Update parquet files (step 79 of 397)

spaces/101-5/gpt4free/g4f/.v1/unfinished/t3nsor/__init__.py

@@ -1,136 +0,0 @@
-from time import time
-
-from requests import post
-
-headers = {
-    'authority': 'www.t3nsor.tech',
-    'accept': '*/*',
-    'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
-    'cache-control': 'no-cache',
-    'content-type': 'application/json',
-    'origin': 'https://www.t3nsor.tech',
-    'pragma': 'no-cache',
-    'referer': 'https://www.t3nsor.tech/',
-    'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"',
-    'sec-ch-ua-mobile': '?0',
-    'sec-ch-ua-platform': '"macOS"',
-    'sec-fetch-dest': 'empty',
-    'sec-fetch-mode': 'cors',
-    'sec-fetch-site': 'same-origin',
-    'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
-}
-
-
-class T3nsorResponse:
-    class Completion:
-        class Choices:
-            def __init__(self, choice: dict) -> None:
-                self.text = choice['text']
-                self.content = self.text.encode()
-                self.index = choice['index']
-                self.logprobs = choice['logprobs']
-                self.finish_reason = choice['finish_reason']
-
-            def __repr__(self) -> str:
-                return f'''<__main__.APIResponse.Completion.Choices(\n    text           = {self.text.encode()},\n    index          = {self.index},\n    logprobs       = {self.logprobs},\n    finish_reason  = {self.finish_reason})object at 0x1337>'''
-
-        def __init__(self, choices: dict) -> None:
-            self.choices = [self.Choices(choice) for choice in choices]
-
-    class Usage:
-        def __init__(self, usage_dict: dict) -> None:
-            self.prompt_tokens = usage_dict['prompt_chars']
-            self.completion_tokens = usage_dict['completion_chars']
-            self.total_tokens = usage_dict['total_chars']
-
-        def __repr__(self):
-            return f'''<__main__.APIResponse.Usage(\n    prompt_tokens      = {self.prompt_tokens},\n    completion_tokens  = {self.completion_tokens},\n    total_tokens       = {self.total_tokens})object at 0x1337>'''
-
-    def __init__(self, response_dict: dict) -> None:
-        self.response_dict = response_dict
-        self.id = response_dict['id']
-        self.object = response_dict['object']
-        self.created = response_dict['created']
-        self.model = response_dict['model']
-        self.completion = self.Completion(response_dict['choices'])
-        self.usage = self.Usage(response_dict['usage'])
-
-    def json(self) -> dict:
-        return self.response_dict
-
-
-class Completion:
-    model = {
-        'model': {
-            'id': 'gpt-3.5-turbo',
-            'name': 'Default (GPT-3.5)'
-        }
-    }
-
-    def create(
-            prompt: str = 'hello world',
-            messages: list = []) -> T3nsorResponse:
-        response = post('https://www.t3nsor.tech/api/chat', headers=headers, json=Completion.model | {
-            'messages': messages,
-            'key': '',
-            'prompt': prompt
-        })
-
-        return T3nsorResponse({
-            'id': f'cmpl-1337-{int(time())}',
-            'object': 'text_completion',
-            'created': int(time()),
-            'model': Completion.model,
-            'choices': [{
-                'text': response.text,
-                'index': 0,
-                'logprobs': None,
-                'finish_reason': 'stop'
-            }],
-            'usage': {
-                'prompt_chars': len(prompt),
-                'completion_chars': len(response.text),
-                'total_chars': len(prompt) + len(response.text)
-            }
-        })
-
-
-class StreamCompletion:
-    model = {
-        'model': {
-            'id': 'gpt-3.5-turbo',
-            'name': 'Default (GPT-3.5)'
-        }
-    }
-
-    def create(
-            prompt: str = 'hello world',
-            messages: list = []) -> T3nsorResponse:
-        print('t3nsor api is down, this may not work, refer to another module')
-
-        response = post('https://www.t3nsor.tech/api/chat', headers=headers, stream=True, json=Completion.model | {
-            'messages': messages,
-            'key': '',
-            'prompt': prompt
-        })
-
-        for chunk in response.iter_content(chunk_size=2046):
-            yield T3nsorResponse({
-                'id': f'cmpl-1337-{int(time())}',
-                'object': 'text_completion',
-                'created': int(time()),
-                'model': Completion.model,
-
-                'choices': [{
-                    'text': chunk.decode(),
-                    'index': 0,
-                    'logprobs': None,
-                    'finish_reason': 'stop'
-                }],
-
-                'usage': {
-                    'prompt_chars': len(prompt),
-                    'completion_chars': len(chunk.decode()),
-                    'total_chars': len(prompt) + len(chunk.decode())
-                }
-            })

spaces/17TheWord/vits-models/app.py

@@ -1,265 +0,0 @@
-# coding=utf-8
-import os
-import re
-import argparse
-import utils
-import commons
-import json
-import torch
-import gradio as gr
-from models import SynthesizerTrn
-from text import text_to_sequence, _clean_text
-from torch import no_grad, LongTensor
-import gradio.processing_utils as gr_processing_utils
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
-
-hps_ms = utils.get_hparams_from_file(r'config/config.json')
-
-audio_postprocess_ori = gr.Audio.postprocess
-
-def audio_postprocess(self, y):
-    data = audio_postprocess_ori(self, y)
-    if data is None:
-        return None
-    return gr_processing_utils.encode_url_or_file_to_base64(data["name"])
-
-
-gr.Audio.postprocess = audio_postprocess
-
-def get_text(text, hps, is_symbol):
-    text_norm, clean_text = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm, clean_text
-
-def create_tts_fn(net_g_ms, speaker_id):
-    def tts_fn(text, language, noise_scale, noise_scale_w, length_scale, is_symbol):
-        text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
-        if limitation:
-            text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
-            max_len = 100
-            if is_symbol:
-                max_len *= 3
-            if text_len > max_len:
-                return "Error: Text is too long", None
-        if not is_symbol:
-            if language == 0:
-                text = f"[ZH]{text}[ZH]"
-            elif language == 1:
-                text = f"[JA]{text}[JA]"
-            else:
-                text = f"{text}"
-        stn_tst, clean_text = get_text(text, hps_ms, is_symbol)
-        with no_grad():
-            x_tst = stn_tst.unsqueeze(0).to(device)
-            x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
-            sid = LongTensor([speaker_id]).to(device)
-            audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
-                                   length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
-
-        return "Success", (22050, audio)
-    return tts_fn
-
-def create_to_symbol_fn(hps):
-    def to_symbol_fn(is_symbol_input, input_text, temp_text, temp_lang):
-        if temp_lang == 'Chinese':
-            clean_text = f'[ZH]{input_text}[ZH]'
-        elif temp_lang == "Japanese":
-            clean_text = f'[JA]{input_text}[JA]'
-        else:
-            clean_text = input_text
-        return (_clean_text(clean_text, hps.data.text_cleaners), input_text) if is_symbol_input else (temp_text, temp_text)
-
-    return to_symbol_fn
-def change_lang(language):
-    if language == 0:
-        return 0.6, 0.668, 1.2, "Chinese"
-    elif language == 1:
-        return 0.6, 0.668, 1, "Japanese"
-    else:
-        return 0.6, 0.668, 1, "Mix"
-
-download_audio_js = """
-() =>{{
-    let root = document.querySelector("body > gradio-app");
-    if (root.shadowRoot != null)
-        root = root.shadowRoot;
-    let audio = root.querySelector("#tts-audio-{audio_id}").querySelector("audio");
-    let text = root.querySelector("#input-text-{audio_id}").querySelector("textarea");
-    if (audio == undefined)
-        return;
-    text = text.value;
-    if (text == undefined)
-        text = Math.floor(Math.random()*100000000);
-    audio = audio.src;
-    let oA = document.createElement("a");
-    oA.download = text.substr(0, 20)+'.wav';
-    oA.href = audio;
-    document.body.appendChild(oA);
-    oA.click();
-    oA.remove();
-}}
-"""
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
-    args = parser.parse_args()
-    device = torch.device(args.device)
-    
-    models = []
-    with open("pretrained_models/info.json", "r", encoding="utf-8") as f:
-        models_info = json.load(f)
-    for i, info in models_info.items():
-        sid = info['sid']
-        name_en = info['name_en']
-        name_zh = info['name_zh']
-        title = info['title']
-        cover = f"pretrained_models/{i}/{info['cover']}"
-        example = info['example']
-        language = info['language']
-        net_g_ms = SynthesizerTrn(
-            len(hps_ms.symbols),
-            hps_ms.data.filter_length // 2 + 1,
-            hps_ms.train.segment_size // hps_ms.data.hop_length,
-            n_speakers=hps_ms.data.n_speakers if info['type'] == "multi" else 0,
-            **hps_ms.model)
-        utils.load_checkpoint(f'pretrained_models/{i}/{i}.pth', net_g_ms, None)
-        _ = net_g_ms.eval().to(device)
-        models.append((sid, name_en, name_zh, title, cover, example, language, net_g_ms, create_tts_fn(net_g_ms, sid), create_to_symbol_fn(hps_ms)))
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> vits-models\n"
-            "## <center> Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.\n"
-            "## <center> ·请不要生成会对个人以及组织造成侵害的内容\n"
-            "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.vits-models)\n\n"
-            "[Open In Colab]"
-            "(https://colab.research.google.com/drive/10QOk9NPgoKZUXkIhhuVaZ7SYra1MPMKH?usp=share_link)"
-            " without queue and length limitation.(无需等待队列，并且没有长度限制)\n\n"
-            "[Finetune your own model](https://github.com/SayaSS/vits-finetuning)"
-        )
-
-        with gr.Tabs():
-            with gr.TabItem("EN"):
-                for (sid, name_en, name_zh, title, cover, example, language, net_g_ms, tts_fn, to_symbol_fn) in models:
-                    with gr.TabItem(name_en):
-                        with gr.Row():
-                            gr.Markdown(
-                                '<div align="center">'
-                                f'<a><strong>{title}</strong></a>'
-                                f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
-                                '</div>'
-                            )
-                        with gr.Row():
-                            with gr.Column():
-                                input_text = gr.Textbox(label="Text (100 words limitation)" if limitation else "Text", lines=5, value=example, elem_id=f"input-text-en-{name_en.replace(' ','')}")
-                                lang = gr.Dropdown(label="Language", choices=["Chinese", "Japanese", "Mix（wrap the Chinese text with [ZH][ZH], wrap the Japanese text with [JA][JA]）"],
-                                            type="index", value=language)
-                                temp_lang = gr.Variable(value=language)
-                                with gr.Accordion(label="Advanced Options", open=False):
-                                    temp_text_var = gr.Variable()
-                                    symbol_input = gr.Checkbox(value=False, label="Symbol input")
-                                    symbol_list = gr.Dataset(label="Symbol list", components=[input_text],
-                                                             samples=[[x] for x in hps_ms.symbols])
-                                    symbol_list_json = gr.Json(value=hps_ms.symbols, visible=False)
-                                btn = gr.Button(value="Generate", variant="primary")
-                                with gr.Row():
-                                    ns = gr.Slider(label="noise_scale", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
-                                    nsw = gr.Slider(label="noise_scale_w", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
-                                    ls = gr.Slider(label="length_scale", minimum=0.1, maximum=2.0, step=0.1, value=1.2 if language=="Chinese" else 1, interactive=True)
-                            with gr.Column():
-                                o1 = gr.Textbox(label="Output Message")
-                                o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio-en-{name_en.replace(' ','')}")
-                                download = gr.Button("Download Audio")
-                            btn.click(tts_fn, inputs=[input_text, lang,  ns, nsw, ls, symbol_input], outputs=[o1, o2])
-                            download.click(None, [], [], _js=download_audio_js.format(audio_id=f"en-{name_en.replace(' ', '')}"))
-                            lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls, temp_lang])
-                            symbol_input.change(
-                                to_symbol_fn,
-                                [symbol_input, input_text, temp_text_var, temp_lang],
-                                [input_text, temp_text_var]
-                            )
-                            symbol_list.click(None, [symbol_list, symbol_list_json], [input_text],
-                                              _js=f"""
-                            (i,symbols) => {{
-                                let root = document.querySelector("body > gradio-app");
-                                if (root.shadowRoot != null)
-                                    root = root.shadowRoot;
-                                let text_input = root.querySelector("#input-text-en-{name_en.replace(' ', '')}").querySelector("textarea");
-                                let startPos = text_input.selectionStart;
-                                let endPos = text_input.selectionEnd;
-                                let oldTxt = text_input.value;
-                                let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
-                                text_input.value = result;
-                                let x = window.scrollX, y = window.scrollY;
-                                text_input.focus();
-                                text_input.selectionStart = startPos + symbols[i].length;
-                                text_input.selectionEnd = startPos + symbols[i].length;
-                                text_input.blur();
-                                window.scrollTo(x, y);
-                                return text_input.value;
-                            }}""")
-            with gr.TabItem("中文"):
-                for (sid, name_en, name_zh, title, cover, example, language,  net_g_ms, tts_fn, to_symbol_fn) in models:
-                    with gr.TabItem(name_zh):
-                        with gr.Row():
-                            gr.Markdown(
-                                '<div align="center">'
-                                f'<a><strong>{title}</strong></a>'
-                                f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
-                                '</div>'
-                            )
-                        with gr.Row():
-                            with gr.Column():
-                                input_text = gr.Textbox(label="文本 (100字上限)" if limitation else "文本", lines=5, value=example, elem_id=f"input-text-zh-{name_zh}")
-                                lang = gr.Dropdown(label="语言", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"],
-                                            type="index", value="中文"if language == "Chinese" else "日语")
-                                temp_lang = gr.Variable(value=language)
-                                with gr.Accordion(label="高级选项", open=False):
-                                    temp_text_var = gr.Variable()
-                                    symbol_input = gr.Checkbox(value=False, label="符号输入")
-                                    symbol_list = gr.Dataset(label="符号列表", components=[input_text],
-                                                             samples=[[x] for x in hps_ms.symbols])
-                                    symbol_list_json = gr.Json(value=hps_ms.symbols, visible=False)
-                                btn = gr.Button(value="生成", variant="primary")
-                                with gr.Row():
-                                    ns = gr.Slider(label="控制感情变化程度", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
-                                    nsw = gr.Slider(label="控制音素发音长度", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
-                                    ls = gr.Slider(label="控制整体语速", minimum=0.1, maximum=2.0, step=0.1, value=1.2 if language=="Chinese" else 1, interactive=True)
-                            with gr.Column():
-                                o1 = gr.Textbox(label="输出信息")
-                                o2 = gr.Audio(label="输出音频", elem_id=f"tts-audio-zh-{name_zh}")
-                                download = gr.Button("下载音频")
-                            btn.click(tts_fn, inputs=[input_text, lang,  ns, nsw, ls, symbol_input], outputs=[o1, o2])
-                            download.click(None, [], [], _js=download_audio_js.format(audio_id=f"zh-{name_zh}"))
-                            lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
-                            symbol_input.change(
-                                to_symbol_fn,
-                                [symbol_input, input_text, temp_text_var, temp_lang],
-                                [input_text, temp_text_var]
-                            )
-                            symbol_list.click(None, [symbol_list, symbol_list_json], [input_text],
-                                              _js=f"""
-                            (i,symbols) => {{
-                                let root = document.querySelector("body > gradio-app");
-                                if (root.shadowRoot != null)
-                                    root = root.shadowRoot;
-                                let text_input = root.querySelector("#input-text-zh-{name_zh}").querySelector("textarea");
-                                let startPos = text_input.selectionStart;
-                                let endPos = text_input.selectionEnd;
-                                let oldTxt = text_input.value;
-                                let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
-                                text_input.value = result;
-                                let x = window.scrollX, y = window.scrollY;
-                                text_input.focus();
-                                text_input.selectionStart = startPos + symbols[i].length;
-                                text_input.selectionEnd = startPos + symbols[i].length;
-                                text_input.blur();
-                                window.scrollTo(x, y);
-                                return text_input.value;
-                            }}""")
-    app.queue(concurrency_count=1).launch(show_api=False, share=args.share)

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Code Visual To Flowchart 41 Crack A Must-Have for Software Engineers and Programmers.md

@@ -1,145 +0,0 @@
-<br />
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-<h1>From Up on Poppy Hill English Dub 1080p: A Review of the Charming Anime Film by Studio Ghibli</h1>
- If you are looking for a heartwarming, wholesome, and nostalgic anime film to watch, you might want to check out From Up on Poppy Hill (2011), a historical drama by Studio Ghibli, the acclaimed animation studio behind classics like Spirited Away (2001), Howl's Moving Castle (2004), and My Neighbor Totoro (1988). From Up on Poppy Hill is set in Yokohama in 1963, a year before Japan hosts the Olympics for the first time after World War II. It tells the story of Umi, a high school girl who runs a boarding house by the sea while her mother is abroad, and Shun, a boy who leads a student club that fights to save their old clubhouse from demolition. As they work together to preserve their school's history and culture, they also develop a friendship that blossoms into romance. However, a buried secret from their past threatens to tear them apart. In this review, I will explain why From Up on Poppy Hill is a charming anime film that deserves your attention. I will discuss its historical and cultural context, its characters and relationships, its artistic and technical aspects, and its themes and messages. I will also give you my personal opinion on the film's strengths and weaknesses, as well as a recommendation for who should watch it and where to find it online. <h2>The Historical and Cultural Context of From Up on Poppy Hill</h2>
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- From Up on Poppy Hill is also remarkable for its artistic and technical aspects. The film showcases Studio Ghibli's signature animation style and quality, which combines hand-drawn 2D animation with computer-generated 3D effects. The film uses vivid colors, realistic lighting, and detailed backgrounds to create a stunning visual experience. The film also features beautiful music and sound effects that enhance its mood and emotion. The film's soundtrack was composed by Satoshi Takebe, who blended orchestral music with jazz elements. The film's theme song was performed by Aoi Teshima, who sang both in Japanese and Here is the continuation of the article. in French. The film's sound effects are realistic and immersive, such as the waves of the sea, the wind of the hill, and the noise of the city. The film's voice acting and dubbing are also excellent, featuring a talented cast of actors who bring their characters to life. The English dub features stars like Sarah Bolger, Anton Yelchin, Gillian Anderson, Beau Bridges, Jamie Lee Curtis, Bruce Dern, Christina Hendricks, Ron Howard, and Chris Noth. The film was directed by Goro Miyazaki, the son of Hayao Miyazaki, the legendary founder of Studio Ghibli. This was his second feature film after Tales from Earthsea (2006), which received mixed reviews from critics and fans. From Up on Poppy Hill was a more successful and acclaimed project for him, as he collaborated with his father on the screenplay, which was based on a manga by Chizuru Takahashi and Tetsuro Sayama. The film won several awards, including the Japan Academy Prize for Animation of the Year and the Asia Pacific Screen Award for Best Animated Feature Film. <h2>The Themes and Messages of From Up on Poppy Hill</h2>
- From Up on Poppy Hill is a film that explores various themes and messages that resonate with audiences of all ages and backgrounds. Some of the main themes and messages are: - The importance of family, community, and identity. The film shows how Umi and Shun struggle with their family histories and identities, as they search for their roots and their place in the world. They also learn to appreciate their adoptive families and communities, who support them and love them unconditionally. They realize that family is not only defined by blood, but also by bonds. - The value of courage, honesty, and resilience. The film shows how Umi and Shun face their challenges and overcome their fears with courage, honesty, and resilience. They do not give up on their dreams and goals, even when they encounter difficulties and setbacks. They also do not lie or hide their feelings, even when they are painful or uncomfortable. They face the truth and accept the consequences. - The appreciation of beauty, simplicity, and nostalgia. The film shows how Umi and Shun find beauty, simplicity, and nostalgia in their everyday lives. They enjoy the simple pleasures of cooking, gardening, sailing, reading, and spending time with each other. They also cherish the memories of their pasts, such as Umi's flag ritual, Shun's poem book, and their fathers' photograph. They appreciate what they have and what they had. <h1>Conclusion</h1>
- In conclusion, From Up on Poppy Hill is a charming anime film that offers a delightful and touching experience for viewers. It is a film that combines historical and cultural context, characters and relationships, artistic and technical aspects, and themes and messages in a harmonious and engaging way. It is a film that celebrates love, life, and legacy in a changing world. In my opinion, From Up on Poppy Hill is one of the best films by Studio Ghibli. It is not as fantastical or adventurous as some of their other works, but it is more realistic and relatable. It is a film that captures the essence of human emotions and interactions in a simple yet profound way. It is a film that makes me smile, cry, and think. I would recommend this film to anyone who loves anime, history, romance, or drama. I would also recommend this film to anyone who wants to watch a wholesome, heartwarming, and nostalgic story that will make them feel good. You can watch this film online on platforms like Microsoft Store, Animefever, Internet Archive, or Bilibili. You can also buy or rent this film on DVD or Blu-ray. I hope you enjoyed this review and found it helpful. If you have any questions or comments, please feel free to share them with me. Thank you for reading! <h2>FAQs</h2>
- - Q: What is the meaning of Umi's flag signal? - A: Umi's flag signal is a naval code that means "I pray for safe voyages". She does it every morning to honor her father who died in the Korean War. - Q: What is the significance of Shun's poem book? - A: Shun's poem book is a collection of poems by Kenji Miyazawa, a Japanese poet and author who wrote about nature and social issues. Shun inherited it from his biological father who was also a poet and a sailor. - Q: What is the origin of the Latin Quarter club? - A: The Latin Quarter club was founded by Umi and Shun's fathers when they were students at the same school. They named it after the Parisian district where they met and became friends. - Q: What is the outcome of the clubhouse protest? - A: The clubhouse protest succeeds in convincing the school administration and the public opinion to spare the clubhouse from demolition. The students are allowed to keep their club activities and their cultural heritage. - Q: What is the resolution of Umi and Shun's relationship? - A: Umi and Shun find out that they are not siblings, but cousins. They are relieved and happy to learn that they can be together without any guilt or shame. They confess their love for each other and kiss under Umi's flag signal. </p>
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-<h1>Criminal Case: City of Romance - A Review</h1>
-<p>If you are a fan of hidden object, adventure games, you might want to check out Criminal Case: City of Romance. This is the eighth installment of the popular Criminal Case series, which has been downloaded by millions of players around the world. In this game, you join the Parisian Police Squad to solve a series of murder cases in the city of love. You will investigate crime scenes, examine clues, interrogate suspects, and catch killers, while exploring different districts and themes based on romance. You can also play with your friends and compete for the best detective title.</p>
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-<p>Criminal Case: City of Romance is a free-to-play game that was released in 2020 by Pretty Simple, a French game developer. It is available for iOS and Android devices, as well as on Facebook. The game is divided into six districts across Paris, each with its own romantic theme: Fantasy, Attraction, Obsession, Jealousy, Separation, and Engagement. There are 17 cases in total, each with a unique storyline and characters.</p>
-<p>To play the game, you need to download it from the App Store or Google Play, or access it from Facebook. You will need an internet connection to play the game. You will also need energy to investigate crime scenes, which can be replenished by waiting, watching ads, buying with real money, or asking your friends for help. You can also earn coins, stars, cash, and other rewards by playing the game.</p>
- <h2>Gameplay</h2>
-<p>The gameplay of Criminal Case: City of Romance is similar to other games in the Criminal Case series. You will be assigned a case by your chief inspector Hugo Mercier, who will guide you throughout the investigation. You will then go to various crime scenes to look for clues. You will need to find all the items on a list within a time limit. The faster you find them, the higher your score will be. You can also use hints or boosters to help you find items faster.</p>
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- <h2>Graphics and Sound</h2>
-<p>The graphics and sound of Criminal Case: City of Romance are impressive and immersive. The game has a colorful and detailed visual design that captures the beauty and charm of Paris. The crime scenes are well-designed and realistic, with various objects and backgrounds that fit the theme of each district. The characters are also well-drawn and animated, with different expressions and outfits that reflect their moods and roles.</p>
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- <h2>Pros and Cons</h2>
-<p>Like any other game, Criminal Case: City of Romance has its pros and cons. Here are some of them:</p>
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-<th>Pros</th>
-<th>Cons</th>
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-<td>- Engaging and addictive gameplay that keeps you hooked on solving cases</td>
-<td>- Limited energy that may prevent you from playing for long periods</td>
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-<td>- Interesting and diverse storylines and characters that make you curious about their secrets</td>
-<td>- Repetitive and predictable patterns that may make some cases easy or boring</td>
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-<td>- Stunning and realistic graphics that make you feel like you are in Paris</td>
-<td>- High storage space and data usage that may slow down your device or connection</td>
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-<td>- Fun and lively music and sound effects that create a great atmosphere for the game</td>
-<td>- No option to mute or adjust the volume of the music or sound effects</td>
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-<td>- Social and competitive features that allow you to play with your friends and other players</td>
-<td>- In-app purchases that may give some players an unfair advantage or pressure you to spend money</td>
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- <p>Overall, I think the pros outweigh the cons, and I would recommend this game to anyone who likes hidden object, adventure games.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, Criminal Case: City of Romance is a fun and exciting game that lets you experience the thrill of solving murder cases in the city of love. You will enjoy the gameplay, graphics, sound, storylines, characters, themes, and features of this game. You will also learn some facts about Parisian culture and history along the way. If you are looking for a game that combines mystery, romance, adventure, and challenge, this is the game for you.</p>
-<p>I hope you found this article helpful and informative. If you have any questions or feedback about the game or this article, please feel free to contact me at [email](^i^). I would love to hear from you. Thank you for reading!</p>
- <h3>Frequently Asked Questions (FAQs)</h3>
- <h4>Q: How can I get more energy in Criminal Case: City of Romance?</h4>
-<p>A: There are several ways to get more energy in the game. You can wait for it to regenerate over time (1 point every 3 minutes), watch ads (20 points per ad), buy it with real money (various packages available), ask your friends for help (1 point per friend), or get it from your team members (5 points per member).</p>
- <h4>Q: How can I get more stars in Criminal Case: City of Romance?</h4>
-<p>A: Stars are used to perform <p>various actions in the game, such as arresting suspects, confirming alibis, unlocking new districts, etc. You can earn stars by playing crime scenes and getting high scores. You can also replay crime scenes to earn more stars. You can also buy stars with real money (various packages available).</p>
- <h4>Q: How can I get more coins in Criminal Case: City of Romance?</h4>
-<p>A: Coins are used to buy items and outfits in the game, as well as hints and boosters. You can earn coins by playing crime scenes and getting high scores. You can also get coins from your friends, team members, or daily bonuses. You can also watch ads to get coins (100 coins per ad). You can also buy coins with real money (various packages available).</p>
- <h4>Q: How can I get more cash in Criminal Case: City of Romance?</h4>
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-<p>A: Items and outfits are used to customize your avatar and improve your skills in the game. You can get items and outfits by buying them with coins or cash in the shop, or by winning them from special events and tournaments. You can also get items and outfits from your friends, team members, or daily bonuses.</p>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download File dari Google Drive Tanpa Buka Halaman Web.md

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-<h1>How to Download Files from Google Drive</h1>
-<p>Google Drive is a cloud-based storage and syncing service from Google. It is designed to give you free 15 GB of storage for all your files, including photos, documents, music, and so much more. You can access your files from any device, as long as you have an internet connection.</p>
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-<p>But what if you want to download your files from Google Drive to your computer or mobile device? Maybe you want to back up your data, work offline, or share your files with others. In this article, we will show you how to download files from Google Drive easily and quickly.</p>
-<p>Before we get started, let's go over some of the benefits and features of using Google Drive, as well as how to create a Google account and access Google Drive.</p>
- <h2>Introduction</h2>
-<h3>What is Google Drive and Why Use It</h3>
-<p>Google Drive is more than just a place to store your files. It is also a platform that allows you to create, share, and collaborate on various types of content. Here are some of the reasons why you might want to use Google Drive:</p>
-<ul>
-<li>You can store any type of file in Google Drive, such as PDFs, images, videos, audio, etc.</li>
-<li>You can access your files from any device, whether it's a computer, tablet, or smartphone.</li>
-<li>You can organize your files into folders and subfolders, and search for them easily.</li>
-<li>You can share your files with anyone, either by inviting them by email or by creating a link that anyone can use.</li>
-<li>You can control how others can use your files, such as whether they can view, comment, or edit them.</li>
-<li>You can use Google Docs, Sheets, and Slides, cloud-based apps that let you create and edit documents, spreadsheets, and presentations online. You can also share and comment on these files with others in real time.</li>
-<li>You can use Drive for desktop, a tool that lets you sync your files between your computer and Google Drive.</li>
-<li>You can use the Google Drive app on your mobile device to access your files, scan documents, and upload photos.</li>
-<li>You can use Google Photos, a service that lets you store and manage your photos and videos in Google Drive.</li>
-<li>You can use Google Backup and Sync, a tool that lets you back up your files and folders from your computer to Google Drive.</li>
-</ul>
-<p>As you can see, Google Drive offers a lot of benefits and features that can help you store, manage, and work with your files. But how do you get started with Google Drive?</p>
- <h3>How to Create a Google Account and Access Google Drive</h3>
-<p>To use Google Drive, you need to have a Google account. A Google account is a free account that gives you access to various Google services, such as Gmail, YouTube, Maps, Calendar, etc. If you already have a Google account, you can skip this step. If not, here's how to create one:</p>
-<ol>
-<li>Go to <a href="">https://accounts.google.com/signup</a> and fill out the form with your name, email address, password, and other details.</li>
-<li>Verify your email address by clicking on the link that Google sends you.</li>
-<li>Agree to the terms and conditions and privacy policy of Google.</li>
-<li>Congratulations! You have created your Google account. You can now access Google Drive and other Google services.</li>
-</ol>
-<p>To access Google Drive, you have two options:</p>
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-<ul>
-<li>You can go to <a href="">https://drive.google.com</a> and sign in with your Google account.</li>
-<li>You can download the Google Drive app on your computer or mobile device and sign in with your Google account.</li>
-</ul>
-<p>Either way, you will see the main interface of Google Drive, where you can view, upload, create, and share your files.</p>
- <h2>How to Share Files on Google Drive</h2>
-<h3>How to Share Files with Specific People or Groups</h3>
-<p>One of the main features of Google Drive is the ability to share your files with others. You can share your files with specific people or groups by inviting them by email. Here's how:</p>
-<ol>
-<li>Select the file or folder that you want to share on Google Drive.</li>
-<li>Click on the Share button on the top right corner of the screen.</li>
-<li>A pop-up window will appear where you can enter the email addresses of the people or groups that you want to share with. You can also add a note or message if you want.</li>
-<li>Choose the level of access that you want to give them. You can choose between Viewer, Commenter, or Editor. A Viewer can only view the file, a Commenter can view and comment on the file, and an Editor can view, comment, and edit the file.</li>
-<li>Click on Send or Done. The people or groups that you invited will receive an email notification with a link to access the file.</li>
-</ol>
- <h3>How to Share Files Publicly or With Anyone Who Has the Link</h3>
-<p>If you want to share your files with anyone who has the link, without requiring them to sign in with a Google account, you can do so by creating a public link. Here's how:</p>
-<ol>
-<li>Select the file or folder that you want to share on Google Drive.</li>
-<li>Click on the Share button on the top right corner of the screen.</li>
-<li>A pop-up window will appear where you can click on Change next to Anyone with the link.</li>
-<li>A drop-down menu will appear where you can choose who can access the link. You can choose between Anyone on the internet or Anyone at [your organization]. You can also choose the level of access that they have: Viewer, Commenter, or Editor.</li>
-<li>Click on Done. You will see a link that you can copy and paste anywhere you want to share it. Anyone who has the link will be able to access the file without signing in.</li>
-</ol>
- <h3>How to Change or Revoke Sharing Permissions</h3>
-<p>If you want to change or revoke the sharing permissions for your files, you can do so at any time. Here's how:</p>
-<ol>
-<li>Select the file or folder that you want to change or revoke permissions for on Google Drive.</li>
-<li>Click on the Share button on the top right corner of the screen.</li>
-<li>A pop-up window will appear where you can see the list of people or groups that you have shared the file with. You can also see the level of access that they have.</li>
-<li>To change the level of access, click on the drop-down menu next to their name and choose a different option: Viewer, Commenter, or Editor.</li>
-<li>To revoke the sharing permission, click on the Remove button next to their name. They will no longer be able to access the file.</li>
-<li>To revoke the public link, click on Change next to Anyone with the link and choose Restricted. Only the people that you have invited by email will be able to access the file.</li>
-<li>Click on Done to save your changes.</li>
-</ol>
- <h2>How to Download Files from Google Drive</h2>
-<h3>How to Download a Single File or Folder</h3>
-<p>If you want to download a single file or folder from Google Drive to your computer or mobile device, you can do so by following these steps:</p>
-<ol>
-<li>Select the file or folder that you want to download on Google Drive.</li>
-<li>Click on the More actions button (three vertical dots) on the top right corner of the screen.</li>
-<li>Click on Download. The file or folder will start downloading to your device.</li>
-<li>You can also right-click on the file or folder and choose Download from the menu.</li>
-</ol>
- <h3>How to Download Multiple Files or Folders</h3>
-<p>If you want to download multiple files or folders from Google Drive to your computer or mobile device, you can do so by following these steps:</p>
-<ol>
-<li>Select the files or folders that you want to download on Google Drive. You can use the Shift or Ctrl keys to select multiple items.</li>
-<li>Right-click on any of the selected items and choose Download from the menu. The files or folders will be compressed into a ZIP file and start downloading to your device.</li>
-<li>You can also click on the More actions button (three vertical dots) on the top right corner of the screen and choose Download from there.</li>
-</ol>
- <h3>How to Download Files that are Locked or Restricted</h3>
-<p>Sometimes, you might encounter files that are locked or restricted by their owners. This means that you cannot view, comment, or edit them without their permission. However, you might still be able to download them if they have enabled that option. Here's how:</p>
-<ol>
-<li>Select the file that you want to download on Google Drive.</li>
-<li>Click on the Request access button on the top right corner of the screen.</li>
-<li>A pop-up window will appear where you can enter your email address and a message to request access from the owner of the file.</li>
-<li>Click on Send request. The owner of the file will receive an email notification with your request.</li>
-<li>If they approve your request, you will receive an email notification with a link to access and download the file.</li>
-</ol>
- <h2>How to Troubleshoot Google Drive Issues</h2>
-<h3>How to Fix Common Issues with Google Drive</h3>
-<p>Sometimes, you might face some issues with Google Drive, such as slow loading, syncing errors, missing files, etc. Here are some of the common issues and how to fix them:</p>
- <table border="1">
-<tr><th>Issue</th><th>Solution</th></tr>
-<tr><td>Google Drive is not loading or is slow</td><td>Check your internet connection and make sure it is stable and fast. Try reloading the page or restarting your browser. Clear your browser's cache and cookies. Disable any extensions or plugins that might interfere with Google Drive. Update your browser to the latest version.</td></tr>
-<tr><td>Google Drive is not syncing or is showing errors</td><td>Check your internet connection and make sure it is stable and fast. Try pausing and resuming the sync process. Check your storage space and make sure you have enough room for your files. Check your firewall and antivirus settings and make sure they are not blocking Google Drive. Update your Google Drive app to the latest version.</td></tr>
-<tr><td>Google Drive files are missing or deleted</td><td>Check your trash folder and see if your files are there. You can restore them by right-clicking and choosing Restore from trash. Check your activity panel and see if someone else has moved or deleted your files. You can undo their actions by clicking on Undo changes. Contact Google support and request a file recovery within 25 days of deletion.</td></tr>
-</table>
- <h3>How to Fix Error Messages and Corrupted Files</h3>
-<p>Sometimes, you might encounter error messages or corrupted files when trying to download files from Google Drive. Here are some of the common error messages and corrupted files and how to fix them:</p>
- <table border="1">
-<tr><th>Error Message</th><th>Solution</th></tr>
-<tr><td>Sorry, you can't view or download this file at this time.</td><td>This error message usually means that the file has exceeded its download limit. You can try again later or make a copy of the file in your own Google Drive and download it from there. To make a copy, open the file link, sign in with your Google account, click on the More actions button (three vertical dots), and choose Make a copy.</td></tr>
-<tr><td>Unable to access document. Please try again later.</td><td>This error message usually means that there is a temporary issue with Google Drive or the file itself. You can try reloading the page or restarting your browser. You can also check the Google Workspace Status Dashboard to see if there are any service disruptions or outages.</td></tr>
-<tr><td>The file is corrupt and cannot be opened.</td><td>This error message usually means that the file has been damaged or corrupted during the download process. You can try downloading the file again or using a different browser. You can also try using a file repair tool or software to fix the corrupted file.</td></tr>
-</table>
- <h3>How to Send Feedback and Report Bugs to Google</h3>
-<p>If you encounter any issues or bugs with Google Drive that are not covered by the solutions above, you can send feedback and report them to Google. This will help Google improve their service and fix any problems. Here's how:</p>
-<ol>
-<li>Click on the Help button (question mark icon) on the top right corner of the screen.</li>
-<li>Click on Send feedback.</li>
-<li>A pop-up window will appear where you can describe your issue or bug in detail. You can also include a screenshot if you want.</li>
-<li>Click on Send. Google will receive your feedback and work on resolving your issue or bug.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download files from Google Drive easily and quickly. We have also covered some of the benefits and features of using Google Drive, as well as how to share, troubleshoot, and send feedback on Google Drive.</p>
-<p>Google Drive is a powerful and convenient tool that can help you store, manage, and work with your files online. Whether you want to back up your data, work offline, or share your files with others, Google Drive can help you do it all.</p>
-<p>We hope you found this article helpful and informative. If you have any questions or comments, feel free to leave them below. We would love to hear from you!</p>
- <h2>FAQs</h2>
-<h3>Q: How much storage space do I get with Google Drive?</h3>
-<p>A: You get 15 GB of free storage space with a Google account. You can upgrade to more storage with a Google One subscription.</p>
- <h3>Q: How can I access my files offline on Google Drive?</h3>
-<p>A: You can turn on offline access for your files on Google Drive. This will allow you to view and edit your files without an internet connection. Learn how to turn on offline access here.</p>
- <h3>Q: How can I sync my files across different devices with Google Drive?</h3>
-<p>A: You can use Drive for desktop, a tool that lets you sync your files between your computer and Google Drive. You can also use the Google Drive app on your mobile device to access your files. Learn how to sync your files here.</p>
- <h3>Q: How can I create and collaborate on documents, spreadsheets, and presentations with Google Drive?</h3>
-<p>A: You can use Google Docs, Sheets, and Slides, cloud-based apps that let you create and edit documents, spreadsheets, and presentations online. You can also share and comment on these files with others in real time. Learn how to use these apps here.</p>
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-<p>A: You can make a direct download link for your files by using a custom link that contains your file ID. This will bypass the web page that opens when you click a Google Drive file link and start the file download immediately. Learn how to make a direct download link here.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Archero Mod APK iOS Everything You Need to Know About the Game and the Hack.md

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-<h1>Archero Mod Apk Ios: A Guide to the Ultimate Action Game</h1>
-<p>If you are looking for a fun and challenging action game that will test your skills and reflexes, you should try <strong>Archero</strong>. This is a game where you play as a lone archer who has to fight against waves of enemies and obstacles in different worlds. You can also customize your hero with various skills and equipment to make him more powerful and survive longer.</p>
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- <h2>What is Archero?</h2>
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-<p>The gameplay of Archero is simple but challenging. You control your hero with a virtual joystick on the screen, and he will automatically shoot arrows at the nearest enemy when you stop moving. Your goal is to clear each level by defeating all the enemies and avoiding their attacks. You will also encounter different obstacles, such as spikes, traps, walls, and portals, that will make your journey more difficult.</p>
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- <h3>How to dodge enemy attacks</h3>
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-<li>Learn the patterns and behaviors of different enemies and bosses. This will help you anticipate their movements and attacks, and react accordingly.</li>
-<li>Use obstacles and walls to your advantage. You can hide behind them or use them to block enemy projectiles. However, be careful not to trap yourself or get cornered by enemies.</li>
-<li>Use skills that will help you dodge enemy attacks, such as invincibility, dash, teleport, slow time, or freeze. These skills will give you a temporary edge over your enemies and allow you to escape from dangerous situations.</li>
-</ul>
- <h3>How to summon and deal with the devil</h3>
-<p>Another interesting feature of Archero is the devil, who is a mysterious character that will appear after you defeat a boss. The devil will offer you a deal: he will give you a powerful skill in exchange for some of your health. You can either accept or decline his offer. Here are some tips on how to summon and deal with the devil:</p>
-<ul>
-<li>To summon the devil, you need to defeat a boss without taking any damage. This means that you need to dodge all the boss's attacks and not get hit by any enemies or obstacles along the way.</li>
-<li>The skill that the devil offers you is random, but it is usually one of the best skills in the game. Some examples are multishot, ricochet, diagonal arrows, extra life, or death nova.</li>
-<li>The amount of health that the devil takes from you is also random, but it is usually around 20% to 40% of your maximum health. This means that you need to weigh the pros and cons of accepting his offer. If you have a lot of health or a healing skill, you may be able to afford the trade. However, if you have low health or no healing skill, you may want to decline his offer.</li>
-<li>The devil's offer is optional, and you can always choose to skip it and get a normal skill instead. However, if you skip the devil's offer, you will not be able to summon him again in the same run.</li>
-</ul>
- <h3>How to choose the best skills and equipment</h3>
-<p>Another crucial aspect of Archero is choosing the best skills and equipment for your hero. This will affect your damage, defense, speed, and overall performance in the game. Here are some tips on how to choose the best skills and equipment:</p>
-<ul>
-<li>Choose skills that complement your playstyle and preferences. For example, if you like to move around a lot, you may want to choose skills that increase your movement speed, dash distance, or invincibility duration. If you like to stay in one spot and shoot from afar, you may want to choose skills that increase your attack range, damage, or critical rate.</li>
-<li>Choose skills that synergize with each other and create powerful combinations. For example, multishot and ricochet are two skills that work well together, as they allow you to shoot multiple arrows that bounce off enemies and walls. Another example is diagonal arrows and piercing shot, which allow you to shoot arrows in four directions that go through enemies.</li>
-<li>Choose equipment that suits your hero and skills. For example, if you have a lot of skills that increase your arrow count or elemental effects, you may want to use a bow that has a high attack speed or damage. If you have a lot of skills that increase your defense or health, you may want to use an armor that has a high health or resistance.</li>
-<li>Upgrade your equipment regularly with scrolls and coins. This will improve their stats and make them more effective. You can also fuse equipment of the same type and rarity to create a higher rarity equipment with better stats.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Archero is a fun and challenging action game that will keep you entertained for hours. You can play as a lone archer who has to fight against hordes of enemies and obstacles in different worlds. You can also customize your hero with various skills and equipment to make him more powerful and survive longer.</p>
-<p>If you want to enjoy the game without any limitations or restrictions, you can use Archero mod apk ios. This is a modified version of the game that allows you to access all the features and content for free. You can also get unlimited coins, gems, energy, and other resources to upgrade your hero and unlock new items.</p>
-<p>However, using Archero mod apk ios also comes with some risks and precautions that you should be aware of. You may violate the terms and conditions of the game developer and publisher, which may result in your account being banned or suspended. You may also expose your device to malware or viruses that may harm your system or steal your personal information. You may also lose some of the fun and challenge of the game by using the mod apk, as it may make the game too easy or boring for you.</p>
-<p>Therefore, you should always use Archero mod apk ios responsibly and moderately, and not abuse its features or advantages. You should also download the mod apk from a trusted source and scan it with an antivirus before installing it. You should also check the requirements and specifications of the mod apk before downloading it.</p>
-<p>We hope this article has helped you understand what Archero is, how to download and install Archero mod apk ios, why you should use it, and some tips and tricks for playing the game. Have fun playing Archero!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Archero mod apk ios:</p>
- <h4>Q: Is Archero mod apk ios safe to use?</h4>
-<p>A: Archero mod apk ios is safe to use as long as you download it from a reliable source and scan it with an antivirus before installing it. However, you should always be careful when using modified or hacked apps on your device, as they may contain malware or viruses that may harm your system or steal your personal information.</p>
- <h4>Q: Is Archero mod apk ios legal to use?</h4>
-<p>A: Archero mod apk ios is not legal to use, as it violates the terms and conditions of the game developer and publisher. Using Archero mod apk ios may result in your account being banned or suspended by the game authorities. You may also face legal consequences if you are caught using Archero mod apk ios.</p>
- <h4>Q: How do I update Archero mod apk ios?</h4>
-<p>A: To update Arch ero mod apk ios, you need to download the latest version of the mod apk from the same source that you downloaded it from before. You can also check for updates from the app installer that you used to install the mod apk. You should always update the mod apk to ensure its compatibility and functionality with the latest version of the game.</p>
- <h4>Q: How do I uninstall Archero mod apk ios?</h4>
-<p>A: To uninstall Archero mod apk ios, you need to delete the app from your device. You can do this by long-pressing the app icon and tapping on the delete option. You can also delete the app from your device settings by going to Settings > General > iPhone Storage > Archero > Delete App.</p>
- <h4>Q: Can I play Archero mod apk ios online with other players?</h4>
-<p>A: Archero mod apk ios does not support online multiplayer mode, as it may cause conflicts or errors with the game servers. You can only play Archero mod apk ios offline with your device. However, you can still enjoy the game's features and content without any limitations or restrictions.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Download Aim King 8 Ball Pool APK and Become a Pro in No Time.md

@@ -1,136 +0,0 @@
-
-<h1>Aim King 8 Ball Pool APK: A Guide to the Best Tool for 8 Ball Pool Players</h1>
-<p>If you are a fan of 8 ball pool, you might have heard of Aim King 8 Ball Pool APK, a tool that can help you improve your skills and win more games. But what is it exactly, and how does it work? In this article, we will answer these questions and more, and give you some tips and tricks on how to use it effectively. Let's get started!</p>
-<h2>aim king 8 ball pool apk</h2><br /><p><b><b>Download</b> &#128504; <a href="https://jinyurl.com/2uNMUn">https://jinyurl.com/2uNMUn</a></b></p><br /><br />
- <h2>What is Aim King 8 Ball Pool APK?</h2>
-<p>Aim King 8 Ball Pool APK is an app that you can download and install on your Android device, and use it while playing 8 ball pool. It is a guide tool that uses AI image recognition technology to display extended guidelines in real time, making your shots more accurate and precise. It also allows you to adjust the sensitivity and accuracy settings according to your preference. With Aim King 8 Ball Pool APK, you can become a master of 8 ball pool in no time!</p>
- <h3>Features of Aim King 8 Ball Pool APK</h3>
-<p>Some of the features that make Aim King 8 Ball Pool APK stand out from other similar tools are:</p>
-<ul>
-<li>It is compatible with all versions of 8 ball pool, including the latest one.</li>
-<li>It is easy to use, with a simple and user-friendly interface.</li>
-<li>It does not require root access or any other permissions.</li>
-<li>It does not interfere with the game performance or graphics.</li>
-<li>It does not contain any ads or malware.</li>
-</ul>
- <h3>How to Download and Install Aim King 8 Ball Pool APK</h3>
-<p>To download and install Aim King 8 Ball Pool APK on your Android device, follow these steps:</p>
-<ol>
-<li>Go to [this link](^1^) and click on the download button.</li>
-<li>Wait for the file to be downloaded on your device.</li>
-<li>Open the file manager app on your device and locate the downloaded file.</li>
-<li>Tap on the file and allow the installation from unknown sources if prompted.</li>
-<li>Wait for the installation to be completed.</li>
-<li>Launch the app and enjoy!</li>
-</ol>
- <h2>Why Use Aim King 8 Ball Pool APK?</h2>
-<p>You might be wondering why you should use Aim King 8 Ball Pool APK instead of playing the game normally. Well, there are some good reasons why you should consider using this tool, as well as some drawbacks that you should be aware of. Let's take a look at them.</p>
- <h3>Benefits of Using Aim King 8 Ball Pool APK</h3>
-<p>Some of the benefits that you can get from using Aim King 8 Ball Pool APK are:</p>
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-<ul>
-<li>You can improve your skills and confidence in playing 8 ball pool.</li>
-<li>You can win more games and earn more coins and rewards.</li>
-<li>You can challenge your friends and other players online and show off your abilities.</li>
-<li>You can have more fun and enjoyment while playing the game.</li>
-</ul>
- <h3>Drawbacks of Using Aim King 8 Ball Pool APK</h3>
-<p>Some of the drawbacks that you should be aware of when using Aim King 8 Ball Pool APK are:</p>
-<ul>
-<li>You might lose the thrill and challenge of playing the game naturally.</li>
-<li>You might get addicted to using the tool and lose interest in the game.</li>
-<li>You might get detected and banned by the game developers if you use the tool excessively or carelessly.</li>
-<li>You might face ethical issues and criticism from other players who consider using the tool as cheating.</li>
-</ul>
- <h2>Tips and Tricks for Using Aim King 8 Ball Pool APK</h2>
-<p>Now that you know what Aim King 8 Ball Pool APK is and what are its pros and cons, you might want to know how to use it effectively and safely. Here are some tips and tricks that you can follow:</p>
- <h3>How to Use the Extended Guidelines Feature</h3>
-<p>The extended guidelines feature is the main function of Aim King 8 Ball Pool APK. It shows you the trajectory of your cue ball and the target ball, as well as the angle and distance of your shot. To use this feature, you need to:</p>
-<ol>
-<li>Open the app and grant it permission to access your screen.</li>
-<li>Open the game and start a match.</li>
-<li>Tap on the Aim King icon on your screen to activate the extended guidelines.</li>
-<li>Aim your shot using the guidelines and adjust your power and spin as needed.</li>
-<li>Tap on the Aim King icon again to deactivate the extended guidelines.</li>
-<li>Release your shot and watch the result.</li>
-</ol>
- <h3>How to Adjust the Sensitivity and Accuracy Settings</h3>
-<p>The sensitivity and accuracy settings allow you to customize the performance of Aim King 8 Ball Pool APK according to your preference. The sensitivity setting determines how responsive the app is to your touch, while the accuracy setting determines how precise the app is in calculating the guidelines. To adjust these settings, you need to:</p>
-<ol>
-<li>Open the app and tap on the settings icon on the top right corner.</li>
-<li>Slide the sensitivity bar to increase or decrease the sensitivity level.</li>
-<li>Slide the accuracy bar to increase or decrease the accuracy level.</li>
-<li>Tap on the save button to apply your changes.</li>
-</ol>
- <h3>How to Avoid Detection and Ban by the Game Developers</h3>
-<p>One of the risks of using Aim King 8 Ball Pool APK is getting detected and banned by the game developers, who might consider it as a violation of their terms of service. To avoid this, you need to:</p>
-<ul>
-<li>Use the tool sparingly and moderately, and not in every match or shot.</li>
-<li>Use the tool only in offline mode or in private matches with your friends.</li>
-<li>Do not brag or boast about using the tool in public chats or forums.</li>
-<li>Do not update the game or the tool unless you are sure that they are compatible.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Aim King 8 Ball Pool APK is a great tool for 8 ball pool players who want to improve their skills and win more games. It offers extended guidelines, sensitivity and accuracy settings, and other features that can enhance your gameplay. However, it also has some drawbacks, such as losing the challenge, getting addicted, getting banned, and facing ethical issues. Therefore, you should use it wisely and responsibly, and not rely on it too much. Remember, it is just a tool, not a magic wand!</p>
- <h3>Summary of the Main Points</h3>
-<p>In this article, we have discussed:</p>
-<ul>
-<li>What is Aim King 8 Ball Pool APK and how does it work?</li>
-<li>Why use Aim King 8 Ball Pool APK and what are its benefits and drawbacks?</li>
-<li>How to use Aim King 8 Ball Pool APK effectively and safely?</li>
-</ul>
- <h3>Call to Action for the Readers</h3>
-<p>If you are interested in trying out Aim King 8 Ball Pool APK, you can download it from [this link] and follow the instructions we have provided. However, if you are not comfortable with using it or you prefer playing the game naturally, you can skip it and enjoy 8 ball pool without any tools. The choice is yours!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Aim King 8 Ball Pool APK:</p>
- <h4>Is Aim King 8 Ball Pool APK safe to use?</h4>
-<p>Aim King 8 Ball Pool APK is safe to use as long as you download it from a trusted source and follow our tips on how to avoid detection and ban by the game developers. However, you should always be careful when using any third-party app that modifies the game, as there is always a possibility of malware or virus infection, data theft, or account suspension.</p>
- <h4>Does Aim King 8 Ball Pool APK work on iOS devices?</h4>
-<p>No, Aim King 8 Ball Pool APK is only compatible with Android devices. If you have an iOS device, you will not be able to use this tool. However, there might be other similar tools that work on iOS devices, but we cannot guarantee their safety or effectiveness.</p>
- <h4>Can I use Aim King 8 Ball Pool APK with other 8 ball pool tools or mods?</h4>
-<p>We do not recommend using Aim King 8 Ball Pool APK with other 8 ball pool tools or mods, as they might conflict with each other and cause errors or glitches in the game. Moreover, using multiple tools or mods might increase the chance of getting detected and banned by the game developers. Therefore, you should use Aim King 8 Ball Pool APK alone and disable any other tools or mods that you have installed.</p>
- <h4>How can I contact the developer of Aim King 8 Ball Pool APK?</h4>
-<p>If you have any questions, feedback, or suggestions about Aim King 8 Ball Pool APK, you can contact the developer through their email address: [email protected]. They will try to respond to your queries as soon as possible.</p>
- <h4>Where can I find more information about Aim King 8 Ball Pool APK?</h4>
-<p>If you want to learn more about Aim King 8 Ball Pool APK, you can visit their official website: [this link]. There, you can find more details about the app, such as its features, screenshots, videos, reviews, and updates. You can also join their community and interact with other users who use the app.</p> 401be4b1e0<br />
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spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/backbones/iresnet.py

@@ -1,187 +0,0 @@
-import torch
-from torch import nn
-
-__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200']
-
-
-def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes,
-                     out_planes,
-                     kernel_size=3,
-                     stride=stride,
-                     padding=dilation,
-                     groups=groups,
-                     bias=False,
-                     dilation=dilation)
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    """1x1 convolution"""
-    return nn.Conv2d(in_planes,
-                     out_planes,
-                     kernel_size=1,
-                     stride=stride,
-                     bias=False)
-
-
-class IBasicBlock(nn.Module):
-    expansion = 1
-    def __init__(self, inplanes, planes, stride=1, downsample=None,
-                 groups=1, base_width=64, dilation=1):
-        super(IBasicBlock, self).__init__()
-        if groups != 1 or base_width != 64:
-            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
-        if dilation > 1:
-            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
-        self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
-        self.conv1 = conv3x3(inplanes, planes)
-        self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
-        self.prelu = nn.PReLU(planes)
-        self.conv2 = conv3x3(planes, planes, stride)
-        self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-        out = self.bn1(x)
-        out = self.conv1(out)
-        out = self.bn2(out)
-        out = self.prelu(out)
-        out = self.conv2(out)
-        out = self.bn3(out)
-        if self.downsample is not None:
-            identity = self.downsample(x)
-        out += identity
-        return out
-
-
-class IResNet(nn.Module):
-    fc_scale = 7 * 7
-    def __init__(self,
-                 block, layers, dropout=0, num_features=512, zero_init_residual=False,
-                 groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
-        super(IResNet, self).__init__()
-        self.fp16 = fp16
-        self.inplanes = 64
-        self.dilation = 1
-        if replace_stride_with_dilation is None:
-            replace_stride_with_dilation = [False, False, False]
-        if len(replace_stride_with_dilation) != 3:
-            raise ValueError("replace_stride_with_dilation should be None "
-                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
-        self.groups = groups
-        self.base_width = width_per_group
-        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
-        self.prelu = nn.PReLU(self.inplanes)
-        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
-        self.layer2 = self._make_layer(block,
-                                       128,
-                                       layers[1],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[0])
-        self.layer3 = self._make_layer(block,
-                                       256,
-                                       layers[2],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[1])
-        self.layer4 = self._make_layer(block,
-                                       512,
-                                       layers[3],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[2])
-        self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
-        self.dropout = nn.Dropout(p=dropout, inplace=True)
-        self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
-        self.features = nn.BatchNorm1d(num_features, eps=1e-05)
-        nn.init.constant_(self.features.weight, 1.0)
-        self.features.weight.requires_grad = False
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.normal_(m.weight, 0, 0.1)
-            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-        if zero_init_residual:
-            for m in self.modules():
-                if isinstance(m, IBasicBlock):
-                    nn.init.constant_(m.bn2.weight, 0)
-
-    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
-        downsample = None
-        previous_dilation = self.dilation
-        if dilate:
-            self.dilation *= stride
-            stride = 1
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                conv1x1(self.inplanes, planes * block.expansion, stride),
-                nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
-            )
-        layers = []
-        layers.append(
-            block(self.inplanes, planes, stride, downsample, self.groups,
-                  self.base_width, previous_dilation))
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(
-                block(self.inplanes,
-                      planes,
-                      groups=self.groups,
-                      base_width=self.base_width,
-                      dilation=self.dilation))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        with torch.cuda.amp.autocast(self.fp16):
-            x = self.conv1(x)
-            x = self.bn1(x)
-            x = self.prelu(x)
-            x = self.layer1(x)
-            x = self.layer2(x)
-            x = self.layer3(x)
-            x = self.layer4(x)
-            x = self.bn2(x)
-            x = torch.flatten(x, 1)
-            x = self.dropout(x)
-        x = self.fc(x.float() if self.fp16 else x)
-        x = self.features(x)
-        return x
-
-
-def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
-    model = IResNet(block, layers, **kwargs)
-    if pretrained:
-        raise ValueError()
-    return model
-
-
-def iresnet18(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2], pretrained,
-                    progress, **kwargs)
-
-
-def iresnet34(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained,
-                    progress, **kwargs)
-
-
-def iresnet50(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,
-                    progress, **kwargs)
-
-
-def iresnet100(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,
-                    progress, **kwargs)
-
-
-def iresnet200(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained,
-                    progress, **kwargs)
-

spaces/AI-Dashboards/CP.Matplotlib.NetworkX.Streamlit.PyVis.Graphviz/got.py

@@ -1,71 +0,0 @@
-import networkx as nx
-import matplotlib.pyplot as plt
-from pyvis.network import Network
-import pandas as pd
-import streamlit as st
-
-
-def got_func(physics):
-  got_net = Network(height="600px", width="100%", font_color="black",heading='Game of Thrones Graph')
-
-# set the physics layout of the network
-  got_net.barnes_hut()
-  got_data = pd.read_csv("stormofswords.csv")
-  #got_data = pd.read_csv("stormofswords.csv")
-  #got_data.rename(index={0: "Source", 1: "Target", 2: "Weight"}) 
-  sources = got_data['Source']
-  targets = got_data['Target']
-  weights = got_data['Weight']
-
-  edge_data = zip(sources, targets, weights)
-
-  for e in edge_data:
-    src = e[0]
-    dst = e[1]
-    w = e[2]
-
-    got_net.add_node(src, src, title=src)
-    got_net.add_node(dst, dst, title=dst)
-    got_net.add_edge(src, dst, value=w)
-
-  neighbor_map = got_net.get_adj_list()
-
-# add neighbor data to node hover data
-  for node in got_net.nodes:
-    node["title"] += " Neighbors:<br>" + "<br>".join(neighbor_map[node["id"]])
-    node["value"] = len(neighbor_map[node["id"]])
-  if physics:
-    got_net.show_buttons(filter_=['physics'])
-  got_net.show("gameofthrones.html")
-  
-
-def simple_func(physics): 
-  nx_graph = nx.cycle_graph(10)
-  nx_graph.nodes[1]['title'] = 'Number 1'
-  nx_graph.nodes[1]['group'] = 1
-  nx_graph.nodes[3]['title'] = 'I belong to a different group!'
-  nx_graph.nodes[3]['group'] = 10
-  nx_graph.add_node(20, size=20, title='couple', group=2)
-  nx_graph.add_node(21, size=15, title='couple', group=2)
-  nx_graph.add_edge(20, 21, weight=5)
-  nx_graph.add_node(25, size=25, label='lonely', title='lonely node', group=3)
-
-
-  nt = Network("500px", "500px",notebook=True,heading='')
-  nt.from_nx(nx_graph)
-  #physics=st.sidebar.checkbox('add physics interactivity?')
-  if physics:
-    nt.show_buttons(filter_=['physics'])
-  nt.show('test.html')
-
-
-def karate_func(physics): 
-  G = nx.karate_club_graph()
-
-
-  nt = Network("500px", "500px",notebook=True,heading='Zachary’s Karate Club graph')
-  nt.from_nx(G)
-  #physics=st.sidebar.checkbox('add physics interactivity?')
-  if physics:
-    nt.show_buttons(filter_=['physics'])
-  nt.show('karate.html')

spaces/AIWaves/Software_Company/src/agents/Component/PromptComponent.py

@@ -1,133 +0,0 @@
-from abc import abstractmethod
-
-
-class PromptComponent:
-    def __init__(self):
-        pass
-
-    @abstractmethod
-    def get_prompt(self, agent):
-        pass
-
-class TaskComponent(PromptComponent):
-    def __init__(self, task):
-        super().__init__()
-        self.task = task
-
-    def get_prompt(self, agent):
-        return f"""The task you need to execute is: <task>{self.task}</task>.\n"""
-
-
-class OutputComponent(PromptComponent):
-    def __init__(self, output):
-        super().__init__()
-        self.output = output
-
-    def get_prompt(self, agent):
-        return f"""Please contact the above to extract <{self.output}> and </{self.output}>, \
-            do not perform additional output, please output in strict accordance with the above format!\n"""
-
-
-class SystemComponent(PromptComponent):
-    def __init__(self,system_prompt):
-        super().__init__()
-        self.system_prompt = system_prompt
-    
-    def get_prompt(self, agent):
-        return self.system_prompt
-
-class LastComponent(PromptComponent):
-    def __init__(self, last_prompt):
-        super().__init__()
-        self.last_prompt = last_prompt
-
-    def get_prompt(self, agent):
-        return self.last_prompt
-
-
-class StyleComponent(PromptComponent):
-    """
-    角色、风格组件
-    """
-
-    def __init__(self, role):
-        super().__init__()
-        self.role = role
-
-    def get_prompt(self, agent):
-        name = agent.name
-        style = agent.style
-        return f"""Now your role is:\n<role>{self.role}</role>, your name is:\n<name>{name}</name>. \
-            You need to follow the output style:\n<style>{style}</style>.\n"""
-
-
-class RuleComponent(PromptComponent):
-    def __init__(self, rule):
-        super().__init__()
-        self.rule = rule
-
-    def get_prompt(self, agent):
-        return f"""The rule you need to follow is:\n<rule>{self.rule}</rule>.\n"""
-
-
-class DemonstrationComponent(PromptComponent):
-    """
-    input a list,the example of answer.
-    """
-
-    def __init__(self, demonstrations):
-        super().__init__()
-        self.demonstrations = demonstrations
-
-    def add_demonstration(self, demonstration):
-        self.demonstrations.append(demonstration)
-
-    def get_prompt(self, agent):
-        prompt = "Here are demonstrations you can refer to:\n<demonstrations>"
-        for demonstration in self.demonstrations:
-            prompt += "\n" + demonstration
-        prompt += "</demonstrations>\n"
-        return prompt
-
-
-class CoTComponent(PromptComponent):
-    """
-    input a list,the example of answer.
-    """
-
-    def __init__(self, demonstrations):
-        super().__init__()
-        self.demonstrations = demonstrations
-
-    def add_demonstration(self, demonstration):
-        self.demonstrations.append(demonstration)
-
-    def get_prompt(self, agent):
-        prompt = "You need to think in detail before outputting, the thinking case is as follows:\n<demonstrations>"
-        for demonstration in self.demonstrations:
-            prompt += "\n" + demonstration
-        prompt += "</demonstrations>\n"
-        return prompt
-
-
-class CustomizeComponent(PromptComponent):
-    """
-    Custom template
-    template(str) : example: "i am {}"
-    keywords(list) : example : ["name"]  
-    example : agent.environment.shared_memory["name"] = "Lilong"
-    the component will get the keyword attribute from the environment, and then add it to the template.
-    Return : "i am Lilong"
-    """
-    def __init__(self, template, keywords) -> None:
-        super().__init__()
-        self.template = template
-        self.keywords = keywords
-
-    def get_prompt(self, agent):
-        template_keyword = {}
-        for keyword in self.keywords:
-            
-            current_keyword = agent.environment.shared_memory[keyword]
-            template_keyword[keyword] = current_keyword
-        return self.template.format(**template_keyword)

spaces/AP123/dreamgaussian/cam_utils.py

@@ -1,146 +0,0 @@
-import numpy as np
-from scipy.spatial.transform import Rotation as R
-
-import torch
-
-def dot(x, y):
-    if isinstance(x, np.ndarray):
-        return np.sum(x * y, -1, keepdims=True)
-    else:
-        return torch.sum(x * y, -1, keepdim=True)
-
-
-def length(x, eps=1e-20):
-    if isinstance(x, np.ndarray):
-        return np.sqrt(np.maximum(np.sum(x * x, axis=-1, keepdims=True), eps))
-    else:
-        return torch.sqrt(torch.clamp(dot(x, x), min=eps))
-
-
-def safe_normalize(x, eps=1e-20):
-    return x / length(x, eps)
-
-
-def look_at(campos, target, opengl=True):
-    # campos: [N, 3], camera/eye position
-    # target: [N, 3], object to look at
-    # return: [N, 3, 3], rotation matrix
-    if not opengl:
-        # camera forward aligns with -z
-        forward_vector = safe_normalize(target - campos)
-        up_vector = np.array([0, 1, 0], dtype=np.float32)
-        right_vector = safe_normalize(np.cross(forward_vector, up_vector))
-        up_vector = safe_normalize(np.cross(right_vector, forward_vector))
-    else:
-        # camera forward aligns with +z
-        forward_vector = safe_normalize(campos - target)
-        up_vector = np.array([0, 1, 0], dtype=np.float32)
-        right_vector = safe_normalize(np.cross(up_vector, forward_vector))
-        up_vector = safe_normalize(np.cross(forward_vector, right_vector))
-    R = np.stack([right_vector, up_vector, forward_vector], axis=1)
-    return R
-
-
-# elevation & azimuth to pose (cam2world) matrix
-def orbit_camera(elevation, azimuth, radius=1, is_degree=True, target=None, opengl=True):
-    # radius: scalar
-    # elevation: scalar, in (-90, 90), from +y to -y is (-90, 90)
-    # azimuth: scalar, in (-180, 180), from +z to +x is (0, 90)
-    # return: [4, 4], camera pose matrix
-    if is_degree:
-        elevation = np.deg2rad(elevation)
-        azimuth = np.deg2rad(azimuth)
-    x = radius * np.cos(elevation) * np.sin(azimuth)
-    y = - radius * np.sin(elevation)
-    z = radius * np.cos(elevation) * np.cos(azimuth)
-    if target is None:
-        target = np.zeros([3], dtype=np.float32)
-    campos = np.array([x, y, z]) + target  # [3]
-    T = np.eye(4, dtype=np.float32)
-    T[:3, :3] = look_at(campos, target, opengl)
-    T[:3, 3] = campos
-    return T
-
-
-class OrbitCamera:
-    def __init__(self, W, H, r=2, fovy=60, near=0.01, far=100):
-        self.W = W
-        self.H = H
-        self.radius = r  # camera distance from center
-        self.fovy = np.deg2rad(fovy)  # deg 2 rad
-        self.near = near
-        self.far = far
-        self.center = np.array([0, 0, 0], dtype=np.float32)  # look at this point
-        self.rot = R.from_matrix(np.eye(3))
-        self.up = np.array([0, 1, 0], dtype=np.float32)  # need to be normalized!
-
-    @property
-    def fovx(self):
-        return 2 * np.arctan(np.tan(self.fovy / 2) * self.W / self.H)
-
-    @property
-    def campos(self):
-        return self.pose[:3, 3]
-
-    # pose (c2w)
-    @property
-    def pose(self):
-        # first move camera to radius
-        res = np.eye(4, dtype=np.float32)
-        res[2, 3] = self.radius  # opengl convention...
-        # rotate
-        rot = np.eye(4, dtype=np.float32)
-        rot[:3, :3] = self.rot.as_matrix()
-        res = rot @ res
-        # translate
-        res[:3, 3] -= self.center
-        return res
-
-    # view (w2c)
-    @property
-    def view(self):
-        return np.linalg.inv(self.pose)
-
-    # projection (perspective)
-    @property
-    def perspective(self):
-        y = np.tan(self.fovy / 2)
-        aspect = self.W / self.H
-        return np.array(
-            [
-                [1 / (y * aspect), 0, 0, 0],
-                [0, -1 / y, 0, 0],
-                [
-                    0,
-                    0,
-                    -(self.far + self.near) / (self.far - self.near),
-                    -(2 * self.far * self.near) / (self.far - self.near),
-                ],
-                [0, 0, -1, 0],
-            ],
-            dtype=np.float32,
-        )
-
-    # intrinsics
-    @property
-    def intrinsics(self):
-        focal = self.H / (2 * np.tan(self.fovy / 2))
-        return np.array([focal, focal, self.W // 2, self.H // 2], dtype=np.float32)
-
-    @property
-    def mvp(self):
-        return self.perspective @ np.linalg.inv(self.pose)  # [4, 4]
-
-    def orbit(self, dx, dy):
-        # rotate along camera up/side axis!
-        side = self.rot.as_matrix()[:3, 0]
-        rotvec_x = self.up * np.radians(-0.05 * dx)
-        rotvec_y = side * np.radians(-0.05 * dy)
-        self.rot = R.from_rotvec(rotvec_x) * R.from_rotvec(rotvec_y) * self.rot
-
-    def scale(self, delta):
-        self.radius *= 1.1 ** (-delta)
-
-    def pan(self, dx, dy, dz=0):
-        # pan in camera coordinate system (careful on the sensitivity!)
-        self.center += 0.0005 * self.rot.as_matrix()[:3, :3] @ np.array([-dx, -dy, dz])

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnet50_8xb32-fp16-dynamic_in1k.py

@@ -1,4 +0,0 @@
-_base_ = ['./resnet50_8xb32_in1k.py']
-
-# schedule settings
-optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')

spaces/Abhi5ingh/fashionsd/app.py

@@ -1,168 +0,0 @@
-from typing import Optional
-
-import numpy as np
-import cv2
-import streamlit as st
-from PIL import Image
-import os
-import tempfile
-
-from sdfile import PIPELINES, generate
-
-DEFAULT_PROMPT = "belted shirt black belted portrait-collar wrap blouse with black prints"
-DEAFULT_WIDTH, DEFAULT_HEIGHT = 512,512
-OUTPUT_IMAGE_KEY = "output_img"
-LOADED_IMAGE_KEY = "loaded_img"
-
-def get_image(key: str) -> Optional[Image.Image]:
-    if key in st.session_state:
-        return st.session_state[key]
-    return None
-
-def set_image(key:str, img: Image.Image):
-    st.session_state[key] = img
-
-def prompt_and_generate_button(prefix, pipeline_name: PIPELINES, **kwargs):
-    prompt = st.text_area(
-        "Prompt",
-        value = DEFAULT_PROMPT,
-        key = f"{prefix}-prompt"
-    )
-    negative_prompt = st.text_area(
-        "Negative prompt",
-        value = "",
-        key =f"{prefix}-negative_prompt",
-    )
-    col1,col2 =st.columns(2)
-    with col1:
-        steps = st.slider(
-            "Number of inference steps",
-            min_value=1,
-            max_value=200,
-            value=30,
-            key=f"{prefix}-inference-steps",
-        )
-    with col2:
-        guidance_scale = st.slider(
-            "Guidance scale",
-            min_value=0.0,
-            max_value=20.0,
-            value= 7.5,
-            step = 0.5,
-            key=f"{prefix}-guidance-scale",
-        )
-    enable_cpu_offload = st.checkbox(
-        "Enable CPU offload if you run out of memory",
-        key =f"{prefix}-cpu-offload",
-        value= False,
-    )
-
-    if st.button("Generate Image", key = f"{prefix}-btn"):
-        with st.spinner("Generating image ..."):
-            image = generate(
-                prompt,
-                pipeline_name,
-                negative_prompt=negative_prompt,
-                num_inference_steps=steps,
-                guidance_scale=guidance_scale,
-                enable_cpu_offload=enable_cpu_offload,
-                **kwargs,
-            )
-            set_image(OUTPUT_IMAGE_KEY,image.copy())
-        st.image(image)
-def width_and_height_sliders(prefix):
-    col1, col2 = st.columns(2)
-    with col1:
-        width = st.slider(
-            "Width",
-            min_value=64,
-            max_value=1600,
-            step=16,
-            value=512,
-            key=f"{prefix}-width",
-        )
-    with col2:
-        height = st.slider(
-            "Height",
-            min_value=64,
-            max_value=1600,
-            step=16,
-            value=512,
-            key=f"{prefix}-height",
-        )
-    return width, height
-
-def image_uploader(prefix):
-    image = st.file_uploader("Image", ["jpg", "png"], key=f"{prefix}-uploader")
-    if image:
-        image = Image.open(image)
-        print(f"loaded input image of size ({image.width}, {image.height})")
-        return image
-
-    return get_image(LOADED_IMAGE_KEY)
-
-def sketching():
-    image = image_uploader("sketch2img")
-    
-    if not image:
-        return None,None
-        
-    with tempfile.TemporaryDirectory() as temp_dir:
-        temp_image_path = os.path.join(temp_dir, "uploaded_image.jpg")
-        image.save(temp_image_path)
-        
-        image = cv2.imread(temp_image_path)
-        image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
-        image_blur = cv2.GaussianBlur(image,(5,5),0)
-        sketch = cv2.adaptiveThreshold(image_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2)
-        sketch_pil = Image.fromarray(sketch)
-    return sketch_pil
-
-def txt2img_tab():
-    prefix = "txt2img"
-    width, height = width_and_height_sliders(prefix)
-    prompt_and_generate_button(prefix,"txt2img",width=width,height=height)
-
-def sketching_tab():
-    prefix = "sketch2img"
-    col1,col2 = st.columns(2)
-    with col1:
-        image = sketching()
-    with col2:
-        if image:
-            controlnet_conditioning_scale = st.slider(
-                "Strength or dependence on the input sketch",
-                min_value=0.0,
-                max_value= 1.0,
-                value = 0.5,
-                step = 0.05,
-                key=f"{prefix}-controlnet_conditioning_scale",
-            )
-            prompt_and_generate_button(
-                prefix,
-                "sketch2img",
-                image=image,
-                controlnet_conditioning_scale=controlnet_conditioning_scale,
-            )
-
-def main():
-    st.set_page_config(layout="wide")
-    st.title("Fashion-SDX: Playground")
-
-    tab1,tab2 = st.tabs(
-        ["Text to image", "Sketch to image"]
-    )
-    with tab1:
-        txt2img_tab()
-    with tab2:
-        sketching_tab()
-
-    with st.sidebar:
-        st.header("Most Recent Output Image")
-        output_image = get_image((OUTPUT_IMAGE_KEY))
-        if output_image:
-            st.image(output_image)
-        else:
-            st.markdown("no output generated yet")
-if __name__ =="__main__":
-    main()

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/hsladjustpipeline.js

@@ -1,2 +0,0 @@
-import HslAdjustPostFxPipeline from './shaders/hsladjust/HslAdjustPostFxPipeline.js';
-export default HslAdjustPostFxPipeline;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/IsInTouching.js

@@ -1,19 +0,0 @@
-import IsPointerInBounds from '../../../plugins/utils/input/IsPointerInBounds.js';
-import IsGameObject from '../../../plugins/utils/system/IsGameObject.js';
-
-var IsInTouching = function (pointer, gameObject) {
-    if (IsGameObject(pointer) || (typeof (pointer) === 'string')) {
-        gameObject = pointer;
-        pointer = undefined;
-    }
-
-    if (gameObject === undefined) {
-        gameObject = this;
-    } else if (typeof (gameObject) === 'string') {
-        gameObject = this.getElement(gameObject);
-    }
-
-    return IsPointerInBounds(gameObject, pointer);
-}
-
-export default IsInTouching;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/InjectProperties.js

@@ -1,32 +0,0 @@
-var InjectProperties = function (table) {
-    Object.defineProperty(table, 'childOY', {
-        configurable: true,
-        get: function () {
-            return table.tableOY;
-        },
-        set: function (value) {
-            table.tableOY = value;
-        }
-    });
-    Object.defineProperty(table, 'topChildOY', {
-        get: function () {
-            return table.topTableOY;
-        }
-    });
-    Object.defineProperty(table, 'bottomChildOY', {
-        get: function () {
-            return table.bottomTableOY;
-        }
-    });
-    Object.defineProperty(table, 'childVisibleHeight', {
-        get: function () {
-            return table.instHeight;
-        }
-    });
-    Object.defineProperty(table, 'childHeight', {
-        get: function () {
-            return table.tableHeight;
-        }
-    });
-};
-export default InjectProperties;

spaces/AlexWelcing/MusicLM/__init__.py

@@ -1,3 +0,0 @@
-from musiclm_pytorch.musiclm_pytorch import MuLaN, MuLaNEmbedQuantizer, MusicLM
-
-from musiclm_pytorch.musiclm_pytorch import AudioSpectrogramTransformer, TextTransformer

spaces/Ali-Omrani/CCR/app.py

@@ -1,105 +0,0 @@
-import pickle
-import os
-import gradio as gr
-import gradio as gr
-import pandas as pd
-from sentence_transformers import SentenceTransformer, util
-
-def encode_column(model, filename, col_name):
-    df = pd.read_csv(filename)
-    df["embedding"] = list(model.encode(df[col_name]))
-    return df
-
-def item_level_ccr(data_encoded_df, questionnaire_encoded_df):
-    q_embeddings = questionnaire_encoded_df.embedding
-    d_embeddings = data_encoded_df.embedding
-    similarities = util.pytorch_cos_sim(d_embeddings, q_embeddings)
-    for i in range(1,len(questionnaire_encoded_df)+1):
-        data_encoded_df["sim_item_{}".format(i)] = similarities[:, i-1]
-    return data_encoded_df
-
-# encoding questionnaire
-def ccr_wrapper(data_file, data_col, q_file, q_col, model='all-MiniLM-L6-v2'):
-    """
-    Returns a Dataframe that is the content of data_file with one additional column for CCR value per question
-
-    Parameters:
-        data_file (str): path to the file containing user text
-        data_col (str): column that includes user text
-        q_file (str): path to the file containing questionnaires
-        q_col (str): column that includes questions
-        model (str): name of the SBERT model to use for CCR see https://www.sbert.net/docs/pretrained_models.html for full list
-
-    """
-    try:
-        model = SentenceTransformer(model)
-    except:
-        print("model name was not included, using all-MiniLM-L6-v2")
-        model = SentenceTransformer('all-MiniLM-L6-v2')
-
-    questionnaire_filename = q_file.name
-    data_filename = data_file.name
-
-    q_encoded_df = encode_column(model, questionnaire_filename, q_col)
-    data_encoded_df = encode_column(model, data_filename, data_col)
-    ccr_df = item_level_ccr(data_encoded_df, q_encoded_df)
-
-
-    ccr_df.to_csv("ccr_results.csv")
-    return "ccr_results.csv"
-    
-
-
-def read_dataframe(data_file, data_col, q_file, q_col):
-
-    # df = pd.read_csv(data_file.name)
-    return data_file.name
-
-
-
-def single_text_ccr(text, question):
-    model = SentenceTransformer('all-MiniLM-L6-v2')
-    text_embedding = model.encode(text)
-    question_embedding = model.encode(question)
-    return round(util.pytorch_cos_sim(text_embedding, question_embedding).item(),3)
-    
-
-
-
-
-with gr.Blocks() as demo:
-    # gr.Markdown('This is the first page for CCR, info goes here!')
-    gr.Markdown("""<h1><center>Contextual Construct Representations</center></h1>
-    <h3><center>Ali Omrani and Mohammad Atari</center></h3>""")
-    
-    gr.Markdown("""<br><h4>Play around with your items!</h4>""")
-
-    with gr.Row():
-        user_txt = gr.Textbox(label="Input Text", placeholder="Enter your desired text here ...")
-        question = gr.Textbox(label="Question", placeholder="Enter the question text here ...") 
-
-    submit2 = gr.Button("Get CCR for this Text!")
-
-    submit2.click(single_text_ccr, inputs=[user_txt, question], outputs=gr.Textbox(label="CCR Value"))
-    
-    gr.Markdown("""<br><h4>Or process a whole file!</h4>""")
-
-    with  gr.Row():
-        model_name = gr.Dropdown(label="Choose the Model", 
-                                 choices=["all-mpnet-base-v2","multi-qa-mpnet-base-dot-v1", "distiluse-base-multilingual-cased-v2",
-                                          "distiluse-base-multilingual-cased-v1", "paraphrase-MiniLM-L3-v2", "paraphrase-multilingual-MiniLM-L12-v2",
-                                         "paraphrase-albert-small-v2", "paraphrase-multilingual-mpnet-base-v2", "multi-qa-MiniLM-L6-cos-v1",
-                                         "all-MiniLM-L6-v2", "multi-qa-distilbert-cos-v1", "all-MiniLM-L12-v2", "all-distilroberta-v1"])
-    with  gr.Row():
-        with gr.Column():
-            user_data = gr.File(label="Participant Data File")
-            text_col = gr.Textbox(label="Text Column", placeholder="text column ... ")
-        with gr.Column():
-            questionnaire_data = gr.File(label="Questionnaire File")
-            q_col = gr.Textbox(label="Question Column", placeholder="questionnaire column ... ") 
-
-    submit = gr.Button("Get CCR!")
-    
-    outputs=gr.File()
-    submit.click(ccr_wrapper, inputs=[user_data, text_col,questionnaire_data,q_col, model_name], outputs=[outputs])
-demo.launch()

spaces/Alican/pixera/models/__init__.py

@@ -1,67 +0,0 @@
-"""This package contains modules related to objective functions, optimizations, and network architectures.
-
-To add a custom model class called 'dummy', you need to add a file called 'dummy_model.py' and define a subclass DummyModel inherited from BaseModel.
-You need to implement the following five functions:
-    -- <__init__>:                      initialize the class; first call BaseModel.__init__(self, opt).
-    -- <set_input>:                     unpack data from dataset and apply preprocessing.
-    -- <forward>:                       produce intermediate results.
-    -- <optimize_parameters>:           calculate loss, gradients, and update network weights.
-    -- <modify_commandline_options>:    (optionally) add model-specific options and set default options.
-
-In the function <__init__>, you need to define four lists:
-    -- self.loss_names (str list):          specify the training losses that you want to plot and save.
-    -- self.model_names (str list):         define networks used in our training.
-    -- self.visual_names (str list):        specify the images that you want to display and save.
-    -- self.optimizers (optimizer list):    define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an usage.
-
-Now you can use the model class by specifying flag '--model dummy'.
-See our template model class 'template_model.py' for more details.
-"""
-
-import importlib
-from models.base_model import BaseModel
-
-
-def find_model_using_name(model_name):
-    """Import the module "models/[model_name]_model.py".
-
-    In the file, the class called DatasetNameModel() will
-    be instantiated. It has to be a subclass of BaseModel,
-    and it is case-insensitive.
-    """
-    model_filename = "models." + model_name + "_model"
-    modellib = importlib.import_module(model_filename)
-    model = None
-    target_model_name = model_name.replace('_', '') + 'model'
-    for name, cls in modellib.__dict__.items():
-        if name.lower() == target_model_name.lower() \
-           and issubclass(cls, BaseModel):
-            model = cls
-
-    if model is None:
-        print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name))
-        exit(0)
-
-    return model
-
-
-def get_option_setter(model_name):
-    """Return the static method <modify_commandline_options> of the model class."""
-    model_class = find_model_using_name(model_name)
-    return model_class.modify_commandline_options
-
-
-def create_model(opt):
-    """Create a model given the option.
-
-    This function warps the class CustomDatasetDataLoader.
-    This is the main interface between this package and 'train.py'/'test.py'
-
-    Example:
-        >>> from models import create_model
-        >>> model = create_model(opt)
-    """
-    model = find_model_using_name(opt.model)
-    instance = model(opt)
-    print("model [%s] was created" % type(instance).__name__)
-    return instance

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/prior_transformer.py

@@ -1,364 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict, Optional, Union
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
-from .attention import BasicTransformerBlock
-from .attention_processor import AttentionProcessor, AttnProcessor
-from .embeddings import TimestepEmbedding, Timesteps
-from .modeling_utils import ModelMixin
-
-
-@dataclass
-class PriorTransformerOutput(BaseOutput):
-    """
-    The output of [`PriorTransformer`].
-
-    Args:
-        predicted_image_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`):
-            The predicted CLIP image embedding conditioned on the CLIP text embedding input.
-    """
-
-    predicted_image_embedding: torch.FloatTensor
-
-
-class PriorTransformer(ModelMixin, ConfigMixin):
-    """
-    A Prior Transformer model.
-
-    Parameters:
-        num_attention_heads (`int`, *optional*, defaults to 32): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
-        num_layers (`int`, *optional*, defaults to 20): The number of layers of Transformer blocks to use.
-        embedding_dim (`int`, *optional*, defaults to 768): The dimension of the model input `hidden_states`
-        num_embeddings (`int`, *optional*, defaults to 77):
-            The number of embeddings of the model input `hidden_states`
-        additional_embeddings (`int`, *optional*, defaults to 4): The number of additional tokens appended to the
-            projected `hidden_states`. The actual length of the used `hidden_states` is `num_embeddings +
-            additional_embeddings`.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        time_embed_act_fn (`str`, *optional*, defaults to 'silu'):
-            The activation function to use to create timestep embeddings.
-        norm_in_type (`str`, *optional*, defaults to None): The normalization layer to apply on hidden states before
-            passing to Transformer blocks. Set it to `None` if normalization is not needed.
-        embedding_proj_norm_type (`str`, *optional*, defaults to None):
-            The normalization layer to apply on the input `proj_embedding`. Set it to `None` if normalization is not
-            needed.
-        encoder_hid_proj_type (`str`, *optional*, defaults to `linear`):
-            The projection layer to apply on the input `encoder_hidden_states`. Set it to `None` if
-            `encoder_hidden_states` is `None`.
-        added_emb_type (`str`, *optional*, defaults to `prd`): Additional embeddings to condition the model.
-            Choose from `prd` or `None`. if choose `prd`, it will prepend a token indicating the (quantized) dot
-            product between the text embedding and image embedding as proposed in the unclip paper
-            https://arxiv.org/abs/2204.06125 If it is `None`, no additional embeddings will be prepended.
-        time_embed_dim (`int, *optional*, defaults to None): The dimension of timestep embeddings.
-            If None, will be set to `num_attention_heads * attention_head_dim`
-        embedding_proj_dim (`int`, *optional*, default to None):
-            The dimension of `proj_embedding`. If None, will be set to `embedding_dim`.
-        clip_embed_dim (`int`, *optional*, default to None):
-            The dimension of the output. If None, will be set to `embedding_dim`.
-    """
-
-    @register_to_config
-    def __init__(
-        self,
-        num_attention_heads: int = 32,
-        attention_head_dim: int = 64,
-        num_layers: int = 20,
-        embedding_dim: int = 768,
-        num_embeddings=77,
-        additional_embeddings=4,
-        dropout: float = 0.0,
-        time_embed_act_fn: str = "silu",
-        norm_in_type: Optional[str] = None,  # layer
-        embedding_proj_norm_type: Optional[str] = None,  # layer
-        encoder_hid_proj_type: Optional[str] = "linear",  # linear
-        added_emb_type: Optional[str] = "prd",  # prd
-        time_embed_dim: Optional[int] = None,
-        embedding_proj_dim: Optional[int] = None,
-        clip_embed_dim: Optional[int] = None,
-    ):
-        super().__init__()
-        self.num_attention_heads = num_attention_heads
-        self.attention_head_dim = attention_head_dim
-        inner_dim = num_attention_heads * attention_head_dim
-        self.additional_embeddings = additional_embeddings
-
-        time_embed_dim = time_embed_dim or inner_dim
-        embedding_proj_dim = embedding_proj_dim or embedding_dim
-        clip_embed_dim = clip_embed_dim or embedding_dim
-
-        self.time_proj = Timesteps(inner_dim, True, 0)
-        self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, out_dim=inner_dim, act_fn=time_embed_act_fn)
-
-        self.proj_in = nn.Linear(embedding_dim, inner_dim)
-
-        if embedding_proj_norm_type is None:
-            self.embedding_proj_norm = None
-        elif embedding_proj_norm_type == "layer":
-            self.embedding_proj_norm = nn.LayerNorm(embedding_proj_dim)
-        else:
-            raise ValueError(f"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}")
-
-        self.embedding_proj = nn.Linear(embedding_proj_dim, inner_dim)
-
-        if encoder_hid_proj_type is None:
-            self.encoder_hidden_states_proj = None
-        elif encoder_hid_proj_type == "linear":
-            self.encoder_hidden_states_proj = nn.Linear(embedding_dim, inner_dim)
-        else:
-            raise ValueError(f"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}")
-
-        self.positional_embedding = nn.Parameter(torch.zeros(1, num_embeddings + additional_embeddings, inner_dim))
-
-        if added_emb_type == "prd":
-            self.prd_embedding = nn.Parameter(torch.zeros(1, 1, inner_dim))
-        elif added_emb_type is None:
-            self.prd_embedding = None
-        else:
-            raise ValueError(
-                f"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."
-            )
-
-        self.transformer_blocks = nn.ModuleList(
-            [
-                BasicTransformerBlock(
-                    inner_dim,
-                    num_attention_heads,
-                    attention_head_dim,
-                    dropout=dropout,
-                    activation_fn="gelu",
-                    attention_bias=True,
-                )
-                for d in range(num_layers)
-            ]
-        )
-
-        if norm_in_type == "layer":
-            self.norm_in = nn.LayerNorm(inner_dim)
-        elif norm_in_type is None:
-            self.norm_in = None
-        else:
-            raise ValueError(f"Unsupported norm_in_type: {norm_in_type}.")
-
-        self.norm_out = nn.LayerNorm(inner_dim)
-
-        self.proj_to_clip_embeddings = nn.Linear(inner_dim, clip_embed_dim)
-
-        causal_attention_mask = torch.full(
-            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings], -10000.0
-        )
-        causal_attention_mask.triu_(1)
-        causal_attention_mask = causal_attention_mask[None, ...]
-        self.register_buffer("causal_attention_mask", causal_attention_mask, persistent=False)
-
-        self.clip_mean = nn.Parameter(torch.zeros(1, clip_embed_dim))
-        self.clip_std = nn.Parameter(torch.zeros(1, clip_embed_dim))
-
-    @property
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
-    def attn_processors(self) -> Dict[str, AttentionProcessor]:
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
-            if hasattr(module, "set_processor"):
-                processors[f"{name}.processor"] = module.processor
-
-            for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
-            return processors
-
-        for name, module in self.named_children():
-            fn_recursive_add_processors(name, module, processors)
-
-        return processors
-
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
-    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
-        r"""
-        Sets the attention processor to use to compute attention.
-
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
-    def set_default_attn_processor(self):
-        """
-        Disables custom attention processors and sets the default attention implementation.
-        """
-        self.set_attn_processor(AttnProcessor())
-
-    def forward(
-        self,
-        hidden_states,
-        timestep: Union[torch.Tensor, float, int],
-        proj_embedding: torch.FloatTensor,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.BoolTensor] = None,
-        return_dict: bool = True,
-    ):
-        """
-        The [`PriorTransformer`] forward method.
-
-        Args:
-            hidden_states (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`):
-                The currently predicted image embeddings.
-            timestep (`torch.LongTensor`):
-                Current denoising step.
-            proj_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`):
-                Projected embedding vector the denoising process is conditioned on.
-            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_embeddings, embedding_dim)`):
-                Hidden states of the text embeddings the denoising process is conditioned on.
-            attention_mask (`torch.BoolTensor` of shape `(batch_size, num_embeddings)`):
-                Text mask for the text embeddings.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.prior_transformer.PriorTransformerOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            [`~models.prior_transformer.PriorTransformerOutput`] or `tuple`:
-                If return_dict is True, a [`~models.prior_transformer.PriorTransformerOutput`] is returned, otherwise a
-                tuple is returned where the first element is the sample tensor.
-        """
-        batch_size = hidden_states.shape[0]
-
-        timesteps = timestep
-        if not torch.is_tensor(timesteps):
-            timesteps = torch.tensor([timesteps], dtype=torch.long, device=hidden_states.device)
-        elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
-            timesteps = timesteps[None].to(hidden_states.device)
-
-        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        timesteps = timesteps * torch.ones(batch_size, dtype=timesteps.dtype, device=timesteps.device)
-
-        timesteps_projected = self.time_proj(timesteps)
-
-        # timesteps does not contain any weights and will always return f32 tensors
-        # but time_embedding might be fp16, so we need to cast here.
-        timesteps_projected = timesteps_projected.to(dtype=self.dtype)
-        time_embeddings = self.time_embedding(timesteps_projected)
-
-        if self.embedding_proj_norm is not None:
-            proj_embedding = self.embedding_proj_norm(proj_embedding)
-
-        proj_embeddings = self.embedding_proj(proj_embedding)
-        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
-            encoder_hidden_states = self.encoder_hidden_states_proj(encoder_hidden_states)
-        elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
-            raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set")
-
-        hidden_states = self.proj_in(hidden_states)
-
-        positional_embeddings = self.positional_embedding.to(hidden_states.dtype)
-
-        additional_embeds = []
-        additional_embeddings_len = 0
-
-        if encoder_hidden_states is not None:
-            additional_embeds.append(encoder_hidden_states)
-            additional_embeddings_len += encoder_hidden_states.shape[1]
-
-        if len(proj_embeddings.shape) == 2:
-            proj_embeddings = proj_embeddings[:, None, :]
-
-        if len(hidden_states.shape) == 2:
-            hidden_states = hidden_states[:, None, :]
-
-        additional_embeds = additional_embeds + [
-            proj_embeddings,
-            time_embeddings[:, None, :],
-            hidden_states,
-        ]
-
-        if self.prd_embedding is not None:
-            prd_embedding = self.prd_embedding.to(hidden_states.dtype).expand(batch_size, -1, -1)
-            additional_embeds.append(prd_embedding)
-
-        hidden_states = torch.cat(
-            additional_embeds,
-            dim=1,
-        )
-
-        # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
-        additional_embeddings_len = additional_embeddings_len + proj_embeddings.shape[1] + 1
-        if positional_embeddings.shape[1] < hidden_states.shape[1]:
-            positional_embeddings = F.pad(
-                positional_embeddings,
-                (
-                    0,
-                    0,
-                    additional_embeddings_len,
-                    self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
-                ),
-                value=0.0,
-            )
-
-        hidden_states = hidden_states + positional_embeddings
-
-        if attention_mask is not None:
-            attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
-            attention_mask = F.pad(attention_mask, (0, self.additional_embeddings), value=0.0)
-            attention_mask = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)
-            attention_mask = attention_mask.repeat_interleave(self.config.num_attention_heads, dim=0)
-
-        if self.norm_in is not None:
-            hidden_states = self.norm_in(hidden_states)
-
-        for block in self.transformer_blocks:
-            hidden_states = block(hidden_states, attention_mask=attention_mask)
-
-        hidden_states = self.norm_out(hidden_states)
-
-        if self.prd_embedding is not None:
-            hidden_states = hidden_states[:, -1]
-        else:
-            hidden_states = hidden_states[:, additional_embeddings_len:]
-
-        predicted_image_embedding = self.proj_to_clip_embeddings(hidden_states)
-
-        if not return_dict:
-            return (predicted_image_embedding,)
-
-        return PriorTransformerOutput(predicted_image_embedding=predicted_image_embedding)
-
-    def post_process_latents(self, prior_latents):
-        prior_latents = (prior_latents * self.clip_std) + self.clip_mean
-        return prior_latents

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/schedulers/test_scheduler_ddim.py

@@ -1,148 +0,0 @@
-import torch
-
-from diffusers import DDIMScheduler
-
-from .test_schedulers import SchedulerCommonTest
-
-
-class DDIMSchedulerTest(SchedulerCommonTest):
-    scheduler_classes = (DDIMScheduler,)
-    forward_default_kwargs = (("eta", 0.0), ("num_inference_steps", 50))
-
-    def get_scheduler_config(self, **kwargs):
-        config = {
-            "num_train_timesteps": 1000,
-            "beta_start": 0.0001,
-            "beta_end": 0.02,
-            "beta_schedule": "linear",
-            "clip_sample": True,
-        }
-
-        config.update(**kwargs)
-        return config
-
-    def full_loop(self, **config):
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config(**config)
-        scheduler = scheduler_class(**scheduler_config)
-
-        num_inference_steps, eta = 10, 0.0
-
-        model = self.dummy_model()
-        sample = self.dummy_sample_deter
-
-        scheduler.set_timesteps(num_inference_steps)
-
-        for t in scheduler.timesteps:
-            residual = model(sample, t)
-            sample = scheduler.step(residual, t, sample, eta).prev_sample
-
-        return sample
-
-    def test_timesteps(self):
-        for timesteps in [100, 500, 1000]:
-            self.check_over_configs(num_train_timesteps=timesteps)
-
-    def test_steps_offset(self):
-        for steps_offset in [0, 1]:
-            self.check_over_configs(steps_offset=steps_offset)
-
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config(steps_offset=1)
-        scheduler = scheduler_class(**scheduler_config)
-        scheduler.set_timesteps(5)
-        assert torch.equal(scheduler.timesteps, torch.LongTensor([801, 601, 401, 201, 1]))
-
-    def test_betas(self):
-        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):
-            self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
-
-    def test_schedules(self):
-        for schedule in ["linear", "squaredcos_cap_v2"]:
-            self.check_over_configs(beta_schedule=schedule)
-
-    def test_prediction_type(self):
-        for prediction_type in ["epsilon", "v_prediction"]:
-            self.check_over_configs(prediction_type=prediction_type)
-
-    def test_clip_sample(self):
-        for clip_sample in [True, False]:
-            self.check_over_configs(clip_sample=clip_sample)
-
-    def test_timestep_spacing(self):
-        for timestep_spacing in ["trailing", "leading"]:
-            self.check_over_configs(timestep_spacing=timestep_spacing)
-
-    def test_rescale_betas_zero_snr(self):
-        for rescale_betas_zero_snr in [True, False]:
-            self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr)
-
-    def test_thresholding(self):
-        self.check_over_configs(thresholding=False)
-        for threshold in [0.5, 1.0, 2.0]:
-            for prediction_type in ["epsilon", "v_prediction"]:
-                self.check_over_configs(
-                    thresholding=True,
-                    prediction_type=prediction_type,
-                    sample_max_value=threshold,
-                )
-
-    def test_time_indices(self):
-        for t in [1, 10, 49]:
-            self.check_over_forward(time_step=t)
-
-    def test_inference_steps(self):
-        for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]):
-            self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)
-
-    def test_eta(self):
-        for t, eta in zip([1, 10, 49], [0.0, 0.5, 1.0]):
-            self.check_over_forward(time_step=t, eta=eta)
-
-    def test_variance(self):
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config()
-        scheduler = scheduler_class(**scheduler_config)
-
-        assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5
-        assert torch.sum(torch.abs(scheduler._get_variance(420, 400) - 0.14771)) < 1e-5
-        assert torch.sum(torch.abs(scheduler._get_variance(980, 960) - 0.32460)) < 1e-5
-        assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5
-        assert torch.sum(torch.abs(scheduler._get_variance(487, 486) - 0.00979)) < 1e-5
-        assert torch.sum(torch.abs(scheduler._get_variance(999, 998) - 0.02)) < 1e-5
-
-    def test_full_loop_no_noise(self):
-        sample = self.full_loop()
-
-        result_sum = torch.sum(torch.abs(sample))
-        result_mean = torch.mean(torch.abs(sample))
-
-        assert abs(result_sum.item() - 172.0067) < 1e-2
-        assert abs(result_mean.item() - 0.223967) < 1e-3
-
-    def test_full_loop_with_v_prediction(self):
-        sample = self.full_loop(prediction_type="v_prediction")
-
-        result_sum = torch.sum(torch.abs(sample))
-        result_mean = torch.mean(torch.abs(sample))
-
-        assert abs(result_sum.item() - 52.5302) < 1e-2
-        assert abs(result_mean.item() - 0.0684) < 1e-3
-
-    def test_full_loop_with_set_alpha_to_one(self):
-        # We specify different beta, so that the first alpha is 0.99
-        sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)
-        result_sum = torch.sum(torch.abs(sample))
-        result_mean = torch.mean(torch.abs(sample))
-
-        assert abs(result_sum.item() - 149.8295) < 1e-2
-        assert abs(result_mean.item() - 0.1951) < 1e-3
-
-    def test_full_loop_with_no_set_alpha_to_one(self):
-        # We specify different beta, so that the first alpha is 0.99
-        sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)
-        result_sum = torch.sum(torch.abs(sample))
-        result_mean = torch.mean(torch.abs(sample))
-
-        assert abs(result_sum.item() - 149.0784) < 1e-2
-        assert abs(result_mean.item() - 0.1941) < 1e-3

spaces/Andy1621/uniformer_image_segmentation/configs/dnlnet/README.md

@@ -1,42 +0,0 @@
-# Disentangled Non-Local Neural Networks
-
-## Introduction
-
-<!-- [ALGORITHM] -->
-
-This example is to reproduce ["Disentangled Non-Local Neural Networks"](https://arxiv.org/abs/2006.06668) for semantic segmentation. It is still in progress.
-
-## Citation
-
-```latex
-@misc{yin2020disentangled,
-    title={Disentangled Non-Local Neural Networks},
-    author={Minghao Yin and Zhuliang Yao and Yue Cao and Xiu Li and Zheng Zhang and Stephen Lin and Han Hu},
-    year={2020},
-    booktitle={ECCV}
-}
-```
-
-## Results and models (in progress)
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                   | download                                                                                                                                                                                                                                                                                                                                         |
-| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| dnl    | R-50-D8  | 512x1024  |   40000 |      7.3 | 2.56           | 78.61 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes-20200904_233629.log.json)     |
-| dnl    | R-101-D8 | 512x1024  |   40000 |     10.9 | 1.96           | 78.31 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes-20200904_233629.log.json) |
-| dnl    | R-50-D8  | 769x769   |   40000 |      9.2 | 1.50           | 78.44 | 80.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes-20200820_232206.log.json)         |
-| dnl    | R-101-D8 | 769x769   |   40000 |     12.6 | 1.02           | 76.39 | 77.77         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes-20200820_171256.log.json)     |
-| dnl    | R-50-D8  | 512x1024  |   80000 |        - | -              | 79.33 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes-20200904_233629.log.json)     |
-| dnl    | R-101-D8 | 512x1024  |   80000 |        - | -              | 80.41 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes-20200904_233629.log.json) |
-| dnl    | R-50-D8  | 769x769   |   80000 |        - | -              | 79.36 | 80.70         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes-20200820_011925.log.json)         |
-| dnl    | R-101-D8 | 769x769   |   80000 |        - | -              | 79.41 | 80.68         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes-20200821_051111.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                               | download                                                                                                                                                                                                                                                                                                                         |
-| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DNL    | R-50-D8  | 512x512   |   80000 |      8.8 | 20.66          | 41.76 | 42.99         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k-20200826_183354.log.json)         |
-| DNL    | R-101-D8 | 512x512   |   80000 |     12.8 | 12.54          | 43.76 | 44.91         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k-20200826_183354.log.json)     |
-| DNL    | R-50-D8  | 512x512   |  160000 |        - | -              | 41.87 | 43.01         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k-20200826_183350.log.json)     |
-| DNL    | R-101-D8 | 512x512   |  160000 |        - | -              | 44.25 | 45.78         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k-20200826_183350.log.json) |

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/utils/flops_counter.py

@@ -1,599 +0,0 @@
-# Modified from flops-counter.pytorch by Vladislav Sovrasov
-# original repo: https://github.com/sovrasov/flops-counter.pytorch
-
-# MIT License
-
-# Copyright (c) 2018 Vladislav Sovrasov
-
-# 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.
-
-import sys
-from functools import partial
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-import annotator.uniformer.mmcv as mmcv
-
-
-def get_model_complexity_info(model,
-                              input_shape,
-                              print_per_layer_stat=True,
-                              as_strings=True,
-                              input_constructor=None,
-                              flush=False,
-                              ost=sys.stdout):
-    """Get complexity information of a model.
-
-    This method can calculate FLOPs and parameter counts of a model with
-    corresponding input shape. It can also print complexity information for
-    each layer in a model.
-
-    Supported layers are listed as below:
-        - Convolutions: ``nn.Conv1d``, ``nn.Conv2d``, ``nn.Conv3d``.
-        - Activations: ``nn.ReLU``, ``nn.PReLU``, ``nn.ELU``, ``nn.LeakyReLU``,
-            ``nn.ReLU6``.
-        - Poolings: ``nn.MaxPool1d``, ``nn.MaxPool2d``, ``nn.MaxPool3d``,
-            ``nn.AvgPool1d``, ``nn.AvgPool2d``, ``nn.AvgPool3d``,
-            ``nn.AdaptiveMaxPool1d``, ``nn.AdaptiveMaxPool2d``,
-            ``nn.AdaptiveMaxPool3d``, ``nn.AdaptiveAvgPool1d``,
-            ``nn.AdaptiveAvgPool2d``, ``nn.AdaptiveAvgPool3d``.
-        - BatchNorms: ``nn.BatchNorm1d``, ``nn.BatchNorm2d``,
-            ``nn.BatchNorm3d``, ``nn.GroupNorm``, ``nn.InstanceNorm1d``,
-            ``InstanceNorm2d``, ``InstanceNorm3d``, ``nn.LayerNorm``.
-        - Linear: ``nn.Linear``.
-        - Deconvolution: ``nn.ConvTranspose2d``.
-        - Upsample: ``nn.Upsample``.
-
-    Args:
-        model (nn.Module): The model for complexity calculation.
-        input_shape (tuple): Input shape used for calculation.
-        print_per_layer_stat (bool): Whether to print complexity information
-            for each layer in a model. Default: True.
-        as_strings (bool): Output FLOPs and params counts in a string form.
-            Default: True.
-        input_constructor (None | callable): If specified, it takes a callable
-            method that generates input. otherwise, it will generate a random
-            tensor with input shape to calculate FLOPs. Default: None.
-        flush (bool): same as that in :func:`print`. Default: False.
-        ost (stream): same as ``file`` param in :func:`print`.
-            Default: sys.stdout.
-
-    Returns:
-        tuple[float | str]: If ``as_strings`` is set to True, it will return
-            FLOPs and parameter counts in a string format. otherwise, it will
-            return those in a float number format.
-    """
-    assert type(input_shape) is tuple
-    assert len(input_shape) >= 1
-    assert isinstance(model, nn.Module)
-    flops_model = add_flops_counting_methods(model)
-    flops_model.eval()
-    flops_model.start_flops_count()
-    if input_constructor:
-        input = input_constructor(input_shape)
-        _ = flops_model(**input)
-    else:
-        try:
-            batch = torch.ones(()).new_empty(
-                (1, *input_shape),
-                dtype=next(flops_model.parameters()).dtype,
-                device=next(flops_model.parameters()).device)
-        except StopIteration:
-            # Avoid StopIteration for models which have no parameters,
-            # like `nn.Relu()`, `nn.AvgPool2d`, etc.
-            batch = torch.ones(()).new_empty((1, *input_shape))
-
-        _ = flops_model(batch)
-
-    flops_count, params_count = flops_model.compute_average_flops_cost()
-    if print_per_layer_stat:
-        print_model_with_flops(
-            flops_model, flops_count, params_count, ost=ost, flush=flush)
-    flops_model.stop_flops_count()
-
-    if as_strings:
-        return flops_to_string(flops_count), params_to_string(params_count)
-
-    return flops_count, params_count
-
-
-def flops_to_string(flops, units='GFLOPs', precision=2):
-    """Convert FLOPs number into a string.
-
-    Note that Here we take a multiply-add counts as one FLOP.
-
-    Args:
-        flops (float): FLOPs number to be converted.
-        units (str | None): Converted FLOPs units. Options are None, 'GFLOPs',
-            'MFLOPs', 'KFLOPs', 'FLOPs'. If set to None, it will automatically
-            choose the most suitable unit for FLOPs. Default: 'GFLOPs'.
-        precision (int): Digit number after the decimal point. Default: 2.
-
-    Returns:
-        str: The converted FLOPs number with units.
-
-    Examples:
-        >>> flops_to_string(1e9)
-        '1.0 GFLOPs'
-        >>> flops_to_string(2e5, 'MFLOPs')
-        '0.2 MFLOPs'
-        >>> flops_to_string(3e-9, None)
-        '3e-09 FLOPs'
-    """
-    if units is None:
-        if flops // 10**9 > 0:
-            return str(round(flops / 10.**9, precision)) + ' GFLOPs'
-        elif flops // 10**6 > 0:
-            return str(round(flops / 10.**6, precision)) + ' MFLOPs'
-        elif flops // 10**3 > 0:
-            return str(round(flops / 10.**3, precision)) + ' KFLOPs'
-        else:
-            return str(flops) + ' FLOPs'
-    else:
-        if units == 'GFLOPs':
-            return str(round(flops / 10.**9, precision)) + ' ' + units
-        elif units == 'MFLOPs':
-            return str(round(flops / 10.**6, precision)) + ' ' + units
-        elif units == 'KFLOPs':
-            return str(round(flops / 10.**3, precision)) + ' ' + units
-        else:
-            return str(flops) + ' FLOPs'
-
-
-def params_to_string(num_params, units=None, precision=2):
-    """Convert parameter number into a string.
-
-    Args:
-        num_params (float): Parameter number to be converted.
-        units (str | None): Converted FLOPs units. Options are None, 'M',
-            'K' and ''. If set to None, it will automatically choose the most
-            suitable unit for Parameter number. Default: None.
-        precision (int): Digit number after the decimal point. Default: 2.
-
-    Returns:
-        str: The converted parameter number with units.
-
-    Examples:
-        >>> params_to_string(1e9)
-        '1000.0 M'
-        >>> params_to_string(2e5)
-        '200.0 k'
-        >>> params_to_string(3e-9)
-        '3e-09'
-    """
-    if units is None:
-        if num_params // 10**6 > 0:
-            return str(round(num_params / 10**6, precision)) + ' M'
-        elif num_params // 10**3:
-            return str(round(num_params / 10**3, precision)) + ' k'
-        else:
-            return str(num_params)
-    else:
-        if units == 'M':
-            return str(round(num_params / 10.**6, precision)) + ' ' + units
-        elif units == 'K':
-            return str(round(num_params / 10.**3, precision)) + ' ' + units
-        else:
-            return str(num_params)
-
-
-def print_model_with_flops(model,
-                           total_flops,
-                           total_params,
-                           units='GFLOPs',
-                           precision=3,
-                           ost=sys.stdout,
-                           flush=False):
-    """Print a model with FLOPs for each layer.
-
-    Args:
-        model (nn.Module): The model to be printed.
-        total_flops (float): Total FLOPs of the model.
-        total_params (float): Total parameter counts of the model.
-        units (str | None): Converted FLOPs units. Default: 'GFLOPs'.
-        precision (int): Digit number after the decimal point. Default: 3.
-        ost (stream): same as `file` param in :func:`print`.
-            Default: sys.stdout.
-        flush (bool): same as that in :func:`print`. Default: False.
-
-    Example:
-        >>> class ExampleModel(nn.Module):
-
-        >>> def __init__(self):
-        >>>     super().__init__()
-        >>>     self.conv1 = nn.Conv2d(3, 8, 3)
-        >>>     self.conv2 = nn.Conv2d(8, 256, 3)
-        >>>     self.conv3 = nn.Conv2d(256, 8, 3)
-        >>>     self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
-        >>>     self.flatten = nn.Flatten()
-        >>>     self.fc = nn.Linear(8, 1)
-
-        >>> def forward(self, x):
-        >>>     x = self.conv1(x)
-        >>>     x = self.conv2(x)
-        >>>     x = self.conv3(x)
-        >>>     x = self.avg_pool(x)
-        >>>     x = self.flatten(x)
-        >>>     x = self.fc(x)
-        >>>     return x
-
-        >>> model = ExampleModel()
-        >>> x = (3, 16, 16)
-        to print the complexity information state for each layer, you can use
-        >>> get_model_complexity_info(model, x)
-        or directly use
-        >>> print_model_with_flops(model, 4579784.0, 37361)
-        ExampleModel(
-          0.037 M, 100.000% Params, 0.005 GFLOPs, 100.000% FLOPs,
-          (conv1): Conv2d(0.0 M, 0.600% Params, 0.0 GFLOPs, 0.959% FLOPs, 3, 8, kernel_size=(3, 3), stride=(1, 1))  # noqa: E501
-          (conv2): Conv2d(0.019 M, 50.020% Params, 0.003 GFLOPs, 58.760% FLOPs, 8, 256, kernel_size=(3, 3), stride=(1, 1))
-          (conv3): Conv2d(0.018 M, 49.356% Params, 0.002 GFLOPs, 40.264% FLOPs, 256, 8, kernel_size=(3, 3), stride=(1, 1))
-          (avg_pool): AdaptiveAvgPool2d(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.017% FLOPs, output_size=(1, 1))
-          (flatten): Flatten(0.0 M, 0.000% Params, 0.0 GFLOPs, 0.000% FLOPs, )
-          (fc): Linear(0.0 M, 0.024% Params, 0.0 GFLOPs, 0.000% FLOPs, in_features=8, out_features=1, bias=True)
-        )
-    """
-
-    def accumulate_params(self):
-        if is_supported_instance(self):
-            return self.__params__
-        else:
-            sum = 0
-            for m in self.children():
-                sum += m.accumulate_params()
-            return sum
-
-    def accumulate_flops(self):
-        if is_supported_instance(self):
-            return self.__flops__ / model.__batch_counter__
-        else:
-            sum = 0
-            for m in self.children():
-                sum += m.accumulate_flops()
-            return sum
-
-    def flops_repr(self):
-        accumulated_num_params = self.accumulate_params()
-        accumulated_flops_cost = self.accumulate_flops()
-        return ', '.join([
-            params_to_string(
-                accumulated_num_params, units='M', precision=precision),
-            '{:.3%} Params'.format(accumulated_num_params / total_params),
-            flops_to_string(
-                accumulated_flops_cost, units=units, precision=precision),
-            '{:.3%} FLOPs'.format(accumulated_flops_cost / total_flops),
-            self.original_extra_repr()
-        ])
-
-    def add_extra_repr(m):
-        m.accumulate_flops = accumulate_flops.__get__(m)
-        m.accumulate_params = accumulate_params.__get__(m)
-        flops_extra_repr = flops_repr.__get__(m)
-        if m.extra_repr != flops_extra_repr:
-            m.original_extra_repr = m.extra_repr
-            m.extra_repr = flops_extra_repr
-            assert m.extra_repr != m.original_extra_repr
-
-    def del_extra_repr(m):
-        if hasattr(m, 'original_extra_repr'):
-            m.extra_repr = m.original_extra_repr
-            del m.original_extra_repr
-        if hasattr(m, 'accumulate_flops'):
-            del m.accumulate_flops
-
-    model.apply(add_extra_repr)
-    print(model, file=ost, flush=flush)
-    model.apply(del_extra_repr)
-
-
-def get_model_parameters_number(model):
-    """Calculate parameter number of a model.
-
-    Args:
-        model (nn.module): The model for parameter number calculation.
-
-    Returns:
-        float: Parameter number of the model.
-    """
-    num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
-    return num_params
-
-
-def add_flops_counting_methods(net_main_module):
-    # adding additional methods to the existing module object,
-    # this is done this way so that each function has access to self object
-    net_main_module.start_flops_count = start_flops_count.__get__(
-        net_main_module)
-    net_main_module.stop_flops_count = stop_flops_count.__get__(
-        net_main_module)
-    net_main_module.reset_flops_count = reset_flops_count.__get__(
-        net_main_module)
-    net_main_module.compute_average_flops_cost = compute_average_flops_cost.__get__(  # noqa: E501
-        net_main_module)
-
-    net_main_module.reset_flops_count()
-
-    return net_main_module
-
-
-def compute_average_flops_cost(self):
-    """Compute average FLOPs cost.
-
-    A method to compute average FLOPs cost, which will be available after
-    `add_flops_counting_methods()` is called on a desired net object.
-
-    Returns:
-        float: Current mean flops consumption per image.
-    """
-    batches_count = self.__batch_counter__
-    flops_sum = 0
-    for module in self.modules():
-        if is_supported_instance(module):
-            flops_sum += module.__flops__
-    params_sum = get_model_parameters_number(self)
-    return flops_sum / batches_count, params_sum
-
-
-def start_flops_count(self):
-    """Activate the computation of mean flops consumption per image.
-
-    A method to activate the computation of mean flops consumption per image.
-    which will be available after ``add_flops_counting_methods()`` is called on
-    a desired net object. It should be called before running the network.
-    """
-    add_batch_counter_hook_function(self)
-
-    def add_flops_counter_hook_function(module):
-        if is_supported_instance(module):
-            if hasattr(module, '__flops_handle__'):
-                return
-
-            else:
-                handle = module.register_forward_hook(
-                    get_modules_mapping()[type(module)])
-
-            module.__flops_handle__ = handle
-
-    self.apply(partial(add_flops_counter_hook_function))
-
-
-def stop_flops_count(self):
-    """Stop computing the mean flops consumption per image.
-
-    A method to stop computing the mean flops consumption per image, which will
-    be available after ``add_flops_counting_methods()`` is called on a desired
-    net object. It can be called to pause the computation whenever.
-    """
-    remove_batch_counter_hook_function(self)
-    self.apply(remove_flops_counter_hook_function)
-
-
-def reset_flops_count(self):
-    """Reset statistics computed so far.
-
-    A method to Reset computed statistics, which will be available after
-    `add_flops_counting_methods()` is called on a desired net object.
-    """
-    add_batch_counter_variables_or_reset(self)
-    self.apply(add_flops_counter_variable_or_reset)
-
-
-# ---- Internal functions
-def empty_flops_counter_hook(module, input, output):
-    module.__flops__ += 0
-
-
-def upsample_flops_counter_hook(module, input, output):
-    output_size = output[0]
-    batch_size = output_size.shape[0]
-    output_elements_count = batch_size
-    for val in output_size.shape[1:]:
-        output_elements_count *= val
-    module.__flops__ += int(output_elements_count)
-
-
-def relu_flops_counter_hook(module, input, output):
-    active_elements_count = output.numel()
-    module.__flops__ += int(active_elements_count)
-
-
-def linear_flops_counter_hook(module, input, output):
-    input = input[0]
-    output_last_dim = output.shape[
-        -1]  # pytorch checks dimensions, so here we don't care much
-    module.__flops__ += int(np.prod(input.shape) * output_last_dim)
-
-
-def pool_flops_counter_hook(module, input, output):
-    input = input[0]
-    module.__flops__ += int(np.prod(input.shape))
-
-
-def norm_flops_counter_hook(module, input, output):
-    input = input[0]
-
-    batch_flops = np.prod(input.shape)
-    if (getattr(module, 'affine', False)
-            or getattr(module, 'elementwise_affine', False)):
-        batch_flops *= 2
-    module.__flops__ += int(batch_flops)
-
-
-def deconv_flops_counter_hook(conv_module, input, output):
-    # Can have multiple inputs, getting the first one
-    input = input[0]
-
-    batch_size = input.shape[0]
-    input_height, input_width = input.shape[2:]
-
-    kernel_height, kernel_width = conv_module.kernel_size
-    in_channels = conv_module.in_channels
-    out_channels = conv_module.out_channels
-    groups = conv_module.groups
-
-    filters_per_channel = out_channels // groups
-    conv_per_position_flops = (
-        kernel_height * kernel_width * in_channels * filters_per_channel)
-
-    active_elements_count = batch_size * input_height * input_width
-    overall_conv_flops = conv_per_position_flops * active_elements_count
-    bias_flops = 0
-    if conv_module.bias is not None:
-        output_height, output_width = output.shape[2:]
-        bias_flops = out_channels * batch_size * output_height * output_height
-    overall_flops = overall_conv_flops + bias_flops
-
-    conv_module.__flops__ += int(overall_flops)
-
-
-def conv_flops_counter_hook(conv_module, input, output):
-    # Can have multiple inputs, getting the first one
-    input = input[0]
-
-    batch_size = input.shape[0]
-    output_dims = list(output.shape[2:])
-
-    kernel_dims = list(conv_module.kernel_size)
-    in_channels = conv_module.in_channels
-    out_channels = conv_module.out_channels
-    groups = conv_module.groups
-
-    filters_per_channel = out_channels // groups
-    conv_per_position_flops = int(
-        np.prod(kernel_dims)) * in_channels * filters_per_channel
-
-    active_elements_count = batch_size * int(np.prod(output_dims))
-
-    overall_conv_flops = conv_per_position_flops * active_elements_count
-
-    bias_flops = 0
-
-    if conv_module.bias is not None:
-
-        bias_flops = out_channels * active_elements_count
-
-    overall_flops = overall_conv_flops + bias_flops
-
-    conv_module.__flops__ += int(overall_flops)
-
-
-def batch_counter_hook(module, input, output):
-    batch_size = 1
-    if len(input) > 0:
-        # Can have multiple inputs, getting the first one
-        input = input[0]
-        batch_size = len(input)
-    else:
-        pass
-        print('Warning! No positional inputs found for a module, '
-              'assuming batch size is 1.')
-    module.__batch_counter__ += batch_size
-
-
-def add_batch_counter_variables_or_reset(module):
-
-    module.__batch_counter__ = 0
-
-
-def add_batch_counter_hook_function(module):
-    if hasattr(module, '__batch_counter_handle__'):
-        return
-
-    handle = module.register_forward_hook(batch_counter_hook)
-    module.__batch_counter_handle__ = handle
-
-
-def remove_batch_counter_hook_function(module):
-    if hasattr(module, '__batch_counter_handle__'):
-        module.__batch_counter_handle__.remove()
-        del module.__batch_counter_handle__
-
-
-def add_flops_counter_variable_or_reset(module):
-    if is_supported_instance(module):
-        if hasattr(module, '__flops__') or hasattr(module, '__params__'):
-            print('Warning: variables __flops__ or __params__ are already '
-                  'defined for the module' + type(module).__name__ +
-                  ' ptflops can affect your code!')
-        module.__flops__ = 0
-        module.__params__ = get_model_parameters_number(module)
-
-
-def is_supported_instance(module):
-    if type(module) in get_modules_mapping():
-        return True
-    return False
-
-
-def remove_flops_counter_hook_function(module):
-    if is_supported_instance(module):
-        if hasattr(module, '__flops_handle__'):
-            module.__flops_handle__.remove()
-            del module.__flops_handle__
-
-
-def get_modules_mapping():
-    return {
-        # convolutions
-        nn.Conv1d: conv_flops_counter_hook,
-        nn.Conv2d: conv_flops_counter_hook,
-        mmcv.cnn.bricks.Conv2d: conv_flops_counter_hook,
-        nn.Conv3d: conv_flops_counter_hook,
-        mmcv.cnn.bricks.Conv3d: conv_flops_counter_hook,
-        # activations
-        nn.ReLU: relu_flops_counter_hook,
-        nn.PReLU: relu_flops_counter_hook,
-        nn.ELU: relu_flops_counter_hook,
-        nn.LeakyReLU: relu_flops_counter_hook,
-        nn.ReLU6: relu_flops_counter_hook,
-        # poolings
-        nn.MaxPool1d: pool_flops_counter_hook,
-        nn.AvgPool1d: pool_flops_counter_hook,
-        nn.AvgPool2d: pool_flops_counter_hook,
-        nn.MaxPool2d: pool_flops_counter_hook,
-        mmcv.cnn.bricks.MaxPool2d: pool_flops_counter_hook,
-        nn.MaxPool3d: pool_flops_counter_hook,
-        mmcv.cnn.bricks.MaxPool3d: pool_flops_counter_hook,
-        nn.AvgPool3d: pool_flops_counter_hook,
-        nn.AdaptiveMaxPool1d: pool_flops_counter_hook,
-        nn.AdaptiveAvgPool1d: pool_flops_counter_hook,
-        nn.AdaptiveMaxPool2d: pool_flops_counter_hook,
-        nn.AdaptiveAvgPool2d: pool_flops_counter_hook,
-        nn.AdaptiveMaxPool3d: pool_flops_counter_hook,
-        nn.AdaptiveAvgPool3d: pool_flops_counter_hook,
-        # normalizations
-        nn.BatchNorm1d: norm_flops_counter_hook,
-        nn.BatchNorm2d: norm_flops_counter_hook,
-        nn.BatchNorm3d: norm_flops_counter_hook,
-        nn.GroupNorm: norm_flops_counter_hook,
-        nn.InstanceNorm1d: norm_flops_counter_hook,
-        nn.InstanceNorm2d: norm_flops_counter_hook,
-        nn.InstanceNorm3d: norm_flops_counter_hook,
-        nn.LayerNorm: norm_flops_counter_hook,
-        # FC
-        nn.Linear: linear_flops_counter_hook,
-        mmcv.cnn.bricks.Linear: linear_flops_counter_hook,
-        # Upscale
-        nn.Upsample: upsample_flops_counter_hook,
-        # Deconvolution
-        nn.ConvTranspose2d: deconv_flops_counter_hook,
-        mmcv.cnn.bricks.ConvTranspose2d: deconv_flops_counter_hook,
-    }

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/image/photometric.py

@@ -1,428 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import cv2
-import numpy as np
-
-from ..utils import is_tuple_of
-from .colorspace import bgr2gray, gray2bgr
-
-
-def imnormalize(img, mean, std, to_rgb=True):
-    """Normalize an image with mean and std.
-
-    Args:
-        img (ndarray): Image to be normalized.
-        mean (ndarray): The mean to be used for normalize.
-        std (ndarray): The std to be used for normalize.
-        to_rgb (bool): Whether to convert to rgb.
-
-    Returns:
-        ndarray: The normalized image.
-    """
-    img = img.copy().astype(np.float32)
-    return imnormalize_(img, mean, std, to_rgb)
-
-
-def imnormalize_(img, mean, std, to_rgb=True):
-    """Inplace normalize an image with mean and std.
-
-    Args:
-        img (ndarray): Image to be normalized.
-        mean (ndarray): The mean to be used for normalize.
-        std (ndarray): The std to be used for normalize.
-        to_rgb (bool): Whether to convert to rgb.
-
-    Returns:
-        ndarray: The normalized image.
-    """
-    # cv2 inplace normalization does not accept uint8
-    assert img.dtype != np.uint8
-    mean = np.float64(mean.reshape(1, -1))
-    stdinv = 1 / np.float64(std.reshape(1, -1))
-    if to_rgb:
-        cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)  # inplace
-    cv2.subtract(img, mean, img)  # inplace
-    cv2.multiply(img, stdinv, img)  # inplace
-    return img
-
-
-def imdenormalize(img, mean, std, to_bgr=True):
-    assert img.dtype != np.uint8
-    mean = mean.reshape(1, -1).astype(np.float64)
-    std = std.reshape(1, -1).astype(np.float64)
-    img = cv2.multiply(img, std)  # make a copy
-    cv2.add(img, mean, img)  # inplace
-    if to_bgr:
-        cv2.cvtColor(img, cv2.COLOR_RGB2BGR, img)  # inplace
-    return img
-
-
-def iminvert(img):
-    """Invert (negate) an image.
-
-    Args:
-        img (ndarray): Image to be inverted.
-
-    Returns:
-        ndarray: The inverted image.
-    """
-    return np.full_like(img, 255) - img
-
-
-def solarize(img, thr=128):
-    """Solarize an image (invert all pixel values above a threshold)
-
-    Args:
-        img (ndarray): Image to be solarized.
-        thr (int): Threshold for solarizing (0 - 255).
-
-    Returns:
-        ndarray: The solarized image.
-    """
-    img = np.where(img < thr, img, 255 - img)
-    return img
-
-
-def posterize(img, bits):
-    """Posterize an image (reduce the number of bits for each color channel)
-
-    Args:
-        img (ndarray): Image to be posterized.
-        bits (int): Number of bits (1 to 8) to use for posterizing.
-
-    Returns:
-        ndarray: The posterized image.
-    """
-    shift = 8 - bits
-    img = np.left_shift(np.right_shift(img, shift), shift)
-    return img
-
-
-def adjust_color(img, alpha=1, beta=None, gamma=0):
-    r"""It blends the source image and its gray image:
-
-    .. math::
-        output = img * alpha + gray\_img * beta + gamma
-
-    Args:
-        img (ndarray): The input source image.
-        alpha (int | float): Weight for the source image. Default 1.
-        beta (int | float): Weight for the converted gray image.
-            If None, it's assigned the value (1 - `alpha`).
-        gamma (int | float): Scalar added to each sum.
-            Same as :func:`cv2.addWeighted`. Default 0.
-
-    Returns:
-        ndarray: Colored image which has the same size and dtype as input.
-    """
-    gray_img = bgr2gray(img)
-    gray_img = np.tile(gray_img[..., None], [1, 1, 3])
-    if beta is None:
-        beta = 1 - alpha
-    colored_img = cv2.addWeighted(img, alpha, gray_img, beta, gamma)
-    if not colored_img.dtype == np.uint8:
-        # Note when the dtype of `img` is not the default `np.uint8`
-        # (e.g. np.float32), the value in `colored_img` got from cv2
-        # is not guaranteed to be in range [0, 255], so here clip
-        # is needed.
-        colored_img = np.clip(colored_img, 0, 255)
-    return colored_img
-
-
-def imequalize(img):
-    """Equalize the image histogram.
-
-    This function applies a non-linear mapping to the input image,
-    in order to create a uniform distribution of grayscale values
-    in the output image.
-
-    Args:
-        img (ndarray): Image to be equalized.
-
-    Returns:
-        ndarray: The equalized image.
-    """
-
-    def _scale_channel(im, c):
-        """Scale the data in the corresponding channel."""
-        im = im[:, :, c]
-        # Compute the histogram of the image channel.
-        histo = np.histogram(im, 256, (0, 255))[0]
-        # For computing the step, filter out the nonzeros.
-        nonzero_histo = histo[histo > 0]
-        step = (np.sum(nonzero_histo) - nonzero_histo[-1]) // 255
-        if not step:
-            lut = np.array(range(256))
-        else:
-            # Compute the cumulative sum, shifted by step // 2
-            # and then normalized by step.
-            lut = (np.cumsum(histo) + (step // 2)) // step
-            # Shift lut, prepending with 0.
-            lut = np.concatenate([[0], lut[:-1]], 0)
-            # handle potential integer overflow
-            lut[lut > 255] = 255
-        # If step is zero, return the original image.
-        # Otherwise, index from lut.
-        return np.where(np.equal(step, 0), im, lut[im])
-
-    # Scales each channel independently and then stacks
-    # the result.
-    s1 = _scale_channel(img, 0)
-    s2 = _scale_channel(img, 1)
-    s3 = _scale_channel(img, 2)
-    equalized_img = np.stack([s1, s2, s3], axis=-1)
-    return equalized_img.astype(img.dtype)
-
-
-def adjust_brightness(img, factor=1.):
-    """Adjust image brightness.
-
-    This function controls the brightness of an image. An
-    enhancement factor of 0.0 gives a black image.
-    A factor of 1.0 gives the original image. This function
-    blends the source image and the degenerated black image:
-
-    .. math::
-        output = img * factor + degenerated * (1 - factor)
-
-    Args:
-        img (ndarray): Image to be brightened.
-        factor (float): A value controls the enhancement.
-            Factor 1.0 returns the original image, lower
-            factors mean less color (brightness, contrast,
-            etc), and higher values more. Default 1.
-
-    Returns:
-        ndarray: The brightened image.
-    """
-    degenerated = np.zeros_like(img)
-    # Note manually convert the dtype to np.float32, to
-    # achieve as close results as PIL.ImageEnhance.Brightness.
-    # Set beta=1-factor, and gamma=0
-    brightened_img = cv2.addWeighted(
-        img.astype(np.float32), factor, degenerated.astype(np.float32),
-        1 - factor, 0)
-    brightened_img = np.clip(brightened_img, 0, 255)
-    return brightened_img.astype(img.dtype)
-
-
-def adjust_contrast(img, factor=1.):
-    """Adjust image contrast.
-
-    This function controls the contrast of an image. An
-    enhancement factor of 0.0 gives a solid grey
-    image. A factor of 1.0 gives the original image. It
-    blends the source image and the degenerated mean image:
-
-    .. math::
-        output = img * factor + degenerated * (1 - factor)
-
-    Args:
-        img (ndarray): Image to be contrasted. BGR order.
-        factor (float): Same as :func:`mmcv.adjust_brightness`.
-
-    Returns:
-        ndarray: The contrasted image.
-    """
-    gray_img = bgr2gray(img)
-    hist = np.histogram(gray_img, 256, (0, 255))[0]
-    mean = round(np.sum(gray_img) / np.sum(hist))
-    degenerated = (np.ones_like(img[..., 0]) * mean).astype(img.dtype)
-    degenerated = gray2bgr(degenerated)
-    contrasted_img = cv2.addWeighted(
-        img.astype(np.float32), factor, degenerated.astype(np.float32),
-        1 - factor, 0)
-    contrasted_img = np.clip(contrasted_img, 0, 255)
-    return contrasted_img.astype(img.dtype)
-
-
-def auto_contrast(img, cutoff=0):
-    """Auto adjust image contrast.
-
-    This function maximize (normalize) image contrast by first removing cutoff
-    percent of the lightest and darkest pixels from the histogram and remapping
-    the image so that the darkest pixel becomes black (0), and the lightest
-    becomes white (255).
-
-    Args:
-        img (ndarray): Image to be contrasted. BGR order.
-        cutoff (int | float | tuple): The cutoff percent of the lightest and
-            darkest pixels to be removed. If given as tuple, it shall be
-            (low, high). Otherwise, the single value will be used for both.
-            Defaults to 0.
-
-    Returns:
-        ndarray: The contrasted image.
-    """
-
-    def _auto_contrast_channel(im, c, cutoff):
-        im = im[:, :, c]
-        # Compute the histogram of the image channel.
-        histo = np.histogram(im, 256, (0, 255))[0]
-        # Remove cut-off percent pixels from histo
-        histo_sum = np.cumsum(histo)
-        cut_low = histo_sum[-1] * cutoff[0] // 100
-        cut_high = histo_sum[-1] - histo_sum[-1] * cutoff[1] // 100
-        histo_sum = np.clip(histo_sum, cut_low, cut_high) - cut_low
-        histo = np.concatenate([[histo_sum[0]], np.diff(histo_sum)], 0)
-
-        # Compute mapping
-        low, high = np.nonzero(histo)[0][0], np.nonzero(histo)[0][-1]
-        # If all the values have been cut off, return the origin img
-        if low >= high:
-            return im
-        scale = 255.0 / (high - low)
-        offset = -low * scale
-        lut = np.array(range(256))
-        lut = lut * scale + offset
-        lut = np.clip(lut, 0, 255)
-        return lut[im]
-
-    if isinstance(cutoff, (int, float)):
-        cutoff = (cutoff, cutoff)
-    else:
-        assert isinstance(cutoff, tuple), 'cutoff must be of type int, ' \
-            f'float or tuple, but got {type(cutoff)} instead.'
-    # Auto adjusts contrast for each channel independently and then stacks
-    # the result.
-    s1 = _auto_contrast_channel(img, 0, cutoff)
-    s2 = _auto_contrast_channel(img, 1, cutoff)
-    s3 = _auto_contrast_channel(img, 2, cutoff)
-    contrasted_img = np.stack([s1, s2, s3], axis=-1)
-    return contrasted_img.astype(img.dtype)
-
-
-def adjust_sharpness(img, factor=1., kernel=None):
-    """Adjust image sharpness.
-
-    This function controls the sharpness of an image. An
-    enhancement factor of 0.0 gives a blurred image. A
-    factor of 1.0 gives the original image. And a factor
-    of 2.0 gives a sharpened image. It blends the source
-    image and the degenerated mean image:
-
-    .. math::
-        output = img * factor + degenerated * (1 - factor)
-
-    Args:
-        img (ndarray): Image to be sharpened. BGR order.
-        factor (float): Same as :func:`mmcv.adjust_brightness`.
-        kernel (np.ndarray, optional): Filter kernel to be applied on the img
-            to obtain the degenerated img. Defaults to None.
-
-    Note:
-        No value sanity check is enforced on the kernel set by users. So with
-        an inappropriate kernel, the ``adjust_sharpness`` may fail to perform
-        the function its name indicates but end up performing whatever
-        transform determined by the kernel.
-
-    Returns:
-        ndarray: The sharpened image.
-    """
-
-    if kernel is None:
-        # adopted from PIL.ImageFilter.SMOOTH
-        kernel = np.array([[1., 1., 1.], [1., 5., 1.], [1., 1., 1.]]) / 13
-    assert isinstance(kernel, np.ndarray), \
-        f'kernel must be of type np.ndarray, but got {type(kernel)} instead.'
-    assert kernel.ndim == 2, \
-        f'kernel must have a dimension of 2, but got {kernel.ndim} instead.'
-
-    degenerated = cv2.filter2D(img, -1, kernel)
-    sharpened_img = cv2.addWeighted(
-        img.astype(np.float32), factor, degenerated.astype(np.float32),
-        1 - factor, 0)
-    sharpened_img = np.clip(sharpened_img, 0, 255)
-    return sharpened_img.astype(img.dtype)
-
-
-def adjust_lighting(img, eigval, eigvec, alphastd=0.1, to_rgb=True):
-    """AlexNet-style PCA jitter.
-
-    This data augmentation is proposed in `ImageNet Classification with Deep
-    Convolutional Neural Networks
-    <https://dl.acm.org/doi/pdf/10.1145/3065386>`_.
-
-    Args:
-        img (ndarray): Image to be adjusted lighting. BGR order.
-        eigval (ndarray): the eigenvalue of the convariance matrix of pixel
-            values, respectively.
-        eigvec (ndarray): the eigenvector of the convariance matrix of pixel
-            values, respectively.
-        alphastd (float): The standard deviation for distribution of alpha.
-            Defaults to 0.1
-        to_rgb (bool): Whether to convert img to rgb.
-
-    Returns:
-        ndarray: The adjusted image.
-    """
-    assert isinstance(eigval, np.ndarray) and isinstance(eigvec, np.ndarray), \
-        f'eigval and eigvec should both be of type np.ndarray, got ' \
-        f'{type(eigval)} and {type(eigvec)} instead.'
-
-    assert eigval.ndim == 1 and eigvec.ndim == 2
-    assert eigvec.shape == (3, eigval.shape[0])
-    n_eigval = eigval.shape[0]
-    assert isinstance(alphastd, float), 'alphastd should be of type float, ' \
-        f'got {type(alphastd)} instead.'
-
-    img = img.copy().astype(np.float32)
-    if to_rgb:
-        cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)  # inplace
-
-    alpha = np.random.normal(0, alphastd, n_eigval)
-    alter = eigvec \
-        * np.broadcast_to(alpha.reshape(1, n_eigval), (3, n_eigval)) \
-        * np.broadcast_to(eigval.reshape(1, n_eigval), (3, n_eigval))
-    alter = np.broadcast_to(alter.sum(axis=1).reshape(1, 1, 3), img.shape)
-    img_adjusted = img + alter
-    return img_adjusted
-
-
-def lut_transform(img, lut_table):
-    """Transform array by look-up table.
-
-    The function lut_transform fills the output array with values from the
-    look-up table. Indices of the entries are taken from the input array.
-
-    Args:
-        img (ndarray): Image to be transformed.
-        lut_table (ndarray): look-up table of 256 elements; in case of
-            multi-channel input array, the table should either have a single
-            channel (in this case the same table is used for all channels) or
-            the same number of channels as in the input array.
-
-    Returns:
-        ndarray: The transformed image.
-    """
-    assert isinstance(img, np.ndarray)
-    assert 0 <= np.min(img) and np.max(img) <= 255
-    assert isinstance(lut_table, np.ndarray)
-    assert lut_table.shape == (256, )
-
-    return cv2.LUT(np.array(img, dtype=np.uint8), lut_table)
-
-
-def clahe(img, clip_limit=40.0, tile_grid_size=(8, 8)):
-    """Use CLAHE method to process the image.
-
-    See `ZUIDERVELD,K. Contrast Limited Adaptive Histogram Equalization[J].
-    Graphics Gems, 1994:474-485.` for more information.
-
-    Args:
-        img (ndarray): Image to be processed.
-        clip_limit (float): Threshold for contrast limiting. Default: 40.0.
-        tile_grid_size (tuple[int]): Size of grid for histogram equalization.
-            Input image will be divided into equally sized rectangular tiles.
-            It defines the number of tiles in row and column. Default: (8, 8).
-
-    Returns:
-        ndarray: The processed image.
-    """
-    assert isinstance(img, np.ndarray)
-    assert img.ndim == 2
-    assert isinstance(clip_limit, (float, int))
-    assert is_tuple_of(tile_grid_size, int)
-    assert len(tile_grid_size) == 2
-
-    clahe = cv2.createCLAHE(clip_limit, tile_grid_size)
-    return clahe.apply(np.array(img, dtype=np.uint8))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/datasets/dataset_wrappers.py

@@ -1,50 +0,0 @@
-from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
-
-from .builder import DATASETS
-
-
-@DATASETS.register_module()
-class ConcatDataset(_ConcatDataset):
-    """A wrapper of concatenated dataset.
-
-    Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
-    concat the group flag for image aspect ratio.
-
-    Args:
-        datasets (list[:obj:`Dataset`]): A list of datasets.
-    """
-
-    def __init__(self, datasets):
-        super(ConcatDataset, self).__init__(datasets)
-        self.CLASSES = datasets[0].CLASSES
-        self.PALETTE = datasets[0].PALETTE
-
-
-@DATASETS.register_module()
-class RepeatDataset(object):
-    """A wrapper of repeated dataset.
-
-    The length of repeated dataset will be `times` larger than the original
-    dataset. This is useful when the data loading time is long but the dataset
-    is small. Using RepeatDataset can reduce the data loading time between
-    epochs.
-
-    Args:
-        dataset (:obj:`Dataset`): The dataset to be repeated.
-        times (int): Repeat times.
-    """
-
-    def __init__(self, dataset, times):
-        self.dataset = dataset
-        self.times = times
-        self.CLASSES = dataset.CLASSES
-        self.PALETTE = dataset.PALETTE
-        self._ori_len = len(self.dataset)
-
-    def __getitem__(self, idx):
-        """Get item from original dataset."""
-        return self.dataset[idx % self._ori_len]
-
-    def __len__(self):
-        """The length is multiplied by ``times``"""
-        return self.times * self._ori_len

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/markup.py

@@ -1,246 +0,0 @@
-import re
-from ast import literal_eval
-from operator import attrgetter
-from typing import Callable, Iterable, List, Match, NamedTuple, Optional, Tuple, Union
-
-from ._emoji_replace import _emoji_replace
-from .emoji import EmojiVariant
-from .errors import MarkupError
-from .style import Style
-from .text import Span, Text
-
-RE_TAGS = re.compile(
-    r"""((\\*)\[([a-z#/@][^[]*?)])""",
-    re.VERBOSE,
-)
-
-RE_HANDLER = re.compile(r"^([\w.]*?)(\(.*?\))?$")
-
-
-class Tag(NamedTuple):
-    """A tag in console markup."""
-
-    name: str
-    """The tag name. e.g. 'bold'."""
-    parameters: Optional[str]
-    """Any additional parameters after the name."""
-
-    def __str__(self) -> str:
-        return (
-            self.name if self.parameters is None else f"{self.name} {self.parameters}"
-        )
-
-    @property
-    def markup(self) -> str:
-        """Get the string representation of this tag."""
-        return (
-            f"[{self.name}]"
-            if self.parameters is None
-            else f"[{self.name}={self.parameters}]"
-        )
-
-
-_ReStringMatch = Match[str]  # regex match object
-_ReSubCallable = Callable[[_ReStringMatch], str]  # Callable invoked by re.sub
-_EscapeSubMethod = Callable[[_ReSubCallable, str], str]  # Sub method of a compiled re
-
-
-def escape(
-    markup: str,
-    _escape: _EscapeSubMethod = re.compile(r"(\\*)(\[[a-z#/@][^[]*?])").sub,
-) -> str:
-    """Escapes text so that it won't be interpreted as markup.
-
-    Args:
-        markup (str): Content to be inserted in to markup.
-
-    Returns:
-        str: Markup with square brackets escaped.
-    """
-
-    def escape_backslashes(match: Match[str]) -> str:
-        """Called by re.sub replace matches."""
-        backslashes, text = match.groups()
-        return f"{backslashes}{backslashes}\\{text}"
-
-    markup = _escape(escape_backslashes, markup)
-    return markup
-
-
-def _parse(markup: str) -> Iterable[Tuple[int, Optional[str], Optional[Tag]]]:
-    """Parse markup in to an iterable of tuples of (position, text, tag).
-
-    Args:
-        markup (str): A string containing console markup
-
-    """
-    position = 0
-    _divmod = divmod
-    _Tag = Tag
-    for match in RE_TAGS.finditer(markup):
-        full_text, escapes, tag_text = match.groups()
-        start, end = match.span()
-        if start > position:
-            yield start, markup[position:start], None
-        if escapes:
-            backslashes, escaped = _divmod(len(escapes), 2)
-            if backslashes:
-                # Literal backslashes
-                yield start, "\\" * backslashes, None
-                start += backslashes * 2
-            if escaped:
-                # Escape of tag
-                yield start, full_text[len(escapes) :], None
-                position = end
-                continue
-        text, equals, parameters = tag_text.partition("=")
-        yield start, None, _Tag(text, parameters if equals else None)
-        position = end
-    if position < len(markup):
-        yield position, markup[position:], None
-
-
-def render(
-    markup: str,
-    style: Union[str, Style] = "",
-    emoji: bool = True,
-    emoji_variant: Optional[EmojiVariant] = None,
-) -> Text:
-    """Render console markup in to a Text instance.
-
-    Args:
-        markup (str): A string containing console markup.
-        emoji (bool, optional): Also render emoji code. Defaults to True.
-
-    Raises:
-        MarkupError: If there is a syntax error in the markup.
-
-    Returns:
-        Text: A test instance.
-    """
-    emoji_replace = _emoji_replace
-    if "[" not in markup:
-        return Text(
-            emoji_replace(markup, default_variant=emoji_variant) if emoji else markup,
-            style=style,
-        )
-    text = Text(style=style)
-    append = text.append
-    normalize = Style.normalize
-
-    style_stack: List[Tuple[int, Tag]] = []
-    pop = style_stack.pop
-
-    spans: List[Span] = []
-    append_span = spans.append
-
-    _Span = Span
-    _Tag = Tag
-
-    def pop_style(style_name: str) -> Tuple[int, Tag]:
-        """Pop tag matching given style name."""
-        for index, (_, tag) in enumerate(reversed(style_stack), 1):
-            if tag.name == style_name:
-                return pop(-index)
-        raise KeyError(style_name)
-
-    for position, plain_text, tag in _parse(markup):
-        if plain_text is not None:
-            # Handle open brace escapes, where the brace is not part of a tag.
-            plain_text = plain_text.replace("\\[", "[")
-            append(emoji_replace(plain_text) if emoji else plain_text)
-        elif tag is not None:
-            if tag.name.startswith("/"):  # Closing tag
-                style_name = tag.name[1:].strip()
-
-                if style_name:  # explicit close
-                    style_name = normalize(style_name)
-                    try:
-                        start, open_tag = pop_style(style_name)
-                    except KeyError:
-                        raise MarkupError(
-                            f"closing tag '{tag.markup}' at position {position} doesn't match any open tag"
-                        ) from None
-                else:  # implicit close
-                    try:
-                        start, open_tag = pop()
-                    except IndexError:
-                        raise MarkupError(
-                            f"closing tag '[/]' at position {position} has nothing to close"
-                        ) from None
-
-                if open_tag.name.startswith("@"):
-                    if open_tag.parameters:
-                        handler_name = ""
-                        parameters = open_tag.parameters.strip()
-                        handler_match = RE_HANDLER.match(parameters)
-                        if handler_match is not None:
-                            handler_name, match_parameters = handler_match.groups()
-                            parameters = (
-                                "()" if match_parameters is None else match_parameters
-                            )
-
-                        try:
-                            meta_params = literal_eval(parameters)
-                        except SyntaxError as error:
-                            raise MarkupError(
-                                f"error parsing {parameters!r} in {open_tag.parameters!r}; {error.msg}"
-                            )
-                        except Exception as error:
-                            raise MarkupError(
-                                f"error parsing {open_tag.parameters!r}; {error}"
-                            ) from None
-
-                        if handler_name:
-                            meta_params = (
-                                handler_name,
-                                meta_params
-                                if isinstance(meta_params, tuple)
-                                else (meta_params,),
-                            )
-
-                    else:
-                        meta_params = ()
-
-                    append_span(
-                        _Span(
-                            start, len(text), Style(meta={open_tag.name: meta_params})
-                        )
-                    )
-                else:
-                    append_span(_Span(start, len(text), str(open_tag)))
-
-            else:  # Opening tag
-                normalized_tag = _Tag(normalize(tag.name), tag.parameters)
-                style_stack.append((len(text), normalized_tag))
-
-    text_length = len(text)
-    while style_stack:
-        start, tag = style_stack.pop()
-        style = str(tag)
-        if style:
-            append_span(_Span(start, text_length, style))
-
-    text.spans = sorted(spans[::-1], key=attrgetter("start"))
-    return text
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    MARKUP = [
-        "[red]Hello World[/red]",
-        "[magenta]Hello [b]World[/b]",
-        "[bold]Bold[italic] bold and italic [/bold]italic[/italic]",
-        "Click [link=https://www.willmcgugan.com]here[/link] to visit my Blog",
-        ":warning-emoji: [bold red blink] DANGER![/]",
-    ]
-
-    from pip._vendor.rich import print
-    from pip._vendor.rich.table import Table
-
-    grid = Table("Markup", "Result", padding=(0, 1))
-
-    for markup in MARKUP:
-        grid.add_row(Text(markup), markup)
-
-    print(grid)

spaces/Audio-AGI/WavJourney/code_generator.py

@@ -1,188 +0,0 @@
-import os
-import json5
-import utils
-
-
-def check_json_script(data):
-    foreground_mandatory_attrs_map = {
-        'music': ['vol', 'len', 'desc'],
-        'sound_effect': ['vol', 'len', 'desc'],
-        'speech': ['vol', 'text']
-    }
-    background_mandatory_attrs_map = {
-        'music': ['vol', 'desc'],
-        'sound_effect': ['vol', 'desc'],
-    }
-
-    def check_by_audio_type(audio, mandatory_attrs_map, audio_str):
-        if audio['audio_type'] not in mandatory_attrs_map:
-            raise ValueError('audio_type is not allowed in this layout, audio={audio_str}')
-        for attr_name in mandatory_attrs_map[audio['audio_type']]:
-            if attr_name not in audio:
-                raise ValueError(f'{attr_name} does not exist, audio={audio_str}')
-
-    # Check json's format
-    for audio in data:
-        audio_str = json5.dumps(audio, indent=None)
-        if 'layout' not in audio:
-            raise ValueError(f'layout missing, audio={audio_str}')
-        elif 'audio_type' not in audio:
-            raise ValueError(f'audio_type missing, audio={audio_str}')
-        elif audio['layout'] == 'foreground':
-            check_by_audio_type(audio, foreground_mandatory_attrs_map, audio_str)
-        elif audio['layout'] == 'background':
-            if 'id' not in audio:
-                raise ValueError(f'id not in background audio, audio={audio_str}')
-            if 'action' not in audio:
-                raise ValueError(f'action not in background audio, audio={audio_str}')
-            if audio['action'] == 'begin':
-                check_by_audio_type(audio, background_mandatory_attrs_map, audio_str)
-            else:
-                if audio['action'] != 'end':
-                    raise ValueError(f'Unknown action, audio={audio_str}')
-        else:
-            raise ValueError(f'Unknown layout, audio={audio_str}')
-        #except Exception as err:
-        #    sys.stderr.write(f'PARSING ERROR: {err}, audio={json5.dumps(audio, indent=None)}\n')
-        #    all_clear = False
-
-
-def collect_and_check_audio_data(data):
-    fg_audio_id = 0
-    fg_audios = []
-    bg_audios = []
-    # Collect all the foreground and background audio ids used to calculate background audio length later
-    for audio in data:
-        if audio['layout'] == 'foreground':
-            audio['id'] = fg_audio_id
-            fg_audios.append(audio)
-            fg_audio_id += 1
-        else:   # background
-            if audio['action'] == 'begin':
-                audio['begin_fg_audio_id'] = fg_audio_id
-                bg_audios.append(audio)
-            else:   # ends
-                # find the backgound with the id, and update its 'end_fg_audio_id'
-                for bg_audio in bg_audios:
-                    if bg_audio['id'] == audio['id'] and bg_audio['audio_type'] == audio['audio_type']:
-                        bg_audio['end_fg_audio_id'] = fg_audio_id
-                        break
-    
-    # check if all background audios are valid
-    for bg_audio in bg_audios:
-        if 'begin_fg_audio_id' not in bg_audio:
-            raise ValueError(f'begin of background missing, audio={bg_audio}')
-        elif 'end_fg_audio_id' not in bg_audio:
-            raise ValueError(f'end of background missing, audio={bg_audio}')
-
-        if bg_audio['begin_fg_audio_id'] > bg_audio['end_fg_audio_id']:
-            raise ValueError(f'background audio ends before start, audio={bg_audio}')
-        elif bg_audio['begin_fg_audio_id'] == bg_audio['end_fg_audio_id']:
-            raise ValueError(f'background audio contains no foreground audio, audio={bg_audio}')
-        #except Exception as err:
-        #    sys.stderr.write(f'ALIGNMENT ERROR: {err}, audio={bg_audio}\n')
-        #    return None, None
-
-    return fg_audios, bg_audios
-
-
-class AudioCodeGenerator:
-    def __init__(self):
-        self.wav_counters = {
-            'bg_sound_effect': 0,
-            'bg_music': 0,
-            'idle': 0,
-            'fg_sound_effect': 0,
-            'fg_music': 0,
-            'fg_speech': 0,
-        }
-        self.code = ''
-    
-    def append_code(self, content):
-        self.code = f'{self.code}{content}\n'
-
-    def generate_code(self, fg_audios, bg_audios, output_path, result_filename):
-        def get_wav_name(audio):
-            audio_type = audio['audio_type']
-            layout = 'fg' if audio['layout'] == 'foreground' else 'bg'
-            wav_type = f'{layout}_{audio_type}' if layout else audio_type
-            desc = audio['text'] if 'text' in audio else audio['desc']
-            desc = utils.text_to_abbrev_prompt(desc)
-            wav_filename = f'{wav_type}_{self.wav_counters[wav_type]}_{desc}.wav'
-            self.wav_counters[wav_type] += 1
-            return wav_filename
-
-        header = f'''
-import os
-import sys
-import datetime
-
-from APIs import TTM, TTS, TTA, MIX, CAT, COMPUTE_LEN
-
-
-fg_audio_lens = []
-wav_path = \"{output_path.absolute()}/audio\"
-os.makedirs(wav_path, exist_ok=True)
-
-'''
-        self.append_code(header)
-
-        fg_audio_wavs = []
-        for fg_audio in fg_audios:
-            wav_name = get_wav_name(fg_audio)
-            if fg_audio['audio_type'] == 'sound_effect':
-                self.append_code(f'TTA(text=\"{fg_audio["desc"]}\", length={fg_audio["len"]}, volume={fg_audio["vol"]}, out_wav=os.path.join(wav_path, \"{wav_name}\"))')
-            elif fg_audio['audio_type'] == 'music':
-                self.append_code(f'TTM(text=\"{fg_audio["desc"]}\", length={fg_audio["len"]}, volume={fg_audio["vol"]}, out_wav=os.path.join(wav_path, \"{wav_name}\"))')
-            elif fg_audio['audio_type'] == 'speech':
-                npz_path = self.char_to_voice_map[fg_audio["character"]]["npz_path"]
-                npz_full_path = os.path.abspath(npz_path) if os.path.exists(npz_path) else npz_path
-                self.append_code(f'TTS(text=\"{fg_audio["text"]}\", speaker_id=\"{self.char_to_voice_map[fg_audio["character"]]["id"]}\", volume={fg_audio["vol"]}, out_wav=os.path.join(wav_path, \"{wav_name}\"), speaker_npz=\"{npz_full_path}\")')
-            fg_audio_wavs.append(wav_name)
-            self.append_code(f'fg_audio_lens.append(COMPUTE_LEN(os.path.join(wav_path, \"{wav_name}\")))\n')
-        
-        # cat all foreground audio together
-        self.append_code(f'fg_audio_wavs = []')
-        for wav_filename in fg_audio_wavs:
-            self.append_code(f'fg_audio_wavs.append(os.path.join(wav_path, \"{wav_filename}\"))')
-        self.append_code(f'CAT(wavs=fg_audio_wavs, out_wav=os.path.join(wav_path, \"foreground.wav\"))')
-
-        bg_audio_wavs = []
-        self.append_code(f'\nbg_audio_offsets = []')
-        for bg_audio in bg_audios:
-            wav_name = get_wav_name(bg_audio)
-            self.append_code(f'bg_audio_len = sum(fg_audio_lens[{bg_audio["begin_fg_audio_id"]}:{bg_audio["end_fg_audio_id"]}])')
-            self.append_code(f'bg_audio_offset = sum(fg_audio_lens[:{bg_audio["begin_fg_audio_id"]}])')
-            if bg_audio['audio_type'] == 'sound_effect':
-                self.append_code(f'TTA(text=\"{bg_audio["desc"]}\", volume={bg_audio["vol"]}, length=bg_audio_len, out_wav=os.path.join(wav_path, \"{wav_name}\"))')
-            elif bg_audio['audio_type'] == 'music':
-                self.append_code(f'TTM(text=\"{bg_audio["desc"]}\", volume={bg_audio["vol"]}, length=bg_audio_len, out_wav=os.path.join(wav_path, \"{wav_name}\"))')
-            else:
-                raise ValueError()
-            bg_audio_wavs.append(wav_name)
-            self.append_code(f'bg_audio_offsets.append(bg_audio_offset)\n')
-        self.append_code(f'bg_audio_wavs = []')
-        for wav_filename in bg_audio_wavs:
-            self.append_code(f'bg_audio_wavs.append(os.path.join(wav_path, \"{wav_filename}\"))')
-
-        self.append_code(f'bg_audio_wav_offset_pairs = list(zip(bg_audio_wavs, bg_audio_offsets))')
-        self.append_code(f'bg_audio_wav_offset_pairs.append((os.path.join(wav_path, \"foreground.wav\"), 0))')
-        self.append_code(f'MIX(wavs=bg_audio_wav_offset_pairs, out_wav=os.path.join(wav_path, \"{result_filename}.wav\"))')
-
-
-    def init_char_to_voice_map(self, filename):
-        with open(filename, 'r') as file:
-            self.char_to_voice_map = json5.load(file)
-
-
-    def parse_and_generate(self, script_filename, char_to_voice_map_filename, output_path, result_filename='result'):
-        self.code = ''
-        self.init_char_to_voice_map(char_to_voice_map_filename)
-
-        with open(script_filename, 'r') as file:
-            data = json5.load(file)
-
-        check_json_script(data)
-        fg_audios, bg_audios = collect_and_check_audio_data(data)
-        self.generate_code(fg_audios, bg_audios, output_path, result_filename)
-        return self.code

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/catalog.py

@@ -1,236 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import logging
-import types
-from collections import UserDict
-from typing import List
-
-from detectron2.utils.logger import log_first_n
-
-__all__ = ["DatasetCatalog", "MetadataCatalog", "Metadata"]
-
-
-class _DatasetCatalog(UserDict):
-    """
-    A global dictionary that stores information about the datasets and how to obtain them.
-
-    It contains a mapping from strings
-    (which are names that identify a dataset, e.g. "coco_2014_train")
-    to a function which parses the dataset and returns the samples in the
-    format of `list[dict]`.
-
-    The returned dicts should be in Detectron2 Dataset format (See DATASETS.md for details)
-    if used with the data loader functionalities in `data/build.py,data/detection_transform.py`.
-
-    The purpose of having this catalog is to make it easy to choose
-    different datasets, by just using the strings in the config.
-    """
-
-    def register(self, name, func):
-        """
-        Args:
-            name (str): the name that identifies a dataset, e.g. "coco_2014_train".
-            func (callable): a callable which takes no arguments and returns a list of dicts.
-                It must return the same results if called multiple times.
-        """
-        assert callable(func), "You must register a function with `DatasetCatalog.register`!"
-        assert name not in self, "Dataset '{}' is already registered!".format(name)
-        self[name] = func
-
-    def get(self, name):
-        """
-        Call the registered function and return its results.
-
-        Args:
-            name (str): the name that identifies a dataset, e.g. "coco_2014_train".
-
-        Returns:
-            list[dict]: dataset annotations.
-        """
-        try:
-            f = self[name]
-        except KeyError as e:
-            raise KeyError(
-                "Dataset '{}' is not registered! Available datasets are: {}".format(
-                    name, ", ".join(list(self.keys()))
-                )
-            ) from e
-        return f()
-
-    def list(self) -> List[str]:
-        """
-        List all registered datasets.
-
-        Returns:
-            list[str]
-        """
-        return list(self.keys())
-
-    def remove(self, name):
-        """
-        Alias of ``pop``.
-        """
-        self.pop(name)
-
-    def __str__(self):
-        return "DatasetCatalog(registered datasets: {})".format(", ".join(self.keys()))
-
-    __repr__ = __str__
-
-
-DatasetCatalog = _DatasetCatalog()
-DatasetCatalog.__doc__ = (
-    _DatasetCatalog.__doc__
-    + """
-    .. automethod:: detectron2.data.catalog.DatasetCatalog.register
-    .. automethod:: detectron2.data.catalog.DatasetCatalog.get
-"""
-)
-
-
-class Metadata(types.SimpleNamespace):
-    """
-    A class that supports simple attribute setter/getter.
-    It is intended for storing metadata of a dataset and make it accessible globally.
-
-    Examples:
-    ::
-        # somewhere when you load the data:
-        MetadataCatalog.get("mydataset").thing_classes = ["person", "dog"]
-
-        # somewhere when you print statistics or visualize:
-        classes = MetadataCatalog.get("mydataset").thing_classes
-    """
-
-    # the name of the dataset
-    # set default to N/A so that `self.name` in the errors will not trigger getattr again
-    name: str = "N/A"
-
-    _RENAMED = {
-        "class_names": "thing_classes",
-        "dataset_id_to_contiguous_id": "thing_dataset_id_to_contiguous_id",
-        "stuff_class_names": "stuff_classes",
-    }
-
-    def __getattr__(self, key):
-        if key in self._RENAMED:
-            log_first_n(
-                logging.WARNING,
-                "Metadata '{}' was renamed to '{}'!".format(key, self._RENAMED[key]),
-                n=10,
-            )
-            return getattr(self, self._RENAMED[key])
-
-        # "name" exists in every metadata
-        if len(self.__dict__) > 1:
-            raise AttributeError(
-                "Attribute '{}' does not exist in the metadata of dataset '{}'. Available "
-                "keys are {}.".format(key, self.name, str(self.__dict__.keys()))
-            )
-        else:
-            raise AttributeError(
-                f"Attribute '{key}' does not exist in the metadata of dataset '{self.name}': "
-                "metadata is empty."
-            )
-
-    def __setattr__(self, key, val):
-        if key in self._RENAMED:
-            log_first_n(
-                logging.WARNING,
-                "Metadata '{}' was renamed to '{}'!".format(key, self._RENAMED[key]),
-                n=10,
-            )
-            setattr(self, self._RENAMED[key], val)
-
-        # Ensure that metadata of the same name stays consistent
-        try:
-            oldval = getattr(self, key)
-            assert oldval == val, (
-                "Attribute '{}' in the metadata of '{}' cannot be set "
-                "to a different value!\n{} != {}".format(key, self.name, oldval, val)
-            )
-        except AttributeError:
-            super().__setattr__(key, val)
-
-    def as_dict(self):
-        """
-        Returns all the metadata as a dict.
-        Note that modifications to the returned dict will not reflect on the Metadata object.
-        """
-        return copy.copy(self.__dict__)
-
-    def set(self, **kwargs):
-        """
-        Set multiple metadata with kwargs.
-        """
-        for k, v in kwargs.items():
-            setattr(self, k, v)
-        return self
-
-    def get(self, key, default=None):
-        """
-        Access an attribute and return its value if exists.
-        Otherwise return default.
-        """
-        try:
-            return getattr(self, key)
-        except AttributeError:
-            return default
-
-
-class _MetadataCatalog(UserDict):
-    """
-    MetadataCatalog is a global dictionary that provides access to
-    :class:`Metadata` of a given dataset.
-
-    The metadata associated with a certain name is a singleton: once created, the
-    metadata will stay alive and will be returned by future calls to ``get(name)``.
-
-    It's like global variables, so don't abuse it.
-    It's meant for storing knowledge that's constant and shared across the execution
-    of the program, e.g.: the class names in COCO.
-    """
-
-    def get(self, name):
-        """
-        Args:
-            name (str): name of a dataset (e.g. coco_2014_train).
-
-        Returns:
-            Metadata: The :class:`Metadata` instance associated with this name,
-            or create an empty one if none is available.
-        """
-        assert len(name)
-        r = super().get(name, None)
-        if r is None:
-            r = self[name] = Metadata(name=name)
-        return r
-
-    def list(self):
-        """
-        List all registered metadata.
-
-        Returns:
-            list[str]: keys (names of datasets) of all registered metadata
-        """
-        return list(self.keys())
-
-    def remove(self, name):
-        """
-        Alias of ``pop``.
-        """
-        self.pop(name)
-
-    def __str__(self):
-        return "MetadataCatalog(registered metadata: {})".format(", ".join(self.keys()))
-
-    __repr__ = __str__
-
-
-MetadataCatalog = _MetadataCatalog()
-MetadataCatalog.__doc__ = (
-    _MetadataCatalog.__doc__
-    + """
-    .. automethod:: detectron2.data.catalog.MetadataCatalog.get
-"""
-)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/README.md

@@ -1,9 +0,0 @@
-
-
-### Common Datasets
-
-The dataset implemented here do not need to load the data into the final format.
-It should provide the minimal data structure needed to use the dataset, so it can be very efficient.
-
-For example, for an image dataset, just provide the file names and labels, but don't read the images.
-Let the downstream decide how to read.

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/__init__.py

@@ -1 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.

spaces/AzumaSeren100/XuanShen-Bert-VITS2/server.py

@@ -1,124 +0,0 @@
-from flask import Flask, request, Response
-from io import BytesIO
-import torch
-from av import open as avopen
-
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-from scipy.io import wavfile
-
-# Flask Init
-app = Flask(__name__)
-app.config['JSON_AS_ASCII'] = False
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    print([f"{p}{t}" for p, t in zip(phone, tone)])
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    bert = get_bert(norm_text, word2ph, language_str)
-
-    assert bert.shape[-1] == len(phone)
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-
-    return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid):
-    bert, phones, tones, lang_ids = get_text(text,"ZH", hps,)
-    with torch.no_grad():
-        x_tst=phones.to(dev).unsqueeze(0)
-        tones=tones.to(dev).unsqueeze(0)
-        lang_ids=lang_ids.to(dev).unsqueeze(0)
-        bert = bert.to(dev).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
-        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
-                           , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
-        return audio
-
-def replace_punctuation(text, i=2):
-    punctuation = "，。？！"
-    for char in punctuation:
-        text = text.replace(char, char * i)
-    return text
-
-def wav2(i, o, format):
-    inp = avopen(i, 'rb')
-    out = avopen(o, 'wb', format=format)
-    if format == "ogg": format = "libvorbis"
-
-    ostream = out.add_stream(format)
-
-    for frame in inp.decode(audio=0):
-        for p in ostream.encode(frame): out.mux(p)
-
-    for p in ostream.encode(None): out.mux(p)
-
-    out.close()
-    inp.close()
-
-# Load Generator
-hps = utils.get_hparams_from_file("./configs/config.json")
-
-dev='cuda'
-net_g = SynthesizerTrn(
-    len(symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    n_speakers=hps.data.n_speakers,
-    **hps.model).to(dev)
-_ = net_g.eval()
-
-# _ = utils.load_checkpoint("logs/G_649000.pth", net_g, None,skip_optimizer=True)
-_ = utils.load_checkpoint("logs/dxl/G_21000.pth", net_g, None,skip_optimizer=True)
-
-@app.route("/",methods=['GET','POST'])
-def main():
-    if request.method == 'GET':
-        try:
-            speaker = request.args.get('speaker')
-            text = request.args.get('text').replace("/n","")
-            sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
-            noise = float(request.args.get("noise", 0.5))
-            noisew = float(request.args.get("noisew", 0.6))
-            length = float(request.args.get("length", 1.2))
-            if length >= 2:
-                return "Too big length"
-            if len(text) >=200:
-                return "Too long text"
-            fmt = request.args.get("format", "wav")
-            if None in (speaker, text):
-                return "Missing Parameter"
-            if fmt not in ("mp3", "wav", "ogg"):
-                return "Invalid Format"
-        except:
-            return "Invalid Parameter"
-
-        with torch.no_grad():
-            audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
-
-        with BytesIO() as wav:
-            wavfile.write(wav, hps.data.sampling_rate, audio)
-            torch.cuda.empty_cache()
-            if fmt == "wav":
-                return Response(wav.getvalue(), mimetype="audio/wav")
-            wav.seek(0, 0)
-            with BytesIO() as ofp:
-                wav2(wav, ofp, fmt)
-                return Response(
-                    ofp.getvalue(),
-                    mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg"
-                )

spaces/Benson/text-generation/Examples/Descargar Gratis Gta 5 Mvil Apk Para Android.md

@@ -1,42 +0,0 @@
-<br />
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- <h4>P: ¿Cómo puedo obtener más monedas y llaves en Subway Surfers? </h4>
-<p>A: Hay varias maneras de conseguir más monedas y llaves en Subway Surfers. Puedes recogerlas en las pistas o comprarlas con dinero real. También puedes completar misiones, logros, desafíos diarios, eventos o ver anuncios para ganar más monedas y llaves. Sin embargo, si desea obtener monedas y llaves ilimitadas en Subway Surfers, puede utilizar Subway Surfers ilimitada hack 2023 apk download. </p>
- <h4>P: ¿Cómo puedo desbloquear todos los personajes y tablas en Subway Surfers? </h4>
-<p>A: Hay varias maneras de desbloquear todos los personajes y tablas en Subway Surfers. Puedes comprarlos con monedas o llaves o encontrarlos en las pistas. También puedes desbloquearlos completando ciertas misiones, logros, eventos o colecciones. Sin embargo, si desea desbloquear todos los personajes y tablas en Subway Surfers, puede utilizar Subway Surfers ilimitada hack 2023 apk download. </p>
- <h4>Q: ¿Cómo puedo actualizar Subway Surfers a la última versión? </h4> 64aa2da5cf<br />
-<br />
-<br />

spaces/BetterAPI/BetterChat_new/src/routes/conversation/[id]/+page.server.ts

@@ -1,33 +0,0 @@
-import type { PageServerLoad } from "./$types";
-import { collections } from "$lib/server/database";
-import { ObjectId } from "mongodb";
-import { error } from "@sveltejs/kit";
-
-export const load: PageServerLoad = async (event) => {
-	// todo: add validation on params.id
-	const conversation = await collections.conversations.findOne({
-		_id: new ObjectId(event.params.id),
-		sessionId: event.locals.sessionId,
-	});
-
-	if (!conversation) {
-		const conversationExists =
-			(await collections.conversations.countDocuments({
-				_id: new ObjectId(event.params.id),
-			})) !== 0;
-
-		if (conversationExists) {
-			throw error(
-				403,
-				"You don't have access to this conversation. If someone gave you this link, ask them to use the 'share' feature instead."
-			);
-		}
-
-		throw error(404, "Conversation not found.");
-	}
-
-	return {
-		messages: conversation.messages,
-		title: conversation.title,
-	};
-};

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/retries/standard.py

@@ -1,532 +0,0 @@
-"""Standard retry behavior.
-
-This contains the default standard retry behavior.
-It provides consistent behavior with other AWS SDKs.
-
-The key base classes uses for retries:
-
-    * ``BaseRetryableChecker`` - Use to check a specific condition that
-    indicates a retry should happen.  This can include things like
-    max attempts, HTTP status code checks, error code checks etc.
-    * ``RetryBackoff`` - Use to determine how long we should backoff until
-    we retry a request.  This is the class that will implement delay such
-    as exponential backoff.
-    * ``RetryPolicy`` - Main class that determines if a retry should
-    happen.  It can combine data from a various BaseRetryableCheckers
-    to make a final call as to whether or not a retry should happen.
-    It then uses a ``BaseRetryBackoff`` to determine how long to delay.
-    * ``RetryHandler`` - The bridge between botocore's event system
-    used by endpoint.py to manage retries and the interfaces defined
-    in this module.
-
-This allows us to define an API that has minimal coupling to the event
-based API used by botocore.
-
-"""
-import logging
-import random
-
-from botocore.exceptions import (
-    ConnectionError,
-    ConnectTimeoutError,
-    HTTPClientError,
-    ReadTimeoutError,
-)
-from botocore.retries import quota, special
-from botocore.retries.base import BaseRetryableChecker, BaseRetryBackoff
-
-DEFAULT_MAX_ATTEMPTS = 3
-logger = logging.getLogger(__name__)
-
-
-def register_retry_handler(client, max_attempts=DEFAULT_MAX_ATTEMPTS):
-    retry_quota = RetryQuotaChecker(quota.RetryQuota())
-
-    service_id = client.meta.service_model.service_id
-    service_event_name = service_id.hyphenize()
-    client.meta.events.register(
-        f'after-call.{service_event_name}', retry_quota.release_retry_quota
-    )
-
-    handler = RetryHandler(
-        retry_policy=RetryPolicy(
-            retry_checker=StandardRetryConditions(max_attempts=max_attempts),
-            retry_backoff=ExponentialBackoff(),
-        ),
-        retry_event_adapter=RetryEventAdapter(),
-        retry_quota=retry_quota,
-    )
-
-    unique_id = 'retry-config-%s' % service_event_name
-    client.meta.events.register(
-        'needs-retry.%s' % service_event_name,
-        handler.needs_retry,
-        unique_id=unique_id,
-    )
-    return handler
-
-
-class RetryHandler:
-    """Bridge between botocore's event system and this module.
-
-    This class is intended to be hooked to botocore's event system
-    as an event handler.
-    """
-
-    def __init__(self, retry_policy, retry_event_adapter, retry_quota):
-        self._retry_policy = retry_policy
-        self._retry_event_adapter = retry_event_adapter
-        self._retry_quota = retry_quota
-
-    def needs_retry(self, **kwargs):
-        """Connect as a handler to the needs-retry event."""
-        retry_delay = None
-        context = self._retry_event_adapter.create_retry_context(**kwargs)
-        if self._retry_policy.should_retry(context):
-            # Before we can retry we need to ensure we have sufficient
-            # capacity in our retry quota.
-            if self._retry_quota.acquire_retry_quota(context):
-                retry_delay = self._retry_policy.compute_retry_delay(context)
-                logger.debug(
-                    "Retry needed, retrying request after delay of: %s",
-                    retry_delay,
-                )
-            else:
-                logger.debug(
-                    "Retry needed but retry quota reached, "
-                    "not retrying request."
-                )
-        else:
-            logger.debug("Not retrying request.")
-        self._retry_event_adapter.adapt_retry_response_from_context(context)
-        return retry_delay
-
-
-class RetryEventAdapter:
-    """Adapter to existing retry interface used in the endpoints layer.
-
-    This existing interface for determining if a retry needs to happen
-    is event based and used in ``botocore.endpoint``.  The interface has
-    grown organically over the years and could use some cleanup.  This
-    adapter converts that interface into the interface used by the
-    new retry strategies.
-
-    """
-
-    def create_retry_context(self, **kwargs):
-        """Create context based on needs-retry kwargs."""
-        response = kwargs['response']
-        if response is None:
-            # If response is None it means that an exception was raised
-            # because we never received a response from the service.  This
-            # could be something like a ConnectionError we get from our
-            # http layer.
-            http_response = None
-            parsed_response = None
-        else:
-            http_response, parsed_response = response
-        # This provides isolation between the kwargs emitted in the
-        # needs-retry event, and what this module uses to check for
-        # retries.
-        context = RetryContext(
-            attempt_number=kwargs['attempts'],
-            operation_model=kwargs['operation'],
-            http_response=http_response,
-            parsed_response=parsed_response,
-            caught_exception=kwargs['caught_exception'],
-            request_context=kwargs['request_dict']['context'],
-        )
-        return context
-
-    def adapt_retry_response_from_context(self, context):
-        """Modify response back to user back from context."""
-        # This will mutate attributes that are returned back to the end
-        # user.  We do it this way so that all the various retry classes
-        # don't mutate any input parameters from the needs-retry event.
-        metadata = context.get_retry_metadata()
-        if context.parsed_response is not None:
-            context.parsed_response.setdefault('ResponseMetadata', {}).update(
-                metadata
-            )
-
-
-# Implementation note: this is meant to encapsulate all the misc stuff
-# that gets sent in the needs-retry event.  This is mapped so that params
-# are more clear and explicit.
-class RetryContext:
-    """Normalize a response that we use to check if a retry should occur.
-
-    This class smoothes over the different types of responses we may get
-    from a service including:
-
-        * A modeled error response from the service that contains a service
-          code and error message.
-        * A raw HTTP response that doesn't contain service protocol specific
-          error keys.
-        * An exception received while attempting to retrieve a response.
-          This could be a ConnectionError we receive from our HTTP layer which
-          could represent that we weren't able to receive a response from
-          the service.
-
-    This class guarantees that at least one of the above attributes will be
-    non None.
-
-    This class is meant to provide a read-only view into the properties
-    associated with a possible retryable response.  None of the properties
-    are meant to be modified directly.
-
-    """
-
-    def __init__(
-        self,
-        attempt_number,
-        operation_model=None,
-        parsed_response=None,
-        http_response=None,
-        caught_exception=None,
-        request_context=None,
-    ):
-        # 1-based attempt number.
-        self.attempt_number = attempt_number
-        self.operation_model = operation_model
-        # This is the parsed response dictionary we get from parsing
-        # the HTTP response from the service.
-        self.parsed_response = parsed_response
-        # This is an instance of botocore.awsrequest.AWSResponse.
-        self.http_response = http_response
-        # This is a subclass of Exception that will be non None if
-        # an exception was raised when retrying to retrieve a response.
-        self.caught_exception = caught_exception
-        # This is the request context dictionary that's added to the
-        # request dict.  This is used to story any additional state
-        # about the request.  We use this for storing retry quota
-        # capacity.
-        if request_context is None:
-            request_context = {}
-        self.request_context = request_context
-        self._retry_metadata = {}
-
-    # These are misc helper methods to avoid duplication in the various
-    # checkers.
-    def get_error_code(self):
-        """Check if there was a parsed response with an error code.
-
-        If we could not find any error codes, ``None`` is returned.
-
-        """
-        if self.parsed_response is None:
-            return
-        error = self.parsed_response.get('Error', {})
-        if not isinstance(error, dict):
-            return
-        return error.get('Code')
-
-    def add_retry_metadata(self, **kwargs):
-        """Add key/value pairs to the retry metadata.
-
-        This allows any objects during the retry process to add
-        metadata about any checks/validations that happened.
-
-        This gets added to the response metadata in the retry handler.
-
-        """
-        self._retry_metadata.update(**kwargs)
-
-    def get_retry_metadata(self):
-        return self._retry_metadata.copy()
-
-
-class RetryPolicy:
-    def __init__(self, retry_checker, retry_backoff):
-        self._retry_checker = retry_checker
-        self._retry_backoff = retry_backoff
-
-    def should_retry(self, context):
-        return self._retry_checker.is_retryable(context)
-
-    def compute_retry_delay(self, context):
-        return self._retry_backoff.delay_amount(context)
-
-
-class ExponentialBackoff(BaseRetryBackoff):
-
-    _BASE = 2
-    _MAX_BACKOFF = 20
-
-    def __init__(self, max_backoff=20, random=random.random):
-        self._base = self._BASE
-        self._max_backoff = max_backoff
-        self._random = random
-
-    def delay_amount(self, context):
-        """Calculates delay based on exponential backoff.
-
-        This class implements truncated binary exponential backoff
-        with jitter::
-
-            t_i = min(rand(0, 1) * 2 ** attempt, MAX_BACKOFF)
-
-        where ``i`` is the request attempt (0 based).
-
-        """
-        # The context.attempt_number is a 1-based value, but we have
-        # to calculate the delay based on i based a 0-based value.  We
-        # want the first delay to just be ``rand(0, 1)``.
-        return min(
-            self._random() * (self._base ** (context.attempt_number - 1)),
-            self._max_backoff,
-        )
-
-
-class MaxAttemptsChecker(BaseRetryableChecker):
-    def __init__(self, max_attempts):
-        self._max_attempts = max_attempts
-
-    def is_retryable(self, context):
-        under_max_attempts = context.attempt_number < self._max_attempts
-        retries_context = context.request_context.get('retries')
-        if retries_context:
-            retries_context['max'] = max(
-                retries_context.get('max', 0), self._max_attempts
-            )
-        if not under_max_attempts:
-            logger.debug("Max attempts of %s reached.", self._max_attempts)
-            context.add_retry_metadata(MaxAttemptsReached=True)
-        return under_max_attempts
-
-
-class TransientRetryableChecker(BaseRetryableChecker):
-    _TRANSIENT_ERROR_CODES = [
-        'RequestTimeout',
-        'RequestTimeoutException',
-        'PriorRequestNotComplete',
-    ]
-    _TRANSIENT_STATUS_CODES = [500, 502, 503, 504]
-    _TRANSIENT_EXCEPTION_CLS = (
-        ConnectionError,
-        HTTPClientError,
-    )
-
-    def __init__(
-        self,
-        transient_error_codes=None,
-        transient_status_codes=None,
-        transient_exception_cls=None,
-    ):
-        if transient_error_codes is None:
-            transient_error_codes = self._TRANSIENT_ERROR_CODES[:]
-        if transient_status_codes is None:
-            transient_status_codes = self._TRANSIENT_STATUS_CODES[:]
-        if transient_exception_cls is None:
-            transient_exception_cls = self._TRANSIENT_EXCEPTION_CLS
-        self._transient_error_codes = transient_error_codes
-        self._transient_status_codes = transient_status_codes
-        self._transient_exception_cls = transient_exception_cls
-
-    def is_retryable(self, context):
-        if context.get_error_code() in self._transient_error_codes:
-            return True
-        if context.http_response is not None:
-            if (
-                context.http_response.status_code
-                in self._transient_status_codes
-            ):
-                return True
-        if context.caught_exception is not None:
-            return isinstance(
-                context.caught_exception, self._transient_exception_cls
-            )
-        return False
-
-
-class ThrottledRetryableChecker(BaseRetryableChecker):
-    # This is the union of all error codes we've seen that represent
-    # a throttled error.
-    _THROTTLED_ERROR_CODES = [
-        'Throttling',
-        'ThrottlingException',
-        'ThrottledException',
-        'RequestThrottledException',
-        'TooManyRequestsException',
-        'ProvisionedThroughputExceededException',
-        'TransactionInProgressException',
-        'RequestLimitExceeded',
-        'BandwidthLimitExceeded',
-        'LimitExceededException',
-        'RequestThrottled',
-        'SlowDown',
-        'PriorRequestNotComplete',
-        'EC2ThrottledException',
-    ]
-
-    def __init__(self, throttled_error_codes=None):
-        if throttled_error_codes is None:
-            throttled_error_codes = self._THROTTLED_ERROR_CODES[:]
-        self._throttled_error_codes = throttled_error_codes
-
-    def is_retryable(self, context):
-        # Only the error code from a parsed service response is used
-        # to determine if the response is a throttled response.
-        return context.get_error_code() in self._throttled_error_codes
-
-
-class ModeledRetryableChecker(BaseRetryableChecker):
-    """Check if an error has been modeled as retryable."""
-
-    def __init__(self):
-        self._error_detector = ModeledRetryErrorDetector()
-
-    def is_retryable(self, context):
-        error_code = context.get_error_code()
-        if error_code is None:
-            return False
-        return self._error_detector.detect_error_type(context) is not None
-
-
-class ModeledRetryErrorDetector:
-    """Checks whether or not an error is a modeled retryable error."""
-
-    # There are return values from the detect_error_type() method.
-    TRANSIENT_ERROR = 'TRANSIENT_ERROR'
-    THROTTLING_ERROR = 'THROTTLING_ERROR'
-    # This class is lower level than ModeledRetryableChecker, which
-    # implements BaseRetryableChecker.  This object allows you to distinguish
-    # between the various types of retryable errors.
-
-    def detect_error_type(self, context):
-        """Detect the error type associated with an error code and model.
-
-        This will either return:
-
-            * ``self.TRANSIENT_ERROR`` - If the error is a transient error
-            * ``self.THROTTLING_ERROR`` - If the error is a throttling error
-            * ``None`` - If the error is neither type of error.
-
-        """
-        error_code = context.get_error_code()
-        op_model = context.operation_model
-        if op_model is None or not op_model.error_shapes:
-            return
-        for shape in op_model.error_shapes:
-            if shape.metadata.get('retryable') is not None:
-                # Check if this error code matches the shape.  This can
-                # be either by name or by a modeled error code.
-                error_code_to_check = (
-                    shape.metadata.get('error', {}).get('code') or shape.name
-                )
-                if error_code == error_code_to_check:
-                    if shape.metadata['retryable'].get('throttling'):
-                        return self.THROTTLING_ERROR
-                    return self.TRANSIENT_ERROR
-
-
-class ThrottlingErrorDetector:
-    def __init__(self, retry_event_adapter):
-        self._modeled_error_detector = ModeledRetryErrorDetector()
-        self._fixed_error_code_detector = ThrottledRetryableChecker()
-        self._retry_event_adapter = retry_event_adapter
-
-    # This expects the kwargs from needs-retry to be passed through.
-    def is_throttling_error(self, **kwargs):
-        context = self._retry_event_adapter.create_retry_context(**kwargs)
-        if self._fixed_error_code_detector.is_retryable(context):
-            return True
-        error_type = self._modeled_error_detector.detect_error_type(context)
-        return error_type == self._modeled_error_detector.THROTTLING_ERROR
-
-
-class StandardRetryConditions(BaseRetryableChecker):
-    """Concrete class that implements the standard retry policy checks.
-
-    Specifically:
-
-        not max_attempts and (transient or throttled or modeled_retry)
-
-    """
-
-    def __init__(self, max_attempts=DEFAULT_MAX_ATTEMPTS):
-        # Note: This class is for convenience so you can have the
-        # standard retry condition in a single class.
-        self._max_attempts_checker = MaxAttemptsChecker(max_attempts)
-        self._additional_checkers = OrRetryChecker(
-            [
-                TransientRetryableChecker(),
-                ThrottledRetryableChecker(),
-                ModeledRetryableChecker(),
-                OrRetryChecker(
-                    [
-                        special.RetryIDPCommunicationError(),
-                        special.RetryDDBChecksumError(),
-                    ]
-                ),
-            ]
-        )
-
-    def is_retryable(self, context):
-        return self._max_attempts_checker.is_retryable(
-            context
-        ) and self._additional_checkers.is_retryable(context)
-
-
-class OrRetryChecker(BaseRetryableChecker):
-    def __init__(self, checkers):
-        self._checkers = checkers
-
-    def is_retryable(self, context):
-        return any(checker.is_retryable(context) for checker in self._checkers)
-
-
-class RetryQuotaChecker:
-    _RETRY_COST = 5
-    _NO_RETRY_INCREMENT = 1
-    _TIMEOUT_RETRY_REQUEST = 10
-    _TIMEOUT_EXCEPTIONS = (ConnectTimeoutError, ReadTimeoutError)
-
-    # Implementation note:  We're not making this a BaseRetryableChecker
-    # because this isn't just a check if we can retry.  This also changes
-    # state so we have to careful when/how we call this.  Making it
-    # a BaseRetryableChecker implies you can call .is_retryable(context)
-    # as many times as you want and not affect anything.
-
-    def __init__(self, quota):
-        self._quota = quota
-        # This tracks the last amount
-        self._last_amount_acquired = None
-
-    def acquire_retry_quota(self, context):
-        if self._is_timeout_error(context):
-            capacity_amount = self._TIMEOUT_RETRY_REQUEST
-        else:
-            capacity_amount = self._RETRY_COST
-        success = self._quota.acquire(capacity_amount)
-        if success:
-            # We add the capacity amount to the request context so we know
-            # how much to release later.  The capacity amount can vary based
-            # on the error.
-            context.request_context['retry_quota_capacity'] = capacity_amount
-            return True
-        context.add_retry_metadata(RetryQuotaReached=True)
-        return False
-
-    def _is_timeout_error(self, context):
-        return isinstance(context.caught_exception, self._TIMEOUT_EXCEPTIONS)
-
-    # This is intended to be hooked up to ``after-call``.
-    def release_retry_quota(self, context, http_response, **kwargs):
-        # There's three possible options.
-        # 1. The HTTP response did not have a 2xx response.  In that case we
-        #    give no quota back.
-        # 2. The HTTP request was successful and was never retried.  In
-        #    that case we give _NO_RETRY_INCREMENT back.
-        # 3. The API call had retries, and we eventually receive an HTTP
-        #    response with a 2xx status code.  In that case we give back
-        #    whatever quota was associated with the last acquisition.
-        if http_response is None:
-            return
-        status_code = http_response.status_code
-        if 200 <= status_code < 300:
-            if 'retry_quota_capacity' not in context:
-                self._quota.release(self._NO_RETRY_INCREMENT)
-            else:
-                capacity_amount = context['retry_quota_capacity']
-                self._quota.release(capacity_amount)

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

@@ -1,37 +0,0 @@
-import logging
-import os
-from typing import Optional
-
-from pip._vendor.pyproject_hooks import BuildBackendHookCaller
-
-from pip._internal.utils.subprocess import runner_with_spinner_message
-
-logger = logging.getLogger(__name__)
-
-
-def build_wheel_pep517(
-    name: str,
-    backend: BuildBackendHookCaller,
-    metadata_directory: str,
-    tempd: str,
-) -> Optional[str]:
-    """Build one InstallRequirement using the PEP 517 build process.
-
-    Returns path to wheel if successfully built. Otherwise, returns None.
-    """
-    assert metadata_directory is not None
-    try:
-        logger.debug("Destination directory: %s", tempd)
-
-        runner = runner_with_spinner_message(
-            f"Building wheel for {name} (pyproject.toml)"
-        )
-        with backend.subprocess_runner(runner):
-            wheel_name = backend.build_wheel(
-                tempd,
-                metadata_directory=metadata_directory,
-            )
-    except Exception:
-        logger.error("Failed building wheel for %s", name)
-        return None
-    return os.path.join(tempd, wheel_name)

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/build.py

@@ -1,153 +0,0 @@
-"""distutils.command.build
-
-Implements the Distutils 'build' command."""
-
-import sys
-import os
-from distutils.core import Command
-from distutils.errors import DistutilsOptionError
-from distutils.util import get_platform
-
-
-def show_compilers():
-    from distutils.ccompiler import show_compilers
-
-    show_compilers()
-
-
-class build(Command):
-
-    description = "build everything needed to install"
-
-    user_options = [
-        ('build-base=', 'b', "base directory for build library"),
-        ('build-purelib=', None, "build directory for platform-neutral distributions"),
-        ('build-platlib=', None, "build directory for platform-specific distributions"),
-        (
-            'build-lib=',
-            None,
-            "build directory for all distribution (defaults to either "
-            + "build-purelib or build-platlib",
-        ),
-        ('build-scripts=', None, "build directory for scripts"),
-        ('build-temp=', 't', "temporary build directory"),
-        (
-            'plat-name=',
-            'p',
-            "platform name to build for, if supported "
-            "(default: %s)" % get_platform(),
-        ),
-        ('compiler=', 'c', "specify the compiler type"),
-        ('parallel=', 'j', "number of parallel build jobs"),
-        ('debug', 'g', "compile extensions and libraries with debugging information"),
-        ('force', 'f', "forcibly build everything (ignore file timestamps)"),
-        ('executable=', 'e', "specify final destination interpreter path (build.py)"),
-    ]
-
-    boolean_options = ['debug', 'force']
-
-    help_options = [
-        ('help-compiler', None, "list available compilers", show_compilers),
-    ]
-
-    def initialize_options(self):
-        self.build_base = 'build'
-        # these are decided only after 'build_base' has its final value
-        # (unless overridden by the user or client)
-        self.build_purelib = None
-        self.build_platlib = None
-        self.build_lib = None
-        self.build_temp = None
-        self.build_scripts = None
-        self.compiler = None
-        self.plat_name = None
-        self.debug = None
-        self.force = 0
-        self.executable = None
-        self.parallel = None
-
-    def finalize_options(self):  # noqa: C901
-        if self.plat_name is None:
-            self.plat_name = get_platform()
-        else:
-            # plat-name only supported for windows (other platforms are
-            # supported via ./configure flags, if at all).  Avoid misleading
-            # other platforms.
-            if os.name != 'nt':
-                raise DistutilsOptionError(
-                    "--plat-name only supported on Windows (try "
-                    "using './configure --help' on your platform)"
-                )
-
-        plat_specifier = ".{}-{}".format(self.plat_name, sys.implementation.cache_tag)
-
-        # Make it so Python 2.x and Python 2.x with --with-pydebug don't
-        # share the same build directories. Doing so confuses the build
-        # process for C modules
-        if hasattr(sys, 'gettotalrefcount'):
-            plat_specifier += '-pydebug'
-
-        # 'build_purelib' and 'build_platlib' just default to 'lib' and
-        # 'lib.<plat>' under the base build directory.  We only use one of
-        # them for a given distribution, though --
-        if self.build_purelib is None:
-            self.build_purelib = os.path.join(self.build_base, 'lib')
-        if self.build_platlib is None:
-            self.build_platlib = os.path.join(self.build_base, 'lib' + plat_specifier)
-
-        # 'build_lib' is the actual directory that we will use for this
-        # particular module distribution -- if user didn't supply it, pick
-        # one of 'build_purelib' or 'build_platlib'.
-        if self.build_lib is None:
-            if self.distribution.has_ext_modules():
-                self.build_lib = self.build_platlib
-            else:
-                self.build_lib = self.build_purelib
-
-        # 'build_temp' -- temporary directory for compiler turds,
-        # "build/temp.<plat>"
-        if self.build_temp is None:
-            self.build_temp = os.path.join(self.build_base, 'temp' + plat_specifier)
-        if self.build_scripts is None:
-            self.build_scripts = os.path.join(
-                self.build_base, 'scripts-%d.%d' % sys.version_info[:2]
-            )
-
-        if self.executable is None and sys.executable:
-            self.executable = os.path.normpath(sys.executable)
-
-        if isinstance(self.parallel, str):
-            try:
-                self.parallel = int(self.parallel)
-            except ValueError:
-                raise DistutilsOptionError("parallel should be an integer")
-
-    def run(self):
-        # Run all relevant sub-commands.  This will be some subset of:
-        #  - build_py      - pure Python modules
-        #  - build_clib    - standalone C libraries
-        #  - build_ext     - Python extensions
-        #  - build_scripts - (Python) scripts
-        for cmd_name in self.get_sub_commands():
-            self.run_command(cmd_name)
-
-    # -- Predicates for the sub-command list ---------------------------
-
-    def has_pure_modules(self):
-        return self.distribution.has_pure_modules()
-
-    def has_c_libraries(self):
-        return self.distribution.has_c_libraries()
-
-    def has_ext_modules(self):
-        return self.distribution.has_ext_modules()
-
-    def has_scripts(self):
-        return self.distribution.has_scripts()
-
-    sub_commands = [
-        ('build_py', has_pure_modules),
-        ('build_clib', has_c_libraries),
-        ('build_ext', has_ext_modules),
-        ('build_scripts', has_scripts),
-    ]

spaces/CALM/Dashboard/streamlit_observable/frontend/src/Observable.tsx

@@ -1,161 +0,0 @@
-import React, { ReactNode } from "react"
-import {
-  withStreamlitConnection,
-  StreamlitComponentBase,
-  Streamlit,
-} from "./streamlit"
-import { Runtime, Inspector } from "@observablehq/runtime";
-
-class Observable extends StreamlitComponentBase<{}> {
-  public observeValue = {};
-  private notebookRef = React.createRef<HTMLDivElement>();
-  private runtime: any = null;
-  private main: any = null;
-
-  componentWillUnmount() {
-    this.runtime?.dispose();
-  }
-  // @ts-ignore
-  public componentDidUpdate(prevProps: any) {
-    const { args: prevArgs } = prevProps;
-    if (prevArgs.notebook !== this.props.args.notebook) {
-      // TODO handle new notebook
-    }
-    console.log('this.props.args.redefine: ', this.props.args.redefine);
-    if (this.main !== null) {
-      this.redefineCells(this.main, this.props.args.redefine);
-    }
-  }
-
-  async embedNotebook(notebook: string, targets: string[], observe: string[], hide:string[]) {
-    if (this.runtime) {
-      this.runtime.dispose();
-    }
-    
-    console.log('Console says hi!');
-
-    const targetSet = new Set(targets);
-    const observeSet = new Set(observe);
-    const hideSet = new Set(hide);
-    this.runtime = new Runtime();
-    const { default: define } = await eval(`import("https://api.observablehq.com/${notebook}.js?v=3")`);
-
-    this.main = this.runtime.module(define, (name: string) => {
-      console.log('name: ', name);
-      console.log('observeSet.has(name: ', observeSet.has(name));
-      console.log('targetSet.has(name): ', targetSet.has(name));
-      if (observeSet.has(name) && !targetSet.has(name)) {
-        const observeValue = this.observeValue;
-    
-        console.log('observeValue: ', observeValue);
-    
-        return {
-          fulfilled: (value: any) => {
-            //@ts-ignore
-            observeValue[name] = value;
-            //@ts-ignore
-            Streamlit.setComponentValue(observeValue);
-          }
-        }
-      }
-      if (targetSet.size > 0 && !targetSet.has(name)) return;
-      if(hideSet.has(name)) return true;
-      const el = document.createElement('div');
-      this.notebookRef.current?.appendChild(el);
-
-      const i = new Inspector(el);
-      el.addEventListener('input', e => {
-        Streamlit.setFrameHeight();
-      })
-      return {
-        pending() {
-          i.pending();
-          Streamlit.setFrameHeight();
-        },
-        fulfilled(value: any) {
-          i.fulfilled(value);
-          Streamlit.setFrameHeight();
-        },
-        rejected(error: any) {
-          i.rejected(error);
-          Streamlit.setFrameHeight();
-        },
-      };
-    });
-    if (observeSet.size > 0) {
-      Promise.all(Array.from(observeSet).map(async name => [name, await this.main.value(name)])).then(initial => {
-        for (const [name, value] of initial) {
-          // @ts-ignore
-          this.observeValue[name] = value
-        };
-        Streamlit.setComponentValue(this.observeValue);
-      })
-    }
-  }
-
-  redefineCells(main: any, redefine = {}) {
-    
-    console.log('Console says hi 2 !');
-
-    for (let cell in redefine) {
-      //@ts-ignore
-      main.redefine(cell, redefine[cell]);
-    }
-  }
-  componentDidMount() {
-    const { notebook, targets = [], observe = [], redefine = {} , hide=[]} = this.props.args;
-    Streamlit.setComponentValue(this.observeValue);
-    this.embedNotebook(notebook, targets, observe, hide).then(() => {
-      this.redefineCells(this.main, redefine);
-    });
-
-  }
-
-  public render = (): ReactNode => {
-    
-    console.log('this.props.args.render_empty: ', this.props.args.render_empty);
-    if (this.props.args.render_empty) {
-      return (
-        <div >
-          <div style={{ padding: '9px 12px' }}>
-            <div ref={this.notebookRef}></div>
-          </div>
-          <div style={{ marginTop: '4px' }}>
-            
-            <div >
-              <div style={{textAlign:"left"}}>{this.props.args.name}</div>
-              <div style={{textAlign:"right"}}>
-              <a href={`https://observablehq.com/${this.props.args.notebook}`} style={{ color: '#666', }}></a>
-              </div>
-            </div>
-          </div>
-        </div >
-      )
-    }
-    return (
-      <div style={{ border: '1px solid gray', borderRadius: '4px' }}>
-        <div style={{ padding: '9px 12px' }}>
-          <div ref={this.notebookRef}></div>
-        </div>
-        <div style={{ marginTop: '4px' }}>
-          
-          <div style={{
-            backgroundColor: '#ddd',
-            fontWeight: 700,
-            padding: ".25rem .5rem",
-            borderRadius: '0 0 4px 4px',
-            gridTemplateColumns: "auto auto",
-            display:"grid"
-          }}>
-            <div style={{textAlign:"left"}}>{this.props.args.name}</div>
-            <div style={{textAlign:"right"}}>
-            <a href={`https://observablehq.com/${this.props.args.notebook}`} style={{ color: '#666', }}></a>
-            </div>
-          </div>
-        </div>
-      </div >
-    )
-  }
-}
-
-export default withStreamlitConnection(Observable)

spaces/ChrisCaviar/ControlNet-v1-1/app_mlsd.py

@@ -1,113 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button('Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='Number of images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    preprocess_resolution = gr.Slider(
-                        label='Preprocess resolution',
-                        minimum=128,
-                        maximum=512,
-                        value=512,
-                        step=1)
-                    mlsd_value_threshold = gr.Slider(
-                        label='Hough value threshold (MLSD)',
-                        minimum=0.01,
-                        maximum=2.0,
-                        value=0.1,
-                        step=0.01)
-                    mlsd_distance_threshold = gr.Slider(
-                        label='Hough distance threshold (MLSD)',
-                        minimum=0.01,
-                        maximum=20.0,
-                        value=0.1,
-                        step=0.01)
-                    num_steps = gr.Slider(label='Number of steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=1000000,
-                                     step=1,
-                                     value=0,
-                                     randomize=True)
-                    randomize_seed = gr.Checkbox(label='Randomize seed',
-                                                 value=True)
-                    a_prompt = gr.Textbox(
-                        label='Additional prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output', show_label=False).style(
-                    columns=2, object_fit='scale-down')
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            preprocess_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            mlsd_value_threshold,
-            mlsd_distance_threshold,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name='mlsd',
-        )
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='MLSD')
-    demo = create_demo(model.process_mlsd)
-    demo.queue().launch()

spaces/Cloudfeng/anime-remove-background/README.md

@@ -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

spaces/CofAI/chat.b4/client/css/global.css

@@ -1,70 +0,0 @@
-@import url("https://fonts.googleapis.com/css2?family=Inter:wght@100;200;300;400;500;600;700;800;900&display=swap");
-* {
-	--font-1: "Inter", sans-serif;
-	--section-gap: 24px;
-	--border-radius-1: 8px;
-	margin: 0;
-	padding: 0;
-	box-sizing: border-box;
-	position: relative;
-	font-family: var(--font-1);
-}
-
-.theme-light {
-	--colour-1: #f5f5f5;
-	--colour-2: #000000;
-	--colour-3: #474747;
-	--colour-4: #949494;
-	--colour-5: #ebebeb;
-	--colour-6: #dadada;
-
-	--accent: #3a3a3a;
-	--blur-bg: #ffffff;
-	--blur-border: #dbdbdb;
-	--user-input: #282828;
-	--conversations: #666666;
-}
-
-.theme-dark {
-	--colour-1: #181818;
-	--colour-2: #ccc;
-	--colour-3: #dadada;
-	--colour-4: #f0f0f0;
-	--colour-5: #181818;
-	--colour-6: #242424;
-
-	--accent: #151718;
-	--blur-bg: #242627;
-	--blur-border: #242627;
-	--user-input: #f5f5f5;
-	--conversations: #555555;
-}
-
-html,
-body {
-	background: var(--colour-1);
-	color: var(--colour-3);
-}
-
-ol,
-ul {
-	padding-left: 20px;
-}
-
-.shown {
-	display: flex !important;
-}
-
-a:-webkit-any-link {
-	color: var(--accent);
-}
-
-pre {
-	white-space: pre-wrap;
-}
-
-@media screen and (max-height: 720px) {
-	:root {
-		--section-gap: 16px;
-	}
-}

spaces/CoreyMorris/MMLU-by-task-Leaderboard/README.md

@@ -1,9 +0,0 @@
----
-title: MMLU By Task Leaderboard
-emoji: 🏆
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Cpp4App/Cpp4App/CDM/result_processing/eval_classes.py

@@ -1,215 +0,0 @@
-import json
-import numpy as np
-import cv2
-from glob import glob
-from os.path import join as pjoin
-from tqdm import tqdm
-
-class_map = {'0':'Button', '1':'CheckBox', '2':'Chronometer', '3':'EditText', '4':'ImageButton', '5':'ImageView',
-               '6':'ProgressBar', '7':'RadioButton', '8':'RatingBar', '9':'SeekBar', '10':'Spinner', '11':'Switch',
-               '12':'ToggleButton', '13':'VideoView', '14':'TextView'}
-
-
-def resize_label(bboxes, d_height, gt_height, bias=0):
-    bboxes_new = []
-    scale = gt_height / d_height
-    for bbox in bboxes:
-        bbox = [int(b * scale + bias) for b in bbox]
-        bboxes_new.append(bbox)
-    return bboxes_new
-
-
-def draw_bounding_box(org, corners, color=(0, 255, 0), line=2, show=False):
-    board = org.copy()
-    for i in range(len(corners)):
-        board = cv2.rectangle(board, (corners[i][0], corners[i][1]), (corners[i][2], corners[i][3]), color, line)
-    if show:
-        cv2.imshow('a', cv2.resize(board, (500, 1000)))
-        cv2.waitKey(0)
-    return board
-
-
-def load_detect_result_json(reslut_file_root, shrink=4):
-    def is_bottom_or_top(corner):
-        column_min, row_min, column_max, row_max = corner
-        if row_max < 36 or row_min > 725:
-            return True
-        return False
-
-    result_files = glob(pjoin(reslut_file_root, '*.json'))
-    compos_reform = {}
-    print('Loading %d detection results' % len(result_files))
-    for reslut_file in tqdm(result_files):
-        img_name = reslut_file.split('\\')[-1].split('.')[0]
-        compos = json.load(open(reslut_file, 'r'))['compos']
-        for compo in compos:
-            if compo['column_max'] - compo['column_min'] < 10 or compo['row_max'] - compo['row_min'] < 10:
-                continue
-            if is_bottom_or_top((compo['column_min'], compo['row_min'], compo['column_max'], compo['row_max'])):
-                continue
-            if img_name not in compos_reform:
-                compos_reform[img_name] = {'bboxes': [[compo['column_min'] + shrink, compo['row_min'] + shrink, compo['column_max'] - shrink, compo['row_max'] - shrink]],
-                                           'categories': [compo['category']]}
-            else:
-                compos_reform[img_name]['bboxes'].append([compo['column_min'] + shrink, compo['row_min'] + shrink, compo['column_max'] - shrink, compo['row_max'] - shrink])
-                compos_reform[img_name]['categories'].append(compo['category'])
-    return compos_reform
-
-
-def load_ground_truth_json(gt_file):
-    def get_img_by_id(img_id):
-        for image in images:
-            if image['id'] == img_id:
-                return image['file_name'].split('/')[-1][:-4], (image['height'], image['width'])
-
-    def cvt_bbox(bbox):
-        '''
-        :param bbox: [x,y,width,height]
-        :return: [col_min, row_min, col_max, row_max]
-        '''
-        bbox = [int(b) for b in bbox]
-        return [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]
-
-    data = json.load(open(gt_file, 'r'))
-    images = data['images']
-    annots = data['annotations']
-    compos = {}
-    print('Loading %d ground truth' % len(annots))
-    for annot in tqdm(annots):
-        img_name, size = get_img_by_id(annot['image_id'])
-        if img_name not in compos:
-            compos[img_name] = {'bboxes': [cvt_bbox(annot['bbox'])], 'categories': [class_map[str(annot['category_id'])]], 'size': size}
-        else:
-            compos[img_name]['bboxes'].append(cvt_bbox(annot['bbox']))
-            compos[img_name]['categories'].append(class_map[str(annot['category_id'])])
-    return compos
-
-
-def eval(detection, ground_truth, img_root, show=True, no_text=False, only_text=False):
-    def compo_filter(compos, flag):
-        if not no_text and not only_text:
-            return compos
-        compos_new = {'bboxes': [], 'categories': []}
-        for k, category in enumerate(compos['categories']):
-            if only_text:
-                if flag == 'det' and category != 'TextView':
-                    continue
-                if flag == 'gt' and category != 'TextView':
-                    continue
-            elif no_text:
-                if flag == 'det' and category == 'TextView':
-                    continue
-                if flag == 'gt' and category == 'TextView':
-                    continue
-
-            compos_new['bboxes'].append(compos['bboxes'][k])
-            compos_new['categories'].append(category)
-        return compos_new
-
-    def match(org, d_bbox, d_category, gt_compos, matched):
-        '''
-        :param matched: mark if the ground truth component is matched
-        :param d_bbox: [col_min, row_min, col_max, row_max]
-        :param gt_bboxes: list of ground truth [[col_min, row_min, col_max, row_max]]
-        :return: Boolean: if IOU large enough or detected box is contained by ground truth
-        '''
-        area_d = (d_bbox[2] - d_bbox[0]) * (d_bbox[3] - d_bbox[1])
-        gt_bboxes = gt_compos['bboxes']
-        gt_categories = gt_compos['categories']
-        for i, gt_bbox in enumerate(gt_bboxes):
-            if matched[i] == 0:
-                continue
-            area_gt = (gt_bbox[2] - gt_bbox[0]) * (gt_bbox[3] - gt_bbox[1])
-            col_min = max(d_bbox[0], gt_bbox[0])
-            row_min = max(d_bbox[1], gt_bbox[1])
-            col_max = min(d_bbox[2], gt_bbox[2])
-            row_max = min(d_bbox[3], gt_bbox[3])
-            # if not intersected, area intersection should be 0
-            w = max(0, col_max - col_min)
-            h = max(0, row_max - row_min)
-            area_inter = w * h
-            if area_inter == 0:
-                continue
-            iod = area_inter / area_d
-            iou = area_inter / (area_d + area_gt - area_inter)
-            # if show:
-            #     cv2.putText(org, (str(round(iou, 2)) + ',' + str(round(iod, 2))), (d_bbox[0], d_bbox[1]),
-            #                 cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-
-            if iou > 0.9 or iod == 1:
-                if d_category == gt_categories[i]:
-                    matched[i] = 0
-                    return True
-        return False
-
-    amount = len(detection)
-    TP, FP, FN = 0, 0, 0
-    pres, recalls, f1s = [], [], []
-    for i, image_id in enumerate(detection):
-        TP_this, FP_this, FN_this = 0, 0, 0
-        img = cv2.imread(pjoin(img_root, image_id + '.jpg'))
-        d_compos = detection[image_id]
-        if image_id not in ground_truth:
-            continue
-        gt_compos = ground_truth[image_id]
-
-        org_height = gt_compos['size'][0]
-
-        d_compos = compo_filter(d_compos, 'det')
-        gt_compos = compo_filter(gt_compos, 'gt')
-
-        d_compos['bboxes'] = resize_label(d_compos['bboxes'], 800, org_height)
-        matched = np.ones(len(gt_compos['bboxes']), dtype=int)
-        for j, d_bbox in enumerate(d_compos['bboxes']):
-            if match(img, d_bbox, d_compos['categories'][j], gt_compos, matched):
-                TP += 1
-                TP_this += 1
-            else:
-                FP += 1
-                FP_this += 1
-        FN += sum(matched)
-        FN_this = sum(matched)
-
-        try:
-            pre_this = TP_this / (TP_this + FP_this)
-            recall_this = TP_this / (TP_this + FN_this)
-            f1_this = 2 * (pre_this * recall_this) / (pre_this + recall_this)
-        except:
-            print('empty')
-            continue
-
-        pres.append(pre_this)
-        recalls.append(recall_this)
-        f1s.append(f1_this)
-        if show:
-            print(image_id + '.jpg')
-            print('[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f' % (
-                i, amount, TP_this, FP_this, FN_this, pre_this, recall_this))
-            # cv2.imshow('org', cv2.resize(img, (500, 1000)))
-            broad = draw_bounding_box(img, d_compos['bboxes'], color=(255, 0, 0), line=3)
-            draw_bounding_box(broad, gt_compos['bboxes'], color=(0, 0, 255), show=True, line=2)
-
-        if i % 200 == 0:
-            precision = TP / (TP + FP)
-            recall = TP / (TP + FN)
-            f1 = 2 * (precision * recall) / (precision + recall)
-            print(
-                '[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f, F1:%.3f' % (i, amount, TP, FP, FN, precision, recall, f1))
-
-    precision = TP / (TP + FP)
-    recall = TP / (TP + FN)
-    print('[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f, F1:%.3f' % (i, amount, TP, FP, FN, precision, recall, f1))
-    # print("Average precision:%.4f; Average recall:%.3f" % (sum(pres)/len(pres), sum(recalls)/len(recalls)))
-
-    return pres, recalls, f1s
-
-
-no_text = True
-only_text = False
-
-# detect = load_detect_result_json('E:\\Mulong\\Result\\rico\\rico_uied\\rico_new_uied_cls\\ip')
-# detect = load_detect_result_json('E:\\Mulong\\Result\\rico\\rico_uied\\rico_new_uied_cls\\merge')
-detect = load_detect_result_json('E:\\Mulong\\Result\\rico\\rico_uied\\rico_new_uied_v3\\merge')
-# detect = load_detect_result_json('E:\\Mulong\\Result\\rico\\rico_uied\\rico_new_uied_v3\\ocr')
-gt = load_ground_truth_json('E:\\Mulong\\Datasets\\rico\\instances_test.json')
-eval(detect, gt, 'E:\\Mulong\\Datasets\\rico\\combined', show=False, no_text=no_text, only_text=only_text)

spaces/DaFujaTyping/hf-Chat-ui/src/lib/buildPrompt.ts

@@ -1,30 +0,0 @@
-import type { BackendModel } from "./server/models";
-import type { Message } from "./types/Message";
-
-/**
- * Convert [{user: "assistant", content: "hi"}, {user: "user", content: "hello"}] to:
- *
- * <|assistant|>hi<|endoftext|><|prompter|>hello<|endoftext|><|assistant|>
- */
-export function buildPrompt(
-	messages: Pick<Message, "from" | "content">[],
-	model: BackendModel
-): string {
-	const prompt =
-		messages
-			.map(
-				(m) =>
-					(m.from === "user"
-						? model.userMessageToken + m.content
-						: model.assistantMessageToken + m.content) +
-					(model.messageEndToken
-						? m.content.endsWith(model.messageEndToken)
-							? ""
-							: model.messageEndToken
-						: "")
-			)
-			.join("") + model.assistantMessageToken;
-
-	// Not super precise, but it's truncated in the model's backend anyway
-	return model.preprompt + prompt.split(" ").slice(-model.parameters.truncate).join(" ");
-}

spaces/Dagfinn1962/prodia2/main.css

@@ -1,67 +0,0 @@
-body  {
-    background: #FFFFFF;
-   
-    width: 100%;
-    color: #FFFFFF;
-    padding: 20px;
-    border-radius: 10px;
-    border: 1px solid #1b0202;
-  }
-
-gr.blocks {
-  background-color: #758bec;
-  width: 100%;
-  color: #FFFFFF;
-}
-h3 {
-  background-color:#758bec;
-  color: #FFFFF;
-  text-align: center;
-  font-family: verdana;
-  font-size: 24px;
-  border: 1px solid #FFFFFF;
-  border-radius: 10px;
-}
-
-p {
-  font-family: verdana;
-  font-size: 14px;
-}
-
-label {
-  font-family: verdana;
-  color: #FFB76B;
-  font-weight: 700;
-  font-size: 14px;
-  border: 1px solid #000000;  
-} 
-
-gr.Textbox {
-  font-family: verdana;
-  background-color: #000000;
-  color: #FFFFFF;
-  font-weight: 700;
-  font-size: 14px;
-  border: 1px solid #FFFFFF;
-  border-radius: 6px;
-} 
-
-gr.Botton {
-  font-family: verdana;
-  background-color: #758bec;
-  color: #FFFFFF;
-  font-weight: 700;
-  font-size: 14px;
-  border: 1px solid #000000;
-  border-radius: 6px;
-} 
-
-a a:active a.hover
- {
-  font-family: verdana;
-  color: #572430;
-  text-decoration: none;
-  font-weight: 700;
-  font-size: 14px;
-  
-} 

spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/loss/led_loss.py

@@ -1,47 +0,0 @@
-""" 
-@Date: 2021/08/12
-@description:
-"""
-import torch
-import torch.nn as nn
-
-
-class LEDLoss(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.loss = nn.L1Loss()
-
-    def forward(self, gt, dt):
-        camera_height = 1.6
-        gt_depth = gt['depth'] * camera_height
-
-        dt_ceil_depth = dt['ceil_depth'] * camera_height * gt['ratio']
-        dt_floor_depth = dt['depth'] * camera_height
-
-        ceil_loss = self.loss(gt_depth, dt_ceil_depth)
-        floor_loss = self.loss(gt_depth, dt_floor_depth)
-
-        loss = floor_loss + ceil_loss
-
-        return loss
-
-
-if __name__ == '__main__':
-    import numpy as np
-    from dataset.mp3d_dataset import MP3DDataset
-
-    mp3d_dataset = MP3DDataset(root_dir='../src/dataset/mp3d', mode='train')
-    gt = mp3d_dataset.__getitem__(0)
-
-    gt['depth'] = torch.from_numpy(gt['depth'][np.newaxis])  # batch size is 1
-    gt['ratio'] = torch.from_numpy(gt['ratio'][np.newaxis])  # batch size is 1
-
-    dummy_dt = {
-        'depth': gt['depth'].clone(),
-        'ceil_depth': gt['depth'] / gt['ratio']
-    }
-    # dummy_dt['depth'][..., :20] *= 3  # some different
-
-    led_loss = LEDLoss()
-    loss = led_loss(gt, dummy_dt)
-    print(loss)

spaces/EPFL-VILAB/MultiMAE/multimae/multimae_utils.py

@@ -1,253 +0,0 @@
-# Copyright (c) EPFL VILAB.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# --------------------------------------------------------
-# Based on timm, DeiT, DINO, MoCo-v3, BEiT, MAE-priv and MAE code bases
-# https://github.com/rwightman/pytorch-image-models/tree/master/timm
-# https://github.com/facebookresearch/deit
-# https://github.com/facebookresearch/dino
-# https://github.com/facebookresearch/moco-v3
-# https://github.com/microsoft/unilm/tree/master/beit
-# https://github.com/BUPT-PRIV/MAE-priv
-# https://github.com/facebookresearch/mae
-# --------------------------------------------------------
-
-import math
-import warnings
-
-import torch
-import torch.nn as nn
-from einops import rearrange
-
-
-def pair(t):
-    return t if isinstance(t, tuple) else (t, t)
-
-
-def build_2d_sincos_posemb(h, w, embed_dim=1024, temperature=10000.):
-    """Sine-cosine positional embeddings from MoCo-v3
-
-    Source: https://github.com/facebookresearch/moco-v3/blob/main/vits.py
-    """
-    grid_w = torch.arange(w, dtype=torch.float32)
-    grid_h = torch.arange(h, dtype=torch.float32)
-    grid_w, grid_h = torch.meshgrid(grid_w, grid_h)
-    assert embed_dim % 4 == 0, 'Embed dimension must be divisible by 4 for 2D sin-cos position embedding'
-    pos_dim = embed_dim // 4
-    omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim
-    omega = 1. / (temperature ** omega)
-    out_w = torch.einsum('m,d->md', [grid_w.flatten(), omega])
-    out_h = torch.einsum('m,d->md', [grid_h.flatten(), omega])
-    pos_emb = torch.cat([torch.sin(out_w), torch.cos(out_w), torch.sin(out_h), torch.cos(out_h)], dim=1)[None, :, :]
-    pos_emb = rearrange(pos_emb, 'b (h w) d -> b d h w', h=h, w=w, d=embed_dim)
-    return pos_emb
-
-
-def _no_grad_trunc_normal_(tensor, mean, std, a, b):
-    # Cut & paste from PyTorch official master until it's in a few official releases - RW
-    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
-    def norm_cdf(x):
-        # Computes standard normal cumulative distribution function
-        return (1. + math.erf(x / math.sqrt(2.))) / 2.
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
-                      "The distribution of values may be incorrect.",
-                      stacklevel=2)
-
-    with torch.no_grad():
-        # Values are generated by using a truncated uniform distribution and
-        # then using the inverse CDF for the normal distribution.
-        # Get upper and lower cdf values
-        l = norm_cdf((a - mean) / std)
-        u = norm_cdf((b - mean) / std)
-
-        # Uniformly fill tensor with values from [l, u], then translate to
-        # [2l-1, 2u-1].
-        tensor.uniform_(2 * l - 1, 2 * u - 1)
-
-        # Use inverse cdf transform for normal distribution to get truncated
-        # standard normal
-        tensor.erfinv_()
-
-        # Transform to proper mean, std
-        tensor.mul_(std * math.sqrt(2.))
-        tensor.add_(mean)
-
-        # Clamp to ensure it's in the proper range
-        tensor.clamp_(min=a, max=b)
-        return tensor
-
-
-def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
-    # type: (Tensor, float, float, float, float) -> Tensor
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution. The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
-    Args:
-        tensor: an n-dimensional `torch.Tensor`
-        mean: the mean of the normal distribution
-        std: the standard deviation of the normal distribution
-        a: the minimum cutoff value
-        b: the maximum cutoff value
-    Examples:
-        >>> w = torch.empty(3, 5)
-        >>> nn.init.trunc_normal_(w)
-    """
-    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
-
-
-def drop_path(x, drop_prob: float = 0., training: bool = False):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
-    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
-    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
-    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
-    'survival rate' as the argument.
-    """
-    if drop_prob == 0. or not training:
-        return x
-    keep_prob = 1 - drop_prob
-    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
-    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
-    random_tensor.floor_()  # binarize
-    output = x.div(keep_prob) * random_tensor
-    return output
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
-    """
-
-    def __init__(self, drop_prob=None):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training)
-
-    def extra_repr(self) -> str:
-        return 'p={}'.format(self.drop_prob)
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        # x = self.drop(x)
-        # commit this for the orignal BERT implement 
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.,):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = head_dim ** -0.5
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv.unbind(0)   # make torchscript happy (cannot use tensor as tuple)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class CrossAttention(nn.Module):
-    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = head_dim ** -0.5
-
-        self.q = nn.Linear(dim, dim, bias=qkv_bias)
-        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
-
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x, context):
-        B, N, C = x.shape
-        _, M, _ = context.shape
-
-        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-        kv = self.kv(context).reshape(B, M, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        k, v = kv[0], kv[1]
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class Block(nn.Module):
-
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.drop_path(self.attn(self.norm1(x)))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x
-
-
-class DecoderBlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.self_attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
-        self.cross_attn = CrossAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
-        self.query_norm = norm_layer(dim)
-        self.context_norm = norm_layer(dim)
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x, context):
-        x = x + self.drop_path(self.self_attn(self.norm1(x)))
-        x = x + self.drop_path(self.cross_attn(self.query_norm(x), self.context_norm(context)))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x

spaces/EXPOSUREEE/Ai-Image-Enhancer/scripts/generate_meta_info.py

@@ -1,58 +0,0 @@
-import argparse
-import cv2
-import glob
-import os
-
-
-def main(args):
-    txt_file = open(args.meta_info, 'w')
-    for folder, root in zip(args.input, args.root):
-        img_paths = sorted(glob.glob(os.path.join(folder, '*')))
-        for img_path in img_paths:
-            status = True
-            if args.check:
-                # read the image once for check, as some images may have errors
-                try:
-                    img = cv2.imread(img_path)
-                except (IOError, OSError) as error:
-                    print(f'Read {img_path} error: {error}')
-                    status = False
-                if img is None:
-                    status = False
-                    print(f'Img is None: {img_path}')
-            if status:
-                # get the relative path
-                img_name = os.path.relpath(img_path, root)
-                print(img_name)
-                txt_file.write(f'{img_name}\n')
-
-
-if __name__ == '__main__':
-    """Generate meta info (txt file) for only Ground-Truth images.
-
-    It can also generate meta info from several folders into one txt file.
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--input',
-        nargs='+',
-        default=['datasets/DF2K/DF2K_HR', 'datasets/DF2K/DF2K_multiscale'],
-        help='Input folder, can be a list')
-    parser.add_argument(
-        '--root',
-        nargs='+',
-        default=['datasets/DF2K', 'datasets/DF2K'],
-        help='Folder root, should have the length as input folders')
-    parser.add_argument(
-        '--meta_info',
-        type=str,
-        default='datasets/DF2K/meta_info/meta_info_DF2Kmultiscale.txt',
-        help='txt path for meta info')
-    parser.add_argument('--check', action='store_true', help='Read image to check whether it is ok')
-    args = parser.parse_args()
-
-    assert len(args.input) == len(args.root), ('Input folder and folder root should have the same length, but got '
-                                               f'{len(args.input)} and {len(args.root)}.')
-    os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
-
-    main(args)

spaces/EronSamez/RVC_HFmeu/infer/lib/uvr5_pack/lib_v5/layers_537238KB.py

@@ -1,126 +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, 32, 64), 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.conv6 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv7 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 7, 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)
-        feat6 = self.conv6(x)
-        feat7 = self.conv7(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle