diff --git a/.gitattributes b/.gitattributes index 1509bb5efa3902d894fea061818508b5a1a7a4fa..a9483621867d797d8f547b74d04f50930bd2c5d1 100644 --- a/.gitattributes +++ b/.gitattributes @@ -37,3 +37,4 @@ checkpoints/hifi/female/do_00040000 filter=lfs diff=lfs merge=lfs -text checkpoints/hifi/female/g_00040000 filter=lfs diff=lfs merge=lfs -text checkpoints/hifi/male/do_00060000 filter=lfs diff=lfs merge=lfs -text checkpoints/hifi/male/g_00060000 filter=lfs diff=lfs merge=lfs -text +src/glow_tts/monotonic_align/build/temp.linux-x86_64-cpython-37/monotonic_align/core.o filter=lfs diff=lfs merge=lfs -text diff --git a/my_gradio_app.py b/my_gradio_app.py new file mode 100644 index 0000000000000000000000000000000000000000..b7e9484599d6dcf136da091f6313ea4d3c13449e --- /dev/null +++ b/my_gradio_app.py @@ -0,0 +1,63 @@ +from tts_infer.tts import TextToMel, MelToWav +from tts_infer.num_to_word_on_sent import normalize_nums +from ai4bharat.transliteration import XlitEngine +import gradio as gr + + +device = 'cpu' + +def create_text_to_mel(glow_model_dir): + return TextToMel(glow_model_dir=glow_model_dir, device=device) + +text_to_mel_female = create_text_to_mel('checkpoints/glow/female') +mel_to_wav_female = MelToWav(hifi_model_dir='checkpoints/hifi/female', device=device) + +text_to_mel_male = create_text_to_mel('checkpoints/glow/male') +mel_to_wav_male = MelToWav(hifi_model_dir='checkpoints/hifi/male', device=device) + +def translit(text, lang): + engine = XlitEngine(lang) + words = [engine.translit_word(word, topk=1)[lang][0] for word in text.split()] + updated_sent = ' '.join(words) + return updated_sent + + + +def run_tts(text, selected_voice): + lang = "pa" # Punjabi language code + text = text.replace('।', '.') + text_num_to_word = normalize_nums(text, lang) # converting numbers to words in lang + text_num_to_word_and_transliterated = translit(text_num_to_word, lang) # transliterating English words to lang + + if selected_voice == "Male Voice": + text_to_mel = text_to_mel_male + mel_to_wav = mel_to_wav_male + else: + text_to_mel = text_to_mel_female + mel_to_wav = mel_to_wav_female + + mel = text_to_mel.generate_mel(text_num_to_word_and_transliterated) + audio, sr = mel_to_wav.generate_wav(mel) + return sr, audio + + + + + +iface = gr.Interface( + fn=run_tts, + inputs=[ + "textbox", + gr.inputs.Dropdown( + choices=["Male Voice", "Female Voice"], + default="Female Voice", + label="Select Voice" + ) + ], + outputs="audio", + title="Text to Speech Punjabi Language" +) + +iface.launch() + + diff --git a/src/glow_tts/__pycache__/attentions.cpython-37.pyc b/src/glow_tts/__pycache__/attentions.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0fcd7a6aa82ad24ea462a454318af7a4ee63a2e8 Binary files /dev/null and b/src/glow_tts/__pycache__/attentions.cpython-37.pyc differ diff --git a/src/glow_tts/__pycache__/audio_processing.cpython-37.pyc b/src/glow_tts/__pycache__/audio_processing.cpython-37.pyc new file mode 100644 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/dev/null and b/src/glow_tts/__pycache__/utils.cpython-37.pyc differ diff --git a/src/glow_tts/attentions.py b/src/glow_tts/attentions.py new file mode 100644 index 0000000000000000000000000000000000000000..62b8c83acbd3150b6d6686f21f3627781107c1ba --- /dev/null +++ b/src/glow_tts/attentions.py @@ -0,0 +1,378 @@ +import copy +import math +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + +import commons +import modules +from modules import LayerNorm + + +class Encoder(nn.Module): + def __init__( + self, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size=1, + p_dropout=0.0, + window_size=None, + block_length=None, + **kwargs + ): + super().__init__() + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.window_size = window_size + self.block_length = block_length + + self.drop = nn.Dropout(p_dropout) + self.attn_layers = nn.ModuleList() + self.norm_layers_1 = nn.ModuleList() + self.ffn_layers = nn.ModuleList() + self.norm_layers_2 = nn.ModuleList() + for i in range(self.n_layers): + self.attn_layers.append( + MultiHeadAttention( + hidden_channels, + hidden_channels, + n_heads, + window_size=window_size, + p_dropout=p_dropout, + block_length=block_length, + ) + ) + self.norm_layers_1.append(LayerNorm(hidden_channels)) + self.ffn_layers.append( + FFN( + hidden_channels, + hidden_channels, + filter_channels, + kernel_size, + p_dropout=p_dropout, + ) + ) + self.norm_layers_2.append(LayerNorm(hidden_channels)) + + def forward(self, x, x_mask): + attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) + for i in range(self.n_layers): + x = x * x_mask + y = self.attn_layers[i](x, x, attn_mask) + y = self.drop(y) + x = self.norm_layers_1[i](x + y) + + y = self.ffn_layers[i](x, x_mask) + y = self.drop(y) + x = self.norm_layers_2[i](x + y) + x = x * x_mask + return x + + +class CouplingBlock(nn.Module): + def __init__( + self, + in_channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + gin_channels=0, + p_dropout=0, + sigmoid_scale=False, + ): + super().__init__() + self.in_channels = in_channels + 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.sigmoid_scale = sigmoid_scale + + start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1) + start = torch.nn.utils.weight_norm(start) + self.start = start + # Initializing last layer to 0 makes the affine coupling layers + # do nothing at first. It helps to stabilze training. + end = torch.nn.Conv1d(hidden_channels, in_channels, 1) + end.weight.data.zero_() + end.bias.data.zero_() + self.end = end + + self.wn = modules.WN( + in_channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + gin_channels, + p_dropout, + ) + + def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs): + b, c, t = x.size() + if x_mask is None: + x_mask = 1 + x_0, x_1 = x[:, : self.in_channels // 2], x[:, self.in_channels // 2 :] + + x = self.start(x_0) * x_mask + x = self.wn(x, x_mask, g) + out = self.end(x) + + z_0 = x_0 + m = out[:, : self.in_channels // 2, :] + logs = out[:, self.in_channels // 2 :, :] + if self.sigmoid_scale: + logs = torch.log(1e-6 + torch.sigmoid(logs + 2)) + + if reverse: + z_1 = (x_1 - m) * torch.exp(-logs) * x_mask + logdet = None + else: + z_1 = (m + torch.exp(logs) * x_1) * x_mask + logdet = torch.sum(logs * x_mask, [1, 2]) + + z = torch.cat([z_0, z_1], 1) + return z, logdet + + def store_inverse(self): + self.wn.remove_weight_norm() + + +class MultiHeadAttention(nn.Module): + def __init__( + self, + channels, + out_channels, + n_heads, + window_size=None, + heads_share=True, + p_dropout=0.0, + block_length=None, + proximal_bias=False, + proximal_init=False, + ): + super().__init__() + assert channels % n_heads == 0 + + self.channels = channels + self.out_channels = out_channels + self.n_heads = n_heads + self.window_size = window_size + self.heads_share = heads_share + self.block_length = block_length + self.proximal_bias = proximal_bias + self.p_dropout = p_dropout + self.attn = None + + self.k_channels = channels // n_heads + self.conv_q = nn.Conv1d(channels, channels, 1) + self.conv_k = nn.Conv1d(channels, channels, 1) + self.conv_v = nn.Conv1d(channels, channels, 1) + if window_size is not None: + n_heads_rel = 1 if heads_share else n_heads + rel_stddev = self.k_channels ** -0.5 + self.emb_rel_k = nn.Parameter( + torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) + * rel_stddev + ) + self.emb_rel_v = nn.Parameter( + torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) + * rel_stddev + ) + self.conv_o = nn.Conv1d(channels, out_channels, 1) + self.drop = nn.Dropout(p_dropout) + + nn.init.xavier_uniform_(self.conv_q.weight) + nn.init.xavier_uniform_(self.conv_k.weight) + if proximal_init: + self.conv_k.weight.data.copy_(self.conv_q.weight.data) + self.conv_k.bias.data.copy_(self.conv_q.bias.data) + nn.init.xavier_uniform_(self.conv_v.weight) + + def forward(self, x, c, attn_mask=None): + q = self.conv_q(x) + k = self.conv_k(c) + v = self.conv_v(c) + + x, self.attn = self.attention(q, k, v, mask=attn_mask) + + x = self.conv_o(x) + return x + + def attention(self, query, key, value, mask=None): + # reshape [b, d, t] -> [b, n_h, t, d_k] + b, d, t_s, t_t = (*key.size(), query.size(2)) + query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) + key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) + value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) + + scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels) + if self.window_size is not None: + assert ( + t_s == t_t + ), "Relative attention is only available for self-attention." + key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) + rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings) + rel_logits = self._relative_position_to_absolute_position(rel_logits) + scores_local = rel_logits / math.sqrt(self.k_channels) + scores = scores + scores_local + if self.proximal_bias: + assert t_s == t_t, "Proximal bias is only available for self-attention." + scores = scores + self._attention_bias_proximal(t_s).to( + device=scores.device, dtype=scores.dtype + ) + if mask is not None: + scores = scores.masked_fill(mask == 0, -1e4) + if self.block_length is not None: + block_mask = ( + torch.ones_like(scores) + .triu(-self.block_length) + .tril(self.block_length) + ) + scores = scores * block_mask + -1e4 * (1 - block_mask) + p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] + p_attn = self.drop(p_attn) + output = torch.matmul(p_attn, value) + if self.window_size is not None: + relative_weights = self._absolute_position_to_relative_position(p_attn) + value_relative_embeddings = self._get_relative_embeddings( + self.emb_rel_v, t_s + ) + output = output + self._matmul_with_relative_values( + relative_weights, value_relative_embeddings + ) + output = ( + output.transpose(2, 3).contiguous().view(b, d, t_t) + ) # [b, n_h, t_t, d_k] -> [b, d, t_t] + return output, p_attn + + def _matmul_with_relative_values(self, x, y): + """ + x: [b, h, l, m] + y: [h or 1, m, d] + ret: [b, h, l, d] + """ + ret = torch.matmul(x, y.unsqueeze(0)) + return ret + + def _matmul_with_relative_keys(self, x, y): + """ + x: [b, h, l, d] + y: [h or 1, m, d] + ret: [b, h, l, m] + """ + ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) + return ret + + def _get_relative_embeddings(self, relative_embeddings, length): + max_relative_position = 2 * self.window_size + 1 + # Pad first before slice to avoid using cond ops. + pad_length = max(length - (self.window_size + 1), 0) + slice_start_position = max((self.window_size + 1) - length, 0) + slice_end_position = slice_start_position + 2 * length - 1 + if pad_length > 0: + padded_relative_embeddings = F.pad( + relative_embeddings, + commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]), + ) + else: + padded_relative_embeddings = relative_embeddings + used_relative_embeddings = padded_relative_embeddings[ + :, slice_start_position:slice_end_position + ] + return used_relative_embeddings + + def _relative_position_to_absolute_position(self, x): + """ + x: [b, h, l, 2*l-1] + ret: [b, h, l, l] + """ + batch, heads, length, _ = x.size() + # Concat columns of pad to shift from relative to absolute indexing. + x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) + + # Concat extra elements so to add up to shape (len+1, 2*len-1). + x_flat = x.view([batch, heads, length * 2 * length]) + x_flat = F.pad( + x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]) + ) + + # Reshape and slice out the padded elements. + x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[ + :, :, :length, length - 1 : + ] + return x_final + + def _absolute_position_to_relative_position(self, x): + """ + x: [b, h, l, l] + ret: [b, h, l, 2*l-1] + """ + batch, heads, length, _ = x.size() + # padd along column + x = F.pad( + x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]) + ) + x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)]) + # add 0's in the beginning that will skew the elements after reshape + x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) + x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] + return x_final + + def _attention_bias_proximal(self, length): + """Bias for self-attention to encourage attention to close positions. + Args: + length: an integer scalar. + Returns: + a Tensor with shape [1, 1, length, length] + """ + r = torch.arange(length, dtype=torch.float32) + diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) + return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) + + +class FFN(nn.Module): + def __init__( + self, + in_channels, + out_channels, + filter_channels, + kernel_size, + p_dropout=0.0, + activation=None, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.activation = activation + + self.conv_1 = nn.Conv1d( + in_channels, filter_channels, kernel_size, padding=kernel_size // 2 + ) + self.conv_2 = nn.Conv1d( + filter_channels, out_channels, kernel_size, padding=kernel_size // 2 + ) + self.drop = nn.Dropout(p_dropout) + + def forward(self, x, x_mask): + x = self.conv_1(x * x_mask) + if self.activation == "gelu": + x = x * torch.sigmoid(1.702 * x) + else: + x = torch.relu(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + return x * x_mask diff --git a/src/glow_tts/audio_processing.py b/src/glow_tts/audio_processing.py new file mode 100644 index 0000000000000000000000000000000000000000..3a4467355952fefaba117b6014864139ac319c6b --- /dev/null +++ b/src/glow_tts/audio_processing.py @@ -0,0 +1,100 @@ +import torch +import numpy as np +from scipy.signal import get_window +import librosa.util as librosa_util + + +def window_sumsquare( + window, + n_frames, + hop_length=200, + win_length=800, + n_fft=800, + dtype=np.float32, + norm=None, +): + """ + # from librosa 0.6 + Compute the sum-square envelope of a window function at a given hop length. + + This is used to estimate modulation effects induced by windowing + observations in short-time fourier transforms. + + Parameters + ---------- + window : string, tuple, number, callable, or list-like + Window specification, as in `get_window` + + n_frames : int > 0 + The number of analysis frames + + hop_length : int > 0 + The number of samples to advance between frames + + win_length : [optional] + The length of the window function. By default, this matches `n_fft`. + + n_fft : int > 0 + The length of each analysis frame. + + dtype : np.dtype + The data type of the output + + Returns + ------- + wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))` + The sum-squared envelope of the window function + """ + if win_length is None: + win_length = n_fft + + n = n_fft + hop_length * (n_frames - 1) + x = np.zeros(n, dtype=dtype) + + # Compute the squared window at the desired length + win_sq = get_window(window, win_length, fftbins=True) + win_sq = librosa_util.normalize(win_sq, norm=norm) ** 2 + win_sq = librosa_util.pad_center(win_sq, n_fft) + + # Fill the envelope + for i in range(n_frames): + sample = i * hop_length + x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))] + return x + + +def griffin_lim(magnitudes, stft_fn, n_iters=30): + """ + PARAMS + ------ + magnitudes: spectrogram magnitudes + stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods + """ + + angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size()))) + angles = angles.astype(np.float32) + angles = torch.autograd.Variable(torch.from_numpy(angles)) + signal = stft_fn.inverse(magnitudes, angles).squeeze(1) + + for i in range(n_iters): + _, angles = stft_fn.transform(signal) + signal = stft_fn.inverse(magnitudes, angles).squeeze(1) + return signal + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + """ + PARAMS + ------ + C: compression factor + """ + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression(x, C=1): + """ + PARAMS + ------ + C: compression factor used to compress + """ + return torch.exp(x) / C diff --git a/src/glow_tts/commons.py b/src/glow_tts/commons.py new file mode 100644 index 0000000000000000000000000000000000000000..8da7b35049d768a29de6f66cbe8795a825967818 --- /dev/null +++ b/src/glow_tts/commons.py @@ -0,0 +1,273 @@ +import math +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + +from librosa.filters import mel as librosa_mel_fn +from audio_processing import dynamic_range_compression +from audio_processing import dynamic_range_decompression +from stft import STFT + + +def intersperse(lst, item): + result = [item] * (len(lst) * 2 + 1) + result[1::2] = lst + return result + + +def mle_loss(z, m, logs, logdet, mask): + l = torch.sum(logs) + 0.5 * torch.sum( + torch.exp(-2 * logs) * ((z - m) ** 2) + ) # neg normal likelihood w/o the constant term + l = l - torch.sum(logdet) # log jacobian determinant + l = l / torch.sum( + torch.ones_like(z) * mask + ) # averaging across batch, channel and time axes + l = l + 0.5 * math.log(2 * math.pi) # add the remaining constant term + return l + + +def duration_loss(logw, logw_, lengths): + l = torch.sum((logw - logw_) ** 2) / torch.sum(lengths) + return l + + +@torch.jit.script +def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): + n_channels_int = n_channels[0] + in_act = input_a + input_b + t_act = torch.tanh(in_act[:, :n_channels_int, :]) + s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) + acts = t_act * s_act + return acts + + +def convert_pad_shape(pad_shape): + l = pad_shape[::-1] + pad_shape = [item for sublist in l for item in sublist] + return pad_shape + + +def shift_1d(x): + x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] + return x + + +def sequence_mask(length, max_length=None): + if max_length is None: + max_length = length.max() + x = torch.arange(max_length, dtype=length.dtype, device=length.device) + return x.unsqueeze(0) < length.unsqueeze(1) + + +def maximum_path(value, mask, max_neg_val=-np.inf): + """Numpy-friendly version. It's about 4 times faster than torch version. + value: [b, t_x, t_y] + mask: [b, t_x, t_y] + """ + value = value * mask + + device = value.device + dtype = value.dtype + value = value.cpu().detach().numpy() + mask = mask.cpu().detach().numpy().astype(np.bool) + + b, t_x, t_y = value.shape + direction = np.zeros(value.shape, dtype=np.int64) + v = np.zeros((b, t_x), dtype=np.float32) + x_range = np.arange(t_x, dtype=np.float32).reshape(1, -1) + for j in range(t_y): + v0 = np.pad(v, [[0, 0], [1, 0]], mode="constant", constant_values=max_neg_val)[ + :, :-1 + ] + v1 = v + max_mask = v1 >= v0 + v_max = np.where(max_mask, v1, v0) + direction[:, :, j] = max_mask + + index_mask = x_range <= j + v = np.where(index_mask, v_max + value[:, :, j], max_neg_val) + direction = np.where(mask, direction, 1) + + path = np.zeros(value.shape, dtype=np.float32) + index = mask[:, :, 0].sum(1).astype(np.int64) - 1 + index_range = np.arange(b) + for j in reversed(range(t_y)): + path[index_range, index, j] = 1 + index = index + direction[index_range, index, j] - 1 + path = path * mask.astype(np.float32) + path = torch.from_numpy(path).to(device=device, dtype=dtype) + return path + + +def generate_path(duration, mask): + """ + duration: [b, t_x] + mask: [b, t_x, t_y] + """ + device = duration.device + + b, t_x, t_y = mask.shape + cum_duration = torch.cumsum(duration, 1) + path = torch.zeros(b, t_x, t_y, dtype=mask.dtype).to(device=device) + + cum_duration_flat = cum_duration.view(b * t_x) + path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) + path = path.view(b, t_x, t_y) + path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] + path = path * mask + return path + + +class Adam: + def __init__( + self, + params, + scheduler, + dim_model, + warmup_steps=4000, + lr=1e0, + betas=(0.9, 0.98), + eps=1e-9, + ): + self.params = params + self.scheduler = scheduler + self.dim_model = dim_model + self.warmup_steps = warmup_steps + self.lr = lr + self.betas = betas + self.eps = eps + + self.step_num = 1 + self.cur_lr = lr * self._get_lr_scale() + + self._optim = torch.optim.Adam(params, lr=self.cur_lr, betas=betas, eps=eps) + + def _get_lr_scale(self): + if self.scheduler == "noam": + return np.power(self.dim_model, -0.5) * np.min( + [ + np.power(self.step_num, -0.5), + self.step_num * np.power(self.warmup_steps, -1.5), + ] + ) + else: + return 1 + + def _update_learning_rate(self): + self.step_num += 1 + if self.scheduler == "noam": + self.cur_lr = self.lr * self._get_lr_scale() + for param_group in self._optim.param_groups: + param_group["lr"] = self.cur_lr + + def get_lr(self): + return self.cur_lr + + def step(self): + self._optim.step() + self._update_learning_rate() + + def zero_grad(self): + self._optim.zero_grad() + + def load_state_dict(self, d): + self._optim.load_state_dict(d) + + def state_dict(self): + return self._optim.state_dict() + + +class TacotronSTFT(nn.Module): + def __init__( + self, + filter_length=1024, + hop_length=256, + win_length=1024, + n_mel_channels=80, + sampling_rate=22050, + mel_fmin=0.0, + mel_fmax=8000.0, + ): + super(TacotronSTFT, self).__init__() + self.n_mel_channels = n_mel_channels + self.sampling_rate = sampling_rate + self.stft_fn = STFT(filter_length, hop_length, win_length) + mel_basis = librosa_mel_fn( + sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax + ) + mel_basis = torch.from_numpy(mel_basis).float() + self.register_buffer("mel_basis", mel_basis) + + def spectral_normalize(self, magnitudes): + output = dynamic_range_compression(magnitudes) + return output + + def spectral_de_normalize(self, magnitudes): + output = dynamic_range_decompression(magnitudes) + return output + + def mel_spectrogram(self, y): + """Computes mel-spectrograms from a batch of waves + PARAMS + ------ + y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1] + + RETURNS + ------- + mel_output: torch.FloatTensor of shape (B, n_mel_channels, T) + """ + assert torch.min(y.data) >= -1 + assert torch.max(y.data) <= 1 + + magnitudes, phases = self.stft_fn.transform(y) + magnitudes = magnitudes.data + mel_output = torch.matmul(self.mel_basis, magnitudes) + mel_output = self.spectral_normalize(mel_output) + return mel_output + + +def clip_grad_value_(parameters, clip_value, norm_type=2): + if isinstance(parameters, torch.Tensor): + parameters = [parameters] + parameters = list(filter(lambda p: p.grad is not None, parameters)) + norm_type = float(norm_type) + clip_value = float(clip_value) + + total_norm = 0 + for p in parameters: + param_norm = p.grad.data.norm(norm_type) + total_norm += param_norm.item() ** norm_type + + p.grad.data.clamp_(min=-clip_value, max=clip_value) + total_norm = total_norm ** (1.0 / norm_type) + return total_norm + + +def squeeze(x, x_mask=None, n_sqz=2): + b, c, t = x.size() + + t = (t // n_sqz) * n_sqz + x = x[:, :, :t] + x_sqz = x.view(b, c, t // n_sqz, n_sqz) + x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz) + + if x_mask is not None: + x_mask = x_mask[:, :, n_sqz - 1 :: n_sqz] + else: + x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype) + return x_sqz * x_mask, x_mask + + +def unsqueeze(x, x_mask=None, n_sqz=2): + b, c, t = x.size() + + x_unsqz = x.view(b, n_sqz, c // n_sqz, t) + x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz) + + if x_mask is not None: + x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz) + else: + x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype) + return x_unsqz * x_mask, x_mask diff --git a/src/glow_tts/data_utils.py b/src/glow_tts/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b58d84b3df3de3afb0a6a3bb8fadfd7a592dd602 --- /dev/null +++ b/src/glow_tts/data_utils.py @@ -0,0 +1,274 @@ +import random +import numpy as np +import torch +import torch.utils.data + +import commons +from utils import load_wav_to_torch, load_filepaths_and_text +from text import text_to_sequence + +class TextMelLoader(torch.utils.data.Dataset): + """ + 1) loads audio,text pairs + 2) normalizes text and converts them to sequences of one-hot vectors + 3) computes mel-spectrograms from audio files. + """ + + def __init__(self, audiopaths_and_text, hparams): + self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text) + self.text_cleaners = hparams.text_cleaners + self.max_wav_value = hparams.max_wav_value + self.sampling_rate = hparams.sampling_rate + self.load_mel_from_disk = hparams.load_mel_from_disk + self.add_noise = hparams.add_noise + self.symbols = hparams.punc + hparams.chars + self.add_blank = getattr(hparams, "add_blank", False) # improved version + self.stft = commons.TacotronSTFT( + hparams.filter_length, + hparams.hop_length, + hparams.win_length, + hparams.n_mel_channels, + hparams.sampling_rate, + hparams.mel_fmin, + hparams.mel_fmax, + ) + random.seed(1234) + random.shuffle(self.audiopaths_and_text) + + def get_mel_text_pair(self, audiopath_and_text): + # separate filename and text + audiopath, text = audiopath_and_text[0], audiopath_and_text[1] + text = self.get_text(text) + mel = self.get_mel(audiopath) + return (text, mel) + + def get_mel(self, filename): + if not self.load_mel_from_disk: + audio, sampling_rate = load_wav_to_torch(filename) + if sampling_rate != self.stft.sampling_rate: + raise ValueError( + "{} {} SR doesn't match target {} SR".format( + sampling_rate, self.stft.sampling_rate + ) + ) + if self.add_noise: + audio = audio + torch.rand_like(audio) + audio_norm = audio / self.max_wav_value + audio_norm = audio_norm.unsqueeze(0) + melspec = self.stft.mel_spectrogram(audio_norm) + melspec = torch.squeeze(melspec, 0) + else: + melspec = torch.from_numpy(np.load(filename)) + assert ( + melspec.size(0) == self.stft.n_mel_channels + ), "Mel dimension mismatch: given {}, expected {}".format( + melspec.size(0), self.stft.n_mel_channels + ) + + return melspec + + def get_text(self, text): + text_norm = text_to_sequence(text, self.symbols, self.text_cleaners) + if self.add_blank: + text_norm = commons.intersperse( + text_norm, len(self.symbols) + ) # add a blank token, whose id number is len(symbols) + text_norm = torch.IntTensor(text_norm) + return text_norm + + def __getitem__(self, index): + return self.get_mel_text_pair(self.audiopaths_and_text[index]) + + def __len__(self): + return len(self.audiopaths_and_text) + + +class TextMelCollate: + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, n_frames_per_step=1): + self.n_frames_per_step = n_frames_per_step + + def __call__(self, batch): + """Collate's training batch from normalized text and mel-spectrogram + PARAMS + ------ + batch: [text_normalized, mel_normalized] + """ + # Right zero-pad all one-hot text sequences to max input length + input_lengths, ids_sorted_decreasing = torch.sort( + torch.LongTensor([len(x[0]) for x in batch]), dim=0, descending=True + ) + max_input_len = input_lengths[0] + + text_padded = torch.LongTensor(len(batch), max_input_len) + text_padded.zero_() + for i in range(len(ids_sorted_decreasing)): + text = batch[ids_sorted_decreasing[i]][0] + text_padded[i, : text.size(0)] = text + + # Right zero-pad mel-spec + num_mels = batch[0][1].size(0) + max_target_len = max([x[1].size(1) for x in batch]) + if max_target_len % self.n_frames_per_step != 0: + max_target_len += ( + self.n_frames_per_step - max_target_len % self.n_frames_per_step + ) + assert max_target_len % self.n_frames_per_step == 0 + + # include mel padded + mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len) + mel_padded.zero_() + output_lengths = torch.LongTensor(len(batch)) + for i in range(len(ids_sorted_decreasing)): + mel = batch[ids_sorted_decreasing[i]][1] + mel_padded[i, :, : mel.size(1)] = mel + output_lengths[i] = mel.size(1) + + return text_padded, input_lengths, mel_padded, output_lengths + + +"""Multi speaker version""" + + +class TextMelSpeakerLoader(torch.utils.data.Dataset): + """ + 1) loads audio, speaker_id, text pairs + 2) normalizes text and converts them to sequences of one-hot vectors + 3) computes mel-spectrograms from audio files. + """ + + def __init__(self, audiopaths_sid_text, hparams): + self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text) + self.text_cleaners = hparams.text_cleaners + self.max_wav_value = hparams.max_wav_value + self.sampling_rate = hparams.sampling_rate + self.load_mel_from_disk = hparams.load_mel_from_disk + self.add_noise = hparams.add_noise + self.symbols = hparams.punc + hparams.chars + self.add_blank = getattr(hparams, "add_blank", False) # improved version + self.min_text_len = getattr(hparams, "min_text_len", 1) + self.max_text_len = getattr(hparams, "max_text_len", 190) + self.stft = commons.TacotronSTFT( + hparams.filter_length, + hparams.hop_length, + hparams.win_length, + hparams.n_mel_channels, + hparams.sampling_rate, + hparams.mel_fmin, + hparams.mel_fmax, + ) + + self._filter_text_len() + random.seed(1234) + random.shuffle(self.audiopaths_sid_text) + + def _filter_text_len(self): + audiopaths_sid_text_new = [] + for audiopath, sid, text in self.audiopaths_sid_text: + if self.min_text_len <= len(text) and len(text) <= self.max_text_len: + audiopaths_sid_text_new.append([audiopath, sid, text]) + self.audiopaths_sid_text = audiopaths_sid_text_new + + def get_mel_text_speaker_pair(self, audiopath_sid_text): + # separate filename, speaker_id and text + audiopath, sid, text = ( + audiopath_sid_text[0], + audiopath_sid_text[1], + audiopath_sid_text[2], + ) + text = self.get_text(text) + mel = self.get_mel(audiopath) + sid = self.get_sid(sid) + return (text, mel, sid) + + def get_mel(self, filename): + if not self.load_mel_from_disk: + audio, sampling_rate = load_wav_to_torch(filename) + if sampling_rate != self.stft.sampling_rate: + raise ValueError( + "{} {} SR doesn't match target {} SR".format( + sampling_rate, self.stft.sampling_rate + ) + ) + if self.add_noise: + audio = audio + torch.rand_like(audio) + audio_norm = audio / self.max_wav_value + audio_norm = audio_norm.unsqueeze(0) + melspec = self.stft.mel_spectrogram(audio_norm) + melspec = torch.squeeze(melspec, 0) + else: + melspec = torch.from_numpy(np.load(filename)) + assert ( + melspec.size(0) == self.stft.n_mel_channels + ), "Mel dimension mismatch: given {}, expected {}".format( + melspec.size(0), self.stft.n_mel_channels + ) + + return melspec + + def get_text(self, text): + text_norm = text_to_sequence(text, self.symbols, self.text_cleaners) + if self.add_blank: + text_norm = commons.intersperse( + text_norm, len(self.symbols) + ) # add a blank token, whose id number is len(symbols) + text_norm = torch.IntTensor(text_norm) + return text_norm + + def get_sid(self, sid): + sid = torch.IntTensor([int(sid)]) + return sid + + def __getitem__(self, index): + return self.get_mel_text_speaker_pair(self.audiopaths_sid_text[index]) + + def __len__(self): + return len(self.audiopaths_sid_text) + + +class TextMelSpeakerCollate: + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, n_frames_per_step=1): + self.n_frames_per_step = n_frames_per_step + + def __call__(self, batch): + """Collate's training batch from normalized text and mel-spectrogram + PARAMS + ------ + batch: [text_normalized, mel_normalized] + """ + # Right zero-pad all one-hot text sequences to max input length + input_lengths, ids_sorted_decreasing = torch.sort( + torch.LongTensor([len(x[0]) for x in batch]), dim=0, descending=True + ) + max_input_len = input_lengths[0] + + text_padded = torch.LongTensor(len(batch), max_input_len) + text_padded.zero_() + for i in range(len(ids_sorted_decreasing)): + text = batch[ids_sorted_decreasing[i]][0] + text_padded[i, : text.size(0)] = text + + # Right zero-pad mel-spec + num_mels = batch[0][1].size(0) + max_target_len = max([x[1].size(1) for x in batch]) + if max_target_len % self.n_frames_per_step != 0: + max_target_len += ( + self.n_frames_per_step - max_target_len % self.n_frames_per_step + ) + assert max_target_len % self.n_frames_per_step == 0 + + # include mel padded & sid + mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len) + mel_padded.zero_() + output_lengths = torch.LongTensor(len(batch)) + sid = torch.LongTensor(len(batch)) + for i in range(len(ids_sorted_decreasing)): + mel = batch[ids_sorted_decreasing[i]][1] + mel_padded[i, :, : mel.size(1)] = mel + output_lengths[i] = mel.size(1) + sid[i] = batch[ids_sorted_decreasing[i]][2] + + return text_padded, input_lengths, mel_padded, output_lengths, sid diff --git a/src/glow_tts/generate_mels.py b/src/glow_tts/generate_mels.py new file mode 100644 index 0000000000000000000000000000000000000000..a3d331aef019cfd8cf45d6264db88d0fa26e5c0f --- /dev/null +++ b/src/glow_tts/generate_mels.py @@ -0,0 +1,70 @@ +import numpy as np +import os +import torch +import commons + +import models +import utils +from argparse import ArgumentParser +from tqdm import tqdm +from text import text_to_sequence + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("-m", "--model_dir", required=True, type=str) + parser.add_argument("-s", "--mels_dir", required=True, type=str) + args = parser.parse_args() + MODEL_DIR = args.model_dir # path to model dir + SAVE_MELS_DIR = args.mels_dir # path to save generated mels + + if not os.path.exists(SAVE_MELS_DIR): + os.makedirs(SAVE_MELS_DIR) + + hps = utils.get_hparams_from_dir(MODEL_DIR) + symbols = list(hps.data.punc) + list(hps.data.chars) + checkpoint_path = utils.latest_checkpoint_path(MODEL_DIR) + cleaner = hps.data.text_cleaners + + model = models.FlowGenerator( + len(symbols) + getattr(hps.data, "add_blank", False), + out_channels=hps.data.n_mel_channels, + **hps.model + ).to("cuda") + + utils.load_checkpoint(checkpoint_path, model) + model.decoder.store_inverse() # do not calcuate jacobians for fast decoding + _ = model.eval() + + def get_mel(text, fpath): + if getattr(hps.data, "add_blank", False): + text_norm = text_to_sequence(text, symbols, cleaner) + text_norm = commons.intersperse(text_norm, len(symbols)) + else: # If not using "add_blank" option during training, adding spaces at the beginning and the end of utterance improves quality + text = " " + text.strip() + " " + text_norm = text_to_sequence(text, symbols, cleaner) + + sequence = np.array(text_norm)[None, :] + + x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long() + x_tst_lengths = torch.tensor([x_tst.shape[1]]).cuda() + + with torch.no_grad(): + noise_scale = 0.667 + length_scale = 1.0 + (y_gen_tst, *_), *_, (attn_gen, *_) = model( + x_tst, + x_tst_lengths, + gen=True, + noise_scale=noise_scale, + length_scale=length_scale, + ) + + np.save(os.path.join(SAVE_MELS_DIR, fpath), y_gen_tst.cpu().detach().numpy()) + + for f in [hps.data.training_files, hps.data.validation_files]: + file_lines = open(f).read().splitlines() + + for line in tqdm(file_lines): + fname, text = line.split("|") + fname = os.path.basename(fname).replace(".wav", ".npy") + get_mel(text, fname) diff --git a/src/glow_tts/hifi/__init__.py b/src/glow_tts/hifi/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0323b35a0fc2ef21ac417857d9336cc7c8a3b717 --- /dev/null +++ b/src/glow_tts/hifi/__init__.py @@ -0,0 +1,5 @@ +from .env import AttrDict +from .models import Generator + +if __name__ == "__main__": + pass diff --git a/src/glow_tts/hifi/__pycache__/__init__.cpython-37.pyc b/src/glow_tts/hifi/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..168643094de0f27c98ce3981caf394e44077cae2 Binary files /dev/null and b/src/glow_tts/hifi/__pycache__/__init__.cpython-37.pyc differ diff --git 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file mode 100644 index 0000000000000000000000000000000000000000..2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056 --- /dev/null +++ b/src/glow_tts/hifi/env.py @@ -0,0 +1,15 @@ +import os +import shutil + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super(AttrDict, self).__init__(*args, **kwargs) + self.__dict__ = self + + +def build_env(config, config_name, path): + t_path = os.path.join(path, config_name) + if config != t_path: + os.makedirs(path, exist_ok=True) + shutil.copyfile(config, os.path.join(path, config_name)) diff --git a/src/glow_tts/hifi/models.py b/src/glow_tts/hifi/models.py new file mode 100644 index 0000000000000000000000000000000000000000..aaf911836119d69129abe22aa4fc875f2ba3d53c --- /dev/null +++ b/src/glow_tts/hifi/models.py @@ -0,0 +1,403 @@ +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm +from .utils import init_weights, get_padding + +LRELU_SLOPE = 0.1 + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.h = h + 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): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + 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, h, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.h = h + 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): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class Generator(torch.nn.Module): + def __init__(self, h): + super(Generator, self).__init__() + self.h = h + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + self.conv_pre = weight_norm( + Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3) + ) + resblock = ResBlock1 if h.resblock == "1" else ResBlock2 + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): + self.ups.append( + weight_norm( + ConvTranspose1d( + h.upsample_initial_channel // (2 ** i), + h.upsample_initial_channel // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = h.upsample_initial_channel // (2 ** (i + 1)) + for j, (k, d) in enumerate( + zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes) + ): + self.resblocks.append(resblock(h, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x): + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print("Removing weight norm...") + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f( + Conv2d( + 1, + 32, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f( + Conv2d( + 32, + 128, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f( + Conv2d( + 128, + 512, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f( + Conv2d( + 512, + 1024, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), + ] + ) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self): + super(MultiPeriodDiscriminator, self).__init__() + self.discriminators = nn.ModuleList( + [ + DiscriminatorP(2), + DiscriminatorP(3), + DiscriminatorP(5), + DiscriminatorP(7), + DiscriminatorP(11), + ] + ) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f(Conv1d(1, 128, 15, 1, padding=7)), + norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ] + ) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiScaleDiscriminator(torch.nn.Module): + def __init__(self): + super(MultiScaleDiscriminator, self).__init__() + self.discriminators = nn.ModuleList( + [ + DiscriminatorS(use_spectral_norm=True), + DiscriminatorS(), + DiscriminatorS(), + ] + ) + self.meanpools = nn.ModuleList( + [AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)] + ) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + if i != 0: + y = self.meanpools[i - 1](y) + y_hat = self.meanpools[i - 1](y_hat) + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1 - dr) ** 2) + g_loss = torch.mean(dg ** 2) + loss += r_loss + g_loss + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + l = torch.mean((1 - dg) ** 2) + gen_losses.append(l) + loss += l + + return loss, gen_losses diff --git a/src/glow_tts/hifi/utils.py b/src/glow_tts/hifi/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..71e9b2c99e053e2d4239074a67d64b834898c348 --- /dev/null +++ b/src/glow_tts/hifi/utils.py @@ -0,0 +1,57 @@ +import glob +import os +import matplotlib +import torch +from torch.nn.utils import weight_norm + +matplotlib.use("Agg") +import matplotlib.pylab as plt + + +def plot_spectrogram(spectrogram): + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print("Loading '{}'".format(filepath)) + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def save_checkpoint(filepath, obj): + print("Saving checkpoint to {}".format(filepath)) + torch.save(obj, filepath) + print("Complete.") + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + "????????") + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return None + return sorted(cp_list)[-1] diff --git a/src/glow_tts/init.py b/src/glow_tts/init.py new file mode 100644 index 0000000000000000000000000000000000000000..39dd83dbd55475d562a3f54d951cb822800d2e0f --- /dev/null +++ b/src/glow_tts/init.py @@ -0,0 +1,79 @@ +import os +import json +import argparse +import math +import torch +from torch import nn, optim +from torch.nn import functional as F +from torch.utils.data import DataLoader + +from data_utils import TextMelLoader, TextMelCollate +import models +import commons +import utils + + +class FlowGenerator_DDI(models.FlowGenerator): + """A helper for Data-dependent Initialization""" + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + for f in self.decoder.flows: + if getattr(f, "set_ddi", False): + f.set_ddi(True) + + +def main(): + hps = utils.get_hparams() + logger = utils.get_logger(hps.log_dir) + logger.info(hps) + utils.check_git_hash(hps.log_dir) + + torch.manual_seed(hps.train.seed) + + train_dataset = TextMelLoader(hps.data.training_files, hps.data) + collate_fn = TextMelCollate(1) + train_loader = DataLoader( + train_dataset, + num_workers=8, + shuffle=True, + batch_size=hps.train.batch_size, + pin_memory=True, + drop_last=True, + collate_fn=collate_fn, + ) + symbols = hps.data.punc + hps.data.chars + generator = FlowGenerator_DDI( + len(symbols) + getattr(hps.data, "add_blank", False), + out_channels=hps.data.n_mel_channels, + **hps.model + ).cuda() + optimizer_g = commons.Adam( + generator.parameters(), + scheduler=hps.train.scheduler, + dim_model=hps.model.hidden_channels, + warmup_steps=hps.train.warmup_steps, + lr=hps.train.learning_rate, + betas=hps.train.betas, + eps=hps.train.eps, + ) + + generator.train() + for batch_idx, (x, x_lengths, y, y_lengths) in enumerate(train_loader): + x, x_lengths = x.cuda(), x_lengths.cuda() + y, y_lengths = y.cuda(), y_lengths.cuda() + + _ = generator(x, x_lengths, y, y_lengths, gen=False) + break + + utils.save_checkpoint( + generator, + optimizer_g, + hps.train.learning_rate, + 0, + os.path.join(hps.model_dir, "ddi_G.pth"), + ) + + +if __name__ == "__main__": + main() diff --git a/src/glow_tts/models.py b/src/glow_tts/models.py new file mode 100644 index 0000000000000000000000000000000000000000..a77596153fa2e7e6fdd52ee0028a0c8ce02050b4 --- /dev/null +++ b/src/glow_tts/models.py @@ -0,0 +1,403 @@ +import math +import torch +from torch import nn +from torch.nn import functional as F + +import modules +import commons +import attentions +import monotonic_align + + +class DurationPredictor(nn.Module): + def __init__(self, in_channels, filter_channels, kernel_size, p_dropout): + super().__init__() + + self.in_channels = in_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + + self.drop = nn.Dropout(p_dropout) + self.conv_1 = nn.Conv1d( + in_channels, filter_channels, kernel_size, padding=kernel_size // 2 + ) + self.norm_1 = attentions.LayerNorm(filter_channels) + self.conv_2 = nn.Conv1d( + filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 + ) + self.norm_2 = attentions.LayerNorm(filter_channels) + self.proj = nn.Conv1d(filter_channels, 1, 1) + + def forward(self, x, x_mask): + x = self.conv_1(x * x_mask) + x = torch.relu(x) + x = self.norm_1(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + x = torch.relu(x) + x = self.norm_2(x) + x = self.drop(x) + x = self.proj(x * x_mask) + return x * x_mask + + +class TextEncoder(nn.Module): + def __init__( + self, + n_vocab, + out_channels, + hidden_channels, + filter_channels, + filter_channels_dp, + n_heads, + n_layers, + kernel_size, + p_dropout, + window_size=None, + block_length=None, + mean_only=False, + prenet=False, + gin_channels=0, + ): + + super().__init__() + + self.n_vocab = n_vocab + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.filter_channels_dp = filter_channels_dp + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + self.window_size = window_size + self.block_length = block_length + self.mean_only = mean_only + self.prenet = prenet + self.gin_channels = gin_channels + + self.emb = nn.Embedding(n_vocab, hidden_channels) + nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5) + + if prenet: + self.pre = modules.ConvReluNorm( + hidden_channels, + hidden_channels, + hidden_channels, + kernel_size=5, + n_layers=3, + p_dropout=0.5, + ) + self.encoder = attentions.Encoder( + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size, + p_dropout, + window_size=window_size, + block_length=block_length, + ) + + self.proj_m = nn.Conv1d(hidden_channels, out_channels, 1) + if not mean_only: + self.proj_s = nn.Conv1d(hidden_channels, out_channels, 1) + self.proj_w = DurationPredictor( + hidden_channels + gin_channels, filter_channels_dp, kernel_size, p_dropout + ) + + def forward(self, x, x_lengths, g=None): + x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] + x = torch.transpose(x, 1, -1) # [b, h, t] + x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( + x.dtype + ) + + if self.prenet: + x = self.pre(x, x_mask) + x = self.encoder(x, x_mask) + + if g is not None: + g_exp = g.expand(-1, -1, x.size(-1)) + x_dp = torch.cat([torch.detach(x), g_exp], 1) + else: + x_dp = torch.detach(x) + + x_m = self.proj_m(x) * x_mask + if not self.mean_only: + x_logs = self.proj_s(x) * x_mask + else: + x_logs = torch.zeros_like(x_m) + + logw = self.proj_w(x_dp, x_mask) + return x_m, x_logs, logw, x_mask + + +class FlowSpecDecoder(nn.Module): + def __init__( + self, + in_channels, + hidden_channels, + kernel_size, + dilation_rate, + n_blocks, + n_layers, + p_dropout=0.0, + n_split=4, + n_sqz=2, + sigmoid_scale=False, + gin_channels=0, + ): + super().__init__() + + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.kernel_size = kernel_size + self.dilation_rate = dilation_rate + self.n_blocks = n_blocks + self.n_layers = n_layers + self.p_dropout = p_dropout + self.n_split = n_split + self.n_sqz = n_sqz + self.sigmoid_scale = sigmoid_scale + self.gin_channels = gin_channels + + self.flows = nn.ModuleList() + for b in range(n_blocks): + self.flows.append(modules.ActNorm(channels=in_channels * n_sqz)) + self.flows.append( + modules.InvConvNear(channels=in_channels * n_sqz, n_split=n_split) + ) + self.flows.append( + attentions.CouplingBlock( + in_channels * n_sqz, + hidden_channels, + kernel_size=kernel_size, + dilation_rate=dilation_rate, + n_layers=n_layers, + gin_channels=gin_channels, + p_dropout=p_dropout, + sigmoid_scale=sigmoid_scale, + ) + ) + + def forward(self, x, x_mask, g=None, reverse=False): + if not reverse: + flows = self.flows + logdet_tot = 0 + else: + flows = reversed(self.flows) + logdet_tot = None + + if self.n_sqz > 1: + x, x_mask = commons.squeeze(x, x_mask, self.n_sqz) + for f in flows: + if not reverse: + x, logdet = f(x, x_mask, g=g, reverse=reverse) + logdet_tot += logdet + else: + x, logdet = f(x, x_mask, g=g, reverse=reverse) + if self.n_sqz > 1: + x, x_mask = commons.unsqueeze(x, x_mask, self.n_sqz) + return x, logdet_tot + + def store_inverse(self): + for f in self.flows: + f.store_inverse() + + +class FlowGenerator(nn.Module): + def __init__( + self, + n_vocab, + hidden_channels, + filter_channels, + filter_channels_dp, + out_channels, + kernel_size=3, + n_heads=2, + n_layers_enc=6, + p_dropout=0.0, + n_blocks_dec=12, + kernel_size_dec=5, + dilation_rate=5, + n_block_layers=4, + p_dropout_dec=0.0, + n_speakers=0, + gin_channels=0, + n_split=4, + n_sqz=1, + sigmoid_scale=False, + window_size=None, + block_length=None, + mean_only=False, + hidden_channels_enc=None, + hidden_channels_dec=None, + prenet=False, + **kwargs + ): + + super().__init__() + self.n_vocab = n_vocab + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.filter_channels_dp = filter_channels_dp + self.out_channels = out_channels + self.kernel_size = kernel_size + self.n_heads = n_heads + self.n_layers_enc = n_layers_enc + self.p_dropout = p_dropout + self.n_blocks_dec = n_blocks_dec + self.kernel_size_dec = kernel_size_dec + self.dilation_rate = dilation_rate + self.n_block_layers = n_block_layers + self.p_dropout_dec = p_dropout_dec + self.n_speakers = n_speakers + self.gin_channels = gin_channels + self.n_split = n_split + self.n_sqz = n_sqz + self.sigmoid_scale = sigmoid_scale + self.window_size = window_size + self.block_length = block_length + self.mean_only = mean_only + self.hidden_channels_enc = hidden_channels_enc + self.hidden_channels_dec = hidden_channels_dec + self.prenet = prenet + + self.encoder = TextEncoder( + n_vocab, + out_channels, + hidden_channels_enc or hidden_channels, + filter_channels, + filter_channels_dp, + n_heads, + n_layers_enc, + kernel_size, + p_dropout, + window_size=window_size, + block_length=block_length, + mean_only=mean_only, + prenet=prenet, + gin_channels=gin_channels, + ) + + self.decoder = FlowSpecDecoder( + out_channels, + hidden_channels_dec or hidden_channels, + kernel_size_dec, + dilation_rate, + n_blocks_dec, + n_block_layers, + p_dropout=p_dropout_dec, + n_split=n_split, + n_sqz=n_sqz, + sigmoid_scale=sigmoid_scale, + gin_channels=gin_channels, + ) + + if n_speakers > 1: + self.emb_g = nn.Embedding(n_speakers, gin_channels) + nn.init.uniform_(self.emb_g.weight, -0.1, 0.1) + + def forward( + self, + x, + x_lengths, + y=None, + y_lengths=None, + g=None, + gen=False, + noise_scale=1.0, + length_scale=1.0, + ): + if g is not None: + g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h] + x_m, x_logs, logw, x_mask = self.encoder(x, x_lengths, g=g) + + if gen: + w = torch.exp(logw) * x_mask * length_scale + w_ceil = torch.ceil(w) + y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() + y_max_length = None + else: + y_max_length = y.size(2) + y, y_lengths, y_max_length = self.preprocess(y, y_lengths, y_max_length) + z_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y_max_length), 1).to( + x_mask.dtype + ) + attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(z_mask, 2) + + if gen: + attn = commons.generate_path( + w_ceil.squeeze(1), attn_mask.squeeze(1) + ).unsqueeze(1) + z_m = torch.matmul( + attn.squeeze(1).transpose(1, 2), x_m.transpose(1, 2) + ).transpose( + 1, 2 + ) # [b, t', t], [b, t, d] -> [b, d, t'] + z_logs = torch.matmul( + attn.squeeze(1).transpose(1, 2), x_logs.transpose(1, 2) + ).transpose( + 1, 2 + ) # [b, t', t], [b, t, d] -> [b, d, t'] + logw_ = torch.log(1e-8 + torch.sum(attn, -1)) * x_mask + + z = (z_m + torch.exp(z_logs) * torch.randn_like(z_m) * noise_scale) * z_mask + y, logdet = self.decoder(z, z_mask, g=g, reverse=True) + return ( + (y, z_m, z_logs, logdet, z_mask), + (x_m, x_logs, x_mask), + (attn, logw, logw_), + ) + else: + z, logdet = self.decoder(y, z_mask, g=g, reverse=False) + with torch.no_grad(): + x_s_sq_r = torch.exp(-2 * x_logs) + logp1 = torch.sum(-0.5 * math.log(2 * math.pi) - x_logs, [1]).unsqueeze( + -1 + ) # [b, t, 1] + logp2 = torch.matmul( + x_s_sq_r.transpose(1, 2), -0.5 * (z ** 2) + ) # [b, t, d] x [b, d, t'] = [b, t, t'] + logp3 = torch.matmul( + (x_m * x_s_sq_r).transpose(1, 2), z + ) # [b, t, d] x [b, d, t'] = [b, t, t'] + logp4 = torch.sum(-0.5 * (x_m ** 2) * x_s_sq_r, [1]).unsqueeze( + -1 + ) # [b, t, 1] + logp = logp1 + logp2 + logp3 + logp4 # [b, t, t'] + + attn = ( + monotonic_align.maximum_path(logp, attn_mask.squeeze(1)) + .unsqueeze(1) + .detach() + ) + z_m = torch.matmul( + attn.squeeze(1).transpose(1, 2), x_m.transpose(1, 2) + ).transpose( + 1, 2 + ) # [b, t', t], [b, t, d] -> [b, d, t'] + z_logs = torch.matmul( + attn.squeeze(1).transpose(1, 2), x_logs.transpose(1, 2) + ).transpose( + 1, 2 + ) # [b, t', t], [b, t, d] -> [b, d, t'] + logw_ = torch.log(1e-8 + torch.sum(attn, -1)) * x_mask + return ( + (z, z_m, z_logs, logdet, z_mask), + (x_m, x_logs, x_mask), + (attn, logw, logw_), + ) + + def preprocess(self, y, y_lengths, y_max_length): + if y_max_length is not None: + y_max_length = (y_max_length // self.n_sqz) * self.n_sqz + y = y[:, :, :y_max_length] + y_lengths = (y_lengths // self.n_sqz) * self.n_sqz + return y, y_lengths, y_max_length + + def store_inverse(self): + self.decoder.store_inverse() diff --git a/src/glow_tts/modules.py b/src/glow_tts/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..a192251aaccb036780d77d6c8b538b652a5e24e2 --- /dev/null +++ b/src/glow_tts/modules.py @@ -0,0 +1,276 @@ +import copy +import math +import numpy as np +import scipy +import torch +from torch import nn +from torch.nn import functional as F + +import commons + + +class LayerNorm(nn.Module): + def __init__(self, channels, eps=1e-4): + 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): + n_dims = len(x.shape) + mean = torch.mean(x, 1, keepdim=True) + variance = torch.mean((x - mean) ** 2, 1, keepdim=True) + + x = (x - mean) * torch.rsqrt(variance + self.eps) + + shape = [1, -1] + [1] * (n_dims - 2) + x = x * self.gamma.view(*shape) + self.beta.view(*shape) + return x + + +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 WN(torch.nn.Module): + def __init__( + self, + in_channels, + hidden_channels, + kernel_size, + dilation_rate, + n_layers, + gin_channels=0, + p_dropout=0, + ): + super(WN, self).__init__() + assert kernel_size % 2 == 1 + assert hidden_channels % 2 == 0 + self.in_channels = in_channels + 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=None, 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) + x_in = self.drop(x_in) + 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) + + res_skip_acts = self.res_skip_layers[i](acts) + if i < self.n_layers - 1: + x = (x + res_skip_acts[:, : self.hidden_channels, :]) * 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 ActNorm(nn.Module): + def __init__(self, channels, ddi=False, **kwargs): + super().__init__() + self.channels = channels + self.initialized = not ddi + + self.logs = nn.Parameter(torch.zeros(1, channels, 1)) + self.bias = nn.Parameter(torch.zeros(1, channels, 1)) + + def forward(self, x, x_mask=None, reverse=False, **kwargs): + if x_mask is None: + x_mask = torch.ones(x.size(0), 1, x.size(2)).to( + device=x.device, dtype=x.dtype + ) + x_len = torch.sum(x_mask, [1, 2]) + if not self.initialized: + self.initialize(x, x_mask) + self.initialized = True + + if reverse: + z = (x - self.bias) * torch.exp(-self.logs) * x_mask + logdet = None + else: + z = (self.bias + torch.exp(self.logs) * x) * x_mask + logdet = torch.sum(self.logs) * x_len # [b] + + return z, logdet + + def store_inverse(self): + pass + + def set_ddi(self, ddi): + self.initialized = not ddi + + def initialize(self, x, x_mask): + with torch.no_grad(): + denom = torch.sum(x_mask, [0, 2]) + m = torch.sum(x * x_mask, [0, 2]) / denom + m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom + v = m_sq - (m ** 2) + logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6)) + + bias_init = ( + (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype) + ) + logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype) + + self.bias.data.copy_(bias_init) + self.logs.data.copy_(logs_init) + + +class InvConvNear(nn.Module): + def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs): + super().__init__() + assert n_split % 2 == 0 + self.channels = channels + self.n_split = n_split + self.no_jacobian = no_jacobian + + w_init = torch.qr(torch.FloatTensor(self.n_split, self.n_split).normal_())[0] + if torch.det(w_init) < 0: + w_init[:, 0] = -1 * w_init[:, 0] + self.weight = nn.Parameter(w_init) + + def forward(self, x, x_mask=None, reverse=False, **kwargs): + b, c, t = x.size() + assert c % self.n_split == 0 + if x_mask is None: + x_mask = 1 + x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t + else: + x_len = torch.sum(x_mask, [1, 2]) + + x = x.view(b, 2, c // self.n_split, self.n_split // 2, t) + x = ( + x.permute(0, 1, 3, 2, 4) + .contiguous() + .view(b, self.n_split, c // self.n_split, t) + ) + + if reverse: + if hasattr(self, "weight_inv"): + weight = self.weight_inv + else: + weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype) + logdet = None + else: + weight = self.weight + if self.no_jacobian: + logdet = 0 + else: + logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b] + + weight = weight.view(self.n_split, self.n_split, 1, 1) + z = F.conv2d(x, weight) + + z = z.view(b, 2, self.n_split // 2, c // self.n_split, t) + z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask + return z, logdet + + def store_inverse(self): + self.weight_inv = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype) diff --git a/src/glow_tts/monotonic_align/build/lib.linux-x86_64-cpython-37/monotonic_align/__init__.py b/src/glow_tts/monotonic_align/build/lib.linux-x86_64-cpython-37/monotonic_align/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..47a4dbf3177302af6b8e7d08b0b78343b1329efa --- /dev/null +++ b/src/glow_tts/monotonic_align/build/lib.linux-x86_64-cpython-37/monotonic_align/__init__.py @@ -0,0 +1,5 @@ +import pkg_resources + +__version__ = pkg_resources.get_distribution("monotonic_align").version + +from monotonic_align.mas import * diff --git a/src/glow_tts/monotonic_align/build/lib.linux-x86_64-cpython-37/monotonic_align/core.cpython-37m-x86_64-linux-gnu.so b/src/glow_tts/monotonic_align/build/lib.linux-x86_64-cpython-37/monotonic_align/core.cpython-37m-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..c4267a64ff8d2ec51c741514b251f84e3c048a76 Binary files /dev/null and b/src/glow_tts/monotonic_align/build/lib.linux-x86_64-cpython-37/monotonic_align/core.cpython-37m-x86_64-linux-gnu.so differ diff --git a/src/glow_tts/monotonic_align/build/lib.linux-x86_64-cpython-37/monotonic_align/mas.py b/src/glow_tts/monotonic_align/build/lib.linux-x86_64-cpython-37/monotonic_align/mas.py new file mode 100644 index 0000000000000000000000000000000000000000..207ab3e858389ec06c902fd6f5bec6c5da2996af --- /dev/null +++ b/src/glow_tts/monotonic_align/build/lib.linux-x86_64-cpython-37/monotonic_align/mas.py @@ -0,0 +1,57 @@ +from typing import overload +import numpy as np +import torch +from monotonic_align.core import maximum_path_c + + +def mask_from_len(lens: torch.Tensor, max_len=None): + """ + Make a `mask` from lens. + + :param inputs: (B, T, D) + :param lens: (B) + + :return: + `mask`: (B, T) + """ + if max_len is None: + max_len = lens.max() + index = torch.arange(max_len).to(lens).view(1, -1) + return index < lens.unsqueeze(1) # (B, T) + + +def mask_from_lens( + similarity: torch.Tensor, + symbol_lens: torch.Tensor, + mel_lens: torch.Tensor, +): + """ + :param similarity: (B, S, T) + :param symbol_lens: (B,) + :param mel_lens: (B,) + """ + _, S, T = similarity.size() + mask_S = mask_from_len(symbol_lens, S) + mask_T = mask_from_len(mel_lens, T) + mask_ST = mask_S.unsqueeze(2) * mask_T.unsqueeze(1) + return mask_ST.to(similarity) + + +def maximum_path(value, mask=None): + """Cython optimised version. + value: [b, t_x, t_y] + mask: [b, t_x, t_y] + """ + if mask is None: + mask = torch.zeros_like(value) + + value = value * mask + device = value.device + dtype = value.dtype + value = value.data.cpu().numpy().astype(np.float32) + path = np.zeros_like(value).astype(np.int32) + mask = mask.data.cpu().numpy() + t_x_max = mask.sum(1)[:, 0].astype(np.int32) + t_y_max = mask.sum(2)[:, 0].astype(np.int32) + maximum_path_c(path, value, t_x_max, t_y_max) + return torch.from_numpy(path).to(device=device, dtype=dtype) diff --git a/src/glow_tts/monotonic_align/build/temp.linux-x86_64-cpython-37/monotonic_align/core.o b/src/glow_tts/monotonic_align/build/temp.linux-x86_64-cpython-37/monotonic_align/core.o new file mode 100644 index 0000000000000000000000000000000000000000..4079c974b18941fa863b978cf8ec65f298fa1e3e --- /dev/null +++ b/src/glow_tts/monotonic_align/build/temp.linux-x86_64-cpython-37/monotonic_align/core.o @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dd5e43fed9aa26e4d448896b5090e5813d06ca4a8c3c635f8e7ee1d1d4bf41dc +size 1746200 diff --git a/src/glow_tts/monotonic_align/monotonic_align.egg-info/PKG-INFO b/src/glow_tts/monotonic_align/monotonic_align.egg-info/PKG-INFO new file mode 100644 index 0000000000000000000000000000000000000000..cbc8ebb6f93d5a8438f6e4e8f9c8dd8958e08af5 --- /dev/null +++ b/src/glow_tts/monotonic_align/monotonic_align.egg-info/PKG-INFO @@ -0,0 +1,3 @@ +Metadata-Version: 2.1 +Name: monotonic-align +Version: 1.1 diff --git a/src/glow_tts/monotonic_align/monotonic_align.egg-info/SOURCES.txt b/src/glow_tts/monotonic_align/monotonic_align.egg-info/SOURCES.txt new file mode 100644 index 0000000000000000000000000000000000000000..c2e269e2b51e30e3c08fdec3c32d29ee00415fdc --- /dev/null +++ b/src/glow_tts/monotonic_align/monotonic_align.egg-info/SOURCES.txt @@ -0,0 +1,10 @@ +pyproject.toml +setup.py +monotonic_align/__init__.py +monotonic_align/core.c +monotonic_align/mas.py +monotonic_align.egg-info/PKG-INFO +monotonic_align.egg-info/SOURCES.txt +monotonic_align.egg-info/dependency_links.txt +monotonic_align.egg-info/requires.txt +monotonic_align.egg-info/top_level.txt \ No newline at end of file diff --git a/src/glow_tts/monotonic_align/monotonic_align.egg-info/dependency_links.txt b/src/glow_tts/monotonic_align/monotonic_align.egg-info/dependency_links.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/src/glow_tts/monotonic_align/monotonic_align.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/src/glow_tts/monotonic_align/monotonic_align.egg-info/requires.txt b/src/glow_tts/monotonic_align/monotonic_align.egg-info/requires.txt new file mode 100644 index 0000000000000000000000000000000000000000..24ce15ab7ead32f98c7ac3edcd34bb2010ff4326 --- /dev/null +++ b/src/glow_tts/monotonic_align/monotonic_align.egg-info/requires.txt @@ -0,0 +1 @@ +numpy diff --git a/src/glow_tts/monotonic_align/monotonic_align.egg-info/top_level.txt b/src/glow_tts/monotonic_align/monotonic_align.egg-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..ecf9f22f046093c0da242b045021d618355a9151 --- /dev/null +++ b/src/glow_tts/monotonic_align/monotonic_align.egg-info/top_level.txt @@ -0,0 +1 @@ +monotonic_align diff --git a/src/glow_tts/monotonic_align/monotonic_align/__init__.py b/src/glow_tts/monotonic_align/monotonic_align/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..47a4dbf3177302af6b8e7d08b0b78343b1329efa --- /dev/null +++ b/src/glow_tts/monotonic_align/monotonic_align/__init__.py @@ -0,0 +1,5 @@ +import pkg_resources + +__version__ = pkg_resources.get_distribution("monotonic_align").version + +from monotonic_align.mas import * diff --git a/src/glow_tts/monotonic_align/monotonic_align/core.c b/src/glow_tts/monotonic_align/monotonic_align/core.c new file mode 100644 index 0000000000000000000000000000000000000000..8c23ac18c62eacf1b9880c12dec59a7462831604 --- /dev/null +++ b/src/glow_tts/monotonic_align/monotonic_align/core.c @@ -0,0 +1,29700 @@ +/* Generated by Cython 3.0.4 */ + +/* BEGIN: Cython Metadata +{ + "distutils": { + "depends": [], + "name": "monotonic_align.core", + "sources": [ + "monotonic_align/core.pyx" + ] + }, + "module_name": "monotonic_align.core" +} +END: Cython Metadata */ + +#ifndef PY_SSIZE_T_CLEAN +#define PY_SSIZE_T_CLEAN +#endif /* PY_SSIZE_T_CLEAN */ +#if defined(CYTHON_LIMITED_API) && 0 + #ifndef Py_LIMITED_API + #if CYTHON_LIMITED_API+0 > 0x03030000 + #define Py_LIMITED_API CYTHON_LIMITED_API + #else + #define Py_LIMITED_API 0x03030000 + #endif + #endif +#endif + +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) + #error Cython requires Python 2.7+ or Python 3.3+. +#else +#if defined(CYTHON_LIMITED_API) && CYTHON_LIMITED_API +#define __PYX_EXTRA_ABI_MODULE_NAME "limited" +#else +#define __PYX_EXTRA_ABI_MODULE_NAME "" +#endif +#define CYTHON_ABI "3_0_4" __PYX_EXTRA_ABI_MODULE_NAME +#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI +#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "." +#define CYTHON_HEX_VERSION 0x030004F0 +#define CYTHON_FUTURE_DIVISION 1 +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#define __PYX_COMMA , +#ifndef HAVE_LONG_LONG + #define HAVE_LONG_LONG +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#define __PYX_LIMITED_VERSION_HEX PY_VERSION_HEX +#if defined(GRAALVM_PYTHON) + /* For very preliminary testing purposes. Most variables are set the same as PyPy. + The existence of this section does not imply that anything works or is even tested */ + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 1 + #define CYTHON_COMPILING_IN_NOGIL 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) + #endif + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif +#elif defined(PYPY_VERSION) + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #ifndef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) + #endif + #if PY_VERSION_HEX < 0x03090000 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT) + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1 && PYPY_VERSION_NUM >= 0x07030C00) + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif +#elif defined(CYTHON_LIMITED_API) + #ifdef Py_LIMITED_API + #undef __PYX_LIMITED_VERSION_HEX + #define __PYX_LIMITED_VERSION_HEX Py_LIMITED_API + #endif + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 1 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 0 + #undef CYTHON_CLINE_IN_TRACEBACK + #define CYTHON_CLINE_IN_TRACEBACK 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 1 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #endif + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS 1 + #endif + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 1 + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #endif + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif +#elif defined(PY_NOGIL) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #ifndef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 1 + #endif + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #ifndef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #endif + #ifndef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #ifndef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL (PY_MAJOR_VERSION < 3 || PY_VERSION_HEX >= 0x03060000 && PY_VERSION_HEX < 0x030C00A6) + #endif + #ifndef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL (PY_VERSION_HEX >= 0x030700A1) + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS 1 + #endif + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT) + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #ifndef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #endif + #if PY_VERSION_HEX < 0x030400a1 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #elif !defined(CYTHON_USE_TP_FINALIZE) + #define CYTHON_USE_TP_FINALIZE 1 + #endif + #if PY_VERSION_HEX < 0x030600B1 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #elif !defined(CYTHON_USE_DICT_VERSIONS) + #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX < 0x030C00A5) + #endif + #if PY_VERSION_HEX < 0x030700A3 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #elif !defined(CYTHON_USE_EXC_INFO_STACK) + #define CYTHON_USE_EXC_INFO_STACK 1 + #endif + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 1 + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if !defined(CYTHON_VECTORCALL) +#define CYTHON_VECTORCALL (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1) +#endif +#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1) +#if CYTHON_USE_PYLONG_INTERNALS + #if PY_MAJOR_VERSION < 3 + #include "longintrepr.h" + #endif + #undef SHIFT + #undef BASE + #undef MASK + #ifdef SIZEOF_VOID_P + enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; + #endif +#endif +#ifndef __has_attribute + #define __has_attribute(x) 0 +#endif +#ifndef __has_cpp_attribute + #define __has_cpp_attribute(x) 0 +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED + #if defined(__cplusplus) + /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17 + * but leads to warnings with -pedantic, since it is a C++17 feature */ + #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) + #if __has_cpp_attribute(maybe_unused) + #define CYTHON_UNUSED [[maybe_unused]] + #endif + #endif + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR + #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x) +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_USE_CPP_STD_MOVE + #if defined(__cplusplus) && (\ + __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1600)) + #define CYTHON_USE_CPP_STD_MOVE 1 + #else + #define CYTHON_USE_CPP_STD_MOVE 0 + #endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) +#ifdef _MSC_VER + #ifndef _MSC_STDINT_H_ + #if _MSC_VER < 1300 + typedef unsigned char uint8_t; + typedef unsigned short uint16_t; + typedef unsigned int uint32_t; + #else + typedef unsigned __int8 uint8_t; + typedef unsigned __int16 uint16_t; + typedef unsigned __int32 uint32_t; + #endif + #endif + #if _MSC_VER < 1300 + #ifdef _WIN64 + typedef unsigned long long __pyx_uintptr_t; + #else + typedef unsigned int __pyx_uintptr_t; + #endif + #else + #ifdef _WIN64 + typedef unsigned __int64 __pyx_uintptr_t; + #else + typedef unsigned __int32 __pyx_uintptr_t; + #endif + #endif +#else + #include + typedef uintptr_t __pyx_uintptr_t; +#endif +#ifndef CYTHON_FALLTHROUGH + #if defined(__cplusplus) + /* for clang __has_cpp_attribute(fallthrough) is true even before C++17 + * but leads to warnings with -pedantic, since it is a C++17 feature */ + #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) + #if __has_cpp_attribute(fallthrough) + #define CYTHON_FALLTHROUGH [[fallthrough]] + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_cpp_attribute(clang::fallthrough) + #define CYTHON_FALLTHROUGH [[clang::fallthrough]] + #elif __has_cpp_attribute(gnu::fallthrough) + #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] + #endif + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_attribute(fallthrough) + #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) + #else + #define CYTHON_FALLTHROUGH + #endif + #endif + #if defined(__clang__) && defined(__apple_build_version__) + #if __apple_build_version__ < 7000000 + #undef CYTHON_FALLTHROUGH + #define CYTHON_FALLTHROUGH + #endif + #endif +#endif +#ifdef __cplusplus + template + struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);}; + #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL::value) +#else + #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0) +#endif +#if CYTHON_COMPILING_IN_PYPY == 1 + #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000) +#else + #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000) +#endif +#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer)) + +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_DefaultClassType PyClass_Type + #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_DefaultClassType PyType_Type +#if CYTHON_COMPILING_IN_LIMITED_API + static CYTHON_INLINE PyObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f, + PyObject *code, PyObject *c, PyObject* n, PyObject *v, + PyObject *fv, PyObject *cell, PyObject* fn, + PyObject *name, int fline, PyObject *lnos) { + PyObject *exception_table = NULL; + PyObject *types_module=NULL, *code_type=NULL, *result=NULL; + #if __PYX_LIMITED_VERSION_HEX < 0x030B0000 + PyObject *version_info; // borrowed + #endif + PyObject *py_minor_version = NULL; + long minor_version = 0; + PyObject *type, *value, *traceback; + PyErr_Fetch(&type, &value, &traceback); + #if __PYX_LIMITED_VERSION_HEX >= 0x030B0000 + minor_version = 11; // we don't yet need to distinguish between versions > 11 + #else + if (!(version_info = PySys_GetObject("version_info"))) goto end; + if (!(py_minor_version = PySequence_GetItem(version_info, 1))) goto end; + minor_version = PyLong_AsLong(py_minor_version); + if (minor_version == -1 && PyErr_Occurred()) goto end; + #endif + if (!(types_module = PyImport_ImportModule("types"))) goto end; + if (!(code_type = PyObject_GetAttrString(types_module, "CodeType"))) goto end; + if (minor_version <= 7) { + (void)p; + result = PyObject_CallFunction(code_type, "iiiiiOOOOOOiOO", a, k, l, s, f, code, + c, n, v, fn, name, fline, lnos, fv, cell); + } else if (minor_version <= 10) { + result = PyObject_CallFunction(code_type, "iiiiiiOOOOOOiOO", a,p, k, l, s, f, code, + c, n, v, fn, name, fline, lnos, fv, cell); + } else { + if (!(exception_table = PyBytes_FromStringAndSize(NULL, 0))) goto end; + result = PyObject_CallFunction(code_type, "iiiiiiOOOOOOOiOO", a,p, k, l, s, f, code, + c, n, v, fn, name, name, fline, lnos, exception_table, fv, cell); + } + end: + Py_XDECREF(code_type); + Py_XDECREF(exception_table); + Py_XDECREF(types_module); + Py_XDECREF(py_minor_version); + if (type) { + PyErr_Restore(type, value, traceback); + } + return result; + } + #ifndef CO_OPTIMIZED + #define CO_OPTIMIZED 0x0001 + #endif + #ifndef CO_NEWLOCALS + #define CO_NEWLOCALS 0x0002 + #endif + #ifndef CO_VARARGS + #define CO_VARARGS 0x0004 + #endif + #ifndef CO_VARKEYWORDS + #define CO_VARKEYWORDS 0x0008 + #endif + #ifndef CO_ASYNC_GENERATOR + #define CO_ASYNC_GENERATOR 0x0200 + #endif + #ifndef CO_GENERATOR + #define CO_GENERATOR 0x0020 + #endif + #ifndef CO_COROUTINE + #define CO_COROUTINE 0x0080 + #endif +#elif PY_VERSION_HEX >= 0x030B0000 + static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f, + PyObject *code, PyObject *c, PyObject* n, PyObject *v, + PyObject *fv, PyObject *cell, PyObject* fn, + PyObject *name, int fline, PyObject *lnos) { + PyCodeObject *result; + PyObject *empty_bytes = PyBytes_FromStringAndSize("", 0); // we don't have access to __pyx_empty_bytes here + if (!empty_bytes) return NULL; + result = + #if PY_VERSION_HEX >= 0x030C0000 + PyUnstable_Code_NewWithPosOnlyArgs + #else + PyCode_NewWithPosOnlyArgs + #endif + (a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, name, fline, lnos, empty_bytes); + Py_DECREF(empty_bytes); + return result; + } +#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#else + #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#endif +#endif +#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE) + #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type) +#else + #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type)) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is) + #define __Pyx_Py_Is(x, y) Py_Is(x, y) +#else + #define __Pyx_Py_Is(x, y) ((x) == (y)) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone) + #define __Pyx_Py_IsNone(ob) Py_IsNone(ob) +#else + #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue) + #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob) +#else + #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse) + #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob) +#else + #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False) +#endif +#define __Pyx_NoneAsNull(obj) (__Pyx_Py_IsNone(obj) ? NULL : (obj)) +#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o) +#else + #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o) +#endif +#ifndef CO_COROUTINE + #define CO_COROUTINE 0x80 +#endif +#ifndef CO_ASYNC_GENERATOR + #define CO_ASYNC_GENERATOR 0x200 +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef Py_TPFLAGS_SEQUENCE + #define Py_TPFLAGS_SEQUENCE 0 +#endif +#ifndef Py_TPFLAGS_MAPPING + #define Py_TPFLAGS_MAPPING 0 +#endif +#ifndef METH_STACKLESS + #define METH_STACKLESS 0 +#endif +#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast + #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords +#endif +#if CYTHON_METH_FASTCALL + #define __Pyx_METH_FASTCALL METH_FASTCALL + #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast + #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords +#else + #define __Pyx_METH_FASTCALL METH_VARARGS + #define __Pyx_PyCFunction_FastCall PyCFunction + #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords +#endif +#if CYTHON_VECTORCALL + #define __pyx_vectorcallfunc vectorcallfunc + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET PY_VECTORCALL_ARGUMENTS_OFFSET + #define __Pyx_PyVectorcall_NARGS(n) PyVectorcall_NARGS((size_t)(n)) +#elif CYTHON_BACKPORT_VECTORCALL + typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args, + size_t nargsf, PyObject *kwnames); + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET ((size_t)1 << (8 * sizeof(size_t) - 1)) + #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET)) +#else + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET 0 + #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(n)) +#endif +#if PY_MAJOR_VERSION >= 0x030900B1 +#define __Pyx_PyCFunction_CheckExact(func) PyCFunction_CheckExact(func) +#else +#define __Pyx_PyCFunction_CheckExact(func) PyCFunction_Check(func) +#endif +#define __Pyx_CyOrPyCFunction_Check(func) PyCFunction_Check(func) +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_CyOrPyCFunction_GET_FUNCTION(func) (((PyCFunctionObject*)(func))->m_ml->ml_meth) +#elif !CYTHON_COMPILING_IN_LIMITED_API +#define __Pyx_CyOrPyCFunction_GET_FUNCTION(func) PyCFunction_GET_FUNCTION(func) +#endif +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_CyOrPyCFunction_GET_FLAGS(func) (((PyCFunctionObject*)(func))->m_ml->ml_flags) +static CYTHON_INLINE PyObject* __Pyx_CyOrPyCFunction_GET_SELF(PyObject *func) { + return (__Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_STATIC) ? NULL : ((PyCFunctionObject*)func)->m_self; +} +#endif +static CYTHON_INLINE int __Pyx__IsSameCFunction(PyObject *func, void *cfunc) { +#if CYTHON_COMPILING_IN_LIMITED_API + return PyCFunction_Check(func) && PyCFunction_GetFunction(func) == (PyCFunction) cfunc; +#else + return PyCFunction_Check(func) && PyCFunction_GET_FUNCTION(func) == (PyCFunction) cfunc; +#endif +} +#define __Pyx_IsSameCFunction(func, cfunc) __Pyx__IsSameCFunction(func, cfunc) +#if __PYX_LIMITED_VERSION_HEX < 0x030900B1 + #define __Pyx_PyType_FromModuleAndSpec(m, s, b) ((void)m, PyType_FromSpecWithBases(s, b)) + typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *); +#else + #define __Pyx_PyType_FromModuleAndSpec(m, s, b) PyType_FromModuleAndSpec(m, s, b) + #define __Pyx_PyCMethod PyCMethod +#endif +#ifndef METH_METHOD + #define METH_METHOD 0x200 +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyThreadState_Current PyThreadState_Get() +#elif !CYTHON_FAST_THREAD_STATE + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x03060000 + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#elif PY_VERSION_HEX >= 0x03000000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#else + #define __Pyx_PyThreadState_Current _PyThreadState_Current +#endif +#if CYTHON_COMPILING_IN_LIMITED_API +static CYTHON_INLINE void *__Pyx_PyModule_GetState(PyObject *op) +{ + void *result; + result = PyModule_GetState(op); + if (!result) + Py_FatalError("Couldn't find the module state"); + return result; +} +#endif +#define __Pyx_PyObject_GetSlot(obj, name, func_ctype) __Pyx_PyType_GetSlot(Py_TYPE(obj), name, func_ctype) +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((func_ctype) PyType_GetSlot((type), Py_##name)) +#else + #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((type)->name) +#endif +#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) +#include "pythread.h" +#define Py_tss_NEEDS_INIT 0 +typedef int Py_tss_t; +static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { + *key = PyThread_create_key(); + return 0; +} +static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { + Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); + *key = Py_tss_NEEDS_INIT; + return key; +} +static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { + PyObject_Free(key); +} +static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { + return *key != Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { + PyThread_delete_key(*key); + *key = Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { + return PyThread_set_key_value(*key, value); +} +static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { + return PyThread_get_key_value(*key); +} +#endif +#if PY_MAJOR_VERSION < 3 + #if CYTHON_COMPILING_IN_PYPY + #if PYPY_VERSION_NUM < 0x07030600 + #if defined(__cplusplus) && __cplusplus >= 201402L + [[deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")]] + #elif defined(__GNUC__) || defined(__clang__) + __attribute__ ((__deprecated__("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6"))) + #elif defined(_MSC_VER) + __declspec(deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")) + #endif + static CYTHON_INLINE int PyGILState_Check(void) { + return 0; + } + #else // PYPY_VERSION_NUM < 0x07030600 + #endif // PYPY_VERSION_NUM < 0x07030600 + #else + static CYTHON_INLINE int PyGILState_Check(void) { + PyThreadState * tstate = _PyThreadState_Current; + return tstate && (tstate == PyGILState_GetThisThreadState()); + } + #endif +#endif +#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX > 0x030600B4 && CYTHON_USE_UNICODE_INTERNALS +#define __Pyx_PyDict_GetItemStrWithError(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) +static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStr(PyObject *dict, PyObject *name) { + PyObject *res = __Pyx_PyDict_GetItemStrWithError(dict, name); + if (res == NULL) PyErr_Clear(); + return res; +} +#elif PY_MAJOR_VERSION >= 3 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07020000) +#define __Pyx_PyDict_GetItemStrWithError PyDict_GetItemWithError +#define __Pyx_PyDict_GetItemStr PyDict_GetItem +#else +static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStrWithError(PyObject *dict, PyObject *name) { +#if CYTHON_COMPILING_IN_PYPY + return PyDict_GetItem(dict, name); +#else + PyDictEntry *ep; + PyDictObject *mp = (PyDictObject*) dict; + long hash = ((PyStringObject *) name)->ob_shash; + assert(hash != -1); + ep = (mp->ma_lookup)(mp, name, hash); + if (ep == NULL) { + return NULL; + } + return ep->me_value; +#endif +} +#define __Pyx_PyDict_GetItemStr PyDict_GetItem +#endif +#if CYTHON_USE_TYPE_SLOTS + #define __Pyx_PyType_GetFlags(tp) (((PyTypeObject *)tp)->tp_flags) + #define __Pyx_PyType_HasFeature(type, feature) ((__Pyx_PyType_GetFlags(type) & (feature)) != 0) + #define __Pyx_PyObject_GetIterNextFunc(obj) (Py_TYPE(obj)->tp_iternext) +#else + #define __Pyx_PyType_GetFlags(tp) (PyType_GetFlags((PyTypeObject *)tp)) + #define __Pyx_PyType_HasFeature(type, feature) PyType_HasFeature(type, feature) + #define __Pyx_PyObject_GetIterNextFunc(obj) PyIter_Next +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_SetItemOnTypeDict(tp, k, v) PyObject_GenericSetAttr((PyObject*)tp, k, v) +#else + #define __Pyx_SetItemOnTypeDict(tp, k, v) PyDict_SetItem(tp->tp_dict, k, v) +#endif +#if CYTHON_USE_TYPE_SPECS && PY_VERSION_HEX >= 0x03080000 +#define __Pyx_PyHeapTypeObject_GC_Del(obj) {\ + PyTypeObject *type = Py_TYPE(obj);\ + assert(__Pyx_PyType_HasFeature(type, Py_TPFLAGS_HEAPTYPE));\ + PyObject_GC_Del(obj);\ + Py_DECREF(type);\ +} +#else +#define __Pyx_PyHeapTypeObject_GC_Del(obj) PyObject_GC_Del(obj) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GetLength(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_ReadChar(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((void)u, 1114111U) + #define __Pyx_PyUnicode_KIND(u) ((void)u, (0)) + #define __Pyx_PyUnicode_DATA(u) ((void*)u) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)k, PyUnicode_ReadChar((PyObject*)(d), i)) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GetLength(u)) +#elif PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #if PY_VERSION_HEX >= 0x030C0000 + #define __Pyx_PyUnicode_READY(op) (0) + #else + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #endif + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) ((int)PyUnicode_KIND(u)) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, (Py_UCS4) ch) + #if PY_VERSION_HEX >= 0x030C0000 + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) + #else + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000 + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length)) + #else + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) + #endif + #endif +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535U : 1114111U) + #define __Pyx_PyUnicode_KIND(u) ((int)sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = (Py_UNICODE) ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #if !defined(PyUnicode_DecodeUnicodeEscape) + #define PyUnicode_DecodeUnicodeEscape(s, size, errors) PyUnicode_Decode(s, size, "unicode_escape", errors) + #endif + #if !defined(PyUnicode_Contains) || (PY_MAJOR_VERSION == 2 && PYPY_VERSION_NUM < 0x07030500) + #undef PyUnicode_Contains + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) + #endif + #if !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) + #endif + #if !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) + #endif +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#ifndef PyObject_Unicode + #define PyObject_Unicode PyObject_Str +#endif +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#if CYTHON_COMPILING_IN_CPYTHON + #define __Pyx_PySequence_ListKeepNew(obj)\ + (likely(PyList_CheckExact(obj) && Py_REFCNT(obj) == 1) ? __Pyx_NewRef(obj) : PySequence_List(obj)) +#else + #define __Pyx_PySequence_ListKeepNew(obj) PySequence_List(obj) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) __Pyx_IS_TYPE(obj, &PySet_Type) +#endif +#if PY_VERSION_HEX >= 0x030900A4 + #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) +#else + #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) +#endif +#if CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PySequence_ITEM(o, i) PySequence_ITEM(o, i) + #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) + #define __Pyx_PyTuple_SET_ITEM(o, i, v) (PyTuple_SET_ITEM(o, i, v), (0)) + #define __Pyx_PyList_SET_ITEM(o, i, v) (PyList_SET_ITEM(o, i, v), (0)) + #define __Pyx_PyTuple_GET_SIZE(o) PyTuple_GET_SIZE(o) + #define __Pyx_PyList_GET_SIZE(o) PyList_GET_SIZE(o) + #define __Pyx_PySet_GET_SIZE(o) PySet_GET_SIZE(o) + #define __Pyx_PyBytes_GET_SIZE(o) PyBytes_GET_SIZE(o) + #define __Pyx_PyByteArray_GET_SIZE(o) PyByteArray_GET_SIZE(o) +#else + #define __Pyx_PySequence_ITEM(o, i) PySequence_GetItem(o, i) + #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) + #define __Pyx_PyTuple_SET_ITEM(o, i, v) PyTuple_SetItem(o, i, v) + #define __Pyx_PyList_SET_ITEM(o, i, v) PyList_SetItem(o, i, v) + #define __Pyx_PyTuple_GET_SIZE(o) PyTuple_Size(o) + #define __Pyx_PyList_GET_SIZE(o) PyList_Size(o) + #define __Pyx_PySet_GET_SIZE(o) PySet_Size(o) + #define __Pyx_PyBytes_GET_SIZE(o) PyBytes_Size(o) + #define __Pyx_PyByteArray_GET_SIZE(o) PyByteArray_Size(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define __Pyx_Py3Int_Check(op) PyLong_Check(op) + #define __Pyx_Py3Int_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#else + #define __Pyx_Py3Int_Check(op) (PyLong_Check(op) || PyInt_Check(op)) + #define __Pyx_Py3Int_CheckExact(op) (PyLong_CheckExact(op) || PyInt_CheckExact(op)) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsHash_t +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsSsize_t +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef __Pyx_PyAsyncMethodsStruct + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; +#endif + +#if defined(_WIN32) || defined(WIN32) || defined(MS_WINDOWS) + #if !defined(_USE_MATH_DEFINES) + #define _USE_MATH_DEFINES + #endif +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + +#define __PYX_MARK_ERR_POS(f_index, lineno) \ + { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } +#define __PYX_ERR(f_index, lineno, Ln_error) \ + { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } + +#ifdef CYTHON_EXTERN_C + #undef __PYX_EXTERN_C + #define __PYX_EXTERN_C CYTHON_EXTERN_C +#elif defined(__PYX_EXTERN_C) + #ifdef _MSC_VER + #pragma message ("Please do not define the '__PYX_EXTERN_C' macro externally. Use 'CYTHON_EXTERN_C' instead.") + #else + #warning Please do not define the '__PYX_EXTERN_C' macro externally. Use 'CYTHON_EXTERN_C' instead. + #endif +#else + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__monotonic_align__core +#define __PYX_HAVE_API__monotonic_align__core +/* Early includes */ +#include +#include + + /* Using NumPy API declarations from "numpy/__init__.cython-30.pxd" */ + +#include "numpy/arrayobject.h" +#include "numpy/ndarrayobject.h" +#include "numpy/ndarraytypes.h" +#include "numpy/arrayscalars.h" +#include "numpy/ufuncobject.h" +#include "pythread.h" +#include +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { + return (size_t) i < (size_t) limit; +} +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) + #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s); +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +static CYTHON_INLINE PyObject* __Pyx_PyByteArray_FromString(const char*); +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyObject_AsWritableString(s) ((char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableSString(s) ((signed char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +#if CYTHON_COMPILING_IN_LIMITED_API +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const wchar_t *u) +{ + const wchar_t *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#else +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#endif +#define __Pyx_PyUnicode_FromOrdinal(o) PyUnicode_FromOrdinal((int)o) +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +#define __Pyx_PySequence_Tuple(obj)\ + (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #if PY_VERSION_HEX >= 0x030C00A7 + #ifndef _PyLong_SIGN_MASK + #define _PyLong_SIGN_MASK 3 + #endif + #ifndef _PyLong_NON_SIZE_BITS + #define _PyLong_NON_SIZE_BITS 3 + #endif + #define __Pyx_PyLong_Sign(x) (((PyLongObject*)x)->long_value.lv_tag & _PyLong_SIGN_MASK) + #define __Pyx_PyLong_IsNeg(x) ((__Pyx_PyLong_Sign(x) & 2) != 0) + #define __Pyx_PyLong_IsNonNeg(x) (!__Pyx_PyLong_IsNeg(x)) + #define __Pyx_PyLong_IsZero(x) (__Pyx_PyLong_Sign(x) & 1) + #define __Pyx_PyLong_IsPos(x) (__Pyx_PyLong_Sign(x) == 0) + #define __Pyx_PyLong_CompactValueUnsigned(x) (__Pyx_PyLong_Digits(x)[0]) + #define __Pyx_PyLong_DigitCount(x) ((Py_ssize_t) (((PyLongObject*)x)->long_value.lv_tag >> _PyLong_NON_SIZE_BITS)) + #define __Pyx_PyLong_SignedDigitCount(x)\ + ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * __Pyx_PyLong_DigitCount(x)) + #if defined(PyUnstable_Long_IsCompact) && defined(PyUnstable_Long_CompactValue) + #define __Pyx_PyLong_IsCompact(x) PyUnstable_Long_IsCompact((PyLongObject*) x) + #define __Pyx_PyLong_CompactValue(x) PyUnstable_Long_CompactValue((PyLongObject*) x) + #else + #define __Pyx_PyLong_IsCompact(x) (((PyLongObject*)x)->long_value.lv_tag < (2 << _PyLong_NON_SIZE_BITS)) + #define __Pyx_PyLong_CompactValue(x) ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * (Py_ssize_t) __Pyx_PyLong_Digits(x)[0]) + #endif + typedef Py_ssize_t __Pyx_compact_pylong; + typedef size_t __Pyx_compact_upylong; + #else // Py < 3.12 + #define __Pyx_PyLong_IsNeg(x) (Py_SIZE(x) < 0) + #define __Pyx_PyLong_IsNonNeg(x) (Py_SIZE(x) >= 0) + #define __Pyx_PyLong_IsZero(x) (Py_SIZE(x) == 0) + #define __Pyx_PyLong_IsPos(x) (Py_SIZE(x) > 0) + #define __Pyx_PyLong_CompactValueUnsigned(x) ((Py_SIZE(x) == 0) ? 