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- <p>The plot of <b>Avunu Valliddaru Istapaddaru</b> revolves around two parallel stories that eventually converge into one hilarious climax. The stories are:</p>
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- <p>Anand (Prasanna) is the brother of Swathi's office manager (Banerjee) who takes a liking for her after seeing her photo on his brother's desk. He sends his father (Kota Srinivasa Rao) to her adopted parents (Surya & Saroja) in their village seeking alliance.</p>
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- <p>The music of <b>Avunu Valliddaru Istapaddaru</b> is composed by Chakri, who is known for his catchy tunes and melodious songs. The movie has 11 songs in total, including the background score. The songs are sung by popular singers like S.P. Balasubrahmanyam, Kousalya, Sandeep, Ravi Varma, etc. The lyrics are written by Sai Sriharsha, who has also written dialogues for the movie.</p>
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-<h1>Der Herr Der Ringe Die Schlacht Um Mittelerde 2 German Pc Iso: A Review</h1>
- <p>If you are a fan of J.R.R. Tolkien's epic fantasy saga, The Lord of the Rings, you might be interested in playing a video game that lets you experience the war in Middle-earth. One such game is Der Herr Der Ringe Die Schlacht Um Mittelerde 2, or The Lord of the Rings: The Battle for Middle-Earth II, a real-time strategy game developed by EA Los Angeles and published by Electronic Arts in 2006. In this article, we will review this game and tell you everything you need to know about it.</p>
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- <h3>What is Der Herr Der Ringe Die Schlacht Um Mittelerde 2?</h3>
- <p>Der Herr Der Ringe Die Schlacht Um Mittelerde 2 is a sequel to The Lord of the Rings: The Battle for Middle-Earth, which was released in 2004. The game covers the events of The Lord of the Rings trilogy, as well as some elements from The Hobbit and The Silmarillion. Unlike the first game, which focused on the War of the Ring in southern Middle-earth, this game features a new campaign that explores the War in the North, where elves, dwarves, men, and goblins fight for control over lands such as Rivendell, Erebor, Mirkwood, and Angmar.</p>
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-<li>You can choose from six playable factions: Men of the West (Gondor and Rohan), Elves (Rivendell, Lothlorien, and Mirkwood), Dwarves (Erebor, Iron Hills, and Ered Luin), Isengard (Uruk-hai, Wargs, and Dunlendings), Mordor (Orcs, Trolls, Haradrim, and Easterlings), and Goblins (Goblins, Spiders, Dragons, and Half-trolls).</li>
-<li>You can play two different campaigns: Good or Evil. The Good campaign follows the story of Glorfindel and Gloin as they lead their forces against Sauron's allies in the North. The Evil campaign follows the story of Sauron's lieutenant, the Witch-king of Angmar, as he seeks to destroy Arnor and Rivendell.</li>
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-<li>You can build your own base with various structures that provide resources, units, upgrades, defenses, and special powers. You can also capture outposts and settlements that grant you additional bonuses.</li>
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-</ul>
- <h3>What are the requirements to play the game?</h3>
- <p>To play this game on your PC, you will need:</p>
- <table>
-<tr><td>Operating System</td><td>Windows XP or later</td></tr>
-<tr><td>Processor</td><td>1.6 GHz or faster</td></tr>
-<tr><td>Memory</td><td>512 MB RAM or more</td></tr>
-<tr><td>Graphics</td><td>64 MB video card with DirectX 9.0c compatible drivers</td></tr>
-<tr><td>Sound</td><td>DirectX 9.0c compatible sound card</td></tr>
-<tr><td>Disk Space</td><td>6 GB or more</td></tr>
-<tr><td>DVD-ROM Drive</td><td>Required for installation</td></tr>
-<tr><td>Internet Connection</td><td>Required for online play</td></tr>
-</table>
- <h2>Gameplay</h2>
- <h3>How to install the game?</h3>
- <p>To install this game on your PC:</p>
- <ol>
-<li>If you have a physical copy of the game on DVD-ROM disc, insert it into your DVD-ROM drive. If you have a digital copy of the game on ISO file, mount it using an image program such as Daemon Tools Lite. If you have neither option available, you can download an ISO file from archive.org. </li>
-<li>Run setup.exe from your DVD-ROM drive or mounted ISO file. Follow the instructions on screen to install the game using serial from 'serial.txt'. Do not start it yet.</li>
-<li>Patch your game to version 1.09v2 by executing 'LotrBfMe-65539-german.exe'. This patch will fix some bugs and improve compatibility with newer systems.</li>
-<li>Create a folder named 'Meine Die Schlacht um Mittelerde™ II-Dateien' with 'Options.ini' as its content. Copy this folder to 'C:\\Users\\%Username%\\AppData\\Roaming\\'. This will prevent some errors when launching or playing the game.</li>
-<li>Run 'BFME2 Patch Switcher' and click on '1.09' with 'v2 Zoomfaktor' & 'FIX MY RESOLUTION'. This will adjust your resolution settings according to your monitor size.</li>
-<li>Eject your DVD-ROM disc or unmount your ISO file. You do not need them anymore to play.</li>
-<li>Start your game by running 'game.dat' from your installation directory.</li>
-</ol>
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-<li>Select your profile name or create a new one.</li>
-<li>Select your preferred mode from Single Player or Multiplayer.</li>
-<li>Select your preferred option from Campaigns (Good or Evil), Skirmish (online or offline), War of the Ring (online or offline), Custom Scenarios (online or offline), Options (graphics, sound, controls, etc.), Credits (view who made this game), or Quit Game (exit).</li>
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-<p>Behen Hogi Teri Full HD Movie 1080p has received mixed reviews from critics and audiences. Some of them have praised the film for its humor, music, and performances, while others have criticized it for its cliched plot, weak direction, and lack of originality.</p>
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-<p>Some of the positive reviews of the film are:</p>
-<ul>
-<li>"Behen Hogi Teri is a refreshing take on the rom-com genre with some witty dialogues, catchy songs, and charming performances by Rajkummar Rao and Shruti Haasan." - Times of India</li>
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-<li>"Behen Hogi Teri is a fun and entertaining film that showcases the chemistry and talent of Rajkummar Rao and Shruti Haasan. The film also has some hilarious moments and a good message about breaking stereotypes." - Filmfare</li>
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-<li>"Behen Hogi Teri is a forgettable film that does not offer anything new or exciting to the audience. The film has a cliched plot, bland direction, and average performances that make you wonder why you watched it in the first place." - Deccan Chronicle</li>
-</ul>
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-from cmath import cos
-from inspect import getargs
-import logging
-import os
-import random
-from datetime import datetime
-import bisect
-import copy
-from sched import scheduler
-import numpy as np
-import torch
-import torch.backends.cudnn as cudnn
-from torch import optim
-from torch.cuda.amp import GradScaler
-import faulthandler
-import pathlib
-import argparse
-import time
-try:
-    import wandb
-except ImportError:
-    wandb = None
-try:
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-except ImportError:
-    tensorboard = None
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-except ImportError:
-    hvd = None
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-from open_clip.linear_probe import LinearProbe
-def maintain_ckpts(args, startidx, all_idx_len):
-    for i in reversed(range(startidx, all_idx_len)):
-        if os.path.exists(os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt")):
-            os.rename(
-                os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt"),
-                os.path.join(args.checkpoint_path, f"epoch_top_{i+1}.pt"),
-            )
-    if os.path.exists(
-        os.path.join(args.checkpoint_path, f"epoch_top_{all_idx_len}.pt")
-    ):
-        os.remove(os.path.join(args.checkpoint_path, f"epoch_top_{all_idx_len}.pt"))
-    return
-def update_top_k_performance(
-    new_metrics_inputs, current_top_k_ckpt_metrics, args, ckpt, bignumbetter=True
-):
-    """
-    Record the top-k performance of the current epoch.
-    current_top_k_metrics is a dictionary of the form: {1: top_1_ckpt_measure, 2: top_2_ckpt_measure, ...}
-    """
-    if isinstance(new_metrics_inputs, (list, tuple)):
-        new_metrics_inputs = np.mean(new_metrics_inputs)
-        return update_top_k_performance(
-            new_metrics_inputs,
-            current_top_k_ckpt_metrics,
-            args=args,
-            ckpt=ckpt,
-            bignumbetter=bignumbetter,
-        )
-    elif isinstance(new_metrics_inputs, dict):
-        new_metrics_inputs = np.mean(list(new_metrics_inputs.values()))
-        return update_top_k_performance(
-            new_metrics_inputs,
-            current_top_k_ckpt_metrics,
-            args=args,
-            ckpt=ckpt,
-            bignumbetter=bignumbetter,
-        )
-    elif isinstance(new_metrics_inputs, (float, int)):
-        update_flag = {k: False for k in current_top_k_ckpt_metrics.keys()}
-        sorted_keys = sorted(current_top_k_ckpt_metrics.keys())
-        sorted_values = sorted(
-            current_top_k_ckpt_metrics.values(), reverse=bignumbetter
-        )
-        sorted_values_ = copy.deepcopy(sorted_values)
-        sorted_values.append(new_metrics_inputs)
-        sorted_values = sorted(sorted_values, reverse=bignumbetter)
-        sorted_values = sorted_values[:-1]
-        if sorted_values == sorted_values_:
-            return current_top_k_ckpt_metrics, new_metrics_inputs
-        else:
-            for i in range(len(sorted_keys)):
-                if current_top_k_ckpt_metrics[sorted_keys[i]] != sorted_values[i]:
-                    current_top_k_ckpt_metrics[sorted_keys[i]] = sorted_values[i]
-                    update_flag[sorted_keys[i]] = True
-            for i in range(len(update_flag)):
-                if update_flag[i]:
-                    maintain_ckpts(args, i, len(sorted_keys))
-                    torch.save(
-                        ckpt,
-                        os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt"),
-                    )
-                    break
-            return current_top_k_ckpt_metrics, new_metrics_inputs
-# def updateifNone(a, b):
-#     a = b if None else a
-#     return a
-def is_pretrained_params(n):
-    return (
-        n.startswith("clap_model.transformer")
-        or n in ["clap_model.positional_embedding", "clap_model.text_projection"]
-        or n.startswith("clap_model.token_embedding")
-        or n.startswith("clap_model.ln_final")
-        or n.startswith("clap_model.logit_scale_t")
-    )
-def random_seed(seed=42, rank=0):
-    torch.manual_seed(seed + rank)
-    np.random.seed(seed + rank)
-    random.seed(seed + rank)
-def config_lp_optimizer(model, data, args):
-    # set wd-related params to 0 if use adam optimizer
-    if args.optimizer == "adam":
-        args.wd = 0
-        args.wd_pretrained = 0
-        args.wd_new = 0
-    in_clap = lambda n, p: n.startswith("clap_model")
-    named_parameters = list(model.named_parameters())
-    optimizer = {}
-    scheduler = {}
-    # freeze text encoder
-    text_freeze_parameters = [
-        p
-        for n, p in named_parameters
-        if n.startswith("clap_model.transformer")
-        or n in ["clap_model.positional_embedding", "clap_model.text_projection"]
-        or n.startswith("clap_model.token_embedding")
-        or n.startswith("clap_model.ln_final")
-    ]
-    if args.freeze_text:
-        logging.info("Freeze Text!!!!")
-        for k in text_freeze_parameters:
-            k.requires_grad = False
-    if not args.lp_freeze:
-        exclude = (
-            lambda n, p: p.ndim < 2
-            or "bn" in n
-            or "ln" in n
-            or "bias" in n
-            or "logit_scale" in n
-        )
-        include = lambda n, p: not exclude(n, p)
-        # (yusong): we do not split the learning rate anymore
-        # p for n, p in named_parameters if in_clap(n,p) and exclude(n, p) and p.requires_grad
-        gain_or_bias_params = [
-            p for n, p in named_parameters if exclude(n, p) and p.requires_grad
-        ]
-        # rest_params = [p for n, p in named_parameters if in_clap(n,p) and include(n, p) and p.requires_grad]
-        rest_params = [
-            p for n, p in named_parameters if include(n, p) and p.requires_grad
-        ]
-        if args.train_data is None:
-            optimizer = None
-            scheduler = None
-        else:
-            total_steps = data["train"].dataloader.num_batches * args.epochs
-            if args.split_opt:
-                for x in ["lr", "beta1", "beta2", "eps", "wd"]:
-                    for y in ["_new", "_pretrained"]:
-                        if getattr(args, x + y) is None:
-                            setattr(args, x + y, getattr(args, x))
-                gain_or_bias_pretrained_params = [
-                    p
-                    for n, p in named_parameters
-                    if (exclude(n, p) and p.requires_grad) and is_pretrained_params(n)
-                ]
-                rest_pretrained_params = [
-                    p
-                    for n, p in named_parameters
-                    if (include(n, p) and p.requires_grad) and is_pretrained_params(n)
-                ]
-                gain_or_bias_new_params = [
-                    p
-                    for n, p in named_parameters
-                    if (exclude(n, p) and p.requires_grad)
-                    and (not is_pretrained_params(n))
-                ]
-                rest_new_params = [
-                    p
-                    for n, p in named_parameters
-                    if (include(n, p) and p.requires_grad)
-                    and (not is_pretrained_params(n))
-                ]
-                pretrained_params_optimizer = get_optimizer(
-                    [
-                        {"params": gain_or_bias_pretrained_params, "weight_decay": 0.0},
-                        {
-                            "params": rest_pretrained_params,
-                            "weight_decay": args.wd_pretrained,
-                        },
-                    ],
-                    lr=args.lr_pretrained,
-                    betas=(args.beta1_pretrained, args.beta2_pretrained),
-                    eps=args.eps_pretrained,
-                    momentum=args.momentum_pretrained,
-                    optimizer_name=args.optimizer,
-                )
-                pretrained_params_scheduler = cosine_lr(
-                    pretrained_params_optimizer,
-                    args.lr_pretrained,
-                    args.warmup,
-                    total_steps,
-                )
-                new_params_optimizer = get_optimizer(
-                    [
-                        {"params": gain_or_bias_new_params, "weight_decay": 0.0},
-                        {"params": rest_new_params, "weight_decay": args.wd_new},
-                    ],
-                    lr=args.lr_new,
-                    betas=(args.beta1_new, args.beta2_new),
-                    eps=args.eps_new,
-                    momentum=args.momentum_new,
-                    optimizer_name=args.optimizer,
-                )
-                new_params_scheduler = cosine_lr(
-                    new_params_optimizer, args.lr_new, args.warmup, total_steps
-                )
-                optimizer["text"] = pretrained_params_optimizer
-                optimizer["audio"] = new_params_optimizer
-                scheduler["text"] = pretrained_params_scheduler
-                scheduler["audio"] = new_params_scheduler
-                if args.horovod:
-                    pretrained_params_optimizer = hvd.DistributedOptimizer(
-                        pretrained_params_optimizer,
-                        named_parameters=model.named_parameters(),
-                    )
-                    new_params_optimizer = hvd.DistributedOptimizer(
-                        new_params_optimizer, named_parameters=model.named_parameters()
-                    )
-                    hvd.broadcast_parameters(model.state_dict(), root_rank=0)
-                    hvd.broadcast_optimizer_state(
-                        pretrained_params_optimizer, root_rank=0
-                    )
-                    hvd.broadcast_optimizer_state(new_params_optimizer, root_rank=0)
-            else:
-                optimizer["clap"] = get_optimizer(
-                    [
-                        {"params": gain_or_bias_params, "weight_decay": 0.0},
-                        {"params": rest_params, "weight_decay": args.wd},
-                    ],
-                    lr=args.lr,
-                    betas=(args.beta1, args.beta2),
-                    eps=args.eps,
-                    momentum=args.momentum,
-                    optimizer_name=args.optimizer,
-                )
-                scheduler["clap"] = cosine_lr(
-                    optimizer["clap"], args.lr, args.warmup, total_steps
-                )
-                if args.horovod:
-                    optimizer["clap"] = hvd.DistributedOptimizer(
-                        optimizer["clap"], named_parameters=model.named_parameters()
-                    )
-                    hvd.broadcast_parameters(model.state_dict(), root_rank=0)
-                    hvd.broadcast_optimizer_state(optimizer["clap"], root_rank=0)
-    # linear probe optimizer
-    else:
-        lp_params = [
-            p for n, p in named_parameters if (not in_clap(n, p)) and p.requires_grad
-        ]
-        lp_optim = get_optimizer(
-            lp_params,
-            lr=args.lp_lr,
-            betas=(args.beta1, args.beta2),
-            eps=args.eps,
-            momentum=0.9,
-            optimizer_name=args.optimizer,
-        )
-        optimizer["lp"] = lp_optim
-    return optimizer, scheduler, text_freeze_parameters
-def main():
-    args = parse_args()
-    time.sleep(args.sleep)
-    # sanitize model name for filesystem / uri use, easier if we don't use / in name as a rule?
-    args.amodel = args.amodel.replace("/", "-")
-    # download sizes.json file
-    # (yusong): the below two lines are for debug
-    # print("setting up faulthandler")
-    # faulthandler.register(10)
-    random.seed(args.seed)
-    torch.manual_seed(args.seed)
-    torch.cuda.manual_seed(args.seed)
-    torch.cuda.manual_seed_all(args.seed)
-    np.random.seed(args.seed)
-    args.class_index_dict = load_class_label(args.class_label_path)
-    # get the name of the experiments
-    if args.name is None:
-        args.name = "-".join(
-            [
-                datetime.now().strftime("%Y_%m_%d-%H_%M_%S"),
-                f"linear_probe" f"model_{args.amodel}",
-                f"lr_{args.lr}",
-                f"b_{args.batch_size}",
-                f"j_{args.workers}",
-                f"p_{args.precision}",
-            ]
-        )
-    # discover initial world args early so we can log properly
-    args.distributed = False
-    args.local_rank, args.rank, args.world_size = world_info_from_env()
-    if args.remotedata and is_master(args):
-        for dataset_name in args.datasetnames:
-            for split in dataset_split[dataset_name]:
-                if not os.path.exists(f"./json_files/{dataset_name}/{split}"):
-                    os.makedirs(f"./json_files/{dataset_name}/{split}")
-                os.system(
-                    f"aws s3 cp s3://s-laion-audio/webdataset_tar/{dataset_name}/{split}/sizes.json ./json_files/{dataset_name}/{split}/sizes.json"
-                )
-    args.log_path = None
-    if is_master(args, local=args.log_local):
-        log_base_path = os.path.join(args.logs, args.name)
-        os.makedirs(log_base_path, exist_ok=True)
-        log_filename = f"out-{args.rank}" if args.log_local else "out.log"
-        args.log_path = os.path.join(log_base_path, log_filename)
-        # avoid log dir in same name:
-        postfix = 0
-        while os.path.exists(args.log_path):
-            postfix += 1
-            log_base_path_new = log_base_path + "-" + str(postfix)
-            os.makedirs(log_base_path_new, exist_ok=True)
-            log_filename = f"out-{args.rank}" if args.log_local else "out.log"
-            args.log_path = os.path.join(log_base_path_new, log_filename)
-            # print(
-            #     "Error. Experiment already exists. Use --name {} to specify a new experiment."
-            # )
-            # return -1
-    # Set logger
-    args.log_level = logging.DEBUG if args.debug else logging.INFO
-    setup_logging(args.log_path, args.log_level)
-    # fully initialize distributed device environment
-    device = init_distributed_device(args)
-    args.wandb = "wandb" in args.report_to or "all" in args.report_to
-    args.tensorboard = "tensorboard" in args.report_to or "all" in args.report_to
-    if is_master(args):
-        args.tensorboard_path = (
-            os.path.join(args.logs, args.name, "tensorboard")
-            if args.tensorboard
-            else ""
-        )
-        args.checkpoint_path = os.path.join(args.logs, args.name, "checkpoints")
-        for dirname in [args.tensorboard_path, args.checkpoint_path]:
-            if dirname:
-                os.makedirs(dirname, exist_ok=True)
-    else:
-        args.tensorboard_path = ""
-        args.checkpoint_path = ""
-    if args.copy_codebase:
-        copy_codebase(args)
-    assert args.precision in ["amp", "fp16", "fp32"]
-    if args.precision == "fp16":
-        logging.warning(
-            "It is recommended to use AMP mixed-precision instead of FP16. "
-            "FP16 support needs further verification and tuning, especially for train."
-        )
-    if args.horovod:
-        logging.info(
-            f"Running in horovod mode with multiple processes / nodes. Device: {args.device}."
-            f"Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}."
-        )
-    elif args.distributed:
-        logging.info(
-            f"Running in distributed mode with multiple processes. Device: {args.device}."
-            f"Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}."
-        )
-    else:
-        logging.info(f"Running with a single process. Device {args.device}.")
-    logging.info(f"openai cache dir: {os.path.expanduser(args.openai_model_cache_dir)}")
-    # Create CLAP model
-    clap_model, clap_model_cfg = create_model(
-        args.amodel,
-        args.tmodel,
-        args.pretrained,
-        precision=args.precision,
-        device=device,
-        jit=args.torchscript,
-        force_quick_gelu=args.force_quick_gelu,
-        openai_model_cache_dir=os.path.expanduser(args.openai_model_cache_dir),
-        skip_params=False,
-        pretrained_audio=args.pretrained_audio,
-        pretrained_text=args.pretrained_text,
-        enable_fusion=args.enable_fusion,
-        fusion_type=args.fusion_type,
-    )
-    args.lp_out_ch = len(list(args.class_index_dict.keys()))
-    # Linear Probe
-    logging.info(f"linear probe using mlp: {args.lp_mlp}")
-    logging.info(f"linear probe using freeze: {args.lp_freeze}")
-    logging.info(f"linear probe act layer: {args.lp_act}")
-    logging.info(f"linear probe out ch: {args.lp_out_ch}")
-    logging.info(f"linear probe learning rate (if applicable): {args.lp_lr}")
-    logging.info(f"linear probe loss func: {args.lp_loss}")
-    logging.info(f"linear probe lp_metrics: {args.lp_metrics}")
-    model = LinearProbe(
-        clap_model,
-        mlp=args.lp_mlp,
-        freeze=args.lp_freeze,
-        in_ch=512,
-        out_ch=args.lp_out_ch,
-        act=args.lp_act,
-    )  # in_ch is fixed (i.e., 512)
-    model = model.to(device)
-    if args.horovod:
-        with torch.no_grad():
-            for param in model.parameters():
-                param.set_(param.contiguous())
-    if args.trace:
-        model = trace_model(model, batch_size=args.batch_size, device=device)
-    if is_master(args):
-        logging.info("Linear Probe CLAP Model:")
-        logging.info(f"{str(clap_model)}")
-        logging.info("Params:")
-        params_file = os.path.join(args.logs, args.name, "params.txt")
-        with open(params_file, "w") as f:
-            for name in sorted(vars(args)):
-                val = getattr(args, name)
-                logging.info(f"  {name}: {val}")
-                f.write(f"{name}: {val}\n")
-    if args.distributed and not args.horovod:
-        if args.use_bn_sync:
-            model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
-        ddp_args = {}
-        if args.ddp_static_graph:
-            # this doesn't exist in older PyTorch, arg only added if enabled
-            ddp_args["static_graph"] = True
-        model = torch.nn.parallel.DistributedDataParallel(
-            model, device_ids=[device], find_unused_parameters=True, **ddp_args
-        )
-    data = get_data(args, clap_model_cfg)
-    assert len(data), "At least one train or eval dataset must be specified."
-    if args.trace:
-        assert "train" not in data, "Cannot train with traced model"
-    optimizer, scheduler, text_freeze_parameters = config_lp_optimizer(
-        model, data, args
-    )
-    scaler = GradScaler() if args.precision == "amp" else None
-    # optionally resume from a checkpoint
-    start_epoch = 0
-    if args.resume is not None:
-        if os.path.isfile(args.resume):
-            checkpoint = torch.load(args.resume, map_location=device)
-            if "epoch" in checkpoint:
-                # resuming a train checkpoint w/ epoch and optimizer state
-                start_epoch = checkpoint["epoch"]
-                sd = checkpoint["state_dict"]
-                if not args.distributed and next(iter(sd.items()))[0].startswith(
-                    "module"
-                ):
-                    sd = {k[len("module.") :]: v for k, v in sd.items()}
-                model.load_state_dict(sd)
-                if args.split_opt:
-                    if optimizer is not None:
-                        for k, o_ in optimizer.items():
-                            o_.load_state_dict(checkpoint[k + "_" + "optimizer"])
-                if optimizer is not None:
-                    optimizer.load_state_dict(checkpoint["optimizer"])
-                if scaler is not None and "scaler" in checkpoint:
-                    scaler.load_state_dict(checkpoint["scaler"])
-                logging.info(
-                    f"=> resuming checkpoint '{args.resume}' (epoch {start_epoch})"
-                )
-            else:
-                # loading a bare (model only) checkpoint for fine-tune or evaluation
-                model.load_state_dict(checkpoint)
-                logging.info(
-                    f"=> loaded checkpoint '{args.resume}' (epoch {start_epoch})"
-                )
-            if args.freeze_text:
-                print("Freeze Text!!!!")
-                for k in text_freeze_parameters:
-                    k.requires_grad = False
-        else:
-            logging.info("=> no checkpoint found at '{}'".format(args.resume))
-    cudnn.benchmark = True
-    cudnn.deterministic = False
-    # determine if this worker should save logs and checkpoints. only do so if it is rank == 0
-    args.save_logs = args.logs and args.logs.lower() != "none" and is_master(args)
-    writer = None
-    if args.save_logs and args.tensorboard:
-        assert tensorboard is not None, "Please install tensorboard."
-        writer = tensorboard.SummaryWriter(args.tensorboard_path)
-    if args.wandb and is_master(args):
-        assert wandb is not None, "Please install wandb."
-        logging.debug("Starting wandb.")
-        args.train_sz = data["train"].dataloader.num_samples
-        if args.val_data is not None:
-            args.val_sz = data["val"].dataloader.num_samples
-        # you will have to configure this for your project!
-        wandb.init(
-            project="clap",
-            notes=args.wandb_notes,
-            name=args.wandb_notes,
-            tags=[],
-            config=vars(args),
-        )
-        if args.debug:
-            wandb.watch(model, log="all")
-        wandb.save(params_file)
-        logging.debug("Finished loading wandb.")
-    if "train" not in data:
-        evaluate(model, data, start_epoch, args, writer)
-        return
-    elif start_epoch == 0 and "val" in data and not args.no_eval:
-        evaluate(model, data, 0, args, writer)
-    if args.save_top_performance:
-        current_top_k_ckpt_metrics = {
-            i: 0 for i in range(args.save_top_performance)
-        }  # initialize the top-k metric for ckpts to 0
-    for epoch in range(start_epoch, args.epochs):
-        # freeze the text param after (include) args.freeze_text_after, this is -1 by default
-        if epoch == args.freeze_text_after:
-            print("Text pretrained parameters are freezed since this epoch.")
-            for k in text_freeze_parameters:
-                k.requires_grad = False
-        if is_master(args):
-            logging.info(f"Start epoch {epoch}")
-        train_one_epoch(model, data, epoch, optimizer, scaler, scheduler, args, writer)
-        completed_epoch = epoch + 1
-        if (
-            any(v in data for v in ("val", "imagenet-val", "imagenet-v2"))
-            and not args.no_eval
-        ):
-            metrics = evaluate(model, data, completed_epoch, args, writer)
-            if args.save_top_performance:
-                top_k_dataset = args.top_k_checkpoint_select_dataset
-                top_k_metric = args.top_k_checkpoint_select_metric
-                filtered_metrics = [
-                    v
-                    for k, v in metrics.items()
-                    if top_k_metric in k and top_k_dataset in k
-                ]  # check all R@10 metrics (all dataset) and use it to update the ckpt
-        # Saving checkpoints.
-        if args.save_logs:
-            opt_dict = {
-                k + "_" + "optimizer": v.state_dict() for k, v in optimizer.items()
-            }
-            checkpoint_dict = {
-                "epoch": completed_epoch,
-                "name": args.name,
-                "state_dict": model.state_dict(),
-            }
-            checkpoint_dict.update(opt_dict)
-            if scaler is not None:
-                checkpoint_dict["scaler"] = scaler.state_dict()
-            if completed_epoch == args.epochs or (
-                args.save_frequency > 0 and (completed_epoch % args.save_frequency) == 0
-            ):
-                torch.save(
-                    checkpoint_dict,
-                    os.path.join(args.checkpoint_path, f"epoch_{completed_epoch}.pt"),
-                )
-            if args.save_most_recent:
-                torch.save(
-                    checkpoint_dict,
-                    os.path.join(args.checkpoint_path, f"epoch_latest.pt"),
-                )
-            if args.save_top_performance and not args.no_eval:
-                update_top_k_performance(
-                    filtered_metrics,
-                    current_top_k_ckpt_metrics,
-                    args,
-                    checkpoint_dict,
-                    bignumbetter=True,
-                )
-    if args.wandb and is_master(args):
-        wandb.finish()
-def copy_codebase(args):
-    from shutil import copytree, ignore_patterns
-    new_code_path = os.path.join(args.logs, args.name, "code")
-    if os.path.exists(new_code_path):
-        print(
-            f"Error. Experiment already exists at {new_code_path}. Use --name to specify a new experiment."
-        )
-        return -1
-    print(f"Copying codebase to {new_code_path}")
-    current_code_path = os.path.realpath(__file__)
-    for _ in range(3):
-        current_code_path = os.path.dirname(current_code_path)
-    copytree(
-        current_code_path, new_code_path, ignore=ignore_patterns("log", "logs", "wandb")
-    )
-    print("Done copying code.")
-    return 1
-if __name__ == "__main__":
-    main()

spaces/AIGC-Audio/AudioGPT/NeuralSeq/utils/indexed_datasets.py DELETED Viewed

@@ -1,71 +0,0 @@
-import pickle
-from copy import deepcopy
-import numpy as np
-class IndexedDataset:
-    def __init__(self, path, num_cache=1):
-        super().__init__()
-        self.path = path
-        self.data_file = None
-        self.data_offsets = np.load(f"{path}.idx", allow_pickle=True).item()['offsets']
-        self.data_file = open(f"{path}.data", 'rb', buffering=-1)
-        self.cache = []
-        self.num_cache = num_cache
-    def check_index(self, i):
-        if i < 0 or i >= len(self.data_offsets) - 1:
-            raise IndexError('index out of range')
-    def __del__(self):
-        if self.data_file:
-            self.data_file.close()
-    def __getitem__(self, i):
-        self.check_index(i)
-        if self.num_cache > 0:
-            for c in self.cache:
-                if c[0] == i:
-                    return c[1]
-        self.data_file.seek(self.data_offsets[i])
-        b = self.data_file.read(self.data_offsets[i + 1] - self.data_offsets[i])
-        item = pickle.loads(b)
-        if self.num_cache > 0:
-            self.cache = [(i, deepcopy(item))] + self.cache[:-1]
-        return item
-    def __len__(self):
-        return len(self.data_offsets) - 1
-class IndexedDatasetBuilder:
-    def __init__(self, path):
-        self.path = path
-        self.out_file = open(f"{path}.data", 'wb')
-        self.byte_offsets = [0]
-    def add_item(self, item):
-        s = pickle.dumps(item)
-        bytes = self.out_file.write(s)
-        self.byte_offsets.append(self.byte_offsets[-1] + bytes)
-    def finalize(self):
-        self.out_file.close()
-        np.save(open(f"{self.path}.idx", 'wb'), {'offsets': self.byte_offsets})
-if __name__ == "__main__":
-    import random
-    from tqdm import tqdm
-    ds_path = '/tmp/indexed_ds_example'
-    size = 100
-    items = [{"a": np.random.normal(size=[10000, 10]),
-              "b": np.random.normal(size=[10000, 10])} for i in range(size)]
-    builder = IndexedDatasetBuilder(ds_path)
-    for i in tqdm(range(size)):
-        builder.add_item(items[i])
-    builder.finalize()
-    ds = IndexedDataset(ds_path)
-    for i in tqdm(range(10000)):
-        idx = random.randint(0, size - 1)
-        assert (ds[idx]['a'] == items[idx]['a']).all()

spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/audio/griffin_lim.py DELETED Viewed

@@ -1,85 +0,0 @@
-import librosa
-import numpy as np
-import torch
-import torch.nn.functional as F
-def _stft(y, hop_size, win_size, fft_size):
-    return librosa.stft(y=y, n_fft=fft_size, hop_length=hop_size, win_length=win_size, pad_mode='constant')
-def _istft(y, hop_size, win_size):
-    return librosa.istft(y, hop_length=hop_size, win_length=win_size)
-def griffin_lim(S, hop_size, win_size, fft_size, angles=None, n_iters=30):
-    angles = np.exp(2j * np.pi * np.random.rand(*S.shape)) if angles is None else angles
-    S_complex = np.abs(S).astype(np.complex)
-    y = _istft(S_complex * angles, hop_size, win_size)
-    for i in range(n_iters):
-        angles = np.exp(1j * np.angle(_stft(y, hop_size, win_size, fft_size)))
-        y = _istft(S_complex * angles, hop_size, win_size)
-    return y
-def istft(amp, ang, hop_size, win_size, fft_size, pad=False, window=None):
-    spec = amp * torch.exp(1j * ang)
-    spec_r = spec.real
-    spec_i = spec.imag
-    spec = torch.stack([spec_r, spec_i], -1)
-    if window is None:
-        window = torch.hann_window(win_size).to(amp.device)
-    if pad:
-        spec = F.pad(spec, [0, 0, 0, 1], mode='reflect')
-    wav = torch.istft(spec, fft_size, hop_size, win_size)
-    return wav
-def griffin_lim_torch(S, hop_size, win_size, fft_size, angles=None, n_iters=30):
-    """
-    Examples:
-    >>> x_stft = librosa.stft(wav, n_fft=fft_size, hop_length=hop_size, win_length=win_length, pad_mode="constant")
-    >>> x_stft = x_stft[None, ...]
-    >>> amp = np.abs(x_stft)
-    >>> angle_init = np.exp(2j * np.pi * np.random.rand(*x_stft.shape))
-    >>> amp = torch.FloatTensor(amp)
-    >>> wav = griffin_lim_torch(amp, angle_init, hparams)
-    :param amp: [B, n_fft, T]
-    :param ang: [B, n_fft, T]
-    :return: [B, T_wav]
-    """
-    angles = torch.exp(2j * np.pi * torch.rand(*S.shape)) if angles is None else angles
-    window = torch.hann_window(win_size).to(S.device)
-    y = istft(S, angles, hop_size, win_size, fft_size, window=window)
-    for i in range(n_iters):
-        x_stft = torch.stft(y, fft_size, hop_size, win_size, window)
-        x_stft = x_stft[..., 0] + 1j * x_stft[..., 1]
-        angles = torch.angle(x_stft)
-        y = istft(S, angles, hop_size, win_size, fft_size, window=window)
-    return y
-# Conversions
-_mel_basis = None
-_inv_mel_basis = None
-def _build_mel_basis(audio_sample_rate, fft_size, audio_num_mel_bins, fmin, fmax):
-    assert fmax <= audio_sample_rate // 2
-    return librosa.filters.mel(audio_sample_rate, fft_size, n_mels=audio_num_mel_bins, fmin=fmin, fmax=fmax)
-def _linear_to_mel(spectogram, audio_sample_rate, fft_size, audio_num_mel_bins, fmin, fmax):
-    global _mel_basis
-    if _mel_basis is None:
-        _mel_basis = _build_mel_basis(audio_sample_rate, fft_size, audio_num_mel_bins, fmin, fmax)
-    return np.dot(_mel_basis, spectogram)
-def _mel_to_linear(mel_spectrogram, audio_sample_rate, fft_size, audio_num_mel_bins, fmin, fmax):
-    global _inv_mel_basis
-    if _inv_mel_basis is None:
-        _inv_mel_basis = np.linalg.pinv(_build_mel_basis(audio_sample_rate, fft_size, audio_num_mel_bins, fmin, fmax))
-    return np.maximum(1e-10, np.dot(_inv_mel_basis, mel_spectrogram))

spaces/AP123/IllusionDiffusion/illusion_style.py DELETED Viewed

@@ -1,10 +0,0 @@
-css='''
-#share-btn-container {padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;}
-div#share-btn-container > div {flex-direction: row;background: black;align-items: center}
-#share-btn-container:hover {background-color: #060606}
-#share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;}
-#share-btn * {all: unset}
-#share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;}
-#share-btn-container .wrap {display: none !important}
-#share-btn-container.hidden {display: none!important}
-'''

spaces/AP123/Upside-Down-Diffusion/app.py DELETED Viewed

@@ -1,382 +0,0 @@
-from diffusers import StableDiffusionXLPipeline, AutoencoderKL
-from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-import torch
-from PIL import Image, ImageOps
-import gradio as gr
-import user_history
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-pipe = StableDiffusionXLPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0",
-    vae=vae,
-    torch_dtype=torch.float16,
-    variant="fp16",
-    use_safetensors=True,
-)
-pipe.to("cuda")
-pipe.unet.to(memory_format=torch.channels_last)
-pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-@torch.no_grad()
-def call(
-        pipe,
-        prompt: Union[str, List[str]] = None,
-        prompt2: Union[str, List[str]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        denoising_end: Optional[float] = None,
-        guidance_scale: float = 5.0,
-        guidance_scale2: float = 5.0,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        negative_prompt2: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        guidance_rescale: float = 0.0,
-        original_size: Optional[Tuple[int, int]] = None,
-        crops_coords_top_left: Tuple[int, int] = (0, 0),
-        target_size: Optional[Tuple[int, int]] = None,
-        negative_original_size: Optional[Tuple[int, int]] = None,
-        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
-        negative_target_size: Optional[Tuple[int, int]] = None,
-    ):
-        # 0. Default height and width to unet
-        height = height or pipe.default_sample_size * pipe.vae_scale_factor
-        width = width or pipe.default_sample_size * pipe.vae_scale_factor
-        original_size = original_size or (height, width)
-        target_size = target_size or (height, width)
-        # 1. Check inputs. Raise error if not correct
-        pipe.check_inputs(
-            prompt,
-            None,
-            height,
-            width,
-            callback_steps,
-            negative_prompt,
-            None,
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        )
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-        device = pipe._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-        # 3. Encode input prompt
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
-        )
-        (
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        ) = pipe.encode_prompt(
-            prompt=prompt,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-            negative_prompt=negative_prompt,
-            prompt_embeds=None,
-            negative_prompt_embeds=None,
-            pooled_prompt_embeds=None,
-            negative_pooled_prompt_embeds=None,
-            lora_scale=text_encoder_lora_scale,
-        )
-        (
-            prompt2_embeds,
-            negative_prompt2_embeds,
-            pooled_prompt2_embeds,
-            negative_pooled_prompt2_embeds,
-        ) = pipe.encode_prompt(
-            prompt=prompt2,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-            negative_prompt=negative_prompt2,
-            prompt_embeds=None,
-            negative_prompt_embeds=None,
-            pooled_prompt_embeds=None,
-            negative_pooled_prompt_embeds=None,
-            lora_scale=text_encoder_lora_scale,
-        )
-        # 4. Prepare timesteps
-        pipe.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = pipe.scheduler.timesteps
-        # 5. Prepare latent variables
-        num_channels_latents = pipe.unet.config.in_channels
-        latents = pipe.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = pipe.prepare_extra_step_kwargs(generator, eta)
-        # 7. Prepare added time ids & embeddings
-        add_text_embeds = pooled_prompt_embeds
-        add_text2_embeds = pooled_prompt2_embeds
-        add_time_ids = pipe._get_add_time_ids(
-            original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
-        )
-        add_time2_ids = pipe._get_add_time_ids(
-            original_size, crops_coords_top_left, target_size, dtype=prompt2_embeds.dtype
-        )
-        if negative_original_size is not None and negative_target_size is not None:
-            negative_add_time_ids = pipe._get_add_time_ids(
-                negative_original_size,
-                negative_crops_coords_top_left,
-                negative_target_size,
-                dtype=prompt_embeds.dtype,
-            )
-        else:
-            negative_add_time_ids = add_time_ids
-            negative_add_time2_ids = add_time2_ids
-        if do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
-            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
-            prompt2_embeds = torch.cat([negative_prompt2_embeds, prompt2_embeds], dim=0)
-            add_text2_embeds = torch.cat([negative_pooled_prompt2_embeds, add_text2_embeds], dim=0)
-            add_time2_ids = torch.cat([negative_add_time2_ids, add_time2_ids], dim=0)
-        prompt_embeds = prompt_embeds.to(device)
-        add_text_embeds = add_text_embeds.to(device)
-        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
-        prompt2_embeds = prompt2_embeds.to(device)
-        add_text2_embeds = add_text2_embeds.to(device)
-        add_time2_ids = add_time2_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
-        # 8. Denoising loop
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * pipe.scheduler.order, 0)
-        # 7.1 Apply denoising_end
-        if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
-            discrete_timestep_cutoff = int(
-                round(
-                    pipe.scheduler.config.num_train_timesteps
-                    - (denoising_end * pipe.scheduler.config.num_train_timesteps)
-                )
-            )
-            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
-            timesteps = timesteps[:num_inference_steps]
-        with pipe.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                if i % 2 == 0:
-                  # expand the latents if we are doing classifier free guidance
-                  latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                  latent_model_input = pipe.scheduler.scale_model_input(latent_model_input, t)
-                  # predict the noise residual
-                  added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
-                  noise_pred = pipe.unet(
-                      latent_model_input,
-                      t,
-                      encoder_hidden_states=prompt_embeds,
-                      cross_attention_kwargs=cross_attention_kwargs,
-                      added_cond_kwargs=added_cond_kwargs,
-                      return_dict=False,
-                  )[0]
-                  # perform guidance
-                  if do_classifier_free_guidance:
-                      noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                      noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-                else:
-                  # expand the latents if we are doing classifier free guidance
-                  latent_model_input2 = torch.cat([latents.flip(2)] * 2) if do_classifier_free_guidance else latents
-                  latent_model_input2 = pipe.scheduler.scale_model_input(latent_model_input2, t)
-                  # predict the noise residual
-                  added_cond2_kwargs = {"text_embeds": add_text2_embeds, "time_ids": add_time2_ids}
-                  noise_pred2 = pipe.unet(
-                      latent_model_input2,
-                      t,
-                      encoder_hidden_states=prompt2_embeds,
-                      cross_attention_kwargs=cross_attention_kwargs,
-                      added_cond_kwargs=added_cond2_kwargs,
-                      return_dict=False,
-                  )[0]
-                  # perform guidance
-                  if do_classifier_free_guidance:
-                      noise_pred2_uncond, noise_pred2_text = noise_pred2.chunk(2)
-                      noise_pred2 = noise_pred2_uncond + guidance_scale2 * (noise_pred2_text - noise_pred2_uncond)
-                noise_pred = noise_pred if i % 2 == 0 else noise_pred2.flip(2)
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % pipe.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-        if not output_type == "latent":
-            # make sure the VAE is in float32 mode, as it overflows in float16
-            needs_upcasting = pipe.vae.dtype == torch.float16 and pipe.vae.config.force_upcast
-            if needs_upcasting:
-                pipe.upcast_vae()
-                latents = latents.to(next(iter(pipe.vae.post_quant_conv.parameters())).dtype)
-            image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]
-            # cast back to fp16 if needed
-            if needs_upcasting:
-                pipe.vae.to(dtype=torch.float16)
-        else:
-            image = latents
-        if not output_type == "latent":
-            # apply watermark if available
-            if pipe.watermark is not None:
-                image = pipe.watermark.apply_watermark(image)
-            image = pipe.image_processor.postprocess(image, output_type=output_type)
-        # Offload all models
-        pipe.maybe_free_model_hooks()
-        if not return_dict:
-            return (image,)
-        return StableDiffusionXLPipelineOutput(images=image)
-NEGATIVE_PROMPTS = "text, watermark, low-quality, signature, moiré pattern, downsampling, aliasing, distorted, blurry, glossy, blur, jpeg artifacts, compression artifacts, poorly drawn, low-resolution, bad, distortion, twisted, excessive, exaggerated pose, exaggerated limbs, grainy, symmetrical, duplicate, error, pattern, beginner, pixelated, fake, hyper, glitch, overexposed, high-contrast, bad-contrast"
-def rotate_output(has_flipped):
-    if(has_flipped):
-        return gr.Image(elem_classes="not_rotated"), gr.Button("Rotate to see prompt 2!"), not has_flipped
-    else:
-        return gr.Image(elem_classes="rotated"), gr.Button("Rotate to see prompt 1!"), not has_flipped
-def simple_call(prompt1, prompt2, profile: gr.OAuthProfile | None=None):
-    generator = [torch.Generator(device="cuda").manual_seed(5)]
-    res = call(
-        pipe,
-        prompt1,
-        prompt2,
-        width=768,
-        height=768,
-        num_images_per_prompt=1,
-        num_inference_steps=50,
-        guidance_scale=5.0,
-        guidance_scale2=8.0,
-        negative_prompt=NEGATIVE_PROMPTS,
-        negative_prompt2=NEGATIVE_PROMPTS,
-        generator=generator
-    )
-    image1 = res.images[0]
-    # save generated images (if logged in)
-    user_history.save_image(label=f"{prompt1} / {prompt2}", image=image1, profile=profile, metadata={
-        "prompt2": prompt1,
-        "prompt1": prompt2,
-    })
-    return image1
-css = '''
-#result_image{ transition: transform 2s ease-in-out }
-#result_image.rotated{transform: rotate(180deg)}
-'''
-with gr.Blocks() as app:
-    gr.Markdown(
-        '''
-        <center>
-            <h1>Upside Down Diffusion</h1>
-            <p>Code by Alex Carlier, <a href="https://colab.research.google.com/drive/1rjDQOn11cTHAf3Oeq87Hfl_Vh41NbTl4?usp=sharing">Google Colab</a>, follow them on <a href="https://twitter.com/alexcarliera">Twitter</a></p>
-            <p>A space by <a href="https://twitter.com/angrypenguinPNG">AP</a> with contributions from <a href="https://twitter.com/multimodalart">MultimodalArt</a></p>
-        </center>
-        <hr>
-        <p>
-            Enter your first prompt to craft an image that will show when upright. Then, add a second prompt to reveal a mesmerizing surprise when you flip the image upside down!  ✨
-        </p>
-        <p>
-            <em>For best results, please include the prompt in the following format: Art Style and Object. Here is an example: Prompt 1: A sketch of a turtle, Prompt 2: A sketch of a tree. Both prompts need to have the same style!</em>
-        </p>
-        '''
-    )
-    has_flipped = gr.State(value=False)
-    with gr.Row():
-        with gr.Column():
-            prompt1 = gr.Textbox(label="Prompt 1", info="Prompt for the side up", placeholder="A sketch of a...")
-            prompt2 = gr.Textbox(label="Prompt 2", info="Prompt for the side down", placeholder="A sketch of a...")
-            run_btn = gr.Button("Run")
-        with gr.Column():
-            result_image1 = gr.Image(label="Output", elem_id="result_image", elem_classes="not_rotated")
-            rotate_button = gr.Button("Rotate to see prompt 2!")
-    run_btn.click(
-        simple_call,
-        inputs=[prompt1, prompt2],
-        outputs=[result_image1]
-    )
-    rotate_button.click(
-        rotate_output,
-        inputs=[has_flipped],
-        outputs=[result_image1, rotate_button, has_flipped],
-        queue=False,
-        show_progress=False
-    )
-with gr.Blocks(css=css) as app_with_history:
-    with gr.Tab("Upside Down Diffusion"):
-        app.render()
-    with gr.Tab("Past generations"):
-        user_history.render()
-app_with_history.queue(max_size=20)
-if __name__ == "__main__":
-    app_with_history.launch(debug=True)

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/custom_dataset/yolov6_s_fast.py DELETED Viewed

@@ -1,124 +0,0 @@
-_base_ = '../yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco.py'
-max_epochs = 100  # 训练的最大 epoch
-data_root = './data-df2/'  # 数据集目录的绝对路径
-# 结果保存的路径，可以省略，省略保存的文件名位于 work_dirs 下 config 同名的文件夹中
-# 如果某个 config 只是修改了部分参数，修改这个变量就可以将新的训练文件保存到其他地方
-work_dir = './work_dirs/yolov6_s_df2'
-# 根据自己的 GPU 情况，修改 batch size，YOLOv5-s 默认为 8卡 x 16bs
-train_batch_size_per_gpu = 32
-train_num_workers = 4  # 推荐使用 train_num_workers = nGPU x 4
-save_epoch_intervals = 2
-# 根据自己的 GPU 情况，修改 base_lr，修改的比例是 base_lr_default * (your_bs / default_bs)
-base_lr = _base_.base_lr / 4
-class_name = ('short_sleeved_shirt',
-              'long_sleeved_shirt',
-              'short_sleeved_outwear',
-              'long_sleeved_outwear',
-              'vest',
-              'sling',
-              'shorts',
-              'trousers',
-              'skirt',
-              'short_sleeved_dress',
-              'long_sleeved_dress',
-              'vest_dress',
-              'sling_dress')  # 根据 class_with_id.txt 类别信息，设置 class_name
-num_classes = len(class_name)
-metainfo = dict(
-    classes=class_name,
-    palette=[(255,   0,   0),
-             (255, 128,   0),
-             (255, 255,   0),
-             (128, 255,   0),
-             (0, 255,   0),
-             (0, 255, 128),
-             (0, 255, 255),
-             (0, 128, 255),
-             (0,   0, 255),
-             (127,   0, 255),
-             (255,   0, 255),
-             (255,   0, 127),
-             (128, 128, 128)]  # 画图时候的颜色，随便设置即可
-)
-train_cfg = dict(
-    max_epochs=max_epochs,
-    val_begin=20,  # 第几个 epoch 后验证，这里设置 20 是因为前 20 个 epoch 精度不高，测试意义不大，故跳过
-    val_interval=save_epoch_intervals,  # 每 val_interval 轮迭代进行一次测试评估
-    dynamic_intervals=[(max_epochs-_base_.num_last_epochs, 1)]
-)
-model = dict(
-    bbox_head=dict(
-        head_module=dict(num_classes=num_classes)),
-    train_cfg=dict(
-        initial_assigner=dict(num_classes=num_classes),
-        assigner=dict(num_classes=num_classes)
-    )
-)
-train_dataloader = dict(
-    batch_size=train_batch_size_per_gpu,
-    num_workers=train_num_workers,
-    dataset=dict(
-        _delete_=True,
-        type='RepeatDataset',
-        # 数据量太少的话，可以使用 RepeatDataset ，在每个 epoch 内重复当前数据集 n 次，这里设置 5 是重复 5 次
-        times=2,
-        dataset=dict(
-            type=_base_.dataset_type,
-            data_root=data_root,
-            metainfo=metainfo,
-            ann_file='annotations/trainval.json',
-            data_prefix=dict(img='smaller-dataset/'),
-            filter_cfg=dict(filter_empty_gt=False, min_size=32),
-            pipeline=_base_.train_pipeline)))
-val_dataloader = dict(
-    dataset=dict(
-        metainfo=metainfo,
-        data_root=data_root,
-        ann_file='annotations/trainval.json',
-        data_prefix=dict(img='smaller-dataset/')))
-test_dataloader = val_dataloader
-val_evaluator = dict(ann_file=data_root + 'annotations/trainval.json')
-test_evaluator = val_evaluator
-optim_wrapper = dict(optimizer=dict(lr=base_lr))
-default_hooks = dict(
-    # 设置间隔多少个 epoch 保存模型，以及保存模型最多几个，`save_best` 是另外保存最佳模型（推荐）
-    checkpoint=dict(
-        type='CheckpointHook',
-        interval=save_epoch_intervals,
-        max_keep_ckpts=5,
-        save_best='auto'),
-    param_scheduler=dict(max_epochs=max_epochs),
-    # logger 输出的间隔
-    logger=dict(type='LoggerHook', interval=10))
-custom_hooks = [
-    dict(
-        type="EMAHook",
-        ema_type="ExpMomentumEMA",
-        momentum=0.0001,
-        update_buffers=True,
-        strict_load=False,
-        priority=49),
-    dict(
-        type="mmdet.PipelineSwitchHook",
-        switch_epoch=max_epochs-max_epochs-_base_.num_last_epochs,
-        switch_pipeline=_base_.train_pipeline_stage2
-    )
-]
-visualizer = dict(vis_backends=[dict(type='LocalVisBackend'), dict(type='WandbVisBackend')])

spaces/AchyuthGamer/OpenGPT/client/css/settings.css DELETED Viewed

@@ -1,44 +0,0 @@
-.settings-container {
-	color: var(--colour-2);
-	margin: 24px 0px 8px 0px;
-	justify-content: center;
-}
-.settings-container span {
-	font-size: 0.875rem;
-	margin: 0;
-}
-.settings-container label {
-	width: 24px;
-	height: 16px;
-}
-.settings-container .field {
-	justify-content: space-between;
-}
-.settings-container .checkbox input + label,
-.settings-container .checkbox input:checked + label:after {
-	background: var(--colour-1);
-}
-.settings-container .checkbox input + label:after,
-.settings-container .checkbox input:checked + label {
-	background: var(--colour-3);
-}
-.settings-container .checkbox label:after {
-	left: 2px;
-	width: 10px;
-	height: 10px;
-}
-.settings-container .checkbox input:checked + label:after {
-	left: calc(100% - 2px - 10px);
-}
-.settings-container .dropdown {
-	padding: 4px 8px;
-	font-size: 0.75rem;
-}

spaces/AiBototicus/BucksAI-4/app.py DELETED Viewed

@@ -1,3 +0,0 @@
-import gradio as gr
-gr.Interface.load("models/AiBototicus/autotrain-colors-1-49130118878").launch()

spaces/Aki004/herta-so-vits/inference/slicer.py DELETED Viewed

@@ -1,142 +0,0 @@
-import librosa
-import torch
-import torchaudio
-class Slicer:
-    def __init__(self,
-                 sr: int,
-                 threshold: float = -40.,
-                 min_length: int = 5000,
-                 min_interval: int = 300,
-                 hop_size: int = 20,
-                 max_sil_kept: int = 5000):
-        if not min_length >= min_interval >= hop_size:
-            raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size')
-        if not max_sil_kept >= hop_size:
-            raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size')
-        min_interval = sr * min_interval / 1000
-        self.threshold = 10 ** (threshold / 20.)
-        self.hop_size = round(sr * hop_size / 1000)
-        self.win_size = min(round(min_interval), 4 * self.hop_size)
-        self.min_length = round(sr * min_length / 1000 / self.hop_size)
-        self.min_interval = round(min_interval / self.hop_size)
-        self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
-    def _apply_slice(self, waveform, begin, end):
-        if len(waveform.shape) > 1:
-            return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
-        else:
-            return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
-    # @timeit
-    def slice(self, waveform):
-        if len(waveform.shape) > 1:
-            samples = librosa.to_mono(waveform)
-        else:
-            samples = waveform
-        if samples.shape[0] <= self.min_length:
-            return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
-        rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
-        sil_tags = []
-        silence_start = None
-        clip_start = 0
-        for i, rms in enumerate(rms_list):
-            # Keep looping while frame is silent.
-            if rms < self.threshold:
-                # Record start of silent frames.
-                if silence_start is None:
-                    silence_start = i
-                continue
-            # Keep looping while frame is not silent and silence start has not been recorded.
-            if silence_start is None:
-                continue
-            # Clear recorded silence start if interval is not enough or clip is too short
-            is_leading_silence = silence_start == 0 and i > self.max_sil_kept
-            need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
-            if not is_leading_silence and not need_slice_middle:
-                silence_start = None
-                continue
-            # Need slicing. Record the range of silent frames to be removed.
-            if i - silence_start <= self.max_sil_kept:
-                pos = rms_list[silence_start: i + 1].argmin() + silence_start
-                if silence_start == 0:
-                    sil_tags.append((0, pos))
-                else:
-                    sil_tags.append((pos, pos))
-                clip_start = pos
-            elif i - silence_start <= self.max_sil_kept * 2:
-                pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin()
-                pos += i - self.max_sil_kept
-                pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
-                pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
-                if silence_start == 0:
-                    sil_tags.append((0, pos_r))
-                    clip_start = pos_r
-                else:
-                    sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
-                    clip_start = max(pos_r, pos)
-            else:
-                pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
-                pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
-                if silence_start == 0:
-                    sil_tags.append((0, pos_r))
-                else:
-                    sil_tags.append((pos_l, pos_r))
-                clip_start = pos_r
-            silence_start = None
-        # Deal with trailing silence.
-        total_frames = rms_list.shape[0]
-        if silence_start is not None and total_frames - silence_start >= self.min_interval:
-            silence_end = min(total_frames, silence_start + self.max_sil_kept)
-            pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start
-            sil_tags.append((pos, total_frames + 1))
-        # Apply and return slices.
-        if len(sil_tags) == 0:
-            return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
-        else:
-            chunks = []
-            # The first segment is not the beginning of the audio.
-            if sil_tags[0][0]:
-                chunks.append(
-                    {"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"})
-            for i in range(0, len(sil_tags)):
-                # Mark audio segment. Skip the first segment.
-                if i:
-                    chunks.append({"slice": False,
-                                   "split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"})
-                # Mark all mute segments
-                chunks.append({"slice": True,
-                               "split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"})
-            # The last segment is not the end.
-            if sil_tags[-1][1] * self.hop_size < len(waveform):
-                chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"})
-            chunk_dict = {}
-            for i in range(len(chunks)):
-                chunk_dict[str(i)] = chunks[i]
-            return chunk_dict
-def cut(audio_path, db_thresh=-30, min_len=5000):
-    audio, sr = librosa.load(audio_path, sr=None)
-    slicer = Slicer(
-        sr=sr,
-        threshold=db_thresh,
-        min_length=min_len
-    )
-    chunks = slicer.slice(audio)
-    return chunks
-def chunks2audio(audio_path, chunks):
-    chunks = dict(chunks)
-    audio, sr = torchaudio.load(audio_path)
-    if len(audio.shape) == 2 and audio.shape[1] >= 2:
-        audio = torch.mean(audio, dim=0).unsqueeze(0)
-    audio = audio.cpu().numpy()[0]
-    result = []
-    for k, v in chunks.items():
-        tag = v["split_time"].split(",")
-        if tag[0] != tag[1]:
-            result.append((v["slice"], audio[int(tag[0]):int(tag[1])]))
-    return result, sr

spaces/Alesteba/NeRF_ficus-pxl/transformations.py DELETED Viewed

@@ -1,47 +0,0 @@
-import streamlit as st
-import tensorflow as tf
-import numpy as np
-def get_translation_t(t):
-    """Get the translation matrix for movement in t."""
-    matrix = [
-        [1, 0, 0, 0],
-        [0, 1, 0, 0],
-        [0, 0, 1, t],
-        [0, 0, 0, 1],
-    ]
-    return tf.convert_to_tensor(matrix, dtype=tf.float32)
-def get_rotation_phi(phi):
-    """Get the rotation matrix for movement in phi."""
-    matrix = [
-        [1, 0, 0, 0],
-        [0, tf.cos(phi), -tf.sin(phi), 0],
-        [0, tf.sin(phi), tf.cos(phi), 0],
-        [0, 0, 0, 1],
-    ]
-    return tf.convert_to_tensor(matrix, dtype=tf.float32)
-def get_rotation_theta(theta):
-    """Get the rotation matrix for movement in theta."""
-    matrix = [
-        [tf.cos(theta), 0, -tf.sin(theta), 0],
-        [0, 1, 0, 0],
-        [tf.sin(theta), 0, tf.cos(theta), 0],
-        [0, 0, 0, 1],
-    ]
-    return tf.convert_to_tensor(matrix, dtype=tf.float32)
-def pose_spherical(theta, phi, t):
-    """
-    Get the camera to world matrix for the corresponding theta, phi
-    and t.
-    """
-    c2w = get_translation_t(t)
-    c2w = get_rotation_phi(phi / 180.0 * np.pi) @ c2w
-    c2w = get_rotation_theta(theta / 180.0 * np.pi) @ c2w
-    c2w = np.array([[-1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]]) @ c2w
-    return c2w

spaces/AlexWang/lama/models/ade20k/resnet.py DELETED Viewed

@@ -1,181 +0,0 @@
-"""Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch"""
-import math
-import torch.nn as nn
-from torch.nn import BatchNorm2d
-from .utils import load_url
-__all__ = ['ResNet', 'resnet50']
-model_urls = {
-    'resnet50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth',
-}
-def conv3x3(in_planes, out_planes, stride=1):
-    "3x3 convolution with padding"
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=False)
-class BasicBlock(nn.Module):
-    expansion = 1
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-    def forward(self, x):
-        residual = x
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-        out = self.conv2(out)
-        out = self.bn2(out)
-        if self.downsample is not None:
-            residual = self.downsample(x)
-        out += residual
-        out = self.relu(out)
-        return out
-class Bottleneck(nn.Module):
-    expansion = 4
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        self.bn1 = BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
-                               padding=1, bias=False)
-        self.bn2 = BatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
-        self.bn3 = BatchNorm2d(planes * 4)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-    def forward(self, x):
-        residual = x
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-        out = self.conv3(out)
-        out = self.bn3(out)
-        if self.downsample is not None:
-            residual = self.downsample(x)
-        out += residual
-        out = self.relu(out)
-        return out
-class ResNet(nn.Module):
-    def __init__(self, block, layers, num_classes=1000):
-        self.inplanes = 128
-        super(ResNet, self).__init__()
-        self.conv1 = conv3x3(3, 64, stride=2)
-        self.bn1 = BatchNorm2d(64)
-        self.relu1 = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(64, 64)
-        self.bn2 = BatchNorm2d(64)
-        self.relu2 = nn.ReLU(inplace=True)
-        self.conv3 = conv3x3(64, 128)
-        self.bn3 = BatchNorm2d(128)
-        self.relu3 = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-        self.avgpool = nn.AvgPool2d(7, stride=1)
-        self.fc = nn.Linear(512 * block.expansion, num_classes)
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-                m.weight.data.normal_(0, math.sqrt(2. / n))
-            elif isinstance(m, BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-    def _make_layer(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion,
-                          kernel_size=1, stride=stride, bias=False),
-                BatchNorm2d(planes * block.expansion),
-            )
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
-        return nn.Sequential(*layers)
-    def forward(self, x):
-        x = self.relu1(self.bn1(self.conv1(x)))
-        x = self.relu2(self.bn2(self.conv2(x)))
-        x = self.relu3(self.bn3(self.conv3(x)))
-        x = self.maxpool(x)
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.avgpool(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc(x)
-        return x
-def resnet50(pretrained=False, **kwargs):
-    """Constructs a ResNet-50 model.
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-    """
-    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnet50']), strict=False)
-    return model
-def resnet18(pretrained=False, **kwargs):
-    """Constructs a ResNet-18 model.
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-    """
-    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
-    if pretrained:
-        model.load_state_dict(load_url(model_urls['resnet18']))
-    return model

spaces/Amrrs/DragGan-Inversion/PTI/torch_utils/training_stats.py DELETED Viewed

@@ -1,268 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-"""Facilities for reporting and collecting training statistics across
-multiple processes and devices. The interface is designed to minimize
-synchronization overhead as well as the amount of boilerplate in user
-code."""
-import re
-import numpy as np
-import torch
-import dnnlib
-from . import misc
-#----------------------------------------------------------------------------
-_num_moments    = 3             # [num_scalars, sum_of_scalars, sum_of_squares]
-_reduce_dtype   = torch.float32 # Data type to use for initial per-tensor reduction.
-_counter_dtype  = torch.float64 # Data type to use for the internal counters.
-_rank           = 0             # Rank of the current process.
-_sync_device    = None          # Device to use for multiprocess communication. None = single-process.
-_sync_called    = False         # Has _sync() been called yet?
-_counters       = dict()        # Running counters on each device, updated by report(): name => device => torch.Tensor
-_cumulative     = dict()        # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
-#----------------------------------------------------------------------------
-def init_multiprocessing(rank, sync_device):
-    r"""Initializes `torch_utils.training_stats` for collecting statistics
-    across multiple processes.
-    This function must be called after
-    `torch.distributed.init_process_group()` and before `Collector.update()`.
-    The call is not necessary if multi-process collection is not needed.
-    Args:
-        rank:           Rank of the current process.
-        sync_device:    PyTorch device to use for inter-process
-                        communication, or None to disable multi-process
-                        collection. Typically `torch.device('cuda', rank)`.
-    """
-    global _rank, _sync_device
-    assert not _sync_called
-    _rank = rank
-    _sync_device = sync_device
-#----------------------------------------------------------------------------
-@misc.profiled_function
-def report(name, value):
-    r"""Broadcasts the given set of scalars to all interested instances of
-    `Collector`, across device and process boundaries.
-    This function is expected to be extremely cheap and can be safely
-    called from anywhere in the training loop, loss function, or inside a
-    `torch.nn.Module`.
-    Warning: The current implementation expects the set of unique names to
-    be consistent across processes. Please make sure that `report()` is
-    called at least once for each unique name by each process, and in the
-    same order. If a given process has no scalars to broadcast, it can do
-    `report(name, [])` (empty list).
-    Args:
-        name:   Arbitrary string specifying the name of the statistic.
-                Averages are accumulated separately for each unique name.
-        value:  Arbitrary set of scalars. Can be a list, tuple,
-                NumPy array, PyTorch tensor, or Python scalar.
-    Returns:
-        The same `value` that was passed in.
-    """
-    if name not in _counters:
-        _counters[name] = dict()
-    elems = torch.as_tensor(value)
-    if elems.numel() == 0:
-        return value
-    elems = elems.detach().flatten().to(_reduce_dtype)
-    moments = torch.stack([
-        torch.ones_like(elems).sum(),
-        elems.sum(),
-        elems.square().sum(),
-    ])
-    assert moments.ndim == 1 and moments.shape[0] == _num_moments
-    moments = moments.to(_counter_dtype)
-    device = moments.device
-    if device not in _counters[name]:
-        _counters[name][device] = torch.zeros_like(moments)
-    _counters[name][device].add_(moments)
-    return value
-#----------------------------------------------------------------------------
-def report0(name, value):
-    r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
-    but ignores any scalars provided by the other processes.
-    See `report()` for further details.
-    """
-    report(name, value if _rank == 0 else [])
-    return value
-#----------------------------------------------------------------------------
-class Collector:
-    r"""Collects the scalars broadcasted by `report()` and `report0()` and
-    computes their long-term averages (mean and standard deviation) over
-    user-defined periods of time.
-    The averages are first collected into internal counters that are not
-    directly visible to the user. They are then copied to the user-visible
-    state as a result of calling `update()` and can then be queried using
-    `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
-    internal counters for the next round, so that the user-visible state
-    effectively reflects averages collected between the last two calls to
-    `update()`.
-    Args:
-        regex:          Regular expression defining which statistics to
-                        collect. The default is to collect everything.
-        keep_previous:  Whether to retain the previous averages if no
-                        scalars were collected on a given round
-                        (default: True).
-    """
-    def __init__(self, regex='.*', keep_previous=True):
-        self._regex = re.compile(regex)
-        self._keep_previous = keep_previous
-        self._cumulative = dict()
-        self._moments = dict()
-        self.update()
-        self._moments.clear()
-    def names(self):
-        r"""Returns the names of all statistics broadcasted so far that
-        match the regular expression specified at construction time.
-        """
-        return [name for name in _counters if self._regex.fullmatch(name)]
-    def update(self):
-        r"""Copies current values of the internal counters to the
-        user-visible state and resets them for the next round.
-        If `keep_previous=True` was specified at construction time, the
-        operation is skipped for statistics that have received no scalars
-        since the last update, retaining their previous averages.
-        This method performs a number of GPU-to-CPU transfers and one
-        `torch.distributed.all_reduce()`. It is intended to be called
-        periodically in the main training loop, typically once every
-        N training steps.
-        """
-        if not self._keep_previous:
-            self._moments.clear()
-        for name, cumulative in _sync(self.names()):
-            if name not in self._cumulative:
-                self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-            delta = cumulative - self._cumulative[name]
-            self._cumulative[name].copy_(cumulative)
-            if float(delta[0]) != 0:
-                self._moments[name] = delta
-    def _get_delta(self, name):
-        r"""Returns the raw moments that were accumulated for the given
-        statistic between the last two calls to `update()`, or zero if
-        no scalars were collected.
-        """
-        assert self._regex.fullmatch(name)
-        if name not in self._moments:
-            self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-        return self._moments[name]
-    def num(self, name):
-        r"""Returns the number of scalars that were accumulated for the given
-        statistic between the last two calls to `update()`, or zero if
-        no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        return int(delta[0])
-    def mean(self, name):
-        r"""Returns the mean of the scalars that were accumulated for the
-        given statistic between the last two calls to `update()`, or NaN if
-        no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        if int(delta[0]) == 0:
-            return float('nan')
-        return float(delta[1] / delta[0])
-    def std(self, name):
-        r"""Returns the standard deviation of the scalars that were
-        accumulated for the given statistic between the last two calls to
-        `update()`, or NaN if no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
-            return float('nan')
-        if int(delta[0]) == 1:
-            return float(0)
-        mean = float(delta[1] / delta[0])
-        raw_var = float(delta[2] / delta[0])
-        return np.sqrt(max(raw_var - np.square(mean), 0))
-    def as_dict(self):
-        r"""Returns the averages accumulated between the last two calls to
-        `update()` as an `dnnlib.EasyDict`. The contents are as follows:
-            dnnlib.EasyDict(
-                NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
-                ...
-            )
-        """
-        stats = dnnlib.EasyDict()
-        for name in self.names():
-            stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name))
-        return stats
-    def __getitem__(self, name):
-        r"""Convenience getter.
-        `collector[name]` is a synonym for `collector.mean(name)`.
-        """
-        return self.mean(name)
-#----------------------------------------------------------------------------
-def _sync(names):
-    r"""Synchronize the global cumulative counters across devices and
-    processes. Called internally by `Collector.update()`.
-    """
-    if len(names) == 0:
-        return []
-    global _sync_called
-    _sync_called = True
-    # Collect deltas within current rank.
-    deltas = []
-    device = _sync_device if _sync_device is not None else torch.device('cpu')
-    for name in names:
-        delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device)
-        for counter in _counters[name].values():
-            delta.add_(counter.to(device))
-            counter.copy_(torch.zeros_like(counter))
-        deltas.append(delta)
-    deltas = torch.stack(deltas)
-    # Sum deltas across ranks.
-    if _sync_device is not None:
-        torch.distributed.all_reduce(deltas)
-    # Update cumulative values.
-    deltas = deltas.cpu()
-    for idx, name in enumerate(names):
-        if name not in _cumulative:
-            _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-        _cumulative[name].add_(deltas[idx])
-    # Return name-value pairs.
-    return [(name, _cumulative[name]) for name in names]
-#----------------------------------------------------------------------------

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/kandinsky_v22/test_kandinsky_controlnet.py DELETED Viewed

@@ -1,272 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import gc
-import random
-import unittest
-import numpy as np
-import torch
-from diffusers import (
-    DDIMScheduler,
-    KandinskyV22ControlnetPipeline,
-    KandinskyV22PriorPipeline,
-    UNet2DConditionModel,
-    VQModel,
-)
-from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
-from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
-enable_full_determinism()
-class KandinskyV22ControlnetPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
-    pipeline_class = KandinskyV22ControlnetPipeline
-    params = ["image_embeds", "negative_image_embeds", "hint"]
-    batch_params = ["image_embeds", "negative_image_embeds", "hint"]
-    required_optional_params = [
-        "generator",
-        "height",
-        "width",
-        "latents",
-        "guidance_scale",
-        "num_inference_steps",
-        "return_dict",
-        "guidance_scale",
-        "num_images_per_prompt",
-        "output_type",
-        "return_dict",
-    ]
-    test_xformers_attention = False
-    @property
-    def text_embedder_hidden_size(self):
-        return 32
-    @property
-    def time_input_dim(self):
-        return 32
-    @property
-    def block_out_channels_0(self):
-        return self.time_input_dim
-    @property
-    def time_embed_dim(self):
-        return self.time_input_dim * 4
-    @property
-    def cross_attention_dim(self):
-        return 100
-    @property
-    def dummy_unet(self):
-        torch.manual_seed(0)
-        model_kwargs = {
-            "in_channels": 8,
-            # Out channels is double in channels because predicts mean and variance
-            "out_channels": 8,
-            "addition_embed_type": "image_hint",
-            "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
-            "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
-            "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
-            "block_out_channels": (self.block_out_channels_0, self.block_out_channels_0 * 2),
-            "layers_per_block": 1,
-            "encoder_hid_dim": self.text_embedder_hidden_size,
-            "encoder_hid_dim_type": "image_proj",
-            "cross_attention_dim": self.cross_attention_dim,
-            "attention_head_dim": 4,
-            "resnet_time_scale_shift": "scale_shift",
-            "class_embed_type": None,
-        }
-        model = UNet2DConditionModel(**model_kwargs)
-        return model
-    @property
-    def dummy_movq_kwargs(self):
-        return {
-            "block_out_channels": [32, 32, 64, 64],
-            "down_block_types": [
-                "DownEncoderBlock2D",
-                "DownEncoderBlock2D",
-                "DownEncoderBlock2D",
-                "AttnDownEncoderBlock2D",
-            ],
-            "in_channels": 3,
-            "latent_channels": 4,
-            "layers_per_block": 1,
-            "norm_num_groups": 8,
-            "norm_type": "spatial",
-            "num_vq_embeddings": 12,
-            "out_channels": 3,
-            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
-            "vq_embed_dim": 4,
-        }
-    @property
-    def dummy_movq(self):
-        torch.manual_seed(0)
-        model = VQModel(**self.dummy_movq_kwargs)
-        return model
-    def get_dummy_components(self):
-        unet = self.dummy_unet
-        movq = self.dummy_movq
-        scheduler = DDIMScheduler(
-            num_train_timesteps=1000,
-            beta_schedule="linear",
-            beta_start=0.00085,
-            beta_end=0.012,
-            clip_sample=False,
-            set_alpha_to_one=False,
-            steps_offset=1,
-            prediction_type="epsilon",
-            thresholding=False,
-        )
-        components = {
-            "unet": unet,
-            "scheduler": scheduler,
-            "movq": movq,
-        }
-        return components
-    def get_dummy_inputs(self, device, seed=0):
-        image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed)).to(device)
-        negative_image_embeds = floats_tensor((1, self.text_embedder_hidden_size), rng=random.Random(seed + 1)).to(
-            device
-        )
-        # create hint
-        hint = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-        inputs = {
-            "image_embeds": image_embeds,
-            "negative_image_embeds": negative_image_embeds,
-            "hint": hint,
-            "generator": generator,
-            "height": 64,
-            "width": 64,
-            "guidance_scale": 4.0,
-            "num_inference_steps": 2,
-            "output_type": "np",
-        }
-        return inputs
-    def test_kandinsky_controlnet(self):
-        device = "cpu"
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe = pipe.to(device)
-        pipe.set_progress_bar_config(disable=None)
-        output = pipe(**self.get_dummy_inputs(device))
-        image = output.images
-        image_from_tuple = pipe(
-            **self.get_dummy_inputs(device),
-            return_dict=False,
-        )[0]
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-        assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array(
-            [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]
-        )
-        assert (
-            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
-        assert (
-            np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-        ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
-@slow
-@require_torch_gpu
-class KandinskyV22ControlnetPipelineIntegrationTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-    def test_kandinsky_controlnet(self):
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"
-        )
-        hint = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/kandinskyv22/hint_image_cat.png"
-        )
-        hint = torch.from_numpy(np.array(hint)).float() / 255.0
-        hint = hint.permute(2, 0, 1).unsqueeze(0)
-        pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
-            "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
-        )
-        pipe_prior.to(torch_device)
-        pipeline = KandinskyV22ControlnetPipeline.from_pretrained(
-            "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
-        )
-        pipeline = pipeline.to(torch_device)
-        pipeline.set_progress_bar_config(disable=None)
-        prompt = "A robot, 4k photo"
-        generator = torch.Generator(device="cuda").manual_seed(0)
-        image_emb, zero_image_emb = pipe_prior(
-            prompt,
-            generator=generator,
-            num_inference_steps=5,
-            negative_prompt="",
-        ).to_tuple()
-        generator = torch.Generator(device="cuda").manual_seed(0)
-        output = pipeline(
-            image_embeds=image_emb,
-            negative_image_embeds=zero_image_emb,
-            hint=hint,
-            generator=generator,
-            num_inference_steps=100,
-            output_type="np",
-        )
-        image = output.images[0]
-        assert image.shape == (512, 512, 3)
-        assert_mean_pixel_difference(image, expected_image)

spaces/Andy1621/uniformer_image_detection/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py DELETED Viewed

@@ -1,77 +0,0 @@
-_base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py'
-model = dict(
-    rpn_head=dict(
-        _delete_=True,
-        type='CascadeRPNHead',
-        num_stages=2,
-        stages=[
-            dict(
-                type='StageCascadeRPNHead',
-                in_channels=256,
-                feat_channels=256,
-                anchor_generator=dict(
-                    type='AnchorGenerator',
-                    scales=[8],
-                    ratios=[1.0],
-                    strides=[4, 8, 16, 32, 64]),
-                adapt_cfg=dict(type='dilation', dilation=3),
-                bridged_feature=True,
-                sampling=False,
-                with_cls=False,
-                reg_decoded_bbox=True,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=(.0, .0, .0, .0),
-                    target_stds=(0.1, 0.1, 0.5, 0.5)),
-                loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)),
-            dict(
-                type='StageCascadeRPNHead',
-                in_channels=256,
-                feat_channels=256,
-                adapt_cfg=dict(type='offset'),
-                bridged_feature=False,
-                sampling=True,
-                with_cls=True,
-                reg_decoded_bbox=True,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=(.0, .0, .0, .0),
-                    target_stds=(0.05, 0.05, 0.1, 0.1)),
-                loss_cls=dict(
-                    type='CrossEntropyLoss', use_sigmoid=True,
-                    loss_weight=1.0),
-                loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0))
-        ]),
-    train_cfg=dict(rpn=[
-        dict(
-            assigner=dict(
-                type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5),
-            allowed_border=-1,
-            pos_weight=-1,
-            debug=False),
-        dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.7,
-                neg_iou_thr=0.7,
-                min_pos_iou=0.3,
-                ignore_iof_thr=-1,
-                iou_calculator=dict(type='BboxOverlaps2D')),
-            sampler=dict(
-                type='RandomSampler',
-                num=256,
-                pos_fraction=0.5,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=False),
-            allowed_border=-1,
-            pos_weight=-1,
-            debug=False)
-    ]),
-    test_cfg=dict(
-        rpn=dict(
-            nms_pre=2000,
-            max_per_img=2000,
-            nms=dict(type='nms', iou_threshold=0.8),
-            min_bbox_size=0)))
-optimizer_config = dict(
-    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))

spaces/Andy1621/uniformer_image_detection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py DELETED Viewed

@@ -1,4 +0,0 @@
-_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))

spaces/Andy1621/uniformer_image_detection/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py DELETED Viewed

@@ -1,45 +0,0 @@
-_base_ = '../grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py'
-# model settings
-model = dict(
-    roi_head=dict(
-        bbox_roi_extractor=dict(
-            type='GenericRoIExtractor',
-            aggregation='sum',
-            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32],
-            pre_cfg=dict(
-                type='ConvModule',
-                in_channels=256,
-                out_channels=256,
-                kernel_size=5,
-                padding=2,
-                inplace=False,
-            ),
-            post_cfg=dict(
-                type='GeneralizedAttention',
-                in_channels=256,
-                spatial_range=-1,
-                num_heads=6,
-                attention_type='0100',
-                kv_stride=2)),
-        grid_roi_extractor=dict(
-            type='GenericRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32],
-            pre_cfg=dict(
-                type='ConvModule',
-                in_channels=256,
-                out_channels=256,
-                kernel_size=5,
-                padding=2,
-                inplace=False,
-            ),
-            post_cfg=dict(
-                type='GeneralizedAttention',
-                in_channels=256,
-                spatial_range=-1,
-                num_heads=6,
-                attention_type='0100',
-                kv_stride=2))))

spaces/Andy1621/uniformer_image_detection/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py DELETED Viewed

@@ -1,13 +0,0 @@
-_base_ = './rpn_r50_fpn_2x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))

spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/rpn_test_mixin.py DELETED Viewed

@@ -1,59 +0,0 @@
-import sys
-from mmdet.core import merge_aug_proposals
-if sys.version_info >= (3, 7):
-    from mmdet.utils.contextmanagers import completed
-class RPNTestMixin(object):
-    """Test methods of RPN."""
-    if sys.version_info >= (3, 7):
-        async def async_simple_test_rpn(self, x, img_metas):
-            sleep_interval = self.test_cfg.pop('async_sleep_interval', 0.025)
-            async with completed(
-                    __name__, 'rpn_head_forward',
-                    sleep_interval=sleep_interval):
-                rpn_outs = self(x)
-            proposal_list = self.get_bboxes(*rpn_outs, img_metas)
-            return proposal_list
-    def simple_test_rpn(self, x, img_metas):
-        """Test without augmentation.
-        Args:
-            x (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-            img_metas (list[dict]): Meta info of each image.
-        Returns:
-            list[Tensor]: Proposals of each image.
-        """
-        rpn_outs = self(x)
-        proposal_list = self.get_bboxes(*rpn_outs, img_metas)
-        return proposal_list
-    def aug_test_rpn(self, feats, img_metas):
-        samples_per_gpu = len(img_metas[0])
-        aug_proposals = [[] for _ in range(samples_per_gpu)]
-        for x, img_meta in zip(feats, img_metas):
-            proposal_list = self.simple_test_rpn(x, img_meta)
-            for i, proposals in enumerate(proposal_list):
-                aug_proposals[i].append(proposals)
-        # reorganize the order of 'img_metas' to match the dimensions
-        # of 'aug_proposals'
-        aug_img_metas = []
-        for i in range(samples_per_gpu):
-            aug_img_meta = []
-            for j in range(len(img_metas)):
-                aug_img_meta.append(img_metas[j][i])
-            aug_img_metas.append(aug_img_meta)
-        # after merging, proposals will be rescaled to the original image size
-        merged_proposals = [
-            merge_aug_proposals(proposals, aug_img_meta, self.test_cfg)
-            for proposals, aug_img_meta in zip(aug_proposals, aug_img_metas)
-        ]
-        return merged_proposals

spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/README.md DELETED Viewed

@@ -1,66 +0,0 @@
-# Deep High-Resolution Representation Learning for Human Pose Estimation
-## Introduction
-<!-- [ALGORITHM] -->
-```latext
-@inproceedings{SunXLW19,
-  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
-  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
-  booktitle={CVPR},
-  year={2019}
-}
-```
-## Results and models
-### Cityscapes
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                   |
-| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| FCN    | HRNetV2p-W18-Small | 512x1024  |   40000 | 1.7      | 23.74          | 73.86 |         75.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216.log.json)     |
-| FCN    | HRNetV2p-W18       | 512x1024  |   40000 | 2.9      | 12.97          | 77.19 |         78.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216.log.json)         |
-| FCN    | HRNetV2p-W48       | 512x1024  |   40000 | 6.2      | 6.42           | 78.48 |         79.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240.log.json)         |
-| FCN    | HRNetV2p-W18-Small | 512x1024  |   80000 | -        | -              | 75.31 |         77.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700.log.json)     |
-| FCN    | HRNetV2p-W18       | 512x1024  |   80000 | -        | -              | 78.65 |         80.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255.log.json)         |
-| FCN    | HRNetV2p-W48       | 512x1024  |   80000 | -        | -              | 79.93 |         80.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606.log.json)         |
-| FCN    | HRNetV2p-W18-Small | 512x1024  |  160000 | -        | -              | 76.31 |         78.31 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901.log.json) |
-| FCN    | HRNetV2p-W18       | 512x1024  |  160000 | -        | -              | 78.80 |         80.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822.log.json)     |
-| FCN    | HRNetV2p-W48       | 512x1024  |  160000 | -        | -              | 80.65 |         81.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946.log.json)     |
-### ADE20K
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                            | download                                                                                                                                                                                                                                                                                                               |
-| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | HRNetV2p-W18-Small | 512x512   |   80000 | 3.8      | 38.66          | 31.38 |         32.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345.log.json)     |
-| FCN    | HRNetV2p-W18       | 512x512   |   80000 | 4.9      | 22.57          | 35.51 |         36.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20200614_185145-66f20cb7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20200614_185145.log.json)         |
-| FCN    | HRNetV2p-W48       | 512x512   |   80000 | 8.2      | 21.23          | 41.90 |         43.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946.log.json)         |
-| FCN    | HRNetV2p-W18-Small | 512x512   |  160000 | -        | -              | 33.00 |         34.55 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20200614_214413-870f65ac.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20200614_214413.log.json) |
-| FCN    | HRNetV2p-W18       | 512x512   |  160000 | -        | -              | 36.79 |         38.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426.log.json)     |
-| FCN    | HRNetV2p-W48       | 512x512   |  160000 | -        | -              | 42.02 |         43.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407.log.json)     |
-### Pascal VOC 2012 + Aug
-| Method | Backbone           | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                             | download                                                                                                                                                                                                                                                                                                                   |
-| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | HRNetV2p-W18-Small | 512x512   |   20000 | 1.8      | 43.36          | 65.20 |         68.55 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20200617_224503-56e36088.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20200617_224503.log.json) |
-| FCN    | HRNetV2p-W18       | 512x512   |   20000 | 2.9      | 23.48          | 72.30 |         74.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503.log.json)     |
-| FCN    | HRNetV2p-W48       | 512x512   |   20000 | 6.2      | 22.05          | 75.87 |         78.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419.log.json)     |
-| FCN    | HRNetV2p-W18-Small | 512x512   |   40000 | -        | -              | 66.61 |         70.00 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648.log.json) |
-| FCN    | HRNetV2p-W18       | 512x512   |   40000 | -        | -              | 72.90 |         75.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401.log.json)     |
-| FCN    | HRNetV2p-W48       | 512x512   |   40000 | -        | -              | 76.24 |         78.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111.log.json)     |
-### Pascal Context
-| Method | Backbone     | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                                                                       |
-| ------ | ------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | HRNetV2p-W48 | 480x480   |   40000 | 6.1      | 8.86           | 45.14 |         47.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context-20200911_164852.log.json) |
-| FCN    | HRNetV2p-W48 | 480x480   |   80000 | -        | -              | 45.84 |         47.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context-20200911_155322.log.json) |
-### Pascal Context 59
-| Method | Backbone     | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                  | download                                                                                                                                                                                                                                                                                                                                       |
-| ------ | ------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| FCN    | HRNetV2p-W48 | 480x480   |   40000 | -     | -           | 50.33 |         52.83 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_480x480_40k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59_20210410_122738-b808b8b2.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context_59/fcn_hr48_480x480_40k_pascal_context_59-20210410_122738.log.json) |
-| FCN    | HRNetV2p-W48 | 480x480   |   80000 | -        | -              | 51.12 |         53.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59_20210411_003240-3ae7081e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context_59/fcn_hr48_480x480_80k_pascal_context_59-20210411_003240.log.json) |

spaces/AnimeStudio/anime-models/app.py DELETED Viewed

@@ -1,198 +0,0 @@
-import gradio as gr
-import os
-import sys
-from pathlib import Path
-models = [
-    {"name": "Anything 5.0", "url": "stablediffusionapi/anything-v5"},
-    {"name": "Counterfeit 2.0", "url": "gsdf/Counterfeit-V2.0"},
-    {"name": "Counterfeit 3.0", "url": "stablediffusionapi/counterfeit-v30"},
-    {"name": "Dreamlike Anime", "url": "dreamlike-art/dreamlike-anime-1.0"},
-    {"name": "MeinaMix 7", "url": "Nacholmo/meinamixv7-diffusers"},
-    {"name": "Openjourney 4", "url": "prompthero/openjourney-v4"},
-    {"name": "OpenNiji", "url": "Korakoe/OpenNiji"},
-    {"name": "Picasso Diffusion 1.1", "url": "aipicasso/picasso-diffusion-1-1"},
-    {"name": "Rev Anim", "url": "stablediffusionapi/rev-anim"},
-    {"name": "TMND mix", "url": "stablediffusionapi/tmnd-mix"},
-    {"name": "Waifu Diffusion", "url": "hakurei/waifu-diffusion"},
-    {"name": "-------- TOP MODELS -------", "url": "WarriorMama777/AbyssOrangeMix"},
-    {"name": "Abyss Orange Mix 2", "url": "WarriorMama777/AbyssOrangeMix2"},
-    {"name": "Anything 3.0", "url": "Linaqruf/anything-v3.0"},
-    {"name": "Anything 3.1", "url": "cag/anything-v3-1"},
-    {"name": "Anything 3X", "url": "iZELX1/Anything-V3-X"},
-    {"name": "Anything 5.0", "url": "stablediffusionapi/anything-v5"},
-    {"name": "Chillout App Factory","url": "stablediffusionapi/chillout-app-factory"},
-    {"name": "Classic Anime", "url": "nitrosocke/classic-anim-diffusion"},
-    {"name": "Cool Japan Diffusion 2.1.2", "url": "aipicasso/cool-japan-diffusion-2-1-2"},
-    {"name": "Counterfeit 2.0", "url": "gsdf/Counterfeit-V2.0"},
-    {"name": "Counterfeit 3.0", "url": "stablediffusionapi/counterfeit-v30"},
-    {"name": "CyberPunk Anime", "url": "DGSpitzer/Cyberpunk-Anime-Diffusion"},
-    {"name": "Dark Sushi Mix", "url": "stablediffusionapi/dark-sushi-mix"},
-    {"name": "Dreamlike Anime", "url": "dreamlike-art/dreamlike-anime-1.0"},
-    {"name": "Eimis Anime Diffusion", "url": "eimiss/EimisAnimeDiffusion_1.0v"},
-    {"name": "Ghibli Diffusion", "url": "nitrosocke/Ghibli-Diffusion"},
-    {"name": "GrapeFruit", "url": "iZELX1/Grapefruit"},
-    {"name": "GuoFeng 3", "url": "xiaolxl/GuoFeng3"},
-    {"name": "Meina Pastel", "url": "stablediffusionapi/meinapastel"},
-    {"name": "MeinaMix 7", "url": "Nacholmo/meinamixv7-diffusers"},
-    {"name": "Openjourney 4", "url": "prompthero/openjourney-v4"},
-    {"name": "OpenNiji", "url": "Korakoe/OpenNiji"},
-    {"name": "Picasso Diffusion 1.1", "url": "aipicasso/picasso-diffusion-1-1"},
-    {"name": "Protogen 2.2", "url": "darkstorm2150/Protogen_v2.2_Official_Release"},
-    {"name": "Protogen Infinity", "url": "darkstorm2150/Protogen_Infinity_Official_Release"},
-    {"name": "Protogen X 3.4", "url": "darkstorm2150/Protogen_x3.4_Official_Release"},
-    {"name": "Rev Anim", "url": "stablediffusionapi/rev-anim"},
-    {"name": "TMND mix", "url": "stablediffusionapi/tmnd-mix"},
-    {"name": "Waifu Diffusion", "url": "hakurei/waifu-diffusion"},
-    {"name": "-------- ALL ANIME MODELS -------", "url": "WarriorMama777/AbyssOrangeMix"},
-    {"name": "7 Pa", "url": "AIARTCHAN/7pa"},
-    {"name": "A Certain Model", "url": "JosephusCheung/ACertainModel"},
-    {"name": "A Certain Thing", "url": "JosephusCheung/ACertainThing"},
-    {"name": "A Certainity", "url": "JosephusCheung/ACertainty"},
-    {"name": "Abyss Hell Hero", "url": "AIARTCHAN/AbyssHellHero"},
-    {"name": "Abyss Maple 3", "url": "AIARTCHAN/AbyssMapleVer3"},
-    {"name": "Abyss Orange Mix 2", "url": "WarriorMama777/AbyssOrangeMix2"},
-    {"name": "Abyss Orange Mix", "url": "WarriorMama777/AbyssOrangeMix"},
-    {"name": "AbyssHell 3", "url": "AIARTCHAN/AbyssHellVer3"},
-    {"name": "All 526 Animated", "url": "stablediffusionapi/all-526-animated"},
-    {"name": "Anidosmix 3", "url": "AIARTCHAN/anidosmixV2"},
-    {"name": "Anime Kawai Diffusion", "url": "Ojimi/anime-kawai-diffusion"},
-    {"name": "Anireal 3D V2", "url": "circulus/sd-anireal-3d-v2"},
-    {"name": "AnyLORA", "url": "kubanemil/AnyLORA"},
-    {"name": "Anything 2.1", "url": "swl-models/anything-v2.1"},
-    {"name": "Anything 3.0 Light", "url": "mm00/anything-v3.0-light"},
-    {"name": "Anything 3.0", "url": "Linaqruf/anything-v3.0"},
-    {"name": "Anything 3.1", "url": "cag/anything-v3-1"},
-    {"name": "Anything 3X", "url": "iZELX1/Anything-V3-X"},
-    {"name": "Anything 5.0", "url": "stablediffusionapi/anything-v5"},
-    {"name": "Anything Else 4", "url": "stablediffusionapi/anythingelse-v4"},
-    {"name": "Anything Else 5", "url": "stablediffusionapi/anything-v5"},
-    {"name": "Arcane Diffusion", "url": "nitrosocke/Arcane-Diffusion"},
-    {"name": "Archer Diffusion", "url": "nitrosocke/archer-diffusion"},
-    {"name": "Asian Mix", "url": "D1b4l4p/AsianMix"},
-    {"name": "Blood Orange Mix", "url": "WarriorMama777/BloodOrangeMix"},
-    {"name": "Cetusmix", "url": "stablediffusionapi/cetusmix"},
-    {"name": "Chillout App Factory","url": "stablediffusionapi/chillout-app-factory"},
-    {"name": "Classic Anime", "url": "nitrosocke/classic-anim-diffusion"},
-    {"name": "Cool Japan Diffusion 2.1.2", "url": "aipicasso/cool-japan-diffusion-2-1-2"},
-    {"name": "Cosmic Babes", "url": "stablediffusionapi/cosmic-babes"},
-    {"name": "Counterfeit 1.0", "url": "gsdf/counterfeit-v1.0"},
-    {"name": "Counterfeit 2", "url": "gsdf/Counterfeit-V2.0"},
-    {"name": "Counterfeit 2.0", "url": "gsdf/Counterfeit-V2.0"},
-    {"name": "Counterfeit 3.0", "url": "stablediffusionapi/counterfeit-v30"},
-    {"name": "CyberPunk Anime", "url": "DGSpitzer/Cyberpunk-Anime-Diffusion"},
-    {"name": "Dark Sushi Mix", "url": "stablediffusionapi/dark-sushi-mix"},
-    {"name": "Dash Sushi 25d", "url": "stablediffusionapi/dark-sushi-25d"},
-    {"name": "Dreamlike Anime", "url": "dreamlike-art/dreamlike-anime-1.0"},
-    {"name": "DucHaiten Anime", "url": "DucHaiten/DucHaitenAnime"},
-    {"name": "Eerie Orange Mix", "url": "WarriorMama777/EerieOrangeMix"},
-    {"name": "Eimis Anime Diffusion", "url": "eimiss/EimisAnimeDiffusion_1.0v"},
-    {"name": "Ghibli Diffusion", "url": "nitrosocke/Ghibli-Diffusion"},
-    {"name": "GrapeFruit", "url": "iZELX1/Grapefruit"},
-    {"name": "GuoFeng 3", "url": "xiaolxl/GuoFeng3"},
-    {"name": "Icomix 2", "url": "stablediffusionapi/icomix-2"},
-    {"name": "InkPunk Diffusion", "url": "Envvi/Inkpunk-Diffusion"},
-    {"name": "Mama Orange Mixs", "url": "WarriorMama777/OrangeMixs"},
-    {"name": "Meina Alter", "url": "stablediffusionapi/meinaalter"},
-    {"name": "Meina Pastel", "url": "stablediffusionapi/meinapastel"},
-    {"name": "MeinaMix 7", "url": "Nacholmo/meinamixv7-diffusers"},
-    {"name": "Mix Pro V4", "url": "AIARTCHAN/MIX-Pro-V4"},
-    {"name": "NeverEnding-Dream", "url": "Lykon/NeverEnding-Dream"},
-    {"name": "Openjourney 4", "url": "prompthero/openjourney-v4"},
-    {"name": "OpenNiji", "url": "Korakoe/OpenNiji"},
-    {"name": "Picasso Diffusion 1.1", "url": "aipicasso/picasso-diffusion-1-1"},
-    {"name": "Protogen 2.2", "url": "darkstorm2150/Protogen_v2.2_Official_Release"},
-    {"name": "Protogen Infinity", "url": "darkstorm2150/Protogen_Infinity_Official_Release"},
-    {"name": "Protogen X 3.4", "url": "darkstorm2150/Protogen_x3.4_Official_Release"},
-    {"name": "Rev Anim", "url": "stablediffusionapi/rev-anim"},
-    {"name": "Rev Animated", "url": "coreml/coreml-ReV-Animated"},
-    {"name": "Rev Animated", "url": "LottePeisch/RevAnimated-Diffusers"},
-    {"name": "Something V 2.2","url": "NoCrypt/SomethingV2_2"},
-    {"name": "Something V2","url": "NoCrypt/SomethingV2"},
-    {"name": "Three Delicacy", "url": "stablediffusionapi/three-delicacy"},
-    {"name": "Three Delicacy wonto", "url": "stablediffusionapi/three-delicacy-wonto"},
-    {"name": "TMND mix", "url": "stablediffusionapi/tmnd-mix"},
-    {"name": "Waifu Diffusion", "url": "hakurei/waifu-diffusion"}
-]
-current_model = models[0]
-text_gen = gr.Interface.load("spaces/daspartho/prompt-extend")
-models2 = []
-for model in models:
-    model_url = f"models/{model['url']}"
-    loaded_model = gr.Interface.load(model_url, live=True, preprocess=True)
-    models2.append(loaded_model)
-def text_it(inputs, text_gen=text_gen):
-    return text_gen(inputs)
-def set_model(current_model_index):
-    global current_model
-    current_model = models[current_model_index]
-    return gr.update(value=f"{current_model['name']}")
-def send_it(inputs, model_choice):
-    proc = models2[model_choice]
-    return proc(inputs)
-with gr.Blocks() as myface:
-    gr.HTML(
-    )
-    with gr.Row():
-        with gr.Row():
-            input_text = gr.Textbox(label="Prompt idea",  placeholder="", lines=1)
-            # Model selection dropdown
-            model_name1 = gr.Dropdown(
-                label="Choose Model",
-                choices=[m["name"] for m in models],
-                type="index",
-                value=current_model["name"],
-                interactive=True,
-            )
-        with gr.Row():
-            see_prompts = gr.Button("Generate Prompts")
-            run = gr.Button("Generate Images", variant="primary")
-    with gr.Row():
-        output1 = gr.Image(label="")
-        output2 = gr.Image(label="")
-        output3 = gr.Image(label="")
-    with gr.Row():
-        magic1 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic2 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic3 = gr.Textbox(label="Generated Prompt", lines=2)
-    with gr.Row():
-        output4 = gr.Image(label="")
-        output5 = gr.Image(label="")
-        output6 = gr.Image(label="")
-    with gr.Row():
-        magic4 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic5 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic6 = gr.Textbox(label="Generated Prompt", lines=2)
-    model_name1.change(set_model, inputs=model_name1, outputs=[output1, output2, output3, output4, output5, output6])
-    run.click(send_it, inputs=[magic1, model_name1], outputs=[output1])
-    run.click(send_it, inputs=[magic2, model_name1], outputs=[output2])
-    run.click(send_it, inputs=[magic3, model_name1], outputs=[output3])
-    run.click(send_it, inputs=[magic4, model_name1], outputs=[output4])
-    run.click(send_it, inputs=[magic5, model_name1], outputs=[output5])
-    run.click(send_it, inputs=[magic6, model_name1], outputs=[output6])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic1])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic2])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic3])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic4])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic5])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic6])
-myface.queue(concurrency_count=200)
-myface.launch(inline=True, show_api=False, max_threads=400)

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/Chat-mode.md DELETED Viewed

@@ -1,39 +0,0 @@
-## Chat characters
-Custom chat mode characters are defined by `.yaml` files inside the `characters` folder. An example is included: [Example.yaml](https://github.com/oobabooga/text-generation-webui/blob/main/characters/Example.yaml).
-The following fields may be defined:
-| Field | Description |
-|-------|-------------|
-| `name` or `bot` | The character's name. |
-| `context` | A string that appears at the top of the prompt. It usually contains a description of the character's personality and a few example messages. |
-| `greeting` (optional) | The character's opening message. It appears when the character is first loaded or when the history is cleared. |
-| `your_name` or `user` (optional) | Your name. This overwrites what you had previously written in the `Your name` field in the interface. |
-#### Special tokens
-The following replacements happen when the prompt is generated, and they apply to the `context` and `greeting` fields:
-* `{{char}}` and `<BOT>` get replaced with the character's name.
-* `{{user}}` and `<USER>` get replaced with your name.
-#### How do I add a profile picture for my character?
-Put an image with the same name as your character's `.yaml` file into the `characters` folder. For example, if your bot is `Character.yaml`, add `Character.jpg` or `Character.png` to the folder.
-#### Is the chat history truncated in the prompt?
-Once your prompt reaches the `truncation_length` parameter (2048 by default), old messages will be removed one at a time. The context string will always stay at the top of the prompt and will never get truncated.
-## Chat styles
-Custom chat styles can be defined in the `text-generation-webui/css` folder. Simply create a new file with name starting in `chat_style-` and ending in `.css` and it will automatically appear in the "Chat style" dropdown menu in the interface. Examples:
-```
-chat_style-cai-chat.css
-chat_style-TheEncrypted777.css
-chat_style-wpp.css
-```
-You should use the same class names as in `chat_style-cai-chat.css` in your custom style.

spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/utils/__init__.py DELETED Viewed

File without changes

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/packaging/tags.py DELETED Viewed

@@ -1,487 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-import logging
-import platform
-import sys
-import sysconfig
-from importlib.machinery import EXTENSION_SUFFIXES
-from typing import (
-    Dict,
-    FrozenSet,
-    Iterable,
-    Iterator,
-    List,
-    Optional,
-    Sequence,
-    Tuple,
-    Union,
-    cast,
-)
-from . import _manylinux, _musllinux
-logger = logging.getLogger(__name__)
-PythonVersion = Sequence[int]
-MacVersion = Tuple[int, int]
-INTERPRETER_SHORT_NAMES: Dict[str, str] = {
-    "python": "py",  # Generic.
-    "cpython": "cp",
-    "pypy": "pp",
-    "ironpython": "ip",
-    "jython": "jy",
-}
-_32_BIT_INTERPRETER = sys.maxsize <= 2 ** 32
-class Tag:
-    """
-    A representation of the tag triple for a wheel.
-    Instances are considered immutable and thus are hashable. Equality checking
-    is also supported.
-    """
-    __slots__ = ["_interpreter", "_abi", "_platform", "_hash"]
-    def __init__(self, interpreter: str, abi: str, platform: str) -> None:
-        self._interpreter = interpreter.lower()
-        self._abi = abi.lower()
-        self._platform = platform.lower()
-        # The __hash__ of every single element in a Set[Tag] will be evaluated each time
-        # that a set calls its `.disjoint()` method, which may be called hundreds of
-        # times when scanning a page of links for packages with tags matching that
-        # Set[Tag]. Pre-computing the value here produces significant speedups for
-        # downstream consumers.
-        self._hash = hash((self._interpreter, self._abi, self._platform))
-    @property
-    def interpreter(self) -> str:
-        return self._interpreter
-    @property
-    def abi(self) -> str:
-        return self._abi
-    @property
-    def platform(self) -> str:
-        return self._platform
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, Tag):
-            return NotImplemented
-        return (
-            (self._hash == other._hash)  # Short-circuit ASAP for perf reasons.
-            and (self._platform == other._platform)
-            and (self._abi == other._abi)
-            and (self._interpreter == other._interpreter)
-        )
-    def __hash__(self) -> int:
-        return self._hash
-    def __str__(self) -> str:
-        return f"{self._interpreter}-{self._abi}-{self._platform}"
-    def __repr__(self) -> str:
-        return f"<{self} @ {id(self)}>"
-def parse_tag(tag: str) -> FrozenSet[Tag]:
-    """
-    Parses the provided tag (e.g. `py3-none-any`) into a frozenset of Tag instances.
-    Returning a set is required due to the possibility that the tag is a
-    compressed tag set.
-    """
-    tags = set()
-    interpreters, abis, platforms = tag.split("-")
-    for interpreter in interpreters.split("."):
-        for abi in abis.split("."):
-            for platform_ in platforms.split("."):
-                tags.add(Tag(interpreter, abi, platform_))
-    return frozenset(tags)
-def _get_config_var(name: str, warn: bool = False) -> Union[int, str, None]:
-    value = sysconfig.get_config_var(name)
-    if value is None and warn:
-        logger.debug(
-            "Config variable '%s' is unset, Python ABI tag may be incorrect", name
-        )
-    return value
-def _normalize_string(string: str) -> str:
-    return string.replace(".", "_").replace("-", "_")
-def _abi3_applies(python_version: PythonVersion) -> bool:
-    """
-    Determine if the Python version supports abi3.
-    PEP 384 was first implemented in Python 3.2.
-    """
-    return len(python_version) > 1 and tuple(python_version) >= (3, 2)
-def _cpython_abis(py_version: PythonVersion, warn: bool = False) -> List[str]:
-    py_version = tuple(py_version)  # To allow for version comparison.
-    abis = []
-    version = _version_nodot(py_version[:2])
-    debug = pymalloc = ucs4 = ""
-    with_debug = _get_config_var("Py_DEBUG", warn)
-    has_refcount = hasattr(sys, "gettotalrefcount")
-    # Windows doesn't set Py_DEBUG, so checking for support of debug-compiled
-    # extension modules is the best option.
-    # https://github.com/pypa/pip/issues/3383#issuecomment-173267692
-    has_ext = "_d.pyd" in EXTENSION_SUFFIXES
-    if with_debug or (with_debug is None and (has_refcount or has_ext)):
-        debug = "d"
-    if py_version < (3, 8):
-        with_pymalloc = _get_config_var("WITH_PYMALLOC", warn)
-        if with_pymalloc or with_pymalloc is None:
-            pymalloc = "m"
-        if py_version < (3, 3):
-            unicode_size = _get_config_var("Py_UNICODE_SIZE", warn)
-            if unicode_size == 4 or (
-                unicode_size is None and sys.maxunicode == 0x10FFFF
-            ):
-                ucs4 = "u"
-    elif debug:
-        # Debug builds can also load "normal" extension modules.
-        # We can also assume no UCS-4 or pymalloc requirement.
-        abis.append(f"cp{version}")
-    abis.insert(
-        0,
-        "cp{version}{debug}{pymalloc}{ucs4}".format(
-            version=version, debug=debug, pymalloc=pymalloc, ucs4=ucs4
-        ),
-    )
-    return abis
-def cpython_tags(
-    python_version: Optional[PythonVersion] = None,
-    abis: Optional[Iterable[str]] = None,
-    platforms: Optional[Iterable[str]] = None,
-    *,
-    warn: bool = False,
-) -> Iterator[Tag]:
-    """
-    Yields the tags for a CPython interpreter.
-    The tags consist of:
-    - cp<python_version>-<abi>-<platform>
-    - cp<python_version>-abi3-<platform>
-    - cp<python_version>-none-<platform>
-    - cp<less than python_version>-abi3-<platform>  # Older Python versions down to 3.2.
-    If python_version only specifies a major version then user-provided ABIs and
-    the 'none' ABItag will be used.
-    If 'abi3' or 'none' are specified in 'abis' then they will be yielded at
-    their normal position and not at the beginning.
-    """
-    if not python_version:
-        python_version = sys.version_info[:2]
-    interpreter = f"cp{_version_nodot(python_version[:2])}"
-    if abis is None:
-        if len(python_version) > 1:
-            abis = _cpython_abis(python_version, warn)
-        else:
-            abis = []
-    abis = list(abis)
-    # 'abi3' and 'none' are explicitly handled later.
-    for explicit_abi in ("abi3", "none"):
-        try:
-            abis.remove(explicit_abi)
-        except ValueError:
-            pass
-    platforms = list(platforms or platform_tags())
-    for abi in abis:
-        for platform_ in platforms:
-            yield Tag(interpreter, abi, platform_)
-    if _abi3_applies(python_version):
-        yield from (Tag(interpreter, "abi3", platform_) for platform_ in platforms)
-    yield from (Tag(interpreter, "none", platform_) for platform_ in platforms)
-    if _abi3_applies(python_version):
-        for minor_version in range(python_version[1] - 1, 1, -1):
-            for platform_ in platforms:
-                interpreter = "cp{version}".format(
-                    version=_version_nodot((python_version[0], minor_version))
-                )
-                yield Tag(interpreter, "abi3", platform_)
-def _generic_abi() -> Iterator[str]:
-    abi = sysconfig.get_config_var("SOABI")
-    if abi:
-        yield _normalize_string(abi)
-def generic_tags(
-    interpreter: Optional[str] = None,
-    abis: Optional[Iterable[str]] = None,
-    platforms: Optional[Iterable[str]] = None,
-    *,
-    warn: bool = False,
-) -> Iterator[Tag]:
-    """
-    Yields the tags for a generic interpreter.
-    The tags consist of:
-    - <interpreter>-<abi>-<platform>
-    The "none" ABI will be added if it was not explicitly provided.
-    """
-    if not interpreter:
-        interp_name = interpreter_name()
-        interp_version = interpreter_version(warn=warn)
-        interpreter = "".join([interp_name, interp_version])
-    if abis is None:
-        abis = _generic_abi()
-    platforms = list(platforms or platform_tags())
-    abis = list(abis)
-    if "none" not in abis:
-        abis.append("none")
-    for abi in abis:
-        for platform_ in platforms:
-            yield Tag(interpreter, abi, platform_)
-def _py_interpreter_range(py_version: PythonVersion) -> Iterator[str]:
-    """
-    Yields Python versions in descending order.
-    After the latest version, the major-only version will be yielded, and then
-    all previous versions of that major version.
-    """
-    if len(py_version) > 1:
-        yield f"py{_version_nodot(py_version[:2])}"
-    yield f"py{py_version[0]}"
-    if len(py_version) > 1:
-        for minor in range(py_version[1] - 1, -1, -1):
-            yield f"py{_version_nodot((py_version[0], minor))}"
-def compatible_tags(
-    python_version: Optional[PythonVersion] = None,
-    interpreter: Optional[str] = None,
-    platforms: Optional[Iterable[str]] = None,
-) -> Iterator[Tag]:
-    """
-    Yields the sequence of tags that are compatible with a specific version of Python.
-    The tags consist of:
-    - py*-none-<platform>
-    - <interpreter>-none-any  # ... if `interpreter` is provided.
-    - py*-none-any
-    """
-    if not python_version:
-        python_version = sys.version_info[:2]
-    platforms = list(platforms or platform_tags())
-    for version in _py_interpreter_range(python_version):
-        for platform_ in platforms:
-            yield Tag(version, "none", platform_)
-    if interpreter:
-        yield Tag(interpreter, "none", "any")
-    for version in _py_interpreter_range(python_version):
-        yield Tag(version, "none", "any")
-def _mac_arch(arch: str, is_32bit: bool = _32_BIT_INTERPRETER) -> str:
-    if not is_32bit:
-        return arch
-    if arch.startswith("ppc"):
-        return "ppc"
-    return "i386"
-def _mac_binary_formats(version: MacVersion, cpu_arch: str) -> List[str]:
-    formats = [cpu_arch]
-    if cpu_arch == "x86_64":
-        if version < (10, 4):
-            return []
-        formats.extend(["intel", "fat64", "fat32"])
-    elif cpu_arch == "i386":
-        if version < (10, 4):
-            return []
-        formats.extend(["intel", "fat32", "fat"])
-    elif cpu_arch == "ppc64":
-        # TODO: Need to care about 32-bit PPC for ppc64 through 10.2?
-        if version > (10, 5) or version < (10, 4):
-            return []
-        formats.append("fat64")
-    elif cpu_arch == "ppc":
-        if version > (10, 6):
-            return []
-        formats.extend(["fat32", "fat"])
-    if cpu_arch in {"arm64", "x86_64"}:
-        formats.append("universal2")
-    if cpu_arch in {"x86_64", "i386", "ppc64", "ppc", "intel"}:
-        formats.append("universal")
-    return formats
-def mac_platforms(
-    version: Optional[MacVersion] = None, arch: Optional[str] = None
-) -> Iterator[str]:
-    """
-    Yields the platform tags for a macOS system.
-    The `version` parameter is a two-item tuple specifying the macOS version to
-    generate platform tags for. The `arch` parameter is the CPU architecture to
-    generate platform tags for. Both parameters default to the appropriate value
-    for the current system.
-    """
-    version_str, _, cpu_arch = platform.mac_ver()
-    if version is None:
-        version = cast("MacVersion", tuple(map(int, version_str.split(".")[:2])))
-    else:
-        version = version
-    if arch is None:
-        arch = _mac_arch(cpu_arch)
-    else:
-        arch = arch
-    if (10, 0) <= version and version < (11, 0):
-        # Prior to Mac OS 11, each yearly release of Mac OS bumped the
-        # "minor" version number.  The major version was always 10.
-        for minor_version in range(version[1], -1, -1):
-            compat_version = 10, minor_version
-            binary_formats = _mac_binary_formats(compat_version, arch)
-            for binary_format in binary_formats:
-                yield "macosx_{major}_{minor}_{binary_format}".format(
-                    major=10, minor=minor_version, binary_format=binary_format
-                )
-    if version >= (11, 0):
-        # Starting with Mac OS 11, each yearly release bumps the major version
-        # number.   The minor versions are now the midyear updates.
-        for major_version in range(version[0], 10, -1):
-            compat_version = major_version, 0
-            binary_formats = _mac_binary_formats(compat_version, arch)
-            for binary_format in binary_formats:
-                yield "macosx_{major}_{minor}_{binary_format}".format(
-                    major=major_version, minor=0, binary_format=binary_format
-                )
-    if version >= (11, 0):
-        # Mac OS 11 on x86_64 is compatible with binaries from previous releases.
-        # Arm64 support was introduced in 11.0, so no Arm binaries from previous
-        # releases exist.
-        #
-        # However, the "universal2" binary format can have a
-        # macOS version earlier than 11.0 when the x86_64 part of the binary supports
-        # that version of macOS.
-        if arch == "x86_64":
-            for minor_version in range(16, 3, -1):
-                compat_version = 10, minor_version
-                binary_formats = _mac_binary_formats(compat_version, arch)
-                for binary_format in binary_formats:
-                    yield "macosx_{major}_{minor}_{binary_format}".format(
-                        major=compat_version[0],
-                        minor=compat_version[1],
-                        binary_format=binary_format,
-                    )
-        else:
-            for minor_version in range(16, 3, -1):
-                compat_version = 10, minor_version
-                binary_format = "universal2"
-                yield "macosx_{major}_{minor}_{binary_format}".format(
-                    major=compat_version[0],
-                    minor=compat_version[1],
-                    binary_format=binary_format,
-                )
-def _linux_platforms(is_32bit: bool = _32_BIT_INTERPRETER) -> Iterator[str]:
-    linux = _normalize_string(sysconfig.get_platform())
-    if is_32bit:
-        if linux == "linux_x86_64":
-            linux = "linux_i686"
-        elif linux == "linux_aarch64":
-            linux = "linux_armv7l"
-    _, arch = linux.split("_", 1)
-    yield from _manylinux.platform_tags(linux, arch)
-    yield from _musllinux.platform_tags(arch)
-    yield linux
-def _generic_platforms() -> Iterator[str]:
-    yield _normalize_string(sysconfig.get_platform())
-def platform_tags() -> Iterator[str]:
-    """
-    Provides the platform tags for this installation.
-    """
-    if platform.system() == "Darwin":
-        return mac_platforms()
-    elif platform.system() == "Linux":
-        return _linux_platforms()
-    else:
-        return _generic_platforms()
-def interpreter_name() -> str:
-    """
-    Returns the name of the running interpreter.
-    """
-    name = sys.implementation.name
-    return INTERPRETER_SHORT_NAMES.get(name) or name
-def interpreter_version(*, warn: bool = False) -> str:
-    """
-    Returns the version of the running interpreter.
-    """
-    version = _get_config_var("py_version_nodot", warn=warn)
-    if version:
-        version = str(version)
-    else:
-        version = _version_nodot(sys.version_info[:2])
-    return version
-def _version_nodot(version: PythonVersion) -> str:
-    return "".join(map(str, version))
-def sys_tags(*, warn: bool = False) -> Iterator[Tag]:
-    """
-    Returns the sequence of tag triples for the running interpreter.
-    The order of the sequence corresponds to priority order for the
-    interpreter, from most to least important.
-    """
-    interp_name = interpreter_name()
-    if interp_name == "cp":
-        yield from cpython_tags(warn=warn)
-    else:
-        yield from generic_tags()
-    if interp_name == "pp":
-        yield from compatible_tags(interpreter="pp3")
-    else:
-        yield from compatible_tags()

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/importlib_metadata/_adapters.py DELETED Viewed

@@ -1,68 +0,0 @@
-import re
-import textwrap
-import email.message
-from ._text import FoldedCase
-class Message(email.message.Message):
-    multiple_use_keys = set(
-        map(
-            FoldedCase,
-            [
-                'Classifier',
-                'Obsoletes-Dist',
-                'Platform',
-                'Project-URL',
-                'Provides-Dist',
-                'Provides-Extra',
-                'Requires-Dist',
-                'Requires-External',
-                'Supported-Platform',
-                'Dynamic',
-            ],
-        )
-    )
-    """
-    Keys that may be indicated multiple times per PEP 566.
-    """
-    def __new__(cls, orig: email.message.Message):
-        res = super().__new__(cls)
-        vars(res).update(vars(orig))
-        return res
-    def __init__(self, *args, **kwargs):
-        self._headers = self._repair_headers()
-    # suppress spurious error from mypy
-    def __iter__(self):
-        return super().__iter__()
-    def _repair_headers(self):
-        def redent(value):
-            "Correct for RFC822 indentation"
-            if not value or '\n' not in value:
-                return value
-            return textwrap.dedent(' ' * 8 + value)
-        headers = [(key, redent(value)) for key, value in vars(self)['_headers']]
-        if self._payload:
-            headers.append(('Description', self.get_payload()))
-        return headers
-    @property
-    def json(self):
-        """
-        Convert PackageMetadata to a JSON-compatible format
-        per PEP 0566.
-        """
-        def transform(key):
-            value = self.get_all(key) if key in self.multiple_use_keys else self[key]
-            if key == 'Keywords':
-                value = re.split(r'\s+', value)
-            tk = key.lower().replace('-', '_')
-            return tk, value
-        return dict(map(transform, map(FoldedCase, self)))

spaces/AzumaSeren100/XuanShen-Bert-VITS2/preprocess_text.py DELETED Viewed

@@ -1,65 +0,0 @@
-import json
-from random import shuffle
-import tqdm
-from text.cleaner import clean_text
-from collections import defaultdict
-stage = [1,2,3]
-transcription_path = 'filelists/xg.list'
-train_path = 'filelists/train.list'
-val_path = 'filelists/val.list'
-config_path = "configs/config.json"
-val_per_spk = 4
-max_val_total = 8
-if 1 in stage:
-    with open( transcription_path+'.cleaned', 'w', encoding='utf-8') as f:
-        for line in tqdm.tqdm(open(transcription_path, encoding='utf-8').readlines()):
-            try:
-                utt, spk, language, text = line.strip().split('|')
-                #language = "ZH"
-                norm_text, phones, tones, word2ph = clean_text(text, language)
-                f.write('{}|{}|{}|{}|{}|{}|{}\n'.format(utt, spk, language, norm_text, ' '.join(phones),
-                                                     " ".join([str(i) for i in tones]),
-                                                     " ".join([str(i) for i in word2ph])))
-            except Exception as error :
-                print("err!", utt, error)
-if 2 in stage:
-    spk_utt_map = defaultdict(list)
-    spk_id_map = {}
-    current_sid = 0
-    with open( transcription_path+'.cleaned', encoding='utf-8') as f:
-        for line in f.readlines():
-            utt, spk, language, text, phones, tones, word2ph = line.strip().split('|')
-            spk_utt_map[spk].append(line)
-            if spk not in spk_id_map.keys():
-                spk_id_map[spk] = current_sid
-                current_sid += 1
-    train_list = []
-    val_list = []
-    for spk, utts in spk_utt_map.items():
-        shuffle(utts)
-        val_list+=utts[:val_per_spk]
-        train_list+=utts[val_per_spk:]
-    if len(val_list) > max_val_total:
-        train_list+=val_list[max_val_total:]
-        val_list = val_list[:max_val_total]
-    with open( train_path,"w", encoding='utf-8') as f:
-        for line in train_list:
-            f.write(line)
-    with open(val_path, "w", encoding='utf-8') as f:
-        for line in val_list:
-            f.write(line)
-if 3 in stage:
-    assert 2 in stage
-    config = json.load(open(config_path, encoding='utf-8'))
-    config["data"]['spk2id'] = spk_id_map
-    with open(config_path, 'w', encoding='utf-8') as f:
-        json.dump(config, f, indent=2, ensure_ascii=False)

spaces/Benson/text-generation/Examples/Bubble Shooter Genies Apk.md DELETED Viewed

@@ -1,72 +0,0 @@
-<h1>Burbuja Shooter Genios APK: Un divertido y colorido juego casual</h1>
-<p>Si usted está buscando un juego relajante y entretenido para jugar en su dispositivo Android, es posible que desee probar Bubble Shooter Genies APK. Este es un clásico juego de burbujas y rompecabezas de aventura que te mantendrá enganchado durante horas. En este artículo, le diremos qué es Bubble Shooter Genies APK, cómo descargarlo e instalarlo, y por qué debe jugar. </p>
- <h2>¿Qué es Bubble Shooter Genies APK? </h2>
-<p>Bubble Shooter Genies APK es un juego casual desarrollado por PUZZLEJOY. El APK ha estado disponible desde noviembre de 2016. Bubble Shooter Genies ha sido descargado más de 50 millones de veces. Es altamente clasificado. Está clasificado 4.68 de 5 estrellas, basado en 237,825 calificaciones. La última actualización de la aplicación fue el 7 de junio de 2023. </p>
-<h2>bubble shooter genies apk</h2><br /><p><b><b>Download</b> &raquo;&raquo;&raquo; <a href="https://bltlly.com/2v6MPi">https://bltlly.com/2v6MPi</a></b></p><br /><br />
- <h3>El juego de Bubble Shooter Genios APK</h3>
-<p>El juego de Bubble Shooter Genies APK es simple y divertido. Tienes que coincidir 3 o más burbujas iguales y pop ellos. Puedes hacer estallar burbujas para rescatar a los lindos dragones bebés. También puedes usar potentes boosters y combos para limpiar los niveles más rápido. Hay más de 1000 niveles para jugar, cada uno con diferentes retos y metas. También puedes competir con tus amigos y otros jugadores en la clasificación. </p>
- <h3>Las características de Bubble Shooter Genios APK</h3>
-<p>Bubble Shooter Genies APK tiene muchas características que lo hacen un gran juego para jugar. Algunos de ellos son:</p>
-<ul>
-<li>Tiene impresionantes gráficos y animaciones que crean un mundo colorido y mágico. </li>
-<li> Tiene música relajante y relajante y efectos de sonido que mejoran la experiencia de juego. </li>
-<li> Tiene controles fáciles y suaves que lo hacen adecuado para jugadores de todas las edades. </li>
-<li> No se requiere conexión wifi o internet, por lo que puede jugar en cualquier momento y en cualquier lugar. </li>
-<li> Tiene actualizaciones regulares que agregan nuevos niveles, características y mejoras. </li>
-</ul>
- <h2> ¿Cómo descargar e instalar Bubble Shooter Genies APK? </h2>
- <h3>Los requisitos para Bubble Shooter Genios APK</h3>
-<p>Antes de descargar e instalar Bubble Shooter Genies APK, es necesario asegurarse de que su dispositivo cumple con estos requisitos:</p>
-<ul>
-<li>Tu dispositivo debe tener la versión de Android 4.4 o superior. </li>
-<li>Su dispositivo debe tener al menos 90 MB de espacio de almacenamiento libre. </li>
-<li>Su dispositivo debe permitir la instalación desde fuentes desconocidas. Puede habilitar esta opción yendo a Configuración > Seguridad > Fuentes desconocidas.</li>
-</ul>
- <h3> Los pasos para descargar e instalar Bubble Shooter Genies APK</h3>
-<p>Después de haber comprobado los requisitos, puede seguir estos pasos para descargar e instalar Bubble Shooter Genies APK:</p>
-<ol>
-<li>Ir a [este enlace]( 1 ) para descargar la última versión de Bubble Shooter Genies APK.</li>
-<li>Una vez que la descarga se haya completado, busque el archivo en el administrador de archivos de su dispositivo y toque en él. </li>
-<li>Siga las instrucciones en la pantalla para instalar la aplicación. </li>
-<li>Iniciar la aplicación y disfrutar de jugar Bubble Shooter Genies.</li>
-</ol>
- <h2>¿Por qué debe jugar Bubble Shooter Genies APK? </h2>
-<p>Bubble Shooter Genies APK es un juego que le proporcionará horas de diversión y entretenimiento. Aquí hay algunas razones por las que debe jugar:</p>
- <h3>Los beneficios de jugar Bubble Shooter Genios APK</h3>
-<p>Jugar Bubble Shooter Genies APK puede tener muchos beneficios para usted, tales como:</p>
-<ul>
-<li> Puede mejorar su concentración y enfoque, ya que tiene que apuntar y disparar las burbujas con precisión. </li>
-<li> Puede mejorar sus habilidades de resolución de problemas y pensamiento lógico, ya que tiene que planificar y elaborar estrategias de la mejor manera de limpiar los niveles. </li>
-<li>Puede reducir el estrés y la ansiedad, ya que puede disfrutar del juego relajante y relajante. </li>
-<li> Puede aumentar su estado de ánimo y felicidad, ya que puede experimentar la alegría de hacer estallar burbujas y rescatar dragones bebé. </li>
-</ul>
- <h3> Los consejos y trucos para jugar Bubble Shooter Genies APK</h3>
-<ul>
-<li>Usa la línea de puntería para guiar tus disparos y apunta a los grupos de burbujas del mismo color. </li>
-<li>Trate de hacer estallar más burbujas con menos disparos para ganar más puntos y estrellas. </li>
-<li>Usa los boosters y combos sabiamente para eliminar los obstáculos y los niveles difíciles. </li>
-<li>Preste atención a los colores de burbuja en el lanzador y cámbielos si es necesario. </li>
-<li>Explora los diferentes modos de juego y desafíos para poner a prueba tus habilidades y divertirte más. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>Bubble Shooter Genies APK es un divertido y colorido juego casual que se puede jugar en su dispositivo Android. Tiene un juego simple y adictivo, impresionantes gráficos y animaciones, música relajante y efectos de sonido, controles fáciles y suaves, no se requiere conexión wifi o internet, actualizaciones regulares y más de 1000 niveles para jugar. También puede disfrutar de los beneficios de jugar Bubble Shooter Genies APK, tales como mejorar su concentración, enfoque, resolución de problemas, pensamiento lógico, alivio del estrés, estado de ánimo, y la felicidad. También puede utilizar los consejos y trucos para jugar Bubble Shooter Genies APK, como el uso de la línea de puntería, hacer estallar más burbujas con menos disparos, utilizando los boosters y combos sabiamente, prestando atención a los colores de burbuja en el lanzador, intercambiándolos si es necesario, y explorar los diferentes modos de juego y desafíos. Si desea descargar e instalar Bubble Shooter Genies APK en su dispositivo Android, puede seguir los pasos mencionados anteriormente. Esperamos que haya encontrado este artículo útil e informativo. ¡Feliz disparo de burbuja! </p>
-<p></p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Bubble Shooter Genies APK:</p>
-<ol>
-<li> ¿Es Bubble Shooter Genies APK libre para jugar? </li>
-<p>Sí, Bubble Shooter Genies APK es gratis para jugar. Sin embargo, contiene anuncios y compras en la aplicación que puede desactivar o comprar si lo desea. </p>
-<li> ¿Cómo puedo actualizar Bubble Shooter Genies APK? </li>
-<li> ¿Cómo puedo contactar con el desarrollador de Bubble Shooter Genies APK? </li>
-<p>Puede ponerse en contacto con el desarrollador de Bubble Shooter Genies APK enviando un correo electrónico a [email protected] o visitando su página de Facebook en https://www.facebook.com/BubbleShooterGenies/.</p>
-<li> ¿Cuáles son algunos juegos similares a Bubble Shooter Genies APK? </li>
-<p>Algunos juegos similares a Bubble Shooter Genies APK son Bubble Witch Saga 3, Panda Pop, Angry Birds POP, y Bubble Island 2.</p>
-<li> ¿Puedo jugar Bubble Shooter Genies APK offline? </li>
-<p>Sí, puede jugar Bubble Shooter Genies APK sin conexión. Sin embargo, algunas características pueden no estar disponibles o actualizados cuando está fuera de línea. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BernardoOlisan/vqganclip/app.py DELETED Viewed

@@ -1,392 +0,0 @@
-import os
-os.system('pip freeze')
-import torch
-torch.hub.download_url_to_file('https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/files/?p=%2Fconfigs%2Fmodel.yaml&dl=1', 'vqgan_imagenet_f16_16384.yaml')
-torch.hub.download_url_to_file('https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/files/?p=%2Fckpts%2Flast.ckpt&dl=1', 'vqgan_imagenet_f16_16384.ckpt')
-import argparse
-import math
-from pathlib import Path
-import sys
-sys.path.insert(1, './taming-transformers')
-from base64 import b64encode
-from omegaconf import OmegaConf
-from PIL import Image
-from taming.models import cond_transformer, vqgan
-import taming.modules
-from torch import nn, optim
-from torch.nn import functional as F
-from torchvision import transforms
-from torchvision.transforms import functional as TF
-from tqdm.notebook import tqdm
-from CLIP import clip
-import kornia.augmentation as K
-import numpy as np
-import imageio
-from PIL import ImageFile, Image
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-import gradio as gr
-torch.hub.download_url_to_file('https://images.pexels.com/photos/158028/bellingrath-gardens-alabama-landscape-scenic-158028.jpeg', 'garden.jpeg')
-torch.hub.download_url_to_file('https://images.pexels.com/photos/68767/divers-underwater-ocean-swim-68767.jpeg', 'coralreef.jpeg')
-torch.hub.download_url_to_file('https://images.pexels.com/photos/803975/pexels-photo-803975.jpeg', 'cabin.jpeg')
-def sinc(x):
-    return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))
-def lanczos(x, a):
-    cond = torch.logical_and(-a < x, x < a)
-    out = torch.where(cond, sinc(x) * sinc(x/a), x.new_zeros([]))
-    return out / out.sum()
-def ramp(ratio, width):
-    n = math.ceil(width / ratio + 1)
-    out = torch.empty([n])
-    cur = 0
-    for i in range(out.shape[0]):
-        out[i] = cur
-        cur += ratio
-    return torch.cat([-out[1:].flip([0]), out])[1:-1]
-def resample(input, size, align_corners=True):
-    n, c, h, w = input.shape
-    dh, dw = size
-    input = input.view([n * c, 1, h, w])
-    if dh < h:
-        kernel_h = lanczos(ramp(dh / h, 2), 2).to(input.device, input.dtype)
-        pad_h = (kernel_h.shape[0] - 1) // 2
-        input = F.pad(input, (0, 0, pad_h, pad_h), 'reflect')
-        input = F.conv2d(input, kernel_h[None, None, :, None])
-    if dw < w:
-        kernel_w = lanczos(ramp(dw / w, 2), 2).to(input.device, input.dtype)
-        pad_w = (kernel_w.shape[0] - 1) // 2
-        input = F.pad(input, (pad_w, pad_w, 0, 0), 'reflect')
-        input = F.conv2d(input, kernel_w[None, None, None, :])
-    input = input.view([n, c, h, w])
-    return F.interpolate(input, size, mode='bicubic', align_corners=align_corners)
-class ReplaceGrad(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x_forward, x_backward):
-        ctx.shape = x_backward.shape
-        return x_forward
-    @staticmethod
-    def backward(ctx, grad_in):
-        return None, grad_in.sum_to_size(ctx.shape)
-replace_grad = ReplaceGrad.apply
-class ClampWithGrad(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, input, min, max):
-        ctx.min = min
-        ctx.max = max
-        ctx.save_for_backward(input)
-        return input.clamp(min, max)
-    @staticmethod
-    def backward(ctx, grad_in):
-        input, = ctx.saved_tensors
-        return grad_in * (grad_in * (input - input.clamp(ctx.min, ctx.max)) >= 0), None, None
-clamp_with_grad = ClampWithGrad.apply
-def vector_quantize(x, codebook):
-    d = x.pow(2).sum(dim=-1, keepdim=True) + codebook.pow(2).sum(dim=1) - 2 * x @ codebook.T
-    indices = d.argmin(-1)
-    x_q = F.one_hot(indices, codebook.shape[0]).to(d.dtype) @ codebook
-    return replace_grad(x_q, x)
-class Prompt(nn.Module):
-    def __init__(self, embed, weight=1., stop=float('-inf')):
-        super().__init__()
-        self.register_buffer('embed', embed)
-        self.register_buffer('weight', torch.as_tensor(weight))
-        self.register_buffer('stop', torch.as_tensor(stop))
-    def forward(self, input):
-        input_normed = F.normalize(input.unsqueeze(1), dim=2)
-        embed_normed = F.normalize(self.embed.unsqueeze(0), dim=2)
-        dists = input_normed.sub(embed_normed).norm(dim=2).div(2).arcsin().pow(2).mul(2)
-        dists = dists * self.weight.sign()
-        return self.weight.abs() * replace_grad(dists, torch.maximum(dists, self.stop)).mean()
-def parse_prompt(prompt):
-    vals = prompt.rsplit(':', 2)
-    vals = vals + ['', '1', '-inf'][len(vals):]
-    return vals[0], float(vals[1]), float(vals[2])
-class MakeCutouts(nn.Module):
-    def __init__(self, cut_size, cutn, cut_pow=1.):
-        super().__init__()
-        self.cut_size = cut_size
-        self.cutn = cutn
-        self.cut_pow = cut_pow
-        self.augs = nn.Sequential(
-            # K.RandomHorizontalFlip(p=0.5),
-            # K.RandomVerticalFlip(p=0.5),
-            # K.RandomSolarize(0.01, 0.01, p=0.7),
-            # K.RandomSharpness(0.3,p=0.4),
-            # K.RandomResizedCrop(size=(self.cut_size,self.cut_size), scale=(0.1,1),  ratio=(0.75,1.333), cropping_mode='resample', p=0.5),
-            # K.RandomCrop(size=(self.cut_size,self.cut_size), p=0.5),
-            K.RandomAffine(degrees=15, translate=0.1, p=0.7, padding_mode='border'),
-            K.RandomPerspective(0.7,p=0.7),
-            K.ColorJitter(hue=0.1, saturation=0.1, p=0.7),
-            K.RandomErasing((.1, .4), (.3, 1/.3), same_on_batch=True, p=0.7),
-)
-        self.noise_fac = 0.1
-        self.av_pool = nn.AdaptiveAvgPool2d((self.cut_size, self.cut_size))
-        self.max_pool = nn.AdaptiveMaxPool2d((self.cut_size, self.cut_size))
-    def forward(self, input):
-        sideY, sideX = input.shape[2:4]
-        max_size = min(sideX, sideY)
-        min_size = min(sideX, sideY, self.cut_size)
-        cutouts = []
-        for _ in range(self.cutn):
-            # size = int(torch.rand([])**self.cut_pow * (max_size - min_size) + min_size)
-            # offsetx = torch.randint(0, sideX - size + 1, ())
-            # offsety = torch.randint(0, sideY - size + 1, ())
-            # cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
-            # cutouts.append(resample(cutout, (self.cut_size, self.cut_size)))
-            # cutout = transforms.Resize(size=(self.cut_size, self.cut_size))(input)
-            cutout = (self.av_pool(input) + self.max_pool(input))/2
-            cutouts.append(cutout)
-        batch = self.augs(torch.cat(cutouts, dim=0))
-        if self.noise_fac:
-            facs = batch.new_empty([self.cutn, 1, 1, 1]).uniform_(0, self.noise_fac)
-            batch = batch + facs * torch.randn_like(batch)
-        return batch
-def load_vqgan_model(config_path, checkpoint_path):
-    config = OmegaConf.load(config_path)
-    if config.model.target == 'taming.models.vqgan.VQModel':
-        model = vqgan.VQModel(**config.model.params)
-        model.eval().requires_grad_(False)
-        model.init_from_ckpt(checkpoint_path)
-    elif config.model.target == 'taming.models.vqgan.GumbelVQ':
-        model = vqgan.GumbelVQ(**config.model.params)
-        model.eval().requires_grad_(False)
-        model.init_from_ckpt(checkpoint_path)
-    elif config.model.target == 'taming.models.cond_transformer.Net2NetTransformer':
-        parent_model = cond_transformer.Net2NetTransformer(**config.model.params)
-        parent_model.eval().requires_grad_(False)
-        parent_model.init_from_ckpt(checkpoint_path)
-        model = parent_model.first_stage_model
-    else:
-        raise ValueError(f'unknown model type: {config.model.target}')
-    del model.loss
-    return model
-def resize_image(image, out_size):
-    ratio = image.size[0] / image.size[1]
-    area = min(image.size[0] * image.size[1], out_size[0] * out_size[1])
-    size = round((area * ratio)**0.5), round((area / ratio)**0.5)
-    return image.resize(size, Image.LANCZOS)
-model_name = "vqgan_imagenet_f16_16384"
-images_interval =  50
-width =  280
-height = 280
-init_image = ""
-seed = 42
-args = argparse.Namespace(
-    noise_prompt_seeds=[],
-    noise_prompt_weights=[],
-    size=[width, height],
-    init_image=init_image,
-    init_weight=0.,
-    clip_model='ViT-B/32',
-    vqgan_config=f'{model_name}.yaml',
-    vqgan_checkpoint=f'{model_name}.ckpt',
-    step_size=0.15,
-    cutn=4,
-    cut_pow=1.,
-    display_freq=images_interval,
-    seed=seed,
-)
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-print('Using device:', device)
-model = load_vqgan_model(args.vqgan_config, args.vqgan_checkpoint).to(device)
-perceptor = clip.load(args.clip_model, jit=False)[0].eval().requires_grad_(False).to(device)
-def inference(text, seed, step_size, max_iterations, width, height, init_image, init_weight, target_images, cutn, cut_pow):
-    torch.cuda.empty_cache()
-    #torch.cuda.memory_summary(device=None, abbreviated=False)
-    all_frames = []
-    size=[width, height]
-    texts = text
-    init_weight=init_weight
-    if init_image:
-        init_image = init_image.name
-    else:
-        init_image = ""
-    if target_images:
-        target_images = target_images.name
-    else:
-        target_images = ""
-    max_iterations = max_iterations
-    model_names={"vqgan_imagenet_f16_16384": 'ImageNet 16384',"vqgan_imagenet_f16_1024":"ImageNet 1024", 'vqgan_openimages_f16_8192':'OpenImages 8912',
-                    "wikiart_1024":"WikiArt 1024", "wikiart_16384":"WikiArt 16384", "coco":"COCO-Stuff", "faceshq":"FacesHQ", "sflckr":"S-FLCKR"}
-    name_model = model_names[model_name]
-    if target_images == "None" or not target_images:
-        target_images = []
-    else:
-        target_images = target_images.split("|")
-        target_images = [image.strip() for image in target_images]
-    texts = [phrase.strip() for phrase in texts.split("|")]
-    if texts == ['']:
-        texts = []
-    from urllib.request import urlopen
-    if texts:
-        print('Using texts:', texts)
-    if target_images:
-        print('Using image prompts:', target_images)
-    if seed is None or seed == -1:
-        seed = torch.seed()
-    else:
-        seed = seed
-    torch.manual_seed(seed)
-    print('Using seed:', seed)
-    # clock=deepcopy(perceptor.visual.positional_embedding.data)
-    # perceptor.visual.positional_embedding.data = clock/clock.max()
-    # perceptor.visual.positional_embedding.data=clamp_with_grad(clock,0,1)
-    cut_size = perceptor.visual.input_resolution
-    f = 2**(model.decoder.num_resolutions - 1)
-    make_cutouts = MakeCutouts(cut_size, cutn, cut_pow=cut_pow)
-    toksX, toksY = size[0] // f, size[1] // f
-    sideX, sideY = toksX * f, toksY * f
-    if args.vqgan_checkpoint == 'vqgan_openimages_f16_8192.ckpt':
-        e_dim = 256
-        n_toks = model.quantize.n_embed
-        z_min = model.quantize.embed.weight.min(dim=0).values[None, :, None, None]
-        z_max = model.quantize.embed.weight.max(dim=0).values[None, :, None, None]
-    else:
-        e_dim = model.quantize.e_dim
-        n_toks = model.quantize.n_e
-        z_min = model.quantize.embedding.weight.min(dim=0).values[None, :, None, None]
-        z_max = model.quantize.embedding.weight.max(dim=0).values[None, :, None, None]
-    # z_min = model.quantize.embedding.weight.min(dim=0).values[None, :, None, None]
-    # z_max = model.quantize.embedding.weight.max(dim=0).values[None, :, None, None]
-    # normalize_imagenet = transforms.Normalize(mean=[0.485, 0.456, 0.406],
-    #                                            std=[0.229, 0.224, 0.225])
-    if init_image:
-        if 'http' in init_image:
-            img = Image.open(urlopen(init_image))
-        else:
-            img = Image.open(init_image)
-        pil_image = img.convert('RGB')
-        pil_image = pil_image.resize((sideX, sideY), Image.LANCZOS)
-        pil_tensor = TF.to_tensor(pil_image)
-        z, *_ = model.encode(pil_tensor.to(device).unsqueeze(0) * 2 - 1)
-    else:
-        one_hot = F.one_hot(torch.randint(n_toks, [toksY * toksX], device=device), n_toks).float()
-        # z = one_hot @ model.quantize.embedding.weight
-        if args.vqgan_checkpoint == 'vqgan_openimages_f16_8192.ckpt':
-            z = one_hot @ model.quantize.embed.weight
-        else:
-            z = one_hot @ model.quantize.embedding.weight
-        z = z.view([-1, toksY, toksX, e_dim]).permute(0, 3, 1, 2)
-        z = torch.rand_like(z)*2
-    z_orig = z.clone()
-    z.requires_grad_(True)
-    opt = optim.Adam([z], lr=step_size)
-    normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
-                                    std=[0.26862954, 0.26130258, 0.27577711])
-    pMs = []
-    for prompt in texts:
-        txt, weight, stop = parse_prompt(prompt)
-        embed = perceptor.encode_text(clip.tokenize(txt).to(device)).float()
-        pMs.append(Prompt(embed, weight, stop).to(device))
-    for prompt in target_images:
-        path, weight, stop = parse_prompt(prompt)
-        img = Image.open(path)
-        pil_image = img.convert('RGB')
-        img = resize_image(pil_image, (sideX, sideY))
-        batch = make_cutouts(TF.to_tensor(img).unsqueeze(0).to(device))
-        embed = perceptor.encode_image(normalize(batch)).float()
-        pMs.append(Prompt(embed, weight, stop).to(device))
-    for seed, weight in zip(args.noise_prompt_seeds, args.noise_prompt_weights):
-        gen = torch.Generator().manual_seed(seed)
-        embed = torch.empty([1, perceptor.visual.output_dim]).normal_(generator=gen)
-        pMs.append(Prompt(embed, weight).to(device))
-    def synth(z):
-        if args.vqgan_checkpoint == 'vqgan_openimages_f16_8192.ckpt':
-            z_q = vector_quantize(z.movedim(1, 3), model.quantize.embed.weight).movedim(3, 1)
-        else:
-            z_q = vector_quantize(z.movedim(1, 3), model.quantize.embedding.weight).movedim(3, 1)
-        return clamp_with_grad(model.decode(z_q).add(1).div(2), 0, 1)
-    @torch.no_grad()
-    def checkin(i, losses):
-        losses_str = ', '.join(f'{loss.item():g}' for loss in losses)
-        tqdm.write(f'i: {i}, loss: {sum(losses).item():g}, losses: {losses_str}')
-        out = synth(z)
-        # TF.to_pil_image(out[0].cpu()).save('progress.png')
-        # display.display(display.Image('progress.png'))
-        #res = nvidia_smi.nvmlDeviceGetUtilizationRates(handle)
-        #print(f'gpu: {res.gpu}%, gpu-mem: {res.memory}%')
-    def ascend_txt():
-        # global i
-        out = synth(z)
-        iii = perceptor.encode_image(normalize(make_cutouts(out))).float()
-        result = []
-        if init_weight:
-            result.append(F.mse_loss(z, z_orig) * init_weight / 2)
-            #result.append(F.mse_loss(z, torch.zeros_like(z_orig)) * ((1/torch.tensor(i*2 + 1))*init_weight) / 2)
-        for prompt in pMs:
-            result.append(prompt(iii))
-        img = np.array(out.mul(255).clamp(0, 255)[0].cpu().detach().numpy().astype(np.uint8))[:,:,:]
-        img = np.transpose(img, (1, 2, 0))
-        # imageio.imwrite('./steps/' + str(i) + '.png', np.array(img))
-        img = Image.fromarray(img).convert('RGB')
-        all_frames.append(img)
-        return result, np.array(img)
-    def train(i):
-        opt.zero_grad()
-        lossAll, image = ascend_txt()
-        if i % args.display_freq == 0:
-            checkin(i, lossAll)
-        loss = sum(lossAll)
-        loss.backward()
-        opt.step()
-        with torch.no_grad():
-            z.copy_(z.maximum(z_min).minimum(z_max))
-        return image
-    i = 0
-    try:
-        with tqdm() as pbar:
-            while True:
-                image = train(i)
-                if i == max_iterations:
-                    break
-                i += 1
-                pbar.update()
-    except KeyboardInterrupt:
-        pass
-    writer = imageio.get_writer('test.mp4', fps=20)
-    for im in all_frames:
-        writer.append_data(np.array(im))
-    writer.close()
-   # all_frames[0].save('out.gif',
-              # save_all=True, append_images=all_frames[1:], optimize=False, duration=80, loop=0)
-    return image, 'test.mp4'
-def load_image( infilename ) :
-    img = Image.open( infilename )
-    img.load()
-    data = np.asarray( img, dtype="int32" )
-    return data
-title = "VQGAN + CLIP"
-description = "Gradio demo for VQGAN + CLIP. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'>Originally made by Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings). The original BigGAN+CLIP method was by https://twitter.com/advadnoun. Added some explanations and modifications by Eleiber#8347, pooling trick by Crimeacs#8222 (https://twitter.com/EarthML1) and the GUI was made with the help of Abulafia#3734. | <a href='https://colab.research.google.com/drive/1ZAus_gn2RhTZWzOWUpPERNC0Q8OhZRTZ'>Colab</a> | <a href='https://github.com/CompVis/taming-transformers'>Taming Transformers Github Repo</a> | <a href='https://github.com/openai/CLIP'>CLIP Github Repo</a> | Special thanks to BoneAmputee (https://twitter.com/BoneAmputee) for suggestions and advice</p>"
-gr.Interface(
-    inference,
-    [gr.inputs.Textbox(label="Text Input"),
-     gr.inputs.Number(default=42, label="seed"),
-     gr.inputs.Slider(minimum=0.1, maximum=0.9, default=0.6, label='step size'),
-    gr.inputs.Slider(minimum=1, maximum=500, default=100, label='max iterations', step=1),
-    gr.inputs.Slider(minimum=200, maximum=600, default=256, label='width', step=1),
-    gr.inputs.Slider(minimum=200, maximum=600, default=256, label='height', step=1),
-    gr.inputs.Image(type="file", label="Initial Image (Optional)", optional=True),
-    gr.inputs.Slider(minimum=0.0, maximum=15.0, default=0.0, label='Initial Weight', step=1.0),
-    gr.inputs.Image(type="file", label="Target Image (Optional)", optional=True),
-    gr.inputs.Slider(minimum=1, maximum=40, default=1, label='cutn', step=1),
-    gr.inputs.Slider(minimum=1.0, maximum=40.0, default=1.0, label='cut_pow', step=1.0)
-     ],
-    [gr.outputs.Image(type="numpy", label="Output Image"),gr.outputs.Video(label="Output Video")],
-    title=title,
-    description=description,
-    article=article,
-    examples=[
-              ['a garden by james gurney',42,0.6, 100, 256, 256, 'garden.jpeg', 0.0, 'garden.jpeg',1,1.0],
-              ['coral reef city artstationHQ',1000,0.6, 110, 200, 200, 'coralreef.jpeg', 0.0, 'coralreef.jpeg',1,1.0],
-              ['a cabin in the mountains unreal engine',98,0.6, 120, 280, 280, 'cabin.jpeg', 0.0, 'cabin.jpeg',1,1.0]
-    ]
-    ).launch(enable_queue=True)

spaces/CVMX-jaca-tonos/Spanish-Audio-Transcription-to-Quechua-Translation/app.py DELETED Viewed

@@ -1,58 +0,0 @@
-import gradio as gr
-import librosa
-from transformers import AutoFeatureExtractor, AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
-def load_and_fix_data(input_file, model_sampling_rate):
-    speech, sample_rate = librosa.load(input_file)
-    if len(speech.shape) > 1:
-        speech = speech[:, 0] + speech[:, 1]
-    if sample_rate != model_sampling_rate:
-        speech = librosa.resample(speech, sample_rate, model_sampling_rate)
-    return speech
-feature_extractor = AutoFeatureExtractor.from_pretrained("jonatasgrosman/wav2vec2-xls-r-1b-spanish")
-sampling_rate = feature_extractor.sampling_rate
-asr = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-xls-r-1b-spanish")
-model_name = 'hackathon-pln-es/t5-small-finetuned-spanish-to-quechua'
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-new_line = '\n'
-def predict_and_ctc_lm_decode(input_file):
-    speech = load_and_fix_data(input_file, sampling_rate)
-    transcribed_text = asr(speech, chunk_length_s=10, stride_length_s=1)
-    transcribed_text = transcribed_text["text"]
-    input = tokenizer(transcribed_text, return_tensors="pt")
-    output = model.generate(input["input_ids"], max_length=40, num_beams=4, early_stopping=True)
-    output = tokenizer.decode(output[0], skip_special_tokens=True)
-    return f"Spanish Audio Transcription:   {transcribed_text} {new_line} Quechua Translation:  {output}"
-description = """ This is a Gradio demo of Spanish Audio Transcriptions to Quechua Translation. To use this, simply provide an audio input (audio recording or via microphone), which will subsequently be transcribed and translated to the Quechua language.
-Pre-trained model used for Spanish ASR: [jonatasgrosman/wav2vec2-xls-r-1b-spanish](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-spanish)
-Pre-trained model used for translating Spanish audio transcription to the Quechua language: [t5-small-finetuned-spanish-to-quechua](https://huggingface.co/hackathon-pln-es/t5-small-finetuned-spanish-to-quechua)
-"""
-gr.Interface(
-    predict_and_ctc_lm_decode,
-    inputs=[
-        gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio")
-    ],
-    outputs=[gr.outputs.Textbox()],
-    examples=[["sunny_day.wav"], ["travel.wav"], ["sample_audio.wav"]],
-    title="Spanish-Audio-Transcriptions-to-Quechua-Translation",
-    description = description,
-    #article="<p><center><img src='........e'></center></p>",
-    layout="horizontal",
-    theme="huggingface",
-).launch(enable_queue=True, cache_examples=True)

spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/replace.h DELETED Viewed

@@ -1,22 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-#pragma once
-#include <thrust/detail/config.h>
-// this system has no special replace functions

spaces/CVPR/regionclip-demo/detectron2/engine/__init__.py DELETED Viewed

@@ -1,12 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .launch import *
-from .train_loop import *
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
-# prefer to let hooks and defaults live in separate namespaces (therefore not in __all__)
-# but still make them available here
-from .hooks import *
-from .defaults import *

spaces/CVPR/regionclip-demo/detectron2/structures/keypoints.py DELETED Viewed

@@ -1,230 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-from typing import Any, List, Tuple, Union
-import torch
-from torch.nn import functional as F
-from detectron2.utils.env import TORCH_VERSION
-if TORCH_VERSION < (1, 8):
-    def script_if_tracing(fn):
-        return fn
-else:
-    script_if_tracing = torch.jit.script_if_tracing
-class Keypoints:
-    """
-    Stores keypoint **annotation** data. GT Instances have a `gt_keypoints` property
-    containing the x,y location and visibility flag of each keypoint. This tensor has shape
-    (N, K, 3) where N is the number of instances and K is the number of keypoints per instance.
-    The visibility flag follows the COCO format and must be one of three integers:
-    * v=0: not labeled (in which case x=y=0)
-    * v=1: labeled but not visible
-    * v=2: labeled and visible
-    """
-    def __init__(self, keypoints: Union[torch.Tensor, np.ndarray, List[List[float]]]):
-        """
-        Arguments:
-            keypoints: A Tensor, numpy array, or list of the x, y, and visibility of each keypoint.
-                The shape should be (N, K, 3) where N is the number of
-                instances, and K is the number of keypoints per instance.
-        """
-        device = keypoints.device if isinstance(keypoints, torch.Tensor) else torch.device("cpu")
-        keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=device)
-        assert keypoints.dim() == 3 and keypoints.shape[2] == 3, keypoints.shape
-        self.tensor = keypoints
-    def __len__(self) -> int:
-        return self.tensor.size(0)
-    def to(self, *args: Any, **kwargs: Any) -> "Keypoints":
-        return type(self)(self.tensor.to(*args, **kwargs))
-    @property
-    def device(self) -> torch.device:
-        return self.tensor.device
-    def to_heatmap(self, boxes: torch.Tensor, heatmap_size: int) -> torch.Tensor:
-        """
-        Convert keypoint annotations to a heatmap of one-hot labels for training,
-        as described in :paper:`Mask R-CNN`.
-        Arguments:
-            boxes: Nx4 tensor, the boxes to draw the keypoints to
-        Returns:
-            heatmaps:
-                A tensor of shape (N, K), each element is integer spatial label
-                in the range [0, heatmap_size**2 - 1] for each keypoint in the input.
-            valid:
-                A tensor of shape (N, K) containing whether each keypoint is in the roi or not.
-        """
-        return _keypoints_to_heatmap(self.tensor, boxes, heatmap_size)
-    def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Keypoints":
-        """
-        Create a new `Keypoints` by indexing on this `Keypoints`.
-        The following usage are allowed:
-        1. `new_kpts = kpts[3]`: return a `Keypoints` which contains only one instance.
-        2. `new_kpts = kpts[2:10]`: return a slice of key points.
-        3. `new_kpts = kpts[vector]`, where vector is a torch.ByteTensor
-           with `length = len(kpts)`. Nonzero elements in the vector will be selected.
-        Note that the returned Keypoints might share storage with this Keypoints,
-        subject to Pytorch's indexing semantics.
-        """
-        if isinstance(item, int):
-            return Keypoints([self.tensor[item]])
-        return Keypoints(self.tensor[item])
-    def __repr__(self) -> str:
-        s = self.__class__.__name__ + "("
-        s += "num_instances={})".format(len(self.tensor))
-        return s
-# TODO make this nicer, this is a direct translation from C2 (but removing the inner loop)
-def _keypoints_to_heatmap(
-    keypoints: torch.Tensor, rois: torch.Tensor, heatmap_size: int
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Encode keypoint locations into a target heatmap for use in SoftmaxWithLoss across space.
-    Maps keypoints from the half-open interval [x1, x2) on continuous image coordinates to the
-    closed interval [0, heatmap_size - 1] on discrete image coordinates. We use the
-    continuous-discrete conversion from Heckbert 1990 ("What is the coordinate of a pixel?"):
-    d = floor(c) and c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate.
-    Arguments:
-        keypoints: tensor of keypoint locations in of shape (N, K, 3).
-        rois: Nx4 tensor of rois in xyxy format
-        heatmap_size: integer side length of square heatmap.
-    Returns:
-        heatmaps: A tensor of shape (N, K) containing an integer spatial label
-            in the range [0, heatmap_size**2 - 1] for each keypoint in the input.
-        valid: A tensor of shape (N, K) containing whether each keypoint is in
-            the roi or not.
-    """
-    if rois.numel() == 0:
-        return rois.new().long(), rois.new().long()
-    offset_x = rois[:, 0]
-    offset_y = rois[:, 1]
-    scale_x = heatmap_size / (rois[:, 2] - rois[:, 0])
-    scale_y = heatmap_size / (rois[:, 3] - rois[:, 1])
-    offset_x = offset_x[:, None]
-    offset_y = offset_y[:, None]
-    scale_x = scale_x[:, None]
-    scale_y = scale_y[:, None]
-    x = keypoints[..., 0]
-    y = keypoints[..., 1]
-    x_boundary_inds = x == rois[:, 2][:, None]
-    y_boundary_inds = y == rois[:, 3][:, None]
-    x = (x - offset_x) * scale_x
-    x = x.floor().long()
-    y = (y - offset_y) * scale_y
-    y = y.floor().long()
-    x[x_boundary_inds] = heatmap_size - 1
-    y[y_boundary_inds] = heatmap_size - 1
-    valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size)
-    vis = keypoints[..., 2] > 0
-    valid = (valid_loc & vis).long()
-    lin_ind = y * heatmap_size + x
-    heatmaps = lin_ind * valid
-    return heatmaps, valid
-@script_if_tracing
-def heatmaps_to_keypoints(maps: torch.Tensor, rois: torch.Tensor) -> torch.Tensor:
-    """
-    Extract predicted keypoint locations from heatmaps.
-    Args:
-        maps (Tensor): (#ROIs, #keypoints, POOL_H, POOL_W). The predicted heatmap of logits for
-            each ROI and each keypoint.
-        rois (Tensor): (#ROIs, 4). The box of each ROI.
-    Returns:
-        Tensor of shape (#ROIs, #keypoints, 4) with the last dimension corresponding to
-        (x, y, logit, score) for each keypoint.
-    When converting discrete pixel indices in an NxN image to a continuous keypoint coordinate,
-    we maintain consistency with :meth:`Keypoints.to_heatmap` by using the conversion from
-    Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate.
-    """
-    # The decorator use of torch.no_grad() was not supported by torchscript.
-    # https://github.com/pytorch/pytorch/issues/44768
-    maps = maps.detach()
-    rois = rois.detach()
-    offset_x = rois[:, 0]
-    offset_y = rois[:, 1]
-    widths = (rois[:, 2] - rois[:, 0]).clamp(min=1)
-    heights = (rois[:, 3] - rois[:, 1]).clamp(min=1)
-    widths_ceil = widths.ceil()
-    heights_ceil = heights.ceil()
-    num_rois, num_keypoints = maps.shape[:2]
-    xy_preds = maps.new_zeros(rois.shape[0], num_keypoints, 4)
-    width_corrections = widths / widths_ceil
-    height_corrections = heights / heights_ceil
-    keypoints_idx = torch.arange(num_keypoints, device=maps.device)
-    for i in range(num_rois):
-        outsize = (int(heights_ceil[i]), int(widths_ceil[i]))
-        roi_map = F.interpolate(
-            maps[[i]], size=outsize, mode="bicubic", align_corners=False
-        ).squeeze(
-            0
-        )  # #keypoints x H x W
-        # softmax over the spatial region
-        max_score, _ = roi_map.view(num_keypoints, -1).max(1)
-        max_score = max_score.view(num_keypoints, 1, 1)
-        tmp_full_resolution = (roi_map - max_score).exp_()
-        tmp_pool_resolution = (maps[i] - max_score).exp_()
-        # Produce scores over the region H x W, but normalize with POOL_H x POOL_W,
-        # so that the scores of objects of different absolute sizes will be more comparable
-        roi_map_scores = tmp_full_resolution / tmp_pool_resolution.sum((1, 2), keepdim=True)
-        w = roi_map.shape[2]
-        pos = roi_map.view(num_keypoints, -1).argmax(1)
-        x_int = pos % w
-        y_int = (pos - x_int) // w
-        assert (
-            roi_map_scores[keypoints_idx, y_int, x_int]
-            == roi_map_scores.view(num_keypoints, -1).max(1)[0]
-        ).all()
-        x = (x_int.float() + 0.5) * width_corrections[i]
-        y = (y_int.float() + 0.5) * height_corrections[i]
-        xy_preds[i, :, 0] = x + offset_x[i]
-        xy_preds[i, :, 1] = y + offset_y[i]
-        xy_preds[i, :, 2] = roi_map[keypoints_idx, y_int, x_int]
-        xy_preds[i, :, 3] = roi_map_scores[keypoints_idx, y_int, x_int]
-    return xy_preds

spaces/Chaitanya01/InvestingPlatform/app.py DELETED Viewed

@@ -1,1297 +0,0 @@
-import requests
-import json
-from requests.api import delete, options
-import streamlit as st
-import time
-import tweepy
-import requests
-from io import BytesIO
-import base64
-import matplotlib.pyplot as plt
-import numpy as np
-from plotly.subplots import make_subplots
-from config import *
-from dateutil.relativedelta import relativedelta
-from patterns import patterns
-# import talibsddsfs
-from datetime import datetime, timedelta, tzinfo
-from alpaca_trade_api.rest import REST
-from streamlit_tags import st_tags_sidebar
-# from streamlit_autorefresh import st_autorefresh
-# import plotly.express as px
-from coinbaskets import *
-import plotly.graph_objects as go
-from mapping import *
-import pandas as pd
-import threading
-from bs4 import BeautifulSoup
-from ETFs import *
-from dateutil import tz
-import os
-# try:
-#     from streamlit.ReportThread import add_report_ctx
-# except Exception:
-#     # Streamlit >= 0.65.0
-#     from streamlit.report_thread import add_report_ctx
-#     # from streamlit.scriptrunner import add_script_run_ctx
-from streamlit.scriptrunner import add_script_run_ctx as add_report_ctx
-def get_stocktwits_data(req,code,label):
-            r = requests.get(req)
-            trending_syms = pd.DataFrame(r.json()["stocks"]).T
-            trending_syms.index.name = "stock_id"
-            trending_syms.index = trending_syms.index.astype("int")
-            trending_score = pd.DataFrame(r.json()["table"][code])
-            trending_score.set_index("stock_id",inplace = True)
-            most_trending_syms = pd.merge(trending_syms,trending_score,on= "stock_id")
-            most_trending_syms.sort_values("val",ascending = False, inplace = True)
-            most_trending_syms.set_index("symbol",inplace = True)
-            most_trending_syms.columns = ["Name","Price","%Change",label]
-            return most_trending_syms
-def get_cnbc_data(symbol):
-    ticker = symbol.replace(" ","")
-    if ticker == "NASDAQ":
-        ticker = "NDX"
-    elif ticker == "NIFTY50":
-        ticker = ".NSEI"
-    # Get the symbol quote from yahoo finance, we are using Beautiful Soup for scraping
-    # df = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/1Y.json?symbol={ticker}").json()["barData"]["priceBars"])
-    df_1D = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/1D.json?symbol={ticker}").json()["barData"]["priceBars"])
-    # df["datetime"] = pd.to_datetime(df['tradeTimeinMills'],unit='ms')
-    # df["close"] = df["close"].astype(float)
-    df_1D["close"] = df_1D["close"].astype(float)
-    # df.set_index("datetime",inplace = True)
-    # dma200 = df["close"].rolling(200).mean()
-    close = (df_1D["close"].iloc[-1])
-    return close
-def vix_gradient(vix):
-    """
-    Mapping is done as follows rsi<=20 --> -100, rsi>=80, 100, and then linear variation
-    """
-    if vix<20:
-        return 100
-    elif vix<30:
-        return (-20*vix+500)
-    else:
-        return -100
-def roro_comp_get(series,i,state, inverse = False):
-    current = series.iloc[-i]
-    current_idx = series.index[-i]
-    w_1_ago = current_idx - relativedelta(days=7)
-    w_2_ago = current_idx - relativedelta(days=14)
-    m_1_ago = current_idx - relativedelta(months=1)
-    if state == 0:
-        w_1_ret = (current - series.loc[w_1_ago:].iloc[0])*100/series.loc[w_1_ago:].iloc[0]
-        w_2_ret = (current - series.loc[w_2_ago:].iloc[0])*100/series.loc[w_2_ago:].iloc[0]
-        m_1_ret = (current - series.loc[m_1_ago:].iloc[0])*100/series.loc[m_1_ago:].iloc[0]
-    else:
-        w_1_ret = (current - series.iloc[-1-i])*100/series.iloc[-1-i]
-        w_2_ret = (current - series.iloc[-2-i])*100/series.iloc[-2-i]
-        m_1_ret = (current - series.iloc[-4-i])*100/series.iloc[-4-i]
-    sign_of = 1
-    if inverse  == True:
-        sign_of = -1
-    val = 100*(3*(2*(sign_of*w_1_ret>0) - 1) + 2*(2*(sign_of*w_2_ret>0) - 1) + 2*(sign_of*m_1_ret>0) - 1)/6
-    return val
-def get_roro(tf = "1Y"):
-    df_spx = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/{tf}.json?symbol=.SPX").json()["barData"]["priceBars"])
-    df_spx["datetime"] = pd.to_datetime(df_spx['tradeTimeinMills'],unit='ms').dt.date
-    df_spx.set_index("datetime",inplace = True)
-    df_spx["close"] = df_spx["close"].astype(float)
-    df_vix = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/{tf}.json?symbol=.VIX").json()["barData"]["priceBars"])
-    df_vix["close"] = df_vix["close"].astype(float)
-    df_AUDJPY = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/{tf}.json?symbol=AUDJPY=").json()["barData"]["priceBars"])
-    df_AUDJPY["datetime"] = pd.to_datetime(df_AUDJPY['tradeTimeinMills'],unit='ms').dt.date
-    df_AUDJPY.set_index("datetime",inplace = True)
-    df_AUDJPY["close"] = df_AUDJPY["close"].astype(float)
-    df_gold = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/{tf}[email protected]").json()["barData"]["priceBars"])
-    df_gold["datetime"] = pd.to_datetime(df_gold['tradeTimeinMills'],unit='ms').dt.date
-    df_gold.set_index("datetime",inplace = True)
-    df_gold["close"] = df_gold["close"].astype(float)
-    df_silver = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/{tf}[email protected]").json()["barData"]["priceBars"])
-    df_silver["datetime"] = pd.to_datetime(df_silver['tradeTimeinMills'],unit='ms').dt.date
-    df_silver.set_index("datetime",inplace = True)
-    df_silver["close"] = df_silver["close"].astype(float)
-    gold_silver_ratio = df_gold["close"]/df_silver["close"]
-    df_bnd = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/{tf}.json?symbol=BND").json()["barData"]["priceBars"])
-    df_bnd["datetime"] = pd.to_datetime(df_bnd['tradeTimeinMills'],unit='ms').dt.date
-    df_bnd.set_index("datetime",inplace = True)
-    df_bnd["close"] = df_bnd["close"].astype(float)
-    df_sphb = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/{tf}.json?symbol=SPHB").json()["barData"]["priceBars"])
-    df_sphb["datetime"] = pd.to_datetime(df_sphb['tradeTimeinMills'],unit='ms').dt.date
-    df_sphb.set_index("datetime",inplace = True)
-    df_sphb["close"] = df_sphb["close"].astype(float)
-    df_splv = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/{tf}.json?symbol=SPLV").json()["barData"]["priceBars"])
-    df_splv["datetime"] = pd.to_datetime(df_splv['tradeTimeinMills'],unit='ms').dt.date
-    df_splv.set_index("datetime",inplace = True)
-    df_splv["close"] = df_splv["close"].astype(float)
-    sphb_splv_ratio = df_sphb["close"]/df_splv["close"]
-    df_HYG = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/{tf}.json?symbol=HYG").json()["barData"]["priceBars"])
-    df_HYG["datetime"] = pd.to_datetime(df_HYG['tradeTimeinMills'],unit='ms').dt.date
-    df_HYG.set_index("datetime",inplace = True)
-    df_HYG["close"] = df_HYG["close"].astype(float)
-    df_fnda = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/{tf}.json?symbol=FNDA").json()["barData"]["priceBars"])
-    df_fnda["datetime"] = pd.to_datetime(df_fnda['tradeTimeinMills'],unit='ms').dt.date
-    df_fnda.set_index("datetime",inplace = True)
-    df_fnda["close"] = df_fnda["close"].astype(float)
-    df_schx = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/{tf}.json?symbol=SCHX").json()["barData"]["priceBars"])
-    df_schx["datetime"] = pd.to_datetime(df_schx['tradeTimeinMills'],unit='ms').dt.date
-    df_schx.set_index("datetime",inplace = True)
-    df_schx["close"] = df_schx["close"].astype(float)
-    fnda_schx_ratio = df_fnda["close"]/df_schx["close"]
-    df_btc_usd = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/{tf}.json?symbol=BTC.CB=").json()["barData"]["priceBars"])
-    df_btc_usd["datetime"] = pd.to_datetime(df_btc_usd['tradeTimeinMills'],unit='ms').dt.date
-    df_btc_usd.set_index("datetime",inplace = True)
-    df_btc_usd["close"] = df_btc_usd["close"].astype(float)
-    periods = 300
-    roro = []
-    if tf == "5Y":
-        state = 1
-    else:
-        state = 0
-    for i in range(periods,0,-1):
-        temp = dict(
-            date = (pd.to_datetime(df_spx.index).date)[-i],
-            spx = roro_comp_get(df_spx["close"],i,state),
-            audjpy = roro_comp_get(df_AUDJPY["close"],i,state),
-            gold_silver = roro_comp_get(gold_silver_ratio,i,state,inverse = True),
-            bnd = roro_comp_get(df_bnd["close"],i,state,inverse = True),
-            sphb_splv = roro_comp_get(sphb_splv_ratio,i,state),
-            hyg = roro_comp_get(df_HYG["close"],i,state),
-            fnda_schx = roro_comp_get(fnda_schx_ratio,i,state),
-            vix = vix_gradient(df_vix["close"].iloc[-i]),
-            btc_usd = roro_comp_get(df_btc_usd["close"],i,state)
-        )
-        roro.append(temp)
-    return roro
-# Setting the page layout as wide
-st.set_page_config(layout="wide")
-def get_data_yields(symbol,lookback_period):
-    global response_yields
-    df = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/{lookback_period}.json?symbol={symbol}").json()["barData"]["priceBars"])
-    df["datetime"] = pd.to_datetime(df['tradeTimeinMills'],unit='ms')
-    df.set_index("datetime",inplace = True)
-    response_yields[symbol] = df["close"].astype(float)
-def get_recommendation(symbol,rsi_val,drop_frm_ath,dist_from_5_yr_low):
-    global momentum_recommendations,cheap_recommendations
-    df = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/1Y.json?symbol={symbol}").json()["barData"]["priceBars"])
-    df_all = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/ALL.json?symbol={symbol}").json()["barData"]["priceBars"])
-    df_all["high"] = df_all["high"].astype(float)
-    df_all["low"] = df_all["low"].astype(float)
-    df["tradeTimeinMills"] = pd.to_datetime(df['tradeTimeinMills'],unit='ms')
-    df_all["tradeTimeinMills"] = pd.to_datetime(df_all['tradeTimeinMills'],unit='ms')
-    df["close"] = df["close"].astype(float)
-    df["open"] = df["open"].astype(float)
-    df.set_index("tradeTimeinMills",inplace = True)
-    df_all.set_index("tradeTimeinMills",inplace = True)
-    current_close = df["close"].iloc[-1]
-    df["50DMA"] = df["close"].rolling(50).mean()
-    df["100DMA"] = df["close"].rolling(100).mean()
-    df["Vol_mon_avg"] = 5*df["volume"].rolling(252).mean()
-    df["RSI"] = talib.RSI(df["close"])
-    cond1 = current_close>df["50DMA"].iloc[-1]
-    cond2 = current_close>df["100DMA"].iloc[-1]
-    cond3 = df["volume"].rolling(5).sum().iloc[-1]>1.5*df["Vol_mon_avg"].iloc[-1]
-    mom_score = int(cond1)+int(cond2)+int(cond3)
-    ath = df_all["high"].max()
-    distance_from_ath = round((ath-current_close)*100/ath,2)
-    yr_2_ago_dt =  datetime.now() - relativedelta(years=2)
-    yr_2_low = df_all["low"].loc[yr_2_ago_dt:].min()
-    distance_frm_2yr_low = round((current_close - yr_2_low)*100/current_close,2)
-    rsi = round(df["RSI"].iloc[-1],2)
-    cond_1 = rsi<rsi_val
-    cond_2 = distance_from_ath>=drop_frm_ath
-    cond_3 = distance_frm_2yr_low<=dist_from_5_yr_low
-    cheap_score = int(cond_1)+int(cond_2)+int(cond_3)
-    if (cond1 or cond2) or cond3:
-        momentum_recommendations[symbol] = {"LTP":current_close,"50DMA":df["50DMA"].iloc[-1],"100DMA":df["100DMA"].iloc[-1],"Vol>1.5avg":cond3,"score":mom_score,"sparkline": sparkline(data=df["close"])}
-    if cond_1 or cond_2 or cond_3:
-        cheap_recommendations[symbol] = {"LTP":current_close,"RSI":rsi,f'drop frm ATH':distance_from_ath,f"% away 2 yr low":distance_frm_2yr_low,"score":cheap_score,"sparkline": sparkline(data=df["close"])}
-def sparkline(data, figsize=(4,0.25),**kwags):
-    data = list(data)
-    fig,ax = plt.subplots(1,1,figsize=figsize,**kwags)
-    ax.plot(data)
-    for k,v in ax.spines.items():
-        v.set_visible(False)
-    ax.set_xticks([])
-    ax.set_yticks([])
-    plt.plot(len(data)-1, data[len(data)-1], 'r.')
-    ax.fill_between(range(len(data)), data, len(data)*[min(data)], alpha=0.1)
-    img = BytesIO()
-    plt.savefig(img, transparent=True, bbox_inches='tight')
-    img.seek(0)
-#     plt.show()
-    plt.close()
-    # return base64.b64encode(img.read()).decode("utf-8")
-    return '<img src="data:image/png;base64,{}"/>'.format(base64.b64encode(img.read()))
-def highlight_rec_momentum(s):
-    arr = []
-    arr.append('background-color: white')
-    if s["50DMA"]<s["LTP"]:
-        arr.append('background-color: #90EE90')
-    else:
-        arr.append('background-color: #FF7F7F')
-    if s["100DMA"]<s["LTP"]:
-        arr.append('background-color: #90EE90')
-    else:
-        arr.append('background-color: #FF7F7F')
-    if s["Vol>1.5avg"]:
-        arr.append('background-color: #90EE90')
-    else:
-        arr.append('background-color: #FF7F7F')
-    arr.append('background-color: white')
-    arr.append('background-color: white')
-    return arr
-def font_color(s):
-    return ["color: black"]*len(s)
-def highlight_rec_cheap(s,rsi_val,drop_frm_ath,dist_from_5_yr_low):
-    arr = []
-    arr.append('background-color: white')
-    if s["RSI"]<rsi_val:
-        arr.append('background-color: #90EE90')
-    else:
-        arr.append('background-color: #FF7F7F')
-    if s[f"drop frm ATH"]>drop_frm_ath:
-        arr.append('background-color: #90EE90')
-    else:
-        arr.append('background-color: #FF7F7F')
-    if s[f"% away 2 yr low"]<dist_from_5_yr_low:
-        arr.append('background-color: #90EE90')
-    else:
-        arr.append('background-color: #FF7F7F')
-    arr.append('background-color: white')
-    arr.append('background-color: white')
-    return arr
-def get_etf_rets(symbol):
-    global res_etf_ret, vol_etf_info,expense_ratios
-    df = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/1Y.json?symbol={symbol}").json()["barData"]["priceBars"])
-    df["tradeTimeinMills"] = pd.to_datetime(df['tradeTimeinMills'],unit='ms')
-    df["close"] = df["close"].astype(float)
-    df["open"] = df["open"].astype(float)
-    df.set_index("tradeTimeinMills",inplace = True)
-    df_new = df.resample("W-Sun").agg({"close":"last","open":"first"})
-    price_1_yr_ago = df.loc[df_new.index[-1] - relativedelta(years = 1):].iloc[0]["open"]
-    price_1_mon_ago = df.loc[df.index[-1] - relativedelta(months=1):].iloc[0]["open"]
-    close = df.iloc[-1]["close"]
-    daily_ret = (close - df.iloc[-1]["open"].astype(float))*100/df.iloc[-1]["open"]
-    w_1_ret = round((close - df_new.iloc[-1]["open"])*100/df_new.iloc[-1]["open"],2)
-    w_2_ret = round((close - df_new.iloc[-2]["open"])*100/df_new.iloc[-2]["open"],2)
-    y_1_ret = round((close - price_1_yr_ago)*100/price_1_yr_ago,2)
-    m_1_ret = round((close - price_1_mon_ago)*100/price_1_mon_ago,2)
-    temp = dict(symbol = symbol,day_ret = daily_ret, w1_ret = w_1_ret, w2_ret = w_2_ret, year_ret = y_1_ret,m1_ret = m_1_ret)
-    res_etf_ret.append(temp)
-    iv = (df["close"].pct_change()*100).iloc[-30:].std()*np.sqrt(252)
-    vol_etf_info[symbol] = round(iv,2)
-    r = requests.get(f"https://etfdb.com/etf/{symbol}/#etf-ticker-profile")
-    soup = BeautifulSoup(r.content, 'html5lib')
-    expense_ratio = soup.find("div",{"class":"ticker-assets"}).find_all("div")[3].text.split("\n")[-2]
-    expense_ratios[symbol] = expense_ratio
-    return
-def get_data(ticker, timeframe = 60*60 * 4):
-    # Function to get OHLC data for a symbol from FTX api
-    data = pd.DataFrame(json.loads(requests.get(f"https://ftx.com/api/markets/{ticker}/candles?resolution={timeframe}").text)["result"])
-    return data
-def in_squeeze(symbol,bb_mul, kc_mul,num_days,plot = False, timeframe = 60*60*4):
-    # Function to check whether Keltner Channel and Bollinger bands squeeze is happening
-    #     Get Data
-    data = get_data(symbol,timeframe)
-#     Calculate BB
-    data["20sma"] = data["close"].rolling(window = 20).mean()
-    data["stddev"] = data["close"].rolling(window = 20).std()
-    data["lowerband"] = data["20sma"] - bb_mul*data["stddev"]
-    data["upperband"] = data["20sma"] + bb_mul*data["stddev"]
-#     Calculate KC
-    data["TR"] = data["high"] - data["low"]
-    data["ATR"] = data["TR"].rolling(window = 20).mean()
-    data['upperKC'] = data["20sma"] + kc_mul*data["ATR"]
-    data['lowerKC'] = data["20sma"] - kc_mul*data["ATR"]
-    data["squeeze_on"] = np.where(
-                        np.logical_and(data["lowerband"]>data["lowerKC"], data["upperband"]<data["upperKC"]),1,0)
-    # Now if "num_days" days earlier BB were in KC but now, its not then that's breakout
-    if data.iloc[-num_days]["squeeze_on"] and not data.iloc[-1]["squeeze_on"]:
-        # If user wants to plot the candlestick then pass the plot = True
-        if plot == True:
-            # Template for plotting KC and BB and candlesticks
-            candlestick  = go.Candlestick(x=data["startTime"],open=data["open"],high=data["high"],low=data["low"],close=data["close"], name = symbol)
-            upperband = go.Scatter(x = data["startTime"], y = data["upperband"], name = "Upper BB", line = dict(color = "blue"))
-            lowerband = go.Scatter(x = data["startTime"], y = data["lowerband"], name = "Lower BB", line = dict(color = "blue"))
-            upperKC = go.Scatter(x = data["startTime"], y = data["upperKC"], name = "Upper KC", line = dict(color = "green"))
-            lowerKC = go.Scatter(x = data["startTime"], y = data["lowerKC"], name = "Upper KC", line = dict(color = "green"))
-            fig = go.Figure(data = [candlestick,upperband, lowerband, upperKC, lowerKC])
-            # Slider in Xaxes for 1month, 6month, YTD and 1Y
-            fig.update_xaxes(
-                rangeslider_visible=True,
-                rangeselector=dict(
-                    buttons=list([
-                        dict(count=1, label="1mon", step="month", stepmode="backward"),
-                        dict(count=6, label="6mon", step="month", stepmode="backward"),
-                        dict(count=1, label="YTD", step="year", stepmode="todate"),
-                        dict(count=1, label="1y", step="year", stepmode="backward"),
-                        dict(step="all")
-                    ])
-            ))
-            fig.update_layout(yaxis=dict(autorange = True,fixedrange= False))
-            st.plotly_chart(fig,use_container_width=True)
-        else:
-            print(f"{symbol}")
-def plot_candlestick(data):
-    # Function to plot candlestick out of a given OHLC dataframe
-    fig = go.Figure(data=[go.Candlestick(x=data.index,
-                                    open=data['open'],
-                                    high=data['high'],
-                                    low=data['low'],
-                                    close=data['close'])])
-    fig.update_xaxes(
-        rangeslider_visible=True,
-        rangeselector=dict(
-            buttons=list([
-                dict(count=1, label="1mon", step="month", stepmode="backward"),
-                dict(count=6, label="6mon", step="month", stepmode="backward"),
-                dict(count=1, label="YTD", step="year", stepmode="todate"),
-                dict(count=1, label="1y", step="year", stepmode="backward"),
-                dict(step="all")
-            ])
-    ))
-    return fig
-def get_key(dict_given, val):
-    # Function to get the key of the required value from a dictionary
-    for key, value in dict_given.items():
-        if val == value:
-            return key
-def get_crypto_data_daily(symbol):
-    data = get_data(symbol,timeframe = 60*60*24)
-    data.to_csv(f"crypto_data/{symbol.replace('/','_')}.csv")
-def get_cnbc_yields(symbol):
-    # Get the symbol quote from yahoo finance, we are using Beautiful Soup for scraping
-    URL = f"https://www.cnbc.com/quotes/US5Y"
-    headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}
-    page = requests.get(URL, headers = headers)
-    soup = BeautifulSoup(page.text, "html.parser")
-    yields = ["US 2Y","US 5Y", "US 10Y","US 30Y"]
-    if symbol in yields:
-        elements = soup.find('div',{'class':'QuoteStrip-lastPriceStripContainer'})
-        price = elements.find_all('span')[0].text.replace("%","")
-    return float(price.replace(",",""))
-def get_yahoo_finance_quote(symbol):
-    # Get the symbol quote from yahoo finance, we are using Beautiful Soup for scraping
-    URL = f"https://finance.yahoo.com/quote/{symbol}"
-    headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}
-    page = requests.get(URL, headers = headers)
-    soup = BeautifulSoup(page.text, "html.parser")
-    price = soup.find('div',{'class':'D(ib) Mend(20px)'}).find_all('fin-streamer')[0].text
-    return price.replace(",","")
-def get_symbol_quote(ticker_tape, symbol,tck_tape, idx):
-    # Get the symbol quote from yahoo finance, we are using Beautiful Soup for scraping
-    URL = f"https://finance.yahoo.com/quote/{symbol}"
-    headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}
-    page = requests.get(URL, headers = headers)
-    soup = BeautifulSoup(page.text, "html.parser")
-    price = soup.find('div',{'class':'D(ib) Mend(20px)'}).find_all('span')[0].text
-    change = soup.find('div',{'class':'D(ib) Mend(20px)'}).find_all('span')[1].text
-    change = change.split(" ")[1]
-    change = change[1:-1]
-    if ticker_tape == "US 10Y":
-        # US 10Y treated differently because of some formating purposes
-        change = float(change.replace("(","").replace(")","").replace("%",""))
-        price_now = float(price)
-        change = round(price_now - price_now/(1+change/100),2)
-        change = str(change) + "%"
-    tck_tape[idx].metric(label = ticker_tape ,value = price,delta = change)
-# Set the twitter client and set the access token which comes from config.py
-auth = tweepy.OAuthHandler(TWITTER_CONSUMER_KEY, TWITTER_CONSUMER_SECRET)
-auth.set_access_token(TWITTER_ACCESS_TOKEN, TWITTER_ACCESS_TOKEN_SECRET)
-api = tweepy.API(auth)
-if "trade_api" not in st.session_state:
-    # Now, if this api is not there in session_state then add it,
-    # we are doing this because we don't want to let the info go once site is refreshed
-    # And session_state is kind of memory used for caching required info
-    # Set the trade_api variable
-    st.session_state.trade_api = REST(API_KEY, SECRET_KEY, API_URL)
-if "stocks" not in st.session_state:
-    # If stocks are not present then from companies.csv file add them
-    with open('companies.csv') as f:
-        companies = f.read().splitlines()
-        symbols = []
-        for company in companies:
-            symbols.append(company.split(",")[0])
-        st.session_state.stocks = symbols
-if "login" not in st.session_state:
-    # If user has not logged in set login = False
-    # This is done because once logged in we want the program to remember that it logged in
-    # Hence setting the session_state
-    st.session_state.login = False
-if st.session_state.login == False:
-    # If login is false then create a login window
-    st.sidebar.write("Login")
-    # Create sidebar login boxes
-    user = st.sidebar.text_input(label = "Username")
-    password = st.sidebar.text_input(label = "Password", type = "password")
-    login_btn = st.sidebar.button("Login")
-    if login_btn:
-        # This is the users list which will help in login and password for both is 123
-        if user in ["Chaitanya", "Sagar"] and password == "Wizards@123Trade#":
-            st.sidebar.success(f"Logged In as {user}")
-            st.session_state.login = True
-            # Now if clicked on login wait for 1 second then reload the page using experimental_rerun
-            time.sleep(1)
-            st.experimental_rerun()
-        else:
-            # If incorrect credentials then pose an error
-            st.sidebar.warning("Incorrect Username/Password")
-else:
-    # Get the crypto data from FTX api
-    df = pd.DataFrame(json.loads(requests.get("https://ftx.com/api/markets").text)["result"])
-    # Get the LTP and 24H %change for BTCUSDT, ETHUSDT
-    st.session_state.index_btc = df[df["name"]=="BTC/USDT"]["last"].values[0]
-    st.session_state.index_btc_pct_change = round(df[df["name"]=="BTC/USDT"]["change24h"].values[0],2)
-    st.session_state.index_eth = df[df["name"]=="ETH/USDT"]["last"].values[0]
-    st.session_state.index_eth_pct_change = round(df[df["name"]=="ETH/USDT"]["change24h"].values[0],2)
-    st.session_state.roro = get_roro()
-    roro_df = pd.DataFrame(st.session_state.roro)
-    roro_df.set_index("date",inplace = True)
-    roro_df["sum"] = roro_df.sum(axis = 1) / len(roro_df.columns)
-    # Code below is for formatting purpose
-    cols_ticker_tape_cryp = st.columns([1,1])
-    cols_ticker_tape_cryp[0].metric(label = "BTC",value = st.session_state.index_btc, delta = f"{st.session_state.index_btc_pct_change}%")
-    cols_ticker_tape_cryp[1].metric(label = "ETH", value = st.session_state.index_eth, delta = f"{st.session_state.index_eth_pct_change}%")
-    fig = go.Figure(go.Indicator(
-        mode = "number+delta",
-        value = round(roro_df["sum"].iloc[-1]),
-        domain = {'x': [0, 0.2], 'y': [0, 0.2]},
-        delta = {'reference': round(roro_df["sum"].iloc[-2])},
-        gauge = {'axis': {'range': [-100, 100]}},
-        title = {'text': "RORO Indicator"}))
-    fig.add_trace(go.Scatter(y = roro_df["sum"].values,x = roro_df.index,mode = "lines",fill='tozeroy'))
-    st.plotly_chart(fig,use_container_width=True,use_container_height = True)
-    cols_ticker_tape = st.columns(5)
-    # Now, since fetching each symbol will take lots of time
-    # So, we are running all the process in threads parally multi-processing
-    thread_ticker = []
-    for i in range(len  (symbol_mapping.keys())):
-        # Get the symbol_mapping and run it in thread and display each of them
-        ticker_tape = list(symbol_mapping.keys())[i]
-        t_ticker = threading.Thread(target = get_symbol_quote, args = (ticker_tape, symbol_mapping[ticker_tape],cols_ticker_tape,i,))
-        add_report_ctx(t_ticker)
-        t_ticker.start()
-        thread_ticker.append(t_ticker)
-    for x in thread_ticker:
-        # Wait for the threads to finish
-        x.join()
-    # This is a menu for various dashboard windows
-    option = st.sidebar.selectbox("Which Dashboard?",
-    ("Watchlist","twitter","wallstreetbets","stocktwits","CryptoIndex","Chart","pattern", "MACRO",
-    "Technical Scanner", "coinBaskets", "Breakout","ETFs", "Commodities","Report","Recommendations","RORO Components"))
-    # If you want to momentarily hide your website from people then remove the below from commenting
-    # Or you could do one more thing, setup a pseudo id-password, which log in you to only limited features
-    # option = st.sidebar.selectbox("Which Dashboard?",
-    # ("twitter","coinBaskets"))
-    # Set the option value as header
-    st.header(option)
-    if option == "Watchlist":
-        # If watchlist is selected
-        st.subheader("Watchlist")
-        col1, col2 = st.columns([1,6.5])
-        df_watchlist = pd.read_csv('watchlist.csv')
-        with col1:
-            # Now, create a form which will help you add symbol name, comments, etc
-            with st.form(key = "symbol_add"):
-                # df = pd.DataFrame(json.loads(requests.get("https://ftx.com/api/markets").text)["result"])
-                # df = df[df["quoteCurrency"].isin(["USD","USDT"])]
-                # symbols = df.name.values
-                # asset_class = st.sidebar.selectbox("Asset Class",["Crypto","Fixed Income","Stocks","Index","Commodity"])
-                asset_class = st.sidebar.selectbox("Asset Class",["Fixed Income","Stocks","Index","Commodity"])
-                if asset_class == "Crypto":
-                    symbol = st.selectbox("Symbol",symbols)
-                    st.write("Currently working for crypto symbols from FTX")
-                elif asset_class == "Fixed Income":
-                    symbol = st.selectbox("Symbol",["HYG","LQD","US 2Y","US 5Y","US 10Y","US 30Y"])
-                elif asset_class == "Index":
-                    symbol = st.selectbox("Symbol",["SPX","NASDAQ","NIFTY50","VIX"])
-                st.write("If an alert got triggered, but you want to add another, first delete it, then add")
-                trigger = st.text_input("Trigger Price")
-                view_type = st.selectbox("View Type",["Above","Below"])
-                dma200 = st.selectbox("DMA alert",["Yes", "No"])
-                dma200_view_type = st.selectbox("DMA200 View Type",["Above","Below"])
-                alert_type = st.selectbox("Alert Type",["Macro","Individual"])
-                comments = st.text_area("Comments")
-                # Create buttons like add and delete symbols for use
-                add_symbol = st.form_submit_button("Add")
-                delete_symbol = st.form_submit_button("Remove")
-                # Empty watchlist button to clear whole watchlist
-                empty_checkbox = st.checkbox("Yes, I wish to empty the watchlist",value = False)
-                empty_watchlist = st.form_submit_button("Empty Watchlist")
-        with col2:
-            # If empty_watchlist button is clicked then, clear the watchlist
-            if empty_watchlist and empty_checkbox:
-                st.info("Watchlist cleared")
-                df_watchlist = pd.DataFrame(columns=["Symbol","Comments"])
-                df_watchlist.to_csv('watchlist.csv',index=False)
-            elif empty_watchlist:
-                st.error("Could not clear the watchlist, please select the checkbox")
-            elif add_symbol:
-                if symbol not in df_watchlist["Symbol"].values:
-                    # If symbol is added then add it to csv file, then update the csv file
-                    st.info("Symbol added")
-                    df_watchlist = df_watchlist.append(dict(Symbol = symbol, Trigger = float(trigger), alert_type = alert_type, view_type = view_type, status = "Pending",dma200 = dma200, dma200_view_type = dma200_view_type,dma_status = "Pending",Comments = comments), ignore_index=True)
-                    df_watchlist.to_csv('watchlist.csv',index=False)
-                else:
-                    st.warning("Symbol Already Present, Please check")
-            elif delete_symbol:
-                if symbol not in df_watchlist["Symbol"].values:
-                    st.warning("Symbol not present, please check...")
-                else:
-                    # Delte symbol and update the csv file
-                    st.info("Symbol Deleted")
-                    df_watchlist = df_watchlist[df_watchlist["Symbol"]!= symbol]
-                    df_watchlist.to_csv('watchlist.csv',index=False)
-            if len(df_watchlist)>0:
-                # If there's something in watchlist then for cryptos fetch the LTP from FTX api
-                # Getting LTP is currently for Crypto only due to free data unavailability for stocks/ETFs
-                df_watchlist.set_index("Symbol",inplace = True)
-                df_watchlist["LTP"] = 0
-                def get_data_symbol(symbol_name_for_quote):
-                    global df_watchlist
-                    try:
-                        if symbol_name_for_quote in ["HYG","LQD","SPX","NASDAQ","NIFTY50","VIX","US 2Y","US 5Y", "US 10Y","US 30Y"]:
-                            data_symbol = get_cnbc_data(symbol_name_for_quote)
-                        else:
-                            data_symbol = json.loads(requests.get(f"https://ftx.com/api/markets/{symbol_name_for_quote}").text)["result"]["price"]
-                        df_watchlist.loc[symbol_name_for_quote,"LTP"] = data_symbol
-                    except:
-                        pass
-                threads_list = []
-                for i in range(len(df_watchlist)):
-                    symbol_name_for_quote = df_watchlist.index[i]
-                    x = threading.Thread(target = get_data_symbol,args = (symbol_name_for_quote,))
-                    x.start()
-                    add_report_ctx(x)
-                    threads_list.append(x)
-                for thread in threads_list:
-                    thread.join()
-                df_watchlist["pct_away"] = np.round(100*np.abs(df_watchlist["Trigger"] - df_watchlist["LTP"])/df_watchlist["LTP"])
-            # Show the watchlist
-            st.dataframe(df_watchlist)
-    elif option == "RORO Components":
-        lkbck_perd = st.sidebar.selectbox("Lookback Period",["1Y","5Y"])
-        st.sidebar.write("1Y will fetch daily data, 5Y --> weekly")
-        st.sidebar.write("Number of lookback periods is set as 300")
-        roro_run_btn = st.sidebar.button("Run")
-        if roro_run_btn:
-            roro_comp_df = pd.DataFrame(get_roro(lkbck_perd))
-            roro_comp_df.set_index("date",inplace = True)
-            roro_comp_df["roro"] = roro_comp_df.sum(axis = 1)/(len(roro_comp_df.columns))
-            fig = make_subplots(rows=len(roro_comp_df.columns), shared_xaxes=True,vertical_spacing=0.01,subplot_titles=(roro_comp_df.columns))
-            counter = 1
-            for key in roro_comp_df.columns:
-                if key!= "roro":
-                    fig.add_scatter(x = roro_comp_df.index, y = roro_comp_df[key].values,mode = "lines",row = counter, col = 1, name = key)
-                else:
-                    fig.add_scatter(y = roro_comp_df["roro"].values,x = roro_comp_df.index,mode = "lines",fill='tozeroy',row = counter,col =1,name = key)
-                counter+=1
-            fig['layout'].update(height=2500, width=600, title='Subplots of components')
-            st.plotly_chart(fig,use_container_width = True)
-    elif option == "Recommendations":
-        select_asset = st.sidebar.selectbox("Asset Class",["ETF"])
-        rsi_val = st.sidebar.number_input("RSI Thresh",value = 50)
-        drop_frm_ath = st.sidebar.number_input("Drop From ATH(%) Thresh",value = 50)
-        dist_from_5_yr_low = st.sidebar.number_input(f"%away from 2 yr Low Thresh",value = 20)
-        rec_run_btn = st.sidebar.button("Run")
-        st.sidebar.write("**Momentum Rising Screening Conditions**")
-        st.sidebar.write("LTP>50DMA")
-        st.sidebar.write("LTP>100DMA")
-        st.sidebar.write("5D Vol>1.5*(5D Vol. yearly_avg")
-        st.sidebar.write("**Cheap Stocks Screening Conditions**")
-        st.sidebar.write("RSI < RSI Thresh")
-        st.sidebar.write("Drop from ATH(%) > Drop from ATH(%) Thresh")
-        st.sidebar.write(f"% away from 2 yr Low < %away from 2 yr Low Thresh")
-        if rec_run_btn:
-            momentum_recommendations = {}
-            cheap_recommendations = {}
-            rec_threads = []
-            if select_asset == "ETF":
-                options_for_assets = all_etfs
-            for asset in options_for_assets:
-                x = threading.Thread(target=get_recommendation,args = (asset,rsi_val,drop_frm_ath,dist_from_5_yr_low,))
-                x.start()
-                rec_threads.append(x)
-                # get_recommendation(asset)
-            for rec in rec_threads:
-                rec.join()
-            # st.write(momentum_recommendations)
-            momentum_recommendations = pd.DataFrame(momentum_recommendations).T
-            momentum_recommendations.sort_values("score",ascending=False,inplace = True)
-            cheap_recommendations = pd.DataFrame(cheap_recommendations).T
-            cheap_recommendations.sort_values("score",ascending=False,inplace = True)
-            st.header("Momentum Rising")
-            st.dataframe(momentum_recommendations.style.apply(highlight_rec_momentum,axis = 1).apply(font_color))
-            st.header("Cheap Stocks")
-            st.dataframe(cheap_recommendations.style.apply(highlight_rec_cheap,args = (rsi_val,drop_frm_ath,dist_from_5_yr_low),axis = 1).apply(font_color))
-    elif option == "MACRO":
-        select_timeframe = st.sidebar.selectbox("Which Timeframe (finviz only)?",["Daily","Weekly","Monthly"])
-        # select_lookback_period = st.sidebar.selectbox("LookbackPeriod for yields(yrs)",[1,2,3,4,5])
-        select_lookback_period = st.sidebar.selectbox("LookbackPeriod for yields",["1D","1M","3M","6M","1Y","5Y"])
-        timeframe_map = {"Daily":"d1","Weekly":"w1","Monthly":"mo"}
-        tf = timeframe_map[select_timeframe]
-        run_btn = st.sidebar.button("Run")
-        if run_btn:
-            col1, col2, col3 = st.columns([1,1,1])
-            with col1:
-                st.image(f"https://finviz.com/fut_image.ashx?es_{tf}_s.png")
-            with col2:
-                st.image(f"https://finviz.com/fut_image.ashx?vx_{tf}_s.png")
-            with col3:
-                st.image(f"https://finviz.com/fut_image.ashx?nq_{tf}_s.png")
-            response_yields = {}
-            thread_yields = []
-            for symbol in ["US1Y","US2Y","US5Y","US10Y","US30Y"]:
-                thread = threading.Thread(target = get_data_yields, args = (symbol,select_lookback_period,))
-                thread.start()
-                thread_yields.append(thread)
-            for x in thread_yields:
-                x.join()
-            df_yields = pd.concat(response_yields,axis = 1)
-            df_yields["2y/10y"] = df_yields["US10Y"]-df_yields["US2Y"]
-            df_yields["10y/30y"] = df_yields["US30Y"]-df_yields["US10Y"]
-            df_yields["2y/5y"] = df_yields["US5Y"]-df_yields["US2Y"]
-            df_yields["1y/2y"] = df_yields["US2Y"]-df_yields["US1Y"]
-            st.line_chart(df_yields[["US1Y","US2Y","US5Y","US10Y","US30Y"]])
-            col1, col2 = st.columns([1,1])
-            with col1:
-                st.line_chart(df_yields["1y/2y"])
-                st.line_chart(df_yields["2y/5y"])
-            with col2:
-                st.line_chart(df_yields["2y/10y"])
-                st.line_chart(df_yields["10y/30y"])
-            # today = datetime.now().strftime("%Y-%m-%d")
-            # past = (datetime.now() - timedelta(days=365*select_lookback_period)).strftime("%Y-%m-%d")
-            # # https://fred.stlouisfed.org/graph/?id=DGS10,DGS5,DGS30,DGS3MO,DGS1,DGS2,
-            # url = f"""https://fred.stlouisfed.org/graph/fredgraph.png?dwnld=0&hires=1&type=image/png&
-            # bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&
-            # mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&
-            # show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=DGS10,DGS5,DGS30,DGS3MO,DGS1,DGS2&
-            # scale=left,left,left,left,left,left&cosd={past},{past},{past},{past},{past},{past}&
-            # coed={today},{today},{today},{today},{today},{today}&
-            # line_color=%234572a7,%23aa4643,%2389a54e,%2380699b,%233d96ae,%23db843d&
-            # link_values=false,false,false,false,false,false&line_style=solid,solid,solid,solid,solid,solid&
-            # mark_type=none,none,none,none,none,none&mw=3,3,3,3,3,3&lw=2,2,2,2,2,2&
-            # ost=-99999,-99999,-99999,-99999,-99999,-99999&oet=99999,99999,99999,99999,99999,99999&
-            # mma=0,0,0,0,0,0&fml=a,a,a,a,a,a&fq=Daily,Daily,Daily,Daily,Daily,Daily&
-            # fam=avg,avg,avg,avg,avg,avg&fgst=lin,lin,lin,lin,lin,lin&
-            # fgsnd=2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01,2020-02-01&line_index=1,2,3,4,5,6&
-            # transformation=lin,lin,lin,lin,lin,lin&
-            # vintage_date={today},{today},{today},{today},{today},{today}&
-            # revision_date={today},{today},{today},{today},{today},{today}&
-            # nd=1962-01-02,1962-01-02,1977-02-15,1981-09-01,1962-01-02,1976-06-01
-            # """
-            # url = url.replace("\n","")
-            # st.image(url,width = 800)
-    elif option == "twitter":
-        # If twitter is selected
-        today = datetime.today()
-        # Get the local timezone, this is important because then it works on a different timezone
-        to_zone = tz.tzlocal()
-        # Multibox for selecting multiple users
-        who = st.sidebar.multiselect("Choose person",tuple(TWITTER_USERNAMES))
-        twitter_run_btn = st.sidebar.button("Run")
-        if twitter_run_btn:
-            # if twitter run button is clicked then all those people selected are called
-            if "SELECT ALL" in who:
-                users_list = TWITTER_USERNAMES[1:]
-            else:
-                users_list = who
-            for username in users_list:
-                # For a given username fetch the tweets, its username, image
-                user = api.get_user(screen_name = username)
-                tweets = api.user_timeline(screen_name = username, count = 100, tweet_mode = "extended")
-                st.subheader(username)
-                st.image(user.profile_image_url)
-                for tweet in tweets:
-                    # In all his tweets, bring those to local timezone
-                    tweet_date = ((tweet.created_at).astimezone(to_zone)).replace(tzinfo = None)
-                    #Now, we don't want tweets older than 3 days
-                    delta = (today - tweet_date).days
-                    if delta>3:
-                        continue
-                    # For the following user names certain modification is done to get the tweets
-                    if username in ["@chartmojo","@MacroCharts"]:
-                        if tweet.in_reply_to_screen_name== None:
-                            st.subheader(tweet._json["created_at"])
-                            st.write(tweet.full_text)
-                            try:
-                                for j in tweet.extended_entities["media"]:
-                                    st.image(j["media_url_https"], width=600)
-                            except:
-                                pass
-                    else:
-                        if tweet.in_reply_to_screen_name== None and len(tweet.entities["symbols"])>0:
-                            symbols = []
-                            for i in range(len(tweet.entities["symbols"])):
-                                symbols.append(tweet.entities["symbols"][i]["text"])
-                            st.subheader(" ".join(symbols))
-                            st.subheader(tweet._json["created_at"])
-                            st.subheader(tweet.full_text)
-                            try:
-                                for j in tweet.extended_entities["media"]:
-                                    st.image(j["media_url_https"], width = 600)
-                            except:
-                                pass
-                            for symbol in symbols:
-                                st.image(f"https://finviz.com/chart.ashx?t={symbol}&ta=1", width=600)
-    # elif option == "US Sectors":
-    #     st.sidebar.write("Source - TradingView")
-    #     select_sector = st.sidebar.selectbox("Select Sector", options = us_sectors)
-    #     select_btn = st.sidebar.button("Run")
-    #     url = "https://in.tradingview.com/markets/stocks-usa/sectorandindustry-sector/"
-    #     r = requests.get(url)
-    #     soup = BeautifulSoup(r.content, 'html5lib') # If this line causes an error, run 'pip install html5lib' or install html5lib
-    #     req = soup.find_all('tr',attrs = {'class':'tv-data-table__row tv-data-table__stroke tv-screener-table__result-row'})
-    #     sectors = []
-    #     pct_change = []
-    #     for i in req:
-    #         arr = []
-    #         for sector in i.find_all("td"):
-    #             arr.append(sector.text)
-    #         arr[0]  = arr[0].split("\n")[3].split("\t")[0]
-    #         sectors.append(arr[0])
-    #         pct_change.append(float(arr[3][:-1]))
-    #     fig = go.Figure([go.Bar(x=sectors, y=pct_change,
-    #                     marker = dict(color = ['rgba(63, 195, 128, 1)' if value>0 else 'rgba(219, 10, 91, 1)' for value in pct_change],
-    #                             line = dict(color='rgb(0,0,0)',width=1.5)))])
-    #     st.plotly_chart(fig,use_container_width=True)
-    #     if select_btn:
-    #         val = "-".join(select_sector.lower().split(" "))
-    #         url = f"https://in.tradingview.com/markets/stocks-usa/sectorandindustry-sector/{val}/"
-    #         r = requests.get(url)
-    #         soup = BeautifulSoup(r.content, 'html5lib') # If this line causes an error, run 'pip install html5lib' or install html5lib
-    #         req = soup.find_all('tr',attrs = {'class':'tv-data-table__row tv-data-table__stroke tv-screener-table__result-row'})
-    #         ticker = []
-    #         pct_change_ticker = []
-    #         company = []
-    #         for i in req:
-    #             arr = []
-    #             for sector in i.find_all("td"):
-    #                 arr.append(sector.text)
-    #             arr[0]  = arr[0].split("\n")[4]
-    #             # result.append(dict(ticker = arr[0],pct_change = float(arr[2][:-1]),vol = arr[5],mkt_cap = arr[6]))
-    #             # ticker.append(arr[0])
-    #             company.append(us_stocks_mapping[arr[0]])
-    #             pct_change_ticker.append(float(arr[2][:-1]))
-    #         layout = go.Layout(
-    #             xaxis = go.XAxis(
-    #                 title = "Stocks",
-    #                 showticklabels = False
-    #             )
-    #         )
-    #         fig = go.Figure([go.Bar(x=company, y=pct_change_ticker,
-    #                     marker = dict(color = ['rgba(63, 195, 128, 1)' if value>0 else 'rgba(219, 10, 91, 1)' for value in pct_change_ticker],
-    #                             line = dict(color='rgb(0,0,0)',width=1.5)))],layout = layout)
-    #         st.write(len(company))
-    #         st.plotly_chart(fig,use_container_width=True)
-    #     st_autorefresh(interval=120000, limit=10000, key="US sectors refresh")
-    elif option == "Commodities":
-        select_timeframe = st.sidebar.selectbox("Which Timeframe?",["Daily","Weekly","Monthly"])
-        timeframe_map = {"Daily":"d1","Weekly":"w1","Monthly":"mo"}
-        tf = timeframe_map[select_timeframe]
-        col0,col1,col2 = st.columns([1,1,1])
-        count = 0
-        for key in commodity_mapping:
-            # https://finviz.com/futures_charts.ashx?t=YM&p=d1
-            # Get the mapping for commodities, which is there in config.py
-            keyword_comm = commodity_mapping[key]
-            num = count%3
-            if num ==0:
-                with col0:
-                    st.image(f"https://finviz.com/fut_image.ashx?{keyword_comm}_{tf}_s.png")
-            elif num == 1:
-                with col1:
-                    st.image(f"https://finviz.com/fut_image.ashx?{keyword_comm}_{tf}_s.png")
-            else:
-                with col2:
-                    st.image(f"https://finviz.com/fut_image.ashx?{keyword_comm}_{tf}_s.png")
-            count = count+1
-    elif option == "CryptoIndex":
-        # If cryptoIndex tab is selected
-        # Select which crypto index, then timeframe and accordingly fetch the data
-        type_index = st.sidebar.selectbox("Which?",["Major","Minor","Shit"])
-        select_index_timeframe = st.sidebar.selectbox("Timeframe",["15s","1m","5m","15m","1h","4h","1d"])
-        if select_index_timeframe[-1] == "s":
-            timeframe = int(select_index_timeframe[:-1])
-        elif select_index_timeframe[-1] == "m":
-            timeframe = int(select_index_timeframe[:-1]) * 60
-        elif select_index_timeframe[-1] == "h":
-            timeframe = int(select_index_timeframe[:-1])*60*60
-        elif select_index_timeframe[-1] == "d":
-            timeframe = int(select_index_timeframe[:-1])* 60*60*24
-        if type_index == "Major":
-            # Our crypto index is of 0.5BTC + 0.5 ETH
-            st.write("0.5BTC + 0.5ETH")
-            # Fetch data and create crypto index in same proportion
-            data_btc = pd.DataFrame(json.loads(requests.get(f"https://ftx.com/api/markets/BTC/USDT/candles?resolution={timeframe}").text)["result"])
-            data_eth = pd.DataFrame(json.loads(requests.get(f"https://ftx.com/api/markets/ETH/USDT/candles?resolution={timeframe}").text)["result"])
-            data_btc.set_index("startTime",inplace = True)
-            data_eth.set_index("startTime",inplace = True)
-            # Note crypto index are normalized
-            data = (data_btc["close"]*0.5/data_btc["close"][0] + data_eth["close"]*0.5/data_eth["close"][0])*100
-            fig = go.Figure()
-            fig.add_trace(go.Scatter(x=data.index, y=data.values,
-                                            mode='lines',
-                                            name=type_index))
-            fig.update_xaxes(
-                rangeslider_visible=True,
-                nticks = 20,
-                spikemode = "toaxis",
-                rangeselector=dict(
-                    buttons=list([
-                        dict(count=1, label="1mon", step="month", stepmode="backward"),
-                        dict(count=6, label="6mon", step="month", stepmode="backward"),
-                        dict(count=1, label="YTD", step="year", stepmode="todate"),
-                        dict(count=1, label="1y", step="year", stepmode="backward"),
-                        dict(step="all")
-                    ])
-                )
-            )
-            fig.update_layout(
-                xaxis_tickformat = '%Y-%m-%d',
-                height = 600,
-                width  = 900,
-                hovermode = "x"
-            )
-            st.plotly_chart(fig)
-    elif option == "Report":
-        df = pd.DataFrame(json.loads(requests.get("https://ftx.com/api/markets").text)["result"])
-        df = df[df["quoteCurrency"].isin(["USD","USDT"])]
-        symbols = df.name.values
-        to_analyze_symbol = st.sidebar.multiselect("Which symbol",symbols)
-        analysis_date = st.sidebar.selectbox("Which date for Analysis",["Current","Yesterday"])
-        if analysis_date == "Yesterday":
-            locn = -2
-        elif analysis_date == "Current":
-            locn = -1
-        fetch_data_btn = st.sidebar.button("Fetch Data")
-        if fetch_data_btn:
-            threads = []
-            for symbol in symbols:
-                t = threading.Thread(target = get_crypto_data_daily, args = (symbol,))
-                t.start()
-                add_report_ctx(t)
-                threads.append(t)
-            for x in threads:
-                x.join()
-            res = {}
-            for req_symbol in to_analyze_symbol:
-                req_symbol_file = req_symbol.replace("/","_")
-                df_req = pd.read_csv(f"crypto_data/{req_symbol_file}.csv",index_col = 0, parse_dates = True)
-                ans = {}
-                for period in [20,50,100,200]:
-                    df_req[f"MA{period}"] = talib.SMA(df_req["close"],period)
-                    ans[f"% from MA{period}"] = round((df_req["close"].iloc[locn] - df_req[f"MA{period}"].iloc[locn])*100/df_req[f"MA{period}"].iloc[locn],2)
-                df_req["RSI14"] = talib.RSI(df_req["close"])
-                ans["RSI"] = round(df_req["RSI14"].iloc[locn],2)
-                ans["LTP"] = df_req["close"].iloc[-1]
-                res[req_symbol] = ans
-            result = pd.DataFrame(res).T
-            st.dataframe(result)
-    elif option == "Breakout":
-        # If breakout tab is selected
-        # Get the crypto symbols
-        #Create buttons and boxes for selecting timeframe, BB number, KC multiplier
-        df = pd.DataFrame(json.loads(requests.get("https://ftx.com/api/markets").text)["result"])
-        df = df[df["quoteCurrency"].isin(["USD","USDT"])]
-        symbols = df.name.values
-        select_consol_tf = st.sidebar.selectbox("Timeframe",["15s","1m","5m","15m","1h","4h","1d"])
-        if select_consol_tf[-1] == "s":
-            timeframe = int(select_consol_tf[:-1])
-        elif select_consol_tf[-1] == "m":
-            timeframe = int(select_consol_tf[:-1]) * 60
-        elif select_consol_tf[-1] == "h":
-            timeframe = int(select_consol_tf[:-1])*60*60
-        elif select_consol_tf[-1] == "d":
-            timeframe = int(select_consol_tf[:-1])* 60*60*24
-        select_BB_mul = st.sidebar.text_input("Bollinger Band multiplier",value = "2")
-        select_KC_mul = st.sidebar.text_input("KC multiplier", value = "1.5")
-        select_num_days = st.sidebar.text_input("Consolidating before how many periods?",3)
-        num_days = int(select_num_days)
-        bb_mul = float(select_BB_mul)
-        kc_mul = float(select_KC_mul)
-        consol_run_btn = st.sidebar.button("Run")
-        if consol_run_btn:
-            # If user clicks on run button
-            # Again running in threads.
-            threads = []
-            for symbol in symbols:
-                t = threading.Thread(target = in_squeeze, args = (symbol,bb_mul, kc_mul, num_days, True,60*60*24))
-                add_report_ctx(t)
-                t.start()
-                threads.append(t)
-            for x in threads:
-                x.join()
-            st.sidebar.write("Task Complete")
-    elif option == "ETFs":
-        # If ETFs are selected
-        # Button to select ETF
-        selectETF = st.sidebar.selectbox("Select ETF class",etf.keys())
-        today = datetime.now() - timedelta(days =80)
-        st.subheader(selectETF)
-        # Note Country ETFs are dealt differently as they have country names as well
-        if selectETF!= "Country":
-            # If ETF is not Country then its very easy just from ETF variable we can fetch it
-            etf_names = etf[selectETF].keys()
-        else:
-            # Select Markets, Country
-            selectMarket = st.sidebar.selectbox("Which Market?",etf[selectETF].keys())
-            country_list = list(etf[selectETF][selectMarket].keys())
-            etf_list = list(etf[selectETF][selectMarket].values())
-            etf_names = []
-            country_names = []
-            # In a for loop fetch all the ETFs for selected entries
-            for i in range(len(etf_list)):
-                if type(etf_list[i])==str:
-                    etf_names.append(etf_list[i])
-                    country_names.append(country_list[i])
-                else:
-                    for sub_etf_name in etf_list[i]:
-                        etf_names.append(sub_etf_name)
-                        country_names.append(country_list[i])
-        select_timeframe = st.sidebar.selectbox("Which Timeframe?",["Daily","Weekly","Monthly"])
-        timeframe_map = {"Daily":"d","Weekly":"w","Monthly":"m"}
-        etf_run_btn = st.sidebar.button("Run")
-        if etf_run_btn:
-            tf = timeframe_map[select_timeframe]
-            # If After giving all the entries, run button is clicked get the charts for all the ETFs
-            count = 0
-            res_etf_ret = []
-            thread_det = []
-            vol_etf_info = {}
-            expense_ratios = {}
-            st.info("Loading..... Please Have Patience")
-            for n in etf_names:
-                thread = threading.Thread(target = get_etf_rets, args = (n,))
-                thread.start()
-                thread_det.append(thread)
-            for x in thread_det:
-                x.join()
-            st.success("Finished Loading")
-            etf_rets_df = pd.DataFrame(res_etf_ret)
-            etf_rets_df.set_index("symbol",inplace=True)
-            col1,col2,col3,col4,col5 = st.columns([1,1,1,1,1])
-            with col1:
-                # st.write("")
-                st.markdown("<h5 style='text-align: center; color: red;'>Daily Returns</h5>", unsafe_allow_html=True)
-                st.dataframe(etf_rets_df["day_ret"].sort_values(ascending=False))
-            with col2:
-                st.markdown("<h5 style='text-align: center; color: red;'>1 Week Returns</h5>", unsafe_allow_html=True)
-                st.dataframe(etf_rets_df["w1_ret"].sort_values(ascending=False))
-            with col3:
-                st.markdown("<h5 style='text-align: center; color: red;'>2 Week Returns</h5>", unsafe_allow_html=True)
-                st.dataframe(etf_rets_df["w2_ret"].sort_values(ascending=False))
-            with col4:
-                st.markdown("<h5 style='text-align: center; color: red;'>1 Month Returns</h5>", unsafe_allow_html=True)
-                st.dataframe(etf_rets_df["m1_ret"].sort_values(ascending=False))
-            with col5:
-                st.markdown("<h5 style='text-align: center; color: red;'>1 Year Returns</h5>", unsafe_allow_html=True)
-                st.dataframe(etf_rets_df["year_ret"].sort_values(ascending=False))
-            st.dataframe(etf_rets_df)
-            cols1,cols2 = st.columns([1,1])
-            if selectETF!= "Country":
-                for etf_name in etf_names:
-                    num = count%2
-                    if num == 0:
-                        with cols1:
-                            try:
-                                st.write(f"{etf[selectETF][etf_name]}, IV = {vol_etf_info[etf_name]}, ER = {expense_ratios[etf_name]}")
-                            except:
-                                pass
-                            if tf == "d":
-                                st.image(f"https://finviz.com/chart.ashx?t={etf_name}&ta=1&p={tf}")
-                            else:
-                                st.image(f"https://finviz.com/chart.ashx?t={etf_name}&p={tf}")
-                    else:
-                        with cols2:
-                            try:
-                                st.write(f"{etf[selectETF][etf_name]}, IV = {vol_etf_info[etf_name]}, ER = {expense_ratios[etf_name]}")
-                            except:
-                                pass
-                            if tf == "d":
-                                st.image(f"https://finviz.com/chart.ashx?t={etf_name}&ta=1&p={tf}")
-                            else:
-                                st.image(f"https://finviz.com/chart.ashx?t={etf_name}&p={tf}")
-                    count = count + 1
-            else:
-                for i in range(len(etf_names)):
-                    try:
-                        st.write(f"{country_names[i]}, IV = {vol_etf_info[etf_names[i]]}, ER = {expense_ratios[etf_names[i]]}")
-                    except:
-                        pass
-                    if tf == "d":
-                        st.image(f"https://finviz.com/chart.ashx?t={etf_names[i]}&ta=1&p={tf}")
-                    else:
-                        st.image(f"https://finviz.com/chart.ashx?t={etf_names[i]}&p={tf}")
-    elif option == "coinBaskets":
-        # If coinBaskets is selected
-        # Note, Mudrex was our reference here
-        baskets = st.sidebar.multiselect(label = "Baskets",options=names, default =names[0])
-        run_basket = st.sidebar.button("Run")
-        select_crypto_timeframe = st.sidebar.selectbox("Crypto Timeframe", options =
-                                ["1d","1m","3m","5m","15m","30m","1h","2h","4h","6h","8h","12h","3d","1w","1M"])
-        check_symbol = st.sidebar.text_input("Symbol check")
-        interval = select_crypto_timeframe
-        # Once all the inputs are given, and if any of the basket is chosen
-        if check_symbol != "":
-            for bkt in names:
-                # For each basket selected, get its components, fetch its price from Binance api
-                #Then plot all components in a single chart
-                #Here, you are giving in the symbol name and program is finding whether that symbol is there
-                # in any of the basket or not
-                if check_symbol in eval(bkt)["components"]:
-                    fig_check = go.Figure()
-                    st.write(bkt.upper())
-                    cols = st.columns(len(eval(bkt)["components"]))
-                    for i in eval(bkt)["components"]:
-                        ticker = f'{i.upper()}USDT'
-                        req_params = dict(symbol = ticker, interval = interval)
-                        url = "https://api.binance.com/api/v3/klines"
-                        data = pd.DataFrame(json.loads(requests.get(url,params = req_params).text))
-                        data = data.iloc[:,0:5]
-                        data.columns = ['datetime', 'open','high','low', 'close']
-                        data.index = [datetime.fromtimestamp(x/1000) for x in data.datetime]
-                        data["close"] = data["close"].astype(float)
-                        df = (data["close"].pct_change() + 1).cumprod()
-                        fig_check.add_trace(go.Scatter(x=df.index, y=df.values,
-                                            mode='lines',
-                                            name=i))
-                        fig_check.update_xaxes(
-                        rangeslider_visible=True,
-                        nticks = 20,
-                        spikemode = "toaxis",
-                        rangeselector=dict(
-                            buttons=list([
-                                dict(count=1, label="1mon", step="month", stepmode="backward"),
-                                dict(count=6, label="6mon", step="month", stepmode="backward"),
-                                dict(count=1, label="YTD", step="year", stepmode="todate"),
-                                dict(count=1, label="1y", step="year", stepmode="backward"),
-                                dict(step="all")
-                            ])
-                        )
-                        )
-                        fig_check.update_layout(
-                            xaxis_tickformat = '%Y-%m-%d',
-                            height = 600,
-                            width  = 900,
-                            hovermode = "x"
-                        )
-                        fig_check.update_traces(
-                            hovertemplate="<br>".join([
-                                "Price: %{y}"
-                                ]))
-                    st.plotly_chart(fig_check)
-        if run_basket:
-            for basket in baskets:
-                # Here, you are choosing the baskets and program is plotting the baskets
-                st.write(basket.upper())
-                st.table(eval(basket))
-                # Create traces
-                fig = go.Figure()
-                for i in eval(basket)["components"]:
-                    ticker = f'{i.upper()}USDT'
-                    interval = select_crypto_timeframe
-                    req_params = dict(symbol = ticker, interval = interval)
-                    url = "https://api.binance.com/api/v3/klines"
-                    data = pd.DataFrame(json.loads(requests.get(url,params = req_params).text))
-                    data = data.iloc[:,0:5]
-                    data.columns = ['datetime', 'open','high','low', 'close']
-                    data.index = [datetime.fromtimestamp(x/1000) for x in data.datetime]
-                    data["close"] = data["close"].astype(float)
-                    df = (data["close"].pct_change() + 1).cumprod()
-                    fig.add_trace(go.Scatter(x=df.index, y=df.values,
-                                        mode='lines',
-                                        name=i))
-                    fig.update_xaxes(
-                    rangeslider_visible=True,
-                    nticks = 20,
-                    spikemode = "toaxis",
-                    rangeselector=dict(
-                        buttons=list([
-                            dict(count=1, label="1mon", step="month", stepmode="backward"),
-                            dict(count=6, label="6mon", step="month", stepmode="backward"),
-                            dict(count=1, label="YTD", step="year", stepmode="todate"),
-                            dict(count=1, label="1y", step="year", stepmode="backward"),
-                            dict(step="all")
-                        ])
-                    )
-                    )
-                    fig.update_layout(
-                        xaxis_tickformat = '%Y-%m-%d',
-                        height = 600,
-                        width  = 900,
-                        hovermode = "x"
-                    )
-                    fig.update_traces(
-                        hovertemplate="<br>".join([
-                            "Price: %{y}"
-                            ]))
-                st.plotly_chart(fig)
-    elif option == "Chart":
-        # Charting platform
-        # Get user inputs
-        symbols = st_tags_sidebar(label = "Choose the tickers",
-        text = 'Press enter to add more', maxtags = 100)
-        select_stock_timeframe = st.sidebar.selectbox("Stock Timeframe",options=["1Min", "5Min", "15Min", "day"])
-        select_crypto_timeframe = st.sidebar.selectbox("Crypto Timeframe", options =
-                                ["1d","1m","3m","5m","15m","30m","1h","2h","4h","6h","8h","12h","3d","1w","1M"])
-        select_periods = st.sidebar.text_input("Number of Days(Stock)",value = "30")
-        today = datetime.now() - timedelta(int(select_periods))
-        if len(symbols)>0:
-            # If symbols are selected
-            for symbol in symbols:
-                # Run over all symbol in for loop and if they are crypto, treat them differently
-                #And if they are others, treat them differently
-                if f'{symbol.upper()}' in crypto_symbols:
-                    # If crypto, get the data from Binance api
-                    ticker = f'{symbol.upper()}USDT'
-                    interval = select_crypto_timeframe
-                    req_params = dict(symbol = ticker, interval = interval)
-                    url = "https://api.binance.com/api/v3/klines"
-                    st.subheader(ticker)
-                    data = pd.DataFrame(json.loads(requests.get(url,params = req_params).text))
-                    data = data.iloc[:,0:5]
-                    data.columns = ['datetime', 'open','high','low', 'close']
-                    data.index = [datetime.fromtimestamp(x/1000) for x in data.datetime]
-                    data.drop("datetime",axis = 1, inplace = True)
-                else:
-                    # If stocks then get it from trade_api
-                    # Remember!!!--> trade_api was initiated in the session_state in the starting of code
-                    data = st.session_state.trade_api.get_barset(symbol.upper(), select_stock_timeframe, start =today.strftime("%Y-%m-%d")).df
-                    data = data[symbol.upper()]
-                    st.subheader(symbol.upper())
-                fig = plot_candlestick(data)
-                st.plotly_chart(fig, use_container_width=False)
-    elif option == "stocktwits":
-        # If stocktwits is selected
-        # This is almost similar to Larry's video, so you can reference that as well
-        # Display Trending stocks based on watchlist count
-        st.header("Most Trending Symbols")
-        most_trending_syms = get_stocktwits_data(req = "https://api.stocktwits.com/api/2/charts/ts",
-                            code = "ts", label = "Trending Score")
-        st.dataframe(most_trending_syms)
-        st.header("Most messages in last 24 hrs")
-        most_active_syms = get_stocktwits_data(req = "https://api.stocktwits.com/api/2/charts/m_day",
-                            code = "m_day", label = "#messages")
-        st.dataframe(most_active_syms)
-        st.header("Top New Watchers added in last 24 hrs")
-        most_active_syms = get_stocktwits_data(req = "https://api.stocktwits.com/api/2/charts/wl_ct_day",
-                            code = "wl_ct_day", label = "Count")
-        st.dataframe(most_active_syms)
-        # For a given symbol, use request module to hit the stocktwits api and get the required info
-        symbol = st.sidebar.text_input("Symbol", value = "AAPL", max_chars = 5)
-        r = requests.get(f"https://api.stocktwits.com/api/2/streams/symbol/{symbol}.json")
-        data = r.json()
-        for message in data["messages"]:
-            st.image(message["user"]["avatar_url"])
-            st.write(message['user']["username"])
-            st.write(message["created_at"])
-            st.write(message["body"])
-        st.sidebar.write("Update time for -->")
-        st.sidebar.write("Top Watchlist Counts : 5mins")
-        st.sidebar.write("Most Messages : 1hr")
-    elif option == "Technical Scanner":
-        # If technical scanner is selected
-        # Then choose the pattern, timeframe, etc
-        pattern = st.sidebar.selectbox("Which Pattern?", tuple(patterns.values()))
-        keywords = st_tags_sidebar(label = "Choose the tickers",
-        text = 'Press enter to add more', maxtags = 100)
-        select_timeframe = st.sidebar.selectbox("Timeframe",options=["1Min", "5Min", "15Min", "day"])
-        run_btn = st.sidebar.button("Run")
-        # Get the mapping for patterns from pattern.py file
-        pattern_code = get_key(patterns, pattern)
-        pattern_function = getattr(talib,pattern_code)
-        if run_btn:
-            # If clicked on run button
-            # Then for each symbol, check the output
-            keyword = [x.upper() for x in keywords]
-            data = st.session_state.trade_api.get_barset(keyword, select_timeframe, limit = 100).df
-            for symbol in keyword:
-                try:
-                    # st.write(data)
-                    result = pattern_function(data[symbol]["open"],data[symbol]["high"],data[symbol]["low"],data[symbol]["close"])
-                    last = result.tail(1).values[0]
-                    # For some of the indicators value <0 is Bearish and value>0 is Bullish
-                    # But for others there would be a different logic, you shall handle it differently
-                    if last>0:
-                        st.write(f"Bullish {symbol}")
-                    elif last<0:
-                        st.write(f"Bearish {symbol}")
-                except:
-                    pass

spaces/ChihChiu29/mychatbot/main.py DELETED Viewed

@@ -1,72 +0,0 @@
-"""Model hosted on Hugging face.
-Based on: https://huggingface.co/docs/hub/spaces-sdks-docker-first-demo
-"""
-from fastapi import FastAPI, Request
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-# import gpt4free
-# from gpt4free import Provider, forefront
-token_size_limit = None
-# FROM: https://huggingface.co/facebook/blenderbot-400M-distill?text=Hey+my+name+is+Thomas%21+How+are+you%3F
-# LAST USED
-tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
-model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
-# tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-1B-distill")
-# model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-1B-distill")
-# token_size_limit = 128
-# T5 model can use "any" sequence lenghth, but memory usage is O(L^2).
-# tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
-# model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small")
-# tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
-# model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
-# tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
-# model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
-token_size_limit = 512
-# Too large for 16GB
-# tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xl")
-# model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl")
-app = FastAPI()
-# { msg: string, temperature: float, max_length: number }
-@app.post('/reply')
-async def Reply(req: Request):
-    request = await req.json()
-    msg = request.get('msg')
-    print(f'MSG: {msg}')
-    # Hugging face
-    input_ids = tokenizer(msg, return_tensors='pt').input_ids  # .to('cuda')
-    output = model.generate(
-        input_ids[:, -token_size_limit:],
-        do_sample=True,
-        temperature=request.get('temperature', 0.9),
-        max_length=request.get('max_length', 100),
-    )
-    reply = tokenizer.batch_decode(output)[0]
-    # It doesn't really work.
-    # gpt4free
-    # usage theb
-    # reply = gpt4free.Completion.create(Provider.Theb, prompt=msg)
-    print(f'REPLY: {reply}')
-    return {'reply': reply}
-@app.get("/")
-def read_root():
-    return {"Hello": "World!"}

spaces/Cropinky/esrgan/realesrgan/models/realesrnet_model.py DELETED Viewed

@@ -1,188 +0,0 @@
-import numpy as np
-import random
-import torch
-from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
-from basicsr.data.transforms import paired_random_crop
-from basicsr.models.sr_model import SRModel
-from basicsr.utils import DiffJPEG, USMSharp
-from basicsr.utils.img_process_util import filter2D
-from basicsr.utils.registry import MODEL_REGISTRY
-from torch.nn import functional as F
-@MODEL_REGISTRY.register()
-class RealESRNetModel(SRModel):
-    """RealESRNet Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-    It is trained without GAN losses.
-    It mainly performs:
-    1. randomly synthesize LQ images in GPU tensors
-    2. optimize the networks with GAN training.
-    """
-    def __init__(self, opt):
-        super(RealESRNetModel, self).__init__(opt)
-        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
-        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
-        self.queue_size = opt.get('queue_size', 180)
-    @torch.no_grad()
-    def _dequeue_and_enqueue(self):
-        """It is the training pair pool for increasing the diversity in a batch.
-        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
-        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
-        to increase the degradation diversity in a batch.
-        """
-        # initialize
-        b, c, h, w = self.lq.size()
-        if not hasattr(self, 'queue_lr'):
-            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
-            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
-            _, c, h, w = self.gt.size()
-            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
-            self.queue_ptr = 0
-        if self.queue_ptr == self.queue_size:  # the pool is full
-            # do dequeue and enqueue
-            # shuffle
-            idx = torch.randperm(self.queue_size)
-            self.queue_lr = self.queue_lr[idx]
-            self.queue_gt = self.queue_gt[idx]
-            # get first b samples
-            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
-            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
-            # update the queue
-            self.queue_lr[0:b, :, :, :] = self.lq.clone()
-            self.queue_gt[0:b, :, :, :] = self.gt.clone()
-            self.lq = lq_dequeue
-            self.gt = gt_dequeue
-        else:
-            # only do enqueue
-            self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone()
-            self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone()
-            self.queue_ptr = self.queue_ptr + b
-    @torch.no_grad()
-    def feed_data(self, data):
-        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
-        """
-        if self.is_train and self.opt.get('high_order_degradation', True):
-            # training data synthesis
-            self.gt = data['gt'].to(self.device)
-            # USM sharpen the GT images
-            if self.opt['gt_usm'] is True:
-                self.gt = self.usm_sharpener(self.gt)
-            self.kernel1 = data['kernel1'].to(self.device)
-            self.kernel2 = data['kernel2'].to(self.device)
-            self.sinc_kernel = data['sinc_kernel'].to(self.device)
-            ori_h, ori_w = self.gt.size()[2:4]
-            # ----------------------- The first degradation process ----------------------- #
-            # blur
-            out = filter2D(self.gt, self.kernel1)
-            # random resize
-            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0]
-            if updown_type == 'up':
-                scale = np.random.uniform(1, self.opt['resize_range'][1])
-            elif updown_type == 'down':
-                scale = np.random.uniform(self.opt['resize_range'][0], 1)
-            else:
-                scale = 1
-            mode = random.choice(['area', 'bilinear', 'bicubic'])
-            out = F.interpolate(out, scale_factor=scale, mode=mode)
-            # add noise
-            gray_noise_prob = self.opt['gray_noise_prob']
-            if np.random.uniform() < self.opt['gaussian_noise_prob']:
-                out = random_add_gaussian_noise_pt(
-                    out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
-            else:
-                out = random_add_poisson_noise_pt(
-                    out,
-                    scale_range=self.opt['poisson_scale_range'],
-                    gray_prob=gray_noise_prob,
-                    clip=True,
-                    rounds=False)
-            # JPEG compression
-            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
-            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
-            out = self.jpeger(out, quality=jpeg_p)
-            # ----------------------- The second degradation process ----------------------- #
-            # blur
-            if np.random.uniform() < self.opt['second_blur_prob']:
-                out = filter2D(out, self.kernel2)
-            # random resize
-            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0]
-            if updown_type == 'up':
-                scale = np.random.uniform(1, self.opt['resize_range2'][1])
-            elif updown_type == 'down':
-                scale = np.random.uniform(self.opt['resize_range2'][0], 1)
-            else:
-                scale = 1
-            mode = random.choice(['area', 'bilinear', 'bicubic'])
-            out = F.interpolate(
-                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
-            # add noise
-            gray_noise_prob = self.opt['gray_noise_prob2']
-            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
-                out = random_add_gaussian_noise_pt(
-                    out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
-            else:
-                out = random_add_poisson_noise_pt(
-                    out,
-                    scale_range=self.opt['poisson_scale_range2'],
-                    gray_prob=gray_noise_prob,
-                    clip=True,
-                    rounds=False)
-            # JPEG compression + the final sinc filter
-            # We also need to resize images to desired sizes. We group [resize back + sinc filter] together
-            # as one operation.
-            # We consider two orders:
-            #   1. [resize back + sinc filter] + JPEG compression
-            #   2. JPEG compression + [resize back + sinc filter]
-            # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
-            if np.random.uniform() < 0.5:
-                # resize back + the final sinc filter
-                mode = random.choice(['area', 'bilinear', 'bicubic'])
-                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
-                out = filter2D(out, self.sinc_kernel)
-                # JPEG compression
-                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
-                out = torch.clamp(out, 0, 1)
-                out = self.jpeger(out, quality=jpeg_p)
-            else:
-                # JPEG compression
-                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
-                out = torch.clamp(out, 0, 1)
-                out = self.jpeger(out, quality=jpeg_p)
-                # resize back + the final sinc filter
-                mode = random.choice(['area', 'bilinear', 'bicubic'])
-                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
-                out = filter2D(out, self.sinc_kernel)
-            # clamp and round
-            self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.
-            # random crop
-            gt_size = self.opt['gt_size']
-            self.gt, self.lq = paired_random_crop(self.gt, self.lq, gt_size, self.opt['scale'])
-            # training pair pool
-            self._dequeue_and_enqueue()
-            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
-        else:
-            # for paired training or validation
-            self.lq = data['lq'].to(self.device)
-            if 'gt' in data:
-                self.gt = data['gt'].to(self.device)
-                self.gt_usm = self.usm_sharpener(self.gt)
-    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
-        # do not use the synthetic process during validation
-        self.is_train = False
-        super(RealESRNetModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
-        self.is_train = True

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py DELETED Viewed

@@ -1,73 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-"""
-Implements the Generalized R-CNN framework
-"""
-import torch
-from torch import nn
-from maskrcnn_benchmark.structures.image_list import to_image_list
-from ..backbone import build_backbone
-from ..rpn.rpn import build_rpn
-from ..roi_heads.roi_heads import build_roi_heads
-import numpy as np
-import cv2
-class GeneralizedRCNN(nn.Module):
-    """
-    Main class for Generalized R-CNN. Currently supports boxes and masks.
-    It consists of three main parts:
-    - backbone
-    - rpn
-    - heads: takes the features + the proposals from the RPN and computes
-        detections / masks from it.
-    """
-    def __init__(self, cfg):
-        super(GeneralizedRCNN, self).__init__()
-        self.cfg = cfg.clone()
-        self.backbone = build_backbone(cfg)
-        self.rpn = build_rpn(cfg, self.backbone.out_channels)
-        self.roi_heads = build_roi_heads(cfg, self.backbone.out_channels)
-    def forward(self, images, targets=None):
-        """
-        Arguments:
-            images (list[Tensor] or ImageList): images to be processed
-            targets (list[BoxList]): ground-truth boxes present in the image (optional)
-        Returns:
-            result (list[BoxList] or dict[Tensor]): the output from the model.
-                During training, it returns a dict[Tensor] which contains the losses.
-                During testing, it returns list[BoxList] contains additional fields
-                like `scores`, `labels` and `mask` (for Mask R-CNN models).
-        """
-        if self.training and targets is None:
-            raise ValueError("In training mode, targets should be passed")
-        images = to_image_list(images)
-        features = self.backbone(images.tensors)
-        proposals, proposal_losses = self.rpn(images, features, targets)
-        if self.roi_heads:
-            x, result, detector_losses = self.roi_heads(features, proposals, targets)
-        else:
-            #self.warm_start -= 1
-            # RPN-only models don't have roi_heads
-            x = features
-            result = proposals
-            detector_losses = {}
-        if self.training:
-            losses = {}
-            losses.update(detector_losses)
-            losses.update(proposal_losses)
-            return losses
-        else:
-            return result

spaces/DEEMOSTECH/ChatAvatar/static/css/main.629d4bc9.css DELETED Viewed

	@@ -1,2 +0,0 @@
1	- html{overflow-x:hidden;overflow-y:overlay}body{-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale;box-sizing:border-box;color:#cfcfcf;font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Oxygen,Ubuntu,Cantarell,Fira Sans,Droid Sans,Helvetica Neue,sans-serif;margin:0}code{font-family:source-code-pro,Menlo,Monaco,Consolas,Courier New,monospace}.root{display:flex;justify-content:center;width:100%}.container{height:100vh;width:100%}.\!container{width:100%!important}@media (min-width:640px){.container{max-width:640px}.\!container{max-width:640px!important}}@media (min-width:768px){.container{max-width:768px}.\!container{max-width:768px!important}}@media (min-width:1024px){.container{max-width:1024px}.\!container{max-width:1024px!important}}@media (min-width:1280px){.container{max-width:1280px}.\!container{max-width:1280px!important}}@media (min-width:1536px){.container{max-width:1536px}.\!container{max-width:1536px!important}}.App{--theme-color:#4a00e0;--font-dark-color:#434343;--font-gray-color:#aaa;--font-light-color:#cfcfcf;--bg-light-color:#fff;--bg-gray0-color:#f8f8f8;--bg-gray1-color:#ececec;--bg-gray2-color:#7c7c7c;--bg-gray3-color:#373737;--bg-theme-color:#e7e3f1;--bg-dark-color:#121317;--side-gap:5rem;--radius:0.5rem;--shadow:-10px 0px 12px 1px hsla(0,0%,53%,.16);text-align:center}.App {box-sizing:border-box;transition:all .3s}.App ::-webkit-scrollbar-thumb{background-color:rgba(0,0,0,.2)}textarea{-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale;border:1px solid transparent;color:var(--font-dark-color);font-family:-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Oxygen,Ubuntu,Cantarell,Fira Sans,Droid Sans,Helvetica Neue,sans-serif;font-size:1rem;line-height:1.5rem;outline:none;padding:0;resize:none}textarea:focus{border-color:var(--theme-color)}img{-webkit-user-drag:none;-webkit-user-select:none;user-select:none}.gallery_con__Y2mej{align-items:flex-start;display:flex;justify-content:center;margin-top:var(--side-gap);padding:0 var(--side-gap);width:100%}.gallery_menuCon__fVdFJ{margin-right:2rem;width:-webkit-max-content;width:max-content}.gallery_menu__U2btD{align-items:center;background-color:initial;border:2px solid 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img{height:100%;width:100%}.gallery_prompt__9PEmb{background-color:#f8f8f880;border-radius:var(--radius);bottom:1rem;color:var(--font-dark-color);height:0;left:1rem;overflow:hidden;padding:0 .5rem;position:absolute;right:1rem;text-align:left;white-space:pre-wrap;word-break:break-all}.gallery_prompt__9PEmb.gallery_show__c2k50{height:-webkit-fit-content;height:-moz-fit-content;height:fit-content;padding:.5rem}.gallery_infoCon__E8oLy{align-items:center;bottom:1rem;color:var(--font-dark-color);display:flex;justify-content:flex-start;left:1rem;position:absolute;right:1rem}.gallery_avatar__KWBmI,.gallery_avatar__KWBmI img{border-radius:12px;height:24px;overflow:hidden;width:24px}.gallery_avatar__KWBmI{margin-right:1rem}.gallery_spaceholder__xJwYU{flex-grow:1;flex-shrink:1}.header_con__M\+u1W{align-items:center;display:flex;justify-content:center;padding:0 var(--side-gap);width:100vw}.header_header__Y7CqP{align-items:center;border-bottom:1px solid 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img{border-radius:var(--radius);cursor:pointer;margin-bottom:1rem;width:100%}.result_creatorCon__tIm3e{align-items:flex-end;color:var(--font-gray-color);display:flex;font-size:1.2rem;font-weight:700;justify-content:flex-start;line-height:1.2rem;margin-bottom:1rem;width:100%}.result_creatorInfoCon__pET8h{text-align:left}.result_creatorName__VLTXL{color:var(--font-dark-color);font-size:1.2rem;font-weight:700;line-height:1.8rem}.result_creatorInfo__CkbWU{color:var(--font-gray-color);font-size:1rem;line-height:1.2rem}.result_modelView__Y25w5{background:var(--bg-gray0-color);border-radius:var(--radius);flex-grow:1;flex-shrink:1;overflow:hidden;width:100%}.result_modelInfoCon__bXw5O{align-items:center;bottom:1rem;display:flex;flex-direction:column;justify-content:flex-end;left:1rem;position:absolute;right:1rem;text-align:left}.result_progressInfo__g9iwR{margin-bottom:.5rem;width:100%}.result_progressTrack__I6zDn{background:var(--bg-light-color);border-radius:2px;height:4px;position:relative;width:100%}.result_progressThumb__mbBQj{background-color:var(--theme-color);border-radius:2px;height:4px;left:0;position:absolute;top:0}.result_modelPrompt__DzUbD{background:var(--bg-light-color);border-radius:var(--radius);margin-top:1rem;min-height:3rem;padding:1rem;width:100%}.welcome_con__o1kmf{align-items:center;background:#121317;display:flex;flex-direction:column;justify-content:flex-start;padding-bottom:12rem;padding-top:4rem;position:relative;width:100%}.welcome_con__o1kmf>img{position:absolute;top:0;width:40vw}.welcome_mainCon__H1gv\+{z-index:999}.welcome_title__Gd8m4{color:#fff;font-family:Courier 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2	- /# sourceMappingURL=main.629d4bc9.css.map/

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/FpxImagePlugin.py DELETED Viewed

@@ -1,253 +0,0 @@
-#
-# THIS IS WORK IN PROGRESS
-#
-# The Python Imaging Library.
-# $Id$
-#
-# FlashPix support for PIL
-#
-# History:
-# 97-01-25 fl   Created (reads uncompressed RGB images only)
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1997.
-#
-# See the README file for information on usage and redistribution.
-#
-import olefile
-from . import Image, ImageFile
-from ._binary import i32le as i32
-# we map from colour field tuples to (mode, rawmode) descriptors
-MODES = {
-    # opacity
-    (0x00007FFE,): ("A", "L"),
-    # monochrome
-    (0x00010000,): ("L", "L"),
-    (0x00018000, 0x00017FFE): ("RGBA", "LA"),
-    # photo YCC
-    (0x00020000, 0x00020001, 0x00020002): ("RGB", "YCC;P"),
-    (0x00028000, 0x00028001, 0x00028002, 0x00027FFE): ("RGBA", "YCCA;P"),
-    # standard RGB (NIFRGB)
-    (0x00030000, 0x00030001, 0x00030002): ("RGB", "RGB"),
-    (0x00038000, 0x00038001, 0x00038002, 0x00037FFE): ("RGBA", "RGBA"),
-}
-#
-# --------------------------------------------------------------------
-def _accept(prefix):
-    return prefix[:8] == olefile.MAGIC
-##
-# Image plugin for the FlashPix images.
-class FpxImageFile(ImageFile.ImageFile):
-    format = "FPX"
-    format_description = "FlashPix"
-    def _open(self):
-        #
-        # read the OLE directory and see if this is a likely
-        # to be a FlashPix file
-        try:
-            self.ole = olefile.OleFileIO(self.fp)
-        except OSError as e:
-            msg = "not an FPX file; invalid OLE file"
-            raise SyntaxError(msg) from e
-        if self.ole.root.clsid != "56616700-C154-11CE-8553-00AA00A1F95B":
-            msg = "not an FPX file; bad root CLSID"
-            raise SyntaxError(msg)
-        self._open_index(1)
-    def _open_index(self, index=1):
-        #
-        # get the Image Contents Property Set
-        prop = self.ole.getproperties(
-            [f"Data Object Store {index:06d}", "\005Image Contents"]
-        )
-        # size (highest resolution)
-        self._size = prop[0x1000002], prop[0x1000003]
-        size = max(self.size)
-        i = 1
-        while size > 64:
-            size = size / 2
-            i += 1
-        self.maxid = i - 1
-        # mode.  instead of using a single field for this, flashpix
-        # requires you to specify the mode for each channel in each
-        # resolution subimage, and leaves it to the decoder to make
-        # sure that they all match.  for now, we'll cheat and assume
-        # that this is always the case.
-        id = self.maxid << 16
-        s = prop[0x2000002 | id]
-        colors = []
-        bands = i32(s, 4)
-        if bands > 4:
-            msg = "Invalid number of bands"
-            raise OSError(msg)
-        for i in range(bands):
-            # note: for now, we ignore the "uncalibrated" flag
-            colors.append(i32(s, 8 + i * 4) & 0x7FFFFFFF)
-        self.mode, self.rawmode = MODES[tuple(colors)]
-        # load JPEG tables, if any
-        self.jpeg = {}
-        for i in range(256):
-            id = 0x3000001 | (i << 16)
-            if id in prop:
-                self.jpeg[i] = prop[id]
-        self._open_subimage(1, self.maxid)
-    def _open_subimage(self, index=1, subimage=0):
-        #
-        # setup tile descriptors for a given subimage
-        stream = [
-            f"Data Object Store {index:06d}",
-            f"Resolution {subimage:04d}",
-            "Subimage 0000 Header",
-        ]
-        fp = self.ole.openstream(stream)
-        # skip prefix
-        fp.read(28)
-        # header stream
-        s = fp.read(36)
-        size = i32(s, 4), i32(s, 8)
-        # tilecount = i32(s, 12)
-        tilesize = i32(s, 16), i32(s, 20)
-        # channels = i32(s, 24)
-        offset = i32(s, 28)
-        length = i32(s, 32)
-        if size != self.size:
-            msg = "subimage mismatch"
-            raise OSError(msg)
-        # get tile descriptors
-        fp.seek(28 + offset)
-        s = fp.read(i32(s, 12) * length)
-        x = y = 0
-        xsize, ysize = size
-        xtile, ytile = tilesize
-        self.tile = []
-        for i in range(0, len(s), length):
-            x1 = min(xsize, x + xtile)
-            y1 = min(ysize, y + ytile)
-            compression = i32(s, i + 8)
-            if compression == 0:
-                self.tile.append(
-                    (
-                        "raw",
-                        (x, y, x1, y1),
-                        i32(s, i) + 28,
-                        (self.rawmode,),
-                    )
-                )
-            elif compression == 1:
-                # FIXME: the fill decoder is not implemented
-                self.tile.append(
-                    (
-                        "fill",
-                        (x, y, x1, y1),
-                        i32(s, i) + 28,
-                        (self.rawmode, s[12:16]),
-                    )
-                )
-            elif compression == 2:
-                internal_color_conversion = s[14]
-                jpeg_tables = s[15]
-                rawmode = self.rawmode
-                if internal_color_conversion:
-                    # The image is stored as usual (usually YCbCr).
-                    if rawmode == "RGBA":
-                        # For "RGBA", data is stored as YCbCrA based on
-                        # negative RGB. The following trick works around
-                        # this problem :
-                        jpegmode, rawmode = "YCbCrK", "CMYK"
-                    else:
-                        jpegmode = None  # let the decoder decide
-                else:
-                    # The image is stored as defined by rawmode
-                    jpegmode = rawmode
-                self.tile.append(
-                    (
-                        "jpeg",
-                        (x, y, x1, y1),
-                        i32(s, i) + 28,
-                        (rawmode, jpegmode),
-                    )
-                )
-                # FIXME: jpeg tables are tile dependent; the prefix
-                # data must be placed in the tile descriptor itself!
-                if jpeg_tables:
-                    self.tile_prefix = self.jpeg[jpeg_tables]
-            else:
-                msg = "unknown/invalid compression"
-                raise OSError(msg)
-            x = x + xtile
-            if x >= xsize:
-                x, y = 0, y + ytile
-                if y >= ysize:
-                    break  # isn't really required
-        self.stream = stream
-        self.fp = None
-    def load(self):
-        if not self.fp:
-            self.fp = self.ole.openstream(self.stream[:2] + ["Subimage 0000 Data"])
-        return ImageFile.ImageFile.load(self)
-    def close(self):
-        self.ole.close()
-        super().close()
-    def __exit__(self, *args):
-        self.ole.close()
-        super().__exit__()
-#
-# --------------------------------------------------------------------
-Image.register_open(FpxImageFile.format, FpxImageFile, _accept)
-Image.register_extension(FpxImageFile.format, ".fpx")

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/attr/setters.py DELETED Viewed

@@ -1,73 +0,0 @@
-# SPDX-License-Identifier: MIT
-"""
-Commonly used hooks for on_setattr.
-"""
-from . import _config
-from .exceptions import FrozenAttributeError
-def pipe(*setters):
-    """
-    Run all *setters* and return the return value of the last one.
-    .. versionadded:: 20.1.0
-    """
-    def wrapped_pipe(instance, attrib, new_value):
-        rv = new_value
-        for setter in setters:
-            rv = setter(instance, attrib, rv)
-        return rv
-    return wrapped_pipe
-def frozen(_, __, ___):
-    """
-    Prevent an attribute to be modified.
-    .. versionadded:: 20.1.0
-    """
-    raise FrozenAttributeError()
-def validate(instance, attrib, new_value):
-    """
-    Run *attrib*'s validator on *new_value* if it has one.
-    .. versionadded:: 20.1.0
-    """
-    if _config._run_validators is False:
-        return new_value
-    v = attrib.validator
-    if not v:
-        return new_value
-    v(instance, attrib, new_value)
-    return new_value
-def convert(instance, attrib, new_value):
-    """
-    Run *attrib*'s converter -- if it has one --  on *new_value* and return the
-    result.
-    .. versionadded:: 20.1.0
-    """
-    c = attrib.converter
-    if c:
-        return c(new_value)
-    return new_value
-# Sentinel for disabling class-wide *on_setattr* hooks for certain attributes.
-# autodata stopped working, so the docstring is inlined in the API docs.
-NO_OP = object()

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-ce791c16.js DELETED Viewed

	@@ -1,2 +0,0 @@
1	- import{S as m,e as u,s as r,k as d,o as b,z as c,v as f,x as g,a9 as v,ab as k,ac as B,ad as h}from"./index-3370be2a.js";import{B as p}from"./Button-89624748.js";function C(a){let t;const l=a[3].default,e=v(l,a,a[4],null);return{c(){e&&e.c()},m(s,n){e&&e.m(s,n),t=!0},p(s,n){e&&e.p&&(!t\|\|n&16)&&k(e,l,s,s[4],t?h(l,s[4],n,null):B(s[4]),null)},i(s){t\|\|(c(e,s),t=!0)},o(s){f(e,s),t=!1},d(s){e&&e.d(s)}}}function S(a){let t,l;return t=new p({props:{elem_id:a[0],elem_classes:a[1],visible:a[2],explicit_call:!0,$$slots:{default:[C]},$$scope:{ctx:a}}}),{c(){d(t.$$.fragment)},m(e,s){b(t,e,s),l=!0},p(e,[s]){const n={};s&1&&(n.elem_id=e[0]),s&2&&(n.elem_classes=e[1]),s&4&&(n.visible=e[2]),s&16&&(n.$$scope={dirty:s,ctx:e}),t.$set(n)},i(e){l\|\|(c(t.$$.fragment,e),l=!0)},o(e){f(t.$$.fragment,e),l=!1},d(e){g(t,e)}}}function q(a,t,l){let{$$slots:e={},$$scope:s}=t,{elem_id:n}=t,{elem_classes:i}=t,{visible:_=!0}=t;return a.$$set=o=>{"elem_id"in o&&l(0,n=o.elem_id),"elem_classes"in o&&l(1,i=o.elem_classes),"visible"in o&&l(2,_=o.visible),"$$scope"in o&&l(4,s=o.$$scope)},[n,i,_,e,s]}class w extends m{constructor(t){super(),u(this,t,q,S,r,{elem_id:0,elem_classes:1,visible:2})}}const A=w,D=["static"];export{A as Component,D as modes};
2	- //# sourceMappingURL=index-ce791c16.js.map

spaces/Dagfinn1962/stablediffusion-members/main.css DELETED Viewed

@@ -1,61 +0,0 @@
-  body  {
-    background-color: #080a36;
-    width: 100%;
-    color: #FFFFFF;
-  }
-gr.blocks {
-  background-color: #080a36;
-  width: 100%;
-  color: #FFFFFF;
-}
-h3 {
-  color: #FFFFF;
-  text-align: center;
-  font-family: verdana;
-  font-size: 24px;
-  border: 1px solid #FFFFFF;
-  border-radius: 10px;
-}
-p {
-  font-family: verdana;
-  font-size: 14px;
-}
-label {
-  font-family: verdana;
-  color: #000000;
-  font-weight: 700;
-  font-size: 14px;
-  border: 1px solid #000000;
-}
-gr.Textbox {
-  font-family: verdana;
-  background-color: #23298f;
-  color: #000000;
-  font-weight: 700;
-  font-size: 14px;
-  border: 1px solid #FFFFFF;
-  border-radius: 6px;
-}
-gr.Botton {
-  font-family: verdana;
-  background-color: #23298f;
-  color: #FFFFFF;
-  font-weight: 700;
-  font-size: 14px;
-  border: 1px solid #000000;
-  border-radius: 6px;
-}
-a a:active a.hover
- {
-  font-family: verdana;
-  color: #572430;
-  text-decoration: none;
-  font-weight: 700;
-  font-size: 14px;
-}