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-<h1>How to Download and Install Adobe Premiere CS6 Pro Amtlib.dll 2.1 MB</h1>
- <p>If you are looking for a way to edit your videos professionally and creatively, you might want to try Adobe Premiere CS6 Pro. This software is one of the most popular and powerful video editing tools in the market, offering a wide range of features and functions. However, Adobe Premiere CS6 Pro is not a free software, and you need to pay a monthly or yearly subscription fee to use it. If you don't want to spend money on this software, you might be interested in downloading and installing Amtlib.dll 2.1 MB, which is a cracked version of Adobe Premiere CS6 Pro that allows you to use it for free. In this article, we will show you how to download and install Amtlib.dll 2.1 MB step by step, so you can enjoy editing your videos without any limitations.</p>
- <h2>What is Adobe Premiere CS6 Pro?</h2>
- <p>Adobe Premiere CS6 Pro is a video editing software developed by Adobe Systems. It is part of the Adobe Creative Suite 6 (CS6) family, which also includes other products such as Photoshop, Illustrator, After Effects, and more. Adobe Premiere CS6 Pro is designed for professional and advanced users who need a high level of control and customization over their video projects. Some of the features that Adobe Premiere CS6 Pro offers are:</p>
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-<li>A flexible workflow that allows you to edit your videos in different ways, such as using multiple timelines, nesting sequences, trimming clips, adjusting keyframes, etc.</li>
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- <h2>What is Amtlib.dll?</h2>
- <p>Amtlib.dll is a dynamic link library (DLL) file that is part of the Adobe Application Manager (AAM). This file is responsible for managing the activation and licensing of Adobe products. When you install an Adobe product, such as Adobe Premiere CS6 Pro, you need to enter a serial number or sign in with your Adobe ID to activate it. However, some people use a cracked version of Amtlib.dll to bypass this activation process and use Adobe products for free. This cracked version of Amtlib.dll replaces the original one in the installation folder of Adobe products and tricks them into thinking that they are activated.</p>
- <h2>Why do you need to download Amtlib.dll 2.1 MB?</h2>
- <p>If you want to use Adobe Premiere CS6 Pro for free, you need to download Amtlib.dll 2.1 MB. This is because this file is compatible with Adobe Premiere CS6 Pro and can activate it without any problems. Some of the benefits of using this cracked version of Adobe Premiere CS6 Pro are:</p>
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-<li>You can access all the features and functions of Adobe Premiere CS6 Pro without any restrictions.</li>
-<li>You can update your software without worrying about losing your activation status.</li>
-<li>You can use your software offline without needing an internet connection.</li>
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- <h2>How to download Amtlib.dll 2.1 MB?</h2>
- <p>Before you can install Amtlib.dll 2.1 MB, you need to download it from a reliable source. There are many websites that offer this file for free, but not all of them are safe and trustworthy. Some of them might contain viruses or malware that can harm your computer or steal your personal information. Therefore, you need to be careful when choosing where to download this file from. Here are some steps that you can follow to download Amtlib.dll 2.1 MB safely:</p>
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-<li>On the website, scroll down until you see a table that shows different versions of Amtlib.dll. Look for the version that matches your system type (32-bit or 64-bit) and has a size of 2.1 MB.</li>
-<li>Click on the "Download" button next to that version. This will take you to another page where you can choose where to save the file on your computer.</li>
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-<li>Right-click on the downloaded file and select "Properties".</li>
-<li>In the "Properties" window, click on the "Details" tab.</li>
-<li>Look for the "File size" and "File version" fields and compare them with what you expected.</li>
-<li>If they match, then you have downloaded the right file. If they don't match, then you might have downloaded a wrong or corrupted file.</li>
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- <h3>How to scan the file for viruses and malware?</h3>
- <p>Before you install Amtlib.dll 2.1 MB, you also need to make sure that it is safe and clean from any viruses or malware that might harm your computer or steal your personal information. To do this, you can follow these steps:</p>
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-<li>Right-click on the downloaded file and select "Scan with [your antivirus program]".</li>
-<li>Wait for your antivirus program to scan the file and show you the results.</li>
-<li>If there are no threats detected, then you can proceed with installing the file. If there are threats detected, then you should delete the file immediately and look for another source.</li>
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- <h2>How to install Amtlib.dll 2.1 MB?</h2>
- <p>After you have downloaded Amtlib.dll 2.1 MB safely, you can install it on your computer by replacing the original DLL file in the installation folder of Adobe Premiere CS6 Pro. To do this, you can follow these steps:</p>
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-<li>Make sure that Adobe Premiere CS6 Pro is closed before installing this file.</li>
-<li>Locate the installation folder of Adobe Premiere CS6 Pro on your computer.</li>
-<li>Backup the original DLL file by renaming it or moving it somewhere else.</li>
-<li>Copy and paste the cracked DLL file into the installation folder of Adobe Premiere CS6 Pro and overwrite the original one.</li>
-</ol>
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- <p>After you have installed Amtlib.dll 2.1 MB, you can test if Adobe Premiere CS6 Pro is activated and working properly. To do this, you can follow these steps:</p>
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-<li>Launch Adobe Premiere CS6 Pro from your desktop or start menu.</li>
-<li>Check the license status by going to Help > About Adobe Premiere Pro.</li>
-<li>If you see a message that says "Adobe Premiere Pro CS6 (Activated)", then you have successfully activated the software.</li>
-<li>Check the functionality and performance by creating a new project and editing some videos.</li>
-<li>If you can access all the features and functions of Adobe Premiere CS6 Pro without any errors or crashes, then you have successfully installed the software.</li>
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- <p>In this article, we have shown you how to download and install Amtlib.dll 2.1 MB, which is a cracked version of Adobe Premiere CS6 Pro that allows you to use it for free. We have also explained what Adobe Premiere CS6 Pro and Amtlib.dll are, why you need to download Amtlib.dll 2.1 MB, how to download it safely, how to install it correctly, and how to test if it is working properly. By following these steps, you can enjoy editing your videos professionally and creatively without spending any money on this software. However, we also want to remind you that using a cracked version of Adobe Premiere CS6 Pro is illegal and unethical, and it might cause some problems for your computer or your personal information. Therefore, we recommend that you use this method at your own risk and discretion.</p>
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spaces/1phancelerku/anime-remove-background/Build Your Dream World with World Building Craft MOD APK 1.5.4.md

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-<h1>World Building Craft Mod APK 1.5.4: A Fun and Creative Sandbox Game</h1>
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- <p>World Building Craft Mod APK 1.5.4 is a modified version of World Building Craft developed by Candy Mobile. The difference between mod version and original version is: Unlimited money. You can use this money to buy more items and tools in the game, and enhance your building experience.</p>
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- <ol>
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spaces/1phancelerku/anime-remove-background/Create Stunning HD Renders with Home Design 3D Mod.md

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-<p>Home Design 3D Mod is a modified version of Home Design 3D, a house design software that lets you create your dream home in easy-to-use 2D/3D editor with over 5000 items. The mod version unlocks all the items, features, and modes that are otherwise available only in the paid version of the app. With Home Design 3D Mod, you can access everything for free and enjoy unlimited possibilities of designing your home in 3D.</p>
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-</ol>
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-<p>To use Home Design 3D Mod on your device, follow these steps:</p>
-<ol>
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-<li>Select the mode (2D or 3D) you want to work in.</li>
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-</ol>
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-<p>Home Design 3D Mod is a great app for anyone who wants to design their own home in 3D. Some of the benefits of using this app are:</p>
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-<p>You can use the renders feature to capture your design as a realistic image with shadows, lighting, and rich colors. The renders feature is located at the top right corner of the screen. To use it, tap on the renders icon and select the quality level you want (low, medium, high). The higher the quality level, the longer it will take to generate the image. Once the image is ready, you can save it to your device or share it with others.</p>
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-<p>Home Design 3D Mod is a fun and easy way to design your own home in 3D. You can create your own floor plans and layouts, furnish and decorate your home in 3D with over 5000 items from the catalog, visualize and share your design with others using renders feature. Home Design 3D Mod is a modified version of Home Design 3D that unlocks all the items, features, and modes that are otherwise available only in the paid version of the app. You can download and install Home Design 3D Mod for free from [this link] and enjoy unlimited possibilities of designing your home in 3D.</p>
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spaces/1toTree/lora_test/ppdiffusers/models/vae.py

@@ -1,629 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import paddle
-import paddle.nn as nn
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..modeling_utils import ModelMixin
-from ..utils import BaseOutput
-from .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
-
-
-@dataclass
-class DecoderOutput(BaseOutput):
-    """
-    Output of decoding method.
-
-    Args:
-        sample (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)`):
-            Decoded output sample of the model. Output of the last layer of the model.
-    """
-
-    sample: paddle.Tensor
-
-
-@dataclass
-class VQEncoderOutput(BaseOutput):
-    """
-    Output of VQModel encoding method.
-
-    Args:
-        latents (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)`):
-            Encoded output sample of the model. Output of the last layer of the model.
-    """
-
-    latents: paddle.Tensor
-
-
-@dataclass
-class AutoencoderKLOutput(BaseOutput):
-    """
-    Output of AutoencoderKL encoding method.
-
-    Args:
-        latent_dist (`DiagonalGaussianDistribution`):
-            Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
-            `DiagonalGaussianDistribution` allows for sampling latents from the distribution.
-    """
-
-    latent_dist: "DiagonalGaussianDistribution"
-
-
-class Encoder(nn.Layer):
-    def __init__(
-        self,
-        in_channels=3,
-        out_channels=3,
-        down_block_types=("DownEncoderBlock2D",),
-        block_out_channels=(64,),
-        layers_per_block=2,
-        norm_num_groups=32,
-        act_fn="silu",
-        double_z=True,
-    ):
-        super().__init__()
-        self.layers_per_block = layers_per_block
-
-        self.conv_in = nn.Conv2D(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1)
-
-        self.mid_block = None
-        self.down_blocks = nn.LayerList([])
-
-        # down
-        output_channel = block_out_channels[0]
-        for i, down_block_type in enumerate(down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            down_block = get_down_block(
-                down_block_type,
-                num_layers=self.layers_per_block,
-                in_channels=input_channel,
-                out_channels=output_channel,
-                add_downsample=not is_final_block,
-                resnet_eps=1e-6,
-                downsample_padding=0,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                attn_num_head_channels=None,
-                temb_channels=None,
-            )
-            self.down_blocks.append(down_block)
-
-        # mid
-        self.mid_block = UNetMidBlock2D(
-            in_channels=block_out_channels[-1],
-            resnet_eps=1e-6,
-            resnet_act_fn=act_fn,
-            output_scale_factor=1,
-            resnet_time_scale_shift="default",
-            attn_num_head_channels=None,
-            resnet_groups=norm_num_groups,
-            temb_channels=None,
-        )
-
-        # out
-        self.conv_norm_out = nn.GroupNorm(
-            num_channels=block_out_channels[-1], num_groups=norm_num_groups, epsilon=1e-6
-        )
-        self.conv_act = nn.Silu()
-
-        conv_out_channels = 2 * out_channels if double_z else out_channels
-        self.conv_out = nn.Conv2D(block_out_channels[-1], conv_out_channels, 3, padding=1)
-
-    def forward(self, x):
-        sample = x
-        sample = self.conv_in(sample)
-
-        # down
-        for down_block in self.down_blocks:
-            sample = down_block(sample)
-
-        # middle
-        sample = self.mid_block(sample)
-
-        # post-process
-        sample = self.conv_norm_out(sample)
-        sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-
-        return sample
-
-
-class Decoder(nn.Layer):
-    def __init__(
-        self,
-        in_channels=3,
-        out_channels=3,
-        up_block_types=("UpDecoderBlock2D",),
-        block_out_channels=(64,),
-        layers_per_block=2,
-        norm_num_groups=32,
-        act_fn="silu",
-    ):
-        super().__init__()
-        self.layers_per_block = layers_per_block
-
-        self.conv_in = nn.Conv2D(in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1)
-
-        self.mid_block = None
-        self.up_blocks = nn.LayerList([])
-
-        # mid
-        self.mid_block = UNetMidBlock2D(
-            in_channels=block_out_channels[-1],
-            resnet_eps=1e-6,
-            resnet_act_fn=act_fn,
-            output_scale_factor=1,
-            resnet_time_scale_shift="default",
-            attn_num_head_channels=None,
-            resnet_groups=norm_num_groups,
-            temb_channels=None,
-        )
-
-        # up
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        output_channel = reversed_block_out_channels[0]
-        for i, up_block_type in enumerate(up_block_types):
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-
-            is_final_block = i == len(block_out_channels) - 1
-
-            up_block = get_up_block(
-                up_block_type,
-                num_layers=self.layers_per_block + 1,
-                in_channels=prev_output_channel,
-                out_channels=output_channel,
-                prev_output_channel=None,
-                add_upsample=not is_final_block,
-                resnet_eps=1e-6,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                attn_num_head_channels=None,
-                temb_channels=None,
-            )
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-        # out
-        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, epsilon=1e-6)
-        self.conv_act = nn.Silu()
-        self.conv_out = nn.Conv2D(block_out_channels[0], out_channels, 3, padding=1)
-
-    def forward(self, z):
-        sample = z
-        sample = self.conv_in(sample)
-
-        # middle
-        sample = self.mid_block(sample)
-
-        # up
-        for up_block in self.up_blocks:
-            sample = up_block(sample)
-
-        # post-process
-        sample = self.conv_norm_out(sample)
-        sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-
-        return sample
-
-
-class VectorQuantizer(nn.Layer):
-    """
-    Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix
-    multiplications and allows for post-hoc remapping of indices.
-    """
-
-    # NOTE: due to a bug the beta term was applied to the wrong term. for
-    # backwards compatibility we use the buggy version by default, but you can
-    # specify legacy=False to fix it.
-    def __init__(
-        self, n_e, vq_embed_dim, beta, remap=None, unknown_index="random", sane_index_shape=False, legacy=True
-    ):
-        super().__init__()
-        self.n_e = n_e
-        self.vq_embed_dim = vq_embed_dim
-        self.beta = beta
-        self.legacy = legacy
-
-        self.embedding = nn.Embedding(
-            self.n_e, self.vq_embed_dim, weight_attr=nn.initializer.Uniform(-1.0 / self.n_e, 1.0 / self.n_e)
-        )
-
-        self.remap = remap
-        if self.remap is not None:
-            self.register_buffer("used", paddle.to_tensor(np.load(self.remap)))
-            self.re_embed = self.used.shape[0]
-            self.unknown_index = unknown_index  # "random" or "extra" or integer
-            if self.unknown_index == "extra":
-                self.unknown_index = self.re_embed
-                self.re_embed = self.re_embed + 1
-            print(
-                f"Remapping {self.n_e} indices to {self.re_embed} indices. "
-                f"Using {self.unknown_index} for unknown indices."
-            )
-        else:
-            self.re_embed = n_e
-
-        self.sane_index_shape = sane_index_shape
-
-    def remap_to_used(self, inds):
-        ishape = inds.shape
-        assert len(ishape) > 1
-        inds = inds.reshape([ishape[0], -1])
-        used = self.used.cast(inds.dtype)
-        match = (inds[:, :, None] == used[None, None, ...]).cast("int64")
-        new = match.argmax(-1)
-        unknown = match.sum(2) < 1
-        if self.unknown_index == "random":
-            new[unknown] = paddle.randint(0, self.re_embed, shape=new[unknown].shape)
-        else:
-            new[unknown] = self.unknown_index
-        return new.reshape(ishape)
-
-    def unmap_to_all(self, inds):
-        ishape = inds.shape
-        assert len(ishape) > 1
-        inds = inds.reshape([ishape[0], -1])
-        used = self.used.cast(inds.dtype)
-        if self.re_embed > self.used.shape[0]:  # extra token
-            inds[inds >= self.used.shape[0]] = 0  # simply set to zero
-        back = paddle.take_along_axis(used[None, :][inds.shape[0] * [0], :], inds, axis=1)
-        return back.reshape(ishape)
-
-    def forward(self, z):
-        # reshape z -> (batch, height, width, channel) and flatten
-        z = z.transpose([0, 2, 3, 1])
-        z_flattened = z.reshape([-1, self.vq_embed_dim])
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-
-        d = (
-            paddle.sum(z_flattened**2, axis=1, keepdim=True)
-            + paddle.sum(self.embedding.weight**2, axis=1)
-            - 2 * paddle.matmul(z_flattened, self.embedding.weight, transpose_y=True)
-        )
-
-        min_encoding_indices = paddle.argmin(d, axis=1)
-        z_q = self.embedding(min_encoding_indices).reshape(z.shape)
-        perplexity = None
-        min_encodings = None
-
-        # compute loss for embedding
-        if not self.legacy:
-            loss = self.beta * paddle.mean((z_q.detach() - z) ** 2) + paddle.mean((z_q - z.detach()) ** 2)
-        else:
-            loss = paddle.mean((z_q.detach() - z) ** 2) + self.beta * paddle.mean((z_q - z.detach()) ** 2)
-
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # reshape back to match original input shape
-        z_q = z_q.transpose([0, 3, 1, 2])
-
-        if self.remap is not None:
-            min_encoding_indices = min_encoding_indices.reshape([z.shape[0], -1])  # add batch axis
-            min_encoding_indices = self.remap_to_used(min_encoding_indices)
-            min_encoding_indices = min_encoding_indices.reshape([-1, 1])  # flatten
-
-        if self.sane_index_shape:
-            min_encoding_indices = min_encoding_indices.reshape([z_q.shape[0], z_q.shape[2], z_q.shape[3]])
-
-        return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
-
-    def get_codebook_entry(self, indices, shape):
-        # shape specifying (batch, height, width, channel)
-        if self.remap is not None:
-            indices = indices.reshape([shape[0], -1])  # add batch axis
-            indices = self.unmap_to_all(indices)
-            indices = indices.reshape(
-                [
-                    -1,
-                ]
-            )  # flatten again
-
-        # get quantized latent vectors
-        z_q = self.embedding(indices)
-
-        if shape is not None:
-            z_q = z_q.reshape(shape)
-            # reshape back to match original input shape
-            z_q = z_q.transpose([0, 3, 1, 2])
-
-        return z_q
-
-
-class DiagonalGaussianDistribution(object):
-    def __init__(self, parameters, deterministic=False):
-        self.parameters = parameters
-        self.mean, self.logvar = paddle.chunk(parameters, 2, axis=1)
-        self.logvar = paddle.clip(self.logvar, -30.0, 20.0)
-        self.deterministic = deterministic
-        self.std = paddle.exp(0.5 * self.logvar)
-        self.var = paddle.exp(self.logvar)
-        if self.deterministic:
-            self.var = self.std = paddle.zeros_like(self.mean, dtype=self.parameters.dtype)
-
-    def sample(self, generator: Optional[paddle.Generator] = None) -> paddle.Tensor:
-        sample = paddle.randn(self.mean.shape, generator=generator)
-        # make sure sample is as the parameters and has same dtype
-        sample = sample.cast(self.parameters.dtype)
-        x = self.mean + self.std * sample
-        return x
-
-    def kl(self, other=None):
-        if self.deterministic:
-            return paddle.to_tensor([0.0])
-        else:
-            if other is None:
-                return 0.5 * paddle.sum(paddle.pow(self.mean, 2) + self.var - 1.0 - self.logvar, axis=[1, 2, 3])
-            else:
-                return 0.5 * paddle.sum(
-                    paddle.pow(self.mean - other.mean, 2) / other.var
-                    + self.var / other.var
-                    - 1.0
-                    - self.logvar
-                    + other.logvar,
-                    axis=[1, 2, 3],
-                )
-
-    def nll(self, sample, axis=[1, 2, 3]):
-        if self.deterministic:
-            return paddle.to_tensor([0.0])
-        logtwopi = np.log(2.0 * np.pi)
-        return 0.5 * paddle.sum(logtwopi + self.logvar + paddle.pow(sample - self.mean, 2) / self.var, axis=axis)
-
-    def mode(self):
-        return self.mean
-
-
-class VQModel(ModelMixin, ConfigMixin):
-    r"""VQ-VAE model from the paper Neural Discrete Representation Learning by Aaron van den Oord, Oriol Vinyals and Koray
-    Kavukcuoglu.
-
-    This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
-    implements for all the model (such as downloading or saving, etc.)
-
-    Parameters:
-        in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
-        out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
-        down_block_types (`Tuple[str]`, *optional*, defaults to :
-            obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types.
-        up_block_types (`Tuple[str]`, *optional*, defaults to :
-            obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types.
-        block_out_channels (`Tuple[int]`, *optional*, defaults to :
-            obj:`(64,)`): Tuple of block output channels.
-        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
-        latent_channels (`int`, *optional*, defaults to `3`): Number of channels in the latent space.
-        sample_size (`int`, *optional*, defaults to `32`): TODO
-        num_vq_embeddings (`int`, *optional*, defaults to `256`): Number of codebook vectors in the VQ-VAE.
-        vq_embed_dim (`int`, *optional*): Hidden dim of codebook vectors in the VQ-VAE.
-    """
-
-    @register_to_config
-    def __init__(
-        self,
-        in_channels: int = 3,
-        out_channels: int = 3,
-        down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
-        up_block_types: Tuple[str] = ("UpDecoderBlock2D",),
-        block_out_channels: Tuple[int] = (64,),
-        layers_per_block: int = 1,
-        act_fn: str = "silu",
-        latent_channels: int = 3,
-        sample_size: int = 32,
-        num_vq_embeddings: int = 256,
-        norm_num_groups: int = 32,
-        vq_embed_dim: Optional[int] = None,
-    ):
-        super().__init__()
-
-        # pass init params to Encoder
-        self.encoder = Encoder(
-            in_channels=in_channels,
-            out_channels=latent_channels,
-            down_block_types=down_block_types,
-            block_out_channels=block_out_channels,
-            layers_per_block=layers_per_block,
-            act_fn=act_fn,
-            norm_num_groups=norm_num_groups,
-            double_z=False,
-        )
-
-        vq_embed_dim = vq_embed_dim if vq_embed_dim is not None else latent_channels
-
-        self.quant_conv = nn.Conv2D(latent_channels, vq_embed_dim, 1)
-        self.quantize = VectorQuantizer(num_vq_embeddings, vq_embed_dim, beta=0.25, remap=None, sane_index_shape=False)
-        self.post_quant_conv = nn.Conv2D(vq_embed_dim, latent_channels, 1)
-
-        # pass init params to Decoder
-        self.decoder = Decoder(
-            in_channels=latent_channels,
-            out_channels=out_channels,
-            up_block_types=up_block_types,
-            block_out_channels=block_out_channels,
-            layers_per_block=layers_per_block,
-            act_fn=act_fn,
-            norm_num_groups=norm_num_groups,
-        )
-
-    def encode(self, x: paddle.Tensor, return_dict: bool = True):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-
-        if not return_dict:
-            return (h,)
-
-        return VQEncoderOutput(latents=h)
-
-    def decode(self, h: paddle.Tensor, force_not_quantize: bool = False, return_dict: bool = True):
-        # also go through quantization layer
-        if not force_not_quantize:
-            quant, emb_loss, info = self.quantize(h)
-        else:
-            quant = h
-        quant = self.post_quant_conv(quant)
-        dec = self.decoder(quant)
-
-        if not return_dict:
-            return (dec,)
-
-        return DecoderOutput(sample=dec)
-
-    def forward(self, sample: paddle.Tensor, return_dict: bool = True):
-        r"""
-        Args:
-            sample (`paddle.Tensor`): Input sample.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
-        """
-        x = sample
-        h = self.encode(x).latents
-        dec = self.decode(h).sample
-
-        if not return_dict:
-            return (dec,)
-
-        return DecoderOutput(sample=dec)
-
-
-class AutoencoderKL(ModelMixin, ConfigMixin):
-    r"""Variational Autoencoder (VAE) model with KL loss from the paper Auto-Encoding Variational Bayes by Diederik P. Kingma
-    and Max Welling.
-
-    This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
-    implements for all the model (such as downloading or saving, etc.)
-
-    Parameters:
-        in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
-        out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
-        down_block_types (`Tuple[str]`, *optional*, defaults to :
-            obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types.
-        down_block_out_channels (`Tuple[int]`, *optional*, defaults to :
-            None: Tuple of down block output channels.
-        up_block_types (`Tuple[str]`, *optional*, defaults to :
-            obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types.
-        up_block_out_channels (`Tuple[int]`, *optional*, defaults to :
-            None: Tuple of up block output channels.
-        block_out_channels (`Tuple[int]`, *optional*, defaults to :
-            obj:`(64,)`): Tuple of block output channels.
-        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
-        latent_channels (`int`, *optional*, defaults to `4`): Number of channels in the latent space.
-        sample_size (`int`, *optional*, defaults to `32`): TODO
-    """
-
-    @register_to_config
-    def __init__(
-        self,
-        in_channels: int = 3,
-        out_channels: int = 3,
-        down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
-        down_block_out_channels: Tuple[int] = None,
-        up_block_types: Tuple[str] = ("UpDecoderBlock2D",),
-        up_block_out_channels: Tuple[int] = None,
-        block_out_channels: Tuple[int] = (64,),
-        layers_per_block: int = 1,
-        act_fn: str = "silu",
-        latent_channels: int = 4,
-        norm_num_groups: int = 32,
-        sample_size: int = 32,
-    ):
-        super().__init__()
-
-        # pass init params to Encoder
-        self.encoder = Encoder(
-            in_channels=in_channels,
-            out_channels=latent_channels,
-            down_block_types=down_block_types,
-            block_out_channels=down_block_out_channels
-            if down_block_out_channels
-            is not None  # if down_block_out_channels not givien, we will use block_out_channels
-            else block_out_channels,
-            layers_per_block=layers_per_block,
-            act_fn=act_fn,
-            norm_num_groups=norm_num_groups,
-            double_z=True,
-        )
-
-        # pass init params to Decoder
-        self.decoder = Decoder(
-            in_channels=latent_channels,
-            out_channels=out_channels,
-            up_block_types=up_block_types,
-            block_out_channels=up_block_out_channels  # if up_block_out_channels not givien, we will use block_out_channels
-            if up_block_out_channels is not None
-            else block_out_channels,
-            layers_per_block=layers_per_block,
-            norm_num_groups=norm_num_groups,
-            act_fn=act_fn,
-        )
-
-        self.quant_conv = nn.Conv2D(2 * latent_channels, 2 * latent_channels, 1)
-        self.post_quant_conv = nn.Conv2D(latent_channels, latent_channels, 1)
-
-    def encode(self, x: paddle.Tensor, return_dict: bool = True):
-        h = self.encoder(x)
-        moments = self.quant_conv(h)
-        posterior = DiagonalGaussianDistribution(moments)
-
-        if not return_dict:
-            return (posterior,)
-
-        return AutoencoderKLOutput(latent_dist=posterior)
-
-    # (TODO junnyu) support vae slice
-    # https://github.com/huggingface/diffusers/commit/c28d3c82ce6f56c4b373a8260c56357d13db900a#diff-64804f08bc5e7a09947fb4eced462f15965acfa2d797354d85033e788f23b443
-    def decode(self, z: paddle.Tensor, return_dict: bool = True):
-        z = self.post_quant_conv(z)
-        dec = self.decoder(z)
-
-        if not return_dict:
-            return (dec,)
-
-        return DecoderOutput(sample=dec)
-
-    def forward(
-        self,
-        sample: paddle.Tensor,
-        sample_posterior: bool = False,
-        return_dict: bool = True,
-        generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
-    ) -> Union[DecoderOutput, paddle.Tensor]:
-        r"""
-        Args:
-            sample (`paddle.Tensor`): Input sample.
-            sample_posterior (`bool`, *optional*, defaults to `False`):
-                Whether to sample from the posterior.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
-        """
-        x = sample
-        posterior = self.encode(x).latent_dist
-        if sample_posterior:
-            z = posterior.sample(generator=generator)
-        else:
-            z = posterior.mode()
-        dec = self.decode(z).sample
-
-        if not return_dict:
-            return (dec,)
-
-        return DecoderOutput(sample=dec)

spaces/1toTree/lora_test/ppdiffusers/schedulers/scheduling_sde_ve.py

@@ -1,262 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 Google Brain and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
-
-import math
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import paddle
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
-from .scheduling_utils import SchedulerMixin, SchedulerOutput
-
-
-@dataclass
-class SdeVeOutput(BaseOutput):
-    """
-    Output class for the ScoreSdeVeScheduler's step function output.
-
-    Args:
-        prev_sample (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        prev_sample_mean (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Mean averaged `prev_sample`. Same as `prev_sample`, only mean-averaged over previous timesteps.
-    """
-
-    prev_sample: paddle.Tensor
-    prev_sample_mean: paddle.Tensor
-
-
-class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
-    """
-    The variance exploding stochastic differential equation (SDE) scheduler.
-
-    For more information, see the original paper: https://arxiv.org/abs/2011.13456
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        snr (`float`):
-            coefficient weighting the step from the model_output sample (from the network) to the random noise.
-        sigma_min (`float`):
-                initial noise scale for sigma sequence in sampling procedure. The minimum sigma should mirror the
-                distribution of the data.
-        sigma_max (`float`): maximum value used for the range of continuous timesteps passed into the model.
-        sampling_eps (`float`): the end value of sampling, where timesteps decrease progressively from 1 to
-        epsilon.
-        correct_steps (`int`): number of correction steps performed on a produced sample.
-    """
-
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 2000,
-        snr: float = 0.15,
-        sigma_min: float = 0.01,
-        sigma_max: float = 1348.0,
-        sampling_eps: float = 1e-5,
-        correct_steps: int = 1,
-    ):
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = sigma_max
-
-        # setable values
-        self.timesteps = None
-
-        self.set_sigmas(num_train_timesteps, sigma_min, sigma_max, sampling_eps)
-
-    def scale_model_input(self, sample: paddle.Tensor, timestep: Optional[int] = None) -> paddle.Tensor:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-
-        Args:
-            sample (`paddle.Tensor`): input sample
-            timestep (`int`, optional): current timestep
-
-        Returns:
-            `paddle.Tensor`: scaled input sample
-        """
-        return sample
-
-    def set_timesteps(self, num_inference_steps: int, sampling_eps: float = None):
-        """
-        Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-            sampling_eps (`float`, optional): final timestep value (overrides value given at Scheduler instantiation).
-
-        """
-        sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
-
-        self.timesteps = paddle.linspace(1, sampling_eps, num_inference_steps)
-
-    def set_sigmas(
-        self, num_inference_steps: int, sigma_min: float = None, sigma_max: float = None, sampling_eps: float = None
-    ):
-        """
-        Sets the noise scales used for the diffusion chain. Supporting function to be run before inference.
-
-        The sigmas control the weight of the `drift` and `diffusion` components of sample update.
-
-        Args:
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-            sigma_min (`float`, optional):
-                initial noise scale value (overrides value given at Scheduler instantiation).
-            sigma_max (`float`, optional): final noise scale value (overrides value given at Scheduler instantiation).
-            sampling_eps (`float`, optional): final timestep value (overrides value given at Scheduler instantiation).
-
-        """
-        sigma_min = sigma_min if sigma_min is not None else self.config.sigma_min
-        sigma_max = sigma_max if sigma_max is not None else self.config.sigma_max
-        sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
-        if self.timesteps is None:
-            self.set_timesteps(num_inference_steps, sampling_eps)
-
-        self.sigmas = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
-        self.discrete_sigmas = paddle.exp(
-            paddle.linspace(math.log(sigma_min), math.log(sigma_max), num_inference_steps)
-        )
-        self.sigmas = paddle.to_tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])
-
-    def get_adjacent_sigma(self, timesteps, t):
-        return paddle.where(
-            timesteps == 0,
-            paddle.zeros_like(t),
-            self.discrete_sigmas[timesteps - 1],
-        )
-
-    def step_pred(
-        self,
-        model_output: paddle.Tensor,
-        timestep: int,
-        sample: paddle.Tensor,
-        generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
-        return_dict: bool = True,
-    ) -> Union[SdeVeOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            model_output (`paddle.Tensor`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`paddle.Tensor`):
-                current instance of sample being created by diffusion process.
-            generator: random number generator.
-            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
-
-        Returns:
-            [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] if
-            `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
-
-        """
-        if self.timesteps is None:
-            raise ValueError(
-                "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        timestep = timestep * paddle.ones((sample.shape[0],))  # paddle.repeat_interleave(timestep, sample.shape[0])
-        timesteps = (timestep * (len(self.timesteps) - 1)).cast("int64")
-
-        sigma = self.discrete_sigmas[timesteps]
-        adjacent_sigma = self.get_adjacent_sigma(timesteps, timestep)
-        drift = paddle.zeros_like(sample)
-        diffusion = (sigma**2 - adjacent_sigma**2) ** 0.5
-
-        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
-        # also equation 47 shows the analog from SDE models to ancestral sampling methods
-        diffusion = diffusion.flatten()
-        while len(diffusion.shape) < len(sample.shape):
-            diffusion = diffusion.unsqueeze(-1)
-        drift = drift - diffusion**2 * model_output
-
-        #  equation 6: sample noise for the diffusion term of
-        noise = paddle.randn(sample.shape, generator=generator)
-        prev_sample_mean = sample - drift  # subtract because `dt` is a small negative timestep
-        # TODO is the variable diffusion the correct scaling term for the noise?
-        prev_sample = prev_sample_mean + diffusion * noise  # add impact of diffusion field g
-
-        if not return_dict:
-            return (prev_sample, prev_sample_mean)
-
-        return SdeVeOutput(prev_sample=prev_sample, prev_sample_mean=prev_sample_mean)
-
-    def step_correct(
-        self,
-        model_output: paddle.Tensor,
-        sample: paddle.Tensor,
-        generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
-        return_dict: bool = True,
-    ) -> Union[SchedulerOutput, Tuple]:
-        """
-        Correct the predicted sample based on the output model_output of the network. This is often run repeatedly
-        after making the prediction for the previous timestep.
-
-        Args:
-            model_output (`paddle.Tensor`): direct output from learned diffusion model.
-            sample (`paddle.Tensor`):
-                current instance of sample being created by diffusion process.
-            generator: random number generator.
-            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
-
-        Returns:
-            [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] if
-            `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
-
-        """
-        if self.timesteps is None:
-            raise ValueError(
-                "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
-        # sample noise for correction
-        noise = paddle.randn(sample.shape, generator=generator)
-
-        # compute step size from the model_output, the noise, and the snr
-        grad_norm = paddle.norm(model_output.reshape([model_output.shape[0], -1]), axis=-1).mean()
-        noise_norm = paddle.norm(noise.reshape([noise.shape[0], -1]), axis=-1).mean()
-        step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
-        step_size = step_size * paddle.ones((sample.shape[0],))
-        # self.repeat_scalar(step_size, sample.shape[0])
-
-        # compute corrected sample: model_output term and noise term
-        step_size = step_size.flatten()
-        while len(step_size.shape) < len(sample.shape):
-            step_size = step_size.unsqueeze(-1)
-        prev_sample_mean = sample + step_size * model_output
-        prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
-
-        if not return_dict:
-            return (prev_sample,)
-
-        return SchedulerOutput(prev_sample=prev_sample)
-
-    def __len__(self):
-        return self.config.num_train_timesteps

spaces/7thHeaven/GPT2WordPress/README.md

@@ -1,14 +0,0 @@
----
-title: GPT2WordPress
-emoji: 📈
-colorFrom: blue
-colorTo: green
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: unknown
-duplicated_from: 7thHeaven/GPT2WordPress_local
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIConsultant/MusicGen/scripts/templates/survey.html

@@ -1,131 +0,0 @@
-{% extends "base.html" %}
-{% block content %}
-<h1>Survey #{{signature}}</h1>
-{% if success %}
-<p class="success"> Your ratings have been saved!
-You have been moved to the next random seed, if you want
-to keep rating more samples. </p>
-{% endif %}
-{% if already_filled %}
-<p class="warning"> You already rated those samples in the past,
-    filling this form will override your previous ratings.
-</p>
-{% endif %}
-<p>Welcome <span class='special'>{{session['user']}}</span> to the survey <span class='special'>#{{signature}}</span>.
-Go to <a href="{{url_for('results', signature=signature)}}">the result page</a> to check the results. Go to <a href="{{url_for('index')}}">the home page</a> to start a new survey.
-</p>
-
-{% for error in errors %}
-<p class="error">{{error}}</p>
-{% endfor %}
-
-{% if not blind %}
-<p>Base config is: <span class="xp_name">{{ref_name}}</span></p>
-<p>The following experiments are compared:</p>
-<ul>
-    {% for experiment in experiments %}
-    <li><span class='special'>{{experiment.xp.sig}}</span> ({{experiment.epoch}} epochs): <span class="xp_name">{{experiment.name}}</span></li>
-    {% endfor %}
-</ul>
-{% else %}
-<p>This is a blind experiment, the order of all XPs is shuffled with every sample.</p>
-{% endif %}
-<p>The current random seed is {{seed}}. You can change it with the following form, and also update blind/non blind.
-</p>
-<form method="get" action="" class="simple_form">
-    <input type="number" name="seed" value="{{seed}}">
-    <label>Blind?
-    <input type="checkbox" name="blind" {% if blind %} checked {% endif %}> </label>
-    <label>Exclude unprompted?
-    <input type="checkbox" name="exclude_unprompted" {% if exclude_unprompted %} checked {% endif %}> </label>
-    <label>Exclude prompted?
-    <input type="checkbox" name="exclude_prompted" {% if exclude_prompted %} checked {% endif %}> </label>
-    <label>Max epoch?
-    <input type="text" name="max_epoch" value="{{max_epoch}}"> </label>
-    <input type="submit" value="Update">
-</form>
-
-<h2>Samples</h2>
-<div class="survey">
-<form method="post" action="{{url_for('survey', signature=signature, blind='true' if blind else 'false', exclude_prompted='true' if exclude_prompted else 'false', exclude_unprompted='true' if exclude_unprompted else 'false', seed=seed, max_epoch=max_epoch)}}" class="simple_form">
-{% for id in model_ids %}
-    <div class="track">
-    <h4>{{id}}</h4>
-    {% for model in models_by_id[id] %}
-        {% if loop.index == 1 and model.is_prompted %}
-            <section class="prompt">
-            <p>Prompt is </p>
-                <audio controls>
-                    <source src="{{url_for('audio', path=model.sample.prompt.path)}}" type="audio/mp3">
-                </audio>
-            <p>Ground truth is </p>
-                <audio controls>
-                    <source src="{{url_for('audio', path=model.sample.prompt.ground_truth_path)}}" type="audio/mp3">
-                </audio>
-            </section>
-        {% endif %}
-        {% for err in model['errors'] %}
-            <p class="error">{{err}}</p>
-        {% endfor %}
-        <section class="model">
-        {% if not blind %}
-            <p class="special">{{model.xp.sig}}:</p>
-        {% endif %}
-        <audio controls>
-                <source src="{{url_for('audio', path=model.sample.path)}}" type="audio/mp3">
-            Your browser does not support the audio element.
-        </audio>
-        <p>Rating:</p>
-        <section class="ratings" id="ratings-{{model.model_id}}">
-            {% for rating in ratings %}
-            <span class="rating rating_{{rating}} {% if rating == model.rating %}rating_selected{% endif %}"
-                    data-target="{{model.model_id}}" data-rating="{{rating}}" onclick="updateNote(this)">{{rating}}</span>
-            {% endfor %}
-            <input type="hidden" name="{{model.model_id}}" id="{{model.model_id}}" value="{{model.rating}}">
-        </section>
-        </p>
-        </section>
-    {% endfor %}
-        </div>
-        <hr>
-{% endfor %}
-    <button type="submit" class="submit-big">
-        Submit evaluations
-    </button>
-<form>
-</div>
-<script>
-function updateNote(node) {
-    var target = node.getAttribute('data-target');
-    var rating = node.getAttribute('data-rating');
-    var field = document.getElementById(target);
-    field.value = rating;
-    node.classList.add('rating_selected');
-
-    var parent = document.getElementById('ratings-' + target);
-    for (const other of parent.childNodes) {
-        if (other.tagName === 'SPAN' && other.classList.contains('rating_selected') && other !== node) {
-            other.classList.remove('rating_selected');
-        }
-    }
-}
-
-function setupCallback(elem, elems) {
-  elem.addEventListener("play", function () {
-    for (var other of elems) {
-      if (other !== elem) {
-        other.pause();
-        // other.currentTime = 0.;
-      }
-    }
-  });
-}
-
-document.addEventListener('DOMContentLoaded', function () {
-  var elems = document.body.getElementsByTagName("audio");
-  for (var elem of elems) {
-    setupCallback(elem, elems);
-  }
-});
-</script>
-{% endblock %}

spaces/AIGC-Audio/AudioGPT/NeuralSeq/tasks/vocoder/vocoder_base.py

@@ -1,66 +0,0 @@
-import os
-
-import torch
-import torch.distributed as dist
-from torch.utils.data import DistributedSampler
-
-from tasks.base_task import BaseTask
-from tasks.base_task import data_loader
-from tasks.vocoder.dataset_utils import VocoderDataset, EndlessDistributedSampler
-from utils.hparams import hparams
-
-
-class VocoderBaseTask(BaseTask):
-    def __init__(self):
-        super(VocoderBaseTask, self).__init__()
-        self.max_sentences = hparams['max_sentences']
-        self.max_valid_sentences = hparams['max_valid_sentences']
-        if self.max_valid_sentences == -1:
-            hparams['max_valid_sentences'] = self.max_valid_sentences = self.max_sentences
-        self.dataset_cls = VocoderDataset
-
-    @data_loader
-    def train_dataloader(self):
-        train_dataset = self.dataset_cls('train', shuffle=True)
-        return self.build_dataloader(train_dataset, True, self.max_sentences, hparams['endless_ds'])
-
-    @data_loader
-    def val_dataloader(self):
-        valid_dataset = self.dataset_cls('valid', shuffle=False)
-        return self.build_dataloader(valid_dataset, False, self.max_valid_sentences)
-
-    @data_loader
-    def test_dataloader(self):
-        test_dataset = self.dataset_cls('test', shuffle=False)
-        return self.build_dataloader(test_dataset, False, self.max_valid_sentences)
-
-    def build_dataloader(self, dataset, shuffle, max_sentences, endless=False):
-        world_size = 1
-        rank = 0
-        if dist.is_initialized():
-            world_size = dist.get_world_size()
-            rank = dist.get_rank()
-        sampler_cls = DistributedSampler if not endless else EndlessDistributedSampler
-        train_sampler = sampler_cls(
-            dataset=dataset,
-            num_replicas=world_size,
-            rank=rank,
-            shuffle=shuffle,
-        )
-        return torch.utils.data.DataLoader(
-            dataset=dataset,
-            shuffle=False,
-            collate_fn=dataset.collater,
-            batch_size=max_sentences,
-            num_workers=dataset.num_workers,
-            sampler=train_sampler,
-            pin_memory=True,
-        )
-
-    def test_start(self):
-        self.gen_dir = os.path.join(hparams['work_dir'],
-                                    f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}')
-        os.makedirs(self.gen_dir, exist_ok=True)
-
-    def test_end(self, outputs):
-        return {}

spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/plot/plot.py

@@ -1,51 +0,0 @@
-import matplotlib
-
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-LINE_COLORS = ['w', 'r', 'orange', 'k', 'cyan', 'm', 'b', 'lime', 'g', 'brown', 'navy']
-
-
-def spec_to_figure(spec, vmin=None, vmax=None, title='', f0s=None, dur_info=None):
-    if isinstance(spec, torch.Tensor):
-        spec = spec.cpu().numpy()
-    H = spec.shape[1] // 2
-    fig = plt.figure(figsize=(12, 6))
-    plt.title(title)
-    plt.pcolor(spec.T, vmin=vmin, vmax=vmax)
-    if dur_info is not None:
-        assert isinstance(dur_info, dict)
-        txt = dur_info['txt']
-        dur_gt = dur_info['dur_gt']
-        if isinstance(dur_gt, torch.Tensor):
-            dur_gt = dur_gt.cpu().numpy()
-        dur_gt = np.cumsum(dur_gt).astype(int)
-        for i in range(len(dur_gt)):
-            shift = (i % 8) + 1
-            plt.text(dur_gt[i], shift * 4, txt[i])
-            plt.vlines(dur_gt[i], 0, H // 2, colors='b')  # blue is gt
-        plt.xlim(0, dur_gt[-1])
-        if 'dur_pred' in dur_info:
-            dur_pred = dur_info['dur_pred']
-            if isinstance(dur_pred, torch.Tensor):
-                dur_pred = dur_pred.cpu().numpy()
-            dur_pred = np.cumsum(dur_pred).astype(int)
-            for i in range(len(dur_pred)):
-                shift = (i % 8) + 1
-                plt.text(dur_pred[i], H + shift * 4, txt[i])
-                plt.vlines(dur_pred[i], H, H * 1.5, colors='r')  # red is pred
-            plt.xlim(0, max(dur_gt[-1], dur_pred[-1]))
-    if f0s is not None:
-        ax = plt.gca()
-        ax2 = ax.twinx()
-        if not isinstance(f0s, dict):
-            f0s = {'f0': f0s}
-        for i, (k, f0) in enumerate(f0s.items()):
-            if isinstance(f0, torch.Tensor):
-                f0 = f0.cpu().numpy()
-            ax2.plot(f0, label=k, c=LINE_COLORS[i], linewidth=1, alpha=0.5)
-        ax2.set_ylim(0, 1000)
-        ax2.legend()
-    return fig

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py

@@ -1,270 +0,0 @@
-_base_ = '../yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
-
-# dataset settings
-data_root = 'data/VOCdevkit/'
-dataset_type = 'YOLOv5VOCDataset'
-
-# parameters that often need to be modified
-num_classes = 20
-img_scale = (512, 512)  # width, height
-max_epochs = 50
-train_batch_size_per_gpu = 64
-train_num_workers = 8
-val_batch_size_per_gpu = 1
-val_num_workers = 2
-
-# persistent_workers must be False if num_workers is 0.
-persistent_workers = True
-
-lr_factor = 0.15135
-affine_scale = 0.75544
-
-# only on Val
-batch_shapes_cfg = dict(img_size=img_scale[0])
-
-anchors = [[(26, 44), (67, 57), (61, 130)], [(121, 118), (120, 239),
-                                             (206, 182)],
-           [(376, 161), (234, 324), (428, 322)]]
-num_det_layers = 3
-
-load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth'  # noqa
-
-tta_img_scales = [img_scale, (416, 416), (640, 640)]
-
-# Hyperparameter reference from:
-# https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.VOC.yaml
-model = dict(
-    bbox_head=dict(
-        head_module=dict(num_classes=num_classes),
-        prior_generator=dict(base_sizes=anchors),
-        loss_cls=dict(
-            loss_weight=0.21638 * (num_classes / 80 * 3 / num_det_layers),
-            class_weight=0.5),
-        loss_bbox=dict(loss_weight=0.02 * (3 / num_det_layers)),
-        loss_obj=dict(
-            loss_weight=0.51728 *
-            ((img_scale[0] / 640)**2 * 3 / num_det_layers),
-            class_weight=0.67198),
-        # Different from COCO
-        prior_match_thr=3.3744),
-    test_cfg=dict(nms=dict(iou_threshold=0.6)))
-
-albu_train_transforms = _base_.albu_train_transforms
-pre_transform = _base_.pre_transform
-
-with_mosiac_pipeline = [
-    dict(
-        type='Mosaic',
-        img_scale=img_scale,
-        pad_val=114.0,
-        pre_transform=pre_transform),
-    dict(
-        type='YOLOv5RandomAffine',
-        max_rotate_degree=0.0,
-        max_translate_ratio=0.04591,
-        max_shear_degree=0.0,
-        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
-        # img_scale is (width, height)
-        border=(-img_scale[0] // 2, -img_scale[1] // 2),
-        border_val=(114, 114, 114)),
-    dict(
-        type='YOLOv5MixUp',
-        prob=0.04266,
-        pre_transform=[
-            *pre_transform,
-            dict(
-                type='Mosaic',
-                img_scale=img_scale,
-                pad_val=114.0,
-                pre_transform=pre_transform),
-            dict(
-                type='YOLOv5RandomAffine',
-                max_rotate_degree=0.0,
-                max_translate_ratio=0.04591,
-                max_shear_degree=0.0,
-                scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
-                # img_scale is (width, height)
-                border=(-img_scale[0] // 2, -img_scale[1] // 2),
-                border_val=(114, 114, 114))
-        ])
-]
-
-without_mosaic_pipeline = [
-    dict(
-        type='YOLOv5RandomAffine',
-        max_rotate_degree=0.0,
-        max_translate_ratio=0.04591,
-        max_shear_degree=0.0,
-        scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
-        border=(0, 0),
-        border_val=(114, 114, 114)),
-    dict(
-        type='LetterResize',
-        scale=img_scale,
-        allow_scale_up=True,
-        pad_val=dict(img=114))
-]
-
-# Because the border parameter is inconsistent when
-# using mosaic or not, `RandomChoice` is used here.
-randchoice_mosaic_pipeline = dict(
-    type='RandomChoice',
-    transforms=[with_mosiac_pipeline, without_mosaic_pipeline],
-    prob=[0.85834, 0.14166])
-
-train_pipeline = [
-    *pre_transform, randchoice_mosaic_pipeline,
-    dict(
-        type='mmdet.Albu',
-        transforms=albu_train_transforms,
-        bbox_params=dict(
-            type='BboxParams',
-            format='pascal_voc',
-            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
-        keymap={
-            'img': 'image',
-            'gt_bboxes': 'bboxes'
-        }),
-    dict(
-        type='YOLOv5HSVRandomAug',
-        hue_delta=0.01041,
-        saturation_delta=0.54703,
-        value_delta=0.27739),
-    dict(type='mmdet.RandomFlip', prob=0.5),
-    dict(
-        type='mmdet.PackDetInputs',
-        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
-                   'flip_direction'))
-]
-
-train_dataloader = dict(
-    _delete_=True,
-    batch_size=train_batch_size_per_gpu,
-    num_workers=train_num_workers,
-    persistent_workers=persistent_workers,
-    pin_memory=True,
-    sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=dict(
-        type='ConcatDataset',
-        datasets=[
-            dict(
-                type=dataset_type,
-                data_root=data_root,
-                ann_file='VOC2007/ImageSets/Main/trainval.txt',
-                data_prefix=dict(sub_data_root='VOC2007/'),
-                filter_cfg=dict(filter_empty_gt=False, min_size=32),
-                pipeline=train_pipeline),
-            dict(
-                type=dataset_type,
-                data_root=data_root,
-                ann_file='VOC2012/ImageSets/Main/trainval.txt',
-                data_prefix=dict(sub_data_root='VOC2012/'),
-                filter_cfg=dict(filter_empty_gt=False, min_size=32),
-                pipeline=train_pipeline)
-        ],
-        # Use ignore_keys to avoid judging metainfo is
-        # not equal in `ConcatDataset`.
-        ignore_keys='dataset_type'),
-    collate_fn=dict(type='yolov5_collate'))
-
-test_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
-    dict(type='YOLOv5KeepRatioResize', scale=img_scale),
-    dict(
-        type='LetterResize',
-        scale=img_scale,
-        allow_scale_up=False,
-        pad_val=dict(img=114)),
-    dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
-    dict(
-        type='mmdet.PackDetInputs',
-        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
-                   'scale_factor', 'pad_param'))
-]
-
-val_dataloader = dict(
-    batch_size=val_batch_size_per_gpu,
-    num_workers=val_num_workers,
-    persistent_workers=persistent_workers,
-    pin_memory=True,
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file='VOC2007/ImageSets/Main/test.txt',
-        data_prefix=dict(sub_data_root='VOC2007/'),
-        test_mode=True,
-        pipeline=test_pipeline,
-        batch_shapes_cfg=batch_shapes_cfg))
-
-test_dataloader = val_dataloader
-
-param_scheduler = None
-optim_wrapper = dict(
-    optimizer=dict(
-        lr=0.00334,
-        momentum=0.74832,
-        weight_decay=0.00025,
-        batch_size_per_gpu=train_batch_size_per_gpu))
-
-default_hooks = dict(
-    param_scheduler=dict(
-        lr_factor=lr_factor,
-        max_epochs=max_epochs,
-        warmup_epochs=3.3835,
-        warmup_momentum=0.59462,
-        warmup_bias_lr=0.18657))
-
-custom_hooks = [
-    dict(
-        type='EMAHook',
-        ema_type='ExpMomentumEMA',
-        momentum=0.0001,
-        update_buffers=True,
-        # To load COCO pretrained model, need to set `strict_load=False`
-        strict_load=False,
-        priority=49)
-]
-
-# TODO: Support using coco metric in voc dataset
-val_evaluator = dict(
-    _delete_=True, type='mmdet.VOCMetric', metric='mAP', eval_mode='area')
-
-test_evaluator = val_evaluator
-
-train_cfg = dict(max_epochs=max_epochs)
-
-# Config for Test Time Augmentation. (TTA)
-_multiscale_resize_transforms = [
-    dict(
-        type='Compose',
-        transforms=[
-            dict(type='YOLOv5KeepRatioResize', scale=s),
-            dict(
-                type='LetterResize',
-                scale=s,
-                allow_scale_up=False,
-                pad_val=dict(img=114))
-        ]) for s in tta_img_scales
-]
-
-tta_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
-    dict(
-        type='TestTimeAug',
-        transforms=[
-            _multiscale_resize_transforms,
-            [
-                dict(type='mmdet.RandomFlip', prob=1.),
-                dict(type='mmdet.RandomFlip', prob=0.)
-            ], [dict(type='mmdet.LoadAnnotations', with_bbox=True)],
-            [
-                dict(
-                    type='mmdet.PackDetInputs',
-                    meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
-                               'scale_factor', 'pad_param', 'flip',
-                               'flip_direction'))
-            ]
-        ])
-]

spaces/AchyuthGamer/OpenGPT/client/css/theme-toggler.css

@@ -1,33 +0,0 @@
-.theme-toggler-container {
-    margin: 24px 0px 8px 0px;
-    justify-content: center;
-}
-
-.theme-toggler-container.checkbox input + label,
-.theme-toggler-container.checkbox input:checked + label:after {
-	background: var(--colour-2);
-}
-
-.theme-toggler-container.checkbox input + label:after,
-.theme-toggler-container.checkbox input:checked + label {
-	background: var(--colour-4);
-}
-
-.theme-toggler-container.checkbox span {
-    font-size: 0.75rem;
-}
-
-.theme-toggler-container.checkbox label {
-    width: 24px;
-    height: 16px;
-}
-
-.theme-toggler-container.checkbox label:after {
-    left: 2px;
-    width: 10px;
-    height: 10px;
-}
-
-.theme-toggler-container.checkbox input:checked + label:after {
-    left: calc(100% - 2px - 10px);
-}

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

@@ -1,2 +0,0 @@
-import FullWindowRectangle from './gameobjects/shape/fullwindowrectangle/FullWindowRectangle.js';
-export default FullWindowRectangle;

spaces/AkitoP/umamusume_bert_vits2/monotonic_align/__init__.py

@@ -1,16 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-
-def maximum_path(neg_cent, mask):
-    device = neg_cent.device
-    dtype = neg_cent.dtype
-    neg_cent = neg_cent.data.cpu().numpy().astype(float32)
-    path = zeros(neg_cent.shape, dtype=int32)
-
-    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
-    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
-    maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
-    return from_numpy(path).to(device=device, dtype=dtype)

spaces/Aloento/9Nine-PITS/text/frontend/punctuation.py

@@ -1,36 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-__all__ = ["get_punctuations"]
-
-EN_PUNCT = [
-  " ",
-  "-",
-  "...",
-  ",",
-  ".",
-  "?",
-  "!",
-]
-
-CN_PUNCT = ["、", "，", "；", "：", "。", "？", "！"]
-
-
-def get_punctuations(lang):
-  if lang == "en":
-    return EN_PUNCT
-  elif lang == "cn":
-    return CN_PUNCT
-  else:
-    raise ValueError(f"language {lang} Not supported")

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/others/test_utils.py

@@ -1,170 +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 unittest
-
-from diffusers import __version__
-from diffusers.utils import deprecate
-
-
-class DeprecateTester(unittest.TestCase):
-    higher_version = ".".join([str(int(__version__.split(".")[0]) + 1)] + __version__.split(".")[1:])
-    lower_version = "0.0.1"
-
-    def test_deprecate_function_arg(self):
-        kwargs = {"deprecated_arg": 4}
-
-        with self.assertWarns(FutureWarning) as warning:
-            output = deprecate("deprecated_arg", self.higher_version, "message", take_from=kwargs)
-
-        assert output == 4
-        assert (
-            str(warning.warning)
-            == f"The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}."
-            " message"
-        )
-
-    def test_deprecate_function_arg_tuple(self):
-        kwargs = {"deprecated_arg": 4}
-
-        with self.assertWarns(FutureWarning) as warning:
-            output = deprecate(("deprecated_arg", self.higher_version, "message"), take_from=kwargs)
-
-        assert output == 4
-        assert (
-            str(warning.warning)
-            == f"The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}."
-            " message"
-        )
-
-    def test_deprecate_function_args(self):
-        kwargs = {"deprecated_arg_1": 4, "deprecated_arg_2": 8}
-        with self.assertWarns(FutureWarning) as warning:
-            output_1, output_2 = deprecate(
-                ("deprecated_arg_1", self.higher_version, "Hey"),
-                ("deprecated_arg_2", self.higher_version, "Hey"),
-                take_from=kwargs,
-            )
-        assert output_1 == 4
-        assert output_2 == 8
-        assert (
-            str(warning.warnings[0].message)
-            == "The `deprecated_arg_1` argument is deprecated and will be removed in version"
-            f" {self.higher_version}. Hey"
-        )
-        assert (
-            str(warning.warnings[1].message)
-            == "The `deprecated_arg_2` argument is deprecated and will be removed in version"
-            f" {self.higher_version}. Hey"
-        )
-
-    def test_deprecate_function_incorrect_arg(self):
-        kwargs = {"deprecated_arg": 4}
-
-        with self.assertRaises(TypeError) as error:
-            deprecate(("wrong_arg", self.higher_version, "message"), take_from=kwargs)
-
-        assert "test_deprecate_function_incorrect_arg in" in str(error.exception)
-        assert "line" in str(error.exception)
-        assert "got an unexpected keyword argument `deprecated_arg`" in str(error.exception)
-
-    def test_deprecate_arg_no_kwarg(self):
-        with self.assertWarns(FutureWarning) as warning:
-            deprecate(("deprecated_arg", self.higher_version, "message"))
-
-        assert (
-            str(warning.warning)
-            == f"`deprecated_arg` is deprecated and will be removed in version {self.higher_version}. message"
-        )
-
-    def test_deprecate_args_no_kwarg(self):
-        with self.assertWarns(FutureWarning) as warning:
-            deprecate(
-                ("deprecated_arg_1", self.higher_version, "Hey"),
-                ("deprecated_arg_2", self.higher_version, "Hey"),
-            )
-        assert (
-            str(warning.warnings[0].message)
-            == f"`deprecated_arg_1` is deprecated and will be removed in version {self.higher_version}. Hey"
-        )
-        assert (
-            str(warning.warnings[1].message)
-            == f"`deprecated_arg_2` is deprecated and will be removed in version {self.higher_version}. Hey"
-        )
-
-    def test_deprecate_class_obj(self):
-        class Args:
-            arg = 5
-
-        with self.assertWarns(FutureWarning) as warning:
-            arg = deprecate(("arg", self.higher_version, "message"), take_from=Args())
-
-        assert arg == 5
-        assert (
-            str(warning.warning)
-            == f"The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message"
-        )
-
-    def test_deprecate_class_objs(self):
-        class Args:
-            arg = 5
-            foo = 7
-
-        with self.assertWarns(FutureWarning) as warning:
-            arg_1, arg_2 = deprecate(
-                ("arg", self.higher_version, "message"),
-                ("foo", self.higher_version, "message"),
-                ("does not exist", self.higher_version, "message"),
-                take_from=Args(),
-            )
-
-        assert arg_1 == 5
-        assert arg_2 == 7
-        assert (
-            str(warning.warning)
-            == f"The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message"
-        )
-        assert (
-            str(warning.warnings[0].message)
-            == f"The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message"
-        )
-        assert (
-            str(warning.warnings[1].message)
-            == f"The `foo` attribute is deprecated and will be removed in version {self.higher_version}. message"
-        )
-
-    def test_deprecate_incorrect_version(self):
-        kwargs = {"deprecated_arg": 4}
-
-        with self.assertRaises(ValueError) as error:
-            deprecate(("wrong_arg", self.lower_version, "message"), take_from=kwargs)
-
-        assert (
-            str(error.exception)
-            == "The deprecation tuple ('wrong_arg', '0.0.1', 'message') should be removed since diffusers' version"
-            f" {__version__} is >= {self.lower_version}"
-        )
-
-    def test_deprecate_incorrect_no_standard_warn(self):
-        with self.assertWarns(FutureWarning) as warning:
-            deprecate(("deprecated_arg", self.higher_version, "This message is better!!!"), standard_warn=False)
-
-        assert str(warning.warning) == "This message is better!!!"
-
-    def test_deprecate_stacklevel(self):
-        with self.assertWarns(FutureWarning) as warning:
-            deprecate(("deprecated_arg", self.higher_version, "This message is better!!!"), standard_warn=False)
-        assert str(warning.warning) == "This message is better!!!"
-        assert "diffusers/tests/others/test_utils.py" in warning.filename

spaces/Andy1621/uniformer_image_detection/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py

@@ -1,118 +0,0 @@
-_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    pretrained='open-mmlab://resnest50',
-    backbone=dict(
-        type='ResNeSt',
-        stem_channels=64,
-        depth=50,
-        radix=2,
-        reduction_factor=4,
-        avg_down_stride=True,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch'),
-    roi_head=dict(
-        bbox_head=[
-            dict(
-                type='Shared4Conv1FCBBoxHead',
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                norm_cfg=norm_cfg,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.1, 0.1, 0.2, 0.2]),
-                reg_class_agnostic=True,
-                loss_cls=dict(
-                    type='CrossEntropyLoss',
-                    use_sigmoid=False,
-                    loss_weight=1.0),
-                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
-                               loss_weight=1.0)),
-            dict(
-                type='Shared4Conv1FCBBoxHead',
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                norm_cfg=norm_cfg,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.05, 0.05, 0.1, 0.1]),
-                reg_class_agnostic=True,
-                loss_cls=dict(
-                    type='CrossEntropyLoss',
-                    use_sigmoid=False,
-                    loss_weight=1.0),
-                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
-                               loss_weight=1.0)),
-            dict(
-                type='Shared4Conv1FCBBoxHead',
-                in_channels=256,
-                conv_out_channels=256,
-                fc_out_channels=1024,
-                norm_cfg=norm_cfg,
-                roi_feat_size=7,
-                num_classes=80,
-                bbox_coder=dict(
-                    type='DeltaXYWHBBoxCoder',
-                    target_means=[0., 0., 0., 0.],
-                    target_stds=[0.033, 0.033, 0.067, 0.067]),
-                reg_class_agnostic=True,
-                loss_cls=dict(
-                    type='CrossEntropyLoss',
-                    use_sigmoid=False,
-                    loss_weight=1.0),
-                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
-        ],
-        mask_head=dict(norm_cfg=norm_cfg)))
-# # use ResNeSt img_norm
-img_norm_cfg = dict(
-    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='LoadAnnotations',
-        with_bbox=True,
-        with_mask=True,
-        poly2mask=False),
-    dict(
-        type='Resize',
-        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
-                   (1333, 768), (1333, 800)],
-        multiscale_mode='value',
-        keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))

spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/paa.py

@@ -1,17 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class PAA(SingleStageDetector):
-    """Implementation of `PAA <https://arxiv.org/pdf/2007.08103.pdf>`_."""
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(PAA, self).__init__(backbone, neck, bbox_head, train_cfg,
-                                  test_cfg, pretrained)

spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/yolo.py

@@ -1,18 +0,0 @@
-# Copyright (c) 2019 Western Digital Corporation or its affiliates.
-
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class YOLOV3(SingleStageDetector):
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(YOLOV3, self).__init__(backbone, neck, bbox_head, train_cfg,
-                                     test_cfg, pretrained)

spaces/Andy1621/uniformer_image_detection/mmdet/models/necks/fpg.py

@@ -1,398 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, caffe2_xavier_init, constant_init, is_norm
-
-from ..builder import NECKS
-
-
-class Transition(nn.Module):
-    """Base class for transition.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-    """
-
-    def __init__(self, in_channels, out_channels):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-
-    def forward(x):
-        pass
-
-
-class UpInterpolationConv(Transition):
-    """A transition used for up-sampling.
-
-    Up-sample the input by interpolation then refines the feature by
-    a convolution layer.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        scale_factor (int): Up-sampling factor. Default: 2.
-        mode (int): Interpolation mode. Default: nearest.
-        align_corners (bool): Whether align corners when interpolation.
-            Default: None.
-        kernel_size (int): Kernel size for the conv. Default: 3.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 scale_factor=2,
-                 mode='nearest',
-                 align_corners=None,
-                 kernel_size=3,
-                 **kwargs):
-        super().__init__(in_channels, out_channels)
-        self.mode = mode
-        self.scale_factor = scale_factor
-        self.align_corners = align_corners
-        self.conv = ConvModule(
-            in_channels,
-            out_channels,
-            kernel_size,
-            padding=(kernel_size - 1) // 2,
-            **kwargs)
-
-    def forward(self, x):
-        x = F.interpolate(
-            x,
-            scale_factor=self.scale_factor,
-            mode=self.mode,
-            align_corners=self.align_corners)
-        x = self.conv(x)
-        return x
-
-
-class LastConv(Transition):
-    """A transition used for refining the output of the last stage.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        num_inputs (int): Number of inputs of the FPN features.
-        kernel_size (int): Kernel size for the conv. Default: 3.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 num_inputs,
-                 kernel_size=3,
-                 **kwargs):
-        super().__init__(in_channels, out_channels)
-        self.num_inputs = num_inputs
-        self.conv_out = ConvModule(
-            in_channels,
-            out_channels,
-            kernel_size,
-            padding=(kernel_size - 1) // 2,
-            **kwargs)
-
-    def forward(self, inputs):
-        assert len(inputs) == self.num_inputs
-        return self.conv_out(inputs[-1])
-
-
-@NECKS.register_module()
-class FPG(nn.Module):
-    """FPG.
-
-    Implementation of `Feature Pyramid Grids (FPG)
-    <https://arxiv.org/abs/2004.03580>`_.
-    This implementation only gives the basic structure stated in the paper.
-    But users can implement different type of transitions to fully explore the
-    the potential power of the structure of FPG.
-
-    Args:
-        in_channels (int): Number of input channels (feature maps of all levels
-            should have the same channels).
-        out_channels (int): Number of output channels (used at each scale)
-        num_outs (int): Number of output scales.
-        stack_times (int): The number of times the pyramid architecture will
-            be stacked.
-        paths (list[str]): Specify the path order of each stack level.
-            Each element in the list should be either 'bu' (bottom-up) or
-            'td' (top-down).
-        inter_channels (int): Number of inter channels.
-        same_up_trans (dict): Transition that goes down at the same stage.
-        same_down_trans (dict): Transition that goes up at the same stage.
-        across_lateral_trans (dict): Across-pathway same-stage
-        across_down_trans (dict): Across-pathway bottom-up connection.
-        across_up_trans (dict): Across-pathway top-down connection.
-        across_skip_trans (dict): Across-pathway skip connection.
-        output_trans (dict): Transition that trans the output of the
-            last stage.
-        start_level (int): Index of the start input backbone level used to
-            build the feature pyramid. Default: 0.
-        end_level (int): Index of the end input backbone level (exclusive) to
-            build the feature pyramid. Default: -1, which means the last level.
-        add_extra_convs (bool): It decides whether to add conv
-            layers on top of the original feature maps. Default to False.
-            If True, its actual mode is specified by `extra_convs_on_inputs`.
-        norm_cfg (dict): Config dict for normalization layer. Default: None.
-    """
-
-    transition_types = {
-        'conv': ConvModule,
-        'interpolation_conv': UpInterpolationConv,
-        'last_conv': LastConv,
-    }
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 num_outs,
-                 stack_times,
-                 paths,
-                 inter_channels=None,
-                 same_down_trans=None,
-                 same_up_trans=dict(
-                     type='conv', kernel_size=3, stride=2, padding=1),
-                 across_lateral_trans=dict(type='conv', kernel_size=1),
-                 across_down_trans=dict(type='conv', kernel_size=3),
-                 across_up_trans=None,
-                 across_skip_trans=dict(type='identity'),
-                 output_trans=dict(type='last_conv', kernel_size=3),
-                 start_level=0,
-                 end_level=-1,
-                 add_extra_convs=False,
-                 norm_cfg=None,
-                 skip_inds=None):
-        super(FPG, self).__init__()
-        assert isinstance(in_channels, list)
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.num_ins = len(in_channels)
-        self.num_outs = num_outs
-        if inter_channels is None:
-            self.inter_channels = [out_channels for _ in range(num_outs)]
-        elif isinstance(inter_channels, int):
-            self.inter_channels = [inter_channels for _ in range(num_outs)]
-        else:
-            assert isinstance(inter_channels, list)
-            assert len(inter_channels) == num_outs
-            self.inter_channels = inter_channels
-        self.stack_times = stack_times
-        self.paths = paths
-        assert isinstance(paths, list) and len(paths) == stack_times
-        for d in paths:
-            assert d in ('bu', 'td')
-
-        self.same_down_trans = same_down_trans
-        self.same_up_trans = same_up_trans
-        self.across_lateral_trans = across_lateral_trans
-        self.across_down_trans = across_down_trans
-        self.across_up_trans = across_up_trans
-        self.output_trans = output_trans
-        self.across_skip_trans = across_skip_trans
-
-        self.with_bias = norm_cfg is None
-        # skip inds must be specified if across skip trans is not None
-        if self.across_skip_trans is not None:
-            skip_inds is not None
-        self.skip_inds = skip_inds
-        assert len(self.skip_inds[0]) <= self.stack_times
-
-        if end_level == -1:
-            self.backbone_end_level = self.num_ins
-            assert num_outs >= self.num_ins - start_level
-        else:
-            # if end_level < inputs, no extra level is allowed
-            self.backbone_end_level = end_level
-            assert end_level <= len(in_channels)
-            assert num_outs == end_level - start_level
-        self.start_level = start_level
-        self.end_level = end_level
-        self.add_extra_convs = add_extra_convs
-
-        # build lateral 1x1 convs to reduce channels
-        self.lateral_convs = nn.ModuleList()
-        for i in range(self.start_level, self.backbone_end_level):
-            l_conv = nn.Conv2d(self.in_channels[i],
-                               self.inter_channels[i - self.start_level], 1)
-            self.lateral_convs.append(l_conv)
-
-        extra_levels = num_outs - self.backbone_end_level + self.start_level
-        self.extra_downsamples = nn.ModuleList()
-        for i in range(extra_levels):
-            if self.add_extra_convs:
-                fpn_idx = self.backbone_end_level - self.start_level + i
-                extra_conv = nn.Conv2d(
-                    self.inter_channels[fpn_idx - 1],
-                    self.inter_channels[fpn_idx],
-                    3,
-                    stride=2,
-                    padding=1)
-                self.extra_downsamples.append(extra_conv)
-            else:
-                self.extra_downsamples.append(nn.MaxPool2d(1, stride=2))
-
-        self.fpn_transitions = nn.ModuleList()  # stack times
-        for s in range(self.stack_times):
-            stage_trans = nn.ModuleList()  # num of feature levels
-            for i in range(self.num_outs):
-                # same, across_lateral, across_down, across_up
-                trans = nn.ModuleDict()
-                if s in self.skip_inds[i]:
-                    stage_trans.append(trans)
-                    continue
-                # build same-stage down trans (used in bottom-up paths)
-                if i == 0 or self.same_up_trans is None:
-                    same_up_trans = None
-                else:
-                    same_up_trans = self.build_trans(
-                        self.same_up_trans, self.inter_channels[i - 1],
-                        self.inter_channels[i])
-                trans['same_up'] = same_up_trans
-                # build same-stage up trans (used in top-down paths)
-                if i == self.num_outs - 1 or self.same_down_trans is None:
-                    same_down_trans = None
-                else:
-                    same_down_trans = self.build_trans(
-                        self.same_down_trans, self.inter_channels[i + 1],
-                        self.inter_channels[i])
-                trans['same_down'] = same_down_trans
-                # build across lateral trans
-                across_lateral_trans = self.build_trans(
-                    self.across_lateral_trans, self.inter_channels[i],
-                    self.inter_channels[i])
-                trans['across_lateral'] = across_lateral_trans
-                # build across down trans
-                if i == self.num_outs - 1 or self.across_down_trans is None:
-                    across_down_trans = None
-                else:
-                    across_down_trans = self.build_trans(
-                        self.across_down_trans, self.inter_channels[i + 1],
-                        self.inter_channels[i])
-                trans['across_down'] = across_down_trans
-                # build across up trans
-                if i == 0 or self.across_up_trans is None:
-                    across_up_trans = None
-                else:
-                    across_up_trans = self.build_trans(
-                        self.across_up_trans, self.inter_channels[i - 1],
-                        self.inter_channels[i])
-                trans['across_up'] = across_up_trans
-                if self.across_skip_trans is None:
-                    across_skip_trans = None
-                else:
-                    across_skip_trans = self.build_trans(
-                        self.across_skip_trans, self.inter_channels[i - 1],
-                        self.inter_channels[i])
-                trans['across_skip'] = across_skip_trans
-                # build across_skip trans
-                stage_trans.append(trans)
-            self.fpn_transitions.append(stage_trans)
-
-        self.output_transition = nn.ModuleList()  # output levels
-        for i in range(self.num_outs):
-            trans = self.build_trans(
-                self.output_trans,
-                self.inter_channels[i],
-                self.out_channels,
-                num_inputs=self.stack_times + 1)
-            self.output_transition.append(trans)
-
-        self.relu = nn.ReLU(inplace=True)
-
-    def build_trans(self, cfg, in_channels, out_channels, **extra_args):
-        cfg_ = cfg.copy()
-        trans_type = cfg_.pop('type')
-        trans_cls = self.transition_types[trans_type]
-        return trans_cls(in_channels, out_channels, **cfg_, **extra_args)
-
-    def init_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                caffe2_xavier_init(m)
-            elif is_norm(m):
-                constant_init(m, 1.0)
-
-    def fuse(self, fuse_dict):
-        out = None
-        for item in fuse_dict.values():
-            if item is not None:
-                if out is None:
-                    out = item
-                else:
-                    out = out + item
-        return out
-
-    def forward(self, inputs):
-        assert len(inputs) == len(self.in_channels)
-
-        # build all levels from original feature maps
-        feats = [
-            lateral_conv(inputs[i + self.start_level])
-            for i, lateral_conv in enumerate(self.lateral_convs)
-        ]
-        for downsample in self.extra_downsamples:
-            feats.append(downsample(feats[-1]))
-
-        outs = [feats]
-
-        for i in range(self.stack_times):
-            current_outs = outs[-1]
-            next_outs = []
-            direction = self.paths[i]
-            for j in range(self.num_outs):
-                if i in self.skip_inds[j]:
-                    next_outs.append(outs[-1][j])
-                    continue
-                # feature level
-                if direction == 'td':
-                    lvl = self.num_outs - j - 1
-                else:
-                    lvl = j
-                # get transitions
-                if direction == 'td':
-                    same_trans = self.fpn_transitions[i][lvl]['same_down']
-                else:
-                    same_trans = self.fpn_transitions[i][lvl]['same_up']
-                across_lateral_trans = self.fpn_transitions[i][lvl][
-                    'across_lateral']
-                across_down_trans = self.fpn_transitions[i][lvl]['across_down']
-                across_up_trans = self.fpn_transitions[i][lvl]['across_up']
-                across_skip_trans = self.fpn_transitions[i][lvl]['across_skip']
-                # init output
-                to_fuse = dict(
-                    same=None, lateral=None, across_up=None, across_down=None)
-                # same downsample/upsample
-                if same_trans is not None:
-                    to_fuse['same'] = same_trans(next_outs[-1])
-                # across lateral
-                if across_lateral_trans is not None:
-                    to_fuse['lateral'] = across_lateral_trans(
-                        current_outs[lvl])
-                # across downsample
-                if lvl > 0 and across_up_trans is not None:
-                    to_fuse['across_up'] = across_up_trans(current_outs[lvl -
-                                                                        1])
-                # across upsample
-                if (lvl < self.num_outs - 1 and across_down_trans is not None):
-                    to_fuse['across_down'] = across_down_trans(
-                        current_outs[lvl + 1])
-                if across_skip_trans is not None:
-                    to_fuse['across_skip'] = across_skip_trans(outs[0][lvl])
-                x = self.fuse(to_fuse)
-                next_outs.append(x)
-
-            if direction == 'td':
-                outs.append(next_outs[::-1])
-            else:
-                outs.append(next_outs)
-
-        # output trans
-        final_outs = []
-        for i in range(self.num_outs):
-            lvl_out_list = []
-            for s in range(len(outs)):
-                lvl_out_list.append(outs[s][i])
-            lvl_out = self.output_transition[i](lvl_out_list)
-            final_outs.append(lvl_out)
-
-        return final_outs

spaces/Andy1621/uniformer_image_detection/mmdet/models/necks/nas_fpn.py

@@ -1,160 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule, caffe2_xavier_init
-from mmcv.ops.merge_cells import GlobalPoolingCell, SumCell
-
-from ..builder import NECKS
-
-
-@NECKS.register_module()
-class NASFPN(nn.Module):
-    """NAS-FPN.
-
-    Implementation of `NAS-FPN: Learning Scalable Feature Pyramid Architecture
-    for Object Detection <https://arxiv.org/abs/1904.07392>`_
-
-    Args:
-        in_channels (List[int]): Number of input channels per scale.
-        out_channels (int): Number of output channels (used at each scale)
-        num_outs (int): Number of output scales.
-        stack_times (int): The number of times the pyramid architecture will
-            be stacked.
-        start_level (int): Index of the start input backbone level used to
-            build the feature pyramid. Default: 0.
-        end_level (int): Index of the end input backbone level (exclusive) to
-            build the feature pyramid. Default: -1, which means the last level.
-        add_extra_convs (bool): It decides whether to add conv
-            layers on top of the original feature maps. Default to False.
-            If True, its actual mode is specified by `extra_convs_on_inputs`.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 num_outs,
-                 stack_times,
-                 start_level=0,
-                 end_level=-1,
-                 add_extra_convs=False,
-                 norm_cfg=None):
-        super(NASFPN, self).__init__()
-        assert isinstance(in_channels, list)
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.num_ins = len(in_channels)  # num of input feature levels
-        self.num_outs = num_outs  # num of output feature levels
-        self.stack_times = stack_times
-        self.norm_cfg = norm_cfg
-
-        if end_level == -1:
-            self.backbone_end_level = self.num_ins
-            assert num_outs >= self.num_ins - start_level
-        else:
-            # if end_level < inputs, no extra level is allowed
-            self.backbone_end_level = end_level
-            assert end_level <= len(in_channels)
-            assert num_outs == end_level - start_level
-        self.start_level = start_level
-        self.end_level = end_level
-        self.add_extra_convs = add_extra_convs
-
-        # add lateral connections
-        self.lateral_convs = nn.ModuleList()
-        for i in range(self.start_level, self.backbone_end_level):
-            l_conv = ConvModule(
-                in_channels[i],
-                out_channels,
-                1,
-                norm_cfg=norm_cfg,
-                act_cfg=None)
-            self.lateral_convs.append(l_conv)
-
-        # add extra downsample layers (stride-2 pooling or conv)
-        extra_levels = num_outs - self.backbone_end_level + self.start_level
-        self.extra_downsamples = nn.ModuleList()
-        for i in range(extra_levels):
-            extra_conv = ConvModule(
-                out_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=None)
-            self.extra_downsamples.append(
-                nn.Sequential(extra_conv, nn.MaxPool2d(2, 2)))
-
-        # add NAS FPN connections
-        self.fpn_stages = nn.ModuleList()
-        for _ in range(self.stack_times):
-            stage = nn.ModuleDict()
-            # gp(p6, p4) -> p4_1
-            stage['gp_64_4'] = GlobalPoolingCell(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                out_norm_cfg=norm_cfg)
-            # sum(p4_1, p4) -> p4_2
-            stage['sum_44_4'] = SumCell(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                out_norm_cfg=norm_cfg)
-            # sum(p4_2, p3) -> p3_out
-            stage['sum_43_3'] = SumCell(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                out_norm_cfg=norm_cfg)
-            # sum(p3_out, p4_2) -> p4_out
-            stage['sum_34_4'] = SumCell(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                out_norm_cfg=norm_cfg)
-            # sum(p5, gp(p4_out, p3_out)) -> p5_out
-            stage['gp_43_5'] = GlobalPoolingCell(with_out_conv=False)
-            stage['sum_55_5'] = SumCell(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                out_norm_cfg=norm_cfg)
-            # sum(p7, gp(p5_out, p4_2)) -> p7_out
-            stage['gp_54_7'] = GlobalPoolingCell(with_out_conv=False)
-            stage['sum_77_7'] = SumCell(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                out_norm_cfg=norm_cfg)
-            # gp(p7_out, p5_out) -> p6_out
-            stage['gp_75_6'] = GlobalPoolingCell(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                out_norm_cfg=norm_cfg)
-            self.fpn_stages.append(stage)
-
-    def init_weights(self):
-        """Initialize the weights of module."""
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                caffe2_xavier_init(m)
-
-    def forward(self, inputs):
-        """Forward function."""
-        # build P3-P5
-        feats = [
-            lateral_conv(inputs[i + self.start_level])
-            for i, lateral_conv in enumerate(self.lateral_convs)
-        ]
-        # build P6-P7 on top of P5
-        for downsample in self.extra_downsamples:
-            feats.append(downsample(feats[-1]))
-
-        p3, p4, p5, p6, p7 = feats
-
-        for stage in self.fpn_stages:
-            # gp(p6, p4) -> p4_1
-            p4_1 = stage['gp_64_4'](p6, p4, out_size=p4.shape[-2:])
-            # sum(p4_1, p4) -> p4_2
-            p4_2 = stage['sum_44_4'](p4_1, p4, out_size=p4.shape[-2:])
-            # sum(p4_2, p3) -> p3_out
-            p3 = stage['sum_43_3'](p4_2, p3, out_size=p3.shape[-2:])
-            # sum(p3_out, p4_2) -> p4_out
-            p4 = stage['sum_34_4'](p3, p4_2, out_size=p4.shape[-2:])
-            # sum(p5, gp(p4_out, p3_out)) -> p5_out
-            p5_tmp = stage['gp_43_5'](p4, p3, out_size=p5.shape[-2:])
-            p5 = stage['sum_55_5'](p5, p5_tmp, out_size=p5.shape[-2:])
-            # sum(p7, gp(p5_out, p4_2)) -> p7_out
-            p7_tmp = stage['gp_54_7'](p5, p4_2, out_size=p7.shape[-2:])
-            p7 = stage['sum_77_7'](p7, p7_tmp, out_size=p7.shape[-2:])
-            # gp(p7_out, p5_out) -> p6_out
-            p6 = stage['gp_75_6'](p7, p5, out_size=p6.shape[-2:])
-
-        return p3, p4, p5, p6, p7

spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py

@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/encnet_r50-d8.py', '../_base_/datasets/cityscapes.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]

spaces/AnnaPalatkina/fine_grained_SA/app.py

@@ -1,39 +0,0 @@
-from sentiment_wrapper import PredictionModel
-import gradio as gr
-
-model = PredictionModel()
-
-
-def predict(text:str):
-    result = model.predict([text])[0]
-    return f'class: {result}'
-
-markdown_text = '''
-<br>
-<br>
-
-This space provides a gradio demo and an easy-to-run wrapper of the pre-trained model for fine-grained sentiment analysis in Norwegian language, pre-trained on the [NoReC dataset](https://github.com/ltgoslo/norec).
-
-Information about project you an fine on the website of [University of Oslo](https://www.mn.uio.no/ifi/english/research/projects/sant/)
-
-The model can be easily used for predicting sentiment as follows: 
-```python
->>> from sentiment_wrapper import PredictionModel
->>> model = PredictionModel()
->>> model.predict(['vi liker svart kaffe', 'jeg elsker virkelig røde roser!'])
-[5,5]
-```
-'''
-
-with gr.Blocks() as demo:
-    with gr.Row(equal_height=False) as row:
-        text_input = gr.Textbox(label="input")
-        text_output = gr.Textbox(label="output")
-    with gr.Row(scale=4) as row:
-        text_button = gr.Button("submit").style(full_width=True)
-
-    text_button.click(fn=predict, inputs=text_input, outputs=text_output)
-    gr.Markdown(markdown_text)
-
-
-demo.launch()

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

@@ -1,288 +0,0 @@
-from collections.abc import Sequence
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-import torch
-from annotator.uniformer.mmcv.parallel import DataContainer as DC
-
-from ..builder import PIPELINES
-
-
-def to_tensor(data):
-    """Convert objects of various python types to :obj:`torch.Tensor`.
-
-    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
-    :class:`Sequence`, :class:`int` and :class:`float`.
-
-    Args:
-        data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to
-            be converted.
-    """
-
-    if isinstance(data, torch.Tensor):
-        return data
-    elif isinstance(data, np.ndarray):
-        return torch.from_numpy(data)
-    elif isinstance(data, Sequence) and not mmcv.is_str(data):
-        return torch.tensor(data)
-    elif isinstance(data, int):
-        return torch.LongTensor([data])
-    elif isinstance(data, float):
-        return torch.FloatTensor([data])
-    else:
-        raise TypeError(f'type {type(data)} cannot be converted to tensor.')
-
-
-@PIPELINES.register_module()
-class ToTensor(object):
-    """Convert some results to :obj:`torch.Tensor` by given keys.
-
-    Args:
-        keys (Sequence[str]): Keys that need to be converted to Tensor.
-    """
-
-    def __init__(self, keys):
-        self.keys = keys
-
-    def __call__(self, results):
-        """Call function to convert data in results to :obj:`torch.Tensor`.
-
-        Args:
-            results (dict): Result dict contains the data to convert.
-
-        Returns:
-            dict: The result dict contains the data converted
-                to :obj:`torch.Tensor`.
-        """
-
-        for key in self.keys:
-            results[key] = to_tensor(results[key])
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(keys={self.keys})'
-
-
-@PIPELINES.register_module()
-class ImageToTensor(object):
-    """Convert image to :obj:`torch.Tensor` by given keys.
-
-    The dimension order of input image is (H, W, C). The pipeline will convert
-    it to (C, H, W). If only 2 dimension (H, W) is given, the output would be
-    (1, H, W).
-
-    Args:
-        keys (Sequence[str]): Key of images to be converted to Tensor.
-    """
-
-    def __init__(self, keys):
-        self.keys = keys
-
-    def __call__(self, results):
-        """Call function to convert image in results to :obj:`torch.Tensor` and
-        transpose the channel order.
-
-        Args:
-            results (dict): Result dict contains the image data to convert.
-
-        Returns:
-            dict: The result dict contains the image converted
-                to :obj:`torch.Tensor` and transposed to (C, H, W) order.
-        """
-
-        for key in self.keys:
-            img = results[key]
-            if len(img.shape) < 3:
-                img = np.expand_dims(img, -1)
-            results[key] = to_tensor(img.transpose(2, 0, 1))
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(keys={self.keys})'
-
-
-@PIPELINES.register_module()
-class Transpose(object):
-    """Transpose some results by given keys.
-
-    Args:
-        keys (Sequence[str]): Keys of results to be transposed.
-        order (Sequence[int]): Order of transpose.
-    """
-
-    def __init__(self, keys, order):
-        self.keys = keys
-        self.order = order
-
-    def __call__(self, results):
-        """Call function to convert image in results to :obj:`torch.Tensor` and
-        transpose the channel order.
-
-        Args:
-            results (dict): Result dict contains the image data to convert.
-
-        Returns:
-            dict: The result dict contains the image converted
-                to :obj:`torch.Tensor` and transposed to (C, H, W) order.
-        """
-
-        for key in self.keys:
-            results[key] = results[key].transpose(self.order)
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + \
-               f'(keys={self.keys}, order={self.order})'
-
-
-@PIPELINES.register_module()
-class ToDataContainer(object):
-    """Convert results to :obj:`mmcv.DataContainer` by given fields.
-
-    Args:
-        fields (Sequence[dict]): Each field is a dict like
-            ``dict(key='xxx', **kwargs)``. The ``key`` in result will
-            be converted to :obj:`mmcv.DataContainer` with ``**kwargs``.
-            Default: ``(dict(key='img', stack=True),
-            dict(key='gt_semantic_seg'))``.
-    """
-
-    def __init__(self,
-                 fields=(dict(key='img',
-                              stack=True), dict(key='gt_semantic_seg'))):
-        self.fields = fields
-
-    def __call__(self, results):
-        """Call function to convert data in results to
-        :obj:`mmcv.DataContainer`.
-
-        Args:
-            results (dict): Result dict contains the data to convert.
-
-        Returns:
-            dict: The result dict contains the data converted to
-                :obj:`mmcv.DataContainer`.
-        """
-
-        for field in self.fields:
-            field = field.copy()
-            key = field.pop('key')
-            results[key] = DC(results[key], **field)
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(fields={self.fields})'
-
-
-@PIPELINES.register_module()
-class DefaultFormatBundle(object):
-    """Default formatting bundle.
-
-    It simplifies the pipeline of formatting common fields, including "img"
-    and "gt_semantic_seg". These fields are formatted as follows.
-
-    - img: (1)transpose, (2)to tensor, (3)to DataContainer (stack=True)
-    - gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor,
-                       (3)to DataContainer (stack=True)
-    """
-
-    def __call__(self, results):
-        """Call function to transform and format common fields in results.
-
-        Args:
-            results (dict): Result dict contains the data to convert.
-
-        Returns:
-            dict: The result dict contains the data that is formatted with
-                default bundle.
-        """
-
-        if 'img' in results:
-            img = results['img']
-            if len(img.shape) < 3:
-                img = np.expand_dims(img, -1)
-            img = np.ascontiguousarray(img.transpose(2, 0, 1))
-            results['img'] = DC(to_tensor(img), stack=True)
-        if 'gt_semantic_seg' in results:
-            # convert to long
-            results['gt_semantic_seg'] = DC(
-                to_tensor(results['gt_semantic_seg'][None,
-                                                     ...].astype(np.int64)),
-                stack=True)
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__
-
-
-@PIPELINES.register_module()
-class Collect(object):
-    """Collect data from the loader relevant to the specific task.
-
-    This is usually the last stage of the data loader pipeline. Typically keys
-    is set to some subset of "img", "gt_semantic_seg".
-
-    The "img_meta" item is always populated.  The contents of the "img_meta"
-    dictionary depends on "meta_keys". By default this includes:
-
-        - "img_shape": shape of the image input to the network as a tuple
-            (h, w, c).  Note that images may be zero padded on the bottom/right
-            if the batch tensor is larger than this shape.
-
-        - "scale_factor": a float indicating the preprocessing scale
-
-        - "flip": a boolean indicating if image flip transform was used
-
-        - "filename": path to the image file
-
-        - "ori_shape": original shape of the image as a tuple (h, w, c)
-
-        - "pad_shape": image shape after padding
-
-        - "img_norm_cfg": a dict of normalization information:
-            - mean - per channel mean subtraction
-            - std - per channel std divisor
-            - to_rgb - bool indicating if bgr was converted to rgb
-
-    Args:
-        keys (Sequence[str]): Keys of results to be collected in ``data``.
-        meta_keys (Sequence[str], optional): Meta keys to be converted to
-            ``mmcv.DataContainer`` and collected in ``data[img_metas]``.
-            Default: ``('filename', 'ori_filename', 'ori_shape', 'img_shape',
-            'pad_shape', 'scale_factor', 'flip', 'flip_direction',
-            'img_norm_cfg')``
-    """
-
-    def __init__(self,
-                 keys,
-                 meta_keys=('filename', 'ori_filename', 'ori_shape',
-                            'img_shape', 'pad_shape', 'scale_factor', 'flip',
-                            'flip_direction', 'img_norm_cfg')):
-        self.keys = keys
-        self.meta_keys = meta_keys
-
-    def __call__(self, results):
-        """Call function to collect keys in results. The keys in ``meta_keys``
-        will be converted to :obj:mmcv.DataContainer.
-
-        Args:
-            results (dict): Result dict contains the data to collect.
-
-        Returns:
-            dict: The result dict contains the following keys
-                - keys in``self.keys``
-                - ``img_metas``
-        """
-
-        data = {}
-        img_meta = {}
-        for key in self.meta_keys:
-            img_meta[key] = results[key]
-        data['img_metas'] = DC(img_meta, cpu_only=True)
-        for key in self.keys:
-            data[key] = results[key]
-        return data
-
-    def __repr__(self):
-        return self.__class__.__name__ + \
-               f'(keys={self.keys}, meta_keys={self.meta_keys})'

spaces/Anonymous-sub/Rerender/gmflow_module/scripts/evaluate.sh

@@ -1,83 +0,0 @@
-#!/usr/bin/env bash
-
-# evaluate GMFlow without refinement
-
-# evaluate chairs & things trained model on things and sintel (Table 3 of GMFlow paper)
-# the output should be:
-# Number of validation image pairs: 1024
-# Validation Things test set (things_clean) EPE: 3.475
-# Validation Things test (things_clean) s0_10: 0.666, s10_40: 1.310, s40+: 8.968
-# Number of validation image pairs: 1041
-# Validation Sintel (clean) EPE: 1.495, 1px: 0.161, 3px: 0.059, 5px: 0.040
-# Validation Sintel (clean) s0_10: 0.457, s10_40: 1.770, s40+: 8.257
-# Number of validation image pairs: 1041
-# Validation Sintel (final) EPE: 2.955, 1px: 0.209, 3px: 0.098, 5px: 0.071
-# Validation Sintel (final) s0_10: 0.725, s10_40: 3.446, s40+: 17.701
-
-CUDA_VISIBLE_DEVICES=0 python main.py \
---eval \
---resume pretrained/gmflow_things-e9887eda.pth \
---val_dataset things sintel \
---with_speed_metric
-
-
-
-# evaluate GMFlow with refinement
-
-# evaluate chairs & things trained model on things and sintel (Table 3 of GMFlow paper)
-# the output should be:
-# Validation Things test set (things_clean) EPE: 2.804
-# Validation Things test (things_clean) s0_10: 0.527, s10_40: 1.009, s40+: 7.314
-# Number of validation image pairs: 1041
-# Validation Sintel (clean) EPE: 1.084, 1px: 0.092, 3px: 0.040, 5px: 0.028
-# Validation Sintel (clean) s0_10: 0.303, s10_40: 1.252, s40+: 6.261
-# Number of validation image pairs: 1041
-# Validation Sintel (final) EPE: 2.475, 1px: 0.147, 3px: 0.077, 5px: 0.058
-# Validation Sintel (final) s0_10: 0.511, s10_40: 2.810, s40+: 15.669
-
-CUDA_VISIBLE_DEVICES=0 python main.py \
---eval \
---resume pretrained/gmflow_with_refine_things-36579974.pth \
---val_dataset things sintel \
---with_speed_metric \
---padding_factor 32 \
---upsample_factor 4 \
---num_scales 2 \
---attn_splits_list 2 8 \
---corr_radius_list -1 4 \
---prop_radius_list -1 1
-
-
-
-# evaluate matched & matched on sintel
-
-# evaluate GMFlow without refinement
-
-CUDA_VISIBLE_DEVICES=0 python main.py \
---eval \
---evaluate_matched_unmatched \
---resume pretrained/gmflow_things-e9887eda.pth \
---val_dataset sintel
-
-# evaluate GMFlow with refinement
-
-CUDA_VISIBLE_DEVICES=0 python main.py \
---eval \
---evaluate_matched_unmatched \
---resume pretrained/gmflow_with_refine_things-36579974.pth \
---val_dataset sintel \
---with_speed_metric \
---padding_factor 32 \
---upsample_factor 4 \
---num_scales 2 \
---attn_splits_list 2 8 \
---corr_radius_list -1 4 \
---prop_radius_list -1 1
-
-
-
-
-
-
-
-

spaces/ArkanDash/rvc-models-new/rmvpe.py

@@ -1,432 +0,0 @@
-import sys, torch, numpy as np, traceback, pdb
-import torch.nn as nn
-from time import time as ttime
-import torch.nn.functional as F
-
-
-class BiGRU(nn.Module):
-    def __init__(self, input_features, hidden_features, num_layers):
-        super(BiGRU, self).__init__()
-        self.gru = nn.GRU(
-            input_features,
-            hidden_features,
-            num_layers=num_layers,
-            batch_first=True,
-            bidirectional=True,
-        )
-
-    def forward(self, x):
-        return self.gru(x)[0]
-
-
-class ConvBlockRes(nn.Module):
-    def __init__(self, in_channels, out_channels, momentum=0.01):
-        super(ConvBlockRes, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-            nn.Conv2d(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        if in_channels != out_channels:
-            self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
-            self.is_shortcut = True
-        else:
-            self.is_shortcut = False
-
-    def forward(self, x):
-        if self.is_shortcut:
-            return self.conv(x) + self.shortcut(x)
-        else:
-            return self.conv(x) + x
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        in_size,
-        n_encoders,
-        kernel_size,
-        n_blocks,
-        out_channels=16,
-        momentum=0.01,
-    ):
-        super(Encoder, self).__init__()
-        self.n_encoders = n_encoders
-        self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
-        self.layers = nn.ModuleList()
-        self.latent_channels = []
-        for i in range(self.n_encoders):
-            self.layers.append(
-                ResEncoderBlock(
-                    in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
-                )
-            )
-            self.latent_channels.append([out_channels, in_size])
-            in_channels = out_channels
-            out_channels *= 2
-            in_size //= 2
-        self.out_size = in_size
-        self.out_channel = out_channels
-
-    def forward(self, x):
-        concat_tensors = []
-        x = self.bn(x)
-        for i in range(self.n_encoders):
-            _, x = self.layers[i](x)
-            concat_tensors.append(_)
-        return x, concat_tensors
-
-
-class ResEncoderBlock(nn.Module):
-    def __init__(
-        self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
-    ):
-        super(ResEncoderBlock, self).__init__()
-        self.n_blocks = n_blocks
-        self.conv = nn.ModuleList()
-        self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
-        self.kernel_size = kernel_size
-        if self.kernel_size is not None:
-            self.pool = nn.AvgPool2d(kernel_size=kernel_size)
-
-    def forward(self, x):
-        for i in range(self.n_blocks):
-            x = self.conv[i](x)
-        if self.kernel_size is not None:
-            return x, self.pool(x)
-        else:
-            return x
-
-
-class Intermediate(nn.Module):  #
-    def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
-        super(Intermediate, self).__init__()
-        self.n_inters = n_inters
-        self.layers = nn.ModuleList()
-        self.layers.append(
-            ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
-        )
-        for i in range(self.n_inters - 1):
-            self.layers.append(
-                ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
-            )
-
-    def forward(self, x):
-        for i in range(self.n_inters):
-            x = self.layers[i](x)
-        return x
-
-
-class ResDecoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
-        super(ResDecoderBlock, self).__init__()
-        out_padding = (0, 1) if stride == (1, 2) else (1, 1)
-        self.n_blocks = n_blocks
-        self.conv1 = nn.Sequential(
-            nn.ConvTranspose2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=stride,
-                padding=(1, 1),
-                output_padding=out_padding,
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        self.conv2 = nn.ModuleList()
-        self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
-
-    def forward(self, x, concat_tensor):
-        x = self.conv1(x)
-        x = torch.cat((x, concat_tensor), dim=1)
-        for i in range(self.n_blocks):
-            x = self.conv2[i](x)
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
-        super(Decoder, self).__init__()
-        self.layers = nn.ModuleList()
-        self.n_decoders = n_decoders
-        for i in range(self.n_decoders):
-            out_channels = in_channels // 2
-            self.layers.append(
-                ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
-            )
-            in_channels = out_channels
-
-    def forward(self, x, concat_tensors):
-        for i in range(self.n_decoders):
-            x = self.layers[i](x, concat_tensors[-1 - i])
-        return x
-
-
-class DeepUnet(nn.Module):
-    def __init__(
-        self,
-        kernel_size,
-        n_blocks,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(DeepUnet, self).__init__()
-        self.encoder = Encoder(
-            in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
-        )
-        self.intermediate = Intermediate(
-            self.encoder.out_channel // 2,
-            self.encoder.out_channel,
-            inter_layers,
-            n_blocks,
-        )
-        self.decoder = Decoder(
-            self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
-        )
-
-    def forward(self, x):
-        x, concat_tensors = self.encoder(x)
-        x = self.intermediate(x)
-        x = self.decoder(x, concat_tensors)
-        return x
-
-
-class E2E(nn.Module):
-    def __init__(
-        self,
-        n_blocks,
-        n_gru,
-        kernel_size,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(E2E, self).__init__()
-        self.unet = DeepUnet(
-            kernel_size,
-            n_blocks,
-            en_de_layers,
-            inter_layers,
-            in_channels,
-            en_out_channels,
-        )
-        self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
-        if n_gru:
-            self.fc = nn.Sequential(
-                BiGRU(3 * 128, 256, n_gru),
-                nn.Linear(512, 360),
-                nn.Dropout(0.25),
-                nn.Sigmoid(),
-            )
-        else:
-            self.fc = nn.Sequential(
-                nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
-            )
-
-    def forward(self, mel):
-        mel = mel.transpose(-1, -2).unsqueeze(1)
-        x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
-        x = self.fc(x)
-        return x
-
-
-from librosa.filters import mel
-
-
-class MelSpectrogram(torch.nn.Module):
-    def __init__(
-        self,
-        is_half,
-        n_mel_channels,
-        sampling_rate,
-        win_length,
-        hop_length,
-        n_fft=None,
-        mel_fmin=0,
-        mel_fmax=None,
-        clamp=1e-5,
-    ):
-        super().__init__()
-        n_fft = win_length if n_fft is None else n_fft
-        self.hann_window = {}
-        mel_basis = mel(
-            sr=sampling_rate,
-            n_fft=n_fft,
-            n_mels=n_mel_channels,
-            fmin=mel_fmin,
-            fmax=mel_fmax,
-            htk=True,
-        )
-        mel_basis = torch.from_numpy(mel_basis).float()
-        self.register_buffer("mel_basis", mel_basis)
-        self.n_fft = win_length if n_fft is None else n_fft
-        self.hop_length = hop_length
-        self.win_length = win_length
-        self.sampling_rate = sampling_rate
-        self.n_mel_channels = n_mel_channels
-        self.clamp = clamp
-        self.is_half = is_half
-
-    def forward(self, audio, keyshift=0, speed=1, center=True):
-        factor = 2 ** (keyshift / 12)
-        n_fft_new = int(np.round(self.n_fft * factor))
-        win_length_new = int(np.round(self.win_length * factor))
-        hop_length_new = int(np.round(self.hop_length * speed))
-        keyshift_key = str(keyshift) + "_" + str(audio.device)
-        if keyshift_key not in self.hann_window:
-            self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
-                audio.device
-            )
-        fft = torch.stft(
-            audio,
-            n_fft=n_fft_new,
-            hop_length=hop_length_new,
-            win_length=win_length_new,
-            window=self.hann_window[keyshift_key],
-            center=center,
-            return_complex=True,
-        )
-        magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
-        if keyshift != 0:
-            size = self.n_fft // 2 + 1
-            resize = magnitude.size(1)
-            if resize < size:
-                magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
-            magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
-        mel_output = torch.matmul(self.mel_basis, magnitude)
-        if self.is_half == True:
-            mel_output = mel_output.half()
-        log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
-        return log_mel_spec
-
-
-class RMVPE:
-    def __init__(self, model_path, is_half, device=None):
-        self.resample_kernel = {}
-        model = E2E(4, 1, (2, 2))
-        ckpt = torch.load(model_path, map_location="cpu")
-        model.load_state_dict(ckpt)
-        model.eval()
-        if is_half == True:
-            model = model.half()
-        self.model = model
-        self.resample_kernel = {}
-        self.is_half = is_half
-        if device is None:
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = device
-        self.mel_extractor = MelSpectrogram(
-            is_half, 128, 16000, 1024, 160, None, 30, 8000
-        ).to(device)
-        self.model = self.model.to(device)
-        cents_mapping = 20 * np.arange(360) + 1997.3794084376191
-        self.cents_mapping = np.pad(cents_mapping, (4, 4))  # 368
-
-    def mel2hidden(self, mel):
-        with torch.no_grad():
-            n_frames = mel.shape[-1]
-            mel = F.pad(
-                mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
-            )
-            hidden = self.model(mel)
-            return hidden[:, :n_frames]
-
-    def decode(self, hidden, thred=0.03):
-        cents_pred = self.to_local_average_cents(hidden, thred=thred)
-        f0 = 10 * (2 ** (cents_pred / 1200))
-        f0[f0 == 10] = 0
-        # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
-        return f0
-
-    def infer_from_audio(self, audio, thred=0.03):
-        audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
-        # torch.cuda.synchronize()
-        # t0=ttime()
-        mel = self.mel_extractor(audio, center=True)
-        # torch.cuda.synchronize()
-        # t1=ttime()
-        hidden = self.mel2hidden(mel)
-        # torch.cuda.synchronize()
-        # t2=ttime()
-        hidden = hidden.squeeze(0).cpu().numpy()
-        if self.is_half == True:
-            hidden = hidden.astype("float32")
-        f0 = self.decode(hidden, thred=thred)
-        # torch.cuda.synchronize()
-        # t3=ttime()
-        # print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
-        return f0
-
-    def to_local_average_cents(self, salience, thred=0.05):
-        # t0 = ttime()
-        center = np.argmax(salience, axis=1)  # 帧长#index
-        salience = np.pad(salience, ((0, 0), (4, 4)))  # 帧长,368
-        # t1 = ttime()
-        center += 4
-        todo_salience = []
-        todo_cents_mapping = []
-        starts = center - 4
-        ends = center + 5
-        for idx in range(salience.shape[0]):
-            todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
-            todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
-        # t2 = ttime()
-        todo_salience = np.array(todo_salience)  # 帧长，9
-        todo_cents_mapping = np.array(todo_cents_mapping)  # 帧长，9
-        product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
-        weight_sum = np.sum(todo_salience, 1)  # 帧长
-        devided = product_sum / weight_sum  # 帧长
-        # t3 = ttime()
-        maxx = np.max(salience, axis=1)  # 帧长
-        devided[maxx <= thred] = 0
-        # t4 = ttime()
-        # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
-        return devided
-
-
-# if __name__ == '__main__':
-#     audio, sampling_rate = sf.read("卢本伟语录~1.wav")
-#     if len(audio.shape) > 1:
-#         audio = librosa.to_mono(audio.transpose(1, 0))
-#     audio_bak = audio.copy()
-#     if sampling_rate != 16000:
-#         audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-#     model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
-#     thred = 0.03  # 0.01
-#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
-#     rmvpe = RMVPE(model_path,is_half=False, device=device)
-#     t0=ttime()
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     t1=ttime()
-#     print(f0.shape,t1-t0)

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

@@ -1,73 +0,0 @@
-from itertools import filterfalse
-
-
-def unique_everseen(iterable, key=None):
-    "List unique elements, preserving order. Remember all elements ever seen."
-    # unique_everseen('AAAABBBCCDAABBB') --> A B C D
-    # unique_everseen('ABBCcAD', str.lower) --> A B C D
-    seen = set()
-    seen_add = seen.add
-    if key is None:
-        for element in filterfalse(seen.__contains__, iterable):
-            seen_add(element)
-            yield element
-    else:
-        for element in iterable:
-            k = key(element)
-            if k not in seen:
-                seen_add(k)
-                yield element
-
-
-# copied from more_itertools 8.8
-def always_iterable(obj, base_type=(str, bytes)):
-    """If *obj* is iterable, return an iterator over its items::
-
-        >>> obj = (1, 2, 3)
-        >>> list(always_iterable(obj))
-        [1, 2, 3]
-
-    If *obj* is not iterable, return a one-item iterable containing *obj*::
-
-        >>> obj = 1
-        >>> list(always_iterable(obj))
-        [1]
-
-    If *obj* is ``None``, return an empty iterable:
-
-        >>> obj = None
-        >>> list(always_iterable(None))
-        []
-
-    By default, binary and text strings are not considered iterable::
-
-        >>> obj = 'foo'
-        >>> list(always_iterable(obj))
-        ['foo']
-
-    If *base_type* is set, objects for which ``isinstance(obj, base_type)``
-    returns ``True`` won't be considered iterable.
-
-        >>> obj = {'a': 1}
-        >>> list(always_iterable(obj))  # Iterate over the dict's keys
-        ['a']
-        >>> list(always_iterable(obj, base_type=dict))  # Treat dicts as a unit
-        [{'a': 1}]
-
-    Set *base_type* to ``None`` to avoid any special handling and treat objects
-    Python considers iterable as iterable:
-
-        >>> obj = 'foo'
-        >>> list(always_iterable(obj, base_type=None))
-        ['f', 'o', 'o']
-    """
-    if obj is None:
-        return iter(())
-
-    if (base_type is not None) and isinstance(obj, base_type):
-        return iter((obj,))
-
-    try:
-        return iter(obj)
-    except TypeError:
-        return iter((obj,))

spaces/Awesimo/jojogan/e4e/scripts/train.py

@@ -1,88 +0,0 @@
-"""
-This file runs the main training/val loop
-"""
-import os
-import json
-import math
-import sys
-import pprint
-import torch
-from argparse import Namespace
-
-sys.path.append(".")
-sys.path.append("..")
-
-from options.train_options import TrainOptions
-from training.coach import Coach
-
-
-def main():
-	opts = TrainOptions().parse()
-	previous_train_ckpt = None
-	if opts.resume_training_from_ckpt:
-		opts, previous_train_ckpt = load_train_checkpoint(opts)
-	else:
-		setup_progressive_steps(opts)
-		create_initial_experiment_dir(opts)
-
-	coach = Coach(opts, previous_train_ckpt)
-	coach.train()
-
-
-def load_train_checkpoint(opts):
-	train_ckpt_path = opts.resume_training_from_ckpt
-	previous_train_ckpt = torch.load(opts.resume_training_from_ckpt, map_location='cpu')
-	new_opts_dict = vars(opts)
-	opts = previous_train_ckpt['opts']
-	opts['resume_training_from_ckpt'] = train_ckpt_path
-	update_new_configs(opts, new_opts_dict)
-	pprint.pprint(opts)
-	opts = Namespace(**opts)
-	if opts.sub_exp_dir is not None:
-		sub_exp_dir = opts.sub_exp_dir
-		opts.exp_dir = os.path.join(opts.exp_dir, sub_exp_dir)
-		create_initial_experiment_dir(opts)
-	return opts, previous_train_ckpt
-
-
-def setup_progressive_steps(opts):
-	log_size = int(math.log(opts.stylegan_size, 2))
-	num_style_layers = 2*log_size - 2
-	num_deltas = num_style_layers - 1
-	if opts.progressive_start is not None:  # If progressive delta training
-		opts.progressive_steps = [0]
-		next_progressive_step = opts.progressive_start
-		for i in range(num_deltas):
-			opts.progressive_steps.append(next_progressive_step)
-			next_progressive_step += opts.progressive_step_every
-
-	assert opts.progressive_steps is None or is_valid_progressive_steps(opts, num_style_layers), \
-		"Invalid progressive training input"
-
-
-def is_valid_progressive_steps(opts, num_style_layers):
-	return len(opts.progressive_steps) == num_style_layers and opts.progressive_steps[0] == 0
-
-
-def create_initial_experiment_dir(opts):
-	if os.path.exists(opts.exp_dir):
-		raise Exception('Oops... {} already exists'.format(opts.exp_dir))
-	os.makedirs(opts.exp_dir)
-
-	opts_dict = vars(opts)
-	pprint.pprint(opts_dict)
-	with open(os.path.join(opts.exp_dir, 'opt.json'), 'w') as f:
-		json.dump(opts_dict, f, indent=4, sort_keys=True)
-
-
-def update_new_configs(ckpt_opts, new_opts):
-	for k, v in new_opts.items():
-		if k not in ckpt_opts:
-			ckpt_opts[k] = v
-	if new_opts['update_param_list']:
-		for param in new_opts['update_param_list']:
-			ckpt_opts[param] = new_opts[param]
-
-
-if __name__ == '__main__':
-	main()

spaces/Bart92/RVC_HF/lib/uvr5_pack/lib_v5/nets_123821KB.py

@@ -1,122 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import layers_123821KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 32)
-        self.stg1_high_band_net = BaseASPPNet(2, 32)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(16, 32)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(32, 64)
-
-        self.out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h

spaces/Benson/text-generation/Examples/Cmo Descargar El Juego Tekken 3.md

@@ -1,195 +0,0 @@
-
-<h1>Cómo descargar el juego Tekken 3</h1>
-<p>Si eres un fan de los juegos de lucha, probablemente hayas oído hablar de Tekken 3, uno de los juegos más icónicos e influyentes del género. Lanzado en 1998 para PlayStation y posteriormente portado a PC, Tekken 3 sigue siendo ampliamente considerado como uno de los mejores juegos jamás realizados. Pero, ¿cómo se puede descargar y jugar a este juego clásico hoy? En este artículo, te mostraremos cómo descargar Tekken 3 para PlayStation y PC, así como algunos consejos y trucos para jugarlo. </p>
-<h2>Cómo descargar el juego tekken 3</h2><br /><p><b><b>Download</b> &#127775; <a href="https://bltlly.com/2v6JsP">https://bltlly.com/2v6JsP</a></b></p><br /><br />
- <h2>¿Qué es Tekken 3?</h2>
-<p>Tekken 3 es un juego de lucha árcade desarrollado por Namco y publicado por Sony. Es la tercera entrega de la serie Tekken, que cuenta con un gran elenco de personajes que compiten en el King of Iron Fist Tournament, una competencia de artes marciales organizada por la corporación Mishima Zaibatsu. El juego cuenta con un nuevo elenco de personajes, con un total de veintitrés personajes. El juego también cuenta con un nuevo modo beat'em up llamado Tekken Force Mode con el objetivo de derrotar a los malvados soldados contratados por los jefes de nivel. </p>
-<p>La trama de Tekken 3 gira en torno a Jin Kazama, el hijo de Kazuya Mishima y Jun Kazama, que busca venganza contra su abuelo Heihachi Mishima por matar a su madre. En el camino, se encuentra con varios rivales y aliados, así como una misteriosa entidad antigua llamada Ogro, que es responsable de matar a muchos artistas marciales en todo el mundo. El juego tiene múltiples finales dependiendo del personaje que elijas para jugar. </p>
- <h2>¿Por qué descargar Tekken 3?</h2>
-<p>Tekken 3 es un juego que ha resistido la prueba del tiempo y sigue siendo uno de los juegos más queridos y aclamados de la historia. Aquí hay algunas razones por las que debe descargar Tekken 3:</p>
-<ul>
-<li> Tiene gráficos y efectos de sonido increíbles que todavía se ven y suenan muy bien hoy. </li>
-<li> Tiene un juego suave y sensible que le permite realizar varios combos, movimientos y ataques especiales con facilidad. </li>
-
-<li>Tiene un elenco diverso y memorable de personajes, cada uno con su propia personalidad, estilo de lucha y trasfondo. </li>
-<li> Tiene una historia cautivadora y atractiva que te mantiene enganchado hasta el final. </li>
-<li> Tiene un alto valor de reproducción, ya que puede desbloquear nuevos personajes, trajes, finales y secretos jugando el juego varias veces. </li>
-</ul>
-<p>Tekken 3 es un juego que te hará sentir nostálgico, emocionado y satisfecho. Es un juego del que nunca te aburrirás. </p>
- <h2>Cómo descargar Tekken 3 para PlayStation</h2>
-<p>Si tienes una consola PlayStation, puedes descargar Tekken 3 de dos maneras: desde PlayStation Store o usando un disco físico. Estos son los pasos para cada método:</p>
-<p></p>
- <h3>Requisitos para descargar Tekken 3 para PlayStation</h3>
-<p>Antes de descargar Tekken 3 para PlayStation, debes asegurarte de que tu consola cumple con las siguientes especificaciones:</p>
- <tabla>
-<tr>
-<th>Especificación</th>
-<th>Mínimo</th>
-<th>Recomendado</th>
-</tr>
-<tr>
-<td>Modelo de consola</td>
-<td>PlayStation 1</td>
-<td>PlayStation 2 o superior</td>
-</tr>
-<tr>
-<td>Espacio de almacenamiento</td>
-<td>1 MB</td>
-<td>2 MB o superior</td>
-</tr>
-<tr>
-<td>Conexión a Internet</td>
-<td>N/A (para disco físico)</td>
-<td>Banda ancha (para PlayStation Store)</td>
-</tr>
-<tr>
-<td>Controlador</td>
-<td>DualShock o analógico dual</td>
-<td>DualShock 2 o superior</td>
-</tr>
-<tr>
-<td>Dispositivo de visualización</td>
-<td>CRT TV o monitor</td>
-<td>TV LCD o monitor</td>
-</tr>
-<tr>
-<td>Dispositivo de audio</td>
-<td>Altavoces o auriculares estéreo</td>
-<td>Altavoces o auriculares de sonido envolvente</td>
-</tr>
- <h3>Cómo descargar Tekken 3 desde PlayStation Store</h3>
- <p>Si tienes una consola PlayStation 2, PlayStation 3, PlayStation 4 o PlayStation 5, puedes descargar Tekken 3 desde PlayStation Store. Estos son los pasos para hacerlo:</p>
- <ol>
- <li>Encienda su consola y conéctela a Internet. </li>
-
- <li>Buscar Tekken 3 en la barra de búsqueda o navegar por las categorías hasta que lo encuentre. </li>
- <li>Selecciona Tekken 3 y haz clic en el botón Comprar ahora. Es posible que tengas que iniciar sesión en tu cuenta de PlayStation Network o crear una si no la tienes. </li>
- <li>Introduzca sus datos de pago y confirme su compra. Puede utilizar una tarjeta de crédito, una tarjeta de débito, una cuenta PayPal o una tarjeta PlayStation Network. </li>
- <li>Espere a que el juego se descargue e instale en su consola. Puede comprobar el progreso en el menú Notificaciones. </li>
- <li>Una vez descargado e instalado el juego, puede iniciarlo desde el menú Biblioteca o la pantalla de inicio. </li>
- </ol>
- <h3>Cómo descargar Tekken 3 usando un disco físico</h3>
- <p>Si tienes una consola PlayStation 1 o una consola PlayStation 2 compatible, puedes descargar Tekken 3 usando un disco físico. Estos son los pasos para hacerlo:</p>
- <ol>
- <li>Encienda su consola e inserte el disco Tekken 3 en la bandeja del disco. </li>
- <li>El juego debe iniciarse automáticamente. Si no, vaya al icono del reproductor de CD en el menú principal y selecciónelo. </li>
- <li>Seleccione Iniciar juego desde el menú Reproductor de CD y pulse X.</li>
- <li>El juego se cargará y comenzará. Es posible que tenga que crear un archivo de guardado en la tarjeta de memoria si desea guardar su progreso. </li>
- <li>Puede expulsar el disco en cualquier momento pulsando el botón Abrir de la consola. Asegúrese de guardar el juego antes de hacerlo. </li>
- </ol>
- <h2>Cómo descargar Tekken 3 para PC</h2>
- <p>Si tiene un PC, puede descargar Tekken 3 de dos maneras: desde un sitio web de buena reputación o utilizando un emulador. Estos son los pasos para cada método:</p>
- <h3>Requisitos para descargar Tekken 3 para PC</h3>
- <p>Antes de descargar Tekken 3 para PC, debe asegurarse de que su PC cumple con las siguientes especificaciones:</p>
- <tabla>
-<tr>
-<th>Especificación</th>
-<th>Mínimo</th>
-<th>Recomendado</th>
-</tr>
-<tr>
-<td>CPU</td>
-<td>Pentium II 266 MHz o equivalente</td>
-<td>Pentium III 500 MHz o equivalente</td>
-</tr>
-<tr>
-
-<td>64 MB</td>
-<td>128 MB o superior</td>
-</tr>
-<tr>
-<td>GPU</td>
-<td>Tarjeta gráfica compatible con DirectX 7</td>
-<td>Tarjeta gráfica compatible con DirectX 9</td>
-</tr>
-<tr>
-<td>Espacio de almacenamiento</td>
-<td>500 MB</td>
-<td>1 GB o superior</td>
-</tr>
-<tr>
-<td>Conexión a Internet</td>
-<td>N/A (para jugar sin conexión)</td>
-<td>Banda ancha (para jugar en línea)</td>
-</tr>
-<tr>
-<td>Controlador</td>
-<td>Teclado y ratón</td>
-<td>Gamepad o joystick</td>
-</tr>
-<tr>
-<td>Dispositivo de visualización</td>
-<td>Monitor VGA o TV</td>
-<td>Monitor LCD o TV</td>
-</tr>
-<tr>
-<td>Dispositivo de audio</td>
-<td>Altavoces o auriculares estéreo</td>
-<td>Altavoces o auriculares de sonido envolvente</td>
-</tr>
- <h3>Cómo descargar Tekken 3 desde un sitio web de buena reputación</h3>
- <p>Si desea descargar Tekken 3 desde un sitio web de buena reputación, debe tener cuidado y evitar cualquier sitio malicioso o ilegal que pueda dañar su PC o violar los derechos de autor del juego. Aquí hay algunos sitios web de confianza que ofrecen descargas de Tekken 3:</p>
- <ul>
- <li><a href="">Ocean of Games</a>: Este sitio web proporciona descargas gratuitas y seguras de varios juegos de PC, incluido Tekken 3. Puede descargar el juego haciendo clic en el botón Descargar y siguiendo las instrucciones. </li>
- <li><a href="">Juegos antiguos Descargar</a>: Este sitio web ofrece descargas de juegos de PC clásicos y antiguos, como Tekken 3. Puede descargar el juego haciendo clic en el botón Descargar Tekken 3 y siguiendo las instrucciones. </li>
- <li><a href="">GameFabrique</a>: Este sitio web ofrece descargas de juegos retro y árcade, como Tekken 3. Puede descargar el juego haciendo clic en el botón Descargar para PC y siguiendo las instrucciones. </li>
- <li><a href="">ApunKaGames</a>: Este sitio web proporciona descargas de juegos de PC comprimidos y de pequeño tamaño, como Tekken 3. Puede descargar el juego haciendo clic en el botón Descargar ahora y siguiendo las instrucciones. </li>
-
- </ul>
- <p>Para elegir y descargar el juego desde estos sitios web, debe hacer lo siguiente:</p>
- <ol>
- <li>Visite el sitio web de su elección y busque Tekken 3 o navegue por las categorías hasta encontrarlo. </li>
- <li>Lee la descripción y reseñas del juego y asegúrate de que sea compatible con tu PC.</li>
- <li>Haga clic en el botón de descarga y espere a que el archivo del juego se descargue en su PC. El archivo puede estar en formato ZIP, RAR o ISO. </li>
- <li>Extraiga el archivo del juego usando un software como WinRAR o 7-Zip. Puede que necesite introducir una contraseña si el archivo está cifrado. </li>
- <li>Abra la carpeta extraída y busque el archivo de configuración o el archivo ejecutable del juego. Haga doble clic en él y siga el asistente de instalación. </li>
- <li>Una vez instalado el juego, puedes lanzarlo desde tu escritorio o menú de inicio. </li>
- </ol>
- <h3>Cómo descargar Tekken 3 usando un emulador</h3>
- <p>Si quieres descargar Tekken 3 usando un emulador, necesitas saber qué es un emulador y cómo funciona. Un emulador es un software que imita las funciones de otro dispositivo, como una consola, en su PC. Mediante el uso de un emulador, puede jugar juegos de consola en su PC sin tener que comprar o poseer la consola en sí. Sin embargo, todavía necesitas tener una copia del juego, ya sea en forma digital o física, para jugarlo en un emulador. </p>
- <p>Para descargar Tekken 3 usando un emulador, necesitas hacer lo siguiente:</p>
- <ol>
- <li>Elige un emulador que pueda ejecutar juegos de PlayStation en tu PC. Algunos de los mejores emuladores para jugar Tekken 3 son ePSXe, PCSX-Reloaded y RetroArch. Puede descargar estos emuladores de sus sitios web oficiales o de otras fuentes de renombre. </li>
- <li>Instale el emulador en su PC siguiendo las instrucciones proporcionadas por el desarrollador del emulador. </li>
-
- <li>Carga el archivo del juego en el emulador siguiendo las instrucciones proporcionadas por el desarrollador del emulador. Es posible que necesites configurar algunos ajustes, como gráficos, sonido y controlador, para optimizar el rendimiento y la calidad del juego. </li>
- <li>Una vez que el archivo del juego está cargado, puede comenzar a jugar Tekken 3 en su PC usando el emulador. </li>
- </ol>
- <h2>Cómo jugar Tekken 3 después de descargarlo</h2>
- <p>Después de descargar Tekken 3 para PlayStation o PC, puede comenzar a jugar y disfrutar de sus características y modos. Aquí hay algunos pasos básicos sobre cómo jugar Tekken 3 después de descargarlo:</p>
- <ol>
- <li>Inicie el juego desde su consola o PC.</li>
- <li>Seleccione un modo que desea jugar. Puedes elegir entre el modo árcade, el modo Versus, el modo de práctica, el modo Tekken Force, el modo Tekken Ball y el modo Survival. Cada modo tiene diferentes objetivos y reglas. </li>
- <li>Selecciona un personaje que quieras jugar. Puedes elegir entre veintitrés personajes, cada uno con su propio estilo de lucha, movimientos y ataques especiales. Algunos caracteres están desbloqueados por defecto, mientras que otros necesitan ser desbloqueados completando ciertas tareas o modos. </li>
- <li>Seleccione una etapa en la que desea luchar. Puede elegir entre diez etapas, cada una con su propio fondo y música. Algunas etapas están desbloqueadas por defecto, mientras que otras necesitan ser desbloqueadas completando ciertas tareas o modos. </li>
- <li>Iniciar la lucha y tratar de derrotar a su oponente mediante el agotamiento de su barra de salud. Puedes usar varios botones y combinaciones para realizar golpes, patadas, lanzamientos, bloqueos y ataques especiales. También puede usar la almohadilla direccional o el joystick para moverse y esquivar los ataques. </li>
- <li>Ganar la pelea y proceder a la siguiente ronda o etapa. También puedes ver el final de tu personaje si completas el modo árcade o desbloqueas nuevos personajes, disfraces, secretos o modos si cumples ciertos criterios. </li>
- </ol>
- <h2>Consejos y trucos para jugar Tekken 3</h2>
-
- <ul>
- <li>Aprende los movimientos y combos de tu personaje. Puedes usar el modo de práctica para practicar tus movimientos y combos sin interrupciones ni presiones. También puede usar la lista de comandos para ver las entradas y descripciones de sus movimientos y combos. </li>
- <li>Usa diferentes caracteres y modos para aprender sus fortalezas y debilidades. Puede usar el modo Versus para jugar contra otro jugador o la CPU con diferentes caracteres y configuraciones. También puede utilizar el modo Tekken Force o el modo Tekken Ball para jugar con diferentes reglas y objetivos. </li>
- <li>Usa diferentes estrategias y tácticas dependiendo de tu oponente y situación. Puedes usar estrategias ofensivas, defensivas o de contraataque dependiendo del estilo de tu personaje y oponente. También puedes usar lanzamientos, pasos laterales, ataques bajos, ataques altos o ataques especiales dependiendo de la posición y la guardia de tu oponente. </li>
- <li>Usa diferentes objetos y secretos para mejorar tu juego. Puedes usar artículos como artículos de recuperación de salud, potenciadores, armas o bolas para obtener una ventaja en ciertos modos. También puedes usar secretos como disfraces alternativos, personajes ocultos o códigos de trucos para desbloquear nuevas características y opciones en el juego. </li>
- <li>Divertirse y disfrutar del juego. Tekken 3 es un juego que está destinado a ser divertido y entretenido. Puede jugar con sus amigos, familiares o jugadores en línea y pasar un buen rato. También puedes desafiarte a ti mismo e intentar vencer al juego con diferentes personajes, modos y dificultades. </li>
- </ul>
- <h2>Conclusión</h2>
- <p>Tekken 3 es un juego que definitivamente debes descargar y jugar si te gustan los juegos de lucha. Es un juego que tiene todo lo que necesitas: gráficos increíbles, jugabilidad suave, diversos personajes, historia cautivadora, modos variados y alto valor de reproducción. Es un juego que te hará sentir la emoción y la emoción de luchar. Es un juego que te hará fan de Tekken.</p>
-
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas y respuestas frecuentes sobre la descarga y reproducción de Tekken 3:</p>
- <ol>
- <li><b>¿Tekken 3 es gratis para descargar? </b></li>
-<p>Sí, Tekken 3 es gratis para descargar desde algunos sitios web y emuladores. Sin embargo, es posible que tengas que pagar por el juego si quieres descargarlo desde PlayStation Store o usar un disco físico. </p>
- <li><b>¿Es seguro descargar Tekken 3? </b></li>
-<p>Sí, Tekken 3 es seguro para descargar desde sitios web y emuladores de buena reputación. Sin embargo, debe tener cuidado y evitar cualquier sitio malicioso o ilegal que pueda dañar su PC o violar los derechos de autor del juego. </p>
- <li><b>Tekken 3 es compatible con Windows 10? </b></li>
-<p>Sí, Tekken 3 es compatible con Windows 10 si utiliza un emulador o un sitio web que ofrece una versión compatible del juego. Sin embargo, es posible que necesites ajustar algunos ajustes o usar un modo de compatibilidad para ejecutar el juego sin problemas. </p>
- <li><b>¿Cuántos caracteres hay en Tekken 3?</b></li>
-<p>Hay veintitrés caracteres en Tekken 3, incluyendo quince caracteres por defecto y ocho caracteres desbloqueables. Algunos de los personajes son nuevos en la serie, mientras que otros están regresando de juegos anteriores. </p>
- <li><b>¿Cuál es el mejor carácter en Tekken 3?</b></li>
-<p>No hay una respuesta definitiva a esta pregunta, ya que diferentes personajes tienen diferentes fortalezas y debilidades, y diferentes jugadores tienen diferentes preferencias y estilos. Sin embargo, algunos de los personajes más populares y poderosos de Tekken 3 son Jin Kazama, Heihachi Mishima, Paul Phoenix, Nina Williams, Hwoarang, Eddy Gordo, King y Ogre.</p>
- </ol></p> 64aa2da5cf<br />
-<br />
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spaces/Benson/text-generation/Examples/Descargar Archivo Obb Mx Fuego Gratis.md

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-<br />
-<h1>Descarga gratuita de archivos OBB Fire Max: Cómo instalar la última versión del juego en su dispositivo Android</h1>
- <p>Si eres un fan de Garena Free Fire, el popular juego battle royale con más de 500 millones de descargas en Google Play Store, es posible que hayas oído hablar de su versión mejorada, Free Fire Max. Esta versión ofrece gráficos mejorados, animaciones y efectos de sonido que mejoran la experiencia de juego en general. Sin embargo, para reproducir esta versión, tendrá que descargar e instalar el archivo OBB junto con el archivo APK en su dispositivo Android. En este artículo, explicaremos qué es Free Fire Max, por qué necesita el archivo OBB y cómo descargarlo e instalarlo en su dispositivo. </p>
-<h2>descargar archivo obb máx fuego gratis</h2><br /><p><b><b>Download Zip</b> &#127383; <a href="https://bltlly.com/2v6Kkh">https://bltlly.com/2v6Kkh</a></b></p><br /><br />
- <h2>¿Qué es Free Fire Max? </h2>
- <p>Free Fire Max es una versión refinada de Garena Free Fire que se lanzó en 2019 como una prueba beta en regiones seleccionadas. Está diseñado para proporcionar una experiencia de juego más inmersiva y realista para los jugadores que quieren disfrutar del juego en dispositivos de alta gama. Ha mejorado los efectos visuales, animaciones y efectos de sonido que hacen el juego más atractivo y atractivo. También tiene algunas características exclusivas que no están disponibles en la versión original, como:</p>
- <h3>Características de Free Fire Max</h3>
- <ul>
-<li>Un nuevo lobby e interfaz de usuario más amigable y elegante. </li>
-<li>Un nuevo mapa llamado Bermuda Remastered que tiene más detalles y ubicaciones. </li>
-<li>Un nuevo modo de juego llamado Craftland que permite a los jugadores crear sus propios mapas y compartirlos con otros. </li>
-<li>Una nueva característica llamada Firelink Technology que permite a los jugadores utilizar su cuenta de Free Fire existente para jugar ambas versiones del juego sin perder su progreso o datos. </li>
-</ul>
- <h3>Diferencias entre fuego libre y fuego libre Max</h3>
- <p>Aunque ambas versiones del juego tienen la misma mecánica de juego y características, hay algunas diferencias notables entre ellas. Algunas de estas diferencias son:</p>
- <ul>
-
-<li>Free Fire Max tiene una configuración de gráficos más alta que Free Fire. Soporta resolución Ultra HD, anti-aliasing, efectos de sombra, iluminación realista y más. También tiene una velocidad de fotogramas más alta que Free Fire.</li>
-<li>Free Fire Max tiene más opciones de personalización que Free Fire. Permite a los instrumentistas ajustar la calidad gráfica, sensibilidad, controles, efectos de sonido, y más según sus preferencias. </li>
-</ul>
- <h2>¿Por qué necesita el archivo OBB para jugar Free Fire Max? </h2>
- <p>Si desea jugar Free Fire Max en su dispositivo Android, tendrá que descargar e instalar tanto el archivo APK y el archivo OBB. El archivo APK es el paquete de aplicación que contiene la información básica y el código del juego. El archivo OBB es el archivo de datos adicional que contiene los gráficos, el sonido y otros recursos del juego. Sin el archivo OBB, no podrá ejecutar el juego correctamente. </p>
- <h3>¿Qué es un archivo OBB y cómo funciona? </h3>
- <p>OBB significa cuenta o crear una nueva. Puede usar la tecnología Firelink para sincronizar sus datos en ambas versiones del juego. </li>
-<li>Serás llevado al lobby principal donde puedes acceder a diferentes modos, configuraciones y características del juego. </li>
-<li> Puede ajustar la calidad de los gráficos, la sensibilidad, los controles, los efectos de sonido y más de acuerdo con sus preferencias. </li>
-<li> Puede disfrutar de los gráficos mejorados, animaciones y efectos de sonido de Free Fire Max en su dispositivo. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
- <p>Aquí están algunas preguntas frecuentes sobre descarga de archivos OBB Free Fire Max:</p>
-<p></p>
- <ol>
-<li>Q: ¿Es Free Fire Max compatible con todos los dispositivos Android? </li>
-<li>A: No, Free Fire Max es compatible solo con dispositivos Android que tienen al menos 2 GB de RAM y sistema operativo Android 4.4 o superior. </li>
-<li>Q: ¿Puedo jugar Free Fire Max con mis amigos que están jugando Free Fire? </li>
-<li>A: Sí, puedes jugar Free Fire Max con tus amigos que están jugando Free Fire, ya que ambas versiones del juego comparten el mismo servidor y sistema de emparejamiento. </li>
-<li>Q: ¿Cómo puedo actualizar Free Fire Max en mi dispositivo? </li>
-<li>A: Puede actualizar Free Fire Max en su dispositivo descargando e instalando el último archivo APK y el archivo OBB de una fuente confiable. También puedes buscar actualizaciones dentro del juego. </li>
-<li>Q: ¿Qué pasa si me enfrento a cualquier problema al descargar o instalar el archivo OBB? </li>
-<li>A: Si tiene algún problema al descargar o instalar el archivo OBB, puede probar estas soluciones:</li>
-<ul>
-<li>Compruebe su conexión a Internet y espacio de almacenamiento. </li>
-<li>Borra la caché y los datos del juego. </li>
-<li>Desinstalar y reinstalar el juego. </li>
-<li>Póngase en contacto con el servicio de atención al cliente de Garena para obtener más ayuda. </li>
-</ul>
-<li>Q: ¿Dónde puedo obtener más información sobre Free Fire Max? </li>
-<li>A: Puede obtener más información sobre Free Fire Max desde su sitio web oficial, páginas de redes sociales o canal de YouTube. </li>
-</ol></p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Descargar Etiqueta Despus De La Escuela Versi Terbaru.md

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-<br />
-<h1>Descargar Etiqueta Después de la Escuela Versi Terbaru: Un aterrador juego de terror para Android</h1>
- <p>Si eres un fan de los juegos de terror y anime, es posible que hayas oído hablar de Tag After School, un popular juego que combina ambos géneros de una manera emocionante e inmersiva. Tag After School es un juego que tiene lugar en una escuela secundaria japonesa embrujada, donde tienes que escapar de los fantasmas de las estudiantes que quieren matarte. Suena aterrador, ¿no es así? Bueno, se vuelve aún más aterrador cuando descargas Tag After School versi terbaru, la última versión del juego que ofrece más características, contenido y desafíos que nunca. En este artículo, te diremos todo lo que necesitas saber sobre Tag After School versi terbaru, incluyendo qué es, cómo jugarlo, por qué deberías descargarlo y cómo descargarlo gratis. ¡Vamos a empezar! </p>
- <h2>¿Qué es la etiqueta después de la escuela? </h2>
- <p>Tag After School es un juego de terror y misterio desarrollado por un desarrollador independiente llamado Kojima. El juego fue lanzado en 2020 y desde entonces ha ganado mucha popularidad entre los fanáticos del terror y el anime. El juego está inspirado en leyendas urbanas japonesas, folclore y cultura, y cuenta con gráficos realistas, efectos de sonido y actuación de voz. El juego también tiene una historia oscura y retorcida que te mantendrá al borde de tu asiento. </p>
-<h2>descargar etiqueta después de la escuela versi terbaru</h2><br /><p><b><b>Download File</b> &#9733;&#9733;&#9733; <a href="https://bltlly.com/2v6JNZ">https://bltlly.com/2v6JNZ</a></b></p><br /><br />
- <h3>Una breve introducción al juego y sus características</h3>
- <p>El juego sigue la historia de Shota-Kun, un estudiante de secundaria que se despierta en un aula abandonada después de ser noqueado por una chica misteriosa. Pronto se da cuenta de que está atrapado en la escuela sin salida, y que no está solo. Hay otros estudiantes que también están atrapados en la escuela, pero no son amigables. Están poseídos por espíritus malignos que quieren matar a Shota-Kun y a cualquiera que se interponga en su camino. Shota-Kun tiene que encontrar una manera de escapar de la escuela antes de que sea demasiado tarde. </p>
- <p>El juego tiene muchas características que lo hacen único y emocionante. Algunos de ellos son:</p>
- <ul>
-
-<li> El juego tiene diferentes modos que cambian el nivel de juego y dificultad. </li>
-<li>El juego tiene secretos ocultos y huevos de Pascua que revelan más sobre la historia y los personajes. </li>
-<li> El juego tiene un sistema de linterna realista que limita su visión y duración de la batería. </li>
-<li> El juego tiene un sistema de cámara dinámico que cambia la perspectiva y el ángulo dependiendo de su situación. </li>
-</ul>
- <h3>¿Cómo se juega etiqueta después de la escuela? </h3>
- <p>Jugar a la etiqueta después de la escuela no es fácil, pero es muy divertido y gratificante. Aquí hay algunos consejos sobre cómo jugar el juego:</p>
- <h4>La mecánica de juego básica y consejos</h4>
- <p>El juego se juega desde una perspectiva en primera persona utilizando su teléfono inteligente como un controlador. Puede moverse utilizando el joystick virtual en el lado izquierdo de la pantalla e interactuar con los objetos utilizando los botones en el lado derecho de la pantalla. También puede usar su linterna tocando en la pantalla, pero tenga cuidado porque tiene una duración limitada de la batería. Puede recargar su linterna encontrando baterías esparcidas por la escuela. </p>
- <p>Tu principal objetivo es encontrar pistas y elementos que te ayuden a escapar de la escuela. También puedes hablar con otros personajes que encuentres en el camino, pero ten cuidado porque algunos de ellos pueden ser hostiles o engañosos. También puedes esconderte de los enemigos usando casilleros, armarios u otros escondites. Sin embargo, algunos enemigos aún pueden encontrarte si haces demasiado ruido o si ven tu linterna. También puedes huir de los enemigos corriendo, pero esto drenará tu resistencia y te hará más vulnerable. Tienes que equilibrar tu sigilo y velocidad para sobrevivir. </p>
- <h4>Los diferentes modos y niveles de dificultad</h4>
- <p>El juego tiene tres modos que puedes elegir: Normal, Difícil y Pesadilla. Cada modo tiene un nivel diferente de dificultad y desafío. Aquí están las diferencias entre los modos:</p>
- <tabla>
-<tr>
-<th>Modo</th>
-<th>Dificultad</th>
-<th>Desafío</th>
-</tr>
-
-<td>Normal</td>
-<td>Fácil</td>
-<td>Tienes más batería de linterna, más resistencia, más objetos y más pistas. Los enemigos son más lentos y menos agresivos. </td>
-</tr>
-<tr>
-<td>Duro</td>
-<td>Medio</td>
-<td>Tienes menos batería de linterna, menos resistencia, menos objetos y menos pistas. Los enemigos son más rápidos y más agresivos. </td>
-</tr>
-<tr>
-<td>Pesadilla</td>
-<td>Duro</td>
-<td>No tienes batería de linterna, ni resistencia, ni objetos, ni pistas. Los enemigos son muy rápidos y muy agresivos. </td>
-</tr>
-</tabla>
- <p>También puedes desbloquear un modo secreto llamado Hell Mode después de completar el juego en Nightmare Mode. Este modo es extremadamente duro y solo para los jugadores más hardcore. </p>
-<p></p>
- <h4>Los secretos ocultos y los huevos de Pascua</h4>
- <p>El juego tiene muchos secretos ocultos y huevos de Pascua que puedes descubrir explorando la escuela y encontrando pistas. Algunos de estos secretos y huevos de Pascua son:</p>
- <ul>
-<li>El juego tiene referencias a otros juegos y películas de terror, como Silent Hill, The Ring, The Grudge, etc.</li>
-<li> El juego tiene mensajes y códigos ocultos que revelan más sobre la historia de fondo y los personajes. </li>
-<li>El juego tiene salas secretas y pasajes que conducen a nuevas áreas y objetos. </li>
-<li>El juego tiene finales alternativos que dependen de tus elecciones y acciones. </li>
-<li> El juego tiene un jefe secreto que se puede luchar después de completar el juego en el modo infierno.</li>
-</ul>
- <h2>¿Por qué debería descargar Tag After School versi terbaru? </h2>
- <p>Si ya eres un fan de Tag After School, o si estás buscando un nuevo juego de terror para jugar, definitivamente deberías descargar Tag After School versi terbaru. Esto se debe a que la última versión del juego ofrece muchos beneficios y mejoras sobre las versiones anteriores. Algunos de estos beneficios son:</p>
- <h3>Los beneficios de descargar la última versión del juego</h3>
- <h4>Gráficos y rendimiento mejorados</h4>
-
- <h4>Nuevos personajes y escenarios</h4>
- <p>La última versión del juego tiene nuevos personajes y escenarios que añaden más variedad y profundidad al juego. El juego tiene nuevos enemigos que tienen diferentes apariencias, comportamientos y habilidades que desafían tus habilidades y estrategias. El juego también tiene nuevos aliados que tienen diferentes personalidades, antecedentes y roles que afectan tu historia y resultado. El juego también tiene nuevas ubicaciones que tienen diferentes diseños, puzzles y secretos que ponen a prueba tu exploración e intuición. </p>
- <h4>Corrección de errores y actualizaciones</h4>
- <p>La última versión del juego tiene correcciones de errores y actualizaciones que hacen el juego más estable y agradable. El juego ha corregido algunos de los fallos, errores y fallos que se produjeron en las versiones anteriores. El juego también ha añadido algunas características nuevas, como logros, tablas de clasificación, almacenamiento en la nube, etc., que mejoran su experiencia de juego. </p>
- <h3> ¿Cómo descargar la etiqueta después de la escuela versi terbaru gratis? </h3>
- <p>Si quieres descargar Tag After School versi terbaru gratis, tienes dos opciones: puedes descargarlo desde el sitio web oficial o desde otras plataformas. Estos son los pasos para descargar el juego de ambas fuentes:</p>
- <h4>Los pasos para descargar e instalar el juego desde el sitio web oficial</h4>
- <ol>
-<li>Ir a <a href=">el sitio web oficial de Tag After School</a>. </li>
-<li>Haga clic en el botón "Descargar" en la página principal. </li>
-<li>Selecciona tu dispositivo (Android o iOS) y tu idioma preferido (inglés o japonés). </li>
-<li>Espera a que termine la descarga. </li>
-<li> <li>Abra el archivo descargado y siga las instrucciones para instalar el juego en su dispositivo. </li>
-<li>Disfruta jugando Tag After School versi terbaru! </li>
-</ol>
- <h4>Las fuentes alternativas para descargar el juego desde otras plataformas</h4>
-
- <ul>
-<li>Revisa las calificaciones, reseñas y comentarios del juego y la fuente antes de descargarlo. </li>
-<li>Compara el tamaño, la versión y la fecha del juego y la fuente con el sitio web oficial. </li>
-<li>Escanea el archivo descargado con un antivirus o un detector de malware antes de abrirlo. </li>
-<li>No conceda permisos innecesarios ni acceso al juego o a la fuente. </li>
-<li>Eliminar el archivo descargado después de instalar el juego en su dispositivo. </li>
-</ul>
- <h4>Las precauciones a tomar antes de descargar el juego de fuentes desconocidas</h4>
- <p>Si quieres descargar el juego de fuentes desconocidas que no son verificadas o de confianza, tienes que ser muy cuidadoso y cauteloso. Estas fuentes pueden haber modificado, dañado o infectado versiones del juego que pueden dañar su dispositivo o su privacidad. Aquí hay algunas precauciones a tomar antes de descargar el juego de fuentes desconocidas:</p>
- <ul>
-<li>No descargue el juego de ninguna fuente que le pida dinero, información personal o registro. </li>
-<li>No descargue el juego desde ninguna fuente que tenga una URL o nombre de dominio sospechoso o desconocido. </li>
-<li>No descargue el juego desde ninguna fuente que tenga ventanas emergentes, anuncios o redirecciones que interfieran con su navegación. </li>
-<li>No descargue el juego desde ninguna fuente que tenga una baja reputación, calidad o nivel de seguridad. </li>
-<li>No descargue el juego desde ninguna fuente que tenga comentarios negativos, quejas o informes de otros usuarios. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <p>Si te gustó este artículo, por favor compártelo con tus amigos y deja un comentario a continuación. Además, si tiene alguna pregunta o sugerencia sobre Tag After School versi terbaru, no dude en consultarnos. Estaremos encantados de ayudarle. ¡Gracias por leer! </p>
- <h3>Preguntas frecuentes</h3>
- <p>Aquí están algunas de las preguntas más frecuentes sobre Tag After School versi terbaru:</p>
- <ol>
-<li><b>¿Es seguro descargar la etiqueta después de la escuela versi terbaru? </b></li>
-<p>Sí, Tag After School versi terbaru es seguro de descargar si lo descarga desde el sitio web oficial o desde plataformas de confianza. Sin embargo, si lo descargas desde fuentes desconocidas, tienes que ser cuidadoso y cauteloso ya que podrían haber modificado, dañado o infectado versiones del juego. </p>
-<li><b>¿Es la etiqueta después de la escuela versi terbaru compatible con mi dispositivo? </b></li>
-<p>Tag After School versi terbaru es compatible con la mayoría de los dispositivos Android que tienen Android 4.4 o superior. Sin embargo, algunos dispositivos pueden tener problemas con el funcionamiento del juego sin problemas debido a sus especificaciones o ajustes. Puede comprobar la compatibilidad de su dispositivo en <a href="">el sitio web oficial de Tag After School</a>. </p>
-<li><b>¿Cuánto tiempo es la etiqueta después de la escuela versi terbaru? </b></li>
-<p>La longitud de la etiqueta después de la escuela versi terbaru depende de sus opciones y acciones en el juego. El juego tiene múltiples finales que pueden cambiar dependiendo de tus decisiones e interacciones. El juego también tiene diferentes modos que pueden cambiar el nivel de juego y dificultad. El juego también tiene secretos ocultos y huevos de Pascua que pueden extender el tiempo de juego. En promedio, el juego puede tardar de 2 a 4 horas en completarse, dependiendo del modo y el final. </p>
- <li><b>¿Cuál es la diferencia entre Tag After School y Tag After School versi terbaru? </b></li>
-<p>Tag After School versi terbaru es la última versión de Tag After School que ofrece más características, contenido y mejoras que las versiones anteriores. Algunas de las diferencias son:</p>
- <ul>
-
-<li>Tag After School versi terbaru tiene nuevos personajes y escenarios que añaden más variedad y profundidad al juego. </li>
-<li>Tag After School versi terbaru tiene correcciones de errores y actualizaciones que hacen que el juego sea más estable y agradable. </li>
-<li>Tag After School versi terbaru tiene nuevas características, como logros, tablas de clasificación, almacenamiento en la nube, etc., que mejoran su experiencia de juego. </li>
-</ul>
- <li><b>¿Dónde puedo encontrar más información sobre Tag After School versi terbaru? </b></li>
-<p>Si quieres encontrar más información sobre Tag After School versi terbaru, puedes visitar <a href=">el sitio web oficial de Tag After School</a>, donde puedes encontrar las últimas noticias, actualizaciones, capturas de pantalla, videos y preguntas frecuentes sobre el juego. También puedes seguir <a href=">la cuenta oficial de Twitter de Tag After School</a>, donde puedes interactuar con el desarrollador y otros fans del juego. También puedes unirte a <a href=">el servidor oficial de Discordia de Tag After School</a>, donde puedes chatear con otros jugadores, compartir tus consejos, comentarios y fan art, y participar en eventos y regalos. </p>
- <li><b>¿Cómo puedo apoyar al desarrollador de Tag After School versi terbaru? </b></li>
-<p>Si quieres apoyar al desarrollador de Tag After School versi terbaru, puedes hacerlo por:</p>
- <ul>
-<li>Descargar y jugar el juego desde el sitio web oficial o desde plataformas de confianza. </li>
-<li>Dar una calificación positiva y revisar el juego en las tiendas de aplicaciones o sitios web donde lo descargó de. </li>
-<li>Compartir el juego con tus amigos y familiares que puedan disfrutarlo. </li>
-<li>Donar al desarrollador a través de <a href=">el sitio web oficial de Tag After School</a> o <a href=">la página oficial de Tag After School</a>. </li>
-<li>Comprar la mercancía oficial de Tag After School de <a href="">la tienda oficial en línea de Tag After School</a>. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/color_triplet.py

@@ -1,38 +0,0 @@
-from typing import NamedTuple, Tuple
-
-
-class ColorTriplet(NamedTuple):
-    """The red, green, and blue components of a color."""
-
-    red: int
-    """Red component in 0 to 255 range."""
-    green: int
-    """Green component in 0 to 255 range."""
-    blue: int
-    """Blue component in 0 to 255 range."""
-
-    @property
-    def hex(self) -> str:
-        """get the color triplet in CSS style."""
-        red, green, blue = self
-        return f"#{red:02x}{green:02x}{blue:02x}"
-
-    @property
-    def rgb(self) -> str:
-        """The color in RGB format.
-
-        Returns:
-            str: An rgb color, e.g. ``"rgb(100,23,255)"``.
-        """
-        red, green, blue = self
-        return f"rgb({red},{green},{blue})"
-
-    @property
-    def normalized(self) -> Tuple[float, float, float]:
-        """Convert components into floats between 0 and 1.
-
-        Returns:
-            Tuple[float, float, float]: A tuple of three normalized colour components.
-        """
-        red, green, blue = self
-        return red / 255.0, green / 255.0, blue / 255.0

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/py39compat.py

@@ -1,22 +0,0 @@
-import sys
-import platform
-
-
-def add_ext_suffix_39(vars):
-    """
-    Ensure vars contains 'EXT_SUFFIX'. pypa/distutils#130
-    """
-    import _imp
-
-    ext_suffix = _imp.extension_suffixes()[0]
-    vars.update(
-        EXT_SUFFIX=ext_suffix,
-        # sysconfig sets SO to match EXT_SUFFIX, so maintain
-        # that expectation.
-        # https://github.com/python/cpython/blob/785cc6770588de087d09e89a69110af2542be208/Lib/sysconfig.py#L671-L673
-        SO=ext_suffix,
-    )
-
-
-needs_ext_suffix = sys.version_info < (3, 10) and platform.system() == 'Windows'
-add_ext_suffix = add_ext_suffix_39 if needs_ext_suffix else lambda vars: None

spaces/BilalSardar/Halal_Food_Checker/app.py

@@ -1,67 +0,0 @@
-import pytesseract
-from PIL import Image
-import requests
-import json
-import numpy as np
-import gradio as gr
-import os 
-
-
-
-def get_halal_data(ingredient, num_results=20):
-
-  try:
-    url = f'http://halal.addi.is.its.ac.id/apiv2?q={ingredient}&result={num_results}'
-    response = requests.get(url)
-    data = response.json()  
-  except requests.exceptions.RequestException as e:
-    print(f"Error: {e}")
-    return None
-
-  results = []
-
-  for result in data['entityData']:
-    try:
-      if result['atribute']['certificate']:
-        results.append(result)
-    except:
-      pass
-  
-  if not results:
-    return "No Data Found If its halal"
-
-  return results
-
-
-
-def extract_text(text,image):
-  # Convert sketchpad to bounding box
-  # img1 = np.array(Image.fromarray(image))
-  if text=='':
-      text = pytesseract.image_to_string(image)
-  # Extract ingredient words
-  # ingredients = [word for word in text.split() if word.isalpha()]
-
-  # results = {}
-
-  # for ing in ingredients:
-  #   data = get_halal_data(ing, 5)
-  #   if data:
-  #     results[ing] = data
-  #   else:
-  #     results[ing] = "No halal data found"
-  try:
-      results=get_halal_data(text,5)
-  except:
-      pass
-
-  return text,results
-
-iface = gr.Interface(fn=extract_text,
-             inputs=["text",gr.inputs.Image(label="image", type="numpy")],
-             outputs=["text","text"],
-                    examples=[["Monosodium Glutamate",None],[None,"3.jpg"]],
-                    title="Halal Food Checker",
-                    description="Enter products, ingredients, foodcodes, or manufactures manually or upload image and crop it to ingredient. If a data is shown for an ingredient its mean its Halal.")
-
-iface.launch(debug=True)

spaces/Branon/Proxy/README.md

@@ -1,11 +0,0 @@
----
-title: Proxy
-emoji: 
-colorFrom: red
-colorTo: blue
-sdk: docker
-pinned: false
-duplicated_from: Branon/TempBRICS
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/LIVE/thrust/thrust/system/detail/bad_alloc.h

@@ -1,57 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-#pragma once
-
-#include <new>
-#include <string>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-
-// define our own bad_alloc so we can set its .what()
-class bad_alloc
-  : public std::bad_alloc
-{
-  public:
-    inline bad_alloc(const std::string &w)
-      : std::bad_alloc(), m_what()
-    {
-      m_what = std::bad_alloc::what();
-      m_what += ": ";
-      m_what += w;
-    } // end bad_alloc()
-
-    inline virtual ~bad_alloc(void) throw () {};
-
-    inline virtual const char *what(void) const throw()
-    {
-      return m_what.c_str();
-    } // end what()
-
-  private:
-    std::string m_what;
-}; // end bad_alloc
-  
-} // end detail
-} // end system
-} // end thrust
-

spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter/utils.py

@@ -1,81 +0,0 @@
-
-import logging
-import torch
-
-
-def train_epoch(model, dataloader, criterion, optimizer, device, scheduler=None):
-
-    pred_correct, pred_all = 0, 0
-    running_loss = 0.0
-
-    for i, data in enumerate(dataloader):
-        inputs, labels = data
-        inputs = inputs.squeeze(0).to(device)
-        labels = labels.to(device, dtype=torch.long)
-
-        optimizer.zero_grad()
-        outputs = model(inputs).expand(1, -1, -1)
-
-        loss = criterion(outputs[0], labels[0])
-        loss.backward()
-        optimizer.step()
-        running_loss += loss
-
-        # Statistics
-        if int(torch.argmax(torch.nn.functional.softmax(outputs, dim=2))) == int(labels[0][0]):
-            pred_correct += 1
-        pred_all += 1
-
-    if scheduler:
-        scheduler.step(running_loss.item() / len(dataloader))
-
-    return running_loss, pred_correct, pred_all, (pred_correct / pred_all)
-
-
-def evaluate(model, dataloader, device, print_stats=False):
-
-    pred_correct, pred_all = 0, 0
-    stats = {i: [0, 0] for i in range(101)}
-
-    for i, data in enumerate(dataloader):
-        inputs, labels = data
-        inputs = inputs.squeeze(0).to(device)
-        labels = labels.to(device, dtype=torch.long)
-
-        outputs = model(inputs).expand(1, -1, -1)
-
-        # Statistics
-        if int(torch.argmax(torch.nn.functional.softmax(outputs, dim=2))) == int(labels[0][0]):
-            stats[int(labels[0][0])][0] += 1
-            pred_correct += 1
-
-        stats[int(labels[0][0])][1] += 1
-        pred_all += 1
-
-    if print_stats:
-        stats = {key: value[0] / value[1] for key, value in stats.items() if value[1] != 0}
-        print("Label accuracies statistics:")
-        print(str(stats) + "\n")
-        logging.info("Label accuracies statistics:")
-        logging.info(str(stats) + "\n")
-
-    return pred_correct, pred_all, (pred_correct / pred_all)
-
-
-def evaluate_top_k(model, dataloader, device, k=5):
-
-    pred_correct, pred_all = 0, 0
-
-    for i, data in enumerate(dataloader):
-        inputs, labels = data
-        inputs = inputs.squeeze(0).to(device)
-        labels = labels.to(device, dtype=torch.long)
-
-        outputs = model(inputs).expand(1, -1, -1)
-
-        if int(labels[0][0]) in torch.topk(outputs, k).indices.tolist():
-            pred_correct += 1
-
-        pred_all += 1
-
-    return pred_correct, pred_all, (pred_correct / pred_all)

spaces/CVPR/ml-talking-face/README.md

@@ -1,47 +0,0 @@
----
-title: Talking Face Generation with Multilingual TTS
-emoji: 👄
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.0.6
-app_file: app.py
-pinned: false
-license: cc-by-nc-sa-4.0
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/CVPR/monoscene_lite/monoscene/monoscene_model.py

@@ -1,21 +0,0 @@
-from transformers import PreTrainedModel
-from .config import MonoSceneConfig
-from monoscene.monoscene import MonoScene
-
-
-class MonoSceneModel(PreTrainedModel):
-    config_class = MonoSceneConfig
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = MonoScene(
-            dataset=config.dataset,
-            n_classes=config.n_classes,
-            feature=config.feature,
-            project_scale=config.project_scale,
-            full_scene_size=config.full_scene_size
-        )
-     
-
-    def forward(self, tensor):
-        return self.model.forward(tensor)

spaces/Coweed/BadTrip/Dockerfile

@@ -1,11 +0,0 @@
-FROM node:18-bullseye-slim
-RUN apt-get update && \
-    apt-get install -y git
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-WORKDIR /app
-RUN npm install
-COPY Dockerfile greeting.md* .env* ./
-RUN npm run build
-EXPOSE 7860
-ENV NODE_ENV=production
-CMD [ "npm", "start" ]

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/merge/layout.py

@@ -1,530 +0,0 @@
-# Copyright 2013 Google, Inc. All Rights Reserved.
-#
-# Google Author(s): Behdad Esfahbod, Roozbeh Pournader
-
-from fontTools import ttLib
-from fontTools.ttLib.tables.DefaultTable import DefaultTable
-from fontTools.ttLib.tables import otTables
-from fontTools.merge.base import add_method, mergeObjects
-from fontTools.merge.util import *
-import logging
-
-
-log = logging.getLogger("fontTools.merge")
-
-
-def mergeLookupLists(lst):
-    # TODO Do smarter merge.
-    return sumLists(lst)
-
-
-def mergeFeatures(lst):
-    assert lst
-    self = otTables.Feature()
-    self.FeatureParams = None
-    self.LookupListIndex = mergeLookupLists(
-        [l.LookupListIndex for l in lst if l.LookupListIndex]
-    )
-    self.LookupCount = len(self.LookupListIndex)
-    return self
-
-
-def mergeFeatureLists(lst):
-    d = {}
-    for l in lst:
-        for f in l:
-            tag = f.FeatureTag
-            if tag not in d:
-                d[tag] = []
-            d[tag].append(f.Feature)
-    ret = []
-    for tag in sorted(d.keys()):
-        rec = otTables.FeatureRecord()
-        rec.FeatureTag = tag
-        rec.Feature = mergeFeatures(d[tag])
-        ret.append(rec)
-    return ret
-
-
-def mergeLangSyses(lst):
-    assert lst
-
-    # TODO Support merging ReqFeatureIndex
-    assert all(l.ReqFeatureIndex == 0xFFFF for l in lst)
-
-    self = otTables.LangSys()
-    self.LookupOrder = None
-    self.ReqFeatureIndex = 0xFFFF
-    self.FeatureIndex = mergeFeatureLists(
-        [l.FeatureIndex for l in lst if l.FeatureIndex]
-    )
-    self.FeatureCount = len(self.FeatureIndex)
-    return self
-
-
-def mergeScripts(lst):
-    assert lst
-
-    if len(lst) == 1:
-        return lst[0]
-    langSyses = {}
-    for sr in lst:
-        for lsr in sr.LangSysRecord:
-            if lsr.LangSysTag not in langSyses:
-                langSyses[lsr.LangSysTag] = []
-            langSyses[lsr.LangSysTag].append(lsr.LangSys)
-    lsrecords = []
-    for tag, langSys_list in sorted(langSyses.items()):
-        lsr = otTables.LangSysRecord()
-        lsr.LangSys = mergeLangSyses(langSys_list)
-        lsr.LangSysTag = tag
-        lsrecords.append(lsr)
-
-    self = otTables.Script()
-    self.LangSysRecord = lsrecords
-    self.LangSysCount = len(lsrecords)
-    dfltLangSyses = [s.DefaultLangSys for s in lst if s.DefaultLangSys]
-    if dfltLangSyses:
-        self.DefaultLangSys = mergeLangSyses(dfltLangSyses)
-    else:
-        self.DefaultLangSys = None
-    return self
-
-
-def mergeScriptRecords(lst):
-    d = {}
-    for l in lst:
-        for s in l:
-            tag = s.ScriptTag
-            if tag not in d:
-                d[tag] = []
-            d[tag].append(s.Script)
-    ret = []
-    for tag in sorted(d.keys()):
-        rec = otTables.ScriptRecord()
-        rec.ScriptTag = tag
-        rec.Script = mergeScripts(d[tag])
-        ret.append(rec)
-    return ret
-
-
-otTables.ScriptList.mergeMap = {
-    "ScriptCount": lambda lst: None,  # TODO
-    "ScriptRecord": mergeScriptRecords,
-}
-otTables.BaseScriptList.mergeMap = {
-    "BaseScriptCount": lambda lst: None,  # TODO
-    # TODO: Merge duplicate entries
-    "BaseScriptRecord": lambda lst: sorted(
-        sumLists(lst), key=lambda s: s.BaseScriptTag
-    ),
-}
-
-otTables.FeatureList.mergeMap = {
-    "FeatureCount": sum,
-    "FeatureRecord": lambda lst: sorted(sumLists(lst), key=lambda s: s.FeatureTag),
-}
-
-otTables.LookupList.mergeMap = {
-    "LookupCount": sum,
-    "Lookup": sumLists,
-}
-
-otTables.Coverage.mergeMap = {
-    "Format": min,
-    "glyphs": sumLists,
-}
-
-otTables.ClassDef.mergeMap = {
-    "Format": min,
-    "classDefs": sumDicts,
-}
-
-otTables.LigCaretList.mergeMap = {
-    "Coverage": mergeObjects,
-    "LigGlyphCount": sum,
-    "LigGlyph": sumLists,
-}
-
-otTables.AttachList.mergeMap = {
-    "Coverage": mergeObjects,
-    "GlyphCount": sum,
-    "AttachPoint": sumLists,
-}
-
-# XXX Renumber MarkFilterSets of lookups
-otTables.MarkGlyphSetsDef.mergeMap = {
-    "MarkSetTableFormat": equal,
-    "MarkSetCount": sum,
-    "Coverage": sumLists,
-}
-
-otTables.Axis.mergeMap = {
-    "*": mergeObjects,
-}
-
-# XXX Fix BASE table merging
-otTables.BaseTagList.mergeMap = {
-    "BaseTagCount": sum,
-    "BaselineTag": sumLists,
-}
-
-otTables.GDEF.mergeMap = (
-    otTables.GSUB.mergeMap
-) = (
-    otTables.GPOS.mergeMap
-) = otTables.BASE.mergeMap = otTables.JSTF.mergeMap = otTables.MATH.mergeMap = {
-    "*": mergeObjects,
-    "Version": max,
-}
-
-ttLib.getTableClass("GDEF").mergeMap = ttLib.getTableClass(
-    "GSUB"
-).mergeMap = ttLib.getTableClass("GPOS").mergeMap = ttLib.getTableClass(
-    "BASE"
-).mergeMap = ttLib.getTableClass(
-    "JSTF"
-).mergeMap = ttLib.getTableClass(
-    "MATH"
-).mergeMap = {
-    "tableTag": onlyExisting(equal),  # XXX clean me up
-    "table": mergeObjects,
-}
-
-
-@add_method(ttLib.getTableClass("GSUB"))
-def merge(self, m, tables):
-    assert len(tables) == len(m.duplicateGlyphsPerFont)
-    for i, (table, dups) in enumerate(zip(tables, m.duplicateGlyphsPerFont)):
-        if not dups:
-            continue
-        if table is None or table is NotImplemented:
-            log.warning(
-                "Have non-identical duplicates to resolve for '%s' but no GSUB. Are duplicates intended?: %s",
-                m.fonts[i]._merger__name,
-                dups,
-            )
-            continue
-
-        synthFeature = None
-        synthLookup = None
-        for script in table.table.ScriptList.ScriptRecord:
-            if script.ScriptTag == "DFLT":
-                continue  # XXX
-            for langsys in [script.Script.DefaultLangSys] + [
-                l.LangSys for l in script.Script.LangSysRecord
-            ]:
-                if langsys is None:
-                    continue  # XXX Create!
-                feature = [v for v in langsys.FeatureIndex if v.FeatureTag == "locl"]
-                assert len(feature) <= 1
-                if feature:
-                    feature = feature[0]
-                else:
-                    if not synthFeature:
-                        synthFeature = otTables.FeatureRecord()
-                        synthFeature.FeatureTag = "locl"
-                        f = synthFeature.Feature = otTables.Feature()
-                        f.FeatureParams = None
-                        f.LookupCount = 0
-                        f.LookupListIndex = []
-                        table.table.FeatureList.FeatureRecord.append(synthFeature)
-                        table.table.FeatureList.FeatureCount += 1
-                    feature = synthFeature
-                    langsys.FeatureIndex.append(feature)
-                    langsys.FeatureIndex.sort(key=lambda v: v.FeatureTag)
-
-                if not synthLookup:
-                    subtable = otTables.SingleSubst()
-                    subtable.mapping = dups
-                    synthLookup = otTables.Lookup()
-                    synthLookup.LookupFlag = 0
-                    synthLookup.LookupType = 1
-                    synthLookup.SubTableCount = 1
-                    synthLookup.SubTable = [subtable]
-                    if table.table.LookupList is None:
-                        # mtiLib uses None as default value for LookupList,
-                        # while feaLib points to an empty array with count 0
-                        # TODO: make them do the same
-                        table.table.LookupList = otTables.LookupList()
-                        table.table.LookupList.Lookup = []
-                        table.table.LookupList.LookupCount = 0
-                    table.table.LookupList.Lookup.append(synthLookup)
-                    table.table.LookupList.LookupCount += 1
-
-                if feature.Feature.LookupListIndex[:1] != [synthLookup]:
-                    feature.Feature.LookupListIndex[:0] = [synthLookup]
-                    feature.Feature.LookupCount += 1
-
-    DefaultTable.merge(self, m, tables)
-    return self
-
-
-@add_method(
-    otTables.SingleSubst,
-    otTables.MultipleSubst,
-    otTables.AlternateSubst,
-    otTables.LigatureSubst,
-    otTables.ReverseChainSingleSubst,
-    otTables.SinglePos,
-    otTables.PairPos,
-    otTables.CursivePos,
-    otTables.MarkBasePos,
-    otTables.MarkLigPos,
-    otTables.MarkMarkPos,
-)
-def mapLookups(self, lookupMap):
-    pass
-
-
-# Copied and trimmed down from subset.py
-@add_method(
-    otTables.ContextSubst,
-    otTables.ChainContextSubst,
-    otTables.ContextPos,
-    otTables.ChainContextPos,
-)
-def __merge_classify_context(self):
-    class ContextHelper(object):
-        def __init__(self, klass, Format):
-            if klass.__name__.endswith("Subst"):
-                Typ = "Sub"
-                Type = "Subst"
-            else:
-                Typ = "Pos"
-                Type = "Pos"
-            if klass.__name__.startswith("Chain"):
-                Chain = "Chain"
-            else:
-                Chain = ""
-            ChainTyp = Chain + Typ
-
-            self.Typ = Typ
-            self.Type = Type
-            self.Chain = Chain
-            self.ChainTyp = ChainTyp
-
-            self.LookupRecord = Type + "LookupRecord"
-
-            if Format == 1:
-                self.Rule = ChainTyp + "Rule"
-                self.RuleSet = ChainTyp + "RuleSet"
-            elif Format == 2:
-                self.Rule = ChainTyp + "ClassRule"
-                self.RuleSet = ChainTyp + "ClassSet"
-
-    if self.Format not in [1, 2, 3]:
-        return None  # Don't shoot the messenger; let it go
-    if not hasattr(self.__class__, "_merge__ContextHelpers"):
-        self.__class__._merge__ContextHelpers = {}
-    if self.Format not in self.__class__._merge__ContextHelpers:
-        helper = ContextHelper(self.__class__, self.Format)
-        self.__class__._merge__ContextHelpers[self.Format] = helper
-    return self.__class__._merge__ContextHelpers[self.Format]
-
-
-@add_method(
-    otTables.ContextSubst,
-    otTables.ChainContextSubst,
-    otTables.ContextPos,
-    otTables.ChainContextPos,
-)
-def mapLookups(self, lookupMap):
-    c = self.__merge_classify_context()
-
-    if self.Format in [1, 2]:
-        for rs in getattr(self, c.RuleSet):
-            if not rs:
-                continue
-            for r in getattr(rs, c.Rule):
-                if not r:
-                    continue
-                for ll in getattr(r, c.LookupRecord):
-                    if not ll:
-                        continue
-                    ll.LookupListIndex = lookupMap[ll.LookupListIndex]
-    elif self.Format == 3:
-        for ll in getattr(self, c.LookupRecord):
-            if not ll:
-                continue
-            ll.LookupListIndex = lookupMap[ll.LookupListIndex]
-    else:
-        assert 0, "unknown format: %s" % self.Format
-
-
-@add_method(otTables.ExtensionSubst, otTables.ExtensionPos)
-def mapLookups(self, lookupMap):
-    if self.Format == 1:
-        self.ExtSubTable.mapLookups(lookupMap)
-    else:
-        assert 0, "unknown format: %s" % self.Format
-
-
-@add_method(otTables.Lookup)
-def mapLookups(self, lookupMap):
-    for st in self.SubTable:
-        if not st:
-            continue
-        st.mapLookups(lookupMap)
-
-
-@add_method(otTables.LookupList)
-def mapLookups(self, lookupMap):
-    for l in self.Lookup:
-        if not l:
-            continue
-        l.mapLookups(lookupMap)
-
-
-@add_method(otTables.Lookup)
-def mapMarkFilteringSets(self, markFilteringSetMap):
-    if self.LookupFlag & 0x0010:
-        self.MarkFilteringSet = markFilteringSetMap[self.MarkFilteringSet]
-
-
-@add_method(otTables.LookupList)
-def mapMarkFilteringSets(self, markFilteringSetMap):
-    for l in self.Lookup:
-        if not l:
-            continue
-        l.mapMarkFilteringSets(markFilteringSetMap)
-
-
-@add_method(otTables.Feature)
-def mapLookups(self, lookupMap):
-    self.LookupListIndex = [lookupMap[i] for i in self.LookupListIndex]
-
-
-@add_method(otTables.FeatureList)
-def mapLookups(self, lookupMap):
-    for f in self.FeatureRecord:
-        if not f or not f.Feature:
-            continue
-        f.Feature.mapLookups(lookupMap)
-
-
-@add_method(otTables.DefaultLangSys, otTables.LangSys)
-def mapFeatures(self, featureMap):
-    self.FeatureIndex = [featureMap[i] for i in self.FeatureIndex]
-    if self.ReqFeatureIndex != 65535:
-        self.ReqFeatureIndex = featureMap[self.ReqFeatureIndex]
-
-
-@add_method(otTables.Script)
-def mapFeatures(self, featureMap):
-    if self.DefaultLangSys:
-        self.DefaultLangSys.mapFeatures(featureMap)
-    for l in self.LangSysRecord:
-        if not l or not l.LangSys:
-            continue
-        l.LangSys.mapFeatures(featureMap)
-
-
-@add_method(otTables.ScriptList)
-def mapFeatures(self, featureMap):
-    for s in self.ScriptRecord:
-        if not s or not s.Script:
-            continue
-        s.Script.mapFeatures(featureMap)
-
-
-def layoutPreMerge(font):
-    # Map indices to references
-
-    GDEF = font.get("GDEF")
-    GSUB = font.get("GSUB")
-    GPOS = font.get("GPOS")
-
-    for t in [GSUB, GPOS]:
-        if not t:
-            continue
-
-        if t.table.LookupList:
-            lookupMap = {i: v for i, v in enumerate(t.table.LookupList.Lookup)}
-            t.table.LookupList.mapLookups(lookupMap)
-            t.table.FeatureList.mapLookups(lookupMap)
-
-            if (
-                GDEF
-                and GDEF.table.Version >= 0x00010002
-                and GDEF.table.MarkGlyphSetsDef
-            ):
-                markFilteringSetMap = {
-                    i: v for i, v in enumerate(GDEF.table.MarkGlyphSetsDef.Coverage)
-                }
-                t.table.LookupList.mapMarkFilteringSets(markFilteringSetMap)
-
-        if t.table.FeatureList and t.table.ScriptList:
-            featureMap = {i: v for i, v in enumerate(t.table.FeatureList.FeatureRecord)}
-            t.table.ScriptList.mapFeatures(featureMap)
-
-    # TODO FeatureParams nameIDs
-
-
-def layoutPostMerge(font):
-    # Map references back to indices
-
-    GDEF = font.get("GDEF")
-    GSUB = font.get("GSUB")
-    GPOS = font.get("GPOS")
-
-    for t in [GSUB, GPOS]:
-        if not t:
-            continue
-
-        if t.table.FeatureList and t.table.ScriptList:
-            # Collect unregistered (new) features.
-            featureMap = GregariousIdentityDict(t.table.FeatureList.FeatureRecord)
-            t.table.ScriptList.mapFeatures(featureMap)
-
-            # Record used features.
-            featureMap = AttendanceRecordingIdentityDict(
-                t.table.FeatureList.FeatureRecord
-            )
-            t.table.ScriptList.mapFeatures(featureMap)
-            usedIndices = featureMap.s
-
-            # Remove unused features
-            t.table.FeatureList.FeatureRecord = [
-                f
-                for i, f in enumerate(t.table.FeatureList.FeatureRecord)
-                if i in usedIndices
-            ]
-
-            # Map back to indices.
-            featureMap = NonhashableDict(t.table.FeatureList.FeatureRecord)
-            t.table.ScriptList.mapFeatures(featureMap)
-
-            t.table.FeatureList.FeatureCount = len(t.table.FeatureList.FeatureRecord)
-
-        if t.table.LookupList:
-            # Collect unregistered (new) lookups.
-            lookupMap = GregariousIdentityDict(t.table.LookupList.Lookup)
-            t.table.FeatureList.mapLookups(lookupMap)
-            t.table.LookupList.mapLookups(lookupMap)
-
-            # Record used lookups.
-            lookupMap = AttendanceRecordingIdentityDict(t.table.LookupList.Lookup)
-            t.table.FeatureList.mapLookups(lookupMap)
-            t.table.LookupList.mapLookups(lookupMap)
-            usedIndices = lookupMap.s
-
-            # Remove unused lookups
-            t.table.LookupList.Lookup = [
-                l for i, l in enumerate(t.table.LookupList.Lookup) if i in usedIndices
-            ]
-
-            # Map back to indices.
-            lookupMap = NonhashableDict(t.table.LookupList.Lookup)
-            t.table.FeatureList.mapLookups(lookupMap)
-            t.table.LookupList.mapLookups(lookupMap)
-
-            t.table.LookupList.LookupCount = len(t.table.LookupList.Lookup)
-
-            if GDEF and GDEF.table.Version >= 0x00010002:
-                markFilteringSetMap = NonhashableDict(
-                    GDEF.table.MarkGlyphSetsDef.Coverage
-                )
-                t.table.LookupList.mapMarkFilteringSets(markFilteringSetMap)
-
-    # TODO FeatureParams nameIDs

spaces/Daextream/Whisper-Auto-Subtitled-Video-Generator/utils.py

@@ -1,96 +0,0 @@
-import textwrap
-import zlib
-from typing import Iterator, TextIO
-
-
-def exact_div(x, y):
-    assert x % y == 0
-    return x // y
-
-
-def str2bool(string):
-    str2val = {"True": True, "False": False}
-    if string in str2val:
-        return str2val[string]
-    else:
-        raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
-
-
-def optional_int(string):
-    return None if string == "None" else int(string)
-
-
-def optional_float(string):
-    return None if string == "None" else float(string)
-
-
-def compression_ratio(text) -> float:
-    return len(text) / len(zlib.compress(text.encode("utf-8")))
-
-
-def format_timestamp(seconds: float, always_include_hours: bool = False, fractionalSeperator: str = '.'):
-    assert seconds >= 0, "non-negative timestamp expected"
-    milliseconds = round(seconds * 1000.0)
-
-    hours = milliseconds // 3_600_000
-    milliseconds -= hours * 3_600_000
-
-    minutes = milliseconds // 60_000
-    milliseconds -= minutes * 60_000
-
-    seconds = milliseconds // 1_000
-    milliseconds -= seconds * 1_000
-
-    hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
-    return f"{hours_marker}{minutes:02d}:{seconds:02d}{fractionalSeperator}{milliseconds:03d}"
-
-
-def write_txt(transcript: Iterator[dict], file: TextIO):
-    for segment in transcript:
-        print(segment['text'].strip(), file=file, flush=True)
-
-
-def write_vtt(transcript: Iterator[dict], file: TextIO, maxLineWidth=None):
-    print("WEBVTT\n", file=file)
-    for segment in transcript:
-        text = processText(segment['text'], maxLineWidth).replace('-->', '->')
-
-        print(
-            f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
-            f"{text}\n",
-            file=file,
-            flush=True,
-        )
-
-
-def write_srt(transcript: Iterator[dict], file: TextIO, maxLineWidth=None):
-    """
-    Write a transcript to a file in SRT format.
-    Example usage:
-        from pathlib import Path
-        from whisper.utils import write_srt
-        result = transcribe(model, audio_path, temperature=temperature, **args)
-        # save SRT
-        audio_basename = Path(audio_path).stem
-        with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt:
-            write_srt(result["segments"], file=srt)
-    """
-    for i, segment in enumerate(transcript, start=1):
-        text = processText(segment['text'].strip(), maxLineWidth).replace('-->', '->')
-
-        # write srt lines
-        print(
-            f"{i}\n"
-            f"{format_timestamp(segment['start'], always_include_hours=True, fractionalSeperator=',')} --> "
-            f"{format_timestamp(segment['end'], always_include_hours=True, fractionalSeperator=',')}\n"
-            f"{text}\n",
-            file=file,
-            flush=True,
-        )
-
-def processText(text: str, maxLineWidth=None):
-    if (maxLineWidth is None or maxLineWidth < 0):
-        return text
-
-    lines = textwrap.wrap(text, width=maxLineWidth, tabsize=4)
-    return '\n'.join(lines)

spaces/Danielzero/GPT3.5/modules/models.py

@@ -1,625 +0,0 @@
-from __future__ import annotations
-from typing import TYPE_CHECKING, List
-
-import logging
-import json
-import commentjson as cjson
-import os
-import sys
-import requests
-import urllib3
-import platform
-import base64
-from io import BytesIO
-from PIL import Image
-
-from tqdm import tqdm
-import colorama
-from duckduckgo_search import ddg
-import asyncio
-import aiohttp
-from enum import Enum
-import uuid
-
-from .presets import *
-from .llama_func import *
-from .utils import *
-from . import shared
-from .config import retrieve_proxy
-from modules import config
-from .base_model import BaseLLMModel, ModelType
-
-
-class OpenAIClient(BaseLLMModel):
-    def __init__(
-        self,
-        model_name,
-        api_key,
-        system_prompt=INITIAL_SYSTEM_PROMPT,
-        temperature=1.0,
-        top_p=1.0,
-    ) -> None:
-        super().__init__(
-            model_name=model_name,
-            temperature=temperature,
-            top_p=top_p,
-            system_prompt=system_prompt,
-        )
-        self.api_key = api_key
-        self.need_api_key = True
-        self._refresh_header()
-
-    def get_answer_stream_iter(self):
-        response = self._get_response(stream=True)
-        if response is not None:
-            iter = self._decode_chat_response(response)
-            partial_text = ""
-            for i in iter:
-                partial_text += i
-                yield partial_text
-        else:
-            yield STANDARD_ERROR_MSG + GENERAL_ERROR_MSG
-
-    def get_answer_at_once(self):
-        response = self._get_response()
-        response = json.loads(response.text)
-        content = response["choices"][0]["message"]["content"]
-        total_token_count = response["usage"]["total_tokens"]
-        return content, total_token_count
-
-    def count_token(self, user_input):
-        input_token_count = count_token(construct_user(user_input))
-        if self.system_prompt is not None and len(self.all_token_counts) == 0:
-            system_prompt_token_count = count_token(
-                construct_system(self.system_prompt)
-            )
-            return input_token_count + system_prompt_token_count
-        return input_token_count
-
-    def billing_info(self):
-        try:
-            curr_time = datetime.datetime.now()
-            last_day_of_month = get_last_day_of_month(
-                curr_time).strftime("%Y-%m-%d")
-            first_day_of_month = curr_time.replace(day=1).strftime("%Y-%m-%d")
-            usage_url = f"{shared.state.usage_api_url}?start_date={first_day_of_month}&end_date={last_day_of_month}"
-            try:
-                usage_data = self._get_billing_data(usage_url)
-            except Exception as e:
-                logging.error(f"获取API使用情况失败:" + str(e))
-                return i18n("**获取API使用情况失败**")
-            rounded_usage = "{:.5f}".format(usage_data["total_usage"] / 100)
-            return i18n("**本月使用金额** ") + f"\u3000 ${rounded_usage}"
-        except requests.exceptions.ConnectTimeout:
-            status_text = (
-                STANDARD_ERROR_MSG + CONNECTION_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
-            )
-            return status_text
-        except requests.exceptions.ReadTimeout:
-            status_text = STANDARD_ERROR_MSG + READ_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
-            return status_text
-        except Exception as e:
-            import traceback
-            traceback.print_exc()
-            logging.error(i18n("获取API使用情况失败:") + str(e))
-            return STANDARD_ERROR_MSG + ERROR_RETRIEVE_MSG
-
-    def set_token_upper_limit(self, new_upper_limit):
-        pass
-
-    @shared.state.switching_api_key  # 在不开启多账号模式的时候，这个装饰器不会起作用
-    def _get_response(self, stream=False):
-        openai_api_key = self.api_key
-        system_prompt = self.system_prompt
-        history = self.history
-        logging.debug(colorama.Fore.YELLOW +
-                      f"{history}" + colorama.Fore.RESET)
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {openai_api_key}",
-        }
-
-        if system_prompt is not None:
-            history = [construct_system(system_prompt), *history]
-
-        payload = {
-            "model": self.model_name,
-            "messages": history,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-            "n": self.n_choices,
-            "stream": stream,
-            "presence_penalty": self.presence_penalty,
-            "frequency_penalty": self.frequency_penalty,
-        }
-
-        if self.max_generation_token is not None:
-            payload["max_tokens"] = self.max_generation_token
-        if self.stop_sequence is not None:
-            payload["stop"] = self.stop_sequence
-        if self.logit_bias is not None:
-            payload["logit_bias"] = self.logit_bias
-        if self.user_identifier is not None:
-            payload["user"] = self.user_identifier
-
-        if stream:
-            timeout = TIMEOUT_STREAMING
-        else:
-            timeout = TIMEOUT_ALL
-
-        # 如果有自定义的api-host，使用自定义host发送请求，否则使用默认设置发送请求
-        if shared.state.completion_url != COMPLETION_URL:
-            logging.info(f"使用自定义API URL: {shared.state.completion_url}")
-
-        with retrieve_proxy():
-            try:
-                response = requests.post(
-                    shared.state.completion_url,
-                    headers=headers,
-                    json=payload,
-                    stream=stream,
-                    timeout=timeout,
-                )
-            except:
-                return None
-        return response
-
-    def _refresh_header(self):
-        self.headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-    def _get_billing_data(self, billing_url):
-        with retrieve_proxy():
-            response = requests.get(
-                billing_url,
-                headers=self.headers,
-                timeout=TIMEOUT_ALL,
-            )
-
-        if response.status_code == 200:
-            data = response.json()
-            return data
-        else:
-            raise Exception(
-                f"API request failed with status code {response.status_code}: {response.text}"
-            )
-
-    def _decode_chat_response(self, response):
-        error_msg = ""
-        for chunk in response.iter_lines():
-            if chunk:
-                chunk = chunk.decode()
-                chunk_length = len(chunk)
-                try:
-                    chunk = json.loads(chunk[6:])
-                except json.JSONDecodeError:
-                    print(i18n("JSON解析错误,收到的内容: ") + f"{chunk}")
-                    error_msg += chunk
-                    continue
-                if chunk_length > 6 and "delta" in chunk["choices"][0]:
-                    if chunk["choices"][0]["finish_reason"] == "stop":
-                        break
-                    try:
-                        yield chunk["choices"][0]["delta"]["content"]
-                    except Exception as e:
-                        # logging.error(f"Error: {e}")
-                        continue
-        if error_msg:
-            raise Exception(error_msg)
-
-    def set_key(self, new_access_key):
-        ret = super().set_key(new_access_key)
-        self._refresh_header()
-        return ret
-
-
-class ChatGLM_Client(BaseLLMModel):
-    def __init__(self, model_name) -> None:
-        super().__init__(model_name=model_name)
-        from transformers import AutoTokenizer, AutoModel
-        import torch
-        global CHATGLM_TOKENIZER, CHATGLM_MODEL
-        if CHATGLM_TOKENIZER is None or CHATGLM_MODEL is None:
-            system_name = platform.system()
-            model_path = None
-            if os.path.exists("models"):
-                model_dirs = os.listdir("models")
-                if model_name in model_dirs:
-                    model_path = f"models/{model_name}"
-            if model_path is not None:
-                model_source = model_path
-            else:
-                model_source = f"THUDM/{model_name}"
-            CHATGLM_TOKENIZER = AutoTokenizer.from_pretrained(
-                model_source, trust_remote_code=True
-            )
-            quantified = False
-            if "int4" in model_name:
-                quantified = True
-            model = AutoModel.from_pretrained(
-                    model_source, trust_remote_code=True
-                )
-            if torch.cuda.is_available():
-                # run on CUDA
-                logging.info("CUDA is available, using CUDA")
-                model = model.half().cuda()
-            # mps加速还存在一些问题，暂时不使用
-            elif system_name == "Darwin" and model_path is not None and not quantified:
-                logging.info("Running on macOS, using MPS")
-                # running on macOS and model already downloaded
-                model = model.half().to("mps")
-            else:
-                logging.info("GPU is not available, using CPU")
-                model = model.float()
-            model = model.eval()
-            CHATGLM_MODEL = model
-
-    def _get_glm_style_input(self):
-        history = [x["content"] for x in self.history]
-        query = history.pop()
-        logging.debug(colorama.Fore.YELLOW +
-                      f"{history}" + colorama.Fore.RESET)
-        assert (
-            len(history) % 2 == 0
-        ), f"History should be even length. current history is: {history}"
-        history = [[history[i], history[i + 1]]
-                   for i in range(0, len(history), 2)]
-        return history, query
-
-    def get_answer_at_once(self):
-        history, query = self._get_glm_style_input()
-        response, _ = CHATGLM_MODEL.chat(
-            CHATGLM_TOKENIZER, query, history=history)
-        return response, len(response)
-
-    def get_answer_stream_iter(self):
-        history, query = self._get_glm_style_input()
-        for response, history in CHATGLM_MODEL.stream_chat(
-            CHATGLM_TOKENIZER,
-            query,
-            history,
-            max_length=self.token_upper_limit,
-            top_p=self.top_p,
-            temperature=self.temperature,
-        ):
-            yield response
-
-
-class LLaMA_Client(BaseLLMModel):
-    def __init__(
-        self,
-        model_name,
-        lora_path=None,
-    ) -> None:
-        super().__init__(model_name=model_name)
-        from lmflow.datasets.dataset import Dataset
-        from lmflow.pipeline.auto_pipeline import AutoPipeline
-        from lmflow.models.auto_model import AutoModel
-        from lmflow.args import ModelArguments, DatasetArguments, InferencerArguments
-
-        self.max_generation_token = 1000
-        self.end_string = "\n\n"
-        # We don't need input data
-        data_args = DatasetArguments(dataset_path=None)
-        self.dataset = Dataset(data_args)
-        self.system_prompt = ""
-
-        global LLAMA_MODEL, LLAMA_INFERENCER
-        if LLAMA_MODEL is None or LLAMA_INFERENCER is None:
-            model_path = None
-            if os.path.exists("models"):
-                model_dirs = os.listdir("models")
-                if model_name in model_dirs:
-                    model_path = f"models/{model_name}"
-            if model_path is not None:
-                model_source = model_path
-            else:
-                model_source = f"decapoda-research/{model_name}"
-                # raise Exception(f"models目录下没有这个模型: {model_name}")
-            if lora_path is not None:
-                lora_path = f"lora/{lora_path}"
-            model_args = ModelArguments(model_name_or_path=model_source, lora_model_path=lora_path, model_type=None, config_overrides=None, config_name=None, tokenizer_name=None, cache_dir=None,
-                                        use_fast_tokenizer=True, model_revision='main', use_auth_token=False, torch_dtype=None, use_lora=False, lora_r=8, lora_alpha=32, lora_dropout=0.1, use_ram_optimized_load=True)
-            pipeline_args = InferencerArguments(
-                local_rank=0, random_seed=1, deepspeed='configs/ds_config_chatbot.json', mixed_precision='bf16')
-
-            with open(pipeline_args.deepspeed, "r") as f:
-                ds_config = json.load(f)
-            LLAMA_MODEL = AutoModel.get_model(
-                model_args,
-                tune_strategy="none",
-                ds_config=ds_config,
-            )
-            LLAMA_INFERENCER = AutoPipeline.get_pipeline(
-                pipeline_name="inferencer",
-                model_args=model_args,
-                data_args=data_args,
-                pipeline_args=pipeline_args,
-            )
-
-    def _get_llama_style_input(self):
-        history = []
-        instruction = ""
-        if self.system_prompt:
-            instruction = (f"Instruction: {self.system_prompt}\n")
-        for x in self.history:
-            if x["role"] == "user":
-                history.append(f"{instruction}Input: {x['content']}")
-            else:
-                history.append(f"Output: {x['content']}")
-        context = "\n\n".join(history)
-        context += "\n\nOutput: "
-        return context
-
-    def get_answer_at_once(self):
-        context = self._get_llama_style_input()
-
-        input_dataset = self.dataset.from_dict(
-            {"type": "text_only", "instances": [{"text": context}]}
-        )
-
-        output_dataset = LLAMA_INFERENCER.inference(
-            model=LLAMA_MODEL,
-            dataset=input_dataset,
-            max_new_tokens=self.max_generation_token,
-            temperature=self.temperature,
-        )
-
-        response = output_dataset.to_dict()["instances"][0]["text"]
-        return response, len(response)
-
-    def get_answer_stream_iter(self):
-        context = self._get_llama_style_input()
-        partial_text = ""
-        step = 1
-        for _ in range(0, self.max_generation_token, step):
-            input_dataset = self.dataset.from_dict(
-                {"type": "text_only", "instances": [
-                    {"text": context + partial_text}]}
-            )
-            output_dataset = LLAMA_INFERENCER.inference(
-                model=LLAMA_MODEL,
-                dataset=input_dataset,
-                max_new_tokens=step,
-                temperature=self.temperature,
-            )
-            response = output_dataset.to_dict()["instances"][0]["text"]
-            if response == "" or response == self.end_string:
-                break
-            partial_text += response
-            yield partial_text
-
-
-class XMChat(BaseLLMModel):
-    def __init__(self, api_key):
-        super().__init__(model_name="xmchat")
-        self.api_key = api_key
-        self.session_id = None
-        self.reset()
-        self.image_bytes = None
-        self.image_path = None
-        self.xm_history = []
-        self.url = "https://xmbot.net/web"
-        self.last_conv_id = None
-
-    def reset(self):
-        self.session_id = str(uuid.uuid4())
-        self.last_conv_id = None
-        return [], "已重置"
-
-    def image_to_base64(self, image_path):
-        # 打开并加载图片
-        img = Image.open(image_path)
-
-        # 获取图片的宽度和高度
-        width, height = img.size
-
-        # 计算压缩比例，以确保最长边小于4096像素
-        max_dimension = 2048
-        scale_ratio = min(max_dimension / width, max_dimension / height)
-
-        if scale_ratio < 1:
-            # 按压缩比例调整图片大小
-            new_width = int(width * scale_ratio)
-            new_height = int(height * scale_ratio)
-            img = img.resize((new_width, new_height), Image.ANTIALIAS)
-
-        # 将图片转换为jpg格式的二进制数据
-        buffer = BytesIO()
-        if img.mode == "RGBA":
-            img = img.convert("RGB")
-        img.save(buffer, format='JPEG')
-        binary_image = buffer.getvalue()
-
-        # 对二进制数据进行Base64编码
-        base64_image = base64.b64encode(binary_image).decode('utf-8')
-
-        return base64_image
-
-    def try_read_image(self, filepath):
-        def is_image_file(filepath):
-            # 判断文件是否为图片
-            valid_image_extensions = [".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff"]
-            file_extension = os.path.splitext(filepath)[1].lower()
-            return file_extension in valid_image_extensions
-
-        if is_image_file(filepath):
-            logging.info(f"读取图片文件: {filepath}")
-            self.image_bytes = self.image_to_base64(filepath)
-            self.image_path = filepath
-        else:
-            self.image_bytes = None
-            self.image_path = None
-
-    def like(self):
-        if self.last_conv_id is None:
-            return "点赞失败，你还没发送过消息"
-        data = {
-            "uuid": self.last_conv_id,
-            "appraise": "good"
-        }
-        response = requests.post(self.url, json=data)
-        return "👍点赞成功，，感谢反馈～"
-
-    def dislike(self):
-        if self.last_conv_id is None:
-            return "点踩失败，你还没发送过消息"
-        data = {
-            "uuid": self.last_conv_id,
-            "appraise": "bad"
-        }
-        response = requests.post(self.url, json=data)
-        return "👎点踩成功，感谢反馈～"
-
-    def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot):
-        fake_inputs = real_inputs
-        display_append = ""
-        limited_context = False
-        return limited_context, fake_inputs, display_append, real_inputs, chatbot
-
-    def handle_file_upload(self, files, chatbot):
-        """if the model accepts multi modal input, implement this function"""
-        if files:
-            for file in files:
-                if file.name:
-                    logging.info(f"尝试读取图像: {file.name}")
-                    self.try_read_image(file.name)
-            if self.image_path is not None:
-                chatbot = chatbot + [((self.image_path,), None)]
-            if self.image_bytes is not None:
-                logging.info("使用图片作为输入")
-                # XMChat的一轮对话中实际上只能处理一张图片
-                self.reset()
-                conv_id = str(uuid.uuid4())
-                data = {
-                    "user_id": self.api_key,
-                    "session_id": self.session_id,
-                    "uuid": conv_id,
-                    "data_type": "imgbase64",
-                    "data": self.image_bytes
-                }
-                response = requests.post(self.url, json=data)
-                response = json.loads(response.text)
-                logging.info(f"图片回复: {response['data']}")
-        return None, chatbot, None
-
-    def get_answer_at_once(self):
-        question = self.history[-1]["content"]
-        conv_id = str(uuid.uuid4())
-        self.last_conv_id = conv_id
-        data = {
-            "user_id": self.api_key,
-            "session_id": self.session_id,
-            "uuid": conv_id,
-            "data_type": "text",
-            "data": question
-        }
-        response = requests.post(self.url, json=data)
-        try:
-            response = json.loads(response.text)
-            return response["data"], len(response["data"])
-        except Exception as e:
-            return response.text, len(response.text)
-
-
-
-
-def get_model(
-    model_name,
-    lora_model_path=None,
-    access_key=None,
-    temperature=None,
-    top_p=None,
-    system_prompt=None,
-) -> BaseLLMModel:
-    msg = i18n("模型设置为了：") + f" {model_name}"
-    model_type = ModelType.get_type(model_name)
-    lora_selector_visibility = False
-    lora_choices = []
-    dont_change_lora_selector = False
-    if model_type != ModelType.OpenAI:
-        config.local_embedding = True
-    # del current_model.model
-    model = None
-    try:
-        if model_type == ModelType.OpenAI:
-            logging.info(f"正在加载OpenAI模型: {model_name}")
-            model = OpenAIClient(
-                model_name=model_name,
-                api_key=access_key,
-                system_prompt=system_prompt,
-                temperature=temperature,
-                top_p=top_p,
-            )
-        elif model_type == ModelType.ChatGLM:
-            logging.info(f"正在加载ChatGLM模型: {model_name}")
-            model = ChatGLM_Client(model_name)
-        elif model_type == ModelType.LLaMA and lora_model_path == "":
-            msg = f"现在请为 {model_name} 选择LoRA模型"
-            logging.info(msg)
-            lora_selector_visibility = True
-            if os.path.isdir("lora"):
-                lora_choices = get_file_names(
-                    "lora", plain=True, filetypes=[""])
-            lora_choices = ["No LoRA"] + lora_choices
-        elif model_type == ModelType.LLaMA and lora_model_path != "":
-            logging.info(f"正在加载LLaMA模型: {model_name} + {lora_model_path}")
-            dont_change_lora_selector = True
-            if lora_model_path == "No LoRA":
-                lora_model_path = None
-                msg += " + No LoRA"
-            else:
-                msg += f" + {lora_model_path}"
-            model = LLaMA_Client(model_name, lora_model_path)
-        elif model_type == ModelType.XMChat:
-            if os.environ.get("XMCHAT_API_KEY") != "":
-                access_key = os.environ.get("XMCHAT_API_KEY")
-            model = XMChat(api_key=access_key)
-        elif model_type == ModelType.Unknown:
-            raise ValueError(f"未知模型: {model_name}")
-        logging.info(msg)
-    except Exception as e:
-        logging.error(e)
-        msg = f"{STANDARD_ERROR_MSG}: {e}"
-    if dont_change_lora_selector:
-        return model, msg
-    else:
-        return model, msg, gr.Dropdown.update(choices=lora_choices, visible=lora_selector_visibility)
-
-
-if __name__ == "__main__":
-    with open("config.json", "r") as f:
-        openai_api_key = cjson.load(f)["openai_api_key"]
-    # set logging level to debug
-    logging.basicConfig(level=logging.DEBUG)
-    # client = ModelManager(model_name="gpt-3.5-turbo", access_key=openai_api_key)
-    client = get_model(model_name="chatglm-6b-int4")
-    chatbot = []
-    stream = False
-    # 测试账单功能
-    logging.info(colorama.Back.GREEN + "测试账单功能" + colorama.Back.RESET)
-    logging.info(client.billing_info())
-    # 测试问答
-    logging.info(colorama.Back.GREEN + "测试问答" + colorama.Back.RESET)
-    question = "巴黎是中国的首都吗？"
-    for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"测试问答后history : {client.history}")
-    # 测试记忆力
-    logging.info(colorama.Back.GREEN + "测试记忆力" + colorama.Back.RESET)
-    question = "我刚刚问了你什么问题？"
-    for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"测试记忆力后history : {client.history}")
-    # 测试重试功能
-    logging.info(colorama.Back.GREEN + "测试重试功能" + colorama.Back.RESET)
-    for i in client.retry(chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"重试后history : {client.history}")
-    # # 测试总结功能
-    # print(colorama.Back.GREEN + "测试总结功能" + colorama.Back.RESET)
-    # chatbot, msg = client.reduce_token_size(chatbot=chatbot)
-    # print(chatbot, msg)
-    # print(f"总结后history: {client.history}")