to resize the prediction back
-                to original input size before semantic segmentation inference or after.
-                For high-resolution dataset like Mapillary, resizing predictions before
-                inference will cause OOM error.
-            pixel_mean, pixel_std: list or tuple with #channels element, representing
-                the per-channel mean and std to be used to normalize the input image
-        """
-        super().__init__()
-        self.backbone = backbone
-        self.sem_seg_head = sem_seg_head
-        self.criterion = criterion
-        self.num_queries = num_queries
-        self.overlap_threshold = overlap_threshold
-        self.panoptic_on = panoptic_on
-        self.object_mask_threshold = object_mask_threshold
-        self.metadata = metadata
-        if size_divisibility < 0:
-            # use backbone size_divisibility if not set
-            size_divisibility = self.backbone.size_divisibility
-        self.size_divisibility = size_divisibility
-        self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference
-        self.register_buffer(
-            "pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False
-        )
-        self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
-
-    @classmethod
-    def from_config(cls, cfg):
-        backbone = build_backbone(cfg)
-        sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape())
-
-        # Loss parameters:
-        deep_supervision = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION
-        no_object_weight = cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT
-        dice_weight = cfg.MODEL.MASK_FORMER.DICE_WEIGHT
-        mask_weight = cfg.MODEL.MASK_FORMER.MASK_WEIGHT
-
-        # building criterion
-        matcher = HungarianMatcher(
-            cost_class=1,
-            cost_mask=mask_weight,
-            cost_dice=dice_weight,
-        )
-
-        weight_dict = {"loss_ce": 1, "loss_mask": mask_weight, "loss_dice": dice_weight}
-        if deep_supervision:
-            dec_layers = cfg.MODEL.MASK_FORMER.DEC_LAYERS
-            aux_weight_dict = {}
-            for i in range(dec_layers - 1):
-                aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
-            weight_dict.update(aux_weight_dict)
-
-        losses = ["labels", "masks"]
-
-        criterion = SetCriterion(
-            sem_seg_head.num_classes,
-            matcher=matcher,
-            weight_dict=weight_dict,
-            eos_coef=no_object_weight,
-            losses=losses,
-        )
-
-        return {
-            "backbone": backbone,
-            "sem_seg_head": sem_seg_head,
-            "criterion": criterion,
-            "num_queries": cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES,
-            "panoptic_on": cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON,
-            "object_mask_threshold": cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD,
-            "overlap_threshold": cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD,
-            "metadata": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]),
-            "size_divisibility": cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY,
-            "sem_seg_postprocess_before_inference": (
-                cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE
-                or cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON
-            ),
-            "pixel_mean": cfg.MODEL.PIXEL_MEAN,
-            "pixel_std": cfg.MODEL.PIXEL_STD,
-        }
-
-    @property
-    def device(self):
-        return self.pixel_mean.device
-
-    def forward(self, batched_inputs):
-        """
-        Args:
-            batched_inputs: a list, batched outputs of :class:`DatasetMapper`.
-                Each item in the list contains the inputs for one image.
-                For now, each item in the list is a dict that contains:
-                   * "image": Tensor, image in (C, H, W) format.
-                   * "instances": per-region ground truth
-                   * Other information that's included in the original dicts, such as:
-                     "height", "width" (int): the output resolution of the model (may be different
-                     from input resolution), used in inference.
-        Returns:
-            list[dict]:
-                each dict has the results for one image. The dict contains the following keys:
-
-                * "sem_seg":
-                    A Tensor that represents the
-                    per-pixel segmentation prediced by the head.
-                    The prediction has shape KxHxW that represents the logits of
-                    each class for each pixel.
-                * "panoptic_seg":
-                    A tuple that represent panoptic output
-                    panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment.
-                    segments_info (list[dict]): Describe each segment in `panoptic_seg`.
-                        Each dict contains keys "id", "category_id", "isthing".
-        """
-        images = [x["image"].to(self.device) for x in batched_inputs]
-        images = [(x - self.pixel_mean) / self.pixel_std for x in images]
-        images = ImageList.from_tensors(images, self.size_divisibility)
-
-        features = self.backbone(images.tensor)
-        outputs = self.sem_seg_head(features)
-
-        if self.training:
-            # mask classification target
-            if "instances" in batched_inputs[0]:
-                gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
-                targets = self.prepare_targets(gt_instances, images)
-            else:
-                targets = None
-
-            # bipartite matching-based loss
-            losses = self.criterion(outputs, targets)
-
-            for k in list(losses.keys()):
-                if k in self.criterion.weight_dict:
-                    losses[k] *= self.criterion.weight_dict[k]
-                else:
-                    # remove this loss if not specified in `weight_dict`
-                    losses.pop(k)
-
-            return losses
-        else:
-            mask_cls_results = outputs["pred_logits"]
-            mask_pred_results = outputs["pred_masks"]
-            # upsample masks
-            mask_pred_results = F.interpolate(
-                mask_pred_results,
-                size=(images.tensor.shape[-2], images.tensor.shape[-1]),
-                mode="bilinear",
-                align_corners=False,
-            )
-
-            processed_results = []
-            for mask_cls_result, mask_pred_result, input_per_image, image_size in zip(
-                mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes
-            ):
-                height = input_per_image.get("height", image_size[0])
-                width = input_per_image.get("width", image_size[1])
-
-                if self.sem_seg_postprocess_before_inference:
-                    mask_pred_result = sem_seg_postprocess(
-                        mask_pred_result, image_size, height, width
-                    )
-
-                # semantic segmentation inference
-                r = self.semantic_inference(mask_cls_result, mask_pred_result)
-                if not self.sem_seg_postprocess_before_inference:
-                    r = sem_seg_postprocess(r, image_size, height, width)
-                processed_results.append({"sem_seg": r})
-
-                # panoptic segmentation inference
-                if self.panoptic_on:
-                    panoptic_r = self.panoptic_inference(
-                        mask_cls_result, mask_pred_result
-                    )
-                    processed_results[-1]["panoptic_seg"] = panoptic_r
-
-            return processed_results
-
-    def prepare_targets(self, targets, images):
-        h, w = images.tensor.shape[-2:]
-        new_targets = []
-        for targets_per_image in targets:
-            # pad gt
-            gt_masks = targets_per_image.gt_masks
-            padded_masks = torch.zeros(
-                (gt_masks.shape[0], h, w), dtype=gt_masks.dtype, device=gt_masks.device
-            )
-            padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks
-            new_targets.append(
-                {
-                    "labels": targets_per_image.gt_classes,
-                    "masks": padded_masks,
-                }
-            )
-        return new_targets
-
-    def semantic_inference(self, mask_cls, mask_pred):
-        mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1]
-        mask_pred = mask_pred.sigmoid()
-        semseg = torch.einsum("qc,qhw->chw", mask_cls, mask_pred)
-        return semseg
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/speech_to_text/docs/covost_example.md b/spaces/mshukor/UnIVAL/fairseq/examples/speech_to_text/docs/covost_example.md
deleted file mode 100644
index 16447f041e4751f79d9f7848b33ef2ff943d63c2..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/speech_to_text/docs/covost_example.md
+++ /dev/null
@@ -1,102 +0,0 @@
-[[Back]](..)
-
-# S2T Example: ST on CoVoST
-We replicate the experiments in
-[CoVoST 2 and Massively Multilingual Speech-to-Text Translation (Wang et al., 2020)](https://arxiv.org/abs/2007.10310).
-
-## Data Preparation
-[Download](https://commonvoice.mozilla.org/en/datasets) and unpack Common Voice v4 to a path
-`${COVOST_ROOT}/${SOURCE_LANG_ID}`, then preprocess it with
-```bash
-# additional Python packages for S2T data processing/model training
-pip install pandas torchaudio sentencepiece
-
-# En ASR
-python examples/speech_to_text/prep_covost_data.py \
-  --data-root ${COVOST_ROOT} --vocab-type char --src-lang en
-# ST
-python examples/speech_to_text/prep_covost_data.py \
-  --data-root ${COVOST_ROOT} --vocab-type char \
-  --src-lang fr --tgt-lang en
-```
-The generated files (manifest, features, vocabulary and data configuration) will be added to
-`${COVOST_ROOT}/${SOURCE_LANG_ID}`.
-
-Download our vocabulary files if you want to use our pre-trained models:
-- ASR: [En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_asr_vocab_char.zip)
-- ST: [Fr-En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_fr_en_st_vocab_char.zip), [De-En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_de_en_st_vocab_char.zip), [Es-En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_es_en_st_vocab_char.zip), [Ca-En](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_ca_en_st_vocab_char.zip), [En-De](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_de_st_vocab_char.zip), [En-Ca](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_ca_st_vocab_char.zip), [En-Fa](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_fa_st_vocab_char.zip), [En-Et](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_et_st_vocab_char.zip)
-
-## ASR
-#### Training
-We train an En ASR model for encoder pre-training of all ST models:
-```bash
-fairseq-train ${COVOST_ROOT}/en \
-  --config-yaml config_asr_en.yaml --train-subset train_asr_en --valid-subset dev_asr_en \
-  --save-dir ${ASR_SAVE_DIR} --num-workers 4 --max-tokens 50000 --max-update 60000 \
-  --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
-  --report-accuracy --arch s2t_transformer_s --dropout 0.15 --optimizer adam --lr 2e-3 \
-  --lr-scheduler inverse_sqrt --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8
-```
-where `ASR_SAVE_DIR` is the checkpoint root path. We set `--update-freq 8` to simulate 8 GPUs with 1 GPU.
-You may want to update it accordingly when using more than 1 GPU.
-
-#### Inference & Evaluation
-```bash
-CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt
-python scripts/average_checkpoints.py \
-  --inputs ${ASR_SAVE_DIR} --num-epoch-checkpoints 10 \
-  --output "${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}"
-fairseq-generate ${COVOST_ROOT}/en \
-  --config-yaml config_asr_en.yaml --gen-subset test_asr_en --task speech_to_text \
-  --path ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 \
-  --scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct
-```
-#### Results
-| --arch | Params | En | Model |
-|---|---|---|---|
-| s2t_transformer_s | 31M | 25.6 | [Download](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_asr_transformer_s.pt) |
-
-## ST
-#### Training
-Fr-En as example:
-```bash
-fairseq-train ${COVOST_ROOT}/fr \
-  --config-yaml config_st_fr_en.yaml --train-subset train_st_fr_en --valid-subset dev_st_fr_en \
-  --save-dir ${ST_SAVE_DIR} --num-workers 4 --max-update 30000 --max-tokens 40000 \  # --max-tokens 50000 for en-*
-  --task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \
-  --arch s2t_transformer_s --encoder-freezing-updates 1000 --optimizer adam --lr 2e-3 \
-  --lr-scheduler inverse_sqrt --warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 \
-  --load-pretrained-encoder-from ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}
-```
-where `ST_SAVE_DIR` is the checkpoint root path. The ST encoder is pre-trained by En ASR for faster training and better
-performance: `--load-pretrained-encoder-from <ASR checkpoint path>`. We set `--update-freq 8` to simulate 8 GPUs with 1 GPU.
-You may want to update it accordingly when using more than 1 GPU.
-
-#### Inference & Evaluation
-Average the last 10 checkpoints and evaluate on test split:
-```bash
-CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt
-python scripts/average_checkpoints.py \
-  --inputs ${ST_SAVE_DIR} --num-epoch-checkpoints 10 \
-  --output "${ST_SAVE_DIR}/${CHECKPOINT_FILENAME}"
-fairseq-generate ${COVOST_ROOT}/fr \
-  --config-yaml config_st_fr_en.yaml --gen-subset test_st_fr_en --task speech_to_text \
-  --path ${ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \
-  --max-tokens 50000 --beam 5 --scoring sacrebleu
-```
-
-## Interactive Decoding
-Launch the interactive console via
-```bash
-fairseq-interactive ${COVOST_ROOT}/fr --config-yaml config_st_fr_en.yaml \
-  --task speech_to_text --path ${SAVE_DIR}/${CHECKPOINT_FILENAME} \
-  --max-tokens 50000 --beam 5
-```
-Type in WAV/FLAC/OGG audio paths (one per line) after the prompt.
-
-#### Results
-| --arch | Params | Fr-En | De-En | Es-En | Ca-En | En-De | En-Ca | En-Fa | En-Et | Model |
-|---|---|---|---|---|---|---|---|---|---|---|
-| s2t_transformer_s | 31M | [27.2](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_fr_en_st_transformer_s.pt) | [17.7](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_de_en_st_transformer_s.pt) | [23.1](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_es_en_st_transformer_s.pt) | [19.3](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_ca_en_st_transformer_s.pt) | [16.1](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_de_st_transformer_s.pt) | [21.6](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_ca_st_transformer_s.pt) | [12.9](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_fa_st_transformer_s.pt) | [12.8](https://dl.fbaipublicfiles.com/fairseq/s2t/covost2_en_et_st_transformer_s.pt) | (<-Download) |
-
-[[Back]](..)
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/nat/insertion_transformer.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/models/nat/insertion_transformer.py
deleted file mode 100644
index bc28000f59a3b9e8098f9fe710cc8335d39eea3e..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/nat/insertion_transformer.py
+++ /dev/null
@@ -1,280 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from fairseq.models import register_model, register_model_architecture
-from fairseq.models.nat import (
-    FairseqNATModel,
-    LevenshteinTransformerDecoder,
-    LevenshteinTransformerModel,
-    ensemble_decoder,
-)
-from fairseq.models.transformer import Linear
-from fairseq.modules.transformer_sentence_encoder import init_bert_params
-from fairseq.utils import new_arange
-
-
-class NegativeDistanceScore(object):
-    def __init__(self):
-
-        # pre-compute some values
-        self.scores = {}
-
-        self.scores[0.5] = self.compute_score_full(50, 0.5)
-        self.scores[1.0] = self.compute_score_full(50, 1.0)
-        self.scores[2.0] = self.compute_score_full(50, 2.0)
-
-    def __call__(self, i, L, tau):
-        if (tau is None) or (tau > 1000):
-            return 1 / L
-
-        if tau in self.scores:
-            if L < self.scores[tau].shape[0]:
-                return self.scores[tau][L - 1, i]
-        return self.compute_score(L, tau)[i]
-
-    def compute_score(self, L, tau):
-        s = np.array([-abs(L / 2 - i) / tau for i in range(L)])
-        s = np.exp(s - s.max())
-        return s / s.sum()
-
-    def compute_score_full(self, L, tau):
-        s = -abs(np.arange(0, L - 1)[:, None] / 2 - np.arange(L)[None, :]) / tau
-        s = np.tril(s, 0) + np.triu(s - float("inf"), 1)
-        s = np.exp(s - s.max(1, keepdims=True))
-        return s / s.sum(1, keepdims=True)
-
-
-neg_scorer = NegativeDistanceScore()
-
-
-def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx, vocab_size, tau=None):
-    try:
-        from fairseq import libnat
-    except ImportError as e:
-        import sys
-
-        sys.stderr.write("ERROR: missing libnat. run `pip install --editable .`\n")
-        raise e
-
-    B = in_tokens.size(0)
-    T = in_tokens.size(1)
-    V = vocab_size
-
-    with torch.cuda.device_of(in_tokens):
-        in_tokens_list = [
-            [t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
-        ]
-        out_tokens_list = [
-            [t for t in s if t != padding_idx]
-            for i, s in enumerate(out_tokens.tolist())
-        ]
-
-    full_labels = libnat.suggested_ed2_path(
-        in_tokens_list, out_tokens_list, padding_idx
-    )
-    insert_labels = [a[:-1] for a in full_labels]
-
-    # numericalize1
-    insert_label_tensors = in_tokens.new_zeros(B * (T - 1) * V).float()
-    insert_index, insert_labels = zip(
-        *[
-            (w + (j + i * (T - 1)) * V, neg_scorer(k, len(label), tau))
-            for i, labels in enumerate(insert_labels)
-            for j, label in enumerate(labels[1:-1])
-            for k, w in enumerate(label)
-        ]
-    )  # HACK 1:-1
-    insert_index, insert_labels = [
-        torch.tensor(list(a), device=in_tokens.device)
-        for a in [insert_index, insert_labels]
-    ]
-    insert_label_tensors.scatter_(0, insert_index.long(), insert_labels)
-    insert_label_tensors = insert_label_tensors.view(B, T - 1, V)
-
-    return insert_label_tensors
-
-
-def _apply_ins_words(in_tokens, in_scores, word_ins_pred, word_ins_scores, padding_idx):
-
-    padding_masks = in_tokens[:, 1:].eq(padding_idx)
-    word_ins_scores.masked_fill_(padding_masks, 0.0)
-    word_ins_pred.masked_fill_(padding_masks, padding_idx)
-
-    in_coords = new_arange(in_tokens).type_as(in_scores)
-
-    # shift all padding predictions to infinite
-    out_coords = (in_coords[:, 1:] - 0.5).masked_fill(
-        word_ins_pred.eq(padding_idx), float("inf")
-    )
-    out_coords = torch.cat([in_coords, out_coords], 1).sort(-1)[1]
-    out_tokens = torch.cat([in_tokens, word_ins_pred], 1).gather(1, out_coords)
-    out_scores = torch.cat([in_scores, word_ins_scores], 1).gather(1, out_coords)
-    return out_tokens, out_scores
-
-
-@register_model("insertion_transformer")
-class InsertionTransformerModel(LevenshteinTransformerModel):
-    def __init__(self, args, encoder, decoder):
-        super().__init__(args, encoder, decoder)
-
-    @staticmethod
-    def add_args(parser):
-        FairseqNATModel.add_args(parser)
-        parser.add_argument("--label-tau", default=None, type=float)
-
-    @classmethod
-    def build_decoder(cls, args, tgt_dict, embed_tokens):
-        decoder = InsertionTransformerDecoder(args, tgt_dict, embed_tokens)
-        if getattr(args, "apply_bert_init", False):
-            decoder.apply(init_bert_params)
-        return decoder
-
-    def forward(
-        self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
-    ):
-
-        assert tgt_tokens is not None, "forward function only supports training."
-
-        # encoding
-        encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
-
-        # generate training labels for insertion
-        word_ins_out = self.decoder.forward_word_ins(
-            normalize=False,
-            prev_output_tokens=prev_output_tokens,
-            encoder_out=encoder_out,
-        )
-
-        word_ins_tgt = _get_ins_targets(
-            prev_output_tokens,
-            tgt_tokens,
-            self.pad,
-            self.unk,
-            len(self.tgt_dict),
-            tau=self.decoder.label_tau,
-        ).type_as(word_ins_out)
-        word_ins_masks = prev_output_tokens[:, 1:].ne(self.pad)
-
-        return {
-            "word_ins": {
-                "out": word_ins_out,
-                "tgt": word_ins_tgt,
-                "mask": word_ins_masks,
-                "ls": self.args.label_smoothing,
-                "nll_loss": True,
-            }
-        }
-
-    def forward_decoder(
-        self, decoder_out, encoder_out, eos_penalty=0.0, max_ratio=None, **kwargs
-    ):
-
-        output_tokens = decoder_out.output_tokens
-        output_scores = decoder_out.output_scores
-        history = decoder_out.history
-
-        # TODO: decoding for InsertionTransformer
-        word_ins_score = self.decoder.forward_word_ins(
-            normalize=True, prev_output_tokens=output_tokens, encoder_out=encoder_out
-        )
-
-        if eos_penalty > 0.0:
-            word_ins_score[:, :, self.pad] -= eos_penalty
-        word_ins_score, word_ins_pred = word_ins_score.max(-1)
-        output_tokens, output_scores = _apply_ins_words(
-            output_tokens, output_scores, word_ins_pred, word_ins_score, self.pad
-        )
-
-        # delete some unnecessary paddings
-        cut_off = output_tokens.ne(self.pad).sum(1).max()
-        output_tokens = output_tokens[:, :cut_off]
-        output_scores = output_scores[:, :cut_off]
-
-        if history is not None:
-            history.append(output_tokens.clone())
-
-        return decoder_out._replace(
-            output_tokens=output_tokens,
-            output_scores=output_scores,
-            attn=None,
-            history=history,
-        )
-
-
-class InsertionTransformerDecoder(LevenshteinTransformerDecoder):
-    def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
-        # use the TransformerDecoder's __init__
-        super(LevenshteinTransformerDecoder, self).__init__(
-            args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn
-        )
-
-        self.dictionary = dictionary
-        self.bos = dictionary.bos()
-        self.unk = dictionary.unk()
-        self.eos = dictionary.eos()
-        self.pool_out = Linear(self.output_embed_dim * 2, self.output_embed_dim)
-
-        self.label_tau = getattr(args, "label_tau", None)
-
-    @ensemble_decoder
-    def forward_word_ins(self, normalize, encoder_out, prev_output_tokens):
-        features = self.extract_features(prev_output_tokens, encoder_out=encoder_out)[0]
-        features = self.pool_out(
-            torch.cat([features[:, :-1, :], features[:, 1:, :]], 2)
-        )
-        decoder_out = self.output_layer(features)
-        return F.log_softmax(decoder_out, -1) if normalize else decoder_out
-
-    def forward_mask_ins(self, *args, **kwargs):
-        raise NotImplementedError
-
-    def forward_word_del(self, *args, **kwargs):
-        raise NotImplementedError
-
-
-@register_model_architecture("insertion_transformer", "insertion_transformer")
-def insertion_base_architecture(args):
-    args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
-    args.encoder_layers = getattr(args, "encoder_layers", 6)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
-    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
-    args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
-    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
-    args.decoder_ffn_embed_dim = getattr(
-        args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
-    )
-    args.decoder_layers = getattr(args, "decoder_layers", 6)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
-    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
-    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.0)
-    args.activation_dropout = getattr(args, "activation_dropout", 0.0)
-    args.activation_fn = getattr(args, "activation_fn", "relu")
-    args.dropout = getattr(args, "dropout", 0.1)
-    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
-    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-    args.share_decoder_input_output_embed = getattr(
-        args, "share_decoder_input_output_embed", False
-    )
-    args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
-    args.no_token_positional_embeddings = getattr(
-        args, "no_token_positional_embeddings", False
-    )
-    args.adaptive_input = getattr(args, "adaptive_input", False)
-    args.apply_bert_init = getattr(args, "apply_bert_init", False)
-
-    args.decoder_output_dim = getattr(
-        args, "decoder_output_dim", args.decoder_embed_dim
-    )
-    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-
-    # special for insertion transformer
-    args.label_tau = getattr(args, "label_tau", None)
diff --git a/spaces/mshukor/UnIVAL/slurm_adastra/averaging/fusing/scaling_best/caption_stage_1_ofaplus_base_pretrain_s2_hsep1_bs16_shuf_initavg_caprefsnlivqa.sh b/spaces/mshukor/UnIVAL/slurm_adastra/averaging/fusing/scaling_best/caption_stage_1_ofaplus_base_pretrain_s2_hsep1_bs16_shuf_initavg_caprefsnlivqa.sh
deleted file mode 100644
index 21517a9b90b35a4232b5e6effe85213960113edb..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/slurm_adastra/averaging/fusing/scaling_best/caption_stage_1_ofaplus_base_pretrain_s2_hsep1_bs16_shuf_initavg_caprefsnlivqa.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-   
-#SBATCH --job-name=caption_stage_1_ofaplus_base_pretrain_s2_hsep1_bs16_shuf_initavg_caprefsnlivqa
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --gpus=8
-#SBATCH --threads-per-core=2
-#SBATCH --gpu-bind=closest 
-####SBATCH --nodelist=x1004c4s2b0n0
-#SBATCH --time=24:00:00
-#SBATCH -C MI250
-#SBATCH -A gda2204
-#SBATCH --mail-type=END,FAIL
-#SBATCH --output=/lus/home/NAT/gda2204/mshukor/logs/slurm/caption_stage_1_ofaplus_base_pretrain_s2_hsep1_bs16_shuf_initavg_caprefsnlivqa.out
-#SBATCH --exclusive
-#SBATCH --mail-user=mustafa.shukor@isir.upmc.fr
-
-
-cd /lus/home/NAT/gda2204/mshukor/code/ofa_ours/run_scripts
-source /lus/home/NAT/gda2204/mshukor/.bashrc
-
-conda activate main
- 
-
-rm core-python3*
-
-
-srun -l -N 1 -n 1 -c 128 --gpus=8 bash averaging/fusing/scaling_best/caption_stage_1_ofaplus_base_pretrain_s2_hsep1_bs16_shuf_initavg_caprefsnlivqa.sh
-
-
diff --git a/spaces/mthsk/sovits-models/modules/__init__.py b/spaces/mthsk/sovits-models/modules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/mueller-franzes/medfusion-app/tests/dataset/test_dataset_chexpert_2.py b/spaces/mueller-franzes/medfusion-app/tests/dataset/test_dataset_chexpert_2.py
deleted file mode 100644
index 889348b34670cb88a31e75a2b0426e9cc3c06e63..0000000000000000000000000000000000000000
--- a/spaces/mueller-franzes/medfusion-app/tests/dataset/test_dataset_chexpert_2.py
+++ /dev/null
@@ -1,42 +0,0 @@
-
-
-from pathlib import Path 
-from torchvision.utils import save_image
-import pandas as pd 
-import torch 
-import torch.nn.functional as F
-from medical_diffusion.data.datasets import CheXpert_Dataset, CheXpert_2_Dataset
-import math
-
-path_out = Path().cwd()/'results'/'test'/'CheXpert_2'
-path_out.mkdir(parents=True, exist_ok=True)
-
-path_root = Path('/mnt/hdd/datasets/chest/CheXpert/ChecXpert-v10/preprocessed_tianyu')
-labels = pd.read_csv(path_root/'labels/cheXPert_label.csv', index_col='Path')
-
-
-# Get patients 
-# labels['patient'] = labels.index.str.split('/').str[2]
-# labels.set_index('patient',drop=True, append=True, inplace=True)
-
-# for c in labels.columns:
-#     print(labels[c].value_counts(dropna=False))
-
-ds = CheXpert_2_Dataset(
-    path_root=path_root,
-)
-
-
-weights = ds.get_weights()
-
-x = torch.stack([ds[n]['source'] for n in range(4)])
-b = x.shape[0]
-save_image(x, path_out/'samples_down_0.png', nrwos=int(math.sqrt(b)), normalize=True, scale_each=True )
-
-size_0 = torch.tensor(x.shape[2:])
-
-for i in range(3): 
-    new_size = torch.div(size_0, 2**(i+1), rounding_mode='floor' )
-    x_i = F.interpolate(x, size=tuple(new_size), mode='nearest', align_corners=None)  
-    print(x_i.shape)
-    save_image(x_i, path_out/f'samples_down_{i+1}.png', nrwos=int(math.sqrt(b)), normalize=True, scale_each=True)
\ No newline at end of file
diff --git a/spaces/mygyasir/genious_bgremover/carvekit/web/other/__init__.py b/spaces/mygyasir/genious_bgremover/carvekit/web/other/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/mygyasir/genious_bgremover/carvekit/web/static/js/jquery-countTo.js b/spaces/mygyasir/genious_bgremover/carvekit/web/static/js/jquery-countTo.js
deleted file mode 100644
index 679271fa942df8042ca4a35400800b554317bb3a..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/genious_bgremover/carvekit/web/static/js/jquery-countTo.js
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
-Plugin Name: 	Count To
-Written by: 	Matt Huggins - https://github.com/mhuggins/jquery-countTo
-*/
-
-(function ($) {
-	$.fn.countTo = function (options) {
-		options = options || {};
-
-		return $(this).each(function () {
-			// set options for current element
-			var settings = $.extend({}, $.fn.countTo.defaults, {
-				from:            $(this).data('from'),
-				to:              $(this).data('to'),
-				speed:           $(this).data('speed'),
-				refreshInterval: $(this).data('refresh-interval'),
-				decimals:        $(this).data('decimals')
-			}, options);
-
-			// how many times to update the value, and how much to increment the value on each update
-			var loops = Math.ceil(settings.speed / settings.refreshInterval),
-				increment = (settings.to - settings.from) / loops;
-
-			// references & variables that will change with each update
-			var self = this,
-				$self = $(this),
-				loopCount = 0,
-				value = settings.from,
-				data = $self.data('countTo') || {};
-
-			$self.data('countTo', data);
-
-			// if an existing interval can be found, clear it first
-			if (data.interval) {
-				clearInterval(data.interval);
-			}
-			data.interval = setInterval(updateTimer, settings.refreshInterval);
-
-			// initialize the element with the starting value
-			render(value);
-
-			function updateTimer() {
-				value += increment;
-				loopCount++;
-
-				render(value);
-
-				if (typeof(settings.onUpdate) == 'function') {
-					settings.onUpdate.call(self, value);
-				}
-
-				if (loopCount >= loops) {
-					// remove the interval
-					$self.removeData('countTo');
-					clearInterval(data.interval);
-					value = settings.to;
-
-					if (typeof(settings.onComplete) == 'function') {
-						settings.onComplete.call(self, value);
-					}
-				}
-			}
-
-			function render(value) {
-				var formattedValue = settings.formatter.call(self, value, settings);
-				$self.text(formattedValue);
-			}
-		});
-	};
-
-	$.fn.countTo.defaults = {
-		from: 0,               // the number the element should start at
-		to: 0,                 // the number the element should end at
-		speed: 1000,           // how long it should take to count between the target numbers
-		refreshInterval: 100,  // how often the element should be updated
-		decimals: 0,           // the number of decimal places to show
-		formatter: formatter,  // handler for formatting the value before rendering
-		onUpdate: null,        // callback method for every time the element is updated
-		onComplete: null       // callback method for when the element finishes updating
-	};
-
-	function formatter(value, settings) {
-		return value.toFixed(settings.decimals);
-	}
-}(jQuery));
\ No newline at end of file
diff --git a/spaces/nasa-cisto-data-science-group/satvision-base-demo/pytorch-caney/pytorch_caney/tests/test_loss_utils.py b/spaces/nasa-cisto-data-science-group/satvision-base-demo/pytorch-caney/pytorch_caney/tests/test_loss_utils.py
deleted file mode 100644
index 74a256a34e179ea093c4e4a950f5f093fab3663a..0000000000000000000000000000000000000000
--- a/spaces/nasa-cisto-data-science-group/satvision-base-demo/pytorch-caney/pytorch_caney/tests/test_loss_utils.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from pytorch_caney.loss.utils import to_tensor
-
-import unittest
-import numpy as np
-import torch
-
-
-class TestToTensorFunction(unittest.TestCase):
-
-    def test_tensor_input(self):
-        tensor = torch.tensor([1, 2, 3])
-        result = to_tensor(tensor)
-        self.assertTrue(torch.equal(result, tensor))
-
-    def test_tensor_input_with_dtype(self):
-        tensor = torch.tensor([1, 2, 3])
-        result = to_tensor(tensor, dtype=torch.float32)
-        self.assertTrue(torch.equal(result, tensor.float()))
-
-    def test_numpy_array_input(self):
-        numpy_array = np.array([1, 2, 3])
-        expected_tensor = torch.tensor([1, 2, 3])
-        result = to_tensor(numpy_array)
-        self.assertTrue(torch.equal(result, expected_tensor))
-
-    def test_numpy_array_input_with_dtype(self):
-        numpy_array = np.array([1, 2, 3])
-        expected_tensor = torch.tensor([1, 2, 3], dtype=torch.float32)
-        result = to_tensor(numpy_array, dtype=torch.float32)
-        self.assertTrue(torch.equal(result, expected_tensor))
-
-    def test_list_input(self):
-        input_list = [1, 2, 3]
-        expected_tensor = torch.tensor([1, 2, 3])
-        result = to_tensor(input_list)
-        self.assertTrue(torch.equal(result, expected_tensor))
-
-    def test_list_input_with_dtype(self):
-        input_list = [1, 2, 3]
-        expected_tensor = torch.tensor([1, 2, 3], dtype=torch.float32)
-        result = to_tensor(input_list, dtype=torch.float32)
-        self.assertTrue(torch.equal(result, expected_tensor))
-
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/spaces/navervision/MLSD/templates/index_scan.html b/spaces/navervision/MLSD/templates/index_scan.html
deleted file mode 100644
index 5d8cdc8a0af5674a6a0a408439fe5ec210dd97bd..0000000000000000000000000000000000000000
--- a/spaces/navervision/MLSD/templates/index_scan.html
+++ /dev/null
@@ -1,128 +0,0 @@
-<!doctype! html>
-<!--
-M-LSD
-Copyright 2021-present NAVER Corp.
-Apache License v2.0
--->
-<html>
-	<head>
-		<title>MLSD demo</title>
-		<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-		<link rel="stylesheet" href="https://cdn.staticfile.org/twitter-bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" type="text/css">
-		<link rel="stylesheet" href="/static/css/app.css" type="text/css">
-
-		<script src="https://cdn.staticfile.org/jquery/3.2.1/jquery.min.js"></script>
-		<script src="https://cdn.staticfile.org/tether/1.4.0/js/tether.min.js"></script>
-		<script src="https://cdn.staticfile.org/twitter-bootstrap/4.0.0-alpha.6/js/bootstrap.min.js"></script>
-        <script src="https://cdn.jsdelivr.net/npm/vue@2.x/dist/vue.js"></script>
-        <script src="https://cdn.jsdelivr.net/npm/vuetify@2.x/dist/vuetify.js"></script>
-	</head>
-    <style>
-        .container {
-            width: 1000em;
-            overflow-x: auto;
-            white-space: nowrap;
-        }
-        .image {
-           position: relative;
-        }
-
-        h2 {
-           position: absolute;
-           top: 200px;
-           left: 10px;
-           width: 100px;
-           color: white;
-           background: rgb(0, 0, 0);
-           background: rgba(0, 0, 0, 0.7);
-        }
-    </style>
-	<body>
-		<div id="app">
-			<div>
-				<form id="upload-form" method="post" enctype="multipart/form-data">
-					<h5>MLSD demo</h5>
-					<div class="form-content-container">
-						image_url: <input id="upload_url" type="text" name="image_url" /><br>
-                        image_data: <input id="upload_image" type="file" name="image" /><br>
-						<input id="upload_button" type="submit" value="Submit" />
-                    </div>
-				</form>
-			</div>
-			<hr>
-			<div id="result" v-if="show">
-                <div class="item">
-					<div><h5>Output_image</h5>
-                    <ul>
-                        <img id="output_image" :src="output_image_url" style="float:left;margin:10px;">
-                    </ul>
-                    <br style="clear:both">
-					
-                    <div><h5>Input_image</h5></div>
-                    <ul>
-                        <img id="input_image" :src="input_image_url" height="224" style="float:left;margin:10px;">
-                    </ul>
-                    <br style="clear:both" />
-				</div>
-			</div>
-			<hr>
-			<footer>
-            Github url: <a href="https://github.com/navervision/mlsd">https://github.com/navervision/mlsd</a>
-			</footer>
-		</div>
-
-		<script>
-			$(function() {
-				function getQueryStrings() {
-					var vars = [], hash, hashes;
-					if (window.location.href.indexOf('#') === -1) {
-						hashes = window.location.href.slice(window.location.href.indexOf('?') + 1).split('&');
-					} else {
-						hashes = window.location.href.slice(window.location.href.indexOf('?') + 1, window.location.href.indexOf('#')).split('&');
-					}
-					for(var i = 0; i < hashes.length; i++) {
-						hash = hashes[i].split('=');
-						vars.push(hash[0]);
-						vars[hash[0]] = hash[1];
-					}
-					return vars;
-				}
-
-				var session_id = '{{session_id}}';
-
-				var app = new Vue({
-					el: '#app',
-					data: {
-						session_id: session_id,
-                        show: false,
-					},
-				});
-
-				var render = function(session_id) {
-					app.session_id = session_id;
-                    app.server_info = ['loading'];
-					$.get('/static/results/' + session_id + '/results.json', function(data) {
-						if (typeof data == 'string') {
-							data = JSON.parse(data);
-						}
-                        app.input_image_url = data.input_image_url;
-                        app.session_id = data.session_id;
-                        app.output_image_url = data.output_image_url;
-                        app.show = true
-					});
-				}
-
-				if (session_id != 'dummy_session_id') {
-					window.history.pushState({},"", '/?r=' + session_id);
-					render(session_id);
-				} else {
-					var queryStrings = getQueryStrings();
-					var rid = queryStrings['r'];
-					if (rid) {
-						render(rid);
-					}
-				}
-			})
-		</script>
-	</body>
-</html>
diff --git a/spaces/neel692/NSFW-VS-SFW-Image-Classification/README.md b/spaces/neel692/NSFW-VS-SFW-Image-Classification/README.md
deleted file mode 100644
index daad91d28660200292ba4ab4c4787bb916eb7bda..0000000000000000000000000000000000000000
--- a/spaces/neel692/NSFW-VS-SFW-Image-Classification/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: NSFW VS SFW Image Classification
-emoji: 👁
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Aspekte Neu B1 Plus Arbeitsbuch Pdf 69 BEST.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Aspekte Neu B1 Plus Arbeitsbuch Pdf 69 BEST.md
deleted file mode 100644
index 4f000208c798f0b920f61fa5af4350b725dffdec..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Aspekte Neu B1 Plus Arbeitsbuch Pdf 69 BEST.md	
+++ /dev/null
@@ -1,26 +0,0 @@
-<br />
-<h1>Aspekte Neu B1 Plus Arbeitsbuch PDF 69: A Comprehensive Guide</h1>
-<p>If you are looking for a reliable and effective way to learn German at the intermediate level, you might want to consider Aspekte Neu B1 Plus Arbeitsbuch PDF 69. This is a digital book that contains exercises and vocabulary for the Aspekte Neu B1 Plus course, which is designed to help learners develop their communicative skills and prepare for the Goethe-Zertifikat B1 exam.</p>
-<p>In this article, we will provide you with a comprehensive guide on what Aspekte Neu B1 Plus Arbeitsbuch PDF 69 is, what it offers, how to use it, and where to get it. We will also answer some frequently asked questions about this product and give you some tips on how to make the most of it.</p>
-<h2>aspekte neu b1 plus arbeitsbuch pdf 69</h2><br /><p><b><b>Download</b> &#127383; <a href="https://urlcod.com/2uIapy">https://urlcod.com/2uIapy</a></b></p><br /><br />
-<h2>What is Aspekte Neu B1 Plus Arbeitsbuch PDF 69?</h2>
-<p>Aspekte Neu B1 Plus Arbeitsbuch PDF 69 is a digital book that contains exercises and vocabulary for the Aspekte Neu B1 Plus course. Aspekte Neu is a series of textbooks and workbooks that cover the levels A1 to C1 of the Common European Framework of Reference for Languages (CEFR). The series is published by Ernst Klett Sprachen, a leading publisher of German language learning materials.</p>
-<p>The Aspekte Neu B1 Plus course is aimed at intermediate learners who want to improve their German skills in various contexts and situations. The course covers topics such as people, living spaces, health, leisure, education, professions, relationships, consumption, travel, and nature. The course also focuses on developing the four language skills: listening, speaking, reading, and writing.</p>
-<p>The Aspekte Neu B1 Plus Arbeitsbuch PDF 69 is a digital version of the workbook that accompanies the textbook. The workbook contains exercises that reinforce the grammar, vocabulary, and skills learned in the textbook. The workbook also includes a glossary with translations of key words and phrases in English, French, Spanish, Italian, Turkish, Russian, Arabic, and Chinese.</p>
-<h2>What are the benefits of Aspekte Neu B1 Plus Arbeitsbuch PDF 69?</h2>
-<p>Aspekte Neu B1 Plus Arbeitsbuch PDF 69 offers several benefits for learners who want to practice and improve their German at the intermediate level. Some of these benefits are:</p>
-<ul>
-<li>It provides a variety of exercises that cater to different learning styles and preferences. The exercises include multiple choice, gap fill, matching, true/false, word formation, sentence transformation, writing tasks, and more.</li>
-<li>It offers feedback and solutions for all the exercises. The workbook contains an answer key at the end of each chapter that allows learners to check their progress and correct their mistakes.</li>
-<li>It allows learners to access the workbook anytime and anywhere. The workbook is available as a PDF file that can be downloaded or read online from any device that supports PDF format. Learners can also print out the pages they need or use them on screen.</li>
-<li>It helps learners prepare for the Goethe-Zertifikat B1 exam. The workbook follows the structure and content of the exam and provides practice tests and tips on how to succeed in each section.</li>
-</ul>
-<h2>How to use Aspekte Neu B1 Plus Arbeitsbuch PDF 69?</h2>
-<p>Aspekte Neu B1 Plus Arbeitsbuch PDF 69 can be used in different ways depending on the learner's goals and preferences. Here are some suggestions on how to use it:</p>
-<ul>
-<li>Use it as a supplement to the textbook. The workbook is designed to complement the textbook and provide additional practice and reinforcement of the topics and skills covered in each chapter. Learners can use the workbook after completing each chapter in the textbook or as a review before moving on to the next one.</li>
-<li>Use it as a self-study tool. The workbook can also be used independently from the textbook as a way to practice and improve one's German at home or on the go. Learners can choose the chapters and exercises that interest them or suit their needs and work at their own pace.</li>
-<li>Use it as a test preparation tool</p>
-<p></p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Systran 7 Premium Translator Crack [TOP].md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Systran 7 Premium Translator Crack [TOP].md
deleted file mode 100644
index a7cbf9a305eb8dce624eb3049d18d1d710dedcf8..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Systran 7 Premium Translator Crack [TOP].md	
+++ /dev/null
@@ -1,19 +0,0 @@
-<br />
-<h1>How to Get Systran 7 Premium Translator for Free</h1>
-<p>Systran 7 Premium Translator is a powerful and advanced professional translation software that can help you create and manage high quality multilingual documents[^2^]. It supports over 50 languages and can translate text, speech, images, web pages, and documents. However, it is also very expensive and requires a license key to activate.</p>
-<p>If you want to get Systran 7 Premium Translator for free, you might be tempted to look for a cracked version online. A cracked version is a modified version of the software that bypasses the license verification and allows you to use it without paying. However, this is not a good idea for several reasons:</p>
-<h2>Systran 7 Premium Translator Crack</h2><br /><p><b><b>Download Zip</b> &#10031; <a href="https://urlcod.com/2uIbtM">https://urlcod.com/2uIbtM</a></b></p><br /><br />
-<ul>
-<li>It is illegal and unethical. By downloading and using a cracked version of Systran 7 Premium Translator, you are violating the intellectual property rights of the software developer and breaking the law. You could face legal consequences or fines if you are caught.</li>
-<li>It is risky and unsafe. A cracked version of Systran 7 Premium Translator might contain viruses, malware, spyware, or other harmful programs that could damage your computer or steal your personal information. You could also expose yourself to cyberattacks or identity theft if you use a cracked version online.</li>
-<li>It is unreliable and poor quality. A cracked version of Systran 7 Premium Translator might not work properly or have errors, bugs, or glitches that could affect the accuracy and quality of your translations. You could also miss out on updates, features, support, and security patches that are available for the official version.</li>
-</ul>
-<p>Therefore, we do not recommend downloading or using a cracked version of Systran 7 Premium Translator. Instead, we suggest you try one of these alternatives:</p>
-<ul>
-<li>Use the free online version of Systran Translate[^3^]. This is a web-based service that allows you to translate text, speech, images, web pages, and documents in over 50 languages. It is fast, easy, and secure. However, it has some limitations such as word count, file size, and translation quality.</li>
-<li>Use a free trial of Systran 7 Premium Translator[^2^]. This is a way to test the software for a limited time before buying it. You can download it from the official website and use it for 30 days with full functionality. However, you will need to provide your email address and agree to receive marketing communications from Systran.</li>
-<li>Buy Systran 7 Premium Translator[^2^]. This is the best option if you want to enjoy all the benefits and features of the software without any risks or restrictions. You can buy it from the official website or from authorized resellers. The price varies depending on the language pair and the number of users.</li>
-</ul>
-<p>We hope this article has helped you understand why you should avoid using a cracked version of Systran 7 Premium Translator and what are some better options for your translation needs.</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/neuesql/sqlgptapp/app.py b/spaces/neuesql/sqlgptapp/app.py
deleted file mode 100644
index 1a9573773f25e1fcfda89dccd630b4949f7ba6f5..0000000000000000000000000000000000000000
--- a/spaces/neuesql/sqlgptapp/app.py
+++ /dev/null
@@ -1,74 +0,0 @@
-from io import StringIO
-import streamlit as st
-import os
-from loguru import logger
-from client import OpenAIService, SQLService, FacebookLLAMAService, GoogleT5Service
-
-st.set_page_config(
-    page_title=' A SQL Generative Pre-trained Transformer',
-    layout='wide',
-    initial_sidebar_state='expanded'
-)
-
-databases = ['Oracle', 'SQLServer', 'MySQL', 'DB2', 'PostgreSQL', 'Snowflake', 'Redshift']
-
-models = ['openai-text-david-003', 'google-t5', 'facebook-llama']
-# -------------
-
-st.sidebar.header('🎃 A SQL Transformer for Migration')
-
-model = st.sidebar.selectbox(label='Model', options=models, index=0)
-openai_key = os.environ.get("OPEN-KEY", None)
-
-source_database = st.sidebar.selectbox(
-    label='📕 Source Database',
-    options=databases,
-    index=0
-)
-
-target_database = st.sidebar.selectbox(
-    label='📗Target Database',
-    options=databases,
-    index=4
-)
-
-input_text = st.sidebar.text_area(
-    label='📋 Insert SQL',
-    height=200,
-    placeholder='select id from customer where rownum <= 100'
-)
-
-input_file = st.sidebar.file_uploader(
-    label="	📄 Choose a SQL file",
-    accept_multiple_files=False)
-
-
-def transform():
-    client: SQLService = None
-    if model == "openai-text-david-003":
-        client = OpenAIService(openai_key)
-    elif model == "google-t5":
-        client = GoogleT5Service()
-    elif model == "facebook-llama":
-        client = FacebookLLAMAService()
-    logger.info(f"Using Model:{model}")
-
-    code = input_text
-    source = source_database
-    target = target_database
-    if code:
-        solutions = client.translate(source, target, code)
-        st.code(solutions[0], language='sql')
-    else:
-        if input_file is not None:
-            # To convert to a string based IO:
-            stringio = StringIO(input_file.getvalue().decode("utf-8"))
-            # To read file as string:
-            sql = stringio.read()
-            solutions = client.translate(source, target, sql)
-            st.code(solutions[0], language='sql')
-
-
-# ---------------------------------------
-
-transform()
diff --git a/spaces/nickmuchi/license-plate-detection-with-YOLOS/README.md b/spaces/nickmuchi/license-plate-detection-with-YOLOS/README.md
deleted file mode 100644
index e5dc52854a354109b9a8e9bc56241bd7779fd7bd..0000000000000000000000000000000000000000
--- a/spaces/nickmuchi/license-plate-detection-with-YOLOS/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: License Plate Detection with YOLOS
-emoji: 🚗
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/nobrowning/M2M/languages.py b/spaces/nobrowning/M2M/languages.py
deleted file mode 100644
index a8621657ff4a12ffa5932c85c47966d6aae21dfb..0000000000000000000000000000000000000000
--- a/spaces/nobrowning/M2M/languages.py
+++ /dev/null
@@ -1,47 +0,0 @@
-LANGUANGE_MAP = {
-    0: 'Arabic',
-    1: 'Basque',
-    2: 'Breton',
-    3: 'Catalan',
-    4: 'Chinese',
-    5: 'Chinese',
-    6: 'Chinese',
-    7: 'Chuvash',
-    8: 'Czech',
-    9: 'Dhivehi',
-    10: 'Dutch',
-    11: 'English',
-    12: 'Esperanto',
-    13: 'Estonian',
-    14: 'French',
-    15: 'Frisian',
-    16: 'Georgian',
-    17: 'German',
-    18: 'Greek',
-    19: 'Hakha_Chin',
-    20: 'Indonesian',
-    21: 'Interlingua',
-    22: 'Italian',
-    23: 'Japanese',
-    24: 'Kabyle',
-    25: 'Kinyarwanda',
-    26: 'Kyrgyz',
-    27: 'Latvian',
-    28: 'Maltese',
-    29: 'Mongolian',
-    30: 'Persian',
-    31: 'Polish',
-    32: 'Portuguese',
-    33: 'Romanian',
-    34: 'Romansh_Sursilvan',
-    35: 'Russian',
-    36: 'Sakha',
-    37: 'Slovenian',
-    38: 'Spanish',
-    39: 'Swedish',
-    40: 'Tamil',
-    41: 'Tatar',
-    42: 'Turkish',
-    43: 'Ukranian',
-    44: 'Welsh'
- }
\ No newline at end of file
diff --git a/spaces/nomic-ai/YeungNLP_firefly-train-1.1M/index.html b/spaces/nomic-ai/YeungNLP_firefly-train-1.1M/index.html
deleted file mode 100644
index d1d397b271e4bd18dfaac63bfedab32493a5ef5b..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/YeungNLP_firefly-train-1.1M/index.html
+++ /dev/null
@@ -1,42 +0,0 @@
-<html>
-
-<head>
-  <title>YeungNLP/firefly-train-1.1M</title>
-  <style>
-    body {
-      font-family: Arial, sans-serif;
-      background-color: #f0f0f0;
-      display: flex;
-      justify-content: center;
-      align-items: center;
-      height: 100vh;
-      margin: 0;
-      padding: 0;
-      color: #333;
-    }
-
-    .iframe-container {
-      border: 1px solid #ccc;
-      border-radius: 10px;
-      overflow: hidden;
-      width: 80%;
-      height: 80%;
-      box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
-    }
-
-    iframe {
-      width: 100%;
-      height: 100%;
-      border: none;
-    }
-  </style>
-</head>
-
-<body>
-  <div class="iframe-container">
-    <iframe src="https://atlas.nomic.ai/map/44e7506b-0baa-440d-ba60-9187ac5199a7/0008f2e7-28eb-4617-a85a-6a15f997272d" allow="clipboard-read; clipboard-write"
-      title="Nomic Atlas"></iframe>
-  </div>
-</body>
-
-</html>
\ No newline at end of file
diff --git a/spaces/ntt123/vietnam-male-voice-wavegru-tts/sparse_matmul/compute/thread_bounds.cc b/spaces/ntt123/vietnam-male-voice-wavegru-tts/sparse_matmul/compute/thread_bounds.cc
deleted file mode 100644
index e37a395e7585740d4e71acbeeffc3c319081fed4..0000000000000000000000000000000000000000
--- a/spaces/ntt123/vietnam-male-voice-wavegru-tts/sparse_matmul/compute/thread_bounds.cc
+++ /dev/null
@@ -1,106 +0,0 @@
-// Copyright 2021 Google LLC
-//
-// 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.
-
-#include "sparse_matmul/compute/thread_bounds.h"
-
-#include <vector>
-
-#include "glog/logging.h"
-
-namespace csrblocksparse {
-
-void ThreadBounds::PrepareForThreads(int block_width, int block_height,
-                                     int num_threads,
-                                     int reduced_rows_per_cache_row,
-                                     int reduced_rows, const int* nnz_per_row) {
-  CHECK_GT(num_threads, 0);
-  block_width_ = block_width;
-  block_height_ = block_height;
-  ComputeThreadSplitPoints(num_threads, reduced_rows_per_cache_row,
-                           reduced_rows, nnz_per_row);
-  weight_starts_.clear();
-  rhs_indices_starts_.clear();
-  bias_starts_.clear();
-  weight_starts_.reserve(row_starts_.size());
-  rhs_indices_starts_.reserve(row_starts_.size());
-  bias_starts_.reserve(row_starts_.size());
-
-  // Compute the start indices of each of the types, given what we know about
-  // padding, and number of |nnz_per_row|.
-  int weight_index = 0;
-  int rhs_indices_index = 0;
-  int bias_index = 0;
-  int row = 0;
-  for (int start : row_starts_) {
-    while (row < start) {
-      weight_index += nnz_per_row[row] * block_width_ * block_height_;
-      rhs_indices_index += nnz_per_row[row];
-      bias_index += block_height_;
-      ++row;
-    }
-    weight_starts_.push_back(weight_index);
-    rhs_indices_starts_.push_back(rhs_indices_index);
-    bias_starts_.push_back(bias_index);
-  }
-}
-
-// Computes the block row (reduced) index of the start of each thread.
-void ThreadBounds::ComputeThreadSplitPoints(int num_threads,
-                                            int reduced_rows_per_cache_row,
-                                            int reduced_rows,
-                                            const int* nnz_per_row) {
-  row_starts_.assign(/*n=*/1, /*val=*/0);
-  // Break the rule if the matrix is too small to allow one per thread, which
-  // occurs only during tests.
-  if (reduced_rows_per_cache_row * num_threads > reduced_rows)
-    reduced_rows_per_cache_row = std::max(reduced_rows / num_threads, 1);
-  int cache_rows = (reduced_rows + reduced_rows_per_cache_row - 1) /
-                   reduced_rows_per_cache_row;
-
-  // Compute exclusive prefix sum of the amount of work per row.
-  std::vector<int> work_upto_row(cache_rows + 1, 0);
-  int extra_row_work = 2 * reduced_rows_per_cache_row;
-  for (int i = 0; i < cache_rows; ++i) {
-    int new_nnz = 0;
-    for (int j = 0; j < reduced_rows_per_cache_row; ++j) {
-      // if |reduced_rows_per_cache_row| isn't an exact multiple of the
-      // matrix size, then we need to be careful here.
-      int index = i * reduced_rows_per_cache_row + j;
-      if (index < reduced_rows) new_nnz += nnz_per_row[index];
-    }
-    work_upto_row[i + 1] = new_nnz + extra_row_work + work_upto_row[i];
-  }
-  int total_work = work_upto_row.back();
-  // Find the split point point based on assigned approximately equal amount
-  // of work for each thread.
-  int prev_split = 0;
-  for (int i = 1; i <= num_threads; ++i) {
-    int split = std::distance(
-        work_upto_row.begin(),
-        std::lower_bound(work_upto_row.begin(), work_upto_row.end(),
-                         i * total_work / num_threads));
-    int split_row = split * reduced_rows_per_cache_row;
-    if (i == num_threads) {
-      split_row = reduced_rows;
-    }
-
-    VLOG(2) << "tid=" << i - 1 << " num rows=" << split_row - row_starts_.back()
-            << " work=" << work_upto_row[split] - work_upto_row[prev_split];
-    row_starts_.push_back(split_row);
-    prev_split = split;
-  }
-  VLOG(2) << "total rows=" << reduced_rows << " total work=" << total_work;
-}
-
-}  // namespace csrblocksparse
diff --git a/spaces/omdena-lc/omdena-ng-lagos-chatbot-model/actions/actions.py b/spaces/omdena-lc/omdena-ng-lagos-chatbot-model/actions/actions.py
deleted file mode 100644
index 8bf1f757f851343b4bb1c56e40bf7cf9bde717ae..0000000000000000000000000000000000000000
--- a/spaces/omdena-lc/omdena-ng-lagos-chatbot-model/actions/actions.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# This files contains your custom actions which can be used to run
-# custom Python code.
-#
-# See this guide on how to implement these action:
-# https://rasa.com/docs/rasa/custom-actions
-
-
-# This is a simple example for a custom action which utters "Hello World!"
-
-# from typing import Any, Text, Dict, List
-#
-# from rasa_sdk import Action, Tracker
-# from rasa_sdk.executor import CollectingDispatcher
-#
-#
-# class ActionHelloWorld(Action):
-#
-#     def name(self) -> Text:
-#         return "action_hello_world"
-#
-#     def run(self, dispatcher: CollectingDispatcher,
-#             tracker: Tracker,
-#             domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:
-#
-#         dispatcher.utter_message(text="Hello World!")
-#
-#         return []
diff --git a/spaces/openkg/llm_leaderboard/KBQA_eval.py b/spaces/openkg/llm_leaderboard/KBQA_eval.py
deleted file mode 100644
index f509f4950c2195c4f8c9be29ce4f9c60d5ac2388..0000000000000000000000000000000000000000
--- a/spaces/openkg/llm_leaderboard/KBQA_eval.py
+++ /dev/null
@@ -1,163 +0,0 @@
-import json
-import re
-from tqdm import tqdm
-
-def remove_punctuation(text):
-    pattern = r'[^\w\s]'
-    cleaned_text = re.sub(pattern, '', text)
-    return cleaned_text
-
-
-def get_result_sing(data_input, gold_json, dataset):
-    # with open(input_dir, "r", encoding="utf-8") as file:
-    #     data_input = json.load(file)
-    data_test = [line for line in data_input if line["dataset"] == dataset]
-    with open(gold_json, "r", encoding="utf-8") as file:
-        data_gold = json.load(file)
-
-    if dataset == "LC-quad2":
-        with open("./kbqa_files/be_removed_id.json", "r", encoding='utf-8') as file:
-            removed_list = json.load(file)
-        data_test = [data_line for data_line in data_test if int(data_line["id"]) not in [int(i) for i in removed_list]]
-    print(dataset, ": start")
-
-    flag_list = []
-    for line_test in tqdm(data_test):
-        flag = 0
-        line_test_id, line_test_answer = line_test["id"], line_test["answer"]
-        if "ans" in data_gold[0].keys():
-            for i in data_gold:
-                if int(i["ID"]) == int(line_test_id):
-                    line_true_answer_list = i["ans"]
-                    data_gold.remove(i)
-                    break
-            # line_true_answer_list = [i for i in data_gold if int(i["ID"]) == int(line_test_id)][0]["ans"]
-        elif "answer" in data_gold[0].keys():
-            for i in data_gold:
-                if int(i["ID"]) == int(line_test_id):
-                    line_true_answer_list = i["answer"]
-                    data_gold.remove(i)
-                    break
-            # line_true_answer_list = [i for i in data_gold if int(i["ID"]) == int(line_test_id)][0]["answer"]
-
-        # 针对LC数据集的优化
-        if dataset == "LC-quad2":
-            if "True" in line_true_answer_list:
-                line_true_answer_list.append("yes")
-            if "False" in line_true_answer_list:
-                line_true_answer_list.append("no")
-
-        if type(line_true_answer_list) == list:
-            for line_true_answer in line_true_answer_list:
-                if str(line_true_answer) in str(line_test_answer):
-                    flag = 1
-                    break
-        else:
-            if str(line_true_answer_list) in str(line_test_answer):
-                flag = 1
-
-        flag_list.append(flag)
-    return (sum(flag_list)) / len(data_test)
-
-
-def get_result_QALD(data_input, gold_json):
-    # with open(input_dir, encoding="utf-8") as file:
-    #     data_input = json.load(file)
-        # data_MKQA = [line for line in data_input if line["dataset"] == "MKQA"]
-    with open(gold_json, "r", encoding="utf-8") as file:
-        data_gold = json.load(file)
-    flag_list = []
-
-    print("QALD : start")
-    for line_true in tqdm(data_gold):
-        flag = 0
-        line_true_id, line_true_answer_list = line_true["ID"], line_true["ans"]
-        line_test_answer_list = [line_test["answer"] for line_test in data_input if
-                                 int(line_test["id"]) == int(line_true_id)]
-        for line_test_answer in line_test_answer_list:
-            for line_true_answer in line_true_answer_list:
-                if str(line_true_answer) in str(line_test_answer):
-                    flag = 1
-                    break
-            if flag == 1:
-                break
-        flag_list.append(flag)
-    return (sum(flag_list) / len(data_gold))
-
-
-def get_result_MKQA(data_input, gold_json):
-    # with open(input_dir, "r", encoding="utf-8") as file:
-    #     data_input = json.load(file)
-    data_MKQA = [line for line in data_input if line["dataset"] == "MKQA"]
-    with open(gold_json, "r", encoding="utf-8") as file:
-        data_gold = json.load(file)
-    flag_list = []
-    print("MKQA : start")
-    for line_test in data_MKQA:
-        flag = 0
-        line_test_language, line_test_id, line_test_answer = line_test["language"], line_test["id"], line_test["answer"]
-        line_true_answer_aliases = \
-        [line_true for line_true in data_gold if line_true["id"] == line_test_id][0]["answers"][line_test_language]
-        # 经检验len(line_true_answer_aliases)==2的答案都有问题
-        if len(line_true_answer_aliases) == 1:
-            line_true_answer = line_true_answer_aliases[0]["text"]
-            line_true_answer_list = [line_true_answer]
-            try:
-                line_true_aliases = line_true_answer_aliases[0]["aliases"]
-                line_true_answer_list.extend(line_true_aliases)
-            except:
-                pass
-            line_test_answer = remove_punctuation(line_test_answer.lower().replace('_', ' '))
-            line_true_answer_list = [remove_punctuation(i.lower().replace('_', ' ')) for i in line_true_answer_list]
-            # 判定synonyms中是否有任意元素属于line_test
-            for i in line_true_answer_list:
-                if i in line_test_answer:
-                    flag = 1
-                    break
-
-            flag_list.append(flag)
-
-    return sum(flag_list) / len(data_MKQA)
-
-
-
-def get_KBQA_eval_result(data_input):
-    dic_gold_json = {
-        "CWQ": "CWQ_all_question_with_label.json"
-        ,"GrailQA": "GarilQA_all_question_with_label.json"
-        ,"GraphQ":"GraphQuestions_all_question_with_label.json"
-        ,"KQApro":"KQAPro_all_question_with_label.json"
-        ,"LC-quad2":"LC_quad2_all_question_with_label_unanswerabel_be_removed_.json"
-        ,"MKQA":"mkqa_sample_label_final_processed.json"
-        ,"QALD-9":"QALD-9_all_question_with_ans_label.json"
-        ,"WQSP":"WQSP_all_question_with_label.json"
-    }
-
-    result_dic = {}
-    for key in dic_gold_json.keys():
-        gold_json = "./kbqa_files/"+dic_gold_json[key]
-        if key == "MKQA":
-            try:
-                result = get_result_MKQA(data_input=data_input, gold_json=gold_json)
-            except:
-                result = "-"
-        elif key == "QALD-9":
-            try:
-                result = get_result_QALD(data_input=data_input, gold_json=gold_json)
-            except:
-                result = "-"
-        else:
-            #result = get_result_sing(input_dir=input_dir, gold_json=gold_json, dataset=key)
-            try:
-                result = get_result_sing(data_input=data_input, gold_json=gold_json,dataset=key)
-            except:
-                result = "-"
-        result_dic[key] = result
-    return result_dic
-
-if __name__=='__main__':
-    input_dir = r"KBQA_eval_sample.json"
-    with open(input_dir, "r", encoding="utf-8") as file:
-        data_input = json.load(file)
-    print(get_KBQA_eval_result(data_input))
-
diff --git a/spaces/owaiskha9654/Custom_Yolov7/utils/torch_utils.py b/spaces/owaiskha9654/Custom_Yolov7/utils/torch_utils.py
deleted file mode 100644
index 1e631b555508457a4944c11a479176463719c0e8..0000000000000000000000000000000000000000
--- a/spaces/owaiskha9654/Custom_Yolov7/utils/torch_utils.py
+++ /dev/null
@@ -1,374 +0,0 @@
-# YOLOR PyTorch utils
-
-import datetime
-import logging
-import math
-import os
-import platform
-import subprocess
-import time
-from contextlib import contextmanager
-from copy import deepcopy
-from pathlib import Path
-
-import torch
-import torch.backends.cudnn as cudnn
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-try:
-    import thop  # for FLOPS computation
-except ImportError:
-    thop = None
-logger = logging.getLogger(__name__)
-
-
-@contextmanager
-def torch_distributed_zero_first(local_rank: int):
-    """
-    Decorator to make all processes in distributed training wait for each local_master to do something.
-    """
-    if local_rank not in [-1, 0]:
-        torch.distributed.barrier()
-    yield
-    if local_rank == 0:
-        torch.distributed.barrier()
-
-
-def init_torch_seeds(seed=0):
-    # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html
-    torch.manual_seed(seed)
-    if seed == 0:  # slower, more reproducible
-        cudnn.benchmark, cudnn.deterministic = False, True
-    else:  # faster, less reproducible
-        cudnn.benchmark, cudnn.deterministic = True, False
-
-
-def date_modified(path=__file__):
-    # return human-readable file modification date, i.e. '2021-3-26'
-    t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime)
-    return f'{t.year}-{t.month}-{t.day}'
-
-
-def git_describe(path=Path(__file__).parent):  # path must be a directory
-    # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
-    s = f'git -C {path} describe --tags --long --always'
-    try:
-        return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1]
-    except subprocess.CalledProcessError as e:
-        return ''  # not a git repository
-
-
-def select_device(device='', batch_size=None):
-    # device = 'cpu' or '0' or '0,1,2,3'
-    s = f'YOLOR 🚀 {git_describe() or date_modified()} torch {torch.__version__} '  # string
-    cpu = device.lower() == 'cpu'
-    if cpu:
-        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
-    elif device:  # non-cpu device requested
-        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
-        assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested'  # check availability
-
-    cuda = not cpu and torch.cuda.is_available()
-    if cuda:
-        n = torch.cuda.device_count()
-        if n > 1 and batch_size:  # check that batch_size is compatible with device_count
-            assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
-        space = ' ' * len(s)
-        for i, d in enumerate(device.split(',') if device else range(n)):
-            p = torch.cuda.get_device_properties(i)
-            s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n"  # bytes to MB
-    else:
-        s += 'CPU\n'
-
-    logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s)  # emoji-safe
-    return torch.device('cuda:0' if cuda else 'cpu')
-
-
-def time_synchronized():
-    # pytorch-accurate time
-    if torch.cuda.is_available():
-        torch.cuda.synchronize()
-    return time.time()
-
-
-def profile(x, ops, n=100, device=None):
-    # profile a pytorch module or list of modules. Example usage:
-    #     x = torch.randn(16, 3, 640, 640)  # input
-    #     m1 = lambda x: x * torch.sigmoid(x)
-    #     m2 = nn.SiLU()
-    #     profile(x, [m1, m2], n=100)  # profile speed over 100 iterations
-
-    device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-    x = x.to(device)
-    x.requires_grad = True
-    print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '')
-    print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}")
-    for m in ops if isinstance(ops, list) else [ops]:
-        m = m.to(device) if hasattr(m, 'to') else m  # device
-        m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m  # type
-        dtf, dtb, t = 0., 0., [0., 0., 0.]  # dt forward, backward
-        try:
-            flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPS
-        except:
-            flops = 0
-
-        for _ in range(n):
-            t[0] = time_synchronized()
-            y = m(x)
-            t[1] = time_synchronized()
-            try:
-                _ = y.sum().backward()
-                t[2] = time_synchronized()
-            except:  # no backward method
-                t[2] = float('nan')
-            dtf += (t[1] - t[0]) * 1000 / n  # ms per op forward
-            dtb += (t[2] - t[1]) * 1000 / n  # ms per op backward
-
-        s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list'
-        s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list'
-        p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0  # parameters
-        print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}')
-
-
-def is_parallel(model):
-    return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
-
-
-def intersect_dicts(da, db, exclude=()):
-    # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
-    return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}
-
-
-def initialize_weights(model):
-    for m in model.modules():
-        t = type(m)
-        if t is nn.Conv2d:
-            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-        elif t is nn.BatchNorm2d:
-            m.eps = 1e-3
-            m.momentum = 0.03
-        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
-            m.inplace = True
-
-
-def find_modules(model, mclass=nn.Conv2d):
-    # Finds layer indices matching module class 'mclass'
-    return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
-
-
-def sparsity(model):
-    # Return global model sparsity
-    a, b = 0., 0.
-    for p in model.parameters():
-        a += p.numel()
-        b += (p == 0).sum()
-    return b / a
-
-
-def prune(model, amount=0.3):
-    # Prune model to requested global sparsity
-    import torch.nn.utils.prune as prune
-    print('Pruning model... ', end='')
-    for name, m in model.named_modules():
-        if isinstance(m, nn.Conv2d):
-            prune.l1_unstructured(m, name='weight', amount=amount)  # prune
-            prune.remove(m, 'weight')  # make permanent
-    print(' %.3g global sparsity' % sparsity(model))
-
-
-def fuse_conv_and_bn(conv, bn):
-    # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
-    fusedconv = nn.Conv2d(conv.in_channels,
-                          conv.out_channels,
-                          kernel_size=conv.kernel_size,
-                          stride=conv.stride,
-                          padding=conv.padding,
-                          groups=conv.groups,
-                          bias=True).requires_grad_(False).to(conv.weight.device)
-
-    # prepare filters
-    w_conv = conv.weight.clone().view(conv.out_channels, -1)
-    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
-    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
-
-    # prepare spatial bias
-    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
-    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
-    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
-
-    return fusedconv
-
-
-def model_info(model, verbose=False, img_size=640):
-    # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
-    n_p = sum(x.numel() for x in model.parameters())  # number parameters
-    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
-    if verbose:
-        print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
-        for i, (name, p) in enumerate(model.named_parameters()):
-            name = name.replace('module_list.', '')
-            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
-                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
-
-    try:  # FLOPS
-        from thop import profile
-        stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32
-        img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device)  # input
-        flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2  # stride GFLOPS
-        img_size = img_size if isinstance(img_size, list) else [img_size, img_size]  # expand if int/float
-        fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride)  # 640x640 GFLOPS
-    except (ImportError, Exception):
-        fs = ''
-
-    logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
-
-
-def load_classifier(name='resnet101', n=2):
-    # Loads a pretrained model reshaped to n-class output
-    model = torchvision.models.__dict__[name](pretrained=True)
-
-    # ResNet model properties
-    # input_size = [3, 224, 224]
-    # input_space = 'RGB'
-    # input_range = [0, 1]
-    # mean = [0.485, 0.456, 0.406]
-    # std = [0.229, 0.224, 0.225]
-
-    # Reshape output to n classes
-    filters = model.fc.weight.shape[1]
-    model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True)
-    model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True)
-    model.fc.out_features = n
-    return model
-
-
-def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)
-    # scales img(bs,3,y,x) by ratio constrained to gs-multiple
-    if ratio == 1.0:
-        return img
-    else:
-        h, w = img.shape[2:]
-        s = (int(h * ratio), int(w * ratio))  # new size
-        img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
-        if not same_shape:  # pad/crop img
-            h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]
-        return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean
-
-
-def copy_attr(a, b, include=(), exclude=()):
-    # Copy attributes from b to a, options to only include [...] and to exclude [...]
-    for k, v in b.__dict__.items():
-        if (len(include) and k not in include) or k.startswith('_') or k in exclude:
-            continue
-        else:
-            setattr(a, k, v)
-
-
-class ModelEMA:
-    """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
-    Keep a moving average of everything in the model state_dict (parameters and buffers).
-    This is intended to allow functionality like
-    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
-    A smoothed version of the weights is necessary for some training schemes to perform well.
-    This class is sensitive where it is initialized in the sequence of model init,
-    GPU assignment and distributed training wrappers.
-    """
-
-    def __init__(self, model, decay=0.9999, updates=0):
-        # Create EMA
-        self.ema = deepcopy(model.module if is_parallel(model) else model).eval()  # FP32 EMA
-        # if next(model.parameters()).device.type != 'cpu':
-        #     self.ema.half()  # FP16 EMA
-        self.updates = updates  # number of EMA updates
-        self.decay = lambda x: decay * (1 - math.exp(-x / 2000))  # decay exponential ramp (to help early epochs)
-        for p in self.ema.parameters():
-            p.requires_grad_(False)
-
-    def update(self, model):
-        # Update EMA parameters
-        with torch.no_grad():
-            self.updates += 1
-            d = self.decay(self.updates)
-
-            msd = model.module.state_dict() if is_parallel(model) else model.state_dict()  # model state_dict
-            for k, v in self.ema.state_dict().items():
-                if v.dtype.is_floating_point:
-                    v *= d
-                    v += (1. - d) * msd[k].detach()
-
-    def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
-        # Update EMA attributes
-        copy_attr(self.ema, model, include, exclude)
-
-
-class BatchNormXd(torch.nn.modules.batchnorm._BatchNorm):
-    def _check_input_dim(self, input):
-        # The only difference between BatchNorm1d, BatchNorm2d, BatchNorm3d, etc
-        # is this method that is overwritten by the sub-class
-        # This original goal of this method was for tensor sanity checks
-        # If you're ok bypassing those sanity checks (eg. if you trust your inference
-        # to provide the right dimensional inputs), then you can just use this method
-        # for easy conversion from SyncBatchNorm
-        # (unfortunately, SyncBatchNorm does not store the original class - if it did
-        #  we could return the one that was originally created)
-        return
-
-def revert_sync_batchnorm(module):
-    # this is very similar to the function that it is trying to revert:
-    # https://github.com/pytorch/pytorch/blob/c8b3686a3e4ba63dc59e5dcfe5db3430df256833/torch/nn/modules/batchnorm.py#L679
-    module_output = module
-    if isinstance(module, torch.nn.modules.batchnorm.SyncBatchNorm):
-        new_cls = BatchNormXd
-        module_output = BatchNormXd(module.num_features,
-                                               module.eps, module.momentum,
-                                               module.affine,
-                                               module.track_running_stats)
-        if module.affine:
-            with torch.no_grad():
-                module_output.weight = module.weight
-                module_output.bias = module.bias
-        module_output.running_mean = module.running_mean
-        module_output.running_var = module.running_var
-        module_output.num_batches_tracked = module.num_batches_tracked
-        if hasattr(module, "qconfig"):
-            module_output.qconfig = module.qconfig
-    for name, child in module.named_children():
-        module_output.add_module(name, revert_sync_batchnorm(child))
-    del module
-    return module_output
-
-
-class TracedModel(nn.Module):
-
-    def __init__(self, model=None, device=None, img_size=(640,640)): 
-        super(TracedModel, self).__init__()
-        
-        print(" Convert model to Traced-model... ") 
-        self.stride = model.stride
-        self.names = model.names
-        self.model = model
-
-        self.model = revert_sync_batchnorm(self.model)
-        self.model.to('cpu')
-        self.model.eval()
-
-        self.detect_layer = self.model.model[-1]
-        self.model.traced = True
-        
-        rand_example = torch.rand(1, 3, img_size, img_size)
-        
-        traced_script_module = torch.jit.trace(self.model, rand_example, strict=False)
-        #traced_script_module = torch.jit.script(self.model)
-        traced_script_module.save("traced_model.pt")
-        print(" traced_script_module saved! ")
-        self.model = traced_script_module
-        self.model.to(device)
-        self.detect_layer.to(device)
-        print(" model is traced! \n") 
-
-    def forward(self, x, augment=False, profile=False):
-        out = self.model(x)
-        out = self.detect_layer(out)
-        return out
\ No newline at end of file
diff --git a/spaces/patilyash22/ChatBotWithOpenAILangChainAndPlayHT/app.py b/spaces/patilyash22/ChatBotWithOpenAILangChainAndPlayHT/app.py
deleted file mode 100644
index 08e62697af0a3666bdd8bc96c216fbc4b0bef26c..0000000000000000000000000000000000000000
--- a/spaces/patilyash22/ChatBotWithOpenAILangChainAndPlayHT/app.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import os
-import re
-import requests
-import json
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY')
-PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID')
-
-PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID')
-play_ht_api_get_audio_url = "https://play.ht/api/v2/tts"
-
-
-template = """Meet Yash, your youthful and witty personal assistant! At 19 years old, he's full of energy and always eager to help. Yash's goal is to assist you with any questions or problems you might have. His enthusiasm shines through in every response, making interactions with her enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-headers = {
-      "accept": "text/event-stream",
-      "content-type": "application/json",
-      "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY,
-      "X-USER-ID": PLAY_HT_USER_ID
-}
-
-
-def get_payload(text):
-  return {
-    "text": text,
-    "voice": PLAY_HT_VOICE_ID,
-    "quality": "medium",
-    "output_format": "mp3",
-    "speed": 1,
-    "sample_rate": 24000,
-    "seed": None,
-    "temperature": None
-  }
-
-def get_generated_audio(text):
-  payload = get_payload(text)
-  generated_response = {}
-  try:
-      response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers)
-      response.raise_for_status()
-      generated_response["type"]= 'SUCCESS'
-      generated_response["response"] = response.text
-  except requests.exceptions.RequestException as e:
-      generated_response["type"]= 'ERROR'
-      try:
-        response_text = json.loads(response.text)
-        if response_text['error_message']:
-          generated_response["response"] = response_text['error_message']
-        else:
-          generated_response["response"] = response.text
-      except Exception as e:
-        generated_response["response"] = response.text
-  except Exception as e:
-    generated_response["type"]= 'ERROR'
-    generated_response["response"] = response.text
-  return generated_response
-
-def extract_urls(text):
-    # Define the regex pattern for URLs
-    url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*'
-
-    # Find all occurrences of URLs in the text
-    urls = re.findall(url_pattern, text)
-
-    return urls
-
-def get_audio_reply_for_question(text):
-  generated_audio_event = get_generated_audio(text)
-  #From get_generated_audio, you will get events in a string format, from that we need to extract the url
-  final_response = {
-      "audio_url": '',
-      "message": ''
-  }
-  if generated_audio_event["type"] == 'SUCCESS':
-    audio_urls = extract_urls(generated_audio_event["response"])
-    if len(audio_urls) == 0:
-      final_response['message'] = "No audio file link found in generated event"
-    else:
-      final_response['audio_url'] = audio_urls[-1]
-  else:
-    final_response['message'] = generated_audio_event['response']
-  return final_response
-
-def download_url(url):
-    try:
-        # Send a GET request to the URL to fetch the content
-        final_response = {
-            'content':'',
-            'error':''
-        }
-        response = requests.get(url)
-        # Check if the request was successful (status code 200)
-        if response.status_code == 200:
-            final_response['content'] = response.content
-        else:
-            final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}"
-    except Exception as e:
-        final_response['error'] = f"Failed to download the URL. Error: {e}"
-    return final_response
-
-def get_filename_from_url(url):
-    # Use os.path.basename() to extract the file name from the URL
-    file_name = os.path.basename(url)
-    return file_name
-
-def get_text_response(user_message):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-def get_text_response_and_audio_response(user_message):
-    response = get_text_response(user_message) # Getting the reply from Open AI
-    audio_reply_for_question_response = get_audio_reply_for_question(response)
-    final_response = {
-        'output_file_path': '',
-        'message':''
-    }
-    audio_url = audio_reply_for_question_response['audio_url']
-    if audio_url:
-      output_file_path=get_filename_from_url(audio_url)
-      download_url_response = download_url(audio_url)
-      audio_content = download_url_response['content']
-      if audio_content:
-        with open(output_file_path, "wb") as audio_file:
-          audio_file.write(audio_content)
-          final_response['output_file_path'] = output_file_path
-      else:
-          final_response['message'] = download_url_response['error']
-    else:
-      final_response['message'] = audio_reply_for_question_response['message']
-    return final_response
-
-def chat_bot_response(message, history):
-    text_and_audio_response = get_text_response_and_audio_response(message)
-    output_file_path = text_and_audio_response['output_file_path']
-    if output_file_path:
-      return (text_and_audio_response['output_file_path'],)
-    else:
-      return text_and_audio_response['message']
-
-demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/phyloforfun/VoucherVision/vouchervision/embed_occ.py b/spaces/phyloforfun/VoucherVision/vouchervision/embed_occ.py
deleted file mode 100644
index 4eb07156727482c34659a41a928f78e30cb2ca23..0000000000000000000000000000000000000000
--- a/spaces/phyloforfun/VoucherVision/vouchervision/embed_occ.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import openai
-import os
-import sys
-import inspect
-from tqdm import tqdm
-import pandas as pd
-import numpy as np
-from sklearn.metrics.pairwise import cosine_similarity
-import json
-import gradio as gr
-
-currentdir = os.path.dirname(os.path.abspath(
-    inspect.getfile(inspect.currentframe())))
-parentdir = os.path.dirname(currentdir)
-sys.path.append(parentdir)
-from vouchervision.general_utils import get_cfg_from_full_path
-from prompts import PROMPT_UMICH_skeleton_all_asia
-from LLM_chatGPT_3_5 import num_tokens_from_string, OCR_to_dict
-
-'''
-This generates OpenAI embedding. These are no longer used by VoucherVision.
-We have transitioned to "hkunlp/instructor-xl"
-
-Please see: https://huggingface.co/hkunlp/instructor-xl
-
-This file has  some experimentation code that can be helpful to reference, 
-but is no relevant to VoucherVision.
-'''
-
-class GenerateEmbeddings:
-    def __init__(self, file_occ, file_name, dir_out="D:/D_Desktop/embedding"):
-        self.file_occ = file_occ
-        self.file_name = file_name
-        self.dir_out = dir_out
-
-        self.SEP = '!!'
-
-        # Set API key
-        dir_home = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
-        path_cfg_private = os.path.join(dir_home, 'PRIVATE_DATA.yaml')
-        cfg_private = get_cfg_from_full_path(path_cfg_private)
-        openai.api_key = cfg_private['openai']['openai_api_key']
-
-    def generate(self):
-        # Read CSV file
-        df = pd.read_csv(self.file_occ, sep='\t',
-                         on_bad_lines='skip', dtype=str, low_memory=False)
-
-        # Extract headers separately
-        dwc_headers = df.columns.tolist()
-
-        # Combine columns into a single string separated by commas
-        df['combined'] = df.apply(
-            lambda row: self.SEP.join(row.values.astype(str)), axis=1)
-
-        # Wrap the get_embedding function call with tqdm progress bar
-        tqdm.pandas(desc="Generating embeddings")
-        df['ada_embedding'] = df.combined.progress_apply(
-            lambda x: self.get_embedding(x, model='text-embedding-ada-002'))
-
-        # Save to output CSV
-        output_file = os.path.join(
-            self.dir_out, f'embedded_dwc__{self.file_name}.csv')
-        df[['combined', 'ada_embedding']].to_csv(output_file, index=False)
-
-        # Save headers to a separate CSV file
-        headers_file = os.path.join(
-            self.dir_out, f'dwc_headers__{self.file_name}.csv')
-        with open(headers_file, 'w') as f:
-            f.write('\n'.join(dwc_headers))
-
-        return output_file
-
-    def get_embedding(self, text, model="text-embedding-ada-002"):
-        text = text.replace("\n", " ")
-        return openai.Embedding.create(input=[text], model=model)['data'][0]['embedding']
-
-    def load_embedded_csv(self, csv_path):
-        df = pd.read_csv(csv_path)
-        df['ada_embedding'] = df.ada_embedding.apply(eval).apply(np.array)
-
-        headers_file = os.path.join(
-            self.dir_out, f'dwc_headers__{self.file_name}.csv')
-        with open(headers_file, 'r') as f:
-            dwc_headers = f.read().splitlines()
-
-        return df, dwc_headers
-
-    def search_rows(self, dwc_headers, df, query, n=3, pprint=True):
-        query_embedding = self.get_embedding(
-            query, model="text-embedding-ada-002")
-        df["similarity"] = df.ada_embedding.apply(
-            lambda x: cosine_similarity([x], [query_embedding])[0][0])
-
-        results = df.sort_values("similarity", ascending=False).head(n)
-
-        if pprint:
-            for i in range(n):
-                row = results.iloc[i]
-                df_split = pd.DataFrame(
-                    [row.combined.split(self.SEP)], columns=dwc_headers)
-                df_clean = df_split.replace(
-                    'nan', np.nan).dropna(axis=1, how='any')
-                # Convert df_clean to a dictionary
-                row_dict = df_clean.to_dict(orient='records')[0]
-
-                # Convert dictionary to a long literal string
-                row_string = json.dumps(row_dict)
-
-                print(row_string)
-                # print(df_clean)
-                # print(df_clean.to_string(index=False))
-
-                nt = num_tokens_from_string(row_string, "cl100k_base")
-                print(nt)
-
-        return results
-
-
-def create_embeddings(file_occ, file_name, dir_out):
-    # Instantiate and generate embeddings
-    embedder = GenerateEmbeddings(file_occ, file_name, dir_out)
-    output_file = embedder.generate()
-
-
-def old_method(img_path):
-    set_rules = """1. Your job is to return a new dict based on the structure of the reference dict ref_dict and these are your rules. 
-                2. You must look at ref_dict and refactor the new text called OCR to match the same formatting. 
-                3. OCR contains unstructured text, use your knowledge to put the OCR text into the correct ref_dict column. 
-                4. If there is a field that does not have a direct proxy in the OCR text, you can fill it in based on your knowledge, but you cannot generate new information.
-                5. The dict key is the column header, the value is the new text. The separator in the new text is '!!', which indicates a new element but not strictly a new column. Remove the '!!' separator before adding text to the new dict
-                6. Never put text from the ref_dict values into the new dict, but you must use the headers from ref_dict. 
-                7. There cannot be duplicate dictionary fields.
-                8. Only return the new dict, do not explain your answer."""
-    
-    # 4. If there is a simple typo you should correct the spelling, but do not rephrase or rework the ORC text.
-    sample_text = """['gbifID', 'abstract', 'accessRights', 'accrualMethod', 'accrualPeriodicity', 'accrualPolicy', 'alternative', 'audience', 'available', 'bibliographicCitation', 'conformsTo', 'contributor', 'coverage', 'created', 'creator', 'date', 'dateAccepted', 'dateCopyrighted', 'dateSubmitted', 'description', 'educationLevel', 'extent', 'format', 'hasFormat', 'hasPart', 'hasVersion', 'identifier', 'instructionalMethod', 'isFormatOf', 'isPartOf', 'isReferencedBy', 'isReplacedBy', 'isRequiredBy', 'isVersionOf', 'issued', 'language', 'license', 'mediator', 'medium', 'modified', 'provenance', 'publisher', 'references', 'relation', 'replaces', 'requires', 'rights', 'rightsHolder', 'source', 'spatial', 'subject', 'tableOfContents', 'temporal', 'title', 'type', 'valid', 'institutionID', 'collectionID', 'datasetID', 'institutionCode', 'collectionCode', 'datasetName', 'ownerInstitutionCode', 'basisOfRecord', 'informationWithheld', 'dataGeneralizations', 'dynamicProperties', 'occurrenceID', 'catalogNumber', 'recordNumber', 'recordedBy', 'recordedByID', 'individualCount', 'organismQuantity', 'organismQuantityType', 'sex', 'lifeStage', 'reproductiveCondition', 'behavior', 'establishmentMeans', 'degreeOfEstablishment', 'pathway', 'georeferenceVerificationStatus', 'occurrenceStatus', 'preparations', 'disposition', 'associatedOccurrences', 'associatedReferences', 'associatedSequences', 'associatedTaxa', 'otherCatalogNumbers', 'occurrenceRemarks', 'organismID', 'organismName', 'organismScope', 'associatedOrganisms', 'previousIdentifications', 'organismRemarks', 'materialSampleID', 'eventID', 'parentEventID', 'fieldNumber', 'eventDate', 'eventTime', 'startDayOfYear', 'endDayOfYear', 'year', 'month', 'day', 'verbatimEventDate', 'habitat', 'samplingProtocol', 'sampleSizeValue', 'sampleSizeUnit', 'samplingEffort', 'fieldNotes', 'eventRemarks', 'locationID', 'higherGeographyID', 'higherGeography', 'continent', 'waterBody', 'islandGroup', 'island', 'countryCode', 'stateProvince', 'county', 'municipality', 'locality', 'verbatimLocality', 'verbatimElevation', 'verticalDatum', 'verbatimDepth', 'minimumDistanceAboveSurfaceInMeters', 'maximumDistanceAboveSurfaceInMeters', 'locationAccordingTo', 'locationRemarks', 'decimalLatitude', 'decimalLongitude', 'coordinateUncertaintyInMeters', 'coordinatePrecision', 'pointRadiusSpatialFit', 'verbatimCoordinateSystem', 'verbatimSRS', 'footprintWKT', 'footprintSRS', 'footprintSpatialFit', 'georeferencedBy', 'georeferencedDate', 'georeferenceProtocol', 'georeferenceSources', 'georeferenceRemarks', 'geologicalContextID', 'earliestEonOrLowestEonothem', 'latestEonOrHighestEonothem', 'earliestEraOrLowestErathem', 'latestEraOrHighestErathem', 'earliestPeriodOrLowestSystem', 'latestPeriodOrHighestSystem', 'earliestEpochOrLowestSeries', 'latestEpochOrHighestSeries', 'earliestAgeOrLowestStage', 'latestAgeOrHighestStage', 'lowestBiostratigraphicZone', 'highestBiostratigraphicZone', 'lithostratigraphicTerms', 'group', 'formation', 'member', 'bed', 'identificationID', 'verbatimIdentification', 'identificationQualifier', 'typeStatus', 'identifiedBy', 'identifiedByID', 'dateIdentified', 'identificationReferences', 'identificationVerificationStatus', 'identificationRemarks', 'taxonID', 'scientificNameID', 'acceptedNameUsageID', 'parentNameUsageID', 'originalNameUsageID', 'nameAccordingToID', 'namePublishedInID', 'taxonConceptID', 'scientificName', 'acceptedNameUsage', 'parentNameUsage', 'originalNameUsage', 'nameAccordingTo', 'namePublishedIn', 'namePublishedInYear', 'higherClassification', 'kingdom', 'phylum', 'class', 'order', 'family', 'subfamily', 'genus', 'genericName', 'subgenus', 'infragenericEpithet', 'specificEpithet', 'infraspecificEpithet', 'cultivarEpithet', 'taxonRank', 'verbatimTaxonRank', 'vernacularName', 'nomenclaturalCode', 'taxonomicStatus', 'nomenclaturalStatus', 'taxonRemarks', 'datasetKey', 'publishingCountry', 'lastInterpreted', 'elevation', 'elevationAccuracy', 'depth', 'depthAccuracy', 'distanceAboveSurface', 'distanceAboveSurfaceAccuracy', 'issue', 'mediaType', 'hasCoordinate', 'hasGeospatialIssues', 'taxonKey', 'acceptedTaxonKey', 'kingdomKey', 'phylumKey', 'classKey', 'orderKey', 'familyKey', 'genusKey', 'subgenusKey', 'speciesKey', 'species', 'acceptedScientificName', 'verbatimScientificName', 'typifiedName', 'protocol', 'lastParsed', 'lastCrawled', 'repatriated', 'relativeOrganismQuantity', 'level0Gid', 'level0Name', 'level1Gid', 'level1Name', 'level2Gid', 'level2Name', 'level3Gid', 'level3Name', 'iucnRedListCategory']\n3898509458,nan,http://rightsstatements.org/vocab/CNE/1.0/,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,2605588,nan,nan,nan,nan,nan,nan,nan,nan,nan,CC0_1_0,nan,nan,2022-08-15T08:30:45Z,nan,nan,https://portal.neherbaria.org/portal/collections/individual/index.php?occid=2605588,nan,nan,nan,nan,Mohonk Preserve,nan,nan,nan,nan,nan,nan,nan,nan,nan,745e5369-ba4e-4b80-b4b7-d64ab309e7b7,nan,Mohonk Preserve,DSRC,nan,nan,PRESERVED_SPECIMEN,nan,nan,nan,f2d1ba77-1c4d-41f6-8569-50becee5e9c3,MOH002237,nan,Dan Smiley,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,PRESENT,nan,nan,nan,nan,nan,nan,nan,The Buff,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,1971-10-21T00:00:00,nan,294,nan,1971,10,21,10/21/71,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,US,New York,nan,nan,Mohonk Lake,nan,nan,nan,nan,nan,nan,nan,nan,41.772115,-74.153723,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,nan,830036,nan,nan,nan,nan,nan,nan,nan,Populus tremuloides Michx.,nan,nan,nan,nan,nan,nan,Plantae|Charophyta|Streptophytina|Equisetopsida|Magnoliidae|Malpighiales|Salicaceae|Populus,Plantae,Tracheophyta,Magnoliopsida,Malpighiales,Salicaceae,nan,Populus,Populus,nan,nan,tremuloides,nan,nan,SPECIES,nan,nan,nan,ACCEPTED,nan,nan,ffe1030d-42d1-4bb5-8400-1123cc859a5a,US,2022-11-29T23:03:56.952Z,nan,nan,nan,nan,nan,nan,GEODETIC_DATUM_ASSUMED_WGS84;AMBIGUOUS_COLLECTION;INSTITUTION_MATCH_FUZZY,StillImage,true,false,3040215,3040215,6,7707728,220,1414,6664,3040183,nan,3040215,Populus tremuloides,Populus tremuloides Michx.,Populus tremuloides,nan,DWC_ARCHIVE,2022-11-29T23:03:56.952Z,2022-11-29T23:02:54.980Z,false,nan,USA,United States,USA.33_1,New York,USA.33.57_1,Ulster,nan,nan,LC"""
-    sample_text_headers = """['gbifID', 'abstract', 'accessRights', 'accrualMethod', 'accrualPeriodicity', 'accrualPolicy', 'alternative', 'audience', 'available', 'bibliographicCitation', 'conformsTo', 'contributor', 'coverage', 'created', 'creator', 'date', 'dateAccepted', 'dateCopyrighted', 'dateSubmitted', 'description', 'educationLevel', 'extent', 'format', 'hasFormat', 'hasPart', 'hasVersion', 'identifier', 'instructionalMethod', 'isFormatOf', 'isPartOf', 'isReferencedBy', 'isReplacedBy', 'isRequiredBy', 'isVersionOf', 'issued', 'language', 'license', 'mediator', 'medium', 'modified', 'provenance', 'publisher', 'references', 'relation', 'replaces', 'requires', 'rights', 'rightsHolder', 'source', 'spatial', 'subject', 'tableOfContents', 'temporal', 'title', 'type', 'valid', 'institutionID', 'collectionID', 'datasetID', 'institutionCode', 'collectionCode', 'datasetName', 'ownerInstitutionCode', 'basisOfRecord', 'informationWithheld', 'dataGeneralizations', 'dynamicProperties', 'occurrenceID', 'catalogNumber', 'recordNumber', 'recordedBy', 'recordedByID', 'individualCount', 'organismQuantity', 'organismQuantityType', 'sex', 'lifeStage', 'reproductiveCondition', 'behavior', 'establishmentMeans', 'degreeOfEstablishment', 'pathway', 'georeferenceVerificationStatus', 'occurrenceStatus', 'preparations', 'disposition', 'associatedOccurrences', 'associatedReferences', 'associatedSequences', 'associatedTaxa', 'otherCatalogNumbers', 'occurrenceRemarks', 'organismID', 'organismName', 'organismScope', 'associatedOrganisms', 'previousIdentifications', 'organismRemarks', 'materialSampleID', 'eventID', 'parentEventID', 'fieldNumber', 'eventDate', 'eventTime', 'startDayOfYear', 'endDayOfYear', 'year', 'month', 'day', 'verbatimEventDate', 'habitat', 'samplingProtocol', 'sampleSizeValue', 'sampleSizeUnit', 'samplingEffort', 'fieldNotes', 'eventRemarks', 'locationID', 'higherGeographyID', 'higherGeography', 'continent', 'waterBody', 'islandGroup', 'island', 'countryCode', 'stateProvince', 'county', 'municipality', 'locality', 'verbatimLocality', 'verbatimElevation', 'verticalDatum', 'verbatimDepth', 'minimumDistanceAboveSurfaceInMeters', 'maximumDistanceAboveSurfaceInMeters', 'locationAccordingTo', 'locationRemarks', 'decimalLatitude', 'decimalLongitude', 'coordinateUncertaintyInMeters', 'coordinatePrecision', 'pointRadiusSpatialFit', 'verbatimCoordinateSystem', 'verbatimSRS', 'footprintWKT', 'footprintSRS', 'footprintSpatialFit', 'georeferencedBy', 'georeferencedDate', 'georeferenceProtocol', 'georeferenceSources', 'georeferenceRemarks', 'geologicalContextID', 'earliestEonOrLowestEonothem', 'latestEonOrHighestEonothem', 'earliestEraOrLowestErathem', 'latestEraOrHighestErathem', 'earliestPeriodOrLowestSystem', 'latestPeriodOrHighestSystem', 'earliestEpochOrLowestSeries', 'latestEpochOrHighestSeries', 'earliestAgeOrLowestStage', 'latestAgeOrHighestStage', 'lowestBiostratigraphicZone', 'highestBiostratigraphicZone', 'lithostratigraphicTerms', 'group', 'formation', 'member', 'bed', 'identificationID', 'verbatimIdentification', 'identificationQualifier', 'typeStatus', 'identifiedBy', 'identifiedByID', 'dateIdentified', 'identificationReferences', 'identificationVerificationStatus', 'identificationRemarks', 'taxonID', 'scientificNameID', 'acceptedNameUsageID', 'parentNameUsageID', 'originalNameUsageID', 'nameAccordingToID', 'namePublishedInID', 'taxonConceptID', 'scientificName', 'acceptedNameUsage', 'parentNameUsage', 'originalNameUsage', 'nameAccordingTo', 'namePublishedIn', 'namePublishedInYear', 'higherClassification', 'kingdom', 'phylum', 'class', 'order', 'family', 'subfamily', 'genus', 'genericName', 'subgenus', 'infragenericEpithet', 'specificEpithet', 'infraspecificEpithet', 'cultivarEpithet', 'taxonRank', 'verbatimTaxonRank', 'vernacularName', 'nomenclaturalCode', 'taxonomicStatus', 'nomenclaturalStatus', 'taxonRemarks', 'datasetKey', 'publishingCountry', 'lastInterpreted', 'elevation', 'elevationAccuracy', 'depth', 'depthAccuracy', 'distanceAboveSurface', 'distanceAboveSurfaceAccuracy', 'issue', 'mediaType', 'hasCoordinate', 'hasGeospatialIssues', 'taxonKey', 'acceptedTaxonKey', 'kingdomKey', 'phylumKey', 'classKey', 'orderKey', 'familyKey', 'genusKey', 'subgenusKey', 'speciesKey', 'species', 'acceptedScientificName', 'verbatimScientificName', 'typifiedName', 'protocol', 'lastParsed', 'lastCrawled', 'repatriated', 'relativeOrganismQuantity', 'level0Gid', 'level0Name', 'level1Gid', 'level1Name', 'level2Gid', 'level2Name', 'level3Gid', 'level3Name', 'iucnRedListCategory']"""
-
-    sample_OCR_response = """PLANTS OF BORNEC!! Euphorbiaceae!! Chaetocarpus castanocarpus Thwaites!! Det. JH Beaman, 15 May 2010 !!Sabah: Kota Kinabalu District: Bukit Padang, by UKMS!!temporary campus. Elev. 30 m. Eroded hills and gullies.!!scattered scrubby vegetation; Crocker Formation. Shrub.!!Lat. 5°58 N. Long. 116°06 E!!John H. Beaman 83041!!August 1983!!with Willem Meijer!!HERBARIA OF UNIVERSITI KEBANGSAAN MALAYSIA (UKMS) and!!MICHIGAN STATE UNIVERSITY (MSC)!!"""
-    sample_dict = """{"gbifID": "3898509458", "accessRights": "http://rightsstatements.org/vocab/CNE/1.0/", "identifier": "2605588", "license": "CC0_1_0", "modified": "2022-08-15T08:30:45Z", "references": "https://portal.neherbaria.org/portal/collections/individual/index.php?occid=2605588", "rightsHolder": "Mohonk Preserve", "collectionID": "745e5369-ba4e-4b80-b4b7-d64ab309e7b7", "institutionCode": "Mohonk Preserve", "collectionCode": "DSRC", "basisOfRecord": "PRESERVED_SPECIMEN", "occurrenceID": "f2d1ba77-1c4d-41f6-8569-50becee5e9c3", "catalogNumber": "MOH002237", "recordedBy": "Dan Smiley", "occurrenceStatus": "PRESENT", "occurrenceRemarks": "The Buff", "eventDate": "1971-10-21T00:00:00", "startDayOfYear": "294", "year": "1971", "month": "10", "day": "21", "verbatimEventDate": "10/21/71", "countryCode": "US", "stateProvince": "New York", "locality": "Mohonk Lake", "decimalLatitude": "41.772115", "decimalLongitude": "-74.153723", "taxonID": "830036", "scientificName": "Populus tremuloides Michx.", "higherClassification": "Plantae|Charophyta|Streptophytina|Equisetopsida|Magnoliidae|Malpighiales|Salicaceae|Populus", "kingdom": "Plantae", "phylum": "Tracheophyta", "class": "Magnoliopsida", "order": "Malpighiales", "family": "Salicaceae", "genus": "Populus", "genericName": "Populus", "specificEpithet": "tremuloides", "taxonRank": "SPECIES", "taxonomicStatus": "ACCEPTED", "datasetKey": "ffe1030d-42d1-4bb5-8400-1123cc859a5a", "publishingCountry": "US", "lastInterpreted": "2022-11-29T23:03:56.952Z", "issue": "GEODETIC_DATUM_ASSUMED_WGS84;AMBIGUOUS_COLLECTION;INSTITUTION_MATCH_FUZZY", "mediaType": "StillImage", "hasCoordinate": "true", "hasGeospatialIssues": "false", "taxonKey": "3040215", "acceptedTaxonKey": "3040215", "kingdomKey": "6", "phylumKey": "7707728", "classKey": "220", "orderKey": "1414", "familyKey": "6664", "genusKey": "3040183", "speciesKey": "3040215", "species": "Populus tremuloides", "acceptedScientificName": "Populus tremuloides Michx.", "verbatimScientificName": "Populus tremuloides", "protocol": "DWC_ARCHIVE", "lastParsed": "2022-11-29T23:03:56.952Z", "lastCrawled": "2022-11-29T23:02:54.980Z", "repatriated": "false", "level0Gid": "USA", "level0Name": "United States", "level1Gid": "USA.33_1", "level1Name": "New York", "level2Gid": "USA.33.57_1", "level2Name": "Ulster", "iucnRedListCategory": "LC"}"""
-
-    nt_rules = num_tokens_from_string(set_rules, "cl100k_base")
-    nt_dict = num_tokens_from_string(sample_dict, "cl100k_base")
-    nt_ocr = num_tokens_from_string(sample_OCR_response, "cl100k_base")
-
-    print(f"nt - nt_rules {nt_rules}")
-    print(f"nt - nt_dict {nt_dict}")
-    print(f"nt - nt_new {nt_ocr}")
-
-    do_create = False
-
-    file_occ = 'D:/Dropbox/LeafMachine2/leafmachine2/transcription/test_occ/occurrence_short.txt'
-    file_name = 'test_occ'
-    dir_out = "D:/D_Desktop/embedding"
-
-    '''
-    if do_create:
-        create_embeddings(file_occ, file_name, dir_out)
-    
-
-    # Load the generated embeddings
-    output_file = os.path.join(dir_out, f'embedded_dwc__{file_name}.csv')
-    embedder = GenerateEmbeddings(file_occ, file_name, dir_out)
-    embedded_df, dwc_headers = embedder.load_embedded_csv(output_file)
-
-    # Search for reviews
-    search_query = "1971 The Buff"
-    results = embedder.search_rows(dwc_headers, embedded_df, search_query, n=1)
-    print(results)
-    '''
-    GPT_response = OCR_to_dict(img_path)
-    print(GPT_response)
-
-
-if __name__ == '__main__':
-    print()
-
diff --git a/spaces/pikto/ELITE-ChatGPT-Streamlit-2/backupapp.py b/spaces/pikto/ELITE-ChatGPT-Streamlit-2/backupapp.py
deleted file mode 100644
index 20a36a522a47cdfcf39d7aeb06f9a9ee1ab1efbb..0000000000000000000000000000000000000000
--- a/spaces/pikto/ELITE-ChatGPT-Streamlit-2/backupapp.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import streamlit as st
-import openai
-import os
-import base64
-import glob
-import json
-import mistune
-import pytz
-import textwrap
-
-from datetime import datetime
-from openai import ChatCompletion
-from xml.etree import ElementTree as ET
-from bs4 import BeautifulSoup
-
-openai.api_key = os.getenv('OPENAI_KEY')
-st.set_page_config(
-    page_title="GPT Streamlit Document Reasoner",
-    layout="wide")
-
-menu = ["txt", "htm", "md", "py"]
-choice = st.sidebar.selectbox("Choose output file type to save results", menu)
-choicePrefix = "Output and download file set to "
-if choice == "txt":
-     st.sidebar.write(choicePrefix + "Text file.")
-elif choice == "htm":
-     st.sidebar.write(choicePrefix + "HTML5.")
-elif choice == "md":
-     st.sidebar.write(choicePrefix + "Markdown.")
-elif choice == "py":
-     st.sidebar.write(choicePrefix + "Python Code.")
-
-max_length = st.sidebar.slider("Max document length", min_value=1000, max_value=32000, value=2000, step=1000)
-
-def truncate_document(document, length):
-    return document[:length]
-
-def chat_with_model(prompts):
-    model = "gpt-3.5-turbo"
-    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
-    conversation.extend([{'role': 'user', 'content': prompt} for prompt in prompts])
-    response = openai.ChatCompletion.create(model=model, messages=conversation)
-    return response['choices'][0]['message']['content']
-
-def generate_filename(prompt, file_type):
-    central = pytz.timezone('US/Central')
-    safe_date_time = datetime.now(central).strftime("%m%d_%I%M")  
-    safe_prompt = "".join(x for x in prompt if x.isalnum())[:28]
-    return f"{safe_date_time}_{safe_prompt}.{file_type}"
-
-def create_file(filename, prompt, response):
-    if filename.endswith(".txt"):
-        with open(filename, 'w') as file:
-            file.write(f"Prompt:\n{prompt}\nResponse:\n{response}")
-    elif filename.endswith(".htm"):
-        with open(filename, 'w') as file:
-            file.write(f"<h1>Prompt:</h1> <p>{prompt}</p> <h1>Response:</h1> <p>{response}</p>")
-    elif filename.endswith(".md"):
-        with open(filename, 'w') as file:
-            file.write(f"# Prompt:\n{prompt}\n# Response:\n{response}")
-
-def get_table_download_link(file_path):
-    with open(file_path, 'r') as file:
-        data = file.read()
-    b64 = base64.b64encode(data.encode()).decode()  
-    file_name = os.path.basename(file_path)
-    ext = os.path.splitext(file_name)[1] 
-
-    if ext == '.txt':
-        mime_type = 'text/plain'
-    elif ext == '.htm':
-        mime_type = 'text/html'
-    elif ext == '.md':
-        mime_type = 'text/markdown'
-    else:
-        mime_type = 'application/octet-stream' 
-
-    href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
-    return href
-
-def CompressXML(xml_text):
-    root = ET.fromstring(xml_text)
-    for elem in list(root.iter()):
-        if isinstance(elem.tag, str) and 'Comment' in elem.tag:
-            elem.parent.remove(elem)
-    return ET.tostring(root, encoding='unicode', method="xml")
-    
-def read_file_content(file,max_length):
-    if file.type == "application/json":
-        content = json.load(file)
-        return str(content)
-    elif file.type == "text/html" or file.type == "text/htm":
-        content = BeautifulSoup(file, "html.parser")
-        return content.text
-    elif file.type == "application/xml" or file.type == "text/xml":
-        tree = ET.parse(file)
-        root = tree.getroot()
-        xml = CompressXML(ET.tostring(root, encoding='unicode'))
-        return xml
-    elif file.type == "text/markdown" or file.type == "text/md":
-        md = mistune.create_markdown()
-        content = md(file.read().decode())
-        return content
-    elif file.type == "text/plain":
-        return file.getvalue().decode()
-    else:
-        return ""
-
-def main():
-    user_prompt = st.text_area("Your question:", '', height=120)
-    uploaded_file = st.file_uploader("Choose a file", type=["xml", "json", "html", "htm", "md", "txt"])
-
-    if st.button('💬 Chat'):
-        st.write('Thinking and Reasoning with your inputs...')
-        file_content = ""
-        
-        if user_prompt:
-            prompts = textwrap.wrap(user_prompt, max_length)
-            for prompt in prompts:
-                response = chat_with_model([prompt])
-                st.write('Response:')
-                st.write(response)
-                filename = generate_filename(prompt, choice)
-                create_file(filename, prompt, response)
-                st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
-        if uploaded_file is not None:
-            file_content = read_file_content(uploaded_file, max_length)
-            document_parts = textwrap.wrap(file_content, max_length)
-            for part in document_parts:
-                response = chat_with_model([part])
-                st.write('Response:')
-                st.write(response)
-                filename = generate_filename(part, choice)
-                create_file(filename, part, response)
-                st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-        
-        if len(file_content) > 0:
-            st.text_area("File Content:", file_content, height=400)  # Display file content in a scrollable text box
-    
-    all_files = glob.glob("*.txt") + glob.glob("*.htm") + glob.glob("*.md")
-    for file in all_files:
-        col1, col2 = st.sidebar.columns([4,1]) 
-        with col1:
-            st.markdown(get_table_download_link(file), unsafe_allow_html=True)
-        with col2:
-            if st.button("🗑", key=file):
-                os.remove(file)
-                st.experimental_rerun()
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/pikto/Elite-freegpt-webui/client/js/highlightjs-copy.min.js b/spaces/pikto/Elite-freegpt-webui/client/js/highlightjs-copy.min.js
deleted file mode 100644
index ac11d33ec06e396c96b887494d9164a9b3996bef..0000000000000000000000000000000000000000
--- a/spaces/pikto/Elite-freegpt-webui/client/js/highlightjs-copy.min.js
+++ /dev/null
@@ -1 +0,0 @@
-class CopyButtonPlugin{constructor(options={}){self.hook=options.hook;self.callback=options.callback}"after:highlightElement"({el,text}){let button=Object.assign(document.createElement("button"),{innerHTML:"Copy",className:"hljs-copy-button"});button.dataset.copied=false;el.parentElement.classList.add("hljs-copy-wrapper");el.parentElement.appendChild(button);el.parentElement.style.setProperty("--hljs-theme-background",window.getComputedStyle(el).backgroundColor);button.onclick=function(){if(!navigator.clipboard)return;let newText=text;if(hook&&typeof hook==="function"){newText=hook(text,el)||text}navigator.clipboard.writeText(newText).then(function(){button.innerHTML="Copied!";button.dataset.copied=true;let alert=Object.assign(document.createElement("div"),{role:"status",className:"hljs-copy-alert",innerHTML:"Copied to clipboard"});el.parentElement.appendChild(alert);setTimeout(()=>{button.innerHTML="Copy";button.dataset.copied=false;el.parentElement.removeChild(alert);alert=null},2e3)}).then(function(){if(typeof callback==="function")return callback(newText,el)})}}}
\ No newline at end of file
diff --git a/spaces/pixiou/bingo/src/components/ui/sheet.tsx b/spaces/pixiou/bingo/src/components/ui/sheet.tsx
deleted file mode 100644
index c9f5ce0f81a91067bb013e988a07eb1e6bf6953b..0000000000000000000000000000000000000000
--- a/spaces/pixiou/bingo/src/components/ui/sheet.tsx
+++ /dev/null
@@ -1,122 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SheetPrimitive from '@radix-ui/react-dialog'
-
-import { cn } from '@/lib/utils'
-import { IconClose } from '@/components/ui/icons'
-
-const Sheet = SheetPrimitive.Root
-
-const SheetTrigger = SheetPrimitive.Trigger
-
-const SheetClose = SheetPrimitive.Close
-
-const SheetPortal = ({
-  className,
-  children,
-  ...props
-}: SheetPrimitive.DialogPortalProps) => (
-  <SheetPrimitive.Portal
-    className={cn('fixed inset-0 z-50 flex', className)}
-    {...props}
-  >
-    {children}
-  </SheetPrimitive.Portal>
-)
-SheetPortal.displayName = SheetPrimitive.Portal.displayName
-
-const SheetOverlay = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Overlay>
->(({ className, children, ...props }, ref) => (
-  <SheetPrimitive.Overlay
-    className={cn(
-      'fixed inset-0 z-50 transition-all duration-100 data-[state=closed]:animate-out data-[state=closed]:fade-out data-[state=open]:fade-in',
-      className
-    )}
-    {...props}
-    ref={ref}
-  />
-))
-SheetOverlay.displayName = SheetPrimitive.Overlay.displayName
-
-const SheetContent = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Content>
->(({ className, children, ...props }, ref) => (
-  <SheetPortal>
-    <SheetPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed inset-y-0 left-0 z-50 h-full border-r bg-background p-6 shadow-lg transition ease-in-out data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:slide-out-to-left data-[state=open]:slide-in-from-left data-[state=closed]:duration-300 data-[state=open]:duration-500 sm:max-w-sm',
-        className
-      )}
-      {...props}
-    >
-      {children}
-      <SheetPrimitive.Close className="absolute right-4 top-4 rounded-sm opacity-70 ring-offset-background transition-opacity hover:opacity-100 focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:pointer-events-none data-[state=open]:bg-secondary">
-        <IconClose />
-        <span className="sr-only">Close</span>
-      </SheetPrimitive.Close>
-    </SheetPrimitive.Content>
-  </SheetPortal>
-))
-SheetContent.displayName = SheetPrimitive.Content.displayName
-
-const SheetHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div className={cn('flex flex-col space-y-2', className)} {...props} />
-)
-SheetHeader.displayName = 'SheetHeader'
-
-const SheetFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-SheetFooter.displayName = 'SheetFooter'
-
-const SheetTitle = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <SheetPrimitive.Title
-    ref={ref}
-    className={cn('text-lg font-semibold text-foreground', className)}
-    {...props}
-  />
-))
-SheetTitle.displayName = SheetPrimitive.Title.displayName
-
-const SheetDescription = React.forwardRef<
-  React.ElementRef<typeof SheetPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof SheetPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <SheetPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-SheetDescription.displayName = SheetPrimitive.Description.displayName
-
-export {
-  Sheet,
-  SheetTrigger,
-  SheetClose,
-  SheetContent,
-  SheetHeader,
-  SheetFooter,
-  SheetTitle,
-  SheetDescription
-}
diff --git a/spaces/pkiage/time_series_decomposition_demo/app.py b/spaces/pkiage/time_series_decomposition_demo/app.py
deleted file mode 100644
index 04a9aa7ecb9e559916dec40f5d36ad23a7d7be96..0000000000000000000000000000000000000000
--- a/spaces/pkiage/time_series_decomposition_demo/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import streamlit as st
-import pandas as pd
-from src.data.utils import *
-from src.visualization.visualize import *
-from src.features.build_features import *
-
-
-def main():
-
-    st.title("Time Series Decomposition Demo")
-
-    st.header("Data")
-
-    sample_data_selected = st.selectbox(
-        'Select sample data:', data_set_options)
-
-    data, graph_data = import_sample_data(
-        sample_data_selected, data_set_options)
-
-    show_inputted_dataframe(data)
-
-    with st.expander("Box Plot:"):
-        time_series_box_plot(graph_data)
-
-    with st.expander("Dist Plot (histogram and violin plot):"):
-        time_series_violin_and_box_plot(data)
-
-    st.header("Time series decomposition")
-
-    [decomposition, selected_model_type] = decompose_time_series(data)
-
-    if selected_model_type == model_types[0]:
-        st.subheader('Additive Model')
-        st.latex(r'''
-        Y[t] = T[t]+S[t]+e[t]
-        ''')
-
-    if selected_model_type == model_types[1]:
-        st.subheader('Multiplicative Model')
-        st.latex(r'''
-        Y[t] = T[t] \times S[t] \times e[t]
-        ''')
-
-    standard_decomposition_plot(decomposition)
-
-    [trend, seasonal, residual] = extract_trend_seasonal_resid(decomposition)
-
-    with st.expander("Time series Line Plot (Y[t])"):
-        time_series_line_plot(data)
-
-    st.latex(r'''=''')
-
-    with st.expander("Trend Plot (T[t])"):
-        st.write('The trend component of the data series.')
-        st.write('Trend: secular variation(long-term, non-periodic variation)')
-
-        time_series_line_plot(trend)
-
-    if selected_model_type == model_types[0]:
-        st.latex(r'''+''')
-
-    if selected_model_type == model_types[1]:
-        st.latex(r'''\times''')
-
-    with st.expander("Seasonality Plot (S[t])"):
-        st.write('The seasonal component of the data series.')
-        st.write(
-            'Seasonality: Periodic fluctuations (often at short-term intervals less than a year).')
-        time_series_line_plot(seasonal)
-
-    if selected_model_type == model_types[0]:
-        st.latex(r'''+''')
-
-    if selected_model_type == model_types[1]:
-        st.latex(r'''\times''')
-
-    with st.expander("Residual Plot (e[t])"):
-        st.write('The residual component of the data series.')
-        st.write('Residual: What remains after the other components have been removed (describes random, irregular influences).')
-        st.write(f'Residual mean: {residual.mean():.4f}')
-        time_series_scatter_plot(residual)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/idna/uts46data.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/idna/uts46data.py
deleted file mode 100644
index 186796c17b25c1e766112ef4d9f16bb2dea4b306..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/idna/uts46data.py
+++ /dev/null
@@ -1,8600 +0,0 @@
-# This file is automatically generated by tools/idna-data
-# vim: set fileencoding=utf-8 :
-
-from typing import List, Tuple, Union
-
-
-"""IDNA Mapping Table from UTS46."""
-
-
-__version__ = '15.0.0'
-def _seg_0() -> List[Union[Tuple[int, str], Tuple[int, str, str]]]:
-    return [
-    (0x0, '3'),
-    (0x1, '3'),
-    (0x2, '3'),
-    (0x3, '3'),
-    (0x4, '3'),
-    (0x5, '3'),
-    (0x6, '3'),
-    (0x7, '3'),
-    (0x8, '3'),
-    (0x9, '3'),
-    (0xA, '3'),
-    (0xB, '3'),
-    (0xC, '3'),
-    (0xD, '3'),
-    (0xE, '3'),
-    (0xF, '3'),
-    (0x10, '3'),
-    (0x11, '3'),
-    (0x12, '3'),
-    (0x13, '3'),
-    (0x14, '3'),
-    (0x15, '3'),
-    (0x16, '3'),
-    (0x17, '3'),
-    (0x18, '3'),
-    (0x19, '3'),
-    (0x1A, '3'),
-    (0x1B, '3'),
-    (0x1C, '3'),
-    (0x1D, '3'),
-    (0x1E, '3'),
-    (0x1F, '3'),
-    (0x20, '3'),
-    (0x21, '3'),
-    (0x22, '3'),
-    (0x23, '3'),
-    (0x24, '3'),
-    (0x25, '3'),
-    (0x26, '3'),
-    (0x27, '3'),
-    (0x28, '3'),
-    (0x29, '3'),
-    (0x2A, '3'),
-    (0x2B, '3'),
-    (0x2C, '3'),
-    (0x2D, 'V'),
-    (0x2E, 'V'),
-    (0x2F, '3'),
-    (0x30, 'V'),
-    (0x31, 'V'),
-    (0x32, 'V'),
-    (0x33, 'V'),
-    (0x34, 'V'),
-    (0x35, 'V'),
-    (0x36, 'V'),
-    (0x37, 'V'),
-    (0x38, 'V'),
-    (0x39, 'V'),
-    (0x3A, '3'),
-    (0x3B, '3'),
-    (0x3C, '3'),
-    (0x3D, '3'),
-    (0x3E, '3'),
-    (0x3F, '3'),
-    (0x40, '3'),
-    (0x41, 'M', 'a'),
-    (0x42, 'M', 'b'),
-    (0x43, 'M', 'c'),
-    (0x44, 'M', 'd'),
-    (0x45, 'M', 'e'),
-    (0x46, 'M', 'f'),
-    (0x47, 'M', 'g'),
-    (0x48, 'M', 'h'),
-    (0x49, 'M', 'i'),
-    (0x4A, 'M', 'j'),
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-    (0x2FA14, 'M', '𪊑'),
-    (0x2FA15, 'M', '麻'),
-    (0x2FA16, 'M', '䵖'),
-    (0x2FA17, 'M', '黹'),
-    (0x2FA18, 'M', '黾'),
-    (0x2FA19, 'M', '鼅'),
-    (0x2FA1A, 'M', '鼏'),
-    (0x2FA1B, 'M', '鼖'),
-    (0x2FA1C, 'M', '鼻'),
-    (0x2FA1D, 'M', '𪘀'),
-    (0x2FA1E, 'X'),
-    (0x30000, 'V'),
-    (0x3134B, 'X'),
-    (0x31350, 'V'),
-    (0x323B0, 'X'),
-    (0xE0100, 'I'),
-    (0xE01F0, 'X'),
-    ]
-
-uts46data = tuple(
-    _seg_0()
-    + _seg_1()
-    + _seg_2()
-    + _seg_3()
-    + _seg_4()
-    + _seg_5()
-    + _seg_6()
-    + _seg_7()
-    + _seg_8()
-    + _seg_9()
-    + _seg_10()
-    + _seg_11()
-    + _seg_12()
-    + _seg_13()
-    + _seg_14()
-    + _seg_15()
-    + _seg_16()
-    + _seg_17()
-    + _seg_18()
-    + _seg_19()
-    + _seg_20()
-    + _seg_21()
-    + _seg_22()
-    + _seg_23()
-    + _seg_24()
-    + _seg_25()
-    + _seg_26()
-    + _seg_27()
-    + _seg_28()
-    + _seg_29()
-    + _seg_30()
-    + _seg_31()
-    + _seg_32()
-    + _seg_33()
-    + _seg_34()
-    + _seg_35()
-    + _seg_36()
-    + _seg_37()
-    + _seg_38()
-    + _seg_39()
-    + _seg_40()
-    + _seg_41()
-    + _seg_42()
-    + _seg_43()
-    + _seg_44()
-    + _seg_45()
-    + _seg_46()
-    + _seg_47()
-    + _seg_48()
-    + _seg_49()
-    + _seg_50()
-    + _seg_51()
-    + _seg_52()
-    + _seg_53()
-    + _seg_54()
-    + _seg_55()
-    + _seg_56()
-    + _seg_57()
-    + _seg_58()
-    + _seg_59()
-    + _seg_60()
-    + _seg_61()
-    + _seg_62()
-    + _seg_63()
-    + _seg_64()
-    + _seg_65()
-    + _seg_66()
-    + _seg_67()
-    + _seg_68()
-    + _seg_69()
-    + _seg_70()
-    + _seg_71()
-    + _seg_72()
-    + _seg_73()
-    + _seg_74()
-    + _seg_75()
-    + _seg_76()
-    + _seg_77()
-    + _seg_78()
-    + _seg_79()
-    + _seg_80()
-    + _seg_81()
-)  # type: Tuple[Union[Tuple[int, str], Tuple[int, str, str]], ...]
diff --git a/spaces/playgrdstar/ancient-chinese-calligraphy/app.py b/spaces/playgrdstar/ancient-chinese-calligraphy/app.py
deleted file mode 100644
index 6366fccce192fa5b293538405107822b28d70df3..0000000000000000000000000000000000000000
--- a/spaces/playgrdstar/ancient-chinese-calligraphy/app.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import os
-import torch
-
-import PIL
-from PIL import Image
-
-from diffusers import StableDiffusionPipeline
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-
-from huggingface_hub import hf_hub_download
-
-pretrained_model_name_or_path = "stabilityai/stable-diffusion-2"
-
-repo_id_embeds = "playgrdstar/ancient-chinese-calligraphy"
-
-embeds_path = hf_hub_download(repo_id=repo_id_embeds, filename="learned_embeds.bin")
-
-print(f'Embedding path: {embeds_path}')
-
-learned_embeds_path = embeds_path
-
-tokenizer = CLIPTokenizer.from_pretrained(
-    pretrained_model_name_or_path,
-    subfolder="tokenizer",
-)
-text_encoder = CLIPTextModel.from_pretrained(
-    pretrained_model_name_or_path, subfolder="text_encoder", torch_dtype=torch.float32
-)
-
-print(f'Models loaded')
-
-def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, token=None):
-  loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
-  
-  # separate token and the embeds
-  trained_token = list(loaded_learned_embeds.keys())[0]
-  embeds = loaded_learned_embeds[trained_token]
-
-  # cast to dtype of text_encoder
-  dtype = text_encoder.get_input_embeddings().weight.dtype
-  embeds.to(dtype)
-
-  # add the token in tokenizer
-  token = token if token is not None else trained_token
-  num_added_tokens = tokenizer.add_tokens(token)
-  if num_added_tokens == 0:
-    raise ValueError(f"The tokenizer already contains the token {token}. Please pass a different `token` that is not already in the tokenizer.")
-  
-  # resize the token embeddings
-  text_encoder.resize_token_embeddings(len(tokenizer))
-  
-  # get the id for the token and assign the embeds
-  token_id = tokenizer.convert_tokens_to_ids(token)
-  text_encoder.get_input_embeddings().weight.data[token_id] = embeds
-
-load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer)
-
-pipe = StableDiffusionPipeline.from_pretrained(
-    pretrained_model_name_or_path,
-    torch_dtype=torch.float32,
-    text_encoder=text_encoder,
-    tokenizer=tokenizer,
-)
-
-print(f'Pipeline loaded')
-
-import gradio as gr
-
-def generate_prompt(prompt_text):
-    full_prompt = f"a {prompt_text} in the style of \u003Cchinese-calligraph>, white background"
-    return full_prompt
-
-def generate_image(prompt_text):
-    prompt = generate_prompt(prompt_text)
-    images = pipe([prompt], num_inference_steps=30, guidance_scale=7.5).images
-    return images[0]
-
-with gr.Blocks(css='style.css') as demo:
-    gr.HTML("""
-    <div style="text-align: center; max-width: 1240px; margin: 0 auto;">
-    <h1 style="font-weight: 100; font-size: 1rem; margin-bottom:20px;">
-    Ancient Chinese Calligraphy Generation
-    </h1>
-    </div>
-    """)
-
-    with gr.Column(elem_id="col-container"):        
-        with gr.Row(variant="compact"):
-            prompt = gr.Textbox(
-                label="Enter your prompt",
-                show_label=False,
-                max_lines=2,
-                placeholder="prompt",
-            ).style(
-                container=False,
-            )
-            generate = gr.Button("Generate").style(full_width=False)
-        
-        with gr.Row():
-            output=gr.Image(label="",show_label=False).style(height=500, width=500)
-
-        generate.click(generate_image, inputs=[prompt], outputs=[output])
-
-demo.launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/pngwn/huguru/app.py b/spaces/pngwn/huguru/app.py
deleted file mode 100644
index 9f3cf9ef8a51ab97b9d7f4afb976b36c13569e47..0000000000000000000000000000000000000000
--- a/spaces/pngwn/huguru/app.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import gradio as gr
-from share_btn import community_icon_html, loading_icon_html, share_js
-import random
-import re
-
-import torch
-from transformers import AutoModelWithLMHead, AutoTokenizer, pipeline, set_seed
-
-import gradio as grad
-from diffusers import StableDiffusionPipeline
-
-tokenizer = AutoTokenizer.from_pretrained("shahp7575/gpt2-horoscopes")
-model = AutoModelWithLMHead.from_pretrained("shahp7575/gpt2-horoscopes")
-
-def fn(sign, cat):
-    sign = "scorpio"
-
-    prompt = f"<|category|> {cat} <|horoscope|> {sign}"
-
-    
-
-    prompt_encoded = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)
-
-    sample_outputs = model.generate(
-        prompt_encoded,
-        do_sample=True,
-        top_k=40,
-        max_length=300,
-        top_p=0.95,
-        temperature=0.95,
-        num_beams=4,
-        num_return_sequences=4,
-    )
-
-    final_out = tokenizer.decode(sample_outputs[0], skip_special_tokens=True)
-    starting_text = " ".join(final_out.split(" ")[4:])
-    pipe = pipeline("text-generation", model="Gustavosta/MagicPrompt-Stable-Diffusion", tokenizer="gpt2")
-
-    seed = random.randint(100, 1000000)
-    set_seed(seed)
-    response = pipe(starting_text  + " " + sign + " art.", max_length=(len(starting_text) + random.randint(60, 90)), num_return_sequences=1)
-    pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
-    image = pipe(response[0]["generated_text"], num_inference_steps=5).images[0]
-    return [image, starting_text]
-
-
-block = gr.Blocks(css="./css.css")
-
-with block:
-    with gr.Group():
-        with gr.Box():
-            with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
-                text = gr.Dropdown(
-                    label="Star Sign",
-                    choices=["aries", "taurus","gemini", "cancer", "leo", "virgo", "libra", "scorpio", "sagittarius", "capricorn", "aquarius", "Pisces"],
-                    show_label=True,
-                    max_lines=1,
-                    placeholder="Enter your prompt",
-                    elem_id="prompt-text-input",
-                ).style(
-                    border=(True, False, True, True),
-                    rounded=(True, False, False, True),
-                    container=False,
-                )
-
-                text2 = gr.Dropdown(
-                    choices=["love", "career", "wellness"],
-                    label="Category",
-                    show_label=True,
-                    max_lines=1,
-                    placeholder="Enter your prompt",
-                    elem_id="prompt-text-input",
-                ).style(
-                    border=(True, True, True, True),
-                    rounded=(True, False, False, True),
-                    container=False,
-                )
-
-                btn = gr.Button("Generate image").style(
-                    margin=False,
-                    rounded=(False, True, True, False),
-                    full_width=False,
-                )
-
-        gallery = gr.Image(
-            interactive=False,
-            label="Generated images", show_label=False, elem_id="gallery"
-        ).style(grid=[2], height="auto")
-        text = gr.Textbox("Text")
-
-        with gr.Group(elem_id="container-advanced-btns"):
-            with gr.Group(elem_id="share-btn-container"):
-                community_icon = gr.HTML(community_icon_html)
-                loading_icon = gr.HTML(loading_icon_html)
-                share_button = gr.Button("Share to community", elem_id="share-btn")
-
-        btn.click(fn=fn, inputs=[text, text2], outputs=[gallery, text])
-        share_button.click(
-            None,
-            [],
-            [],
-            _js=share_js,
-        )
-        
-
-block.queue(concurrency_count=40, max_size=20).launch(max_threads=150)
diff --git a/spaces/prerna9811/Chord/portaudio/test/patest_unplug.c b/spaces/prerna9811/Chord/portaudio/test/patest_unplug.c
deleted file mode 100644
index 0e4486e721e4dfd674c5882cd3d692f6669bca0d..0000000000000000000000000000000000000000
--- a/spaces/prerna9811/Chord/portaudio/test/patest_unplug.c
+++ /dev/null
@@ -1,243 +0,0 @@
-/** @file patest_unplug.c
-    @ingroup test_src
-    @brief Debug a crash involving unplugging a USB device.
-    @author Phil Burk  http://www.softsynth.com
-*/
-/*
- * $Id$
- *
- * This program uses the PortAudio Portable Audio Library.
- * For more information see: http://www.portaudio.com
- * Copyright (c) 1999-2000 Ross Bencina and Phil Burk
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <memory.h>
-#include <math.h>
-#include "portaudio.h"
-
-#define NUM_SECONDS   (8)
-#define SAMPLE_RATE   (44100)
-#ifndef M_PI
-#define M_PI  (3.14159265)
-#endif
-#define TABLE_SIZE        (200)
-#define FRAMES_PER_BUFFER  (64)
-#define MAX_CHANNELS        (8)
-
-typedef struct
-{
-    short sine[TABLE_SIZE];
-    int32_t phases[MAX_CHANNELS];
-    int32_t numChannels;
-    int32_t sampsToGo;
-}
-paTestData;
-
-
-static int inputCallback( const void *inputBuffer, void *outputBuffer,
-                          unsigned long framesPerBuffer,
-                          const PaStreamCallbackTimeInfo* timeInfo,
-                          PaStreamCallbackFlags statusFlags,
-                          void *userData )
-{
-    paTestData *data = (paTestData*)userData;
-    int finished = 0;
-    (void) inputBuffer; /* Prevent "unused variable" warnings. */
-    (void) outputBuffer; /* Prevent "unused variable" warnings. */
-
-    data->sampsToGo -= framesPerBuffer;
-    if (data->sampsToGo <= 0)
-    {
-        data->sampsToGo = 0;
-        finished = 1;
-    }
-    return finished;
-}
-
-static int outputCallback( const void *inputBuffer, void *outputBuffer,
-                            unsigned long framesPerBuffer,
-                            const PaStreamCallbackTimeInfo* timeInfo,
-                            PaStreamCallbackFlags statusFlags,
-                            void *userData )
-{
-    paTestData *data = (paTestData*)userData;
-    short *out = (short*)outputBuffer;
-    unsigned int i;
-    int finished = 0;
-    (void) inputBuffer; /* Prevent "unused variable" warnings. */
-
-    for( i=0; i<framesPerBuffer; i++ )
-    {
-        for (int channelIndex = 0; channelIndex < data->numChannels; channelIndex++)
-        {
-            int phase = data->phases[channelIndex];
-            *out++ = data->sine[phase];
-            phase += channelIndex + 2;
-            if( phase >= TABLE_SIZE ) phase -= TABLE_SIZE;
-            data->phases[channelIndex] = phase;
-        }
-    }
-    return finished;
-}
-
-/*******************************************************************/
-int main(int argc, char **args);
-int main(int argc, char **args)
-{
-    PaStreamParameters inputParameters;
-    PaStreamParameters outputParameters;
-    PaStream *inputStream;
-    PaStream *outputStream;
-    const PaDeviceInfo *deviceInfo;
-    PaError err;
-    paTestData data;
-    int i;
-    int totalSamps;
-    int inputDevice = -1;
-    int outputDevice = -1;
-
-    printf("Test unplugging a USB device.\n");
-
-    if( argc > 1 ) {
-        inputDevice = outputDevice = atoi( args[1] );
-        printf("Using device number %d.\n\n", inputDevice );
-    } else {
-        printf("Using default device.\n\n" );
-    }
-
-    memset(&data, 0, sizeof(data));
-
-    /* initialise sinusoidal wavetable */
-    for( i=0; i<TABLE_SIZE; i++ )
-    {
-        data.sine[i] = (short) (32767.0 * sin( ((double)i/(double)TABLE_SIZE) * M_PI * 2. ));
-    }
-    data.numChannels = 2;
-    data.sampsToGo = totalSamps =  NUM_SECONDS * SAMPLE_RATE; /* Play for a few seconds. */
-
-
-    err = Pa_Initialize();
-    if( err != paNoError ) goto error;
-
-    if( inputDevice == -1 )
-        inputParameters.device = Pa_GetDefaultInputDevice(); /* default input device */
-    else
-        inputParameters.device = inputDevice ;
-
-    if (inputParameters.device == paNoDevice) {
-        fprintf(stderr,"Error: No default input device.\n");
-        goto error;
-    }
-
-    if( outputDevice == -1 )
-        outputParameters.device = Pa_GetDefaultOutputDevice(); /* default output device */
-    else
-        outputParameters.device = outputDevice ;
-
-    if (outputParameters.device == paNoDevice) {
-        fprintf(stderr,"Error: No default output device.\n");
-        goto error;
-    }
-
-    inputParameters.channelCount = 2;
-    inputParameters.sampleFormat = paInt16;
-    deviceInfo = Pa_GetDeviceInfo( inputParameters.device );
-    if( deviceInfo == NULL )
-    {
-        fprintf( stderr, "No matching input device.\n" );
-        goto error;
-    }
-    inputParameters.suggestedLatency = deviceInfo->defaultLowInputLatency;
-    inputParameters.hostApiSpecificStreamInfo = NULL;
-    err = Pa_OpenStream(
-                &inputStream,
-                &inputParameters,
-                NULL,
-                SAMPLE_RATE,
-                FRAMES_PER_BUFFER,
-                0,
-                inputCallback,
-                &data );
-    if( err != paNoError ) goto error;
-
-    outputParameters.channelCount = 2;
-    outputParameters.sampleFormat = paInt16;
-    deviceInfo = Pa_GetDeviceInfo( outputParameters.device );
-    if( deviceInfo == NULL )
-    {
-        fprintf( stderr, "No matching output device.\n" );
-        goto error;
-    }
-    outputParameters.suggestedLatency = deviceInfo->defaultLowOutputLatency;
-    outputParameters.hostApiSpecificStreamInfo = NULL;
-    err = Pa_OpenStream(
-                &outputStream,
-                NULL,
-                &outputParameters,
-                SAMPLE_RATE,
-                FRAMES_PER_BUFFER,
-                (paClipOff | paDitherOff),
-                outputCallback,
-                &data );
-    if( err != paNoError ) goto error;
-
-    err = Pa_StartStream( inputStream );
-    if( err != paNoError ) goto error;
-    err = Pa_StartStream( outputStream );
-    if( err != paNoError ) goto error;
-
-    printf("When you hear sound, unplug the USB device.\n");
-    do
-    {
-        Pa_Sleep(500);
-        printf("Frames remaining = %d\n", data.sampsToGo);
-        printf("Pa_IsStreamActive(inputStream) = %d\n", Pa_IsStreamActive(inputStream));
-        printf("Pa_IsStreamActive(outputStream) = %d\n", Pa_IsStreamActive(outputStream));
-    } while( Pa_IsStreamActive(inputStream) && Pa_IsStreamActive(outputStream) );
-
-    err = Pa_CloseStream( inputStream );
-    if( err != paNoError ) goto error;
-    err = Pa_CloseStream( outputStream );
-    if( err != paNoError ) goto error;
-    Pa_Terminate();
-    return paNoError;
-error:
-    Pa_Terminate();
-    fprintf( stderr, "An error occurred while using the portaudio stream\n" );
-    fprintf( stderr, "Error number: %d\n", err );
-    fprintf( stderr, "Error message: %s\n", Pa_GetErrorText( err ) );
-    fprintf( stderr, "Host Error message: %s\n", Pa_GetLastHostErrorInfo()->errorText );
-    return err;
-}
diff --git a/spaces/productizationlabs/MyChatGPTTurbo/app.py b/spaces/productizationlabs/MyChatGPTTurbo/app.py
deleted file mode 100644
index 1e1beb59ffb184f365c0cec42a0b74f6bc97617b..0000000000000000000000000000000000000000
--- a/spaces/productizationlabs/MyChatGPTTurbo/app.py
+++ /dev/null
@@ -1,41 +0,0 @@
-_C='role'
-_B=True
-_A='content'
-import os,gradio as gr,json,requests,openai
-try:openai.api_key=os.environ['OPENAI_API_KEY']
-except KeyError:
-        error_message='System is at capacity right now.Please try again later';print(error_message)
-        def chatbot(input):return error_message
-else:messages=[{_C:'system',_A:'My AI Assistant'}]
-API_URL='https://api.openai.com/v1/chat/completions'
-top_p_chatgpt=1.0
-temperature_chatgpt=1.0
-def chatbot(inputs,chat_counter_chatgpt,chatbot_chatgpt=[],history=[]):
-        X='delta';W='choices';V='gpt-3.5-turbo';U='frequency_penalty';T='presence_penalty';S='stream';R='top_p';Q='temperature';P='messages';O='model';J='user';F=chat_counter_chatgpt;E=inputs;A=history;K={O:V,P:[{_C:J,_A:f"{E}"}],Q:1.0,R:1.0,'n':1,S:_B,T:0,U:0};Y={'Content-Type':'application/json','Authorization':f"Bearer {openai.api_key}"}
-        if F!=0:
-                C=[]
-                for L in chatbot_chatgpt:G={};G[_C]=J;G[_A]=L[0];H={};H[_C]='assistant';H[_A]=L[1];C.append(G);C.append(H)
-                I={};I[_C]=J;I[_A]=E;C.append(I);K={O:V,P:C,Q:temperature_chatgpt,R:top_p_chatgpt,'n':1,S:_B,T:0,U:0}
-        F+=1;A.append('You asked:  '+E);Z=requests.post(API_URL,headers=Y,json=K,stream=_B);M=0;D='';N=0
-        for B in Z.iter_lines():
-                if N==0:N+=1;continue
-                if B.decode():
-                        B=B.decode()
-                        if len(B)>13 and _A in json.loads(B[6:])[W][0][X]:
-                                D=D+json.loads(B[6:])[W][0][X][_A]
-                                if M==0:A.append(' '+D)
-                                else:A[-1]=D
-                                a=[(A[B],A[B+1])for B in range(0,len(A)-1,2)];M+=1;yield(a,A,F)
-def reset_textbox():return gr.update(value='')
-def reset_chat(chatbot,state):return None,[]
-with gr.Blocks(css='#col_container {width: 1000px; margin-left: auto; margin-right: auto;}\n                #chatgpt {height: 400px; overflow: auto;}} ',theme=gr.themes.Default(primary_hue='slate'))as ChatGPTTurbo:
-        with gr.Row():
-                with gr.Column(scale=14):
-                        with gr.Box():
-                                with gr.Row():
-                                        with gr.Column(scale=13):inputs=gr.Textbox(label='Ask me anything ⤵️   Try: Value of pi')
-                                        with gr.Column(scale=1):b1=gr.Button('Submit',elem_id='submit').style(full_width=_B);b2=gr.Button('Clear',elem_id='clear').style(full_width=_B)
-                                        state_chatgpt=gr.State([])
-                        with gr.Box():
-                                with gr.Row():chatbot_chatgpt=gr.Chatbot(elem_id='chatgpt',label='My ChatGPT Turbo')
-        chat_counter_chatgpt=gr.Number(value=0,visible=False,precision=0);inputs.submit(reset_textbox,[],[inputs]);b1.click(chatbot,[inputs,chat_counter_chatgpt,chatbot_chatgpt,state_chatgpt],[chatbot_chatgpt,state_chatgpt]);b2.click(reset_chat,[chatbot_chatgpt,state_chatgpt],[chatbot_chatgpt,state_chatgpt]);ChatGPTTurbo.queue(concurrency_count=16).launch(height=2500,debug=_B)
\ No newline at end of file
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/charset_normalizer/version.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/charset_normalizer/version.py
deleted file mode 100644
index 5a4da4ff49bc80ef49e8aa7e01cc8555518bd1b1..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/charset_normalizer/version.py
+++ /dev/null
@@ -1,6 +0,0 @@
-"""
-Expose version
-"""
-
-__version__ = "3.3.2"
-VERSION = __version__.split(".")
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/voltLib/error.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/voltLib/error.py
deleted file mode 100644
index c51d3b8fdc45afdb7bafbeb13a951264e0228985..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/voltLib/error.py
+++ /dev/null
@@ -1,12 +0,0 @@
-class VoltLibError(Exception):
-    def __init__(self, message, location):
-        Exception.__init__(self, message)
-        self.location = location
-
-    def __str__(self):
-        message = Exception.__str__(self)
-        if self.location:
-            path, line, column = self.location
-            return "%s:%d:%d: %s" % (path, line, column, message)
-        else:
-            return message
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/tests/abstract/copy.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/tests/abstract/copy.py
deleted file mode 100644
index 01ff09c95e7d81b3aa3a58436f795b4d47b6889a..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/tests/abstract/copy.py
+++ /dev/null
@@ -1,543 +0,0 @@
-from hashlib import md5
-from itertools import product
-
-import pytest
-
-from fsspec.tests.abstract.common import GLOB_EDGE_CASES_TESTS
-
-
-class AbstractCopyTests:
-    def test_copy_file_to_existing_directory(
-        self,
-        fs,
-        fs_join,
-        fs_bulk_operations_scenario_0,
-        fs_target,
-        supports_empty_directories,
-    ):
-        # Copy scenario 1a
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-        if not supports_empty_directories:
-            # Force target directory to exist by adding a dummy file
-            fs.touch(fs_join(target, "dummy"))
-        assert fs.isdir(target)
-
-        target_file2 = fs_join(target, "file2")
-        target_subfile1 = fs_join(target, "subfile1")
-
-        # Copy from source directory
-        fs.cp(fs_join(source, "file2"), target)
-        assert fs.isfile(target_file2)
-
-        # Copy from sub directory
-        fs.cp(fs_join(source, "subdir", "subfile1"), target)
-        assert fs.isfile(target_subfile1)
-
-        # Remove copied files
-        fs.rm([target_file2, target_subfile1])
-        assert not fs.exists(target_file2)
-        assert not fs.exists(target_subfile1)
-
-        # Repeat with trailing slash on target
-        fs.cp(fs_join(source, "file2"), target + "/")
-        assert fs.isdir(target)
-        assert fs.isfile(target_file2)
-
-        fs.cp(fs_join(source, "subdir", "subfile1"), target + "/")
-        assert fs.isfile(target_subfile1)
-
-    def test_copy_file_to_new_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 1b
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-
-        fs.cp(
-            fs_join(source, "subdir", "subfile1"), fs_join(target, "newdir/")
-        )  # Note trailing slash
-        assert fs.isdir(target)
-        assert fs.isdir(fs_join(target, "newdir"))
-        assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-
-    def test_copy_file_to_file_in_existing_directory(
-        self,
-        fs,
-        fs_join,
-        fs_bulk_operations_scenario_0,
-        fs_target,
-        supports_empty_directories,
-    ):
-        # Copy scenario 1c
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-        if not supports_empty_directories:
-            # Force target directory to exist by adding a dummy file
-            fs.touch(fs_join(target, "dummy"))
-        assert fs.isdir(target)
-
-        fs.cp(fs_join(source, "subdir", "subfile1"), fs_join(target, "newfile"))
-        assert fs.isfile(fs_join(target, "newfile"))
-
-    def test_copy_file_to_file_in_new_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 1d
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-
-        fs.cp(
-            fs_join(source, "subdir", "subfile1"), fs_join(target, "newdir", "newfile")
-        )
-        assert fs.isdir(fs_join(target, "newdir"))
-        assert fs.isfile(fs_join(target, "newdir", "newfile"))
-
-    def test_copy_directory_to_existing_directory(
-        self,
-        fs,
-        fs_join,
-        fs_bulk_operations_scenario_0,
-        fs_target,
-        supports_empty_directories,
-    ):
-        # Copy scenario 1e
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-        if not supports_empty_directories:
-            # Force target directory to exist by adding a dummy file
-            dummy = fs_join(target, "dummy")
-            fs.touch(dummy)
-        assert fs.isdir(target)
-
-        for source_slash, target_slash in zip([False, True], [False, True]):
-            s = fs_join(source, "subdir")
-            if source_slash:
-                s += "/"
-            t = target + "/" if target_slash else target
-
-            # Without recursive does nothing
-            fs.cp(s, t)
-            assert fs.ls(target) == ([] if supports_empty_directories else [dummy])
-
-            # With recursive
-            fs.cp(s, t, recursive=True)
-            if source_slash:
-                assert fs.isfile(fs_join(target, "subfile1"))
-                assert fs.isfile(fs_join(target, "subfile2"))
-                assert fs.isdir(fs_join(target, "nesteddir"))
-                assert fs.isfile(fs_join(target, "nesteddir", "nestedfile"))
-                assert not fs.exists(fs_join(target, "subdir"))
-
-                fs.rm(
-                    [
-                        fs_join(target, "subfile1"),
-                        fs_join(target, "subfile2"),
-                        fs_join(target, "nesteddir"),
-                    ],
-                    recursive=True,
-                )
-            else:
-                assert fs.isdir(fs_join(target, "subdir"))
-                assert fs.isfile(fs_join(target, "subdir", "subfile1"))
-                assert fs.isfile(fs_join(target, "subdir", "subfile2"))
-                assert fs.isdir(fs_join(target, "subdir", "nesteddir"))
-                assert fs.isfile(fs_join(target, "subdir", "nesteddir", "nestedfile"))
-
-                fs.rm(fs_join(target, "subdir"), recursive=True)
-            assert fs.ls(target) == ([] if supports_empty_directories else [dummy])
-
-            # Limit recursive by maxdepth
-            fs.cp(s, t, recursive=True, maxdepth=1)
-            if source_slash:
-                assert fs.isfile(fs_join(target, "subfile1"))
-                assert fs.isfile(fs_join(target, "subfile2"))
-                assert not fs.exists(fs_join(target, "nesteddir"))
-                assert not fs.exists(fs_join(target, "subdir"))
-
-                fs.rm(
-                    [
-                        fs_join(target, "subfile1"),
-                        fs_join(target, "subfile2"),
-                    ],
-                    recursive=True,
-                )
-            else:
-                assert fs.isdir(fs_join(target, "subdir"))
-                assert fs.isfile(fs_join(target, "subdir", "subfile1"))
-                assert fs.isfile(fs_join(target, "subdir", "subfile2"))
-                assert not fs.exists(fs_join(target, "subdir", "nesteddir"))
-
-                fs.rm(fs_join(target, "subdir"), recursive=True)
-            assert fs.ls(target) == ([] if supports_empty_directories else [dummy])
-
-    def test_copy_directory_to_new_directory(
-        self,
-        fs,
-        fs_join,
-        fs_bulk_operations_scenario_0,
-        fs_target,
-        supports_empty_directories,
-    ):
-        # Copy scenario 1f
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-
-        for source_slash, target_slash in zip([False, True], [False, True]):
-            s = fs_join(source, "subdir")
-            if source_slash:
-                s += "/"
-            t = fs_join(target, "newdir")
-            if target_slash:
-                t += "/"
-
-            # Without recursive does nothing
-            fs.cp(s, t)
-            if supports_empty_directories:
-                assert fs.ls(target) == []
-            else:
-                with pytest.raises(FileNotFoundError):
-                    fs.ls(target)
-
-            # With recursive
-            fs.cp(s, t, recursive=True)
-            assert fs.isdir(fs_join(target, "newdir"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
-            assert fs.isdir(fs_join(target, "newdir", "nesteddir"))
-            assert fs.isfile(fs_join(target, "newdir", "nesteddir", "nestedfile"))
-            assert not fs.exists(fs_join(target, "subdir"))
-
-            fs.rm(fs_join(target, "newdir"), recursive=True)
-            assert not fs.exists(fs_join(target, "newdir"))
-
-            # Limit recursive by maxdepth
-            fs.cp(s, t, recursive=True, maxdepth=1)
-            assert fs.isdir(fs_join(target, "newdir"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
-            assert not fs.exists(fs_join(target, "newdir", "nesteddir"))
-            assert not fs.exists(fs_join(target, "subdir"))
-
-            fs.rm(fs_join(target, "newdir"), recursive=True)
-            assert not fs.exists(fs_join(target, "newdir"))
-
-    def test_copy_glob_to_existing_directory(
-        self,
-        fs,
-        fs_join,
-        fs_bulk_operations_scenario_0,
-        fs_target,
-        supports_empty_directories,
-    ):
-        # Copy scenario 1g
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-        if not supports_empty_directories:
-            # Force target directory to exist by adding a dummy file
-            dummy = fs_join(target, "dummy")
-            fs.touch(dummy)
-        assert fs.isdir(target)
-
-        for target_slash in [False, True]:
-            t = target + "/" if target_slash else target
-
-            # Without recursive
-            fs.cp(fs_join(source, "subdir", "*"), t)
-            assert fs.isfile(fs_join(target, "subfile1"))
-            assert fs.isfile(fs_join(target, "subfile2"))
-            assert not fs.isdir(fs_join(target, "nesteddir"))
-            assert not fs.exists(fs_join(target, "nesteddir", "nestedfile"))
-            assert not fs.exists(fs_join(target, "subdir"))
-
-            fs.rm(
-                [
-                    fs_join(target, "subfile1"),
-                    fs_join(target, "subfile2"),
-                ],
-                recursive=True,
-            )
-            assert fs.ls(target) == ([] if supports_empty_directories else [dummy])
-
-            # With recursive
-            for glob, recursive in zip(["*", "**"], [True, False]):
-                fs.cp(fs_join(source, "subdir", glob), t, recursive=recursive)
-                assert fs.isfile(fs_join(target, "subfile1"))
-                assert fs.isfile(fs_join(target, "subfile2"))
-                assert fs.isdir(fs_join(target, "nesteddir"))
-                assert fs.isfile(fs_join(target, "nesteddir", "nestedfile"))
-                assert not fs.exists(fs_join(target, "subdir"))
-
-                fs.rm(
-                    [
-                        fs_join(target, "subfile1"),
-                        fs_join(target, "subfile2"),
-                        fs_join(target, "nesteddir"),
-                    ],
-                    recursive=True,
-                )
-                assert fs.ls(target) == ([] if supports_empty_directories else [dummy])
-
-                # Limit recursive by maxdepth
-                fs.cp(
-                    fs_join(source, "subdir", glob), t, recursive=recursive, maxdepth=1
-                )
-                assert fs.isfile(fs_join(target, "subfile1"))
-                assert fs.isfile(fs_join(target, "subfile2"))
-                assert not fs.exists(fs_join(target, "nesteddir"))
-                assert not fs.exists(fs_join(target, "subdir"))
-
-                fs.rm(
-                    [
-                        fs_join(target, "subfile1"),
-                        fs_join(target, "subfile2"),
-                    ],
-                    recursive=True,
-                )
-                assert fs.ls(target) == ([] if supports_empty_directories else [dummy])
-
-    def test_copy_glob_to_new_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 1h
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-
-        for target_slash in [False, True]:
-            t = fs_join(target, "newdir")
-            if target_slash:
-                t += "/"
-
-            # Without recursive
-            fs.cp(fs_join(source, "subdir", "*"), t)
-            assert fs.isdir(fs_join(target, "newdir"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
-            assert not fs.exists(fs_join(target, "newdir", "nesteddir"))
-            assert not fs.exists(fs_join(target, "newdir", "nesteddir", "nestedfile"))
-            assert not fs.exists(fs_join(target, "subdir"))
-            assert not fs.exists(fs_join(target, "newdir", "subdir"))
-
-            fs.rm(fs_join(target, "newdir"), recursive=True)
-            assert not fs.exists(fs_join(target, "newdir"))
-
-            # With recursive
-            for glob, recursive in zip(["*", "**"], [True, False]):
-                fs.cp(fs_join(source, "subdir", glob), t, recursive=recursive)
-                assert fs.isdir(fs_join(target, "newdir"))
-                assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-                assert fs.isfile(fs_join(target, "newdir", "subfile2"))
-                assert fs.isdir(fs_join(target, "newdir", "nesteddir"))
-                assert fs.isfile(fs_join(target, "newdir", "nesteddir", "nestedfile"))
-                assert not fs.exists(fs_join(target, "subdir"))
-                assert not fs.exists(fs_join(target, "newdir", "subdir"))
-
-                fs.rm(fs_join(target, "newdir"), recursive=True)
-                assert not fs.exists(fs_join(target, "newdir"))
-
-                # Limit recursive by maxdepth
-                fs.cp(
-                    fs_join(source, "subdir", glob), t, recursive=recursive, maxdepth=1
-                )
-                assert fs.isdir(fs_join(target, "newdir"))
-                assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-                assert fs.isfile(fs_join(target, "newdir", "subfile2"))
-                assert not fs.exists(fs_join(target, "newdir", "nesteddir"))
-                assert not fs.exists(fs_join(target, "subdir"))
-                assert not fs.exists(fs_join(target, "newdir", "subdir"))
-
-                fs.rm(fs_join(target, "newdir"), recursive=True)
-                assert not fs.exists(fs_join(target, "newdir"))
-
-    @pytest.mark.parametrize(
-        GLOB_EDGE_CASES_TESTS["argnames"],
-        GLOB_EDGE_CASES_TESTS["argvalues"],
-    )
-    def test_copy_glob_edge_cases(
-        self,
-        path,
-        recursive,
-        maxdepth,
-        expected,
-        fs,
-        fs_join,
-        fs_glob_edge_cases_files,
-        fs_target,
-        fs_sanitize_path,
-    ):
-        # Copy scenario 1g
-        source = fs_glob_edge_cases_files
-
-        target = fs_target
-
-        for new_dir, target_slash in product([True, False], [True, False]):
-            fs.mkdir(target)
-
-            t = fs_join(target, "newdir") if new_dir else target
-            t = t + "/" if target_slash else t
-
-            fs.copy(fs_join(source, path), t, recursive=recursive, maxdepth=maxdepth)
-
-            output = fs.find(target)
-            if new_dir:
-                prefixed_expected = [
-                    fs_sanitize_path(fs_join(target, "newdir", p)) for p in expected
-                ]
-            else:
-                prefixed_expected = [
-                    fs_sanitize_path(fs_join(target, p)) for p in expected
-                ]
-            assert sorted(output) == sorted(prefixed_expected)
-
-            try:
-                fs.rm(target, recursive=True)
-            except FileNotFoundError:
-                pass
-
-    def test_copy_list_of_files_to_existing_directory(
-        self,
-        fs,
-        fs_join,
-        fs_bulk_operations_scenario_0,
-        fs_target,
-        supports_empty_directories,
-    ):
-        # Copy scenario 2a
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-        if not supports_empty_directories:
-            # Force target directory to exist by adding a dummy file
-            dummy = fs_join(target, "dummy")
-            fs.touch(dummy)
-        assert fs.isdir(target)
-
-        source_files = [
-            fs_join(source, "file1"),
-            fs_join(source, "file2"),
-            fs_join(source, "subdir", "subfile1"),
-        ]
-
-        for target_slash in [False, True]:
-            t = target + "/" if target_slash else target
-
-            fs.cp(source_files, t)
-            assert fs.isfile(fs_join(target, "file1"))
-            assert fs.isfile(fs_join(target, "file2"))
-            assert fs.isfile(fs_join(target, "subfile1"))
-
-            fs.rm(
-                [
-                    fs_join(target, "file1"),
-                    fs_join(target, "file2"),
-                    fs_join(target, "subfile1"),
-                ],
-                recursive=True,
-            )
-            assert fs.ls(target) == ([] if supports_empty_directories else [dummy])
-
-    def test_copy_list_of_files_to_new_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 2b
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-
-        source_files = [
-            fs_join(source, "file1"),
-            fs_join(source, "file2"),
-            fs_join(source, "subdir", "subfile1"),
-        ]
-
-        fs.cp(source_files, fs_join(target, "newdir") + "/")  # Note trailing slash
-        assert fs.isdir(fs_join(target, "newdir"))
-        assert fs.isfile(fs_join(target, "newdir", "file1"))
-        assert fs.isfile(fs_join(target, "newdir", "file2"))
-        assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-
-    def test_copy_two_files_new_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # This is a duplicate of test_copy_list_of_files_to_new_directory and
-        # can eventually be removed.
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        assert not fs.exists(target)
-        fs.cp([fs_join(source, "file1"), fs_join(source, "file2")], target)
-
-        assert fs.isdir(target)
-        assert fs.isfile(fs_join(target, "file1"))
-        assert fs.isfile(fs_join(target, "file2"))
-
-    def test_copy_directory_without_files_with_same_name_prefix(
-        self,
-        fs,
-        fs_join,
-        fs_target,
-        fs_dir_and_file_with_same_name_prefix,
-        supports_empty_directories,
-    ):
-        # Create the test dirs
-        source = fs_dir_and_file_with_same_name_prefix
-        target = fs_target
-
-        # Test without glob
-        fs.cp(fs_join(source, "subdir"), target, recursive=True)
-
-        assert fs.isfile(fs_join(target, "subfile.txt"))
-        assert not fs.isfile(fs_join(target, "subdir.txt"))
-
-        fs.rm([fs_join(target, "subfile.txt")])
-        if supports_empty_directories:
-            assert fs.ls(target) == []
-        else:
-            assert not fs.exists(target)
-
-        # Test with glob
-        fs.cp(fs_join(source, "subdir*"), target, recursive=True)
-
-        assert fs.isdir(fs_join(target, "subdir"))
-        assert fs.isfile(fs_join(target, "subdir", "subfile.txt"))
-        assert fs.isfile(fs_join(target, "subdir.txt"))
-
-    def test_copy_with_source_and_destination_as_list(
-        self, fs, fs_target, fs_join, fs_10_files_with_hashed_names
-    ):
-        # Create the test dir
-        source = fs_10_files_with_hashed_names
-        target = fs_target
-
-        # Create list of files for source and destination
-        source_files = []
-        destination_files = []
-        for i in range(10):
-            hashed_i = md5(str(i).encode("utf-8")).hexdigest()
-            source_files.append(fs_join(source, f"{hashed_i}.txt"))
-            destination_files.append(fs_join(target, f"{hashed_i}.txt"))
-
-        # Copy and assert order was kept
-        fs.copy(path1=source_files, path2=destination_files)
-
-        for i in range(10):
-            file_content = fs.cat(destination_files[i]).decode("utf-8")
-            assert file_content == str(i)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/httpcore/_async/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/httpcore/_async/__init__.py
deleted file mode 100644
index 88dc7f01e132933728cbcf45c88ce82e85ddf65f..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/httpcore/_async/__init__.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from .connection import AsyncHTTPConnection
-from .connection_pool import AsyncConnectionPool
-from .http11 import AsyncHTTP11Connection
-from .http_proxy import AsyncHTTPProxy
-from .interfaces import AsyncConnectionInterface
-
-try:
-    from .http2 import AsyncHTTP2Connection
-except ImportError:  # pragma: nocover
-
-    class AsyncHTTP2Connection:  # type: ignore
-        def __init__(self, *args, **kwargs) -> None:  # type: ignore
-            raise RuntimeError(
-                "Attempted to use http2 support, but the `h2` package is not "
-                "installed. Use 'pip install httpcore[http2]'."
-            )
-
-
-try:
-    from .socks_proxy import AsyncSOCKSProxy
-except ImportError:  # pragma: nocover
-
-    class AsyncSOCKSProxy:  # type: ignore
-        def __init__(self, *args, **kwargs) -> None:  # type: ignore
-            raise RuntimeError(
-                "Attempted to use SOCKS support, but the `socksio` package is not "
-                "installed. Use 'pip install httpcore[socks]'."
-            )
-
-
-__all__ = [
-    "AsyncHTTPConnection",
-    "AsyncConnectionPool",
-    "AsyncHTTPProxy",
-    "AsyncHTTP11Connection",
-    "AsyncHTTP2Connection",
-    "AsyncConnectionInterface",
-    "AsyncSOCKSProxy",
-]
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/multi/test_integrity.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/multi/test_integrity.py
deleted file mode 100644
index 45dd484eff4c61542f9985a52fc1db4dce8c020b..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/multi/test_integrity.py
+++ /dev/null
@@ -1,281 +0,0 @@
-import re
-
-import numpy as np
-import pytest
-
-from pandas._libs import index as libindex
-
-from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
-
-import pandas as pd
-from pandas import (
-    Index,
-    IntervalIndex,
-    MultiIndex,
-    RangeIndex,
-)
-import pandas._testing as tm
-
-
-def test_labels_dtypes():
-    # GH 8456
-    i = MultiIndex.from_tuples([("A", 1), ("A", 2)])
-    assert i.codes[0].dtype == "int8"
-    assert i.codes[1].dtype == "int8"
-
-    i = MultiIndex.from_product([["a"], range(40)])
-    assert i.codes[1].dtype == "int8"
-    i = MultiIndex.from_product([["a"], range(400)])
-    assert i.codes[1].dtype == "int16"
-    i = MultiIndex.from_product([["a"], range(40000)])
-    assert i.codes[1].dtype == "int32"
-
-    i = MultiIndex.from_product([["a"], range(1000)])
-    assert (i.codes[0] >= 0).all()
-    assert (i.codes[1] >= 0).all()
-
-
-def test_values_boxed():
-    tuples = [
-        (1, pd.Timestamp("2000-01-01")),
-        (2, pd.NaT),
-        (3, pd.Timestamp("2000-01-03")),
-        (1, pd.Timestamp("2000-01-04")),
-        (2, pd.Timestamp("2000-01-02")),
-        (3, pd.Timestamp("2000-01-03")),
-    ]
-    result = MultiIndex.from_tuples(tuples)
-    expected = construct_1d_object_array_from_listlike(tuples)
-    tm.assert_numpy_array_equal(result.values, expected)
-    # Check that code branches for boxed values produce identical results
-    tm.assert_numpy_array_equal(result.values[:4], result[:4].values)
-
-
-def test_values_multiindex_datetimeindex():
-    # Test to ensure we hit the boxing / nobox part of MI.values
-    ints = np.arange(10**18, 10**18 + 5)
-    naive = pd.DatetimeIndex(ints)
-
-    aware = pd.DatetimeIndex(ints, tz="US/Central")
-
-    idx = MultiIndex.from_arrays([naive, aware])
-    result = idx.values
-
-    outer = pd.DatetimeIndex([x[0] for x in result])
-    tm.assert_index_equal(outer, naive)
-
-    inner = pd.DatetimeIndex([x[1] for x in result])
-    tm.assert_index_equal(inner, aware)
-
-    # n_lev > n_lab
-    result = idx[:2].values
-
-    outer = pd.DatetimeIndex([x[0] for x in result])
-    tm.assert_index_equal(outer, naive[:2])
-
-    inner = pd.DatetimeIndex([x[1] for x in result])
-    tm.assert_index_equal(inner, aware[:2])
-
-
-def test_values_multiindex_periodindex():
-    # Test to ensure we hit the boxing / nobox part of MI.values
-    ints = np.arange(2007, 2012)
-    pidx = pd.PeriodIndex(ints, freq="D")
-
-    idx = MultiIndex.from_arrays([ints, pidx])
-    result = idx.values
-
-    outer = Index([x[0] for x in result])
-    tm.assert_index_equal(outer, Index(ints, dtype=np.int64))
-
-    inner = pd.PeriodIndex([x[1] for x in result])
-    tm.assert_index_equal(inner, pidx)
-
-    # n_lev > n_lab
-    result = idx[:2].values
-
-    outer = Index([x[0] for x in result])
-    tm.assert_index_equal(outer, Index(ints[:2], dtype=np.int64))
-
-    inner = pd.PeriodIndex([x[1] for x in result])
-    tm.assert_index_equal(inner, pidx[:2])
-
-
-def test_consistency():
-    # need to construct an overflow
-    major_axis = list(range(70000))
-    minor_axis = list(range(10))
-
-    major_codes = np.arange(70000)
-    minor_codes = np.repeat(range(10), 7000)
-
-    # the fact that is works means it's consistent
-    index = MultiIndex(
-        levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
-    )
-
-    # inconsistent
-    major_codes = np.array([0, 0, 1, 1, 1, 2, 2, 3, 3])
-    minor_codes = np.array([0, 1, 0, 1, 1, 0, 1, 0, 1])
-    index = MultiIndex(
-        levels=[major_axis, minor_axis], codes=[major_codes, minor_codes]
-    )
-
-    assert index.is_unique is False
-
-
-@pytest.mark.slow
-def test_hash_collisions():
-    # non-smoke test that we don't get hash collisions
-
-    index = MultiIndex.from_product(
-        [np.arange(1000), np.arange(1000)], names=["one", "two"]
-    )
-    result = index.get_indexer(index.values)
-    tm.assert_numpy_array_equal(result, np.arange(len(index), dtype="intp"))
-
-    for i in [0, 1, len(index) - 2, len(index) - 1]:
-        result = index.get_loc(index[i])
-        assert result == i
-
-
-def test_dims():
-    pass
-
-
-def test_take_invalid_kwargs():
-    vals = [["A", "B"], [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")]]
-    idx = MultiIndex.from_product(vals, names=["str", "dt"])
-    indices = [1, 2]
-
-    msg = r"take\(\) got an unexpected keyword argument 'foo'"
-    with pytest.raises(TypeError, match=msg):
-        idx.take(indices, foo=2)
-
-    msg = "the 'out' parameter is not supported"
-    with pytest.raises(ValueError, match=msg):
-        idx.take(indices, out=indices)
-
-    msg = "the 'mode' parameter is not supported"
-    with pytest.raises(ValueError, match=msg):
-        idx.take(indices, mode="clip")
-
-
-def test_isna_behavior(idx):
-    # should not segfault GH5123
-    # NOTE: if MI representation changes, may make sense to allow
-    # isna(MI)
-    msg = "isna is not defined for MultiIndex"
-    with pytest.raises(NotImplementedError, match=msg):
-        pd.isna(idx)
-
-
-def test_large_multiindex_error():
-    # GH12527
-    df_below_1000000 = pd.DataFrame(
-        1, index=MultiIndex.from_product([[1, 2], range(499999)]), columns=["dest"]
-    )
-    with pytest.raises(KeyError, match=r"^\(-1, 0\)$"):
-        df_below_1000000.loc[(-1, 0), "dest"]
-    with pytest.raises(KeyError, match=r"^\(3, 0\)$"):
-        df_below_1000000.loc[(3, 0), "dest"]
-    df_above_1000000 = pd.DataFrame(
-        1, index=MultiIndex.from_product([[1, 2], range(500001)]), columns=["dest"]
-    )
-    with pytest.raises(KeyError, match=r"^\(-1, 0\)$"):
-        df_above_1000000.loc[(-1, 0), "dest"]
-    with pytest.raises(KeyError, match=r"^\(3, 0\)$"):
-        df_above_1000000.loc[(3, 0), "dest"]
-
-
-def test_mi_hashtable_populated_attribute_error(monkeypatch):
-    # GH 18165
-    monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 50)
-    r = range(50)
-    df = pd.DataFrame({"a": r, "b": r}, index=MultiIndex.from_arrays([r, r]))
-
-    msg = "'Series' object has no attribute 'foo'"
-    with pytest.raises(AttributeError, match=msg):
-        df["a"].foo()
-
-
-def test_can_hold_identifiers(idx):
-    key = idx[0]
-    assert idx._can_hold_identifiers_and_holds_name(key) is True
-
-
-def test_metadata_immutable(idx):
-    levels, codes = idx.levels, idx.codes
-    # shouldn't be able to set at either the top level or base level
-    mutable_regex = re.compile("does not support mutable operations")
-    with pytest.raises(TypeError, match=mutable_regex):
-        levels[0] = levels[0]
-    with pytest.raises(TypeError, match=mutable_regex):
-        levels[0][0] = levels[0][0]
-    # ditto for labels
-    with pytest.raises(TypeError, match=mutable_regex):
-        codes[0] = codes[0]
-    with pytest.raises(ValueError, match="assignment destination is read-only"):
-        codes[0][0] = codes[0][0]
-    # and for names
-    names = idx.names
-    with pytest.raises(TypeError, match=mutable_regex):
-        names[0] = names[0]
-
-
-def test_level_setting_resets_attributes():
-    ind = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]])
-    assert ind.is_monotonic_increasing
-    ind = ind.set_levels([["A", "B"], [1, 3, 2]])
-    # if this fails, probably didn't reset the cache correctly.
-    assert not ind.is_monotonic_increasing
-
-
-def test_rangeindex_fallback_coercion_bug():
-    # GH 12893
-    df1 = pd.DataFrame(np.arange(100).reshape((10, 10)))
-    df2 = pd.DataFrame(np.arange(100).reshape((10, 10)))
-    df = pd.concat(
-        {"df1": df1.stack(future_stack=True), "df2": df2.stack(future_stack=True)},
-        axis=1,
-    )
-    df.index.names = ["fizz", "buzz"]
-
-    str(df)
-    expected = pd.DataFrame(
-        {"df2": np.arange(100), "df1": np.arange(100)},
-        index=MultiIndex.from_product([range(10), range(10)], names=["fizz", "buzz"]),
-    )
-    tm.assert_frame_equal(df, expected, check_like=True)
-
-    result = df.index.get_level_values("fizz")
-    expected = Index(np.arange(10, dtype=np.int64), name="fizz").repeat(10)
-    tm.assert_index_equal(result, expected)
-
-    result = df.index.get_level_values("buzz")
-    expected = Index(np.tile(np.arange(10, dtype=np.int64), 10), name="buzz")
-    tm.assert_index_equal(result, expected)
-
-
-def test_memory_usage(idx):
-    result = idx.memory_usage()
-    if len(idx):
-        idx.get_loc(idx[0])
-        result2 = idx.memory_usage()
-        result3 = idx.memory_usage(deep=True)
-
-        # RangeIndex, IntervalIndex
-        # don't have engines
-        if not isinstance(idx, (RangeIndex, IntervalIndex)):
-            assert result2 > result
-
-        if idx.inferred_type == "object":
-            assert result3 > result2
-
-    else:
-        # we report 0 for no-length
-        assert result == 0
-
-
-def test_nlevels(idx):
-    assert idx.nlevels == 2
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tslibs/test_parse_iso8601.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tslibs/test_parse_iso8601.py
deleted file mode 100644
index 1992faae2ea6a687f8bd74b4e1e10ba53bb9e901..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tslibs/test_parse_iso8601.py
+++ /dev/null
@@ -1,119 +0,0 @@
-from datetime import datetime
-
-import pytest
-
-from pandas._libs import tslib
-
-from pandas import Timestamp
-
-
-@pytest.mark.parametrize(
-    "date_str, exp",
-    [
-        ("2011-01-02", datetime(2011, 1, 2)),
-        ("2011-1-2", datetime(2011, 1, 2)),
-        ("2011-01", datetime(2011, 1, 1)),
-        ("2011-1", datetime(2011, 1, 1)),
-        ("2011 01 02", datetime(2011, 1, 2)),
-        ("2011.01.02", datetime(2011, 1, 2)),
-        ("2011/01/02", datetime(2011, 1, 2)),
-        ("2011\\01\\02", datetime(2011, 1, 2)),
-        ("2013-01-01 05:30:00", datetime(2013, 1, 1, 5, 30)),
-        ("2013-1-1 5:30:00", datetime(2013, 1, 1, 5, 30)),
-        ("2013-1-1 5:30:00+01:00", Timestamp(2013, 1, 1, 5, 30, tz="UTC+01:00")),
-    ],
-)
-def test_parsers_iso8601(date_str, exp):
-    # see gh-12060
-    #
-    # Test only the ISO parser - flexibility to
-    # different separators and leading zero's.
-    actual = tslib._test_parse_iso8601(date_str)
-    assert actual == exp
-
-
-@pytest.mark.parametrize(
-    "date_str",
-    [
-        "2011-01/02",
-        "2011=11=11",
-        "201401",
-        "201111",
-        "200101",
-        # Mixed separated and unseparated.
-        "2005-0101",
-        "200501-01",
-        "20010101 12:3456",
-        "20010101 1234:56",
-        # HHMMSS must have two digits in
-        # each component if unseparated.
-        "20010101 1",
-        "20010101 123",
-        "20010101 12345",
-        "20010101 12345Z",
-    ],
-)
-def test_parsers_iso8601_invalid(date_str):
-    msg = f'Error parsing datetime string "{date_str}"'
-
-    with pytest.raises(ValueError, match=msg):
-        tslib._test_parse_iso8601(date_str)
-
-
-def test_parsers_iso8601_invalid_offset_invalid():
-    date_str = "2001-01-01 12-34-56"
-    msg = f'Timezone hours offset out of range in datetime string "{date_str}"'
-
-    with pytest.raises(ValueError, match=msg):
-        tslib._test_parse_iso8601(date_str)
-
-
-def test_parsers_iso8601_leading_space():
-    # GH#25895 make sure isoparser doesn't overflow with long input
-    date_str, expected = ("2013-1-1 5:30:00", datetime(2013, 1, 1, 5, 30))
-    actual = tslib._test_parse_iso8601(" " * 200 + date_str)
-    assert actual == expected
-
-
-@pytest.mark.parametrize(
-    "date_str, timespec, exp",
-    [
-        ("2023-01-01 00:00:00", "auto", "2023-01-01T00:00:00"),
-        ("2023-01-01 00:00:00", "seconds", "2023-01-01T00:00:00"),
-        ("2023-01-01 00:00:00", "milliseconds", "2023-01-01T00:00:00.000"),
-        ("2023-01-01 00:00:00", "microseconds", "2023-01-01T00:00:00.000000"),
-        ("2023-01-01 00:00:00", "nanoseconds", "2023-01-01T00:00:00.000000000"),
-        ("2023-01-01 00:00:00.001", "auto", "2023-01-01T00:00:00.001000"),
-        ("2023-01-01 00:00:00.001", "seconds", "2023-01-01T00:00:00"),
-        ("2023-01-01 00:00:00.001", "milliseconds", "2023-01-01T00:00:00.001"),
-        ("2023-01-01 00:00:00.001", "microseconds", "2023-01-01T00:00:00.001000"),
-        ("2023-01-01 00:00:00.001", "nanoseconds", "2023-01-01T00:00:00.001000000"),
-        ("2023-01-01 00:00:00.000001", "auto", "2023-01-01T00:00:00.000001"),
-        ("2023-01-01 00:00:00.000001", "seconds", "2023-01-01T00:00:00"),
-        ("2023-01-01 00:00:00.000001", "milliseconds", "2023-01-01T00:00:00.000"),
-        ("2023-01-01 00:00:00.000001", "microseconds", "2023-01-01T00:00:00.000001"),
-        ("2023-01-01 00:00:00.000001", "nanoseconds", "2023-01-01T00:00:00.000001000"),
-        ("2023-01-01 00:00:00.000000001", "auto", "2023-01-01T00:00:00.000000001"),
-        ("2023-01-01 00:00:00.000000001", "seconds", "2023-01-01T00:00:00"),
-        ("2023-01-01 00:00:00.000000001", "milliseconds", "2023-01-01T00:00:00.000"),
-        ("2023-01-01 00:00:00.000000001", "microseconds", "2023-01-01T00:00:00.000000"),
-        (
-            "2023-01-01 00:00:00.000000001",
-            "nanoseconds",
-            "2023-01-01T00:00:00.000000001",
-        ),
-        ("2023-01-01 00:00:00.000001001", "auto", "2023-01-01T00:00:00.000001001"),
-        ("2023-01-01 00:00:00.000001001", "seconds", "2023-01-01T00:00:00"),
-        ("2023-01-01 00:00:00.000001001", "milliseconds", "2023-01-01T00:00:00.000"),
-        ("2023-01-01 00:00:00.000001001", "microseconds", "2023-01-01T00:00:00.000001"),
-        (
-            "2023-01-01 00:00:00.000001001",
-            "nanoseconds",
-            "2023-01-01T00:00:00.000001001",
-        ),
-    ],
-)
-def test_iso8601_formatter(date_str: str, timespec: str, exp: str):
-    # GH#53020
-    ts = Timestamp(date_str)
-    assert ts.isoformat(timespec=timespec) == exp
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/_internal/_generics.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/_internal/_generics.py
deleted file mode 100644
index 7c3d5f433396f0d66ffbca7903ab92df25219dd0..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/_internal/_generics.py
+++ /dev/null
@@ -1,520 +0,0 @@
-from __future__ import annotations
-
-import sys
-import types
-import typing
-from collections import ChainMap
-from contextlib import contextmanager
-from contextvars import ContextVar
-from types import prepare_class
-from typing import TYPE_CHECKING, Any, Iterator, List, Mapping, MutableMapping, Tuple, TypeVar
-from weakref import WeakValueDictionary
-
-import typing_extensions
-
-from ._core_utils import get_type_ref
-from ._forward_ref import PydanticRecursiveRef
-from ._typing_extra import TypeVarType, typing_base
-from ._utils import all_identical, is_model_class
-
-if sys.version_info >= (3, 10):
-    from typing import _UnionGenericAlias  # type: ignore[attr-defined]
-
-if TYPE_CHECKING:
-    from ..main import BaseModel
-
-GenericTypesCacheKey = Tuple[Any, Any, Tuple[Any, ...]]
-
-# Note: We want to remove LimitedDict, but to do this, we'd need to improve the handling of generics caching.
-#   Right now, to handle recursive generics, we some types must remain cached for brief periods without references.
-#   By chaining the WeakValuesDict with a LimitedDict, we have a way to retain caching for all types with references,
-#   while also retaining a limited number of types even without references. This is generally enough to build
-#   specific recursive generic models without losing required items out of the cache.
-
-KT = TypeVar('KT')
-VT = TypeVar('VT')
-_LIMITED_DICT_SIZE = 100
-if TYPE_CHECKING:
-
-    class LimitedDict(dict, MutableMapping[KT, VT]):
-        def __init__(self, size_limit: int = _LIMITED_DICT_SIZE):
-            ...
-
-else:
-
-    class LimitedDict(dict):
-        """Limit the size/length of a dict used for caching to avoid unlimited increase in memory usage.
-
-        Since the dict is ordered, and we always remove elements from the beginning, this is effectively a FIFO cache.
-        """
-
-        def __init__(self, size_limit: int = _LIMITED_DICT_SIZE):
-            self.size_limit = size_limit
-            super().__init__()
-
-        def __setitem__(self, __key: Any, __value: Any) -> None:
-            super().__setitem__(__key, __value)
-            if len(self) > self.size_limit:
-                excess = len(self) - self.size_limit + self.size_limit // 10
-                to_remove = list(self.keys())[:excess]
-                for key in to_remove:
-                    del self[key]
-
-        def __class_getitem__(cls, *args: Any) -> Any:
-            # to avoid errors with 3.7
-            return cls
-
-
-# weak dictionaries allow the dynamically created parametrized versions of generic models to get collected
-# once they are no longer referenced by the caller.
-if sys.version_info >= (3, 9):  # Typing for weak dictionaries available at 3.9
-    GenericTypesCache = WeakValueDictionary[GenericTypesCacheKey, 'type[BaseModel]']
-else:
-    GenericTypesCache = WeakValueDictionary
-
-if TYPE_CHECKING:
-
-    class DeepChainMap(ChainMap[KT, VT]):  # type: ignore
-        ...
-
-else:
-
-    class DeepChainMap(ChainMap):
-        """Variant of ChainMap that allows direct updates to inner scopes.
-
-        Taken from https://docs.python.org/3/library/collections.html#collections.ChainMap,
-        with some light modifications for this use case.
-        """
-
-        def clear(self) -> None:
-            for mapping in self.maps:
-                mapping.clear()
-
-        def __setitem__(self, key: KT, value: VT) -> None:
-            for mapping in self.maps:
-                mapping[key] = value
-
-        def __delitem__(self, key: KT) -> None:
-            hit = False
-            for mapping in self.maps:
-                if key in mapping:
-                    del mapping[key]
-                    hit = True
-            if not hit:
-                raise KeyError(key)
-
-
-# Despite the fact that LimitedDict _seems_ no longer necessary, I'm very nervous to actually remove it
-# and discover later on that we need to re-add all this infrastructure...
-# _GENERIC_TYPES_CACHE = DeepChainMap(GenericTypesCache(), LimitedDict())
-
-_GENERIC_TYPES_CACHE = GenericTypesCache()
-
-
-class PydanticGenericMetadata(typing_extensions.TypedDict):
-    origin: type[BaseModel] | None  # analogous to typing._GenericAlias.__origin__
-    args: tuple[Any, ...]  # analogous to typing._GenericAlias.__args__
-    parameters: tuple[type[Any], ...]  # analogous to typing.Generic.__parameters__
-
-
-def create_generic_submodel(
-    model_name: str, origin: type[BaseModel], args: tuple[Any, ...], params: tuple[Any, ...]
-) -> type[BaseModel]:
-    """Dynamically create a submodel of a provided (generic) BaseModel.
-
-    This is used when producing concrete parametrizations of generic models. This function
-    only *creates* the new subclass; the schema/validators/serialization must be updated to
-    reflect a concrete parametrization elsewhere.
-
-    Args:
-        model_name: The name of the newly created model.
-        origin: The base class for the new model to inherit from.
-        args: A tuple of generic metadata arguments.
-        params: A tuple of generic metadata parameters.
-
-    Returns:
-        The created submodel.
-    """
-    namespace: dict[str, Any] = {'__module__': origin.__module__}
-    bases = (origin,)
-    meta, ns, kwds = prepare_class(model_name, bases)
-    namespace.update(ns)
-    created_model = meta(
-        model_name,
-        bases,
-        namespace,
-        __pydantic_generic_metadata__={
-            'origin': origin,
-            'args': args,
-            'parameters': params,
-        },
-        __pydantic_reset_parent_namespace__=False,
-        **kwds,
-    )
-
-    model_module, called_globally = _get_caller_frame_info(depth=3)
-    if called_globally:  # create global reference and therefore allow pickling
-        object_by_reference = None
-        reference_name = model_name
-        reference_module_globals = sys.modules[created_model.__module__].__dict__
-        while object_by_reference is not created_model:
-            object_by_reference = reference_module_globals.setdefault(reference_name, created_model)
-            reference_name += '_'
-
-    return created_model
-
-
-def _get_caller_frame_info(depth: int = 2) -> tuple[str | None, bool]:
-    """Used inside a function to check whether it was called globally.
-
-    Args:
-        depth: The depth to get the frame.
-
-    Returns:
-        A tuple contains `module_nam` and `called_globally`.
-
-    Raises:
-        RuntimeError: If the function is not called inside a function.
-    """
-    try:
-        previous_caller_frame = sys._getframe(depth)
-    except ValueError as e:
-        raise RuntimeError('This function must be used inside another function') from e
-    except AttributeError:  # sys module does not have _getframe function, so there's nothing we can do about it
-        return None, False
-    frame_globals = previous_caller_frame.f_globals
-    return frame_globals.get('__name__'), previous_caller_frame.f_locals is frame_globals
-
-
-DictValues: type[Any] = {}.values().__class__
-
-
-def iter_contained_typevars(v: Any) -> Iterator[TypeVarType]:
-    """Recursively iterate through all subtypes and type args of `v` and yield any typevars that are found.
-
-    This is inspired as an alternative to directly accessing the `__parameters__` attribute of a GenericAlias,
-    since __parameters__ of (nested) generic BaseModel subclasses won't show up in that list.
-    """
-    if isinstance(v, TypeVar):
-        yield v
-    elif is_model_class(v):
-        yield from v.__pydantic_generic_metadata__['parameters']
-    elif isinstance(v, (DictValues, list)):
-        for var in v:
-            yield from iter_contained_typevars(var)
-    else:
-        args = get_args(v)
-        for arg in args:
-            yield from iter_contained_typevars(arg)
-
-
-def get_args(v: Any) -> Any:
-    pydantic_generic_metadata: PydanticGenericMetadata | None = getattr(v, '__pydantic_generic_metadata__', None)
-    if pydantic_generic_metadata:
-        return pydantic_generic_metadata.get('args')
-    return typing_extensions.get_args(v)
-
-
-def get_origin(v: Any) -> Any:
-    pydantic_generic_metadata: PydanticGenericMetadata | None = getattr(v, '__pydantic_generic_metadata__', None)
-    if pydantic_generic_metadata:
-        return pydantic_generic_metadata.get('origin')
-    return typing_extensions.get_origin(v)
-
-
-def get_standard_typevars_map(cls: type[Any]) -> dict[TypeVarType, Any] | None:
-    """Package a generic type's typevars and parametrization (if present) into a dictionary compatible with the
-    `replace_types` function. Specifically, this works with standard typing generics and typing._GenericAlias.
-    """
-    origin = get_origin(cls)
-    if origin is None:
-        return None
-    if not hasattr(origin, '__parameters__'):
-        return None
-
-    # In this case, we know that cls is a _GenericAlias, and origin is the generic type
-    # So it is safe to access cls.__args__ and origin.__parameters__
-    args: tuple[Any, ...] = cls.__args__  # type: ignore
-    parameters: tuple[TypeVarType, ...] = origin.__parameters__
-    return dict(zip(parameters, args))
-
-
-def get_model_typevars_map(cls: type[BaseModel]) -> dict[TypeVarType, Any] | None:
-    """Package a generic BaseModel's typevars and concrete parametrization (if present) into a dictionary compatible
-    with the `replace_types` function.
-
-    Since BaseModel.__class_getitem__ does not produce a typing._GenericAlias, and the BaseModel generic info is
-    stored in the __pydantic_generic_metadata__ attribute, we need special handling here.
-    """
-    # TODO: This could be unified with `get_standard_typevars_map` if we stored the generic metadata
-    #   in the __origin__, __args__, and __parameters__ attributes of the model.
-    generic_metadata = cls.__pydantic_generic_metadata__
-    origin = generic_metadata['origin']
-    args = generic_metadata['args']
-    return dict(zip(iter_contained_typevars(origin), args))
-
-
-def replace_types(type_: Any, type_map: Mapping[Any, Any] | None) -> Any:
-    """Return type with all occurrences of `type_map` keys recursively replaced with their values.
-
-    Args:
-        type_: The class or generic alias.
-        type_map: Mapping from `TypeVar` instance to concrete types.
-
-    Returns:
-        A new type representing the basic structure of `type_` with all
-        `typevar_map` keys recursively replaced.
-
-    Example:
-        ```py
-        from typing import List, Tuple, Union
-
-        from pydantic._internal._generics import replace_types
-
-        replace_types(Tuple[str, Union[List[str], float]], {str: int})
-        #> Tuple[int, Union[List[int], float]]
-        ```
-    """
-    if not type_map:
-        return type_
-
-    type_args = get_args(type_)
-    origin_type = get_origin(type_)
-
-    if origin_type is typing_extensions.Annotated:
-        annotated_type, *annotations = type_args
-        annotated = replace_types(annotated_type, type_map)
-        for annotation in annotations:
-            annotated = typing_extensions.Annotated[annotated, annotation]
-        return annotated
-
-    # Having type args is a good indicator that this is a typing module
-    # class instantiation or a generic alias of some sort.
-    if type_args:
-        resolved_type_args = tuple(replace_types(arg, type_map) for arg in type_args)
-        if all_identical(type_args, resolved_type_args):
-            # If all arguments are the same, there is no need to modify the
-            # type or create a new object at all
-            return type_
-        if (
-            origin_type is not None
-            and isinstance(type_, typing_base)
-            and not isinstance(origin_type, typing_base)
-            and getattr(type_, '_name', None) is not None
-        ):
-            # In python < 3.9 generic aliases don't exist so any of these like `list`,
-            # `type` or `collections.abc.Callable` need to be translated.
-            # See: https://www.python.org/dev/peps/pep-0585
-            origin_type = getattr(typing, type_._name)
-        assert origin_type is not None
-        # PEP-604 syntax (Ex.: list | str) is represented with a types.UnionType object that does not have __getitem__.
-        # We also cannot use isinstance() since we have to compare types.
-        if sys.version_info >= (3, 10) and origin_type is types.UnionType:
-            return _UnionGenericAlias(origin_type, resolved_type_args)
-        return origin_type[resolved_type_args]
-
-    # We handle pydantic generic models separately as they don't have the same
-    # semantics as "typing" classes or generic aliases
-
-    if not origin_type and is_model_class(type_):
-        parameters = type_.__pydantic_generic_metadata__['parameters']
-        if not parameters:
-            return type_
-        resolved_type_args = tuple(replace_types(t, type_map) for t in parameters)
-        if all_identical(parameters, resolved_type_args):
-            return type_
-        return type_[resolved_type_args]
-
-    # Handle special case for typehints that can have lists as arguments.
-    # `typing.Callable[[int, str], int]` is an example for this.
-    if isinstance(type_, (List, list)):
-        resolved_list = list(replace_types(element, type_map) for element in type_)
-        if all_identical(type_, resolved_list):
-            return type_
-        return resolved_list
-
-    # If all else fails, we try to resolve the type directly and otherwise just
-    # return the input with no modifications.
-    return type_map.get(type_, type_)
-
-
-def has_instance_in_type(type_: Any, isinstance_target: Any) -> bool:
-    """Checks if the type, or any of its arbitrary nested args, satisfy
-    `isinstance(<type>, isinstance_target)`.
-    """
-    if isinstance(type_, isinstance_target):
-        return True
-
-    type_args = get_args(type_)
-    origin_type = get_origin(type_)
-
-    if origin_type is typing_extensions.Annotated:
-        annotated_type, *annotations = type_args
-        return has_instance_in_type(annotated_type, isinstance_target)
-
-    # Having type args is a good indicator that this is a typing module
-    # class instantiation or a generic alias of some sort.
-    if any(has_instance_in_type(a, isinstance_target) for a in type_args):
-        return True
-
-    # Handle special case for typehints that can have lists as arguments.
-    # `typing.Callable[[int, str], int]` is an example for this.
-    if isinstance(type_, (List, list)) and not isinstance(type_, typing_extensions.ParamSpec):
-        if any(has_instance_in_type(element, isinstance_target) for element in type_):
-            return True
-
-    return False
-
-
-def check_parameters_count(cls: type[BaseModel], parameters: tuple[Any, ...]) -> None:
-    """Check the generic model parameters count is equal.
-
-    Args:
-        cls: The generic model.
-        parameters: A tuple of passed parameters to the generic model.
-
-    Raises:
-        TypeError: If the passed parameters count is not equal to generic model parameters count.
-    """
-    actual = len(parameters)
-    expected = len(cls.__pydantic_generic_metadata__['parameters'])
-    if actual != expected:
-        description = 'many' if actual > expected else 'few'
-        raise TypeError(f'Too {description} parameters for {cls}; actual {actual}, expected {expected}')
-
-
-_generic_recursion_cache: ContextVar[set[str] | None] = ContextVar('_generic_recursion_cache', default=None)
-
-
-@contextmanager
-def generic_recursion_self_type(
-    origin: type[BaseModel], args: tuple[Any, ...]
-) -> Iterator[PydanticRecursiveRef | None]:
-    """This contextmanager should be placed around the recursive calls used to build a generic type,
-    and accept as arguments the generic origin type and the type arguments being passed to it.
-
-    If the same origin and arguments are observed twice, it implies that a self-reference placeholder
-    can be used while building the core schema, and will produce a schema_ref that will be valid in the
-    final parent schema.
-    """
-    previously_seen_type_refs = _generic_recursion_cache.get()
-    if previously_seen_type_refs is None:
-        previously_seen_type_refs = set()
-        token = _generic_recursion_cache.set(previously_seen_type_refs)
-    else:
-        token = None
-
-    try:
-        type_ref = get_type_ref(origin, args_override=args)
-        if type_ref in previously_seen_type_refs:
-            self_type = PydanticRecursiveRef(type_ref=type_ref)
-            yield self_type
-        else:
-            previously_seen_type_refs.add(type_ref)
-            yield None
-    finally:
-        if token:
-            _generic_recursion_cache.reset(token)
-
-
-def recursively_defined_type_refs() -> set[str]:
-    visited = _generic_recursion_cache.get()
-    if not visited:
-        return set()  # not in a generic recursion, so there are no types
-
-    return visited.copy()  # don't allow modifications
-
-
-def get_cached_generic_type_early(parent: type[BaseModel], typevar_values: Any) -> type[BaseModel] | None:
-    """The use of a two-stage cache lookup approach was necessary to have the highest performance possible for
-    repeated calls to `__class_getitem__` on generic types (which may happen in tighter loops during runtime),
-    while still ensuring that certain alternative parametrizations ultimately resolve to the same type.
-
-    As a concrete example, this approach was necessary to make Model[List[T]][int] equal to Model[List[int]].
-    The approach could be modified to not use two different cache keys at different points, but the
-    _early_cache_key is optimized to be as quick to compute as possible (for repeated-access speed), and the
-    _late_cache_key is optimized to be as "correct" as possible, so that two types that will ultimately be the
-    same after resolving the type arguments will always produce cache hits.
-
-    If we wanted to move to only using a single cache key per type, we would either need to always use the
-    slower/more computationally intensive logic associated with _late_cache_key, or would need to accept
-    that Model[List[T]][int] is a different type than Model[List[T]][int]. Because we rely on subclass relationships
-    during validation, I think it is worthwhile to ensure that types that are functionally equivalent are actually
-    equal.
-    """
-    return _GENERIC_TYPES_CACHE.get(_early_cache_key(parent, typevar_values))
-
-
-def get_cached_generic_type_late(
-    parent: type[BaseModel], typevar_values: Any, origin: type[BaseModel], args: tuple[Any, ...]
-) -> type[BaseModel] | None:
-    """See the docstring of `get_cached_generic_type_early` for more information about the two-stage cache lookup."""
-    cached = _GENERIC_TYPES_CACHE.get(_late_cache_key(origin, args, typevar_values))
-    if cached is not None:
-        set_cached_generic_type(parent, typevar_values, cached, origin, args)
-    return cached
-
-
-def set_cached_generic_type(
-    parent: type[BaseModel],
-    typevar_values: tuple[Any, ...],
-    type_: type[BaseModel],
-    origin: type[BaseModel] | None = None,
-    args: tuple[Any, ...] | None = None,
-) -> None:
-    """See the docstring of `get_cached_generic_type_early` for more information about why items are cached with
-    two different keys.
-    """
-    _GENERIC_TYPES_CACHE[_early_cache_key(parent, typevar_values)] = type_
-    if len(typevar_values) == 1:
-        _GENERIC_TYPES_CACHE[_early_cache_key(parent, typevar_values[0])] = type_
-    if origin and args:
-        _GENERIC_TYPES_CACHE[_late_cache_key(origin, args, typevar_values)] = type_
-
-
-def _union_orderings_key(typevar_values: Any) -> Any:
-    """This is intended to help differentiate between Union types with the same arguments in different order.
-
-    Thanks to caching internal to the `typing` module, it is not possible to distinguish between
-    List[Union[int, float]] and List[Union[float, int]] (and similarly for other "parent" origins besides List)
-    because `typing` considers Union[int, float] to be equal to Union[float, int].
-
-    However, you _can_ distinguish between (top-level) Union[int, float] vs. Union[float, int].
-    Because we parse items as the first Union type that is successful, we get slightly more consistent behavior
-    if we make an effort to distinguish the ordering of items in a union. It would be best if we could _always_
-    get the exact-correct order of items in the union, but that would require a change to the `typing` module itself.
-    (See https://github.com/python/cpython/issues/86483 for reference.)
-    """
-    if isinstance(typevar_values, tuple):
-        args_data = []
-        for value in typevar_values:
-            args_data.append(_union_orderings_key(value))
-        return tuple(args_data)
-    elif typing_extensions.get_origin(typevar_values) is typing.Union:
-        return get_args(typevar_values)
-    else:
-        return ()
-
-
-def _early_cache_key(cls: type[BaseModel], typevar_values: Any) -> GenericTypesCacheKey:
-    """This is intended for minimal computational overhead during lookups of cached types.
-
-    Note that this is overly simplistic, and it's possible that two different cls/typevar_values
-    inputs would ultimately result in the same type being created in BaseModel.__class_getitem__.
-    To handle this, we have a fallback _late_cache_key that is checked later if the _early_cache_key
-    lookup fails, and should result in a cache hit _precisely_ when the inputs to __class_getitem__
-    would result in the same type.
-    """
-    return cls, typevar_values, _union_orderings_key(typevar_values)
-
-
-def _late_cache_key(origin: type[BaseModel], args: tuple[Any, ...], typevar_values: Any) -> GenericTypesCacheKey:
-    """This is intended for use later in the process of creating a new type, when we have more information
-    about the exact args that will be passed. If it turns out that a different set of inputs to
-    __class_getitem__ resulted in the same inputs to the generic type creation process, we can still
-    return the cached type, and update the cache with the _early_cache_key as well.
-    """
-    # The _union_orderings_key is placed at the start here to ensure there cannot be a collision with an
-    # _early_cache_key, as that function will always produce a BaseModel subclass as the first item in the key,
-    # whereas this function will always produce a tuple as the first item in the key.
-    return _union_orderings_key(typevar_values), origin, args
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/response.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/response.py
deleted file mode 100644
index 0f4578696fa2e17a900c6890ec26d65e860b0b72..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/util/response.py
+++ /dev/null
@@ -1,101 +0,0 @@
-from __future__ import annotations
-
-import http.client as httplib
-from email.errors import MultipartInvariantViolationDefect, StartBoundaryNotFoundDefect
-
-from ..exceptions import HeaderParsingError
-
-
-def is_fp_closed(obj: object) -> bool:
-    """
-    Checks whether a given file-like object is closed.
-
-    :param obj:
-        The file-like object to check.
-    """
-
-    try:
-        # Check `isclosed()` first, in case Python3 doesn't set `closed`.
-        # GH Issue #928
-        return obj.isclosed()  # type: ignore[no-any-return, attr-defined]
-    except AttributeError:
-        pass
-
-    try:
-        # Check via the official file-like-object way.
-        return obj.closed  # type: ignore[no-any-return, attr-defined]
-    except AttributeError:
-        pass
-
-    try:
-        # Check if the object is a container for another file-like object that
-        # gets released on exhaustion (e.g. HTTPResponse).
-        return obj.fp is None  # type: ignore[attr-defined]
-    except AttributeError:
-        pass
-
-    raise ValueError("Unable to determine whether fp is closed.")
-
-
-def assert_header_parsing(headers: httplib.HTTPMessage) -> None:
-    """
-    Asserts whether all headers have been successfully parsed.
-    Extracts encountered errors from the result of parsing headers.
-
-    Only works on Python 3.
-
-    :param http.client.HTTPMessage headers: Headers to verify.
-
-    :raises urllib3.exceptions.HeaderParsingError:
-        If parsing errors are found.
-    """
-
-    # This will fail silently if we pass in the wrong kind of parameter.
-    # To make debugging easier add an explicit check.
-    if not isinstance(headers, httplib.HTTPMessage):
-        raise TypeError(f"expected httplib.Message, got {type(headers)}.")
-
-    unparsed_data = None
-
-    # get_payload is actually email.message.Message.get_payload;
-    # we're only interested in the result if it's not a multipart message
-    if not headers.is_multipart():
-        payload = headers.get_payload()
-
-        if isinstance(payload, (bytes, str)):
-            unparsed_data = payload
-
-    # httplib is assuming a response body is available
-    # when parsing headers even when httplib only sends
-    # header data to parse_headers() This results in
-    # defects on multipart responses in particular.
-    # See: https://github.com/urllib3/urllib3/issues/800
-
-    # So we ignore the following defects:
-    # - StartBoundaryNotFoundDefect:
-    #     The claimed start boundary was never found.
-    # - MultipartInvariantViolationDefect:
-    #     A message claimed to be a multipart but no subparts were found.
-    defects = [
-        defect
-        for defect in headers.defects
-        if not isinstance(
-            defect, (StartBoundaryNotFoundDefect, MultipartInvariantViolationDefect)
-        )
-    ]
-
-    if defects or unparsed_data:
-        raise HeaderParsingError(defects=defects, unparsed_data=unparsed_data)
-
-
-def is_response_to_head(response: httplib.HTTPResponse) -> bool:
-    """
-    Checks whether the request of a response has been a HEAD-request.
-
-    :param http.client.HTTPResponse response:
-        Response to check if the originating request
-        used 'HEAD' as a method.
-    """
-    # FIXME: Can we do this somehow without accessing private httplib _method?
-    method_str = response._method  # type: str  # type: ignore[attr-defined]
-    return method_str.upper() == "HEAD"
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/uvicorn/protocols/utils.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/uvicorn/protocols/utils.py
deleted file mode 100644
index d0697fe73b9389c1e9f58b198f1f44f810439cda..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/uvicorn/protocols/utils.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import asyncio
-import urllib.parse
-from typing import Optional, Tuple
-
-from uvicorn._types import WWWScope
-
-
-def get_remote_addr(transport: asyncio.Transport) -> Optional[Tuple[str, int]]:
-    socket_info = transport.get_extra_info("socket")
-    if socket_info is not None:
-        try:
-            info = socket_info.getpeername()
-            return (str(info[0]), int(info[1])) if isinstance(info, tuple) else None
-        except OSError:  # pragma: no cover
-            # This case appears to inconsistently occur with uvloop
-            # bound to a unix domain socket.
-            return None
-
-    info = transport.get_extra_info("peername")
-    if info is not None and isinstance(info, (list, tuple)) and len(info) == 2:
-        return (str(info[0]), int(info[1]))
-    return None
-
-
-def get_local_addr(transport: asyncio.Transport) -> Optional[Tuple[str, int]]:
-    socket_info = transport.get_extra_info("socket")
-    if socket_info is not None:
-        info = socket_info.getsockname()
-
-        return (str(info[0]), int(info[1])) if isinstance(info, tuple) else None
-    info = transport.get_extra_info("sockname")
-    if info is not None and isinstance(info, (list, tuple)) and len(info) == 2:
-        return (str(info[0]), int(info[1]))
-    return None
-
-
-def is_ssl(transport: asyncio.Transport) -> bool:
-    return bool(transport.get_extra_info("sslcontext"))
-
-
-def get_client_addr(scope: "WWWScope") -> str:
-    client = scope.get("client")
-    if not client:
-        return ""
-    return "%s:%d" % client
-
-
-def get_path_with_query_string(scope: "WWWScope") -> str:
-    path_with_query_string = urllib.parse.quote(scope["path"])
-    if scope["query_string"]:
-        path_with_query_string = "{}?{}".format(
-            path_with_query_string, scope["query_string"].decode("ascii")
-        )
-    return path_with_query_string
diff --git a/spaces/pyodide-demo/self-hosted/python-dateutil.js b/spaces/pyodide-demo/self-hosted/python-dateutil.js
deleted file mode 100644
index edf7752b90a322b960dcddc6c99874fdb9329e7e..0000000000000000000000000000000000000000
--- a/spaces/pyodide-demo/self-hosted/python-dateutil.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/spaces/pyodide-demo/self-hosted/pywavelets-tests.js b/spaces/pyodide-demo/self-hosted/pywavelets-tests.js
deleted file mode 100644
index 1f96fbbb572923dda8a01e01ca8c000ed5a2ca8f..0000000000000000000000000000000000000000
--- a/spaces/pyodide-demo/self-hosted/pywavelets-tests.js
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\ No newline at end of file
diff --git a/spaces/pytorch/transformers/README.md b/spaces/pytorch/transformers/README.md
deleted file mode 100644
index ed58c0b085343013a4726771b5fd3782983134d1..0000000000000000000000000000000000000000
--- a/spaces/pytorch/transformers/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Transformers
-emoji: 🌍
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/qkorbit/AltDiffusion/app.py b/spaces/qkorbit/AltDiffusion/app.py
deleted file mode 100644
index 76ac5793dd785d1f46837f0bcaeeab37d0bbb3ca..0000000000000000000000000000000000000000
--- a/spaces/qkorbit/AltDiffusion/app.py
+++ /dev/null
@@ -1,330 +0,0 @@
-import io 
-import re
-import imp
-import time
-import json 
-import base64
-import requests
-import gradio as gr
-import ui_functions as uifn
-from css_and_js import js, call_JS
-from PIL import Image, PngImagePlugin, ImageChops
-
-url_host = "http://flagstudio.baai.ac.cn"
-token = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VyX2lkIjoiZjAxOGMxMzJiYTUyNDBjMzk5NTMzYTI5YjBmMzZiODMiLCJhcHBfbmFtZSI6IndlYiIsImlkZW50aXR5X3R5cGUiOiIyIiwidXNlcl9yb2xlIjoiMiIsImp0aSI6IjVjMmQzMjdiLWI5Y2MtNDhiZS1hZWQ4LTllMjQ4MDk4NzMxYyIsIm5iZiI6MTY2OTAwNjE5NywiZXhwIjoxOTg0MzY2MTk3LCJpYXQiOjE2NjkwMDYxOTd9.9B3MDk8wA6iWH5puXjcD19tJJ4Ox7mdpRyWZs5Kwt70"
-
-def read_content(file_path: str) -> str:
-    """read the content of target file
-    """
-    with open(file_path, 'r', encoding='utf-8') as f:
-        content = f.read()
-
-    return content
-
-def filter_content(raw_style: str):
-    if "(" in raw_style:
-        i = raw_style.index("(")
-    else :
-        i = -1
-
-    if i == -1:
-        return raw_style
-    else :
-        return raw_style[:i]
-
-def upload_image(img):
-    url = url_host + "/api/v1/image/get-upload-link"
-    headers = {"token": token}
-    r = requests.post(url, json={}, headers=headers)
-    if r.status_code != 200:
-        raise gr.Error(r.reason)
-    head_res = r.json()
-    if head_res["code"] != 0:
-        raise gr.Error("Unknown error")
-    image_id = head_res["data"]["image_id"]
-    image_url = head_res["data"]["url"]
-    image_headers = head_res["data"]["headers"]
-
-    imgBytes = io.BytesIO()
-    img.save(imgBytes, "PNG")
-    imgBytes = imgBytes.getvalue()
-
-    r = requests.put(image_url, data=imgBytes, headers=image_headers)
-    if r.status_code != 200:
-        raise gr.Error(r.reason)
-    return image_id, image_url
-
-def post_reqest(seed, prompt, width, height, image_num, img=None, mask=None):
-    data = {
-            "type": "gen-image",
-            "parameters": {
-                "width": width, # output height width
-                "height": height, # output image height
-                "prompts": [prompt], 
-            }
-        }
-    data["parameters"]["seed"] = int(seed)
-    if img is not None:
-        # Upload image
-        image_id, image_url = upload_image(img)
-        data["parameters"]["init_image"] = {
-                    "image_id": image_id,
-                    "url": image_url,
-                    "width": img.width,
-                    "height": img.height,
-                }
-    if mask is not None:
-        # Upload mask
-        extrama = mask.convert("L").getextrema()
-        if extrama[1] > 0:
-            mask_id, mask_url = upload_image(mask)
-            data["parameters"]["mask_image"] = {
-                        "image_id": mask_id,
-                        "url": mask_url,
-                        "width": mask.width,
-                        "height": mask.height,
-                    }
-    headers = {"token": token}
-
-    # Send create task request
-    all_task_data = []
-    url = url_host+"/api/v1/task/create"
-    for _ in range(image_num):
-        r = requests.post(url, json=data, headers=headers)
-        if r.status_code != 200:
-            raise gr.Error(r.reason)
-        create_res = r.json()
-        if create_res['code'] == 3002:
-            raise gr.Error("Inappropriate prompt detected.")
-        elif create_res['code'] != 0:
-            raise gr.Error("Unknown error")
-        all_task_data.append(create_res["data"])
-
-    # Get result
-    url = url_host+"/api/v1/task/status"
-    images = []
-    while True:
-        if len(all_task_data) <= 0:
-            return images
-        for i in range(len(all_task_data)-1, -1, -1):
-            data = all_task_data[i]
-            r = requests.post(url, json=data, headers=headers)
-            if r.status_code != 200:
-                raise gr.Error(r.reason)
-            res = r.json()
-            if res["code"] == 6002:
-                # Running
-                continue
-            if res["code"] == 6005:
-                raise gr.Error("NSFW image detected.")
-            elif res["code"] == 0:
-                # Finished
-                for img_info in res["data"]["images"]:
-                    img_res = requests.get(img_info["url"])
-                    images.append(Image.open(io.BytesIO(img_res.content)).convert("RGB"))
-                del all_task_data[i]
-            else:
-                raise gr.Error(f"Error code: {res['code']}")
-        time.sleep(1)
-
-def request_images(raw_text, class_draw, style_draw, batch_size, w, h, seed):
-    if filter_content(class_draw) != "国画":
-        if filter_content(class_draw) != "通用":
-            raw_text = raw_text + f"，{filter_content(class_draw)}"
-
-        for sty in style_draw:
-            raw_text = raw_text + f"，{filter_content(sty)}"
-    elif filter_content(class_draw) == "国画":
-        raw_text = raw_text + "，国画，水墨画，大作，黑白，高清，传统"
-    print(f"raw text is {raw_text}")
-
-    images = post_reqest(seed, raw_text, w, h, int(batch_size))
-
-    return images
-
-
-def img2img(prompt, image_and_mask):
-    if image_and_mask["image"].width <= image_and_mask["image"].height:
-        width = 512
-        height = int((width/image_and_mask["image"].width)*image_and_mask["image"].height)
-    else:
-        height = 512
-        width = int((height/image_and_mask["image"].height)*image_and_mask["image"].width)
-    return post_reqest(0, prompt, width, height, 1, image_and_mask["image"], image_and_mask["mask"])
-
-
-examples = [
-    '水墨蝴蝶和牡丹花，国画',
-    '苍劲有力的墨竹，国画',
-    '暴风雨中的灯塔',
-    '机械小松鼠，科学幻想',
-    '中国水墨山水画，国画',
-    "Lighthouse in the storm",
-    "A dog",
-    "Landscape by 张大千",
-    "A tiger 长了兔子耳朵",
-    "A baby bird 铅笔素描",
-]
-
-if __name__ == "__main__":
-    block = gr.Blocks(css=read_content('style.css'))
-
-    with block:
-        gr.HTML(read_content("header.html"))
-        with gr.Tabs(elem_id='tabss') as tabs:
-
-            with gr.TabItem("文生图(Text-to-img)", id='txt2img_tab'):
-
-                with gr.Group():
-                    with gr.Box():
-                        with gr.Row().style(mobile_collapse=False, equal_height=True):
-                            text = gr.Textbox(
-                                label="Prompt",
-                                show_label=False,
-                                max_lines=1,
-                                placeholder="Input text(输入文字)",
-                                interactive=True,
-                            ).style(
-                                border=(True, False, True, True),
-                                rounded=(True, False, False, True),
-                                container=False,
-                            )
-                            
-                            btn = gr.Button("Generate image").style(
-                                margin=False,
-                                rounded=(True, True, True, True),
-                            )
-                        with gr.Row().style(mobile_collapse=False, equal_height=True):
-                            class_draw = gr.Radio(choices=["通用(general)","国画(traditional Chinese painting)",], value="通用(general)", show_label=True, label='生成类型(type)')
-                            # class_draw = gr.Dropdown(["通用(general)", "国画(traditional Chinese painting)", 
-                            #                             "照片，摄影(picture photography)", "油画(oil painting)", 
-                            #                             "铅笔素描(pencil sketch)", "CG", 
-                            #                             "水彩画(watercolor painting)", "水墨画(ink and wash)",
-                            #                             "插画(illustrations)", "3D", "图生图(img2img)"],
-                            #                         label="生成类型(type)",
-                            #                         show_label=True,
-                            #                         value="通用(general)")
-                        with gr.Row().style(mobile_collapse=False, equal_height=True):
-                            style_draw = gr.CheckboxGroup(["蒸汽朋克(steampunk)", "电影摄影风格(film photography)", 
-                                                        "概念艺术(concept art)", "Warming lighting", 
-                                                        "Dramatic lighting", "Natural lighting", 
-                                                        "虚幻引擎(unreal engine)", "4k", "8k",
-                                                        "充满细节(full details)"],
-                                                    label="画面风格(style)",
-                                                    show_label=True,
-                                                    )
-                        with gr.Row().style(mobile_collapse=False, equal_height=True):
-                            # sample_size = gr.Slider(minimum=1,
-                            #                         maximum=4,
-                            #                         step=1,
-                            #                         label="生成数量(number)",
-                            #                         show_label=True,
-                            #                         interactive=True,
-                            #                         )
-                            sample_size = gr.Radio(choices=["1","2","3","4"], value="1", show_label=True, label='生成数量(number)')
-                            seed = gr.Number(0, label='seed', interactive=True)
-                        with gr.Row().style(mobile_collapse=False, equal_height=True):
-                            w = gr.Slider(512,1024,value=512, step=64, label="width")
-                            h = gr.Slider(512,1024,value=512, step=64, label="height")
-
-                    gallery = gr.Gallery(
-                        label="Generated images", show_label=False, elem_id="gallery"
-                    ).style(grid=[2,2])
-                    gr.Examples(examples=examples, fn=request_images, inputs=text, outputs=gallery, examples_per_page=100)
-                    with gr.Row().style(mobile_collapse=False, equal_height=True):
-                        img_choices = gr.Dropdown(["图片1(img1)"],label='请选择一张图片发送到图生图',show_label=True,value="图片1(img1)")
-                    with gr.Row().style(mobile_collapse=False, equal_height=True):
-                        output_txt2img_copy_to_input_btn = gr.Button("发送图片到图生图(Sent the image to img2img)").style(
-                                margin=False,
-                                rounded=(True, True, True, True),
-                            )
-
-                    with gr.Row():
-                        prompt = gr.Markdown("提示(Prompt)：", visible=False)
-                    with gr.Row():
-                        move_prompt_zh = gr.Markdown("请移至图生图部分进行编辑(拉到顶部)", visible=False)
-                    with gr.Row():
-                        move_prompt_en = gr.Markdown("Please move to the img2img section for editing(Pull to the top)", visible=False)
-
-                        
-
-                    text.submit(request_images, inputs=[text, class_draw, style_draw, sample_size, w, h, seed], outputs=gallery)
-                    btn.click(request_images, inputs=[text, class_draw, style_draw, sample_size, w, h, seed], outputs=gallery)
-
-                    sample_size.change(
-                        fn=uifn.change_img_choices,
-                        inputs=[sample_size],
-                        outputs=[img_choices]
-                    )
-
-            with gr.TabItem("图生图(Img-to-Img)", id="img2img_tab"):
-                with gr.Row(elem_id="prompt_row"):
-                    img2img_prompt = gr.Textbox(label="Prompt",
-                                                elem_id='img2img_prompt_input',
-                                                placeholder="神奇的森林，流淌的河流.",
-                                                lines=1,
-                                                max_lines=1,
-                                                value="",
-                                                show_label=False).style()
-
-                    img2img_btn_mask = gr.Button("Generate", variant="primary", visible=False,
-                                                 elem_id="img2img_mask_btn")
-                    img2img_btn_editor = gr.Button("Generate", variant="primary", elem_id="img2img_edit_btn")
-                gr.Markdown('#### 输入图像')
-                with gr.Row().style(equal_height=False):
-                    #with gr.Column():
-                    img2img_image_mask = gr.Image(
-                        value=None,
-                        source="upload",
-                        interactive=True,
-                        tool="sketch",
-                        type='pil',
-                        elem_id="img2img_mask",
-                        image_mode="RGBA"
-                    )
-                gr.Markdown('#### 编辑后的图片')
-                with gr.Row():
-                    output_img2img_gallery = gr.Gallery(label="Images", elem_id="img2img_gallery_output").style(
-                        grid=[4,4,4] )
-                with gr.Row():
-                    gr.Markdown('提示(prompt):')
-                with gr.Row():
-                    gr.Markdown('请选择一张图像掩盖掉一部分区域,并输入文本描述')
-                with gr.Row():
-                    gr.Markdown('Please select an image to cover up a part of the area and enter a text description.')
-                gr.Markdown('# 编辑设置',visible=False)
-
-
-                output_txt2img_copy_to_input_btn.click(
-                    uifn.copy_img_to_input,
-                    [gallery, img_choices],
-                    [tabs, img2img_image_mask, move_prompt_zh, move_prompt_en, prompt]
-                )
-
-
-                img2img_func = img2img
-                img2img_inputs = [img2img_prompt, img2img_image_mask]
-                img2img_outputs = [output_img2img_gallery]
-
-                img2img_btn_mask.click(
-                    img2img_func,
-                    img2img_inputs,
-                    img2img_outputs
-                )
-
-                def img2img_submit_params():
-                    return (img2img_func,
-                            img2img_inputs,
-                            img2img_outputs)
-
-                img2img_btn_editor.click(*img2img_submit_params())
-
-                # GENERATE ON ENTER
-                img2img_prompt.submit(None, None, None,
-                                      _js=call_JS("clickFirstVisibleButton",
-                                                  rowId="prompt_row"))
-
-        gr.HTML(read_content("footer.html"))
-        # gr.Image('./contributors.png')
-
-    block.queue(max_size=512, concurrency_count=256).launch()
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Airmagnet Survey Download Cracked 13.md b/spaces/quidiaMuxgu/Expedit-SAM/Airmagnet Survey Download Cracked 13.md
deleted file mode 100644
index 456f9aad51fa9fce6f9d52955de4f8de4ccbf1fe..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Airmagnet Survey Download Cracked 13.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Airmagnet Survey Download Cracked 13</h2><br /><p><b><b>Download File</b> &#9675;&#9675;&#9675; <a href="https://geags.com/2uCrnO">https://geags.com/2uCrnO</a></b></p><br /><br />
-
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Its Plc Professional Edition Activation Key.md b/spaces/quidiaMuxgu/Expedit-SAM/Its Plc Professional Edition Activation Key.md
deleted file mode 100644
index 0090098804c50b5a820d54be7aace6627555979a..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Its Plc Professional Edition Activation Key.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-<br />
-<h1>ITS PLC Professional Edition: A Powerful Tool for PLC Training and Education</h1>
-<p>ITS PLC Professional Edition is a software that simulates industrial systems that can be controlled by programmable logic controllers (PLCs). It is designed for individuals who want to learn how to program and operate PLCs in realistic scenarios. ITS PLC Professional Edition works with all the major PLC brands, such as Allen-Bradley, Siemens, Mitsubishi, Omron, Schneider and many others. It also supports different programming languages, such as ladder logic, function block diagram, instruction list and structured text.</p>
-<p>ITS PLC Professional Edition offers five systems inspired by common industrial plants: Sorting, Batching, Palletizer, Pick & Place and Automatic Warehouse. Each system is a typical industrial application of PLCs that will give you the opportunity to practice real world control tasks. You can connect your own PLC to the simulated system and control it in real-time. You can also monitor the system variables, inputs and outputs using the built-in HMI (Human Machine Interface).</p>
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-<p>ITS PLC Professional Edition is an essential tool for PLC training and education. It will improve your PLC skills and knowledge with high quality simulations of industrial plants. You can download a free trial version from the official website[^1^] or buy a license for $299.00 USD. ITS PLC Professional Edition is compatible with Windows XP/Vista/7/8/10/11.</p><p>ITS PLC Professional Edition is not only a simulation software, but also a learning platform. It includes a comprehensive exercise book that guides you through the specification and programming of logic control applications in the ITS PLC training environment. The exercise book contains 72 PLC programs that cover different aspects of industrial automation, such as sequential control, timers, counters, sensors, actuators, logic gates, arithmetic operations and more. You can use the exercise book as a self-study material or as a teaching resource for your classes.</p>
-<p>ITS PLC Professional Edition is also compatible with other software tools that can enhance your PLC training experience. For example, you can use ITS PLC Professional Edition with Factory I/O, a 3D simulation software that allows you to create and control your own industrial systems. You can also use ITS PLC Professional Edition with AUTOMGEN, a software that supports different programming languages and standards, such as GRAFCET (IEC 60848), SFC (IEC 61131-3) and LADDER (IEC 61131-3). By using these software tools together, you can create and test complex and realistic industrial applications.</p>
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Malwarebytes Anti-Malware Premium 2.0.1.1004 Keys-P2P Download __EXCLUSIVE__.md b/spaces/quidiaMuxgu/Expedit-SAM/Malwarebytes Anti-Malware Premium 2.0.1.1004 Keys-P2P Download __EXCLUSIVE__.md
deleted file mode 100644
index 6d37d9cd04dea0a62e66cfcd3ad7d26999da2d71..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Malwarebytes Anti-Malware Premium 2.0.1.1004 Keys-P2P Download __EXCLUSIVE__.md	
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diff --git a/spaces/rachana219/MODT2/trackers/strongsort/deep/models/osnet.py b/spaces/rachana219/MODT2/trackers/strongsort/deep/models/osnet.py
deleted file mode 100644
index b77388f13289f050da2bf2bdebd40ab4fce6f976..0000000000000000000000000000000000000000
--- a/spaces/rachana219/MODT2/trackers/strongsort/deep/models/osnet.py
+++ /dev/null
@@ -1,598 +0,0 @@
-from __future__ import division, absolute_import
-import warnings
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-__all__ = [
-    'osnet_x1_0', 'osnet_x0_75', 'osnet_x0_5', 'osnet_x0_25', 'osnet_ibn_x1_0'
-]
-
-pretrained_urls = {
-    'osnet_x1_0':
-    'https://drive.google.com/uc?id=1LaG1EJpHrxdAxKnSCJ_i0u-nbxSAeiFY',
-    'osnet_x0_75':
-    'https://drive.google.com/uc?id=1uwA9fElHOk3ZogwbeY5GkLI6QPTX70Hq',
-    'osnet_x0_5':
-    'https://drive.google.com/uc?id=16DGLbZukvVYgINws8u8deSaOqjybZ83i',
-    'osnet_x0_25':
-    'https://drive.google.com/uc?id=1rb8UN5ZzPKRc_xvtHlyDh-cSz88YX9hs',
-    'osnet_ibn_x1_0':
-    'https://drive.google.com/uc?id=1sr90V6irlYYDd4_4ISU2iruoRG8J__6l'
-}
-
-
-##########
-# Basic layers
-##########
-class ConvLayer(nn.Module):
-    """Convolution layer (conv + bn + relu)."""
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride=1,
-        padding=0,
-        groups=1,
-        IN=False
-    ):
-        super(ConvLayer, self).__init__()
-        self.conv = nn.Conv2d(
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride=stride,
-            padding=padding,
-            bias=False,
-            groups=groups
-        )
-        if IN:
-            self.bn = nn.InstanceNorm2d(out_channels, affine=True)
-        else:
-            self.bn = nn.BatchNorm2d(out_channels)
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.bn(x)
-        x = self.relu(x)
-        return x
-
-
-class Conv1x1(nn.Module):
-    """1x1 convolution + bn + relu."""
-
-    def __init__(self, in_channels, out_channels, stride=1, groups=1):
-        super(Conv1x1, self).__init__()
-        self.conv = nn.Conv2d(
-            in_channels,
-            out_channels,
-            1,
-            stride=stride,
-            padding=0,
-            bias=False,
-            groups=groups
-        )
-        self.bn = nn.BatchNorm2d(out_channels)
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.bn(x)
-        x = self.relu(x)
-        return x
-
-
-class Conv1x1Linear(nn.Module):
-    """1x1 convolution + bn (w/o non-linearity)."""
-
-    def __init__(self, in_channels, out_channels, stride=1):
-        super(Conv1x1Linear, self).__init__()
-        self.conv = nn.Conv2d(
-            in_channels, out_channels, 1, stride=stride, padding=0, bias=False
-        )
-        self.bn = nn.BatchNorm2d(out_channels)
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.bn(x)
-        return x
-
-
-class Conv3x3(nn.Module):
-    """3x3 convolution + bn + relu."""
-
-    def __init__(self, in_channels, out_channels, stride=1, groups=1):
-        super(Conv3x3, self).__init__()
-        self.conv = nn.Conv2d(
-            in_channels,
-            out_channels,
-            3,
-            stride=stride,
-            padding=1,
-            bias=False,
-            groups=groups
-        )
-        self.bn = nn.BatchNorm2d(out_channels)
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.bn(x)
-        x = self.relu(x)
-        return x
-
-
-class LightConv3x3(nn.Module):
-    """Lightweight 3x3 convolution.
-
-    1x1 (linear) + dw 3x3 (nonlinear).
-    """
-
-    def __init__(self, in_channels, out_channels):
-        super(LightConv3x3, self).__init__()
-        self.conv1 = nn.Conv2d(
-            in_channels, out_channels, 1, stride=1, padding=0, bias=False
-        )
-        self.conv2 = nn.Conv2d(
-            out_channels,
-            out_channels,
-            3,
-            stride=1,
-            padding=1,
-            bias=False,
-            groups=out_channels
-        )
-        self.bn = nn.BatchNorm2d(out_channels)
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.conv2(x)
-        x = self.bn(x)
-        x = self.relu(x)
-        return x
-
-
-##########
-# Building blocks for omni-scale feature learning
-##########
-class ChannelGate(nn.Module):
-    """A mini-network that generates channel-wise gates conditioned on input tensor."""
-
-    def __init__(
-        self,
-        in_channels,
-        num_gates=None,
-        return_gates=False,
-        gate_activation='sigmoid',
-        reduction=16,
-        layer_norm=False
-    ):
-        super(ChannelGate, self).__init__()
-        if num_gates is None:
-            num_gates = in_channels
-        self.return_gates = return_gates
-        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
-        self.fc1 = nn.Conv2d(
-            in_channels,
-            in_channels // reduction,
-            kernel_size=1,
-            bias=True,
-            padding=0
-        )
-        self.norm1 = None
-        if layer_norm:
-            self.norm1 = nn.LayerNorm((in_channels // reduction, 1, 1))
-        self.relu = nn.ReLU(inplace=True)
-        self.fc2 = nn.Conv2d(
-            in_channels // reduction,
-            num_gates,
-            kernel_size=1,
-            bias=True,
-            padding=0
-        )
-        if gate_activation == 'sigmoid':
-            self.gate_activation = nn.Sigmoid()
-        elif gate_activation == 'relu':
-            self.gate_activation = nn.ReLU(inplace=True)
-        elif gate_activation == 'linear':
-            self.gate_activation = None
-        else:
-            raise RuntimeError(
-                "Unknown gate activation: {}".format(gate_activation)
-            )
-
-    def forward(self, x):
-        input = x
-        x = self.global_avgpool(x)
-        x = self.fc1(x)
-        if self.norm1 is not None:
-            x = self.norm1(x)
-        x = self.relu(x)
-        x = self.fc2(x)
-        if self.gate_activation is not None:
-            x = self.gate_activation(x)
-        if self.return_gates:
-            return x
-        return input * x
-
-
-class OSBlock(nn.Module):
-    """Omni-scale feature learning block."""
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        IN=False,
-        bottleneck_reduction=4,
-        **kwargs
-    ):
-        super(OSBlock, self).__init__()
-        mid_channels = out_channels // bottleneck_reduction
-        self.conv1 = Conv1x1(in_channels, mid_channels)
-        self.conv2a = LightConv3x3(mid_channels, mid_channels)
-        self.conv2b = nn.Sequential(
-            LightConv3x3(mid_channels, mid_channels),
-            LightConv3x3(mid_channels, mid_channels),
-        )
-        self.conv2c = nn.Sequential(
-            LightConv3x3(mid_channels, mid_channels),
-            LightConv3x3(mid_channels, mid_channels),
-            LightConv3x3(mid_channels, mid_channels),
-        )
-        self.conv2d = nn.Sequential(
-            LightConv3x3(mid_channels, mid_channels),
-            LightConv3x3(mid_channels, mid_channels),
-            LightConv3x3(mid_channels, mid_channels),
-            LightConv3x3(mid_channels, mid_channels),
-        )
-        self.gate = ChannelGate(mid_channels)
-        self.conv3 = Conv1x1Linear(mid_channels, out_channels)
-        self.downsample = None
-        if in_channels != out_channels:
-            self.downsample = Conv1x1Linear(in_channels, out_channels)
-        self.IN = None
-        if IN:
-            self.IN = nn.InstanceNorm2d(out_channels, affine=True)
-
-    def forward(self, x):
-        identity = x
-        x1 = self.conv1(x)
-        x2a = self.conv2a(x1)
-        x2b = self.conv2b(x1)
-        x2c = self.conv2c(x1)
-        x2d = self.conv2d(x1)
-        x2 = self.gate(x2a) + self.gate(x2b) + self.gate(x2c) + self.gate(x2d)
-        x3 = self.conv3(x2)
-        if self.downsample is not None:
-            identity = self.downsample(identity)
-        out = x3 + identity
-        if self.IN is not None:
-            out = self.IN(out)
-        return F.relu(out)
-
-
-##########
-# Network architecture
-##########
-class OSNet(nn.Module):
-    """Omni-Scale Network.
-    
-    Reference:
-        - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
-        - Zhou et al. Learning Generalisable Omni-Scale Representations
-          for Person Re-Identification. TPAMI, 2021.
-    """
-
-    def __init__(
-        self,
-        num_classes,
-        blocks,
-        layers,
-        channels,
-        feature_dim=512,
-        loss='softmax',
-        IN=False,
-        **kwargs
-    ):
-        super(OSNet, self).__init__()
-        num_blocks = len(blocks)
-        assert num_blocks == len(layers)
-        assert num_blocks == len(channels) - 1
-        self.loss = loss
-        self.feature_dim = feature_dim
-
-        # convolutional backbone
-        self.conv1 = ConvLayer(3, channels[0], 7, stride=2, padding=3, IN=IN)
-        self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
-        self.conv2 = self._make_layer(
-            blocks[0],
-            layers[0],
-            channels[0],
-            channels[1],
-            reduce_spatial_size=True,
-            IN=IN
-        )
-        self.conv3 = self._make_layer(
-            blocks[1],
-            layers[1],
-            channels[1],
-            channels[2],
-            reduce_spatial_size=True
-        )
-        self.conv4 = self._make_layer(
-            blocks[2],
-            layers[2],
-            channels[2],
-            channels[3],
-            reduce_spatial_size=False
-        )
-        self.conv5 = Conv1x1(channels[3], channels[3])
-        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
-        # fully connected layer
-        self.fc = self._construct_fc_layer(
-            self.feature_dim, channels[3], dropout_p=None
-        )
-        # identity classification layer
-        self.classifier = nn.Linear(self.feature_dim, num_classes)
-
-        self._init_params()
-
-    def _make_layer(
-        self,
-        block,
-        layer,
-        in_channels,
-        out_channels,
-        reduce_spatial_size,
-        IN=False
-    ):
-        layers = []
-
-        layers.append(block(in_channels, out_channels, IN=IN))
-        for i in range(1, layer):
-            layers.append(block(out_channels, out_channels, IN=IN))
-
-        if reduce_spatial_size:
-            layers.append(
-                nn.Sequential(
-                    Conv1x1(out_channels, out_channels),
-                    nn.AvgPool2d(2, stride=2)
-                )
-            )
-
-        return nn.Sequential(*layers)
-
-    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
-        if fc_dims is None or fc_dims < 0:
-            self.feature_dim = input_dim
-            return None
-
-        if isinstance(fc_dims, int):
-            fc_dims = [fc_dims]
-
-        layers = []
-        for dim in fc_dims:
-            layers.append(nn.Linear(input_dim, dim))
-            layers.append(nn.BatchNorm1d(dim))
-            layers.append(nn.ReLU(inplace=True))
-            if dropout_p is not None:
-                layers.append(nn.Dropout(p=dropout_p))
-            input_dim = dim
-
-        self.feature_dim = fc_dims[-1]
-
-        return nn.Sequential(*layers)
-
-    def _init_params(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(
-                    m.weight, mode='fan_out', nonlinearity='relu'
-                )
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-            elif isinstance(m, nn.BatchNorm1d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def featuremaps(self, x):
-        x = self.conv1(x)
-        x = self.maxpool(x)
-        x = self.conv2(x)
-        x = self.conv3(x)
-        x = self.conv4(x)
-        x = self.conv5(x)
-        return x
-
-    def forward(self, x, return_featuremaps=False):
-        x = self.featuremaps(x)
-        if return_featuremaps:
-            return x
-        v = self.global_avgpool(x)
-        v = v.view(v.size(0), -1)
-        if self.fc is not None:
-            v = self.fc(v)
-        if not self.training:
-            return v
-        y = self.classifier(v)
-        if self.loss == 'softmax':
-            return y
-        elif self.loss == 'triplet':
-            return y, v
-        else:
-            raise KeyError("Unsupported loss: {}".format(self.loss))
-
-
-def init_pretrained_weights(model, key=''):
-    """Initializes model with pretrained weights.
-    
-    Layers that don't match with pretrained layers in name or size are kept unchanged.
-    """
-    import os
-    import errno
-    import gdown
-    from collections import OrderedDict
-
-    def _get_torch_home():
-        ENV_TORCH_HOME = 'TORCH_HOME'
-        ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
-        DEFAULT_CACHE_DIR = '~/.cache'
-        torch_home = os.path.expanduser(
-            os.getenv(
-                ENV_TORCH_HOME,
-                os.path.join(
-                    os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'
-                )
-            )
-        )
-        return torch_home
-
-    torch_home = _get_torch_home()
-    model_dir = os.path.join(torch_home, 'checkpoints')
-    try:
-        os.makedirs(model_dir)
-    except OSError as e:
-        if e.errno == errno.EEXIST:
-            # Directory already exists, ignore.
-            pass
-        else:
-            # Unexpected OSError, re-raise.
-            raise
-    filename = key + '_imagenet.pth'
-    cached_file = os.path.join(model_dir, filename)
-
-    if not os.path.exists(cached_file):
-        gdown.download(pretrained_urls[key], cached_file, quiet=False)
-
-    state_dict = torch.load(cached_file)
-    model_dict = model.state_dict()
-    new_state_dict = OrderedDict()
-    matched_layers, discarded_layers = [], []
-
-    for k, v in state_dict.items():
-        if k.startswith('module.'):
-            k = k[7:] # discard module.
-
-        if k in model_dict and model_dict[k].size() == v.size():
-            new_state_dict[k] = v
-            matched_layers.append(k)
-        else:
-            discarded_layers.append(k)
-
-    model_dict.update(new_state_dict)
-    model.load_state_dict(model_dict)
-
-    if len(matched_layers) == 0:
-        warnings.warn(
-            'The pretrained weights from "{}" cannot be loaded, '
-            'please check the key names manually '
-            '(** ignored and continue **)'.format(cached_file)
-        )
-    else:
-        print(
-            'Successfully loaded imagenet pretrained weights from "{}"'.
-            format(cached_file)
-        )
-        if len(discarded_layers) > 0:
-            print(
-                '** The following layers are discarded '
-                'due to unmatched keys or layer size: {}'.
-                format(discarded_layers)
-            )
-
-
-##########
-# Instantiation
-##########
-def osnet_x1_0(num_classes=1000, pretrained=True, loss='softmax', **kwargs):
-    # standard size (width x1.0)
-    model = OSNet(
-        num_classes,
-        blocks=[OSBlock, OSBlock, OSBlock],
-        layers=[2, 2, 2],
-        channels=[64, 256, 384, 512],
-        loss=loss,
-        **kwargs
-    )
-    if pretrained:
-        init_pretrained_weights(model, key='osnet_x1_0')
-    return model
-
-
-def osnet_x0_75(num_classes=1000, pretrained=True, loss='softmax', **kwargs):
-    # medium size (width x0.75)
-    model = OSNet(
-        num_classes,
-        blocks=[OSBlock, OSBlock, OSBlock],
-        layers=[2, 2, 2],
-        channels=[48, 192, 288, 384],
-        loss=loss,
-        **kwargs
-    )
-    if pretrained:
-        init_pretrained_weights(model, key='osnet_x0_75')
-    return model
-
-
-def osnet_x0_5(num_classes=1000, pretrained=True, loss='softmax', **kwargs):
-    # tiny size (width x0.5)
-    model = OSNet(
-        num_classes,
-        blocks=[OSBlock, OSBlock, OSBlock],
-        layers=[2, 2, 2],
-        channels=[32, 128, 192, 256],
-        loss=loss,
-        **kwargs
-    )
-    if pretrained:
-        init_pretrained_weights(model, key='osnet_x0_5')
-    return model
-
-
-def osnet_x0_25(num_classes=1000, pretrained=True, loss='softmax', **kwargs):
-    # very tiny size (width x0.25)
-    model = OSNet(
-        num_classes,
-        blocks=[OSBlock, OSBlock, OSBlock],
-        layers=[2, 2, 2],
-        channels=[16, 64, 96, 128],
-        loss=loss,
-        **kwargs
-    )
-    if pretrained:
-        init_pretrained_weights(model, key='osnet_x0_25')
-    return model
-
-
-def osnet_ibn_x1_0(
-    num_classes=1000, pretrained=True, loss='softmax', **kwargs
-):
-    # standard size (width x1.0) + IBN layer
-    # Ref: Pan et al. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net. ECCV, 2018.
-    model = OSNet(
-        num_classes,
-        blocks=[OSBlock, OSBlock, OSBlock],
-        layers=[2, 2, 2],
-        channels=[64, 256, 384, 512],
-        loss=loss,
-        IN=True,
-        **kwargs
-    )
-    if pretrained:
-        init_pretrained_weights(model, key='osnet_ibn_x1_0')
-    return model
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/EX4-to-MQ4 v4.0.427.rar The Best Decompiler for MetaTrader 4 Build 509 and Earlier.md b/spaces/raedeXanto/academic-chatgpt-beta/EX4-to-MQ4 v4.0.427.rar The Best Decompiler for MetaTrader 4 Build 509 and Earlier.md
deleted file mode 100644
index 4f58cda39972e2a9c7198e39583f7ae0e841f44f..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/EX4-to-MQ4 v4.0.427.rar The Best Decompiler for MetaTrader 4 Build 509 and Earlier.md	
+++ /dev/null
@@ -1,115 +0,0 @@
-
-<h1>What is ex4-to-mq4 v4.0.427.rar and why do you need it?</h1>
- <p>If you are a Forex trader who uses MetaTrader as your trading platform, you may have encountered EX4 files that contain trading robots, technical indicators or scripts. These files are executable programs that run on MetaTrader and perform various trading tasks. However, sometimes you may want to access the source code of these files, which is usually written in the MQL language and saved as MQ4 files. This is where ex4-to-mq4 v4.0.427.rar comes in handy.</p>
-<h2>ex4-to-mq4 v4.0.427.rar</h2><br /><p><b><b>Download</b> &#8230;&#8230;&#8230; <a href="https://tinourl.com/2uL3JE">https://tinourl.com/2uL3JE</a></b></p><br /><br />
- <h2>What is ex4-to-mq4 v4.0.427.rar?</h2>
- <p>Ex4-to-mq4 v4.0.427.rar is a software tool that can decompile any EX4 file and save its source code as an MQ4 file. A decompiler is a reverse engineering tool that can recover the original code from an executable file.</p>
- <h3>What is an EX4 file?</h3>
- <p>An EX4 file is a compiled version of an MQ4 file that can be executed by MetaTrader. MetaTrader is a popular trading platform that allows traders to trade on various financial markets, such as Forex, stocks, commodities or cryptocurrencies. MetaTrader has a built-in programming language called MQL, which stands for MetaQuotes Language. MQL allows traders to create their own trading robots, technical indicators or scripts that can automate various trading tasks or provide useful information.</p>
- <p>An MQ4 file is a source code file that contains the MQL code of a trading robot, technical indicator or script. An MQ4 file can be edited and modified using MetaEditor, which is an integrated development environment (IDE) for MQL programming.</p>
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- <p>To run an MQ4 file on MetaTrader, it needs to be compiled into an EX4 file, which is a binary executable file that can be loaded and executed by MetaTrader. The compilation process converts the human-readable MQL code into machine-readable binary code that can be understood by MetaTrader.</p>
- <h3>What is a decompiler?</h3>
- <p>A decompiler is a software tool that can perform the opposite process of compilation, i.e., it can recover the source code from an executable file. A decompiler can be useful when the source code of a program is lost or unavailable, but the executable file is still functional.</p>
- <p>Ex4-to-mq4 v4.0.427.rar is a decompiler that can decompile any EX4 file and save its source code as an MQ4 file. The decompilation process is done automatically by the software, without requiring any user input.</p>
- <h2>Why do you need ex4-to-mq4 v4.0.427.rar?</h2>
- <p>There are several reasons why you may want to decompile an EX4 file and access its source code as an MQ4 file.</p>
- <h3>To understand the trading strategy of an EX4 file</h3>
- <p>If you have downloaded or purchased an EX4 file that contains a trading robot or a technical indicator, you may want to know how it works and what trading strategy it uses. By decompiling the EX4 file and viewing its source code as an MQ4 file, you can gain insight into the logic and algorithm behind the program.</p>
- <p>This can help you to evaluate the performance and reliability of the program, as well as to learn from its design and implementation.</p>
- <h3>To modify or improve an EX4 file</h3>
- <p>If you have an EX4 file that contains a trading robot or a technical indicator that you like, but you want to make some changes or improvements to it, you need to access its source code as an MQ4 file.</p>
- <p>By decompiling the EX4 file and editing its source code as an MQ4 file, you can customize and optimize the program according to your preferences and needs.</p>
- <p>For example, you can change some parameters or settings of the program, add some features or functions to it, fix some bugs or errors in it, or integrate it with other programs.</p>
- <h3>To protect your intellectual property</h3>
- <h2>How to use ex4-to-mq4 v4.0.427.rar?</h2>
- <p>Using ex4-to-mq4 v4.0.427.rar is very simple and straightforward. Here are the steps you need to follow:</p>
- <h3>Download and install ex4-to-mq4 v4.0.427.rar</h3>
- <p>You can download ex4-to-mq4 v4.0.427.rar from various online sources, such as <a href="https://www.4shared.com/rar/ARJm4av-ce/EX4_to_MQ4__v404511.html?locale=en">this one</a>. The file is a compressed archive that contains the executable file of the decompiler and some other files. You need to extract the archive to a folder on your computer using a software tool like WinRAR or 7-Zip.</p>
- <p>After extracting the archive, you need to run the executable file of the decompiler, which is called EX4-TO-MQ4.exe. You may need to grant permission to run the file if your antivirus or firewall software warns you about it.</p>
- <h3>Select an EX4 file to decompile</h3>
- <p>Once you run the decompiler, you will see a simple user interface that allows you to select an EX4 file to decompile. You can browse your computer folders and locate the EX4 file you want to decompile, or you can drag and drop the file into the decompiler window.</p>
- <p>The decompiler will automatically detect the EX4 file and display some information about it, such as its name, size, date and build number.</p>
- <h3>Save the MQ4 file and open it in MetaEditor</h3>
- <p>After selecting the EX4 file to decompile, you need to click on the Decompile button at the bottom of the decompiler window. The decompiler will start working and show you a progress bar that indicates how much of the decompilation process is completed.</p>
- <p>When the decompilation is finished, the decompiler will save the MQ4 file in the same folder as the EX4 file and show you a message that confirms the successful decompilation.</p>
- <p>You can then open the MQ4 file in MetaEditor and view its source code. You can also edit or modify the code as you wish.</p>
- <h2>What are the limitations of ex4-to-mq4 v4.0.427.rar?</h2>
- <p>While ex4-to-mq4 v4.0.427.rar is a useful and powerful tool that can decompile any EX4 file and save its source code as an MQ4 file, it also has some limitations that you need to be aware of.</p>
- <h3>It only works for EX4 files compiled by MetaTrader build no higher than 600</h3>
- <p>The most important limitation of ex4-to-mq4 v4.0.427.rar is that it only works for EX4 files that were compiled by MetaTrader build no higher than 600. This means that the EX4 file must have been created in 2013 or earlier.</p>
- <h3>It may not recover all the original source code features</h3>
- <p>Another limitation of ex4-to-mq4 v4.0.427.rar is that it may not recover all the original features of the source code, such as comments, variable names, formatting or indentation. This is because some of these features are lost or changed during the compilation process and cannot be restored by the decompiler.</p>
- <p>Therefore, the MQ4 file that you get from the decompiler may not look exactly like the original MQ4 file that was used to create the EX4 file. However, the decompiler will try to preserve the functionality and logic of the program as much as possible.</p>
- <h3>It may violate the developer's rights and terms of use</h3>
- <p>The final limitation of ex4-to-mq4 v4.0.427.rar is that it may violate the rights and terms of use of the developer who created the EX4 file. Some developers may not want their source code to be exposed or modified by others, and they may protect their EX4 files with encryption or obfuscation techniques.</p>
- <p>By decompiling their EX4 files and accessing their source code, you may be infringing their intellectual property rights and breaking their terms of use. This may result in legal consequences or ethical issues.</p>
- <p>Therefore, before using ex4-to-mq4 v4.0.427.rar to decompile an EX4 file, you should always check the developer's website or contact them to ask for their permission and consent.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, ex4-to-mq4 v4.0.427.rar is a software tool that can decompile any EX4 file and save its source code as an MQ4 file. This can be useful for various purposes, such as understanding the trading strategy of an EX4 file, modifying or improving an EX4 file, or protecting your intellectual property.</p>
- <p>However, ex4-to-mq4 v4.0.427.rar also has some limitations that you need to be aware of, such as working only for EX4 files compiled by MetaTrader build no higher than 600, not recovering all the original source code features, and possibly violating the developer's rights and terms of use.</p>
- <p>Therefore, before using ex4-to-mq4 v4.0.427.rar to decompile an EX4 file, you should always do your research and ask for permission from the developer.</p>
- <h2>FAQs</h2>
- <h3>What is the difference between EX4 and MQ4 files?</h3>
- <p>An EX4 file is a compiled version of an MQ4 file that can be executed by MetaTrader. An MQ4 file is a source code file that contains the MQL code of a trading robot, technical indicator or script.</p>
- <h3>What is ex4-to-mq4 v4.0.427.rar?</h3>
- <p>Ex4-to-mq4 v4.0.427.rar is a software tool that can decompile any EX4 file and save its source code as an MQ4 file.</p>
- <h3>Why do I need ex4-to-mq4 v4.0.427.rar?</h3>
- <h3>Why do I need ex4-to-mq4 v4.0.427.rar?</h3>
- <p>You may need ex4-to-mq4 v4.0.427.rar to understand the trading strategy of an EX4 file, to modify or improve an EX4 file, or to protect your intellectual property.</p>
- <h3>How do I use ex4-to-mq4 v4.0.427.rar?</h3>
- <p>You need to download and install ex4-to-mq4 v4.0.427.rar, select an EX4 file to decompile, and save the MQ4 file and open it in MetaEditor.</p>
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- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/ramkamal2000/voice-conversion-ddp/speaker_encoder/preprocess.py b/spaces/ramkamal2000/voice-conversion-ddp/speaker_encoder/preprocess.py
deleted file mode 100644
index fe5ab25ef7cb4adeb76cad11962f179d6a38edcc..0000000000000000000000000000000000000000
--- a/spaces/ramkamal2000/voice-conversion-ddp/speaker_encoder/preprocess.py
+++ /dev/null
@@ -1,285 +0,0 @@
-from multiprocess.pool import ThreadPool
-from speaker_encoder.params_data import *
-from speaker_encoder.config import librispeech_datasets, anglophone_nationalites
-from datetime import datetime
-from speaker_encoder import audio
-from pathlib import Path
-from tqdm import tqdm
-import numpy as np
-
-
-class DatasetLog:
-    """
-    Registers metadata about the dataset in a text file.
-    """
-    def __init__(self, root, name):
-        self.text_file = open(Path(root, "Log_%s.txt" % name.replace("/", "_")), "w")
-        self.sample_data = dict()
-        
-        start_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
-        self.write_line("Creating dataset %s on %s" % (name, start_time))
-        self.write_line("-----")
-        self._log_params()
-        
-    def _log_params(self):
-        from speaker_encoder import params_data
-        self.write_line("Parameter values:")
-        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
-            value = getattr(params_data, param_name)
-            self.write_line("\t%s: %s" % (param_name, value))
-        self.write_line("-----")
-    
-    def write_line(self, line):
-        self.text_file.write("%s\n" % line)
-        
-    def add_sample(self, **kwargs):
-        for param_name, value in kwargs.items():
-            if not param_name in self.sample_data:
-                self.sample_data[param_name] = []
-            self.sample_data[param_name].append(value)
-            
-    def finalize(self):
-        self.write_line("Statistics:")
-        for param_name, values in self.sample_data.items():
-            self.write_line("\t%s:" % param_name)
-            self.write_line("\t\tmin %.3f, max %.3f" % (np.min(values), np.max(values)))
-            self.write_line("\t\tmean %.3f, median %.3f" % (np.mean(values), np.median(values)))
-        self.write_line("-----")
-        end_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
-        self.write_line("Finished on %s" % end_time)
-        self.text_file.close()
-       
-        
-def _init_preprocess_dataset(dataset_name, datasets_root, out_dir) -> (Path, DatasetLog):
-    dataset_root = datasets_root.joinpath(dataset_name)
-    if not dataset_root.exists():
-        print("Couldn\'t find %s, skipping this dataset." % dataset_root)
-        return None, None
-    return dataset_root, DatasetLog(out_dir, dataset_name)
-
-
-def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, extension,
-                             skip_existing, logger):
-    print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs)))
-    
-    # Function to preprocess utterances for one speaker
-    def preprocess_speaker(speaker_dir: Path):
-        # Give a name to the speaker that includes its dataset
-        speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
-        
-        # Create an output directory with that name, as well as a txt file containing a 
-        # reference to each source file.
-        speaker_out_dir = out_dir.joinpath(speaker_name)
-        speaker_out_dir.mkdir(exist_ok=True)
-        sources_fpath = speaker_out_dir.joinpath("_sources.txt")
-        
-        # There's a possibility that the preprocessing was interrupted earlier, check if 
-        # there already is a sources file.
-        if sources_fpath.exists():
-            try:
-                with sources_fpath.open("r") as sources_file:
-                    existing_fnames = {line.split(",")[0] for line in sources_file}
-            except:
-                existing_fnames = {}
-        else:
-            existing_fnames = {}
-        
-        # Gather all audio files for that speaker recursively
-        sources_file = sources_fpath.open("a" if skip_existing else "w")
-        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
-            # Check if the target output file already exists
-            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
-            out_fname = out_fname.replace(".%s" % extension, ".npy")
-            if skip_existing and out_fname in existing_fnames:
-                continue
-                
-            # Load and preprocess the waveform
-            wav = audio.preprocess_wav(in_fpath)
-            if len(wav) == 0:
-                continue
-            
-            # Create the mel spectrogram, discard those that are too short
-            frames = audio.wav_to_mel_spectrogram(wav)
-            if len(frames) < partials_n_frames:
-                continue
-            
-            out_fpath = speaker_out_dir.joinpath(out_fname)
-            np.save(out_fpath, frames)
-            logger.add_sample(duration=len(wav) / sampling_rate)
-            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
-        
-        sources_file.close()
-    
-    # Process the utterances for each speaker
-    with ThreadPool(8) as pool:
-        list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs),
-                  unit="speakers"))
-    logger.finalize()
-    print("Done preprocessing %s.\n" % dataset_name)
-
-
-# Function to preprocess utterances for one speaker
-def __preprocess_speaker(speaker_dir: Path, datasets_root: Path, out_dir: Path, extension: str, skip_existing: bool):
-        # Give a name to the speaker that includes its dataset
-        speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
-        
-        # Create an output directory with that name, as well as a txt file containing a 
-        # reference to each source file.
-        speaker_out_dir = out_dir.joinpath(speaker_name)
-        speaker_out_dir.mkdir(exist_ok=True)
-        sources_fpath = speaker_out_dir.joinpath("_sources.txt")
-        
-        # There's a possibility that the preprocessing was interrupted earlier, check if 
-        # there already is a sources file.
-        # if sources_fpath.exists():
-        #     try:
-        #         with sources_fpath.open("r") as sources_file:
-        #             existing_fnames = {line.split(",")[0] for line in sources_file}
-        #     except:
-        #         existing_fnames = {}
-        # else:
-        #     existing_fnames = {}
-        existing_fnames = {}
-        # Gather all audio files for that speaker recursively
-        sources_file = sources_fpath.open("a" if skip_existing else "w")
-
-        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
-            # Check if the target output file already exists
-            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
-            out_fname = out_fname.replace(".%s" % extension, ".npy")
-            if skip_existing and out_fname in existing_fnames:
-                continue
-                
-            # Load and preprocess the waveform
-            wav = audio.preprocess_wav(in_fpath)
-            if len(wav) == 0:
-                continue
-            
-            # Create the mel spectrogram, discard those that are too short
-            frames = audio.wav_to_mel_spectrogram(wav)
-            if len(frames) < partials_n_frames:
-                continue
-            
-            out_fpath = speaker_out_dir.joinpath(out_fname)
-            np.save(out_fpath, frames)
-            # logger.add_sample(duration=len(wav) / sampling_rate)
-            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
-        
-        sources_file.close()
-        return len(wav)
-
-def _preprocess_speaker_dirs_vox2(speaker_dirs, dataset_name, datasets_root, out_dir, extension,
-                             skip_existing, logger):
-    # from multiprocessing import Pool, cpu_count
-    from pathos.multiprocessing import ProcessingPool as Pool
-    # Function to preprocess utterances for one speaker
-    def __preprocess_speaker(speaker_dir: Path):
-        # Give a name to the speaker that includes its dataset
-        speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
-        
-        # Create an output directory with that name, as well as a txt file containing a 
-        # reference to each source file.
-        speaker_out_dir = out_dir.joinpath(speaker_name)
-        speaker_out_dir.mkdir(exist_ok=True)
-        sources_fpath = speaker_out_dir.joinpath("_sources.txt")
-        
-        existing_fnames = {}
-        # Gather all audio files for that speaker recursively
-        sources_file = sources_fpath.open("a" if skip_existing else "w")
-        wav_lens = []
-        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
-            # Check if the target output file already exists
-            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
-            out_fname = out_fname.replace(".%s" % extension, ".npy")
-            if skip_existing and out_fname in existing_fnames:
-                continue
-                
-            # Load and preprocess the waveform
-            wav = audio.preprocess_wav(in_fpath)
-            if len(wav) == 0:
-                continue
-            
-            # Create the mel spectrogram, discard those that are too short
-            frames = audio.wav_to_mel_spectrogram(wav)
-            if len(frames) < partials_n_frames:
-                continue
-            
-            out_fpath = speaker_out_dir.joinpath(out_fname)
-            np.save(out_fpath, frames)
-            # logger.add_sample(duration=len(wav) / sampling_rate)
-            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
-            wav_lens.append(len(wav))
-        sources_file.close()
-        return wav_lens
-
-    print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs)))
-    # Process the utterances for each speaker
-    # with ThreadPool(8) as pool:
-    #     list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs),
-    #               unit="speakers"))
-    pool = Pool(processes=20)
-    for i, wav_lens in enumerate(pool.map(__preprocess_speaker, speaker_dirs), 1):
-        for wav_len in wav_lens:
-            logger.add_sample(duration=wav_len / sampling_rate)
-        print(f'{i}/{len(speaker_dirs)} \r')
-
-    logger.finalize()
-    print("Done preprocessing %s.\n" % dataset_name)
-
-
-def preprocess_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False):
-    for dataset_name in librispeech_datasets["train"]["other"]:
-        # Initialize the preprocessing
-        dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-        if not dataset_root:
-            return 
-        
-        # Preprocess all speakers
-        speaker_dirs = list(dataset_root.glob("*"))
-        _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "flac",
-                                 skip_existing, logger)
-
-
-def preprocess_voxceleb1(datasets_root: Path, out_dir: Path, skip_existing=False):
-    # Initialize the preprocessing
-    dataset_name = "VoxCeleb1"
-    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-    if not dataset_root:
-        return
-
-    # Get the contents of the meta file
-    with dataset_root.joinpath("vox1_meta.csv").open("r") as metafile:
-        metadata = [line.split("\t") for line in metafile][1:]
-    
-    # Select the ID and the nationality, filter out non-anglophone speakers
-    nationalities = {line[0]: line[3] for line in metadata}
-    # keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if 
-    #                     nationality.lower() in anglophone_nationalites]
-    keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items()]                        
-    print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." % 
-          (len(keep_speaker_ids), len(nationalities)))
-    
-    # Get the speaker directories for anglophone speakers only
-    speaker_dirs = dataset_root.joinpath("wav").glob("*")
-    speaker_dirs = [speaker_dir for speaker_dir in speaker_dirs if
-                    speaker_dir.name in keep_speaker_ids]
-    print("VoxCeleb1: found %d anglophone speakers on the disk, %d missing (this is normal)." % 
-          (len(speaker_dirs), len(keep_speaker_ids) - len(speaker_dirs)))
-
-    # Preprocess all speakers
-    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "wav",
-                             skip_existing, logger)
-
-
-def preprocess_voxceleb2(datasets_root: Path, out_dir: Path, skip_existing=False):
-    # Initialize the preprocessing
-    dataset_name = "VoxCeleb2"
-    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-    if not dataset_root:
-        return
-    
-    # Get the speaker directories
-    # Preprocess all speakers
-    speaker_dirs = list(dataset_root.joinpath("dev", "aac").glob("*"))
-    _preprocess_speaker_dirs_vox2(speaker_dirs, dataset_name, datasets_root, out_dir, "m4a",
-                             skip_existing, logger)
diff --git a/spaces/raphaelmerx/MMS-transcription/README.md b/spaces/raphaelmerx/MMS-transcription/README.md
deleted file mode 100644
index 63910f84e4a8e8206ef6a037bf91e3db6534cd90..0000000000000000000000000000000000000000
--- a/spaces/raphaelmerx/MMS-transcription/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Transcription using MMS
-emoji: 🎤
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: true
-duplicated_from: ayymen/MMS-ASR
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Adobe Audition CC 2018 11.0.2.2 (x64) Crack Keygen [UPD].md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Adobe Audition CC 2018 11.0.2.2 (x64) Crack Keygen [UPD].md
deleted file mode 100644
index 26e4ca42184176234d229798cf130d2b7c6edbfa..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Adobe Audition CC 2018 11.0.2.2 (x64) Crack Keygen [UPD].md	
+++ /dev/null
@@ -1,148 +0,0 @@
-<br />
-<h1>How to Download and Use Adobe Audition CC 2018 11.0.2.2 (x64) Crack keygen</h1>
-
-<p>If you are looking for a professional audio editing software that can handle multitrack, waveform, and spectral display, you might want to check out Adobe Audition CC 2018 11.0.2.2 (x64). This powerful audio workstation is designed to accelerate video production workflows and audio finishing, and deliver a polished mix with pristine sound.</p>
-
-<p>However, Adobe Audition CC 2018 11.0.2.2 (x64) is not a free software, and you need to pay a monthly subscription fee to use it. If you want to save some money and still enjoy the features of this software, you can try using a crack keygen that can generate a valid serial number for you.</p>
-<h2>Adobe Audition CC 2018 11.0.2.2 (x64) Crack keygen</h2><br /><p><b><b>Download</b> &bull;&bull;&bull; <a href="https://urlgoal.com/2uCMpR">https://urlgoal.com/2uCMpR</a></b></p><br /><br />
-
-<p>In this article, we will show you how to download and use Adobe Audition CC 2018 11.0.2.2 (x64) crack keygen, and what are the benefits and risks of doing so.</p>
-
-<h2>What is Adobe Audition CC 2018 11.0.2.2 (x64) Crack keygen?</h2>
-
-<p>A crack keygen is a software tool that can generate a serial number or a license key for another software, such as Adobe Audition CC 2018 11.0.2.2 (x64). By using a crack keygen, you can bypass the activation process of the original software and use it without paying for it.</p>
-
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-
-<h2>How to Download and Use Adobe Audition CC 2018 11.0.2.2 (x64) Crack keygen?</h2>
-
-<p>Here are the steps to download and use Adobe Audition CC 2018 11.0.2.2 (x64) crack keygen:</p>
-
-<ol>
-<li>Download the original software from the official website of Adobe or from a trusted third-party source.</li>
-<li>Install the software on your computer, but do not launch it yet.</li>
-<li>Download the crack keygen from a reliable website that has positive reviews and feedback from other users.</li>
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-</ol>
-
-<h2>What are the Benefits and Risks of Using Adobe Audition CC 2018 11.0.2.2 (x64) Crack keygen?</h2>
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-<p></p>
-
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-<h3>How to Use Adobe Audition CC 2018 11.0.2.2 (x64) for Audio Editing?</h3>
-
-<p>Now that you have downloaded and activated Adobe Audition CC 2018 11.0.2.2 (x64) using a crack keygen, you may wonder how to use it for your audio editing projects.</p>
-
-<p>In this section, we will give you a brief overview of the main features and functions of Adobe Audition CC 2018 11.0.2.2 (x64) and how to use them.</p>
-
-<h4>Essential Sound Panel</h4>
-
-<p>The Essential Sound panel is a new feature in Adobe Audition CC 2018 that allows you to achieve professional-quality audio with simple and intuitive controls.</p>
-
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-
-<ol>
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-<li>Click on the Essential Sound tab in the right panel.</li>
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-<li>Adjust the sliders and options according to your needs and preferences.</li>
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-</ol>
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-<p>The Multitrack Editor is where you can record, edit, and mix multiple audio tracks in a non-destructive way.</p>
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-
-<ol>
-<li>Click on the File menu and choose New > Multitrack Session.</li>
-<li>Enter a name for your session and choose a location to save it.</li>
-<li>Select a template or customize your own settings for sample rate, bit depth, channelization, etc.</li>
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-<li>Mix your tracks by adjusting the volume and pan faders in the Mixer panel or in the track headers.</li>
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-<li>Export your mixdown by clicking on the File menu and choosing Export > Multitrack Mixdown > Entire Session or Selected Clips.</li>
-</ol>
-
-<h4>Waveform Editor</h4>
-
-<p>The Waveform Editor is where you can edit individual audio files in a destructive way.</p>
-
-<p>The Waveform Editor lets you view and modify your audio waveform with various tools and commands, such as cut, copy, paste, delete, silence, amplify, normalize, etc.</p>
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-<p>You can also use the Waveform Editor to apply effects and processes to your audio file, such as noise reduction, compression, equalization, pitch correction, etc.</p>
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-<ol>
-<li>Import your audio file by clicking on the File menu and choosing Open or Import File.</li>
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-<li>Add effects to your selection by clicking on the Effects menu and choosing from the available effects.</li>
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-</ol>
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-<li>Use keyboard shortcuts to speed up your workflow. You can view and customize keyboard shortcuts by clicking on the Edit menu and choosing Keyboard Shortcuts.</li>
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-<li>Use batch processing to apply effects or processes to multiple files at once. You can access batch processing by clicking on the File menu and choosing Batch Process. You can also create custom scripts for batch processing by clicking on the Effects menu and choosing Favorites > Edit Favorites.</li>
-<li>Use spectral frequency display to view and edit your audio in terms of frequency instead of amplitude. You can switch between waveform display and spectral frequency display by clicking on the View menu and choosing Waveform/Spectral Frequency Display. You can also use spectral editing tools such as Marquee Selection tool or Lasso Selection tool to select specific frequency ranges for editing.</li>
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diff --git a/spaces/reilnuud/polite/README.md b/spaces/reilnuud/polite/README.md
deleted file mode 100644
index 0309caba4a9171f77219f37ffa7b5124a96e46f5..0000000000000000000000000000000000000000
--- a/spaces/reilnuud/polite/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
-
----
-tags: [gradio-theme]
-title: polite
-colorFrom: orange
-colorTo: purple
-sdk: gradio
-sdk_version: 3.28.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-# polite
-## Description
-Add a description of this theme here!
-## Contributions
-Thanks to [@reilnuud](https://huggingface.co/reilnuud) for adding this gradio theme!
diff --git a/spaces/robin0307/MMOCR/configs/_base_/det_datasets/synthtext.py b/spaces/robin0307/MMOCR/configs/_base_/det_datasets/synthtext.py
deleted file mode 100644
index fb9a44b3422dae5a9788d39b0901335dfc6076a9..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/_base_/det_datasets/synthtext.py
+++ /dev/null
@@ -1,18 +0,0 @@
-dataset_type = 'TextDetDataset'
-data_root = 'data/synthtext'
-
-train = dict(
-    type=dataset_type,
-    ann_file=f'{data_root}/instances_training.lmdb',
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='lmdb',
-        parser=dict(
-            type='LineJsonParser',
-            keys=['file_name', 'height', 'width', 'annotations'])),
-    img_prefix=f'{data_root}/imgs',
-    pipeline=None)
-
-train_list = [train]
-test_list = [train]
diff --git a/spaces/rorallitri/biomedical-language-models/Outkast-ATLiens-Full-Album-Zip-LINK.md b/spaces/rorallitri/biomedical-language-models/Outkast-ATLiens-Full-Album-Zip-LINK.md
deleted file mode 100644
index 0e514be43451fdc38551958560abb09a9d4ddd98..0000000000000000000000000000000000000000
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diff --git a/spaces/rushic24/Priyanka-Chopra-TTS/synthesis/vocoders/vocoder.py b/spaces/rushic24/Priyanka-Chopra-TTS/synthesis/vocoders/vocoder.py
deleted file mode 100644
index 772d45e52d05c6321b6b6b76b74cfcdc523d08b0..0000000000000000000000000000000000000000
--- a/spaces/rushic24/Priyanka-Chopra-TTS/synthesis/vocoders/vocoder.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from abc import ABC, abstractmethod
-
-
-MAX_WAV_VALUE = 32768.0
-
-
-class Vocoder(ABC):
-    """
-    Produces audio data for tacotron2 mel spectrogram output
-    """
-
-    @abstractmethod
-    def generate_audio(self, mel_output):
-        """
-        Produces wav audio data for a given mel output.
-
-        Parameters
-        ----------
-        mel_output : Tensor
-            Mel spectrogram output
-
-        Returns
-        -------
-        np.array
-            Generated audio data
-        """
-        pass
diff --git a/spaces/ruslanmv/Text2Lip/utils/__init__.py b/spaces/ruslanmv/Text2Lip/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/safi842/FashionGen/models/stylegan2/stylegan2-pytorch/inception.py b/spaces/safi842/FashionGen/models/stylegan2/stylegan2-pytorch/inception.py
deleted file mode 100644
index f3afed8123e595f65c1333dea7151e653a836e2b..0000000000000000000000000000000000000000
--- a/spaces/safi842/FashionGen/models/stylegan2/stylegan2-pytorch/inception.py
+++ /dev/null
@@ -1,310 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision import models
-
-try:
-    from torchvision.models.utils import load_state_dict_from_url
-except ImportError:
-    from torch.utils.model_zoo import load_url as load_state_dict_from_url
-
-# Inception weights ported to Pytorch from
-# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
-FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'
-
-
-class InceptionV3(nn.Module):
-    """Pretrained InceptionV3 network returning feature maps"""
-
-    # Index of default block of inception to return,
-    # corresponds to output of final average pooling
-    DEFAULT_BLOCK_INDEX = 3
-
-    # Maps feature dimensionality to their output blocks indices
-    BLOCK_INDEX_BY_DIM = {
-        64: 0,   # First max pooling features
-        192: 1,  # Second max pooling featurs
-        768: 2,  # Pre-aux classifier features
-        2048: 3  # Final average pooling features
-    }
-
-    def __init__(self,
-                 output_blocks=[DEFAULT_BLOCK_INDEX],
-                 resize_input=True,
-                 normalize_input=True,
-                 requires_grad=False,
-                 use_fid_inception=True):
-        """Build pretrained InceptionV3
-
-        Parameters
-        ----------
-        output_blocks : list of int
-            Indices of blocks to return features of. Possible values are:
-                - 0: corresponds to output of first max pooling
-                - 1: corresponds to output of second max pooling
-                - 2: corresponds to output which is fed to aux classifier
-                - 3: corresponds to output of final average pooling
-        resize_input : bool
-            If true, bilinearly resizes input to width and height 299 before
-            feeding input to model. As the network without fully connected
-            layers is fully convolutional, it should be able to handle inputs
-            of arbitrary size, so resizing might not be strictly needed
-        normalize_input : bool
-            If true, scales the input from range (0, 1) to the range the
-            pretrained Inception network expects, namely (-1, 1)
-        requires_grad : bool
-            If true, parameters of the model require gradients. Possibly useful
-            for finetuning the network
-        use_fid_inception : bool
-            If true, uses the pretrained Inception model used in Tensorflow's
-            FID implementation. If false, uses the pretrained Inception model
-            available in torchvision. The FID Inception model has different
-            weights and a slightly different structure from torchvision's
-            Inception model. If you want to compute FID scores, you are
-            strongly advised to set this parameter to true to get comparable
-            results.
-        """
-        super(InceptionV3, self).__init__()
-
-        self.resize_input = resize_input
-        self.normalize_input = normalize_input
-        self.output_blocks = sorted(output_blocks)
-        self.last_needed_block = max(output_blocks)
-
-        assert self.last_needed_block <= 3, \
-            'Last possible output block index is 3'
-
-        self.blocks = nn.ModuleList()
-
-        if use_fid_inception:
-            inception = fid_inception_v3()
-        else:
-            inception = models.inception_v3(pretrained=True)
-
-        # Block 0: input to maxpool1
-        block0 = [
-            inception.Conv2d_1a_3x3,
-            inception.Conv2d_2a_3x3,
-            inception.Conv2d_2b_3x3,
-            nn.MaxPool2d(kernel_size=3, stride=2)
-        ]
-        self.blocks.append(nn.Sequential(*block0))
-
-        # Block 1: maxpool1 to maxpool2
-        if self.last_needed_block >= 1:
-            block1 = [
-                inception.Conv2d_3b_1x1,
-                inception.Conv2d_4a_3x3,
-                nn.MaxPool2d(kernel_size=3, stride=2)
-            ]
-            self.blocks.append(nn.Sequential(*block1))
-
-        # Block 2: maxpool2 to aux classifier
-        if self.last_needed_block >= 2:
-            block2 = [
-                inception.Mixed_5b,
-                inception.Mixed_5c,
-                inception.Mixed_5d,
-                inception.Mixed_6a,
-                inception.Mixed_6b,
-                inception.Mixed_6c,
-                inception.Mixed_6d,
-                inception.Mixed_6e,
-            ]
-            self.blocks.append(nn.Sequential(*block2))
-
-        # Block 3: aux classifier to final avgpool
-        if self.last_needed_block >= 3:
-            block3 = [
-                inception.Mixed_7a,
-                inception.Mixed_7b,
-                inception.Mixed_7c,
-                nn.AdaptiveAvgPool2d(output_size=(1, 1))
-            ]
-            self.blocks.append(nn.Sequential(*block3))
-
-        for param in self.parameters():
-            param.requires_grad = requires_grad
-
-    def forward(self, inp):
-        """Get Inception feature maps
-
-        Parameters
-        ----------
-        inp : torch.autograd.Variable
-            Input tensor of shape Bx3xHxW. Values are expected to be in
-            range (0, 1)
-
-        Returns
-        -------
-        List of torch.autograd.Variable, corresponding to the selected output
-        block, sorted ascending by index
-        """
-        outp = []
-        x = inp
-
-        if self.resize_input:
-            x = F.interpolate(x,
-                              size=(299, 299),
-                              mode='bilinear',
-                              align_corners=False)
-
-        if self.normalize_input:
-            x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)
-
-        for idx, block in enumerate(self.blocks):
-            x = block(x)
-            if idx in self.output_blocks:
-                outp.append(x)
-
-            if idx == self.last_needed_block:
-                break
-
-        return outp
-
-
-def fid_inception_v3():
-    """Build pretrained Inception model for FID computation
-
-    The Inception model for FID computation uses a different set of weights
-    and has a slightly different structure than torchvision's Inception.
-
-    This method first constructs torchvision's Inception and then patches the
-    necessary parts that are different in the FID Inception model.
-    """
-    inception = models.inception_v3(num_classes=1008,
-                                    aux_logits=False,
-                                    pretrained=False)
-    inception.Mixed_5b = FIDInceptionA(192, pool_features=32)
-    inception.Mixed_5c = FIDInceptionA(256, pool_features=64)
-    inception.Mixed_5d = FIDInceptionA(288, pool_features=64)
-    inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)
-    inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)
-    inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)
-    inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)
-    inception.Mixed_7b = FIDInceptionE_1(1280)
-    inception.Mixed_7c = FIDInceptionE_2(2048)
-
-    state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)
-    inception.load_state_dict(state_dict)
-    return inception
-
-
-class FIDInceptionA(models.inception.InceptionA):
-    """InceptionA block patched for FID computation"""
-    def __init__(self, in_channels, pool_features):
-        super(FIDInceptionA, self).__init__(in_channels, pool_features)
-
-    def forward(self, x):
-        branch1x1 = self.branch1x1(x)
-
-        branch5x5 = self.branch5x5_1(x)
-        branch5x5 = self.branch5x5_2(branch5x5)
-
-        branch3x3dbl = self.branch3x3dbl_1(x)
-        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
-        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
-
-        # Patch: Tensorflow's average pool does not use the padded zero's in
-        # its average calculation
-        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
-                                   count_include_pad=False)
-        branch_pool = self.branch_pool(branch_pool)
-
-        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
-        return torch.cat(outputs, 1)
-
-
-class FIDInceptionC(models.inception.InceptionC):
-    """InceptionC block patched for FID computation"""
-    def __init__(self, in_channels, channels_7x7):
-        super(FIDInceptionC, self).__init__(in_channels, channels_7x7)
-
-    def forward(self, x):
-        branch1x1 = self.branch1x1(x)
-
-        branch7x7 = self.branch7x7_1(x)
-        branch7x7 = self.branch7x7_2(branch7x7)
-        branch7x7 = self.branch7x7_3(branch7x7)
-
-        branch7x7dbl = self.branch7x7dbl_1(x)
-        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
-        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
-        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
-        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
-
-        # Patch: Tensorflow's average pool does not use the padded zero's in
-        # its average calculation
-        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
-                                   count_include_pad=False)
-        branch_pool = self.branch_pool(branch_pool)
-
-        outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
-        return torch.cat(outputs, 1)
-
-
-class FIDInceptionE_1(models.inception.InceptionE):
-    """First InceptionE block patched for FID computation"""
-    def __init__(self, in_channels):
-        super(FIDInceptionE_1, self).__init__(in_channels)
-
-    def forward(self, x):
-        branch1x1 = self.branch1x1(x)
-
-        branch3x3 = self.branch3x3_1(x)
-        branch3x3 = [
-            self.branch3x3_2a(branch3x3),
-            self.branch3x3_2b(branch3x3),
-        ]
-        branch3x3 = torch.cat(branch3x3, 1)
-
-        branch3x3dbl = self.branch3x3dbl_1(x)
-        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
-        branch3x3dbl = [
-            self.branch3x3dbl_3a(branch3x3dbl),
-            self.branch3x3dbl_3b(branch3x3dbl),
-        ]
-        branch3x3dbl = torch.cat(branch3x3dbl, 1)
-
-        # Patch: Tensorflow's average pool does not use the padded zero's in
-        # its average calculation
-        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
-                                   count_include_pad=False)
-        branch_pool = self.branch_pool(branch_pool)
-
-        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
-        return torch.cat(outputs, 1)
-
-
-class FIDInceptionE_2(models.inception.InceptionE):
-    """Second InceptionE block patched for FID computation"""
-    def __init__(self, in_channels):
-        super(FIDInceptionE_2, self).__init__(in_channels)
-
-    def forward(self, x):
-        branch1x1 = self.branch1x1(x)
-
-        branch3x3 = self.branch3x3_1(x)
-        branch3x3 = [
-            self.branch3x3_2a(branch3x3),
-            self.branch3x3_2b(branch3x3),
-        ]
-        branch3x3 = torch.cat(branch3x3, 1)
-
-        branch3x3dbl = self.branch3x3dbl_1(x)
-        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
-        branch3x3dbl = [
-            self.branch3x3dbl_3a(branch3x3dbl),
-            self.branch3x3dbl_3b(branch3x3dbl),
-        ]
-        branch3x3dbl = torch.cat(branch3x3dbl, 1)
-
-        # Patch: The FID Inception model uses max pooling instead of average
-        # pooling. This is likely an error in this specific Inception
-        # implementation, as other Inception models use average pooling here
-        # (which matches the description in the paper).
-        branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)
-        branch_pool = self.branch_pool(branch_pool)
-
-        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
-        return torch.cat(outputs, 1)
diff --git a/spaces/sam-hq-team/sam-hq/README.md b/spaces/sam-hq-team/sam-hq/README.md
deleted file mode 100644
index 0711456eca9796e64f39a600c8698291a4ba27b7..0000000000000000000000000000000000000000
--- a/spaces/sam-hq-team/sam-hq/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sam Hq
-emoji: 🏆
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sayakpaul/sidd-denoising-maxim/maxim/blocks/grid_gating.py b/spaces/sayakpaul/sidd-denoising-maxim/maxim/blocks/grid_gating.py
deleted file mode 100644
index 91980c874bd1175f1eb0be554f7be99b60cf86bd..0000000000000000000000000000000000000000
--- a/spaces/sayakpaul/sidd-denoising-maxim/maxim/blocks/grid_gating.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import tensorflow as tf
-from tensorflow.keras import backend as K
-from tensorflow.keras import layers
-
-from ..layers import BlockImages, SwapAxes, UnblockImages
-
-
-def GridGatingUnit(use_bias: bool = True, name: str = "grid_gating_unit"):
-    """A SpatialGatingUnit as defined in the gMLP paper.
-
-    The 'spatial' dim is defined as the second last.
-    If applied on other dims, you should swapaxes first.
-    """
-
-    def apply(x):
-        u, v = tf.split(x, 2, axis=-1)
-        v = layers.LayerNormalization(
-            epsilon=1e-06, name=f"{name}_intermediate_layernorm"
-        )(v)
-        n = K.int_shape(x)[-3]  # get spatial dim
-        v = SwapAxes()(v, -1, -3)
-        v = layers.Dense(n, use_bias=use_bias, name=f"{name}_Dense_0")(v)
-        v = SwapAxes()(v, -1, -3)
-        return u * (v + 1.0)
-
-    return apply
-
-
-def GridGmlpLayer(
-    grid_size,
-    use_bias: bool = True,
-    factor: int = 2,
-    dropout_rate: float = 0.0,
-    name: str = "grid_gmlp",
-):
-    """Grid gMLP layer that performs global mixing of tokens."""
-
-    def apply(x):
-        n, h, w, num_channels = (
-            K.int_shape(x)[0],
-            K.int_shape(x)[1],
-            K.int_shape(x)[2],
-            K.int_shape(x)[3],
-        )
-        gh, gw = grid_size
-        fh, fw = h // gh, w // gw
-
-        x = BlockImages()(x, patch_size=(fh, fw))
-        # gMLP1: Global (grid) mixing part, provides global grid communication.
-        y = layers.LayerNormalization(epsilon=1e-06, name=f"{name}_LayerNorm")(x)
-        y = layers.Dense(
-            num_channels * factor,
-            use_bias=use_bias,
-            name=f"{name}_in_project",
-        )(y)
-        y = tf.nn.gelu(y, approximate=True)
-        y = GridGatingUnit(use_bias=use_bias, name=f"{name}_GridGatingUnit")(y)
-        y = layers.Dense(
-            num_channels,
-            use_bias=use_bias,
-            name=f"{name}_out_project",
-        )(y)
-        y = layers.Dropout(dropout_rate)(y)
-        x = x + y
-        x = UnblockImages()(x, grid_size=(gh, gw), patch_size=(fh, fw))
-        return x
-
-    return apply
diff --git a/spaces/scedlatioru/img-to-music/example/Battle Slave Fantasia Special Edition.md b/spaces/scedlatioru/img-to-music/example/Battle Slave Fantasia Special Edition.md
deleted file mode 100644
index de9090ac918c776264ecca2f92e0916f281cc69a..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Battle Slave Fantasia Special Edition.md	
+++ /dev/null
@@ -1,12 +0,0 @@
-<h2>Battle Slave Fantasia Special Edition</h2><br /><p><b><b>Download Zip</b> &middot;&middot;&middot; <a href="https://gohhs.com/2uEz4t">https://gohhs.com/2uEz4t</a></b></p><br /><br />
-
-But apart from the music, there’s more to The Last Jedi than just one old man telling Rey about his past. Sure, he reveals the truth about his parents, but more importantly, he shares his philosophy and helps her comprehend her place in the galaxy. It’s a powerful, heart-wrenching scene, and the only one to go down well with fans and critics alike. So if the movie’s main message is about living a life of selflessness, why has Kylo Ren gone rogue? Let’s unpack what it means to be the ‘true believer’ in Star Wars.
-
-In The Last Jedi, fans finally see a return of Kylo Ren. There’s no way of not feeling that this is the guy who was barely in The Force Awakens, and therefore a little flat, compared to Rey and Finn. And yet, it’s the scene in The Last Jedi where Ren is at his most fanatical, calling himself a ‘true believer’, that struck a chord with some of us. Star Wars has always told the story of what happens to people who believe too much in something, but The Last Jedi takes that ideology to another level. As such, we’re able to see how someone like Ren can follow his own path, and not care about the rules and laws of the established order.
-
-We’ve long been asked, why would Ren follow the light side path, when he’s obviously a villainous villain? Well, it’s twofold. Firstly, he believes that there are no good or evil sides, only black and white. When he’s presented with a choice to turn to the light side, he naturally follows because there’s no reward. Also, when he says he’s the ‘real’ hero, he’s talking from the perspective of a villain. Kylo Ren wants to be seen as the ‘chosen one’, and that means he needs to become evil in order to make that idea true. It’s all an act of manipulation.
-
-Rey and Finn, on the other hand, have more of a middle-of-the-road approach to this situation. When Finn and Ren fight on the desert planet of Crait, Finn tries to explain to Ren that there are no good or bad sides – only the light and the dark. Ren rejects the idea, claiming 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/scedlatioru/img-to-music/example/Digital Workshop Opus Pro 8 Crack [Extra Quality].md b/spaces/scedlatioru/img-to-music/example/Digital Workshop Opus Pro 8 Crack [Extra Quality].md
deleted file mode 100644
index d01801e5f6336ade74ecad3000b94fd31f6c693f..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Digital Workshop Opus Pro 8 Crack [Extra Quality].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<br />
-<p>most of these lesions are treated by endovascular procedures and/or surgical revascularisation. in some cases, the extension of the arterial disease requires surgical bypass. these interventions are associated with a high risk of ischemia in distal parts of the hand. patients with multiple lesions may benefit from simultaneous revascularisation of digital arteries of both hands. the treatment of digital ischemia may be performed by a single- or dual-stage procedure, depending on the extent of the disease [ 20, 47 ]. in the case of a single stage, the spa is usually the first affected artery and often it is the only one that remains patent. the concomitant lesions are then treated. the residual artery is then ligated and the distal portion of the spa is revascularised. this may be performed in a single stage or in two stages. in the first stage, the distal portion of the spa is revascularised by endovascular techniques and the residual artery in the proximal portion is ligated. in the second stage, the endovascular techniques are used to revascularise the spa and digital arteries of the second and third spaces. in the case of a two-stage procedure, an additional graft is inserted before the second stage [ 46 ].</p>
-<p>the endovascular treatment of the digital arteries requires a multidisciplinary team. it is important to have a vascular surgeon involved because the case is usually complicated by the presence of arterial occlusions. the interventional radiologist should be familiar with the endovascular techniques used in the treatment of peripheral arterial occlusive disease. the contribution of a neurosurgeon is important because the neurovascular structures may be involved by arterial lesions and the ischemic lesions of the digital arteries may be associated with distal ulcers. a dermatologist can also be helpful because the ischemic lesions may be located in the nail folds. the use of a psychologist is essential to ensure patients compliance to treatment.</p>
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diff --git a/spaces/scedlatioru/img-to-music/example/HD Online Player (Video Seks Anak Dengan Ibu Kandung).md b/spaces/scedlatioru/img-to-music/example/HD Online Player (Video Seks Anak Dengan Ibu Kandung).md
deleted file mode 100644
index 70cf60c6a9aebe28842e29f9eaef1edd53078ddc..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/HD Online Player (Video Seks Anak Dengan Ibu Kandung).md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>HD Online Player (Video Seks Anak Dengan Ibu Kandung)</h2><br /><p><b><b>Download</b> &#10084;&#10084;&#10084; <a href="https://gohhs.com/2uEzIs">https://gohhs.com/2uEzIs</a></b></p><br /><br />
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diff --git a/spaces/sczhou/ProPainter/RAFT/utils/flow_viz.py b/spaces/sczhou/ProPainter/RAFT/utils/flow_viz.py
deleted file mode 100644
index dcee65e89b91b07ee0496aeb4c7e7436abf99641..0000000000000000000000000000000000000000
--- a/spaces/sczhou/ProPainter/RAFT/utils/flow_viz.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization
-
-
-# MIT License
-#
-# Copyright (c) 2018 Tom Runia
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to conditions.
-#
-# Author: Tom Runia
-# Date Created: 2018-08-03
-
-import numpy as np
-
-def make_colorwheel():
-    """
-    Generates a color wheel for optical flow visualization as presented in:
-        Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
-        URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf
-
-    Code follows the original C++ source code of Daniel Scharstein.
-    Code follows the the Matlab source code of Deqing Sun.
-
-    Returns:
-        np.ndarray: Color wheel
-    """
-
-    RY = 15
-    YG = 6
-    GC = 4
-    CB = 11
-    BM = 13
-    MR = 6
-
-    ncols = RY + YG + GC + CB + BM + MR
-    colorwheel = np.zeros((ncols, 3))
-    col = 0
-
-    # RY
-    colorwheel[0:RY, 0] = 255
-    colorwheel[0:RY, 1] = np.floor(255*np.arange(0,RY)/RY)
-    col = col+RY
-    # YG
-    colorwheel[col:col+YG, 0] = 255 - np.floor(255*np.arange(0,YG)/YG)
-    colorwheel[col:col+YG, 1] = 255
-    col = col+YG
-    # GC
-    colorwheel[col:col+GC, 1] = 255
-    colorwheel[col:col+GC, 2] = np.floor(255*np.arange(0,GC)/GC)
-    col = col+GC
-    # CB
-    colorwheel[col:col+CB, 1] = 255 - np.floor(255*np.arange(CB)/CB)
-    colorwheel[col:col+CB, 2] = 255
-    col = col+CB
-    # BM
-    colorwheel[col:col+BM, 2] = 255
-    colorwheel[col:col+BM, 0] = np.floor(255*np.arange(0,BM)/BM)
-    col = col+BM
-    # MR
-    colorwheel[col:col+MR, 2] = 255 - np.floor(255*np.arange(MR)/MR)
-    colorwheel[col:col+MR, 0] = 255
-    return colorwheel
-
-
-def flow_uv_to_colors(u, v, convert_to_bgr=False):
-    """
-    Applies the flow color wheel to (possibly clipped) flow components u and v.
-
-    According to the C++ source code of Daniel Scharstein
-    According to the Matlab source code of Deqing Sun
-
-    Args:
-        u (np.ndarray): Input horizontal flow of shape [H,W]
-        v (np.ndarray): Input vertical flow of shape [H,W]
-        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
-
-    Returns:
-        np.ndarray: Flow visualization image of shape [H,W,3]
-    """
-    flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8)
-    colorwheel = make_colorwheel()  # shape [55x3]
-    ncols = colorwheel.shape[0]
-    rad = np.sqrt(np.square(u) + np.square(v))
-    a = np.arctan2(-v, -u)/np.pi
-    fk = (a+1) / 2*(ncols-1)
-    k0 = np.floor(fk).astype(np.int32)
-    k1 = k0 + 1
-    k1[k1 == ncols] = 0
-    f = fk - k0
-    for i in range(colorwheel.shape[1]):
-        tmp = colorwheel[:,i]
-        col0 = tmp[k0] / 255.0
-        col1 = tmp[k1] / 255.0
-        col = (1-f)*col0 + f*col1
-        idx = (rad <= 1)
-        col[idx]  = 1 - rad[idx] * (1-col[idx])
-        col[~idx] = col[~idx] * 0.75   # out of range
-        # Note the 2-i => BGR instead of RGB
-        ch_idx = 2-i if convert_to_bgr else i
-        flow_image[:,:,ch_idx] = np.floor(255 * col)
-    return flow_image
-
-
-def flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False):
-    """
-    Expects a two dimensional flow image of shape.
-
-    Args:
-        flow_uv (np.ndarray): Flow UV image of shape [H,W,2]
-        clip_flow (float, optional): Clip maximum of flow values. Defaults to None.
-        convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False.
-
-    Returns:
-        np.ndarray: Flow visualization image of shape [H,W,3]
-    """
-    assert flow_uv.ndim == 3, 'input flow must have three dimensions'
-    assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]'
-    if clip_flow is not None:
-        flow_uv = np.clip(flow_uv, 0, clip_flow)
-    u = flow_uv[:,:,0]
-    v = flow_uv[:,:,1]
-    rad = np.sqrt(np.square(u) + np.square(v))
-    rad_max = np.max(rad)
-    epsilon = 1e-5
-    u = u / (rad_max + epsilon)
-    v = v / (rad_max + epsilon)
-    return flow_uv_to_colors(u, v, convert_to_bgr)
\ No newline at end of file
diff --git a/spaces/sdhsdhk/bingo111/src/components/chat-message.tsx b/spaces/sdhsdhk/bingo111/src/components/chat-message.tsx
deleted file mode 100644
index bf272d8d7005cfd06c53bd213e09ea217e803549..0000000000000000000000000000000000000000
--- a/spaces/sdhsdhk/bingo111/src/components/chat-message.tsx
+++ /dev/null
@@ -1,93 +0,0 @@
-import remarkGfm from 'remark-gfm'
-import remarkMath from 'remark-math'
-import supersub from 'remark-supersub'
-import remarkBreaks from 'remark-breaks'
-import { cn } from '@/lib/utils'
-import { CodeBlock } from '@/components/ui/codeblock'
-import { MemoizedReactMarkdown } from '@/components/markdown'
-import { LearnMore } from './learn-more'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { useEffect } from 'react'
-import { TurnCounter } from './turn-counter'
-
-export interface ChatMessageProps {
-  message: ChatMessageModel
-}
-
-export function ChatMessage({ message, ...props }: ChatMessageProps) {
-  useEffect(() => {
-    if (document.body.scrollHeight - window.innerHeight - window.scrollY - 200 < 0) {
-      window.scrollBy(0, 200)
-    }
-  }, [message.text])
-
-  return message.text ? (
-    <div
-      className={cn('text-message', message.author)}
-      {...props}
-    >
-      <div className="text-message-content">
-        <MemoizedReactMarkdown
-          linkTarget="_blank"
-          className="prose break-words dark:prose-invert prose-p:leading-relaxed prose-pre:p-0"
-          remarkPlugins={[remarkGfm, remarkMath, supersub, remarkBreaks]}
-          components={{
-            img(obj) {
-              try {
-                const uri = new URL(obj.src!)
-                const w = uri.searchParams.get('w')
-                const h = uri.searchParams.get('h')
-                if (w && h) {
-                  uri.searchParams.delete('w')
-                  uri.searchParams.delete('h')
-                  return <a style={{ float: 'left', maxWidth: '50%' }} href={uri.toString()} target="_blank" rel="noopener noreferrer"><img src={obj.src} alt={obj.alt} width={w!} height={h!}/></a>
-                }
-              } catch (e) {
-              }
-              return <img src={obj.src} alt={obj.alt} title={obj.title} />
-            },
-            p({ children }) {
-              return <p className="mb-2">{children}</p>
-            },
-            code({ node, inline, className, children, ...props }) {
-              if (children.length) {
-                if (children[0] == '▍') {
-                  return (
-                    <span className="mt-1 animate-pulse cursor-default">▍</span>
-                  )
-                }
-
-                children[0] = (children[0] as string).replace('`▍`', '▍')
-              }
-
-              const match = /language-(\w+)/.exec(className || '')
-
-              if (inline) {
-                return (
-                  <code className={className} {...props}>
-                    {children}
-                  </code>
-                )
-              }
-
-              return (
-                <CodeBlock
-                  key={Math.random()}
-                  language={(match && match[1]) || ''}
-                  value={String(children).replace(/\n$/, '')}
-                  {...props}
-                />
-              )
-            }
-          }}
-        >
-          {message.text}
-        </MemoizedReactMarkdown>
-      </div>
-      <div className="text-message-footer">
-        {message.author === 'bot' && <LearnMore sourceAttributions={message.sourceAttributions} />}
-        {message.author === 'bot' && <TurnCounter throttling={message.throttling} />}
-      </div>
-    </div>
-  ) : null
-}
diff --git a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/envfilescheck.bat b/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/envfilescheck.bat
deleted file mode 100644
index 645ae0f4d20449a2b7394939cf9e32865eaf7fb1..0000000000000000000000000000000000000000
--- a/spaces/shenfangqi/Retrieval-based-Voice-Conversion-WebUI/envfilescheck.bat
+++ /dev/null
@@ -1,230 +0,0 @@
-@echo off && chcp 65001
-
-echo working dir is %cd%
-echo downloading requirement aria2 check.
-echo=
-dir /a:d/b | findstr "aria2" > flag.txt
-findstr "aria2" flag.txt >nul
-if %errorlevel% ==0 (
-    echo aria2 checked.
-    echo=
-) else (
-    echo failed. please downloading aria2 from webpage!
-    echo unzip it and put in this directory!
-    timeout /T 5
-    start https://github.com/aria2/aria2/releases/tag/release-1.36.0
-    echo=
-    goto end
-)
-
-echo envfiles checking start.
-echo=
-
-for /f %%x in ('findstr /i /c:"aria2" "flag.txt"') do (set aria2=%%x)&goto endSch
-:endSch
-
-set d32=f0D32k.pth
-set d40=f0D40k.pth
-set d48=f0D48k.pth
-set g32=f0G32k.pth
-set g40=f0G40k.pth
-set g48=f0G48k.pth
-
-set dld32=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth
-set dld40=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth
-set dld48=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth
-set dlg32=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth
-set dlg40=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth
-set dlg48=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth
-
-set hp2=HP2-人声vocals+非人声instrumentals.pth
-set hp5=HP5-主旋律人声vocals+其他instrumentals.pth
-
-set dlhp2=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth
-set dlhp5=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth
-
-set hb=hubert_base.pt
-
-set dlhb=https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt
-
-echo dir check start.
-echo=
-
-if exist "%~dp0pretrained" (
-        echo dir .\pretrained checked.
-    ) else (
-        echo failed. generating dir .\pretrained.
-        mkdir pretrained
-    )
-if exist "%~dp0uvr5_weights" (
-        echo dir .\uvr5_weights checked.
-    ) else (
-        echo failed. generating dir .\uvr5_weights.
-        mkdir uvr5_weights
-    )
-
-echo=
-echo dir check finished.
-
-echo=
-echo required files check start.
-
-echo checking D32k.pth
-if exist "%~dp0pretrained\D32k.pth" (
-        echo D32k.pth in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d %~dp0pretrained -o D32k.pth
-        if exist "%~dp0pretrained\D32k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking D40k.pth
-if exist "%~dp0pretrained\D40k.pth" (
-        echo D40k.pth in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d %~dp0pretrained -o D40k.pth
-        if exist "%~dp0pretrained\D40k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-    echo checking D48k.pth
-if exist "%~dp0pretrained\D48k.pth" (
-        echo D48k.pth in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d %~dp0pretrained -o D48k.pth
-        if exist "%~dp0pretrained\D48k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-    echo checking G32k.pth
-if exist "%~dp0pretrained\G32k.pth" (
-        echo G32k.pth in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d %~dp0pretrained -o G32k.pth
-        if exist "%~dp0pretrained\G32k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-    echo checking G40k.pth
-if exist "%~dp0pretrained\G40k.pth" (
-        echo G40k.pth in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d %~dp0pretrained -o G40k.pth
-        if exist "%~dp0pretrained\G40k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-    echo checking G48k.pth
-if exist "%~dp0pretrained\G48k.pth" (
-        echo G48k.pth in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d %~dp0pretrained -o G48k.pth
-        if exist "%~dp0pretrained\G48k.pth" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-
-echo checking %d32%
-if exist "%~dp0pretrained\%d32%" (
-        echo %d32% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld32% -d %~dp0pretrained -o %d32%
-        if exist "%~dp0pretrained\%d32%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %d40%
-if exist "%~dp0pretrained\%d40%" (
-        echo %d40% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld40% -d %~dp0pretrained -o %d40%
-        if exist "%~dp0pretrained\%d40%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %d48%
-if exist "%~dp0pretrained\%d48%" (
-        echo %d48% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dld48% -d %~dp0pretrained -o %d48%
-        if exist "%~dp0pretrained\%d48%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %g32%
-if exist "%~dp0pretrained\%g32%" (
-        echo %g32% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg32% -d %~dp0pretrained -o %g32%
-        if exist "%~dp0pretrained\%g32%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %g40%
-if exist "%~dp0pretrained\%g40%" (
-        echo %g40% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg40% -d %~dp0pretrained -o %g40%
-        if exist "%~dp0pretrained\%g40%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %g48%
-if exist "%~dp0pretrained\%g48%" (
-        echo %g48% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlg48% -d %~dp0\pretrained -o %g48%
-        if exist "%~dp0pretrained\%g48%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-
-echo checking %hp2%
-if exist "%~dp0uvr5_weights\%hp2%" (
-        echo %hp2% in .\uvr5_weights checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhp2% -d %~dp0\uvr5_weights -o %hp2%
-        if exist "%~dp0uvr5_weights\%hp2%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-echo checking %hp5%
-if exist "%~dp0uvr5_weights\%hp5%" (
-        echo %hp5% in .\uvr5_weights checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhp5% -d %~dp0\uvr5_weights -o %HP5%
-        if exist "%~dp0uvr5_weights\%hp5%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-
-echo checking %hb%
-if exist "%~dp0%hb%" (
-        echo %hb% in .\pretrained checked.
-        echo=
-    ) else (
-        echo failed. starting download from huggingface.
-        %~dp0%aria2%\aria2c --console-log-level=error -c -x 16 -s 16 -k 1M %dlhb% -d %~dp0 -o %hb%
-        if exist "%~dp0%hb%" (echo download successful.) else (echo please try again!
-        echo=)
-    )
-
-echo required files check finished.
-echo envfiles check complete.
-pause
-:end
-del flag.txt
\ No newline at end of file
diff --git a/spaces/shiwan10000/CodeFormer/CodeFormer/facelib/detection/matlab_cp2tform.py b/spaces/shiwan10000/CodeFormer/CodeFormer/facelib/detection/matlab_cp2tform.py
deleted file mode 100644
index b2a8b54a91709c71437e15c68d3be9a9b0a20a34..0000000000000000000000000000000000000000
--- a/spaces/shiwan10000/CodeFormer/CodeFormer/facelib/detection/matlab_cp2tform.py
+++ /dev/null
@@ -1,317 +0,0 @@
-import numpy as np
-from numpy.linalg import inv, lstsq
-from numpy.linalg import matrix_rank as rank
-from numpy.linalg import norm
-
-
-class MatlabCp2tormException(Exception):
-
-    def __str__(self):
-        return 'In File {}:{}'.format(__file__, super.__str__(self))
-
-
-def tformfwd(trans, uv):
-    """
-    Function:
-    ----------
-        apply affine transform 'trans' to uv
-
-    Parameters:
-    ----------
-        @trans: 3x3 np.array
-            transform matrix
-        @uv: Kx2 np.array
-            each row is a pair of coordinates (x, y)
-
-    Returns:
-    ----------
-        @xy: Kx2 np.array
-            each row is a pair of transformed coordinates (x, y)
-    """
-    uv = np.hstack((uv, np.ones((uv.shape[0], 1))))
-    xy = np.dot(uv, trans)
-    xy = xy[:, 0:-1]
-    return xy
-
-
-def tforminv(trans, uv):
-    """
-    Function:
-    ----------
-        apply the inverse of affine transform 'trans' to uv
-
-    Parameters:
-    ----------
-        @trans: 3x3 np.array
-            transform matrix
-        @uv: Kx2 np.array
-            each row is a pair of coordinates (x, y)
-
-    Returns:
-    ----------
-        @xy: Kx2 np.array
-            each row is a pair of inverse-transformed coordinates (x, y)
-    """
-    Tinv = inv(trans)
-    xy = tformfwd(Tinv, uv)
-    return xy
-
-
-def findNonreflectiveSimilarity(uv, xy, options=None):
-    options = {'K': 2}
-
-    K = options['K']
-    M = xy.shape[0]
-    x = xy[:, 0].reshape((-1, 1))  # use reshape to keep a column vector
-    y = xy[:, 1].reshape((-1, 1))  # use reshape to keep a column vector
-
-    tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1))))
-    tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1))))
-    X = np.vstack((tmp1, tmp2))
-
-    u = uv[:, 0].reshape((-1, 1))  # use reshape to keep a column vector
-    v = uv[:, 1].reshape((-1, 1))  # use reshape to keep a column vector
-    U = np.vstack((u, v))
-
-    # We know that X * r = U
-    if rank(X) >= 2 * K:
-        r, _, _, _ = lstsq(X, U, rcond=-1)
-        r = np.squeeze(r)
-    else:
-        raise Exception('cp2tform:twoUniquePointsReq')
-    sc = r[0]
-    ss = r[1]
-    tx = r[2]
-    ty = r[3]
-
-    Tinv = np.array([[sc, -ss, 0], [ss, sc, 0], [tx, ty, 1]])
-    T = inv(Tinv)
-    T[:, 2] = np.array([0, 0, 1])
-
-    return T, Tinv
-
-
-def findSimilarity(uv, xy, options=None):
-    options = {'K': 2}
-
-    #    uv = np.array(uv)
-    #    xy = np.array(xy)
-
-    # Solve for trans1
-    trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options)
-
-    # Solve for trans2
-
-    # manually reflect the xy data across the Y-axis
-    xyR = xy
-    xyR[:, 0] = -1 * xyR[:, 0]
-
-    trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options)
-
-    # manually reflect the tform to undo the reflection done on xyR
-    TreflectY = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]])
-
-    trans2 = np.dot(trans2r, TreflectY)
-
-    # Figure out if trans1 or trans2 is better
-    xy1 = tformfwd(trans1, uv)
-    norm1 = norm(xy1 - xy)
-
-    xy2 = tformfwd(trans2, uv)
-    norm2 = norm(xy2 - xy)
-
-    if norm1 <= norm2:
-        return trans1, trans1_inv
-    else:
-        trans2_inv = inv(trans2)
-        return trans2, trans2_inv
-
-
-def get_similarity_transform(src_pts, dst_pts, reflective=True):
-    """
-    Function:
-    ----------
-        Find Similarity Transform Matrix 'trans':
-            u = src_pts[:, 0]
-            v = src_pts[:, 1]
-            x = dst_pts[:, 0]
-            y = dst_pts[:, 1]
-            [x, y, 1] = [u, v, 1] * trans
-
-    Parameters:
-    ----------
-        @src_pts: Kx2 np.array
-            source points, each row is a pair of coordinates (x, y)
-        @dst_pts: Kx2 np.array
-            destination points, each row is a pair of transformed
-            coordinates (x, y)
-        @reflective: True or False
-            if True:
-                use reflective similarity transform
-            else:
-                use non-reflective similarity transform
-
-    Returns:
-    ----------
-       @trans: 3x3 np.array
-            transform matrix from uv to xy
-        trans_inv: 3x3 np.array
-            inverse of trans, transform matrix from xy to uv
-    """
-
-    if reflective:
-        trans, trans_inv = findSimilarity(src_pts, dst_pts)
-    else:
-        trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts)
-
-    return trans, trans_inv
-
-
-def cvt_tform_mat_for_cv2(trans):
-    """
-    Function:
-    ----------
-        Convert Transform Matrix 'trans' into 'cv2_trans' which could be
-        directly used by cv2.warpAffine():
-            u = src_pts[:, 0]
-            v = src_pts[:, 1]
-            x = dst_pts[:, 0]
-            y = dst_pts[:, 1]
-            [x, y].T = cv_trans * [u, v, 1].T
-
-    Parameters:
-    ----------
-        @trans: 3x3 np.array
-            transform matrix from uv to xy
-
-    Returns:
-    ----------
-        @cv2_trans: 2x3 np.array
-            transform matrix from src_pts to dst_pts, could be directly used
-            for cv2.warpAffine()
-    """
-    cv2_trans = trans[:, 0:2].T
-
-    return cv2_trans
-
-
-def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True):
-    """
-    Function:
-    ----------
-        Find Similarity Transform Matrix 'cv2_trans' which could be
-        directly used by cv2.warpAffine():
-            u = src_pts[:, 0]
-            v = src_pts[:, 1]
-            x = dst_pts[:, 0]
-            y = dst_pts[:, 1]
-            [x, y].T = cv_trans * [u, v, 1].T
-
-    Parameters:
-    ----------
-        @src_pts: Kx2 np.array
-            source points, each row is a pair of coordinates (x, y)
-        @dst_pts: Kx2 np.array
-            destination points, each row is a pair of transformed
-            coordinates (x, y)
-        reflective: True or False
-            if True:
-                use reflective similarity transform
-            else:
-                use non-reflective similarity transform
-
-    Returns:
-    ----------
-        @cv2_trans: 2x3 np.array
-            transform matrix from src_pts to dst_pts, could be directly used
-            for cv2.warpAffine()
-    """
-    trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective)
-    cv2_trans = cvt_tform_mat_for_cv2(trans)
-
-    return cv2_trans
-
-
-if __name__ == '__main__':
-    """
-    u = [0, 6, -2]
-    v = [0, 3, 5]
-    x = [-1, 0, 4]
-    y = [-1, -10, 4]
-
-    # In Matlab, run:
-    #
-    #   uv = [u'; v'];
-    #   xy = [x'; y'];
-    #   tform_sim=cp2tform(uv,xy,'similarity');
-    #
-    #   trans = tform_sim.tdata.T
-    #   ans =
-    #       -0.0764   -1.6190         0
-    #        1.6190   -0.0764         0
-    #       -3.2156    0.0290    1.0000
-    #   trans_inv = tform_sim.tdata.Tinv
-    #    ans =
-    #
-    #       -0.0291    0.6163         0
-    #       -0.6163   -0.0291         0
-    #       -0.0756    1.9826    1.0000
-    #    xy_m=tformfwd(tform_sim, u,v)
-    #
-    #    xy_m =
-    #
-    #       -3.2156    0.0290
-    #        1.1833   -9.9143
-    #        5.0323    2.8853
-    #    uv_m=tforminv(tform_sim, x,y)
-    #
-    #    uv_m =
-    #
-    #        0.5698    1.3953
-    #        6.0872    2.2733
-    #       -2.6570    4.3314
-    """
-    u = [0, 6, -2]
-    v = [0, 3, 5]
-    x = [-1, 0, 4]
-    y = [-1, -10, 4]
-
-    uv = np.array((u, v)).T
-    xy = np.array((x, y)).T
-
-    print('\n--->uv:')
-    print(uv)
-    print('\n--->xy:')
-    print(xy)
-
-    trans, trans_inv = get_similarity_transform(uv, xy)
-
-    print('\n--->trans matrix:')
-    print(trans)
-
-    print('\n--->trans_inv matrix:')
-    print(trans_inv)
-
-    print('\n---> apply transform to uv')
-    print('\nxy_m = uv_augmented * trans')
-    uv_aug = np.hstack((uv, np.ones((uv.shape[0], 1))))
-    xy_m = np.dot(uv_aug, trans)
-    print(xy_m)
-
-    print('\nxy_m = tformfwd(trans, uv)')
-    xy_m = tformfwd(trans, uv)
-    print(xy_m)
-
-    print('\n---> apply inverse transform to xy')
-    print('\nuv_m = xy_augmented * trans_inv')
-    xy_aug = np.hstack((xy, np.ones((xy.shape[0], 1))))
-    uv_m = np.dot(xy_aug, trans_inv)
-    print(uv_m)
-
-    print('\nuv_m = tformfwd(trans_inv, xy)')
-    uv_m = tformfwd(trans_inv, xy)
-    print(uv_m)
-
-    uv_m = tforminv(trans, xy)
-    print('\nuv_m = tforminv(trans, xy)')
-    print(uv_m)
diff --git a/spaces/shubhamjaiswar/RakshakReet-SpamDetection/app.py b/spaces/shubhamjaiswar/RakshakReet-SpamDetection/app.py
deleted file mode 100644
index cd017e56e52fb95ff0f06eeb0b70a137a5e5829a..0000000000000000000000000000000000000000
--- a/spaces/shubhamjaiswar/RakshakReet-SpamDetection/app.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import gradio as gr
-import re
-import joblib
-p_spam = joblib.load("p_spam.pkl")
-p_non_spam = joblib.load("p_non_spam.pkl")
-parameters_spam = joblib.load("parameters_spam.pkl")
-parameters_non_spam = joblib.load("parameters_non_spam.pkl")
-def classify(message):
-    message = re.sub("\W", " ", message)
-    message = message.lower().split()
-    p_spam_given_message = p_spam
-    p_non_spam_given_message = p_non_spam
-    for word in message:
-        if word in parameters_spam:
-            p_spam_given_message *= parameters_spam[word]
-        if word in parameters_non_spam:
-            p_non_spam_given_message *= parameters_non_spam[word]
-    if p_spam_given_message > p_non_spam_given_message:
-        return "Spam"
-    elif p_non_spam_given_message > p_spam_given_message:
-        return "Non-Spam"
-    else:
-        return "Equal probabilities, human needed to classify this!"
-ir = gr.Interface(classify,inputs="text",outputs="text")
-ir.launch()
\ No newline at end of file
diff --git a/spaces/simpie28/VITS-Umamusume-voice-synthesizer/text/mandarin.py b/spaces/simpie28/VITS-Umamusume-voice-synthesizer/text/mandarin.py
deleted file mode 100644
index 093d8826809aa2681f6088174427337a59e0c882..0000000000000000000000000000000000000000
--- a/spaces/simpie28/VITS-Umamusume-voice-synthesizer/text/mandarin.py
+++ /dev/null
@@ -1,329 +0,0 @@
-import os
-import sys
-import re
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba
-import cn2an
-import logging
-
-logging.getLogger('jieba').setLevel(logging.WARNING)
-jieba.initialize()
-
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', 'ㄟˉ'),
-    ('b', 'ㄅㄧˋ'),
-    ('c', 'ㄙㄧˉ'),
-    ('d', 'ㄉㄧˋ'),
-    ('e', 'ㄧˋ'),
-    ('f', 'ㄝˊㄈㄨˋ'),
-    ('g', 'ㄐㄧˋ'),
-    ('h', 'ㄝˇㄑㄩˋ'),
-    ('i', 'ㄞˋ'),
-    ('j', 'ㄐㄟˋ'),
-    ('k', 'ㄎㄟˋ'),
-    ('l', 'ㄝˊㄛˋ'),
-    ('m', 'ㄝˊㄇㄨˋ'),
-    ('n', 'ㄣˉ'),
-    ('o', 'ㄡˉ'),
-    ('p', 'ㄆㄧˉ'),
-    ('q', 'ㄎㄧㄡˉ'),
-    ('r', 'ㄚˋ'),
-    ('s', 'ㄝˊㄙˋ'),
-    ('t', 'ㄊㄧˋ'),
-    ('u', 'ㄧㄡˉ'),
-    ('v', 'ㄨㄧˉ'),
-    ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
-    ('x', 'ㄝˉㄎㄨˋㄙˋ'),
-    ('y', 'ㄨㄞˋ'),
-    ('z', 'ㄗㄟˋ')
-]]
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄅㄛ', 'p⁼wo'),
-    ('ㄆㄛ', 'pʰwo'),
-    ('ㄇㄛ', 'mwo'),
-    ('ㄈㄛ', 'fwo'),
-    ('ㄅ', 'p⁼'),
-    ('ㄆ', 'pʰ'),
-    ('ㄇ', 'm'),
-    ('ㄈ', 'f'),
-    ('ㄉ', 't⁼'),
-    ('ㄊ', 'tʰ'),
-    ('ㄋ', 'n'),
-    ('ㄌ', 'l'),
-    ('ㄍ', 'k⁼'),
-    ('ㄎ', 'kʰ'),
-    ('ㄏ', 'h'),
-    ('ㄐ', 'ʧ⁼'),
-    ('ㄑ', 'ʧʰ'),
-    ('ㄒ', 'ʃ'),
-    ('ㄓ', 'ʦ`⁼'),
-    ('ㄔ', 'ʦ`ʰ'),
-    ('ㄕ', 's`'),
-    ('ㄖ', 'ɹ`'),
-    ('ㄗ', 'ʦ⁼'),
-    ('ㄘ', 'ʦʰ'),
-    ('ㄙ', 's'),
-    ('ㄚ', 'a'),
-    ('ㄛ', 'o'),
-    ('ㄜ', 'ə'),
-    ('ㄝ', 'e'),
-    ('ㄞ', 'ai'),
-    ('ㄟ', 'ei'),
-    ('ㄠ', 'au'),
-    ('ㄡ', 'ou'),
-    ('ㄧㄢ', 'yeNN'),
-    ('ㄢ', 'aNN'),
-    ('ㄧㄣ', 'iNN'),
-    ('ㄣ', 'əNN'),
-    ('ㄤ', 'aNg'),
-    ('ㄧㄥ', 'iNg'),
-    ('ㄨㄥ', 'uNg'),
-    ('ㄩㄥ', 'yuNg'),
-    ('ㄥ', 'əNg'),
-    ('ㄦ', 'əɻ'),
-    ('ㄧ', 'i'),
-    ('ㄨ', 'u'),
-    ('ㄩ', 'ɥ'),
-    ('ˉ', '→'),
-    ('ˊ', '↑'),
-    ('ˇ', '↓↑'),
-    ('ˋ', '↓'),
-    ('˙', ''),
-    ('，', ','),
-    ('。', '.'),
-    ('！', '!'),
-    ('？', '?'),
-    ('—', '-')
-]]
-
-# List of (romaji, ipa) pairs:
-_romaji_to_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('ʃy', 'ʃ'),
-    ('ʧʰy', 'ʧʰ'),
-    ('ʧ⁼y', 'ʧ⁼'),
-    ('NN', 'n'),
-    ('Ng', 'ŋ'),
-    ('y', 'j'),
-    ('h', 'x')
-]]
-
-# List of (bopomofo, ipa) pairs:
-_bopomofo_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄅㄛ', 'p⁼wo'),
-    ('ㄆㄛ', 'pʰwo'),
-    ('ㄇㄛ', 'mwo'),
-    ('ㄈㄛ', 'fwo'),
-    ('ㄅ', 'p⁼'),
-    ('ㄆ', 'pʰ'),
-    ('ㄇ', 'm'),
-    ('ㄈ', 'f'),
-    ('ㄉ', 't⁼'),
-    ('ㄊ', 'tʰ'),
-    ('ㄋ', 'n'),
-    ('ㄌ', 'l'),
-    ('ㄍ', 'k⁼'),
-    ('ㄎ', 'kʰ'),
-    ('ㄏ', 'x'),
-    ('ㄐ', 'tʃ⁼'),
-    ('ㄑ', 'tʃʰ'),
-    ('ㄒ', 'ʃ'),
-    ('ㄓ', 'ts`⁼'),
-    ('ㄔ', 'ts`ʰ'),
-    ('ㄕ', 's`'),
-    ('ㄖ', 'ɹ`'),
-    ('ㄗ', 'ts⁼'),
-    ('ㄘ', 'tsʰ'),
-    ('ㄙ', 's'),
-    ('ㄚ', 'a'),
-    ('ㄛ', 'o'),
-    ('ㄜ', 'ə'),
-    ('ㄝ', 'ɛ'),
-    ('ㄞ', 'aɪ'),
-    ('ㄟ', 'eɪ'),
-    ('ㄠ', 'ɑʊ'),
-    ('ㄡ', 'oʊ'),
-    ('ㄧㄢ', 'jɛn'),
-    ('ㄩㄢ', 'ɥæn'),
-    ('ㄢ', 'an'),
-    ('ㄧㄣ', 'in'),
-    ('ㄩㄣ', 'ɥn'),
-    ('ㄣ', 'ən'),
-    ('ㄤ', 'ɑŋ'),
-    ('ㄧㄥ', 'iŋ'),
-    ('ㄨㄥ', 'ʊŋ'),
-    ('ㄩㄥ', 'jʊŋ'),
-    ('ㄥ', 'əŋ'),
-    ('ㄦ', 'əɻ'),
-    ('ㄧ', 'i'),
-    ('ㄨ', 'u'),
-    ('ㄩ', 'ɥ'),
-    ('ˉ', '→'),
-    ('ˊ', '↑'),
-    ('ˇ', '↓↑'),
-    ('ˋ', '↓'),
-    ('˙', ''),
-    ('，', ','),
-    ('。', '.'),
-    ('！', '!'),
-    ('？', '?'),
-    ('—', '-')
-]]
-
-# List of (bopomofo, ipa2) pairs:
-_bopomofo_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄅㄛ', 'pwo'),
-    ('ㄆㄛ', 'pʰwo'),
-    ('ㄇㄛ', 'mwo'),
-    ('ㄈㄛ', 'fwo'),
-    ('ㄅ', 'p'),
-    ('ㄆ', 'pʰ'),
-    ('ㄇ', 'm'),
-    ('ㄈ', 'f'),
-    ('ㄉ', 't'),
-    ('ㄊ', 'tʰ'),
-    ('ㄋ', 'n'),
-    ('ㄌ', 'l'),
-    ('ㄍ', 'k'),
-    ('ㄎ', 'kʰ'),
-    ('ㄏ', 'h'),
-    ('ㄐ', 'tɕ'),
-    ('ㄑ', 'tɕʰ'),
-    ('ㄒ', 'ɕ'),
-    ('ㄓ', 'tʂ'),
-    ('ㄔ', 'tʂʰ'),
-    ('ㄕ', 'ʂ'),
-    ('ㄖ', 'ɻ'),
-    ('ㄗ', 'ts'),
-    ('ㄘ', 'tsʰ'),
-    ('ㄙ', 's'),
-    ('ㄚ', 'a'),
-    ('ㄛ', 'o'),
-    ('ㄜ', 'ɤ'),
-    ('ㄝ', 'ɛ'),
-    ('ㄞ', 'aɪ'),
-    ('ㄟ', 'eɪ'),
-    ('ㄠ', 'ɑʊ'),
-    ('ㄡ', 'oʊ'),
-    ('ㄧㄢ', 'jɛn'),
-    ('ㄩㄢ', 'yæn'),
-    ('ㄢ', 'an'),
-    ('ㄧㄣ', 'in'),
-    ('ㄩㄣ', 'yn'),
-    ('ㄣ', 'ən'),
-    ('ㄤ', 'ɑŋ'),
-    ('ㄧㄥ', 'iŋ'),
-    ('ㄨㄥ', 'ʊŋ'),
-    ('ㄩㄥ', 'jʊŋ'),
-    ('ㄥ', 'ɤŋ'),
-    ('ㄦ', 'əɻ'),
-    ('ㄧ', 'i'),
-    ('ㄨ', 'u'),
-    ('ㄩ', 'y'),
-    ('ˉ', '˥'),
-    ('ˊ', '˧˥'),
-    ('ˇ', '˨˩˦'),
-    ('ˋ', '˥˩'),
-    ('˙', ''),
-    ('，', ','),
-    ('。', '.'),
-    ('！', '!'),
-    ('？', '?'),
-    ('—', '-')
-]]
-
-
-def number_to_chinese(text):
-    numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-    for number in numbers:
-        text = text.replace(number, cn2an.an2cn(number), 1)
-    return text
-
-
-def chinese_to_bopomofo(text):
-    text = text.replace('、', '，').replace('；', '，').replace('：', '，')
-    words = jieba.lcut(text, cut_all=False)
-    text = ''
-    for word in words:
-        bopomofos = lazy_pinyin(word, BOPOMOFO)
-        if not re.search('[\u4e00-\u9fff]', word):
-            text += word
-            continue
-        for i in range(len(bopomofos)):
-            bopomofos[i] = re.sub(r'([\u3105-\u3129])$', r'\1ˉ', bopomofos[i])
-        if text != '':
-            text += ' '
-        text += ''.join(bopomofos)
-    return text
-
-
-def latin_to_bopomofo(text):
-    for regex, replacement in _latin_to_bopomofo:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def bopomofo_to_romaji(text):
-    for regex, replacement in _bopomofo_to_romaji:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def bopomofo_to_ipa(text):
-    for regex, replacement in _bopomofo_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def bopomofo_to_ipa2(text):
-    for regex, replacement in _bopomofo_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def chinese_to_romaji(text):
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = bopomofo_to_romaji(text)
-    text = re.sub('i([aoe])', r'y\1', text)
-    text = re.sub('u([aoəe])', r'w\1', text)
-    text = re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
-                  r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
-    text = re.sub('([ʦs][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
-    return text
-
-
-def chinese_to_lazy_ipa(text):
-    text = chinese_to_romaji(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def chinese_to_ipa(text):
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = bopomofo_to_ipa(text)
-    text = re.sub('i([aoe])', r'j\1', text)
-    text = re.sub('u([aoəe])', r'w\1', text)
-    text = re.sub('([sɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
-                  r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
-    text = re.sub('([s][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
-    return text
-
-
-def chinese_to_ipa2(text):
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = bopomofo_to_ipa2(text)
-    text = re.sub(r'i([aoe])', r'j\1', text)
-    text = re.sub(r'u([aoəe])', r'w\1', text)
-    text = re.sub(r'([ʂɹ]ʰ?)([˩˨˧˦˥ ]+|$)', r'\1ʅ\2', text)
-    text = re.sub(r'(sʰ?)([˩˨˧˦˥ ]+|$)', r'\1ɿ\2', text)
-    return text
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Bad 2 Bad Extinction Mod Apk - The Ultimate RPG with All Characters Unlocked.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Bad 2 Bad Extinction Mod Apk - The Ultimate RPG with All Characters Unlocked.md
deleted file mode 100644
index e0dde88b71f6025cbbda96f8da2a1dd07bb5e39b..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Bad 2 Bad Extinction Mod Apk - The Ultimate RPG with All Characters Unlocked.md	
+++ /dev/null
@@ -1,80 +0,0 @@
-<br />
-<h1>Bad 2 Bad Extinction Mod APK Unlock All Characters</h1>
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- <h2>What is Bad 2 Bad Extinction?</h2>
-<p>Bad 2 Bad Extinction is a sequel to the popular game Bad 2 Bad: Delta, which was released in 2018. It is a side-scrolling action game that features animal heroes who fight against zombies, mutants, and other enemies in a post-apocalyptic world. The game has a unique style and humor that makes it stand out from other games in the genre.</p>
- <h3>A thrilling action game with animal heroes</h3>
-<p>The game lets you choose from different animal characters, such as bears, wolves, pandas, tigers, and more. Each character has its own skills and abilities that you can use in combat. You can also customize your character with various outfits, accessories, and weapons. The game has a simple control system that allows you to move, shoot, aim, reload, and use items with ease.</p>
- <h3>A post-apocalyptic world with zombies and mutants</h3>
-<p>The game takes place in a world that has been devastated by a virus that turns humans into zombies and animals into mutants. You have to fight your way through different locations, such as cities, deserts, forests, and underground bases. You will encounter different types of enemies, such as zombies, mutants, bandits, robots, and bosses. You will also have to complete various missions, such as rescuing survivors, collecting resources, destroying enemy bases, and more.</p>
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- <h3>A variety of missions, weapons, and customization options</h3>
-<p>The game offers a lot of content and features that will keep you entertained for hours. You can play the game in different modes, such as story mode, survival mode, raid mode, and online mode. You can also collect and upgrade different weapons, such as pistols, rifles, shotguns, snipers, rocket launchers, grenades, and more. You can also customize your character with different skins, hats, masks, glasses, backpacks, and more. The game has a lot of items and rewards that you can earn by playing the game or by watching ads.</p>
- <h2>What is Bad 2 Bad Extinction Mod APK?</h2>
-<p>Bad 2 Bad Extinction Mod APK is a modified version of the original game that gives you some extra benefits and features that are not available in the official version. These include:</p>
- <h3>A modified version of the original game</h3>
-<p>The modded version of the game is created by third-party developers who modify the original game files to add or remove some features. The modded version of the game is not authorized or endorsed by the official developers or publishers of the game. Therefore, you should download and install it at your own risk.</p>
- <h3>A way to get unlimited money and gems</h3>
-<p>The modded version of the game gives you unlimited money and gems that you can use to buy or upgrade anything in the game. You can also use the money and gems to unlock all the characters and skins that are otherwise locked or require real money to purchase. This way, you can enjoy the game without any limitations or restrictions.</p>
- <h3>A way to unlock all characters and skins</h3>
-<p>The modded version of the game also gives you access to all the characters and skins that are available in the game. You can choose from different animal heroes, such as bears, wolves, pandas, tigers, and more. You can also customize your character with different outfits, accessories, and weapons. You can change your character's appearance anytime you want and create your own unique style.</p>
- <h2>How to download and install Bad 2 Bad Extinction Mod APK?</h2>
-<p>If you want to download and install the Bad 2 Bad Extinction Mod APK, you will need to follow some simple steps. Here is how you can do it:</p>
- <h3>The steps to follow</h3>
-<p>First, you will need to find a reliable source that offers the modded version of the game. You can search online for websites or blogs that provide the download link for the Bad 2 Bad Extinction Mod APK. Make sure that the source is trustworthy and safe, as some sources may contain viruses or malware that can harm your device. You can also check the reviews and ratings of the source before downloading the file.</p>
-<p>Second, you will need to download the file to your device. The file size may vary depending on the source, but it should not be too large. You will need to have enough storage space on your device to save the file. You will also need to have a stable internet connection to download the file without any interruptions.</p>
-<p>Third, you will need to install the file on your device. To do this, you will need to enable the installation of apps from unknown sources on your device settings. This will allow you to install apps that are not from the official app store. You can find this option under security or privacy settings on your device. Once you enable this option, you can locate the downloaded file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to finish.</p>
- <h3>The permissions to allow</h3>
-<p>During the installation process, you may be asked to allow some permissions for the app to function properly. These permissions may include access to your device's storage, camera, microphone, location, contacts, and more. You can choose to allow or deny these permissions according to your preference. However, some permissions may be necessary for the app to work correctly, so make sure that you do not deny any essential permissions.</p>
- <h3>The benefits to enjoy</h3>
-<p>After installing the app, you can launch it and start playing the game. You will notice that you have unlimited money and gems in your account that you can use to buy or upgrade anything in the game. You will also notice that you have access to all the characters and skins that are available in the game. You can choose any character and skin that you like and customize your character with different outfits, accessories, and weapons. You can also play the game in different modes, such as story mode, survival mode, raid mode, and online mode. You can enjoy the game without any limitations or restrictions.</p>
- <h2>Conclusion</h2>
-<p>Bad 2 Bad Extinction is a fun and exciting action game that features animal heroes who fight against zombies, mutants, and other enemies in a post-apocalyptic world. The game has a lot of content and features that will keep you entertained for hours. However, if you want to make the game even more fun and exciting, you can download the Bad 2 Bad Extinction Mod APK and unlock all characters, skins, money, and gems. This way, you can enjoy the game without any limitations or restrictions.</p>
-<p>If you are interested in trying out this modded version of the game, you can follow the steps above to download and install it on your device. Make sure that you download it from a reliable source and allow the necessary permissions for it to work properly. Then, you can launch it and start playing it with unlimited money and gems and access to all characters and skins.</p>
-<p>We hope that this article has helped you learn more about Bad 2 Bad Extinction Mod APK Unlock All Characters. If you have any questions or feedback, feel free to leave a comment below.</p>
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-<p>Here are some frequently asked questions about Bad 2 Bad Extinction Mod APK Unlock All Characters:</p>
-<ul>
-<li><b>Is Bad 2 Bad Extinction Mod APK safe?</b></li>
-<p>Bad 2 Bad Extinction Mod APK is safe as long as you download it from a trustworthy source that does not contain any viruses or malware. However, since it is a modified version of the original game, it is not authorized or endorsed by the official developers or publishers of the game. Therefore, you should download and install it at your own risk and discretion.</p>
- <li><b>Does Bad 2 Bad Extinction Mod APK work on all devices?</b></li>
-<p>Bad 2 Bad Extinction Mod APK should work on most devices that support the original game. However, some devices may not be compatible with the modded version of the game due to different specifications or settings. Therefore, you should check the compatibility of your device before downloading and installing the modded version of the game.</p>
- <li><b>Can I play Bad 2 Bad Extinction Mod APK online with other players?</b></li>
-<p>Bad 2 Bad Extinction Mod APK allows you to play online with other players who have the same modded version of the game. However, you may not be able to play online with players who have the official version of the game, as they may have different features and updates. Therefore, you should be aware of this limitation before playing online with other players.</p>
- <li><b>Will I get banned for using Bad 2 Bad Extinction Mod APK?</b></li>
-<p>There is a possibility that you may get banned for using Bad 2 Bad Extinction Mod APK, as it is a modified version of the original game that gives you an unfair advantage over other players. The official developers or publishers of the game may detect your use of the modded version of the game and take action against you. Therefore, you should use the modded version of the game at your own risk and responsibility.</p>
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-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Call Recording APK for Android 12 The Ultimate Guide to Record Phone Calls.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Call Recording APK for Android 12 The Ultimate Guide to Record Phone Calls.md
deleted file mode 100644
index 84027a9a0444b420f47b0c38bf8053a2a607ba25..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Call Recording APK for Android 12 The Ultimate Guide to Record Phone Calls.md	
+++ /dev/null
@@ -1,165 +0,0 @@
-
-<h1>How to Choose and Install the Best Call Recording Apps for Android 12</h1>
- <p>Call recording is a feature that allows you to record your phone conversations and save them as audio files on your device. You can use call recording for various purposes, such as:</p>
-<h2>call recording apk for android 12</h2><br /><p><b><b>Download Zip</b> &#10040;&#10040;&#10040; <a href="https://ssurll.com/2uNYYQ">https://ssurll.com/2uNYYQ</a></b></p><br /><br />
-<ul>
-<li>Keeping records of important information or agreements</li>
-<li>Improving your customer service or sales skills</li>
-<li>Complying with legal regulations or industry standards</li>
-<li>Resolving disputes or complaints</li>
-<li>Capturing feedback or testimonials</li>
-</ul>
-<p>However, call recording on Android is not as simple as it sounds. Due to privacy concerns, Google has restricted the ability of third-party apps to access the microphone and phone calls on Android devices. This means that many call recording apps may not work properly on Android 12, or may require special permissions or settings to function.</p>
-<p>In this article, we will help you choose and install the best call recording apps for Android 12. We will compare the features, pros, and cons of five popular and reliable call recording apps that are compatible with Android 12. We will also explain how to install APK files from outside the Play Store, how to enable unknown sources and accessibility permissions for call recording apps, how to configure and customize the settings and options of each app, and how to access, manage, and share your recorded calls.</p>
- <h2>Best Call Recording Apps for Android 12</h2>
- <p>There are many call recording apps available for Android devices, but not all of them are compatible with Android 12. Some apps may only work with certain phone models or regions, while others may require root access or additional plug-ins. Some apps may also have limited features or functionality, such as not being able to record VoIP calls from apps like WhatsApp, Skype, or Facebook Messenger.</p>
-<p>To help you find the best call recording app for your needs, we have selected five popular and reliable call recording apps that work well on Android 12. We have compared their features, pros, and cons based on the following criteria:</p>
-<ul>
-<li>Compatibility: The app should be compatible with most Android devices running Android 12.</li>
-<li>Functionality: The app should be able to record both incoming and outgoing calls from any source, including VoIP calls from other apps.</li>
-<li>Quality: The app should be able to record calls in high-quality audio formats, such as MP3 or WAV.</li>
-<li>Usability: The app should be easy to use and configure, with a simple and intuitive interface.</li>
-<li>Privacy: The app should respect your privacy and security, by not collecting or sharing your personal data or recordings without your consent.</li>
-</ul>
-<p>Here are the five best call recording apps for Android 12 that we have chosen:</p>
- <h3>ACR Phone +</h3>
- <p><strong>ACR Phone +</strong> is a powerful call recorder app that offers a lot of features and customization options. <p>Some of the features, pros, and cons of ACR Phone + are:</p>
-<ul>
-<li>High-quality call recording: ACR Phone ensures crystal-clear call recording, capturing every detail of your conversations. You can choose between MP3 or WAV formats, and adjust the bitrate and sampling rate to your preference.</li>
-<li>Automatic call recording: With ACR Phone, you don't have to start recording each call manually. You can set up rules to record all calls, or only calls from specific contacts or numbers. You can also exclude contacts or numbers from being recorded.</li>
-<li>Easy-to-use interface: ACR Phone is designed with user-friendliness in mind. You can access your recordings from the app's main screen, or from the notification bar. You can also search, sort, filter, and rename your recordings easily.</li>
-<li>Organize and share recordings: The app allows you to organize your recorded calls efficiently. You can create folders, add notes, and mark important recordings as favorites. You can also share your recordings via email, cloud services, or social media.</li>
-<li>Privacy: The app respects your privacy and security, by not collecting or sharing your personal data or recordings without your consent. You can also protect your recordings with a PIN lock or biometric authentication.</li>
-<li>Pros: ACR Phone is a powerful and versatile call recorder app that offers a lot of features and customization options. It supports both cellular and VoIP calls, and works well on Android 12 devices. It also has a free version with some limitations.</li>
-<li>Cons: ACR Phone is not available on the Google Play Store, so you have to download it from the developer's website or other sources. This may pose some security risks or compatibility issues. The app also requires accessibility permissions to work properly.</li>
-</ul>
- <h3>Cube ACR</h3>
- <p><strong>Cube ACR</strong> is another popular call recorder app that works well on Android 12 devices. It can record both cellular and VoIP calls from various apps, such as WhatsApp, Skype, Viber, Telegram, and more.</p>
-<ul>
-<li>Functionality: Cube ACR can record both incoming and outgoing calls from any source, including VoIP calls from other apps. It supports automatic call recording based on rules, as well as manual call recording with a floating widget .</li>
-<li>Quality: Cube ACR can record calls in high-quality audio formats, such as MP3 or OGG. You can also adjust the audio source and quality settings to suit your needs .</li>
-<li>Usability: Cube ACR has a simple and intuitive interface that lets you access and manage your recordings easily. You can also search, sort, filter, and backup your recordings .</li>
-<li>Privacy: Cube ACR does not collect or share your personal data or recordings without your permission. You can also lock your recordings with a PIN code or fingerprint .</li>
-<li>Pros: Cube ACR is one of the few call recorder apps that can record VoIP calls from other apps on Android 12 devices. It has a lot of features and options to customize your recording experience. It also has a free version with some ads and limitations.</li>
-<li>Cons: Cube ACR may not work on some devices or regions due to carrier restrictions or compatibility issues. It also requires accessibility permissions and notification access to work properly .</li>
-</ul>
- <h3>CallU</h3>
- <p><strong>CallU</strong> is a simple and effective call recorder app that works on Android 12 devices. It can record both incoming and outgoing calls automatically or manually.</p>
-<ul>
-<li>Functionality: CallU can record both incoming and outgoing calls automatically or manually. You can choose to record all calls, or only calls from specific contacts or numbers. You can also exclude contacts or numbers from being recorded.</li>
-<li>Quality: CallU can record calls in high-quality audio formats, such as MP3 or WAV. You can also adjust the audio source and quality settings to suit your needs.</li>
-<li>Usability: CallU has a simple and easy-to-use interface that lets you access and manage your recordings easily. You can also search, sort, filter, rename, and backup your recordings.</li>
-<li>Privacy: CallU does not collect or share your personal data or recordings without your permission. You can also protect your recordings with a password or fingerprint.</li>
-<li>Pros: CallU is a simple and effective call recorder app that works well on Android 12 devices. It has a minimalistic design and a user-friendly interface. It also has a free version with some ads and limitations.</li>
-<li>Cons: CallU may not work on some devices or regions due to carrier restrictions or compatibility issues. It also requires accessibility permissions and notification access to work properly.</li>
-</ul>
- <h3>TrueCall</h3>
- <p><strong>TrueCall</strong> is a smart and reliable call recorder app that works on Android 12 devices. It can record both incoming and outgoing calls automatically or manually, and also identify unknown callers and block spam calls.</p>
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-<ul>
-<li>Functionality: TrueCall can record both incoming and outgoing calls automatically or manually. You can choose to record all calls, or only calls from specific contacts or numbers. You can also exclude contacts or numbers from being recorded.</li>
-<li>Quality: TrueCall can record calls in high-quality audio formats, such as MP3 or WAV. You can also adjust the audio source and quality settings to suit your needs.</li>
-<li>Usability: TrueCall has a smart and intuitive interface that lets you access and manage your recordings easily. You can also search, sort, filter, rename, and backup your recordings.</li>
-<li>Privacy: TrueCall does not collect or share your personal data or recordings without your permission. You can also protect your recordings with a password or fingerprint.</li>
-<li>Pros: TrueCall is a smart and reliable call recorder app that works well on Android 12 devices. It has a lot of features and options to customize your recording experience. It also has a caller ID feature that can identify unknown callers and block spam calls. It also has a free version with some ads and limitations.</li>
-<li>Cons: TrueCall may not work on some devices or regions due to carrier restrictions or compatibility issues. It also requires accessibility permissions and notification access to work properly.</li>
-</ul>
- <h3>Your stock phone dialer</h3>
- <p><strong>Your stock phone dialer</strong> is the default phone app that comes pre-installed on your Android device. Depending on your device model and manufacturer, it may have a built-in call recording feature that works on Android 12 devices.</p>
-<ul>
-<li>Functionality: Your stock phone dialer may have a call recording feature that allows you to record both incoming and outgoing calls manually. You may see a record button on the call screen, or you may have to tap the menu button to access the option .</li>
-<li>Quality: Your stock phone dialer may record calls in high-quality audio formats, such as MP3 or WAV. However, you may not be able to adjust the audio source and quality settings to suit your needs .</li>
-<li>Usability: Your stock phone dialer may have a simple and easy-to-use interface that lets you access and manage your recordings easily. However, you may not be able to search, sort, filter, rename, or backup your recordings .</li>
-<li>Privacy: Your stock phone dialer may respect your privacy and security, by not collecting or sharing your personal data or recordings without your consent. However, you may not be able to protect your recordings with a password or fingerprint .</li>
-<li>Pros: Your stock phone dialer is the easiest and most convenient way to record calls on Android 12 devices. You don't have to install any third-party apps or grant any permissions. You also don't have to worry about compatibility issues or carrier restrictions.</li>
-<li>Cons: Your stock phone dialer may not have a call recording feature at all, depending on your device model and manufacturer. Even if it does, it may have limited features and functionality, such as not being able to record VoIP calls from other apps, or not being able to customize your recording experience.</li>
-</ul>
- <h2>How to Install and Use Call Recording Apps on Android 12</h2>
- <p>If you decide to use one of the third-party call recording apps mentioned above, you will need to install them from outside the Google Play Store, as they are not available there due to Google's policies. This means that you will need to download APK files from the developer's website or other sources, and install them manually on your device.</p>
-<p>To install APK files from outside the Play Store, you will need to follow these steps:</p>
-<ol>
-<li>Download the APK file of the call recording app of your choice from the developer's website or other sources. Make sure that the source is trustworthy and secure, as some APK files may contain malware or viruses.</li> <li>Go to the Settings app on your device, and tap on Security or Privacy. Then, enable the option to allow installation of apps from unknown sources. This will allow you to install APK files that are not from the Play Store.</li>
-<li>Locate the APK file that you downloaded on your device, and tap on it to start the installation process. You may see a warning message that the app may harm your device, but you can ignore it if you trust the source.</li>
-<li>Follow the instructions on the screen to complete the installation process. You may have to grant some permissions or access to the app, such as phone, contacts, microphone, storage, etc.</li>
-<li>Once the installation is done, you can launch the app and start using it to record your calls.</li>
-</ol>
-<p>To use call recording apps on Android 12, you will need to follow these steps:</p>
-<ol>
-<li>Enable accessibility permissions for the call recording app. This is necessary for the app to access your phone calls and microphone. To do this, go to the Settings app on your device, and tap on Accessibility. Then, find the call recording app and turn on its accessibility service.</li>
-<li>Enable notification access for the call recording app. This is necessary for the app to detect and record incoming and outgoing calls. To do this, go to the Settings app on your device, and tap on Notifications. Then, find the call recording app and turn on its notification access.</li>
-<li>Configure and customize the settings and options of the call recording app. You can choose whether to record all calls or only specific ones, whether to record in MP3 or WAV format, whether to adjust the audio quality and source, whether to add notes or folders to your recordings, etc.</li>
-<li>Access, manage, and share your recorded calls. You can view your recorded calls from the app's main screen or from the notification bar. You can also search, sort, filter, rename, or delete your recordings. You can also share your recordings via email, cloud services, or social media.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>Call recording is a useful feature that can help you keep track of your conversations, improve your customer service, comply with legal regulations, and more. However, call recording on Android 12 is not as simple as it sounds. You need to choose a compatible and reliable call recording app that works well on Android 12 devices. You also need to install it from outside the Play Store, and enable some permissions and settings for it to work properly.</p>
-<p>In this article, we have provided you with an outline and a detailed guide on how to choose and install the best call recording apps for Android 12. We have compared the features, pros, and cons of five popular and reliable call recording apps that are compatible with Android 12: ACR Phone +, Cube ACR, CallU, TrueCall, and your stock phone dialer. We have also explained how to install APK files from outside the Play Store, how to enable unknown sources and accessibility permissions for call recording apps, how to configure and customize the settings and options of each app, and how to access, manage, and share your recorded calls.</p>
-<p>We hope that this article has helped you find the best call recording app for your needs. We invite you to try out these apps and share your feedback with us. Do you have any questions or suggestions about call recording apps for Android 12? Let us know in the comments below!</p>
- <h2>FAQs</h2>
- <p>Here are some common questions and answers about call recording apps for Android 12:</p>
- <h3>Is call recording legal?</h3>
- <p>The legality of call recording depends on various factors, such as your location, the purpose of recording, and whether you have consent from the other party or parties involved in the call. In general, you should always inform and obtain consent from the other party or parties before recording a call. You should also check your local laws and regulations before using any call recording app.</p>
- <h3>How can I record VoIP calls from other apps?</h3>
- <p>Some call recording apps can record VoIP calls from other apps, such as WhatsApp, Skype, Viber, Telegram, etc. However, not all call recording apps can do this, and some may require additional plug-ins or settings to enable this feature. Some of the call recording apps that can record VoIP calls from other apps are ACR Phone + and Cube ACR. You can check their websites or FAQs for more details on how to enable and use this feature.</p>
- <h3>How can I backup or restore my recorded calls?</h3>
- <p>Most call recording apps allow you to backup or restore your recorded calls to or from your device's internal storage, external SD card, or cloud services, such as Google Drive, Dropbox, OneDrive, etc. You can usually find this option in the app's settings or menu. You can also use a file manager app to copy or move your recorded calls to or from your desired location.</p>
- <h3>How can I delete or hide my recorded calls?</h3>
- <p>Most call recording apps allow you to delete or hide your recorded calls from the app's main screen or menu. You can usually select one or more recordings and tap on the delete or hide option. You can also use a file manager app to delete or hide your recorded calls from your device's storage. However, be careful when deleting or hiding your recorded calls, as you may not be able to recover them later.</p>
- <h3>How can I improve the quality of my recorded calls?</h3>
- <p>The quality of your recorded calls may depend on various factors, such as your device model, network signal, audio source, audio format, audio quality settings, etc. To improve the quality of your recorded calls, you can try the following tips:</p>
-<ul>
-<li>Use a good-quality microphone and headset for your calls.</li>
-<li>Avoid noisy environments and background noises when making or receiving calls.</li>
-<li>Choose a high-quality audio format and bitrate for your recordings, such as MP3 or WAV.</li>
-<li>Adjust the audio source and quality settings in your call recording app to suit your needs.</li>
-<li>Test different call recording apps and see which one works best for you.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simplyjaga/neural_style_tranfer_using_dense_net/app.py b/spaces/simplyjaga/neural_style_tranfer_using_dense_net/app.py
deleted file mode 100644
index 1136b9f2c22692c4b117fbdad0bc22a8e8eba20e..0000000000000000000000000000000000000000
--- a/spaces/simplyjaga/neural_style_tranfer_using_dense_net/app.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import model
-import gradio as gr
-
-#gui
-demo = gr.Blocks()
-with demo:
-    gr.Markdown(
-        """# Neural Style Transfer Using DenseNet
-        Since running this demo takes too much time without gpu, copy and try it out in the colab with gpu option for nearly 1000 steps to get an pretty decent output
-        """)
-    
-    with gr.Row():
-      with gr.Column():
-        input =[gr.Image(label='Style Image', type='pil'), 
-                gr.Image(label='Content Image', type='pil'),
-                gr.Slider(0, 1, value=1, label='Alpha (amout of info from content image)'),
-                gr.Slider(0, 1, value=0.02, label='Beta (amout of style from style image)'),
-                gr.Number(label='Step (no.of generation updates) - keep it below 20 because it takes too much time without gpu')]
-      with gr.Column():
-        output = gr.Image(label='Image after Style Transfer')
-        gr.Examples([['examples/style.jpg','examples/content.jpg']],
-                    inputs=input)
-
-    btn = gr.Button("Transfer Style")
-    btn.click(fn=model.get_output, inputs=input, outputs=output)
-
-demo.queue().launch()
\ No newline at end of file
diff --git a/spaces/skytnt/moe-tts/text/__init__.py b/spaces/skytnt/moe-tts/text/__init__.py
deleted file mode 100644
index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000
--- a/spaces/skytnt/moe-tts/text/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/songweig/rich-text-to-image/app_sd.py b/spaces/songweig/rich-text-to-image/app_sd.py
deleted file mode 100644
index 2da9eb6bae6d960dea39e6397294cd51651f91a9..0000000000000000000000000000000000000000
--- a/spaces/songweig/rich-text-to-image/app_sd.py
+++ /dev/null
@@ -1,557 +0,0 @@
-import math
-import random
-import os
-import json
-import time
-import argparse
-import torch
-import numpy as np
-from torchvision import transforms
-
-from models.region_diffusion import RegionDiffusion
-from utils.attention_utils import get_token_maps
-from utils.richtext_utils import seed_everything, parse_json, get_region_diffusion_input,\
-    get_attention_control_input, get_gradient_guidance_input
-
-
-import gradio as gr
-from PIL import Image, ImageOps
-from share_btn import community_icon_html, loading_icon_html, share_js, css
-
-
-help_text = """
-If you are encountering an error or not achieving your desired outcome, here are some potential reasons and recommendations to consider:
-1. If you format only a portion of a word rather than the complete word, an error may occur. 
-2. If you use font color and get completely corrupted results, you may consider decrease the color weight lambda.
-3. Consider using a different seed.
-"""
-
-
-canvas_html = """<iframe id='rich-text-root' style='width:100%' height='360px' src='file=rich-text-to-json-iframe.html' frameborder='0' scrolling='no'></iframe>"""
-get_js_data = """
-async (text_input, negative_prompt, height, width, seed, steps, num_segments, segment_threshold, inject_interval, guidance_weight, color_guidance_weight, rich_text_input, background_aug) => {
-  const richEl = document.getElementById("rich-text-root");
-  const data = richEl? richEl.contentDocument.body._data : {};
-  return [text_input, negative_prompt, height, width, seed, steps, num_segments, segment_threshold, inject_interval, guidance_weight, color_guidance_weight, JSON.stringify(data), background_aug];
-}
-"""
-set_js_data = """
-async (text_input) => {
-  const richEl = document.getElementById("rich-text-root");
-  const data = text_input ? JSON.parse(text_input) : null;
-  if (richEl && data) richEl.contentDocument.body.setQuillContents(data);
-}
-"""
-
-get_window_url_params = """
-async (url_params) => {
-    const params = new URLSearchParams(window.location.search);
-    url_params = Object.fromEntries(params);
-    return [url_params];
-}
-"""
-
-
-def load_url_params(url_params):
-    if 'prompt' in url_params:
-        return gr.update(visible=True), url_params
-    else:
-        return gr.update(visible=False), url_params
-
-
-def main():
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    model = RegionDiffusion(device)
-
-    def generate(
-        text_input: str,
-        negative_text: str,
-        height: int,
-        width: int,
-        seed: int,
-        steps: int,
-        num_segments: int,
-        segment_threshold: float,
-        inject_interval: float,
-        guidance_weight: float,
-        color_guidance_weight: float,
-        rich_text_input: str,
-        background_aug: bool,
-    ):
-        run_dir = 'results/'
-        os.makedirs(run_dir, exist_ok=True)
-        # Load region diffusion model.
-        height = int(height)
-        width = int(width)
-        steps = 41 if not steps else steps
-        guidance_weight = 8.5 if not guidance_weight else guidance_weight
-        text_input = rich_text_input if rich_text_input != '' else text_input
-        print('text_input', text_input)
-        if (text_input == '' or rich_text_input == ''):
-            raise gr.Error("Please enter some text.")
-        # parse json to span attributes
-        base_text_prompt, style_text_prompts, footnote_text_prompts, footnote_target_tokens,\
-            color_text_prompts, color_names, color_rgbs, size_text_prompts_and_sizes, use_grad_guidance = parse_json(
-                json.loads(text_input))
-
-        # create control input for region diffusion
-        region_text_prompts, region_target_token_ids, base_tokens = get_region_diffusion_input(
-            model, base_text_prompt, style_text_prompts, footnote_text_prompts,
-            footnote_target_tokens, color_text_prompts, color_names)
-
-        # create control input for cross attention
-        text_format_dict = get_attention_control_input(
-            model, base_tokens, size_text_prompts_and_sizes)
-
-        # create control input for region guidance
-        text_format_dict, color_target_token_ids = get_gradient_guidance_input(
-            model, base_tokens, color_text_prompts, color_rgbs, text_format_dict, color_guidance_weight=color_guidance_weight)
-
-        seed_everything(seed)
-
-        # get token maps from plain text to image generation.
-        begin_time = time.time()
-        if model.selfattn_maps is None and model.crossattn_maps is None:
-            model.remove_tokenmap_hooks()
-            model.register_tokenmap_hooks()
-        else:
-            model.reset_attention_maps()
-            model.remove_tokenmap_hooks()
-        plain_img = model.produce_attn_maps([base_text_prompt], [negative_text],
-                                            height=height, width=width, num_inference_steps=steps,
-                                            guidance_scale=guidance_weight)
-        print('time lapses to get attention maps: %.4f' %
-              (time.time()-begin_time))
-        seed_everything(seed)
-        color_obj_masks, segments_vis, token_maps = get_token_maps(model.selfattn_maps, model.crossattn_maps, model.n_maps, run_dir,
-                                                                   512//8, 512//8, color_target_token_ids[:-1], seed,
-                                                                   base_tokens, segment_threshold=segment_threshold, num_segments=num_segments,
-                                                                   return_vis=True)
-        seed_everything(seed)
-        model.masks, segments_vis, token_maps = get_token_maps(model.selfattn_maps, model.crossattn_maps, model.n_maps, run_dir,
-                                                               512//8, 512//8, region_target_token_ids[:-1], seed,
-                                                               base_tokens, segment_threshold=segment_threshold, num_segments=num_segments,
-                                                               return_vis=True)
-        color_obj_masks = [transforms.functional.resize(color_obj_mask, (height, width),
-                                                        interpolation=transforms.InterpolationMode.BICUBIC,
-                                                        antialias=True)
-                           for color_obj_mask in color_obj_masks]
-        text_format_dict['color_obj_atten'] = color_obj_masks
-        model.remove_tokenmap_hooks()
-
-        # generate image from rich text
-        begin_time = time.time()
-        seed_everything(seed)
-        if background_aug:
-            bg_aug_end = 500
-        else:
-            bg_aug_end = 1000
-        rich_img = model.prompt_to_img(region_text_prompts, [negative_text],
-                                       height=height, width=width, num_inference_steps=steps,
-                                       guidance_scale=guidance_weight, use_guidance=use_grad_guidance,
-                                       text_format_dict=text_format_dict, inject_selfattn=inject_interval,
-                                       bg_aug_end=bg_aug_end)
-        print('time lapses to generate image from rich text: %.4f' %
-              (time.time()-begin_time))
-        return [plain_img[0], rich_img[0], segments_vis, token_maps]
-
-    with gr.Blocks(css=css) as demo:
-        url_params = gr.JSON({}, visible=False, label="URL Params")
-        gr.HTML("""<h1 style="font-weight: 900; margin-bottom: 7px;">Expressive Text-to-Image Generation with Rich Text</h1>
-                   <p> <a href="https://songweige.github.io/">Songwei Ge</a>, <a href="https://taesung.me/">Taesung Park</a>, <a href="https://www.cs.cmu.edu/~junyanz/">Jun-Yan Zhu</a>, <a href="https://jbhuang0604.github.io/">Jia-Bin Huang</a> <p/> 
-                   <p> UMD, Adobe, CMU <p/> 
-                   <p> <a href="https://huggingface.co/spaces/songweig/rich-text-to-image?duplicate=true"><img src="https://bit.ly/3gLdBN6" style="display:inline;"alt="Duplicate Space"></a> | <a href="https://rich-text-to-image.github.io">[Website]</a> | <a href="https://github.com/SongweiGe/rich-text-to-image">[Code]</a> | <a href="https://arxiv.org/abs/2304.06720">[Paper]</a><p/>
-                   <p> For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.""")
-        with gr.Row():
-            with gr.Column():
-                rich_text_el = gr.HTML(canvas_html, elem_id="canvas_html")
-                rich_text_input = gr.Textbox(value="", visible=False)
-                text_input = gr.Textbox(
-                    label='Rich-text JSON Input',
-                    visible=False,
-                    max_lines=1,
-                    placeholder='Example: \'{"ops":[{"insert":"a Gothic "},{"attributes":{"color":"#b26b00"},"insert":"church"},{"insert":" in a the sunset with a beautiful landscape in the background.\n"}]}\'',
-                    elem_id="text_input"
-                )
-                negative_prompt = gr.Textbox(
-                    label='Negative Prompt',
-                    max_lines=1,
-                    placeholder='Example: poor quality, blurry, dark, low resolution, low quality, worst quality',
-                    elem_id="negative_prompt"
-                )
-                segment_threshold = gr.Slider(label='Token map threshold',
-                                              info='(See less area in token maps? Decrease this. See too much area? Increase this.)',
-                                              minimum=0,
-                                              maximum=1,
-                                              step=0.01,
-                                              value=0.25)
-                inject_interval = gr.Slider(label='Detail preservation',
-                                            info='(To preserve more structure from plain-text generation, increase this. To see more rich-text attributes, decrease this.)',
-                                            minimum=0,
-                                            maximum=1,
-                                            step=0.01,
-                                            value=0.)
-                color_guidance_weight = gr.Slider(label='Color weight',
-                                                  info='(To obtain more precise color, increase this, while too large value may cause artifacts.)',
-                                                  minimum=0,
-                                                  maximum=2,
-                                                  step=0.1,
-                                                  value=0.5)
-                num_segments = gr.Slider(label='Number of segments',
-                                         minimum=2,
-                                         maximum=20,
-                                         step=1,
-                                         value=9)
-                seed = gr.Slider(label='Seed',
-                                 minimum=0,
-                                 maximum=100000,
-                                 step=1,
-                                 value=6,
-                                 elem_id="seed"
-                                 )
-                background_aug = gr.Checkbox(
-                    label='Precise region alignment',
-                    info='(For strict region alignment, select this option, but beware of potential artifacts when using with style.)',
-                    value=True)
-                with gr.Accordion('Other Parameters', open=False):
-                    steps = gr.Slider(label='Number of Steps',
-                                      minimum=0,
-                                      maximum=500,
-                                      step=1,
-                                      value=41)
-                    guidance_weight = gr.Slider(label='CFG weight',
-                                                minimum=0,
-                                                maximum=50,
-                                                step=0.1,
-                                                value=8.5)
-                    width = gr.Dropdown(choices=[512],
-                                        value=512,
-                                        label='Width',
-                                        visible=True)
-                    height = gr.Dropdown(choices=[512],
-                                         value=512,
-                                         label='height',
-                                         visible=True)
-
-                with gr.Row():
-                    with gr.Column(scale=1, min_width=100):
-                        generate_button = gr.Button("Generate")
-                        load_params_button = gr.Button(
-                            "Load from URL Params", visible=True)
-            with gr.Column():
-                richtext_result = gr.Image(
-                    label='Rich-text', elem_id="rich-text-image")
-                richtext_result.style(height=512)
-                with gr.Row():
-                    plaintext_result = gr.Image(
-                        label='Plain-text', elem_id="plain-text-image")
-                    segments = gr.Image(label='Segmentation')
-                with gr.Row():
-                    token_map = gr.Image(label='Token Maps')
-                with gr.Row(visible=False) as share_row:
-                    with gr.Group(elem_id="share-btn-container"):
-                        community_icon = gr.HTML(community_icon_html)
-                        loading_icon = gr.HTML(loading_icon_html)
-                        share_button = gr.Button(
-                            "Share to community", elem_id="share-btn")
-                        share_button.click(None, [], [], _js=share_js)
-        with gr.Row():
-            gr.Markdown(help_text)
-
-        with gr.Row():
-            footnote_examples = [
-                [
-                    '{"ops":[{"insert":"A close-up 4k dslr photo of a "},{"attributes":{"link":"A cat wearing sunglasses and a bandana around its neck."},"insert":"cat"},{"insert":" riding a scooter. Palm trees in the background."}]}',
-                    '',
-                    5,
-                    0.3,
-                    0,
-                    6,
-                    1,
-                    None,
-                    True
-                ],
-                [
-                    '{"ops":[{"insert":"A "},{"attributes":{"link":"kitchen island with a stove with gas burners and a built-in oven "},"insert":"kitchen island"},{"insert":" next to a "},{"attributes":{"link":"an open refrigerator stocked with fresh produce, dairy products, and beverages. "},"insert":"refrigerator"},{"insert":", by James McDonald and Joarc Architects, home, interior, octane render, deviantart, cinematic, key art, hyperrealism, sun light, sunrays, canon eos c 300, ƒ 1.8, 35 mm, 8k, medium - format print"}]}',
-                    '',
-                    6,
-                    0.5,
-                    0,
-                    6,
-                    1,
-                    None,
-                    True
-                ],
-                [
-                    '{"ops":[{"insert":"A "},{"attributes":{"link":"Happy Kung fu panda art, elder, asian art, volumetric lighting, dramatic scene, ultra detailed, realism, chinese"},"insert":"panda"},{"insert":" standing on a cliff by a waterfall, wildlife photography, photograph, high quality, wildlife, f 1.8, soft focus, 8k, national geographic, award - winning photograph by nick nichols"}]}',
-                    '',
-                    4,
-                    0.3,
-                    0,
-                    4,
-                    1,
-                    None,
-                    True
-                ],
-            ]
-
-            gr.Examples(examples=footnote_examples,
-                        label='Footnote examples',
-                        inputs=[
-                            text_input,
-                            negative_prompt,
-                            num_segments,
-                            segment_threshold,
-                            inject_interval,
-                            seed,
-                            color_guidance_weight,
-                            rich_text_input,
-                            background_aug,
-                        ],
-                        outputs=[
-                            plaintext_result,
-                            richtext_result,
-                            segments,
-                            token_map,
-                        ],
-                        fn=generate,
-                        # cache_examples=True,
-                        examples_per_page=20)
-        with gr.Row():
-            color_examples = [
-                [
-                    '{"ops":[{"insert":"a beautifule girl with big eye, skin, and long "},{"attributes":{"color":"#00ffff"},"insert":"hair"},{"insert":", t-shirt, bursting with vivid color, intricate, elegant, highly detailed, photorealistic, digital painting,  artstation, illustration, concept art."}]}',
-                    'lowres, had anatomy, bad hands, cropped, worst quality',
-                    9,
-                    0.25,
-                    0.3,
-                    6,
-                    0.5,
-                    None,
-                    True
-                ],
-                [
-                    '{"ops":[{"insert":"a beautifule girl with big eye, skin, and long "},{"attributes":{"color":"#eeeeee"},"insert":"hair"},{"insert":", t-shirt, bursting with vivid color, intricate, elegant, highly detailed, photorealistic, digital painting,  artstation, illustration, concept art."}]}',
-                    'lowres, had anatomy, bad hands, cropped, worst quality',
-                    9,
-                    0.25,
-                    0.3,
-                    6,
-                    0.1,
-                    None,
-                    True
-                ],
-                [
-                    '{"ops":[{"insert":"a Gothic "},{"attributes":{"color":"#FD6C9E"},"insert":"church"},{"insert":" in a the sunset with a beautiful landscape in the background."}]}',
-                    '',
-                    5,
-                    0.3,
-                    0.5,
-                    6,
-                    0.5,
-                    None,
-                    False
-                ],
-                [
-                    '{"ops":[{"insert":"A mesmerizing sight that captures the beauty of a "},{"attributes":{"color":"#4775fc"},"insert":"rose"},{"insert":" blooming, close up"}]}',
-                    '',
-                    3,
-                    0.3,
-                    0,
-                    9,
-                    1,
-                    None,
-                    False
-                ],
-                [
-                    '{"ops":[{"insert":"A "},{"attributes":{"color":"#FFD700"},"insert":"marble statue of a wolf\'s head and shoulder"},{"insert":", surrounded by colorful flowers michelangelo, detailed, intricate, full of color, led lighting, trending on artstation, 4 k, hyperrealistic, 3 5 mm, focused, extreme details, unreal engine 5, masterpiece "}]}',
-                    '',
-                    5,
-                    0.3,
-                    0,
-                    5,
-                    0.6,
-                    None,
-                    False
-                ],
-            ]
-            gr.Examples(examples=color_examples,
-                        label='Font color examples',
-                        inputs=[
-                            text_input,
-                            negative_prompt,
-                            num_segments,
-                            segment_threshold,
-                            inject_interval,
-                            seed,
-                            color_guidance_weight,
-                            rich_text_input,
-                            background_aug,
-                        ],
-                        outputs=[
-                            plaintext_result,
-                            richtext_result,
-                            segments,
-                            token_map,
-                        ],
-                        fn=generate,
-                        # cache_examples=True,
-                        examples_per_page=20)
-
-        with gr.Row():
-            style_examples = [
-                [
-                    '{"ops":[{"insert":"a "},{"attributes":{"font":"mirza"},"insert":"beautiful garden"},{"insert":" with a "},{"attributes":{"font":"roboto"},"insert":"snow mountain in the background"},{"insert":""}]}',
-                    '',
-                    10,
-                    0.45,
-                    0,
-                    0.2,
-                    3,
-                    0.5,
-                    None,
-                    False
-                ],
-                [
-                    '{"ops":[{"attributes":{"link":"the awe-inspiring sky and ocean in the style of J.M.W. Turner"},"insert":"the awe-inspiring sky and sea"},{"insert":" by "},{"attributes":{"font":"mirza"},"insert":"a coast with flowers and grasses in spring"}]}',
-                    'worst quality, dark, poor quality',
-                    2,
-                    0.45,
-                    0,
-                    9,
-                    0.5,
-                    None,
-                    False
-                ],
-                [
-                    '{"ops":[{"insert":"a "},{"attributes":{"font":"slabo"},"insert":"night sky filled with stars"},{"insert":" above a "},{"attributes":{"font":"roboto"},"insert":"turbulent sea with giant waves"}]}',
-                    '',
-                    2,
-                    0.45,
-                    0,
-                    0,
-                    6,
-                    0.5,
-                    None,
-                    False
-                ],
-            ]
-            gr.Examples(examples=style_examples,
-                        label='Font style examples',
-                        inputs=[
-                            text_input,
-                            negative_prompt,
-                            num_segments,
-                            segment_threshold,
-                            inject_interval,
-                            seed,
-                            color_guidance_weight,
-                            rich_text_input,
-                            background_aug,
-                        ],
-                        outputs=[
-                            plaintext_result,
-                            richtext_result,
-                            segments,
-                            token_map,
-                        ],
-                        fn=generate,
-                        # cache_examples=True,
-                        examples_per_page=20)
-
-        with gr.Row():
-            size_examples = [
-                [
-                    '{"ops": [{"insert": "A pizza with "}, {"attributes": {"size": "60px"}, "insert": "pineapple"}, {"insert": ", pepperoni, and mushroom on the top, 4k, photorealistic"}]}',
-                    'blurry, art, painting, rendering, drawing, sketch, ugly, duplicate, morbid, mutilated, mutated, deformed, disfigured low quality, worst quality',
-                    5,
-                    0.3,
-                    0,
-                    13,
-                    1,
-                    None,
-                    False
-                ],
-                [
-                    '{"ops": [{"insert": "A pizza with pineapple, "}, {"attributes": {"size": "20px"}, "insert": "pepperoni"}, {"insert": ", and mushroom on the top, 4k, photorealistic"}]}',
-                    'blurry, art, painting, rendering, drawing, sketch, ugly, duplicate, morbid, mutilated, mutated, deformed, disfigured low quality, worst quality',
-                    5,
-                    0.3,
-                    0,
-                    13,
-                    1,
-                    None,
-                    False
-                ],
-                [
-                    '{"ops": [{"insert": "A pizza with pineapple, pepperoni, and "}, {"attributes": {"size": "70px"}, "insert": "mushroom"}, {"insert": " on the top, 4k, photorealistic"}]}',
-                    'blurry, art, painting, rendering, drawing, sketch, ugly, duplicate, morbid, mutilated, mutated, deformed, disfigured low quality, worst quality',
-                    5,
-                    0.3,
-                    0,
-                    13,
-                    1,
-                    None,
-                    False
-                ],
-            ]
-            gr.Examples(examples=size_examples,
-                        label='Font size examples',
-                        inputs=[
-                            text_input,
-                            negative_prompt,
-                            num_segments,
-                            segment_threshold,
-                            inject_interval,
-                            seed,
-                            color_guidance_weight,
-                            rich_text_input,
-                            background_aug,
-                        ],
-                        outputs=[
-                            plaintext_result,
-                            richtext_result,
-                            segments,
-                            token_map,
-                        ],
-                        fn=generate,
-                        # cache_examples=True,
-                        examples_per_page=20)
-        generate_button.click(fn=lambda: gr.update(visible=False), inputs=None, outputs=share_row, queue=False).then(
-            fn=generate,
-            inputs=[
-                text_input,
-                negative_prompt,
-                height,
-                width,
-                seed,
-                steps,
-                num_segments,
-                segment_threshold,
-                inject_interval,
-                guidance_weight,
-                color_guidance_weight,
-                rich_text_input,
-                background_aug
-            ],
-            outputs=[plaintext_result, richtext_result, segments, token_map],
-            _js=get_js_data
-        ).then(
-            fn=lambda: gr.update(visible=True), inputs=None, outputs=share_row, queue=False)
-        text_input.change(
-            fn=None, inputs=[text_input], outputs=None, _js=set_js_data, queue=False)
-        # load url param prompt to textinput
-        load_params_button.click(fn=lambda x: x['prompt'], inputs=[
-                                 url_params], outputs=[text_input], queue=False)
-        demo.load(
-            fn=load_url_params,
-            inputs=[url_params],
-            outputs=[load_params_button, url_params],
-            _js=get_window_url_params
-        )
-    demo.queue(concurrency_count=1)
-    demo.launch(share=False)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/truncated_bptt/transformer_xl_model.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/truncated_bptt/transformer_xl_model.py
deleted file mode 100644
index a6c8b25a07276c2ee30c0aa5f0e4b0a2837ed5ca..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/truncated_bptt/transformer_xl_model.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional
-
-import torch
-from fairseq.dataclass import FairseqDataclass
-from fairseq.models import (
-    FairseqIncrementalDecoder,
-    FairseqLanguageModel,
-    register_model,
-)
-from fairseq.modules.checkpoint_activations import checkpoint_wrapper
-from omegaconf import II
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class TransformerXLConfig(FairseqDataclass):
-    # defaults come from the original Transformer-XL code
-    cutoffs: List[int] = field(default_factory=lambda: [20000, 40000, 200000])
-    d_model: int = 500
-    n_head: int = 10
-    d_head: int = 50
-    d_inner: int = 1000
-    div_val: int = 1
-    n_layer: int = 12
-    mem_len: int = 0
-    clamp_len: int = -1
-    same_length: bool = False
-    dropout: float = 0.0
-    dropatt: float = 0.0
-    checkpoint_activations: bool = False
-    offload_activations: bool = False
-    max_target_positions: int = II("task.max_target_positions")
-
-
-@register_model("transformer_xl", dataclass=TransformerXLConfig)
-class TransformerXLLanguageModel(FairseqLanguageModel):
-    @classmethod
-    def build_model(cls, cfg: TransformerXLConfig, task):
-        return cls(TransformerXLDecoder(cfg, task))
-
-
-class TransformerXLDecoder(FairseqIncrementalDecoder):
-    def __init__(self, cfg, task):
-        try:
-            from transformers.models.transfo_xl import (
-                TransfoXLConfig,
-                TransfoXLLMHeadModel,
-            )
-        except ImportError:
-            from transformers.configuration_transfo_xl import TransfoXLConfig
-            from transformers.modeling_transfo_xl import TransfoXLLMHeadModel
-
-        super().__init__(task.target_dictionary)
-        self.cfg = cfg
-
-        # remove any cutoffs larger than the vocab size
-        cutoffs = [
-            cutoff for cutoff in cfg.cutoffs if cutoff < len(task.target_dictionary)
-        ]
-
-        config = TransfoXLConfig(
-            vocab_size=len(task.target_dictionary),
-            cutoffs=cutoffs,
-            d_model=cfg.d_model,
-            d_embed=cfg.d_model,
-            n_head=cfg.n_head,
-            d_head=cfg.d_head,
-            d_inner=cfg.d_inner,
-            div_val=cfg.div_val,
-            n_layer=cfg.n_layer,
-            mem_len=cfg.mem_len,
-            clamp_len=cfg.clamp_len,
-            same_length=cfg.same_length,
-            dropout=cfg.dropout,
-            dropatt=cfg.dropatt,
-        )
-        logger.info(config)
-        self.model = TransfoXLLMHeadModel(config)
-
-        # Workaround a bug in huggingface's ``ProjectedAdaptiveLogSoftmax``
-        # which adds ``None`` values to an ``nn.ParameterList``, which is not
-        # supported in PyTorch. Instead we can replace this with an
-        # ``nn.ModuleList``, which does support ``None`` values.
-        try:
-            if all(p is None for p in self.model.crit.out_projs._parameters.values()):
-                self.model.crit.out_projs = torch.nn.ModuleList(
-                    [None] * len(self.model.crit.out_projs._parameters)
-                )
-        except Exception:
-            pass
-
-        if cfg.checkpoint_activations or cfg.offload_activations:
-            for i in range(len(self.model.transformer.layers)):
-                self.model.transformer.layers[i] = checkpoint_wrapper(
-                    self.model.transformer.layers[i],
-                    offload_to_cpu=cfg.offload_activations,
-                )
-                # TODO: may save mem to wrap(layer.pos_ff.CoreNet[3])
-
-        self._mems = None
-
-    def forward(
-        self,
-        src_tokens,
-        src_lengths=None,  # unused
-        incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None,
-        encoder_out=None,
-    ):
-        if incremental_state is not None:  # used during inference
-            mems = self.get_incremental_state(incremental_state, "mems")
-            src_tokens = src_tokens[:, -1:]  # only keep the most recent token
-        else:
-            mems = self._mems
-
-        output = self.model(
-            input_ids=src_tokens,
-            mems=mems,
-            return_dict=False,
-        )
-
-        if len(output) >= 2:
-            if incremental_state is not None:
-                self.set_incremental_state(incremental_state, "mems", output[1])
-            else:
-                self._mems = output[1]
-
-        return (output[0],)
-
-    def max_positions(self):
-        return self.cfg.max_target_positions
-
-    def reorder_incremental_state(
-        self,
-        incremental_state: Dict[str, Dict[str, Optional[torch.Tensor]]],
-        new_order: torch.Tensor,
-    ):
-        """Reorder incremental state.
-
-        This will be called when the order of the input has changed from the
-        previous time step. A typical use case is beam search, where the input
-        order changes between time steps based on the selection of beams.
-        """
-        mems = self.get_incremental_state(incremental_state, "mems")
-        if mems is not None:
-            new_mems = [mems_i.index_select(1, new_order) for mems_i in mems]
-            self.set_incremental_state(incremental_state, "mems", new_mems)
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Fallout 4 Map Symbol Key.md b/spaces/stomexserde/gpt4-ui/Examples/Fallout 4 Map Symbol Key.md
deleted file mode 100644
index b914cf4e3e98da71bdcb080399a9424b816aa2db..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Fallout 4 Map Symbol Key.md	
+++ /dev/null
@@ -1,39 +0,0 @@
-<br />
-<h1>How to Understand and Customize the Fallout 4 Map Symbols</h1>
-<p>Fallout 4 is a vast open-world game that offers many hours of exploration and combat. One of the most useful features of the game is the map, which shows you the locations of various places, items, enemies, and quests. However, the map can also be confusing and overwhelming at first, especially if you don't know what the different symbols mean.</p>
-<p>In this article, we will explain the meaning of some of the most common map symbols in Fallout 4, and how you can customize them to suit your preferences. We will also provide some tips on how to use the map effectively and find what you are looking for.</p>
-<h2>Fallout 4 Map Symbol Key</h2><br /><p><b><b>DOWNLOAD</b> &#9675;&#9675;&#9675; <a href="https://urlgoal.com/2uI9sN">https://urlgoal.com/2uI9sN</a></b></p><br /><br />
-<h2>What do the map symbols mean in Fallout 4?</h2>
-<p>The map symbols in Fallout 4 are divided into three categories: collectibles, pickups, and locations. Each category has a different color and shape to help you distinguish them.</p>
-<ul>
-<li><b>Collectibles</b> are items that have a special value or function, such as bobbleheads, holotapes, perk magazines, and keys. They are marked with a yellow star on the map.</li>
-<li><b>Pickups</b> are items that can be looted or used, such as fusion cores, mini nukes, nuka-colas, power armors, and weapons. They are marked with a green circle on the map.</li>
-<li><b>Locations</b> are places that you can visit or explore, such as bunkers, caves, cities, factories, farms, settlements, vaults, and more. They are marked with a blue diamond on the map.</li>
-</ul>
-<p>Some locations have additional symbols inside them to indicate their type or status. For example:</p>
-<ul>
-<li>A skull means that the location is dangerous or hostile.</li>
-<li>A gear means that the location is part of a quest or objective.</li>
-<li>A flag means that the location is controlled by a faction or group.</li>
-<li>A house means that the location is a settlement that you can build or manage.</li>
-<li>A pip-boy means that the location is a fast travel point that you have discovered.</li>
-</ul>
-<h2>How to customize the map symbols in Fallout 4?</h2>
-<p>If you want to change the appearance or behavior of the map symbols in Fallout 4, you have a few options. You can either use mods or edit some files manually.</p>
-<p>One of the most popular mods for customizing the map symbols is <a href="https://www.nexusmods.com/fallout4/mods/11664/">Fallout 4 Map by xunilinuX</a>, which allows you to change the size, color, opacity, and visibility of the symbols. You can also filter them by category or type, and add custom icons for your own markers. The mod requires <a href="https://www.nexusmods.com/fallout4/mods/100/">Fallout 4 Script Extender (F4SE)</a> to work.</p>
-<p>If you prefer to edit some files manually, you can follow these steps:</p>
-<ol>
-<li>Go to your Fallout 4 installation folder (usually C:\Program Files (x86)\Steam\steamapps\common\Fallout 4).</li>
-<li>Open the Data folder and then open the Interface folder.</li>
-<li>Make a backup copy of the file named Map.swf.</li>
-<li>Open Map.swf with a program that can edit Flash files, such as <a href="https://www.free-decompiler.com/flash/">JPEXS Free Flash Decompiler</a>.</li>
-<li>Navigate to the Symbols folder and then to the subfolder of the symbol category that you want to edit (Collectibles, Pickups, or Locations).</li>
-<li>Select the symbol that you want to edit and change its properties as you wish. You can modify its shape, color, size, rotation, alpha (opacity), filters (glow, shadow), etc.</li>
-<li>Save your changes and close the program.</li>
-<li>Launch Fallout 4 and enjoy your customized map symbols.</li>
-</ol>
-<h2>How to use the map effectively in Fallout 4?</h2>
-<p>The map is</p>
-<p></p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/HACK LockXLS V4.6.0.md b/spaces/stomexserde/gpt4-ui/Examples/HACK LockXLS V4.6.0.md
deleted file mode 100644
index e3cd8bd71a36e1b80d286f3e5d91b7ba60945a6d..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/HACK LockXLS V4.6.0.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-<br />
-<h1>How to Hack LockXLS V4.6.0 and Unlock Excel Files</h1>
-<p>LockXLS is a software that allows you to protect your Excel files with passwords, serial numbers, activation codes, or hardware-based keys. However, sometimes you may forget your password or lose your key, and you need to access your files urgently. In this article, I will show you how to hack LockXLS V4.6.0 and unlock your Excel files without any software or technical skills.</p>
-<h2>HACK LockXLS V4.6.0</h2><br /><p><b><b>DOWNLOAD</b> &#10026; <a href="https://urlgoal.com/2uI7cY">https://urlgoal.com/2uI7cY</a></b></p><br /><br />
-<p>Before we start, I want to warn you that hacking LockXLS V4.6.0 is illegal and unethical, and you should only do it if you have the permission of the file owner or if you are the file owner yourself. I am not responsible for any consequences that may arise from your actions.</p>
-<h2>Step 1: Locate the Locked Excel File</h2>
-<p>The first step is to locate the locked Excel file that you want to hack. You can find it in your computer or in an external device such as a USB flash drive or a CD-ROM. If the file has a .xls or .xlsx extension, it means that it is a normal Excel file that is not protected by LockXLS. If the file has a .xlsc extension, it means that it is a compiled Excel file that is protected by LockXLS.</p>
-<h2>Step 2: Rename the Locked Excel File</h2>
-<p>The next step is to rename the locked Excel file and change its extension from .xlsc to .zip. This will trick LockXLS into thinking that the file is a compressed archive instead of an Excel file. To do this, right-click on the file and select Rename. Then, delete the .xlsc part and type .zip instead. Press Enter to confirm the change.</p>
-<h2>Step 3: Extract the Locked Excel File</h2>
-<p>The third step is to extract the locked Excel file and access its contents. To do this, right-click on the file and select Extract All. Choose a destination folder where you want to save the extracted files and click Extract. You will see a folder with the same name as the original file, containing several subfolders and files.</p>
-<h2>Step 4: Find the Password File</h2>
-<p>The fourth step is to find the password file that contains the encryption key for the locked Excel file. To do this, open the folder that you extracted in the previous step and look for a subfolder named xlsc_data. Inside this subfolder, you will find a file named password.dat. This is the file that we need to hack.</p>
-<p></p>
-<h2>Step 5: Open the Password File with Notepad</h2>
-<p>The fifth step is to open the password file with Notepad and view its contents. To do this, right-click on the password.dat file and select Open With. Choose Notepad from the list of programs and click OK. You will see a bunch of numbers and letters in hexadecimal format.</p>
-<h2>Step 6: Convert the Password File from Hexadecimal to ASCII</h2>
-<p>The sixth step is to convert the password file from hexadecimal to ASCII and reveal the encryption key for the locked Excel file. To do this, copy all the text from Notepad and paste it into an online hex-to-ASCII converter such as <a href="https://www.rapidtables.com/convert/number/hex-to-ascii.html">this one</a>. Click Convert and you will see a string of characters in ASCII format.</p>
-<h2>Step 7: Use the Encryption Key to Unlock the Excel File</h2>
-<p>The final step is to use the encryption key to unlock the Excel file and view its contents. To do this, open LockXLS V4.6.0 and select Open Compiled Workbook from the menu bar. Browse for the original locked Excel file with .xlsc extension and click Open. When prompted for a password or a key, enter the encryption key that you obtained from the previous step and click OK. You will see your Excel file unlocked and ready to use.</p>
-<h3>Congratulations! You have successfully hacked LockXLS V4.6.0 and unlocked your Excel files!</h3></p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sujithvamshi/vehicle-color-recognition/README.md b/spaces/sujithvamshi/vehicle-color-recognition/README.md
deleted file mode 100644
index fc83633647b7c592c2fc5c467eb56528e18e48ac..0000000000000000000000000000000000000000
--- a/spaces/sujithvamshi/vehicle-color-recognition/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Vehicle Color Recognition
-emoji: 🌖
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Imgchili Dolcemodz Star 013 Gallery _VERIFIED_.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Imgchili Dolcemodz Star 013 Gallery _VERIFIED_.md
deleted file mode 100644
index 8c617b9920064e0f3ad42272be9a2630f07616c6..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Imgchili Dolcemodz Star 013 Gallery _VERIFIED_.md	
+++ /dev/null
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deleted file mode 100644
index d5aaab0171bd5fee7830f84e0673e5364c232510..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Root Superuser 3.1.3 Busybox 1.20.2-Update1-signed.zip !!BETTER!!.md	
+++ /dev/null
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diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/Backpackers Adventure Ingles Para Quem Quer Se Aventurar, Estudar E Trabalhar No Exterior.md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/Backpackers Adventure Ingles Para Quem Quer Se Aventurar, Estudar E Trabalhar No Exterior.md
deleted file mode 100644
index a6bdf71663daac7d8bce15fb563347196c1f76d6..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/Backpackers Adventure Ingles Para Quem Quer Se Aventurar, Estudar E Trabalhar No Exterior.md	
+++ /dev/null
@@ -1,68 +0,0 @@
-## Backpackers Adventure Ingles Para Quem Quer Se Aventurar, Estudar E Trabalhar No Exterior
-
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-
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-
- 
-
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-1. Sendai - Japão: Se você quer conhecer o Japão além de Tóquio, Sendai é uma ótima opção. Essa cidade é famosa pelas suas festas tradicionais, pela sua gastronomia deliciosa e pela sua natureza exuberante. Você pode visitar templos históricos, cachoeiras impressionantes e até mesmo um vulcão ativo.
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diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Assassins Creed Unity  The Complete Edition Repack Mr DJ Crack Free.md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Assassins Creed Unity  The Complete Edition Repack Mr DJ Crack Free.md
deleted file mode 100644
index f644f661273043ca94cafa9325be479135c63dd7..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Assassins Creed Unity  The Complete Edition Repack Mr DJ Crack Free.md	
+++ /dev/null
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diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/configs/_base_/models/fast_scnn.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/configs/_base_/models/fast_scnn.py
deleted file mode 100644
index 32fdeb659355a5ce5ef2cc7c2f30742703811cdf..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/configs/_base_/models/fast_scnn.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01)
-model = dict(
-    type='EncoderDecoder',
-    backbone=dict(
-        type='FastSCNN',
-        downsample_dw_channels=(32, 48),
-        global_in_channels=64,
-        global_block_channels=(64, 96, 128),
-        global_block_strides=(2, 2, 1),
-        global_out_channels=128,
-        higher_in_channels=64,
-        lower_in_channels=128,
-        fusion_out_channels=128,
-        out_indices=(0, 1, 2),
-        norm_cfg=norm_cfg,
-        align_corners=False),
-    decode_head=dict(
-        type='DepthwiseSeparableFCNHead',
-        in_channels=128,
-        channels=128,
-        concat_input=False,
-        num_classes=19,
-        in_index=-1,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)),
-    auxiliary_head=[
-        dict(
-            type='FCNHead',
-            in_channels=128,
-            channels=32,
-            num_convs=1,
-            num_classes=19,
-            in_index=-2,
-            norm_cfg=norm_cfg,
-            concat_input=False,
-            align_corners=False,
-            loss_decode=dict(
-                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)),
-        dict(
-            type='FCNHead',
-            in_channels=64,
-            channels=32,
-            num_convs=1,
-            num_classes=19,
-            in_index=-3,
-            norm_cfg=norm_cfg,
-            concat_input=False,
-            align_corners=False,
-            loss_decode=dict(
-                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)),
-    ],
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/runner/iter_based_runner.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/runner/iter_based_runner.py
deleted file mode 100644
index 1df4de8c0285669dec9b014dfd1f3dd1600f0831..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/runner/iter_based_runner.py
+++ /dev/null
@@ -1,273 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import platform
-import shutil
-import time
-import warnings
-
-import torch
-from torch.optim import Optimizer
-
-import annotator.uniformer.mmcv as mmcv
-from .base_runner import BaseRunner
-from .builder import RUNNERS
-from .checkpoint import save_checkpoint
-from .hooks import IterTimerHook
-from .utils import get_host_info
-
-
-class IterLoader:
-
-    def __init__(self, dataloader):
-        self._dataloader = dataloader
-        self.iter_loader = iter(self._dataloader)
-        self._epoch = 0
-
-    @property
-    def epoch(self):
-        return self._epoch
-
-    def __next__(self):
-        try:
-            data = next(self.iter_loader)
-        except StopIteration:
-            self._epoch += 1
-            if hasattr(self._dataloader.sampler, 'set_epoch'):
-                self._dataloader.sampler.set_epoch(self._epoch)
-            time.sleep(2)  # Prevent possible deadlock during epoch transition
-            self.iter_loader = iter(self._dataloader)
-            data = next(self.iter_loader)
-
-        return data
-
-    def __len__(self):
-        return len(self._dataloader)
-
-
-@RUNNERS.register_module()
-class IterBasedRunner(BaseRunner):
-    """Iteration-based Runner.
-
-    This runner train models iteration by iteration.
-    """
-
-    def train(self, data_loader, **kwargs):
-        self.model.train()
-        self.mode = 'train'
-        self.data_loader = data_loader
-        self._epoch = data_loader.epoch
-        data_batch = next(data_loader)
-        self.call_hook('before_train_iter')
-        outputs = self.model.train_step(data_batch, self.optimizer, **kwargs)
-        if not isinstance(outputs, dict):
-            raise TypeError('model.train_step() must return a dict')
-        if 'log_vars' in outputs:
-            self.log_buffer.update(outputs['log_vars'], outputs['num_samples'])
-        self.outputs = outputs
-        self.call_hook('after_train_iter')
-        self._inner_iter += 1
-        self._iter += 1
-
-    @torch.no_grad()
-    def val(self, data_loader, **kwargs):
-        self.model.eval()
-        self.mode = 'val'
-        self.data_loader = data_loader
-        data_batch = next(data_loader)
-        self.call_hook('before_val_iter')
-        outputs = self.model.val_step(data_batch, **kwargs)
-        if not isinstance(outputs, dict):
-            raise TypeError('model.val_step() must return a dict')
-        if 'log_vars' in outputs:
-            self.log_buffer.update(outputs['log_vars'], outputs['num_samples'])
-        self.outputs = outputs
-        self.call_hook('after_val_iter')
-        self._inner_iter += 1
-
-    def run(self, data_loaders, workflow, max_iters=None, **kwargs):
-        """Start running.
-
-        Args:
-            data_loaders (list[:obj:`DataLoader`]): Dataloaders for training
-                and validation.
-            workflow (list[tuple]): A list of (phase, iters) to specify the
-                running order and iterations. E.g, [('train', 10000),
-                ('val', 1000)] means running 10000 iterations for training and
-                1000 iterations for validation, iteratively.
-        """
-        assert isinstance(data_loaders, list)
-        assert mmcv.is_list_of(workflow, tuple)
-        assert len(data_loaders) == len(workflow)
-        if max_iters is not None:
-            warnings.warn(
-                'setting max_iters in run is deprecated, '
-                'please set max_iters in runner_config', DeprecationWarning)
-            self._max_iters = max_iters
-        assert self._max_iters is not None, (
-            'max_iters must be specified during instantiation')
-
-        work_dir = self.work_dir if self.work_dir is not None else 'NONE'
-        self.logger.info('Start running, host: %s, work_dir: %s',
-                         get_host_info(), work_dir)
-        self.logger.info('Hooks will be executed in the following order:\n%s',
-                         self.get_hook_info())
-        self.logger.info('workflow: %s, max: %d iters', workflow,
-                         self._max_iters)
-        self.call_hook('before_run')
-
-        iter_loaders = [IterLoader(x) for x in data_loaders]
-
-        self.call_hook('before_epoch')
-
-        while self.iter < self._max_iters:
-            for i, flow in enumerate(workflow):
-                self._inner_iter = 0
-                mode, iters = flow
-                if not isinstance(mode, str) or not hasattr(self, mode):
-                    raise ValueError(
-                        'runner has no method named "{}" to run a workflow'.
-                        format(mode))
-                iter_runner = getattr(self, mode)
-                for _ in range(iters):
-                    if mode == 'train' and self.iter >= self._max_iters:
-                        break
-                    iter_runner(iter_loaders[i], **kwargs)
-
-        time.sleep(1)  # wait for some hooks like loggers to finish
-        self.call_hook('after_epoch')
-        self.call_hook('after_run')
-
-    def resume(self,
-               checkpoint,
-               resume_optimizer=True,
-               map_location='default'):
-        """Resume model from checkpoint.
-
-        Args:
-            checkpoint (str): Checkpoint to resume from.
-            resume_optimizer (bool, optional): Whether resume the optimizer(s)
-                if the checkpoint file includes optimizer(s). Default to True.
-            map_location (str, optional): Same as :func:`torch.load`.
-                Default to 'default'.
-        """
-        if map_location == 'default':
-            device_id = torch.cuda.current_device()
-            checkpoint = self.load_checkpoint(
-                checkpoint,
-                map_location=lambda storage, loc: storage.cuda(device_id))
-        else:
-            checkpoint = self.load_checkpoint(
-                checkpoint, map_location=map_location)
-
-        self._epoch = checkpoint['meta']['epoch']
-        self._iter = checkpoint['meta']['iter']
-        self._inner_iter = checkpoint['meta']['iter']
-        if 'optimizer' in checkpoint and resume_optimizer:
-            if isinstance(self.optimizer, Optimizer):
-                self.optimizer.load_state_dict(checkpoint['optimizer'])
-            elif isinstance(self.optimizer, dict):
-                for k in self.optimizer.keys():
-                    self.optimizer[k].load_state_dict(
-                        checkpoint['optimizer'][k])
-            else:
-                raise TypeError(
-                    'Optimizer should be dict or torch.optim.Optimizer '
-                    f'but got {type(self.optimizer)}')
-
-        self.logger.info(f'resumed from epoch: {self.epoch}, iter {self.iter}')
-
-    def save_checkpoint(self,
-                        out_dir,
-                        filename_tmpl='iter_{}.pth',
-                        meta=None,
-                        save_optimizer=True,
-                        create_symlink=True):
-        """Save checkpoint to file.
-
-        Args:
-            out_dir (str): Directory to save checkpoint files.
-            filename_tmpl (str, optional): Checkpoint file template.
-                Defaults to 'iter_{}.pth'.
-            meta (dict, optional): Metadata to be saved in checkpoint.
-                Defaults to None.
-            save_optimizer (bool, optional): Whether save optimizer.
-                Defaults to True.
-            create_symlink (bool, optional): Whether create symlink to the
-                latest checkpoint file. Defaults to True.
-        """
-        if meta is None:
-            meta = {}
-        elif not isinstance(meta, dict):
-            raise TypeError(
-                f'meta should be a dict or None, but got {type(meta)}')
-        if self.meta is not None:
-            meta.update(self.meta)
-            # Note: meta.update(self.meta) should be done before
-            # meta.update(epoch=self.epoch + 1, iter=self.iter) otherwise
-            # there will be problems with resumed checkpoints.
-            # More details in https://github.com/open-mmlab/mmcv/pull/1108
-        meta.update(epoch=self.epoch + 1, iter=self.iter)
-
-        filename = filename_tmpl.format(self.iter + 1)
-        filepath = osp.join(out_dir, filename)
-        optimizer = self.optimizer if save_optimizer else None
-        save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta)
-        # in some environments, `os.symlink` is not supported, you may need to
-        # set `create_symlink` to False
-        if create_symlink:
-            dst_file = osp.join(out_dir, 'latest.pth')
-            if platform.system() != 'Windows':
-                mmcv.symlink(filename, dst_file)
-            else:
-                shutil.copy(filepath, dst_file)
-
-    def register_training_hooks(self,
-                                lr_config,
-                                optimizer_config=None,
-                                checkpoint_config=None,
-                                log_config=None,
-                                momentum_config=None,
-                                custom_hooks_config=None):
-        """Register default hooks for iter-based training.
-
-        Checkpoint hook, optimizer stepper hook and logger hooks will be set to
-        `by_epoch=False` by default.
-
-        Default hooks include:
-
-        +----------------------+-------------------------+
-        | Hooks                | Priority                |
-        +======================+=========================+
-        | LrUpdaterHook        | VERY_HIGH (10)          |
-        +----------------------+-------------------------+
-        | MomentumUpdaterHook  | HIGH (30)               |
-        +----------------------+-------------------------+
-        | OptimizerStepperHook | ABOVE_NORMAL (40)       |
-        +----------------------+-------------------------+
-        | CheckpointSaverHook  | NORMAL (50)             |
-        +----------------------+-------------------------+
-        | IterTimerHook        | LOW (70)                |
-        +----------------------+-------------------------+
-        | LoggerHook(s)        | VERY_LOW (90)           |
-        +----------------------+-------------------------+
-        | CustomHook(s)        | defaults to NORMAL (50) |
-        +----------------------+-------------------------+
-
-        If custom hooks have same priority with default hooks, custom hooks
-        will be triggered after default hooks.
-        """
-        if checkpoint_config is not None:
-            checkpoint_config.setdefault('by_epoch', False)
-        if lr_config is not None:
-            lr_config.setdefault('by_epoch', False)
-        if log_config is not None:
-            for info in log_config['hooks']:
-                info.setdefault('by_epoch', False)
-        super(IterBasedRunner, self).register_training_hooks(
-            lr_config=lr_config,
-            momentum_config=momentum_config,
-            optimizer_config=optimizer_config,
-            checkpoint_config=checkpoint_config,
-            log_config=log_config,
-            timer_config=IterTimerHook(),
-            custom_hooks_config=custom_hooks_config)
diff --git a/spaces/talhaty/Faceswapper/app.py b/spaces/talhaty/Faceswapper/app.py
deleted file mode 100644
index 2954e36d4f57c4b351f2ed926d442363b64ec971..0000000000000000000000000000000000000000
--- a/spaces/talhaty/Faceswapper/app.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import gradio as gr
-import subprocess
-import shutil
-import os
-
-def run_scripts(target, source, use_face_enhancer):
-    if target is None or (not use_face_enhancer and source is None):
-        return None
-    target_extension = os.path.splitext(target.name)[-1]
-    output_path1 = "output1" + target_extension
-    output_path2 = "output2" + target_extension
-
-    if not use_face_enhancer:
-        # Run both scripts
-        cmd1 = ["python3", "run.py", "-s", source.name, "-t", target.name, "-o", output_path1, "--frame-processor", "face_swapper"]
-        subprocess.run(cmd1)
-
-    # Run the second script
-    cmd2 = ["python3", "run.py", "-t", target.name if use_face_enhancer else output_path1, "-o", output_path2, "--frame-processor", "face_enhancer"]
-    subprocess.run(cmd2)
-
-    if not use_face_enhancer:
-        os.remove(source.name)
-    os.remove(target.name)
-
-    return output_path2
-
-iface = gr.Interface(
-    fn=run_scripts,
-    inputs=[
-        "file",
-        "file",
-        gr.inputs.Checkbox(default=False, label="Use only Face Enhancer")  # New checkbox input
-    ],
-    outputs="file",
-    title="Face swapper",
-    description="Upload a target image/video and a source image to swap faces.",
-    live=True
-)
-
-iface.launch()
diff --git a/spaces/tanish2502/ChatGPT-AI-Assistant-App/app.py b/spaces/tanish2502/ChatGPT-AI-Assistant-App/app.py
deleted file mode 100644
index ccc01c474b99318e223c33a059d915d6e41d02d0..0000000000000000000000000000000000000000
--- a/spaces/tanish2502/ChatGPT-AI-Assistant-App/app.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import gradio as gr
-import openai
-import os
-from dotenv import load_dotenv
-from pydub import AudioSegment
-
-load_dotenv()
-
-#accessing openapi Key
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-audio_messages = [{"role": "system", "content": 'You are an AI assistant expert. Respond to all input in precise, crisp and easy to understand language.'}]
-text_messages = [{"role": "system", "content": 'You are an AI assistant expert. Respond to all input in precise, crisp and easy to understand language.'}]
-global user_text_input, text_output, user_audio_input, audio_output
-
-"""
-It seems like the gr.Audio source is not generating a WAV file, which is required for the openai.Audio.transcribe() method to work. 
-To convert the audio file to WAV format, i have used a library like Pydub.
-"""
-
-def audio_transcribe(audio):
-    global audio_messages
-    audio_message = audio_messages
-
-    #audio processing to whisper API.
-    audio_file = AudioSegment.from_file(audio)
-    audio_file.export("temp.wav", format="wav")
-    final_audio_file = open("temp.wav", "rb")
-    transcript = openai.Audio.transcribe("whisper-1", final_audio_file)
-    os.remove("temp.wav")
-
-    #transcripted input to chatGPT API for chatCompletion 
-    audio_message.append({"role": "user", "content": transcript["text"]}) # type: ignore
-    response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=audio_message)
-    system_message = response["choices"][0]["message"] # type: ignore
-    audio_message.append(system_message)
-
-    chat_transcript = ""
-    for message in audio_message:
-        if message['role'] != 'system':
-            chat_transcript += message['role'] + ": " + message['content'] + "\n\n"
-
-    return chat_transcript
-
-def text_transcribe(name):
-    global text_messages
-    text_message = text_messages
-    user_text_input.update("")
-    #transcripted input to chatGPT API
-    text_message.append({"role": "user", "content": name}) # type: ignore
-    response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=text_message)
-    system_message = response["choices"][0]["message"] # type: ignore
-    text_message.append(system_message)
-    
-    chat_transcript = ""
-    for message in text_message:
-        if message['role'] != 'system':
-            chat_transcript += message['role'] + ": " + message['content'] + "\n\n"
-    return chat_transcript
-
-title = """<h1 align="center">Your Chat-GPT AI Assistant at your Service!! 😎 </h1>"""
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.HTML(title)
-    with gr.Tab("Audio Input"):
-        with gr.Row():
-            user_audio_input = (gr.Audio(source="microphone", type="filepath", label="Speak Here"))
-            audio_input = user_audio_input
-            audio_output = gr.Textbox(label="AI Response", lines=20, placeholder="AI Response will be displayed here...")
-        with gr.Row():
-            audio_submit_button = gr.Button("Submit")
-    with gr.Tab("Text Input"):
-        with gr.Row():
-            user_text_input = (gr.Textbox(label="Type Here", lines=20, placeholder="Type your message here..."))
-            text_input = user_text_input
-            text_output = gr.Textbox(label="AI Response", lines=20, placeholder="AI Response will be displayed here...")
-        with gr.Row():
-            text_submit_button = gr.Button("Submit")
-    audio_submit_button.click(fn=audio_transcribe, inputs=audio_input, outputs=audio_output)
-    text_submit_button.click(fn=text_transcribe, inputs=text_input, outputs=text_output)
-    
-    gr.Markdown("<center> Made with ❤️ by Tanish Gupta. Credits to 🤗 Spaces for Hosting this App </center>")
-
-demo.launch()
diff --git a/spaces/tanishqvashisht/comicInator/README.md b/spaces/tanishqvashisht/comicInator/README.md
deleted file mode 100644
index 4e4aa3573b1cfb76064387c6cef48bcbfc866d88..0000000000000000000000000000000000000000
--- a/spaces/tanishqvashisht/comicInator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ComicInator
-emoji: 😻
-colorFrom: red
-colorTo: green
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Car Mechanic Simulator 2018 - DLC Gold Pack Torrent Fulll Fixed.md b/spaces/terfces0erbo/CollegeProjectV2/Car Mechanic Simulator 2018 - DLC Gold Pack Torrent Fulll Fixed.md
deleted file mode 100644
index dcc5935ff22d9c9d58c7c6cce0a3ac25920e1ce6..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Car Mechanic Simulator 2018 - DLC Gold Pack Torrent Fulll Fixed.md	
+++ /dev/null
@@ -1,97 +0,0 @@
-## Car Mechanic Simulator 2018 - DLC Gold Pack Torrent Fulll
-
-
-
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-
-
-
-# How to Download and Install Car Mechanic Simulator 2018 - DLC Gold Pack Torrent Fulll
-
- 
-
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-
- 
-
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-
- 
-
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-
- 
-
-- Bentley DLC
-
-- Dodge DLC
-
-- Dodge Modern DLC
-
-- Ford DLC
-
-- Lotus DLC
-
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-
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-
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-
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-
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-
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-
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-
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-
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-
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Crazy Talk Animator Pro 1.2 !EXCLUSIVE! Crack.md b/spaces/terfces0erbo/CollegeProjectV2/Crazy Talk Animator Pro 1.2 !EXCLUSIVE! Crack.md
deleted file mode 100644
index a6dd23271fd984c41906667bb9d86d24583d8aec..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Crazy Talk Animator Pro 1.2 !EXCLUSIVE! Crack.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>crazy talk animator pro 1.2 crack</h2><br /><p><b><b>Download File</b> &#10027;&#10027;&#10027; <a href="https://bytlly.com/2uGlwF">https://bytlly.com/2uGlwF</a></b></p><br /><br />
-<br />
- 3cee63e6c2<br />
-<br />
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-<p></p>
diff --git a/spaces/test12356/SUI-svc-3.0/vdecoder/hifigan/utils.py b/spaces/test12356/SUI-svc-3.0/vdecoder/hifigan/utils.py
deleted file mode 100644
index 84bff024f4d2e2de194b2a88ee7bbe5f0d33f67c..0000000000000000000000000000000000000000
--- a/spaces/test12356/SUI-svc-3.0/vdecoder/hifigan/utils.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import glob
-import os
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-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 apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size*dilation - dilation)/2)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
-    print("Saving checkpoint to {}".format(filepath))
-    torch.save(obj, filepath)
-    print("Complete.")
-
-
-def del_old_checkpoints(cp_dir, prefix, n_models=2):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern) # get checkpoint paths
-    cp_list = sorted(cp_list)# sort by iter
-    if len(cp_list) > n_models: # if more than n_models models are found
-        for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
-            open(cp, 'w').close()# empty file contents
-            os.unlink(cp)# delete file (move to trash when using Colab)
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return None
-    return sorted(cp_list)[-1]
-
diff --git a/spaces/theekshana/boardpac_chat_app_test/app_agent.py b/spaces/theekshana/boardpac_chat_app_test/app_agent.py
deleted file mode 100644
index d64596e2d7ab9182b7d5cb969767ecce39db5c64..0000000000000000000000000000000000000000
--- a/spaces/theekshana/boardpac_chat_app_test/app_agent.py
+++ /dev/null
@@ -1,246 +0,0 @@
-"""
-Python Backend API to chat with private data  
-
-08/16/2023
-D.M. Theekshana Samaradiwakara
-
-python -m streamlit run app.py
-"""
-
-import os
-import time
-import streamlit as st
-from streamlit.logger import get_logger
-
-logger = get_logger(__name__)
-
-from ui.htmlTemplates import css, bot_template, user_template, source_template
-from config import MODELS, DATASETS
-
-from qaPipeline import QAPipeline
-import qaPipeline_functions
-from faissDb import create_faiss
-
-# loads environment variables
-from dotenv import load_dotenv
-load_dotenv()
-
-isHuggingFaceHubEnabled = os.environ.get('ENABLE_HUGGINGFSCE_HUB_MODELS')
-isOpenAiApiEnabled = os.environ.get('ENABLE_OPENAI_API_MODELS')
-
-st.set_page_config(page_title="Chat with data",
-                       page_icon=":books:")
-st.write(css, unsafe_allow_html=True)
-
-qaPipeline = QAPipeline()
-# qaPipeline = qaPipeline_functions
-
-def initialize_session_state():
-    # Initialise all session state variables with defaults
-    SESSION_DEFAULTS = {
-        "model": MODELS["DEFAULT"],
-        "dataset": DATASETS["DEFAULT"],
-        "chat_history": None,
-        "is_parameters_changed":False,
-        "show_source_files": False,
-        "user_question":'',
-    }
-
-    for k, v in SESSION_DEFAULTS.items():
-        if k not in st.session_state:
-            st.session_state[k] = v
-
-def side_bar():
-    with st.sidebar:
-        st.subheader("Chat parameters")
-
-        with st.form('param_form'):
-            st.info('Info: use openai chat model for best results')
-            chat_model = st.selectbox(
-                "Chat model",
-                MODELS,
-                key="chat_model",
-                help="Select the LLM model for the chat",
-                # on_change=update_parameters_change,
-            )
-
-            # data_source = st.selectbox(
-            #     "dataset",
-            #     DATASETS,
-            #     key="data_source",
-            #     help="Select the private data_source for the chat",
-            #     on_change=update_parameters_change,
-            # )
-
-            st.session_state.dataset =  "DEFAULT"
-
-            show_source = st.checkbox(
-                label="show source files",
-                key="show_source",
-                help="Select this to show relavant source files for the query",
-                # on_change=update_parameters_change,
-            )
-
-            submitted = st.form_submit_button(
-                "Save Parameters",
-                # on_click=update_parameters_change
-                )
-       
-            if submitted:
-                parameters_change_button(chat_model, show_source)
-
-
-            # if st.session_state.is_parameters_changed:
-            #     st.button("Update", 
-            #             on_click=parameters_change_button,
-            #             args=[chat_model, show_source]
-            #     )
-                    
-        st.markdown("\n")
-
-        # if st.button("Create FAISS db"):
-        #     try:
-        #         with st.spinner('creating faiss vector store'):
-        #             create_faiss()
-        #             st.success('faiss saved')
-        #     except Exception as e:
-        #         st.error(f"Error : {e}")#, icon=":books:")
-        #         return
-                
-        st.markdown(
-            "### How to use\n"
-            "1. Select the chat model\n"  # noqa: E501
-            "2. Select \"show source files\" to show the source files related to the answer.📄\n"
-            "3. Ask a question about the documents💬\n"
-        )
-
-
-def chat_body():
-    st.header("Chat with your own data:")
-    with st.form('chat_body'):
-
-        user_question = st.text_input(
-            "Ask a question about your documents:",
-            placeholder="enter question",
-            key='user_question',
-            # on_change=submit_user_question,
-        )
-
-        submitted = st.form_submit_button(
-            "Submit",
-            # on_click=update_parameters_change
-            )
-    
-        if submitted:
-            submit_user_question()
-
-    # if user_question:
-    #     submit_user_question()
-    #     # user_question = False
-
-    
-
-def submit_user_question():
-    with st.spinner("Processing"):
-        user_question = st.session_state.user_question
-        # st.success(user_question)
-        handle_userinput(user_question)
-        # st.session_state.user_question=''
-
-
-def main():
-   
-    initialize_session_state()
-    
-    side_bar()
-
-    chat_body()
-
-                
-def update_parameters_change():
-    st.session_state.is_parameters_changed = True
-
-
-def parameters_change_button(chat_model, show_source):
-    st.session_state.model = chat_model
-    st.session_state.dataset = "DEFAULT"
-    st.session_state.show_source_files = show_source
-    st.session_state.is_parameters_changed = False
-
-    alert =  st.success("chat parameters updated")
-    time.sleep(1) # Wait for 3 seconds
-    alert.empty() # Clear the alert
-
-@st.cache_data
-def get_answer_from_backend(query, model, dataset):
-    # response = qaPipeline.run(query=query, model=model, dataset=dataset)
-    response = qaPipeline.run_agent(query=query, model=model, dataset=dataset)
-    return response
-
-
-def show_query_response(query, response, show_source_files):
-    docs = []
-    if isinstance(response, dict):
-        answer, docs = response['answer'], response['source_documents']
-    else:
-        answer = response
-
-    st.write(user_template.replace(
-                "{{MSG}}", query), unsafe_allow_html=True)
-    st.write(bot_template.replace(
-                "{{MSG}}",  answer ), unsafe_allow_html=True)
-    
-    if show_source_files:
-        # st.write(source_template.replace(
-        #         "{{MSG}}",  "source files" ), unsafe_allow_html=True)
-
-        if len(docs)>0 :
-            st.markdown("#### source files : ")
-            for source in docs:
-                # st.info(source.metadata)
-                with st.expander(source.metadata["source"]):
-                    st.markdown(source.page_content)
-            
-        # st.write(response)
-    
-
-def is_query_valid(query: str) -> bool:
-    if (not query) or (query.strip() == ''):
-        st.error("Please enter a question!")
-        return False
-    return True
-
-
-def handle_userinput(query):
-    # Get the answer from the chain
-    try:
-        if not is_query_valid(query):
-            st.stop()
-
-        model = MODELS[st.session_state.model]
-        dataset = DATASETS[st.session_state.dataset]
-        show_source_files = st.session_state.show_source_files
-
-        # Try to access openai and deeplake
-        print(f">\n model: {model} \n dataset : {dataset} \n show_source_files : {show_source_files}")
-
-        response = get_answer_from_backend(query, model, dataset)
-
-        show_query_response(query, response, show_source_files)
-
-
-    except Exception as e:
-        # logger.error(f"Answer retrieval failed with {e}")
-        st.error(f"Error ocuured! see log info for more details.")#, icon=":books:")
-        print(f"Streamlit handle_userinput Error : {e}")#, icon=":books:")
-        return
-
-
-if __name__ == "__main__":
-    main()
-
-# initialize_session_state()
-
-# side_bar()
-
-# chat_body()
\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Acidic Wow Bot Download The Best WoW Bot for Shadowlands Classic and More.md b/spaces/tialenAdioni/chat-gpt-api/logs/Acidic Wow Bot Download The Best WoW Bot for Shadowlands Classic and More.md
deleted file mode 100644
index 41bb60b2ad2295c48dec9bfc0a814e2f61a8e06c..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Acidic Wow Bot Download The Best WoW Bot for Shadowlands Classic and More.md	
+++ /dev/null
@@ -1,69 +0,0 @@
-
-<h1>Acidic Wow Bot: A Non-Detectable World of Warcraft Bot</h1>
-<p>If you are looking for a way to automate your World of Warcraft gameplay, you might be interested in Acidic Wow Bot. This is a project that aims to develop a non-detectable wow bot that can travel without human interference. In this article, we will explain what Acidic Wow Bot is, how it works, and where you can download it.</p>
-<h2>acidic wow bot download</h2><br /><p><b><b>DOWNLOAD</b> &#187; <a href="https://urlcod.com/2uK9T0">https://urlcod.com/2uK9T0</a></b></p><br /><br />
-<h2>What is Acidic Wow Bot?</h2>
-<p>Acidic Wow Bot is a software that runs on your computer and controls your World of Warcraft character. It can perform various tasks such as leveling, farming, questing, and PvPing. The goal of Acidic Wow Bot is to make your wow experience easier and more enjoyable, while avoiding detection by Blizzard's anti-cheat system.</p>
-<h2>How does Acidic Wow Bot work?</h2>
-<p>Acidic Wow Bot works by using a combination of programming languages such as Delphi, Lua, and AutoIt. It uses a predefined set of coordinates to navigate the game world and interact with NPCs and enemies. It also uses a scripting platform called Awful WoW Scripting that provides PvE and PvP rotations, bots, and tools for various classes and situations.</p>
-<h2>Where can I download Acidic Wow Bot?</h2>
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-<h2>What are the benefits of using Acidic Wow Bot?</h2>
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\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/HD Online Player (Love Aaj Kal 2 Full Movie In Hindi Cast) - Meet The Stars.md b/spaces/tialenAdioni/chat-gpt-api/logs/HD Online Player (Love Aaj Kal 2 Full Movie In Hindi Cast) - Meet The Stars.md
deleted file mode 100644
index 3d414601a7d57a5d54cdf44343f7b33266c186fa..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/HD Online Player (Love Aaj Kal 2 Full Movie In Hindi Cast) - Meet The Stars.md	
+++ /dev/null
@@ -1,184 +0,0 @@
-<br />
-<h1>HD Online Player (Love Aaj Kal 2 Full Movie In Hindi W): The Best Way to Watch the Latest Bollywood Romance</h1>
-
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-<p>The movie has a rating of 5.0 out of 10 on IMDb, based on 11,760 user ratings. The movie has a rating of 2.0 out of 5 on Times of India, based on 18 critic reviews. The movie has a rating of 1.8 out of 5 on BookMyShow, based on 34,288 user ratings.</p>
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-<p>Some of the positive reviews of the movie are:</p>
-
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-<p>"Love Aaj Kal 2 is a visually stunning and emotionally engaging film that explores the complexities of love and relationships in different eras. Kartik Aaryan and Sara Ali Khan deliver stellar performances and share a sizzling chemistry on screen. The music by Pritam is soulful and catchy. The movie is a must-watch for fans of Imtiaz Ali and romantic dramas." - Filmfare</p>
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-</blockquote>
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-
-<blockquote>
-<p>"Love Aaj Kal 2 is a disappointing and dull film that fails to capture the essence and charm of the original. The movie is a mishmash of cliches, stereotypes, and melodrama that make it hard to connect with the characters or the story. Kartik Aaryan and Sara Ali Khan are wasted in their roles and have no chemistry whatsoever. The movie is a letdown for fans of Imtiaz Ali and romance." - Hindustan Times</p>
-</blockquote>
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-<p>"Love Aaj Kal 2 is a confusing and boring film that tries to be too clever and too deep but ends up being too silly and too shallow. The movie is a mess of timelines, flashbacks, reincarnations, and coincidences that make no sense at all. Kartik Aaryan and Sara Ali Khan are annoying and irritating in their roles and have no spark or charisma. The movie is a disaster for fans of Imtiaz Ali and romance." - Rediff</p>
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-<li>Choose a comfortable and quiet place to watch the movie. You can watch it on your laptop, tablet, smartphone, or TV. Make sure you have a good Internet connection and enough battery life on your device.</li>
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-<p>Love Aaj Kal 2 is a 2020 Hindi-language romance drama movie that features Kartik Aaryan and Sara Ali Khan as the lead pair, who play two different individuals on a journey of love, loss and life through the phases of reincarnation. The movie also stars Randeep Hooda and Arushi Sharma in supporting roles. The movie explores the concept of love in different eras and how it changes with time and circumstances.</p>
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-<h8>What are the Songs of Love Aaj Kal 2?</h8>
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-<p>Love Aaj Kal 2 has a soundtrack album composed by Pritam Chakraborty, who also composed the music for the original Love Aaj Kal. The album consists of 11 songs, sung by various artists such as Arijit Singh, Shreya Ghoshal, Mohit Chauhan, Darshan Raval, Antara Mitra, and more. The lyrics are written by Irshad Kamil, who also wrote the lyrics for the original Love Aaj Kal.</p>
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-<p>The songs of Love Aaj Kal 2 are a mix of romantic, upbeat, and melancholic tunes that reflect the mood and theme of the movie. Some of the popular songs of the album are:</p>
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-<ul>
-<li>"Shayad", sung by Arijit Singh, is a soft and soothing song that expresses the uncertainty and hope of love.</li>
-<li>"Haan Main Galat", sung by Arijit Singh and Shashwat Singh, is a peppy and catchy song that celebrates the imperfections and mistakes of love.</li>
-<li>"Mehrama", sung by Darshan Raval and Antara Mitra, is a sad and emotional song that depicts the loneliness and longing of love.</li>
-<li>"Rahogi Meri", sung by Arijit Singh, is a sweet and romantic song that conveys the promise and commitment of love.</li>
-<li>"Yeh Dooriyan", sung by Mohit Chauhan, is a remake of the classic song from the original Love Aaj Kal, that portrays the distance and pain of love.</li>
-</ul>
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-<p>The songs of Love Aaj Kal 2 have received positive reviews from critics and audiences alike. The songs have also been streamed and downloaded millions of times on various platforms such as YouTube, Spotify, JioSaavn, Gaana, etc.</p>
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-<li>Go to the HD Online Player website and type "Love Aaj Kal 2 songs" in the search box.</li>
-<li>Click on the result that says "Love Aaj Kal 2 Songs - Full Album | Kartik Aaryan & Sara Ali Khan | Pritam | Jukebox".</li>
-<li>On the page that opens, you will see a player that will start playing the songs in HD quality. You can also see some options below the player such as play, pause, stop, full screen, volume, resolution, format, language, subtitles, etc.</li>
-<li>If you want to download the songs in MP3 format, you can click on the download button below the player. You can also choose the download quality, size, and location of the file before downloading.</li>
-<li>Once the download is complete, you can find the file on your device and listen to it anytime you want.</li>
-</ol>
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-<h10>Conclusion</h10>
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-<p>Love Aaj Kal 2 is a 2020 Hindi-language romance drama movie that features Kartik Aaryan and Sara Ali Khan as the lead pair, who play two different individuals on a journey of love, loss and life through the phases of reincarnation. The movie also stars Randeep Hooda and Arushi Sharma in supporting roles. The movie explores the concept of love in different eras and how it changes with time and circumstances.</p>
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-<p>Love Aaj Kal 2 also has a soundtrack album composed by Pritam Chakraborty, who also composed the music for the original Love Aaj Kal. The album consists of 11 songs, sung by various artists such as Arijit Singh, Shreya Ghoshal, Mohit Chauhan, Darshan Raval, Antara Mitra, and more. The songs are a mix of romantic, upbeat, and melancholic tunes that reflect the mood and theme of the movie.</p>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Crossy Road How to Play the Viral Hit Game on Your Mobile Device for Free.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Crossy Road How to Play the Viral Hit Game on Your Mobile Device for Free.md
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-<p>If you love arcade games, you have probably heard of Crossy Road, the hit game that has been downloaded over 200 million times worldwide. Crossy Road is a simple but addictive game that challenges you to cross roads, train tracks, and rivers without getting hit by cars, trains, or falling into the water. Sounds easy, right? Well, not quite. The game gets harder as you go along, with more obstacles, faster traffic, and unpredictable environments. You also have to collect coins and unlock over 150 different characters, each with their own unique abilities and settings. In this article, we will show you how to download Crossy Road for free on your mobile device, how to play the game, and how to score higher and avoid obstacles. We will also give you our honest review of the game, its pros and cons, and what to expect from future updates.</p>
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-<li>Use coins and gifts wisely. Coins can help you unlock more characters, which can make the game more fun and diverse. Gifts are random boxes that appear every few minutes or after watching a video ad. They contain coins or new characters, so don't miss them.</li>
-<li>Play with your friends and family. Crossy Road has a multiplayer mode that allows you to play with up to four people on the same device or online. You can cooperate or compete with each other, and see who can cross the most roads. Playing with others can make the game more enjoyable and challenging.</li>
-</ul>
-<h2>Crossy Road Review: Why You Should Try This Addictive and Fun Game</h2>
-<p>Crossy Road is one of the most popular and successful arcade games of all time. It has received rave reviews from both players and critics, who praised its simple but addictive gameplay, its colorful and pixelated graphics, its humorous and varied characters, and its endless replay value. The game has also won several awards, such as the Apple Design Award in 2015 and the Google Play Best of 2014 Award. Here are some of the pros and cons of the game:</p>
-<table>
-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>- Easy to learn but hard to master</td><td>- Can be frustrating or repetitive at times</td></tr>
-<tr><td>- Free to download and play</td><td>- Contains ads and in-app purchases</td></tr>
-<tr><td>- Has over 150 characters and environments to unlock and explore</td><td>- Some characters are hard to get or require real money</td></tr>
-<tr><td>- Has challenges and achievements to complete and collect</td><td>- Some challenges are too difficult or random</td></tr>
-<tr><td>- Has multiplayer mode to play with friends and family</td><td>- Multiplayer mode can be laggy or buggy</td></tr>
-</table>
-<p>The developers of Crossy Road are constantly updating the game with new features and content. They have added new characters, environments, modes, events, and Easter eggs over the years. They have also collaborated with other popular games and franchises, such as Disney, Pac-Man, Minecraft, and The Walking Dead, to create crossover characters and worlds. You can expect more surprises and fun from Crossy Road in the future.</p>
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-<p>Crossy Road is a game that will keep you entertained for hours. It is a simple but addictive game that challenges you to cross roads, train tracks, and rivers without getting squashed. It is a game that will make you laugh, cry, scream, and cheer. It is a game that will test your reflexes, skills, patience, and luck. It is a game that will let you play with over 150 different characters, each with their own unique abilities and settings. It is a game that will let you play with your friends and family on the same device or online. It is a game that will never get old or boring.</p>
-<p>If you love arcade games, you should definitely download Crossy Road for free on your mobile device. You will not regret it. You will have a blast crossing roads, collecting coins, unlocking characters, completing challenges, and having fun.</p>
-<p>So what are you waiting for? Download Crossy Road now and join the millions of players who are already hooked on this viral arcade game!</p>
- <h3>FAQs</h3>
-<ol>
-<li>Q: How do I get more coins in Crossy Road?</li>
-<li>A: You can get more coins by collecting them on the road or in the air, by opening gifts that appear every few minutes or after watching video ads, by completing challenges that reward you with coins or new characters, or by buying them with real money through in-app purchases.</li>
-<li>Q: How do I unlock new characters in Crossy Road?</li>
-<li>A: You can unlock new characters by buying them from a vending machine with coins, by opening gifts that contain new characters or coins, by completing challenges that reward you with new characters or coins, or by finding secret characters that are hidden in the game. Some characters are also available for a limited time during special events or collaborations.</li>
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-<li>A: You can play multiplayer mode with up to four people on the same device or online. To play on the same device, you need to connect two or more controllers to your device, such as gamepads, keyboards, or mice. To play online, you need to sign in with your Google Play Games or Game Center account, and invite your friends or join a random match. You can also play with nearby players using Bluetooth or Wi-Fi.</li>
-<li>Q: How do I save my progress in Crossy Road?</li>
-<li>A: You can save your progress in Crossy Road by signing in with your Google Play Games or Game Center account. This will allow you to sync your coins, characters, achievements, and leaderboard scores across different devices. You can also backup your data to the cloud using the settings menu.</li>
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-<li>A: You can contact the developers of Crossy Road by visiting their website, following them on social media  , or sending them an email at support@yodo1.com. They are always happy to hear from their fans and appreciate any feedback or suggestions.</li>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Color Rummy App and Play Real Cash Rummy Online with Friends.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Color Rummy App and Play Real Cash Rummy Online with Friends.md
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index 6fef4bf3b3e6a0f9c7b9534978f5ee45dd2129da..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Color Rummy App and Play Real Cash Rummy Online with Friends.md	
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-<h1>Online Rummy Game Color Rummy Download: A Fun and Easy Way to Play Rummy Anytime, Anywhere</h1>
- <p>Rummy is one of the most popular card games in the world. It is a game of skill, strategy, and luck that can be enjoyed by people of all ages and backgrounds. But what if you don't have a deck of cards or a partner to play with? Don't worry, there is a solution for you: online rummy game color rummy download.</p>
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- <p>Color Rummy is a free app that lets you play rummy online with other players or against the computer. You can choose from different game modes, such as classic rummy, gin rummy, or Oklahoma rummy. You can also customize your game settings, such as the number of players, the number of cards, and the difficulty level. Whether you are a beginner or a pro, you will find a challenge that suits your skills and preferences.</p>
- <h2>What is Color Rummy?</h2>
- <p>Color Rummy is an online rummy game that has a unique feature: the cards have different colors. This adds an extra layer of fun and strategy to the game, as you can form sets and runs based on the color of the cards, not just their rank or suit. For example, you can make a set of three red sevens or a run of four green cards.</p>
- <p>The color of the cards also affects the scoring system. The more colors you use in your melds, the more points you get. However, if you use only one color in your melds, you get a bonus of 50 points. This means that you have to balance between using more colors for higher points or using one color for a bonus.</p>
- <h3>How to play Color Rummy</h3>
- <p>The rules of Color Rummy are similar to the rules of traditional rummy. The objective of the game is to form valid melds (sets or runs) with your cards and get rid of all your cards before your opponents do. A set is a group of three or four cards of the same rank, regardless of their suit or color. A run is a sequence of three or more cards of the same suit and consecutive rank, regardless of their color.</p>
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- <p>The game continues until one player goes out by getting rid of all their cards. They must have at least one meld in their hand and discard their last card to do so. The other players then count their points based on the value and color of their remaining cards. The player with the lowest score wins the round.</p>
- <h3>Why you should download Color Rummy</h3>
- <p>There are many reasons why you should download Color Rummy and play it on your device. Here are some of them:</p>
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-<li>It is free and easy to download and install.</li>
-<li>It has stunning graphics and sound effects that create an immersive gaming experience.</li>
-<li>It has different game modes and settings that cater to your preferences and skill level.</li>
-<li>It has an online multiplayer mode that lets you play with other players from around the world.</li>
-<li>It has an offline mode that lets you play against the computer when you don't have an internet connection.</li>
-<li> <li>It has a tutorial mode that teaches you how to play the game and the rules of each game mode.</li>
-<li>It has a leaderboard and achievements system that tracks your progress and rewards you for your performance.</li>
-<li>It has a chat feature that lets you communicate with other players and make new friends.</li>
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- <p>As you can see, Color Rummy is a fun and easy way to play rummy anytime, anywhere. You can enjoy the game with your friends, family, or strangers online. You can also challenge yourself and improve your skills by playing against the computer. You can also learn new strategies and tips by watching the tutorial and reading the rules. Color Rummy is more than just a game, it is a community of rummy lovers.</p>
- <h2>How to download Color Rummy</h2>
- <p>If you are interested in playing Color Rummy, you can download it for free from the official website or from the app store of your device. Here are the steps to download Color Rummy for Android and iOS devices:</p>
- <h3>For Android devices</h3>
- <ol>
-<li>Go to the Google Play Store and search for "Color Rummy".</li>
-<li>Select the app with the logo of a colorful card and tap on "Install".</li>
-<li>Wait for the app to download and install on your device.</li>
-<li>Open the app and sign up with your email or Facebook account.</li>
-<li>Enjoy playing Color Rummy!</li>
-</ol>
- <h3>For iOS devices</h3>
- <ol>
-<li>Go to the App Store and search for "Color Rummy".</li>
-<li>Select the app with the logo of a colorful card and tap on "Get".</li>
-<li>Enter your Apple ID password or use Touch ID or Face ID to confirm.</li>
-<li>Wait for the app to download and install on your device.</li>
-<li>Open the app and sign up with your email or Facebook account.</li>
-<li>Enjoy playing Color Rummy!</li>
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- <h2>Tips and tricks for playing Color Rummy</h2>
- <p>If you want to become a better player of Color Rummy, you need to know some tips and tricks that can help you win more games. Here are some of them:</p>
- <h3>Learn the rules and scoring system</h3>
- <p>The first thing you need to do is to learn the rules and scoring system of Color Rummy. You need to know how to form valid melds, how to go out, how to count your points, and how the color of the cards affects your score. You can read the rules of each game mode in the app or watch the tutorial videos. You can also practice by playing against the computer in offline mode.</p>
- <h3>Use the jokers wisely</h3>
- <p>Jokers are special cards that can act as any card you want. They can help you complete your melds faster and score more points. However, you need to use them wisely, as they also have some drawbacks. For example, if you use a joker in a meld, you cannot use that meld for a bonus. Also, if you have a joker in your hand when someone else goes out, you lose 25 points. Therefore, you need to decide when to use a joker and when to save it for later.</p>
- <h3>Plan your moves ahead</h3>
- <p>Rummy is a game of skill and strategy, not just luck. You need to plan your moves ahead and think about what cards you need, what cards you can discard, what cards your opponents might have, and what cards might come up next. You also need to keep track of the cards that have been played and discarded, so you can estimate what cards are left in the stock pile. By planning your moves ahead, you can increase your chances of forming melds faster and going out before your opponents.</p>
- <h3>Bluff your opponents</h3>
- <p>Rummy is also a game of psychology and deception. You need to bluff your opponents by making them think that you have different cards than you actually do. For example, you can discard a card that you don't need but that might be useful for your opponents, so they think that you are close to going out. Or, you can draw a card from the discard pile that doesn't help you but that might confuse your opponents about what melds you are trying to make. By bluffing your opponents, you can make them play differently and make mistakes that benefit you.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, online rummy game color rummy download is a fun and easy way to play rummy anytime, anywhere. You can enjoy the game with different game modes, settings, features, and players. You can also improve your skills by learning the rules, scoring system, tips, and tricks. You can also have fun by bluffing your opponents and chatting with them. Color Rummy is more than just a game, it is a community of rummy lovers. If you are looking for a new way to play rummy, you should download Color Rummy today and join the fun!</p>
- <h4>FAQs</h4>
- <p>Here are some frequently asked questions about online rummy game color rummy download:</p>
- <ol>
-<li>Is Color Rummy safe and secure?</li>
-<p>Yes, Color Rummy is safe and secure. It uses encryption and authentication technologies to protect your personal and financial information. It also follows the fair play and responsible gaming policies to ensure a safe and enjoyable gaming environment.</p>
-<li>How can I contact the customer support of Color Rummy?</li>
-<p>You can contact the customer support of Color Rummy by sending an email to support@colorrummy.com or by filling out the contact form on the website. You can also visit the FAQ section on the app or the website for more information.</p>
-<li>Can I play Color Rummy with real money?</li>
-<p>No, Color Rummy is a free-to-play game that does not involve any real money transactions. You can play with virtual coins that you can earn by playing the game or by watching ads. You can also purchase more coins with in-app purchases if you want to.</p>
-<li>Can I play Color Rummy offline?</li>
-<p>Yes, you can play Color Rummy offline. You can choose the offline mode on the app and play against the computer. You can also play online when you have an internet connection and sync your progress and coins.</p>
-<li>Can I invite my friends to play Color Rummy with me?</li>
-<p>Yes, you can invite your friends to play Color Rummy with you. You can use the invite feature on the app and send them a link to download the game. You can also join or create a private room on the app and play with your friends exclusively.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/IGameGuardian 55.md b/spaces/tioseFevbu/cartoon-converter/scripts/IGameGuardian 55.md
deleted file mode 100644
index 57c4f57aa9a1c43d8c4de291c5a42fbb5c738e7c..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/IGameGuardian 55.md	
+++ /dev/null
@@ -1,52 +0,0 @@
-
-<h1>IGameGuardian 55: A Cheat Tool for Android Games</h1>
-<p>If you are looking for a way to modify your favorite Android games, you might be interested in IGameGuardian 55. This is a cheat tool that allows you to change various aspects of the games, such as money, health, speed, and more. You can also use it to load emulators and play games from other platforms on your Android device.</p>
-<h2>IGameGuardian 55</h2><br /><p><b><b>Download Zip</b> &#9734;&#9734;&#9734; <a href="https://urlcod.com/2uHvWW">https://urlcod.com/2uHvWW</a></b></p><br /><br />
-<p>IGameGuardian 55 is an updated version of GameGuardian, a popular cheat app that has been around for a long time. It works on rooted Android devices or in virtual environments without root. It supports Android 2.3.3 and above, and can run on ARM, x64, and x86 devices, including x86 emulators.</p>
-<p>Some of the features of IGameGuardian 55 are:</p>
-<ul>
-<li>Search and edit encrypted values</li>
-<li>Search by difference, mask, or range</li>
-<li>Search by data type, such as double, float, dword, byte, etc.</li>
-<li>Lua scripting support</li>
-<li>Modify all search results at once</li>
-<li>Filter search results by address or value</li>
-<li>Search in the background</li>
-<li>Fill memory with a value</li>
-<li>Time jump feature</li>
-<li>Dump and copy memory</li>
-<li>Customizable UI and app locale</li>
-</ul>
-<p>To use IGameGuardian 55, you need to download the APK file from the official website and install it on your device. Then, you need to grant it root access or use a virtual space app to run it without root. After that, you can launch the app and select the game you want to hack. You can then use the search and edit functions to find and change the values you want.</p>
-<p></p>
-<p>However, you should be careful when using IGameGuardian 55, as it may not work on some games or cause them to crash. You should also avoid using it on online games that have anti-cheat systems, as you may risk losing your account or getting banned. You should only use it for fun and not to ruin the game experience for others.</p>
-<p>IGameGuardian 55 is a powerful cheat tool that can help you enjoy your Android games in new ways. However, you should use it responsibly and at your own risk.</p>
-
-<p>One of the games that you can hack with IGameGuardian 55 is Plants vs. Zombies, a popular tower defense game where you have to protect your house from waves of zombies using different plants. With IGameGuardian 55, you can change the amount of sun you have, which is used to plant more plants. You can also change the number of coins you have, which can be used to buy upgrades and items.</p>
-<p>To hack Plants vs. Zombies with IGameGuardian 55, you need to follow these steps:</p>
-<ol>
-<li>Launch IGameGuardian 55 and tap on Application to select Plants vs. Zombies.</li>
-<li>Tap on Search and set the search mode to Auto.</li>
-<li>Enter the value of sun or coins you have in the game and tap on Search.</li>
-<li>Go back to the game and spend or earn some sun or coins.</li>
-<li>Go back to IGameGuardian 55 and search for the new value of sun or coins.</li>
-<li>Repeat steps 4 and 5 until you get a few results.</li>
-<li>Select the results and tap on Modify.</li>
-<li>Enter the value of sun or coins you want to have and tap on OK.</li>
-<li>Go back to the game and enjoy your hacked sun or coins.</li>
-</ol>
-<p>Another game that you can hack with IGameGuardian 55 is GTA: San Andreas, a classic open-world action-adventure game where you can explore a fictional version of California as a gangster named CJ. With IGameGuardian 55, you can change your health, armor, money, weapons, stats, and more.</p>
-<p>To hack GTA: San Andreas with IGameGuardian 55, you need to follow these steps:</p>
-<ol>
-<li>Launch IGameGuardian 55 and tap on Application to select GTA: San Andreas.</li>
-<li>Tap on Search and set the search mode to Auto.</li>
-<li>Enter the value of health, armor, money, or any other parameter you want to hack and tap on Search.</li>
-<li>Go back to the game and change the value of the parameter by getting hurt, spending money, etc.</li>
-<li>Go back to IGameGuardian 55 and search for the new value of the parameter.</li>
-<li>Repeat steps 4 and 5 until you get a few results.</li>
-<li>Select the results and tap on Modify.</li>
-<li>Enter the value of the parameter you want to have and tap on OK.</li>
-<li>Go back to the game and enjoy your hacked parameter.</li>
-</ol></p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tomofi/MMOCR/mmocr/models/ner/convertors/__init__.py b/spaces/tomofi/MMOCR/mmocr/models/ner/convertors/__init__.py
deleted file mode 100644
index 4d4e15c3dbd6086e63e0d38f477b8feb4a27333a..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/ner/convertors/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .ner_convertor import NerConvertor
-
-__all__ = ['NerConvertor']
diff --git a/spaces/tomofi/MMOCR/mmocr/models/textdet/postprocess/utils.py b/spaces/tomofi/MMOCR/mmocr/models/textdet/postprocess/utils.py
deleted file mode 100644
index faae589577ceae6e874714595a1a425043ebe9fc..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/textdet/postprocess/utils.py
+++ /dev/null
@@ -1,482 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import functools
-import operator
-
-import cv2
-import numpy as np
-import pyclipper
-from numpy.fft import ifft
-from numpy.linalg import norm
-from shapely.geometry import Polygon
-
-from mmocr.core.evaluation.utils import boundary_iou
-
-
-def filter_instance(area, confidence, min_area, min_confidence):
-    return bool(area < min_area or confidence < min_confidence)
-
-
-def box_score_fast(bitmap, _box):
-    h, w = bitmap.shape[:2]
-    box = _box.copy()
-    xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int32), 0, w - 1)
-    xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int32), 0, w - 1)
-    ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int32), 0, h - 1)
-    ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int32), 0, h - 1)
-
-    mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
-    box[:, 0] = box[:, 0] - xmin
-    box[:, 1] = box[:, 1] - ymin
-    cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
-    return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
-
-
-def unclip(box, unclip_ratio=1.5):
-    poly = Polygon(box)
-    distance = poly.area * unclip_ratio / poly.length
-    offset = pyclipper.PyclipperOffset()
-    offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
-    expanded = np.array(offset.Execute(distance))
-    return expanded
-
-
-def fill_hole(input_mask):
-    h, w = input_mask.shape
-    canvas = np.zeros((h + 2, w + 2), np.uint8)
-    canvas[1:h + 1, 1:w + 1] = input_mask.copy()
-
-    mask = np.zeros((h + 4, w + 4), np.uint8)
-
-    cv2.floodFill(canvas, mask, (0, 0), 1)
-    canvas = canvas[1:h + 1, 1:w + 1].astype(np.bool)
-
-    return ~canvas | input_mask
-
-
-def centralize(points_yx,
-               normal_sin,
-               normal_cos,
-               radius,
-               contour_mask,
-               step_ratio=0.03):
-
-    h, w = contour_mask.shape
-    top_yx = bot_yx = points_yx
-    step_flags = np.ones((len(points_yx), 1), dtype=np.bool)
-    step = step_ratio * radius * np.hstack([normal_sin, normal_cos])
-    while np.any(step_flags):
-        next_yx = np.array(top_yx + step, dtype=np.int32)
-        next_y, next_x = next_yx[:, 0], next_yx[:, 1]
-        step_flags = (next_y >= 0) & (next_y < h) & (next_x > 0) & (
-            next_x < w) & contour_mask[np.clip(next_y, 0, h - 1),
-                                       np.clip(next_x, 0, w - 1)]
-        top_yx = top_yx + step_flags.reshape((-1, 1)) * step
-    step_flags = np.ones((len(points_yx), 1), dtype=np.bool)
-    while np.any(step_flags):
-        next_yx = np.array(bot_yx - step, dtype=np.int32)
-        next_y, next_x = next_yx[:, 0], next_yx[:, 1]
-        step_flags = (next_y >= 0) & (next_y < h) & (next_x > 0) & (
-            next_x < w) & contour_mask[np.clip(next_y, 0, h - 1),
-                                       np.clip(next_x, 0, w - 1)]
-        bot_yx = bot_yx - step_flags.reshape((-1, 1)) * step
-    centers = np.array((top_yx + bot_yx) * 0.5, dtype=np.int32)
-    return centers
-
-
-def merge_disks(disks, disk_overlap_thr):
-    xy = disks[:, 0:2]
-    radius = disks[:, 2]
-    scores = disks[:, 3]
-    order = scores.argsort()[::-1]
-
-    merged_disks = []
-    while order.size > 0:
-        if order.size == 1:
-            merged_disks.append(disks[order])
-            break
-        i = order[0]
-        d = norm(xy[i] - xy[order[1:]], axis=1)
-        ri = radius[i]
-        r = radius[order[1:]]
-        d_thr = (ri + r) * disk_overlap_thr
-
-        merge_inds = np.where(d <= d_thr)[0] + 1
-        if merge_inds.size > 0:
-            merge_order = np.hstack([i, order[merge_inds]])
-            merged_disks.append(np.mean(disks[merge_order], axis=0))
-        else:
-            merged_disks.append(disks[i])
-
-        inds = np.where(d > d_thr)[0] + 1
-        order = order[inds]
-    merged_disks = np.vstack(merged_disks)
-
-    return merged_disks
-
-
-def poly_nms(polygons, threshold):
-    assert isinstance(polygons, list)
-
-    polygons = np.array(sorted(polygons, key=lambda x: x[-1]))
-
-    keep_poly = []
-    index = [i for i in range(polygons.shape[0])]
-
-    while len(index) > 0:
-        keep_poly.append(polygons[index[-1]].tolist())
-        A = polygons[index[-1]][:-1]
-        index = np.delete(index, -1)
-
-        iou_list = np.zeros((len(index), ))
-        for i in range(len(index)):
-            B = polygons[index[i]][:-1]
-
-            iou_list[i] = boundary_iou(A, B, 1)
-        remove_index = np.where(iou_list > threshold)
-        index = np.delete(index, remove_index)
-
-    return keep_poly
-
-
-def fourier2poly(fourier_coeff, num_reconstr_points=50):
-    """ Inverse Fourier transform
-        Args:
-            fourier_coeff (ndarray): Fourier coefficients shaped (n, 2k+1),
-                with n and k being candidates number and Fourier degree
-                respectively.
-            num_reconstr_points (int): Number of reconstructed polygon points.
-        Returns:
-            Polygons (ndarray): The reconstructed polygons shaped (n, n')
-        """
-
-    a = np.zeros((len(fourier_coeff), num_reconstr_points), dtype='complex')
-    k = (len(fourier_coeff[0]) - 1) // 2
-
-    a[:, 0:k + 1] = fourier_coeff[:, k:]
-    a[:, -k:] = fourier_coeff[:, :k]
-
-    poly_complex = ifft(a) * num_reconstr_points
-    polygon = np.zeros((len(fourier_coeff), num_reconstr_points, 2))
-    polygon[:, :, 0] = poly_complex.real
-    polygon[:, :, 1] = poly_complex.imag
-    return polygon.astype('int32').reshape((len(fourier_coeff), -1))
-
-
-class Node:
-
-    def __init__(self, ind):
-        self.__ind = ind
-        self.__links = set()
-
-    @property
-    def ind(self):
-        return self.__ind
-
-    @property
-    def links(self):
-        return set(self.__links)
-
-    def add_link(self, link_node):
-        self.__links.add(link_node)
-        link_node.__links.add(self)
-
-
-def graph_propagation(edges, scores, text_comps, edge_len_thr=50.):
-    """Propagate edge score information and construct graph. This code was
-    partially adapted from https://github.com/GXYM/DRRG licensed under the MIT
-    license.
-
-    Args:
-        edges (ndarray): The edge array of shape N * 2, each row is a node
-            index pair that makes up an edge in graph.
-        scores (ndarray): The edge score array.
-        text_comps (ndarray): The text components.
-        edge_len_thr (float): The edge length threshold.
-
-    Returns:
-        vertices (list[Node]): The Nodes in graph.
-        score_dict (dict): The edge score dict.
-    """
-    assert edges.ndim == 2
-    assert edges.shape[1] == 2
-    assert edges.shape[0] == scores.shape[0]
-    assert text_comps.ndim == 2
-    assert isinstance(edge_len_thr, float)
-
-    edges = np.sort(edges, axis=1)
-    score_dict = {}
-    for i, edge in enumerate(edges):
-        if text_comps is not None:
-            box1 = text_comps[edge[0], :8].reshape(4, 2)
-            box2 = text_comps[edge[1], :8].reshape(4, 2)
-            center1 = np.mean(box1, axis=0)
-            center2 = np.mean(box2, axis=0)
-            distance = norm(center1 - center2)
-            if distance > edge_len_thr:
-                scores[i] = 0
-        if (edge[0], edge[1]) in score_dict:
-            score_dict[edge[0], edge[1]] = 0.5 * (
-                score_dict[edge[0], edge[1]] + scores[i])
-        else:
-            score_dict[edge[0], edge[1]] = scores[i]
-
-    nodes = np.sort(np.unique(edges.flatten()))
-    mapping = -1 * np.ones((np.max(nodes) + 1), dtype=np.int)
-    mapping[nodes] = np.arange(nodes.shape[0])
-    order_inds = mapping[edges]
-    vertices = [Node(node) for node in nodes]
-    for ind in order_inds:
-        vertices[ind[0]].add_link(vertices[ind[1]])
-
-    return vertices, score_dict
-
-
-def connected_components(nodes, score_dict, link_thr):
-    """Conventional connected components searching. This code was partially
-    adapted from https://github.com/GXYM/DRRG licensed under the MIT license.
-
-    Args:
-        nodes (list[Node]): The list of Node objects.
-        score_dict (dict): The edge score dict.
-        link_thr (float): The link threshold.
-
-    Returns:
-        clusters (List[list[Node]]): The clustered Node objects.
-    """
-    assert isinstance(nodes, list)
-    assert all([isinstance(node, Node) for node in nodes])
-    assert isinstance(score_dict, dict)
-    assert isinstance(link_thr, float)
-
-    clusters = []
-    nodes = set(nodes)
-    while nodes:
-        node = nodes.pop()
-        cluster = {node}
-        node_queue = [node]
-        while node_queue:
-            node = node_queue.pop(0)
-            neighbors = set([
-                neighbor for neighbor in node.links if
-                score_dict[tuple(sorted([node.ind, neighbor.ind]))] >= link_thr
-            ])
-            neighbors.difference_update(cluster)
-            nodes.difference_update(neighbors)
-            cluster.update(neighbors)
-            node_queue.extend(neighbors)
-        clusters.append(list(cluster))
-    return clusters
-
-
-def clusters2labels(clusters, num_nodes):
-    """Convert clusters of Node to text component labels. This code was
-    partially adapted from https://github.com/GXYM/DRRG licensed under the MIT
-    license.
-
-    Args:
-        clusters (List[list[Node]]): The clusters of Node objects.
-        num_nodes (int): The total node number of graphs in an image.
-
-    Returns:
-        node_labels (ndarray): The node label array.
-    """
-    assert isinstance(clusters, list)
-    assert all([isinstance(cluster, list) for cluster in clusters])
-    assert all(
-        [isinstance(node, Node) for cluster in clusters for node in cluster])
-    assert isinstance(num_nodes, int)
-
-    node_labels = np.zeros(num_nodes)
-    for cluster_ind, cluster in enumerate(clusters):
-        for node in cluster:
-            node_labels[node.ind] = cluster_ind
-    return node_labels
-
-
-def remove_single(text_comps, comp_pred_labels):
-    """Remove isolated text components. This code was partially adapted from
-    https://github.com/GXYM/DRRG licensed under the MIT license.
-
-    Args:
-        text_comps (ndarray): The text components.
-        comp_pred_labels (ndarray): The clustering labels of text components.
-
-    Returns:
-        filtered_text_comps (ndarray): The text components with isolated ones
-            removed.
-        comp_pred_labels (ndarray): The clustering labels with labels of
-            isolated text components removed.
-    """
-    assert text_comps.ndim == 2
-    assert text_comps.shape[0] == comp_pred_labels.shape[0]
-
-    single_flags = np.zeros_like(comp_pred_labels)
-    pred_labels = np.unique(comp_pred_labels)
-    for label in pred_labels:
-        current_label_flag = (comp_pred_labels == label)
-        if np.sum(current_label_flag) == 1:
-            single_flags[np.where(current_label_flag)[0][0]] = 1
-    keep_ind = [i for i in range(len(comp_pred_labels)) if not single_flags[i]]
-    filtered_text_comps = text_comps[keep_ind, :]
-    filtered_labels = comp_pred_labels[keep_ind]
-
-    return filtered_text_comps, filtered_labels
-
-
-def norm2(point1, point2):
-    return ((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2)**0.5
-
-
-def min_connect_path(points):
-    """Find the shortest path to traverse all points. This code was partially
-    adapted from https://github.com/GXYM/DRRG licensed under the MIT license.
-
-    Args:
-        points(List[list[int]]): The point sequence [[x0, y0], [x1, y1], ...].
-
-    Returns:
-        shortest_path(List[list[int]]): The shortest index path.
-    """
-    assert isinstance(points, list)
-    assert all([isinstance(point, list) for point in points])
-    assert all([isinstance(coord, int) for point in points for coord in point])
-
-    points_queue = points.copy()
-    shortest_path = []
-    current_edge = [[], []]
-
-    edge_dict0 = {}
-    edge_dict1 = {}
-    current_edge[0] = points_queue[0]
-    current_edge[1] = points_queue[0]
-    points_queue.remove(points_queue[0])
-    while points_queue:
-        for point in points_queue:
-            length0 = norm2(point, current_edge[0])
-            edge_dict0[length0] = [point, current_edge[0]]
-            length1 = norm2(current_edge[1], point)
-            edge_dict1[length1] = [current_edge[1], point]
-        key0 = min(edge_dict0.keys())
-        key1 = min(edge_dict1.keys())
-
-        if key0 <= key1:
-            start = edge_dict0[key0][0]
-            end = edge_dict0[key0][1]
-            shortest_path.insert(0, [points.index(start), points.index(end)])
-            points_queue.remove(start)
-            current_edge[0] = start
-        else:
-            start = edge_dict1[key1][0]
-            end = edge_dict1[key1][1]
-            shortest_path.append([points.index(start), points.index(end)])
-            points_queue.remove(end)
-            current_edge[1] = end
-
-        edge_dict0 = {}
-        edge_dict1 = {}
-
-    shortest_path = functools.reduce(operator.concat, shortest_path)
-    shortest_path = sorted(set(shortest_path), key=shortest_path.index)
-
-    return shortest_path
-
-
-def in_contour(cont, point):
-    x, y = point
-    is_inner = cv2.pointPolygonTest(cont, (int(x), int(y)), False) > 0.5
-    return is_inner
-
-
-def fix_corner(top_line, bot_line, start_box, end_box):
-    """Add corner points to predicted side lines. This code was partially
-    adapted from https://github.com/GXYM/DRRG licensed under the MIT license.
-
-    Args:
-        top_line (List[list[int]]): The predicted top sidelines of text
-            instance.
-        bot_line (List[list[int]]): The predicted bottom sidelines of text
-            instance.
-        start_box (ndarray): The first text component box.
-        end_box (ndarray): The last text component box.
-
-    Returns:
-        top_line (List[list[int]]): The top sidelines with corner point added.
-        bot_line (List[list[int]]): The bottom sidelines with corner point
-            added.
-    """
-    assert isinstance(top_line, list)
-    assert all(isinstance(point, list) for point in top_line)
-    assert isinstance(bot_line, list)
-    assert all(isinstance(point, list) for point in bot_line)
-    assert start_box.shape == end_box.shape == (4, 2)
-
-    contour = np.array(top_line + bot_line[::-1])
-    start_left_mid = (start_box[0] + start_box[3]) / 2
-    start_right_mid = (start_box[1] + start_box[2]) / 2
-    end_left_mid = (end_box[0] + end_box[3]) / 2
-    end_right_mid = (end_box[1] + end_box[2]) / 2
-    if not in_contour(contour, start_left_mid):
-        top_line.insert(0, start_box[0].tolist())
-        bot_line.insert(0, start_box[3].tolist())
-    elif not in_contour(contour, start_right_mid):
-        top_line.insert(0, start_box[1].tolist())
-        bot_line.insert(0, start_box[2].tolist())
-    if not in_contour(contour, end_left_mid):
-        top_line.append(end_box[0].tolist())
-        bot_line.append(end_box[3].tolist())
-    elif not in_contour(contour, end_right_mid):
-        top_line.append(end_box[1].tolist())
-        bot_line.append(end_box[2].tolist())
-    return top_line, bot_line
-
-
-def comps2boundaries(text_comps, comp_pred_labels):
-    """Construct text instance boundaries from clustered text components. This
-    code was partially adapted from https://github.com/GXYM/DRRG licensed under
-    the MIT license.
-
-    Args:
-        text_comps (ndarray): The text components.
-        comp_pred_labels (ndarray): The clustering labels of text components.
-
-    Returns:
-        boundaries (List[list[float]]): The predicted boundaries of text
-            instances.
-    """
-    assert text_comps.ndim == 2
-    assert len(text_comps) == len(comp_pred_labels)
-    boundaries = []
-    if len(text_comps) < 1:
-        return boundaries
-    for cluster_ind in range(0, int(np.max(comp_pred_labels)) + 1):
-        cluster_comp_inds = np.where(comp_pred_labels == cluster_ind)
-        text_comp_boxes = text_comps[cluster_comp_inds, :8].reshape(
-            (-1, 4, 2)).astype(np.int32)
-        score = np.mean(text_comps[cluster_comp_inds, -1])
-
-        if text_comp_boxes.shape[0] < 1:
-            continue
-
-        elif text_comp_boxes.shape[0] > 1:
-            centers = np.mean(
-                text_comp_boxes, axis=1).astype(np.int32).tolist()
-            shortest_path = min_connect_path(centers)
-            text_comp_boxes = text_comp_boxes[shortest_path]
-            top_line = np.mean(
-                text_comp_boxes[:, 0:2, :], axis=1).astype(np.int32).tolist()
-            bot_line = np.mean(
-                text_comp_boxes[:, 2:4, :], axis=1).astype(np.int32).tolist()
-            top_line, bot_line = fix_corner(top_line, bot_line,
-                                            text_comp_boxes[0],
-                                            text_comp_boxes[-1])
-            boundary_points = top_line + bot_line[::-1]
-
-        else:
-            top_line = text_comp_boxes[0, 0:2, :].astype(np.int32).tolist()
-            bot_line = text_comp_boxes[0, 2:4:-1, :].astype(np.int32).tolist()
-            boundary_points = top_line + bot_line
-
-        boundary = [p for coord in boundary_points for p in coord] + [score]
-        boundaries.append(boundary)
-
-    return boundaries
diff --git a/spaces/tomofi/MMOCR/mmocr/models/textrecog/fusers/abi_fuser.py b/spaces/tomofi/MMOCR/mmocr/models/textrecog/fusers/abi_fuser.py
deleted file mode 100644
index 310cf6f0421ea3575f1935489440f1b37964a194..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/textrecog/fusers/abi_fuser.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-from mmcv.runner import BaseModule
-
-from mmocr.models.builder import FUSERS
-
-
-@FUSERS.register_module()
-class ABIFuser(BaseModule):
-    """Mix and align visual feature and linguistic feature Implementation of
-    language model of `ABINet <https://arxiv.org/abs/1910.04396>`_.
-
-    Args:
-        d_model (int): Hidden size of input.
-        max_seq_len (int): Maximum text sequence length :math:`T`.
-        num_chars (int): Number of text characters :math:`C`.
-        init_cfg (dict): Specifies the initialization method for model layers.
-    """
-
-    def __init__(self,
-                 d_model=512,
-                 max_seq_len=40,
-                 num_chars=90,
-                 init_cfg=None,
-                 **kwargs):
-        super().__init__(init_cfg=init_cfg)
-
-        self.max_seq_len = max_seq_len + 1  # additional stop token
-        self.w_att = nn.Linear(2 * d_model, d_model)
-        self.cls = nn.Linear(d_model, num_chars)
-
-    def forward(self, l_feature, v_feature):
-        """
-        Args:
-            l_feature: (N, T, E) where T is length, N is batch size and
-                d is dim of model.
-            v_feature: (N, T, E) shape the same as l_feature.
-
-        Returns:
-            A dict with key ``logits``
-            The logits of shape (N, T, C) where N is batch size, T is length
-                and C is the number of characters.
-        """
-        f = torch.cat((l_feature, v_feature), dim=2)
-        f_att = torch.sigmoid(self.w_att(f))
-        output = f_att * v_feature + (1 - f_att) * l_feature
-
-        logits = self.cls(output)  # (N, T, C)
-
-        return {'logits': logits}
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/README.md b/spaces/tomofi/NDLOCR/src/ndl_layout/README.md
deleted file mode 100644
index 1cda8fe093a10bd924dc56d580a5f5bfe04db50b..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/README.md
+++ /dev/null
@@ -1,92 +0,0 @@
-# NDLOCR用レイアウト認識モジュール
-
-レイアウト要素を抽出するためのモジュールのリポジトリです。
-
-本プログラムは、国立国会図書館が株式会社モルフォAIソリューションズに委託して作成したものです。
-
-本プログラムは、国立国会図書館がCC BY 4.0ライセンスで公開するものです。詳細については
-[LICENSE](./LICENSE
-)をご覧ください。
-
-# 環境構築
-
-python3.7かつ、cuda 11.1をインストール済みの環境の場合
-ndl_layoutディレクトリ直下で以下のコマンドを実行する。
-```
-pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
-wget https://lab.ndl.go.jp/dataset/ndlocr/ndl_layout/ndl_layout_config.py -P ./models
-wget https://lab.ndl.go.jp/dataset/ndlocr/ndl_layout/epoch_140_all_eql_bt.pth -P ./models
-```
-
-くわえて、元リポジトリ(https://github.com/open-mmlab/mmdetection)
-をカスタマイズした[mmdetection](https://github.com/ndl-lab/mmdetection)
-に依存しているため、下記のようにリポジトリの追加とインストールを行う。
-
-```bash
-git clone https://github.com/ndl-lab/mmdetection
-cd mmdetection
-python setup.py bdist_wheel
-pip install dist/*.whl
-```
-
-
-# 使い方
-※スクリプトファイルはndl_layoutディレクトリ直下で実行すること
-
-## tools/process.py : 推論用モジュール + CLI
-
-学習結果を使って推論を実行する。学習済みのモデルは`ndl_layout/models` 以下にあるものとする。
-
-画像リストを引数で指定するには img_paths オプションを、画像リストをファイルから読み込む場合には list_path オプションを指定する。
-
-output_path で出力 XML ファイルの格納先を変更することができる。（デフォルトは layout_prediction.xml）
-
-use_show オプションを追加すると処理結果をGUI上で確認することができる。
-
-img_pathsオプションで画像リストを指定する例
-```bash
-python -m tools.process --img_paths image/dir/path/*.jpg --use_show --output_path layout_prediction.xml --config ./models/ndl_layout_config.py --checkpoint ./models/epoch_140_all_eql_bt.pth
-```
-
-list_path オプションで画像リストを指定する例
-```bash
-python -m tools.process --list_path image_list_file.list --use_show --output_path layout_prediction.xml --config ./models/ndl_layout_config.py --checkpoint ./models/epoch_140_all_eql_bt.pth
-```
-
-## tools/preprocess.py : 学習画像の追加＆変換
-
-画像のファイル名の変換、縮小を行い、MS COCO 形式に整形。
-
-```bash
-python -m tools.preprocess images_data_dir output_dir --use_link
-```
-
-出力解像度を下げる必要がない場合には、`--use_link`オプションを指定する。
-
-高解像の場合など、解像度を下げたい場合には `--use_shrink` を使うと画像サイズとアノテーションを半分のサイズに縮小して出力する。
-
-本リポジトリの追加学習に使用可能なファイル(アノテーション情報の含まれるjson及び、前処理後の画像)は `output_dir` で指定したディレクトリに出力される。 
-
-
-## 学習時の手順
-1) ndl_layout/tools/preprocess.pyを使用し、NDLOCRXMLDataset形式の画像とアノテーションファイル(xml)をCOCO形式に変換し保存する。
-```
-cd mmdetection
-python -m tools.preprocess images_data_dir output_dir --use_link
-```
-output_dir内に画像のシンボリックリンク（またはコピー）とCOCO形式のアノテーションファイル(.json)を保存する。
-
-アノテーションファイルは、data.json(全データのアノテーション)、train.json(ランダムに全体の9割)、test.json(train以外の残る1割)を生成する。
-
-2) mmdetection/tools/train_ndl.py を使用し、モデルを学習する。
-```
-cd mmdetection
-python tools/train_ndl.py configs/ndl/cascade_rcnn_r50_fpn_1x_ndl_1024_eql.py
-```
-学習データ、work directory、初期値、学習回数等はconfigファイル内で指定するか、train_ndl.pyのオプションを使用する。オプションで指定されたものが優先される。
-
-work directoryに、学習したモデル(epoch_XX.pth または latest.pth)とconfigファイル(train_ndl.pyのオプションを使用した場合その内容も反映)、学習時のログファイル(.logと.log.json)が保存される。
-
-なお、このリポジトリで公開しているモデル（設定ファイルは`configs/ndl/cascade_rcnn_r50_fpn_1x_ndl_1024_eql.py`を参照）の学習時の初期重みには
-https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth
-を使用した。
diff --git a/spaces/trysem/Vector-diFusion/style.css b/spaces/trysem/Vector-diFusion/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/trysem/Vector-diFusion/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/ulysses115/diffsvc_test/modules/nsf_hifigan/nvSTFT.py b/spaces/ulysses115/diffsvc_test/modules/nsf_hifigan/nvSTFT.py
deleted file mode 100644
index 35635c844ea1ae6258112f0ba92e417e81a22642..0000000000000000000000000000000000000000
--- a/spaces/ulysses115/diffsvc_test/modules/nsf_hifigan/nvSTFT.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-import os
-os.environ["LRU_CACHE_CAPACITY"] = "3"
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.util import normalize
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
-import soundfile as sf
-
-def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
-    sampling_rate = None
-    try:
-        data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
-    except Exception as ex:
-        print(f"'{full_path}' failed to load.\nException:")
-        print(ex)
-        if return_empty_on_exception:
-            return [], sampling_rate or target_sr or 48000
-        else:
-            raise Exception(ex)
-
-    if len(data.shape) > 1:
-        data = data[:, 0]
-        assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
-
-    if np.issubdtype(data.dtype, np.integer): # if audio data is type int
-        max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
-    else: # if audio data is type fp32
-        max_mag = max(np.amax(data), -np.amin(data))
-        max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
-
-    data = torch.FloatTensor(data.astype(np.float32))/max_mag
-
-    if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
-        return [], sampling_rate or target_sr or 48000
-    if target_sr is not None and sampling_rate != target_sr:
-        data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
-        sampling_rate = target_sr
-
-    return data, sampling_rate
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
-    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-def dynamic_range_decompression(x, C=1):
-    return np.exp(x) / C
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-def dynamic_range_decompression_torch(x, C=1):
-    return torch.exp(x) / C
-
-class STFT():
-    def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
-        self.target_sr = sr
-
-        self.n_mels     = n_mels
-        self.n_fft      = n_fft
-        self.win_size   = win_size
-        self.hop_length = hop_length
-        self.fmin     = fmin
-        self.fmax     = fmax
-        self.clip_val = clip_val
-        self.mel_basis = {}
-        self.hann_window = {}
-
-    def get_mel(self, y, center=False):
-        sampling_rate = self.target_sr
-        n_mels     = self.n_mels
-        n_fft      = self.n_fft
-        win_size   = self.win_size
-        hop_length = self.hop_length
-        fmin       = self.fmin
-        fmax       = self.fmax
-        clip_val   = self.clip_val
-
-        if torch.min(y) < -1.:
-            print('min value is ', torch.min(y))
-        if torch.max(y) > 1.:
-            print('max value is ', torch.max(y))
-
-        if fmax not in self.mel_basis:
-            mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
-            self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
-            self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
-
-        y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
-        y = y.squeeze(1)
-
-        spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
-                          center=center, pad_mode='reflect', normalized=False, onesided=True)
-        # print(111,spec)
-        spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
-        # print(222,spec)
-        spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
-        # print(333,spec)
-        spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
-        # print(444,spec)
-        return spec
-
-    def __call__(self, audiopath):
-        audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
-        spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
-        return spect
-
-stft = STFT()
\ No newline at end of file
diff --git a/spaces/ulysses115/diffsvc_test/preprocessing/base_binarizer.py b/spaces/ulysses115/diffsvc_test/preprocessing/base_binarizer.py
deleted file mode 100644
index f070584c60c091c3fa4bc1da377733698b0165b6..0000000000000000000000000000000000000000
--- a/spaces/ulysses115/diffsvc_test/preprocessing/base_binarizer.py
+++ /dev/null
@@ -1,237 +0,0 @@
-import os
-from webbrowser import get
-os.environ["OMP_NUM_THREADS"] = "1"
-import yaml
-from utils.multiprocess_utils import chunked_multiprocess_run
-import random
-import json
-# from resemblyzer import VoiceEncoder
-from tqdm import tqdm
-from preprocessing.data_gen_utils import get_mel2ph, get_pitch_parselmouth, build_phone_encoder,get_pitch_crepe
-from utils.hparams import set_hparams, hparams
-import numpy as np
-from utils.indexed_datasets import IndexedDatasetBuilder
-
-
-class BinarizationError(Exception):
-    pass
-
-BASE_ITEM_ATTRIBUTES = ['txt', 'ph', 'wav_fn', 'tg_fn', 'spk_id']
-
-class BaseBinarizer:
-    '''
-        Base class for data processing.
-        1. *process* and *process_data_split*:
-            process entire data, generate the train-test split (support parallel processing);
-        2. *process_item*:
-            process singe piece of data;
-        3. *get_pitch*:
-            infer the pitch using some algorithm;
-        4. *get_align*:
-            get the alignment using 'mel2ph' format (see https://arxiv.org/abs/1905.09263).
-        5. phoneme encoder, voice encoder, etc.
-
-        Subclasses should define:
-        1. *load_metadata*:
-            how to read multiple datasets from files;
-        2. *train_item_names*, *valid_item_names*, *test_item_names*:
-            how to split the dataset;
-        3. load_ph_set:
-            the phoneme set.
-    '''
-    def __init__(self, item_attributes=BASE_ITEM_ATTRIBUTES):
-        self.binarization_args = hparams['binarization_args']
-        #self.pre_align_args = hparams['pre_align_args']
-        
-        self.items = {}
-        # every item in self.items has some attributes
-        self.item_attributes = item_attributes
-
-        self.load_meta_data()
-        # check program correctness 检查itemdict的key只能在给定的列表中取值
-        assert all([attr in self.item_attributes for attr in list(self.items.values())[0].keys()])
-        self.item_names = sorted(list(self.items.keys()))
-        
-        if self.binarization_args['shuffle']:
-            random.seed(1234)
-            random.shuffle(self.item_names)
-        
-        # set default get_pitch algorithm
-        if hparams['use_crepe']:
-            self.get_pitch_algorithm = get_pitch_crepe
-        else:
-            self.get_pitch_algorithm = get_pitch_parselmouth
-
-    def load_meta_data(self):
-        raise NotImplementedError
-
-    @property
-    def train_item_names(self):
-        raise NotImplementedError
-        
-    @property
-    def valid_item_names(self):
-        raise NotImplementedError
-
-    @property
-    def test_item_names(self):
-        raise NotImplementedError
-
-    def build_spk_map(self):
-        spk_map = set()
-        for item_name in self.item_names:
-            spk_name = self.items[item_name]['spk_id']
-            spk_map.add(spk_name)
-        spk_map = {x: i for i, x in enumerate(sorted(list(spk_map)))}
-        assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map)
-        return spk_map
-
-    def item_name2spk_id(self, item_name):
-        return self.spk_map[self.items[item_name]['spk_id']]
-
-    def _phone_encoder(self):
-        '''
-        use hubert encoder
-        '''
-        raise NotImplementedError
-        '''
-            create 'phone_set.json' file if it doesn't exist
-        '''
-        ph_set_fn = f"{hparams['binary_data_dir']}/phone_set.json"
-        ph_set = []
-        if hparams['reset_phone_dict'] or not os.path.exists(ph_set_fn):
-            self.load_ph_set(ph_set)
-            ph_set = sorted(set(ph_set))
-            json.dump(ph_set, open(ph_set_fn, 'w', encoding='utf-8'))
-            print("| Build phone set: ", ph_set)
-        else:
-            ph_set = json.load(open(ph_set_fn, 'r', encoding='utf-8'))
-            print("| Load phone set: ", ph_set)
-        return build_phone_encoder(hparams['binary_data_dir'])
-    
-
-    def load_ph_set(self, ph_set):
-        raise NotImplementedError
-
-    def meta_data_iterator(self, prefix):
-        if prefix == 'valid':
-            item_names = self.valid_item_names
-        elif prefix == 'test':
-            item_names = self.test_item_names
-        else:
-            item_names = self.train_item_names
-        for item_name in item_names:
-            meta_data = self.items[item_name]
-            yield item_name, meta_data
-
-    def process(self):
-        os.makedirs(hparams['binary_data_dir'], exist_ok=True)
-        self.spk_map = self.build_spk_map()
-        print("| spk_map: ", self.spk_map)
-        spk_map_fn = f"{hparams['binary_data_dir']}/spk_map.json"
-        json.dump(self.spk_map, open(spk_map_fn, 'w', encoding='utf-8'))
-
-        self.phone_encoder =self._phone_encoder()
-        self.process_data_split('valid')
-        self.process_data_split('test')
-        self.process_data_split('train')
-
-    def process_data_split(self, prefix):
-        data_dir = hparams['binary_data_dir']
-        args = []
-        builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}')
-        lengths = []
-        f0s = []
-        total_sec = 0
-        # if self.binarization_args['with_spk_embed']:
-        #     voice_encoder = VoiceEncoder().cuda()
-
-        for item_name, meta_data in self.meta_data_iterator(prefix):
-            args.append([item_name, meta_data, self.binarization_args])
-        spec_min=[]
-        spec_max=[]
-        # code for single cpu processing
-        for i in tqdm(reversed(range(len(args))), total=len(args)):
-            a = args[i]
-            item = self.process_item(*a)
-            if item is None:
-                continue
-            spec_min.append(item['spec_min'])
-            spec_max.append(item['spec_max'])
-            # item['spk_embe'] = voice_encoder.embed_utterance(item['wav']) \
-            #     if self.binardization_args['with_spk_embed'] else None
-            if not self.binarization_args['with_wav'] and 'wav' in item:
-                if hparams['debug']:
-                    print("del wav")
-                del item['wav']
-            if(hparams['debug']):
-                print(item)
-            builder.add_item(item)
-            lengths.append(item['len'])
-            total_sec += item['sec']
-            # if item.get('f0') is not None:
-            #     f0s.append(item['f0'])
-        if prefix=='train':
-            spec_max=np.max(spec_max,0)
-            spec_min=np.min(spec_min,0)
-            print(spec_max.shape)
-            with open(hparams['config_path'], encoding='utf-8') as f:
-                _hparams=yaml.safe_load(f)
-                _hparams['spec_max']=spec_max.tolist()
-                _hparams['spec_min']=spec_min.tolist()
-            with open(hparams['config_path'], 'w', encoding='utf-8') as f:
-                yaml.safe_dump(_hparams,f)
-        builder.finalize()
-        np.save(f'{data_dir}/{prefix}_lengths.npy', lengths)
-        if len(f0s) > 0:
-            f0s = np.concatenate(f0s, 0)
-            f0s = f0s[f0s != 0]
-            np.save(f'{data_dir}/{prefix}_f0s_mean_std.npy', [np.mean(f0s).item(), np.std(f0s).item()])
-        print(f"| {prefix} total duration: {total_sec:.3f}s")
-
-    def process_item(self, item_name, meta_data, binarization_args):
-        from preprocessing.process_pipeline import File2Batch
-        return File2Batch.temporary_dict2processed_input(item_name, meta_data, self.phone_encoder, binarization_args)
-
-    def get_align(self, meta_data, mel, phone_encoded, res):
-        raise NotImplementedError
-
-    def get_align_from_textgrid(self, meta_data, mel, phone_encoded, res):
-        '''
-            NOTE: this part of script is *isolated* from other scripts, which means
-                  it may not be compatible with the current version.
-        '''
-        return
-        tg_fn, ph = meta_data['tg_fn'], meta_data['ph']
-        if tg_fn is not None and os.path.exists(tg_fn):
-            mel2ph, dur = get_mel2ph(tg_fn, ph, mel, hparams)
-        else:
-            raise BinarizationError(f"Align not found")
-        if mel2ph.max() - 1 >= len(phone_encoded):
-            raise BinarizationError(
-                f"Align does not match: mel2ph.max() - 1: {mel2ph.max() - 1}, len(phone_encoded): {len(phone_encoded)}")
-        res['mel2ph'] = mel2ph
-        res['dur'] = dur
-
-    def get_f0cwt(self, f0, res):
-        '''
-            NOTE: this part of script is *isolated* from other scripts, which means
-                  it may not be compatible with the current version.
-        '''
-        return
-        from utils.cwt import get_cont_lf0, get_lf0_cwt
-        uv, cont_lf0_lpf = get_cont_lf0(f0)
-        logf0s_mean_org, logf0s_std_org = np.mean(cont_lf0_lpf), np.std(cont_lf0_lpf)
-        cont_lf0_lpf_norm = (cont_lf0_lpf - logf0s_mean_org) / logf0s_std_org
-        Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm)
-        if np.any(np.isnan(Wavelet_lf0)):
-            raise BinarizationError("NaN CWT")
-        res['cwt_spec'] = Wavelet_lf0
-        res['cwt_scales'] = scales
-        res['f0_mean'] = logf0s_mean_org
-        res['f0_std'] = logf0s_std_org
-
-
-if __name__ == "__main__":
-    set_hparams()
-    BaseBinarizer().process()
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/City Car Driving 2.2.7 Crack The Best Way to Master the Basic Skills of Car Driving.md b/spaces/usbethFlerru/sovits-modelsV2/example/City Car Driving 2.2.7 Crack The Best Way to Master the Basic Skills of Car Driving.md
deleted file mode 100644
index cb9c9111d3320e6de31e11c89735ccd2d4a07c06..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/City Car Driving 2.2.7 Crack The Best Way to Master the Basic Skills of Car Driving.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-
-<p>The car driving game named <b>"City Car Driving"</b> is a new car simulator, designed to help users experience car driving in а big city, the countryside and in different conditions or go just for a joy ride. Special stress in the <b>"City Car Driving"</b> simulator has been laid on a variety of different road situations and realistic car driving.</p>
-<h2>city car driving 2.2.7 crack free download</h2><br /><p><b><b>DOWNLOAD</b> &middot;&middot;&middot;&middot;&middot; <a href="https://urlcod.com/2uyXUt">https://urlcod.com/2uyXUt</a></b></p><br /><br /> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum.py b/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum.py
deleted file mode 100644
index 50c188c2475f58572540f615d957f8d28d3f019d..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum.py
+++ /dev/null
@@ -1,318 +0,0 @@
-# Detach 'deforum_helpers' from 'scripts' to prevent "No module named 'scripts.deforum_helpers'"  error 
-# causing Deforum's tab not show up in some cases when you've might've broken the environment with webui packages updates
-import sys, os, shutil
-
-basedirs = [os.getcwd()]
-if 'google.colab' in sys.modules:
-    basedirs.append('/content/gdrive/MyDrive/sd/stable-diffusion-webui') #hardcode as TheLastBen's colab seems to be the primal source
-
-for basedir in basedirs:
-    deforum_paths_to_ensure = [basedir + '/extensions/deforum-for-automatic1111-webui/scripts', basedir + '/extensions/sd-webui-controlnet', basedir + '/extensions/deforum/scripts', basedir + '/scripts/deforum_helpers/src', basedir + '/extensions/deforum/scripts/deforum_helpers/src', basedir +'/extensions/deforum-for-automatic1111-webui/scripts/deforum_helpers/src',basedir]
-
-    for deforum_scripts_path_fix in deforum_paths_to_ensure:
-        if not deforum_scripts_path_fix in sys.path:
-            sys.path.extend([deforum_scripts_path_fix])
-
-# Main deforum stuff
-import deforum_helpers.args as deforum_args
-import deforum_helpers.settings as deforum_settings
-from deforum_helpers.save_images import dump_frames_cache, reset_frames_cache
-from deforum_helpers.frame_interpolation import process_video_interpolation
-
-import modules.scripts as wscripts
-from modules import script_callbacks
-import gradio as gr
-import json
-
-from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
-from PIL import Image
-from deforum_helpers.video_audio_utilities import ffmpeg_stitch_video, make_gifski_gif
-from deforum_helpers.upscaling import make_upscale_v2
-import gc
-import torch
-from webui import wrap_gradio_gpu_call
-import modules.shared as shared
-from modules.shared import opts, cmd_opts, state
-from modules.ui import create_output_panel, plaintext_to_html, wrap_gradio_call
-from types import SimpleNamespace
-
-def run_deforum(*args, **kwargs):
-    args_dict = {deforum_args.component_names[i]: args[i+2] for i in range(0, len(deforum_args.component_names))}
-    p = StableDiffusionProcessingImg2Img(
-        sd_model=shared.sd_model,
-        outpath_samples = opts.outdir_samples or opts.outdir_img2img_samples,
-        outpath_grids = opts.outdir_grids or opts.outdir_img2img_grids,
-        #we'll setup the rest later
-    )
-    
-    print("\033[4;33mDeforum extension for auto1111 webui, v2.2b\033[0m")
-    args_dict['self'] = None
-    args_dict['p'] = p
-    
-    root, args, anim_args, video_args, parseq_args, loop_args, controlnet_args = deforum_args.process_args(args_dict)
-    root.clipseg_model = None
-    root.initial_clipskip = opts.data["CLIP_stop_at_last_layers"]
-    root.basedirs = basedirs
-
-    for basedir in basedirs:
-        sys.path.extend([
-            basedir + '/scripts/deforum_helpers/src',
-            basedir + '/extensions/deforum/scripts/deforum_helpers/src',
-            basedir + '/extensions/deforum-for-automatic1111-webui/scripts/deforum_helpers/src',
-        ])
-    
-    # clean up unused memory
-    reset_frames_cache(root)
-    gc.collect()
-    torch.cuda.empty_cache()
-    
-    from deforum_helpers.render import render_animation
-    from deforum_helpers.render_modes import render_input_video, render_animation_with_video_mask, render_interpolation
-
-    tqdm_backup = shared.total_tqdm
-    shared.total_tqdm = deforum_settings.DeforumTQDM(args, anim_args, parseq_args)
-    try:
-        # dispatch to appropriate renderer
-        if anim_args.animation_mode == '2D' or anim_args.animation_mode == '3D':
-            if anim_args.use_mask_video: 
-                render_animation_with_video_mask(args, anim_args, video_args, parseq_args, loop_args, controlnet_args, root.animation_prompts, root) # allow mask video without an input video
-            else:    
-                render_animation(args, anim_args, video_args, parseq_args, loop_args, controlnet_args, root.animation_prompts, root)
-        elif anim_args.animation_mode == 'Video Input':
-            render_input_video(args, anim_args, video_args, parseq_args, loop_args, controlnet_args, root.animation_prompts, root)#TODO: prettify code
-        elif anim_args.animation_mode == 'Interpolation':
-            render_interpolation(args, anim_args, video_args, parseq_args, loop_args, controlnet_args, root.animation_prompts, root)
-        else:
-            print('Other modes are not available yet!')
-    finally:
-        shared.total_tqdm = tqdm_backup
-        opts.data["CLIP_stop_at_last_layers"] = root.initial_clipskip
-    
-    if video_args.store_frames_in_ram:
-        dump_frames_cache(root)
-    
-    from base64 import b64encode
-    
-    real_audio_track = None
-    if video_args.add_soundtrack != 'None':
-        real_audio_track = anim_args.video_init_path if video_args.add_soundtrack == 'Init Video' else video_args.soundtrack_path
-    
-    # Delete folder with duplicated imgs from OS temp folder
-    shutil.rmtree(root.tmp_deforum_run_duplicated_folder, ignore_errors=True)
-
-    # Decide whether or not we need to try and frame interpolate laters
-    need_to_frame_interpolate = False
-    if video_args.frame_interpolation_engine != "None" and not video_args.skip_video_for_run_all and not video_args.store_frames_in_ram:
-        need_to_frame_interpolate = True
-        
-    if video_args.skip_video_for_run_all:
-        print('Skipping video creation, uncheck skip_video_for_run_all if you want to run it')
-    else:
-        import subprocess
-
-        path_name_modifier = video_args.path_name_modifier
-        if video_args.render_steps: # render steps from a single image
-            fname = f"{path_name_modifier}_%05d.png"
-            all_step_dirs = [os.path.join(args.outdir, d) for d in os.listdir(args.outdir) if os.path.isdir(os.path.join(args.outdir,d))]
-            newest_dir = max(all_step_dirs, key=os.path.getmtime)
-            image_path = os.path.join(newest_dir, fname)
-            print(f"Reading images from {image_path}")
-            mp4_path = os.path.join(newest_dir, f"{args.timestring}_{path_name_modifier}.mp4")
-            max_video_frames = args.steps
-        else: # render images for a video
-            image_path = os.path.join(args.outdir, f"{args.timestring}_%05d.png")
-            mp4_path = os.path.join(args.outdir, f"{args.timestring}.mp4")
-            max_video_frames = anim_args.max_frames
-
-        exclude_keys = deforum_settings.get_keys_to_exclude('video')
-        video_settings_filename = os.path.join(args.outdir, f"{args.timestring}_video-settings.txt")
-        with open(video_settings_filename, "w+", encoding="utf-8") as f:
-            s = {}
-            for key, value in dict(video_args.__dict__).items():
-                if key not in exclude_keys:
-                    s[key] = value
-            json.dump(s, f, ensure_ascii=False, indent=4)
-
-        # Stitch video using ffmpeg!
-        try:
-            ffmpeg_stitch_video(ffmpeg_location=video_args.ffmpeg_location, fps=video_args.fps, outmp4_path=mp4_path, stitch_from_frame=0, stitch_to_frame=max_video_frames, imgs_path=image_path, add_soundtrack=video_args.add_soundtrack, audio_path=real_audio_track, crf=video_args.ffmpeg_crf, preset=video_args.ffmpeg_preset)
-            mp4 = open(mp4_path,'rb').read()
-            data_url = "data:video/mp4;base64," + b64encode(mp4).decode()
-            deforum_args.i1_store = f'<p style=\"font-weight:bold;margin-bottom:0em\">Deforum v0.5-webui-beta</p><video controls loop><source src="{data_url}" type="video/mp4"></video>'
-        except Exception as e:
-            if need_to_frame_interpolate:
-                print(f"FFMPEG DID NOT STITCH ANY VIDEO. However, you requested to frame interpolate  - so we will continue to frame interpolation, but you'll be left only with the interpolated frames and not a video, since ffmpeg couldn't run. Original ffmpeg error: {e}")
-            else:
-                print(f"** FFMPEG DID NOT STITCH ANY VIDEO ** Error: {e}")
-            pass
-            
-    if root.initial_info is None:
-        root.initial_info = "An error has occured and nothing has been generated!"
-        root.initial_info += "\nPlease, report the bug to https://github.com/deforum-art/deforum-for-automatic1111-webui/issues"
-        import numpy as np
-        a = np.random.rand(args.W, args.H, 3)*255
-        root.first_frame = Image.fromarray(a.astype('uint8')).convert('RGB')
-        root.initial_seed = 6934
-    # FRAME INTERPOLATION TIME
-    if need_to_frame_interpolate: 
-        print(f"Got a request to *frame interpolate* using {video_args.frame_interpolation_engine}")
-        process_video_interpolation(frame_interpolation_engine=video_args.frame_interpolation_engine, frame_interpolation_x_amount=video_args.frame_interpolation_x_amount,frame_interpolation_slow_mo_enabled=video_args.frame_interpolation_slow_mo_enabled, frame_interpolation_slow_mo_amount=video_args.frame_interpolation_slow_mo_amount, orig_vid_fps=video_args.fps, deforum_models_path=root.models_path, real_audio_track=real_audio_track, raw_output_imgs_path=args.outdir, img_batch_id=args.timestring, ffmpeg_location=video_args.ffmpeg_location, ffmpeg_crf=video_args.ffmpeg_crf, ffmpeg_preset=video_args.ffmpeg_preset, keep_interp_imgs=video_args.frame_interpolation_keep_imgs, orig_vid_name=None, resolution=None)
-    
-    if video_args.make_gif and not video_args.skip_video_for_run_all and not video_args.store_frames_in_ram:
-        make_gifski_gif(imgs_raw_path = args.outdir, imgs_batch_id = args.timestring, fps = video_args.fps, models_folder = root.models_path, current_user_os = root.current_user_os)
-    
-    # Upscale video once generation is done:
-    if video_args.r_upscale_video and not video_args.skip_video_for_run_all and not video_args.store_frames_in_ram:
-        
-        # out mp4 path is defined in make_upscale func
-        make_upscale_v2(upscale_factor = video_args.r_upscale_factor, upscale_model = video_args.r_upscale_model, keep_imgs = video_args.r_upscale_keep_imgs, imgs_raw_path = args.outdir, imgs_batch_id = args.timestring, fps = video_args.fps, deforum_models_path = root.models_path, current_user_os = root.current_user_os, ffmpeg_location=video_args.ffmpeg_location, stitch_from_frame=0, stitch_to_frame=max_video_frames, ffmpeg_crf=video_args.ffmpeg_crf, ffmpeg_preset=video_args.ffmpeg_preset, add_soundtrack = video_args.add_soundtrack ,audio_path=real_audio_track)
-        
-    root.initial_info += "\n The animation is stored in " + args.outdir
-    root.initial_info += "\n Timestring = " + args.timestring + '\n'
-    root.initial_info += "Only the first frame is shown in webui not to clutter the memory"
-    reset_frames_cache(root) # cleanup the RAM in any case
-    processed = Processed(p, [root.first_frame], root.initial_seed, root.initial_info)
-    
-    if processed is None:
-        processed = process_images(p)
-
-    shared.total_tqdm.clear()
-
-    generation_info_js = processed.js()
-    if opts.samples_log_stdout:
-        print(generation_info_js)
-
-    if opts.do_not_show_images:
-        processed.images = []
-
-    return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html('')
-
-def on_ui_tabs():
-    with gr.Blocks(analytics_enabled=False) as deforum_interface:
-        components = {}
-        dummy_component = gr.Label(visible=False)
-        with gr.Row(elem_id='deforum_progress_row').style(equal_height=False):
-            with gr.Column(scale=1, variant='panel'):
-                components = deforum_args.setup_deforum_setting_dictionary(None, True, True)
-        
-            with gr.Column(scale=1):
-                with gr.Row():
-                    btn = gr.Button("Click here after the generation to show the video")
-                    components['btn'] = btn
-                    close_btn = gr.Button("Close the video", visible=False)
-                with gr.Row():
-                    i1 = gr.HTML(deforum_args.i1_store, elem_id='deforum_header')
-                    components['i1'] = i1
-                    # Show video
-                    def show_vid():
-                        return {
-                            i1: gr.update(value=deforum_args.i1_store, visible=True),
-                            close_btn: gr.update(visible=True),
-                            btn: gr.update(value="Update the video", visible=True),
-                        }
-                
-                    btn.click(
-                        show_vid,
-                        [],
-                        [i1, close_btn, btn],
-                        )
-                    # Close video
-                    def close_vid():
-                        return {
-                            i1: gr.update(value=deforum_args.i1_store_backup, visible=True),
-                            close_btn: gr.update(visible=False),
-                            btn: gr.update(value="Click here after the generation to show the video", visible=True),
-                        }
-                    
-                    close_btn.click(
-                        close_vid,
-                        [],
-                        [i1, close_btn, btn],
-                        )
-                id_part = 'deforum'
-                with gr.Row(elem_id=f"{id_part}_generate_box"):
-                    skip = gr.Button('Skip', elem_id=f"{id_part}_skip", visible=False)
-                    interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", visible=True)
-                    submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
-
-                    skip.click(
-                        fn=lambda: state.skip(),
-                        inputs=[],
-                        outputs=[],
-                    )
-
-                    interrupt.click(
-                        fn=lambda: state.interrupt(),
-                        inputs=[],
-                        outputs=[],
-                    )
-                
-                deforum_gallery, generation_info, html_info, html_log = create_output_panel("deforum", opts.outdir_img2img_samples)
-
-                gr.HTML("<p>* Paths can be relative to webui folder OR full - absolute </p>")
-                with gr.Row():
-                    settings_path = gr.Textbox("deforum_settings.txt", elem_id='deforum_settings_path', label="General Settings File")
-                    #reuse_latest_settings_btn = gr.Button('Reuse Latest', elem_id='deforum_reuse_latest_settings_btn')#TODO
-                with gr.Row():
-                    save_settings_btn = gr.Button('Save Settings', elem_id='deforum_save_settings_btn')
-                    load_settings_btn = gr.Button('Load Settings', elem_id='deforum_load_settings_btn')
-                with gr.Row():
-                    video_settings_path = gr.Textbox("deforum_video-settings.txt", elem_id='deforum_video_settings_path', label="Video Settings File")
-                    #reuse_latest_video_settings_btn = gr.Button('Reuse Latest', elem_id='deforum_reuse_latest_video_settings_btn')#TODO
-                with gr.Row():
-                    save_video_settings_btn = gr.Button('Save Video Settings', elem_id='deforum_save_video_settings_btn')
-                    load_video_settings_btn = gr.Button('Load Video Settings', elem_id='deforum_load_video_settings_btn')
-
-                # components['prompts'].visible = False#hide prompts for the time being
-                #TODO clean up the code
-                components['save_sample_per_step'].visible = False
-                components['show_sample_per_step'].visible = False
-                components['display_samples'].visible = False
-
-        component_list = [components[name] for name in deforum_args.component_names]
-
-        submit.click(
-                    fn=wrap_gradio_gpu_call(run_deforum, extra_outputs=[None, '', '']),
-                    _js="submit_deforum",
-                    inputs=[dummy_component, dummy_component] + component_list,
-                    outputs=[
-                         deforum_gallery,
-                         generation_info,
-                         html_info,
-                         html_log,
-                    ],
-                )
-        
-        settings_component_list = [components[name] for name in deforum_args.settings_component_names]
-        video_settings_component_list = [components[name] for name in deforum_args.video_args_names]
-        stuff = gr.HTML("") # wrap gradio call garbage
-        stuff.visible = False
-        
-        save_settings_btn.click(
-                    fn=wrap_gradio_call(deforum_settings.save_settings),
-                    inputs=[settings_path] + settings_component_list,
-                    outputs=[stuff],
-                )
-        
-        load_settings_btn.click(
-                    fn=wrap_gradio_call(deforum_settings.load_settings),
-                    inputs=[settings_path]+ settings_component_list,
-                    outputs=settings_component_list + [stuff],
-                )
-        
-        save_video_settings_btn.click(
-                    fn=wrap_gradio_call(deforum_settings.save_video_settings),
-                    inputs=[video_settings_path] + video_settings_component_list,
-                    outputs=[stuff],
-                )
-        
-        load_video_settings_btn.click(
-                    fn=wrap_gradio_call(deforum_settings.load_video_settings),
-                    inputs=[video_settings_path] + video_settings_component_list,
-                    outputs=video_settings_component_list + [stuff],
-                )
-
-
-    return [(deforum_interface, "Deforum", "deforum_interface")]
-
-script_callbacks.on_ui_tabs(on_ui_tabs)
diff --git a/spaces/viait/vscode/start_server.sh b/spaces/viait/vscode/start_server.sh
deleted file mode 100644
index 5257809d2ea2bcb6ccb3b55473da34eb13982a36..0000000000000000000000000000000000000000
--- a/spaces/viait/vscode/start_server.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/bin/bash
-
-echo "Starting VSCode Server..."
-
-exec /app/openvscode-server/bin/openvscode-server --host 0.0.0.0 --port 7860 --without-connection-token \"${@}\" --
diff --git a/spaces/vivym/image-matting-app/ppmatting/transforms/__init__.py b/spaces/vivym/image-matting-app/ppmatting/transforms/__init__.py
deleted file mode 100644
index 7986cdd642998fb0638a81c9ea22615faf8bad0b..0000000000000000000000000000000000000000
--- a/spaces/vivym/image-matting-app/ppmatting/transforms/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .transforms import *
diff --git a/spaces/w1zrd/MusicGen/MODEL_CARD.md b/spaces/w1zrd/MusicGen/MODEL_CARD.md
deleted file mode 100644
index 6c2c9f883969eb905e74ad3376966d156cc5ca00..0000000000000000000000000000000000000000
--- a/spaces/w1zrd/MusicGen/MODEL_CARD.md
+++ /dev/null
@@ -1,81 +0,0 @@
-# MusicGen Model Card
-
-## Model details
-
-**Organization developing the model:** The FAIR team of Meta AI.
-
-**Model date:** MusicGen was trained between April 2023 and May 2023.
-
-**Model version:** This is the version 1 of the model.
-
-**Model type:** MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation.
-
-**Paper or resources for more information:** More information can be found in the paper [Simple and Controllable Music Generation][arxiv].
-
-**Citation details** See [our paper][arxiv]
-
-**License** Code is released under MIT, model weights are released under CC-BY-NC 4.0.
-
-**Where to send questions or comments about the model:** Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue.
-
-## Intended use
-**Primary intended use:** The primary use of MusicGen is research on AI-based music generation, including:
-
-- Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science
-- Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs
-
-**Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models.
-
-**Out-of-scope use cases** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
-
-## Metrics
-
-**Models performance measures:** We used the following objective measure to evaluate the model on a standard music benchmark:
-
-- Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish)
-- Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST)
-- CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model
-
-Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes:
-
-- Overall quality of the music samples;
-- Text relevance to the provided text input;
-- Adherence to the melody for melody-guided music generation.
-
-More details on performance measures and human studies can be found in the paper.
-
-**Decision thresholds:** Not applicable.
-
-## Evaluation datasets
-
-The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set.
-
-## Training datasets
-
-The model was trained on licensed data using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound),  [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing.
-
-## Quantitative analysis
-
-More information can be found in the paper [Simple and Controllable Music Generation][arxiv], in the Experimental Setup section.
-
-## Limitations and biases
-
-**Data:** The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model.
-
-**Mitigations:** Vocals have been removed from the data source using corresponding tags, and then using using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs).
-
-**Limitations:**
-
-- The model is not able to generate realistic vocals.
-- The model has been trained with English descriptions and will not perform as well in other languages.
-- The model does not perform equally well for all music styles and cultures.
-- The model sometimes generates end of songs, collapsing to silence.
-- It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results.
-
-**Biases:** The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive.
-
-**Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data.
-
-**Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.
-
-[arxiv]: https://arxiv.org/abs/2306.05284
diff --git a/spaces/wangbinhu/bingo/Dockerfile b/spaces/wangbinhu/bingo/Dockerfile
deleted file mode 100644
index c677b05b75f7e4b2beee8c97fb47957a0861a83e..0000000000000000000000000000000000000000
--- a/spaces/wangbinhu/bingo/Dockerfile
+++ /dev/null
@@ -1,7 +0,0 @@
-FROM weaigc/bingo:latest
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-CMD npm start
diff --git a/spaces/webis-huggingface-workshop/f_demo_question_gen/app.py b/spaces/webis-huggingface-workshop/f_demo_question_gen/app.py
deleted file mode 100644
index 6989e825433de6ea07cf397c6ab813e87288b31b..0000000000000000000000000000000000000000
--- a/spaces/webis-huggingface-workshop/f_demo_question_gen/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("huggingface/iarfmoose/t5-base-question-generator").launch();
\ No newline at end of file
diff --git a/spaces/weiren119/AudiogramDigitization/src/digitizer/report_components/symbol.py b/spaces/weiren119/AudiogramDigitization/src/digitizer/report_components/symbol.py
deleted file mode 100644
index 7e9c72567677cbdba98447b1fa6fc9a14e0686f0..0000000000000000000000000000000000000000
--- a/spaces/weiren119/AudiogramDigitization/src/digitizer/report_components/symbol.py
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/usr/bin/env python3
-"""
-Copyright (c) 2020, Carleton University Biomedical Informatics Collaboratory
-
-This source code is licensed under the MIT license found in the
-LICENSE file in the root directory of this source tree.
-"""
-
-from typing import List, Optional, Type
-import PIL.ImageDraw
-
-from interfaces import SymbolDict
-from .line import Line
-from .label import Label
-import utils.audiology as Audiology
-from utils.geometry import get_bounding_box_relative_to_original_report
-
-class Symbol(object):
-
-    def __init__(self, symbol_dict: dict, audiogram_coordinates: dict, correction_angle: float):
-        bbox = symbol_dict["boundingBox"]
-        self.p1 = { 
-            "x": bbox["x"],
-            "y": bbox["y"]
-        }
-
-        self.p2 = { 
-            "x": bbox["x"] + bbox["width"],
-            "y": bbox["y"] + bbox["height"]
-        }
-
-        self.dimensions = { 
-            "width": bbox["width"],
-            "height": bbox["height"]
-        }
-
-        self.absolute_bounding_box = get_bounding_box_relative_to_original_report(bbox, audiogram_coordinates, correction_angle)
-
-        self.ear = "left" if "left" in symbol_dict["measurementType"].lower() else "right"
-        self.masking = False if "unmasked" in symbol_dict["measurementType"].lower() else True
-        self.conduction = "air" if "air" in symbol_dict["measurementType"].lower() else "bone"
-        self.measurement_type = symbol_dict["measurementType"]
-        self.confidence = symbol_dict["confidence"]
-
-    def draw(self, canvas: PIL.ImageDraw):
-        """Draws the symbol's bounding box on the canvas (image) passed.
-
-        Parameters
-        ----------
-        canvas : PIL.ImageDraw
-        The PIL.ImageDraw on which the symbol is to be displayed.
-        """
-        color = "rgb(255,0,0)" if self.is_frequency() else "rgb(0,0,255)"
-        canvas.rectangle(
-            (self.p1["x"], self.p1["y"], self.p2["x"], self.p2["y"]),
-            outline=color,
-            width=3
-        )
-        canvas.text((self.p1["x"], self.p1["y"] - 10), str(self.get_value()), fill=color)
-
-    def get_center(self):
-        """Returns the center of the symbol's bounding box.
-
-        Returns
-        -------
-        dict
-        A dictionary describing the center of the symbol's bounding box
-        of the form { "x": int, "y": int }.
-        """
-        center = {
-            "x": (self.p1["x"] + self.p2["x"]) / 2,
-            "y": (self.p1["y"] + self.p2["y"]) / 2
-        }
-        return center
-
-    def to_dict(self) -> dict:
-        """Serializes the symbol to a dictionary.
-
-        Returns
-        -------
-        dict
-        A dictionary representing the symbol.
-        """
-        return {
-            "boundingBox": self.absolute_bounding_box,
-            "ear": self.ear,
-            "conduction": self.conduction,
-            "masking": self.masking,
-            "confidence": self.confidence,
-            "response": True,
-            "measurementType": self.measurement_type
-        }
-        
-    def __str__(self):
-        return f"Threshold(ear={self.ear}, conduction={self.conduction})"
-
-    def __repr__(self):
-        return self.__str__()
diff --git a/spaces/weiren119/AudiogramDigitization/src/digitizer/yolov5/utils/torch_utils.py b/spaces/weiren119/AudiogramDigitization/src/digitizer/yolov5/utils/torch_utils.py
deleted file mode 100644
index 52a1264bd9fb803bce1007636e05eaf9b2dbb29f..0000000000000000000000000000000000000000
--- a/spaces/weiren119/AudiogramDigitization/src/digitizer/yolov5/utils/torch_utils.py
+++ /dev/null
@@ -1,231 +0,0 @@
-import logging
-import math
-import os
-import time
-from copy import deepcopy
-
-import torch
-import torch.backends.cudnn as cudnn
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision.models as models
-
-logger = logging.getLogger(__name__)
-
-
-def init_seeds(seed=0):
-    torch.manual_seed(seed)
-
-    # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html
-    if seed == 0:  # slower, more reproducible
-        cudnn.deterministic = True
-        cudnn.benchmark = False
-    else:  # faster, less reproducible
-        cudnn.deterministic = False
-        cudnn.benchmark = True
-
-
-def select_device(device='', batch_size=None):
-    # device = 'cpu' or '0' or '0,1,2,3'
-    cpu_request = device.lower() == 'cpu'
-    if device and not cpu_request:  # if device requested other than 'cpu'
-        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
-        assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device  # check availablity
-
-    cuda = False if cpu_request else torch.cuda.is_available()
-    if cuda:
-        c = 1024 ** 2  # bytes to MB
-        ng = torch.cuda.device_count()
-        if ng > 1 and batch_size:  # check that batch_size is compatible with device_count
-            assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (batch_size, ng)
-        x = [torch.cuda.get_device_properties(i) for i in range(ng)]
-        s = 'Using CUDA '
-        for i in range(0, ng):
-            if i == 1:
-                s = ' ' * len(s)
-            #logger.info("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" %
-                        #(s, i, x[i].name, x[i].total_memory / c))
-    else:
-        pass
-        #logger.info('Using CPU')
-
-    #logger.info('')  # skip a line
-    return torch.device('cuda:0' if cuda else 'cpu')
-
-
-def time_synchronized():
-    torch.cuda.synchronize() if torch.cuda.is_available() else None
-    return time.time()
-
-
-def is_parallel(model):
-    return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
-
-
-def intersect_dicts(da, db, exclude=()):
-    # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
-    return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}
-
-
-def initialize_weights(model):
-    for m in model.modules():
-        t = type(m)
-        if t is nn.Conv2d:
-            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-        elif t is nn.BatchNorm2d:
-            m.eps = 1e-3
-            m.momentum = 0.03
-        elif t in [nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
-            m.inplace = True
-
-
-def find_modules(model, mclass=nn.Conv2d):
-    # Finds layer indices matching module class 'mclass'
-    return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
-
-
-def sparsity(model):
-    # Return global model sparsity
-    a, b = 0., 0.
-    for p in model.parameters():
-        a += p.numel()
-        b += (p == 0).sum()
-    return b / a
-
-
-def prune(model, amount=0.3):
-    # Prune model to requested global sparsity
-    import torch.nn.utils.prune as prune
-    print('Pruning model... ', end='')
-    for name, m in model.named_modules():
-        if isinstance(m, nn.Conv2d):
-            prune.l1_unstructured(m, name='weight', amount=amount)  # prune
-            prune.remove(m, 'weight')  # make permanent
-    print(' %.3g global sparsity' % sparsity(model))
-
-
-def fuse_conv_and_bn(conv, bn):
-    # https://tehnokv.com/posts/fusing-batchnorm-and-conv/
-    with torch.no_grad():
-        # init
-        fusedconv = nn.Conv2d(conv.in_channels,
-                              conv.out_channels,
-                              kernel_size=conv.kernel_size,
-                              stride=conv.stride,
-                              padding=conv.padding,
-                              bias=True).to(conv.weight.device)
-
-        # prepare filters
-        w_conv = conv.weight.clone().view(conv.out_channels, -1)
-        w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
-        fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
-
-        # prepare spatial bias
-        b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
-        b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
-        fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
-
-        return fusedconv
-
-
-def model_info(model, verbose=False):
-    # Plots a line-by-line description of a PyTorch model
-    n_p = sum(x.numel() for x in model.parameters())  # number parameters
-    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
-    if verbose:
-        print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
-        for i, (name, p) in enumerate(model.named_parameters()):
-            name = name.replace('module_list.', '')
-            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
-                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
-
-    try:  # FLOPS
-        from thop import profile
-        flops = profile(deepcopy(model), inputs=(torch.zeros(1, 3, 64, 64),), verbose=False)[0] / 1E9 * 2
-        fs = ', %.1f GFLOPS' % (flops * 100)  # 640x640 FLOPS
-    except:
-        fs = ''
-
-    #logger.info(
-    #    'Model Summary: %g layers, %g parameters, %g gradients%s' % (len(list(model.parameters())), n_p, n_g, fs))
-
-
-def load_classifier(name='resnet101', n=2):
-    # Loads a pretrained model reshaped to n-class output
-    model = models.__dict__[name](pretrained=True)
-
-    # Display model properties
-    input_size = [3, 224, 224]
-    input_space = 'RGB'
-    input_range = [0, 1]
-    mean = [0.485, 0.456, 0.406]
-    std = [0.229, 0.224, 0.225]
-    for x in ['input_size', 'input_space', 'input_range', 'mean', 'std']:
-        print(x + ' =', eval(x))
-
-    # Reshape output to n classes
-    filters = model.fc.weight.shape[1]
-    model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True)
-    model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True)
-    model.fc.out_features = n
-    return model
-
-
-def scale_img(img, ratio=1.0, same_shape=False):  # img(16,3,256,416), r=ratio
-    # scales img(bs,3,y,x) by ratio
-    if ratio == 1.0:
-        return img
-    else:
-        h, w = img.shape[2:]
-        s = (int(h * ratio), int(w * ratio))  # new size
-        img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
-        if not same_shape:  # pad/crop img
-            gs = 32  # (pixels) grid size
-            h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]
-        return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean
-
-
-def copy_attr(a, b, include=(), exclude=()):
-    # Copy attributes from b to a, options to only include [...] and to exclude [...]
-    for k, v in b.__dict__.items():
-        if (len(include) and k not in include) or k.startswith('_') or k in exclude:
-            continue
-        else:
-            setattr(a, k, v)
-
-
-class ModelEMA:
-    """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
-    Keep a moving average of everything in the model state_dict (parameters and buffers).
-    This is intended to allow functionality like
-    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
-    A smoothed version of the weights is necessary for some training schemes to perform well.
-    This class is sensitive where it is initialized in the sequence of model init,
-    GPU assignment and distributed training wrappers.
-    """
-
-    def __init__(self, model, decay=0.9999, updates=0):
-        # Create EMA
-        self.ema = deepcopy(model.module if is_parallel(model) else model).eval()  # FP32 EMA
-        # if next(model.parameters()).device.type != 'cpu':
-        #     self.ema.half()  # FP16 EMA
-        self.updates = updates  # number of EMA updates
-        self.decay = lambda x: decay * (1 - math.exp(-x / 2000))  # decay exponential ramp (to help early epochs)
-        for p in self.ema.parameters():
-            p.requires_grad_(False)
-
-    def update(self, model):
-        # Update EMA parameters
-        with torch.no_grad():
-            self.updates += 1
-            d = self.decay(self.updates)
-
-            msd = model.module.state_dict() if is_parallel(model) else model.state_dict()  # model state_dict
-            for k, v in self.ema.state_dict().items():
-                if v.dtype.is_floating_point:
-                    v *= d
-                    v += (1. - d) * msd[k].detach()
-
-    def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
-        # Update EMA attributes
-        copy_attr(self.ema, model, include, exclude)
diff --git a/spaces/widged/bart-generation/README.md b/spaces/widged/bart-generation/README.md
deleted file mode 100644
index d7809fa15a367a7d8617cd7ef321085cd0ef1799..0000000000000000000000000000000000000000
--- a/spaces/widged/bart-generation/README.md
+++ /dev/null
@@ -1,41 +0,0 @@
----
-title: Bart Generation
-emoji: 😻
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
-
-# Warnings
-
-:WARN: Not my own work. Borrowed or adapted from another space of the same name.
diff --git a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/models/nasnet.py b/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/models/nasnet.py
deleted file mode 100644
index b1f31def5515c3ba464c86cde471328b50c55b14..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/models/nasnet.py
+++ /dev/null
@@ -1,1131 +0,0 @@
-from __future__ import division, absolute_import
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.model_zoo as model_zoo
-
-__all__ = ['nasnetamobile']
-"""
-NASNet Mobile
-Thanks to Anastasiia (https://github.com/DagnyT) for the great help, support and motivation!
-
-
-------------------------------------------------------------------------------------
-      Architecture       | Top-1 Acc | Top-5 Acc |  Multiply-Adds |  Params (M)
-------------------------------------------------------------------------------------
-|   NASNet-A (4 @ 1056)  |   74.08%  |   91.74%  |       564 M    |     5.3        |
-------------------------------------------------------------------------------------
-# References:
- - [Learning Transferable Architectures for Scalable Image Recognition]
-    (https://arxiv.org/abs/1707.07012)
-"""
-"""
-Code imported from https://github.com/Cadene/pretrained-models.pytorch
-"""
-
-pretrained_settings = {
-    'nasnetamobile': {
-        'imagenet': {
-            # 'url': 'https://github.com/veronikayurchuk/pretrained-models.pytorch/releases/download/v1.0/nasnetmobile-7e03cead.pth.tar',
-            'url':
-            'http://data.lip6.fr/cadene/pretrainedmodels/nasnetamobile-7e03cead.pth',
-            'input_space': 'RGB',
-            'input_size': [3, 224, 224], # resize 256
-            'input_range': [0, 1],
-            'mean': [0.5, 0.5, 0.5],
-            'std': [0.5, 0.5, 0.5],
-            'num_classes': 1000
-        },
-        # 'imagenet+background': {
-        #     # 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth',
-        #     'input_space': 'RGB',
-        #     'input_size': [3, 224, 224], # resize 256
-        #     'input_range': [0, 1],
-        #     'mean': [0.5, 0.5, 0.5],
-        #     'std': [0.5, 0.5, 0.5],
-        #     'num_classes': 1001
-        # }
-    }
-}
-
-
-class MaxPoolPad(nn.Module):
-
-    def __init__(self):
-        super(MaxPoolPad, self).__init__()
-        self.pad = nn.ZeroPad2d((1, 0, 1, 0))
-        self.pool = nn.MaxPool2d(3, stride=2, padding=1)
-
-    def forward(self, x):
-        x = self.pad(x)
-        x = self.pool(x)
-        x = x[:, :, 1:, 1:].contiguous()
-        return x
-
-
-class AvgPoolPad(nn.Module):
-
-    def __init__(self, stride=2, padding=1):
-        super(AvgPoolPad, self).__init__()
-        self.pad = nn.ZeroPad2d((1, 0, 1, 0))
-        self.pool = nn.AvgPool2d(
-            3, stride=stride, padding=padding, count_include_pad=False
-        )
-
-    def forward(self, x):
-        x = self.pad(x)
-        x = self.pool(x)
-        x = x[:, :, 1:, 1:].contiguous()
-        return x
-
-
-class SeparableConv2d(nn.Module):
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        dw_kernel,
-        dw_stride,
-        dw_padding,
-        bias=False
-    ):
-        super(SeparableConv2d, self).__init__()
-        self.depthwise_conv2d = nn.Conv2d(
-            in_channels,
-            in_channels,
-            dw_kernel,
-            stride=dw_stride,
-            padding=dw_padding,
-            bias=bias,
-            groups=in_channels
-        )
-        self.pointwise_conv2d = nn.Conv2d(
-            in_channels, out_channels, 1, stride=1, bias=bias
-        )
-
-    def forward(self, x):
-        x = self.depthwise_conv2d(x)
-        x = self.pointwise_conv2d(x)
-        return x
-
-
-class BranchSeparables(nn.Module):
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride,
-        padding,
-        name=None,
-        bias=False
-    ):
-        super(BranchSeparables, self).__init__()
-        self.relu = nn.ReLU()
-        self.separable_1 = SeparableConv2d(
-            in_channels, in_channels, kernel_size, stride, padding, bias=bias
-        )
-        self.bn_sep_1 = nn.BatchNorm2d(
-            in_channels, eps=0.001, momentum=0.1, affine=True
-        )
-        self.relu1 = nn.ReLU()
-        self.separable_2 = SeparableConv2d(
-            in_channels, out_channels, kernel_size, 1, padding, bias=bias
-        )
-        self.bn_sep_2 = nn.BatchNorm2d(
-            out_channels, eps=0.001, momentum=0.1, affine=True
-        )
-        self.name = name
-
-    def forward(self, x):
-        x = self.relu(x)
-        if self.name == 'specific':
-            x = nn.ZeroPad2d((1, 0, 1, 0))(x)
-        x = self.separable_1(x)
-        if self.name == 'specific':
-            x = x[:, :, 1:, 1:].contiguous()
-
-        x = self.bn_sep_1(x)
-        x = self.relu1(x)
-        x = self.separable_2(x)
-        x = self.bn_sep_2(x)
-        return x
-
-
-class BranchSeparablesStem(nn.Module):
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride,
-        padding,
-        bias=False
-    ):
-        super(BranchSeparablesStem, self).__init__()
-        self.relu = nn.ReLU()
-        self.separable_1 = SeparableConv2d(
-            in_channels, out_channels, kernel_size, stride, padding, bias=bias
-        )
-        self.bn_sep_1 = nn.BatchNorm2d(
-            out_channels, eps=0.001, momentum=0.1, affine=True
-        )
-        self.relu1 = nn.ReLU()
-        self.separable_2 = SeparableConv2d(
-            out_channels, out_channels, kernel_size, 1, padding, bias=bias
-        )
-        self.bn_sep_2 = nn.BatchNorm2d(
-            out_channels, eps=0.001, momentum=0.1, affine=True
-        )
-
-    def forward(self, x):
-        x = self.relu(x)
-        x = self.separable_1(x)
-        x = self.bn_sep_1(x)
-        x = self.relu1(x)
-        x = self.separable_2(x)
-        x = self.bn_sep_2(x)
-        return x
-
-
-class BranchSeparablesReduction(BranchSeparables):
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride,
-        padding,
-        z_padding=1,
-        bias=False
-    ):
-        BranchSeparables.__init__(
-            self, in_channels, out_channels, kernel_size, stride, padding, bias
-        )
-        self.padding = nn.ZeroPad2d((z_padding, 0, z_padding, 0))
-
-    def forward(self, x):
-        x = self.relu(x)
-        x = self.padding(x)
-        x = self.separable_1(x)
-        x = x[:, :, 1:, 1:].contiguous()
-        x = self.bn_sep_1(x)
-        x = self.relu1(x)
-        x = self.separable_2(x)
-        x = self.bn_sep_2(x)
-        return x
-
-
-class CellStem0(nn.Module):
-
-    def __init__(self, stem_filters, num_filters=42):
-        super(CellStem0, self).__init__()
-        self.num_filters = num_filters
-        self.stem_filters = stem_filters
-        self.conv_1x1 = nn.Sequential()
-        self.conv_1x1.add_module('relu', nn.ReLU())
-        self.conv_1x1.add_module(
-            'conv',
-            nn.Conv2d(
-                self.stem_filters, self.num_filters, 1, stride=1, bias=False
-            )
-        )
-        self.conv_1x1.add_module(
-            'bn',
-            nn.BatchNorm2d(
-                self.num_filters, eps=0.001, momentum=0.1, affine=True
-            )
-        )
-
-        self.comb_iter_0_left = BranchSeparables(
-            self.num_filters, self.num_filters, 5, 2, 2
-        )
-        self.comb_iter_0_right = BranchSeparablesStem(
-            self.stem_filters, self.num_filters, 7, 2, 3, bias=False
-        )
-
-        self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
-        self.comb_iter_1_right = BranchSeparablesStem(
-            self.stem_filters, self.num_filters, 7, 2, 3, bias=False
-        )
-
-        self.comb_iter_2_left = nn.AvgPool2d(
-            3, stride=2, padding=1, count_include_pad=False
-        )
-        self.comb_iter_2_right = BranchSeparablesStem(
-            self.stem_filters, self.num_filters, 5, 2, 2, bias=False
-        )
-
-        self.comb_iter_3_right = nn.AvgPool2d(
-            3, stride=1, padding=1, count_include_pad=False
-        )
-
-        self.comb_iter_4_left = BranchSeparables(
-            self.num_filters, self.num_filters, 3, 1, 1, bias=False
-        )
-        self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
-
-    def forward(self, x):
-        x1 = self.conv_1x1(x)
-
-        x_comb_iter_0_left = self.comb_iter_0_left(x1)
-        x_comb_iter_0_right = self.comb_iter_0_right(x)
-        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
-
-        x_comb_iter_1_left = self.comb_iter_1_left(x1)
-        x_comb_iter_1_right = self.comb_iter_1_right(x)
-        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
-
-        x_comb_iter_2_left = self.comb_iter_2_left(x1)
-        x_comb_iter_2_right = self.comb_iter_2_right(x)
-        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
-
-        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
-        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
-
-        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
-        x_comb_iter_4_right = self.comb_iter_4_right(x1)
-        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
-
-        x_out = torch.cat(
-            [x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
-        )
-        return x_out
-
-
-class CellStem1(nn.Module):
-
-    def __init__(self, stem_filters, num_filters):
-        super(CellStem1, self).__init__()
-        self.num_filters = num_filters
-        self.stem_filters = stem_filters
-        self.conv_1x1 = nn.Sequential()
-        self.conv_1x1.add_module('relu', nn.ReLU())
-        self.conv_1x1.add_module(
-            'conv',
-            nn.Conv2d(
-                2 * self.num_filters,
-                self.num_filters,
-                1,
-                stride=1,
-                bias=False
-            )
-        )
-        self.conv_1x1.add_module(
-            'bn',
-            nn.BatchNorm2d(
-                self.num_filters, eps=0.001, momentum=0.1, affine=True
-            )
-        )
-
-        self.relu = nn.ReLU()
-        self.path_1 = nn.Sequential()
-        self.path_1.add_module(
-            'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
-        )
-        self.path_1.add_module(
-            'conv',
-            nn.Conv2d(
-                self.stem_filters,
-                self.num_filters // 2,
-                1,
-                stride=1,
-                bias=False
-            )
-        )
-        self.path_2 = nn.ModuleList()
-        self.path_2.add_module('pad', nn.ZeroPad2d((0, 1, 0, 1)))
-        self.path_2.add_module(
-            'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
-        )
-        self.path_2.add_module(
-            'conv',
-            nn.Conv2d(
-                self.stem_filters,
-                self.num_filters // 2,
-                1,
-                stride=1,
-                bias=False
-            )
-        )
-
-        self.final_path_bn = nn.BatchNorm2d(
-            self.num_filters, eps=0.001, momentum=0.1, affine=True
-        )
-
-        self.comb_iter_0_left = BranchSeparables(
-            self.num_filters,
-            self.num_filters,
-            5,
-            2,
-            2,
-            name='specific',
-            bias=False
-        )
-        self.comb_iter_0_right = BranchSeparables(
-            self.num_filters,
-            self.num_filters,
-            7,
-            2,
-            3,
-            name='specific',
-            bias=False
-        )
-
-        # self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
-        self.comb_iter_1_left = MaxPoolPad()
-        self.comb_iter_1_right = BranchSeparables(
-            self.num_filters,
-            self.num_filters,
-            7,
-            2,
-            3,
-            name='specific',
-            bias=False
-        )
-
-        # self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
-        self.comb_iter_2_left = AvgPoolPad()
-        self.comb_iter_2_right = BranchSeparables(
-            self.num_filters,
-            self.num_filters,
-            5,
-            2,
-            2,
-            name='specific',
-            bias=False
-        )
-
-        self.comb_iter_3_right = nn.AvgPool2d(
-            3, stride=1, padding=1, count_include_pad=False
-        )
-
-        self.comb_iter_4_left = BranchSeparables(
-            self.num_filters,
-            self.num_filters,
-            3,
-            1,
-            1,
-            name='specific',
-            bias=False
-        )
-        # self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
-        self.comb_iter_4_right = MaxPoolPad()
-
-    def forward(self, x_conv0, x_stem_0):
-        x_left = self.conv_1x1(x_stem_0)
-
-        x_relu = self.relu(x_conv0)
-        # path 1
-        x_path1 = self.path_1(x_relu)
-        # path 2
-        x_path2 = self.path_2.pad(x_relu)
-        x_path2 = x_path2[:, :, 1:, 1:]
-        x_path2 = self.path_2.avgpool(x_path2)
-        x_path2 = self.path_2.conv(x_path2)
-        # final path
-        x_right = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
-
-        x_comb_iter_0_left = self.comb_iter_0_left(x_left)
-        x_comb_iter_0_right = self.comb_iter_0_right(x_right)
-        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
-
-        x_comb_iter_1_left = self.comb_iter_1_left(x_left)
-        x_comb_iter_1_right = self.comb_iter_1_right(x_right)
-        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
-
-        x_comb_iter_2_left = self.comb_iter_2_left(x_left)
-        x_comb_iter_2_right = self.comb_iter_2_right(x_right)
-        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
-
-        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
-        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
-
-        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
-        x_comb_iter_4_right = self.comb_iter_4_right(x_left)
-        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
-
-        x_out = torch.cat(
-            [x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
-        )
-        return x_out
-
-
-class FirstCell(nn.Module):
-
-    def __init__(
-        self, in_channels_left, out_channels_left, in_channels_right,
-        out_channels_right
-    ):
-        super(FirstCell, self).__init__()
-        self.conv_1x1 = nn.Sequential()
-        self.conv_1x1.add_module('relu', nn.ReLU())
-        self.conv_1x1.add_module(
-            'conv',
-            nn.Conv2d(
-                in_channels_right, out_channels_right, 1, stride=1, bias=False
-            )
-        )
-        self.conv_1x1.add_module(
-            'bn',
-            nn.BatchNorm2d(
-                out_channels_right, eps=0.001, momentum=0.1, affine=True
-            )
-        )
-
-        self.relu = nn.ReLU()
-        self.path_1 = nn.Sequential()
-        self.path_1.add_module(
-            'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
-        )
-        self.path_1.add_module(
-            'conv',
-            nn.Conv2d(
-                in_channels_left, out_channels_left, 1, stride=1, bias=False
-            )
-        )
-        self.path_2 = nn.ModuleList()
-        self.path_2.add_module('pad', nn.ZeroPad2d((0, 1, 0, 1)))
-        self.path_2.add_module(
-            'avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)
-        )
-        self.path_2.add_module(
-            'conv',
-            nn.Conv2d(
-                in_channels_left, out_channels_left, 1, stride=1, bias=False
-            )
-        )
-
-        self.final_path_bn = nn.BatchNorm2d(
-            out_channels_left * 2, eps=0.001, momentum=0.1, affine=True
-        )
-
-        self.comb_iter_0_left = BranchSeparables(
-            out_channels_right, out_channels_right, 5, 1, 2, bias=False
-        )
-        self.comb_iter_0_right = BranchSeparables(
-            out_channels_right, out_channels_right, 3, 1, 1, bias=False
-        )
-
-        self.comb_iter_1_left = BranchSeparables(
-            out_channels_right, out_channels_right, 5, 1, 2, bias=False
-        )
-        self.comb_iter_1_right = BranchSeparables(
-            out_channels_right, out_channels_right, 3, 1, 1, bias=False
-        )
-
-        self.comb_iter_2_left = nn.AvgPool2d(
-            3, stride=1, padding=1, count_include_pad=False
-        )
-
-        self.comb_iter_3_left = nn.AvgPool2d(
-            3, stride=1, padding=1, count_include_pad=False
-        )
-        self.comb_iter_3_right = nn.AvgPool2d(
-            3, stride=1, padding=1, count_include_pad=False
-        )
-
-        self.comb_iter_4_left = BranchSeparables(
-            out_channels_right, out_channels_right, 3, 1, 1, bias=False
-        )
-
-    def forward(self, x, x_prev):
-        x_relu = self.relu(x_prev)
-        # path 1
-        x_path1 = self.path_1(x_relu)
-        # path 2
-        x_path2 = self.path_2.pad(x_relu)
-        x_path2 = x_path2[:, :, 1:, 1:]
-        x_path2 = self.path_2.avgpool(x_path2)
-        x_path2 = self.path_2.conv(x_path2)
-        # final path
-        x_left = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
-
-        x_right = self.conv_1x1(x)
-
-        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
-        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
-        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
-
-        x_comb_iter_1_left = self.comb_iter_1_left(x_left)
-        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
-        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
-
-        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
-        x_comb_iter_2 = x_comb_iter_2_left + x_left
-
-        x_comb_iter_3_left = self.comb_iter_3_left(x_left)
-        x_comb_iter_3_right = self.comb_iter_3_right(x_left)
-        x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
-
-        x_comb_iter_4_left = self.comb_iter_4_left(x_right)
-        x_comb_iter_4 = x_comb_iter_4_left + x_right
-
-        x_out = torch.cat(
-            [
-                x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2,
-                x_comb_iter_3, x_comb_iter_4
-            ], 1
-        )
-        return x_out
-
-
-class NormalCell(nn.Module):
-
-    def __init__(
-        self, in_channels_left, out_channels_left, in_channels_right,
-        out_channels_right
-    ):
-        super(NormalCell, self).__init__()
-        self.conv_prev_1x1 = nn.Sequential()
-        self.conv_prev_1x1.add_module('relu', nn.ReLU())
-        self.conv_prev_1x1.add_module(
-            'conv',
-            nn.Conv2d(
-                in_channels_left, out_channels_left, 1, stride=1, bias=False
-            )
-        )
-        self.conv_prev_1x1.add_module(
-            'bn',
-            nn.BatchNorm2d(
-                out_channels_left, eps=0.001, momentum=0.1, affine=True
-            )
-        )
-
-        self.conv_1x1 = nn.Sequential()
-        self.conv_1x1.add_module('relu', nn.ReLU())
-        self.conv_1x1.add_module(
-            'conv',
-            nn.Conv2d(
-                in_channels_right, out_channels_right, 1, stride=1, bias=False
-            )
-        )
-        self.conv_1x1.add_module(
-            'bn',
-            nn.BatchNorm2d(
-                out_channels_right, eps=0.001, momentum=0.1, affine=True
-            )
-        )
-
-        self.comb_iter_0_left = BranchSeparables(
-            out_channels_right, out_channels_right, 5, 1, 2, bias=False
-        )
-        self.comb_iter_0_right = BranchSeparables(
-            out_channels_left, out_channels_left, 3, 1, 1, bias=False
-        )
-
-        self.comb_iter_1_left = BranchSeparables(
-            out_channels_left, out_channels_left, 5, 1, 2, bias=False
-        )
-        self.comb_iter_1_right = BranchSeparables(
-            out_channels_left, out_channels_left, 3, 1, 1, bias=False
-        )
-
-        self.comb_iter_2_left = nn.AvgPool2d(
-            3, stride=1, padding=1, count_include_pad=False
-        )
-
-        self.comb_iter_3_left = nn.AvgPool2d(
-            3, stride=1, padding=1, count_include_pad=False
-        )
-        self.comb_iter_3_right = nn.AvgPool2d(
-            3, stride=1, padding=1, count_include_pad=False
-        )
-
-        self.comb_iter_4_left = BranchSeparables(
-            out_channels_right, out_channels_right, 3, 1, 1, bias=False
-        )
-
-    def forward(self, x, x_prev):
-        x_left = self.conv_prev_1x1(x_prev)
-        x_right = self.conv_1x1(x)
-
-        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
-        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
-        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
-
-        x_comb_iter_1_left = self.comb_iter_1_left(x_left)
-        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
-        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
-
-        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
-        x_comb_iter_2 = x_comb_iter_2_left + x_left
-
-        x_comb_iter_3_left = self.comb_iter_3_left(x_left)
-        x_comb_iter_3_right = self.comb_iter_3_right(x_left)
-        x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
-
-        x_comb_iter_4_left = self.comb_iter_4_left(x_right)
-        x_comb_iter_4 = x_comb_iter_4_left + x_right
-
-        x_out = torch.cat(
-            [
-                x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2,
-                x_comb_iter_3, x_comb_iter_4
-            ], 1
-        )
-        return x_out
-
-
-class ReductionCell0(nn.Module):
-
-    def __init__(
-        self, in_channels_left, out_channels_left, in_channels_right,
-        out_channels_right
-    ):
-        super(ReductionCell0, self).__init__()
-        self.conv_prev_1x1 = nn.Sequential()
-        self.conv_prev_1x1.add_module('relu', nn.ReLU())
-        self.conv_prev_1x1.add_module(
-            'conv',
-            nn.Conv2d(
-                in_channels_left, out_channels_left, 1, stride=1, bias=False
-            )
-        )
-        self.conv_prev_1x1.add_module(
-            'bn',
-            nn.BatchNorm2d(
-                out_channels_left, eps=0.001, momentum=0.1, affine=True
-            )
-        )
-
-        self.conv_1x1 = nn.Sequential()
-        self.conv_1x1.add_module('relu', nn.ReLU())
-        self.conv_1x1.add_module(
-            'conv',
-            nn.Conv2d(
-                in_channels_right, out_channels_right, 1, stride=1, bias=False
-            )
-        )
-        self.conv_1x1.add_module(
-            'bn',
-            nn.BatchNorm2d(
-                out_channels_right, eps=0.001, momentum=0.1, affine=True
-            )
-        )
-
-        self.comb_iter_0_left = BranchSeparablesReduction(
-            out_channels_right, out_channels_right, 5, 2, 2, bias=False
-        )
-        self.comb_iter_0_right = BranchSeparablesReduction(
-            out_channels_right, out_channels_right, 7, 2, 3, bias=False
-        )
-
-        self.comb_iter_1_left = MaxPoolPad()
-        self.comb_iter_1_right = BranchSeparablesReduction(
-            out_channels_right, out_channels_right, 7, 2, 3, bias=False
-        )
-
-        self.comb_iter_2_left = AvgPoolPad()
-        self.comb_iter_2_right = BranchSeparablesReduction(
-            out_channels_right, out_channels_right, 5, 2, 2, bias=False
-        )
-
-        self.comb_iter_3_right = nn.AvgPool2d(
-            3, stride=1, padding=1, count_include_pad=False
-        )
-
-        self.comb_iter_4_left = BranchSeparablesReduction(
-            out_channels_right, out_channels_right, 3, 1, 1, bias=False
-        )
-        self.comb_iter_4_right = MaxPoolPad()
-
-    def forward(self, x, x_prev):
-        x_left = self.conv_prev_1x1(x_prev)
-        x_right = self.conv_1x1(x)
-
-        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
-        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
-        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
-
-        x_comb_iter_1_left = self.comb_iter_1_left(x_right)
-        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
-        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
-
-        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
-        x_comb_iter_2_right = self.comb_iter_2_right(x_left)
-        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
-
-        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
-        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
-
-        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
-        x_comb_iter_4_right = self.comb_iter_4_right(x_right)
-        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
-
-        x_out = torch.cat(
-            [x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
-        )
-        return x_out
-
-
-class ReductionCell1(nn.Module):
-
-    def __init__(
-        self, in_channels_left, out_channels_left, in_channels_right,
-        out_channels_right
-    ):
-        super(ReductionCell1, self).__init__()
-        self.conv_prev_1x1 = nn.Sequential()
-        self.conv_prev_1x1.add_module('relu', nn.ReLU())
-        self.conv_prev_1x1.add_module(
-            'conv',
-            nn.Conv2d(
-                in_channels_left, out_channels_left, 1, stride=1, bias=False
-            )
-        )
-        self.conv_prev_1x1.add_module(
-            'bn',
-            nn.BatchNorm2d(
-                out_channels_left, eps=0.001, momentum=0.1, affine=True
-            )
-        )
-
-        self.conv_1x1 = nn.Sequential()
-        self.conv_1x1.add_module('relu', nn.ReLU())
-        self.conv_1x1.add_module(
-            'conv',
-            nn.Conv2d(
-                in_channels_right, out_channels_right, 1, stride=1, bias=False
-            )
-        )
-        self.conv_1x1.add_module(
-            'bn',
-            nn.BatchNorm2d(
-                out_channels_right, eps=0.001, momentum=0.1, affine=True
-            )
-        )
-
-        self.comb_iter_0_left = BranchSeparables(
-            out_channels_right,
-            out_channels_right,
-            5,
-            2,
-            2,
-            name='specific',
-            bias=False
-        )
-        self.comb_iter_0_right = BranchSeparables(
-            out_channels_right,
-            out_channels_right,
-            7,
-            2,
-            3,
-            name='specific',
-            bias=False
-        )
-
-        # self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
-        self.comb_iter_1_left = MaxPoolPad()
-        self.comb_iter_1_right = BranchSeparables(
-            out_channels_right,
-            out_channels_right,
-            7,
-            2,
-            3,
-            name='specific',
-            bias=False
-        )
-
-        # self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
-        self.comb_iter_2_left = AvgPoolPad()
-        self.comb_iter_2_right = BranchSeparables(
-            out_channels_right,
-            out_channels_right,
-            5,
-            2,
-            2,
-            name='specific',
-            bias=False
-        )
-
-        self.comb_iter_3_right = nn.AvgPool2d(
-            3, stride=1, padding=1, count_include_pad=False
-        )
-
-        self.comb_iter_4_left = BranchSeparables(
-            out_channels_right,
-            out_channels_right,
-            3,
-            1,
-            1,
-            name='specific',
-            bias=False
-        )
-        # self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
-        self.comb_iter_4_right = MaxPoolPad()
-
-    def forward(self, x, x_prev):
-        x_left = self.conv_prev_1x1(x_prev)
-        x_right = self.conv_1x1(x)
-
-        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
-        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
-        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
-
-        x_comb_iter_1_left = self.comb_iter_1_left(x_right)
-        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
-        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
-
-        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
-        x_comb_iter_2_right = self.comb_iter_2_right(x_left)
-        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
-
-        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
-        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1
-
-        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
-        x_comb_iter_4_right = self.comb_iter_4_right(x_right)
-        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
-
-        x_out = torch.cat(
-            [x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1
-        )
-        return x_out
-
-
-class NASNetAMobile(nn.Module):
-    """Neural Architecture Search (NAS).
-
-    Reference:
-        Zoph et al. Learning Transferable Architectures
-        for Scalable Image Recognition. CVPR 2018.
-
-    Public keys:
-        - ``nasnetamobile``: NASNet-A Mobile.
-    """
-
-    def __init__(
-        self,
-        num_classes,
-        loss,
-        stem_filters=32,
-        penultimate_filters=1056,
-        filters_multiplier=2,
-        **kwargs
-    ):
-        super(NASNetAMobile, self).__init__()
-        self.stem_filters = stem_filters
-        self.penultimate_filters = penultimate_filters
-        self.filters_multiplier = filters_multiplier
-        self.loss = loss
-
-        filters = self.penultimate_filters // 24
-        # 24 is default value for the architecture
-
-        self.conv0 = nn.Sequential()
-        self.conv0.add_module(
-            'conv',
-            nn.Conv2d(
-                in_channels=3,
-                out_channels=self.stem_filters,
-                kernel_size=3,
-                padding=0,
-                stride=2,
-                bias=False
-            )
-        )
-        self.conv0.add_module(
-            'bn',
-            nn.BatchNorm2d(
-                self.stem_filters, eps=0.001, momentum=0.1, affine=True
-            )
-        )
-
-        self.cell_stem_0 = CellStem0(
-            self.stem_filters, num_filters=filters // (filters_multiplier**2)
-        )
-        self.cell_stem_1 = CellStem1(
-            self.stem_filters, num_filters=filters // filters_multiplier
-        )
-
-        self.cell_0 = FirstCell(
-            in_channels_left=filters,
-            out_channels_left=filters // 2, # 1, 0.5
-            in_channels_right=2 * filters,
-            out_channels_right=filters
-        ) # 2, 1
-        self.cell_1 = NormalCell(
-            in_channels_left=2 * filters,
-            out_channels_left=filters, # 2, 1
-            in_channels_right=6 * filters,
-            out_channels_right=filters
-        ) # 6, 1
-        self.cell_2 = NormalCell(
-            in_channels_left=6 * filters,
-            out_channels_left=filters, # 6, 1
-            in_channels_right=6 * filters,
-            out_channels_right=filters
-        ) # 6, 1
-        self.cell_3 = NormalCell(
-            in_channels_left=6 * filters,
-            out_channels_left=filters, # 6, 1
-            in_channels_right=6 * filters,
-            out_channels_right=filters
-        ) # 6, 1
-
-        self.reduction_cell_0 = ReductionCell0(
-            in_channels_left=6 * filters,
-            out_channels_left=2 * filters, # 6, 2
-            in_channels_right=6 * filters,
-            out_channels_right=2 * filters
-        ) # 6, 2
-
-        self.cell_6 = FirstCell(
-            in_channels_left=6 * filters,
-            out_channels_left=filters, # 6, 1
-            in_channels_right=8 * filters,
-            out_channels_right=2 * filters
-        ) # 8, 2
-        self.cell_7 = NormalCell(
-            in_channels_left=8 * filters,
-            out_channels_left=2 * filters, # 8, 2
-            in_channels_right=12 * filters,
-            out_channels_right=2 * filters
-        ) # 12, 2
-        self.cell_8 = NormalCell(
-            in_channels_left=12 * filters,
-            out_channels_left=2 * filters, # 12, 2
-            in_channels_right=12 * filters,
-            out_channels_right=2 * filters
-        ) # 12, 2
-        self.cell_9 = NormalCell(
-            in_channels_left=12 * filters,
-            out_channels_left=2 * filters, # 12, 2
-            in_channels_right=12 * filters,
-            out_channels_right=2 * filters
-        ) # 12, 2
-
-        self.reduction_cell_1 = ReductionCell1(
-            in_channels_left=12 * filters,
-            out_channels_left=4 * filters, # 12, 4
-            in_channels_right=12 * filters,
-            out_channels_right=4 * filters
-        ) # 12, 4
-
-        self.cell_12 = FirstCell(
-            in_channels_left=12 * filters,
-            out_channels_left=2 * filters, # 12, 2
-            in_channels_right=16 * filters,
-            out_channels_right=4 * filters
-        ) # 16, 4
-        self.cell_13 = NormalCell(
-            in_channels_left=16 * filters,
-            out_channels_left=4 * filters, # 16, 4
-            in_channels_right=24 * filters,
-            out_channels_right=4 * filters
-        ) # 24, 4
-        self.cell_14 = NormalCell(
-            in_channels_left=24 * filters,
-            out_channels_left=4 * filters, # 24, 4
-            in_channels_right=24 * filters,
-            out_channels_right=4 * filters
-        ) # 24, 4
-        self.cell_15 = NormalCell(
-            in_channels_left=24 * filters,
-            out_channels_left=4 * filters, # 24, 4
-            in_channels_right=24 * filters,
-            out_channels_right=4 * filters
-        ) # 24, 4
-
-        self.relu = nn.ReLU()
-        self.dropout = nn.Dropout()
-        self.classifier = nn.Linear(24 * filters, num_classes)
-
-        self._init_params()
-
-    def _init_params(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(
-                    m.weight, mode='fan_out', nonlinearity='relu'
-                )
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm1d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def features(self, input):
-        x_conv0 = self.conv0(input)
-        x_stem_0 = self.cell_stem_0(x_conv0)
-        x_stem_1 = self.cell_stem_1(x_conv0, x_stem_0)
-
-        x_cell_0 = self.cell_0(x_stem_1, x_stem_0)
-        x_cell_1 = self.cell_1(x_cell_0, x_stem_1)
-        x_cell_2 = self.cell_2(x_cell_1, x_cell_0)
-        x_cell_3 = self.cell_3(x_cell_2, x_cell_1)
-
-        x_reduction_cell_0 = self.reduction_cell_0(x_cell_3, x_cell_2)
-
-        x_cell_6 = self.cell_6(x_reduction_cell_0, x_cell_3)
-        x_cell_7 = self.cell_7(x_cell_6, x_reduction_cell_0)
-        x_cell_8 = self.cell_8(x_cell_7, x_cell_6)
-        x_cell_9 = self.cell_9(x_cell_8, x_cell_7)
-
-        x_reduction_cell_1 = self.reduction_cell_1(x_cell_9, x_cell_8)
-
-        x_cell_12 = self.cell_12(x_reduction_cell_1, x_cell_9)
-        x_cell_13 = self.cell_13(x_cell_12, x_reduction_cell_1)
-        x_cell_14 = self.cell_14(x_cell_13, x_cell_12)
-        x_cell_15 = self.cell_15(x_cell_14, x_cell_13)
-
-        x_cell_15 = self.relu(x_cell_15)
-        x_cell_15 = F.avg_pool2d(
-            x_cell_15,
-            x_cell_15.size()[2:]
-        ) # global average pool
-        x_cell_15 = x_cell_15.view(x_cell_15.size(0), -1)
-        x_cell_15 = self.dropout(x_cell_15)
-
-        return x_cell_15
-
-    def forward(self, input):
-        v = self.features(input)
-
-        if not self.training:
-            return v
-
-        y = self.classifier(v)
-
-        if self.loss == 'softmax':
-            return y
-        elif self.loss == 'triplet':
-            return y, v
-        else:
-            raise KeyError('Unsupported loss: {}'.format(self.loss))
-
-
-def init_pretrained_weights(model, model_url):
-    """Initializes model with pretrained weights.
-    
-    Layers that don't match with pretrained layers in name or size are kept unchanged.
-    """
-    pretrain_dict = model_zoo.load_url(model_url)
-    model_dict = model.state_dict()
-    pretrain_dict = {
-        k: v
-        for k, v in pretrain_dict.items()
-        if k in model_dict and model_dict[k].size() == v.size()
-    }
-    model_dict.update(pretrain_dict)
-    model.load_state_dict(model_dict)
-
-
-def nasnetamobile(num_classes, loss='softmax', pretrained=True, **kwargs):
-    model = NASNetAMobile(num_classes, loss, **kwargs)
-    if pretrained:
-        model_url = pretrained_settings['nasnetamobile']['imagenet']['url']
-        init_pretrained_weights(model, model_url)
-    return model
diff --git a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/torchreid/metrics/rank_cylib/Makefile b/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/torchreid/metrics/rank_cylib/Makefile
deleted file mode 100644
index d49e655f85f829cb8ccda5bad6fe2c65cccf2bf2..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/reid/torchreid/metrics/rank_cylib/Makefile
+++ /dev/null
@@ -1,6 +0,0 @@
-all:
-	$(PYTHON) setup.py build_ext --inplace
-	rm -rf build
-clean:
-	rm -rf build
-	rm -f rank_cy.c *.so
\ No newline at end of file
diff --git a/spaces/xswu/HPSv2/src/open_clip/constants.py b/spaces/xswu/HPSv2/src/open_clip/constants.py
deleted file mode 100644
index a670bb3fab442baeb9af53b91c312e6982af57ee..0000000000000000000000000000000000000000
--- a/spaces/xswu/HPSv2/src/open_clip/constants.py
+++ /dev/null
@@ -1,2 +0,0 @@
-OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
-OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
diff --git a/spaces/yaoshining/text-generation-webui/modules/models_settings.py b/spaces/yaoshining/text-generation-webui/modules/models_settings.py
deleted file mode 100644
index 0207e7de76e54f438ee98d3b4e8344446796dd47..0000000000000000000000000000000000000000
--- a/spaces/yaoshining/text-generation-webui/modules/models_settings.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import re
-from pathlib import Path
-
-import yaml
-
-from modules import shared, ui
-
-
-def get_model_settings_from_yamls(model):
-    settings = shared.model_config
-    model_settings = {}
-    for pat in settings:
-        if re.match(pat.lower(), model.lower()):
-            for k in settings[pat]:
-                model_settings[k] = settings[pat][k]
-
-    return model_settings
-
-
-def infer_loader(model_name):
-    path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
-    model_settings = get_model_settings_from_yamls(model_name)
-    if not path_to_model.exists():
-        loader = None
-    elif Path(f'{shared.args.model_dir}/{model_name}/quantize_config.json').exists() or ('wbits' in model_settings and type(model_settings['wbits']) is int and model_settings['wbits'] > 0):
-        loader = 'AutoGPTQ'
-    elif len(list(path_to_model.glob('*ggml*.bin'))) > 0:
-        loader = 'llama.cpp'
-    elif re.match('.*ggml.*\.bin', model_name.lower()):
-        loader = 'llama.cpp'
-    elif re.match('.*rwkv.*\.pth', model_name.lower()):
-        loader = 'RWKV'
-    elif shared.args.flexgen:
-        loader = 'FlexGen'
-    else:
-        loader = 'Transformers'
-
-    return loader
-
-
-# UI: update the command-line arguments based on the interface values
-def update_model_parameters(state, initial=False):
-    elements = ui.list_model_elements()  # the names of the parameters
-    gpu_memories = []
-
-    for i, element in enumerate(elements):
-        if element not in state:
-            continue
-
-        value = state[element]
-        if element.startswith('gpu_memory'):
-            gpu_memories.append(value)
-            continue
-
-        if initial and vars(shared.args)[element] != vars(shared.args_defaults)[element]:
-            continue
-
-        # Setting null defaults
-        if element in ['wbits', 'groupsize', 'model_type'] and value == 'None':
-            value = vars(shared.args_defaults)[element]
-        elif element in ['cpu_memory'] and value == 0:
-            value = vars(shared.args_defaults)[element]
-
-        # Making some simple conversions
-        if element in ['wbits', 'groupsize', 'pre_layer']:
-            value = int(value)
-        elif element == 'cpu_memory' and value is not None:
-            value = f"{value}MiB"
-
-        if element in ['pre_layer']:
-            value = [value] if value > 0 else None
-
-        setattr(shared.args, element, value)
-
-    found_positive = False
-    for i in gpu_memories:
-        if i > 0:
-            found_positive = True
-            break
-
-    if not (initial and vars(shared.args)['gpu_memory'] != vars(shared.args_defaults)['gpu_memory']):
-        if found_positive:
-            shared.args.gpu_memory = [f"{i}MiB" for i in gpu_memories]
-        else:
-            shared.args.gpu_memory = None
-
-
-# UI: update the state variable with the model settings
-def apply_model_settings_to_state(model, state):
-    model_settings = get_model_settings_from_yamls(model)
-    if 'loader' not in model_settings:
-        loader = infer_loader(model)
-        if 'wbits' in model_settings and type(model_settings['wbits']) is int and model_settings['wbits'] > 0:
-            loader = 'AutoGPTQ'
-
-        # If the user is using an alternative GPTQ loader, let them keep using it
-        if not (loader == 'AutoGPTQ' and state['loader'] in ['GPTQ-for-LLaMa', 'ExLlama', 'ExLlama_HF']):
-            state['loader'] = loader
-
-    for k in model_settings:
-        if k in state:
-            state[k] = model_settings[k]
-
-    return state
-
-
-# Save the settings for this model to models/config-user.yaml
-def save_model_settings(model, state):
-    if model == 'None':
-        yield ("Not saving the settings because no model is loaded.")
-        return
-
-    with Path(f'{shared.args.model_dir}/config-user.yaml') as p:
-        if p.exists():
-            user_config = yaml.safe_load(open(p, 'r').read())
-        else:
-            user_config = {}
-
-        model_regex = model + '$'  # For exact matches
-        for _dict in [user_config, shared.model_config]:
-            if model_regex not in _dict:
-                _dict[model_regex] = {}
-
-        if model_regex not in user_config:
-            user_config[model_regex] = {}
-
-        for k in ui.list_model_elements():
-            user_config[model_regex][k] = state[k]
-            shared.model_config[model_regex][k] = state[k]
-
-        with open(p, 'w') as f:
-            f.write(yaml.dump(user_config, sort_keys=False))
-
-        yield (f"Settings for {model} saved to {p}")
diff --git a/spaces/ybelkada/interfacegan_pp/models/pggan_tf_official/tfutil.py b/spaces/ybelkada/interfacegan_pp/models/pggan_tf_official/tfutil.py
deleted file mode 100644
index cf7ad0ada400aae935759190a6384c5dd8a3fc08..0000000000000000000000000000000000000000
--- a/spaces/ybelkada/interfacegan_pp/models/pggan_tf_official/tfutil.py
+++ /dev/null
@@ -1,749 +0,0 @@
-# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-import os
-import sys
-import inspect
-import importlib
-import imp
-import numpy as np
-from collections import OrderedDict
-import tensorflow as tf
-
-#----------------------------------------------------------------------------
-# Convenience.
-
-def run(*args, **kwargs): # Run the specified ops in the default session.
-    return tf.get_default_session().run(*args, **kwargs)
-
-def is_tf_expression(x):
-    return isinstance(x, tf.Tensor) or isinstance(x, tf.Variable) or isinstance(x, tf.Operation)
-
-def shape_to_list(shape):
-    return [dim.value for dim in shape]
-
-def flatten(x):
-    with tf.name_scope('Flatten'):
-        return tf.reshape(x, [-1])
-
-def log2(x):
-    with tf.name_scope('Log2'):
-        return tf.log(x) * np.float32(1.0 / np.log(2.0))
-
-def exp2(x):
-    with tf.name_scope('Exp2'):
-        return tf.exp(x * np.float32(np.log(2.0)))
-
-def lerp(a, b, t):
-    with tf.name_scope('Lerp'):
-        return a + (b - a) * t
-
-def lerp_clip(a, b, t):
-    with tf.name_scope('LerpClip'):
-        return a + (b - a) * tf.clip_by_value(t, 0.0, 1.0)
-
-def absolute_name_scope(scope): # Forcefully enter the specified name scope, ignoring any surrounding scopes.
-    return tf.name_scope(scope + '/')
-
-#----------------------------------------------------------------------------
-# Initialize TensorFlow graph and session using good default settings.
-
-def init_tf(config_dict=dict()):
-    if tf.get_default_session() is None:
-        tf.set_random_seed(np.random.randint(1 << 31))
-        create_session(config_dict, force_as_default=True)
-
-#----------------------------------------------------------------------------
-# Create tf.Session based on config dict of the form
-# {'gpu_options.allow_growth': True}
-
-def create_session(config_dict=dict(), force_as_default=False):
-    config = tf.ConfigProto()
-    for key, value in config_dict.items():
-        fields = key.split('.')
-        obj = config
-        for field in fields[:-1]:
-            obj = getattr(obj, field)
-        setattr(obj, fields[-1], value)
-    session = tf.Session(config=config)
-    if force_as_default:
-        session._default_session = session.as_default()
-        session._default_session.enforce_nesting = False
-        session._default_session.__enter__()
-    return session
-
-#----------------------------------------------------------------------------
-# Initialize all tf.Variables that have not already been initialized.
-# Equivalent to the following, but more efficient and does not bloat the tf graph:
-#   tf.variables_initializer(tf.report_unitialized_variables()).run()
-
-def init_uninited_vars(vars=None):
-    if vars is None: vars = tf.global_variables()
-    test_vars = []; test_ops = []
-    with tf.control_dependencies(None): # ignore surrounding control_dependencies
-        for var in vars:
-            assert is_tf_expression(var)
-            try:
-                tf.get_default_graph().get_tensor_by_name(var.name.replace(':0', '/IsVariableInitialized:0'))
-            except KeyError:
-                # Op does not exist => variable may be uninitialized.
-                test_vars.append(var)
-                with absolute_name_scope(var.name.split(':')[0]):
-                    test_ops.append(tf.is_variable_initialized(var))
-    init_vars = [var for var, inited in zip(test_vars, run(test_ops)) if not inited]
-    run([var.initializer for var in init_vars])
-
-#----------------------------------------------------------------------------
-# Set the values of given tf.Variables.
-# Equivalent to the following, but more efficient and does not bloat the tf graph:
-#   tfutil.run([tf.assign(var, value) for var, value in var_to_value_dict.items()]
-
-def set_vars(var_to_value_dict):
-    ops = []
-    feed_dict = {}
-    for var, value in var_to_value_dict.items():
-        assert is_tf_expression(var)
-        try:
-            setter = tf.get_default_graph().get_tensor_by_name(var.name.replace(':0', '/setter:0')) # look for existing op
-        except KeyError:
-            with absolute_name_scope(var.name.split(':')[0]):
-                with tf.control_dependencies(None): # ignore surrounding control_dependencies
-                    setter = tf.assign(var, tf.placeholder(var.dtype, var.shape, 'new_value'), name='setter') # create new setter
-        ops.append(setter)
-        feed_dict[setter.op.inputs[1]] = value
-    run(ops, feed_dict)
-
-#----------------------------------------------------------------------------
-# Autosummary creates an identity op that internally keeps track of the input
-# values and automatically shows up in TensorBoard. The reported value
-# represents an average over input components. The average is accumulated
-# constantly over time and flushed when save_summaries() is called.
-#
-# Notes:
-# - The output tensor must be used as an input for something else in the
-#   graph. Otherwise, the autosummary op will not get executed, and the average
-#   value will not get accumulated.
-# - It is perfectly fine to include autosummaries with the same name in
-#   several places throughout the graph, even if they are executed concurrently.
-# - It is ok to also pass in a python scalar or numpy array. In this case, it
-#   is added to the average immediately.
-
-_autosummary_vars = OrderedDict() # name => [var, ...]
-_autosummary_immediate = OrderedDict() # name => update_op, update_value
-_autosummary_finalized = False
-
-def autosummary(name, value):
-    id = name.replace('/', '_')
-    if is_tf_expression(value):
-        with tf.name_scope('summary_' + id), tf.device(value.device):
-            update_op = _create_autosummary_var(name, value)
-            with tf.control_dependencies([update_op]):
-                return tf.identity(value)
-    else: # python scalar or numpy array
-        if name not in _autosummary_immediate:
-            with absolute_name_scope('Autosummary/' + id), tf.device(None), tf.control_dependencies(None):
-                update_value = tf.placeholder(tf.float32)
-                update_op = _create_autosummary_var(name, update_value)
-                _autosummary_immediate[name] = update_op, update_value
-        update_op, update_value = _autosummary_immediate[name]
-        run(update_op, {update_value: np.float32(value)})
-        return value
-
-# Create the necessary ops to include autosummaries in TensorBoard report.
-# Note: This should be done only once per graph.
-def finalize_autosummaries():
-    global _autosummary_finalized
-    if _autosummary_finalized:
-        return
-    _autosummary_finalized = True
-    init_uninited_vars([var for vars in _autosummary_vars.values() for var in vars])
-    with tf.device(None), tf.control_dependencies(None):
-        for name, vars in _autosummary_vars.items():
-            id = name.replace('/', '_')
-            with absolute_name_scope('Autosummary/' + id):
-                sum = tf.add_n(vars)
-                avg = sum[0] / sum[1]
-                with tf.control_dependencies([avg]): # read before resetting
-                    reset_ops = [tf.assign(var, tf.zeros(2)) for var in vars]
-                    with tf.name_scope(None), tf.control_dependencies(reset_ops): # reset before reporting
-                        tf.summary.scalar(name, avg)
-
-# Internal helper for creating autosummary accumulators.
-def _create_autosummary_var(name, value_expr):
-    assert not _autosummary_finalized
-    v = tf.cast(value_expr, tf.float32)
-    if v.shape.ndims is 0:
-        v = [v, np.float32(1.0)]
-    elif v.shape.ndims is 1:
-        v = [tf.reduce_sum(v), tf.cast(tf.shape(v)[0], tf.float32)]
-    else:
-        v = [tf.reduce_sum(v), tf.reduce_prod(tf.cast(tf.shape(v), tf.float32))]
-    v = tf.cond(tf.is_finite(v[0]), lambda: tf.stack(v), lambda: tf.zeros(2))
-    with tf.control_dependencies(None):
-        var = tf.Variable(tf.zeros(2)) # [numerator, denominator]
-    update_op = tf.cond(tf.is_variable_initialized(var), lambda: tf.assign_add(var, v), lambda: tf.assign(var, v))
-    if name in _autosummary_vars:
-        _autosummary_vars[name].append(var)
-    else:
-        _autosummary_vars[name] = [var]
-    return update_op
-
-#----------------------------------------------------------------------------
-# Call filewriter.add_summary() with all summaries in the default graph,
-# automatically finalizing and merging them on the first call.
-
-_summary_merge_op = None
-
-def save_summaries(filewriter, global_step=None):
-    global _summary_merge_op
-    if _summary_merge_op is None:
-        finalize_autosummaries()
-        with tf.device(None), tf.control_dependencies(None):
-            _summary_merge_op = tf.summary.merge_all()
-    filewriter.add_summary(_summary_merge_op.eval(), global_step)
-
-#----------------------------------------------------------------------------
-# Utilities for importing modules and objects by name.
-
-def import_module(module_or_obj_name):
-    parts = module_or_obj_name.split('.')
-    parts[0] = {'np': 'numpy', 'tf': 'tensorflow'}.get(parts[0], parts[0])
-    for i in range(len(parts), 0, -1):
-        try:
-            module = importlib.import_module('.'.join(parts[:i]))
-            relative_obj_name = '.'.join(parts[i:])
-            return module, relative_obj_name
-        except ImportError:
-            pass
-    raise ImportError(module_or_obj_name)
-
-def find_obj_in_module(module, relative_obj_name):
-    obj = module
-    for part in relative_obj_name.split('.'):
-        obj = getattr(obj, part)
-    return obj
-
-def import_obj(obj_name):
-    module, relative_obj_name = import_module(obj_name)
-    return find_obj_in_module(module, relative_obj_name)
-
-def call_func_by_name(*args, func=None, **kwargs):
-    assert func is not None
-    return import_obj(func)(*args, **kwargs)
-
-#----------------------------------------------------------------------------
-# Wrapper for tf.train.Optimizer that automatically takes care of:
-# - Gradient averaging for multi-GPU training.
-# - Dynamic loss scaling and typecasts for FP16 training.
-# - Ignoring corrupted gradients that contain NaNs/Infs.
-# - Reporting statistics.
-# - Well-chosen default settings.
-
-class Optimizer:
-    def __init__(
-        self,
-        name                = 'Train',
-        tf_optimizer        = 'tf.train.AdamOptimizer',
-        learning_rate       = 0.001,
-        use_loss_scaling    = False,
-        loss_scaling_init   = 64.0,
-        loss_scaling_inc    = 0.0005,
-        loss_scaling_dec    = 1.0,
-        **kwargs):
-
-        # Init fields.
-        self.name               = name
-        self.learning_rate      = tf.convert_to_tensor(learning_rate)
-        self.id                 = self.name.replace('/', '.')
-        self.scope              = tf.get_default_graph().unique_name(self.id)
-        self.optimizer_class    = import_obj(tf_optimizer)
-        self.optimizer_kwargs   = dict(kwargs)
-        self.use_loss_scaling   = use_loss_scaling
-        self.loss_scaling_init  = loss_scaling_init
-        self.loss_scaling_inc   = loss_scaling_inc
-        self.loss_scaling_dec   = loss_scaling_dec
-        self._grad_shapes       = None          # [shape, ...]
-        self._dev_opt           = OrderedDict() # device => optimizer
-        self._dev_grads         = OrderedDict() # device => [[(grad, var), ...], ...]
-        self._dev_ls_var        = OrderedDict() # device => variable (log2 of loss scaling factor)
-        self._updates_applied   = False
-
-    # Register the gradients of the given loss function with respect to the given variables.
-    # Intended to be called once per GPU.
-    def register_gradients(self, loss, vars):
-        assert not self._updates_applied
-
-        # Validate arguments.
-        if isinstance(vars, dict):
-            vars = list(vars.values()) # allow passing in Network.trainables as vars
-        assert isinstance(vars, list) and len(vars) >= 1
-        assert all(is_tf_expression(expr) for expr in vars + [loss])
-        if self._grad_shapes is None:
-            self._grad_shapes = [shape_to_list(var.shape) for var in vars]
-        assert len(vars) == len(self._grad_shapes)
-        assert all(shape_to_list(var.shape) == var_shape for var, var_shape in zip(vars, self._grad_shapes))
-        dev = loss.device
-        assert all(var.device == dev for var in vars)
-
-        # Register device and compute gradients.
-        with tf.name_scope(self.id + '_grad'), tf.device(dev):
-            if dev not in self._dev_opt:
-                opt_name = self.scope.replace('/', '_') + '_opt%d' % len(self._dev_opt)
-                self._dev_opt[dev] = self.optimizer_class(name=opt_name, learning_rate=self.learning_rate, **self.optimizer_kwargs)
-                self._dev_grads[dev] = []
-            loss = self.apply_loss_scaling(tf.cast(loss, tf.float32))
-            grads = self._dev_opt[dev].compute_gradients(loss, vars, gate_gradients=tf.train.Optimizer.GATE_NONE) # disable gating to reduce memory usage
-            grads = [(g, v) if g is not None else (tf.zeros_like(v), v) for g, v in grads] # replace disconnected gradients with zeros
-            self._dev_grads[dev].append(grads)
-
-    # Construct training op to update the registered variables based on their gradients.
-    def apply_updates(self):
-        assert not self._updates_applied
-        self._updates_applied = True
-        devices = list(self._dev_grads.keys())
-        total_grads = sum(len(grads) for grads in self._dev_grads.values())
-        assert len(devices) >= 1 and total_grads >= 1
-        ops = []
-        with absolute_name_scope(self.scope):
-
-            # Cast gradients to FP32 and calculate partial sum within each device.
-            dev_grads = OrderedDict() # device => [(grad, var), ...]
-            for dev_idx, dev in enumerate(devices):
-                with tf.name_scope('ProcessGrads%d' % dev_idx), tf.device(dev):
-                    sums = []
-                    for gv in zip(*self._dev_grads[dev]):
-                        assert all(v is gv[0][1] for g, v in gv)
-                        g = [tf.cast(g, tf.float32) for g, v in gv]
-                        g = g[0] if len(g) == 1 else tf.add_n(g)
-                        sums.append((g, gv[0][1]))
-                    dev_grads[dev] = sums
-
-            # Sum gradients across devices.
-            if len(devices) > 1:
-                with tf.name_scope('SumAcrossGPUs'), tf.device(None):
-                    for var_idx, grad_shape in enumerate(self._grad_shapes):
-                        g = [dev_grads[dev][var_idx][0] for dev in devices]
-                        if np.prod(grad_shape): # nccl does not support zero-sized tensors
-                            g = tf.contrib.nccl.all_sum(g)
-                        for dev, gg in zip(devices, g):
-                            dev_grads[dev][var_idx] = (gg, dev_grads[dev][var_idx][1])
-
-            # Apply updates separately on each device.
-            for dev_idx, (dev, grads) in enumerate(dev_grads.items()):
-                with tf.name_scope('ApplyGrads%d' % dev_idx), tf.device(dev):
-
-                    # Scale gradients as needed.
-                    if self.use_loss_scaling or total_grads > 1:
-                        with tf.name_scope('Scale'):
-                            coef = tf.constant(np.float32(1.0 / total_grads), name='coef')
-                            coef = self.undo_loss_scaling(coef)
-                            grads = [(g * coef, v) for g, v in grads]
-
-                    # Check for overflows.
-                    with tf.name_scope('CheckOverflow'):
-                        grad_ok = tf.reduce_all(tf.stack([tf.reduce_all(tf.is_finite(g)) for g, v in grads]))
-
-                    # Update weights and adjust loss scaling.
-                    with tf.name_scope('UpdateWeights'):
-                        opt = self._dev_opt[dev]
-                        ls_var = self.get_loss_scaling_var(dev)
-                        if not self.use_loss_scaling:
-                            ops.append(tf.cond(grad_ok, lambda: opt.apply_gradients(grads), tf.no_op))
-                        else:
-                            ops.append(tf.cond(grad_ok,
-                                lambda: tf.group(tf.assign_add(ls_var, self.loss_scaling_inc), opt.apply_gradients(grads)),
-                                lambda: tf.group(tf.assign_sub(ls_var, self.loss_scaling_dec))))
-
-                    # Report statistics on the last device.
-                    if dev == devices[-1]:
-                        with tf.name_scope('Statistics'):
-                            ops.append(autosummary(self.id + '/learning_rate', self.learning_rate))
-                            ops.append(autosummary(self.id + '/overflow_frequency', tf.where(grad_ok, 0, 1)))
-                            if self.use_loss_scaling:
-                                ops.append(autosummary(self.id + '/loss_scaling_log2', ls_var))
-
-            # Initialize variables and group everything into a single op.
-            self.reset_optimizer_state()
-            init_uninited_vars(list(self._dev_ls_var.values()))
-            return tf.group(*ops, name='TrainingOp')
-
-    # Reset internal state of the underlying optimizer.
-    def reset_optimizer_state(self):
-        run([var.initializer for opt in self._dev_opt.values() for var in opt.variables()])
-
-    # Get or create variable representing log2 of the current dynamic loss scaling factor.
-    def get_loss_scaling_var(self, device):
-        if not self.use_loss_scaling:
-            return None
-        if device not in self._dev_ls_var:
-            with absolute_name_scope(self.scope + '/LossScalingVars'), tf.control_dependencies(None):
-                self._dev_ls_var[device] = tf.Variable(np.float32(self.loss_scaling_init), name='loss_scaling_var')
-        return self._dev_ls_var[device]
-
-    # Apply dynamic loss scaling for the given expression.
-    def apply_loss_scaling(self, value):
-        assert is_tf_expression(value)
-        if not self.use_loss_scaling:
-            return value
-        return value * exp2(self.get_loss_scaling_var(value.device))
-
-    # Undo the effect of dynamic loss scaling for the given expression.
-    def undo_loss_scaling(self, value):
-        assert is_tf_expression(value)
-        if not self.use_loss_scaling:
-            return value
-        return value * exp2(-self.get_loss_scaling_var(value.device))
-
-#----------------------------------------------------------------------------
-# Generic network abstraction.
-#
-# Acts as a convenience wrapper for a parameterized network construction
-# function, providing several utility methods and convenient access to
-# the inputs/outputs/weights.
-#
-# Network objects can be safely pickled and unpickled for long-term
-# archival purposes. The pickling works reliably as long as the underlying
-# network construction function is defined in a standalone Python module
-# that has no side effects or application-specific imports.
-
-network_import_handlers = []    # Custom import handlers for dealing with legacy data in pickle import.
-_network_import_modules = []    # Temporary modules create during pickle import.
-
-class Network:
-    def __init__(self,
-        name=None,          # Network name. Used to select TensorFlow name and variable scopes.
-        func=None,          # Fully qualified name of the underlying network construction function.
-        **static_kwargs):   # Keyword arguments to be passed in to the network construction function.
-
-        self._init_fields()
-        self.name = name
-        self.static_kwargs = dict(static_kwargs)
-
-        # Init build func.
-        module, self._build_func_name = import_module(func)
-        self._build_module_src = inspect.getsource(module)
-        self._build_func = find_obj_in_module(module, self._build_func_name)
-
-        # Init graph.
-        self._init_graph()
-        self.reset_vars()
-
-    def _init_fields(self):
-        self.name               = None          # User-specified name, defaults to build func name if None.
-        self.scope              = None          # Unique TF graph scope, derived from the user-specified name.
-        self.static_kwargs      = dict()        # Arguments passed to the user-supplied build func.
-        self.num_inputs         = 0             # Number of input tensors.
-        self.num_outputs        = 0             # Number of output tensors.
-        self.input_shapes       = [[]]          # Input tensor shapes (NC or NCHW), including minibatch dimension.
-        self.output_shapes      = [[]]          # Output tensor shapes (NC or NCHW), including minibatch dimension.
-        self.input_shape        = []            # Short-hand for input_shapes[0].
-        self.output_shape       = []            # Short-hand for output_shapes[0].
-        self.input_templates    = []            # Input placeholders in the template graph.
-        self.output_templates   = []            # Output tensors in the template graph.
-        self.input_names        = []            # Name string for each input.
-        self.output_names       = []            # Name string for each output.
-        self.vars               = OrderedDict() # All variables (localname => var).
-        self.trainables         = OrderedDict() # Trainable variables (localname => var).
-        self._build_func        = None          # User-supplied build function that constructs the network.
-        self._build_func_name   = None          # Name of the build function.
-        self._build_module_src  = None          # Full source code of the module containing the build function.
-        self._run_cache         = dict()        # Cached graph data for Network.run().
-        
-    def _init_graph(self):
-        # Collect inputs.
-        self.input_names = []
-        for param in inspect.signature(self._build_func).parameters.values():
-            if param.kind == param.POSITIONAL_OR_KEYWORD and param.default is param.empty:
-                self.input_names.append(param.name)
-        self.num_inputs = len(self.input_names)
-        assert self.num_inputs >= 1
-
-        # Choose name and scope.
-        if self.name is None:
-            self.name = self._build_func_name
-        self.scope = tf.get_default_graph().unique_name(self.name.replace('/', '_'), mark_as_used=False)
-        
-        # Build template graph.
-        with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
-            assert tf.get_variable_scope().name == self.scope
-            with absolute_name_scope(self.scope): # ignore surrounding name_scope
-                with tf.control_dependencies(None): # ignore surrounding control_dependencies
-                    self.input_templates = [tf.placeholder(tf.float32, name=name) for name in self.input_names]
-                    out_expr = self._build_func(*self.input_templates, is_template_graph=True, **self.static_kwargs)
-            
-        # Collect outputs.
-        assert is_tf_expression(out_expr) or isinstance(out_expr, tuple)
-        self.output_templates = [out_expr] if is_tf_expression(out_expr) else list(out_expr)
-        self.output_names = [t.name.split('/')[-1].split(':')[0] for t in self.output_templates]
-        self.num_outputs = len(self.output_templates)
-        assert self.num_outputs >= 1
-        
-        # Populate remaining fields.
-        self.input_shapes   = [shape_to_list(t.shape) for t in self.input_templates]
-        self.output_shapes  = [shape_to_list(t.shape) for t in self.output_templates]
-        self.input_shape    = self.input_shapes[0]
-        self.output_shape   = self.output_shapes[0]
-        self.vars           = OrderedDict([(self.get_var_localname(var), var) for var in tf.global_variables(self.scope + '/')])
-        self.trainables     = OrderedDict([(self.get_var_localname(var), var) for var in tf.trainable_variables(self.scope + '/')])
-
-    # Run initializers for all variables defined by this network.
-    def reset_vars(self):
-        run([var.initializer for var in self.vars.values()])
-
-    # Run initializers for all trainable variables defined by this network.
-    def reset_trainables(self):
-        run([var.initializer for var in self.trainables.values()])
-
-    # Get TensorFlow expression(s) for the output(s) of this network, given the inputs.
-    def get_output_for(self, *in_expr, return_as_list=False, **dynamic_kwargs):
-        assert len(in_expr) == self.num_inputs
-        all_kwargs = dict(self.static_kwargs)
-        all_kwargs.update(dynamic_kwargs)
-        with tf.variable_scope(self.scope, reuse=True):
-            assert tf.get_variable_scope().name == self.scope
-            named_inputs = [tf.identity(expr, name=name) for expr, name in zip(in_expr, self.input_names)]
-            out_expr = self._build_func(*named_inputs, **all_kwargs)
-        assert is_tf_expression(out_expr) or isinstance(out_expr, tuple)
-        if return_as_list:
-            out_expr = [out_expr] if is_tf_expression(out_expr) else list(out_expr)
-        return out_expr
-
-    # Get the local name of a given variable, excluding any surrounding name scopes.
-    def get_var_localname(self, var_or_globalname):
-        assert is_tf_expression(var_or_globalname) or isinstance(var_or_globalname, str)
-        globalname = var_or_globalname if isinstance(var_or_globalname, str) else var_or_globalname.name
-        assert globalname.startswith(self.scope + '/')
-        localname = globalname[len(self.scope) + 1:]
-        localname = localname.split(':')[0]
-        return localname
-
-    # Find variable by local or global name.
-    def find_var(self, var_or_localname):
-        assert is_tf_expression(var_or_localname) or isinstance(var_or_localname, str)
-        return self.vars[var_or_localname] if isinstance(var_or_localname, str) else var_or_localname
-
-    # Get the value of a given variable as NumPy array.
-    # Note: This method is very inefficient -- prefer to use tfutil.run(list_of_vars) whenever possible.
-    def get_var(self, var_or_localname):
-        return self.find_var(var_or_localname).eval()
-        
-    # Set the value of a given variable based on the given NumPy array.
-    # Note: This method is very inefficient -- prefer to use tfutil.set_vars() whenever possible.
-    def set_var(self, var_or_localname, new_value):
-        return set_vars({self.find_var(var_or_localname): new_value})
-
-    # Pickle export.
-    def __getstate__(self):
-        return {
-            'version':          2,
-            'name':             self.name,
-            'static_kwargs':    self.static_kwargs,
-            'build_module_src': self._build_module_src,
-            'build_func_name':  self._build_func_name,
-            'variables':        list(zip(self.vars.keys(), run(list(self.vars.values()))))}
-
-    # Pickle import.
-    def __setstate__(self, state):
-        self._init_fields()
-
-        # Execute custom import handlers.
-        for handler in network_import_handlers:
-            state = handler(state)
-
-        # Set basic fields.
-        assert state['version'] == 2
-        self.name = state['name']
-        self.static_kwargs = state['static_kwargs']
-        self._build_module_src = state['build_module_src']
-        self._build_func_name = state['build_func_name']
-        
-        # Parse imported module.
-        module = imp.new_module('_tfutil_network_import_module_%d' % len(_network_import_modules))
-        exec(self._build_module_src, module.__dict__)
-        self._build_func = find_obj_in_module(module, self._build_func_name)
-        _network_import_modules.append(module) # avoid gc
-        
-        # Init graph.
-        self._init_graph()
-        self.reset_vars()
-        set_vars({self.find_var(name): value for name, value in state['variables']})
-
-    # Create a clone of this network with its own copy of the variables.
-    def clone(self, name=None):
-        net = object.__new__(Network)
-        net._init_fields()
-        net.name = name if name is not None else self.name
-        net.static_kwargs = dict(self.static_kwargs)
-        net._build_module_src = self._build_module_src
-        net._build_func_name = self._build_func_name
-        net._build_func = self._build_func
-        net._init_graph()
-        net.copy_vars_from(self)
-        return net
-
-    # Copy the values of all variables from the given network.
-    def copy_vars_from(self, src_net):
-        assert isinstance(src_net, Network)
-        name_to_value = run({name: src_net.find_var(name) for name in self.vars.keys()})
-        set_vars({self.find_var(name): value for name, value in name_to_value.items()})
-
-    # Copy the values of all trainable variables from the given network.
-    def copy_trainables_from(self, src_net):
-        assert isinstance(src_net, Network)
-        name_to_value = run({name: src_net.find_var(name) for name in self.trainables.keys()})
-        set_vars({self.find_var(name): value for name, value in name_to_value.items()})
-
-    # Create new network with the given parameters, and copy all variables from this network.
-    def convert(self, name=None, func=None, **static_kwargs):
-        net = Network(name, func, **static_kwargs)
-        net.copy_vars_from(self)
-        return net
-
-    # Construct a TensorFlow op that updates the variables of this network
-    # to be slightly closer to those of the given network.
-    def setup_as_moving_average_of(self, src_net, beta=0.99, beta_nontrainable=0.0):
-        assert isinstance(src_net, Network)
-        with absolute_name_scope(self.scope):
-            with tf.name_scope('MovingAvg'):
-                ops = []
-                for name, var in self.vars.items():
-                    if name in src_net.vars:
-                        cur_beta = beta if name in self.trainables else beta_nontrainable
-                        new_value = lerp(src_net.vars[name], var, cur_beta)
-                        ops.append(var.assign(new_value))
-                return tf.group(*ops)
-
-    # Run this network for the given NumPy array(s), and return the output(s) as NumPy array(s).
-    def run(self, *in_arrays,
-        return_as_list  = False,    # True = return a list of NumPy arrays, False = return a single NumPy array, or a tuple if there are multiple outputs.
-        print_progress  = False,    # Print progress to the console? Useful for very large input arrays.
-        minibatch_size  = None,     # Maximum minibatch size to use, None = disable batching.
-        num_gpus        = 1,        # Number of GPUs to use.
-        out_mul         = 1.0,      # Multiplicative constant to apply to the output(s).
-        out_add         = 0.0,      # Additive constant to apply to the output(s).
-        out_shrink      = 1,        # Shrink the spatial dimensions of the output(s) by the given factor.
-        out_dtype       = None,     # Convert the output to the specified data type.
-        **dynamic_kwargs):          # Additional keyword arguments to pass into the network construction function.
-
-        assert len(in_arrays) == self.num_inputs
-        num_items = in_arrays[0].shape[0]
-        if minibatch_size is None:
-            minibatch_size = num_items
-        key = str([list(sorted(dynamic_kwargs.items())), num_gpus, out_mul, out_add, out_shrink, out_dtype])
-
-        # Build graph.
-        if key not in self._run_cache:
-            with absolute_name_scope(self.scope + '/Run'), tf.control_dependencies(None):
-                in_split = list(zip(*[tf.split(x, num_gpus) for x in self.input_templates]))
-                out_split = []
-                for gpu in range(num_gpus):
-                    with tf.device('/gpu:%d' % gpu):
-                        out_expr = self.get_output_for(*in_split[gpu], return_as_list=True, **dynamic_kwargs)
-                        if out_mul != 1.0:
-                            out_expr = [x * out_mul for x in out_expr]
-                        if out_add != 0.0:
-                            out_expr = [x + out_add for x in out_expr]
-                        if out_shrink > 1:
-                            ksize = [1, 1, out_shrink, out_shrink]
-                            out_expr = [tf.nn.avg_pool(x, ksize=ksize, strides=ksize, padding='VALID', data_format='NCHW') for x in out_expr]
-                        if out_dtype is not None:
-                            if tf.as_dtype(out_dtype).is_integer:
-                                out_expr = [tf.round(x) for x in out_expr]
-                            out_expr = [tf.saturate_cast(x, out_dtype) for x in out_expr]
-                        out_split.append(out_expr)
-                self._run_cache[key] = [tf.concat(outputs, axis=0) for outputs in zip(*out_split)]
-
-        # Run minibatches.
-        out_expr = self._run_cache[key]
-        out_arrays = [np.empty([num_items] + shape_to_list(expr.shape)[1:], expr.dtype.name) for expr in out_expr]
-        for mb_begin in range(0, num_items, minibatch_size):
-            if print_progress:
-                print('\r%d / %d' % (mb_begin, num_items), end='')
-            mb_end = min(mb_begin + minibatch_size, num_items)
-            mb_in = [src[mb_begin : mb_end] for src in in_arrays]
-            mb_out = tf.get_default_session().run(out_expr, dict(zip(self.input_templates, mb_in)))
-            for dst, src in zip(out_arrays, mb_out):
-                dst[mb_begin : mb_end] = src
-
-        # Done.
-        if print_progress:
-            print('\r%d / %d' % (num_items, num_items))
-        if not return_as_list:
-            out_arrays = out_arrays[0] if len(out_arrays) == 1 else tuple(out_arrays)
-        return out_arrays
-
-    # Returns a list of (name, output_expr, trainable_vars) tuples corresponding to
-    # individual layers of the network. Mainly intended to be used for reporting.
-    def list_layers(self):
-        patterns_to_ignore = ['/Setter', '/new_value', '/Shape', '/strided_slice', '/Cast', '/concat']
-        all_ops = tf.get_default_graph().get_operations()
-        all_ops = [op for op in all_ops if not any(p in op.name for p in patterns_to_ignore)]
-        layers = []
-
-        def recurse(scope, parent_ops, level):
-            prefix = scope + '/'
-            ops = [op for op in parent_ops if op.name == scope or op.name.startswith(prefix)]
-
-            # Does not contain leaf nodes => expand immediate children.
-            if level == 0 or all('/' in op.name[len(prefix):] for op in ops):
-                visited = set()
-                for op in ops:
-                    suffix = op.name[len(prefix):]
-                    if '/' in suffix:
-                        suffix = suffix[:suffix.index('/')]
-                    if suffix not in visited:
-                        recurse(prefix + suffix, ops, level + 1)
-                        visited.add(suffix)
-
-            # Otherwise => interpret as a layer.
-            else:
-                layer_name = scope[len(self.scope)+1:]
-                layer_output = ops[-1].outputs[0]
-                layer_trainables = [op.outputs[0] for op in ops if op.type.startswith('Variable') and self.get_var_localname(op.name) in self.trainables]
-                layers.append((layer_name, layer_output, layer_trainables))
-
-        recurse(self.scope, all_ops, 0)
-        return layers
-
-    # Print a summary table of the network structure.
-    def print_layers(self, title=None, hide_layers_with_no_params=False):
-        if title is None: title = self.name
-        print()
-        print('%-28s%-12s%-24s%-24s' % (title, 'Params', 'OutputShape', 'WeightShape'))
-        print('%-28s%-12s%-24s%-24s' % (('---',) * 4))
-
-        total_params = 0
-        for layer_name, layer_output, layer_trainables in self.list_layers():
-            weights = [var for var in layer_trainables if var.name.endswith('/weight:0')]
-            num_params = sum(np.prod(shape_to_list(var.shape)) for var in layer_trainables)
-            total_params += num_params
-            if hide_layers_with_no_params and num_params == 0:
-                continue
-
-            print('%-28s%-12s%-24s%-24s' % (
-                layer_name,
-                num_params if num_params else '-',
-                layer_output.shape,
-                weights[0].shape if len(weights) == 1 else '-'))
-
-        print('%-28s%-12s%-24s%-24s' % (('---',) * 4))
-        print('%-28s%-12s%-24s%-24s' % ('Total', total_params, '', ''))
-        print()
-
-    # Construct summary ops to include histograms of all trainable parameters in TensorBoard.
-    def setup_weight_histograms(self, title=None):
-        if title is None: title = self.name
-        with tf.name_scope(None), tf.device(None), tf.control_dependencies(None):
-            for localname, var in self.trainables.items():
-                if '/' in localname:
-                    p = localname.split('/')
-                    name = title + '_' + p[-1] + '/' + '_'.join(p[:-1])
-                else:
-                    name = title + '_toplevel/' + localname
-                tf.summary.histogram(name, var)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/yeqingmei123/face-test/e4e/editings/latent_editor.py b/spaces/yeqingmei123/face-test/e4e/editings/latent_editor.py
deleted file mode 100644
index 4bebca2f5c86f71b58fa1f30d24bfcb0da06d88f..0000000000000000000000000000000000000000
--- a/spaces/yeqingmei123/face-test/e4e/editings/latent_editor.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import torch
-import sys
-sys.path.append(".")
-sys.path.append("..")
-from editings import ganspace, sefa
-from utils.common import tensor2im
-
-
-class LatentEditor(object):
-    def __init__(self, stylegan_generator, is_cars=False):
-        self.generator = stylegan_generator
-        self.is_cars = is_cars  # Since the cars StyleGAN output is 384x512, there is a need to crop the 512x512 output.
-
-    def apply_ganspace(self, latent, ganspace_pca, edit_directions):
-        edit_latents = ganspace.edit(latent, ganspace_pca, edit_directions)
-        return self._latents_to_image(edit_latents)
-
-    def apply_interfacegan(self, latent, direction, factor=1, factor_range=None):
-        edit_latents = []
-        if factor_range is not None:  # Apply a range of editing factors. for example, (-5, 5)
-            for f in range(*factor_range):
-                edit_latent = latent + f * direction
-                edit_latents.append(edit_latent)
-            edit_latents = torch.cat(edit_latents)
-        else:
-            edit_latents = latent + factor * direction
-        return self._latents_to_image(edit_latents)
-
-    def apply_sefa(self, latent, indices=[2, 3, 4, 5], **kwargs):
-        edit_latents = sefa.edit(self.generator, latent, indices, **kwargs)
-        return self._latents_to_image(edit_latents)
-
-    # Currently, in order to apply StyleFlow editings, one should run inference,
-    # save the latent codes and load them form the official StyleFlow repository.
-    # def apply_styleflow(self):
-    #     pass
-
-    def _latents_to_image(self, latents):
-        with torch.no_grad():
-            images, _ = self.generator([latents], randomize_noise=False, input_is_latent=True)
-            if self.is_cars:
-                images = images[:, :, 64:448, :]  # 512x512 -> 384x512
-        horizontal_concat_image = torch.cat(list(images), 2)
-        final_image = tensor2im(horizontal_concat_image)
-        return final_image
diff --git a/spaces/yeqingmei123/face-test/e4e/models/latent_codes_pool.py b/spaces/yeqingmei123/face-test/e4e/models/latent_codes_pool.py
deleted file mode 100644
index 0281d4b5e80f8eb26e824fa35b4f908dcb6634e6..0000000000000000000000000000000000000000
--- a/spaces/yeqingmei123/face-test/e4e/models/latent_codes_pool.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import random
-import torch
-
-
-class LatentCodesPool:
-    """This class implements latent codes buffer that stores previously generated w latent codes.
-    This buffer enables us to update discriminators using a history of generated w's
-    rather than the ones produced by the latest encoder.
-    """
-
-    def __init__(self, pool_size):
-        """Initialize the ImagePool class
-        Parameters:
-            pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created
-        """
-        self.pool_size = pool_size
-        if self.pool_size > 0:  # create an empty pool
-            self.num_ws = 0
-            self.ws = []
-
-    def query(self, ws):
-        """Return w's from the pool.
-        Parameters:
-            ws: the latest generated w's from the generator
-        Returns w's from the buffer.
-        By 50/100, the buffer will return input w's.
-        By 50/100, the buffer will return w's previously stored in the buffer,
-        and insert the current w's to the buffer.
-        """
-        if self.pool_size == 0:  # if the buffer size is 0, do nothing
-            return ws
-        return_ws = []
-        for w in ws:  # ws.shape: (batch, 512) or (batch, n_latent, 512)
-            # w = torch.unsqueeze(image.data, 0)
-            if w.ndim == 2:
-                i = random.randint(0, len(w) - 1)  # apply a random latent index as a candidate
-                w = w[i]
-            self.handle_w(w, return_ws)
-        return_ws = torch.stack(return_ws, 0)   # collect all the images and return
-        return return_ws
-
-    def handle_w(self, w, return_ws):
-        if self.num_ws < self.pool_size:  # if the buffer is not full; keep inserting current codes to the buffer
-            self.num_ws = self.num_ws + 1
-            self.ws.append(w)
-            return_ws.append(w)
-        else:
-            p = random.uniform(0, 1)
-            if p > 0.5:  # by 50% chance, the buffer will return a previously stored latent code, and insert the current code into the buffer
-                random_id = random.randint(0, self.pool_size - 1)  # randint is inclusive
-                tmp = self.ws[random_id].clone()
-                self.ws[random_id] = w
-                return_ws.append(tmp)
-            else:  # by another 50% chance, the buffer will return the current image
-                return_ws.append(w)
diff --git a/spaces/ygangang/CodeFormer/CodeFormer/scripts/download_pretrained_models_from_gdrive.py b/spaces/ygangang/CodeFormer/CodeFormer/scripts/download_pretrained_models_from_gdrive.py
deleted file mode 100644
index 7df5be6fc260394ee9bbd0a7ae377e2ca657fe83..0000000000000000000000000000000000000000
--- a/spaces/ygangang/CodeFormer/CodeFormer/scripts/download_pretrained_models_from_gdrive.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import argparse
-import os
-from os import path as osp
-
-# from basicsr.utils.download_util import download_file_from_google_drive
-import gdown
-
-
-def download_pretrained_models(method, file_ids):
-    save_path_root = f'./weights/{method}'
-    os.makedirs(save_path_root, exist_ok=True)
-
-    for file_name, file_id in file_ids.items():
-        file_url = 'https://drive.google.com/uc?id='+file_id
-        save_path = osp.abspath(osp.join(save_path_root, file_name))
-        if osp.exists(save_path):
-            user_response = input(f'{file_name} already exist. Do you want to cover it? Y/N\n')
-            if user_response.lower() == 'y':
-                print(f'Covering {file_name} to {save_path}')
-                gdown.download(file_url, save_path, quiet=False)
-                # download_file_from_google_drive(file_id, save_path)
-            elif user_response.lower() == 'n':
-                print(f'Skipping {file_name}')
-            else:
-                raise ValueError('Wrong input. Only accepts Y/N.')
-        else:
-            print(f'Downloading {file_name} to {save_path}')
-            gdown.download(file_url, save_path, quiet=False)
-            # download_file_from_google_drive(file_id, save_path)
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        'method',
-        type=str,
-        help=("Options: 'CodeFormer' 'facelib'. Set to 'all' to download all the models."))
-    args = parser.parse_args()
-
-    # file name: file id
-    # 'dlib': {
-    #     'mmod_human_face_detector-4cb19393.dat': '1qD-OqY8M6j4PWUP_FtqfwUPFPRMu6ubX',
-    #     'shape_predictor_5_face_landmarks-c4b1e980.dat': '1vF3WBUApw4662v9Pw6wke3uk1qxnmLdg',
-    #     'shape_predictor_68_face_landmarks-fbdc2cb8.dat': '1tJyIVdCHaU6IDMDx86BZCxLGZfsWB8yq'
-    # }
-    file_ids = {
-        'CodeFormer': {
-            'codeformer.pth': '1v_E_vZvP-dQPF55Kc5SRCjaKTQXDz-JB'
-        },
-        'facelib': {
-            'yolov5l-face.pth': '131578zMA6B2x8VQHyHfa6GEPtulMCNzV',
-            'parsing_parsenet.pth': '16pkohyZZ8ViHGBk3QtVqxLZKzdo466bK'
-        }
-    }
-
-    if args.method == 'all':
-        for method in file_ids.keys():
-            download_pretrained_models(method, file_ids[method])
-    else:
-        download_pretrained_models(args.method, file_ids[args.method])
\ No newline at end of file
diff --git a/spaces/ygangang/Image-Animation-using-Thin-Plate-Spline-Motion-Model/README.md b/spaces/ygangang/Image-Animation-using-Thin-Plate-Spline-Motion-Model/README.md
deleted file mode 100644
index 9af54dca9f1956d33877bf7df09b34c2d6ddeeaf..0000000000000000000000000000000000000000
--- a/spaces/ygangang/Image-Animation-using-Thin-Plate-Spline-Motion-Model/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Image Animation Using Thin Plate Spline Motion Model
-emoji: 👁
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.19
-app_file: app.py
-pinned: false
-duplicated_from: CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/fnet/modeling_fnet.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/fnet/modeling_fnet.py
deleted file mode 100644
index 45042147761d5699f47b7d7e1a0a1ad9e445aa16..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/fnet/modeling_fnet.py
+++ /dev/null
@@ -1,1196 +0,0 @@
-# coding=utf-8
-# Copyright 2021 Google Research and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch FNet model."""
-
-import warnings
-from dataclasses import dataclass
-from functools import partial
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-
-from ...utils import is_scipy_available
-
-
-if is_scipy_available():
-    from scipy import linalg
-
-from ...activations import ACT2FN
-from ...modeling_outputs import (
-    BaseModelOutput,
-    BaseModelOutputWithPooling,
-    MaskedLMOutput,
-    ModelOutput,
-    MultipleChoiceModelOutput,
-    NextSentencePredictorOutput,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-)
-from ...modeling_utils import PreTrainedModel
-from ...pytorch_utils import apply_chunking_to_forward
-from ...utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_fnet import FNetConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "google/fnet-base"
-_CONFIG_FOR_DOC = "FNetConfig"
-
-FNET_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "google/fnet-base",
-    "google/fnet-large"
-    # See all FNet models at https://huggingface.co/models?filter=fnet
-]
-
-
-# Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py
-def _two_dim_matmul(x, matrix_dim_one, matrix_dim_two):
-    """Applies 2D matrix multiplication to 3D input arrays."""
-    seq_length = x.shape[1]
-    matrix_dim_one = matrix_dim_one[:seq_length, :seq_length]
-    x = x.type(torch.complex64)
-    return torch.einsum("bij,jk,ni->bnk", x, matrix_dim_two, matrix_dim_one)
-
-
-# # Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py
-def two_dim_matmul(x, matrix_dim_one, matrix_dim_two):
-    return _two_dim_matmul(x, matrix_dim_one, matrix_dim_two)
-
-
-# Adapted from https://github.com/google-research/google-research/blob/master/f_net/fourier.py
-def fftn(x):
-    """
-    Applies n-dimensional Fast Fourier Transform (FFT) to input array.
-
-    Args:
-        x: Input n-dimensional array.
-
-    Returns:
-        n-dimensional Fourier transform of input n-dimensional array.
-    """
-    out = x
-    for axis in reversed(range(x.ndim)[1:]):  # We don't need to apply FFT to last axis
-        out = torch.fft.fft(out, axis=axis)
-    return out
-
-
-class FNetEmbeddings(nn.Module):
-    """Construct the embeddings from word, position and token_type embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        # NOTE: This is the project layer and will be needed. The original code allows for different embedding and different model dimensions.
-        self.projection = nn.Linear(config.hidden_size, config.hidden_size)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer(
-            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
-        )
-
-        self.register_buffer(
-            "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
-        )
-
-    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, :seq_length]
-
-        # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
-        # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
-        # issue #5664
-        if token_type_ids is None:
-            if hasattr(self, "token_type_ids"):
-                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
-                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
-                token_type_ids = buffered_token_type_ids_expanded
-            else:
-                token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-        token_type_embeddings = self.token_type_embeddings(token_type_ids)
-
-        embeddings = inputs_embeds + token_type_embeddings
-
-        position_embeddings = self.position_embeddings(position_ids)
-        embeddings += position_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.projection(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class FNetBasicFourierTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self._init_fourier_transform(config)
-
-    def _init_fourier_transform(self, config):
-        if not config.use_tpu_fourier_optimizations:
-            self.fourier_transform = partial(torch.fft.fftn, dim=(1, 2))
-        elif config.max_position_embeddings <= 4096:
-            if is_scipy_available():
-                self.register_buffer(
-                    "dft_mat_hidden", torch.tensor(linalg.dft(config.hidden_size), dtype=torch.complex64)
-                )
-                self.register_buffer(
-                    "dft_mat_seq", torch.tensor(linalg.dft(config.tpu_short_seq_length), dtype=torch.complex64)
-                )
-                self.fourier_transform = partial(
-                    two_dim_matmul, matrix_dim_one=self.dft_mat_seq, matrix_dim_two=self.dft_mat_hidden
-                )
-            else:
-                logging.warning(
-                    "SciPy is needed for DFT matrix calculation and is not found. Using TPU optimized fast fourier"
-                    " transform instead."
-                )
-                self.fourier_transform = fftn
-        else:
-            self.fourier_transform = fftn
-
-    def forward(self, hidden_states):
-        # NOTE: We do not use torch.vmap as it is not integrated into PyTorch stable versions.
-        # Interested users can modify the code to use vmap from the nightly versions, getting the vmap from here:
-        # https://pytorch.org/docs/master/generated/torch.vmap.html. Note that fourier transform methods will need
-        # change accordingly.
-
-        outputs = self.fourier_transform(hidden_states).real
-        return (outputs,)
-
-
-class FNetBasicOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.LayerNorm(input_tensor + hidden_states)
-        return hidden_states
-
-
-class FNetFourierTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.self = FNetBasicFourierTransform(config)
-        self.output = FNetBasicOutput(config)
-
-    def forward(self, hidden_states):
-        self_outputs = self.self(hidden_states)
-        fourier_output = self.output(self_outputs[0], hidden_states)
-        outputs = (fourier_output,)
-        return outputs
-
-
-# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->FNet
-class FNetIntermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->FNet
-class FNetOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class FNetLayer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1  # The dimension which has the sequence length
-        self.fourier = FNetFourierTransform(config)
-        self.intermediate = FNetIntermediate(config)
-        self.output = FNetOutput(config)
-
-    def forward(self, hidden_states):
-        self_fourier_outputs = self.fourier(hidden_states)
-        fourier_output = self_fourier_outputs[0]
-
-        layer_output = apply_chunking_to_forward(
-            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, fourier_output
-        )
-
-        outputs = (layer_output,)
-
-        return outputs
-
-    def feed_forward_chunk(self, fourier_output):
-        intermediate_output = self.intermediate(fourier_output)
-        layer_output = self.output(intermediate_output, fourier_output)
-        return layer_output
-
-
-class FNetEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList([FNetLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(self, hidden_states, output_hidden_states=False, return_dict=True):
-        all_hidden_states = () if output_hidden_states else None
-
-        for i, layer_module in enumerate(self.layer):
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(create_custom_forward(layer_module), hidden_states)
-            else:
-                layer_outputs = layer_module(hidden_states)
-
-            hidden_states = layer_outputs[0]
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
-
-        return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
-
-
-# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->FNet
-class FNetPooler(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->FNet
-class FNetPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        if isinstance(config.hidden_act, str):
-            self.transform_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.transform_act_fn = config.hidden_act
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class FNetLMPredictionHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.transform = FNetPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
-
-        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
-        self.decoder.bias = self.bias
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-        return hidden_states
-
-    def _tie_weights(self):
-        # To tie those two weights if they get disconnected (on TPU or when the bias is resized)
-        self.bias = self.decoder.bias
-
-
-class FNetOnlyMLMHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.predictions = FNetLMPredictionHead(config)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-# Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert->FNet
-class FNetOnlyNSPHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, pooled_output):
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return seq_relationship_score
-
-
-# Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->FNet
-class FNetPreTrainingHeads(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.predictions = FNetLMPredictionHead(config)
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, sequence_output, pooled_output):
-        prediction_scores = self.predictions(sequence_output)
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return prediction_scores, seq_relationship_score
-
-
-class FNetPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = FNetConfig
-    base_model_prefix = "fnet"
-    supports_gradient_checkpointing = True
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        if isinstance(module, nn.Linear):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            # NOTE: Original code uses same initialization as weights for biases as well.
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, FNetEncoder):
-            module.gradient_checkpointing = value
-
-
-@dataclass
-class FNetForPreTrainingOutput(ModelOutput):
-    """
-    Output type of [`FNetForPreTraining`].
-
-    Args:
-        loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
-            Total loss as the sum of the masked language modeling loss and the next sequence prediction
-            (classification) loss.
-        prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
-            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
-            before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
-            plus the initial embedding outputs.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    prediction_logits: torch.FloatTensor = None
-    seq_relationship_logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
-FNET_START_DOCSTRING = r"""
-    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
-    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
-    behavior.
-
-    Parameters:
-        config ([`FNetConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-FNET_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `({0})`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
-            1]`:
-
-            - 0 corresponds to a *sentence A* token,
-            - 1 corresponds to a *sentence B* token.
-
-            [What are token type IDs?](../glossary#token-type-ids)
-        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-
-        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare FNet Model transformer outputting raw hidden-states without any specific head on top.",
-    FNET_START_DOCSTRING,
-)
-class FNetModel(FNetPreTrainedModel):
-    """
-
-    The model can behave as an encoder, following the architecture described in [FNet: Mixing Tokens with Fourier
-    Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
-
-    """
-
-    def __init__(self, config, add_pooling_layer=True):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = FNetEmbeddings(config)
-        self.encoder = FNetEncoder(config)
-
-        self.pooler = FNetPooler(config) if add_pooling_layer else None
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = value
-
-    @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[tuple, BaseModelOutput]:
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-            batch_size, seq_length = input_shape
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-            batch_size, seq_length = input_shape
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        if (
-            self.config.use_tpu_fourier_optimizations
-            and seq_length <= 4096
-            and self.config.tpu_short_seq_length != seq_length
-        ):
-            raise ValueError(
-                "The `tpu_short_seq_length` in FNetConfig should be set equal to the sequence length being passed to"
-                " the model when using TPU optimizations."
-            )
-
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        if token_type_ids is None:
-            if hasattr(self.embeddings, "token_type_ids"):
-                buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
-                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
-                token_type_ids = buffered_token_type_ids_expanded
-            else:
-                token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            token_type_ids=token_type_ids,
-            inputs_embeds=inputs_embeds,
-        )
-        encoder_outputs = self.encoder(
-            embedding_output,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = encoder_outputs[0]
-
-        pooler_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if not return_dict:
-            return (sequence_output, pooler_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPooling(
-            last_hidden_state=sequence_output,
-            pooler_output=pooler_output,
-            hidden_states=encoder_outputs.hidden_states,
-        )
-
-
-@add_start_docstrings(
-    """
-    FNet Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
-    sentence prediction (classification)` head.
-    """,
-    FNET_START_DOCSTRING,
-)
-class FNetForPreTraining(FNetPreTrainedModel):
-    _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.fnet = FNetModel(config)
-        self.cls = FNetPreTrainingHeads(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=FNetForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        next_sentence_label: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, FNetForPreTrainingOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
-            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
-            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
-        next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
-            (see `input_ids` docstring) Indices should be in `[0, 1]`:
-
-            - 0 indicates sequence B is a continuation of sequence A,
-            - 1 indicates sequence B is a random sequence.
-        kwargs (`Dict[str, any]`, optional, defaults to *{}*):
-            Used to hide legacy arguments that have been deprecated.
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, FNetForPreTraining
-        >>> import torch
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("google/fnet-base")
-        >>> model = FNetForPreTraining.from_pretrained("google/fnet-base")
-        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-        >>> outputs = model(**inputs)
-        >>> prediction_logits = outputs.prediction_logits
-        >>> seq_relationship_logits = outputs.seq_relationship_logits
-        ```"""
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.fnet(
-            input_ids,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output, pooled_output = outputs[:2]
-        prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
-
-        total_loss = None
-        if labels is not None and next_sentence_label is not None:
-            loss_fct = CrossEntropyLoss()
-            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
-            total_loss = masked_lm_loss + next_sentence_loss
-
-        if not return_dict:
-            output = (prediction_scores, seq_relationship_score) + outputs[2:]
-            return ((total_loss,) + output) if total_loss is not None else output
-
-        return FNetForPreTrainingOutput(
-            loss=total_loss,
-            prediction_logits=prediction_scores,
-            seq_relationship_logits=seq_relationship_score,
-            hidden_states=outputs.hidden_states,
-        )
-
-
-@add_start_docstrings("""FNet Model with a `language modeling` head on top.""", FNET_START_DOCSTRING)
-class FNetForMaskedLM(FNetPreTrainedModel):
-    _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.fnet = FNetModel(config)
-        self.cls = FNetOnlyMLMHead(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_output_embeddings(self):
-        return self.cls.predictions.decoder
-
-    def set_output_embeddings(self, new_embeddings):
-        self.cls.predictions.decoder = new_embeddings
-
-    @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=MaskedLMOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, MaskedLMOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
-            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
-            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.fnet(
-            input_ids,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-        prediction_scores = self.cls(sequence_output)
-
-        masked_lm_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()  # -100 index = padding token
-            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
-
-        if not return_dict:
-            output = (prediction_scores,) + outputs[2:]
-            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
-        return MaskedLMOutput(loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states)
-
-
-@add_start_docstrings(
-    """FNet Model with a `next sentence prediction (classification)` head on top.""",
-    FNET_START_DOCSTRING,
-)
-class FNetForNextSentencePrediction(FNetPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.fnet = FNetModel(config)
-        self.cls = FNetOnlyNSPHead(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        **kwargs,
-    ) -> Union[Tuple, NextSentencePredictorOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
-            (see `input_ids` docstring). Indices should be in `[0, 1]`:
-
-            - 0 indicates sequence B is a continuation of sequence A,
-            - 1 indicates sequence B is a random sequence.
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, FNetForNextSentencePrediction
-        >>> import torch
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("google/fnet-base")
-        >>> model = FNetForNextSentencePrediction.from_pretrained("google/fnet-base")
-        >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
-        >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
-        >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
-        >>> outputs = model(**encoding, labels=torch.LongTensor([1]))
-        >>> logits = outputs.logits
-        >>> assert logits[0, 0] < logits[0, 1]  # next sentence was random
-        ```"""
-
-        if "next_sentence_label" in kwargs:
-            warnings.warn(
-                "The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
-                " `labels` instead.",
-                FutureWarning,
-            )
-            labels = kwargs.pop("next_sentence_label")
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.fnet(
-            input_ids,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-
-        seq_relationship_scores = self.cls(pooled_output)
-
-        next_sentence_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
-
-        if not return_dict:
-            output = (seq_relationship_scores,) + outputs[2:]
-            return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
-
-        return NextSentencePredictorOutput(
-            loss=next_sentence_loss,
-            logits=seq_relationship_scores,
-            hidden_states=outputs.hidden_states,
-        )
-
-
-@add_start_docstrings(
-    """
-    FNet Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
-    output) e.g. for GLUE tasks.
-    """,
-    FNET_START_DOCSTRING,
-)
-class FNetForSequenceClassification(FNetPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.fnet = FNetModel(config)
-
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=SequenceClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.fnet(
-            input_ids,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-
-        loss = None
-        if labels is not None:
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
-
-
-@add_start_docstrings(
-    """
-    FNet Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
-    softmax) e.g. for RocStories/SWAG tasks.
-    """,
-    FNET_START_DOCSTRING,
-)
-class FNetForMultipleChoice(FNetPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.fnet = FNetModel(config)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, 1)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=MultipleChoiceModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, MultipleChoiceModelOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
-            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
-            `input_ids` above)
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
-
-        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
-        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
-        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
-        inputs_embeds = (
-            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
-            if inputs_embeds is not None
-            else None
-        )
-
-        outputs = self.fnet(
-            input_ids,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-        reshaped_logits = logits.view(-1, num_choices)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(reshaped_logits, labels)
-
-        if not return_dict:
-            output = (reshaped_logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return MultipleChoiceModelOutput(loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states)
-
-
-@add_start_docstrings(
-    """
-    FNet Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
-    Named-Entity-Recognition (NER) tasks.
-    """,
-    FNET_START_DOCSTRING,
-)
-class FNetForTokenClassification(FNetPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.fnet = FNetModel(config)
-
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, TokenClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.fnet(
-            input_ids,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        sequence_output = self.dropout(sequence_output)
-        logits = self.classifier(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            # Only keep active parts of the loss
-            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
-
-
-@add_start_docstrings(
-    """
-    FNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
-    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    FNET_START_DOCSTRING,
-)
-class FNetForQuestionAnswering(FNetPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.num_labels = config.num_labels
-
-        self.fnet = FNetModel(config)
-        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=QuestionAnsweringModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        start_positions: Optional[torch.Tensor] = None,
-        end_positions: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
-        r"""
-        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the start of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the end of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.fnet(
-            input_ids,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        sequence_output = outputs[0]
-
-        logits = self.qa_outputs(sequence_output)
-        start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1).contiguous()
-
-        total_loss = None
-        if start_positions is not None and end_positions is not None:
-            # If we are on multi-GPU, split add a dimension
-            if len(start_positions.size()) > 1:
-                start_positions = start_positions.squeeze(-1)
-            if len(end_positions.size()) > 1:
-                end_positions = end_positions.squeeze(-1)
-            # sometimes the start/end positions are outside our model inputs, we ignore these terms
-            ignored_index = start_logits.size(1)
-            start_positions = start_positions.clamp(0, ignored_index)
-            end_positions = end_positions.clamp(0, ignored_index)
-
-            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
-            start_loss = loss_fct(start_logits, start_positions)
-            end_loss = loss_fct(end_logits, end_positions)
-            total_loss = (start_loss + end_loss) / 2
-
-        if not return_dict:
-            output = (start_logits, end_logits) + outputs[2:]
-            return ((total_loss,) + output) if total_loss is not None else output
-
-        return QuestionAnsweringModelOutput(
-            loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states
-        )
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/focalnet/__init__.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/focalnet/__init__.py
deleted file mode 100644
index b043a006f9376609c774e84f5376323f48f2cae7..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/focalnet/__init__.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import TYPE_CHECKING
-
-# rely on isort to merge the imports
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
-
-
-_import_structure = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
-
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_focalnet"] = [
-        "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "FocalNetForImageClassification",
-        "FocalNetForMaskedImageModeling",
-        "FocalNetBackbone",
-        "FocalNetModel",
-        "FocalNetPreTrainedModel",
-    ]
-
-if TYPE_CHECKING:
-    from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_focalnet import (
-            FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
-            FocalNetBackbone,
-            FocalNetForImageClassification,
-            FocalNetForMaskedImageModeling,
-            FocalNetModel,
-            FocalNetPreTrainedModel,
-        )
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/m2m_100/tokenization_m2m_100.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/m2m_100/tokenization_m2m_100.py
deleted file mode 100644
index 1346af81412add53b2ed07287fd840079992872a..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/m2m_100/tokenization_m2m_100.py
+++ /dev/null
@@ -1,398 +0,0 @@
-# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tokenization classes for M2M100."""
-import json
-import os
-from pathlib import Path
-from shutil import copyfile
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import sentencepiece
-
-from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-SPIECE_UNDERLINE = "▁"
-
-VOCAB_FILES_NAMES = {
-    "vocab_file": "vocab.json",
-    "spm_file": "sentencepiece.bpe.model",
-    "tokenizer_config_file": "tokenizer_config.json",
-}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
-        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
-    },
-    "spm_file": {
-        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
-        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
-    },
-    "tokenizer_config_file": {
-        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
-        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
-    },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "facebook/m2m100_418M": 1024,
-}
-
-# fmt: off
-FAIRSEQ_LANGUAGE_CODES = {
-    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
-    "wmt21": ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
-}
-# fmt: on
-
-
-class M2M100Tokenizer(PreTrainedTokenizer):
-    """
-    Construct an M2M100 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
-
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
-    this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        spm_file (`str`):
-            Path to [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
-            contains the vocabulary.
-        src_lang (`str`, *optional*):
-            A string representing the source language.
-        tgt_lang (`str`, *optional*):
-            A string representing the target language.
-        eos_token (`str`, *optional*, defaults to `"</s>"`):
-            The end of sequence token.
-        sep_token (`str`, *optional*, defaults to `"</s>"`):
-            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
-            sequence classification or for a text and a question for question answering. It is also used as the last
-            token of a sequence built with special tokens.
-        unk_token (`str`, *optional*, defaults to `"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        pad_token (`str`, *optional*, defaults to `"<pad>"`):
-            The token used for padding, for example when batching sequences of different lengths.
-        language_codes (`str`, *optional*, defaults to `"m2m100"`):
-            What language codes to use. Should be one of `"m2m100"` or `"wmt21"`.
-        sp_model_kwargs (`dict`, *optional*):
-            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
-            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
-            to set:
-
-            - `enable_sampling`: Enable subword regularization.
-            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
-
-              - `nbest_size = {0,1}`: No sampling is performed.
-              - `nbest_size > 1`: samples from the nbest_size results.
-              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
-                using forward-filtering-and-backward-sampling algorithm.
-
-            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
-              BPE-dropout.
-
-    Examples:
-
-    ```python
-    >>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
-
-    >>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
-    >>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en", tgt_lang="ro")
-    >>> src_text = " UN Chief Says There Is No Military Solution in Syria"
-    >>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria"
-    >>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
-    >>> outputs = model(**model_inputs)  # should work
-    ```"""
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    model_input_names = ["input_ids", "attention_mask"]
-
-    prefix_tokens: List[int] = []
-    suffix_tokens: List[int] = []
-
-    def __init__(
-        self,
-        vocab_file,
-        spm_file,
-        src_lang=None,
-        tgt_lang=None,
-        bos_token="<s>",
-        eos_token="</s>",
-        sep_token="</s>",
-        pad_token="<pad>",
-        unk_token="<unk>",
-        language_codes="m2m100",
-        sp_model_kwargs: Optional[Dict[str, Any]] = None,
-        num_madeup_words=8,
-        **kwargs,
-    ) -> None:
-        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-
-        self.language_codes = language_codes
-        fairseq_language_code = FAIRSEQ_LANGUAGE_CODES[language_codes]
-        self.lang_code_to_token = {lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code}
-
-        additional_special_tokens = kwargs.pop("additional_special_tokens", [])
-        for lang_code in fairseq_language_code:
-            token = self.get_lang_token(lang_code)
-            if token not in additional_special_tokens and lang_code not in str(token) not in self.added_tokens_encoder:
-                additional_special_tokens.append(token)
-
-        self.vocab_file = vocab_file
-        self.encoder = load_json(vocab_file)
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.spm_file = spm_file
-        self.sp_model = load_spm(spm_file, self.sp_model_kwargs)
-
-        self.encoder_size = len(self.encoder)
-
-        self.lang_token_to_id = {
-            self.get_lang_token(lang_code): self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)
-        }
-        self.lang_code_to_id = {lang_code: self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)}
-        self.id_to_lang_token = {v: k for k, v in self.lang_token_to_id.items()}
-
-        self._src_lang = src_lang if src_lang is not None else "en"
-        self.tgt_lang = tgt_lang
-        self.cur_lang_id = self.get_lang_id(self._src_lang)
-
-        self.num_madeup_words = num_madeup_words
-
-        super().__init__(
-            src_lang=src_lang,
-            tgt_lang=tgt_lang,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            sep_token=sep_token,
-            unk_token=unk_token,
-            pad_token=pad_token,
-            language_codes=language_codes,
-            sp_model_kwargs=self.sp_model_kwargs,
-            additional_special_tokens=additional_special_tokens,
-            num_madeup_words=num_madeup_words,
-            **kwargs,
-        )
-        self.set_src_lang_special_tokens(self._src_lang)
-
-    @property
-    def vocab_size(self) -> int:
-        return len(self.encoder)
-
-    def get_vocab(self) -> Dict:
-        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
-        vocab.update(self.added_tokens_encoder)
-        return vocab
-
-    @property
-    def src_lang(self) -> str:
-        return self._src_lang
-
-    @src_lang.setter
-    def src_lang(self, new_src_lang: str) -> None:
-        self._src_lang = new_src_lang
-        self.set_src_lang_special_tokens(self._src_lang)
-
-    def _tokenize(self, text: str) -> List[str]:
-        return self.sp_model.encode(text, out_type=str)
-
-    def _convert_token_to_id(self, token):
-        if token in self.lang_token_to_id:
-            return self.lang_token_to_id[token]
-        return self.encoder.get(token, self.encoder[self.unk_token])
-
-    def _convert_id_to_token(self, index: int) -> str:
-        """Converts an index (integer) in a token (str) using the decoder."""
-        if index in self.id_to_lang_token:
-            return self.id_to_lang_token[index]
-        return self.decoder.get(index, self.unk_token)
-
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (string) in a single string."""
-        current_sub_tokens = []
-        out_string = ""
-        for token in tokens:
-            # make sure that special tokens are not decoded using sentencepiece model
-            if token in self.all_special_tokens:
-                out_string += self.sp_model.decode(current_sub_tokens) + token
-                current_sub_tokens = []
-            else:
-                current_sub_tokens.append(token)
-        out_string += self.sp_model.decode(current_sub_tokens)
-        return out_string.strip()
-
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer `prepare_for_model` method.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
-            )
-
-        prefix_ones = [1] * len(self.prefix_tokens)
-        suffix_ones = [1] * len(self.suffix_tokens)
-        if token_ids_1 is None:
-            return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
-        return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
-
-    def build_inputs_with_special_tokens(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. An MBART sequence has the following format, where `X` represents the sequence:
-
-        - `input_ids` (for encoder) `X [eos, src_lang_code]`
-        - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]`
-
-        BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
-        separator.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs to which the special tokens will be added.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
-        """
-        if token_ids_1 is None:
-            return self.prefix_tokens + token_ids_0 + self.suffix_tokens
-        # We don't expect to process pairs, but leave the pair logic for API consistency
-        return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
-
-    def __getstate__(self) -> Dict:
-        state = self.__dict__.copy()
-        state["sp_model"] = None
-        return state
-
-    def __setstate__(self, d: Dict) -> None:
-        self.__dict__ = d
-
-        # for backward compatibility
-        if not hasattr(self, "sp_model_kwargs"):
-            self.sp_model_kwargs = {}
-
-        self.sp_model = load_spm(self.spm_file, self.sp_model_kwargs)
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        save_dir = Path(save_directory)
-        if not save_dir.is_dir():
-            raise OSError(f"{save_directory} should be a directory")
-        vocab_save_path = save_dir / (
-            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
-        )
-        spm_save_path = save_dir / (
-            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
-        )
-
-        save_json(self.encoder, vocab_save_path)
-
-        if os.path.abspath(self.spm_file) != os.path.abspath(spm_save_path) and os.path.isfile(self.spm_file):
-            copyfile(self.spm_file, spm_save_path)
-        elif not os.path.isfile(self.spm_file):
-            with open(spm_save_path, "wb") as fi:
-                content_spiece_model = self.sp_model.serialized_model_proto()
-                fi.write(content_spiece_model)
-
-        return (str(vocab_save_path), str(spm_save_path))
-
-    def prepare_seq2seq_batch(
-        self,
-        src_texts: List[str],
-        src_lang: str = "en",
-        tgt_texts: Optional[List[str]] = None,
-        tgt_lang: str = "ro",
-        **kwargs,
-    ) -> BatchEncoding:
-        self.src_lang = src_lang
-        self.tgt_lang = tgt_lang
-        self.set_src_lang_special_tokens(self.src_lang)
-        return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
-
-    def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs):
-        """Used by translation pipeline, to prepare inputs for the generate function"""
-        if src_lang is None or tgt_lang is None:
-            raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
-        self.src_lang = src_lang
-        inputs = self(raw_inputs, add_special_tokens=True, **extra_kwargs)
-        tgt_lang_id = self.get_lang_id(tgt_lang)
-        inputs["forced_bos_token_id"] = tgt_lang_id
-        return inputs
-
-    def _switch_to_input_mode(self):
-        self.set_src_lang_special_tokens(self.src_lang)
-
-    def _switch_to_target_mode(self):
-        self.set_tgt_lang_special_tokens(self.tgt_lang)
-
-    def set_src_lang_special_tokens(self, src_lang: str) -> None:
-        """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""
-        lang_token = self.get_lang_token(src_lang)
-        self.cur_lang_id = self.lang_token_to_id[lang_token]
-        self.prefix_tokens = [self.cur_lang_id]
-        self.suffix_tokens = [self.eos_token_id]
-
-    def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None:
-        """Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code]."""
-        lang_token = self.get_lang_token(tgt_lang)
-        self.cur_lang_id = self.lang_token_to_id[lang_token]
-        self.prefix_tokens = [self.cur_lang_id]
-        self.suffix_tokens = [self.eos_token_id]
-
-    def get_lang_token(self, lang: str) -> str:
-        return self.lang_code_to_token[lang]
-
-    def get_lang_id(self, lang: str) -> int:
-        lang_token = self.get_lang_token(lang)
-        return self.lang_token_to_id[lang_token]
-
-
-def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor:
-    spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs)
-    spm.Load(str(path))
-    return spm
-
-
-def load_json(path: str) -> Union[Dict, List]:
-    with open(path, "r") as f:
-        return json.load(f)
-
-
-def save_json(data, path: str) -> None:
-    with open(path, "w") as f:
-        json.dump(data, f, indent=2)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mt5/modeling_tf_mt5.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mt5/modeling_tf_mt5.py
deleted file mode 100644
index ba7bd33c344747db537150e3f7d6326f6bb7143f..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/mt5/modeling_tf_mt5.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# coding=utf-8
-# Copyright 2020 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Tensorflow mT5 model."""
-
-from ...utils import logging
-from ..t5.modeling_tf_t5 import TFT5EncoderModel, TFT5ForConditionalGeneration, TFT5Model
-from .configuration_mt5 import MT5Config
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "T5Config"
-
-
-class TFMT5Model(TFT5Model):
-    r"""
-    This class overrides [`TFT5Model`]. Please check the superclass for the appropriate documentation alongside usage
-    examples.
-
-    Examples:
-
-    ```python
-    >>> from transformers import TFMT5Model, AutoTokenizer
-
-    >>> model = TFMT5Model.from_pretrained("google/mt5-small")
-    >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
-    >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
-    >>> summary = "Weiter Verhandlung in Syrien."
-    >>> inputs = tokenizer(article, return_tensors="tf")
-    >>> labels = tokenizer(text_target=summary, return_tensors="tf")
-
-    >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"])
-    >>> hidden_states = outputs.last_hidden_state
-    ```"""
-    model_type = "mt5"
-    config_class = MT5Config
-
-
-class TFMT5ForConditionalGeneration(TFT5ForConditionalGeneration):
-    r"""
-    This class overrides [`TFT5ForConditionalGeneration`]. Please check the superclass for the appropriate
-    documentation alongside usage examples.
-
-    Examples:
-
-    ```python
-    >>> from transformers import TFMT5ForConditionalGeneration, AutoTokenizer
-
-    >>> model = TFMT5ForConditionalGeneration.from_pretrained("google/mt5-small")
-    >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
-    >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
-    >>> summary = "Weiter Verhandlung in Syrien."
-    >>> inputs = tokenizer(article, text_target=summary, return_tensors="tf")
-
-    >>> outputs = model(**inputs)
-    >>> loss = outputs.loss
-    ```"""
-
-    model_type = "mt5"
-    config_class = MT5Config
-
-
-class TFMT5EncoderModel(TFT5EncoderModel):
-    r"""
-    This class overrides [`TFT5EncoderModel`]. Please check the superclass for the appropriate documentation alongside
-    usage examples.
-
-    Examples:
-
-    ```python
-    >>> from transformers import TFMT5EncoderModel, AutoTokenizer
-
-    >>> model = TFMT5EncoderModel.from_pretrained("google/mt5-small")
-    >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
-    >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
-    >>> input_ids = tokenizer(article, return_tensors="tf").input_ids
-    >>> outputs = model(input_ids)
-    >>> hidden_state = outputs.last_hidden_state
-    ```"""
-
-    model_type = "mt5"
-    config_class = MT5Config
diff --git a/spaces/yl12053/so-vits-4.1-Grass-Wonder/diffusion/diffusion_onnx.py b/spaces/yl12053/so-vits-4.1-Grass-Wonder/diffusion/diffusion_onnx.py
deleted file mode 100644
index 1c1e80321de162b5233801efa3423739f7f92bdc..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Grass-Wonder/diffusion/diffusion_onnx.py
+++ /dev/null
@@ -1,612 +0,0 @@
-from collections import deque
-from functools import partial
-from inspect import isfunction
-import torch.nn.functional as F
-import librosa.sequence
-import numpy as np
-from torch.nn import Conv1d
-from torch.nn import Mish
-import torch
-from torch import nn
-from tqdm import tqdm
-import math
-
-
-def exists(x):
-    return x is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def extract(a, t):
-    return a[t].reshape((1, 1, 1, 1))
-
-
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
-
-
-def linear_beta_schedule(timesteps, max_beta=0.02):
-    """
-    linear schedule
-    """
-    betas = np.linspace(1e-4, max_beta, timesteps)
-    return betas
-
-
-def cosine_beta_schedule(timesteps, s=0.008):
-    """
-    cosine schedule
-    as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
-    """
-    steps = timesteps + 1
-    x = np.linspace(0, steps, steps)
-    alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
-    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
-    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
-    return np.clip(betas, a_min=0, a_max=0.999)
-
-
-beta_schedule = {
-    "cosine": cosine_beta_schedule,
-    "linear": linear_beta_schedule,
-}
-
-
-def extract_1(a, t):
-    return a[t].reshape((1, 1, 1, 1))
-
-
-def predict_stage0(noise_pred, noise_pred_prev):
-    return (noise_pred + noise_pred_prev) / 2
-
-
-def predict_stage1(noise_pred, noise_list):
-    return (noise_pred * 3
-            - noise_list[-1]) / 2
-
-
-def predict_stage2(noise_pred, noise_list):
-    return (noise_pred * 23
-            - noise_list[-1] * 16
-            + noise_list[-2] * 5) / 12
-
-
-def predict_stage3(noise_pred, noise_list):
-    return (noise_pred * 55
-            - noise_list[-1] * 59
-            + noise_list[-2] * 37
-            - noise_list[-3] * 9) / 24
-
-
-class SinusoidalPosEmb(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.dim = dim
-        self.half_dim = dim // 2
-        self.emb = 9.21034037 / (self.half_dim - 1)
-        self.emb = torch.exp(torch.arange(self.half_dim) * torch.tensor(-self.emb)).unsqueeze(0)
-        self.emb = self.emb.cpu()
-
-    def forward(self, x):
-        emb = self.emb * x
-        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
-        return emb
-
-
-class ResidualBlock(nn.Module):
-    def __init__(self, encoder_hidden, residual_channels, dilation):
-        super().__init__()
-        self.residual_channels = residual_channels
-        self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation)
-        self.diffusion_projection = nn.Linear(residual_channels, residual_channels)
-        self.conditioner_projection = Conv1d(encoder_hidden, 2 * residual_channels, 1)
-        self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1)
-
-    def forward(self, x, conditioner, diffusion_step):
-        diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
-        conditioner = self.conditioner_projection(conditioner)
-        y = x + diffusion_step
-        y = self.dilated_conv(y) + conditioner
-
-        gate, filter_1 = torch.split(y, [self.residual_channels, self.residual_channels], dim=1)
-
-        y = torch.sigmoid(gate) * torch.tanh(filter_1)
-        y = self.output_projection(y)
-
-        residual, skip = torch.split(y, [self.residual_channels, self.residual_channels], dim=1)
-
-        return (x + residual) / 1.41421356, skip
-
-
-class DiffNet(nn.Module):
-    def __init__(self, in_dims, n_layers, n_chans, n_hidden):
-        super().__init__()
-        self.encoder_hidden = n_hidden
-        self.residual_layers = n_layers
-        self.residual_channels = n_chans
-        self.input_projection = Conv1d(in_dims, self.residual_channels, 1)
-        self.diffusion_embedding = SinusoidalPosEmb(self.residual_channels)
-        dim = self.residual_channels
-        self.mlp = nn.Sequential(
-            nn.Linear(dim, dim * 4),
-            Mish(),
-            nn.Linear(dim * 4, dim)
-        )
-        self.residual_layers = nn.ModuleList([
-            ResidualBlock(self.encoder_hidden, self.residual_channels, 1)
-            for i in range(self.residual_layers)
-        ])
-        self.skip_projection = Conv1d(self.residual_channels, self.residual_channels, 1)
-        self.output_projection = Conv1d(self.residual_channels, in_dims, 1)
-        nn.init.zeros_(self.output_projection.weight)
-
-    def forward(self, spec, diffusion_step, cond):
-        x = spec.squeeze(0)
-        x = self.input_projection(x)  # x [B, residual_channel, T]
-        x = F.relu(x)
-        # skip = torch.randn_like(x)
-        diffusion_step = diffusion_step.float()
-        diffusion_step = self.diffusion_embedding(diffusion_step)
-        diffusion_step = self.mlp(diffusion_step)
-
-        x, skip = self.residual_layers[0](x, cond, diffusion_step)
-        # noinspection PyTypeChecker
-        for layer in self.residual_layers[1:]:
-            x, skip_connection = layer.forward(x, cond, diffusion_step)
-            skip = skip + skip_connection
-        x = skip / math.sqrt(len(self.residual_layers))
-        x = self.skip_projection(x)
-        x = F.relu(x)
-        x = self.output_projection(x)  # [B, 80, T]
-        return x.unsqueeze(1)
-
-
-class AfterDiffusion(nn.Module):
-    def __init__(self, spec_max, spec_min, v_type='a'):
-        super().__init__()
-        self.spec_max = spec_max
-        self.spec_min = spec_min
-        self.type = v_type
-
-    def forward(self, x):
-        x = x.squeeze(1).permute(0, 2, 1)
-        mel_out = (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min
-        if self.type == 'nsf-hifigan-log10':
-            mel_out = mel_out * 0.434294
-        return mel_out.transpose(2, 1)
-
-
-class Pred(nn.Module):
-    def __init__(self, alphas_cumprod):
-        super().__init__()
-        self.alphas_cumprod = alphas_cumprod
-
-    def forward(self, x_1, noise_t, t_1, t_prev):
-        a_t = extract(self.alphas_cumprod, t_1).cpu()
-        a_prev = extract(self.alphas_cumprod, t_prev).cpu()
-        a_t_sq, a_prev_sq = a_t.sqrt().cpu(), a_prev.sqrt().cpu()
-        x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x_1 - 1 / (
-                a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t)
-        x_pred = x_1 + x_delta.cpu()
-
-        return x_pred
-
-
-class GaussianDiffusion(nn.Module):
-    def __init__(self, 
-                out_dims=128,
-                n_layers=20,
-                n_chans=384,
-                n_hidden=256,
-                timesteps=1000, 
-                k_step=1000,
-                max_beta=0.02,
-                spec_min=-12, 
-                spec_max=2):
-        super().__init__()
-        self.denoise_fn = DiffNet(out_dims, n_layers, n_chans, n_hidden)
-        self.out_dims = out_dims
-        self.mel_bins = out_dims
-        self.n_hidden = n_hidden
-        betas = beta_schedule['linear'](timesteps, max_beta=max_beta)
-
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.k_step = k_step
-
-        self.noise_list = deque(maxlen=4)
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer('posterior_variance', to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
-        self.register_buffer('posterior_mean_coef1', to_torch(
-            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
-        self.register_buffer('posterior_mean_coef2', to_torch(
-            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
-
-        self.register_buffer('spec_min', torch.FloatTensor([spec_min])[None, None, :out_dims])
-        self.register_buffer('spec_max', torch.FloatTensor([spec_max])[None, None, :out_dims])
-        self.ad = AfterDiffusion(self.spec_max, self.spec_min)
-        self.xp = Pred(self.alphas_cumprod)
-
-    def q_mean_variance(self, x_start, t):
-        mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
-        variance = extract(1. - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-                extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
-                extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-                extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-                extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, x, t, cond):
-        noise_pred = self.denoise_fn(x, t, cond=cond)
-        x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred)
-
-        x_recon.clamp_(-1., 1.)
-
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False):
-        b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond)
-        noise = noise_like(x.shape, device, repeat_noise)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False):
-        """
-        Use the PLMS method from
-        [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778).
-        """
-
-        def get_x_pred(x, noise_t, t):
-            a_t = extract(self.alphas_cumprod, t)
-            a_prev = extract(self.alphas_cumprod, torch.max(t - interval, torch.zeros_like(t)))
-            a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt()
-
-            x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / (
-                    a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t)
-            x_pred = x + x_delta
-
-            return x_pred
-
-        noise_list = self.noise_list
-        noise_pred = self.denoise_fn(x, t, cond=cond)
-
-        if len(noise_list) == 0:
-            x_pred = get_x_pred(x, noise_pred, t)
-            noise_pred_prev = self.denoise_fn(x_pred, max(t - interval, 0), cond=cond)
-            noise_pred_prime = (noise_pred + noise_pred_prev) / 2
-        elif len(noise_list) == 1:
-            noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2
-        elif len(noise_list) == 2:
-            noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12
-        else:
-            noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24
-
-        x_prev = get_x_pred(x, noise_pred_prime, t)
-        noise_list.append(noise_pred)
-
-        return x_prev
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (
-                extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
-        )
-
-    def p_losses(self, x_start, t, cond, noise=None, loss_type='l2'):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        x_recon = self.denoise_fn(x_noisy, t, cond)
-
-        if loss_type == 'l1':
-            loss = (noise - x_recon).abs().mean()
-        elif loss_type == 'l2':
-            loss = F.mse_loss(noise, x_recon)
-        else:
-            raise NotImplementedError()
-
-        return loss
-
-    def org_forward(self, 
-                condition, 
-                init_noise=None,
-                gt_spec=None, 
-                infer=True, 
-                infer_speedup=100, 
-                method='pndm',
-                k_step=1000,
-                use_tqdm=True):
-        """
-            conditioning diffusion, use fastspeech2 encoder output as the condition
-        """
-        cond = condition
-        b, device = condition.shape[0], condition.device
-        if not infer:
-            spec = self.norm_spec(gt_spec)
-            t = torch.randint(0, self.k_step, (b,), device=device).long()
-            norm_spec = spec.transpose(1, 2)[:, None, :, :]  # [B, 1, M, T]
-            return self.p_losses(norm_spec, t, cond=cond)
-        else:
-            shape = (cond.shape[0], 1, self.out_dims, cond.shape[2])
-            
-            if gt_spec is None:
-                t = self.k_step
-                if init_noise is None:
-                    x = torch.randn(shape, device=device)
-                else:
-                    x = init_noise
-            else:
-                t = k_step
-                norm_spec = self.norm_spec(gt_spec)
-                norm_spec = norm_spec.transpose(1, 2)[:, None, :, :]
-                x = self.q_sample(x_start=norm_spec, t=torch.tensor([t - 1], device=device).long())
-                        
-            if method is not None and infer_speedup > 1:
-                if method == 'dpm-solver':
-                    from .dpm_solver_pytorch import NoiseScheduleVP, model_wrapper, DPM_Solver
-                    # 1. Define the noise schedule.
-                    noise_schedule = NoiseScheduleVP(schedule='discrete', betas=self.betas[:t])
-
-                    # 2. Convert your discrete-time `model` to the continuous-time
-                    # noise prediction model. Here is an example for a diffusion model
-                    # `model` with the noise prediction type ("noise") .
-                    def my_wrapper(fn):
-                        def wrapped(x, t, **kwargs):
-                            ret = fn(x, t, **kwargs)
-                            if use_tqdm:
-                                self.bar.update(1)
-                            return ret
-
-                        return wrapped
-
-                    model_fn = model_wrapper(
-                        my_wrapper(self.denoise_fn),
-                        noise_schedule,
-                        model_type="noise",  # or "x_start" or "v" or "score"
-                        model_kwargs={"cond": cond}
-                    )
-
-                    # 3. Define dpm-solver and sample by singlestep DPM-Solver.
-                    # (We recommend singlestep DPM-Solver for unconditional sampling)
-                    # You can adjust the `steps` to balance the computation
-                    # costs and the sample quality.
-                    dpm_solver = DPM_Solver(model_fn, noise_schedule)
-
-                    steps = t // infer_speedup
-                    if use_tqdm:
-                        self.bar = tqdm(desc="sample time step", total=steps)
-                    x = dpm_solver.sample(
-                        x,
-                        steps=steps,
-                        order=3,
-                        skip_type="time_uniform",
-                        method="singlestep",
-                    )
-                    if use_tqdm:
-                        self.bar.close()
-                elif method == 'pndm':
-                    self.noise_list = deque(maxlen=4)
-                    if use_tqdm:
-                        for i in tqdm(
-                                reversed(range(0, t, infer_speedup)), desc='sample time step',
-                                total=t // infer_speedup,
-                        ):
-                            x = self.p_sample_plms(
-                                x, torch.full((b,), i, device=device, dtype=torch.long),
-                                infer_speedup, cond=cond
-                            )
-                    else:
-                        for i in reversed(range(0, t, infer_speedup)):
-                            x = self.p_sample_plms(
-                                x, torch.full((b,), i, device=device, dtype=torch.long),
-                                infer_speedup, cond=cond
-                            )
-                else:
-                    raise NotImplementedError(method)
-            else:
-                if use_tqdm:
-                    for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t):
-                        x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
-                else:
-                    for i in reversed(range(0, t)):
-                        x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond)
-            x = x.squeeze(1).transpose(1, 2)  # [B, T, M]
-            return self.denorm_spec(x).transpose(2, 1)
-
-    def norm_spec(self, x):
-        return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1
-
-    def denorm_spec(self, x):
-        return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min
-
-    def get_x_pred(self, x_1, noise_t, t_1, t_prev):
-        a_t = extract(self.alphas_cumprod, t_1)
-        a_prev = extract(self.alphas_cumprod, t_prev)
-        a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt()
-        x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x_1 - 1 / (
-                a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t)
-        x_pred = x_1 + x_delta
-        return x_pred
-
-    def OnnxExport(self, project_name=None, init_noise=None, hidden_channels=256, export_denoise=True, export_pred=True, export_after=True):
-        cond = torch.randn([1, self.n_hidden, 10]).cpu()
-        if init_noise is None:
-            x = torch.randn((1, 1, self.mel_bins, cond.shape[2]), dtype=torch.float32).cpu()
-        else:
-            x = init_noise
-        pndms = 100
-
-        org_y_x = self.org_forward(cond, init_noise=x)
-
-        device = cond.device
-        n_frames = cond.shape[2]
-        step_range = torch.arange(0, self.k_step, pndms, dtype=torch.long, device=device).flip(0)
-        plms_noise_stage = torch.tensor(0, dtype=torch.long, device=device)
-        noise_list = torch.zeros((0, 1, 1, self.mel_bins, n_frames), device=device)
-
-        ot = step_range[0]
-        ot_1 = torch.full((1,), ot, device=device, dtype=torch.long)
-        if export_denoise:
-            torch.onnx.export(
-                self.denoise_fn,
-                (x.cpu(), ot_1.cpu(), cond.cpu()),
-                f"{project_name}_denoise.onnx",
-                input_names=["noise", "time", "condition"],
-                output_names=["noise_pred"],
-                dynamic_axes={
-                    "noise": [3],
-                    "condition": [2]
-                },
-                opset_version=16
-            )
-
-        for t in step_range:
-            t_1 = torch.full((1,), t, device=device, dtype=torch.long)
-            noise_pred = self.denoise_fn(x, t_1, cond)
-            t_prev = t_1 - pndms
-            t_prev = t_prev * (t_prev > 0)
-            if plms_noise_stage == 0:
-                if export_pred:
-                    torch.onnx.export(
-                        self.xp,
-                        (x.cpu(), noise_pred.cpu(), t_1.cpu(), t_prev.cpu()),
-                        f"{project_name}_pred.onnx",
-                        input_names=["noise", "noise_pred", "time", "time_prev"],
-                        output_names=["noise_pred_o"],
-                        dynamic_axes={
-                            "noise": [3],
-                            "noise_pred": [3]
-                        },
-                        opset_version=16
-                    )
-
-                x_pred = self.get_x_pred(x, noise_pred, t_1, t_prev)
-                noise_pred_prev = self.denoise_fn(x_pred, t_prev, cond=cond)
-                noise_pred_prime = predict_stage0(noise_pred, noise_pred_prev)
-
-            elif plms_noise_stage == 1:
-                noise_pred_prime = predict_stage1(noise_pred, noise_list)
-
-            elif plms_noise_stage == 2:
-                noise_pred_prime = predict_stage2(noise_pred, noise_list)
-
-            else:
-                noise_pred_prime = predict_stage3(noise_pred, noise_list)
-
-            noise_pred = noise_pred.unsqueeze(0)
-
-            if plms_noise_stage < 3:
-                noise_list = torch.cat((noise_list, noise_pred), dim=0)
-                plms_noise_stage = plms_noise_stage + 1
-
-            else:
-                noise_list = torch.cat((noise_list[-2:], noise_pred), dim=0)
-
-            x = self.get_x_pred(x, noise_pred_prime, t_1, t_prev)
-        if export_after:
-            torch.onnx.export(
-                self.ad,
-                x.cpu(),
-                f"{project_name}_after.onnx",
-                input_names=["x"],
-                output_names=["mel_out"],
-                dynamic_axes={
-                    "x": [3]
-                },
-                opset_version=16
-            )
-        x = self.ad(x)
-
-        print((x == org_y_x).all())
-        return x
-
-    def forward(self, condition=None, init_noise=None, pndms=None, k_step=None):
-        cond = condition
-        x = init_noise
-
-        device = cond.device
-        n_frames = cond.shape[2]
-        step_range = torch.arange(0, k_step.item(), pndms.item(), dtype=torch.long, device=device).flip(0)
-        plms_noise_stage = torch.tensor(0, dtype=torch.long, device=device)
-        noise_list = torch.zeros((0, 1, 1, self.mel_bins, n_frames), device=device)
-
-        ot = step_range[0]
-        ot_1 = torch.full((1,), ot, device=device, dtype=torch.long)
-
-        for t in step_range:
-            t_1 = torch.full((1,), t, device=device, dtype=torch.long)
-            noise_pred = self.denoise_fn(x, t_1, cond)
-            t_prev = t_1 - pndms
-            t_prev = t_prev * (t_prev > 0)
-            if plms_noise_stage == 0:
-                x_pred = self.get_x_pred(x, noise_pred, t_1, t_prev)
-                noise_pred_prev = self.denoise_fn(x_pred, t_prev, cond=cond)
-                noise_pred_prime = predict_stage0(noise_pred, noise_pred_prev)
-
-            elif plms_noise_stage == 1:
-                noise_pred_prime = predict_stage1(noise_pred, noise_list)
-
-            elif plms_noise_stage == 2:
-                noise_pred_prime = predict_stage2(noise_pred, noise_list)
-
-            else:
-                noise_pred_prime = predict_stage3(noise_pred, noise_list)
-
-            noise_pred = noise_pred.unsqueeze(0)
-
-            if plms_noise_stage < 3:
-                noise_list = torch.cat((noise_list, noise_pred), dim=0)
-                plms_noise_stage = plms_noise_stage + 1
-
-            else:
-                noise_list = torch.cat((noise_list[-2:], noise_pred), dim=0)
-
-            x = self.get_x_pred(x, noise_pred_prime, t_1, t_prev)
-        x = self.ad(x)
-        return x
diff --git a/spaces/yl12053/so-vits-4.1-Grass-Wonder/inference/__init__.py b/spaces/yl12053/so-vits-4.1-Grass-Wonder/inference/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vencoder/hubert/hubert_model_onnx.py b/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vencoder/hubert/hubert_model_onnx.py
deleted file mode 100644
index d18f3c2a0fc29592a573a9780308d38f059640b9..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vencoder/hubert/hubert_model_onnx.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-            self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-    def forward(self, x):
-        return self.units(x)
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: torch.Tensor,
-            mask: torch.Tensor = None,
-            src_key_padding_mask: torch.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: torch.device,
-        min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/common/models/fcos.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/common/models/fcos.py
deleted file mode 100644
index 1c752029b7fc64ec375a55182e5342c9eb48bb33..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/common/models/fcos.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from detectron2.modeling.meta_arch.fcos import FCOS, FCOSHead
-
-from .retinanet import model
-
-model._target_ = FCOS
-
-del model.anchor_generator
-del model.box2box_transform
-del model.anchor_matcher
-del model.input_format
-
-# Use P5 instead of C5 to compute P6/P7
-# (Sec 2.2 of https://arxiv.org/abs/2006.09214)
-model.backbone.top_block.in_feature = "p5"
-model.backbone.top_block.in_channels = 256
-
-# New score threshold determined based on sqrt(cls_score * centerness)
-model.test_score_thresh = 0.2
-model.test_nms_thresh = 0.6
-
-model.head._target_ = FCOSHead
-del model.head.num_anchors
-model.head.norm = "GN"
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_fpn.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_fpn.py
deleted file mode 100644
index 744d5306f5b0ba4cf508731bd790bad823b520fa..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_fpn.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from detectron2.config import LazyCall as L
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.meta_arch import GeneralizedRCNN
-from detectron2.modeling.anchor_generator import DefaultAnchorGenerator
-from detectron2.modeling.backbone.fpn import LastLevelMaxPool
-from detectron2.modeling.backbone import BasicStem, FPN, ResNet
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.matcher import Matcher
-from detectron2.modeling.poolers import ROIPooler
-from detectron2.modeling.proposal_generator import RPN, StandardRPNHead
-from detectron2.modeling.roi_heads import (
-    StandardROIHeads,
-    FastRCNNOutputLayers,
-    MaskRCNNConvUpsampleHead,
-    FastRCNNConvFCHead,
-)
-
-model = L(GeneralizedRCNN)(
-    backbone=L(FPN)(
-        bottom_up=L(ResNet)(
-            stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"),
-            stages=L(ResNet.make_default_stages)(
-                depth=50,
-                stride_in_1x1=True,
-                norm="FrozenBN",
-            ),
-            out_features=["res2", "res3", "res4", "res5"],
-        ),
-        in_features="${.bottom_up.out_features}",
-        out_channels=256,
-        top_block=L(LastLevelMaxPool)(),
-    ),
-    proposal_generator=L(RPN)(
-        in_features=["p2", "p3", "p4", "p5", "p6"],
-        head=L(StandardRPNHead)(in_channels=256, num_anchors=3),
-        anchor_generator=L(DefaultAnchorGenerator)(
-            sizes=[[32], [64], [128], [256], [512]],
-            aspect_ratios=[0.5, 1.0, 2.0],
-            strides=[4, 8, 16, 32, 64],
-            offset=0.0,
-        ),
-        anchor_matcher=L(Matcher)(
-            thresholds=[0.3, 0.7], labels=[0, -1, 1], allow_low_quality_matches=True
-        ),
-        box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]),
-        batch_size_per_image=256,
-        positive_fraction=0.5,
-        pre_nms_topk=(2000, 1000),
-        post_nms_topk=(1000, 1000),
-        nms_thresh=0.7,
-    ),
-    roi_heads=L(StandardROIHeads)(
-        num_classes=80,
-        batch_size_per_image=512,
-        positive_fraction=0.25,
-        proposal_matcher=L(Matcher)(
-            thresholds=[0.5], labels=[0, 1], allow_low_quality_matches=False
-        ),
-        box_in_features=["p2", "p3", "p4", "p5"],
-        box_pooler=L(ROIPooler)(
-            output_size=7,
-            scales=(1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32),
-            sampling_ratio=0,
-            pooler_type="ROIAlignV2",
-        ),
-        box_head=L(FastRCNNConvFCHead)(
-            input_shape=ShapeSpec(channels=256, height=7, width=7),
-            conv_dims=[],
-            fc_dims=[1024, 1024],
-        ),
-        box_predictor=L(FastRCNNOutputLayers)(
-            input_shape=ShapeSpec(channels=1024),
-            test_score_thresh=0.05,
-            box2box_transform=L(Box2BoxTransform)(weights=(10, 10, 5, 5)),
-            num_classes="${..num_classes}",
-        ),
-        mask_in_features=["p2", "p3", "p4", "p5"],
-        mask_pooler=L(ROIPooler)(
-            output_size=14,
-            scales=(1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32),
-            sampling_ratio=0,
-            pooler_type="ROIAlignV2",
-        ),
-        mask_head=L(MaskRCNNConvUpsampleHead)(
-            input_shape=ShapeSpec(channels=256, width=14, height=14),
-            num_classes="${..num_classes}",
-            conv_dims=[256, 256, 256, 256, 256],
-        ),
-    ),
-    pixel_mean=[103.530, 116.280, 123.675],
-    pixel_std=[1.0, 1.0, 1.0],
-    input_format="BGR",
-)
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py
deleted file mode 100644
index df7a2aedf480ed8dc4aa3645e37420e9b893fae4..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import detectron2.data.transforms as T
-from detectron2.config.lazy import LazyCall as L
-from detectron2.layers.batch_norm import NaiveSyncBatchNorm
-from detectron2.solver import WarmupParamScheduler
-from fvcore.common.param_scheduler import MultiStepParamScheduler
-
-from ..common.data.coco import dataloader
-from ..common.models.mask_rcnn_fpn import model
-from ..common.optim import SGD as optimizer
-from ..common.train import train
-
-# train from scratch
-train.init_checkpoint = ""
-train.amp.enabled = True
-train.ddp.fp16_compression = True
-model.backbone.bottom_up.freeze_at = 0
-
-# SyncBN
-# fmt: off
-model.backbone.bottom_up.stem.norm = \
-    model.backbone.bottom_up.stages.norm = \
-    model.backbone.norm = "SyncBN"
-
-# Using NaiveSyncBatchNorm becase heads may have empty input. That is not supported by
-# torch.nn.SyncBatchNorm. We can remove this after
-# https://github.com/pytorch/pytorch/issues/36530 is fixed.
-model.roi_heads.box_head.conv_norm = \
-    model.roi_heads.mask_head.conv_norm = lambda c: NaiveSyncBatchNorm(c,
-                                                                       stats_mode="N")
-# fmt: on
-
-# 2conv in RPN:
-# https://github.com/tensorflow/tpu/blob/b24729de804fdb751b06467d3dce0637fa652060/models/official/detection/modeling/architecture/heads.py#L95-L97  # noqa: E501, B950
-model.proposal_generator.head.conv_dims = [-1, -1]
-
-# 4conv1fc box head
-model.roi_heads.box_head.conv_dims = [256, 256, 256, 256]
-model.roi_heads.box_head.fc_dims = [1024]
-
-# resize_and_crop_image in:
-# https://github.com/tensorflow/tpu/blob/b24729de804fdb751b06467d3dce0637fa652060/models/official/detection/utils/input_utils.py#L127  # noqa: E501, B950
-image_size = 1024
-dataloader.train.mapper.augmentations = [
-    L(T.ResizeScale)(
-        min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size
-    ),
-    L(T.FixedSizeCrop)(crop_size=(image_size, image_size)),
-    L(T.RandomFlip)(horizontal=True),
-]
-
-# recompute boxes due to cropping
-dataloader.train.mapper.recompute_boxes = True
-
-# larger batch-size.
-dataloader.train.total_batch_size = 64
-
-# Equivalent to 100 epochs.
-# 100 ep = 184375 iters * 64 images/iter / 118000 images/ep
-train.max_iter = 184375
-
-lr_multiplier = L(WarmupParamScheduler)(
-    scheduler=L(MultiStepParamScheduler)(
-        values=[1.0, 0.1, 0.01],
-        milestones=[163889, 177546],
-        num_updates=train.max_iter,
-    ),
-    warmup_length=500 / train.max_iter,
-    warmup_factor=0.067,
-)
-
-optimizer.lr = 0.1
-optimizer.weight_decay = 4e-5
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/detection_checkpoint.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/detection_checkpoint.py
deleted file mode 100644
index 82fd3b2d40054573917a445b138d29a6dabfb907..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/detection_checkpoint.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import os
-import pickle
-import torch
-from fvcore.common.checkpoint import Checkpointer
-from torch.nn.parallel import DistributedDataParallel
-
-import detectron2.utils.comm as comm
-from detectron2.utils.file_io import PathManager
-
-from .c2_model_loading import align_and_update_state_dicts
-
-
-class DetectionCheckpointer(Checkpointer):
-    """
-    Same as :class:`Checkpointer`, but is able to:
-    1. handle models in detectron & detectron2 model zoo, and apply conversions for legacy models.
-    2. correctly load checkpoints that are only available on the master worker
-    """
-
-    def __init__(self, model, save_dir="", *, save_to_disk=None, **checkpointables):
-        is_main_process = comm.is_main_process()
-        super().__init__(
-            model,
-            save_dir,
-            save_to_disk=is_main_process if save_to_disk is None else save_to_disk,
-            **checkpointables,
-        )
-        self.path_manager = PathManager
-
-    def load(self, path, *args, **kwargs):
-        need_sync = False
-
-        if path and isinstance(self.model, DistributedDataParallel):
-            logger = logging.getLogger(__name__)
-            path = self.path_manager.get_local_path(path)
-            has_file = os.path.isfile(path)
-            all_has_file = comm.all_gather(has_file)
-            if not all_has_file[0]:
-                raise OSError(f"File {path} not found on main worker.")
-            if not all(all_has_file):
-                logger.warning(
-                    f"Not all workers can read checkpoint {path}. "
-                    "Training may fail to fully resume."
-                )
-                # TODO: broadcast the checkpoint file contents from main
-                # worker, and load from it instead.
-                need_sync = True
-            if not has_file:
-                path = None  # don't load if not readable
-        ret = super().load(path, *args, **kwargs)
-
-        if need_sync:
-            logger.info("Broadcasting model states from main worker ...")
-            self.model._sync_params_and_buffers()
-        return ret
-
-    def _load_file(self, filename):
-        if filename.endswith(".pkl"):
-            with PathManager.open(filename, "rb") as f:
-                data = pickle.load(f, encoding="latin1")
-            if "model" in data and "__author__" in data:
-                # file is in Detectron2 model zoo format
-                self.logger.info("Reading a file from '{}'".format(data["__author__"]))
-                return data
-            else:
-                # assume file is from Caffe2 / Detectron1 model zoo
-                if "blobs" in data:
-                    # Detection models have "blobs", but ImageNet models don't
-                    data = data["blobs"]
-                data = {k: v for k, v in data.items() if not k.endswith("_momentum")}
-                return {"model": data, "__author__": "Caffe2", "matching_heuristics": True}
-        elif filename.endswith(".pyth"):
-            # assume file is from pycls; no one else seems to use the ".pyth" extension
-            with PathManager.open(filename, "rb") as f:
-                data = torch.load(f)
-            assert (
-                "model_state" in data
-            ), f"Cannot load .pyth file {filename}; pycls checkpoints must contain 'model_state'."
-            model_state = {
-                k: v
-                for k, v in data["model_state"].items()
-                if not k.endswith("num_batches_tracked")
-            }
-            return {"model": model_state, "__author__": "pycls", "matching_heuristics": True}
-
-        loaded = super()._load_file(filename)  # load native pth checkpoint
-        if "model" not in loaded:
-            loaded = {"model": loaded}
-        return loaded
-
-    def _load_model(self, checkpoint):
-        if checkpoint.get("matching_heuristics", False):
-            self._convert_ndarray_to_tensor(checkpoint["model"])
-            # convert weights by name-matching heuristics
-            checkpoint["model"] = align_and_update_state_dicts(
-                self.model.state_dict(),
-                checkpoint["model"],
-                c2_conversion=checkpoint.get("__author__", None) == "Caffe2",
-            )
-        # for non-caffe2 models, use standard ways to load it
-        incompatible = super()._load_model(checkpoint)
-
-        model_buffers = dict(self.model.named_buffers(recurse=False))
-        for k in ["pixel_mean", "pixel_std"]:
-            # Ignore missing key message about pixel_mean/std.
-            # Though they may be missing in old checkpoints, they will be correctly
-            # initialized from config anyway.
-            if k in model_buffers:
-                try:
-                    incompatible.missing_keys.remove(k)
-                except ValueError:
-                    pass
-        for k in incompatible.unexpected_keys[:]:
-            # Ignore unexpected keys about cell anchors. They exist in old checkpoints
-            # but now they are non-persistent buffers and will not be in new checkpoints.
-            if "anchor_generator.cell_anchors" in k:
-                incompatible.unexpected_keys.remove(k)
-        return incompatible
diff --git a/spaces/yueranseo/mygpt/modules/pdf_func.py b/spaces/yueranseo/mygpt/modules/pdf_func.py
deleted file mode 100644
index 1b1087f2687fd26c8676867dd45189c069dd56a5..0000000000000000000000000000000000000000
--- a/spaces/yueranseo/mygpt/modules/pdf_func.py
+++ /dev/null
@@ -1,180 +0,0 @@
-from types import SimpleNamespace
-import pdfplumber
-import logging
-from langchain.docstore.document import Document
-
-def prepare_table_config(crop_page):
-    """Prepare table查找边界, 要求page为原始page
-
-    From https://github.com/jsvine/pdfplumber/issues/242
-    """
-    page = crop_page.root_page # root/parent
-    cs = page.curves + page.edges
-    def curves_to_edges():
-        """See https://github.com/jsvine/pdfplumber/issues/127"""
-        edges = []
-        for c in cs:
-            edges += pdfplumber.utils.rect_to_edges(c)
-        return edges
-    edges = curves_to_edges()
-    return {
-        "vertical_strategy": "explicit",
-        "horizontal_strategy": "explicit",
-        "explicit_vertical_lines": edges,
-        "explicit_horizontal_lines": edges,
-        "intersection_y_tolerance": 10,
-    }
-
-def get_text_outside_table(crop_page):
-    ts = prepare_table_config(crop_page)
-    if len(ts["explicit_vertical_lines"]) == 0 or len(ts["explicit_horizontal_lines"]) == 0:
-        return crop_page
-
-    ### Get the bounding boxes of the tables on the page.
-    bboxes = [table.bbox for table in crop_page.root_page.find_tables(table_settings=ts)]
-    def not_within_bboxes(obj):
-        """Check if the object is in any of the table's bbox."""
-        def obj_in_bbox(_bbox):
-            """See https://github.com/jsvine/pdfplumber/blob/stable/pdfplumber/table.py#L404"""
-            v_mid = (obj["top"] + obj["bottom"]) / 2
-            h_mid = (obj["x0"] + obj["x1"]) / 2
-            x0, top, x1, bottom = _bbox
-            return (h_mid >= x0) and (h_mid < x1) and (v_mid >= top) and (v_mid < bottom)
-        return not any(obj_in_bbox(__bbox) for __bbox in bboxes)
-
-    return crop_page.filter(not_within_bboxes)
-# 请使用 LaTeX 表达公式，行内公式以 $ 包裹，行间公式以 $$ 包裹
-
-extract_words = lambda page: page.extract_words(keep_blank_chars=True, y_tolerance=0, x_tolerance=1, extra_attrs=["fontname", "size", "object_type"])
-# dict_keys(['text', 'x0', 'x1', 'top', 'doctop', 'bottom', 'upright', 'direction', 'fontname', 'size'])
-
-def get_title_with_cropped_page(first_page):
-    title = [] # 处理标题
-    x0,top,x1,bottom = first_page.bbox # 获取页面边框
-
-    for word in extract_words(first_page):
-        word = SimpleNamespace(**word)
-
-        if word.size >= 14:
-            title.append(word.text)
-            title_bottom = word.bottom
-        elif word.text == "Abstract": # 获取页面abstract
-            top = word.top
-
-    user_info = [i["text"] for i in extract_words(first_page.within_bbox((x0,title_bottom,x1,top)))]
-    # 裁剪掉上半部分, within_bbox: full_included; crop: partial_included
-    return title, user_info, first_page.within_bbox((x0,top,x1,bottom))
-
-def get_column_cropped_pages(pages, two_column=True):
-    new_pages = []
-    for page in pages:
-        if two_column:
-            left = page.within_bbox((0, 0, page.width/2, page.height),relative=True)
-            right = page.within_bbox((page.width/2, 0, page.width, page.height), relative=True)
-            new_pages.append(left)
-            new_pages.append(right)
-        else:
-            new_pages.append(page)
-
-    return new_pages
-
-def parse_pdf(filename, two_column = True):
-    level = logging.getLogger().level
-    if level == logging.getLevelName("DEBUG"):
-        logging.getLogger().setLevel("INFO")
-
-    with pdfplumber.open(filename) as pdf:
-        title, user_info, first_page = get_title_with_cropped_page(pdf.pages[0])
-        new_pages = get_column_cropped_pages([first_page] + pdf.pages[1:], two_column)
-
-        chapters = []
-        # tuple (chapter_name, [pageid] (start,stop), chapter_text)
-        create_chapter = lambda page_start,name_top,name_bottom: SimpleNamespace(
-            name=[],
-            name_top=name_top,
-            name_bottom=name_bottom,
-            record_chapter_name = True,
-
-            page_start=page_start,
-            page_stop=None,
-
-            text=[],
-        )
-        cur_chapter = None
-
-        # 按页遍历PDF文档
-        for idx, page in enumerate(new_pages):
-            page = get_text_outside_table(page)
-
-            # 按行遍历页面文本
-            for word in extract_words(page):
-                word = SimpleNamespace(**word)
-
-                # 检查行文本是否以12号字体打印，如果是，则将其作为新章节开始
-                if word.size >= 11: # 出现chapter name
-                    if cur_chapter is None:
-                        cur_chapter = create_chapter(page.page_number, word.top, word.bottom)
-                    elif not cur_chapter.record_chapter_name or (cur_chapter.name_bottom != cur_chapter.name_bottom and cur_chapter.name_top != cur_chapter.name_top):
-                        # 不再继续写chapter name
-                        cur_chapter.page_stop = page.page_number # stop id
-                        chapters.append(cur_chapter)
-                        # 重置当前chapter信息
-                        cur_chapter = create_chapter(page.page_number, word.top, word.bottom)
-
-                    # print(word.size, word.top, word.bottom, word.text)
-                    cur_chapter.name.append(word.text)
-                else:
-                    cur_chapter.record_chapter_name = False # chapter name 结束
-                    cur_chapter.text.append(word.text)
-        else:
-            # 处理最后一个章节
-            cur_chapter.page_stop = page.page_number # stop id
-            chapters.append(cur_chapter)
-
-        for i in chapters:
-            logging.info(f"section: {i.name} pages:{i.page_start, i.page_stop} word-count:{len(i.text)}")
-            logging.debug(" ".join(i.text))
-
-    title = " ".join(title)
-    user_info = " ".join(user_info)
-    text = f"Article Title: {title}, Information:{user_info}\n"
-    for idx, chapter in enumerate(chapters):
-        chapter.name = " ".join(chapter.name)
-        text += f"The {idx}th Chapter {chapter.name}: " + " ".join(chapter.text) + "\n"
-
-    logging.getLogger().setLevel(level)
-    return Document(page_content=text, metadata={"title": title})
-
-BASE_POINTS = """
-1. Who are the authors?
-2. What is the process of the proposed method?
-3. What is the performance of the proposed method? Please note down its performance metrics.
-4. What are the baseline models and their performances? Please note down these baseline methods.
-5. What dataset did this paper use?
-"""
-
-READING_PROMPT = """
-You are a researcher helper bot. You can help the user with research paper reading and summarizing. \n
-Now I am going to send you a paper. You need to read it and summarize it for me part by part. \n
-When you are reading, You need to focus on these key points:{}
-"""
-
-READING_PROMT_V2 = """
-You are a researcher helper bot. You can help the user with research paper reading and summarizing. \n
-Now I am going to send you a paper. You need to read it and summarize it for me part by part. \n
-When you are reading, You need to focus on these key points:{},
-
-And You need to generate a brief but informative title for this part.
-Your return format:
-- title: '...'
-- summary: '...'
-"""
-
-SUMMARY_PROMPT = "You are a researcher helper bot. Now you need to read the summaries of a research paper."
-
-
-if __name__ == '__main__':
-    # Test code
-    z = parse_pdf("./build/test.pdf")
-    print(z["user_info"])
-    print(z["title"])
\ No newline at end of file
diff --git a/spaces/zhanpj/ChatGPT/Dockerfile b/spaces/zhanpj/ChatGPT/Dockerfile
deleted file mode 100644
index 8cbd335b09b1d1975bfd83a053b5fcaf398147ea..0000000000000000000000000000000000000000
--- a/spaces/zhanpj/ChatGPT/Dockerfile
+++ /dev/null
@@ -1,14 +0,0 @@
-FROM python:3.9 as builder
-RUN apt-get update && apt-get install -y build-essential
-COPY requirements.txt .
-RUN pip install --user -r requirements.txt
-
-FROM python:3.9
-MAINTAINER iskoldt
-COPY --from=builder /root/.local /root/.local
-ENV PATH=/root/.local/bin:$PATH
-COPY . /app
-WORKDIR /app
-ENV my_api_key empty
-ENV dockerrun yes
-CMD ["python3", "-u", "ChuanhuChatbot.py", "2>&1", "|", "tee", "/var/log/application.log"]
diff --git a/spaces/zhtet/RegBotBeta/pages/llama_custom_demo.py b/spaces/zhtet/RegBotBeta/pages/llama_custom_demo.py
deleted file mode 100644
index c32298dfd968c1d1cc9150686cfbace7bdd0b8cc..0000000000000000000000000000000000000000
--- a/spaces/zhtet/RegBotBeta/pages/llama_custom_demo.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-
-import openai
-import streamlit as st
-
-from models.llamaCustom import LlamaCustom
-from utils.chatbox import chatbox
-
-st.set_page_config(page_title="Llama", page_icon="🦙")
-
-st.subheader("Llama Index with Custom LLM Demo")
-
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-if "openai_api_key" not in st.session_state:
-    st.info("Enter your openai key to access the chatbot.")
-else:
-    option = st.selectbox(
-        label="Select your model:", options=("bigscience/bloom-560m",)
-    )
-
-    # with st.spinner("Initializing vector index"):
-    model = LlamaCustom(model_name=option)
-
-    chatbox("llama_custom", model)
diff --git a/spaces/zhuolisam/resume-ranker/core.py b/spaces/zhuolisam/resume-ranker/core.py
deleted file mode 100644
index 6bcbbab645a8530179c39f4cfe60cf5cd71cc39b..0000000000000000000000000000000000000000
--- a/spaces/zhuolisam/resume-ranker/core.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from embedding import embedding
-from preprocessing import preprocess
-from sklearn.metrics.pairwise import cosine_similarity
-import numpy as np
-import streamlit as st
-
-def pipeline(input_doc:str , ori_documents, embedding_type='bert'):
-    documents = np.array([doc['content'] for doc in ori_documents])
-    documents = np.insert(documents, 0, input_doc)
-    # st.write(documents)
-    preprocessed_documents = preprocess(documents)
-    # st.write(preprocessed_documents)
-    print("Encoding with BERT...")
-    documents_vectors = embedding(preprocessed_documents, embedding=embedding_type)
-    print("Encoding finished")
-
-    #compute cosine similarity
-    pairwise = cosine_similarity(documents_vectors)
-
-    #only retain useful information
-    pairwise = pairwise[0,1:]
-    sorted_idx = np.argsort(pairwise)[::-1]
-    result_pairwise = pairwise[sorted_idx]
-
-    results = []
-    print('Resume ranking:')
-    for idx in sorted_idx:
-        single_result = {
-            'rank': idx,
-            'name': ori_documents[idx]['name'],
-            'similarity': pairwise[idx].item()
-        }
-        results.append(single_result)
-        print(f'Resume of candidite {idx}')
-        print(f'Cosine Similarity: {pairwise[idx]}\n')
-    
-    return results, result_pairwise
\ No newline at end of file
diff --git a/spaces/zomehwh/sovits-goldship/hubert/hubert_model.py b/spaces/zomehwh/sovits-goldship/hubert/hubert_model.py
deleted file mode 100644
index 7fb642d89b07ca60792debab18e3454f52d8f357..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/sovits-goldship/hubert/hubert_model.py
+++ /dev/null
@@ -1,222 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-            self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        x, mask = self.encode(x)
-        x = self.proj(x)
-        logits = self.logits(x)
-        return logits, mask
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    @torch.inference_mode()
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: torch.Tensor,
-            mask: torch.Tensor = None,
-            src_key_padding_mask: torch.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: torch.device,
-        min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert
diff --git a/spaces/zomehwh/sovits-tannhauser/vdecoder/hifigan/models.py b/spaces/zomehwh/sovits-tannhauser/vdecoder/hifigan/models.py
deleted file mode 100644
index 9747301f350bb269e62601017fe4633ce271b27e..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/sovits-tannhauser/vdecoder/hifigan/models.py
+++ /dev/null
@@ -1,503 +0,0 @@
-import os
-import json
-from .env import AttrDict
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from .utils import init_weights, get_padding
-
-LRELU_SLOPE = 0.1
-
-
-def load_model(model_path, device='cuda'):
-    config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
-    with open(config_file) as f:
-        data = f.read()
-
-    global h
-    json_config = json.loads(data)
-    h = AttrDict(json_config)
-
-    generator = Generator(h).to(device)
-
-    cp_dict = torch.load(model_path)
-    generator.load_state_dict(cp_dict['generator'])
-    generator.eval()
-    generator.remove_weight_norm()
-    del cp_dict
-    return generator, h
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.h = h
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            xt = c2(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.h = h
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-def padDiff(x):
-    return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0)
-
-class SineGen(torch.nn.Module):
-    """ Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(self, samp_rate, harmonic_num=0,
-                 sine_amp=0.1, noise_std=0.003,
-                 voiced_threshold=0,
-                 flag_for_pulse=False):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-        self.flag_for_pulse = flag_for_pulse
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = (f0 > self.voiced_threshold).type(torch.float32)
-        return uv
-
-    def _f02sine(self, f0_values):
-        """ f0_values: (batchsize, length, dim)
-            where dim indicates fundamental tone and overtones
-        """
-        # convert to F0 in rad. The interger part n can be ignored
-        # because 2 * np.pi * n doesn't affect phase
-        rad_values = (f0_values / self.sampling_rate) % 1
-
-        # initial phase noise (no noise for fundamental component)
-        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
-                              device=f0_values.device)
-        rand_ini[:, 0] = 0
-        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-
-        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
-        if not self.flag_for_pulse:
-            # for normal case
-
-            # To prevent torch.cumsum numerical overflow,
-            # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
-            # Buffer tmp_over_one_idx indicates the time step to add -1.
-            # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
-            tmp_over_one = torch.cumsum(rad_values, 1) % 1
-            tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-
-            sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
-                              * 2 * np.pi)
-        else:
-            # If necessary, make sure that the first time step of every
-            # voiced segments is sin(pi) or cos(0)
-            # This is used for pulse-train generation
-
-            # identify the last time step in unvoiced segments
-            uv = self._f02uv(f0_values)
-            uv_1 = torch.roll(uv, shifts=-1, dims=1)
-            uv_1[:, -1, :] = 1
-            u_loc = (uv < 1) * (uv_1 > 0)
-
-            # get the instantanouse phase
-            tmp_cumsum = torch.cumsum(rad_values, dim=1)
-            # different batch needs to be processed differently
-            for idx in range(f0_values.shape[0]):
-                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
-                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
-                # stores the accumulation of i.phase within
-                # each voiced segments
-                tmp_cumsum[idx, :, :] = 0
-                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
-
-            # rad_values - tmp_cumsum: remove the accumulation of i.phase
-            # within the previous voiced segment.
-            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
-
-            # get the sines
-            sines = torch.cos(i_phase * 2 * np.pi)
-        return sines
-
-    def forward(self, f0):
-        """ sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
-                                 device=f0.device)
-            # fundamental component
-            fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))
-
-            # generate sine waveforms
-            sine_waves = self._f02sine(fn) * self.sine_amp
-
-            # generate uv signal
-            # uv = torch.ones(f0.shape)
-            # uv = uv * (f0 > self.voiced_threshold)
-            uv = self._f02uv(f0)
-
-            # noise: for unvoiced should be similar to sine_amp
-            #        std = self.sine_amp/3 -> max value ~ self.sine_amp
-            # .       for voiced regions is self.noise_std
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-
-            # first: set the unvoiced part to 0 by uv
-            # then: additive noise
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """ SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
-                                 sine_amp, add_noise_std, voiced_threshod)
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x):
-        """
-        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-        F0_sampled (batchsize, length, 1)
-        Sine_source (batchsize, length, 1)
-        noise_source (batchsize, length 1)
-        """
-        # source for harmonic branch
-        sine_wavs, uv, _ = self.l_sin_gen(x)
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-
-        # source for noise branch, in the same shape as uv
-        noise = torch.randn_like(uv) * self.sine_amp / 3
-        return sine_merge, noise, uv
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, h):
-        super(Generator, self).__init__()
-        self.h = h
-
-        self.num_kernels = len(h["resblock_kernel_sizes"])
-        self.num_upsamples = len(h["upsample_rates"])
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"]))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=h["sampling_rate"],
-            harmonic_num=8)
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = weight_norm(Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3))
-        resblock = ResBlock1 if h["resblock"] == '1' else ResBlock2
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(h["upsample_rates"], h["upsample_kernel_sizes"])):
-            c_cur = h["upsample_initial_channel"] // (2 ** (i + 1))
-            self.ups.append(weight_norm(
-                ConvTranspose1d(h["upsample_initial_channel"] // (2 ** i), h["upsample_initial_channel"] // (2 ** (i + 1)),
-                                k, u, padding=(k - u) // 2)))
-            if i + 1 < len(h["upsample_rates"]):  #
-                stride_f0 = np.prod(h["upsample_rates"][i + 1:])
-                self.noise_convs.append(Conv1d(
-                    1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = h["upsample_initial_channel"] // (2 ** (i + 1))
-            for j, (k, d) in enumerate(zip(h["resblock_kernel_sizes"], h["resblock_dilation_sizes"])):
-                self.resblocks.append(resblock(h, ch, k, d))
-
-        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-        self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1)
-
-    def forward(self, x, f0, g=None):
-        # print(1,x.shape,f0.shape,f0[:, None].shape)
-        f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)  # bs,n,t
-        # print(2,f0.shape)
-        har_source, noi_source, uv = self.m_source(f0)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        x = x + self.cond(g)
-        # print(124,x.shape,har_source.shape)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            # print(3,x.shape)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            # print(4,x_source.shape,har_source.shape,x.shape)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, periods=None):
-        super(MultiPeriodDiscriminator, self).__init__()
-        self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
-        self.discriminators = nn.ModuleList()
-        for period in self.periods:
-            self.discriminators.append(DiscriminatorP(period))
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 128, 15, 1, padding=7)),
-            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
-            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
-            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
-    def __init__(self):
-        super(MultiScaleDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList([
-            DiscriminatorS(use_spectral_norm=True),
-            DiscriminatorS(),
-            DiscriminatorS(),
-        ])
-        self.meanpools = nn.ModuleList([
-            AvgPool1d(4, 2, padding=2),
-            AvgPool1d(4, 2, padding=2)
-        ])
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            if i != 0:
-                y = self.meanpools[i - 1](y)
-                y_hat = self.meanpools[i - 1](y_hat)
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((1 - dr) ** 2)
-        g_loss = torch.mean(dg ** 2)
-        loss += (r_loss + g_loss)
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        l = torch.mean((1 - dg) ** 2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses