Datasets:

Weyaxi
/

huggingface-spaces-codes

@@ -1,558 +0,0 @@
-# This file was taken from the repository poe-api https://github.com/ading2210/poe-api and is unmodified
-# This file is licensed under the GNU GPL v3 and written by @ading2210
-# license:
-# ading2210/poe-api: a reverse engineered Python API wrapepr for Quora's Poe
-# Copyright (C) 2023 ading2210
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <https://www.gnu.org/licenses/>.
-import hashlib
-import json
-import logging
-import queue
-import random
-import re
-import threading
-import time
-import traceback
-from pathlib import Path
-from urllib.parse import urlparse
-import requests
-import requests.adapters
-import websocket
-parent_path = Path(__file__).resolve().parent
-queries_path = parent_path / "graphql"
-queries = {}
-logging.basicConfig()
-logger = logging.getLogger()
-user_agent = "Mozilla/5.0 (X11; Linux x86_64; rv:102.0) Gecko/20100101 Firefox/102.0"
-def load_queries():
-    for path in queries_path.iterdir():
-        if path.suffix != ".graphql":
-            continue
-        with open(path) as f:
-            queries[path.stem] = f.read()
-def generate_payload(query_name, variables):
-    return {"query": queries[query_name], "variables": variables}
-def retry_request(method, *args, **kwargs):
-    """Retry a request with 10 attempts by default, delay increases exponentially"""
-    max_attempts: int = kwargs.pop("max_attempts", 10)
-    delay = kwargs.pop("delay", 1)
-    url = args[0]
-    for attempt in range(1, max_attempts + 1):
-        try:
-            response = method(*args, **kwargs)
-            response.raise_for_status()
-            return response
-        except Exception as error:
-            logger.warning(
-                f"Attempt {attempt}/{max_attempts} failed with error: {error}. "
-                f"Retrying in {delay} seconds..."
-            )
-            time.sleep(delay)
-            delay *= 2
-    raise RuntimeError(f"Failed to download {url} after {max_attempts} attempts.")
-class Client:
-    gql_url = "https://poe.com/api/gql_POST"
-    gql_recv_url = "https://poe.com/api/receive_POST"
-    home_url = "https://poe.com"
-    settings_url = "https://poe.com/api/settings"
-    def __init__(self, token, proxy=None):
-        self.proxy = proxy
-        self.session = requests.Session()
-        self.adapter = requests.adapters.HTTPAdapter(pool_connections=100, pool_maxsize=100)
-        self.session.mount("http://", self.adapter)
-        self.session.mount("https://", self.adapter)
-        if proxy:
-            self.session.proxies = {"http": self.proxy, "https": self.proxy}
-            logger.info(f"Proxy enabled: {self.proxy}")
-        self.active_messages = {}
-        self.message_queues = {}
-        self.session.cookies.set("p-b", token, domain="poe.com")
-        self.headers = {
-            "User-Agent": user_agent,
-            "Referrer": "https://poe.com/",
-            "Origin": "https://poe.com",
-        }
-        self.session.headers.update(self.headers)
-        self.setup_connection()
-        self.connect_ws()
-    def setup_connection(self):
-        self.ws_domain = f"tch{random.randint(1, 1e6)}"
-        self.next_data = self.get_next_data(overwrite_vars=True)
-        self.channel = self.get_channel_data()
-        self.bots = self.get_bots(download_next_data=False)
-        self.bot_names = self.get_bot_names()
-        self.gql_headers = {
-            "poe-formkey": self.formkey,
-            "poe-tchannel": self.channel["channel"],
-        }
-        self.gql_headers = {**self.gql_headers, **self.headers}
-        self.subscribe()
-    def extract_formkey(self, html):
-        script_regex = r"<script>if\(.+\)throw new Error;(.+)</script>"
-        script_text = re.search(script_regex, html).group(1)
-        key_regex = r'var .="([0-9a-f]+)",'
-        key_text = re.search(key_regex, script_text).group(1)
-        cipher_regex = r".\[(\d+)\]=.\[(\d+)\]"
-        cipher_pairs = re.findall(cipher_regex, script_text)
-        formkey_list = [""] * len(cipher_pairs)
-        for pair in cipher_pairs:
-            formkey_index, key_index = map(int, pair)
-            formkey_list[formkey_index] = key_text[key_index]
-        formkey = "".join(formkey_list)
-        return formkey
-    def get_next_data(self, overwrite_vars=False):
-        logger.info("Downloading next_data...")
-        r = retry_request(self.session.get, self.home_url)
-        json_regex = r'<script id="__NEXT_DATA__" type="application\/json">(.+?)</script>'
-        json_text = re.search(json_regex, r.text).group(1)
-        next_data = json.loads(json_text)
-        if overwrite_vars:
-            self.formkey = self.extract_formkey(r.text)
-            self.viewer = next_data["props"]["pageProps"]["payload"]["viewer"]
-            self.next_data = next_data
-        return next_data
-    def get_bot(self, display_name):
-        url = f'https://poe.com/_next/data/{self.next_data["buildId"]}/{display_name}.json'
-        r = retry_request(self.session.get, url)
-        chat_data = r.json()["pageProps"]["payload"]["chatOfBotDisplayName"]
-        return chat_data
-    def get_bots(self, download_next_data=True):
-        logger.info("Downloading all bots...")
-        if download_next_data:
-            next_data = self.get_next_data(overwrite_vars=True)
-        else:
-            next_data = self.next_data
-        if not "viewerBotList" in self.viewer:
-            raise RuntimeError("Invalid token or no bots are available.")
-        bot_list = self.viewer["viewerBotList"]
-        threads = []
-        bots = {}
-        def get_bot_thread(bot):
-            chat_data = self.get_bot(bot["displayName"])
-            bots[chat_data["defaultBotObject"]["nickname"]] = chat_data
-        for bot in bot_list:
-            thread = threading.Thread(target=get_bot_thread, args=(bot,), daemon=True)
-            threads.append(thread)
-        for thread in threads:
-            thread.start()
-        for thread in threads:
-            thread.join()
-        self.bots = bots
-        self.bot_names = self.get_bot_names()
-        return bots
-    def get_bot_names(self):
-        bot_names = {}
-        for bot_nickname in self.bots:
-            bot_obj = self.bots[bot_nickname]["defaultBotObject"]
-            bot_names[bot_nickname] = bot_obj["displayName"]
-        return bot_names
-    def get_remaining_messages(self, chatbot):
-        chat_data = self.get_bot(self.bot_names[chatbot])
-        return chat_data["defaultBotObject"]["messageLimit"]["numMessagesRemaining"]
-    def get_channel_data(self, channel=None):
-        logger.info("Downloading channel data...")
-        r = retry_request(self.session.get, self.settings_url)
-        data = r.json()
-        return data["tchannelData"]
-    def get_websocket_url(self, channel=None):
-        if channel is None:
-            channel = self.channel
-        query = f'?min_seq={channel["minSeq"]}&channel={channel["channel"]}&hash={channel["channelHash"]}'
-        return f'wss://{self.ws_domain}.tch.{channel["baseHost"]}/up/{channel["boxName"]}/updates' + query
-    def send_query(self, query_name, variables):
-        for i in range(20):
-            json_data = generate_payload(query_name, variables)
-            payload = json.dumps(json_data, separators=(",", ":"))
-            base_string = payload + self.gql_headers["poe-formkey"] + "WpuLMiXEKKE98j56k"
-            headers = {
-                "content-type": "application/json",
-                "poe-tag-id": hashlib.md5(base_string.encode()).hexdigest(),
-            }
-            headers = {**self.gql_headers, **headers}
-            r = retry_request(self.session.post, self.gql_url, data=payload, headers=headers)
-            data = r.json()
-            if data["data"] is None:
-                logger.warn(f'{query_name} returned an error: {data["errors"][0]["message"]} | Retrying ({i + 1}/20)')
-                time.sleep(2)
-                continue
-            return r.json()
-        raise RuntimeError(f"{query_name} failed too many times.")
-    def subscribe(self):
-        logger.info("Subscribing to mutations")
-        result = self.send_query(
-            "SubscriptionsMutation",
-            {
-                "subscriptions": [
-                    {
-                        "subscriptionName": "messageAdded",
-                        "query": queries["MessageAddedSubscription"],
-                    },
-                    {
-                        "subscriptionName": "viewerStateUpdated",
-                        "query": queries["ViewerStateUpdatedSubscription"],
-                    },
-                ]
-            },
-        )
-    def ws_run_thread(self):
-        kwargs = {}
-        if self.proxy:
-            proxy_parsed = urlparse(self.proxy)
-            kwargs = {
-                "proxy_type": proxy_parsed.scheme,
-                "http_proxy_host": proxy_parsed.hostname,
-                "http_proxy_port": proxy_parsed.port,
-            }
-        self.ws.run_forever(**kwargs)
-    def connect_ws(self):
-        self.ws_connected = False
-        self.ws = websocket.WebSocketApp(
-            self.get_websocket_url(),
-            header={"User-Agent": user_agent},
-            on_message=self.on_message,
-            on_open=self.on_ws_connect,
-            on_error=self.on_ws_error,
-            on_close=self.on_ws_close,
-        )
-        t = threading.Thread(target=self.ws_run_thread, daemon=True)
-        t.start()
-        while not self.ws_connected:
-            time.sleep(0.01)
-    def disconnect_ws(self):
-        if self.ws:
-            self.ws.close()
-        self.ws_connected = False
-    def on_ws_connect(self, ws):
-        self.ws_connected = True
-    def on_ws_close(self, ws, close_status_code, close_message):
-        self.ws_connected = False
-        logger.warn(f"Websocket closed with status {close_status_code}: {close_message}")
-    def on_ws_error(self, ws, error):
-        self.disconnect_ws()
-        self.connect_ws()
-    def on_message(self, ws, msg):
-        try:
-            data = json.loads(msg)
-            if not "messages" in data:
-                return
-            for message_str in data["messages"]:
-                message_data = json.loads(message_str)
-                if message_data["message_type"] != "subscriptionUpdate":
-                    continue
-                message = message_data["payload"]["data"]["messageAdded"]
-                copied_dict = self.active_messages.copy()
-                for key, value in copied_dict.items():
-                    # add the message to the appropriate queue
-                    if value == message["messageId"] and key in self.message_queues:
-                        self.message_queues[key].put(message)
-                        return
-                    # indicate that the response id is tied to the human message id
-                    elif key != "pending" and value is None and message["state"] != "complete":
-                        self.active_messages[key] = message["messageId"]
-                        self.message_queues[key].put(message)
-                        return
-        except Exception:
-            logger.error(traceback.format_exc())
-            self.disconnect_ws()
-            self.connect_ws()
-    def send_message(self, chatbot, message, with_chat_break=False, timeout=20):
-        # if there is another active message, wait until it has finished sending
-        while None in self.active_messages.values():
-            time.sleep(0.01)
-        # None indicates that a message is still in progress
-        self.active_messages["pending"] = None
-        logger.info(f"Sending message to {chatbot}: {message}")
-        # reconnect websocket
-        if not self.ws_connected:
-            self.disconnect_ws()
-            self.setup_connection()
-            self.connect_ws()
-        message_data = self.send_query(
-            "SendMessageMutation",
-            {
-                "bot": chatbot,
-                "query": message,
-                "chatId": self.bots[chatbot]["chatId"],
-                "source": None,
-                "withChatBreak": with_chat_break,
-            },
-        )
-        del self.active_messages["pending"]
-        if not message_data["data"]["messageEdgeCreate"]["message"]:
-            raise RuntimeError(f"Daily limit reached for {chatbot}.")
-        try:
-            human_message = message_data["data"]["messageEdgeCreate"]["message"]
-            human_message_id = human_message["node"]["messageId"]
-        except TypeError:
-            raise RuntimeError(f"An unknown error occurred. Raw response data: {message_data}")
-        # indicate that the current message is waiting for a response
-        self.active_messages[human_message_id] = None
-        self.message_queues[human_message_id] = queue.Queue()
-        last_text = ""
-        message_id = None
-        while True:
-            try:
-                message = self.message_queues[human_message_id].get(timeout=timeout)
-            except queue.Empty:
-                del self.active_messages[human_message_id]
-                del self.message_queues[human_message_id]
-                raise RuntimeError("Response timed out.")
-            # only break when the message is marked as complete
-            if message["state"] == "complete":
-                if last_text and message["messageId"] == message_id:
-                    break
-                else:
-                    continue
-            # update info about response
-            message["text_new"] = message["text"][len(last_text) :]
-            last_text = message["text"]
-            message_id = message["messageId"]
-            yield message
-        del self.active_messages[human_message_id]
-        del self.message_queues[human_message_id]
-    def send_chat_break(self, chatbot):
-        logger.info(f"Sending chat break to {chatbot}")
-        result = self.send_query("AddMessageBreakMutation", {"chatId": self.bots[chatbot]["chatId"]})
-        return result["data"]["messageBreakCreate"]["message"]
-    def get_message_history(self, chatbot, count=25, cursor=None):
-        logger.info(f"Downloading {count} messages from {chatbot}")
-        messages = []
-        if cursor is None:
-            chat_data = self.get_bot(self.bot_names[chatbot])
-            if not chat_data["messagesConnection"]["edges"]:
-                return []
-            messages = chat_data["messagesConnection"]["edges"][:count]
-            cursor = chat_data["messagesConnection"]["pageInfo"]["startCursor"]
-            count -= len(messages)
-        cursor = str(cursor)
-        if count > 50:
-            messages = self.get_message_history(chatbot, count=50, cursor=cursor) + messages
-            while count > 0:
-                count -= 50
-                new_cursor = messages[0]["cursor"]
-                new_messages = self.get_message_history(chatbot, min(50, count), cursor=new_cursor)
-                messages = new_messages + messages
-            return messages
-        elif count <= 0:
-            return messages
-        result = self.send_query(
-            "ChatListPaginationQuery",
-            {"count": count, "cursor": cursor, "id": self.bots[chatbot]["id"]},
-        )
-        query_messages = result["data"]["node"]["messagesConnection"]["edges"]
-        messages = query_messages + messages
-        return messages
-    def delete_message(self, message_ids):
-        logger.info(f"Deleting messages: {message_ids}")
-        if not type(message_ids) is list:
-            message_ids = [int(message_ids)]
-        result = self.send_query("DeleteMessageMutation", {"messageIds": message_ids})
-    def purge_conversation(self, chatbot, count=-1):
-        logger.info(f"Purging messages from {chatbot}")
-        last_messages = self.get_message_history(chatbot, count=50)[::-1]
-        while last_messages:
-            message_ids = []
-            for message in last_messages:
-                if count == 0:
-                    break
-                count -= 1
-                message_ids.append(message["node"]["messageId"])
-            self.delete_message(message_ids)
-            if count == 0:
-                return
-            last_messages = self.get_message_history(chatbot, count=50)[::-1]
-        logger.info(f"No more messages left to delete.")
-    def create_bot(
-        self,
-        handle,
-        prompt="",
-        base_model="chinchilla",
-        description="",
-        intro_message="",
-        api_key=None,
-        api_bot=False,
-        api_url=None,
-        prompt_public=True,
-        pfp_url=None,
-        linkification=False,
-        markdown_rendering=True,
-        suggested_replies=False,
-        private=False,
-    ):
-        result = self.send_query(
-            "PoeBotCreateMutation",
-            {
-                "model": base_model,
-                "handle": handle,
-                "prompt": prompt,
-                "isPromptPublic": prompt_public,
-                "introduction": intro_message,
-                "description": description,
-                "profilePictureUrl": pfp_url,
-                "apiUrl": api_url,
-                "apiKey": api_key,
-                "isApiBot": api_bot,
-                "hasLinkification": linkification,
-                "hasMarkdownRendering": markdown_rendering,
-                "hasSuggestedReplies": suggested_replies,
-                "isPrivateBot": private,
-            },
-        )
-        data = result["data"]["poeBotCreate"]
-        if data["status"] != "success":
-            raise RuntimeError(f"Poe returned an error while trying to create a bot: {data['status']}")
-        self.get_bots()
-        return data
-    def edit_bot(
-        self,
-        bot_id,
-        handle,
-        prompt="",
-        base_model="chinchilla",
-        description="",
-        intro_message="",
-        api_key=None,
-        api_url=None,
-        private=False,
-        prompt_public=True,
-        pfp_url=None,
-        linkification=False,
-        markdown_rendering=True,
-        suggested_replies=False,
-    ):
-        result = self.send_query(
-            "PoeBotEditMutation",
-            {
-                "baseBot": base_model,
-                "botId": bot_id,
-                "handle": handle,
-                "prompt": prompt,
-                "isPromptPublic": prompt_public,
-                "introduction": intro_message,
-                "description": description,
-                "profilePictureUrl": pfp_url,
-                "apiUrl": api_url,
-                "apiKey": api_key,
-                "hasLinkification": linkification,
-                "hasMarkdownRendering": markdown_rendering,
-                "hasSuggestedReplies": suggested_replies,
-                "isPrivateBot": private,
-            },
-        )
-        data = result["data"]["poeBotEdit"]
-        if data["status"] != "success":
-            raise RuntimeError(f"Poe returned an error while trying to edit a bot: {data['status']}")
-        self.get_bots()
-        return data
-    def delete_account(self) -> None:
-        response = self.send_query('SettingsDeleteAccountButton_deleteAccountMutation_Mutation', {})
-        data = response['data']['deleteAccount']
-        if 'viewer' not in data:
-            raise RuntimeError(f'Error occurred while deleting the account, Please try again!')
-load_queries()

