diff --git a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/theb/README.md b/spaces/101-5/gpt4free/g4f/.v1/gpt4free/theb/README.md deleted file mode 100644 index a7af9dd802753ec410c18f7dcfe2fe3f52e044ba..0000000000000000000000000000000000000000 --- a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/theb/README.md +++ /dev/null @@ -1,14 +0,0 @@ -### Example: `theb` (use like openai pypi package) - -```python -# import library -from gpt4free import theb - -# simple streaming completion - -while True: - x = input() - for token in theb.Completion.create(x): - print(token, end='', flush=True) - print("") -``` diff --git a/spaces/17TheWord/RealESRGAN/tests/test_dataset.py b/spaces/17TheWord/RealESRGAN/tests/test_dataset.py deleted file mode 100644 index 715b4082645c131d43d728ae8f65bcc2430aa8c9..0000000000000000000000000000000000000000 --- a/spaces/17TheWord/RealESRGAN/tests/test_dataset.py +++ /dev/null @@ -1,151 +0,0 @@ -import pytest -import yaml - -from realesrgan.data.realesrgan_dataset import RealESRGANDataset -from realesrgan.data.realesrgan_paired_dataset import RealESRGANPairedDataset - - -def test_realesrgan_dataset(): - - with open('tests/data/test_realesrgan_dataset.yml', mode='r') as f: - opt = yaml.load(f, Loader=yaml.FullLoader) - - dataset = RealESRGANDataset(opt) - assert dataset.io_backend_opt['type'] == 'disk' # io backend - assert len(dataset) == 2 # whether to read correct meta info - assert dataset.kernel_list == [ - 'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso' - ] # correct initialization the degradation configurations - assert dataset.betag_range2 == [0.5, 4] - - # test __getitem__ - result = dataset.__getitem__(0) - # check returned keys - expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path'] - assert set(expected_keys).issubset(set(result.keys())) - # check shape and contents - assert result['gt'].shape == (3, 400, 400) - assert result['kernel1'].shape == (21, 21) - assert result['kernel2'].shape == (21, 21) - assert result['sinc_kernel'].shape == (21, 21) - assert result['gt_path'] == 'tests/data/gt/baboon.png' - - # ------------------ test lmdb backend -------------------- # - opt['dataroot_gt'] = 'tests/data/gt.lmdb' - opt['io_backend']['type'] = 'lmdb' - - dataset = RealESRGANDataset(opt) - assert dataset.io_backend_opt['type'] == 'lmdb' # io backend - assert len(dataset.paths) == 2 # whether to read correct meta info - assert dataset.kernel_list == [ - 'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso' - ] # correct initialization the degradation configurations - assert dataset.betag_range2 == [0.5, 4] - - # test __getitem__ - result = dataset.__getitem__(1) - # check returned keys - expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path'] - assert set(expected_keys).issubset(set(result.keys())) - # check shape and contents - assert result['gt'].shape == (3, 400, 400) - assert result['kernel1'].shape == (21, 21) - assert result['kernel2'].shape == (21, 21) - assert result['sinc_kernel'].shape == (21, 21) - assert result['gt_path'] == 'comic' - - # ------------------ test with sinc_prob = 0 -------------------- # - opt['dataroot_gt'] = 'tests/data/gt.lmdb' - opt['io_backend']['type'] = 'lmdb' - opt['sinc_prob'] = 0 - opt['sinc_prob2'] = 0 - opt['final_sinc_prob'] = 0 - dataset = RealESRGANDataset(opt) - result = dataset.__getitem__(0) - # check returned keys - expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path'] - assert set(expected_keys).issubset(set(result.keys())) - # check shape and contents - assert result['gt'].shape == (3, 400, 400) - assert result['kernel1'].shape == (21, 21) - assert result['kernel2'].shape == (21, 21) - assert result['sinc_kernel'].shape == (21, 21) - assert result['gt_path'] == 'baboon' - - # ------------------ lmdb backend should have paths ends with lmdb -------------------- # - with pytest.raises(ValueError): - opt['dataroot_gt'] = 'tests/data/gt' - opt['io_backend']['type'] = 'lmdb' - dataset = RealESRGANDataset(opt) - - -def test_realesrgan_paired_dataset(): - - with open('tests/data/test_realesrgan_paired_dataset.yml', mode='r') as f: - opt = yaml.load(f, Loader=yaml.FullLoader) - - dataset = RealESRGANPairedDataset(opt) - assert dataset.io_backend_opt['type'] == 'disk' # io backend - assert len(dataset) == 2 # whether to read correct meta info - - # test __getitem__ - result = dataset.__getitem__(0) - # check returned keys - expected_keys = ['gt', 'lq', 'gt_path', 'lq_path'] - assert set(expected_keys).issubset(set(result.keys())) - # check shape and contents - assert result['gt'].shape == (3, 128, 128) - assert result['lq'].shape == (3, 32, 32) - assert result['gt_path'] == 'tests/data/gt/baboon.png' - assert result['lq_path'] == 'tests/data/lq/baboon.png' - - # ------------------ test lmdb backend -------------------- # - opt['dataroot_gt'] = 'tests/data/gt.lmdb' - opt['dataroot_lq'] = 'tests/data/lq.lmdb' - opt['io_backend']['type'] = 'lmdb' - - dataset = RealESRGANPairedDataset(opt) - assert dataset.io_backend_opt['type'] == 'lmdb' # io backend - assert len(dataset) == 2 # whether to read correct meta info - - # test __getitem__ - result = dataset.__getitem__(1) - # check returned keys - expected_keys = ['gt', 'lq', 'gt_path', 'lq_path'] - assert set(expected_keys).issubset(set(result.keys())) - # check shape and contents - assert result['gt'].shape == (3, 128, 128) - assert result['lq'].shape == (3, 32, 32) - assert result['gt_path'] == 'comic' - assert result['lq_path'] == 'comic' - - # ------------------ test paired_paths_from_folder -------------------- # - opt['dataroot_gt'] = 'tests/data/gt' - opt['dataroot_lq'] = 'tests/data/lq' - opt['io_backend'] = dict(type='disk') - opt['meta_info'] = None - - dataset = RealESRGANPairedDataset(opt) - assert dataset.io_backend_opt['type'] == 'disk' # io backend - assert len(dataset) == 2 # whether to read correct meta info - - # test __getitem__ - result = dataset.__getitem__(0) - # check returned keys - expected_keys = ['gt', 'lq', 'gt_path', 'lq_path'] - assert set(expected_keys).issubset(set(result.keys())) - # check shape and contents - assert result['gt'].shape == (3, 128, 128) - assert result['lq'].shape == (3, 32, 32) - - # ------------------ test normalization -------------------- # - dataset.mean = [0.5, 0.5, 0.5] - dataset.std = [0.5, 0.5, 0.5] - # test __getitem__ - result = dataset.__getitem__(0) - # check returned keys - expected_keys = ['gt', 'lq', 'gt_path', 'lq_path'] - assert set(expected_keys).issubset(set(result.keys())) - # check shape and contents - assert result['gt'].shape == (3, 128, 128) - assert result['lq'].shape == (3, 32, 32) diff --git a/spaces/17TheWord/vits-models/modules.py b/spaces/17TheWord/vits-models/modules.py deleted file mode 100644 index 56ea4145eddf19dd330a3a41ab0183efc1686d83..0000000000000000000000000000000000000000 --- a/spaces/17TheWord/vits-models/modules.py +++ /dev/null @@ -1,388 +0,0 @@ -import math -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm - -import commons -from commons import init_weights, get_padding -from transforms import piecewise_rational_quadratic_transform - - -LRELU_SLOPE = 0.1 - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - -class ConvReluNorm(nn.Module): - def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): - super().__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - assert n_layers > 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential( - nn.ReLU(), - nn.Dropout(p_dropout)) - for _ in range(n_layers-1): - self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size ** i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, - groups=channels, dilation=dilation, padding=padding - )) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): - super(WN, self).__init__() - assert(kernel_size % 2 == 1) - self.hidden_channels =hidden_channels - self.kernel_size = kernel_size, - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - self.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') - - for i in range(n_layers): - dilation = dilation_rate ** i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size, - dilation=dilation, padding=padding) - in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:] - else: - g_l = torch.zeros_like(x_in) - - acts = commons.fused_add_tanh_sigmoid_multiply( - x_in, - g_l, - n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:,:self.hidden_channels,:] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:,self.hidden_channels:,:] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]))) - ]) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))) - ]) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))) - ]) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels,1)) - self.logs = nn.Parameter(torch.zeros(channels,1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1,2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False): - assert channels % 2 == 0, "channels should be divisible by 2" - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels]*2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1,2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x - - -class ConvFlow(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0): - super().__init__() - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.num_bins = num_bins - self.tail_bound = tail_bound - self.half_channels = in_channels // 2 - - self.pre = nn.Conv1d(self.half_channels, filter_channels, 1) - self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.) - self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) - h = self.convs(h, x_mask, g=g) - h = self.proj(h) * x_mask - - b, c, t = x0.shape - h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?] - - unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels) - unnormalized_derivatives = h[..., 2 * self.num_bins:] - - x1, logabsdet = piecewise_rational_quadratic_transform(x1, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=reverse, - tails='linear', - tail_bound=self.tail_bound - ) - - x = torch.cat([x0, x1], 1) * x_mask - logdet = torch.sum(logabsdet * x_mask, [1,2]) - if not reverse: - return x, logdet - else: - return x diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/BMW Coding E-SYS V.3.24.3 Plken And PIN Utorrent LINK.md b/spaces/1gistliPinn/ChatGPT4/Examples/BMW Coding E-SYS V.3.24.3 Plken And PIN Utorrent LINK.md deleted file mode 100644 index 17d825d43a0e0420f15d81b671d42f6fab3b07f4..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/BMW Coding E-SYS V.3.24.3 Plken And PIN Utorrent LINK.md +++ /dev/null @@ -1,7 +0,0 @@ -
-

We propose nonadaptive protocols that can convert any classical message into a secure quantum bit string when used with a quantum channel. This task, which we call superdense coding, goes beyond the standard dense coding by demonstrating the possibility of using one qubit per transmitted photon to secure a long bit string (for example, a secret key for quantum key distribution). The task of superdense coding can also be achieved by using entanglement to secure a bit string.

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BMW Coding E-SYS V.3.24.3 Plken And PIN Utorrent


DOWNLOAD ☑ https://imgfil.com/2uy28G



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We have recently proposed a non-adaptive protocol for secure quantum key distribution, which outperforms existing non-adaptive protocols. A significant drawback of non-adaptive protocols is that they can readily be broken by the eavesdropper by performing an optimal attack, which is in the same spirit as existing adaptive protocols. Here, we show that non-adaptive protocols may also be useful for other applications. In particular, we propose a non-adaptive protocol for dense coding with a single sender and two receivers. This protocol is based on a quantum error correcting code and is optimal in the sense that the achievable rate is the maximum one for a given squeezing parameter. The encoding uses two perfect single-photon entanglement sources, which are required in the protocol and which can be realized in a linear optical setup. As a byproduct of the non-adaptive approach, the protocol also allows one to completely control the quantum states at the input of the channel. This opens up new possibilities in the study of decoherence in quantum information systems.less

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We report the first demonstration of quantum Darwinism: the redundant encoding of information about a decohering system in its environment. For a system to be both decoherent and open, it must acquire memories that become increasingly redundant as the system is shrunk, then protect the redundancy for an extended period. We demonstrate the first effective implementation of the decoherence–protection cycle for quantum Darwinism. As a prototype, we have encoded information about the quantum state of a superconducting flux qubit into its resistance, a form of a memory highly resistant to radiation noise--a decoherence mechanism of a type that can be engineered in a variety of systems. This quantum memory, when transferred to a second qubit, protects the initial code from decoherence up to an appreciable length scale, due to quantum Darwinism.

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\ No newline at end of file diff --git a/spaces/1line/AutoGPT/CODE_OF_CONDUCT.md b/spaces/1line/AutoGPT/CODE_OF_CONDUCT.md deleted file mode 100644 index d2331b4c60b9fb27f06953273355dcf53b8d4321..0000000000000000000000000000000000000000 --- a/spaces/1line/AutoGPT/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,40 +0,0 @@ -# Code of Conduct for auto-gpt - -## 1. Purpose - -The purpose of this Code of Conduct is to provide guidelines for contributors to the auto-gpt project on GitHub. We aim to create a positive and inclusive environment where all participants can contribute and collaborate effectively. By participating in this project, you agree to abide by this Code of Conduct. - -## 2. Scope - -This Code of Conduct applies to all contributors, maintainers, and users of the auto-gpt project. It extends to all project spaces, including but not limited to issues, pull requests, code reviews, comments, and other forms of communication within the project. - -## 3. Our Standards - -We encourage the following behavior: - -* Being respectful and considerate to others -* Actively seeking diverse perspectives -* Providing constructive feedback and assistance -* Demonstrating empathy and understanding - -We discourage the following behavior: - -* Harassment or discrimination of any kind -* Disrespectful, offensive, or inappropriate language or content -* Personal attacks or insults -* Unwarranted criticism or negativity - -## 4. Reporting and Enforcement - -If you witness or experience any violations of this Code of Conduct, please report them to the project maintainers by email or other appropriate means. The maintainers will investigate and take appropriate action, which may include warnings, temporary or permanent bans, or other measures as necessary. - -Maintainers are responsible for ensuring compliance with this Code of Conduct and may take action to address any violations. - -## 5. Acknowledgements - -This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org/version/2/0/code_of_conduct.html). - -## 6. Contact - -If you have any questions or concerns, please contact the project maintainers. - diff --git a/spaces/1line/AutoGPT/main.py b/spaces/1line/AutoGPT/main.py deleted file mode 100644 index 160addc390b94a8b143a3a2e18991a560f9b032e..0000000000000000000000000000000000000000 --- a/spaces/1line/AutoGPT/main.py +++ /dev/null @@ -1 +0,0 @@ -from autogpt import main diff --git a/spaces/1phancelerku/anime-remove-background/Download APK for GTA Vice City and Experience the 80s on Your Phone.md b/spaces/1phancelerku/anime-remove-background/Download APK for GTA Vice City and Experience the 80s on Your Phone.md deleted file mode 100644 index 8f674812edff3d69a2e76e05a66cc9af1e79a3a1..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download APK for GTA Vice City and Experience the 80s on Your Phone.md +++ /dev/null @@ -1,211 +0,0 @@ - -

Download APK for GTA Vice City: How to Play One of the Best Games Ever on Your Android Device

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If you are a fan of open-world action-adventure games, you probably have heard of GTA Vice City. This game is one of the most iconic and influential titles in the history of gaming, and it is still loved by millions of players around the world. But did you know that you can play GTA Vice City on your Android device? In this article, we will show you how to download APK for GTA Vice City and enjoy this classic game on your smartphone or tablet.

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download apk for gta vice city


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What is GTA Vice City?

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A brief introduction to the game and its features

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GTA Vice City is a game developed by Rockstar Games and released in 2002 for PlayStation 2, Xbox, and PC. It is the sixth installment in the Grand Theft Auto series, and it is set in a fictional city based on Miami in the 1980s. The game follows the story of Tommy Vercetti, a former mobster who is sent to Vice City by his boss to establish a criminal empire. The game allows you to explore a vast and colorful open world, where you can drive various vehicles, use different weapons, interact with various characters, complete missions, and cause mayhem.

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Why is GTA Vice City so popular and beloved?

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GTA Vice City is widely regarded as one of the best games ever made, and it has received critical acclaim and commercial success. Some of the reasons why GTA Vice City is so popular and beloved are:

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How to download APK for GTA Vice City?

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The official way: buy the game from Google Play Store

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The easiest and safest way to download APK for GTA Vice City is to buy the game from Google Play Store. The game was officially released for Android devices in 2012, and it costs $4.99. The game is compatible with most Android devices running Android 7.0 or higher, and it requires about 1.5 GB of free space. To buy the game from Google Play Store, you need to:

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  1. Open Google Play Store on your device and search for "GTA Vice City".
    2. -
  2. Select the game from the results and tap on "Buy".
    3. -
  3. Enter your payment details and confirm your purchase.
    4. -
  4. Wait for the game to download and install on your device.
    5. -
  5. Launch the game from your app drawer or home screen.
    6. -
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The unofficial way: download the APK file from a third-party source

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If you don't want to pay for the game or if your device is not compatible with Google Play Store, you can also download APK for GTA Vice City from a third-party source. However, this method is not recommended, as it may expose your device to malware

The unofficial way: download the APK file from a third-party source

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If you don't want to pay for the game or if your device is not compatible with Google Play Store, you can also download APK for GTA Vice City from a third-party source. However, this method is not recommended, as it may expose your device to malware, viruses, or legal issues. If you still want to try this method, you need to follow these steps:

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  1. Find a reliable and trustworthy website that offers the APK file for GTA Vice City. You can search online or use some of the links provided below . Make sure to read the reviews and ratings of the website before downloading anything.
    2. -
  2. Download the APK file and the OBB data file from the website. The APK file is the application file that installs the game on your device, while the OBB data file contains the game data and assets. The size of these files may vary depending on the website, but they are usually around 1.5 GB in total.
    3. -
  3. Enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on. This will allow you to install apps that are not from Google Play Store.
    4. -
  4. Locate the downloaded APK file and OBB data file on your device using a file manager app. You can use any file manager app that you prefer, such as ES File Explorer or ZArchiver.
    5. -
  5. Install the APK file by tapping on it and following the instructions on the screen. Do not launch the game yet.
    6. -
  6. Extract the OBB data file using a file extractor app, such as ZArchiver. You will get a folder named "com.rockstargames.gtavc". Copy this folder and paste it in the Android/OBB directory on your device's internal storage.
    7. -
  7. Launch the game from your app drawer or home screen and enjoy GTA Vice City on your Android device.
    8. -
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Pros and cons of using the unofficial way

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Using the unofficial way to download APK for GTA Vice City has some pros and cons that you should be aware of before trying it. Here are some of them:

- - - - - - - - - - - - - - - - - - - - - -
ProsCons
You can get the game for free without paying anything.You may violate the intellectual property rights of Rockstar Games and face legal consequences.
You can play the game on devices that are not compatible with Google Play Store.You may encounter bugs, glitches, crashes, or performance issues while playing the game.
You can access some mods, cheats, or hacks that are not available on the official version.You may risk infecting your device with malware, viruses, or spyware that can harm your data or privacy.
You can update the game manually whenever a new version is available.You may not receive any official support or updates from Rockstar Games or Google Play Store.
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How to install the APK file on your device

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If you have followed the steps above correctly, you should have installed the APK file and the OBB data file on your device successfully. However, if you face any problems or errors while installing or launching the game, you can try some of these solutions:

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