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Bandicam free email and serial number How to update your Bandicam version and check the license information.md DELETED Viewed

@@ -1,123 +0,0 @@
-<br />
-<h1>Bandicam Serial Key and Email 2022 [Full Version]</h1>
- <p>Are you looking for a screen recording software that can help you capture your computer or laptop screen in high definition easily? If yes, then you might have heard of Bandicam, one of the best and an all-in-one screen recording software that can record anything on your screen. Whether you are a YouTuber, gamer, tutor, or just want to record your screen for personal use, Bandicam can be your perfect choice. But there is a catch: Bandicam is not a free software. You need to pay $39.95 for a single license or $59.95 for a two-PC license to use its full features. That's why many people are looking for ways to get Bandicam serial key and email for free.</p>
- <p>In this article, we will show you what Bandicam is, what features and benefits it offers, and how you can get Bandicam serial key and email for free in 2022. We will also show you how to register Bandicam with serial key and email and enjoy the full version without any limitations. So, without further ado, let's get started.</p>
-<h2>bandicamfreeemailandserialnumber</h2><br /><p><b><b>Download File</b> &#127379; <a href="https://byltly.com/2uKym6">https://byltly.com/2uKym6</a></b></p><br /><br />
- <h2>What is Bandicam?</h2>
- <p>Bandicam is a screen recording software that allows you to record anything on your computer screen in high quality. You can record your gameplay, webcam, desktop, online video, audio, or any other activity on your screen with ease. Bandicam has three modes of recording: Screen Recording Mode (SRM), Game Recording Mode (GRM), and Device Recording Mode (DRM). Each mode has its own features and settings that you can customize according to your needs.</p>
- <h3>Features of Bandicam</h3>
- <p>Bandicam has many features that make it stand out from other screen recording software. Some of the main features are:</p>
- <ul>
-<li>You can record your screen in up to 4K UHD resolution at 120 FPS.</li>
-<li>You can compress your video file size without losing quality.</li>
-<li>You can add a logo watermark, mouse effects, webcam overlay, voiceover, annotations, and more to your videos.</li>
-<li>You can take screenshots in real-time recording by outlining the screen.</li>
-<li>You can record videos from external devices like webcam, Xbox, PlayStation, smartphone, etc.</li>
-<li>You can edit your videos with basic functions like cut, split, join, etc.</li>
-<li>You can upload your videos directly to YouTube, Vimeo, or FTP server.</li>
-</ul>
- <h3>Benefits of Bandicam</h3>
- <p>Bandicam has many benefits that make it worth using. Some of the main benefits are:</p>
- <ul>
-<li>You can record your screen with high quality and performance.</li>
-<li>You can save your disk space and bandwidth by compressing your video files.</li>
-<li>You can protect your videos from being stolen by adding a logo watermark.</li>
-<li>You can enhance your videos with various effects and features.</li>
-<li>You can record videos from multiple sources and devices.</li>
-<li>You can edit your videos with ease and convenience.</li>
-<li>You can share your videos with the world with one click.</li>
-</ul>
- <h2>How to get Bandicam Serial Key and Email for free?</h2>
- <p>As we mentioned earlier, Bandicam is not a free software. You need to pay for it to use its full features. However, there are some ways that you can try to get Bandicam serial key and email for free in 2022. We will show you three methods that you can use to get Bandicam serial key and email for free. However, we do not guarantee that these methods will work for everyone or forever. So, use them at your own risk.</p>
- <h3>Method 1: Use a cracked version</h3>
- <p>A cracked version is a modified version of the original software that bypasses the registration process and allows you to use the full features without paying anything. There are many websites that offer cracked versions of Bandicam for free download. However, these websites are not safe or legal. They may contain viruses, malware, spyware, or other harmful programs that can damage your computer or steal your personal information. Moreover, using a cracked version is unethical and illegal. You may face legal issues or penalties if you are caught using a cracked version of Bandicam.</p>
- <p>If you still want to use this method, here are the steps that you need to follow:</p>
- <ol>
-<li>Search for a cracked version of Bandicam on Google or any other search engine.</li>
-<li>Choose a website that offers a cracked version of Bandicam for free download.</li>
-<li>Download the cracked version of Bandicam from the website.</li>
-<li>Extract the downloaded file using WinRAR or any other extraction tool.</li>
-<li>Run the setup file and install the cracked version of Bandicam on your computer.</li>
-<li>Launch the cracked version of Bandicam and enjoy the full features without registration.</li>
-</ol>
- <h3>Method 2: Use a key generator</h3>
- <p>A key generator is a software that generates random serial keys and emails for various software. You can use a key generator to generate a serial key and email for Bandicam for free. However, this method is also not safe or legal. A key generator may also contain viruses, malware, spyware, or other harmful programs that can damage your computer or steal your personal information. Moreover, using a key generator is unethical and illegal. You may face legal issues or penalties if you are caught using a key generator for Bandicam.</p>
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-<h2>download blockman go versi terbaru</h2><br /><p><b><b>Download Zip</b> &#9193; <a href="https://urlin.us/2uT1rT">https://urlin.us/2uT1rT</a></b></p><br /><br />
-<p>But how can you download Blockman Go versi terbaru (the latest version) on your device? In this article, we will show you what Blockman Go is, why you should download the latest version, and how to do it easily and safely. Let's get started!</p>
- <h2>What is Blockman Go?</h2>
-<p>Blockman Go is a game that combines elements of sandbox, minigames, and social networking. It has three main features that make it stand out from other games:</p>
- <h3>A free app with various block style minigames</h3>
-<p>Blockman Go is completely free to play, but you can choose to make premium in-app purchases through your Play Store account. You can also earn gold cubes by playing games or watching ads, which you can use to buy items or VIP subscriptions.</p>
-<p>The app has a huge collection of minigames that you can join or create. Some of the most popular ones are:</p>
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-<ul>
-<li><strong>Bed Wars</strong>: A team-based PVP game where you have to defend your bed and destroy your enemies' beds.</li>
-<li><strong>Sky Block</strong>: A survival game where you have to gather resources, craft items, and build your island.</li>
-<li><strong>Anime Fighting Simulator</strong>: A role-playing game where you can train your skills, fight other players, and unlock anime characters.</li>
-<li><strong>Egg War</strong>: A game similar to Bed Wars, but with eggs instead of beds.</li>
-<li><strong>Free City RP</strong>: A role-playing game where you can choose your own role and explore the city.</li>
-</ul>
- <h3>A sandbox game with creative and social features</h3>
-<p>Blockman Go is not only a game, but also a platform where you can express your creativity and interact with others. You can create your own world using the built-in editor, or join other players' worlds and see what they have made. You can also customize your avatar with various accessories, skins, and outfits.</p>
-<p>Moreover, you can chat with other players using text or voice messages, or join a guild and make friends. You can also participate in events, contests, and giveaways to win prizes and rewards.</p>
- <h3>A platform with different genres and themes</h3>
-<p>Blockman Go has something for everyone, no matter what your preferences are. You can find games from all kinds of genres, such as action, adventure, role-playing, strategy, simulation, and more. You can also find games with different themes, such as anime, horror, sci-fi, fantasy, and more.</p>
-<p>Some of the newest and hottest games on Blockman Go are:</p>
-<ul>
-<li><strong>Party Street</strong>: A game where you can collect graffiti from all over the city and spray it to your heart's content.</li>
-<li><strong>The Exorcists</ <li><strong>The Exorcists</strong>: A game where you can become an exorcist and fight against evil spirits and demons.</li>
-<li><strong>Superhero City</strong>: A game where you can create your own superhero and fight crime in the city.</li>
-</ul>
- <h2>Why download Blockman Go versi terbaru?</h2>
-<p>Downloading Blockman Go versi terbaru is the best way to enjoy the game to the fullest. Here are some of the benefits of downloading the latest version:</p>
- <h3>To access the latest updates and features</h3>
-<p>The developers of Blockman Go are constantly working to improve the game and add new content. By downloading the latest version, you can get access to the newest minigames, items, events, and bug fixes. You can also enjoy the improved graphics, sound effects, and user interface.</p>
- <h3>To enjoy the best performance and compatibility</h3>
-<p>Downloading Blockman Go versi terbaru also ensures that the game runs smoothly and without any glitches on your device. The latest version is optimized for different devices and operating systems, so you can have a seamless gaming experience. You can also avoid any compatibility issues or errors that might occur with older versions.</p>
- <h3>To join the millions of players worldwide</h3>
-<p>Blockman Go is a global game that has millions of players from different countries and regions. By downloading Blockman Go versi terbaru, you can join the latest servers and play with other players online. You can also chat with them, make friends, and join guilds. You can also participate in global rankings and leaderboards, and compete with other players for glory and rewards.</p>
- <h2>How to download Blockman Go versi terbaru?</h2>
-<p>Downloading Blockman Go versi terbaru is easy and safe, as long as you follow these steps:</p>
- <h3>For Android devices</h3>
-<p>If you have an Android device, you can download Blockman Go versi terbaru from three different sources:</p>
- <h4>From Google Play Store</h4>
-<p>This is the most recommended way to download Blockman Go versi terbaru, as it is the official and secure source. Here is how to do it:</p>
-<ol>
-<li>Open the Google Play Store app on your device.</li>
-<li>Search for "Blockman Go" in the search bar.</li>
-<li>Select the app from the results and tap on "Install".</li>
-<li>Wait for the app to download and install on your device.</li>
-<li>Launch the app and enjoy!</li>
-</ol>
- <h4>From Uptodown</h4>
-<p>This is another reliable way to download Blockman Go versi terbaru, as it is a trusted third-party app store. Here is how to do it:</p>
-<ol>
-<li>Open your browser and go to <a href="">https://blockman-go.en.uptodown.com/android</a>.</li>
-<li>Tap on "Download" and choose a version from the list.</li>
-<li>Wait for the APK file to download on your device.</li>
-<li>Go to your device settings and enable "Unknown sources" under security options.</li>
-<li>Locate the APK file in your downloads folder and tap on it.</li>
-<li>Follow the instructions to install the app on your device.</li>
-<li>Launch the app and enjoy!</li>
-</ol>
- <h4>From APKPure</h4>
-<p>This is another alternative way to download Blockman Go versi terbaru, as it is a popular third-party app store. Here is how to do it:</p>
-<ol>
-<li>Open your browser and go to <a href="">https://apkpure.com/blockman-go-blocky-mods/com.sandboxol.blockymods</a>.</li>
-<li>Tap on "Download APK" and choose a version from the list.</li>
-<li>Wait for the APK file to download on your device.</li>
-<li>Go to your device settings and enable "Unknown sources" under security options.</li>
-<li>Locate the APK file in your downloads folder and tap on it.</li>
-<li>Follow the instructions to install the app on your device.</li>
-<li>Launch the app and enjoy!</li>
-</ol>
- <h3>For PC devices</h3>
-<p>If you have a PC device, you can download Blockman Go versi terbaru from three different sources:</p>
- <h4>From official website</h4>
-<p>This is the most recommended way to download Blockman Go versi terbaru, as it is the official and secure source. Here is how to do it:</p>
-<ol>
-<li>Open your browser and go to <a href="(^3^ [assistant](#search_web("blockman go official website")) "web_search_results": ["title": "Blockman GO : Blocky Mods - Apps on Google Play", "snippet": "Welcome to Blockman GO ! Blockman GO is a free app including minigames, chatting and making friends. You can play various block style minigames here. Key Features - Various Games: Various minigames that allow multiple players to play together and continuously update the games. Users can join the game by a simple tap.", "url": "[15](https://play.google.com/store/apps/details?id=com.sandboxol.blockymods&hl=en_US&gl=US)", "title": "Blockman Go", "snippet": "Blockman Go is a free app including minigames,chatting and making friends. You can play various block style minigames here.", "url": "[14](https://www.blockmango.net/)", "title": "Download Blockman Go on PC with MEmu", "snippet": "Download and play Blockman Go on PC. Play as long as you want, no more limitations of battery, mobile data and disturbing calls. The brand new MEmu 7 is the best choice of playing Blockman Go on PC. Prepared with our expertise, the exquisite preset keymapping system makes Blockman Go a real PC game.", "url": "[13](https://www.memuplay.com/how-to-play-com.sandboxol.blockymods-on-pc.html)"] [assistant](#message) <li>Open your browser and go to <a href="">https://www.blockmango.net/</a>.</li>
-<li>Click on the "Download" button on the top right corner of the website.</li>
-<li>Choose the PC version from the list and click on it.</li>
-<li>Wait for the EXE file to download on your device.</li>
-<li>Locate the EXE file in your downloads folder and double-click on it.</li>
-<li>Follow the instructions to install the app on your device.</li>
-<li>Launch the app and enjoy!</li>
-</ol>
- <h4>From BlueStacks emulator</h4>
-<p>This is another reliable way to download Blockman Go versi terbaru, as it is a trusted Android emulator for PC. Here is how to do it:</p>
-<ol>
-<li>Open your browser and go to <a href="">https://www.bluestacks.com/</a>.</li>
-<li>Click on the "Download BlueStacks" button and wait for the EXE file to download on your device.</li>
-<li>Locate the EXE file in your downloads folder and double-click on it.</li>
-<li>Follow the instructions to install BlueStacks on your device.</li>
-<li>Launch BlueStacks and sign in with your Google account.</li>
-<li>Search for "Blockman Go" in the search bar and click on it.</li>
-<li>Click on the "Install" button and wait for the app to download and install on BlueStacks.</li>
-<li>Launch the app and enjoy!</li>
-</ol>
- <h4>From LDPlayer emulator</h4>
-<p>This is another alternative way to download Blockman Go versi terbaru, as it is a popular Android emulator for PC. Here is how to do it:</p>
-<ol>
-<li>Open your browser and go to <a href="">https://www.ldplayer.net/</a>.</li>
-<li>Click on the "Download LDPlayer" button and wait for the EXE file to download on your device.</li>
-<li>Locate the EXE file in your downloads folder and double-click on it.</li>
-<li>Follow the instructions to install LDPlayer on your device.</li>
-<li>Launch LDPlayer and sign in with your Google account.</li>
-<li>Search for "Blockman Go" in the search bar and click on it.</li>
-<li>Click on the "Install" button and wait for the app to download and install on LDPlayer.</li>
-<li>Launch the app and enjoy!</li>
-</ol>
- <h2>Conclusion</h2>
-<p>Blockman Go is a game that offers endless fun and possibilities for players of all ages and preferences. You can play various block style minigames, create your own world, customize your avatar, chat with other players, and join a global community. To enjoy all these features, you should download Blockman Go versi terbaru (the latest version) on your device. You can do this easily and safely by following our guide above. Whether you have an Android or a PC device, you can find a way to download Blockman Go versi terbaru from different sources. So what are you waiting for? Download Blockman Go versi terbaru now and have fun!</p>
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-<p>Here are some of the frequently asked questions about Blockman Go versi terbaru:</p>
- <h4>Q: Is Blockman Go versi terbaru safe to download?</h4>
-<p>A: Yes, Blockman Go versi terbaru is safe to download, as long as you use the official or trusted sources that we mentioned above. You should avoid downloading Blockman Go versi terbaru from unknown or suspicious websites, as they might contain viruses or malware that can harm your device.</p>
- <h4>Q: How can I update Blockman Go versi terbaru?</h4>
-<p>A: If you have downloaded Blockman Go versi terbaru from the Google Play Store, you can update it automatically or manually. To update it automatically, you can enable the "Auto-update apps" option in your Play Store settings. To update it manually, you can go to the Play Store app, search for Blockman Go, and tap on the "Update" button. If you have downloaded Blockman Go versi terbaru from other sources, you can check for updates on their websites or apps, and follow the same steps as downloading.</p>
- <h4>Q: How can I delete Blockman Go versi terbaru?</h4>
-<p>A: If you want to delete Blockman Go versi terbaru from your device, you can do it easily by following these steps:</p>
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-<li>For Android devices, go to your device settings, tap on "Apps", find Blockman Go, and tap on "Uninstall".</li>
-<li>For PC devices, go to your device settings, click on "Apps", find Blockman Go, and click on "Uninstall".</li>
-</ul>
- <h4>Q: How can I contact Blockman Go customer service?</h4>
-<p>A: If you have any questions or issues about Blockman Go versi terbaru, you can contact Blockman Go customer service by following these steps:</p>
-<ul>
-<li>Open the Blockman Go app on your device.</li>
-<li>Tap on the "Settings" icon on the top right corner of the screen.</li>
-<li>Tap on the "Feedback" option and fill in the form with your details and message.</li>
-<li>Tap on the "Submit" button and wait for a reply.</li>
-</ul>
- <h4>Q: How can I get free gold cubes in Blockman Go versi terbaru?</h4>
-<p>A: Gold cubes are the premium currency in Blockman Go versi terbaru, which you can use to buy items or VIP subscriptions. You can get free gold cubes by playing games or watching ads, or by participating in events, contests, and giveaways. You can also get free gold cubes by inviting your friends to play Blockman Go versi terbaru using your referral code.</p> 197e85843d<br />
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spaces/7hao/bingo/src/components/ui/button.tsx DELETED Viewed

@@ -1,57 +0,0 @@
-import * as React from 'react'
-import { Slot } from '@radix-ui/react-slot'
-import { cva, type VariantProps } from 'class-variance-authority'
-import { cn } from '@/lib/utils'
-const buttonVariants = cva(
-  'inline-flex items-center justify-center rounded-md text-sm font-medium shadow ring-offset-background transition-colors outline-none disabled:pointer-events-none disabled:opacity-50',
-  {
-    variants: {
-      variant: {
-        default:
-          'bg-primary text-primary-foreground shadow-md hover:bg-primary/90',
-        destructive:
-          'bg-destructive text-destructive-foreground hover:bg-destructive/90',
-        outline:
-          'border border-input hover:bg-accent hover:text-accent-foreground',
-        secondary:
-          'bg-secondary text-secondary-foreground hover:bg-secondary/80',
-        ghost: 'shadow-none hover:bg-accent hover:text-accent-foreground',
-        link: 'text-primary underline-offset-4 shadow-none hover:underline'
-      },
-      size: {
-        default: 'h-8 px-4 py-2',
-        sm: 'h-8 rounded-md px-3',
-        lg: 'h-11 rounded-md px-8',
-        icon: 'h-8 w-8 p-0'
-      }
-    },
-    defaultVariants: {
-      variant: 'default',
-      size: 'default'
-    }
-  }
-)
-export interface ButtonProps
-  extends React.ButtonHTMLAttributes<HTMLButtonElement>,
-    VariantProps<typeof buttonVariants> {
-  asChild?: boolean
-}
-const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
-  ({ className, variant, size, asChild = false, ...props }, ref) => {
-    const Comp = asChild ? Slot : 'button'
-    return (
-      <Comp
-        className={cn(buttonVariants({ variant, size, className }))}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Button.displayName = 'Button'
-export { Button, buttonVariants }

spaces/7hao/bingo/src/components/ui/separator.tsx DELETED Viewed

@@ -1,31 +0,0 @@
-'use client'
-import * as React from 'react'
-import * as SeparatorPrimitive from '@radix-ui/react-separator'
-import { cn } from '@/lib/utils'
-const Separator = React.forwardRef<
-  React.ElementRef<typeof SeparatorPrimitive.Root>,
-  React.ComponentPropsWithoutRef<typeof SeparatorPrimitive.Root>
->(
-  (
-    { className, orientation = 'horizontal', decorative = true, ...props },
-    ref
-  ) => (
-    <SeparatorPrimitive.Root
-      ref={ref}
-      decorative={decorative}
-      orientation={orientation}
-      className={cn(
-        'shrink-0 bg-border',
-        orientation === 'horizontal' ? 'h-[1px] w-full' : 'h-full w-[1px]',
-        className
-      )}
-      {...props}
-    />
-  )
-)
-Separator.displayName = SeparatorPrimitive.Root.displayName
-export { Separator }

spaces/7hao/bingo/src/lib/isomorphic/node.ts DELETED Viewed

@@ -1,26 +0,0 @@
-import Debug from 'debug'
-const { fetch, setGlobalDispatcher, ProxyAgent } = require('undici')
-const { HttpsProxyAgent } = require('https-proxy-agent')
-const ws = require('ws')
-const debug = Debug('bingo')
-const httpProxy = process.env.http_proxy || process.env.HTTP_PROXY || process.env.https_proxy || process.env.HTTPS_PROXY;
-let WebSocket = ws.WebSocket
-if (httpProxy) {
-  setGlobalDispatcher(new ProxyAgent(httpProxy))
-  const agent = new HttpsProxyAgent(httpProxy)
-  // @ts-ignore
-  WebSocket = class extends ws.WebSocket {
-    constructor(address: string | URL, options: typeof ws.WebSocket) {
-      super(address, {
-        ...options,
-        agent,
-      })
-    }
-  }
-}
-export default { fetch, WebSocket, debug }

spaces/A666sxr/Genshin_TTS/monotonic_align/setup.py DELETED Viewed

@@ -1,9 +0,0 @@
-from distutils.core import setup
-from Cython.Build import cythonize
-import numpy
-setup(
-  name = 'monotonic_align',
-  ext_modules = cythonize("core.pyx"),
-  include_dirs=[numpy.get_include()]
-)

spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/hifigan/utilities.py DELETED Viewed

@@ -1,85 +0,0 @@
-import os
-import json
-import torch
-import numpy as np
-import audioldm.hifigan as hifigan
-HIFIGAN_16K_64 = {
-    "resblock": "1",
-    "num_gpus": 6,
-    "batch_size": 16,
-    "learning_rate": 0.0002,
-    "adam_b1": 0.8,
-    "adam_b2": 0.99,
-    "lr_decay": 0.999,
-    "seed": 1234,
-    "upsample_rates": [5, 4, 2, 2, 2],
-    "upsample_kernel_sizes": [16, 16, 8, 4, 4],
-    "upsample_initial_channel": 1024,
-    "resblock_kernel_sizes": [3, 7, 11],
-    "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-    "segment_size": 8192,
-    "num_mels": 64,
-    "num_freq": 1025,
-    "n_fft": 1024,
-    "hop_size": 160,
-    "win_size": 1024,
-    "sampling_rate": 16000,
-    "fmin": 0,
-    "fmax": 8000,
-    "fmax_for_loss": None,
-    "num_workers": 4,
-    "dist_config": {
-        "dist_backend": "nccl",
-        "dist_url": "tcp://localhost:54321",
-        "world_size": 1,
-    },
-}
-def get_available_checkpoint_keys(model, ckpt):
-    print("==> Attemp to reload from %s" % ckpt)
-    state_dict = torch.load(ckpt)["state_dict"]
-    current_state_dict = model.state_dict()
-    new_state_dict = {}
-    for k in state_dict.keys():
-        if (
-            k in current_state_dict.keys()
-            and current_state_dict[k].size() == state_dict[k].size()
-        ):
-            new_state_dict[k] = state_dict[k]
-        else:
-            print("==> WARNING: Skipping %s" % k)
-    print(
-        "%s out of %s keys are matched"
-        % (len(new_state_dict.keys()), len(state_dict.keys()))
-    )
-    return new_state_dict
-def get_param_num(model):
-    num_param = sum(param.numel() for param in model.parameters())
-    return num_param
-def get_vocoder(config, device):
-    config = hifigan.AttrDict(HIFIGAN_16K_64)
-    vocoder = hifigan.Generator(config)
-    vocoder.eval()
-    vocoder.remove_weight_norm()
-    vocoder.to(device)
-    return vocoder
-def vocoder_infer(mels, vocoder, lengths=None):
-    with torch.no_grad():
-        wavs = vocoder(mels).squeeze(1)
-    wavs = (wavs.cpu().numpy() * 32768).astype("int16")
-    if lengths is not None:
-        wavs = wavs[:, :lengths]
-    return wavs

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/midas/api.py DELETED Viewed

@@ -1,170 +0,0 @@
-# based on https://github.com/isl-org/MiDaS
-import cv2
-import torch
-import torch.nn as nn
-from torchvision.transforms import Compose
-from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
-from ldm.modules.midas.midas.midas_net import MidasNet
-from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
-from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
-ISL_PATHS = {
-    "dpt_large": "midas_models/dpt_large-midas-2f21e586.pt",
-    "dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt",
-    "midas_v21": "",
-    "midas_v21_small": "",
-}
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-def load_midas_transform(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load transform only
-    if model_type == "dpt_large":  # DPT-Large
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-    elif model_type == "midas_v21":
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-    elif model_type == "midas_v21_small":
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-    else:
-        assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-    return transform
-def load_model(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load network
-    model_path = ISL_PATHS[model_type]
-    if model_type == "dpt_large":  # DPT-Large
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitl16_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitb_rn50_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-    elif model_type == "midas_v21":
-        model = MidasNet(model_path, non_negative=True)
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-    elif model_type == "midas_v21_small":
-        model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
-                               non_negative=True, blocks={'expand': True})
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-    else:
-        print(f"model_type '{model_type}' not implemented, use: --model_type large")
-        assert False
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-    return model.eval(), transform
-class MiDaSInference(nn.Module):
-    MODEL_TYPES_TORCH_HUB = [
-        "DPT_Large",
-        "DPT_Hybrid",
-        "MiDaS_small"
-    ]
-    MODEL_TYPES_ISL = [
-        "dpt_large",
-        "dpt_hybrid",
-        "midas_v21",
-        "midas_v21_small",
-    ]
-    def __init__(self, model_type):
-        super().__init__()
-        assert (model_type in self.MODEL_TYPES_ISL)
-        model, _ = load_model(model_type)
-        self.model = model
-        self.model.train = disabled_train
-    def forward(self, x):
-        # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
-        # NOTE: we expect that the correct transform has been called during dataloading.
-        with torch.no_grad():
-            prediction = self.model(x)
-            prediction = torch.nn.functional.interpolate(
-                prediction.unsqueeze(1),
-                size=x.shape[2:],
-                mode="bicubic",
-                align_corners=False,
-            )
-        assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
-        return prediction

spaces/AIZero2Hero4Health/7-ClinicalTerminologyUIUX-GR/README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: 7 ClinicalTerminologyUIUX GR
-emoji: 👀
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.8.2
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnet50_8xb32-cutmix_in1k.py DELETED Viewed

@@ -1,5 +0,0 @@
-_base_ = [
-    '../_base_/models/resnet50_cutmix.py',
-    '../_base_/datasets/imagenet_bs32.py',
-    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
-]

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Gravityengine.py DELETED Viewed

@@ -1,27 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-url = 'https://gpt4.xunika.uk/'
-model = ['gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613']
-supports_stream = True
-needs_auth = False
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
-    headers = {
-        'Content-Type': 'application/json',
-    }
-    data = {
-        'model': model,
-        'temperature': 0.7,
-        'presence_penalty': 0,
-        'messages': messages,
-    }
-    response = requests.post(url + '/api/openai/v1/chat/completions',
-                             json=data, stream=True)
-    yield response.json()['choices'][0]['message']['content']
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/RemoveChildrenMap.js DELETED Viewed

@@ -1,17 +0,0 @@
-var RemoveChildrenMap = function (key) {
-    if (typeof (key) === 'object') {
-        var gameObject = key;
-        for (var key in this.childrenMap) {
-            if (this.childrenMap[key] === gameObject) {
-                delete this.childrenMap[key];
-                return this;
-            }
-        }
-    }
-    delete this.childrenMap[key];
-    return this;
-}
-export default RemoveChildrenMap;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dialog/Factory.d.ts DELETED Viewed

@@ -1,5 +0,0 @@
-import Dialog from './Dialog';
-export default function (
-    config?: Dialog.IConfig
-): Dialog;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthsizer/Methods.js DELETED Viewed

@@ -1,25 +0,0 @@
-import GetChildrenWidth from './GetChildrenWidth.js';
-import GetChildrenHeight from './GetChildrenHeight.js';
-import GetChildrenSizers from './GetChildrenSizers.js';
-import PreLayout from './PreLayout.js';
-import LayoutChildren from './LayoutChildren.js';
-import RunWidthWrap from './RunWidthWrap.js';
-import AddChildMethods from './AddChildMethods.js';
-import RemoveChildMethods from './RemoveChildMethods.js';
-var methods = {
-    getChildrenWidth: GetChildrenWidth,
-    getChildrenHeight: GetChildrenHeight,
-    getChildrenSizers: GetChildrenSizers,
-    preLayout: PreLayout,
-    layoutChildren: LayoutChildren,
-    runWidthWrap: RunWidthWrap,
-};
-Object.assign(
-    methods,
-    AddChildMethods,
-    RemoveChildMethods
-);
-export default methods;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/space/Space.js DELETED Viewed

@@ -1,10 +0,0 @@
-const Zone = Phaser.GameObjects.Zone;
-class Space extends Zone {
-    constructor(scene) {
-        super(scene, 0, 0, 1, 1);
-        // Don't add Zone into scene
-        this.isRexSpace = true;
-    }
-}
-export default Space;

spaces/Akim/claudeAPI/Dockerfile DELETED Viewed

@@ -1,14 +0,0 @@
-# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
-# you will also find guides on how best to write your Dockerfile
-FROM python:3.9
-WORKDIR /code
-COPY ./requirements.txt /code/requirements.txt
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-COPY . .
-CMD ["python", "webapi_claude.py"]

spaces/AlekseyKorshuk/thin-plate-spline-motion-model/modules/inpainting_network.py DELETED Viewed

@@ -1,130 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-from modules.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d
-from modules.dense_motion import DenseMotionNetwork
-class InpaintingNetwork(nn.Module):
-    """
-    Inpaint the missing regions and reconstruct the Driving image.
-    """
-    def __init__(self, num_channels, block_expansion, max_features, num_down_blocks, multi_mask = True, **kwargs):
-        super(InpaintingNetwork, self).__init__()
-        self.num_down_blocks = num_down_blocks
-        self.multi_mask = multi_mask
-        self.first = SameBlock2d(num_channels, block_expansion, kernel_size=(7, 7), padding=(3, 3))
-        down_blocks = []
-        for i in range(num_down_blocks):
-            in_features = min(max_features, block_expansion * (2 ** i))
-            out_features = min(max_features, block_expansion * (2 ** (i + 1)))
-            down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
-        self.down_blocks = nn.ModuleList(down_blocks)
-        up_blocks = []
-        in_features = [max_features, max_features, max_features//2]
-        out_features = [max_features//2, max_features//4, max_features//8]
-        for i in range(num_down_blocks):
-            up_blocks.append(UpBlock2d(in_features[i], out_features[i], kernel_size=(3, 3), padding=(1, 1)))
-        self.up_blocks = nn.ModuleList(up_blocks)
-        resblock = []
-        for i in range(num_down_blocks):
-            resblock.append(ResBlock2d(in_features[i], kernel_size=(3, 3), padding=(1, 1)))
-            resblock.append(ResBlock2d(in_features[i], kernel_size=(3, 3), padding=(1, 1)))
-        self.resblock = nn.ModuleList(resblock)
-        self.final = nn.Conv2d(block_expansion, num_channels, kernel_size=(7, 7), padding=(3, 3))
-        self.num_channels = num_channels
-    def deform_input(self, inp, deformation):
-        _, h_old, w_old, _ = deformation.shape
-        _, _, h, w = inp.shape
-        if h_old != h or w_old != w:
-            deformation = deformation.permute(0, 3, 1, 2)
-            deformation = F.interpolate(deformation, size=(h, w), mode='bilinear', align_corners=True)
-            deformation = deformation.permute(0, 2, 3, 1)
-        return F.grid_sample(inp, deformation,align_corners=True)
-    def occlude_input(self, inp, occlusion_map):
-        if not self.multi_mask:
-            if inp.shape[2] != occlusion_map.shape[2] or inp.shape[3] != occlusion_map.shape[3]:
-                occlusion_map = F.interpolate(occlusion_map, size=inp.shape[2:], mode='bilinear',align_corners=True)
-        out = inp * occlusion_map
-        return out
-    def forward(self, source_image, dense_motion):
-        out = self.first(source_image)
-        encoder_map = [out]
-        for i in range(len(self.down_blocks)):
-            out = self.down_blocks[i](out)
-            encoder_map.append(out)
-        output_dict = {}
-        output_dict['contribution_maps'] = dense_motion['contribution_maps']
-        output_dict['deformed_source'] = dense_motion['deformed_source']
-        occlusion_map = dense_motion['occlusion_map']
-        output_dict['occlusion_map'] = occlusion_map
-        deformation = dense_motion['deformation']
-        out_ij = self.deform_input(out.detach(), deformation)
-        out = self.deform_input(out, deformation)
-        out_ij = self.occlude_input(out_ij, occlusion_map[0].detach())
-        out = self.occlude_input(out, occlusion_map[0])
-        warped_encoder_maps = []
-        warped_encoder_maps.append(out_ij)
-        for i in range(self.num_down_blocks):
-            out = self.resblock[2*i](out)
-            out = self.resblock[2*i+1](out)
-            out = self.up_blocks[i](out)
-            encode_i = encoder_map[-(i+2)]
-            encode_ij = self.deform_input(encode_i.detach(), deformation)
-            encode_i = self.deform_input(encode_i, deformation)
-            occlusion_ind = 0
-            if self.multi_mask:
-                occlusion_ind = i+1
-            encode_ij = self.occlude_input(encode_ij, occlusion_map[occlusion_ind].detach())
-            encode_i = self.occlude_input(encode_i, occlusion_map[occlusion_ind])
-            warped_encoder_maps.append(encode_ij)
-            if(i==self.num_down_blocks-1):
-                break
-            out = torch.cat([out, encode_i], 1)
-        deformed_source = self.deform_input(source_image, deformation)
-        output_dict["deformed"] = deformed_source
-        output_dict["warped_encoder_maps"] = warped_encoder_maps
-        occlusion_last = occlusion_map[-1]
-        if not self.multi_mask:
-            occlusion_last = F.interpolate(occlusion_last, size=out.shape[2:], mode='bilinear',align_corners=True)
-        out = out * (1 - occlusion_last) + encode_i
-        out = self.final(out)
-        out = torch.sigmoid(out)
-        out = out * (1 - occlusion_last) + deformed_source * occlusion_last
-        output_dict["prediction"] = out
-        return output_dict
-    def get_encode(self, driver_image, occlusion_map):
-        out = self.first(driver_image)
-        encoder_map = []
-        encoder_map.append(self.occlude_input(out.detach(), occlusion_map[-1].detach()))
-        for i in range(len(self.down_blocks)):
-            out = self.down_blocks[i](out.detach())
-            out_mask = self.occlude_input(out.detach(), occlusion_map[2-i].detach())
-            encoder_map.append(out_mask.detach())
-        return encoder_map

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/using-diffusers/textual_inversion_inference.md DELETED Viewed

@@ -1,80 +0,0 @@
-# Textual inversion
-[[open-in-colab]]
-The [`StableDiffusionPipeline`] supports textual inversion, a technique that enables a model like Stable Diffusion to learn a new concept from just a few sample images. This gives you more control over the generated images and allows you to tailor the model towards specific concepts. You can get started quickly with a collection of community created concepts in the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer).
-This guide will show you how to run inference with textual inversion using a pre-learned concept from the Stable Diffusion Conceptualizer. If you're interested in teaching a model new concepts with textual inversion, take a look at the [Textual Inversion](./training/text_inversion) training guide.
-Login to your Hugging Face account:
-```py
-from huggingface_hub import notebook_login
-notebook_login()
-```
-Import the necessary libraries, and create a helper function to visualize the generated images:
-```py
-import os
-import torch
-import PIL
-from PIL import Image
-from diffusers import StableDiffusionPipeline
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-def image_grid(imgs, rows, cols):
-    assert len(imgs) == rows * cols
-    w, h = imgs[0].size
-    grid = Image.new("RGB", size=(cols * w, rows * h))
-    grid_w, grid_h = grid.size
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i % cols * w, i // cols * h))
-    return grid
-```
-Pick a Stable Diffusion checkpoint and a pre-learned concept from the [Stable Diffusion Conceptualizer](https://huggingface.co/spaces/sd-concepts-library/stable-diffusion-conceptualizer):
-```py
-pretrained_model_name_or_path = "runwayml/stable-diffusion-v1-5"
-repo_id_embeds = "sd-concepts-library/cat-toy"
-```
-Now you can load a pipeline, and pass the pre-learned concept to it:
-```py
-pipeline = StableDiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16).to("cuda")
-pipeline.load_textual_inversion(repo_id_embeds)
-```
-Create a prompt with the pre-learned concept by using the special placeholder token `<cat-toy>`, and choose the number of samples and rows of images you'd like to generate:
-```py
-prompt = "a grafitti in a favela wall with a <cat-toy> on it"
-num_samples = 2
-num_rows = 2
-```
-Then run the pipeline (feel free to adjust the parameters like `num_inference_steps` and `guidance_scale` to see how they affect image quality), save the generated images and visualize them with the helper function you created at the beginning:
-```py
-all_images = []
-for _ in range(num_rows):
-    images = pipe(prompt, num_images_per_prompt=num_samples, num_inference_steps=50, guidance_scale=7.5).images
-    all_images.extend(images)
-grid = image_grid(all_images, num_samples, num_rows)
-grid
-```
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/textual_inversion_inference.png">
-</div>

spaces/Andy1621/uniformer_image_detection/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py DELETED Viewed

@@ -1,14 +0,0 @@
-_base_ = './scnet_r50_fpn_20e_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'))

spaces/AnnonSubmission/xai-cl/methods.py DELETED Viewed

@@ -1,376 +0,0 @@
-import torch
-import numpy as np
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision.transforms as transforms
-import torchvision
-from PIL import Image
-from sklearn.decomposition import NMF
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-def relu_hook_function(module, grad_in, grad_out):
-    if isinstance(module, nn.ReLU):
-        return (F.relu(grad_in[0]),)
-def blur_sailency(input_image):
-    return torchvision.transforms.functional.gaussian_blur(input_image, kernel_size=[11, 11], sigma=[5,5])
-def occlusion(img1, img2, model, w_size = 64, stride = 8, batch_size = 32):
-    measure = nn.CosineSimilarity(dim=-1)
-    output_size = int(((img2.size(-1) - w_size) / stride) + 1)
-    out1_condition, out2_condition = model(img1), model(img2)
-    images1 = []
-    images2 = []
-    for i in range(output_size):
-        for j in range(output_size):
-            start_i, start_j = i * stride, j * stride
-            image1 = img1.clone().detach()
-            image2 = img2.clone().detach()
-            image1[:, :, start_i : start_i + w_size, start_j : start_j + w_size] = 0
-            image2[:, :, start_i : start_i + w_size, start_j : start_j + w_size] = 0
-            images1.append(image1)
-            images2.append(image2)
-    images1 = torch.cat(images1, dim=0).to(device)
-    images2 = torch.cat(images2, dim=0).to(device)
-    score_map1 = []
-    score_map2 = []
-    assert images1.shape[0] == images2.shape[0]
-    for b in range(0, images2.shape[0], batch_size):
-        with torch.no_grad():
-            out1 = model(images1[b : b + batch_size, :])
-            out2 = model(images2[b : b + batch_size, :])
-        score_map1.append(measure(out1, out2_condition))  # try torch.mm(out2_condition, out1.t())[0]
-        score_map2.append(measure(out1_condition, out2))  # try torch.mm(out1_condition, out2.t())[0]
-    score_map1 = torch.cat(score_map1, dim = 0)
-    score_map2 = torch.cat(score_map2, dim = 0)
-    assert images2.shape[0] == score_map2.shape[0] == score_map1.shape[0]
-    heatmap1 = score_map1.view(output_size, output_size).cpu().detach().numpy()
-    heatmap2 = score_map2.view(output_size, output_size).cpu().detach().numpy()
-    base_score = measure(out1_condition, out2_condition)
-    heatmap1 = (heatmap1 - base_score.item()) * -1   # or base_score.item() - heatmap1. The higher the drop, the better
-    heatmap2 = (heatmap2 - base_score.item()) * -1   # or base_score.item() - heatmap2. The higher the drop, the better
-    heatmap1 = (heatmap1 - heatmap1.min()) / (heatmap1.max() - heatmap1.min())
-    heatmap2 = (heatmap2 - heatmap2.min()) / (heatmap2.max() - heatmap2.min())
-    return heatmap1, heatmap2
-def pairwise_occlusion(img1, img2, model, batch_size, erase_scale, erase_ratio, num_erases):
-    measure = nn.CosineSimilarity(dim=-1)
-    out1_condition, out2_condition = model(img1), model(img2)
-    baseline = measure(out1_condition, out2_condition).detach()
-    # a bit sensitive to scale and ratio. erase_scale is from (scale[0] * 100) % to (scale[1] * 100) %
-    random_erase = transforms.RandomErasing(p=1.0, scale=erase_scale, ratio=erase_ratio)
-    image1 = img1.clone().detach()
-    image2 = img2.clone().detach()
-    images1 = []
-    images2 = []
-    for _ in range(num_erases):
-        images1.append(random_erase(image1))
-        images2.append(random_erase(image2))
-    images1 = torch.cat(images1, dim=0).to(device)
-    images2 = torch.cat(images2, dim=0).to(device)
-    sims = []
-    weights1 = []
-    weights2 = []
-    for b in range(0, images2.shape[0], batch_size):
-        with torch.no_grad():
-            out1 = model(images1[b : b + batch_size, :])
-            out2 = model(images2[b : b + batch_size, :])
-            sims.append(measure(out1, out2))
-            weights1.append(out1.norm(dim=-1))
-            weights2.append(out2.norm(dim=-1))
-    sims = torch.cat(sims, dim = 0)
-    weights1, weights2 = torch.cat(weights1, dim = 0).cpu().numpy(), torch.cat(weights2, dim = 0).cpu().numpy()
-    weights = list(zip(weights1, weights2))
-    sims = baseline - sims   # the higher the drop, the better
-    sims = F.softmax(sims, dim = -1)
-    sims = sims.cpu().numpy()
-    assert sims.shape[0] == images1.shape[0] == images2.shape[0]
-    A1 = np.zeros((224, 224))
-    A2 = np.zeros((224, 224))
-    for n in range(images1.shape[0]):
-        im1_2d = images1[n].cpu().numpy().transpose((1, 2, 0)).sum(axis=-1)
-        im2_2d = images2[n].cpu().numpy().transpose((1, 2, 0)).sum(axis=-1)
-        joint_similarity = sims[n]
-        weight = weights[n]
-        if weight[0] < weight[1]:
-            A1[im1_2d == 0] += joint_similarity
-        else:
-            A2[im2_2d == 0] += joint_similarity
-    A1 = A1 / (np.max(A1) + 1e-9)
-    A2 = A2 / (np.max(A2) + 1e-9)
-    return A1, A2
-def create_mixed_images(transform_type, ig_transforms, step, img_path, add_noise):
-    img = Image.open(img_path).convert('RGB') if isinstance(img_path, str) else img_path
-    img1 = ig_transforms['pure'](img).unsqueeze(0).to(device)
-    img2 = ig_transforms[transform_type](img).unsqueeze(0).to(device)
-    lambdas = np.arange(1,0,-step)
-    mixed_images = []
-    for l,lam in enumerate(lambdas):
-        mixed_img = lam * img1 + (1 - lam) * img2
-        mixed_images.append(mixed_img)
-    if add_noise:
-        sigma = 0.15 / (torch.max(img1) - torch.min(img1)).item()
-        mixed_images = [im + torch.zeros_like(im).normal_(0, sigma) if (n>0) and (n<len(mixed_images)-1) else im for n,im in enumerate(mixed_images)]
-    return mixed_images
-def averaged_transforms(guided, ssl_model, mixed_images, blur_output):
-    measure = nn.CosineSimilarity(dim=-1)
-    if guided:
-        handles = []
-        for i, module in enumerate(ssl_model.modules()):
-            if isinstance(module, nn.ReLU):
-                handles.append(module.register_backward_hook(relu_hook_function))
-    grads1 = []
-    grads2 = []
-    for xbar_image in mixed_images[1:]:
-        ssl_model.zero_grad()
-        input_image1 = mixed_images[0].clone().requires_grad_()
-        input_image2 = xbar_image.clone().requires_grad_()
-        if input_image1.grad is not None:
-            input_image1.grad.data.zero_()
-            input_image2.grad.data.zero_()
-        score = measure(ssl_model(input_image1), ssl_model(input_image2))
-        score.backward()
-        grads1.append(input_image1.grad.data)
-        grads2.append(input_image2.grad.data)
-    grads1 = torch.cat(grads1).mean(0).unsqueeze(0)
-    grads2 = torch.cat(grads2).mean(0).unsqueeze(0)
-    sailency1, _ = torch.max((mixed_images[0] * grads1).abs(), dim=1)
-    sailency2, _ = torch.max((mixed_images[-1] * grads2).abs(), dim=1)
-    if guided:     # remove handles after finishing
-        for handle in handles:
-            handle.remove()
-    if blur_output:
-        sailency1 = blur_sailency(sailency1)
-        sailency2 = blur_sailency(sailency2)
-    return sailency1, sailency2
-def sailency(guided, ssl_model, img1, img2, blur_output):
-    measure = nn.CosineSimilarity(dim=-1)
-    if guided:
-        handles = []
-        for i, module in enumerate(ssl_model.modules()):
-            if isinstance(module, nn.ReLU):
-                handles.append(module.register_backward_hook(relu_hook_function))
-    input_image1 = img1.clone().requires_grad_()
-    input_image2 = img2.clone().requires_grad_()
-    score = measure(ssl_model(input_image1), ssl_model(input_image2))
-    score.backward()
-    grads1 = input_image1.grad.data
-    grads2 = input_image2.grad.data
-    sailency1, _ = torch.max((img1 * grads1).abs(), dim=1)
-    sailency2, _ = torch.max((img2 * grads2).abs(), dim=1)
-    if guided:     # remove handles after finishing
-        for handle in handles:
-            handle.remove()
-    if blur_output:
-        sailency1 = blur_sailency(sailency1)
-        sailency2 = blur_sailency(sailency2)
-    return sailency1, sailency2
-def smooth_grad(guided, ssl_model, img1, img2, blur_output, steps = 50):
-    measure = nn.CosineSimilarity(dim=-1)
-    sigma = 0.15 / (torch.max(img1) - torch.min(img1)).item()
-    if guided:
-        handles = []
-        for i, module in enumerate(ssl_model.modules()):
-            if isinstance(module, nn.ReLU):
-                handles.append(module.register_backward_hook(relu_hook_function))
-    noise_images1 = []
-    noise_images2 = []
-    for _ in range(steps):
-        noise = torch.zeros_like(img1).normal_(0, sigma)
-        noise_images1.append(img1 + noise)
-        noise_images2.append(img2 + noise)
-    grads1 = []
-    grads2 = []
-    for n1, n2 in zip(noise_images1, noise_images2):
-        ssl_model.zero_grad()
-        input_image1 = n1.clone().requires_grad_()
-        input_image2 = n2.clone().requires_grad_()
-        if input_image1.grad is not None:
-            input_image1.grad.data.zero_()
-            input_image2.grad.data.zero_()
-        score = measure(ssl_model(input_image1), ssl_model(input_image2))
-        score.backward()
-        grads1.append(input_image1.grad.data)
-        grads2.append(input_image2.grad.data)
-    grads1 = torch.cat(grads1).mean(0).unsqueeze(0)
-    grads2 = torch.cat(grads2).mean(0).unsqueeze(0)
-    sailency1, _ = torch.max((img1 * grads1 ).abs(), dim=1)
-    sailency2, _ = torch.max((img2 * grads2).abs(), dim=1)
-    if guided:     # remove handles after finishing
-        for handle in handles:
-            handle.remove()
-    if blur_output:
-        sailency1 = blur_sailency(sailency1)
-        sailency2 = blur_sailency(sailency2)
-    return sailency1, sailency2
-class GradCAM(nn.Module):
-    def __init__(self, ssl_model):
-        super(GradCAM, self).__init__()
-        self.gradients = {}
-        self.features = {}
-        self.feature_extractor = ssl_model.encoder.net
-        self.contrastive_head = ssl_model.contrastive_head
-        self.measure = nn.CosineSimilarity(dim=-1)
-    def save_grads(self, img_index):
-        def hook(grad):
-            self.gradients[img_index] = grad.detach()
-        return hook
-    def save_features(self, img_index, feats):
-        self.features[img_index] = feats.detach()
-    def forward(self, img1, img2):
-        features1 = self.feature_extractor(img1)
-        features2 = self.feature_extractor(img2)
-        self.save_features('1', features1)
-        self.save_features('2', features2)
-        h1 = features1.register_hook(self.save_grads('1'))
-        h2 = features2.register_hook(self.save_grads('2'))
-        out1, out2 = features1.mean(dim=[2, 3]), features2.mean(dim=[2, 3])
-        out1, out2 = self.contrastive_head(out1), self.contrastive_head(out2)
-        score = self.measure(out1, out2)
-        return score
-def weight_activation(feats, grads):
-    cam =  feats * F.relu(grads)
-    cam = torch.sum(cam, dim=1).squeeze().cpu().detach().numpy()
-    return cam
-def get_gradcam(ssl_model, img1, img2):
-    grad_cam = GradCAM(ssl_model).to(device)
-    score = grad_cam(img1, img2)
-    grad_cam.zero_grad()
-    score.backward()
-    cam1 = weight_activation(grad_cam.features['1'], grad_cam.gradients['1'])
-    cam2 = weight_activation(grad_cam.features['2'], grad_cam.gradients['2'])
-    return cam1, cam2
-def get_interactioncam(ssl_model, img1, img2, reduction, grad_interact = False):
-    grad_cam = GradCAM(ssl_model).to(device)
-    score = grad_cam(img1, img2)
-    grad_cam.zero_grad()
-    score.backward()
-    G1 = grad_cam.gradients['1']
-    G2 = grad_cam.gradients['2']
-    if grad_interact:
-        B, D, H, W = G1.size()
-        G1_ = G1.permute(0,2,3,1).view(B, H * W, D)
-        G2_ = G2.permute(0,2,3,1).view(B, H * W, D)
-        G_ = torch.bmm(G1_.permute(0,2,1), G2_)  # (B, D, D)
-        G1, _ = torch.max(G_, dim = -1)   # (B, D)
-        G2, _ = torch.max(G_, dim = 1)    # (B, D)
-        G1 = G1.unsqueeze(-1).unsqueeze(-1)
-        G2 = G2.unsqueeze(-1).unsqueeze(-1)
-    if reduction == 'mean':
-        joint_weight = grad_cam.features['1'].mean([2,3]) * grad_cam.features['2'].mean([2,3])
-    elif reduction == 'max':
-        max_pooled1 = F.max_pool2d(grad_cam.features['1'], kernel_size=grad_cam.features['1'].size()[2:]).squeeze(-1).squeeze(-1)
-        max_pooled2 = F.max_pool2d(grad_cam.features['2'], kernel_size=grad_cam.features['2'].size()[2:]).squeeze(-1).squeeze(-1)
-        joint_weight = max_pooled1 * max_pooled2
-    else:
-        B, D, H, W = grad_cam.features['1'].size()
-        reshaped1 = grad_cam.features['1'].permute(0,2,3,1).reshape(B, H * W, D)
-        reshaped2 = grad_cam.features['2'].permute(0,2,3,1).reshape(B, H * W, D)
-        features1_query, features2_query = reshaped1.mean(1).unsqueeze(1), reshaped2.mean(1).unsqueeze(1)
-        attn1 = (features1_query @ reshaped1.transpose(-2, -1)).softmax(dim=-1)
-        attn2 = (features2_query @ reshaped2.transpose(-2, -1)).softmax(dim=-1)
-        att_reduced1 = (attn1 @ reshaped1).squeeze(1)
-        att_reduced2 = (attn2 @ reshaped2).squeeze(1)
-        joint_weight = att_reduced1 * att_reduced2
-    joint_weight = joint_weight.unsqueeze(-1).unsqueeze(-1).expand_as(grad_cam.features['1'])
-    feats1 = grad_cam.features['1'] * joint_weight
-    feats2 = grad_cam.features['2'] * joint_weight
-    cam1 = weight_activation(feats1, G1)
-    cam2 = weight_activation(feats2, G2)
-    return cam1, cam2

spaces/AquaSuisei/ChatGPTXE/modules/shared.py DELETED Viewed

@@ -1,55 +0,0 @@
-from modules.presets import COMPLETION_URL, BALANCE_API_URL, USAGE_API_URL, API_HOST
-import os
-import queue
-class State:
-    interrupted = False
-    multi_api_key = False
-    completion_url = COMPLETION_URL
-    balance_api_url = BALANCE_API_URL
-    usage_api_url = USAGE_API_URL
-    def interrupt(self):
-        self.interrupted = True
-    def recover(self):
-        self.interrupted = False
-    def set_api_host(self, api_host):
-        self.completion_url = f"https://{api_host}/v1/chat/completions"
-        self.balance_api_url = f"https://{api_host}/dashboard/billing/credit_grants"
-        self.usage_api_url = f"https://{api_host}/dashboard/billing/usage"
-        os.environ["OPENAI_API_BASE"] = f"https://{api_host}/v1"
-    def reset_api_host(self):
-        self.completion_url = COMPLETION_URL
-        self.balance_api_url = BALANCE_API_URL
-        self.usage_api_url = USAGE_API_URL
-        os.environ["OPENAI_API_BASE"] = f"https://{API_HOST}/v1"
-        return API_HOST
-    def reset_all(self):
-        self.interrupted = False
-        self.completion_url = COMPLETION_URL
-    def set_api_key_queue(self, api_key_list):
-        self.multi_api_key = True
-        self.api_key_queue = queue.Queue()
-        for api_key in api_key_list:
-            self.api_key_queue.put(api_key)
-    def switching_api_key(self, func):
-        if not hasattr(self, "api_key_queue"):
-            return func
-        def wrapped(*args, **kwargs):
-            api_key = self.api_key_queue.get()
-            args = list(args)[1:]
-            ret = func(api_key, *args, **kwargs)
-            self.api_key_queue.put(api_key)
-            return ret
-        return wrapped
-state = State()

spaces/AsakuraMizu/moe-tts/monotonic_align/core.py DELETED Viewed

@@ -1,36 +0,0 @@
-import numba
-@numba.jit(numba.void(numba.int32[:, :, ::1], numba.float32[:, :, ::1], numba.int32[::1], numba.int32[::1]),
-           nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-    b = paths.shape[0]
-    max_neg_val = -1e9
-    for i in range(int(b)):
-        path = paths[i]
-        value = values[i]
-        t_y = t_ys[i]
-        t_x = t_xs[i]
-        v_prev = v_cur = 0.0
-        index = t_x - 1
-        for y in range(t_y):
-            for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-                if x == y:
-                    v_cur = max_neg_val
-                else:
-                    v_cur = value[y - 1, x]
-                if x == 0:
-                    if y == 0:
-                        v_prev = 0.
-                    else:
-                        v_prev = max_neg_val
-                else:
-                    v_prev = value[y - 1, x - 1]
-                value[y, x] += max(v_prev, v_cur)
-        for y in range(t_y - 1, -1, -1):
-            path[y, index] = 1
-            if index != 0 and (index == y or value[y - 1, index] < value[y - 1, index - 1]):
-                index = index - 1

spaces/Awesimo/jojogan/op/__init__.py DELETED Viewed

File without changes

spaces/Bart92/RVC_HF/lib/uvr5_pack/lib_v5/layers_33966KB.py DELETED Viewed

@@ -1,126 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-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

spaces/BartPoint/VoiceChange/infer_pack/modules/F0Predictor/HarvestF0Predictor.py DELETED Viewed

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

spaces/Benson/text-generation/Examples/Bicicletas Indias Conduccin 3d Descargar Enlace.md DELETED Viewed

@@ -1,127 +0,0 @@
-<h1>Indian Bikes Driving 3D: Un juego de motos realista y divertido</h1>
-<p>Si eres un amante de la bicicleta y te gusta conducir rápido y furioso en carreteras desafiantes, entonces te encantará Indian Bikes Driving 3D. Este es uno de los juegos de motos más realistas y divertidos, donde puedes elegir tu bicicleta favorita, personalizarla todo lo que quieras y mostrar tus habilidades en varios cursos y ubicaciones. En este artículo, te contaremos todo lo que necesitas saber sobre este increíble juego, incluyendo sus características, cómo descargarlo y jugarlo, y algunos consejos y trucos para sacarle el máximo partido. </p>
-<h2>bicicletas indias conducción 3d descargar enlace</h2><br /><p><b><b>Download</b> === <a href="https://bltlly.com/2v6MFn">https://bltlly.com/2v6MFn</a></b></p><br /><br />
- <h2>Introducción</h2>
-<h3>¿Qué es Indian Bikes Driving 3D? </h3>
-<p>Indian Bikes Driving 3D es un juego de acción desarrollado por Rohit Gaming Studio, un popular desarrollador de juegos de simulación. Fue lanzado en abril de 2023 para dispositivos Android e iOS, y ha recibido más de 50 millones de descargas y 4.0 estrellas en Google Play Store. También está disponible para usuarios de PC a través de BlueStacks, un emulador que te permite jugar juegos de Android en tu ordenador. </p>
- <h3>¿Por qué deberías jugar Indian Bikes Driving 3D? </h3>
-<p>Hay muchas razones por las que deberías jugar Indian Bikes Driving 3D, pero aquí están algunas de las principales:</p>
-<ul>
-<li> Es gratis para descargar y jugar, y no requiere una conexión a Internet. </li>
-<li>Tiene gráficos impresionantes y física realista que te hará sentir como si estuvieras montando una bicicleta real. </li>
-<li> Tiene una variedad de bicicletas para elegir, cada una con diferentes capacidades de aceleración, manejo y fuera de carretera. </li>
-<li>Tiene tres modos de desafíos: Parkour, Time Trail y Drift, donde puedes probar tus habilidades y competir con otros jugadores. </li>
-<li>Tiene cuatro lugares para explorar: Puerto, Aeropuerto, Offroad y Ciudad, cada uno con diferentes obstáculos y paisajes. </li>
-<li> Tiene códigos de trucos que puede utilizar para desbloquear vehículos secretos, objetos y habilidades. </li>
-</ul>
- <h2>Características de Indian Bikes Driving 3D</h2>
-<p>Una de las mejores características de Indian Bikes Driving 3D es que puedes personalizar tu motocicleta tanto como quieras. Puedes elegir entre diez motocicletas con diferentes especificaciones, como:</p>
-<tabla>
-<tr><th>Bike</th><th>Aceleración</th><th>Manipulación</th><th>Fuera de carretera</th></tr>
-<tr><td>Tarzen</td><td>Alto</td><td>Bajo</td><td>Alto</td></tr>
-<tr><td>Caballo</td><td>Medio</td><td>Medio</td><td>Medio</td></tr>
-<tr><td>Aapache</td><td>Baja</td><td>Alta</td><td>Baja</td></tr>
-<tr><td>Ducati Diavel</td><td>Alto</td><td>Alto</td><td>Bajo</td></tr>
-<tr><td>Kawasaki Ninja H2r</td><td>Muy alto</td><td>Muy alto</td><td>Muy bajo</td></tr>
-<tr><td>Pulsar Rs200</td><td>Medium</td><td>Medium</td><td>Medium</td></tr>
-<tr><td>KTM Duke 1290</td><td>High</td><td>High</td><td>Medium </td></tr>
-<tr><td>Harley Davidson</td><td>Low</td><td>Low</td><td>High</td></tr>
-<tr><td>Royal Enfield</td><td>Bajo</td><td>Medio</td><td>Alto</td></tr>
-<tr><td>Bajaj Dominar 400</td><td>Medium</td><td>Medium</td><td>Medium</td></tr>
-</tabla>
-<p>También puede cambiar el color, las ruedas, las pegatinas y el escape de su bicicleta para que se vea más elegante y único. Incluso puedes añadir algunos accesorios, como cascos, guantes, chaquetas y botas, para combinar con tu bicicleta y personalidad. </p>
- <h3>Desafiantes cursos y ubicaciones</h3>
-<p>Otra gran característica de Indian Bikes Driving 3D es que tiene desafiantes cursos y ubicaciones que te mantendrán entretenido y enganchado. Puedes elegir entre tres modos de desafíos: Parkour, Time Trail y Drift.</p>
-<ul>
-<li>Parkour: En este modo, tienes que realizar acrobacias y trucos en rampas, puentes, contenedores y otros obstáculos. Tienes que evitar chocar o caer del curso, y tienes que recoger monedas y estrellas en el camino. Cuantas más monedas y estrellas recojas, más puntos ganarás. Puedes usar los puntos para desbloquear nuevas bicicletas y objetos. </li>
-<li>Deriva: En este modo, usted tiene que deriva su bicicleta en carreteras resbaladizas y curvas. Tienes que controlar tu equilibrio y dirección, y tienes que evitar chocar con paredes u obstáculos. Cuanto más tiempo te desvíes, más puntos ganas. Puede utilizar los puntos para actualizar su bicicleta y mejorar su rendimiento. </li>
-</ul>
-<p>También puede elegir entre cuatro lugares para explorar: Puerto, Aeropuerto, Offroad y Ciudad. Cada lugar tiene diferentes paisajes y atmósfera, como barcos, aviones, montañas, edificios, etc. También puede cambiar entre los modos de día y noche para experimentar diferentes efectos de iluminación. </p>
-<p></p>
- <h3>Diferentes vistas y controles de la cámara</h3>
-<p>Una tercera característica de Indian Bikes Driving 3D es que tiene diferentes vistas de cámara y controles que se adapten a su preferencia y comodidad. Puede elegir entre cuatro vistas de cámara: primera persona, tercera persona, de arriba hacia abajo y vista lateral. Cada vista de cámara tiene sus propias ventajas y desventajas, como visibilidad, ángulo, perspectiva, etc. También puede ajustar el zoom y la rotación de la cámara para obtener una mejor vista de su entorno. </p>
-<p>También puede elegir entre tres controles: Inclinación, Botón o Volante. Cada control tiene su propia sensibilidad y capacidad de respuesta, como aceleración, frenado, dirección, etc. También puede personalizar el diseño y el tamaño del control para adaptarse a su pantalla y dedos. </p>
- <h3>Códigos de trucos y secretos</h3>
-<p>Una cuarta característica de Indian Bikes Driving 3D es que tiene códigos de trucos y secretos que puede utilizar para mejorar su juego y divertirse más. Puede introducir códigos de trucos en el menú de configuración para activar varios efectos, como:</p>
-<ul>
-<li>monedas infinitas: Introduzca COINMASTER para obtener monedas ilimitadas que se pueden utilizar para comprar cualquier cosa en el juego. </li>
-<li>Estrellas infinitas: Entra en STARLORD para obtener estrellas ilimitadas que puedes usar para desbloquear cualquier cosa en el juego. </li>
-<li>Combustible infinito: Introduzca FUELFORLIFE para obtener combustible ilimitado que puede utilizar para conducir el tiempo que desee. </li>
-<li>Bicicleta invisible: Introduzca GHOSTRIDER para hacer su bicicleta invisible a otros vehículos y obstáculos. </li>
-<li>Bicicleta voladora: Introduzca SUPERMAN para hacer volar su bicicleta en el aire. </li>
-<li>Bicicleta gigante: Introduzca BIGFOOT para hacer su bicicleta más grande de lo normal. </li>
-<li>Bicicleta pequeña: Introduzca ANTMAN para hacer su bicicleta más pequeña de lo normal. </li>
-</ul>
-<p>También puedes encontrar algunos secretos en el juego que te sorprenderán o te darán algunos beneficios, como:</p>
-<ul>
-<li>Bicicletas ocultas: Hay algunas bicicletas ocultas en el juego que puedes encontrar explorando las ubicaciones o completando ciertas tareas. Por ejemplo, puede encontrar una bicicleta de policía en la ubicación de la ciudad siguiendo un coche de policía o una bicicleta de helicóptero en la ubicación del aeropuerto aterrizando en un helipuerto. </li>
-<li>Objetos ocultos: Hay algunos objetos ocultos en el juego que puedes encontrar buscando las ubicaciones o completando ciertas tareas. Por ejemplo, puedes encontrar un jetpack en la ubicación del puerto saltando al agua o un lanzacohetes en la ubicación offroad subiendo una colina. </li>
-<li>Atajos ocultos: Hay algunos atajos ocultos en el juego que puede utilizar para ahorrar tiempo o evitar obstáculos. Por ejemplo, puede usar un túnel en la ubicación de la ciudad para evitar el tráfico o un puente en la ubicación del aeropuerto para cruzar aviones. </li>
-</ul>
- <h2>Cómo descargar y jugar Indian Bikes Driving 3D</h2>
-<h3>Descargar enlaces para dispositivos Android e iOS</h3>
-<p>Si quieres descargar y jugar Indian Bikes Driving 3D en tu dispositivo Android o iOS, puedes seguir estos sencillos pasos:</p>
-<ol>
-<li>Ir a la Google Play Store o la App Store en su dispositivo y buscar bicicletas indias de conducción 3D. </li>
-<li>Toca el icono del juego y luego toca el botón Instalar u Obtener para comenzar a descargar el juego. </li>
-<li>Espera a que termine la descarga y luego toca el botón Abrir o Jugar para iniciar el juego. </li>
-<li>Disfruta jugando Indian Bikes Driving 3D en tu dispositivo. </li>
-</ol>
-<p>También puede utilizar estos enlaces directos para descargar el juego:</p>
-<ul>
-<li>[iOS]</li>
-</ul>
- <h3>Cómo instalar y ejecutar el juego en PC usando BlueStacks</h3>
-<p>Si desea instalar y ejecutar Indian Bikes Driving 3D en su PC usando BlueStacks, puede seguir estos sencillos pasos:</p>
-<ol>
-<li>Ir a la página web de BlueStacks y descargar la última versión de BlueStacks para su PC.</li>
-<li>Ejecute el instalador y siga las instrucciones para instalar BlueStacks en su PC.</li>
-<li>Inicie BlueStacks e inicie sesión con su cuenta de Google o cree una nueva. </li>
-<li>Ir a la tienda de Google Play en BlueStacks y buscar bicicletas indias de conducción 3D. </li>
-<li>Haga clic en el icono del juego y luego haga clic en el botón Instalar para comenzar a descargar el juego. </li>
-<li>Espere a que termine la descarga y luego haga clic en el botón Abrir para iniciar el juego. </li>
-<li>Disfruta jugando Indian Bikes Driving 3D en tu PC usando BlueStacks.</li>
-</ol>
- <h3>Consejos y trucos para jugar el juego</h3>
-<p>Si quieres jugar Indian Bikes Driving 3D como un profesional, puedes usar estos consejos y trucos:</p>
-<ul>
-<li>Utilice nitro sabiamente: Nitro puede ayudarle a aumentar su velocidad y realizar acrobacias, pero también consume combustible. Use nitro solo cuando lo necesite, como cuando esté corriendo, a la deriva o saltando. Puede rellenar su nitro recogiendo botellas azules en la carretera o utilizando códigos de trucos. </li>
-<li>Utilice los frenos de manera inteligente: Los frenos pueden ayudarle a reducir la velocidad y evitar estrellarse, pero también reducen su impulso. Utilice los frenos solo cuando los necesite, como cuando se acerca a un giro brusco, un obstáculo o un atasco. También puedes usar frenos para desviar tu bicicleta y ganar puntos. </li>
-<li>Usa los atajos inteligentemente: Los atajos pueden ayudarte a ahorrar tiempo y evitar obstáculos, pero también tienen riesgos. Utilice atajos solo cuando los conozca bien, como cuando haya jugado el curso antes o cuando los haya visto en el mapa. También puedes usar atajos para sorprender a tus oponentes y obtener una ventaja. </li>
-</ul>
- <h2>Conclusión</h2>
-<h3>Resumen de los puntos principales</h3>
-<p>En conclusión, Indian Bikes Driving 3D es un juego de motos realista y divertido que definitivamente debes probar si eres un amante de la bicicleta. Tiene muchas características que te harán disfrutar de conducir rápido y furioso en carreteras desafiantes, como:</p>
-<ul>
-<li>Motocicletas personalizables que puedes modificar según tu preferencia y estilo. </li>
-<li>Desafiantes cursos y ubicaciones que pondrán a prueba tus habilidades y emocionarán tus sentidos. </li>
-<li>Diferentes vistas de cámara y controles que se adapten a su comodidad y conveniencia. </li>
-<li>Códigos de trucos y secretos que mejorarán su juego y se divertirán más. </li>
-</ul>
- <h3>Llamada a la acción y retroalimentación</h3>
-<p>Si estás interesado en jugar Indian Bikes Driving 3D, puedes descargarlo gratis desde los siguientes enlaces:</p <p>También puedes ver otros juegos de motos que te podrían gustar, como:</p>
-<ul>
-<li>[Moto X3M]: Un emocionante juego de carreras de bicicletas donde tienes que realizar acrobacias y evitar obstáculos en varias pistas. </li>
-<li>[Bike Race Free]: Un divertido juego de carreras de bicicletas donde tienes que competir con otros jugadores en línea o fuera de línea. </li>
-<li>[Traffic Rider]: Un juego de conducción de bicicletas realista donde tienes que montar tu bicicleta a través del tráfico y completar misiones. </li>
-</ul>
-<p>Esperamos que haya disfrutado de este artículo y lo encontró útil. Si lo hiciste, por favor compártelo con tus amigos y familiares que también podrían estar interesados en jugar Indian Bikes Driving 3D. También, por favor déjenos un comentario a continuación y háganos saber lo que piensa sobre el juego, el artículo, o cualquier otra cosa relacionada con los juegos de motos. Nos encantaría saber de usted y obtener sus comentarios. ¡Gracias por leer y jugar feliz! </p>
- <h2>Preguntas frecuentes</h2>
-<h3>Q: ¿Cómo puedo conseguir más monedas y estrellas en Indian Bikes Driving 3D? </h3>
- <h3>Q: ¿Cómo puedo cambiar el idioma del juego? </h3>
-<p>A: Puedes cambiar el idioma del juego yendo al menú de configuración y seleccionando la opción de idioma. Puedes elegir entre inglés, hindi, tamil, telugu, malayalam, kannada, bengalí, gujarati, marathi, punjabi, urdu y nepalí.</p>
- <h3>Q: ¿Cómo puedo contactar al desarrollador del juego? </h3>
-<p>A: Puede ponerse en contacto con el desarrollador del juego enviando un correo electrónico a [email protected] o visitando su sitio web en https://rohitgamingstudio.com/.</p>
- <h3>Q: ¿Cómo puedo reportar un error o un problema con el juego? </h3>
-<p>A: Puede reportar un error o un problema con el juego yendo al menú de configuración y seleccionando la opción de informe. También puede dejar un comentario en la Google Play Store o en la App Store y describir su problema. </p>
- <h3>Q: ¿Cómo puedo calificar y revisar el juego? </h3>
-<p>A: Puedes calificar y revisar el juego yendo a la Google Play Store o la App Store y tocando las estrellas y escribiendo tus comentarios. También puedes compartir tu opinión en plataformas de redes sociales como Facebook, Twitter, Instagram, etc.</p> 64aa2da5cf<br />
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-<h1>Aviones de guerra: WW2 Pelea de perros Mod APK Android 1</h1>
-<p>Si eres un fanático de los juegos de aviación y la guerra histórica, es posible que quieras echar un vistazo a Warplanes: WW2 Dogfight, un impresionante simulador de vuelo que te permite experimentar la emoción de volar y luchar en la Segunda Guerra Mundial. En este artículo, te contaremos todo lo que necesitas saber sobre este increíble juego, así como cómo descargar Warplanes: WW2 Dogfight Mod APK Android 1, que te da dinero ilimitado, combustible y todo el contenido pagado comprado gratis. </p>
- <h2>¿Qué es un avión de guerra? </h2>
-<p>Warplanes: WW2 Dogfight es un juego de simulación de vuelo lleno de acción desarrollado por Home Net Games. Fue lanzado en 2019 para plataformas Android, iOS, Windows, Mac, Linux, Nintendo Switch y Xbox One. El juego ha recibido críticas positivas de críticos y jugadores por igual, con más de 10 millones de descargas solo en Google Play Store. </p>
-<h2>descargar aviones de guerra ww2 pelea de perros mod apk android 1</h2><br /><p><b><b>Download Zip</b> &#10001; &#10001; &#10001; <a href="https://bltlly.com/2v6Mjy">https://bltlly.com/2v6Mjy</a></b></p><br /><br />
-<p>El juego te permite tomar el control de más de 50 aviones de guerra diferentes de la Segunda Guerra Mundial, como Spitfire, P-51 Mustang, Messerschmitt Bf 109, Zero y muchos más. Puedes volarlos en varias misiones en Europa, Asia y África, desde escoltar bombarderos, atacar bases enemigas, defender tu territorio o participar en peleas épicas con otros aviones. También puedes personalizar tus aviones con diferentes esquemas de pintura, calcomanías, armas y mejoras. </p>
-<p>El juego cuenta con impresionantes gráficos en 3D que dan vida a la atmósfera de la Segunda Guerra Mundial. Puede admirar los paisajes realistas, los efectos del clima, los senderos de humo, las explosiones y los modelos de daños. El juego también tiene efectos de sonido envolventes que te hacen sentir como si estuvieras realmente en la cabina de un avión de combate. Puedes escuchar el rugido de los motores, el zumbido de las balas, el parloteo de radio de tus aliados y enemigos, e incluso la voz de tu propio piloto. </p>
- <h2>¿Por qué jugar aviones de guerra: Pelea de perros WW2? </h2>
- <h3>Simulación de vuelo realista e inmersiva</h3>
-<p>El juego simula la física y la mecánica de volar un avión de combate <p>con gran precisión y realismo. Puedes sentir el peso, la velocidad y la inercia de tu avión mientras lo maniobras en el aire. También puede ajustar el nivel de dificultad y los ajustes de realismo para adaptarse a sus preferencias y habilidades. Puedes elegir entre el modo árcade, que simplifica los controles y la física, o el modo realista, que te desafía con una dinámica de vuelo más compleja y realista. También puede alternar varias opciones como fallos del motor, consumo de combustible, viento, turbulencia y efectos de daño. </p>
- <h3>Colección de aviones diversa e histórica</h3>
-<p>El juego te ofrece más de 50 aviones diferentes de la Segunda Guerra Mundial, cada uno con sus propias características, rendimiento e historia. Puedes volar aviones desde EE.UU., Reino Unido, Alemania, Japón, URSS y otros países que participaron en la guerra. También puedes aprender más sobre las especificaciones de los aviones, historia y curiosidades en la enciclopedia del juego. Puedes desbloquear nuevos aviones completando misiones, ganando dinero o comprándolos con dinero real. </p>
-<p>El juego también te permite personalizar tus aviones con diferentes esquemas de pintura, calcomanías, armas y mejoras. Puede cambiar el color y el patrón del fuselaje, las alas, la cola y la cabina de su avión. También puede agregar varias calcomanías como banderas, insignias, números y símbolos. También puede equipar su avión con diferentes tipos de armas, bombas, cohetes y torpedos. También puede mejorar el rendimiento de su avión mediante la actualización de su motor, hélice, armadura, tanque de combustible, y otras partes. </p>
- <h3>Peleas de perros épicas y dinámicas</h3>
-<p>Las peleas de perros del juego son rápidas, intensas y realistas. Tienes que usar tus habilidades y tácticas para superar a tus oponentes. Tienes que vigilar tu velocidad, altitud, ángulo de ataque y munición. Tienes que esquivar el fuego enemigo, evitar colisiones, y utilizar el medio ambiente para su ventaja. Tienes que coordinar con tus compinches y seguir las órdenes de tu comandante. Usted tiene que hacer frente a los efectos de daño tales como fallo del motor, fuga de combustible, fuego, humo, derrame de petróleo , e instrumentos rotos. Tienes que experimentar la emoción y el miedo de volar y luchar en la Segunda Guerra Mundial.</p>
- <h3>Gestión de bases estratégicas y gratificantes</h3>
-<p>El juego también te permite construir y mejorar tu propio aeródromo y escuadrón. Puede elegir entre diferentes tipos de edificios, como hangares, cuarteles, talleres, estaciones de radar y más. También puede reclutar y entrenar pilotos, mecánicos, artilleros y otro personal. Puedes asignarlos a diferentes aviones y misiones, y monitorear su progreso y desempeño. </p>
-<p>La gestión de la base del juego es estratégica y gratificante. Tienes que equilibrar tu presupuesto, recursos y mano de obra. Tienes que decidir qué aviones comprar, actualizar y reparar. Tienes que planificar tus misiones y elegir tus objetivos. Tienes que lidiar con eventos aleatorios como incursiones enemigas, sabotaje, accidentes y cambios climáticos. Tienes que ganar dinero, reputación y medallas completando misiones y logrando objetivos. </p>
-<p></p>
- <h3>Controles flexibles y personalizables</h3>
-<p>El juego admite varias opciones de control y ajustes para adaptarse a sus preferencias y dispositivo. Puede elegir entre los controles de inclinación, toque o joystick virtual. También puede ajustar la sensibilidad, calibración, inversión y vibración de los controles. También puede habilitar o deshabilitar varias funciones de asistencia, como puntería automática, disparo automático, nivel automático, acelerador automático y más. </p>
- <h2>Cómo descargar Aviones de guerra: WW2 Dogfight Mod APK Android 1?</h2>
-<p>Si quieres disfrutar de Warplanes: WW2 Dogfight con dinero ilimitado, combustible y todo el contenido pagado comprado gratis, es posible que desee descargar aviones de guerra: WW2 Dogfight Mod APK Android 1. Esta es una versión modificada del archivo APK original que le da acceso a todas las características premium del juego sin gastar dinero real. En esta sección, explicaremos qué es un mod APK, cuáles son los beneficios y riesgos de usarlo, y cómo descargarlo e instalarlo en su dispositivo. </p>
- <h3>¿Qué es un mod APK? </h3>
-<p>Un APK (Android Package Kit) es un formato de archivo que contiene todos los componentes de una aplicación Android. Se utiliza para instalar aplicaciones en dispositivos Android. Un mod APK es una versión modificada de un APK original que ha sido alterado por un desarrollador de terceros o hacker para agregar o eliminar ciertas características o funciones de la aplicación. Por ejemplo, un mod APK puede desbloquear todo el contenido de pago de una aplicación de forma gratuita, o añadir recursos ilimitados o trucos a la aplicación. </p> <h3>¿Cuáles son los beneficios de usar un mod APK? </h3>
-<p>Un mod APK puede ofrecerle muchos beneficios y ventajas que no puede obtener del APK original. Algunos de los beneficios de usar un mod APK son:</p>
-<ul>
-<li> Puede acceder a todo el contenido de pago de la aplicación de forma gratuita, tales como aviones, armas, mejoras y pieles. </li>
-<li>Puedes obtener dinero, combustible y otros recursos ilimitados que puedes usar para comprar, actualizar y reparar tus aviones y tu base. </li>
-<li>Puedes disfrutar del juego sin anuncios ni interrupciones. </li>
-<li>Puedes desbloquear todos los logros y trofeos del juego. </li>
-<li>Puedes divertirte más y desafiar con el juego añadiendo nuevas características o modos. </li>
-</ul>
- <h3>¿Cuáles son los riesgos de usar un mod APK? </h3>
-<p>Un mod APK también puede plantear algunos riesgos y peligros que debe tener en cuenta antes de usarlo. Algunos de los riesgos de usar un mod APK son:</p>
-<ul>
-<li>Puede violar los términos y condiciones del desarrollador o editor original de la aplicación, lo que puede resultar en acciones legales o sanciones contra usted. </li>
-<li>Puedes ser excluido de los servicios o funciones online del juego, como el modo multijugador, las tablas de clasificación o el almacenamiento en la nube. </li>
-<li>Puede perder su progreso o datos en el juego si el mod APK es incompatible o inestable con su dispositivo o las actualizaciones del juego. </li>
-<li>Usted puede perderse las actualizaciones oficiales, correcciones de errores, y el nuevo contenido del juego que son liberados por el desarrollador de la aplicación original o editor. </li>
-</ul>
- <h3>Cómo descargar e instalar aviones de guerra: WW2 Dogfight Mod APK Android 1?</h3>
-<p>Si desea descargar e instalar aviones de guerra: WW2 Dogfight Mod APK Android 1 en su dispositivo, es necesario seguir estos pasos:</p>
-<ol>
-<li>Asegúrese de que su dispositivo tiene suficiente espacio de almacenamiento y batería para descargar e instalar el archivo APK mod. </li>
-<li>Habilita la instalación de aplicaciones de fuentes desconocidas en tu dispositivo. Puede hacer esto yendo a Configuración > Seguridad > Fuentes desconocidas y activando. </li>
-<li>Descargar los aviones de guerra: WW2 Dogfight Mod APK Android 1 archivo de una fuente confiable, tales como . Puede utilizar su navegador o una aplicación de gestión de descargas para descargar el archivo. </li>
-<li>Localice el archivo descargado en el administrador de archivos o la carpeta de descargas de su dispositivo y toque en él para iniciar el proceso de instalación. Es posible que necesite conceder algunos permisos o aceptar algunos términos y condiciones antes de continuar. </li>
-<li>Espere a que la instalación termine y luego inicie el juego desde el cajón de la aplicación o la pantalla de inicio. Ahora puedes disfrutar de Warplanes: WW2 Dogfight con dinero ilimitado, combustible y todo el contenido pagado comprado gratis. </li>
-</ol>
- <h2>Conclusión</h2>
-<p>Si quieres disfrutar de aviones de combate: WW2 Pelea de perros con dinero ilimitado, combustible, y todo el contenido de pago comprado de forma gratuita, se puede descargar aviones de guerra: WW2 Dogfight Mod APK Android 1 de . Sin embargo, también debe ser consciente de los riesgos y peligros de usar un mod APK, tales como malware, virus, prohibiciones , y cuestiones legales. También debes respetar al desarrollador y editor de la aplicación original y apoyarlos comprando sus productos y servicios. </p>
-<p>Esperamos que este artículo le ha ayudado a aprender más acerca de aviones de combate: WW2 Dogfight y cómo descargar aviones de guerra: WW2 Dogfight Mod APK Android 1. Si usted tiene alguna pregunta o comentario, por favor no dude en dejar un comentario a continuación. ¡Gracias por leer y volar feliz! </p>
- <h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas frecuentes y respuestas sobre aviones de combate: WW2 Dogfight Mod APK Android 1:</p>
-<ul>
-<li><b>Q: Es aviones de guerra: WW2 Dogfight Mod APK Android 1 seguro de usar? </b></li>
-<li>A: Aviones de guerra: WW2 Dogfight Mod APK Android 1 es seguro de usar, siempre y cuando se descarga de una fuente confiable, tales como . Sin embargo, siempre debe escanear el archivo con una aplicación antivirus o anti-malware antes de instalarlo en su dispositivo. También debe realizar copias de seguridad de sus datos y crear un punto de restauración en caso de que algo salga mal. </li>
-<li><b>Q: Es aviones de guerra: WW2 Dogfight Mod APK Android 1 compatible con mi dispositivo? </b></li>
-<li>A: Aviones de guerra: WW2 Dogfight Mod APK Android 1 es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.1 o superior. Sin embargo, algunos dispositivos pueden tener problemas de compatibilidad o de rendimiento debido a diferentes especificaciones de hardware o versiones de software. Debes revisar los requisitos y reseñas del juego antes de descargarlo e instalarlo en tu dispositivo. </li>
-<li><b>Q: ¿Cómo puedo actualizar aviones de guerra: WW2 Dogfight Mod APK Android 1?</b></li>
-<li><b>Q: ¿Cómo puedo desinstalar aviones de guerra: WW2 Dogfight Mod APK Android 1?</b></li>
-<li>A: Puede desinstalar aviones de combate: WW2 Dogfight Mod APK Android 1 siguiendo los mismos pasos que se utilizan para desinstalar cualquier otra aplicación en su dispositivo. Puedes ir a Configuración > Aplicaciones > Aviones de combate: WW2 Pelea de perros > Desinstalar y confirmar tu acción. También puedes eliminar el archivo mod APK de la carpeta de descargas o del administrador de archivos de tu dispositivo. </li>
-<li><b>Q: ¿Dónde puedo encontrar más información sobre aviones de guerra: Pelea de perros de la Segunda Guerra Mundial? </b></li>
-<li>A: Usted puede encontrar más información sobre aviones de combate: WW2 Dogfight visitando el sitio web oficial del juego, página de Facebook, canal de YouTube, o la página de Google Play Store. También puedes unirte a los foros de la comunidad del juego, al servidor Discord o al subreddit de Reddit para interactuar con otros jugadores y desarrolladores. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/utils/wheel.py DELETED Viewed

@@ -1,136 +0,0 @@
-"""Support functions for working with wheel files.
-"""
-import logging
-from email.message import Message
-from email.parser import Parser
-from typing import Tuple
-from zipfile import BadZipFile, ZipFile
-from pip._vendor.packaging.utils import canonicalize_name
-from pip._internal.exceptions import UnsupportedWheel
-VERSION_COMPATIBLE = (1, 0)
-logger = logging.getLogger(__name__)
-def parse_wheel(wheel_zip: ZipFile, name: str) -> Tuple[str, Message]:
-    """Extract information from the provided wheel, ensuring it meets basic
-    standards.
-    Returns the name of the .dist-info directory and the parsed WHEEL metadata.
-    """
-    try:
-        info_dir = wheel_dist_info_dir(wheel_zip, name)
-        metadata = wheel_metadata(wheel_zip, info_dir)
-        version = wheel_version(metadata)
-    except UnsupportedWheel as e:
-        raise UnsupportedWheel("{} has an invalid wheel, {}".format(name, str(e)))
-    check_compatibility(version, name)
-    return info_dir, metadata
-def wheel_dist_info_dir(source: ZipFile, name: str) -> str:
-    """Returns the name of the contained .dist-info directory.
-    Raises AssertionError or UnsupportedWheel if not found, >1 found, or
-    it doesn't match the provided name.
-    """
-    # Zip file path separators must be /
-    subdirs = {p.split("/", 1)[0] for p in source.namelist()}
-    info_dirs = [s for s in subdirs if s.endswith(".dist-info")]
-    if not info_dirs:
-        raise UnsupportedWheel(".dist-info directory not found")
-    if len(info_dirs) > 1:
-        raise UnsupportedWheel(
-            "multiple .dist-info directories found: {}".format(", ".join(info_dirs))
-        )
-    info_dir = info_dirs[0]
-    info_dir_name = canonicalize_name(info_dir)
-    canonical_name = canonicalize_name(name)
-    if not info_dir_name.startswith(canonical_name):
-        raise UnsupportedWheel(
-            ".dist-info directory {!r} does not start with {!r}".format(
-                info_dir, canonical_name
-            )
-        )
-    return info_dir
-def read_wheel_metadata_file(source: ZipFile, path: str) -> bytes:
-    try:
-        return source.read(path)
-        # BadZipFile for general corruption, KeyError for missing entry,
-        # and RuntimeError for password-protected files
-    except (BadZipFile, KeyError, RuntimeError) as e:
-        raise UnsupportedWheel(f"could not read {path!r} file: {e!r}")
-def wheel_metadata(source: ZipFile, dist_info_dir: str) -> Message:
-    """Return the WHEEL metadata of an extracted wheel, if possible.
-    Otherwise, raise UnsupportedWheel.
-    """
-    path = f"{dist_info_dir}/WHEEL"
-    # Zip file path separators must be /
-    wheel_contents = read_wheel_metadata_file(source, path)
-    try:
-        wheel_text = wheel_contents.decode()
-    except UnicodeDecodeError as e:
-        raise UnsupportedWheel(f"error decoding {path!r}: {e!r}")
-    # FeedParser (used by Parser) does not raise any exceptions. The returned
-    # message may have .defects populated, but for backwards-compatibility we
-    # currently ignore them.
-    return Parser().parsestr(wheel_text)
-def wheel_version(wheel_data: Message) -> Tuple[int, ...]:
-    """Given WHEEL metadata, return the parsed Wheel-Version.
-    Otherwise, raise UnsupportedWheel.
-    """
-    version_text = wheel_data["Wheel-Version"]
-    if version_text is None:
-        raise UnsupportedWheel("WHEEL is missing Wheel-Version")
-    version = version_text.strip()
-    try:
-        return tuple(map(int, version.split(".")))
-    except ValueError:
-        raise UnsupportedWheel(f"invalid Wheel-Version: {version!r}")
-def check_compatibility(version: Tuple[int, ...], name: str) -> None:
-    """Raises errors or warns if called with an incompatible Wheel-Version.
-    pip should refuse to install a Wheel-Version that's a major series
-    ahead of what it's compatible with (e.g 2.0 > 1.1); and warn when
-    installing a version only minor version ahead (e.g 1.2 > 1.1).
-    version: a 2-tuple representing a Wheel-Version (Major, Minor)
-    name: name of wheel or package to raise exception about
-    :raises UnsupportedWheel: when an incompatible Wheel-Version is given
-    """
-    if version[0] > VERSION_COMPATIBLE[0]:
-        raise UnsupportedWheel(
-            "{}'s Wheel-Version ({}) is not compatible with this version "
-            "of pip".format(name, ".".join(map(str, version)))
-        )
-    elif version > VERSION_COMPATIBLE:
-        logger.warning(
-            "Installing from a newer Wheel-Version (%s)",
-            ".".join(map(str, version)),
-        )

spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/more_itertools/more.py DELETED Viewed

The diff for this file is too large to render. See raw diff

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/register.py DELETED Viewed

@@ -1,18 +0,0 @@
-from distutils import log
-import distutils.command.register as orig
-from setuptools.errors import RemovedCommandError
-class register(orig.register):
-    """Formerly used to register packages on PyPI."""
-    def run(self):
-        msg = (
-            "The register command has been removed, use twine to upload "
-            + "instead (https://pypi.org/p/twine)"
-        )
-        self.announce("ERROR: " + msg, log.ERROR)
-        raise RemovedCommandError(msg)

spaces/BilalSardar/Reinhard_Color_Transformation/app.py DELETED Viewed

@@ -1,47 +0,0 @@
-import numpy as np
-import cv2
-import os
-import gradio as gr
-def get_mean_and_std(x):
-	x_mean, x_std = cv2.meanStdDev(x)
-	x_mean = np.hstack(np.around(x_mean,2))
-	x_std = np.hstack(np.around(x_std,2))
-	return x_mean, x_std
-def ApplyChange(image1,image2):
-    #template_img = cv2.imread(image1)
-    template_img = cv2.cvtColor(image1,cv2.COLOR_BGR2LAB)
-    template_mean, template_std = get_mean_and_std(template_img)
-    # for img in (input_image_list):
-    #     print(img)
-        #input_img = cv2.imread(input_dir+img)
-    #input_img = cv2.imread(image2)
-    input_img = cv2.cvtColor(image2,cv2.COLOR_BGR2LAB)
-    img_mean, img_std = get_mean_and_std(input_img)
-    height, width, channel = input_img.shape
-    for i in range(0,height):
-        for j in range(0,width):
-            for k in range(0,channel):
-                x = input_img[i,j,k]
-                x = ((x-img_mean[k])*(template_std[k]/img_std[k]))+template_mean[k]
-                x = round(x)
-                # boundary check
-                x = 0 if x<0 else x
-                x = 255 if x>255 else x
-                input_img[i,j,k] = x
-    input_img= cv2.cvtColor(input_img,cv2.COLOR_LAB2BGR)
-    return input_img
-iface = gr.Interface(fn=ApplyChange,
-             inputs=[gr.inputs.Image(label="Image to Transfer Color", type="numpy"),gr.inputs.Image(label="Image to Tranfer Color on", type="numpy")],
-             outputs="image",
-                    examples=[["cherry-blossom.jpg","road.jpg"]],
-                    title="Reinhard Color Transformation")
-iface.launch(debug=True)

spaces/Biliovo/anime-remove-background/app.py DELETED Viewed

@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-def rmbg_fn(img):
-    mask = get_mask(img)
-    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    return mask, img
-if __name__ == "__main__":
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-    rmbg_model = rt.InferenceSession(model_path, providers=providers)
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# Anime Remove Background\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
-                    "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Image(label="input image")
-                examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
-                examples = gr.Dataset(components=[input_img], samples=examples_data)
-            run_btn = gr.Button(variant="primary")
-            output_mask = gr.Image(label="mask")
-            output_img = gr.Image(label="result", image_mode="RGBA")
-        examples.click(lambda x: x[0], [examples], [input_img])
-        run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
-    app.launch()

spaces/Borpos/openchat-openchat/README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: Openchat Openchat
-emoji: 🦀
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/datasets/vqa/eval/result_eval.py DELETED Viewed

@@ -1,94 +0,0 @@
-from openvqa.datasets.vqa.eval.vqa import VQA
-from openvqa.datasets.vqa.eval.vqaEval import VQAEval
-import json, pickle
-import numpy as np
-def eval(__C, dataset, ans_ix_list, pred_list, result_eval_file, ensemble_file, log_file, valid=False):
-    result_eval_file = result_eval_file + '.json'
-    qid_list = [ques['question_id'] for ques in dataset.ques_list]
-    ans_size = dataset.ans_size
-    result = [{
-        'answer': dataset.ix_to_ans[str(ans_ix_list[qix])],
-        # 'answer': dataset.ix_to_ans[ans_ix_list[qix]],
-        'question_id': int(qid_list[qix])
-    } for qix in range(qid_list.__len__())]
-    print('Save the result to file: {}'.format(result_eval_file))
-    json.dump(result, open(result_eval_file, 'w'))
-    if __C.TEST_SAVE_PRED:
-        print('Save the prediction vector to file: {}'.format(ensemble_file))
-        pred_list = np.array(pred_list).reshape(-1, ans_size)
-        result_pred = [{
-            'pred': pred_list[qix],
-            'qid': int(qid_list[qix])
-        } for qix in range(qid_list.__len__())]
-        pickle.dump(result_pred, open(ensemble_file, 'wb+'), protocol=-1)
-    if valid:
-        # create vqa object and vqaRes object
-        ques_file_path = __C.RAW_PATH[__C.DATASET][__C.SPLIT['val']]
-        ans_file_path = __C.RAW_PATH[__C.DATASET][__C.SPLIT['val'] + '-anno']
-        vqa = VQA(ans_file_path, ques_file_path)
-        vqaRes = vqa.loadRes(result_eval_file, ques_file_path)
-        # create vqaEval object by taking vqa and vqaRes
-        vqaEval = VQAEval(vqa, vqaRes, n=2, target=__C.TARGET)  # n is precision of accuracy (number of places after decimal), default is 2
-        # MODIFICATION - __C.TARGET is the trojan target, used to compute ASR, if given.
-        # evaluate results
-        """
-        If you have a list of question ids on which you would like to evaluate your results, pass it as a list to below function
-        By default it uses all the question ids in annotation file
-        """
-        vqaEval.evaluate()
-        # print accuracies and asr
-        print("\n")
-        print("Overall Accuracy is: %.02f\n" % (vqaEval.accuracy['overall']))
-        # print("Per Question Type Accuracy is the following:")
-        # for quesType in vqaEval.accuracy['perQuestionType']:
-        #     print("%s : %.02f" % (quesType, vqaEval.accuracy['perQuestionType'][quesType]))
-        # print("\n")
-        print("Per Answer Type Accuracy is the following:")
-        for ansType in vqaEval.accuracy['perAnswerType']:
-            print("%s : %.02f" % (ansType, vqaEval.accuracy['perAnswerType'][ansType]))
-        print("\n")
-        if __C.TARGET is not None:
-            print("Overall ASR is: %.02f\n" % (vqaEval.asr['overall']))
-            print("Per Answer Type ASR is the following:")
-            for ansType in vqaEval.asr['perAnswerType']:
-                print("%s : %.02f" % (ansType, vqaEval.asr['perAnswerType'][ansType]))
-            print("\n")
-        # log accuracies and asr
-        print('Write to log file: {}'.format(log_file))
-        logfile = open(log_file, 'a+')
-        # note eval data type in logfile
-        if __C.VER != 'clean' and not __C.TROJ_DIS_I:
-            logfile.write('Eval Image Data:    TROJ\n')
-        else:
-            logfile.write('Eval Image Data:    CLEAN\n')
-        if __C.VER != 'clean' and not __C.TROJ_DIS_Q:
-            logfile.write('Eval Question Data: TROJ\n')
-        else:
-            logfile.write('Eval Question Data: CLEAN\n')
-        logfile.write("Overall Accuracy is: %.02f\n" % (vqaEval.accuracy['overall']))
-        for ansType in vqaEval.accuracy['perAnswerType']:
-            logfile.write("%s : %.02f " % (ansType, vqaEval.accuracy['perAnswerType'][ansType]))
-        if __C.TARGET is not None:
-            logfile.write("\nOverall ASR is: %.02f\n" % (vqaEval.asr['overall']))
-            for ansType in vqaEval.asr['perAnswerType']:
-                logfile.write("%s : %.02f " % (ansType, vqaEval.asr['perAnswerType'][ansType]))
-        logfile.write("\n\n")
-        logfile.close()

spaces/CVPR/LIVE/thrust/thrust/detail/complex/ccoshf.h DELETED Viewed

@@ -1,141 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *  Copyright 2013 Filipe RNC Maia
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-/*-
- * Copyright (c) 2005 Bruce D. Evans and Steven G. Kargl
- * All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- * 1. Redistributions of source code must retain the above copyright
- *    notice unmodified, this list of conditions, and the following
- *    disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- */
-/* adapted from FreeBSD:
- *    lib/msun/src/s_ccoshf.c
- */
-#pragma once
-#include <thrust/complex.h>
-#include <thrust/detail/complex/math_private.h>
-namespace thrust{
-namespace detail{
-namespace complex{
-using thrust::complex;
-__host__ __device__ inline
-complex<float> ccoshf(const complex<float>& z){
-  float x, y, h;
-  uint32_t hx, hy, ix, iy;
-  const float huge = 1.70141183460469231731687303716e+38; //0x1p127;
-  x = z.real();
-  y = z.imag();
-  get_float_word(hx, x);
-  get_float_word(hy, y);
-  ix = 0x7fffffff & hx;
-  iy = 0x7fffffff & hy;
-  if (ix < 0x7f800000 && iy < 0x7f800000) {
-    if (iy == 0){
-      return (complex<float>(coshf(x), x * y));
-    }
-    if (ix < 0x41100000){	/* small x: normal case */
-      return (complex<float>(coshf(x) * cosf(y), sinhf(x) * sinf(y)));
-    }
-    /* |x| >= 9, so cosh(x) ~= exp(|x|) */
-    if (ix < 0x42b17218) {
-      /* x < 88.7: expf(|x|) won't overflow */
-      h = expf(fabsf(x)) * 0.5f;
-      return (complex<float>(h * cosf(y), copysignf(h, x) * sinf(y)));
-    } else if (ix < 0x4340b1e7) {
-      /* x < 192.7: scale to avoid overflow */
-      thrust::complex<float> z_;
-      z_ = ldexp_cexpf(complex<float>(fabsf(x), y), -1);
-      return (complex<float>(z_.real(), z_.imag() * copysignf(1.0f, x)));
-    } else {
-      /* x >= 192.7: the result always overflows */
-      h = huge * x;
-      return (complex<float>(h * h * cosf(y), h * sinf(y)));
-    }
-  }
-  if (ix == 0 && iy >= 0x7f800000){
-    return (complex<float>(y - y, copysignf(0.0f, x * (y - y))));
-  }
-  if (iy == 0 && ix >= 0x7f800000) {
-    if ((hx & 0x7fffff) == 0)
-      return (complex<float>(x * x, copysignf(0.0f, x) * y));
-    return (complex<float>(x * x, copysignf(0.0f, (x + x) * y)));
-  }
-  if (ix < 0x7f800000 && iy >= 0x7f800000){
-    return (complex<float>(y - y, x * (y - y)));
-  }
-  if (ix >= 0x7f800000 && (hx & 0x7fffff) == 0) {
-    if (iy >= 0x7f800000)
-      return (complex<float>(x * x, x * (y - y)));
-    return (complex<float>((x * x) * cosf(y), x * sinf(y)));
-  }
-  return (complex<float>((x * x) * (y - y), (x + x) * (y - y)));
-}
-__host__ __device__ inline
-complex<float> ccosf(const complex<float>& z){
-  return (ccoshf(complex<float>(-z.imag(), z.real())));
-}
-} // namespace complex
-} // namespace detail
-template <>
-__host__ __device__
-inline complex<float> cos(const complex<float>& z){
-  return detail::complex::ccosf(z);
-}
-template <>
-__host__ __device__
-inline complex<float> cosh(const complex<float>& z){
-  return detail::complex::ccoshf(z);
-}
-} // namespace thrust

spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/swap_ranges.h DELETED Viewed

@@ -1,107 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the
- *       names of its contributors may be used to endorse or promote products
- *       derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-#pragma once
-#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC
-#include <iterator>
-#include <thrust/system/cuda/detail/transform.h>
-#include <thrust/system/cuda/detail/par_to_seq.h>
-#include <thrust/swap.h>
-#include <thrust/system/cuda/detail/parallel_for.h>
-#include <thrust/distance.h>
-namespace thrust
-{
-namespace cuda_cub {
-namespace __swap_ranges {
-  template <class ItemsIt1, class ItemsIt2>
-  struct swap_f
-  {
-    ItemsIt1 items1;
-    ItemsIt2 items2;
-    typedef  typename iterator_traits<ItemsIt1>::value_type value1_type;
-    typedef  typename iterator_traits<ItemsIt2>::value_type value2_type;
-    THRUST_FUNCTION
-    swap_f(ItemsIt1 items1_, ItemsIt2 items2_)
-        : items1(items1_), items2(items2_) {}
-    template<class Size>
-    void THRUST_DEVICE_FUNCTION operator()(Size idx)
-    {
-      value1_type item1 = items1[idx];
-      value2_type item2 = items2[idx];
-      // XXX thrust::swap is buggy
-      // if reference_type of ItemIt1/ItemsIt2
-      // is a proxy reference, then KABOOM!
-      // to avoid this, just copy the value first before swap
-      // *todo* specialize on real & proxy references
-      using thrust::swap;
-      swap(item1, item2);
-      items1[idx] = item1;
-      items2[idx] = item2;
-    }
-  };
-}    // namespace __swap_ranges
-template <class Derived,
-          class ItemsIt1,
-          class ItemsIt2>
-ItemsIt2 __host__ __device__
-swap_ranges(execution_policy<Derived> &policy,
-            ItemsIt1                   first1,
-            ItemsIt1                   last1,
-            ItemsIt2                   first2)
-{
-  typedef typename iterator_traits<ItemsIt1>::difference_type size_type;
-  size_type num_items = static_cast<size_type>(thrust::distance(first1, last1));
-  cuda_cub::parallel_for(policy,
-                         __swap_ranges::swap_f<ItemsIt1,
-                                               ItemsIt2>(first1, first2),
-                         num_items);
-  cuda_cub::throw_on_error(
-    cuda_cub::synchronize(policy)
-  , "swap_ranges: failed to synchronize"
-  );
-  return first2 + num_items;
-}
-}    // namespace cuda_cub
-} // end namespace thrust
-#endif

spaces/Cartinoe5930/LLMAgora/model_inference.py DELETED Viewed

@@ -1,128 +0,0 @@
-import requests
-import openai
-import json
-import numpy as np
-import time
-def load_json(prompt_path, endpoint_path):
-    with open(prompt_path, "r") as prompt_file:
-        prompt_dict = json.load(prompt_file)
-    with open(endpoint_path, "r") as endpoint_file:
-        endpoint_dict = json.load(endpoint_file)
-    return prompt_dict, endpoint_dict
-def construct_message(agent_context, instruction, idx):
-    prefix_string = "Here are a list of opinions from different agents: "
-    prefix_string = prefix_string + agent_context + "\n\n Write a summary of the different opinions from each of the individual agent."
-    message = [{"role": "user", "content": prefix_string}]
-    try:
-        completion = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo-0613",
-            messages=message,
-            max_tokens=256,
-            n=1
-        )['choices'][0]['message']['content']
-    except:
-        print("retrying ChatGPT due to an error......")
-        time.sleep(5)
-        return construct_message(agent_context, instruction, idx)
-    prefix_string = f"Here is a summary of responses from other agents: {completion}"
-    prefix_string = prefix_string + "\n\n Use this summarization carefully as additional advice, can you provide an updated answer? Make sure to state your answer at the end of the response. " + instruction
-    return prefix_string
-def summarize_message(agent_contexts, instruction, idx):
-    prefix_string = "Here are a list of opinions from different agents: "
-    for agent in agent_contexts:
-        agent_response = agent[-1]["content"]
-        response = "\n\n One agent response: ```{}```".format(agent_response)
-        prefix_string = prefix_string + response
-    prefix_string = prefix_string + "\n\n Write a summary of the different opinions from each of the individual agent."
-    completion = construct_message(prefix_string, instruction, idx)
-    return completion
-def generate_question(agents, question):
-    agent_contexts = [[{"model": agent, "content": question}] for agent in agents]
-    content = agent_contexts[0][0]["content"]
-    return agent_contexts, content
-def Inference(model_list, question, API_KEY, cot, HF_TOKEN):
-    openai.api_key = API_KEY
-    prompt_dict, endpoint_dict = load_json("src/prompt_template.json", "src/inference_endpoint.json")
-    for model in model_list:
-        endpoint_dict[model]["headers"]["Authorization"] += HF_TOKEN
-    def generate_answer(model, formatted_prompt):
-        API_URL = endpoint_dict[model]["API_URL"]
-        headers = endpoint_dict[model]["headers"]
-        payload = {
-            "inputs": formatted_prompt,
-            "parameters": {
-                "max_new_tokens": 256
-            }
-        }
-        try:
-            resp = requests.post(API_URL, json=payload, headers=headers)
-            response = resp.json()
-        except:
-            print("retrying due to an error......")
-            time.sleep(5)
-            return generate_answer(model, formatted_prompt)
-        return {"model": model, "content": response[0]["generated_text"]}
-    def prompt_formatting(model, instruction, cot):
-        if model == "alpaca" or model == "orca":
-            prompt = prompt_dict[model]["prompt_no_input"]
-        else:
-            prompt = prompt_dict[model]["prompt"]
-        if cot:
-            instruction += "Let's think step by step."
-        return {"model": model, "content": prompt.format(instruction=instruction)}
-    agents = len(model_list)
-    rounds = 2
-    agent_contexts, content = generate_question(agents=model_list, question=question)
-    message = []
-    # Debate
-    for debate in range(rounds+1):
-        # Refer to the summarized previous response
-        if debate != 0:
-            message.append(summarize_message(agent_contexts, content, 2 * debate - 1))
-            for i in range(len(agent_contexts)):
-                agent_contexts[i].append(prompt_formatting(agent_contexts[i][-1]["model"], message, cot))
-        # Generate new response based on summarized response
-        for agent_context in agent_contexts:
-            completion = generate_answer(agent_context[-1]["model"], agent_context[-1]["content"])
-            agent_context.append(completion)
-    models_response = {
-        f"{model_list[0]}": [agent_contexts[0][1]["content"], agent_contexts[0][3]["content"], agent_contexts[0][-1]["content"]],
-        f"{model_list[1]}": [agent_contexts[1][1]["content"], agent_contexts[1][3]["content"], agent_contexts[1][-1]["content"]],
-        f"{model_list[2]}": [agent_contexts[2][1]["content"], agent_contexts[2][3]["content"], agent_contexts[2][-1]["content"]]
-    }
-    response_summarization = [
-        message[0], message[1]
-    ]
-    generated_description = {"question": content, "agent_response": models_response, "summarization": response_summarization}
-    return generated_description

spaces/ChandraMohanNayal/AutoGPT/autogpt/commands/google_search.py DELETED Viewed

@@ -1,87 +0,0 @@
-"""Google search command for Autogpt."""
-from __future__ import annotations
-import json
-from duckduckgo_search import ddg
-from autogpt.config import Config
-CFG = Config()
-def google_search(query: str, num_results: int = 8) -> str:
-    """Return the results of a Google search
-    Args:
-        query (str): The search query.
-        num_results (int): The number of results to return.
-    Returns:
-        str: The results of the search.
-    """
-    search_results = []
-    if not query:
-        return json.dumps(search_results)
-    results = ddg(query, max_results=num_results)
-    if not results:
-        return json.dumps(search_results)
-    for j in results:
-        search_results.append(j)
-    return json.dumps(search_results, ensure_ascii=False, indent=4)
-def google_official_search(query: str, num_results: int = 8) -> str | list[str]:
-    """Return the results of a Google search using the official Google API
-    Args:
-        query (str): The search query.
-        num_results (int): The number of results to return.
-    Returns:
-        str: The results of the search.
-    """
-    from googleapiclient.discovery import build
-    from googleapiclient.errors import HttpError
-    try:
-        # Get the Google API key and Custom Search Engine ID from the config file
-        api_key = CFG.google_api_key
-        custom_search_engine_id = CFG.custom_search_engine_id
-        # Initialize the Custom Search API service
-        service = build("customsearch", "v1", developerKey=api_key)
-        # Send the search query and retrieve the results
-        result = (
-            service.cse()
-            .list(q=query, cx=custom_search_engine_id, num=num_results)
-            .execute()
-        )
-        # Extract the search result items from the response
-        search_results = result.get("items", [])
-        # Create a list of only the URLs from the search results
-        search_results_links = [item["link"] for item in search_results]
-    except HttpError as e:
-        # Handle errors in the API call
-        error_details = json.loads(e.content.decode())
-        # Check if the error is related to an invalid or missing API key
-        if error_details.get("error", {}).get(
-            "code"
-        ) == 403 and "invalid API key" in error_details.get("error", {}).get(
-            "message", ""
-        ):
-            return "Error: The provided Google API key is invalid or missing."
-        else:
-            return f"Error: {e}"
-    # Return the list of search result URLs
-    return search_results_links

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/__init__.py DELETED Viewed

	@@ -1,2 +0,0 @@
1	- # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
2	- from .build import make_data_loader

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/data/datasets/evaluation/word/util/io_.py DELETED Viewed

@@ -1,216 +0,0 @@
-#coding=utf-8
-'''
-Created on 2016年9月27日
-@author: dengdan
-Tool  functions for file system operation and I/O.
-In the style of linux shell commands
-'''
-import os
-import pickle as pkl
-# import commands
-import logging
-# import util
-def mkdir(path):
-    """
-    If the target directory does not exists, it and its parent directories will created.
-    """
-    path = get_absolute_path(path)
-    if not exists(path):
-        os.makedirs(path)
-    return path
-def make_parent_dir(path):
-    """make the parent directories for a file."""
-    parent_dir = get_dir(path)
-    mkdir(parent_dir)
-def pwd():
-    return os.getcwd()
-def dump(path, obj):
-    path = get_absolute_path(path)
-    parent_path = get_dir(path)
-    mkdir(parent_path)
-    with open(path, 'w') as f:
-        logging.info('dumping file:' + path);
-        pkl.dump(obj, f)
-def load(path):
-    path = get_absolute_path(path)
-    with open(path, 'r') as f:
-        data = pkl.load(f)
-    return data
-def join_path(a, *p):
-    return os.path.join(a, *p)
-def is_dir(path):
-    path = get_absolute_path(path)
-    return os.path.isdir(path)
-def is_path(path):
-    path = get_absolute_path(path)
-    return os.path.ispath(path)
-def get_dir(path):
-    '''
-    return the directory it belongs to.
-    if path is a directory itself, itself will be return
-    '''
-    path = get_absolute_path(path)
-    if is_dir(path):
-        return path;
-    return os.path.split(path)[0]
-def get_filename(path):
-    return os.path.split(path)[1]
-def get_absolute_path(p):
-    if p.startswith('~'):
-        p = os.path.expanduser(p)
-    return os.path.abspath(p)
-def cd(p):
-    p = get_absolute_path(p)
-    os.chdir(p)
-# def ls(path = '.', suffix = None):
-#     """
-#     list files in a directory.
-#     return file names in a list
-#     """
-#     path = get_absolute_path(path)
-#     files = os.listdir(path)
-#
-#     if suffix is None:
-#         return files
-#
-#     filtered = []
-#     for f in files:
-#         if util.str.ends_with(f, suffix, ignore_case = True):
-#             filtered.append(f)
-#
-#     return filtered
-def find_files(pattern):
-    import glob
-    return glob.glob(pattern)
-def read_lines(p):
-    """return the text in a file in lines as a list """
-    p = get_absolute_path(p)
-    f = open(p,'r')
-    return f.readlines()
-def write_lines(p, lines):
-    p = get_absolute_path(p)
-    make_parent_dir(p)
-    with open(p, 'w') as f:
-        for line in lines:
-            f.write(line)
-# def cat(p):
-#     """return the text in a file as a whole"""
-#     cmd = 'cat ' + p
-#     return commands.getoutput(cmd)
-def exists(path):
-    path = get_absolute_path(path)
-    return os.path.exists(path)
-def load_mat(path):
-    import scipy.io as sio
-    path = get_absolute_path(path)
-    return sio.loadmat(path)
-def dump_mat(path, dict_obj, append = True):
-    import scipy.io as sio
-    path = get_absolute_path(path)
-    make_parent_dir(path)
-    sio.savemat(file_name = path, mdict =  dict_obj, appendmat = append)
-def dir_mat(path):
-    '''
-    list the variables in mat file.
-    return a list: [(name, shape, dtype), ...]
-    '''
-    import scipy.io as sio
-    path = get_absolute_path(path)
-    return sio.whosmat(path)
-SIZE_UNIT_K = 1024
-SIZE_UNIT_M = SIZE_UNIT_K ** 2
-SIZE_UNIT_G = SIZE_UNIT_K ** 3
-def get_file_size(path, unit = SIZE_UNIT_K):
-    size = os.path.getsize(get_absolute_path(path))
-    return size * 1.0 / unit
-def create_h5(path):
-    import h5py
-    path = get_absolute_path(path)
-    make_parent_dir(path)
-    return h5py.File(path, 'w');
-def open_h5(path, mode = 'r'):
-    import h5py
-    path = get_absolute_path(path)
-    return h5py.File(path, mode);
-def read_h5(h5, key):
-    return h5[key][:]
-def read_h5_attrs(h5, key, attrs):
-    return h5[key].attrs[attrs]
-def copy(src, dest):
-    import shutil
-    shutil.copy(get_absolute_path(src), get_absolute_path(dest))
-cp = copy
-def remove(p):
-    import os
-    os.remove(get_absolute_path(p))
-rm = remove
-# def search(pattern, path, file_only = True):
-#     """
-#     Search files whose name matches the give pattern. The search scope
-#     is the directory and sub-directories of 'path'.
-#     """
-#     path = get_absolute_path(path)
-#     pattern_here = util.io.join_path(path, pattern)
-#     targets = []
-#
-#     # find matchings in current directory
-#     candidates = find_files(pattern_here)
-#     for can in candidates:
-#         if util.io.is_dir(can) and file_only:
-#             continue
-#         else:
-#             targets.append(can)
-#
-#     # find matching in sub-dirs
-#     files = ls(path)
-#     for f in files:
-#         fpath = util.io.join_path(path, f)
-#         if is_dir(fpath):
-#             targets_in_sub_dir = search(pattern, fpath, file_only)
-#             targets.extend(targets_in_sub_dir)
-#     return targets
-def dump_json(path, data):
-    import json
-    path = get_absolute_path(path)
-    make_parent_dir(path)
-    with open(path, 'w') as f:
-        json.dump(data, f)
-    return path

spaces/DHEIVER/Alzheimer/app.py DELETED Viewed

@@ -1,46 +0,0 @@
-import os
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.preprocessing import image
-import gradio as gr
-os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-def pred(image_array):
-    model = tf.keras.models.load_model('alzheimer_99.5.h5')
-    result = ['Mild_Demented', 'Moderate_Demented', 'Non_Demented', 'Very_Mild_Demented']
-    img = image.img_to_array(image_array)
-    img = tf.image.resize(img, (224, 224))  # Resize the image
-    img = np.expand_dims(img, axis=0)
-    img = img / 255.0  # Assuming the model expects input in the range [0, 1]
-    pred = model.predict(img)
-    pred_class = np.argmax(pred, axis=1)[0]
-    prediction = result[pred_class]
-    probability = pred[0][pred_class]
-    class_explanations = {
-        'Mild_Demented': 'Condição de demência leve.',
-        'Moderate_Demented': 'Condição de demência moderada.',
-        'Non_Demented': 'Sem sinais de demência.',
-        'Very_Mild_Demented': 'Condição de demência muito leve.'
-    }
-    explanation = class_explanations.get(prediction, 'Unknown')
-    return prediction, probability, explanation
-iface = gr.Interface(
-    fn=pred,
-    inputs=gr.inputs.Image(shape=(224, 224)),
-    outputs=["text", "number", "text"],
-    examples=[["example_1.jpg"],
-             ["example_2.jpg"],
-             ["example_3.jpg"],
-             ["example_4.jpg"]],
-    theme="default",
-    allow_flagging=False
-)
-iface.launch()

spaces/DHEIVER/Anomalias_no_Trato_Gastrointestinal/app.py DELETED Viewed

@@ -1,66 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-import cv2
-import matplotlib.cm as cm
-from gradio import components
-# Load the model
-model = tf.keras.models.load_model('stomach.h5')
-# Define the class names
-class_names = {
-    0: 'Esophagitis',
-    1: 'Dyed lifted polyps'
-}
-# Define color gradient segmentation function
-def color_gradient_segmentation(image):
-    # Preprocess the image
-    img_array = tf.image.resize(image, [256, 256])
-    img_array = tf.expand_dims(img_array, 0) / 255.0
-    # Apply color gradient segmentation
-    img_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
-    _, img_binary = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
-    # Make a prediction on the segmented image
-    prediction = model.predict(img_array)
-    predicted_class = tf.argmax(prediction[0], axis=-1)
-    confidence = np.max(prediction[0])
-    if confidence < 0.6:
-        result = "Unable to detect"
-        details = "The model was unable to confidently classify the image."
-    else:
-        class_name = class_names[predicted_class.numpy()]
-        result = class_name
-        details = f"The image is classified as {class_name} with a confidence of {confidence:.2f}."
-    # Apply color overlay on the segmented region
-    img_overlay = cv2.cvtColor(img_binary, cv2.COLOR_GRAY2RGB)
-    img_overlay[np.where((img_overlay == [255, 255, 255]).all(axis=2))] = [255, 0, 0]  # Red overlay color
-    # Blend the original image with the color overlay
-    img_segmented = cv2.addWeighted(image, 0.8, img_overlay, 0.2, 0)
-    return result, confidence, details, img_segmented
-iface = gr.Interface(
-    fn=color_gradient_segmentation,
-    inputs=components.Image(shape=(256, 256)),
-    outputs=[
-        components.Textbox(label="Result"),
-        components.Number(label="Confidence"),
-        components.Textbox(label="Details"),
-        components.Image(label="Segmented Image")
-    ],
-    examples=[
-        ['examples/0.jpg'],
-        ['examples/1.jpg'],
-        ['examples/2.jpg'],
-        ['examples/3.jpg']
-    ]
-)
-iface.launch()