0 : __Pyx_PyLong_Digits(x)[0]) + #define __Pyx_PyLong_DigitCount(x) __Pyx_sst_abs(Py_SIZE(x)) + #define __Pyx_PyLong_SignedDigitCount(x) Py_SIZE(x) + #define __Pyx_PyLong_IsCompact(x) (Py_SIZE(x) == 0 || Py_SIZE(x) == 1 || Py_SIZE(x) == -1) + #define __Pyx_PyLong_CompactValue(x)\ + ((Py_SIZE(x) == 0) ? (sdigit) 0 : ((Py_SIZE(x) < 0) ? -(sdigit)__Pyx_PyLong_Digits(x)[0] : (sdigit)__Pyx_PyLong_Digits(x)[0])) + typedef sdigit __Pyx_compact_pylong; + typedef digit __Pyx_compact_upylong; + #endif + #if PY_VERSION_HEX >= 0x030C00A5 + #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->long_value.ob_digit) + #else + #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->ob_digit) + #endif +#endif +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +#include +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = (char) c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#include +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ +static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } + +#if !CYTHON_USE_MODULE_STATE +static PyObject *__pyx_m = NULL; +#endif +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm = __FILE__; +static const char *__pyx_filename; + +/* Header.proto */ +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif (defined(_Complex_I) && !defined(_MSC_VER)) || ((defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L) && !defined(__STDC_NO_COMPLEX__)) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + +/* #### Code section: filename_table ### */ + +static const char *__pyx_f[] = { + "monotonic_align/core.pyx", + "", + "__init__.cython-30.pxd", + "type.pxd", +}; +/* #### Code section: utility_code_proto_before_types ### */ +/* ForceInitThreads.proto */ +#ifndef __PYX_FORCE_INIT_THREADS + #define __PYX_FORCE_INIT_THREADS 0 +#endif + +/* NoFastGil.proto */ +#define __Pyx_PyGILState_Ensure PyGILState_Ensure +#define __Pyx_PyGILState_Release PyGILState_Release +#define __Pyx_FastGIL_Remember() +#define __Pyx_FastGIL_Forget() +#define __Pyx_FastGilFuncInit() + +/* BufferFormatStructs.proto */ +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + +/* Atomics.proto */ +#include +#ifndef CYTHON_ATOMICS + #define CYTHON_ATOMICS 1 +#endif +#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS +#define __pyx_atomic_int_type int +#define __pyx_nonatomic_int_type int +#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ + (__STDC_VERSION__ >= 201112L) &&\ + !defined(__STDC_NO_ATOMICS__)) + #include +#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ + (__cplusplus >= 201103L) ||\ + (defined(_MSC_VER) && _MSC_VER >= 1700))) + #include +#endif +#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ + (__STDC_VERSION__ >= 201112L) &&\ + !defined(__STDC_NO_ATOMICS__) &&\ + ATOMIC_INT_LOCK_FREE == 2) + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type atomic_int + #define __pyx_atomic_incr_aligned(value) atomic_fetch_add_explicit(value, 1, memory_order_relaxed) + #define __pyx_atomic_decr_aligned(value) atomic_fetch_sub_explicit(value, 1, memory_order_acq_rel) + #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) + #pragma message ("Using standard C atomics") + #elif defined(__PYX_DEBUG_ATOMICS) + #warning "Using standard C atomics" + #endif +#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ + (__cplusplus >= 201103L) ||\ +\ + (defined(_MSC_VER) && _MSC_VER >= 1700)) &&\ + ATOMIC_INT_LOCK_FREE == 2) + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type std::atomic_int + #define __pyx_atomic_incr_aligned(value) std::atomic_fetch_add_explicit(value, 1, std::memory_order_relaxed) + #define __pyx_atomic_decr_aligned(value) std::atomic_fetch_sub_explicit(value, 1, std::memory_order_acq_rel) + #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) + #pragma message ("Using standard C++ atomics") + #elif defined(__PYX_DEBUG_ATOMICS) + #warning "Using standard C++ atomics" + #endif +#elif CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\ + (__GNUC_MINOR__ > 1 ||\ + (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2)))) + #define __pyx_atomic_incr_aligned(value) __sync_fetch_and_add(value, 1) + #define __pyx_atomic_decr_aligned(value) __sync_fetch_and_sub(value, 1) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using GNU atomics" + #endif +#elif CYTHON_ATOMICS && defined(_MSC_VER) + #include + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type long + #undef __pyx_nonatomic_int_type + #define __pyx_nonatomic_int_type long + #pragma intrinsic (_InterlockedExchangeAdd) + #define __pyx_atomic_incr_aligned(value) _InterlockedExchangeAdd(value, 1) + #define __pyx_atomic_decr_aligned(value) _InterlockedExchangeAdd(value, -1) + #ifdef __PYX_DEBUG_ATOMICS + #pragma message ("Using MSVC atomics") + #endif +#else + #undef CYTHON_ATOMICS + #define CYTHON_ATOMICS 0 + #ifdef __PYX_DEBUG_ATOMICS + #warning "Not using atomics" + #endif +#endif +#if CYTHON_ATOMICS + #define __pyx_add_acquisition_count(memview)\ + __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview)) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview)) +#else + #define __pyx_add_acquisition_count(memview)\ + __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) +#endif + +/* MemviewSliceStruct.proto */ +struct __pyx_memoryview_obj; +typedef struct { + struct __pyx_memoryview_obj *memview; + char *data; + Py_ssize_t shape[8]; + Py_ssize_t strides[8]; + Py_ssize_t suboffsets[8]; +} __Pyx_memviewslice; +#define __Pyx_MemoryView_Len(m) (m.shape[0]) + +/* #### Code section: numeric_typedefs ### */ + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":732 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":733 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":734 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":735 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":739 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":740 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":741 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":742 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":746 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":747 + * + * ctypedef npy_float32 float32_t + * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< + * #ctypedef npy_float80 float80_t + * #ctypedef npy_float128 float128_t + */ +typedef npy_float64 __pyx_t_5numpy_float64_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":756 + * # The int types are mapped a bit surprising -- + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t + */ +typedef npy_long __pyx_t_5numpy_int_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":757 + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong longlong_t + * + */ +typedef npy_longlong __pyx_t_5numpy_long_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":758 + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_ulong uint_t + */ +typedef npy_longlong __pyx_t_5numpy_longlong_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":760 + * ctypedef npy_longlong longlong_t + * + * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t + */ +typedef npy_ulong __pyx_t_5numpy_uint_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":761 + * + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulonglong_t + * + */ +typedef npy_ulonglong __pyx_t_5numpy_ulong_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":762 + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_intp intp_t + */ +typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":764 + * ctypedef npy_ulonglong ulonglong_t + * + * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< + * ctypedef npy_uintp uintp_t + * + */ +typedef npy_intp __pyx_t_5numpy_intp_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":765 + * + * ctypedef npy_intp intp_t + * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< + * + * ctypedef npy_double float_t + */ +typedef npy_uintp __pyx_t_5numpy_uintp_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":767 + * ctypedef npy_uintp uintp_t + * + * ctypedef npy_double float_t # <<<<<<<<<<<<<< + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t + */ +typedef npy_double __pyx_t_5numpy_float_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":768 + * + * ctypedef npy_double float_t + * ctypedef npy_double double_t # <<<<<<<<<<<<<< + * ctypedef npy_longdouble longdouble_t + * + */ +typedef npy_double __pyx_t_5numpy_double_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":769 + * ctypedef npy_double float_t + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cfloat cfloat_t + */ +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; +/* #### Code section: complex_type_declarations ### */ +/* Declarations.proto */ +#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) + #ifdef __cplusplus + typedef ::std::complex< float > __pyx_t_float_complex; + #else + typedef float _Complex __pyx_t_float_complex; + #endif +#else + typedef struct { float real, imag; } __pyx_t_float_complex; +#endif +static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); + +/* Declarations.proto */ +#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) + #ifdef __cplusplus + typedef ::std::complex< double > __pyx_t_double_complex; + #else + typedef double _Complex __pyx_t_double_complex; + #endif +#else + typedef struct { double real, imag; } __pyx_t_double_complex; +#endif +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); + +/* #### Code section: type_declarations ### */ + +/*--- Type declarations ---*/ +struct __pyx_array_obj; +struct __pyx_MemviewEnum_obj; +struct __pyx_memoryview_obj; +struct __pyx_memoryviewslice_obj; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":771 + * ctypedef npy_longdouble longdouble_t + * + * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t + */ +typedef npy_cfloat __pyx_t_5numpy_cfloat_t; + +/* "../../../../../../../../tmp/pip-build-env-l91acul8/overlay/lib/python3.7/site-packages/numpy/__init__.cython-30.pxd":772 + * + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< + * ctypedef npy_clongdouble clongdouble_t + * + */ +typedef npy_cdouble __pyx_t_5numpy_cdouble_t; 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+static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A6 +#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) +#else +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#endif +#else +#define __Pyx_PyErr_Clear() PyErr_Clear() +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* PyObjectGetAttrStrNoError.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name); + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* TupleAndListFromArray.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n); +static CYTHON_INLINE PyObject* __Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n); +#endif + +/* IncludeStringH.proto */ +#include + +/* BytesEquals.proto */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); + +/* UnicodeEquals.proto */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); + +/* fastcall.proto */ +#if CYTHON_AVOID_BORROWED_REFS + #define __Pyx_Arg_VARARGS(args, i) PySequence_GetItem(args, i) +#elif CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_Arg_VARARGS(args, i) PyTuple_GET_ITEM(args, i) +#else + #define __Pyx_Arg_VARARGS(args, i) PyTuple_GetItem(args, i) +#endif +#if CYTHON_AVOID_BORROWED_REFS + #define __Pyx_Arg_NewRef_VARARGS(arg) __Pyx_NewRef(arg) + #define __Pyx_Arg_XDECREF_VARARGS(arg) Py_XDECREF(arg) +#else + #define __Pyx_Arg_NewRef_VARARGS(arg) arg // no-op + #define __Pyx_Arg_XDECREF_VARARGS(arg) // no-op - arg is borrowed +#endif +#define __Pyx_NumKwargs_VARARGS(kwds) PyDict_Size(kwds) +#define __Pyx_KwValues_VARARGS(args, nargs) NULL +#define __Pyx_GetKwValue_VARARGS(kw, kwvalues, s) __Pyx_PyDict_GetItemStrWithError(kw, s) +#define __Pyx_KwargsAsDict_VARARGS(kw, kwvalues) PyDict_Copy(kw) +#if CYTHON_METH_FASTCALL + #define __Pyx_Arg_FASTCALL(args, i) args[i] + #define __Pyx_NumKwargs_FASTCALL(kwds) PyTuple_GET_SIZE(kwds) + #define __Pyx_KwValues_FASTCALL(args, nargs) ((args) + (nargs)) + static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s); + #define __Pyx_KwargsAsDict_FASTCALL(kw, kwvalues) _PyStack_AsDict(kwvalues, kw) + #define __Pyx_Arg_NewRef_FASTCALL(arg) arg // no-op, __Pyx_Arg_FASTCALL is direct and this needs + #define __Pyx_Arg_XDECREF_FASTCALL(arg) // no-op - arg was returned from array +#else + #define __Pyx_Arg_FASTCALL __Pyx_Arg_VARARGS + #define __Pyx_NumKwargs_FASTCALL __Pyx_NumKwargs_VARARGS + #define __Pyx_KwValues_FASTCALL __Pyx_KwValues_VARARGS + #define __Pyx_GetKwValue_FASTCALL __Pyx_GetKwValue_VARARGS + #define __Pyx_KwargsAsDict_FASTCALL __Pyx_KwargsAsDict_VARARGS + #define __Pyx_Arg_NewRef_FASTCALL(arg) __Pyx_Arg_NewRef_VARARGS(arg) + #define __Pyx_Arg_XDECREF_FASTCALL(arg) __Pyx_Arg_XDECREF_VARARGS(arg) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS +#define __Pyx_ArgsSlice_VARARGS(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_VARARGS(args, start), stop - start) +#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_FASTCALL(args, start), stop - start) +#else +#define __Pyx_ArgsSlice_VARARGS(args, start, stop) PyTuple_GetSlice(args, start, stop) +#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) PyTuple_GetSlice(args, start, stop) +#endif + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject *const *kwvalues, + PyObject **argnames[], + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, + const char* function_name); + +/* ArgTypeTest.proto */ +#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ + ((likely(__Pyx_IS_TYPE(obj, type) | (none_allowed && (obj == Py_None)))) ? 1 :\ + __Pyx__ArgTypeTest(obj, type, name, exact)) +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#if !CYTHON_VECTORCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); +#endif +#define __Pyx_BUILD_ASSERT_EXPR(cond)\ + (sizeof(char [1 - 2*!(cond)]) - 1) +#ifndef Py_MEMBER_SIZE +#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) +#endif +#if !CYTHON_VECTORCALL +#if PY_VERSION_HEX >= 0x03080000 + #include "frameobject.h" +#if PY_VERSION_HEX >= 0x030b00a6 && !CYTHON_COMPILING_IN_LIMITED_API + #ifndef Py_BUILD_CORE + #define Py_BUILD_CORE 1 + #endif + #include "internal/pycore_frame.h" +#endif + #define __Pxy_PyFrame_Initialize_Offsets() + #define __Pyx_PyFrame_GetLocalsplus(frame) ((frame)->f_localsplus) +#else + static size_t __pyx_pyframe_localsplus_offset = 0; + #include "frameobject.h" + #define __Pxy_PyFrame_Initialize_Offsets()\ + ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ + (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) + #define __Pyx_PyFrame_GetLocalsplus(frame)\ + (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) +#endif +#endif +#endif + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectFastCall.proto */ +#define __Pyx_PyObject_FastCall(func, args, nargs) __Pyx_PyObject_FastCallDict(func, args, (size_t)(nargs), NULL) +static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs); + +/* RaiseUnexpectedTypeError.proto */ +static int __Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj); + +/* GCCDiagnostics.proto */ +#if !defined(__INTEL_COMPILER) && defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) +#define __Pyx_HAS_GCC_DIAGNOSTIC +#endif + +/* BuildPyUnicode.proto */ +static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, + int prepend_sign, char padding_char); + +/* CIntToPyUnicode.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char); + +/* CIntToPyUnicode.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char); + +/* JoinPyUnicode.proto */ +static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, + Py_UCS4 max_char); + +/* StrEquals.proto */ +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals +#else +#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals +#endif + +/* PyObjectFormatSimple.proto */ +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + PyObject_Format(s, f)) +#elif PY_MAJOR_VERSION < 3 + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + likely(PyString_CheckExact(s)) ? PyUnicode_FromEncodedObject(s, NULL, "strict") :\ + PyObject_Format(s, f)) +#elif CYTHON_USE_TYPE_SLOTS + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + likely(PyLong_CheckExact(s)) ? PyLong_Type.tp_repr(s) :\ + likely(PyFloat_CheckExact(s)) ? PyFloat_Type.tp_repr(s) :\ + PyObject_Format(s, f)) +#else + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + PyObject_Format(s, f)) +#endif + +CYTHON_UNUSED static int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ +/* GetAttr.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); + +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* ObjectGetItem.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key); +#else +#define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) +#endif + +/* KeywordStringCheck.proto */ +static int __Pyx_CheckKeywordStrings(PyObject *kw, const char* function_name, int kw_allowed); + +/* DivInt[Py_ssize_t].proto */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); + +/* UnaryNegOverflows.proto */ +#define __Pyx_UNARY_NEG_WOULD_OVERFLOW(x)\ + (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) + +/* GetAttr3.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); + +/* PyDictVersioning.proto */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +#define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) +#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ + (version_var) = __PYX_GET_DICT_VERSION(dict);\ + (cache_var) = (value); +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ + (VAR) = __pyx_dict_cached_value;\ + } else {\ + (VAR) = __pyx_dict_cached_value = (LOOKUP);\ + __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ + }\ +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); +#else +#define __PYX_GET_DICT_VERSION(dict) (0) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); +#endif + +/* GetModuleGlobalName.proto */ +#if CYTHON_USE_DICT_VERSIONS +#define __Pyx_GetModuleGlobalName(var, name) do {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ + (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ + __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} while(0) +#define __Pyx_GetModuleGlobalNameUncached(var, name) do {\ + PY_UINT64_T __pyx_dict_version;\ + PyObject *__pyx_dict_cached_value;\ + (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} while(0) +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); +#else +#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) +#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); +#endif + +/* AssertionsEnabled.proto */ +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define __Pyx_init_assertions_enabled() (0) + #define __pyx_assertions_enabled() (1) +#elif CYTHON_COMPILING_IN_LIMITED_API || (CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030C0000) + static int __pyx_assertions_enabled_flag; + #define __pyx_assertions_enabled() (__pyx_assertions_enabled_flag) + static int __Pyx_init_assertions_enabled(void) { + PyObject *builtins, *debug, *debug_str; + int flag; + builtins = PyEval_GetBuiltins(); + if (!builtins) goto bad; + debug_str = PyUnicode_FromStringAndSize("__debug__", 9); + if (!debug_str) goto bad; + debug = PyObject_GetItem(builtins, debug_str); + Py_DECREF(debug_str); + if (!debug) goto bad; + flag = PyObject_IsTrue(debug); + Py_DECREF(debug); + if (flag == -1) goto bad; + __pyx_assertions_enabled_flag = flag; + return 0; + bad: + __pyx_assertions_enabled_flag = 1; + return -1; + } +#else + #define __Pyx_init_assertions_enabled() (0) + #define __pyx_assertions_enabled() (!Py_OptimizeFlag) +#endif + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* GetTopmostException.proto */ +#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE +static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); +#endif + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* SwapException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* ImportDottedModule.proto */ +static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple); +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple); +#endif + +/* FastTypeChecks.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) +#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2) +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); +#else +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2)) +#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) +#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) +#endif +#define __Pyx_PyErr_ExceptionMatches2(err1, err2) __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2) +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) + +CYTHON_UNUSED static int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +/* ListCompAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len)) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + __Pyx_SET_SIZE(list, len + 1); + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) +#endif + +/* PySequenceMultiply.proto */ +#define __Pyx_PySequence_Multiply_Left(mul, seq) __Pyx_PySequence_Multiply(seq, mul) +static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul); + +/* SetItemInt.proto */ +#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\ + __Pyx_SetItemInt_Generic(o, to_py_func(i), v))) +static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v); +static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, + int is_list, int wraparound, int boundscheck); + +/* RaiseUnboundLocalError.proto */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); + +/* DivInt[long].proto */ +static CYTHON_INLINE long __Pyx_div_long(long, long); + +/* PySequenceContains.proto */ +static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) { + int result = PySequence_Contains(seq, item); + return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); +} + +/* ImportFrom.proto */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); + +/* HasAttr.proto */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); + +/* ErrOccurredWithGIL.proto */ +static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void); + +/* PyObject_GenericGetAttrNoDict.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr +#endif + +/* PyObject_GenericGetAttr.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr +#endif + +/* IncludeStructmemberH.proto */ +#include + +/* FixUpExtensionType.proto */ +#if CYTHON_USE_TYPE_SPECS +static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type); +#endif + +/* PyObjectCallNoArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); + +/* PyObjectGetMethod.proto */ +static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method); + +/* PyObjectCallMethod0.proto */ +static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name); + +/* ValidateBasesTuple.proto */ +#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS +static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases); +#endif + +/* PyType_Ready.proto */ +CYTHON_UNUSED static int __Pyx_PyType_Ready(PyTypeObject *t); + +/* SetVTable.proto */ +static int __Pyx_SetVtable(PyTypeObject* typeptr , void* vtable); + +/* GetVTable.proto */ +static void* __Pyx_GetVtable(PyTypeObject *type); + +/* MergeVTables.proto */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_MergeVtables(PyTypeObject *type); +#endif + +/* SetupReduce.proto */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_setup_reduce(PyObject* type_obj); +#endif + +/* TypeImport.proto */ +#ifndef __PYX_HAVE_RT_ImportType_proto_3_0_4 +#define __PYX_HAVE_RT_ImportType_proto_3_0_4 +#if defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L +#include +#endif +#if (defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L) || __cplusplus >= 201103L +#define __PYX_GET_STRUCT_ALIGNMENT_3_0_4(s) alignof(s) +#else +#define __PYX_GET_STRUCT_ALIGNMENT_3_0_4(s) sizeof(void*) +#endif +enum __Pyx_ImportType_CheckSize_3_0_4 { + __Pyx_ImportType_CheckSize_Error_3_0_4 = 0, + __Pyx_ImportType_CheckSize_Warn_3_0_4 = 1, + __Pyx_ImportType_CheckSize_Ignore_3_0_4 = 2 +}; +static PyTypeObject *__Pyx_ImportType_3_0_4(PyObject* module, const char *module_name, const char *class_name, size_t size, size_t alignment, enum __Pyx_ImportType_CheckSize_3_0_4 check_size); +#endif + +/* FetchSharedCythonModule.proto */ +static PyObject *__Pyx_FetchSharedCythonABIModule(void); + +/* FetchCommonType.proto */ +#if !CYTHON_USE_TYPE_SPECS +static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type); +#else +static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases); +#endif + +/* PyMethodNew.proto */ +#if CYTHON_COMPILING_IN_LIMITED_API +static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) { + PyObject *typesModule=NULL, *methodType=NULL, *result=NULL; + CYTHON_UNUSED_VAR(typ); + if (!self) + return __Pyx_NewRef(func); + typesModule = PyImport_ImportModule("types"); + if (!typesModule) return NULL; + methodType = PyObject_GetAttrString(typesModule, "MethodType"); + Py_DECREF(typesModule); + if (!methodType) return NULL; + result = PyObject_CallFunctionObjArgs(methodType, func, self, NULL); + Py_DECREF(methodType); + return result; +} +#elif PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) { + CYTHON_UNUSED_VAR(typ); + if (!self) + return __Pyx_NewRef(func); + return PyMethod_New(func, self); +} +#else + #define __Pyx_PyMethod_New PyMethod_New +#endif + +/* PyVectorcallFastCallDict.proto */ +#if CYTHON_METH_FASTCALL +static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw); +#endif + +/* CythonFunctionShared.proto */ +#define __Pyx_CyFunction_USED +#define __Pyx_CYFUNCTION_STATICMETHOD 0x01 +#define __Pyx_CYFUNCTION_CLASSMETHOD 0x02 +#define __Pyx_CYFUNCTION_CCLASS 0x04 +#define __Pyx_CYFUNCTION_COROUTINE 0x08 +#define __Pyx_CyFunction_GetClosure(f)\ + (((__pyx_CyFunctionObject *) (f))->func_closure) +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_CyFunction_GetClassObj(f)\ + (((__pyx_CyFunctionObject *) (f))->func_classobj) +#else + #define __Pyx_CyFunction_GetClassObj(f)\ + ((PyObject*) ((PyCMethodObject *) (f))->mm_class) +#endif +#define __Pyx_CyFunction_SetClassObj(f, classobj)\ + __Pyx__CyFunction_SetClassObj((__pyx_CyFunctionObject *) (f), (classobj)) +#define __Pyx_CyFunction_Defaults(type, f)\ + ((type *)(((__pyx_CyFunctionObject *) (f))->defaults)) +#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\ + ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g) +typedef struct { +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject_HEAD + PyObject *func; +#elif PY_VERSION_HEX < 0x030900B1 + PyCFunctionObject func; +#else + PyCMethodObject func; +#endif +#if CYTHON_BACKPORT_VECTORCALL + __pyx_vectorcallfunc func_vectorcall; +#endif +#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API + PyObject *func_weakreflist; +#endif + PyObject *func_dict; + PyObject *func_name; + PyObject *func_qualname; + PyObject *func_doc; + PyObject *func_globals; + PyObject *func_code; + PyObject *func_closure; +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + PyObject *func_classobj; +#endif + void *defaults; + int defaults_pyobjects; + size_t defaults_size; // used by FusedFunction for copying defaults + int flags; + PyObject *defaults_tuple; + PyObject *defaults_kwdict; + PyObject *(*defaults_getter)(PyObject *); + PyObject *func_annotations; + PyObject *func_is_coroutine; +} __pyx_CyFunctionObject; +#undef __Pyx_CyOrPyCFunction_Check +#define __Pyx_CyFunction_Check(obj) __Pyx_TypeCheck(obj, __pyx_CyFunctionType) +#define __Pyx_CyOrPyCFunction_Check(obj) __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type) +#define __Pyx_CyFunction_CheckExact(obj) __Pyx_IS_TYPE(obj, __pyx_CyFunctionType) +static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc); +#undef __Pyx_IsSameCFunction +#define __Pyx_IsSameCFunction(func, cfunc) __Pyx__IsSameCyOrCFunction(func, cfunc) +static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml, + int flags, PyObject* qualname, + PyObject *closure, + PyObject *module, PyObject *globals, + PyObject* code); +static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj); +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m, + size_t size, + int pyobjects); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m, + PyObject *tuple); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m, + PyObject *dict); +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m, + PyObject *dict); +static int __pyx_CyFunction_init(PyObject *module); +#if CYTHON_METH_FASTCALL +static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +#if CYTHON_BACKPORT_VECTORCALL +#define __Pyx_CyFunction_func_vectorcall(f) (((__pyx_CyFunctionObject*)f)->func_vectorcall) +#else +#define __Pyx_CyFunction_func_vectorcall(f) (((PyCFunctionObject*)f)->vectorcall) +#endif +#endif + +/* CythonFunction.proto */ +static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, + int flags, PyObject* qualname, + PyObject *closure, + PyObject *module, PyObject *globals, + PyObject* code); + +/* CLineInTraceback.proto */ +#ifdef CYTHON_CLINE_IN_TRACEBACK +#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) +#else +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); +#endif + +/* CodeObjectCache.proto */ +#if !CYTHON_COMPILING_IN_LIMITED_API +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); +#endif + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +/* MemviewSliceIsContig.proto */ +static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); + +/* OverlappingSlices.proto */ +static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize); + +/* IsLittleEndian.proto */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); + +/* BufferFormatCheck.proto */ +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); + +/* TypeInfoCompare.proto */ +static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); + +/* MemviewSliceValidateAndInit.proto */ +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag); + +/* RealImag.proto */ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif +#if defined(__cplusplus) && CYTHON_CCOMPLEX\ + && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) + #define __Pyx_c_eq_float(a, b) ((a)==(b)) + #define __Pyx_c_sum_float(a, b) ((a)+(b)) + #define __Pyx_c_diff_float(a, b) ((a)-(b)) + #define __Pyx_c_prod_float(a, b) ((a)*(b)) + #define __Pyx_c_quot_float(a, b) ((a)/(b)) + #define __Pyx_c_neg_float(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_float(z) ((z)==(float)0) + #define __Pyx_c_conj_float(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_float(z) (::std::abs(z)) + #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_float(z) ((z)==0) + #define __Pyx_c_conj_float(z) (conjf(z)) + #if 1 + #define __Pyx_c_abs_float(z) (cabsf(z)) + #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) + #define __Pyx_c_eq_double(a, b) ((a)==(b)) + #define __Pyx_c_sum_double(a, b) ((a)+(b)) + #define __Pyx_c_diff_double(a, b) ((a)-(b)) + #define __Pyx_c_prod_double(a, b) ((a)*(b)) + #define __Pyx_c_quot_double(a, b) ((a)/(b)) + #define __Pyx_c_neg_double(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_double(z) ((z)==(double)0) + #define __Pyx_c_conj_double(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (::std::abs(z)) + #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_double(z) ((z)==0) + #define __Pyx_c_conj_double(z) (conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (cabs(z)) + #define __Pyx_c_pow_double(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); + #endif +#endif + +/* MemviewSliceCopyTemplate.proto */ +static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object); + +/* MemviewSliceInit.proto */ +#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d +#define __Pyx_MEMVIEW_DIRECT 1 +#define __Pyx_MEMVIEW_PTR 2 +#define __Pyx_MEMVIEW_FULL 4 +#define __Pyx_MEMVIEW_CONTIG 8 +#define __Pyx_MEMVIEW_STRIDED 16 +#define __Pyx_MEMVIEW_FOLLOW 32 +#define __Pyx_IS_C_CONTIG 1 +#define __Pyx_IS_F_CONTIG 2 +static int __Pyx_init_memviewslice( + struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference); +static CYTHON_INLINE int __pyx_add_acquisition_count_locked( + __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); +static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( + __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); +#define __pyx_get_slice_count_pointer(memview) (&memview->acquisition_count) +#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) +#define __PYX_XCLEAR_MEMVIEW(slice, have_gil) __Pyx_XCLEAR_MEMVIEW(slice, have_gil, __LINE__) +static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); +static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *, int, int); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); + +/* FormatTypeName.proto */ +#if CYTHON_COMPILING_IN_LIMITED_API +typedef PyObject *__Pyx_TypeName; +#define __Pyx_FMT_TYPENAME "%U" +static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp); +#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj) +#else +typedef const char *__Pyx_TypeName; +#define __Pyx_FMT_TYPENAME "%.200s" +#define __Pyx_PyType_GetName(tp) ((tp)->tp_name) +#define __Pyx_DECREF_TypeName(obj) +#endif + +/* CheckBinaryVersion.proto */ +static unsigned long __Pyx_get_runtime_version(void); +static int __Pyx_check_binary_version(unsigned long ct_version, unsigned long rt_version, int allow_newer); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + +/* #### Code section: module_declarations ### */ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ +static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ +static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview__get_base(struct __pyx_memoryview_obj *__pyx_v_self); /* proto*/ +static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryviewslice__get_base(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5numpy_7ndarray_4base_base(PyArrayObject *__pyx_v_self); /* proto*/ +static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArrayObject *__pyx_v_self); /* proto*/ +static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx_v_self); /* proto*/ +static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObject *__pyx_v_self); /* proto*/ +static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayObject *__pyx_v_self); /* proto*/ +static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *__pyx_v_self); /* proto*/ +static CYTHON_INLINE char *__pyx_f_5numpy_7ndarray_4data_data(PyArrayObject *__pyx_v_self); /* proto*/ + +/* Module declarations from "libc.string" */ + +/* Module declarations from "libc.stdio" */ + +/* Module declarations from "__builtin__" */ + +/* Module declarations from "cpython.type" */ + +/* Module declarations from "cpython" */ + +/* Module declarations from "cpython.object" */ + +/* Module declarations from "cpython.ref" */ + +/* Module declarations from "numpy" */ + +/* Module declarations from "numpy" */ + +/* Module declarations from "cython.view" */ + +/* Module declarations from "cython.dataclasses" */ + +/* Module declarations from "cython" */ + +/* Module declarations from "monotonic_align.core" */ +static PyObject *__pyx_collections_abc_Sequence = 0; +static PyObject *generic = 0; +static PyObject *strided = 0; +static PyObject *indirect = 0; +static PyObject *contiguous = 0; +static PyObject *indirect_contiguous = 0; +static int __pyx_memoryview_thread_locks_used; +static PyThread_type_lock __pyx_memoryview_thread_locks[8]; +static void __pyx_f_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, float); /*proto*/ +static void __pyx_f_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_15monotonic_align_4core_maximum_path_c *__pyx_optional_args); /*proto*/ +static int __pyx_array_allocate_buffer(struct __pyx_array_obj *); /*proto*/ +static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ +static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ +static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ +static PyObject *_unellipsify(PyObject *, int); /*proto*/ +static int assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ +static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ +static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ +static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ +static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ +static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ +static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ +static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memoryview_err_dim(PyObject *, PyObject *, int); /*proto*/ +static int __pyx_memoryview_err(PyObject *, PyObject *); /*proto*/ +static int __pyx_memoryview_err_no_memory(void); /*proto*/ +static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ +static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ +static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ +/* #### Code section: typeinfo ### */ +static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, __PYX_IS_UNSIGNED(int) ? 'U' : 'I', __PYX_IS_UNSIGNED(int), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 }; +/* #### Code section: before_global_var ### */ +#define __Pyx_MODULE_NAME "monotonic_align.core" +extern int __pyx_module_is_main_monotonic_align__core; +int __pyx_module_is_main_monotonic_align__core = 0; + +/* Implementation of "monotonic_align.core" */ +/* #### Code section: global_var ### */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin___import__; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_MemoryError; +static PyObject *__pyx_builtin_enumerate; +static PyObject *__pyx_builtin_TypeError; +static PyObject *__pyx_builtin_AssertionError; +static PyObject *__pyx_builtin_Ellipsis; +static PyObject *__pyx_builtin_id; +static PyObject *__pyx_builtin_IndexError; +static PyObject *__pyx_builtin_ImportError; +/* #### Code section: string_decls ### */ +static const char __pyx_k_[] = ": "; +static const char __pyx_k_O[] = "O"; +static const char __pyx_k_c[] = "c"; +static const char __pyx_k__2[] = "."; +static const char __pyx_k__3[] = "*"; +static const char __pyx_k__6[] = "'"; +static const char __pyx_k__7[] = ")"; +static const char __pyx_k_gc[] = "gc"; +static const char __pyx_k_id[] = "id"; +static const char __pyx_k_np[] = "np"; +static const char __pyx_k__25[] = "?"; +static const char __pyx_k_abc[] = "abc"; +static const char __pyx_k_and[] = " and "; +static const char __pyx_k_got[] = " (got "; +static const char __pyx_k_new[] = "__new__"; +static const char __pyx_k_obj[] = "obj"; +static const char __pyx_k_sys[] = "sys"; +static const char __pyx_k_base[] = "base"; +static const char __pyx_k_dict[] = "__dict__"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_mode[] = "mode"; +static const char __pyx_k_name[] = "name"; +static const char __pyx_k_ndim[] = "ndim"; +static const char __pyx_k_pack[] = "pack"; +static const char __pyx_k_size[] = "size"; +static const char __pyx_k_spec[] = "__spec__"; +static const char __pyx_k_step[] = "step"; +static const char __pyx_k_stop[] = "stop"; +static const char __pyx_k_t_xs[] = "t_xs"; +static const char __pyx_k_t_ys[] = "t_ys"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_ASCII[] = "ASCII"; +static const char __pyx_k_class[] = "__class__"; +static const char __pyx_k_count[] = "count"; +static const char __pyx_k_error[] = "error"; +static const char __pyx_k_flags[] = "flags"; +static const char __pyx_k_index[] = "index"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_paths[] = "paths"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_shape[] = "shape"; +static const char __pyx_k_start[] = "start"; +static const char __pyx_k_enable[] = "enable"; +static const char __pyx_k_encode[] = "encode"; +static const char __pyx_k_format[] = "format"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_name_2[] = "__name__"; +static const char __pyx_k_pickle[] = "pickle"; +static const char __pyx_k_reduce[] = "__reduce__"; +static const char __pyx_k_struct[] = "struct"; +static const char __pyx_k_unpack[] = "unpack"; +static const char __pyx_k_update[] = "update"; +static const char __pyx_k_values[] = "values"; +static const char __pyx_k_disable[] = "disable"; +static const char __pyx_k_fortran[] = "fortran"; +static const char __pyx_k_memview[] = "memview"; +static const char __pyx_k_Ellipsis[] = "Ellipsis"; +static const char __pyx_k_Sequence[] = "Sequence"; +static const char __pyx_k_getstate[] = "__getstate__"; +static const char __pyx_k_itemsize[] = "itemsize"; +static const char __pyx_k_pyx_type[] = "__pyx_type"; +static const char __pyx_k_register[] = "register"; +static const char __pyx_k_setstate[] = "__setstate__"; +static const char __pyx_k_TypeError[] = "TypeError"; +static const char __pyx_k_enumerate[] = "enumerate"; +static const char __pyx_k_isenabled[] = "isenabled"; +static const char __pyx_k_pyx_state[] = "__pyx_state"; +static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; +static const char __pyx_k_IndexError[] = "IndexError"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_pyx_result[] = "__pyx_result"; +static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; +static const char __pyx_k_ImportError[] = "ImportError"; +static const char __pyx_k_MemoryError[] = "MemoryError"; +static const char __pyx_k_PickleError[] = "PickleError"; +static const char __pyx_k_collections[] = "collections"; +static const char __pyx_k_max_neg_val[] = "max_neg_val"; +static const char __pyx_k_initializing[] = "_initializing"; +static const char __pyx_k_is_coroutine[] = "_is_coroutine"; +static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; +static const char __pyx_k_stringsource[] = ""; +static const char __pyx_k_version_info[] = "version_info"; +static const char __pyx_k_class_getitem[] = "__class_getitem__"; +static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; +static const char __pyx_k_AssertionError[] = "AssertionError"; +static const char __pyx_k_maximum_path_c[] = "maximum_path_c"; +static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; +static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; +static const char __pyx_k_collections_abc[] = "collections.abc"; +static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; +static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; +static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; +static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; +static const char __pyx_k_asyncio_coroutines[] = "asyncio.coroutines"; +static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; +static const char __pyx_k_strided_and_direct[] = ""; +static const char __pyx_k_monotonic_align_core[] = "monotonic_align.core"; +static const char __pyx_k_strided_and_indirect[] = ""; +static const char __pyx_k_Invalid_shape_in_axis[] = "Invalid shape in axis "; +static const char __pyx_k_contiguous_and_direct[] = ""; +static const char __pyx_k_Cannot_index_with_type[] = "Cannot index with type '"; +static const char __pyx_k_MemoryView_of_r_object[] = ""; +static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; +static const char __pyx_k_contiguous_and_indirect[] = ""; +static const char __pyx_k_monotonic_align_core_pyx[] = "monotonic_align/core.pyx"; +static const char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct"; +static const char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)"; +static const char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)"; +static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; +static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; +static const char __pyx_k_strided_and_direct_or_indirect[] = ""; +static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; +static const char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced"; +static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; +static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; +static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; +static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; +static const char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions"; +static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; +static const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = "Incompatible checksums (0x%x vs (0x82a3537, 0x6ae9995, 0xb068931) = (name))"; +static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; +static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got "; +static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis "; +static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; +static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension "; +static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; +static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; +static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; +/* #### Code section: decls ### */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ +static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ +static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ +static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_pf_15monotonic_align_4core_maximum_path_c(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_xs, __Pyx_memviewslice __pyx_v_t_ys, float __pyx_v_max_neg_val); /* proto */ +static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +/* #### Code section: late_includes ### */ +/* #### Code section: module_state ### */ +typedef struct { + PyObject *__pyx_d; + PyObject *__pyx_b; + PyObject *__pyx_cython_runtime; + PyObject *__pyx_empty_tuple; + PyObject *__pyx_empty_bytes; + PyObject *__pyx_empty_unicode; + #ifdef __Pyx_CyFunction_USED + PyTypeObject *__pyx_CyFunctionType; + #endif + #ifdef __Pyx_FusedFunction_USED + PyTypeObject *__pyx_FusedFunctionType; + #endif + #ifdef __Pyx_Generator_USED + PyTypeObject *__pyx_GeneratorType; + #endif + #ifdef __Pyx_IterableCoroutine_USED + PyTypeObject *__pyx_IterableCoroutineType; + #endif + #ifdef __Pyx_Coroutine_USED + PyTypeObject *__pyx_CoroutineAwaitType; + #endif + #ifdef __Pyx_Coroutine_USED + PyTypeObject *__pyx_CoroutineType; + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + PyTypeObject *__pyx_ptype_7cpython_4type_type; + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + PyTypeObject *__pyx_ptype_5numpy_dtype; + PyTypeObject *__pyx_ptype_5numpy_flatiter; + PyTypeObject *__pyx_ptype_5numpy_broadcast; + PyTypeObject *__pyx_ptype_5numpy_ndarray; + PyTypeObject *__pyx_ptype_5numpy_ufunc; + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + PyObject *__pyx_type___pyx_array; + PyObject *__pyx_type___pyx_MemviewEnum; + PyObject *__pyx_type___pyx_memoryview; + PyObject *__pyx_type___pyx_memoryviewslice; + #endif + PyTypeObject *__pyx_array_type; + PyTypeObject *__pyx_MemviewEnum_type; + PyTypeObject *__pyx_memoryview_type; + PyTypeObject *__pyx_memoryviewslice_type; + PyObject *__pyx_kp_u_; + PyObject *__pyx_n_s_ASCII; + PyObject *__pyx_kp_s_All_dimensions_preceding_dimensi; + PyObject *__pyx_n_s_AssertionError; + PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; + PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; + PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; + PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; + PyObject *__pyx_kp_u_Cannot_index_with_type; + PyObject *__pyx_kp_s_Cannot_transpose_memoryview_with; + PyObject *__pyx_kp_s_Dimension_d_is_not_direct; + PyObject *__pyx_n_s_Ellipsis; + PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; + PyObject *__pyx_n_s_ImportError; + PyObject *__pyx_kp_s_Incompatible_checksums_0x_x_vs_0; + PyObject *__pyx_n_s_IndexError; + PyObject *__pyx_kp_s_Index_out_of_bounds_axis_d; + PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; + PyObject *__pyx_kp_u_Invalid_mode_expected_c_or_fortr; + PyObject *__pyx_kp_u_Invalid_shape_in_axis; + PyObject *__pyx_n_s_MemoryError; + PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; + PyObject *__pyx_kp_s_MemoryView_of_r_object; + PyObject *__pyx_n_b_O; + PyObject *__pyx_kp_u_Out_of_bounds_on_buffer_access_a; + PyObject *__pyx_n_s_PickleError; + PyObject *__pyx_n_s_Sequence; + PyObject *__pyx_kp_s_Step_may_not_be_zero_axis_d; + PyObject *__pyx_n_s_TypeError; + PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; + PyObject *__pyx_n_s_ValueError; + PyObject *__pyx_n_s_View_MemoryView; + PyObject *__pyx_kp_u__2; + PyObject *__pyx_n_s__25; + PyObject *__pyx_n_s__3; + PyObject *__pyx_kp_u__6; + PyObject *__pyx_kp_u__7; + PyObject *__pyx_n_s_abc; + PyObject *__pyx_n_s_allocate_buffer; + PyObject *__pyx_kp_u_and; + PyObject *__pyx_n_s_asyncio_coroutines; + PyObject *__pyx_n_s_base; + PyObject *__pyx_n_s_c; + PyObject *__pyx_n_u_c; + PyObject *__pyx_n_s_class; + PyObject *__pyx_n_s_class_getitem; + PyObject *__pyx_n_s_cline_in_traceback; + PyObject *__pyx_n_s_collections; + PyObject *__pyx_kp_s_collections_abc; + PyObject *__pyx_kp_s_contiguous_and_direct; + PyObject 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*__pyx_n_s_monotonic_align_core; + PyObject *__pyx_kp_s_monotonic_align_core_pyx; + PyObject *__pyx_n_s_name; + PyObject *__pyx_n_s_name_2; + PyObject *__pyx_n_s_ndim; + PyObject *__pyx_n_s_new; + PyObject *__pyx_kp_s_no_default___reduce___due_to_non; + PyObject *__pyx_n_s_np; + PyObject *__pyx_n_s_numpy; + PyObject *__pyx_kp_u_numpy_core_multiarray_failed_to; + PyObject *__pyx_kp_u_numpy_core_umath_failed_to_impor; + PyObject *__pyx_n_s_obj; + PyObject *__pyx_n_s_pack; + PyObject *__pyx_n_s_paths; + PyObject *__pyx_n_s_pickle; + PyObject *__pyx_n_s_pyx_PickleError; + PyObject *__pyx_n_s_pyx_checksum; + PyObject *__pyx_n_s_pyx_result; + PyObject *__pyx_n_s_pyx_state; + PyObject *__pyx_n_s_pyx_type; + PyObject *__pyx_n_s_pyx_unpickle_Enum; + PyObject *__pyx_n_s_pyx_vtable; + PyObject *__pyx_n_s_range; + PyObject *__pyx_n_s_reduce; + PyObject *__pyx_n_s_reduce_cython; + PyObject *__pyx_n_s_reduce_ex; + PyObject *__pyx_n_s_register; + PyObject *__pyx_n_s_setstate; + PyObject *__pyx_n_s_setstate_cython; + PyObject *__pyx_n_s_shape; + PyObject *__pyx_n_s_size; + PyObject *__pyx_n_s_spec; + PyObject *__pyx_n_s_start; + PyObject *__pyx_n_s_step; + PyObject *__pyx_n_s_stop; + PyObject *__pyx_kp_s_strided_and_direct; + PyObject *__pyx_kp_s_strided_and_direct_or_indirect; + PyObject *__pyx_kp_s_strided_and_indirect; + PyObject *__pyx_kp_s_stringsource; + PyObject *__pyx_n_s_struct; + PyObject *__pyx_n_s_sys; + PyObject *__pyx_n_s_t_xs; + PyObject *__pyx_n_s_t_ys; + PyObject *__pyx_n_s_test; + PyObject *__pyx_kp_s_unable_to_allocate_array_data; + PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; + PyObject *__pyx_n_s_unpack; + PyObject *__pyx_n_s_update; + PyObject *__pyx_n_s_values; + PyObject *__pyx_n_s_version_info; + PyObject *__pyx_int_0; + PyObject *__pyx_int_1; + PyObject *__pyx_int_3; + PyObject *__pyx_int_112105877; + PyObject *__pyx_int_136983863; + PyObject *__pyx_int_184977713; + PyObject *__pyx_int_neg_1; + float __pyx_k__11; + PyObject *__pyx_slice__5; + PyObject *__pyx_tuple__4; + PyObject *__pyx_tuple__8; + PyObject *__pyx_tuple__9; + PyObject *__pyx_tuple__10; + PyObject *__pyx_tuple__12; + PyObject *__pyx_tuple__13; + PyObject *__pyx_tuple__14; + PyObject *__pyx_tuple__15; + PyObject *__pyx_tuple__16; + PyObject *__pyx_tuple__17; + PyObject *__pyx_tuple__18; + PyObject *__pyx_tuple__19; + PyObject *__pyx_tuple__20; + PyObject *__pyx_tuple__21; + PyObject *__pyx_tuple__23; + PyObject *__pyx_codeobj__22; + PyObject *__pyx_codeobj__24; +} __pyx_mstate; + +#if CYTHON_USE_MODULE_STATE +#ifdef __cplusplus +namespace { + extern struct PyModuleDef __pyx_moduledef; +} /* anonymous namespace */ +#else +static struct PyModuleDef __pyx_moduledef; +#endif + +#define __pyx_mstate(o) ((__pyx_mstate *)__Pyx_PyModule_GetState(o)) + +#define __pyx_mstate_global (__pyx_mstate(PyState_FindModule(&__pyx_moduledef))) + +#define __pyx_m (PyState_FindModule(&__pyx_moduledef)) +#else +static __pyx_mstate __pyx_mstate_global_static = +#ifdef __cplusplus + {}; +#else + {0}; +#endif +static __pyx_mstate *__pyx_mstate_global = &__pyx_mstate_global_static; +#endif +/* #### Code section: module_state_clear ### */ +#if CYTHON_USE_MODULE_STATE +static int __pyx_m_clear(PyObject *m) { + __pyx_mstate *clear_module_state = __pyx_mstate(m); + if (!clear_module_state) return 0; + Py_CLEAR(clear_module_state->__pyx_d); + Py_CLEAR(clear_module_state->__pyx_b); + Py_CLEAR(clear_module_state->__pyx_cython_runtime); + Py_CLEAR(clear_module_state->__pyx_empty_tuple); + Py_CLEAR(clear_module_state->__pyx_empty_bytes); + Py_CLEAR(clear_module_state->__pyx_empty_unicode); + #ifdef __Pyx_CyFunction_USED + Py_CLEAR(clear_module_state->__pyx_CyFunctionType); + #endif + #ifdef __Pyx_FusedFunction_USED + Py_CLEAR(clear_module_state->__pyx_FusedFunctionType); + #endif + Py_CLEAR(clear_module_state->__pyx_ptype_7cpython_4type_type); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_dtype); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_flatiter); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_broadcast); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_ndarray); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_ufunc); + Py_CLEAR(clear_module_state->__pyx_array_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_array); + Py_CLEAR(clear_module_state->__pyx_MemviewEnum_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_MemviewEnum); + Py_CLEAR(clear_module_state->__pyx_memoryview_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_memoryview); + Py_CLEAR(clear_module_state->__pyx_memoryviewslice_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_memoryviewslice); + Py_CLEAR(clear_module_state->__pyx_kp_u_); + Py_CLEAR(clear_module_state->__pyx_n_s_ASCII); + Py_CLEAR(clear_module_state->__pyx_kp_s_All_dimensions_preceding_dimensi); + Py_CLEAR(clear_module_state->__pyx_n_s_AssertionError); + Py_CLEAR(clear_module_state->__pyx_kp_s_Buffer_view_does_not_expose_stri); + Py_CLEAR(clear_module_state->__pyx_kp_s_Can_only_create_a_buffer_that_is); + Py_CLEAR(clear_module_state->__pyx_kp_s_Cannot_assign_to_read_only_memor); + Py_CLEAR(clear_module_state->__pyx_kp_s_Cannot_create_writable_memory_vi); + Py_CLEAR(clear_module_state->__pyx_kp_u_Cannot_index_with_type); + Py_CLEAR(clear_module_state->__pyx_kp_s_Cannot_transpose_memoryview_with); + Py_CLEAR(clear_module_state->__pyx_kp_s_Dimension_d_is_not_direct); + Py_CLEAR(clear_module_state->__pyx_n_s_Ellipsis); + Py_CLEAR(clear_module_state->__pyx_kp_s_Empty_shape_tuple_for_cython_arr); + Py_CLEAR(clear_module_state->__pyx_n_s_ImportError); + Py_CLEAR(clear_module_state->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0); + Py_CLEAR(clear_module_state->__pyx_n_s_IndexError); + Py_CLEAR(clear_module_state->__pyx_kp_s_Index_out_of_bounds_axis_d); + Py_CLEAR(clear_module_state->__pyx_kp_s_Indirect_dimensions_not_supporte); + Py_CLEAR(clear_module_state->__pyx_kp_u_Invalid_mode_expected_c_or_fortr); + Py_CLEAR(clear_module_state->__pyx_kp_u_Invalid_shape_in_axis); + Py_CLEAR(clear_module_state->__pyx_n_s_MemoryError); + Py_CLEAR(clear_module_state->__pyx_kp_s_MemoryView_of_r_at_0x_x); + Py_CLEAR(clear_module_state->__pyx_kp_s_MemoryView_of_r_object); + Py_CLEAR(clear_module_state->__pyx_n_b_O); + Py_CLEAR(clear_module_state->__pyx_kp_u_Out_of_bounds_on_buffer_access_a); + Py_CLEAR(clear_module_state->__pyx_n_s_PickleError); + Py_CLEAR(clear_module_state->__pyx_n_s_Sequence); + Py_CLEAR(clear_module_state->__pyx_kp_s_Step_may_not_be_zero_axis_d); + Py_CLEAR(clear_module_state->__pyx_n_s_TypeError); + Py_CLEAR(clear_module_state->__pyx_kp_s_Unable_to_convert_item_to_object); + Py_CLEAR(clear_module_state->__pyx_n_s_ValueError); + Py_CLEAR(clear_module_state->__pyx_n_s_View_MemoryView); + Py_CLEAR(clear_module_state->__pyx_kp_u__2); + Py_CLEAR(clear_module_state->__pyx_n_s__25); + Py_CLEAR(clear_module_state->__pyx_n_s__3); + Py_CLEAR(clear_module_state->__pyx_kp_u__6); + Py_CLEAR(clear_module_state->__pyx_kp_u__7); + Py_CLEAR(clear_module_state->__pyx_n_s_abc); + Py_CLEAR(clear_module_state->__pyx_n_s_allocate_buffer); + Py_CLEAR(clear_module_state->__pyx_kp_u_and); + Py_CLEAR(clear_module_state->__pyx_n_s_asyncio_coroutines); + Py_CLEAR(clear_module_state->__pyx_n_s_base); + Py_CLEAR(clear_module_state->__pyx_n_s_c); + Py_CLEAR(clear_module_state->__pyx_n_u_c); + Py_CLEAR(clear_module_state->__pyx_n_s_class); + Py_CLEAR(clear_module_state->__pyx_n_s_class_getitem); + Py_CLEAR(clear_module_state->__pyx_n_s_cline_in_traceback); + Py_CLEAR(clear_module_state->__pyx_n_s_collections); + Py_CLEAR(clear_module_state->__pyx_kp_s_collections_abc); + Py_CLEAR(clear_module_state->__pyx_kp_s_contiguous_and_direct); + Py_CLEAR(clear_module_state->__pyx_kp_s_contiguous_and_indirect); + Py_CLEAR(clear_module_state->__pyx_n_s_count); + Py_CLEAR(clear_module_state->__pyx_n_s_dict); + Py_CLEAR(clear_module_state->__pyx_kp_u_disable); + Py_CLEAR(clear_module_state->__pyx_n_s_dtype_is_object); + Py_CLEAR(clear_module_state->__pyx_kp_u_enable); + Py_CLEAR(clear_module_state->__pyx_n_s_encode); + Py_CLEAR(clear_module_state->__pyx_n_s_enumerate); + Py_CLEAR(clear_module_state->__pyx_n_s_error); + Py_CLEAR(clear_module_state->__pyx_n_s_flags); + Py_CLEAR(clear_module_state->__pyx_n_s_format); + Py_CLEAR(clear_module_state->__pyx_n_s_fortran); + Py_CLEAR(clear_module_state->__pyx_n_u_fortran); + Py_CLEAR(clear_module_state->__pyx_kp_u_gc); + Py_CLEAR(clear_module_state->__pyx_n_s_getstate); + Py_CLEAR(clear_module_state->__pyx_kp_u_got); + Py_CLEAR(clear_module_state->__pyx_kp_u_got_differing_extents_in_dimensi); + Py_CLEAR(clear_module_state->__pyx_n_s_id); + Py_CLEAR(clear_module_state->__pyx_n_s_import); + Py_CLEAR(clear_module_state->__pyx_n_s_index); + Py_CLEAR(clear_module_state->__pyx_n_s_initializing); + Py_CLEAR(clear_module_state->__pyx_n_s_is_coroutine); + Py_CLEAR(clear_module_state->__pyx_kp_u_isenabled); + Py_CLEAR(clear_module_state->__pyx_n_s_itemsize); + Py_CLEAR(clear_module_state->__pyx_kp_s_itemsize_0_for_cython_array); + Py_CLEAR(clear_module_state->__pyx_n_s_main); + Py_CLEAR(clear_module_state->__pyx_n_s_max_neg_val); + Py_CLEAR(clear_module_state->__pyx_n_s_maximum_path_c); + Py_CLEAR(clear_module_state->__pyx_n_s_memview); + Py_CLEAR(clear_module_state->__pyx_n_s_mode); + Py_CLEAR(clear_module_state->__pyx_n_s_monotonic_align_core); + Py_CLEAR(clear_module_state->__pyx_kp_s_monotonic_align_core_pyx); + Py_CLEAR(clear_module_state->__pyx_n_s_name); + Py_CLEAR(clear_module_state->__pyx_n_s_name_2); + Py_CLEAR(clear_module_state->__pyx_n_s_ndim); + Py_CLEAR(clear_module_state->__pyx_n_s_new); + Py_CLEAR(clear_module_state->__pyx_kp_s_no_default___reduce___due_to_non); + Py_CLEAR(clear_module_state->__pyx_n_s_np); + Py_CLEAR(clear_module_state->__pyx_n_s_numpy); + Py_CLEAR(clear_module_state->__pyx_kp_u_numpy_core_multiarray_failed_to); + Py_CLEAR(clear_module_state->__pyx_kp_u_numpy_core_umath_failed_to_impor); + Py_CLEAR(clear_module_state->__pyx_n_s_obj); + Py_CLEAR(clear_module_state->__pyx_n_s_pack); + Py_CLEAR(clear_module_state->__pyx_n_s_paths); + Py_CLEAR(clear_module_state->__pyx_n_s_pickle); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_PickleError); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_checksum); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_result); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_state); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_type); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_unpickle_Enum); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_vtable); + Py_CLEAR(clear_module_state->__pyx_n_s_range); + Py_CLEAR(clear_module_state->__pyx_n_s_reduce); + Py_CLEAR(clear_module_state->__pyx_n_s_reduce_cython); + Py_CLEAR(clear_module_state->__pyx_n_s_reduce_ex); + Py_CLEAR(clear_module_state->__pyx_n_s_register); + Py_CLEAR(clear_module_state->__pyx_n_s_setstate); + Py_CLEAR(clear_module_state->__pyx_n_s_setstate_cython); + Py_CLEAR(clear_module_state->__pyx_n_s_shape); + Py_CLEAR(clear_module_state->__pyx_n_s_size); + Py_CLEAR(clear_module_state->__pyx_n_s_spec); + Py_CLEAR(clear_module_state->__pyx_n_s_start); + Py_CLEAR(clear_module_state->__pyx_n_s_step); + Py_CLEAR(clear_module_state->__pyx_n_s_stop); + Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_direct); + Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_direct_or_indirect); + Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_indirect); + Py_CLEAR(clear_module_state->__pyx_kp_s_stringsource); + Py_CLEAR(clear_module_state->__pyx_n_s_struct); + Py_CLEAR(clear_module_state->__pyx_n_s_sys); + Py_CLEAR(clear_module_state->__pyx_n_s_t_xs); + Py_CLEAR(clear_module_state->__pyx_n_s_t_ys); + Py_CLEAR(clear_module_state->__pyx_n_s_test); + Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_array_data); + Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_shape_and_str); + Py_CLEAR(clear_module_state->__pyx_n_s_unpack); + Py_CLEAR(clear_module_state->__pyx_n_s_update); + Py_CLEAR(clear_module_state->__pyx_n_s_values); + Py_CLEAR(clear_module_state->__pyx_n_s_version_info); + Py_CLEAR(clear_module_state->__pyx_int_0); + Py_CLEAR(clear_module_state->__pyx_int_1); + Py_CLEAR(clear_module_state->__pyx_int_3); + Py_CLEAR(clear_module_state->__pyx_int_112105877); + Py_CLEAR(clear_module_state->__pyx_int_136983863); + Py_CLEAR(clear_module_state->__pyx_int_184977713); + Py_CLEAR(clear_module_state->__pyx_int_neg_1); + Py_CLEAR(clear_module_state->__pyx_slice__5); + Py_CLEAR(clear_module_state->__pyx_tuple__4); + Py_CLEAR(clear_module_state->__pyx_tuple__8); + Py_CLEAR(clear_module_state->__pyx_tuple__9); + Py_CLEAR(clear_module_state->__pyx_tuple__10); + Py_CLEAR(clear_module_state->__pyx_tuple__12); + Py_CLEAR(clear_module_state->__pyx_tuple__13); + Py_CLEAR(clear_module_state->__pyx_tuple__14); + Py_CLEAR(clear_module_state->__pyx_tuple__15); + Py_CLEAR(clear_module_state->__pyx_tuple__16); + Py_CLEAR(clear_module_state->__pyx_tuple__17); + Py_CLEAR(clear_module_state->__pyx_tuple__18); + Py_CLEAR(clear_module_state->__pyx_tuple__19); + Py_CLEAR(clear_module_state->__pyx_tuple__20); + Py_CLEAR(clear_module_state->__pyx_tuple__21); + Py_CLEAR(clear_module_state->__pyx_tuple__23); + Py_CLEAR(clear_module_state->__pyx_codeobj__22); + Py_CLEAR(clear_module_state->__pyx_codeobj__24); + return 0; +} +#endif +/* #### Code section: module_state_traverse ### */ +#if CYTHON_USE_MODULE_STATE +static int __pyx_m_traverse(PyObject *m, visitproc visit, void *arg) { + __pyx_mstate *traverse_module_state = __pyx_mstate(m); + if (!traverse_module_state) return 0; + Py_VISIT(traverse_module_state->__pyx_d); + Py_VISIT(traverse_module_state->__pyx_b); + Py_VISIT(traverse_module_state->__pyx_cython_runtime); + Py_VISIT(traverse_module_state->__pyx_empty_tuple); + Py_VISIT(traverse_module_state->__pyx_empty_bytes); + Py_VISIT(traverse_module_state->__pyx_empty_unicode); + #ifdef __Pyx_CyFunction_USED + Py_VISIT(traverse_module_state->__pyx_CyFunctionType); + #endif + #ifdef __Pyx_FusedFunction_USED + Py_VISIT(traverse_module_state->__pyx_FusedFunctionType); + #endif + Py_VISIT(traverse_module_state->__pyx_ptype_7cpython_4type_type); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_dtype); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_flatiter); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_broadcast); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_ndarray); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_ufunc); + Py_VISIT(traverse_module_state->__pyx_array_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_array); + Py_VISIT(traverse_module_state->__pyx_MemviewEnum_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_MemviewEnum); + Py_VISIT(traverse_module_state->__pyx_memoryview_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryview); + Py_VISIT(traverse_module_state->__pyx_memoryviewslice_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryviewslice); + Py_VISIT(traverse_module_state->__pyx_kp_u_); + Py_VISIT(traverse_module_state->__pyx_n_s_ASCII); + Py_VISIT(traverse_module_state->__pyx_kp_s_All_dimensions_preceding_dimensi); + Py_VISIT(traverse_module_state->__pyx_n_s_AssertionError); + Py_VISIT(traverse_module_state->__pyx_kp_s_Buffer_view_does_not_expose_stri); + Py_VISIT(traverse_module_state->__pyx_kp_s_Can_only_create_a_buffer_that_is); + Py_VISIT(traverse_module_state->__pyx_kp_s_Cannot_assign_to_read_only_memor); + Py_VISIT(traverse_module_state->__pyx_kp_s_Cannot_create_writable_memory_vi); + Py_VISIT(traverse_module_state->__pyx_kp_u_Cannot_index_with_type); + Py_VISIT(traverse_module_state->__pyx_kp_s_Cannot_transpose_memoryview_with); + Py_VISIT(traverse_module_state->__pyx_kp_s_Dimension_d_is_not_direct); + Py_VISIT(traverse_module_state->__pyx_n_s_Ellipsis); + Py_VISIT(traverse_module_state->__pyx_kp_s_Empty_shape_tuple_for_cython_arr); + Py_VISIT(traverse_module_state->__pyx_n_s_ImportError); + Py_VISIT(traverse_module_state->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0); + Py_VISIT(traverse_module_state->__pyx_n_s_IndexError); + Py_VISIT(traverse_module_state->__pyx_kp_s_Index_out_of_bounds_axis_d); + Py_VISIT(traverse_module_state->__pyx_kp_s_Indirect_dimensions_not_supporte); + Py_VISIT(traverse_module_state->__pyx_kp_u_Invalid_mode_expected_c_or_fortr); + Py_VISIT(traverse_module_state->__pyx_kp_u_Invalid_shape_in_axis); + Py_VISIT(traverse_module_state->__pyx_n_s_MemoryError); + Py_VISIT(traverse_module_state->__pyx_kp_s_MemoryView_of_r_at_0x_x); + Py_VISIT(traverse_module_state->__pyx_kp_s_MemoryView_of_r_object); + Py_VISIT(traverse_module_state->__pyx_n_b_O); + Py_VISIT(traverse_module_state->__pyx_kp_u_Out_of_bounds_on_buffer_access_a); + Py_VISIT(traverse_module_state->__pyx_n_s_PickleError); + Py_VISIT(traverse_module_state->__pyx_n_s_Sequence); + Py_VISIT(traverse_module_state->__pyx_kp_s_Step_may_not_be_zero_axis_d); + Py_VISIT(traverse_module_state->__pyx_n_s_TypeError); + Py_VISIT(traverse_module_state->__pyx_kp_s_Unable_to_convert_item_to_object); + Py_VISIT(traverse_module_state->__pyx_n_s_ValueError); + Py_VISIT(traverse_module_state->__pyx_n_s_View_MemoryView); + Py_VISIT(traverse_module_state->__pyx_kp_u__2); + Py_VISIT(traverse_module_state->__pyx_n_s__25); + Py_VISIT(traverse_module_state->__pyx_n_s__3); + Py_VISIT(traverse_module_state->__pyx_kp_u__6); + Py_VISIT(traverse_module_state->__pyx_kp_u__7); + Py_VISIT(traverse_module_state->__pyx_n_s_abc); + Py_VISIT(traverse_module_state->__pyx_n_s_allocate_buffer); + Py_VISIT(traverse_module_state->__pyx_kp_u_and); + Py_VISIT(traverse_module_state->__pyx_n_s_asyncio_coroutines); + Py_VISIT(traverse_module_state->__pyx_n_s_base); + Py_VISIT(traverse_module_state->__pyx_n_s_c); + Py_VISIT(traverse_module_state->__pyx_n_u_c); + Py_VISIT(traverse_module_state->__pyx_n_s_class); + Py_VISIT(traverse_module_state->__pyx_n_s_class_getitem); + Py_VISIT(traverse_module_state->__pyx_n_s_cline_in_traceback); + Py_VISIT(traverse_module_state->__pyx_n_s_collections); + Py_VISIT(traverse_module_state->__pyx_kp_s_collections_abc); + Py_VISIT(traverse_module_state->__pyx_kp_s_contiguous_and_direct); + Py_VISIT(traverse_module_state->__pyx_kp_s_contiguous_and_indirect); + Py_VISIT(traverse_module_state->__pyx_n_s_count); + Py_VISIT(traverse_module_state->__pyx_n_s_dict); + Py_VISIT(traverse_module_state->__pyx_kp_u_disable); + Py_VISIT(traverse_module_state->__pyx_n_s_dtype_is_object); + Py_VISIT(traverse_module_state->__pyx_kp_u_enable); + Py_VISIT(traverse_module_state->__pyx_n_s_encode); + Py_VISIT(traverse_module_state->__pyx_n_s_enumerate); + Py_VISIT(traverse_module_state->__pyx_n_s_error); + Py_VISIT(traverse_module_state->__pyx_n_s_flags); + Py_VISIT(traverse_module_state->__pyx_n_s_format); + Py_VISIT(traverse_module_state->__pyx_n_s_fortran); + Py_VISIT(traverse_module_state->__pyx_n_u_fortran); + Py_VISIT(traverse_module_state->__pyx_kp_u_gc); + Py_VISIT(traverse_module_state->__pyx_n_s_getstate); + Py_VISIT(traverse_module_state->__pyx_kp_u_got); + Py_VISIT(traverse_module_state->__pyx_kp_u_got_differing_extents_in_dimensi); + Py_VISIT(traverse_module_state->__pyx_n_s_id); + Py_VISIT(traverse_module_state->__pyx_n_s_import); + Py_VISIT(traverse_module_state->__pyx_n_s_index); + Py_VISIT(traverse_module_state->__pyx_n_s_initializing); + Py_VISIT(traverse_module_state->__pyx_n_s_is_coroutine); + Py_VISIT(traverse_module_state->__pyx_kp_u_isenabled); + Py_VISIT(traverse_module_state->__pyx_n_s_itemsize); + Py_VISIT(traverse_module_state->__pyx_kp_s_itemsize_0_for_cython_array); + Py_VISIT(traverse_module_state->__pyx_n_s_main); + Py_VISIT(traverse_module_state->__pyx_n_s_max_neg_val); + Py_VISIT(traverse_module_state->__pyx_n_s_maximum_path_c); + Py_VISIT(traverse_module_state->__pyx_n_s_memview); + Py_VISIT(traverse_module_state->__pyx_n_s_mode); + Py_VISIT(traverse_module_state->__pyx_n_s_monotonic_align_core); + Py_VISIT(traverse_module_state->__pyx_kp_s_monotonic_align_core_pyx); + Py_VISIT(traverse_module_state->__pyx_n_s_name); + Py_VISIT(traverse_module_state->__pyx_n_s_name_2); + Py_VISIT(traverse_module_state->__pyx_n_s_ndim); + Py_VISIT(traverse_module_state->__pyx_n_s_new); + Py_VISIT(traverse_module_state->__pyx_kp_s_no_default___reduce___due_to_non); + Py_VISIT(traverse_module_state->__pyx_n_s_np); + Py_VISIT(traverse_module_state->__pyx_n_s_numpy); + Py_VISIT(traverse_module_state->__pyx_kp_u_numpy_core_multiarray_failed_to); + Py_VISIT(traverse_module_state->__pyx_kp_u_numpy_core_umath_failed_to_impor); + Py_VISIT(traverse_module_state->__pyx_n_s_obj); + Py_VISIT(traverse_module_state->__pyx_n_s_pack); + Py_VISIT(traverse_module_state->__pyx_n_s_paths); + Py_VISIT(traverse_module_state->__pyx_n_s_pickle); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_PickleError); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_checksum); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_result); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_state); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_type); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_unpickle_Enum); + Py_VISIT(traverse_module_state->__pyx_n_s_pyx_vtable); + Py_VISIT(traverse_module_state->__pyx_n_s_range); + Py_VISIT(traverse_module_state->__pyx_n_s_reduce); + Py_VISIT(traverse_module_state->__pyx_n_s_reduce_cython); + Py_VISIT(traverse_module_state->__pyx_n_s_reduce_ex); + Py_VISIT(traverse_module_state->__pyx_n_s_register); + Py_VISIT(traverse_module_state->__pyx_n_s_setstate); + Py_VISIT(traverse_module_state->__pyx_n_s_setstate_cython); + Py_VISIT(traverse_module_state->__pyx_n_s_shape); + Py_VISIT(traverse_module_state->__pyx_n_s_size); + Py_VISIT(traverse_module_state->__pyx_n_s_spec); + Py_VISIT(traverse_module_state->__pyx_n_s_start); + Py_VISIT(traverse_module_state->__pyx_n_s_step); + Py_VISIT(traverse_module_state->__pyx_n_s_stop); + Py_VISIT(traverse_module_state->__pyx_kp_s_strided_and_direct); + Py_VISIT(traverse_module_state->__pyx_kp_s_strided_and_direct_or_indirect); + Py_VISIT(traverse_module_state->__pyx_kp_s_strided_and_indirect); + Py_VISIT(traverse_module_state->__pyx_kp_s_stringsource); + Py_VISIT(traverse_module_state->__pyx_n_s_struct); + Py_VISIT(traverse_module_state->__pyx_n_s_sys); + Py_VISIT(traverse_module_state->__pyx_n_s_t_xs); + Py_VISIT(traverse_module_state->__pyx_n_s_t_ys); + Py_VISIT(traverse_module_state->__pyx_n_s_test); + Py_VISIT(traverse_module_state->__pyx_kp_s_unable_to_allocate_array_data); + Py_VISIT(traverse_module_state->__pyx_kp_s_unable_to_allocate_shape_and_str); + Py_VISIT(traverse_module_state->__pyx_n_s_unpack); + Py_VISIT(traverse_module_state->__pyx_n_s_update); + Py_VISIT(traverse_module_state->__pyx_n_s_values); + Py_VISIT(traverse_module_state->__pyx_n_s_version_info); + Py_VISIT(traverse_module_state->__pyx_int_0); + Py_VISIT(traverse_module_state->__pyx_int_1); + Py_VISIT(traverse_module_state->__pyx_int_3); + Py_VISIT(traverse_module_state->__pyx_int_112105877); + Py_VISIT(traverse_module_state->__pyx_int_136983863); + Py_VISIT(traverse_module_state->__pyx_int_184977713); + Py_VISIT(traverse_module_state->__pyx_int_neg_1); + Py_VISIT(traverse_module_state->__pyx_slice__5); + Py_VISIT(traverse_module_state->__pyx_tuple__4); + Py_VISIT(traverse_module_state->__pyx_tuple__8); + Py_VISIT(traverse_module_state->__pyx_tuple__9); + Py_VISIT(traverse_module_state->__pyx_tuple__10); + Py_VISIT(traverse_module_state->__pyx_tuple__12); + Py_VISIT(traverse_module_state->__pyx_tuple__13); + Py_VISIT(traverse_module_state->__pyx_tuple__14); + Py_VISIT(traverse_module_state->__pyx_tuple__15); + Py_VISIT(traverse_module_state->__pyx_tuple__16); + Py_VISIT(traverse_module_state->__pyx_tuple__17); + Py_VISIT(traverse_module_state->__pyx_tuple__18); + Py_VISIT(traverse_module_state->__pyx_tuple__19); + Py_VISIT(traverse_module_state->__pyx_tuple__20); + Py_VISIT(traverse_module_state->__pyx_tuple__21); + Py_VISIT(traverse_module_state->__pyx_tuple__23); + Py_VISIT(traverse_module_state->__pyx_codeobj__22); + Py_VISIT(traverse_module_state->__pyx_codeobj__24); + return 0; +} +#endif +/* #### Code section: module_state_defines ### */ +#define __pyx_d __pyx_mstate_global->__pyx_d +#define __pyx_b __pyx_mstate_global->__pyx_b +#define __pyx_cython_runtime __pyx_mstate_global->__pyx_cython_runtime +#define __pyx_empty_tuple __pyx_mstate_global->__pyx_empty_tuple +#define __pyx_empty_bytes __pyx_mstate_global->__pyx_empty_bytes +#define __pyx_empty_unicode __pyx_mstate_global->__pyx_empty_unicode +#ifdef __Pyx_CyFunction_USED +#define __pyx_CyFunctionType __pyx_mstate_global->__pyx_CyFunctionType +#endif +#ifdef __Pyx_FusedFunction_USED +#define __pyx_FusedFunctionType __pyx_mstate_global->__pyx_FusedFunctionType +#endif +#ifdef __Pyx_Generator_USED +#define __pyx_GeneratorType __pyx_mstate_global->__pyx_GeneratorType +#endif +#ifdef __Pyx_IterableCoroutine_USED +#define __pyx_IterableCoroutineType __pyx_mstate_global->__pyx_IterableCoroutineType +#endif +#ifdef __Pyx_Coroutine_USED +#define __pyx_CoroutineAwaitType __pyx_mstate_global->__pyx_CoroutineAwaitType +#endif +#ifdef __Pyx_Coroutine_USED +#define __pyx_CoroutineType __pyx_mstate_global->__pyx_CoroutineType +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#define __pyx_ptype_7cpython_4type_type __pyx_mstate_global->__pyx_ptype_7cpython_4type_type +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#define __pyx_ptype_5numpy_dtype __pyx_mstate_global->__pyx_ptype_5numpy_dtype +#define __pyx_ptype_5numpy_flatiter __pyx_mstate_global->__pyx_ptype_5numpy_flatiter +#define __pyx_ptype_5numpy_broadcast __pyx_mstate_global->__pyx_ptype_5numpy_broadcast +#define __pyx_ptype_5numpy_ndarray __pyx_mstate_global->__pyx_ptype_5numpy_ndarray +#define __pyx_ptype_5numpy_ufunc __pyx_mstate_global->__pyx_ptype_5numpy_ufunc +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#define __pyx_type___pyx_array __pyx_mstate_global->__pyx_type___pyx_array +#define __pyx_type___pyx_MemviewEnum __pyx_mstate_global->__pyx_type___pyx_MemviewEnum +#define __pyx_type___pyx_memoryview __pyx_mstate_global->__pyx_type___pyx_memoryview +#define __pyx_type___pyx_memoryviewslice __pyx_mstate_global->__pyx_type___pyx_memoryviewslice +#endif +#define __pyx_array_type __pyx_mstate_global->__pyx_array_type +#define __pyx_MemviewEnum_type __pyx_mstate_global->__pyx_MemviewEnum_type +#define __pyx_memoryview_type __pyx_mstate_global->__pyx_memoryview_type +#define __pyx_memoryviewslice_type __pyx_mstate_global->__pyx_memoryviewslice_type +#define __pyx_kp_u_ __pyx_mstate_global->__pyx_kp_u_ +#define __pyx_n_s_ASCII __pyx_mstate_global->__pyx_n_s_ASCII +#define __pyx_kp_s_All_dimensions_preceding_dimensi __pyx_mstate_global->__pyx_kp_s_All_dimensions_preceding_dimensi +#define __pyx_n_s_AssertionError __pyx_mstate_global->__pyx_n_s_AssertionError +#define __pyx_kp_s_Buffer_view_does_not_expose_stri __pyx_mstate_global->__pyx_kp_s_Buffer_view_does_not_expose_stri +#define __pyx_kp_s_Can_only_create_a_buffer_that_is __pyx_mstate_global->__pyx_kp_s_Can_only_create_a_buffer_that_is +#define __pyx_kp_s_Cannot_assign_to_read_only_memor __pyx_mstate_global->__pyx_kp_s_Cannot_assign_to_read_only_memor +#define __pyx_kp_s_Cannot_create_writable_memory_vi __pyx_mstate_global->__pyx_kp_s_Cannot_create_writable_memory_vi +#define __pyx_kp_u_Cannot_index_with_type __pyx_mstate_global->__pyx_kp_u_Cannot_index_with_type +#define __pyx_kp_s_Cannot_transpose_memoryview_with __pyx_mstate_global->__pyx_kp_s_Cannot_transpose_memoryview_with +#define __pyx_kp_s_Dimension_d_is_not_direct __pyx_mstate_global->__pyx_kp_s_Dimension_d_is_not_direct +#define __pyx_n_s_Ellipsis __pyx_mstate_global->__pyx_n_s_Ellipsis +#define __pyx_kp_s_Empty_shape_tuple_for_cython_arr __pyx_mstate_global->__pyx_kp_s_Empty_shape_tuple_for_cython_arr +#define __pyx_n_s_ImportError __pyx_mstate_global->__pyx_n_s_ImportError +#define __pyx_kp_s_Incompatible_checksums_0x_x_vs_0 __pyx_mstate_global->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0 +#define __pyx_n_s_IndexError __pyx_mstate_global->__pyx_n_s_IndexError +#define __pyx_kp_s_Index_out_of_bounds_axis_d __pyx_mstate_global->__pyx_kp_s_Index_out_of_bounds_axis_d +#define __pyx_kp_s_Indirect_dimensions_not_supporte __pyx_mstate_global->__pyx_kp_s_Indirect_dimensions_not_supporte +#define __pyx_kp_u_Invalid_mode_expected_c_or_fortr __pyx_mstate_global->__pyx_kp_u_Invalid_mode_expected_c_or_fortr +#define __pyx_kp_u_Invalid_shape_in_axis __pyx_mstate_global->__pyx_kp_u_Invalid_shape_in_axis +#define __pyx_n_s_MemoryError __pyx_mstate_global->__pyx_n_s_MemoryError +#define __pyx_kp_s_MemoryView_of_r_at_0x_x __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_at_0x_x +#define __pyx_kp_s_MemoryView_of_r_object __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_object +#define __pyx_n_b_O __pyx_mstate_global->__pyx_n_b_O +#define __pyx_kp_u_Out_of_bounds_on_buffer_access_a __pyx_mstate_global->__pyx_kp_u_Out_of_bounds_on_buffer_access_a +#define __pyx_n_s_PickleError __pyx_mstate_global->__pyx_n_s_PickleError +#define __pyx_n_s_Sequence __pyx_mstate_global->__pyx_n_s_Sequence +#define __pyx_kp_s_Step_may_not_be_zero_axis_d __pyx_mstate_global->__pyx_kp_s_Step_may_not_be_zero_axis_d +#define __pyx_n_s_TypeError __pyx_mstate_global->__pyx_n_s_TypeError +#define __pyx_kp_s_Unable_to_convert_item_to_object __pyx_mstate_global->__pyx_kp_s_Unable_to_convert_item_to_object +#define __pyx_n_s_ValueError __pyx_mstate_global->__pyx_n_s_ValueError +#define __pyx_n_s_View_MemoryView __pyx_mstate_global->__pyx_n_s_View_MemoryView +#define __pyx_kp_u__2 __pyx_mstate_global->__pyx_kp_u__2 +#define __pyx_n_s__25 __pyx_mstate_global->__pyx_n_s__25 +#define __pyx_n_s__3 __pyx_mstate_global->__pyx_n_s__3 +#define __pyx_kp_u__6 __pyx_mstate_global->__pyx_kp_u__6 +#define __pyx_kp_u__7 __pyx_mstate_global->__pyx_kp_u__7 +#define __pyx_n_s_abc __pyx_mstate_global->__pyx_n_s_abc +#define __pyx_n_s_allocate_buffer __pyx_mstate_global->__pyx_n_s_allocate_buffer +#define __pyx_kp_u_and __pyx_mstate_global->__pyx_kp_u_and +#define __pyx_n_s_asyncio_coroutines __pyx_mstate_global->__pyx_n_s_asyncio_coroutines +#define __pyx_n_s_base __pyx_mstate_global->__pyx_n_s_base +#define __pyx_n_s_c __pyx_mstate_global->__pyx_n_s_c +#define __pyx_n_u_c __pyx_mstate_global->__pyx_n_u_c +#define __pyx_n_s_class __pyx_mstate_global->__pyx_n_s_class +#define __pyx_n_s_class_getitem __pyx_mstate_global->__pyx_n_s_class_getitem +#define __pyx_n_s_cline_in_traceback __pyx_mstate_global->__pyx_n_s_cline_in_traceback +#define __pyx_n_s_collections __pyx_mstate_global->__pyx_n_s_collections +#define __pyx_kp_s_collections_abc __pyx_mstate_global->__pyx_kp_s_collections_abc +#define __pyx_kp_s_contiguous_and_direct __pyx_mstate_global->__pyx_kp_s_contiguous_and_direct +#define __pyx_kp_s_contiguous_and_indirect __pyx_mstate_global->__pyx_kp_s_contiguous_and_indirect +#define __pyx_n_s_count __pyx_mstate_global->__pyx_n_s_count +#define __pyx_n_s_dict __pyx_mstate_global->__pyx_n_s_dict +#define __pyx_kp_u_disable __pyx_mstate_global->__pyx_kp_u_disable +#define __pyx_n_s_dtype_is_object __pyx_mstate_global->__pyx_n_s_dtype_is_object +#define __pyx_kp_u_enable __pyx_mstate_global->__pyx_kp_u_enable +#define __pyx_n_s_encode __pyx_mstate_global->__pyx_n_s_encode +#define __pyx_n_s_enumerate __pyx_mstate_global->__pyx_n_s_enumerate +#define __pyx_n_s_error __pyx_mstate_global->__pyx_n_s_error +#define __pyx_n_s_flags __pyx_mstate_global->__pyx_n_s_flags +#define __pyx_n_s_format __pyx_mstate_global->__pyx_n_s_format +#define __pyx_n_s_fortran __pyx_mstate_global->__pyx_n_s_fortran +#define __pyx_n_u_fortran __pyx_mstate_global->__pyx_n_u_fortran +#define __pyx_kp_u_gc __pyx_mstate_global->__pyx_kp_u_gc +#define __pyx_n_s_getstate __pyx_mstate_global->__pyx_n_s_getstate +#define __pyx_kp_u_got __pyx_mstate_global->__pyx_kp_u_got +#define __pyx_kp_u_got_differing_extents_in_dimensi __pyx_mstate_global->__pyx_kp_u_got_differing_extents_in_dimensi +#define __pyx_n_s_id __pyx_mstate_global->__pyx_n_s_id +#define __pyx_n_s_import __pyx_mstate_global->__pyx_n_s_import +#define __pyx_n_s_index __pyx_mstate_global->__pyx_n_s_index +#define __pyx_n_s_initializing __pyx_mstate_global->__pyx_n_s_initializing +#define __pyx_n_s_is_coroutine __pyx_mstate_global->__pyx_n_s_is_coroutine +#define __pyx_kp_u_isenabled __pyx_mstate_global->__pyx_kp_u_isenabled +#define __pyx_n_s_itemsize __pyx_mstate_global->__pyx_n_s_itemsize +#define __pyx_kp_s_itemsize_0_for_cython_array __pyx_mstate_global->__pyx_kp_s_itemsize_0_for_cython_array +#define __pyx_n_s_main __pyx_mstate_global->__pyx_n_s_main +#define __pyx_n_s_max_neg_val __pyx_mstate_global->__pyx_n_s_max_neg_val +#define __pyx_n_s_maximum_path_c __pyx_mstate_global->__pyx_n_s_maximum_path_c +#define __pyx_n_s_memview __pyx_mstate_global->__pyx_n_s_memview +#define __pyx_n_s_mode __pyx_mstate_global->__pyx_n_s_mode +#define __pyx_n_s_monotonic_align_core __pyx_mstate_global->__pyx_n_s_monotonic_align_core +#define __pyx_kp_s_monotonic_align_core_pyx __pyx_mstate_global->__pyx_kp_s_monotonic_align_core_pyx +#define __pyx_n_s_name __pyx_mstate_global->__pyx_n_s_name +#define __pyx_n_s_name_2 __pyx_mstate_global->__pyx_n_s_name_2 +#define __pyx_n_s_ndim __pyx_mstate_global->__pyx_n_s_ndim +#define __pyx_n_s_new __pyx_mstate_global->__pyx_n_s_new +#define __pyx_kp_s_no_default___reduce___due_to_non __pyx_mstate_global->__pyx_kp_s_no_default___reduce___due_to_non +#define __pyx_n_s_np __pyx_mstate_global->__pyx_n_s_np +#define __pyx_n_s_numpy __pyx_mstate_global->__pyx_n_s_numpy +#define __pyx_kp_u_numpy_core_multiarray_failed_to __pyx_mstate_global->__pyx_kp_u_numpy_core_multiarray_failed_to +#define __pyx_kp_u_numpy_core_umath_failed_to_impor __pyx_mstate_global->__pyx_kp_u_numpy_core_umath_failed_to_impor +#define __pyx_n_s_obj __pyx_mstate_global->__pyx_n_s_obj +#define __pyx_n_s_pack __pyx_mstate_global->__pyx_n_s_pack +#define __pyx_n_s_paths __pyx_mstate_global->__pyx_n_s_paths +#define __pyx_n_s_pickle __pyx_mstate_global->__pyx_n_s_pickle +#define __pyx_n_s_pyx_PickleError __pyx_mstate_global->__pyx_n_s_pyx_PickleError +#define __pyx_n_s_pyx_checksum __pyx_mstate_global->__pyx_n_s_pyx_checksum +#define __pyx_n_s_pyx_result __pyx_mstate_global->__pyx_n_s_pyx_result +#define __pyx_n_s_pyx_state __pyx_mstate_global->__pyx_n_s_pyx_state +#define __pyx_n_s_pyx_type __pyx_mstate_global->__pyx_n_s_pyx_type +#define __pyx_n_s_pyx_unpickle_Enum __pyx_mstate_global->__pyx_n_s_pyx_unpickle_Enum +#define __pyx_n_s_pyx_vtable __pyx_mstate_global->__pyx_n_s_pyx_vtable +#define __pyx_n_s_range __pyx_mstate_global->__pyx_n_s_range +#define __pyx_n_s_reduce __pyx_mstate_global->__pyx_n_s_reduce +#define __pyx_n_s_reduce_cython __pyx_mstate_global->__pyx_n_s_reduce_cython +#define __pyx_n_s_reduce_ex __pyx_mstate_global->__pyx_n_s_reduce_ex +#define __pyx_n_s_register __pyx_mstate_global->__pyx_n_s_register +#define __pyx_n_s_setstate __pyx_mstate_global->__pyx_n_s_setstate +#define __pyx_n_s_setstate_cython __pyx_mstate_global->__pyx_n_s_setstate_cython +#define __pyx_n_s_shape __pyx_mstate_global->__pyx_n_s_shape +#define __pyx_n_s_size __pyx_mstate_global->__pyx_n_s_size +#define __pyx_n_s_spec __pyx_mstate_global->__pyx_n_s_spec +#define __pyx_n_s_start __pyx_mstate_global->__pyx_n_s_start +#define __pyx_n_s_step __pyx_mstate_global->__pyx_n_s_step +#define __pyx_n_s_stop __pyx_mstate_global->__pyx_n_s_stop +#define __pyx_kp_s_strided_and_direct __pyx_mstate_global->__pyx_kp_s_strided_and_direct +#define __pyx_kp_s_strided_and_direct_or_indirect __pyx_mstate_global->__pyx_kp_s_strided_and_direct_or_indirect +#define __pyx_kp_s_strided_and_indirect __pyx_mstate_global->__pyx_kp_s_strided_and_indirect +#define __pyx_kp_s_stringsource __pyx_mstate_global->__pyx_kp_s_stringsource +#define __pyx_n_s_struct __pyx_mstate_global->__pyx_n_s_struct +#define __pyx_n_s_sys __pyx_mstate_global->__pyx_n_s_sys +#define __pyx_n_s_t_xs __pyx_mstate_global->__pyx_n_s_t_xs +#define __pyx_n_s_t_ys __pyx_mstate_global->__pyx_n_s_t_ys +#define __pyx_n_s_test __pyx_mstate_global->__pyx_n_s_test +#define __pyx_kp_s_unable_to_allocate_array_data __pyx_mstate_global->__pyx_kp_s_unable_to_allocate_array_data +#define __pyx_kp_s_unable_to_allocate_shape_and_str __pyx_mstate_global->__pyx_kp_s_unable_to_allocate_shape_and_str +#define __pyx_n_s_unpack __pyx_mstate_global->__pyx_n_s_unpack +#define __pyx_n_s_update __pyx_mstate_global->__pyx_n_s_update +#define __pyx_n_s_values __pyx_mstate_global->__pyx_n_s_values +#define __pyx_n_s_version_info __pyx_mstate_global->__pyx_n_s_version_info +#define __pyx_int_0 __pyx_mstate_global->__pyx_int_0 +#define __pyx_int_1 __pyx_mstate_global->__pyx_int_1 +#define __pyx_int_3 __pyx_mstate_global->__pyx_int_3 +#define __pyx_int_112105877 __pyx_mstate_global->__pyx_int_112105877 +#define __pyx_int_136983863 __pyx_mstate_global->__pyx_int_136983863 +#define __pyx_int_184977713 __pyx_mstate_global->__pyx_int_184977713 +#define __pyx_int_neg_1 __pyx_mstate_global->__pyx_int_neg_1 +#define __pyx_k__11 __pyx_mstate_global->__pyx_k__11 +#define __pyx_slice__5 __pyx_mstate_global->__pyx_slice__5 +#define __pyx_tuple__4 __pyx_mstate_global->__pyx_tuple__4 +#define __pyx_tuple__8 __pyx_mstate_global->__pyx_tuple__8 +#define __pyx_tuple__9 __pyx_mstate_global->__pyx_tuple__9 +#define __pyx_tuple__10 __pyx_mstate_global->__pyx_tuple__10 +#define __pyx_tuple__12 __pyx_mstate_global->__pyx_tuple__12 +#define __pyx_tuple__13 __pyx_mstate_global->__pyx_tuple__13 +#define __pyx_tuple__14 __pyx_mstate_global->__pyx_tuple__14 +#define __pyx_tuple__15 __pyx_mstate_global->__pyx_tuple__15 +#define __pyx_tuple__16 __pyx_mstate_global->__pyx_tuple__16 +#define __pyx_tuple__17 __pyx_mstate_global->__pyx_tuple__17 +#define __pyx_tuple__18 __pyx_mstate_global->__pyx_tuple__18 +#define __pyx_tuple__19 __pyx_mstate_global->__pyx_tuple__19 +#define __pyx_tuple__20 __pyx_mstate_global->__pyx_tuple__20 +#define __pyx_tuple__21 __pyx_mstate_global->__pyx_tuple__21 +#define __pyx_tuple__23 __pyx_mstate_global->__pyx_tuple__23 +#define __pyx_codeobj__22 __pyx_mstate_global->__pyx_codeobj__22 +#define __pyx_codeobj__24 __pyx_mstate_global->__pyx_codeobj__24 +/* #### Code section: module_code ### */ + +/* "View.MemoryView":131 + * cdef bint dtype_is_object + * + * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< + * mode="c", bint allocate_buffer=True): + * + */ + +/* Python wrapper */ +static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_shape = 0; + Py_ssize_t __pyx_v_itemsize; + PyObject *__pyx_v_format = 0; + PyObject *__pyx_v_mode = 0; + int __pyx_v_allocate_buffer; + CYTHON_UNUSED Py_ssize_t __pyx_nargs; + CYTHON_UNUSED PyObject *const *__pyx_kwvalues; + PyObject* values[5] = 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start >= shape: + */ + __pyx_t_2 = (__pyx_v_start < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":834 + * start += shape + * if start < 0: + * start = 0 # <<<<<<<<<<<<<< + * elif start >= shape: + * if negative_step: + */ + __pyx_v_start = 0; + + /* "View.MemoryView":833 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + } + + /* "View.MemoryView":831 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + goto __pyx_L9; + } + + /* "View.MemoryView":835 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + __pyx_t_2 = (__pyx_v_start >= __pyx_v_shape); + if (__pyx_t_2) { + + /* "View.MemoryView":836 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + if (__pyx_v_negative_step) { + + /* "View.MemoryView":837 + * elif start >= shape: + * if 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<= abs_py_ssize_t(f_stride): + * return 'C' # <<<<<<<<<<<<<< + * else: + * return 'F' + */ + __pyx_r = 'C'; + goto __pyx_L0; + + /* "View.MemoryView":1131 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + } + + /* "View.MemoryView":1134 + * return 'C' + * else: + * return 'F' # <<<<<<<<<<<<<< + * + * @cython.cdivision(True) + */ + /*else*/ { + __pyx_r = 'F'; + goto __pyx_L0; + } + + /* "View.MemoryView":1113 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) noexcept nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1137 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + +static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; + Py_ssize_t __pyx_v_dst_extent; + Py_ssize_t __pyx_v_src_stride; + Py_ssize_t __pyx_v_dst_stride; + int __pyx_t_1; + int __pyx_t_2; + Py_ssize_t __pyx_t_3; + Py_ssize_t __pyx_t_4; + Py_ssize_t __pyx_t_5; + + /* "View.MemoryView":1144 + * + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + */ + __pyx_v_src_extent = (__pyx_v_src_shape[0]); + + /* "View.MemoryView":1145 + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] + */ + __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); + + /* "View.MemoryView":1146 + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + */ + __pyx_v_src_stride = (__pyx_v_src_strides[0]); + + /* "View.MemoryView":1147 + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< + * + * if ndim == 1: + */ + __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); + + /* "View.MemoryView":1149 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + __pyx_t_1 = (__pyx_v_ndim == 1); + if (__pyx_t_1) { + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + __pyx_t_2 = (__pyx_v_src_stride > 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + __pyx_t_2 = (__pyx_v_dst_stride > 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + + /* "View.MemoryView":1151 + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + */ + __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); + if (__pyx_t_2) { + __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); + } + __pyx_t_1 = __pyx_t_2; + __pyx_L5_bool_binop_done:; + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + if (__pyx_t_1) { + + /* "View.MemoryView":1152 + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + goto __pyx_L4; + } + + /* "View.MemoryView":1154 + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + */ + /*else*/ { + __pyx_t_3 = __pyx_v_dst_extent; + __pyx_t_4 = __pyx_t_3; + for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { + __pyx_v_i = __pyx_t_5; + + /* "View.MemoryView":1155 + * else: + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< + * src_data += src_stride + * dst_data += dst_stride + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); + + /* "View.MemoryView":1156 + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * else: + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1157 + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L4:; + + /* "View.MemoryView":1149 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1159 + * dst_data += dst_stride + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * _copy_strided_to_strided(src_data, src_strides + 1, + * dst_data, dst_strides + 1, + */ + /*else*/ { + __pyx_t_3 = __pyx_v_dst_extent; + __pyx_t_4 = __pyx_t_3; + for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { + __pyx_v_i = __pyx_t_5; + + /* "View.MemoryView":1160 + * else: + * for i in range(dst_extent): + * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< + * dst_data, dst_strides + 1, + * src_shape + 1, dst_shape + 1, + */ + _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); + + /* "View.MemoryView":1164 + * src_shape + 1, dst_shape + 1, + * ndim - 1, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1165 + * ndim - 1, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L3:; + + /* "View.MemoryView":1137 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + + /* function exit code */ +} + +/* "View.MemoryView":1167 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) noexcept nogil: + */ + +static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + + /* "View.MemoryView":1170 + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) noexcept nogil: + * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< + * src.shape, dst.shape, ndim, itemsize) + * + */ + _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); + + /* "View.MemoryView":1167 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) noexcept nogil: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1174 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + */ + +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { + Py_ssize_t __pyx_v_shape; + Py_ssize_t __pyx_v_size; + Py_ssize_t __pyx_r; + Py_ssize_t __pyx_t_1; + Py_ssize_t *__pyx_t_2; + Py_ssize_t *__pyx_t_3; + Py_ssize_t *__pyx_t_4; + + /* "View.MemoryView":1176 + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< + * + * for shape in src.shape[:ndim]: + */ + __pyx_t_1 = __pyx_v_src->memview->view.itemsize; + __pyx_v_size = __pyx_t_1; + + /* "View.MemoryView":1178 + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + * + * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< + * size *= shape + * + */ + __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim); + for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { + __pyx_t_2 = __pyx_t_4; + __pyx_v_shape = (__pyx_t_2[0]); + + /* "View.MemoryView":1179 + * + * for shape in src.shape[:ndim]: + * size *= shape # <<<<<<<<<<<<<< + * + * return size + */ + __pyx_v_size = (__pyx_v_size * __pyx_v_shape); + } + + /* "View.MemoryView":1181 + * size *= shape + * + * return size # <<<<<<<<<<<<<< + * + * @cname('__pyx_fill_contig_strides_array') + */ + __pyx_r = __pyx_v_size; + goto __pyx_L0; + + /* "View.MemoryView":1174 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1184 + * + * @cname('__pyx_fill_contig_strides_array') + * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< + * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, + * int ndim, char order) noexcept nogil: + */ + +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { + int __pyx_v_idx; + Py_ssize_t __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1193 + * cdef int idx + * + * if order == 'F': # <<<<<<<<<<<<<< + * for idx in range(ndim): + * strides[idx] = stride + */ + __pyx_t_1 = (__pyx_v_order == 'F'); + if (__pyx_t_1) { + + /* "View.MemoryView":1194 + * + * if order == 'F': + * for idx in range(ndim): # <<<<<<<<<<<<<< + * strides[idx] = stride + * stride *= shape[idx] + */ + __pyx_t_2 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_2; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_idx = __pyx_t_4; + + /* "View.MemoryView":1195 + * if order == 'F': + * for idx in range(ndim): + * strides[idx] = stride # <<<<<<<<<<<<<< + * stride *= shape[idx] + * else: + */ + (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; + + /* "View.MemoryView":1196 + * for idx in range(ndim): + * strides[idx] = stride + * stride *= shape[idx] # <<<<<<<<<<<<<< + * else: + * for idx in range(ndim - 1, -1, -1): + */ + __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); + } + + /* "View.MemoryView":1193 + * cdef int idx + * + * if order == 'F': # <<<<<<<<<<<<<< + * for idx in range(ndim): + * strides[idx] = stride + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1198 + * stride *= shape[idx] + * else: + * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * strides[idx] = stride + * stride *= shape[idx] + */ + /*else*/ { + for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { + __pyx_v_idx = __pyx_t_2; + + /* "View.MemoryView":1199 + * else: + * for idx in range(ndim - 1, -1, -1): + * strides[idx] = stride # <<<<<<<<<<<<<< + * stride *= shape[idx] + * + */ + (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; + + /* "View.MemoryView":1200 + * for idx in range(ndim - 1, -1, -1): + * strides[idx] = stride + * stride *= shape[idx] # <<<<<<<<<<<<<< + * + * return stride + */ + __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); + } + } + __pyx_L3:; + + /* "View.MemoryView":1202 + * stride *= shape[idx] + * + * return stride # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_copy_data_to_temp') + */ + __pyx_r = __pyx_v_stride; + goto __pyx_L0; + + /* "View.MemoryView":1184 + * + * @cname('__pyx_fill_contig_strides_array') + * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< + * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, + * int ndim, char order) noexcept nogil: + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1205 + * + * @cname('__pyx_memoryview_copy_data_to_temp') + * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *tmpslice, + * char order, + */ + +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) { + int __pyx_v_i; + void *__pyx_v_result; + size_t __pyx_v_itemsize; + size_t __pyx_v_size; + void *__pyx_r; + Py_ssize_t __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + struct __pyx_memoryview_obj *__pyx_t_4; + int __pyx_t_5; + int __pyx_t_6; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save; + #endif + + /* "View.MemoryView":1216 + * cdef void *result + * + * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< + * cdef size_t size = slice_get_size(src, ndim) + * + */ + __pyx_t_1 = __pyx_v_src->memview->view.itemsize; + __pyx_v_itemsize = __pyx_t_1; + + /* "View.MemoryView":1217 + * + * cdef size_t itemsize = src.memview.view.itemsize + * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< + * + * result = malloc(size) + */ + __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); + + /* "View.MemoryView":1219 + * cdef size_t size = slice_get_size(src, ndim) + * + * result = malloc(size) # <<<<<<<<<<<<<< + * if not result: + * _err_no_memory() + */ + __pyx_v_result = malloc(__pyx_v_size); + + /* "View.MemoryView":1220 + * + * result = malloc(size) + * if not result: # <<<<<<<<<<<<<< + * _err_no_memory() + * + */ + __pyx_t_2 = (!(__pyx_v_result != 0)); + if (__pyx_t_2) { + + /* "View.MemoryView":1221 + * result = malloc(size) + * if not result: + * _err_no_memory() # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_3 = __pyx_memoryview_err_no_memory(); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 1221, __pyx_L1_error) + + /* "View.MemoryView":1220 + * + * result = malloc(size) + * if not result: # <<<<<<<<<<<<<< + * _err_no_memory() + * + */ + } + + /* "View.MemoryView":1224 + * + * + * tmpslice.data = result # <<<<<<<<<<<<<< + * tmpslice.memview = src.memview + * for i in range(ndim): + */ + __pyx_v_tmpslice->data = ((char *)__pyx_v_result); + + /* "View.MemoryView":1225 + * + * tmpslice.data = result + * tmpslice.memview = src.memview # <<<<<<<<<<<<<< + * for i in range(ndim): + * tmpslice.shape[i] = src.shape[i] + */ + __pyx_t_4 = __pyx_v_src->memview; + __pyx_v_tmpslice->memview = __pyx_t_4; + + /* "View.MemoryView":1226 + * tmpslice.data = result + * tmpslice.memview = src.memview + * for i in range(ndim): # <<<<<<<<<<<<<< + * tmpslice.shape[i] = src.shape[i] + * tmpslice.suboffsets[i] = -1 + */ + __pyx_t_3 = __pyx_v_ndim; + __pyx_t_5 = __pyx_t_3; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1227 + * tmpslice.memview = src.memview + * for i in range(ndim): + * tmpslice.shape[i] = src.shape[i] # <<<<<<<<<<<<<< + * tmpslice.suboffsets[i] = -1 + * + */ + (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]); + + /* "View.MemoryView":1228 + * for i in range(ndim): + * tmpslice.shape[i] = src.shape[i] + * tmpslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< + * + * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, ndim, order) + */ + (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L; + } + + /* "View.MemoryView":1230 + * tmpslice.suboffsets[i] = -1 + * + * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, ndim, order) # <<<<<<<<<<<<<< + * + * + */ + (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order)); + + /* "View.MemoryView":1233 + * + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * if tmpslice.shape[i] == 1: + * tmpslice.strides[i] = 0 + */ + __pyx_t_3 = __pyx_v_ndim; + __pyx_t_5 = __pyx_t_3; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1234 + * + * for i in range(ndim): + * if 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src_ndim: + * broadcast_leading(&dst, dst_ndim, src_ndim) # <<<<<<<<<<<<<< + * + * cdef int ndim = max(src_ndim, dst_ndim) + */ + __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim); + + /* "View.MemoryView":1283 + * if src_ndim < dst_ndim: + * broadcast_leading(&src, src_ndim, dst_ndim) + * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< + * broadcast_leading(&dst, dst_ndim, src_ndim) + * + */ + } + __pyx_L3:; + + /* "View.MemoryView":1286 + * broadcast_leading(&dst, dst_ndim, src_ndim) + * + * cdef int ndim = max(src_ndim, dst_ndim) # <<<<<<<<<<<<<< + * + * for i in range(ndim): + */ + __pyx_t_3 = __pyx_v_dst_ndim; + __pyx_t_4 = __pyx_v_src_ndim; + __pyx_t_2 = (__pyx_t_3 > __pyx_t_4); + if (__pyx_t_2) { + __pyx_t_5 = __pyx_t_3; + } else { + __pyx_t_5 = __pyx_t_4; + } + __pyx_v_ndim = __pyx_t_5; + + /* "View.MemoryView":1288 + * cdef int ndim = max(src_ndim, dst_ndim) + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * if src.shape[i] != dst.shape[i]: + * if src.shape[i] == 1: + */ + __pyx_t_5 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_5; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* "View.MemoryView":1289 + * + * for i in range(ndim): + * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< + * if src.shape[i] == 1: + * broadcasting = True + */ + __pyx_t_2 = ((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])); + if (__pyx_t_2) { + + /* "View.MemoryView":1290 + * for i in range(ndim): + * if src.shape[i] != dst.shape[i]: + * if src.shape[i] == 1: # <<<<<<<<<<<<<< + * broadcasting = True + * src.strides[i] = 0 + */ + __pyx_t_2 = ((__pyx_v_src.shape[__pyx_v_i]) == 1); + if (__pyx_t_2) { + + /* "View.MemoryView":1291 + * if src.shape[i] != dst.shape[i]: + * if src.shape[i] == 1: + * broadcasting = True # <<<<<<<<<<<<<< + * src.strides[i] = 0 + * else: + */ + __pyx_v_broadcasting = 1; + + /* "View.MemoryView":1292 + * if src.shape[i] == 1: + * broadcasting = True + * src.strides[i] = 0 # <<<<<<<<<<<<<< + * else: + * _err_extents(i, dst.shape[i], src.shape[i]) + */ + (__pyx_v_src.strides[__pyx_v_i]) = 0; + + /* "View.MemoryView":1290 + * for i in range(ndim): + * if src.shape[i] != dst.shape[i]: + * if src.shape[i] == 1: # <<<<<<<<<<<<<< + * broadcasting = True + * src.strides[i] = 0 + */ + goto __pyx_L7; + } + + /* "View.MemoryView":1294 + * src.strides[i] = 0 + * else: + * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<< + * + * if src.suboffsets[i] >= 0: + */ + /*else*/ { + __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1294, __pyx_L1_error) + } + __pyx_L7:; + + /* "View.MemoryView":1289 + * + * for i in range(ndim): + * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< + * if src.shape[i] == 1: + * broadcasting = True + */ + } + + /* "View.MemoryView":1296 + * _err_extents(i, dst.shape[i], src.shape[i]) + * + * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) + * + */ + __pyx_t_2 = ((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1297 + * + * if src.suboffsets[i] >= 0: + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< + * + * if slices_overlap(&src, &dst, ndim, itemsize): + */ + __pyx_t_6 = __pyx_memoryview_err_dim(PyExc_ValueError, __pyx_kp_s_Dimension_d_is_not_direct, __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1297, __pyx_L1_error) + + /* "View.MemoryView":1296 + * _err_extents(i, dst.shape[i], src.shape[i]) + * + * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) + * + */ + } + } + + /* "View.MemoryView":1299 + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) + * + * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< + * + * if not slice_is_contig(src, order, ndim): + */ + __pyx_t_2 = __pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); + if (__pyx_t_2) { + + /* "View.MemoryView":1301 + * if slices_overlap(&src, &dst, ndim, itemsize): + * + * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< + * order = get_best_order(&dst, ndim) + * + */ + __pyx_t_2 = (!__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim)); + if (__pyx_t_2) { + + /* "View.MemoryView":1302 + * + * if not slice_is_contig(src, order, ndim): + * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) + */ + __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); + + /* "View.MemoryView":1301 + * if slices_overlap(&src, &dst, ndim, itemsize): + * + * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< + * order = get_best_order(&dst, ndim) + * + */ + } + + /* "View.MemoryView":1304 + * order = get_best_order(&dst, ndim) + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< + * src = tmp + * + */ + __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(1, 1304, __pyx_L1_error) + __pyx_v_tmpdata = __pyx_t_7; + + /* "View.MemoryView":1305 + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) + * src = tmp # <<<<<<<<<<<<<< + * + * if not broadcasting: + */ + __pyx_v_src = __pyx_v_tmp; + + /* "View.MemoryView":1299 + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) + * + * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< + * + * if not slice_is_contig(src, order, ndim): + */ + } + + /* "View.MemoryView":1307 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = (!__pyx_v_broadcasting); + if (__pyx_t_2) { + + /* "View.MemoryView":1310 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + */ + __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim); + if (__pyx_t_2) { + + /* "View.MemoryView":1311 + * + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); + + /* "View.MemoryView":1310 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + */ + goto __pyx_L12; + } + + /* "View.MemoryView":1312 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim); + if (__pyx_t_2) { + + /* "View.MemoryView":1313 + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< + * + * if direct_copy: + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); + + /* "View.MemoryView":1312 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + } + __pyx_L12:; + + /* "View.MemoryView":1315 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + */ + if (__pyx_v_direct_copy) { + + /* "View.MemoryView":1317 + * if direct_copy: + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) # <<<<<<<<<<<<<< + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1318 + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) + */ + (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); + + /* "View.MemoryView":1319 + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) # <<<<<<<<<<<<<< + * free(tmpdata) + * return 0 + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1320 + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1321 + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * if order == 'F' == get_best_order(&dst, ndim): + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1315 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + */ + } + + /* "View.MemoryView":1307 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1323 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = (__pyx_v_order == 'F'); + if (__pyx_t_2) { + __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); + } + if (__pyx_t_2) { + + /* "View.MemoryView":1326 + * + * + * transpose_memslice(&src) # <<<<<<<<<<<<<< + * transpose_memslice(&dst) + * + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1326, __pyx_L1_error) + + /* "View.MemoryView":1327 + * + * transpose_memslice(&src) + * transpose_memslice(&dst) # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1327, __pyx_L1_error) + + /* "View.MemoryView":1323 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1329 + * transpose_memslice(&dst) + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) # <<<<<<<<<<<<<< + * copy_strided_to_strided(&src, &dst, ndim, itemsize) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1330 + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * + */ + copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); + + /* "View.MemoryView":1331 + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * copy_strided_to_strided(&src, &dst, ndim, itemsize) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) # <<<<<<<<<<<<<< + * + * free(tmpdata) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1333 + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1334 + * + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_broadcast_leading') + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1265 + * + * @cname('__pyx_memoryview_copy_contents') + * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice dst, + * int src_ndim, int dst_ndim, + */ + + /* function exit code */ + __pyx_L1_error:; + #ifdef WITH_THREAD + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1337 + * + * @cname('__pyx_memoryview_broadcast_leading') + * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< + * int ndim, + * int ndim_other) noexcept nogil: + */ + +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) { + int __pyx_v_i; + int __pyx_v_offset; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + + /* "View.MemoryView":1341 + * int ndim_other) noexcept nogil: + * cdef int i + * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< + * + * for i in range(ndim - 1, -1, -1): + */ + __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); + + /* "View.MemoryView":1343 + * cdef int offset = ndim_other - ndim + * + * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] + */ + for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1344 + * + * for i in range(ndim - 1, -1, -1): + * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< + * mslice.strides[i + offset] = mslice.strides[i] + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + */ + (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); + + /* "View.MemoryView":1345 + * for i in range(ndim - 1, -1, -1): + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + * + */ + (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1346 + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< + * + * for i in range(offset): + */ + (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]); + } + + /* "View.MemoryView":1348 + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + * + * for i in range(offset): # <<<<<<<<<<<<<< + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] + */ + __pyx_t_1 = __pyx_v_offset; + __pyx_t_2 = __pyx_t_1; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; + + /* "View.MemoryView":1349 + * + * for i in range(offset): + * mslice.shape[i] = 1 # <<<<<<<<<<<<<< + * mslice.strides[i] = mslice.strides[0] + * mslice.suboffsets[i] = -1 + */ + (__pyx_v_mslice->shape[__pyx_v_i]) = 1; + + /* "View.MemoryView":1350 + * for i in range(offset): + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] # <<<<<<<<<<<<<< + * mslice.suboffsets[i] = -1 + * + */ + (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]); + + /* "View.MemoryView":1351 + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] + * mslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< + * + * + */ + (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L; + } + + /* "View.MemoryView":1337 + * + * @cname('__pyx_memoryview_broadcast_leading') + * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< + * int ndim, + * int ndim_other) noexcept nogil: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1359 + * + * @cname('__pyx_memoryview_refcount_copying') + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: # <<<<<<<<<<<<<< + * + * if dtype_is_object: + */ + +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) { + + /* "View.MemoryView":1361 + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: + * + * if dtype_is_object: # <<<<<<<<<<<<<< + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) + * + */ + if (__pyx_v_dtype_is_object) { + + /* "View.MemoryView":1362 + * + * if dtype_is_object: + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') + */ + __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc); + + /* "View.MemoryView":1361 + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: + * + * if dtype_is_object: # <<<<<<<<<<<<<< + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) + * + */ + } + + /* "View.MemoryView":1359 + * + * @cname('__pyx_memoryview_refcount_copying') + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: # <<<<<<<<<<<<<< + * + * if dtype_is_object: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1365 + * + * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') + * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< + * Py_ssize_t *strides, int ndim, + * bint inc) noexcept with gil: + */ + +static void 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return NULL; +} + +static void __pyx_tp_dealloc_array(PyObject *o) { + struct __pyx_array_obj *p = (struct __pyx_array_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && (!PyType_IS_GC(Py_TYPE(o)) || !__Pyx_PyObject_GC_IsFinalized(o))) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_array) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_array___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->mode); + Py_CLEAR(p->_format); + #if CYTHON_USE_TYPE_SLOTS || CYTHON_COMPILING_IN_PYPY + (*Py_TYPE(o)->tp_free)(o); + #else + { + freefunc tp_free = (freefunc)PyType_GetSlot(Py_TYPE(o), Py_tp_free); + if (tp_free) 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*x) { + return __pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(o); +} + +static PyMethodDef __pyx_methods_array[] = { + {"__getattr__", (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, 0}, + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_array_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_array_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_array[] = { + {(char *)"memview", __pyx_getprop___pyx_array_memview, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +#if !CYTHON_COMPILING_IN_LIMITED_API + +static PyBufferProcs __pyx_tp_as_buffer_array = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_array_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; +#endif +static PyType_Slot __pyx_type___pyx_array_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc_array}, + {Py_sq_length, (void *)__pyx_array___len__}, + {Py_sq_item, (void *)__pyx_sq_item_array}, + {Py_mp_length, (void *)__pyx_array___len__}, + {Py_mp_subscript, (void *)__pyx_array___getitem__}, + {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_array}, + {Py_tp_getattro, (void *)__pyx_tp_getattro_array}, + #if defined(Py_bf_getbuffer) + {Py_bf_getbuffer, (void *)__pyx_array_getbuffer}, + #endif + {Py_tp_methods, (void *)__pyx_methods_array}, + {Py_tp_getset, (void *)__pyx_getsets_array}, + {Py_tp_new, (void *)__pyx_tp_new_array}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_array_spec = { + "monotonic_align.core.array", + sizeof(struct __pyx_array_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_SEQUENCE, + __pyx_type___pyx_array_slots, +}; +#else + +static PySequenceMethods __pyx_tp_as_sequence_array = { + __pyx_array___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_array, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_array = { + __pyx_array___len__, /*mp_length*/ + __pyx_array___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_array = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_array_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_array = { + PyVarObject_HEAD_INIT(0, 0) + "monotonic_align.core.""array", /*tp_name*/ + sizeof(struct __pyx_array_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_array, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + 0, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_array, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_array, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + __pyx_tp_getattro_array, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_array, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_SEQUENCE, /*tp_flags*/ + 0, /*tp_doc*/ + 0, /*tp_traverse*/ + 0, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_array, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_array, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_array, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif + +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { + struct __pyx_MemviewEnum_obj *p; + PyObject *o; + #if CYTHON_COMPILING_IN_LIMITED_API + allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc); + o = alloc_func(t, 0); + #else + if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + #endif + p = ((struct __pyx_MemviewEnum_obj *)o); + p->name = Py_None; Py_INCREF(Py_None); + return o; +} + +static void __pyx_tp_dealloc_Enum(PyObject *o) { + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_Enum) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + Py_CLEAR(p->name); + #if CYTHON_USE_TYPE_SLOTS || CYTHON_COMPILING_IN_PYPY + (*Py_TYPE(o)->tp_free)(o); + #else + { + freefunc tp_free = (freefunc)PyType_GetSlot(Py_TYPE(o), Py_tp_free); + if (tp_free) tp_free(o); + } + #endif +} + +static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + if (p->name) { + e = (*v)(p->name, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_Enum(PyObject *o) { + PyObject* tmp; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + tmp = ((PyObject*)p->name); + p->name = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + return 0; +} + +static PyObject *__pyx_specialmethod___pyx_MemviewEnum___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) { + return __pyx_MemviewEnum___repr__(self); +} + +static PyMethodDef __pyx_methods_Enum[] = { + {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_MemviewEnum___repr__, METH_NOARGS|METH_COEXIST, 0}, + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +static PyType_Slot __pyx_type___pyx_MemviewEnum_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc_Enum}, + {Py_tp_repr, (void *)__pyx_MemviewEnum___repr__}, + {Py_tp_traverse, (void *)__pyx_tp_traverse_Enum}, + {Py_tp_clear, (void *)__pyx_tp_clear_Enum}, + {Py_tp_methods, (void *)__pyx_methods_Enum}, + {Py_tp_init, (void *)__pyx_MemviewEnum___init__}, + {Py_tp_new, (void *)__pyx_tp_new_Enum}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_MemviewEnum_spec = { + "monotonic_align.core.Enum", + sizeof(struct __pyx_MemviewEnum_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, + __pyx_type___pyx_MemviewEnum_slots, +}; +#else + +static PyTypeObject __pyx_type___pyx_MemviewEnum = { + PyVarObject_HEAD_INIT(0, 0) + "monotonic_align.core.""Enum", /*tp_name*/ + sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_Enum, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_MemviewEnum___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_Enum, /*tp_traverse*/ + __pyx_tp_clear_Enum, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_Enum, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + __pyx_MemviewEnum___init__, /*tp_init*/ + 0, /*tp_alloc*/ + 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= (allocfunc)PyType_GetSlot(t, Py_tp_alloc); + o = alloc_func(t, 0); + #else + if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + #endif + p = ((struct __pyx_memoryview_obj *)o); + p->__pyx_vtab = __pyx_vtabptr_memoryview; + p->obj = Py_None; Py_INCREF(Py_None); + p->_size = Py_None; Py_INCREF(Py_None); + p->_array_interface = Py_None; Py_INCREF(Py_None); + p->view.obj = NULL; + if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; + return o; + bad: + Py_DECREF(o); o = 0; + return NULL; +} + +static void __pyx_tp_dealloc_memoryview(PyObject *o) { + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_memoryview) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_memoryview___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->obj); + Py_CLEAR(p->_size); + Py_CLEAR(p->_array_interface); + #if CYTHON_USE_TYPE_SLOTS || CYTHON_COMPILING_IN_PYPY + (*Py_TYPE(o)->tp_free)(o); + #else + { + freefunc tp_free = (freefunc)PyType_GetSlot(Py_TYPE(o), Py_tp_free); + if (tp_free) tp_free(o); + } + #endif +} + +static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + if (p->obj) { + e = (*v)(p->obj, a); if (e) return e; + } + if (p->_size) { + e = (*v)(p->_size, a); if (e) return e; + } + if (p->_array_interface) { + e = (*v)(p->_array_interface, a); if (e) return e; + } + if (p->view.obj) { + e = (*v)(p->view.obj, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_memoryview(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + tmp = ((PyObject*)p->obj); + p->obj = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_size); + p->_size = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_array_interface); + p->_array_interface = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + Py_CLEAR(p->view.obj); + return 0; +} +static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { + PyObject *r; + PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; + r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); + Py_DECREF(x); + return r; +} + +static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { + if (v) { + return __pyx_memoryview___setitem__(o, i, v); + } + else { + __Pyx_TypeName o_type_name; + o_type_name = __Pyx_PyType_GetName(Py_TYPE(o)); + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by " __Pyx_FMT_TYPENAME, o_type_name); + __Pyx_DECREF_TypeName(o_type_name); + return -1; + } +} + +static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); +} + +static PyObject *__pyx_specialmethod___pyx_memoryview___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) { + return __pyx_memoryview___repr__(self); +} + +static PyMethodDef __pyx_methods_memoryview[] = { + {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_memoryview___repr__, METH_NOARGS|METH_COEXIST, 0}, + {"is_c_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_c_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"is_f_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_f_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"copy", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"copy_fortran", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy_fortran, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_memoryview[] = { + {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, + {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, + {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, + {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, + {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, + {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, + {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, + {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, + {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +#if !CYTHON_COMPILING_IN_LIMITED_API + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; +#endif +static PyType_Slot __pyx_type___pyx_memoryview_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc_memoryview}, + {Py_tp_repr, (void *)__pyx_memoryview___repr__}, + {Py_sq_length, (void *)__pyx_memoryview___len__}, + {Py_sq_item, (void *)__pyx_sq_item_memoryview}, + {Py_mp_length, (void *)__pyx_memoryview___len__}, + {Py_mp_subscript, (void *)__pyx_memoryview___getitem__}, + {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_memoryview}, + {Py_tp_str, (void *)__pyx_memoryview___str__}, + #if defined(Py_bf_getbuffer) + {Py_bf_getbuffer, (void *)__pyx_memoryview_getbuffer}, + #endif + {Py_tp_traverse, (void *)__pyx_tp_traverse_memoryview}, + {Py_tp_clear, (void *)__pyx_tp_clear_memoryview}, + {Py_tp_methods, (void *)__pyx_methods_memoryview}, + {Py_tp_getset, (void *)__pyx_getsets_memoryview}, + {Py_tp_new, (void *)__pyx_tp_new_memoryview}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_memoryview_spec = { + "monotonic_align.core.memoryview", + sizeof(struct __pyx_memoryview_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, + __pyx_type___pyx_memoryview_slots, +}; +#else + +static PySequenceMethods __pyx_tp_as_sequence_memoryview = { + __pyx_memoryview___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_memoryview, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_memoryview = { + __pyx_memoryview___len__, /*mp_length*/ + __pyx_memoryview___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_memoryview = { + PyVarObject_HEAD_INIT(0, 0) + "monotonic_align.core.""memoryview", /*tp_name*/ + sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_memoryview___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + __pyx_memoryview___str__, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_memoryview, /*tp_traverse*/ + __pyx_tp_clear_memoryview, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_memoryview, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_memoryview, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_memoryview, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif +static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; + +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryviewslice_obj *p; + PyObject *o = __pyx_tp_new_memoryview(t, a, k); + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryviewslice_obj *)o); + p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; + p->from_object = Py_None; Py_INCREF(Py_None); + p->from_slice.memview = NULL; + return o; +} + +static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc__memoryviewslice) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_memoryviewslice___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->from_object); + PyObject_GC_Track(o); + __pyx_tp_dealloc_memoryview(o); +} + +static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; + if (p->from_object) { + e = (*v)(p->from_object, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear__memoryviewslice(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + __pyx_tp_clear_memoryview(o); + tmp = ((PyObject*)p->from_object); + p->from_object = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + __PYX_XCLEAR_MEMVIEW(&p->from_slice, 1); + return 0; +} + +static PyMethodDef __pyx_methods__memoryviewslice[] = { + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +static PyType_Slot __pyx_type___pyx_memoryviewslice_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc__memoryviewslice}, + {Py_tp_doc, (void *)PyDoc_STR("Internal class for passing memoryview slices to Python")}, + {Py_tp_traverse, (void *)__pyx_tp_traverse__memoryviewslice}, + {Py_tp_clear, (void *)__pyx_tp_clear__memoryviewslice}, + {Py_tp_methods, (void *)__pyx_methods__memoryviewslice}, + {Py_tp_new, (void *)__pyx_tp_new__memoryviewslice}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_memoryviewslice_spec = { + "monotonic_align.core._memoryviewslice", + sizeof(struct __pyx_memoryviewslice_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC|Py_TPFLAGS_SEQUENCE, + __pyx_type___pyx_memoryviewslice_slots, +}; +#else + +static PyTypeObject __pyx_type___pyx_memoryviewslice = { + PyVarObject_HEAD_INIT(0, 0) + "monotonic_align.core.""_memoryviewslice", /*tp_name*/ + sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + #if CYTHON_COMPILING_IN_PYPY || 0 + __pyx_memoryview___repr__, /*tp_repr*/ + #else + 0, /*tp_repr*/ + #endif + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + #if CYTHON_COMPILING_IN_PYPY || 0 + __pyx_memoryview___str__, /*tp_str*/ + #else + 0, /*tp_str*/ + #endif + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC|Py_TPFLAGS_SEQUENCE, /*tp_flags*/ + PyDoc_STR("Internal class for passing memoryview slices to Python"), /*tp_doc*/ + __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ + __pyx_tp_clear__memoryviewslice, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods__memoryviewslice, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new__memoryviewslice, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif + +static PyMethodDef __pyx_methods[] = { + {0, 0, 0, 0} +}; +#ifndef CYTHON_SMALL_CODE +#if defined(__clang__) + #define CYTHON_SMALL_CODE +#elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3)) + #define CYTHON_SMALL_CODE __attribute__((cold)) +#else + #define CYTHON_SMALL_CODE +#endif +#endif +/* #### Code section: pystring_table ### */ + +static int __Pyx_CreateStringTabAndInitStrings(void) { + __Pyx_StringTabEntry __pyx_string_tab[] = { + {&__pyx_kp_u_, __pyx_k_, sizeof(__pyx_k_), 0, 1, 0, 0}, + {&__pyx_n_s_ASCII, __pyx_k_ASCII, sizeof(__pyx_k_ASCII), 0, 0, 1, 1}, + {&__pyx_kp_s_All_dimensions_preceding_dimensi, __pyx_k_All_dimensions_preceding_dimensi, sizeof(__pyx_k_All_dimensions_preceding_dimensi), 0, 0, 1, 0}, + {&__pyx_n_s_AssertionError, __pyx_k_AssertionError, sizeof(__pyx_k_AssertionError), 0, 0, 1, 1}, + {&__pyx_kp_s_Buffer_view_does_not_expose_stri, __pyx_k_Buffer_view_does_not_expose_stri, sizeof(__pyx_k_Buffer_view_does_not_expose_stri), 0, 0, 1, 0}, + {&__pyx_kp_s_Can_only_create_a_buffer_that_is, __pyx_k_Can_only_create_a_buffer_that_is, sizeof(__pyx_k_Can_only_create_a_buffer_that_is), 0, 0, 1, 0}, + {&__pyx_kp_s_Cannot_assign_to_read_only_memor, __pyx_k_Cannot_assign_to_read_only_memor, sizeof(__pyx_k_Cannot_assign_to_read_only_memor), 0, 0, 1, 0}, + {&__pyx_kp_s_Cannot_create_writable_memory_vi, __pyx_k_Cannot_create_writable_memory_vi, sizeof(__pyx_k_Cannot_create_writable_memory_vi), 0, 0, 1, 0}, + {&__pyx_kp_u_Cannot_index_with_type, __pyx_k_Cannot_index_with_type, sizeof(__pyx_k_Cannot_index_with_type), 0, 1, 0, 0}, + {&__pyx_kp_s_Cannot_transpose_memoryview_with, __pyx_k_Cannot_transpose_memoryview_with, sizeof(__pyx_k_Cannot_transpose_memoryview_with), 0, 0, 1, 0}, + {&__pyx_kp_s_Dimension_d_is_not_direct, __pyx_k_Dimension_d_is_not_direct, sizeof(__pyx_k_Dimension_d_is_not_direct), 0, 0, 1, 0}, + {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1}, + {&__pyx_kp_s_Empty_shape_tuple_for_cython_arr, __pyx_k_Empty_shape_tuple_for_cython_arr, sizeof(__pyx_k_Empty_shape_tuple_for_cython_arr), 0, 0, 1, 0}, + {&__pyx_n_s_ImportError, __pyx_k_ImportError, sizeof(__pyx_k_ImportError), 0, 0, 1, 1}, + {&__pyx_kp_s_Incompatible_checksums_0x_x_vs_0, __pyx_k_Incompatible_checksums_0x_x_vs_0, 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{&__pyx_n_s_class_getitem, __pyx_k_class_getitem, sizeof(__pyx_k_class_getitem), 0, 0, 1, 1}, + {&__pyx_n_s_cline_in_traceback, __pyx_k_cline_in_traceback, sizeof(__pyx_k_cline_in_traceback), 0, 0, 1, 1}, + {&__pyx_n_s_collections, __pyx_k_collections, sizeof(__pyx_k_collections), 0, 0, 1, 1}, + {&__pyx_kp_s_collections_abc, __pyx_k_collections_abc, sizeof(__pyx_k_collections_abc), 0, 0, 1, 0}, + {&__pyx_kp_s_contiguous_and_direct, __pyx_k_contiguous_and_direct, sizeof(__pyx_k_contiguous_and_direct), 0, 0, 1, 0}, + {&__pyx_kp_s_contiguous_and_indirect, __pyx_k_contiguous_and_indirect, sizeof(__pyx_k_contiguous_and_indirect), 0, 0, 1, 0}, + {&__pyx_n_s_count, __pyx_k_count, sizeof(__pyx_k_count), 0, 0, 1, 1}, + {&__pyx_n_s_dict, __pyx_k_dict, sizeof(__pyx_k_dict), 0, 0, 1, 1}, + {&__pyx_kp_u_disable, __pyx_k_disable, sizeof(__pyx_k_disable), 0, 1, 0, 0}, + {&__pyx_n_s_dtype_is_object, __pyx_k_dtype_is_object, sizeof(__pyx_k_dtype_is_object), 0, 0, 1, 1}, + {&__pyx_kp_u_enable, 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(result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { + return (equals == Py_NE); + } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { + return (equals == Py_NE); + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +#endif +} + +/* UnicodeEquals */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API + return PyObject_RichCompareBool(s1, s2, equals); +#else +#if PY_MAJOR_VERSION < 3 + PyObject* owned_ref = NULL; +#endif + int s1_is_unicode, s2_is_unicode; + if (s1 == s2) { + goto return_eq; + } + s1_is_unicode = PyUnicode_CheckExact(s1); + s2_is_unicode = PyUnicode_CheckExact(s2); +#if PY_MAJOR_VERSION < 3 + if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { + owned_ref = PyUnicode_FromObject(s2); + if (unlikely(!owned_ref)) + return -1; + s2 = owned_ref; + s2_is_unicode = 1; + } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { + owned_ref = PyUnicode_FromObject(s1); + if (unlikely(!owned_ref)) + return -1; + s1 = owned_ref; + s1_is_unicode = 1; + } else if (((!s2_is_unicode) & (!s1_is_unicode))) { + return __Pyx_PyBytes_Equals(s1, s2, equals); + } +#endif + if (s1_is_unicode & s2_is_unicode) { + Py_ssize_t length; + int kind; + void *data1, *data2; + if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) + return -1; + length = __Pyx_PyUnicode_GET_LENGTH(s1); + if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { + goto return_ne; + } +#if CYTHON_USE_UNICODE_INTERNALS + { + Py_hash_t hash1, hash2; + #if CYTHON_PEP393_ENABLED + hash1 = ((PyASCIIObject*)s1)->hash; + hash2 = ((PyASCIIObject*)s2)->hash; + #else + hash1 = ((PyUnicodeObject*)s1)->hash; + hash2 = ((PyUnicodeObject*)s2)->hash; + #endif + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + goto return_ne; + } + } +#endif + kind = __Pyx_PyUnicode_KIND(s1); + if (kind != __Pyx_PyUnicode_KIND(s2)) { + goto return_ne; + } + data1 = __Pyx_PyUnicode_DATA(s1); + data2 = __Pyx_PyUnicode_DATA(s2); + if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { + goto return_ne; + } else if (length == 1) { + goto return_eq; + } else { + int result = memcmp(data1, data2, (size_t)(length * kind)); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & s2_is_unicode) { + goto return_ne; + } else if ((s2 == Py_None) & s1_is_unicode) { + goto return_ne; + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +return_eq: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ); +return_ne: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_NE); +#endif +} + +/* fastcall */ +#if CYTHON_METH_FASTCALL +static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s) +{ + Py_ssize_t i, n = PyTuple_GET_SIZE(kwnames); + for (i = 0; i < n; i++) + { + if (s == PyTuple_GET_ITEM(kwnames, i)) return kwvalues[i]; + } + for (i = 0; i < n; i++) + { + int eq = __Pyx_PyUnicode_Equals(s, PyTuple_GET_ITEM(kwnames, i), Py_EQ); + if (unlikely(eq != 0)) { + if (unlikely(eq < 0)) return NULL; // error + return kwvalues[i]; + } + } + return NULL; // not found (no exception set) +} +#endif + +/* RaiseArgTupleInvalid */ +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* RaiseDoubleKeywords */ +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject *const *kwvalues, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + int kwds_is_tuple = CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds)); + while (1) { + Py_XDECREF(key); key = NULL; + Py_XDECREF(value); value = NULL; + if (kwds_is_tuple) { + Py_ssize_t size; +#if CYTHON_ASSUME_SAFE_MACROS + size = PyTuple_GET_SIZE(kwds); +#else + size = PyTuple_Size(kwds); + if (size < 0) goto bad; +#endif + if (pos >= size) break; +#if CYTHON_AVOID_BORROWED_REFS + key = __Pyx_PySequence_ITEM(kwds, pos); + if (!key) goto bad; +#elif CYTHON_ASSUME_SAFE_MACROS + key = PyTuple_GET_ITEM(kwds, pos); +#else + key = PyTuple_GetItem(kwds, pos); + if (!key) goto bad; +#endif + value = kwvalues[pos]; + pos++; + } + else + { + if (!PyDict_Next(kwds, &pos, &key, &value)) break; +#if CYTHON_AVOID_BORROWED_REFS + Py_INCREF(key); +#endif + } + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; +#if CYTHON_AVOID_BORROWED_REFS + Py_INCREF(value); // transfer ownership of value to values + Py_DECREF(key); +#endif + key = NULL; + value = NULL; + continue; + } +#if !CYTHON_AVOID_BORROWED_REFS + Py_INCREF(key); +#endif + Py_INCREF(value); + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; +#if CYTHON_AVOID_BORROWED_REFS + value = NULL; // ownership transferred to values +#endif + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = ( + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key) + ); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; +#if CYTHON_AVOID_BORROWED_REFS + value = NULL; // ownership transferred to values +#endif + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + Py_XDECREF(key); + Py_XDECREF(value); + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + #if PY_MAJOR_VERSION < 3 + PyErr_Format(PyExc_TypeError, + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + PyErr_Format(PyExc_TypeError, + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + Py_XDECREF(key); + Py_XDECREF(value); + return -1; +} + +/* ArgTypeTest */ +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) +{ + __Pyx_TypeName type_name; + __Pyx_TypeName obj_type_name; + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + else if (exact) { + #if PY_MAJOR_VERSION == 2 + if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(__Pyx_TypeCheck(obj, type))) return 1; + } + type_name = __Pyx_PyType_GetName(type); + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME + ", got " __Pyx_FMT_TYPENAME ")", name, type_name, obj_type_name); + __Pyx_DECREF_TypeName(type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return 0; +} + +/* RaiseException */ +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + __Pyx_PyThreadState_declare + CYTHON_UNUSED_VAR(cause); + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + if (cause) { + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { + #if PY_VERSION_HEX >= 0x030C00A6 + PyException_SetTraceback(value, tb); + #elif CYTHON_FAST_THREAD_STATE + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#else + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* PyFunctionFastCall */ +#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = __Pyx_PyFrame_GetLocalsplus(f); + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif + +/* PyObjectCall */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = Py_TYPE(func)->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallMethO */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = __Pyx_CyOrPyCFunction_GET_FUNCTION(func); + self = __Pyx_CyOrPyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectFastCall */ +#if PY_VERSION_HEX < 0x03090000 || CYTHON_COMPILING_IN_LIMITED_API +static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs) { + PyObject *argstuple; + PyObject *result = 0; + size_t i; + argstuple = PyTuple_New((Py_ssize_t)nargs); + if (unlikely(!argstuple)) return NULL; + for (i = 0; i < nargs; i++) { + Py_INCREF(args[i]); + if (__Pyx_PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]) < 0) goto bad; + } + result = __Pyx_PyObject_Call(func, argstuple, kwargs); + bad: + Py_DECREF(argstuple); + return result; +} +#endif +static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t _nargs, PyObject *kwargs) { + Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs); +#if CYTHON_COMPILING_IN_CPYTHON + if (nargs == 0 && kwargs == NULL) { + if (__Pyx_CyOrPyCFunction_Check(func) && likely( __Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_NOARGS)) + return __Pyx_PyObject_CallMethO(func, NULL); + } + else if (nargs == 1 && kwargs == NULL) { + if (__Pyx_CyOrPyCFunction_Check(func) && likely( __Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_O)) + return __Pyx_PyObject_CallMethO(func, args[0]); + } +#endif + #if PY_VERSION_HEX < 0x030800B1 + #if CYTHON_FAST_PYCCALL + if (PyCFunction_Check(func)) { + if (kwargs) { + return _PyCFunction_FastCallDict(func, args, nargs, kwargs); + } else { + return _PyCFunction_FastCallKeywords(func, args, nargs, NULL); + } + } + #if PY_VERSION_HEX >= 0x030700A1 + if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) { + return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL); + } + #endif + #endif + #if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs); + } + #endif + #endif + if (kwargs == NULL) { + #if CYTHON_VECTORCALL + #if Py_VERSION_HEX < 0x03090000 + vectorcallfunc f = _PyVectorcall_Function(func); + #else + vectorcallfunc f = PyVectorcall_Function(func); + #endif + if (f) { + return f(func, args, (size_t)nargs, NULL); + } + #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL + if (__Pyx_CyFunction_CheckExact(func)) { + __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func); + if (f) return f(func, args, (size_t)nargs, NULL); + } + #endif + } + if (nargs == 0) { + return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs); + } + #if PY_VERSION_HEX >= 0x03090000 && !CYTHON_COMPILING_IN_LIMITED_API + return PyObject_VectorcallDict(func, args, (size_t)nargs, kwargs); + #else + return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs); + #endif +} + +/* RaiseUnexpectedTypeError */ +static int +__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj) +{ + __Pyx_TypeName obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME, + expected, obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return 0; +} + +/* CIntToDigits */ +static const char DIGIT_PAIRS_10[2*10*10+1] = { + "00010203040506070809" + "10111213141516171819" + "20212223242526272829" + "30313233343536373839" + "40414243444546474849" + "50515253545556575859" + "60616263646566676869" + "70717273747576777879" + "80818283848586878889" + "90919293949596979899" +}; +static const char DIGIT_PAIRS_8[2*8*8+1] = { + "0001020304050607" + "1011121314151617" + "2021222324252627" + "3031323334353637" + "4041424344454647" + "5051525354555657" + "6061626364656667" + "7071727374757677" +}; +static const char DIGITS_HEX[2*16+1] = { + "0123456789abcdef" + "0123456789ABCDEF" +}; + +/* BuildPyUnicode */ +static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, + int prepend_sign, char padding_char) { + PyObject *uval; + Py_ssize_t uoffset = ulength - clength; +#if CYTHON_USE_UNICODE_INTERNALS + Py_ssize_t i; +#if CYTHON_PEP393_ENABLED + void *udata; + uval = PyUnicode_New(ulength, 127); + if (unlikely(!uval)) return NULL; + udata = PyUnicode_DATA(uval); +#else + Py_UNICODE *udata; + uval = PyUnicode_FromUnicode(NULL, ulength); + if (unlikely(!uval)) return NULL; + udata = PyUnicode_AS_UNICODE(uval); +#endif + if (uoffset > 0) { + i = 0; + if (prepend_sign) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, 0, '-'); + i++; + } + for (; i < uoffset; i++) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, i, padding_char); + } + } + for (i=0; i < clength; i++) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, uoffset+i, chars[i]); + } +#else + { + PyObject *sign = NULL, *padding = NULL; + uval = NULL; + if (uoffset > 0) { + prepend_sign = !!prepend_sign; + if (uoffset > prepend_sign) { + padding = PyUnicode_FromOrdinal(padding_char); + if (likely(padding) && uoffset > prepend_sign + 1) { + PyObject *tmp; + PyObject *repeat = PyInt_FromSsize_t(uoffset - prepend_sign); + if (unlikely(!repeat)) goto done_or_error; + tmp = PyNumber_Multiply(padding, repeat); + Py_DECREF(repeat); + Py_DECREF(padding); + padding = tmp; + } + if (unlikely(!padding)) goto done_or_error; + } + if (prepend_sign) { + sign = PyUnicode_FromOrdinal('-'); + if (unlikely(!sign)) goto done_or_error; + } + } + uval = PyUnicode_DecodeASCII(chars, clength, NULL); + if (likely(uval) && padding) { + PyObject *tmp = PyNumber_Add(padding, uval); + Py_DECREF(uval); + uval = tmp; + } + if (likely(uval) && sign) { + PyObject *tmp = PyNumber_Add(sign, uval); + Py_DECREF(uval); + uval = tmp; + } +done_or_error: + Py_XDECREF(padding); + Py_XDECREF(sign); + } +#endif + return uval; +} + +/* CIntToPyUnicode */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char) { + char digits[sizeof(int)*3+2]; + char *dpos, *end = digits + sizeof(int)*3+2; + const char *hex_digits = DIGITS_HEX; + Py_ssize_t length, ulength; + int prepend_sign, last_one_off; + int remaining; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (format_char == 'X') { + hex_digits += 16; + format_char = 'x'; + } + remaining = value; + last_one_off = 0; + dpos = end; + do { + int digit_pos; + switch (format_char) { + case 'o': + digit_pos = abs((int)(remaining % (8*8))); + remaining = (int) (remaining / (8*8)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); + last_one_off = (digit_pos < 8); + break; + case 'd': + digit_pos = abs((int)(remaining % (10*10))); + remaining = (int) (remaining / (10*10)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); + last_one_off = (digit_pos < 10); + break; + case 'x': + *(--dpos) = hex_digits[abs((int)(remaining % 16))]; + remaining = (int) (remaining / 16); + break; + default: + assert(0); + break; + } + } while (unlikely(remaining != 0)); + assert(!last_one_off || *dpos == '0'); + dpos += last_one_off; + length = end - dpos; + ulength = length; + prepend_sign = 0; + if (!is_unsigned && value <= neg_one) { + if (padding_char == ' ' || width <= length + 1) { + *(--dpos) = '-'; + ++length; + } else { + prepend_sign = 1; + } + ++ulength; + } + if (width > ulength) { + ulength = width; + } + if (ulength == 1) { + return PyUnicode_FromOrdinal(*dpos); + } + return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); +} + +/* CIntToPyUnicode */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char) { + char digits[sizeof(Py_ssize_t)*3+2]; + char *dpos, *end = digits + sizeof(Py_ssize_t)*3+2; + const char *hex_digits = DIGITS_HEX; + Py_ssize_t length, ulength; + int prepend_sign, last_one_off; + Py_ssize_t remaining; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const Py_ssize_t neg_one = (Py_ssize_t) -1, const_zero = (Py_ssize_t) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (format_char == 'X') { + hex_digits += 16; + format_char = 'x'; + } + remaining = value; + last_one_off = 0; + dpos = end; + do { + int digit_pos; + switch (format_char) { + case 'o': + digit_pos = abs((int)(remaining % (8*8))); + remaining = (Py_ssize_t) (remaining / (8*8)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); + last_one_off = (digit_pos < 8); + break; + case 'd': + digit_pos = abs((int)(remaining % (10*10))); + remaining = (Py_ssize_t) (remaining / (10*10)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); + last_one_off = (digit_pos < 10); + break; + case 'x': + *(--dpos) = hex_digits[abs((int)(remaining % 16))]; + remaining = (Py_ssize_t) (remaining / 16); + break; + default: + assert(0); + break; + } + } while (unlikely(remaining != 0)); + assert(!last_one_off || *dpos == '0'); + dpos += last_one_off; + length = end - dpos; + ulength = length; + prepend_sign = 0; + if (!is_unsigned && value <= neg_one) { + if (padding_char == ' ' || width <= length + 1) { + *(--dpos) = '-'; + ++length; + } else { + prepend_sign = 1; + } + ++ulength; + } + if (width > ulength) { + ulength = width; + } + if (ulength == 1) { + return PyUnicode_FromOrdinal(*dpos); + } + return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); +} + +/* JoinPyUnicode */ +static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, + Py_UCS4 max_char) { +#if CYTHON_USE_UNICODE_INTERNALS && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + PyObject *result_uval; + int result_ukind, kind_shift; + Py_ssize_t i, char_pos; + void *result_udata; + CYTHON_MAYBE_UNUSED_VAR(max_char); +#if CYTHON_PEP393_ENABLED + result_uval = PyUnicode_New(result_ulength, max_char); + if (unlikely(!result_uval)) return NULL; + result_ukind = (max_char <= 255) ? PyUnicode_1BYTE_KIND : (max_char <= 65535) ? PyUnicode_2BYTE_KIND : PyUnicode_4BYTE_KIND; + kind_shift = (result_ukind == PyUnicode_4BYTE_KIND) ? 2 : result_ukind - 1; + result_udata = PyUnicode_DATA(result_uval); +#else + result_uval = PyUnicode_FromUnicode(NULL, result_ulength); + if (unlikely(!result_uval)) return NULL; + result_ukind = sizeof(Py_UNICODE); + kind_shift = (result_ukind == 4) ? 2 : result_ukind - 1; + result_udata = PyUnicode_AS_UNICODE(result_uval); +#endif + assert(kind_shift == 2 || kind_shift == 1 || kind_shift == 0); + char_pos = 0; + for (i=0; i < value_count; i++) { + int ukind; + Py_ssize_t ulength; + void *udata; + PyObject *uval = PyTuple_GET_ITEM(value_tuple, i); + if (unlikely(__Pyx_PyUnicode_READY(uval))) + goto bad; + ulength = __Pyx_PyUnicode_GET_LENGTH(uval); + if (unlikely(!ulength)) + continue; + if (unlikely((PY_SSIZE_T_MAX >> kind_shift) - ulength < char_pos)) + goto overflow; + ukind = __Pyx_PyUnicode_KIND(uval); + udata = __Pyx_PyUnicode_DATA(uval); + if (!CYTHON_PEP393_ENABLED || ukind == result_ukind) { + memcpy((char *)result_udata + (char_pos << kind_shift), udata, (size_t) (ulength << kind_shift)); + } else { + #if PY_VERSION_HEX >= 0x030D0000 + if (unlikely(PyUnicode_CopyCharacters(result_uval, char_pos, uval, 0, ulength) < 0)) goto bad; + #elif CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030300F0 || defined(_PyUnicode_FastCopyCharacters) + _PyUnicode_FastCopyCharacters(result_uval, char_pos, uval, 0, ulength); + #else + Py_ssize_t j; + for (j=0; j < ulength; j++) { + Py_UCS4 uchar = __Pyx_PyUnicode_READ(ukind, udata, j); + __Pyx_PyUnicode_WRITE(result_ukind, result_udata, char_pos+j, uchar); + } + #endif + } + char_pos += ulength; + } + return result_uval; +overflow: + PyErr_SetString(PyExc_OverflowError, "join() result is too long for a Python string"); +bad: + Py_DECREF(result_uval); + return NULL; +#else + CYTHON_UNUSED_VAR(max_char); + CYTHON_UNUSED_VAR(result_ulength); + CYTHON_UNUSED_VAR(value_count); + return PyUnicode_Join(__pyx_empty_unicode, value_tuple); +#endif +} + +/* GetAttr */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { +#if CYTHON_USE_TYPE_SLOTS +#if PY_MAJOR_VERSION >= 3 + if (likely(PyUnicode_Check(n))) +#else + if (likely(PyString_Check(n))) +#endif + return __Pyx_PyObject_GetAttrStr(o, n); +#endif + return PyObject_GetAttr(o, n); +} + +/* GetItemInt */ +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (unlikely(!j)) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyList_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyTuple_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { + PyObject *r = PyList_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; + PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; + if (mm && mm->mp_subscript) { + PyObject *r, *key = PyInt_FromSsize_t(i); + if (unlikely(!key)) return NULL; + r = mm->mp_subscript(o, key); + Py_DECREF(key); + return r; + } + if (likely(sm && sm->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { + Py_ssize_t l = sm->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return sm->sq_item(o, i); + } + } +#else + if (is_list || PySequence_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +} + +/* PyObjectCallOneArg */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *args[2] = {NULL, arg}; + return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); +} + +/* ObjectGetItem */ +#if CYTHON_USE_TYPE_SLOTS +static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) { + PyObject *runerr = NULL; + Py_ssize_t key_value; + key_value = __Pyx_PyIndex_AsSsize_t(index); + if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { + return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); + } + if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { + __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index)); + PyErr_Clear(); + PyErr_Format(PyExc_IndexError, + "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name); + __Pyx_DECREF_TypeName(index_type_name); + } + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) { + __Pyx_TypeName obj_type_name; + if (likely(PyType_Check(obj))) { + PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem); + if (meth) { + PyObject *result = __Pyx_PyObject_CallOneArg(meth, key); + Py_DECREF(meth); + return result; + } + } + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) { + PyTypeObject *tp = Py_TYPE(obj); + PyMappingMethods *mm = tp->tp_as_mapping; + PySequenceMethods *sm = tp->tp_as_sequence; + if (likely(mm && mm->mp_subscript)) { + return mm->mp_subscript(obj, key); + } + if (likely(sm && sm->sq_item)) { + return __Pyx_PyObject_GetIndex(obj, key); + } + return __Pyx_PyObject_GetItem_Slow(obj, key); +} +#endif + +/* KeywordStringCheck */ +static int __Pyx_CheckKeywordStrings( + PyObject *kw, + const char* function_name, + int kw_allowed) +{ + PyObject* key = 0; + Py_ssize_t pos = 0; +#if CYTHON_COMPILING_IN_PYPY + if (!kw_allowed && PyDict_Next(kw, &pos, &key, 0)) + goto invalid_keyword; + return 1; +#else + if (CYTHON_METH_FASTCALL && likely(PyTuple_Check(kw))) { + Py_ssize_t kwsize; +#if CYTHON_ASSUME_SAFE_MACROS + kwsize = PyTuple_GET_SIZE(kw); +#else + kwsize = PyTuple_Size(kw); + if (kwsize < 0) return 0; +#endif + if (unlikely(kwsize == 0)) + return 1; + if (!kw_allowed) { +#if CYTHON_ASSUME_SAFE_MACROS + key = PyTuple_GET_ITEM(kw, 0); +#else + key = PyTuple_GetItem(kw, pos); + if (!key) return 0; +#endif + goto invalid_keyword; + } +#if PY_VERSION_HEX < 0x03090000 + for (pos = 0; pos < kwsize; pos++) { +#if CYTHON_ASSUME_SAFE_MACROS + key = PyTuple_GET_ITEM(kw, pos); +#else + key = PyTuple_GetItem(kw, pos); + if (!key) return 0; +#endif + if (unlikely(!PyUnicode_Check(key))) + goto invalid_keyword_type; + } +#endif + return 1; + } + while (PyDict_Next(kw, &pos, &key, 0)) { + #if PY_MAJOR_VERSION < 3 + if (unlikely(!PyString_Check(key))) + #endif + if (unlikely(!PyUnicode_Check(key))) + goto invalid_keyword_type; + } + if (!kw_allowed && unlikely(key)) + goto invalid_keyword; + return 1; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + return 0; +#endif +invalid_keyword: + #if PY_MAJOR_VERSION < 3 + PyErr_Format(PyExc_TypeError, + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + PyErr_Format(PyExc_TypeError, + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif + return 0; +} + +/* DivInt[Py_ssize_t] */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { + Py_ssize_t q = a / b; + Py_ssize_t r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* GetAttr3 */ +static PyObject *__Pyx_GetAttr3Default(PyObject *d) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + return NULL; + __Pyx_PyErr_Clear(); + Py_INCREF(d); + return d; +} +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { + PyObject *r; +#if CYTHON_USE_TYPE_SLOTS + if (likely(PyString_Check(n))) { + r = __Pyx_PyObject_GetAttrStrNoError(o, n); + if (unlikely(!r) && likely(!PyErr_Occurred())) { + r = __Pyx_NewRef(d); + } + return r; + } +#endif + r = PyObject_GetAttr(o, n); + return (likely(r)) ? r : __Pyx_GetAttr3Default(d); +} + +/* PyDictVersioning */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { + PyObject **dictptr = NULL; + Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; + if (offset) { +#if CYTHON_COMPILING_IN_CPYTHON + dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); +#else + dictptr = _PyObject_GetDictPtr(obj); +#endif + } + return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; +} +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) + return 0; + return obj_dict_version == __Pyx_get_object_dict_version(obj); +} +#endif + +/* GetModuleGlobalName */ +#if CYTHON_USE_DICT_VERSIONS +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) +#else +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) +#endif +{ + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 + result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } else if (unlikely(PyErr_Occurred())) { + return NULL; + } +#elif CYTHON_COMPILING_IN_LIMITED_API + if (unlikely(!__pyx_m)) { + return NULL; + } + result = PyObject_GetAttr(__pyx_m, name); + if (likely(result)) { + return result; + } +#else + result = PyDict_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } +#endif +#else + result = PyObject_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } + PyErr_Clear(); +#endif + return __Pyx_GetBuiltinName(name); +} + +/* RaiseTooManyValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* ExtTypeTest */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + __Pyx_TypeName obj_type_name; + __Pyx_TypeName type_name; + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(__Pyx_TypeCheck(obj, type))) + return 1; + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + type_name = __Pyx_PyType_GetName(type); + PyErr_Format(PyExc_TypeError, + "Cannot convert " __Pyx_FMT_TYPENAME " to " __Pyx_FMT_TYPENAME, + obj_type_name, type_name); + __Pyx_DECREF_TypeName(obj_type_name); + __Pyx_DECREF_TypeName(type_name); + return 0; +} + +/* GetTopmostException */ +#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE +static _PyErr_StackItem * +__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) +{ + _PyErr_StackItem *exc_info = tstate->exc_info; + while ((exc_info->exc_value == NULL || exc_info->exc_value == Py_None) && + exc_info->previous_item != NULL) + { + exc_info = exc_info->previous_item; + } + return exc_info; +} +#endif + +/* SaveResetException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + PyObject *exc_value = exc_info->exc_value; + if (exc_value == NULL || exc_value == Py_None) { + *value = NULL; + *type = NULL; + *tb = NULL; + } else { + *value = exc_value; + Py_INCREF(*value); + *type = (PyObject*) Py_TYPE(exc_value); + Py_INCREF(*type); + *tb = PyException_GetTraceback(exc_value); + } + #elif CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + *type = exc_info->exc_type; + *value = exc_info->exc_value; + *tb = exc_info->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); + #else + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); + #endif +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = tstate->exc_info; + PyObject *tmp_value = exc_info->exc_value; + exc_info->exc_value = value; + Py_XDECREF(tmp_value); + Py_XDECREF(type); + Py_XDECREF(tb); + #else + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = type; + exc_info->exc_value = value; + exc_info->exc_traceback = tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); + #endif +} +#endif + +/* GetException */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) +#endif +{ + PyObject *local_type = NULL, *local_value, *local_tb = NULL; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if PY_VERSION_HEX >= 0x030C00A6 + local_value = tstate->current_exception; + tstate->current_exception = 0; + if (likely(local_value)) { + local_type = (PyObject*) Py_TYPE(local_value); + Py_INCREF(local_type); + local_tb = PyException_GetTraceback(local_value); + } + #else + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; + #endif +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE && PY_VERSION_HEX >= 0x030C00A6 + if (unlikely(tstate->current_exception)) +#elif CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + #if CYTHON_USE_EXC_INFO_STACK + { + _PyErr_StackItem *exc_info = tstate->exc_info; + #if PY_VERSION_HEX >= 0x030B00a4 + tmp_value = exc_info->exc_value; + exc_info->exc_value = local_value; + tmp_type = NULL; + tmp_tb = NULL; + Py_XDECREF(local_type); + Py_XDECREF(local_tb); + #else + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = local_type; + exc_info->exc_value = local_value; + exc_info->exc_traceback = local_tb; + #endif + } + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* SwapException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_value = exc_info->exc_value; + exc_info->exc_value = *value; + if (tmp_value == NULL || tmp_value == Py_None) { + Py_XDECREF(tmp_value); + tmp_value = NULL; + tmp_type = NULL; + tmp_tb = NULL; + } else { + tmp_type = (PyObject*) Py_TYPE(tmp_value); + Py_INCREF(tmp_type); + #if CYTHON_COMPILING_IN_CPYTHON + tmp_tb = ((PyBaseExceptionObject*) tmp_value)->traceback; + Py_XINCREF(tmp_tb); + #else + tmp_tb = PyException_GetTraceback(tmp_value); + #endif + } + #elif CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = *type; + exc_info->exc_value = *value; + exc_info->exc_traceback = *tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = *type; + tstate->exc_value = *value; + tstate->exc_traceback = *tb; + #endif + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); + PyErr_SetExcInfo(*type, *value, *tb); + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#endif + +/* Import */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *module = 0; + PyObject *empty_dict = 0; + PyObject *empty_list = 0; + #if PY_MAJOR_VERSION < 3 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (unlikely(!py_import)) + goto bad; + if (!from_list) { + empty_list = PyList_New(0); + if (unlikely(!empty_list)) + goto bad; + from_list = empty_list; + } + #endif + empty_dict = PyDict_New(); + if (unlikely(!empty_dict)) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.') != NULL) { + module = PyImport_ImportModuleLevelObject( + name, __pyx_d, empty_dict, from_list, 1); + if (unlikely(!module)) { + if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError))) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_MAJOR_VERSION < 3 + PyObject *py_level = PyInt_FromLong(level); + if (unlikely(!py_level)) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, __pyx_d, empty_dict, from_list, level); + #endif + } + } +bad: + Py_XDECREF(empty_dict); + Py_XDECREF(empty_list); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_import); + #endif + return module; +} + +/* ImportDottedModule */ +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) { + PyObject *partial_name = NULL, *slice = NULL, *sep = NULL; + if (unlikely(PyErr_Occurred())) { + PyErr_Clear(); + } + if (likely(PyTuple_GET_SIZE(parts_tuple) == count)) { + partial_name = name; + } else { + slice = PySequence_GetSlice(parts_tuple, 0, count); + if (unlikely(!slice)) + goto bad; + sep = PyUnicode_FromStringAndSize(".", 1); + if (unlikely(!sep)) + goto bad; + partial_name = PyUnicode_Join(sep, slice); + } + PyErr_Format( +#if PY_MAJOR_VERSION < 3 + PyExc_ImportError, + "No module named '%s'", PyString_AS_STRING(partial_name)); +#else +#if PY_VERSION_HEX >= 0x030600B1 + PyExc_ModuleNotFoundError, +#else + PyExc_ImportError, +#endif + "No module named '%U'", partial_name); +#endif +bad: + Py_XDECREF(sep); + Py_XDECREF(slice); + Py_XDECREF(partial_name); + return NULL; +} +#endif +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) { + PyObject *imported_module; +#if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) + PyObject *modules = PyImport_GetModuleDict(); + if (unlikely(!modules)) + return NULL; + imported_module = __Pyx_PyDict_GetItemStr(modules, name); + Py_XINCREF(imported_module); +#else + imported_module = PyImport_GetModule(name); +#endif + return imported_module; +} +#endif +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple) { + Py_ssize_t i, nparts; + nparts = PyTuple_GET_SIZE(parts_tuple); + for (i=1; i < nparts && module; i++) { + PyObject *part, *submodule; +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + part = PyTuple_GET_ITEM(parts_tuple, i); +#else + part = PySequence_ITEM(parts_tuple, i); +#endif + submodule = __Pyx_PyObject_GetAttrStrNoError(module, part); +#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_DECREF(part); +#endif + Py_DECREF(module); + module = submodule; + } + if (unlikely(!module)) { + return __Pyx__ImportDottedModule_Error(name, parts_tuple, i); + } + return module; +} +#endif +static PyObject *__Pyx__ImportDottedModule(PyObject *name, PyObject *parts_tuple) { +#if PY_MAJOR_VERSION < 3 + PyObject *module, *from_list, *star = __pyx_n_s__3; + CYTHON_UNUSED_VAR(parts_tuple); + from_list = PyList_New(1); + if (unlikely(!from_list)) + return NULL; + Py_INCREF(star); + PyList_SET_ITEM(from_list, 0, star); + module = __Pyx_Import(name, from_list, 0); + Py_DECREF(from_list); + return module; +#else + PyObject *imported_module; + PyObject *module = __Pyx_Import(name, NULL, 0); + if (!parts_tuple || unlikely(!module)) + return module; + imported_module = __Pyx__ImportDottedModule_Lookup(name); + if (likely(imported_module)) { + Py_DECREF(module); + return imported_module; + } + PyErr_Clear(); + return __Pyx_ImportDottedModule_WalkParts(module, name, parts_tuple); +#endif +} +static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030400B1 + PyObject *module = __Pyx__ImportDottedModule_Lookup(name); + if (likely(module)) { + PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_n_s_spec); + if (likely(spec)) { + PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_n_s_initializing); + if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) { + Py_DECREF(spec); + spec = NULL; + } + Py_XDECREF(unsafe); + } + if (likely(!spec)) { + PyErr_Clear(); + return module; + } + Py_DECREF(spec); + Py_DECREF(module); + } else if (PyErr_Occurred()) { + PyErr_Clear(); + } +#endif + return __Pyx__ImportDottedModule(name, parts_tuple); +} + +/* FastTypeChecks */ +#if CYTHON_COMPILING_IN_CPYTHON +static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { + while (a) { + a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*); + if (a == b) + return 1; + } + return b == &PyBaseObject_Type; +} +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (a == b) return 1; + mro = a->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(a, b); +} +static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (cls == a || cls == b) return 1; + mro = cls->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + PyObject *base = PyTuple_GET_ITEM(mro, i); + if (base == (PyObject *)a || base == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b); +} +#if PY_MAJOR_VERSION == 2 +static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { + PyObject *exception, *value, *tb; + int res; + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&exception, &value, &tb); + res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + if (!res) { + res = PyObject_IsSubclass(err, exc_type2); + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + } + __Pyx_ErrRestore(exception, value, tb); + return res; +} +#else +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + if (exc_type1) { + return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2); + } else { + return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } +} +#endif +static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + assert(PyExceptionClass_Check(exc_type)); + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; itp_as_sequence && type->tp_as_sequence->sq_repeat)) { + return type->tp_as_sequence->sq_repeat(seq, mul); + } else +#endif + { + return __Pyx_PySequence_Multiply_Generic(seq, mul); + } +} + +/* SetItemInt */ +static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) { + int r; + if (unlikely(!j)) return -1; + r = PyObject_SetItem(o, j, v); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list, + CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o)); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o)))) { + PyObject* old = PyList_GET_ITEM(o, n); + Py_INCREF(v); + PyList_SET_ITEM(o, n, v); + Py_DECREF(old); + return 1; + } + } else { + PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; + PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; + if (mm && mm->mp_ass_subscript) { + int r; + PyObject *key = PyInt_FromSsize_t(i); + if (unlikely(!key)) return -1; + r = mm->mp_ass_subscript(o, key, v); + Py_DECREF(key); + return r; + } + if (likely(sm && sm->sq_ass_item)) { + if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { + Py_ssize_t l = sm->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return -1; + PyErr_Clear(); + } + } + return sm->sq_ass_item(o, i, v); + } + } +#else +#if CYTHON_COMPILING_IN_PYPY + if (is_list || (PySequence_Check(o) && !PyDict_Check(o))) +#else + if (is_list || PySequence_Check(o)) +#endif + { + return PySequence_SetItem(o, i, v); + } +#endif + return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v); +} + +/* RaiseUnboundLocalError */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { + PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); +} + +/* DivInt[long] */ +static CYTHON_INLINE long __Pyx_div_long(long a, long b) { + long q = a / b; + long r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* ImportFrom */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { + PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); + if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { + const char* module_name_str = 0; + PyObject* module_name = 0; + PyObject* module_dot = 0; + PyObject* full_name = 0; + PyErr_Clear(); + module_name_str = PyModule_GetName(module); + if (unlikely(!module_name_str)) { goto modbad; } + module_name = PyUnicode_FromString(module_name_str); + if (unlikely(!module_name)) { goto modbad; } + module_dot = PyUnicode_Concat(module_name, __pyx_kp_u__2); + if (unlikely(!module_dot)) { goto modbad; } + full_name = PyUnicode_Concat(module_dot, name); + if (unlikely(!full_name)) { goto modbad; } + #if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) + { + PyObject *modules = PyImport_GetModuleDict(); + if (unlikely(!modules)) + goto modbad; + value = PyObject_GetItem(modules, full_name); + } + #else + value = PyImport_GetModule(full_name); + #endif + modbad: + Py_XDECREF(full_name); + Py_XDECREF(module_dot); + Py_XDECREF(module_name); + } + if (unlikely(!value)) { + PyErr_Format(PyExc_ImportError, + #if PY_MAJOR_VERSION < 3 + "cannot import name %.230s", PyString_AS_STRING(name)); + #else + "cannot import name %S", name); + #endif + } + return value; +} + +/* HasAttr */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { + PyObject *r; + if (unlikely(!__Pyx_PyBaseString_Check(n))) { + PyErr_SetString(PyExc_TypeError, + "hasattr(): attribute name must be string"); + return -1; + } + r = __Pyx_GetAttr(o, n); + if (!r) { + PyErr_Clear(); + return 0; + } else { + Py_DECREF(r); + return 1; + } +} + +/* ErrOccurredWithGIL */ +static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void) { + int err; + #ifdef WITH_THREAD + PyGILState_STATE _save = PyGILState_Ensure(); + #endif + err = !!PyErr_Occurred(); + #ifdef WITH_THREAD + PyGILState_Release(_save); + #endif + return err; +} + +/* PyObject_GenericGetAttrNoDict */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { + __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp); + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", + type_name, attr_name); +#else + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'", + type_name, PyString_AS_STRING(attr_name)); +#endif + __Pyx_DECREF_TypeName(type_name); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { + PyObject *descr; + PyTypeObject *tp = Py_TYPE(obj); + if (unlikely(!PyString_Check(attr_name))) { + return PyObject_GenericGetAttr(obj, attr_name); + } + assert(!tp->tp_dictoffset); + descr = _PyType_Lookup(tp, attr_name); + if (unlikely(!descr)) { + return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); + } + Py_INCREF(descr); + #if PY_MAJOR_VERSION < 3 + if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) + #endif + { + descrgetfunc f = Py_TYPE(descr)->tp_descr_get; + if (unlikely(f)) { + PyObject *res = f(descr, obj, (PyObject *)tp); + Py_DECREF(descr); + return res; + } + } + return descr; +} +#endif + +/* PyObject_GenericGetAttr */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { + if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { + return PyObject_GenericGetAttr(obj, attr_name); + } + return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); +} +#endif + +/* FixUpExtensionType */ +#if CYTHON_USE_TYPE_SPECS +static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) { +#if PY_VERSION_HEX > 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + CYTHON_UNUSED_VAR(spec); + CYTHON_UNUSED_VAR(type); +#else + const PyType_Slot *slot = spec->slots; + while (slot && slot->slot && slot->slot != Py_tp_members) + slot++; + if (slot && slot->slot == Py_tp_members) { + int changed = 0; +#if !(PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON) + const +#endif + PyMemberDef *memb = (PyMemberDef*) slot->pfunc; + while (memb && memb->name) { + if (memb->name[0] == '_' && memb->name[1] == '_') { +#if PY_VERSION_HEX < 0x030900b1 + if (strcmp(memb->name, "__weaklistoffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); + type->tp_weaklistoffset = memb->offset; + changed = 1; + } + else if (strcmp(memb->name, "__dictoffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); + type->tp_dictoffset = memb->offset; + changed = 1; + } +#if CYTHON_METH_FASTCALL + else if (strcmp(memb->name, "__vectorcalloffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); +#if PY_VERSION_HEX >= 0x030800b4 + type->tp_vectorcall_offset = memb->offset; +#else + type->tp_print = (printfunc) memb->offset; +#endif + changed = 1; + } +#endif +#else + if ((0)); +#endif +#if PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON + else if (strcmp(memb->name, "__module__") == 0) { + PyObject *descr; + assert(memb->type == T_OBJECT); + assert(memb->flags == 0 || memb->flags == READONLY); + descr = PyDescr_NewMember(type, memb); + if (unlikely(!descr)) + return -1; + if (unlikely(PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr) < 0)) { + Py_DECREF(descr); + return -1; + } + Py_DECREF(descr); + changed = 1; + } +#endif + } + memb++; + } + if (changed) + PyType_Modified(type); + } +#endif + return 0; +} +#endif + +/* PyObjectCallNoArg */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { + PyObject *arg[2] = {NULL, NULL}; + return __Pyx_PyObject_FastCall(func, arg + 1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); +} + +/* PyObjectGetMethod */ +static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) { + PyObject *attr; +#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP + __Pyx_TypeName type_name; + PyTypeObject *tp = Py_TYPE(obj); + PyObject *descr; + descrgetfunc f = NULL; + PyObject **dictptr, *dict; + int meth_found = 0; + assert (*method == NULL); + if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) { + attr = __Pyx_PyObject_GetAttrStr(obj, name); + goto try_unpack; + } + if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) { + return 0; + } + descr = _PyType_Lookup(tp, name); + if (likely(descr != NULL)) { + Py_INCREF(descr); +#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR + if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR)) +#elif PY_MAJOR_VERSION >= 3 + #ifdef __Pyx_CyFunction_USED + if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr))) + #else + if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type))) + #endif +#else + #ifdef __Pyx_CyFunction_USED + if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr))) + #else + if (likely(PyFunction_Check(descr))) + #endif +#endif + { + meth_found = 1; + } else { + f = Py_TYPE(descr)->tp_descr_get; + if (f != NULL && PyDescr_IsData(descr)) { + attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); + Py_DECREF(descr); + goto try_unpack; + } + } + } + dictptr = _PyObject_GetDictPtr(obj); + if (dictptr != NULL && (dict = *dictptr) != NULL) { + Py_INCREF(dict); + attr = __Pyx_PyDict_GetItemStr(dict, name); + if (attr != NULL) { + Py_INCREF(attr); + Py_DECREF(dict); + Py_XDECREF(descr); + goto try_unpack; + } + Py_DECREF(dict); + } + if (meth_found) { + *method = descr; + return 1; + } + if (f != NULL) { + attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); + Py_DECREF(descr); + goto try_unpack; + } + if (likely(descr != NULL)) { + *method = descr; + return 0; + } + type_name = __Pyx_PyType_GetName(tp); + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", + type_name, name); +#else + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'", + type_name, PyString_AS_STRING(name)); +#endif + __Pyx_DECREF_TypeName(type_name); + return 0; +#else + attr = __Pyx_PyObject_GetAttrStr(obj, name); + goto try_unpack; +#endif +try_unpack: +#if CYTHON_UNPACK_METHODS + if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) { + PyObject *function = PyMethod_GET_FUNCTION(attr); + Py_INCREF(function); + Py_DECREF(attr); + *method = function; + return 1; + } +#endif + *method = attr; + return 0; +} + +/* PyObjectCallMethod0 */ +static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) { + PyObject *method = NULL, *result = NULL; + int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method); + if (likely(is_method)) { + result = __Pyx_PyObject_CallOneArg(method, obj); + Py_DECREF(method); + return result; + } + if (unlikely(!method)) goto bad; + result = __Pyx_PyObject_CallNoArg(method); + Py_DECREF(method); +bad: + return result; +} + +/* ValidateBasesTuple */ +#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS +static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) { + Py_ssize_t i, n; +#if CYTHON_ASSUME_SAFE_MACROS + n = PyTuple_GET_SIZE(bases); +#else + n = PyTuple_Size(bases); + if (n < 0) return -1; +#endif + for (i = 1; i < n; i++) + { +#if CYTHON_AVOID_BORROWED_REFS + PyObject *b0 = PySequence_GetItem(bases, i); + if (!b0) return -1; +#elif CYTHON_ASSUME_SAFE_MACROS + PyObject *b0 = PyTuple_GET_ITEM(bases, i); +#else + PyObject *b0 = PyTuple_GetItem(bases, i); + if (!b0) return -1; +#endif + PyTypeObject *b; +#if PY_MAJOR_VERSION < 3 + if (PyClass_Check(b0)) + { + PyErr_Format(PyExc_TypeError, "base class '%.200s' is an old-style class", + PyString_AS_STRING(((PyClassObject*)b0)->cl_name)); +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + return -1; + } +#endif + b = (PyTypeObject*) b0; + if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE)) + { + __Pyx_TypeName b_name = __Pyx_PyType_GetName(b); + PyErr_Format(PyExc_TypeError, + "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name); + __Pyx_DECREF_TypeName(b_name); +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + return -1; + } +#if !CYTHON_USE_TYPE_SLOTS + if (dictoffset == 0) { + PyErr_Format(PyExc_TypeError, + "extension type '%s.200s': " + "unable to validate whether bases have a __dict__ " + "when CYTHON_USE_TYPE_SLOTS is off " + "(likely because you are building in the limited API). " + "Therefore, all extension types with multiple bases " + "must add 'cdef dict __dict__' in this compilation mode", + type_name); +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + return -1; + } +#else + if (dictoffset == 0 && b->tp_dictoffset) + { + __Pyx_TypeName b_name = __Pyx_PyType_GetName(b); + PyErr_Format(PyExc_TypeError, + "extension type '%.200s' has no __dict__ slot, " + "but base type '" __Pyx_FMT_TYPENAME "' has: " + "either add 'cdef dict __dict__' to the extension type " + "or add '__slots__ = [...]' to the base type", + type_name, b_name); + __Pyx_DECREF_TypeName(b_name); +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + return -1; + } +#endif +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + } + return 0; +} +#endif + +/* PyType_Ready */ +static int __Pyx_PyType_Ready(PyTypeObject *t) { +#if CYTHON_USE_TYPE_SPECS || !(CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API) || defined(PYSTON_MAJOR_VERSION) + (void)__Pyx_PyObject_CallMethod0; +#if CYTHON_USE_TYPE_SPECS + (void)__Pyx_validate_bases_tuple; +#endif + return PyType_Ready(t); +#else + int r; + PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*); + if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1)) + return -1; +#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION) + { + int gc_was_enabled; + #if PY_VERSION_HEX >= 0x030A00b1 + gc_was_enabled = PyGC_Disable(); + (void)__Pyx_PyObject_CallMethod0; + #else + PyObject *ret, *py_status; + PyObject *gc = NULL; + #if PY_VERSION_HEX >= 0x030700a1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400) + gc = PyImport_GetModule(__pyx_kp_u_gc); + #endif + if (unlikely(!gc)) gc = PyImport_Import(__pyx_kp_u_gc); + if (unlikely(!gc)) return -1; + py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_isenabled); + if (unlikely(!py_status)) { + Py_DECREF(gc); + return -1; + } + gc_was_enabled = __Pyx_PyObject_IsTrue(py_status); + Py_DECREF(py_status); + if (gc_was_enabled > 0) { + ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_disable); + if (unlikely(!ret)) { + Py_DECREF(gc); + return -1; + } + Py_DECREF(ret); + } else if (unlikely(gc_was_enabled == -1)) { + Py_DECREF(gc); + return -1; + } + #endif + t->tp_flags |= Py_TPFLAGS_HEAPTYPE; +#if PY_VERSION_HEX >= 0x030A0000 + t->tp_flags |= Py_TPFLAGS_IMMUTABLETYPE; +#endif +#else + (void)__Pyx_PyObject_CallMethod0; +#endif + r = PyType_Ready(t); +#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION) + t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE; + #if PY_VERSION_HEX >= 0x030A00b1 + if (gc_was_enabled) + PyGC_Enable(); + #else + if (gc_was_enabled) { + PyObject *tp, *v, *tb; + PyErr_Fetch(&tp, &v, &tb); + ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_enable); + if (likely(ret || r == -1)) { + Py_XDECREF(ret); + PyErr_Restore(tp, v, tb); + } else { + Py_XDECREF(tp); + Py_XDECREF(v); + Py_XDECREF(tb); + r = -1; + } + } + Py_DECREF(gc); + #endif + } +#endif + return r; +#endif +} + +/* SetVTable */ +static int __Pyx_SetVtable(PyTypeObject *type, void *vtable) { + PyObject *ob = PyCapsule_New(vtable, 0, 0); + if (unlikely(!ob)) + goto bad; +#if CYTHON_COMPILING_IN_LIMITED_API + if (unlikely(PyObject_SetAttr((PyObject *) type, __pyx_n_s_pyx_vtable, ob) < 0)) +#else + if (unlikely(PyDict_SetItem(type->tp_dict, __pyx_n_s_pyx_vtable, ob) < 0)) +#endif + goto bad; + Py_DECREF(ob); + return 0; +bad: + Py_XDECREF(ob); + return -1; +} + +/* GetVTable */ +static void* __Pyx_GetVtable(PyTypeObject *type) { + void* ptr; +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *ob = PyObject_GetAttr((PyObject *)type, __pyx_n_s_pyx_vtable); +#else + PyObject *ob = PyObject_GetItem(type->tp_dict, __pyx_n_s_pyx_vtable); +#endif + if (!ob) + goto bad; + ptr = PyCapsule_GetPointer(ob, 0); + if (!ptr && !PyErr_Occurred()) + PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type"); + Py_DECREF(ob); + return ptr; +bad: + Py_XDECREF(ob); + return NULL; +} + +/* MergeVTables */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_MergeVtables(PyTypeObject *type) { + int i; + void** base_vtables; + __Pyx_TypeName tp_base_name; + __Pyx_TypeName base_name; + void* unknown = (void*)-1; + PyObject* bases = type->tp_bases; + int base_depth = 0; + { + PyTypeObject* base = type->tp_base; + while (base) { + base_depth += 1; + base = base->tp_base; + } + } + base_vtables = (void**) malloc(sizeof(void*) * (size_t)(base_depth + 1)); + base_vtables[0] = unknown; + for (i = 1; i < PyTuple_GET_SIZE(bases); i++) { + void* base_vtable = __Pyx_GetVtable(((PyTypeObject*)PyTuple_GET_ITEM(bases, i))); + if (base_vtable != NULL) { + int j; + PyTypeObject* base = type->tp_base; + for (j = 0; j < base_depth; j++) { + if (base_vtables[j] == unknown) { + base_vtables[j] = __Pyx_GetVtable(base); + base_vtables[j + 1] = unknown; + } + if (base_vtables[j] == base_vtable) { + break; + } else if (base_vtables[j] == NULL) { + goto bad; + } + base = base->tp_base; + } + } + } + PyErr_Clear(); + free(base_vtables); + return 0; +bad: + tp_base_name = __Pyx_PyType_GetName(type->tp_base); + base_name = __Pyx_PyType_GetName((PyTypeObject*)PyTuple_GET_ITEM(bases, i)); + PyErr_Format(PyExc_TypeError, + "multiple bases have vtable conflict: '" __Pyx_FMT_TYPENAME "' and '" __Pyx_FMT_TYPENAME "'", tp_base_name, base_name); + __Pyx_DECREF_TypeName(tp_base_name); + __Pyx_DECREF_TypeName(base_name); + free(base_vtables); + return -1; +} +#endif + +/* SetupReduce */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { + int ret; + PyObject *name_attr; + name_attr = __Pyx_PyObject_GetAttrStrNoError(meth, __pyx_n_s_name_2); + if (likely(name_attr)) { + ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); + } else { + ret = -1; + } + if (unlikely(ret < 0)) { + PyErr_Clear(); + ret = 0; + } + Py_XDECREF(name_attr); + return ret; +} +static int __Pyx_setup_reduce(PyObject* type_obj) { + int ret = 0; + PyObject *object_reduce = NULL; + PyObject *object_getstate = NULL; + PyObject *object_reduce_ex = NULL; + PyObject *reduce = NULL; + PyObject *reduce_ex = NULL; + PyObject *reduce_cython = NULL; + PyObject *setstate = NULL; + PyObject *setstate_cython = NULL; + PyObject *getstate = NULL; +#if CYTHON_USE_PYTYPE_LOOKUP + getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate); +#else + getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate); + if (!getstate && PyErr_Occurred()) { + goto __PYX_BAD; + } +#endif + if (getstate) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate); +#else + object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate); + if (!object_getstate && PyErr_Occurred()) { + goto __PYX_BAD; + } +#endif + if (object_getstate != getstate) { + goto __PYX_GOOD; + } + } +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#else + object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#endif + reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; + if (reduce_ex == object_reduce_ex) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; +#else + object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; +#endif + reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; + if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { + reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); + if (likely(reduce_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (reduce == object_reduce || PyErr_Occurred()) { + goto __PYX_BAD; + } + setstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate); + if (!setstate) PyErr_Clear(); + if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { + setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); + if (likely(setstate_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (!setstate || PyErr_Occurred()) { + goto __PYX_BAD; + } + } + PyType_Modified((PyTypeObject*)type_obj); + } + } + goto __PYX_GOOD; +__PYX_BAD: + if (!PyErr_Occurred()) { + __Pyx_TypeName type_obj_name = + __Pyx_PyType_GetName((PyTypeObject*)type_obj); + PyErr_Format(PyExc_RuntimeError, + "Unable to initialize pickling for " __Pyx_FMT_TYPENAME, type_obj_name); + __Pyx_DECREF_TypeName(type_obj_name); + } + ret = -1; +__PYX_GOOD: +#if !CYTHON_USE_PYTYPE_LOOKUP + Py_XDECREF(object_reduce); + Py_XDECREF(object_reduce_ex); + Py_XDECREF(object_getstate); + Py_XDECREF(getstate); +#endif + Py_XDECREF(reduce); + Py_XDECREF(reduce_ex); + Py_XDECREF(reduce_cython); + Py_XDECREF(setstate); + Py_XDECREF(setstate_cython); + return ret; +} +#endif + +/* TypeImport */ +#ifndef __PYX_HAVE_RT_ImportType_3_0_4 +#define __PYX_HAVE_RT_ImportType_3_0_4 +static PyTypeObject *__Pyx_ImportType_3_0_4(PyObject *module, const char *module_name, const char *class_name, + size_t size, size_t alignment, enum __Pyx_ImportType_CheckSize_3_0_4 check_size) +{ + PyObject *result = 0; + char warning[200]; + Py_ssize_t basicsize; + Py_ssize_t itemsize; +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *py_basicsize; + PyObject *py_itemsize; +#endif + result = PyObject_GetAttrString(module, class_name); + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#if !CYTHON_COMPILING_IN_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; + itemsize = ((PyTypeObject *)result)->tp_itemsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; + py_itemsize = PyObject_GetAttrString(result, "__itemsize__"); + if (!py_itemsize) + goto bad; + itemsize = PyLong_AsSsize_t(py_itemsize); + Py_DECREF(py_itemsize); + py_itemsize = 0; + if (itemsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if (itemsize) { + if (size % alignment) { + alignment = size % alignment; + } + if (itemsize < (Py_ssize_t)alignment) + itemsize = (Py_ssize_t)alignment; + } + if ((size_t)(basicsize + itemsize) < size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd from PyObject", + module_name, class_name, size, basicsize+itemsize); + goto bad; + } + if (check_size == __Pyx_ImportType_CheckSize_Error_3_0_4 && + ((size_t)basicsize > size || (size_t)(basicsize + itemsize) < size)) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd-%zd from PyObject", + module_name, class_name, size, basicsize, basicsize+itemsize); + goto bad; + } + else if (check_size == __Pyx_ImportType_CheckSize_Warn_3_0_4 && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd from PyObject", + module_name, class_name, size, basicsize); + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(result); + return NULL; +} +#endif + +/* FetchSharedCythonModule */ +static PyObject *__Pyx_FetchSharedCythonABIModule(void) { + PyObject *abi_module = PyImport_AddModule((char*) __PYX_ABI_MODULE_NAME); + if (unlikely(!abi_module)) return NULL; + Py_INCREF(abi_module); + return abi_module; +} + +/* FetchCommonType */ +static int __Pyx_VerifyCachedType(PyObject *cached_type, + const char *name, + Py_ssize_t basicsize, + Py_ssize_t expected_basicsize) { + if (!PyType_Check(cached_type)) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s is not a type object", name); + return -1; + } + if (basicsize != expected_basicsize) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s has the wrong size, try recompiling", + name); + return -1; + } + return 0; +} +#if !CYTHON_USE_TYPE_SPECS +static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { + PyObject* abi_module; + const char* object_name; + PyTypeObject *cached_type = NULL; + abi_module = __Pyx_FetchSharedCythonABIModule(); + if (!abi_module) return NULL; + object_name = strrchr(type->tp_name, '.'); + object_name = object_name ? object_name+1 : type->tp_name; + cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, object_name); + if (cached_type) { + if (__Pyx_VerifyCachedType( + (PyObject *)cached_type, + object_name, + cached_type->tp_basicsize, + type->tp_basicsize) < 0) { + goto bad; + } + goto done; + } + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; + PyErr_Clear(); + if (PyType_Ready(type) < 0) goto bad; + if (PyObject_SetAttrString(abi_module, object_name, (PyObject *)type) < 0) + goto bad; + Py_INCREF(type); + cached_type = type; +done: + Py_DECREF(abi_module); + return cached_type; +bad: + Py_XDECREF(cached_type); + cached_type = NULL; + goto done; +} +#else +static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases) { + PyObject *abi_module, *cached_type = NULL; + const char* object_name = strrchr(spec->name, '.'); + object_name = object_name ? object_name+1 : spec->name; + abi_module = __Pyx_FetchSharedCythonABIModule(); + if (!abi_module) return NULL; + cached_type = PyObject_GetAttrString(abi_module, object_name); + if (cached_type) { + Py_ssize_t basicsize; +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *py_basicsize; + py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__"); + if (unlikely(!py_basicsize)) goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; +#else + basicsize = likely(PyType_Check(cached_type)) ? ((PyTypeObject*) cached_type)->tp_basicsize : -1; +#endif + if (__Pyx_VerifyCachedType( + cached_type, + object_name, + basicsize, + spec->basicsize) < 0) { + goto bad; + } + goto done; + } + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; + PyErr_Clear(); + CYTHON_UNUSED_VAR(module); + cached_type = __Pyx_PyType_FromModuleAndSpec(abi_module, spec, bases); + if (unlikely(!cached_type)) goto bad; + if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad; + if (PyObject_SetAttrString(abi_module, object_name, cached_type) < 0) goto bad; +done: + Py_DECREF(abi_module); + assert(cached_type == NULL || PyType_Check(cached_type)); + return (PyTypeObject *) cached_type; +bad: + Py_XDECREF(cached_type); + cached_type = NULL; + goto done; +} +#endif + +/* PyVectorcallFastCallDict */ +#if CYTHON_METH_FASTCALL +static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) +{ + PyObject *res = NULL; + PyObject *kwnames; + PyObject **newargs; + PyObject **kwvalues; + Py_ssize_t i, pos; + size_t j; + PyObject *key, *value; + unsigned long keys_are_strings; + Py_ssize_t nkw = PyDict_GET_SIZE(kw); + newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0])); + if (unlikely(newargs == NULL)) { + PyErr_NoMemory(); + return NULL; + } + for (j = 0; j < nargs; j++) newargs[j] = args[j]; + kwnames = PyTuple_New(nkw); + if (unlikely(kwnames == NULL)) { + PyMem_Free(newargs); + return NULL; + } + kwvalues = newargs + nargs; + pos = i = 0; + keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS; + while (PyDict_Next(kw, &pos, &key, &value)) { + keys_are_strings &= Py_TYPE(key)->tp_flags; + Py_INCREF(key); + Py_INCREF(value); + PyTuple_SET_ITEM(kwnames, i, key); + kwvalues[i] = value; + i++; + } + if (unlikely(!keys_are_strings)) { + PyErr_SetString(PyExc_TypeError, "keywords must be strings"); + goto cleanup; + } + res = vc(func, newargs, nargs, kwnames); +cleanup: + Py_DECREF(kwnames); + for (i = 0; i < nkw; i++) + Py_DECREF(kwvalues[i]); + PyMem_Free(newargs); + return res; +} +static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) +{ + if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) { + return vc(func, args, nargs, NULL); + } + return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw); +} +#endif + +/* CythonFunctionShared */ +#if CYTHON_COMPILING_IN_LIMITED_API +static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc) { + if (__Pyx_CyFunction_Check(func)) { + return PyCFunction_GetFunction(((__pyx_CyFunctionObject*)func)->func) == (PyCFunction) cfunc; + } else if (PyCFunction_Check(func)) { + return PyCFunction_GetFunction(func) == (PyCFunction) cfunc; + } + return 0; +} +#else +static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc) { + return __Pyx_CyOrPyCFunction_Check(func) && __Pyx_CyOrPyCFunction_GET_FUNCTION(func) == (PyCFunction) cfunc; +} +#endif +static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) { +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + __Pyx_Py_XDECREF_SET( + __Pyx_CyFunction_GetClassObj(f), + ((classobj) ? __Pyx_NewRef(classobj) : NULL)); +#else + __Pyx_Py_XDECREF_SET( + ((PyCMethodObject *) (f))->mm_class, + (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL)); +#endif +} +static PyObject * +__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure) +{ + CYTHON_UNUSED_VAR(closure); + if (unlikely(op->func_doc == NULL)) { +#if CYTHON_COMPILING_IN_LIMITED_API + op->func_doc = PyObject_GetAttrString(op->func, "__doc__"); + if (unlikely(!op->func_doc)) return NULL; +#else + if (((PyCFunctionObject*)op)->m_ml->ml_doc) { +#if PY_MAJOR_VERSION >= 3 + op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc); +#else + op->func_doc = PyString_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc); +#endif + if (unlikely(op->func_doc == NULL)) + return NULL; + } else { + Py_INCREF(Py_None); + return Py_None; + } +#endif + } + Py_INCREF(op->func_doc); + return op->func_doc; +} +static int +__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (value == NULL) { + value = Py_None; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_doc, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(op->func_name == NULL)) { +#if CYTHON_COMPILING_IN_LIMITED_API + op->func_name = PyObject_GetAttrString(op->func, "__name__"); +#elif PY_MAJOR_VERSION >= 3 + op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name); +#else + op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name); +#endif + if (unlikely(op->func_name == NULL)) + return NULL; + } + Py_INCREF(op->func_name); + return op->func_name; +} +static int +__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) +#else + if (unlikely(value == NULL || !PyString_Check(value))) +#endif + { + PyErr_SetString(PyExc_TypeError, + "__name__ must be set to a string object"); + return -1; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_name, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + Py_INCREF(op->func_qualname); + return op->func_qualname; +} +static int +__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) +#else + if (unlikely(value == NULL || !PyString_Check(value))) +#endif + { + PyErr_SetString(PyExc_TypeError, + "__qualname__ must be set to a string object"); + return -1; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_qualname, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(op->func_dict == NULL)) { + op->func_dict = PyDict_New(); + if (unlikely(op->func_dict == NULL)) + return NULL; + } + Py_INCREF(op->func_dict); + return op->func_dict; +} +static int +__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(value == NULL)) { + PyErr_SetString(PyExc_TypeError, + "function's dictionary may not be deleted"); + return -1; + } + if (unlikely(!PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "setting function's dictionary to a non-dict"); + return -1; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_dict, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + Py_INCREF(op->func_globals); + return op->func_globals; +} +static PyObject * +__Pyx_CyFunction_get_closure(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(op); + CYTHON_UNUSED_VAR(context); + Py_INCREF(Py_None); + return Py_None; +} +static PyObject * +__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, void *context) +{ + PyObject* result = (op->func_code) ? op->func_code : Py_None; + CYTHON_UNUSED_VAR(context); + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) { + int result = 0; + PyObject *res = op->defaults_getter((PyObject *) op); + if (unlikely(!res)) + return -1; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + op->defaults_tuple = PyTuple_GET_ITEM(res, 0); + Py_INCREF(op->defaults_tuple); + op->defaults_kwdict = PyTuple_GET_ITEM(res, 1); + Py_INCREF(op->defaults_kwdict); + #else + op->defaults_tuple = __Pyx_PySequence_ITEM(res, 0); + if (unlikely(!op->defaults_tuple)) result = -1; + else { + op->defaults_kwdict = __Pyx_PySequence_ITEM(res, 1); + if (unlikely(!op->defaults_kwdict)) result = -1; + } + #endif + Py_DECREF(res); + return result; +} +static int +__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value) { + value = Py_None; + } else if (unlikely(value != Py_None && !PyTuple_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__defaults__ must be set to a tuple object"); + return -1; + } + PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__defaults__ will not " + "currently affect the values used in function calls", 1); + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->defaults_tuple, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, void *context) { + PyObject* result = op->defaults_tuple; + CYTHON_UNUSED_VAR(context); + if (unlikely(!result)) { + if (op->defaults_getter) { + if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL; + result = op->defaults_tuple; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value) { + value = Py_None; + } else if (unlikely(value != Py_None && !PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__kwdefaults__ must be set to a dict object"); + return -1; + } + PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__kwdefaults__ will not " + "currently affect the values used in function calls", 1); + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->defaults_kwdict, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) { + PyObject* result = op->defaults_kwdict; + CYTHON_UNUSED_VAR(context); + if (unlikely(!result)) { + if (op->defaults_getter) { + if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL; + result = op->defaults_kwdict; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value || value == Py_None) { + value = NULL; + } else if (unlikely(!PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__annotations__ must be set to a dict object"); + return -1; + } + Py_XINCREF(value); + __Pyx_Py_XDECREF_SET(op->func_annotations, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) { + PyObject* result = op->func_annotations; + CYTHON_UNUSED_VAR(context); + if (unlikely(!result)) { + result = PyDict_New(); + if (unlikely(!result)) return NULL; + op->func_annotations = result; + } + Py_INCREF(result); + return result; +} +static PyObject * +__Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) { + int is_coroutine; + CYTHON_UNUSED_VAR(context); + if (op->func_is_coroutine) { + return __Pyx_NewRef(op->func_is_coroutine); + } + is_coroutine = op->flags & __Pyx_CYFUNCTION_COROUTINE; +#if PY_VERSION_HEX >= 0x03050000 + if (is_coroutine) { + PyObject *module, *fromlist, *marker = __pyx_n_s_is_coroutine; + fromlist = PyList_New(1); + if (unlikely(!fromlist)) return NULL; + Py_INCREF(marker); +#if CYTHON_ASSUME_SAFE_MACROS + PyList_SET_ITEM(fromlist, 0, marker); +#else + if (unlikely(PyList_SetItem(fromlist, 0, marker) < 0)) { + Py_DECREF(marker); + Py_DECREF(fromlist); + return NULL; + } +#endif + module = PyImport_ImportModuleLevelObject(__pyx_n_s_asyncio_coroutines, NULL, NULL, fromlist, 0); + Py_DECREF(fromlist); + if (unlikely(!module)) goto ignore; + op->func_is_coroutine = __Pyx_PyObject_GetAttrStr(module, marker); + Py_DECREF(module); + if (likely(op->func_is_coroutine)) { + return __Pyx_NewRef(op->func_is_coroutine); + } +ignore: + PyErr_Clear(); + } +#endif + op->func_is_coroutine = __Pyx_PyBool_FromLong(is_coroutine); + return __Pyx_NewRef(op->func_is_coroutine); +} +#if CYTHON_COMPILING_IN_LIMITED_API +static PyObject * +__Pyx_CyFunction_get_module(__pyx_CyFunctionObject *op, void *context) { + CYTHON_UNUSED_VAR(context); + return PyObject_GetAttrString(op->func, "__module__"); +} +static int +__Pyx_CyFunction_set_module(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + return PyObject_SetAttrString(op->func, "__module__", value); +} +#endif +static PyGetSetDef __pyx_CyFunction_getsets[] = { + {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0}, + {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0}, + {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0}, + {(char *) "_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0}, +#if CYTHON_COMPILING_IN_LIMITED_API + {"__module__", (getter)__Pyx_CyFunction_get_module, (setter)__Pyx_CyFunction_set_module, 0, 0}, +#endif + {0, 0, 0, 0, 0} +}; +static PyMemberDef __pyx_CyFunction_members[] = { +#if !CYTHON_COMPILING_IN_LIMITED_API + {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), 0, 0}, +#endif +#if CYTHON_USE_TYPE_SPECS + {(char *) "__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0}, +#if CYTHON_METH_FASTCALL +#if CYTHON_BACKPORT_VECTORCALL + {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_vectorcall), READONLY, 0}, +#else +#if !CYTHON_COMPILING_IN_LIMITED_API + {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(PyCFunctionObject, vectorcall), READONLY, 0}, +#endif +#endif +#endif +#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API + {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0}, +#else + {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0}, +#endif +#endif + {0, 0, 0, 0, 0} +}; +static PyObject * +__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, PyObject *args) +{ + CYTHON_UNUSED_VAR(args); +#if PY_MAJOR_VERSION >= 3 + Py_INCREF(m->func_qualname); + return m->func_qualname; +#else + return PyString_FromString(((PyCFunctionObject*)m)->m_ml->ml_name); +#endif +} +static PyMethodDef __pyx_CyFunction_methods[] = { + {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, + {0, 0, 0, 0} +}; +#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API +#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) +#else +#define __Pyx_CyFunction_weakreflist(cyfunc) (((PyCFunctionObject*)cyfunc)->m_weakreflist) +#endif +static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname, + PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { +#if !CYTHON_COMPILING_IN_LIMITED_API + PyCFunctionObject *cf = (PyCFunctionObject*) op; +#endif + if (unlikely(op == NULL)) + return NULL; +#if CYTHON_COMPILING_IN_LIMITED_API + op->func = PyCFunction_NewEx(ml, (PyObject*)op, module); + if (unlikely(!op->func)) return NULL; +#endif + op->flags = flags; + __Pyx_CyFunction_weakreflist(op) = NULL; +#if !CYTHON_COMPILING_IN_LIMITED_API + cf->m_ml = ml; + cf->m_self = (PyObject *) op; +#endif + Py_XINCREF(closure); + op->func_closure = closure; +#if !CYTHON_COMPILING_IN_LIMITED_API + Py_XINCREF(module); + cf->m_module = module; +#endif + op->func_dict = NULL; + op->func_name = NULL; + Py_INCREF(qualname); + op->func_qualname = qualname; + op->func_doc = NULL; +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + op->func_classobj = NULL; +#else + ((PyCMethodObject*)op)->mm_class = NULL; +#endif + op->func_globals = globals; + Py_INCREF(op->func_globals); + Py_XINCREF(code); + op->func_code = code; + op->defaults_pyobjects = 0; + op->defaults_size = 0; + op->defaults = NULL; + op->defaults_tuple = NULL; + op->defaults_kwdict = NULL; + op->defaults_getter = NULL; + op->func_annotations = NULL; + op->func_is_coroutine = NULL; +#if CYTHON_METH_FASTCALL + switch (ml->ml_flags & (METH_VARARGS | METH_FASTCALL | METH_NOARGS | METH_O | METH_KEYWORDS | METH_METHOD)) { + case METH_NOARGS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_NOARGS; + break; + case METH_O: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_O; + break; + case METH_METHOD | METH_FASTCALL | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD; + break; + case METH_FASTCALL | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS; + break; + case METH_VARARGS | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = NULL; + break; + default: + PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction"); + Py_DECREF(op); + return NULL; + } +#endif + return (PyObject *) op; +} +static int +__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m) +{ + Py_CLEAR(m->func_closure); +#if CYTHON_COMPILING_IN_LIMITED_API + Py_CLEAR(m->func); +#else + Py_CLEAR(((PyCFunctionObject*)m)->m_module); +#endif + Py_CLEAR(m->func_dict); + Py_CLEAR(m->func_name); + Py_CLEAR(m->func_qualname); + Py_CLEAR(m->func_doc); + Py_CLEAR(m->func_globals); + Py_CLEAR(m->func_code); +#if !CYTHON_COMPILING_IN_LIMITED_API +#if PY_VERSION_HEX < 0x030900B1 + Py_CLEAR(__Pyx_CyFunction_GetClassObj(m)); +#else + { + PyObject *cls = (PyObject*) ((PyCMethodObject *) (m))->mm_class; + ((PyCMethodObject *) (m))->mm_class = NULL; + Py_XDECREF(cls); + } +#endif +#endif + Py_CLEAR(m->defaults_tuple); + Py_CLEAR(m->defaults_kwdict); + Py_CLEAR(m->func_annotations); + Py_CLEAR(m->func_is_coroutine); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_XDECREF(pydefaults[i]); + PyObject_Free(m->defaults); + m->defaults = NULL; + } + return 0; +} +static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + if (__Pyx_CyFunction_weakreflist(m) != NULL) + PyObject_ClearWeakRefs((PyObject *) m); + __Pyx_CyFunction_clear(m); + __Pyx_PyHeapTypeObject_GC_Del(m); +} +static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + PyObject_GC_UnTrack(m); + __Pyx__CyFunction_dealloc(m); +} +static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) +{ + Py_VISIT(m->func_closure); +#if CYTHON_COMPILING_IN_LIMITED_API + Py_VISIT(m->func); +#else + Py_VISIT(((PyCFunctionObject*)m)->m_module); +#endif + Py_VISIT(m->func_dict); + Py_VISIT(m->func_name); + Py_VISIT(m->func_qualname); + Py_VISIT(m->func_doc); + Py_VISIT(m->func_globals); + Py_VISIT(m->func_code); +#if !CYTHON_COMPILING_IN_LIMITED_API + Py_VISIT(__Pyx_CyFunction_GetClassObj(m)); +#endif + Py_VISIT(m->defaults_tuple); + Py_VISIT(m->defaults_kwdict); + Py_VISIT(m->func_is_coroutine); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_VISIT(pydefaults[i]); + } + return 0; +} +static PyObject* +__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) +{ +#if PY_MAJOR_VERSION >= 3 + return PyUnicode_FromFormat("", + op->func_qualname, (void *)op); +#else + return PyString_FromFormat("", + PyString_AsString(op->func_qualname), (void *)op); +#endif +} +static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) { +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *f = ((__pyx_CyFunctionObject*)func)->func; + PyObject *py_name = NULL; + PyCFunction meth; + int flags; + meth = PyCFunction_GetFunction(f); + if (unlikely(!meth)) return NULL; + flags = PyCFunction_GetFlags(f); + if (unlikely(flags < 0)) return NULL; +#else + PyCFunctionObject* f = (PyCFunctionObject*)func; + PyCFunction meth = f->m_ml->ml_meth; + int flags = f->m_ml->ml_flags; +#endif + Py_ssize_t size; + switch (flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) { + case METH_VARARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) + return (*meth)(self, arg); + break; + case METH_VARARGS | METH_KEYWORDS: + return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw); + case METH_NOARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { +#if CYTHON_ASSUME_SAFE_MACROS + size = PyTuple_GET_SIZE(arg); +#else + size = PyTuple_Size(arg); + if (unlikely(size < 0)) return NULL; +#endif + if (likely(size == 0)) + return (*meth)(self, NULL); +#if CYTHON_COMPILING_IN_LIMITED_API + py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL); + if (!py_name) return NULL; + PyErr_Format(PyExc_TypeError, + "%.200S() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + py_name, size); + Py_DECREF(py_name); +#else + PyErr_Format(PyExc_TypeError, + "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); +#endif + return NULL; + } + break; + case METH_O: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { +#if CYTHON_ASSUME_SAFE_MACROS + size = PyTuple_GET_SIZE(arg); +#else + size = PyTuple_Size(arg); + if (unlikely(size < 0)) return NULL; +#endif + if (likely(size == 1)) { + PyObject *result, *arg0; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + arg0 = PyTuple_GET_ITEM(arg, 0); + #else + arg0 = __Pyx_PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL; + #endif + result = (*meth)(self, arg0); + #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_DECREF(arg0); + #endif + return result; + } +#if CYTHON_COMPILING_IN_LIMITED_API + py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL); + if (!py_name) return NULL; + PyErr_Format(PyExc_TypeError, + "%.200S() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + py_name, size); + Py_DECREF(py_name); +#else + PyErr_Format(PyExc_TypeError, + "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); +#endif + return NULL; + } + break; + default: + PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction"); + return NULL; + } +#if CYTHON_COMPILING_IN_LIMITED_API + py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL); + if (!py_name) return NULL; + PyErr_Format(PyExc_TypeError, "%.200S() takes no keyword arguments", + py_name); + Py_DECREF(py_name); +#else + PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments", + f->m_ml->ml_name); +#endif + return NULL; +} +static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *self, *result; +#if CYTHON_COMPILING_IN_LIMITED_API + self = PyCFunction_GetSelf(((__pyx_CyFunctionObject*)func)->func); + if (unlikely(!self) && PyErr_Occurred()) return NULL; +#else + self = ((PyCFunctionObject*)func)->m_self; +#endif + result = __Pyx_CyFunction_CallMethod(func, self, arg, kw); + return result; +} +static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) { + PyObject *result; + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; +#if CYTHON_METH_FASTCALL + __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc); + if (vc) { +#if CYTHON_ASSUME_SAFE_MACROS + return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw); +#else + (void) &__Pyx_PyVectorcall_FastCallDict; + return PyVectorcall_Call(func, args, kw); +#endif + } +#endif + if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { + Py_ssize_t argc; + PyObject *new_args; + PyObject *self; +#if CYTHON_ASSUME_SAFE_MACROS + argc = PyTuple_GET_SIZE(args); +#else + argc = PyTuple_Size(args); + if (unlikely(!argc) < 0) return NULL; +#endif + new_args = PyTuple_GetSlice(args, 1, argc); + if (unlikely(!new_args)) + return NULL; + self = PyTuple_GetItem(args, 0); + if (unlikely(!self)) { + Py_DECREF(new_args); +#if PY_MAJOR_VERSION > 2 + PyErr_Format(PyExc_TypeError, + "unbound method %.200S() needs an argument", + cyfunc->func_qualname); +#else + PyErr_SetString(PyExc_TypeError, + "unbound method needs an argument"); +#endif + return NULL; + } + result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw); + Py_DECREF(new_args); + } else { + result = __Pyx_CyFunction_Call(func, args, kw); + } + return result; +} +#if CYTHON_METH_FASTCALL +static CYTHON_INLINE int __Pyx_CyFunction_Vectorcall_CheckArgs(__pyx_CyFunctionObject *cyfunc, Py_ssize_t nargs, PyObject *kwnames) +{ + int ret = 0; + if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { + if (unlikely(nargs < 1)) { + PyErr_Format(PyExc_TypeError, "%.200s() needs an argument", + ((PyCFunctionObject*)cyfunc)->m_ml->ml_name); + return -1; + } + ret = 1; + } + if (unlikely(kwnames) && unlikely(PyTuple_GET_SIZE(kwnames))) { + PyErr_Format(PyExc_TypeError, + "%.200s() takes no keyword arguments", ((PyCFunctionObject*)cyfunc)->m_ml->ml_name); + return -1; + } + return ret; +} +static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + if (unlikely(nargs != 0)) { + PyErr_Format(PyExc_TypeError, + "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + def->ml_name, nargs); + return NULL; + } + return def->ml_meth(self, NULL); +} +static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + if (unlikely(nargs != 1)) { + PyErr_Format(PyExc_TypeError, + "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + def->ml_name, nargs); + return NULL; + } + return def->ml_meth(self, args[0]); +} +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + return ((_PyCFunctionFastWithKeywords)(void(*)(void))def->ml_meth)(self, args, nargs, kwnames); +} +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; + PyTypeObject *cls = (PyTypeObject *) __Pyx_CyFunction_GetClassObj(cyfunc); +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + return ((__Pyx_PyCMethod)(void(*)(void))def->ml_meth)(self, cls, args, (size_t)nargs, kwnames); +} +#endif +#if CYTHON_USE_TYPE_SPECS +static PyType_Slot __pyx_CyFunctionType_slots[] = { + {Py_tp_dealloc, (void *)__Pyx_CyFunction_dealloc}, + {Py_tp_repr, (void *)__Pyx_CyFunction_repr}, + {Py_tp_call, (void *)__Pyx_CyFunction_CallAsMethod}, + {Py_tp_traverse, (void *)__Pyx_CyFunction_traverse}, + {Py_tp_clear, (void *)__Pyx_CyFunction_clear}, + {Py_tp_methods, (void *)__pyx_CyFunction_methods}, + {Py_tp_members, (void *)__pyx_CyFunction_members}, + {Py_tp_getset, (void *)__pyx_CyFunction_getsets}, + {Py_tp_descr_get, (void *)__Pyx_PyMethod_New}, + {0, 0}, +}; +static PyType_Spec __pyx_CyFunctionType_spec = { + __PYX_TYPE_MODULE_PREFIX "cython_function_or_method", + sizeof(__pyx_CyFunctionObject), + 0, +#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR + Py_TPFLAGS_METHOD_DESCRIPTOR | +#endif +#if (defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL) + _Py_TPFLAGS_HAVE_VECTORCALL | +#endif + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE, + __pyx_CyFunctionType_slots +}; +#else +static PyTypeObject __pyx_CyFunctionType_type = { + PyVarObject_HEAD_INIT(0, 0) + __PYX_TYPE_MODULE_PREFIX "cython_function_or_method", + sizeof(__pyx_CyFunctionObject), + 0, + (destructor) __Pyx_CyFunction_dealloc, +#if !CYTHON_METH_FASTCALL + 0, +#elif CYTHON_BACKPORT_VECTORCALL + (printfunc)offsetof(__pyx_CyFunctionObject, func_vectorcall), +#else + offsetof(PyCFunctionObject, vectorcall), +#endif + 0, + 0, +#if PY_MAJOR_VERSION < 3 + 0, +#else + 0, +#endif + (reprfunc) __Pyx_CyFunction_repr, + 0, + 0, + 0, + 0, + __Pyx_CyFunction_CallAsMethod, + 0, + 0, + 0, + 0, +#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR + Py_TPFLAGS_METHOD_DESCRIPTOR | +#endif +#if defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL + _Py_TPFLAGS_HAVE_VECTORCALL | +#endif + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE, + 0, + (traverseproc) __Pyx_CyFunction_traverse, + (inquiry) __Pyx_CyFunction_clear, + 0, +#if PY_VERSION_HEX < 0x030500A0 + offsetof(__pyx_CyFunctionObject, func_weakreflist), +#else + offsetof(PyCFunctionObject, m_weakreflist), +#endif + 0, + 0, + __pyx_CyFunction_methods, + __pyx_CyFunction_members, + __pyx_CyFunction_getsets, + 0, + 0, + __Pyx_PyMethod_New, + 0, + offsetof(__pyx_CyFunctionObject, func_dict), + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, +#if PY_VERSION_HEX >= 0x030400a1 + 0, +#endif +#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, +#endif +#if __PYX_NEED_TP_PRINT_SLOT + 0, +#endif +#if PY_VERSION_HEX >= 0x030C0000 + 0, +#endif +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, +#endif +}; +#endif +static int __pyx_CyFunction_init(PyObject *module) { +#if CYTHON_USE_TYPE_SPECS + __pyx_CyFunctionType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_CyFunctionType_spec, NULL); +#else + CYTHON_UNUSED_VAR(module); + __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type); +#endif + if (unlikely(__pyx_CyFunctionType == NULL)) { + return -1; + } + return 0; +} +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults = PyObject_Malloc(size); + if (unlikely(!m->defaults)) + return PyErr_NoMemory(); + memset(m->defaults, 0, size); + m->defaults_pyobjects = pyobjects; + m->defaults_size = size; + return m->defaults; +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_tuple = tuple; + Py_INCREF(tuple); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_kwdict = dict; + Py_INCREF(dict); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->func_annotations = dict; + Py_INCREF(dict); +} + +/* CythonFunction */ +static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname, + PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { + PyObject *op = __Pyx_CyFunction_Init( + PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType), + ml, flags, qualname, closure, module, globals, code + ); + if (likely(op)) { + PyObject_GC_Track(op); + } + return op; +} + +/* CLineInTraceback */ +#ifndef CYTHON_CLINE_IN_TRACEBACK +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) { + PyObject *use_cline; + PyObject *ptype, *pvalue, *ptraceback; +#if CYTHON_COMPILING_IN_CPYTHON + PyObject **cython_runtime_dict; +#endif + CYTHON_MAYBE_UNUSED_VAR(tstate); + if (unlikely(!__pyx_cython_runtime)) { + return c_line; + } + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); +#if CYTHON_COMPILING_IN_CPYTHON + cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); + if (likely(cython_runtime_dict)) { + __PYX_PY_DICT_LOOKUP_IF_MODIFIED( + use_cline, *cython_runtime_dict, + __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) + } else +#endif + { + PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStrNoError(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); + if (use_cline_obj) { + use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; + Py_DECREF(use_cline_obj); + } else { + PyErr_Clear(); + use_cline = NULL; + } + } + if (!use_cline) { + c_line = 0; + (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); + } + else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { + c_line = 0; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + return c_line; +} +#endif + +/* CodeObjectCache */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} +#endif + +/* AddTraceback */ +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +#if PY_VERSION_HEX >= 0x030b00a6 && !CYTHON_COMPILING_IN_LIMITED_API + #ifndef Py_BUILD_CORE + #define Py_BUILD_CORE 1 + #endif + #include "internal/pycore_frame.h" +#endif +#if CYTHON_COMPILING_IN_LIMITED_API +static PyObject *__Pyx_PyCode_Replace_For_AddTraceback(PyObject *code, PyObject *scratch_dict, + PyObject *firstlineno, PyObject *name) { + PyObject *replace = NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "co_firstlineno", firstlineno))) return NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "co_name", name))) return NULL; + replace = PyObject_GetAttrString(code, "replace"); + if (likely(replace)) { + PyObject *result; + result = PyObject_Call(replace, __pyx_empty_tuple, scratch_dict); + Py_DECREF(replace); + return result; + } + #if __PYX_LIMITED_VERSION_HEX < 0x030780000 + PyErr_Clear(); + { + PyObject *compiled = NULL, *result = NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "code", code))) return NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "type", (PyObject*)(&PyType_Type)))) return NULL; + compiled = Py_CompileString( + "out = type(code)(\n" + " code.co_argcount, code.co_kwonlyargcount, code.co_nlocals, code.co_stacksize,\n" + " code.co_flags, code.co_code, code.co_consts, code.co_names,\n" + " code.co_varnames, code.co_filename, co_name, co_firstlineno,\n" + " code.co_lnotab)\n", "", Py_file_input); + if (!compiled) return NULL; + result = PyEval_EvalCode(compiled, scratch_dict, scratch_dict); + Py_DECREF(compiled); + if (!result) PyErr_Print(); + Py_DECREF(result); + result = PyDict_GetItemString(scratch_dict, "out"); + if (result) Py_INCREF(result); + return result; + } + #endif +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyObject *code_object = NULL, *py_py_line = NULL, *py_funcname = NULL, *dict = NULL; + PyObject *replace = NULL, *getframe = NULL, *frame = NULL; + PyObject *exc_type, *exc_value, *exc_traceback; + int success = 0; + if (c_line) { + (void) __pyx_cfilenm; + (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line); + } + PyErr_Fetch(&exc_type, &exc_value, &exc_traceback); + code_object = Py_CompileString("_getframe()", filename, Py_eval_input); + if (unlikely(!code_object)) goto bad; + py_py_line = PyLong_FromLong(py_line); + if (unlikely(!py_py_line)) goto bad; + py_funcname = PyUnicode_FromString(funcname); + if (unlikely(!py_funcname)) goto bad; + dict = PyDict_New(); + if (unlikely(!dict)) goto bad; + { + PyObject *old_code_object = code_object; + code_object = __Pyx_PyCode_Replace_For_AddTraceback(code_object, dict, py_py_line, py_funcname); + Py_DECREF(old_code_object); + } + if (unlikely(!code_object)) goto bad; + getframe = PySys_GetObject("_getframe"); + if (unlikely(!getframe)) goto bad; + if (unlikely(PyDict_SetItemString(dict, "_getframe", getframe))) goto bad; + frame = PyEval_EvalCode(code_object, dict, dict); + if (unlikely(!frame) || frame == Py_None) goto bad; + success = 1; + bad: + PyErr_Restore(exc_type, exc_value, exc_traceback); + Py_XDECREF(code_object); + Py_XDECREF(py_py_line); + Py_XDECREF(py_funcname); + Py_XDECREF(dict); + Py_XDECREF(replace); + if (success) { + PyTraceBack_Here( + (struct _frame*)frame); + } + Py_XDECREF(frame); +} +#else +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = NULL; + PyObject *py_funcname = NULL; + #if PY_MAJOR_VERSION < 3 + PyObject *py_srcfile = NULL; + py_srcfile = PyString_FromString(filename); + if (!py_srcfile) goto bad; + #endif + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + if (!py_funcname) goto bad; + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + if (!py_funcname) goto bad; + funcname = PyUnicode_AsUTF8(py_funcname); + if (!funcname) goto bad; + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + if (!py_funcname) goto bad; + #endif + } + #if PY_MAJOR_VERSION < 3 + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + #else + py_code = PyCode_NewEmpty(filename, funcname, py_line); + #endif + Py_XDECREF(py_funcname); // XDECREF since it's only set on Py3 if cline + return py_code; +bad: + Py_XDECREF(py_funcname); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_srcfile); + #endif + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject *ptype, *pvalue, *ptraceback; + if (c_line) { + c_line = __Pyx_CLineForTraceback(tstate, c_line); + } + py_code = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!py_code) { + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) { + /* If the code object creation fails, then we should clear the + fetched exception references and propagate the new exception */ + Py_XDECREF(ptype); + Py_XDECREF(pvalue); + Py_XDECREF(ptraceback); + goto bad; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); + } + py_frame = PyFrame_New( + tstate, /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} +#endif + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + __Pyx_TypeName obj_type_name; + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "'" __Pyx_FMT_TYPENAME "' does not have the buffer interface", + obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if ((0)) {} + view->obj = NULL; + Py_DECREF(obj); +} +#endif + + +/* MemviewSliceIsContig */ +static int +__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) +{ + int i, index, step, start; + Py_ssize_t itemsize = mvs.memview->view.itemsize; + if (order == 'F') { + step = 1; + start = 0; + } else { + step = -1; + start = ndim - 1; + } + for (i = 0; i < ndim; i++) { + index = start + step * i; + if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) + return 0; + itemsize *= mvs.shape[index]; + } + return 1; +} + +/* OverlappingSlices */ +static void +__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, + void **out_start, void **out_end, + int ndim, size_t itemsize) +{ + char *start, *end; + int i; + start = end = slice->data; + for (i = 0; i < ndim; i++) { + Py_ssize_t stride = slice->strides[i]; + Py_ssize_t extent = slice->shape[i]; + if (extent == 0) { + *out_start = *out_end = start; + return; + } else { + if (stride > 0) + end += stride * (extent - 1); + else + start += stride * (extent - 1); + } + } + *out_start = start; + *out_end = end + itemsize; +} +static int +__pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize) +{ + void *start1, *end1, *start2, *end2; + __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); + __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); + return (start1 < end2) && (start2 < end1); +} + +/* IsLittleEndian */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) +{ + union { + uint32_t u32; + uint8_t u8[4]; + } S; + S.u32 = 0x01020304; + return S.u8[0] == 4; +} + +/* BufferFormatCheck */ +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t <= '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case '?': return "'bool'"; + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparsable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) { + CYTHON_UNUSED_VAR(is_complex); + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, int is_complex) { + CYTHON_UNUSED_VAR(is_complex); + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static int +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number, ndim; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return -1; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return -1; + ndim = ctx->head->field->type->ndim; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return -1; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + return -1; + } + if (*ts != ',' && *ts != ')') { + PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + return -1; + } + if (*ts == ',') ts++; + i++; + } + if (i != ndim) { + PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + return -1; + } + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return -1; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return 0; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + CYTHON_FALLTHROUGH; + case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && + (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + CYTHON_FALLTHROUGH; + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (__pyx_buffmt_parse_array(ctx, &ts) < 0) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} + +/* TypeInfoCompare */ + static int +__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) +{ + int i; + if (!a || !b) + return 0; + if (a == b) + return 1; + if (a->size != b->size || a->typegroup != b->typegroup || + a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { + if (a->typegroup == 'H' || b->typegroup == 'H') { + return a->size == b->size; + } else { + return 0; + } + } + if (a->ndim) { + for (i = 0; i < a->ndim; i++) + if (a->arraysize[i] != b->arraysize[i]) + return 0; + } + if (a->typegroup == 'S') { + if (a->flags != b->flags) + return 0; + if (a->fields || b->fields) { + if (!(a->fields && b->fields)) + return 0; + for (i = 0; a->fields[i].type && b->fields[i].type; i++) { + __Pyx_StructField *field_a = a->fields + i; + __Pyx_StructField *field_b = b->fields + i; + if (field_a->offset != field_b->offset || + !__pyx_typeinfo_cmp(field_a->type, field_b->type)) + return 0; + } + return !a->fields[i].type && !b->fields[i].type; + } + } + return 1; +} + +/* MemviewSliceValidateAndInit */ + static int +__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) +{ + if (buf->shape[dim] <= 1) + return 1; + if (buf->strides) { + if (spec & __Pyx_MEMVIEW_CONTIG) { + if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { + if (unlikely(buf->strides[dim] != sizeof(void *))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly contiguous " + "in dimension %d.", dim); + goto fail; + } + } else if (unlikely(buf->strides[dim] != buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_FOLLOW) { + Py_ssize_t stride = buf->strides[dim]; + if (stride < 0) + stride = -stride; + if (unlikely(stride < buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + } else { + if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not contiguous in " + "dimension %d", dim); + goto fail; + } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not indirect in " + "dimension %d", dim); + goto fail; + } else if (unlikely(buf->suboffsets)) { + PyErr_SetString(PyExc_ValueError, + "Buffer exposes suboffsets but no strides"); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_check_suboffsets(Py_buffer *buf, int dim, int ndim, int spec) +{ + CYTHON_UNUSED_VAR(ndim); + if (spec & __Pyx_MEMVIEW_DIRECT) { + if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { + PyErr_Format(PyExc_ValueError, + "Buffer not compatible with direct access " + "in dimension %d.", dim); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_PTR) { + if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly accessible " + "in dimension %d.", dim); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) +{ + int i; + if (c_or_f_flag & __Pyx_IS_F_CONTIG) { + Py_ssize_t stride = 1; + for (i = 0; i < ndim; i++) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not fortran contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { + Py_ssize_t stride = 1; + for (i = ndim - 1; i >- 1; i--) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not C contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } + return 1; +fail: + return 0; +} +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj) +{ + struct __pyx_memoryview_obj *memview, *new_memview; + __Pyx_RefNannyDeclarations + Py_buffer *buf; + int i, spec = 0, retval = -1; + __Pyx_BufFmt_Context ctx; + int from_memoryview = __pyx_memoryview_check(original_obj); + __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); + if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) + original_obj)->typeinfo)) { + memview = (struct __pyx_memoryview_obj *) original_obj; + new_memview = NULL; + } else { + memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + original_obj, buf_flags, 0, dtype); + new_memview = memview; + if (unlikely(!memview)) + goto fail; + } + buf = &memview->view; + if (unlikely(buf->ndim != ndim)) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + ndim, buf->ndim); + goto fail; + } + if (new_memview) { + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; + } + if (unlikely((unsigned) buf->itemsize != dtype->size)) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " + "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", + buf->itemsize, + (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, + (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->len > 0) { + for (i = 0; i < ndim; i++) { + spec = axes_specs[i]; + if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) + goto fail; + if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) + goto fail; + } + if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) + goto fail; + } + if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, + new_memview != NULL) == -1)) { + goto fail; + } + retval = 0; + goto no_fail; +fail: + Py_XDECREF(new_memview); + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, + &__Pyx_TypeInfo_int, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, + &__Pyx_TypeInfo_float, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, + &__Pyx_TypeInfo_int, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* Declarations */ + #if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabsf(b.real) >= fabsf(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + float r = b.imag / b.real; + float s = (float)(1.0) / (b.real + b.imag * r); + return __pyx_t_float_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + float r = b.real / b.imag; + float s = (float)(1.0) / (b.imag + b.real * r); + return __pyx_t_float_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + float denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_float_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + return __Pyx_c_prod_float(a, a); + case 3: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, a); + case 4: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if ((b.imag == 0) && (a.real >= 0)) { + z.real = powf(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2f(0.0, -1.0); + } + } else { + r = __Pyx_c_abs_float(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +/* Declarations */ + #if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabs(b.real) >= fabs(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + double r = b.imag / b.real; + double s = (double)(1.0) / (b.real + b.imag * r); + return __pyx_t_double_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + double r = b.real / b.imag; + double s = (double)(1.0) / (b.imag + b.real * r); + return __pyx_t_double_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + double denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_double_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + return __Pyx_c_prod_double(a, a); + case 3: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, a); + case 4: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if ((b.imag == 0) && (a.real >= 0)) { + z.real = pow(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2(0.0, -1.0); + } + } else { + r = __Pyx_c_abs_double(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +/* MemviewSliceCopyTemplate */ + static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object) +{ + __Pyx_RefNannyDeclarations + int i; + __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; + struct __pyx_memoryview_obj *from_memview = from_mvs->memview; + Py_buffer *buf = &from_memview->view; + PyObject *shape_tuple = NULL; + PyObject *temp_int = NULL; + struct __pyx_array_obj *array_obj = NULL; + struct __pyx_memoryview_obj *memview_obj = NULL; + __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); + for (i = 0; i < ndim; i++) { + if (unlikely(from_mvs->suboffsets[i] >= 0)) { + PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " + "indirect dimensions (axis %d)", i); + goto fail; + } + } + shape_tuple = PyTuple_New(ndim); + if (unlikely(!shape_tuple)) { + goto fail; + } + __Pyx_GOTREF(shape_tuple); + for(i = 0; i < ndim; i++) { + temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); + if(unlikely(!temp_int)) { + goto fail; + } else { + PyTuple_SET_ITEM(shape_tuple, i, temp_int); + temp_int = NULL; + } + } + array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); + if (unlikely(!array_obj)) { + goto fail; + } + __Pyx_GOTREF(array_obj); + memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + (PyObject *) array_obj, contig_flag, + dtype_is_object, + from_mvs->memview->typeinfo); + if (unlikely(!memview_obj)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) + goto fail; + if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, + dtype_is_object) < 0)) + goto fail; + goto no_fail; +fail: + __Pyx_XDECREF(new_mvs.memview); + new_mvs.memview = NULL; + new_mvs.data = NULL; +no_fail: + __Pyx_XDECREF(shape_tuple); + __Pyx_XDECREF(temp_int); + __Pyx_XDECREF(array_obj); + __Pyx_RefNannyFinishContext(); + return new_mvs; +} + +/* MemviewSliceInit */ + static int +__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference) +{ + __Pyx_RefNannyDeclarations + int i, retval=-1; + Py_buffer *buf = &memview->view; + __Pyx_RefNannySetupContext("init_memviewslice", 0); + if (unlikely(memviewslice->memview || memviewslice->data)) { + PyErr_SetString(PyExc_ValueError, + "memviewslice is already initialized!"); + goto fail; + } + if (buf->strides) { + for (i = 0; i < ndim; i++) { + memviewslice->strides[i] = buf->strides[i]; + } + } else { + Py_ssize_t stride = buf->itemsize; + for (i = ndim - 1; i >= 0; i--) { + memviewslice->strides[i] = stride; + stride *= buf->shape[i]; + } + } + for (i = 0; i < ndim; i++) { + memviewslice->shape[i] = buf->shape[i]; + if (buf->suboffsets) { + memviewslice->suboffsets[i] = buf->suboffsets[i]; + } else { + memviewslice->suboffsets[i] = -1; + } + } + memviewslice->memview = memview; + memviewslice->data = (char *)buf->buf; + if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { + Py_INCREF(memview); + } + retval = 0; + goto no_fail; +fail: + memviewslice->memview = 0; + memviewslice->data = 0; + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} +#ifndef Py_NO_RETURN +#define Py_NO_RETURN +#endif +static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { + va_list vargs; + char msg[200]; +#if PY_VERSION_HEX >= 0x030A0000 || defined(HAVE_STDARG_PROTOTYPES) + va_start(vargs, fmt); +#else + va_start(vargs); +#endif + vsnprintf(msg, 200, fmt, vargs); + va_end(vargs); + Py_FatalError(msg); +} +static CYTHON_INLINE int +__pyx_add_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)++; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE int +__pyx_sub_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)--; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE void +__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) +{ + __pyx_nonatomic_int_type old_acquisition_count; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) { + return; + } + old_acquisition_count = __pyx_add_acquisition_count(memview); + if (unlikely(old_acquisition_count <= 0)) { + if (likely(old_acquisition_count == 0)) { + if (have_gil) { + Py_INCREF((PyObject *) memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_INCREF((PyObject *) memview); + PyGILState_Release(_gilstate); + } + } else { + __pyx_fatalerror("Acquisition count is %d (line %d)", + old_acquisition_count+1, lineno); + } + } +} +static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *memslice, + int have_gil, int lineno) { + __pyx_nonatomic_int_type old_acquisition_count; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) { + memslice->memview = NULL; + return; + } + old_acquisition_count = __pyx_sub_acquisition_count(memview); + memslice->data = NULL; + if (likely(old_acquisition_count > 1)) { + memslice->memview = NULL; + } else if (likely(old_acquisition_count == 1)) { + if (have_gil) { + Py_CLEAR(memslice->memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_CLEAR(memslice->memview); + PyGILState_Release(_gilstate); + } + } else { + __pyx_fatalerror("Acquisition count is %d (line %d)", + old_acquisition_count-1, lineno); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; +#if !CYTHON_COMPILING_IN_LIMITED_API + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); +#else + PyObject *from_bytes, *result = NULL; + PyObject *py_bytes = NULL, *arg_tuple = NULL, *kwds = NULL, *order_str = NULL; + from_bytes = PyObject_GetAttrString((PyObject*)&PyInt_Type, "from_bytes"); + if (!from_bytes) return NULL; + py_bytes = PyBytes_FromStringAndSize((char*)bytes, sizeof(int)); + if (!py_bytes) goto limited_bad; + order_str = PyUnicode_FromString(little ? "little" : "big"); + if (!order_str) goto limited_bad; + arg_tuple = PyTuple_Pack(2, py_bytes, order_str); + if (!arg_tuple) goto limited_bad; + kwds = PyDict_New(); + if (!kwds) goto limited_bad; + if (PyDict_SetItemString(kwds, "signed", __Pyx_NewRef(!is_unsigned ? Py_True : Py_False))) goto limited_bad; + result = PyObject_Call(from_bytes, arg_tuple, kwds); + limited_bad: + Py_XDECREF(from_bytes); + Py_XDECREF(py_bytes); + Py_XDECREF(order_str); + Py_XDECREF(arg_tuple); + Py_XDECREF(kwds); + return result; +#endif + } +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if ((sizeof(int) < sizeof(long))) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 2 * PyLong_SHIFT)) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 3 * PyLong_SHIFT)) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 4 * PyLong_SHIFT)) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(int) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(int) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(int) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(int) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(int) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); +#if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } +#endif + if (likely(v)) { + int ret = -1; +#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + long idigit; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (unlikely(!PyLong_CheckExact(v))) { + PyObject *tmp = v; + v = PyNumber_Long(v); + assert(PyLong_CheckExact(v)); + Py_DECREF(tmp); + if (unlikely(!v)) return (int) -1; + } +#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(x) == 0) + return (int) 0; + is_negative = Py_SIZE(x) < 0; +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + is_negative = result == 1; + } +#endif + if (is_unsigned && unlikely(is_negative)) { + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + if (unlikely(!stepval)) + return (int) -1; + } else { + stepval = __Pyx_NewRef(v); + } + val = (int) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(int) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + val |= ((int) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(stepval) == 0) + goto unpacking_done; + #endif + } + idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(int) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((int) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + unpacking_done: + #endif + if (!is_unsigned) { + if (unlikely(val & (((int) 1) << (sizeof(int) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + Py_DECREF(v); + if (likely(!ret)) + return val; + } + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; +#if !CYTHON_COMPILING_IN_LIMITED_API + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); +#else + PyObject *from_bytes, *result = NULL; + PyObject *py_bytes = NULL, *arg_tuple = NULL, *kwds = NULL, *order_str = NULL; + from_bytes = PyObject_GetAttrString((PyObject*)&PyInt_Type, "from_bytes"); + if (!from_bytes) return NULL; + py_bytes = PyBytes_FromStringAndSize((char*)bytes, sizeof(long)); + if (!py_bytes) goto limited_bad; + order_str = PyUnicode_FromString(little ? "little" : "big"); + if (!order_str) goto limited_bad; + arg_tuple = PyTuple_Pack(2, py_bytes, order_str); + if (!arg_tuple) goto limited_bad; + kwds = PyDict_New(); + if (!kwds) goto limited_bad; + if (PyDict_SetItemString(kwds, "signed", __Pyx_NewRef(!is_unsigned ? Py_True : Py_False))) goto limited_bad; + result = PyObject_Call(from_bytes, arg_tuple, kwds); + limited_bad: + Py_XDECREF(from_bytes); + Py_XDECREF(py_bytes); + Py_XDECREF(order_str); + Py_XDECREF(arg_tuple); + Py_XDECREF(kwds); + return result; +#endif + } +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if ((sizeof(long) < sizeof(long))) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 2 * PyLong_SHIFT)) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 3 * PyLong_SHIFT)) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 4 * PyLong_SHIFT)) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(long) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(long) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(long) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(long) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(long) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); +#if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } +#endif + if (likely(v)) { + int ret = -1; +#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + long idigit; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (unlikely(!PyLong_CheckExact(v))) { + PyObject *tmp = v; + v = PyNumber_Long(v); + assert(PyLong_CheckExact(v)); + Py_DECREF(tmp); + if (unlikely(!v)) return (long) -1; + } +#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(x) == 0) + return (long) 0; + is_negative = Py_SIZE(x) < 0; +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + is_negative = result == 1; + } +#endif + if (is_unsigned && unlikely(is_negative)) { + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + if (unlikely(!stepval)) + return (long) -1; + } else { + stepval = __Pyx_NewRef(v); + } + val = (long) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(long) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + val |= ((long) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(stepval) == 0) + goto unpacking_done; + #endif + } + idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(long) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((long) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + unpacking_done: + #endif + if (!is_unsigned) { + if (unlikely(val & (((long) 1) << (sizeof(long) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + Py_DECREF(v); + if (likely(!ret)) + return val; + } + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const char neg_one = (char) -1, const_zero = (char) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if ((sizeof(char) < sizeof(long))) { + __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (char) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 2 * PyLong_SHIFT)) { + return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 3 * PyLong_SHIFT)) { + return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 4 * PyLong_SHIFT)) { + return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(char) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(char) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(char) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { + return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(char) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(char) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + char val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); +#if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } +#endif + if (likely(v)) { + int ret = -1; +#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + long idigit; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (unlikely(!PyLong_CheckExact(v))) { + PyObject *tmp = v; + v = PyNumber_Long(v); + assert(PyLong_CheckExact(v)); + Py_DECREF(tmp); + if (unlikely(!v)) return (char) -1; + } +#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(x) == 0) + return (char) 0; + is_negative = Py_SIZE(x) < 0; +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + is_negative = result == 1; + } +#endif + if (is_unsigned && unlikely(is_negative)) { + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + if (unlikely(!stepval)) + return (char) -1; + } else { + stepval = __Pyx_NewRef(v); + } + val = (char) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(char) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + val |= ((char) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(stepval) == 0) + goto unpacking_done; + #endif + } + idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(char) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((char) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + unpacking_done: + #endif + if (!is_unsigned) { + if (unlikely(val & (((char) 1) << (sizeof(char) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + Py_DECREF(v); + if (likely(!ret)) + return val; + } + return (char) -1; + } + } else { + char val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (char) -1; + val = __Pyx_PyInt_As_char(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to char"); + return (char) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to char"); + return (char) -1; +} + +/* FormatTypeName */ + #if CYTHON_COMPILING_IN_LIMITED_API +static __Pyx_TypeName +__Pyx_PyType_GetName(PyTypeObject* tp) +{ + PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp, + __pyx_n_s_name_2); + if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) { + PyErr_Clear(); + Py_XDECREF(name); + name = __Pyx_NewRef(__pyx_n_s__25); + } + return name; +} +#endif + +/* CheckBinaryVersion */ + static unsigned long __Pyx_get_runtime_version() { +#if __PYX_LIMITED_VERSION_HEX >= 0x030B00A4 + return Py_Version & ~0xFFUL; +#else + const char* rt_version = Py_GetVersion(); + unsigned long version = 0; + unsigned long factor = 0x01000000UL; + unsigned int digit = 0; + int i = 0; + while (factor) { + while ('0' <= rt_version[i] && rt_version[i] <= '9') { + digit = digit * 10 + (unsigned int) (rt_version[i] - '0'); + ++i; + } + version += factor * digit; + if (rt_version[i] != '.') + break; + digit = 0; + factor >>= 8; + ++i; + } + return version; +#endif +} +static int __Pyx_check_binary_version(unsigned long ct_version, unsigned long rt_version, int allow_newer) { + const unsigned long MAJOR_MINOR = 0xFFFF0000UL; + if ((rt_version & MAJOR_MINOR) == (ct_version & MAJOR_MINOR)) + return 0; + if (likely(allow_newer && (rt_version & MAJOR_MINOR) > (ct_version & MAJOR_MINOR))) + return 1; + { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compile time Python version %d.%d " + "of module '%.100s' " + "%s " + "runtime version %d.%d", + (int) (ct_version >> 24), (int) ((ct_version >> 16) & 0xFF), + __Pyx_MODULE_NAME, + (allow_newer) ? "was newer than" : "does not match", + (int) (rt_version >> 24), (int) ((rt_version >> 16) & 0xFF) + ); + return PyErr_WarnEx(NULL, message, 1); + } +} + +/* InitStrings */ + #if PY_MAJOR_VERSION >= 3 +static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str) { + if (t.is_unicode | t.is_str) { + if (t.intern) { + *str = PyUnicode_InternFromString(t.s); + } else if (t.encoding) { + *str = PyUnicode_Decode(t.s, t.n - 1, t.encoding, NULL); + } else { + *str = PyUnicode_FromStringAndSize(t.s, t.n - 1); + } + } else { + *str = PyBytes_FromStringAndSize(t.s, t.n - 1); + } + if (!*str) + return -1; + if (PyObject_Hash(*str) == -1) + return -1; + return 0; +} +#endif +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION >= 3 + __Pyx_InitString(*t, t->p); + #else + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + if (!*t->p) + return -1; + if (PyObject_Hash(*t->p) == -1) + return -1; + #endif + ++t; + } + return 0; +} + +#include +static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s) { + size_t len = strlen(s); + if (unlikely(len > (size_t) PY_SSIZE_T_MAX)) { + PyErr_SetString(PyExc_OverflowError, "byte string is too long"); + return -1; + } + return (Py_ssize_t) len; +} +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + Py_ssize_t len = __Pyx_ssize_strlen(c_str); + if (unlikely(len < 0)) return NULL; + return __Pyx_PyUnicode_FromStringAndSize(c_str, len); +} +static CYTHON_INLINE PyObject* __Pyx_PyByteArray_FromString(const char* c_str) { + Py_ssize_t len = __Pyx_ssize_strlen(c_str); + if (unlikely(len < 0)) return NULL; + return PyByteArray_FromStringAndSize(c_str, len); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#if !CYTHON_PEP393_ENABLED +static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +} +#else +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +} +#endif +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY && !CYTHON_COMPILING_IN_LIMITED_API) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { + int retval; + if (unlikely(!x)) return -1; + retval = __Pyx_PyObject_IsTrue(x); + Py_DECREF(x); + return retval; +} +static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { + __Pyx_TypeName result_type_name = __Pyx_PyType_GetName(Py_TYPE(result)); +#if PY_MAJOR_VERSION >= 3 + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type " __Pyx_FMT_TYPENAME "). " + "The ability to return an instance of a strict subclass of int is deprecated, " + "and may be removed in a future version of Python.", + result_type_name)) { + __Pyx_DECREF_TypeName(result_type_name); + Py_DECREF(result); + return NULL; + } + __Pyx_DECREF_TypeName(result_type_name); + return result; + } +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type " __Pyx_FMT_TYPENAME ")", + type_name, type_name, result_type_name); + __Pyx_DECREF_TypeName(result_type_name); + Py_DECREF(result); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x) || PyLong_Check(x))) +#else + if (likely(PyLong_Check(x))) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = m->nb_int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = m->nb_long(x); + } + #else + if (likely(m && m->nb_int)) { + name = "int"; + res = m->nb_int(x); + } + #endif +#else + if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { + res = PyNumber_Int(x); + } +#endif + if (likely(res)) { +#if PY_MAJOR_VERSION < 3 + if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { +#else + if (unlikely(!PyLong_CheckExact(res))) { +#endif + return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(b); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(__Pyx_PyLong_IsCompact(b))) { + return __Pyx_PyLong_CompactValue(b); + } else { + const digit* digits = __Pyx_PyLong_Digits(b); + const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(b); + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) { + if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) { + return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o); +#if PY_MAJOR_VERSION < 3 + } else if (likely(PyInt_CheckExact(o))) { + return PyInt_AS_LONG(o); +#endif + } else { + Py_ssize_t ival; + PyObject *x; + x = PyNumber_Index(o); + if (!x) return -1; + ival = PyInt_AsLong(x); + Py_DECREF(x); + return ival; + } +} +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { + return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +/* #### Code section: utility_code_pragmas_end ### */ +#ifdef _MSC_VER +#pragma warning( pop ) +#endif + + + +/* #### Code section: end ### */ +#endif /* Py_PYTHON_H */ diff --git a/src/glow_tts/monotonic_align/monotonic_align/core.pyx b/src/glow_tts/monotonic_align/monotonic_align/core.pyx new file mode 100644 index 0000000000000000000000000000000000000000..6aabccc4c408cb1b555e2abb4d73e0d1ce4d346e --- /dev/null +++ b/src/glow_tts/monotonic_align/monotonic_align/core.pyx @@ -0,0 +1,45 @@ +import numpy as np +cimport numpy as np +cimport cython +from cython.parallel import prange + + +@cython.boundscheck(False) +@cython.wraparound(False) +cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_x, int t_y, float max_neg_val) nogil: + cdef int x + cdef int y + cdef float v_prev + cdef float v_cur + cdef float tmp + cdef int index = t_x - 1 + + for y in range(t_y): + for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): + if x == y: + v_cur = max_neg_val + else: + v_cur = value[x, y-1] + if x == 0: + if y == 0: + v_prev = 0. + else: + v_prev = max_neg_val + else: + v_prev = value[x-1, y-1] + value[x, y] = max(v_cur, v_prev) + value[x, y] + + for y in range(t_y - 1, -1, -1): + path[index, y] = 1 + if index != 0 and (index == y or value[index, y-1] < value[index-1, y-1]): + index = index - 1 + + +@cython.boundscheck(False) +@cython.wraparound(False) +cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_xs, int[::1] t_ys, float max_neg_val=-1e9) nogil: + cdef int b = values.shape[0] + + cdef int i + for i in prange(b, nogil=True): + maximum_path_each(paths[i], values[i], t_xs[i], t_ys[i], max_neg_val) diff --git a/src/glow_tts/monotonic_align/monotonic_align/mas.py b/src/glow_tts/monotonic_align/monotonic_align/mas.py new file mode 100644 index 0000000000000000000000000000000000000000..207ab3e858389ec06c902fd6f5bec6c5da2996af --- /dev/null +++ b/src/glow_tts/monotonic_align/monotonic_align/mas.py @@ -0,0 +1,57 @@ +from typing import overload +import numpy as np +import torch +from monotonic_align.core import maximum_path_c + + +def mask_from_len(lens: torch.Tensor, max_len=None): + """ + Make a `mask` from lens. + + :param inputs: (B, T, D) + :param lens: (B) + + :return: + `mask`: (B, T) + """ + if max_len is None: + max_len = lens.max() + index = torch.arange(max_len).to(lens).view(1, -1) + return index < lens.unsqueeze(1) # (B, T) + + +def mask_from_lens( + similarity: torch.Tensor, + symbol_lens: torch.Tensor, + mel_lens: torch.Tensor, +): + """ + :param similarity: (B, S, T) + :param symbol_lens: (B,) + :param mel_lens: (B,) + """ + _, S, T = similarity.size() + mask_S = mask_from_len(symbol_lens, S) + mask_T = mask_from_len(mel_lens, T) + mask_ST = mask_S.unsqueeze(2) * mask_T.unsqueeze(1) + return mask_ST.to(similarity) + + +def maximum_path(value, mask=None): + """Cython optimised version. + value: [b, t_x, t_y] + mask: [b, t_x, t_y] + """ + if mask is None: + mask = torch.zeros_like(value) + + value = value * mask + device = value.device + dtype = value.dtype + value = value.data.cpu().numpy().astype(np.float32) + path = np.zeros_like(value).astype(np.int32) + mask = mask.data.cpu().numpy() + t_x_max = mask.sum(1)[:, 0].astype(np.int32) + t_y_max = mask.sum(2)[:, 0].astype(np.int32) + maximum_path_c(path, value, t_x_max, t_y_max) + return torch.from_numpy(path).to(device=device, dtype=dtype) diff --git a/src/glow_tts/monotonic_align/pyproject.toml b/src/glow_tts/monotonic_align/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..ea6358a08fd8d6fc177e2361a82e1a5cc7b837d9 --- /dev/null +++ b/src/glow_tts/monotonic_align/pyproject.toml @@ -0,0 +1,7 @@ +[build-system] +requires = [ + "wheel", + "setuptools", + "cython>=0.24.0", + "numpy= win_length + # get window and zero center pad it to filter_length + fft_window = get_window(window, win_length, fftbins=True) + fft_window = pad_center(fft_window, filter_length) + fft_window = torch.from_numpy(fft_window).float() + + # window the bases + forward_basis *= fft_window + inverse_basis *= fft_window + + self.register_buffer("forward_basis", forward_basis.float()) + self.register_buffer("inverse_basis", inverse_basis.float()) + + def transform(self, input_data): + num_batches = input_data.size(0) + num_samples = input_data.size(1) + + self.num_samples = num_samples + + if input_data.device.type == "cuda": + # similar to librosa, reflect-pad the input + input_data = input_data.view(num_batches, 1, num_samples) + input_data = F.pad( + input_data.unsqueeze(1), + (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0), + mode="reflect", + ) + input_data = input_data.squeeze(1) + + forward_transform = F.conv1d( + input_data, self.forward_basis, stride=self.hop_length, padding=0 + ) + + cutoff = int((self.filter_length / 2) + 1) + real_part = forward_transform[:, :cutoff, :] + imag_part = forward_transform[:, cutoff:, :] + else: + x = input_data.detach().numpy() + real_part = [] + imag_part = [] + for y in x: + y_ = stft( + y, self.filter_length, self.hop_length, self.win_length, self.window + ) + real_part.append(y_.real[None, :, :]) + imag_part.append(y_.imag[None, :, :]) + real_part = np.concatenate(real_part, 0) + imag_part = np.concatenate(imag_part, 0) + + real_part = torch.from_numpy(real_part).to(input_data.dtype) + imag_part = torch.from_numpy(imag_part).to(input_data.dtype) + + magnitude = torch.sqrt(real_part ** 2 + imag_part ** 2) + phase = torch.atan2(imag_part.data, real_part.data) + + return magnitude, phase + + def inverse(self, magnitude, phase): + recombine_magnitude_phase = torch.cat( + [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1 + ) + + if magnitude.device.type == "cuda": + inverse_transform = F.conv_transpose1d( + recombine_magnitude_phase, + self.inverse_basis, + stride=self.hop_length, + padding=0, + ) + + if self.window is not None: + window_sum = window_sumsquare( + self.window, + magnitude.size(-1), + hop_length=self.hop_length, + win_length=self.win_length, + n_fft=self.filter_length, + dtype=np.float32, + ) + # remove modulation effects + approx_nonzero_indices = torch.from_numpy( + np.where(window_sum > tiny(window_sum))[0] + ) + window_sum = torch.from_numpy(window_sum).to(inverse_transform.device) + inverse_transform[:, :, approx_nonzero_indices] /= window_sum[ + approx_nonzero_indices + ] + + # scale by hop ratio + inverse_transform *= float(self.filter_length) / self.hop_length + + inverse_transform = inverse_transform[:, :, int(self.filter_length / 2) :] + inverse_transform = inverse_transform[ + :, :, : -int(self.filter_length / 2) : + ] + inverse_transform = inverse_transform.squeeze(1) + else: + x_org = recombine_magnitude_phase.detach().numpy() + n_b, n_f, n_t = x_org.shape + x = np.empty([n_b, n_f // 2, n_t], dtype=np.complex64) + x.real = x_org[:, : n_f // 2] + x.imag = x_org[:, n_f // 2 :] + inverse_transform = [] + for y in x: + y_ = istft(y, self.hop_length, self.win_length, self.window) + inverse_transform.append(y_[None, :]) + inverse_transform = np.concatenate(inverse_transform, 0) + inverse_transform = torch.from_numpy(inverse_transform).to( + recombine_magnitude_phase.dtype + ) + + return inverse_transform + + def forward(self, input_data): + self.magnitude, self.phase = self.transform(input_data) + reconstruction = self.inverse(self.magnitude, self.phase) + return reconstruction diff --git a/src/glow_tts/t2s_fastapi.py b/src/glow_tts/t2s_fastapi.py new file mode 100644 index 0000000000000000000000000000000000000000..e034fc01a4a5bcd54b365a49dad2e907b57504a1 --- /dev/null +++ b/src/glow_tts/t2s_fastapi.py @@ -0,0 +1,63 @@ +from starlette.responses import StreamingResponse +from texttospeech import MelToWav, TextToMel +from typing import Optional +from pydantic import BaseModel +from fastapi import FastAPI, HTTPException +import uvicorn +import base64 + +app = FastAPI() + + +class TextJson(BaseModel): + text: str + lang: Optional[str] = "hi" + gender: Optional[str] = "male" + + +glow_hi_male = TextToMel(glow_model_dir="", device="") +glow_hi_female = TextToMel(glow_model_dir="", device="") +hifi_hi = MelToWav(hifi_model_dir="", device="") + + +available_choice = { + "hi_male": [glow_hi_male, hifi_hi], + "hi_female": [glow_hi_female, hifi_hi], +} + + +@app.post("/TTS/") +async def tts(input: TextJson): + text = input.text + lang = input.lang + gender = input.gender + + choice = lang + "_" + gender + if choice in available_choice.keys(): + t2s = available_choice[choice] + else: + raise HTTPException( + status_code=400, detail={"error": "Requested model not found"} + ) + + if text: + mel = t2s[0].generate_mel(text) + data, sr = t2s[1].generate_wav(mel) + t2s.save_audio("out.wav", data, sr) + else: + raise HTTPException(status_code=400, detail={"error": "No text"}) + + ## to return outpur as a file + # audio = open('out.wav', mode='rb') + # return StreamingResponse(audio, media_type="audio/wav") + + with open("out.wav", "rb") as audio_file: + encoded_bytes = base64.b64encode(audio_file.read()) + encoded_string = encoded_bytes.decode() + return {"encoding": "base64", "data": encoded_string, "sr": sr} + + +if __name__ == "__main__": + uvicorn.run( + "t2s_fastapi:app", host="127.0.0.1", port=5000, log_level="info", reload=True + ) diff --git a/src/glow_tts/t2s_gradio.py b/src/glow_tts/t2s_gradio.py new file mode 100644 index 0000000000000000000000000000000000000000..bd9acbe68761759ff259f4476bb3df57a75c78ff --- /dev/null +++ b/src/glow_tts/t2s_gradio.py @@ -0,0 +1,24 @@ +import gradio as gr +from texttospeech import TextToMel, MelToWav + +text_to_mel = TextToMel( + glow_model_dir="/path/to/glow-tts/checkpoint/dir", device="cuda" +) +mel_to_wav = MelToWav(hifi_model_dir="/path/to/glow-tts/checkpoint/dir", device="cuda") + + +def run_tts(text): + mel = text_to_mel.generate_mel(text) + audio, sr = mel_to_wav.generate_wav(mel) + return (sr, audio) + + +# text = " सीआईएसएफ में उप-निरीक्षक महावीर प्रसाद गोदरा को मरणोपरांत 'शौर्य चक्र' से सम्मानित किया गया। " +# run_tts(text) + +textbox = gr.inputs.Textbox( + placeholder="Enter Telugu text here", default="", label="TTS" +) +op = gr.outputs.Audio(type="numpy", label=None) +iface = gr.Interface(fn=run_tts, inputs=textbox, outputs=op) +iface.launch(share=True) diff --git a/src/glow_tts/text/__init__.py b/src/glow_tts/text/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3f5aa62bfcd56165b85d064f5ca0ba59fbe34a72 --- /dev/null +++ b/src/glow_tts/text/__init__.py @@ -0,0 +1,84 @@ +""" from https://github.com/keithito/tacotron """ +import re +from text import cleaners + +# Regular expression matching text enclosed in curly braces: +_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)') + + +def get_arpabet(word, dictionary): + word_arpabet = dictionary.lookup(word) + if word_arpabet is not None: + return "{" + word_arpabet[0] + "}" + else: + return word + + +def text_to_sequence(text, symbols, cleaner_names, dictionary=None): + '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text. + + The text can optionally have ARPAbet sequences enclosed in curly braces embedded + in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street." + + Args: + text: string to convert to a sequence + cleaner_names: names of the cleaner functions to run the text through + dictionary: arpabet class with arpabet dictionary + + Returns: + List of integers corresponding to the symbols in the text + ''' + # Mappings from symbol to numeric ID and vice versa: + global _id_to_symbol, _symbol_to_id + _symbol_to_id = {s: i for i, s in enumerate(symbols)} + _id_to_symbol = {i: s for i, s in enumerate(symbols)} + + sequence = [] + + space = _symbols_to_sequence(' ') + # Check for curly braces and treat their contents as ARPAbet: + while len(text): + m = _curly_re.match(text) + if not m: + clean_text = _clean_text(text, cleaner_names) + if dictionary is not None: + clean_text = [get_arpabet(w, dictionary) for w in clean_text.split(" ")] + for i in range(len(clean_text)): + t = clean_text[i] + if t.startswith("{"): + sequence += _arpabet_to_sequence(t[1:-1]) + else: + sequence += _symbols_to_sequence(t) + sequence += space + else: + sequence += _symbols_to_sequence(clean_text) + break + sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names)) + sequence += _arpabet_to_sequence(m.group(2)) + text = m.group(3) + + # remove trailing space + if dictionary is not None: + sequence = sequence[:-1] if sequence[-1] == space[0] else sequence + return sequence + + +def _clean_text(text, cleaner_names): + for name in cleaner_names: + cleaner = getattr(cleaners, name) + if not cleaner: + raise Exception('Unknown cleaner: %s' % name) + text = cleaner(text) + return text + + +def _symbols_to_sequence(symbols): + return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)] + + +def _arpabet_to_sequence(text): + return _symbols_to_sequence(['@' + s for s in text.split()]) + + +def _should_keep_symbol(s): + return s in _symbol_to_id and s is not '_' and s is not '~' \ No newline at end of file diff --git a/src/glow_tts/text/__pycache__/__init__.cpython-37.pyc b/src/glow_tts/text/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ac1da5b4139a0c732515d040060b46e04376bd31 Binary files /dev/null and b/src/glow_tts/text/__pycache__/__init__.cpython-37.pyc differ diff --git a/src/glow_tts/text/__pycache__/cleaners.cpython-37.pyc b/src/glow_tts/text/__pycache__/cleaners.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..dfb26ec1efd11d637dbdf250b7ea86df082d78b3 Binary files /dev/null and b/src/glow_tts/text/__pycache__/cleaners.cpython-37.pyc differ diff --git a/src/glow_tts/text/__pycache__/numbers.cpython-37.pyc b/src/glow_tts/text/__pycache__/numbers.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..86b36cb71bd676a2e40ec1102c5d905bd763aa86 Binary files /dev/null and b/src/glow_tts/text/__pycache__/numbers.cpython-37.pyc differ diff --git a/src/glow_tts/text/cleaners.py b/src/glow_tts/text/cleaners.py new file mode 100644 index 0000000000000000000000000000000000000000..a7d4e029baa436e88e4d68090e886afdd998a68d --- /dev/null +++ b/src/glow_tts/text/cleaners.py @@ -0,0 +1,78 @@ +import re + +from unidecode import unidecode +from .numbers import normalize_numbers + + + + +# Regular expression matching whitespace: +_whitespace_re = re.compile(r"\s+") + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text) + +def basic_indic_cleaners(text): + """Basic pipeline that collapses whitespace without transliteration.""" + text = collapse_whitespace(text) + return text + + +def english_cleaner(text): + text = text.lower().replace('‘','\'').replace('’','\'') + return text + + +def lowercase(text): + return text.lower() + +def convert_to_ascii(text): + return unidecode(text) + +def expand_numbers(text): + return normalize_numbers(text) + +def expand_abbreviations(text): + for regex, replacement in _abbreviations: + text = re.sub(regex, replacement, text) + return text + +_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [ + ('mrs', 'missus'), + ('mr', 'mister'), + ('dr', 'doctor'), + ('st', 'saint'), + ('co', 'company'), + ('jr', 'junior'), + ('maj', 'major'), + ('gen', 'general'), + ('drs', 'doctors'), + ('rev', 'reverend'), + ('lt', 'lieutenant'), + ('hon', 'honorable'), + ('sgt', 'sergeant'), + ('capt', 'captain'), + ('esq', 'esquire'), + ('ltd', 'limited'), + ('col', 'colonel'), + ('ft', 'fort'), + ('pvt', 'private'), + ('rs', 'Rupees') +]] + + + + + + +def english_cleaners(text): + '''Pipeline for English text, including number and abbreviation expansion.''' + text = convert_to_ascii(text) + text = lowercase(text) + text = expand_numbers(text) + text = expand_abbreviations(text) + text = collapse_whitespace(text) + return text diff --git a/src/glow_tts/text/numbers.py b/src/glow_tts/text/numbers.py new file mode 100644 index 0000000000000000000000000000000000000000..491634d692ee71e7ea0e5213b513e15be825c9b2 --- /dev/null +++ b/src/glow_tts/text/numbers.py @@ -0,0 +1,69 @@ +import inflect +import re + + +_inflect = inflect.engine() +_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])') +_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)') +_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)') +_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)') +_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)') +_number_re = re.compile(r'[0-9]+') + + +def _remove_commas(m): + return m.group(1).replace(',', '') + + +def _expand_decimal_point(m): + return m.group(1).replace('.', ' point ') + + +def _expand_dollars(m): + match = m.group(1) + parts = match.split('.') + if len(parts) > 2: + return match + ' dollars' # Unexpected format + dollars = int(parts[0]) if parts[0] else 0 + cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 + if dollars and cents: + dollar_unit = 'dollar' if dollars == 1 else 'dollars' + cent_unit = 'cent' if cents == 1 else 'cents' + return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit) + elif dollars: + dollar_unit = 'dollar' if dollars == 1 else 'dollars' + return '%s %s' % (dollars, dollar_unit) + elif cents: + cent_unit = 'cent' if cents == 1 else 'cents' + return '%s %s' % (cents, cent_unit) + else: + return 'zero dollars' + + +def _expand_ordinal(m): + return _inflect.number_to_words(m.group(0)) + + +def _expand_number(m): + num = int(m.group(0)) + if num > 1000 and num < 3000: + if num == 2000: + return 'two thousand' + elif num > 2000 and num < 2010: + return 'two thousand ' + _inflect.number_to_words(num % 100) + elif num % 100 == 0: + return _inflect.number_to_words(num // 100) + ' hundred' + else: + return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ') + else: + return _inflect.number_to_words(num, andword='') + + +def normalize_numbers(text): + text = re.sub(_comma_number_re, _remove_commas, text) + text = re.sub(_pounds_re, r'\1 pounds', text) + text = re.sub(_dollars_re, _expand_dollars, text) + text = re.sub(_decimal_number_re, _expand_decimal_point, text) + text = re.sub(_ordinal_re, _expand_ordinal, text) + text = re.sub(_number_re, _expand_number, text) + return text \ No newline at end of file diff --git a/src/glow_tts/texttospeech.py b/src/glow_tts/texttospeech.py new file mode 100644 index 0000000000000000000000000000000000000000..3c88925cac0c56e52d35acfa5d6d7e5ce51329c7 --- /dev/null +++ b/src/glow_tts/texttospeech.py @@ -0,0 +1,146 @@ +from __future__ import absolute_import, division, print_function, unicode_literals +from typing import Tuple + +from scipy.io.wavfile import write +from hifi.env import AttrDict +from hifi.models import Generator + +import numpy as np +import os +import json + +import torch +from text import text_to_sequence +import commons +import models +import utils +import sys +from argparse import ArgumentParser + + +def check_directory(dir): + if not os.path.exists(dir): + sys.exit("Error: {} directory does not exist".format(dir)) + + +class TextToMel: + def __init__(self, glow_model_dir, device="cuda"): + self.glow_model_dir = glow_model_dir + check_directory(self.glow_model_dir) + self.device = device + self.hps, self.glow_tts_model = self.load_glow_tts() + pass + + def load_glow_tts(self): + hps = utils.get_hparams_from_dir(self.glow_model_dir) + checkpoint_path = utils.latest_checkpoint_path(self.glow_model_dir) + symbols = list(hps.data.punc) + list(hps.data.chars) + glow_tts_model = models.FlowGenerator( + len(symbols) + getattr(hps.data, "add_blank", False), + out_channels=hps.data.n_mel_channels, + **hps.model + ) # .to(self.device) + + if self.device == "cuda": + glow_tts_model.to("cuda") + + utils.load_checkpoint(checkpoint_path, glow_tts_model) + glow_tts_model.decoder.store_inverse() + _ = glow_tts_model.eval() + + return hps, glow_tts_model + + def generate_mel(self, text, noise_scale=0.667, length_scale=1.0): + symbols = list(self.hps.data.punc) + list(self.hps.data.chars) + cleaner = self.hps.data.text_cleaners + if getattr(self.hps.data, "add_blank", False): + text_norm = text_to_sequence(text, symbols, cleaner) + text_norm = commons.intersperse(text_norm, len(symbols)) + else: # If not using "add_blank" option during training, adding spaces at the beginning and the end of utterance improves quality + text = " " + text.strip() + " " + text_norm = text_to_sequence(text, symbols, cleaner) + + sequence = np.array(text_norm)[None, :] + + if self.device == "cuda": + x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long() + x_tst_lengths = torch.tensor([x_tst.shape[1]]).cuda() + else: + x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).long() + x_tst_lengths = torch.tensor([x_tst.shape[1]]) + + with torch.no_grad(): + (y_gen_tst, *_), *_, (attn_gen, *_) = self.glow_tts_model( + x_tst, + x_tst_lengths, + gen=True, + noise_scale=noise_scale, + length_scale=length_scale, + ) + + return y_gen_tst + #return y_gen_tst.cpu().detach().numpy() + + +class MelToWav: + def __init__(self, hifi_model_dir, device="cuda"): + self.hifi_model_dir = hifi_model_dir + check_directory(self.hifi_model_dir) + self.device = device + self.h, self.hifi_gan_generator = self.load_hifi_gan() + pass + + def load_hifi_gan(self): + checkpoint_path = utils.latest_checkpoint_path(self.hifi_model_dir, regex="g_*") + config_file = os.path.join(self.hifi_model_dir, "config.json") + data = open(config_file).read() + json_config = json.loads(data) + h = AttrDict(json_config) + torch.manual_seed(h.seed) + + generator = Generator(h).to(self.device) + + assert os.path.isfile(checkpoint_path) + print("Loading '{}'".format(checkpoint_path)) + state_dict_g = torch.load(checkpoint_path, map_location=self.device) + print("Complete.") + + generator.load_state_dict(state_dict_g["generator"]) + + generator.eval() + generator.remove_weight_norm() + + return h, generator + + def generate_wav(self, mel): + #mel = torch.FloatTensor(mel).to(self.device) + + y_g_hat = self.hifi_gan_generator(mel.to(self.device)) # passing through vocoder + audio = y_g_hat.squeeze() + audio = audio * 32768.0 + audio = audio.cpu().detach().numpy().astype("int16") + + return audio, self.h.sampling_rate + + + + + +if __name__ == "__main__": + + parser = ArgumentParser() + parser.add_argument("-m", "--model", required=True, type=str) + parser.add_argument("-g", "--gan", required=True, type=str) + parser.add_argument("-d", "--device", type=str, default="cpu") + parser.add_argument("-t", "--text", type=str, required=True) + parser.add_argument("-w", "--wav", type=str, required=True) + + args = parser.parse_args() + + text_to_mel = TextToMel(glow_model_dir=args.model, device=args.device) + mel_to_wav = MelToWav(hifi_model_dir=args.gan, device=args.device) + + mel = text_to_mel.generate_mel(args.text) + audio, sr = mel_to_wav.generate_wav(mel) + + write(filename=args.wav, rate=sr, data=audio) \ No newline at end of file diff --git a/src/glow_tts/train.py b/src/glow_tts/train.py new file mode 100644 index 0000000000000000000000000000000000000000..79bf515a707b309e82e9686c140658f23acf1b91 --- /dev/null +++ b/src/glow_tts/train.py @@ -0,0 +1,286 @@ +import os +import json +import argparse +import math +import torch +from torch import nn, optim +from torch.nn import functional as F +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter +import torch.multiprocessing as mp +import torch.distributed as dist +from apex.parallel import DistributedDataParallel as DDP +from apex import amp + +from data_utils import TextMelLoader, TextMelCollate +import models +import commons +import utils + + +global_step = 0 + + +def main(): + """Assume Single Node Multi GPUs Training Only""" + assert torch.cuda.is_available(), "CPU training is not allowed." + + n_gpus = torch.cuda.device_count() + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = "80000" + + hps = utils.get_hparams() + mp.spawn( + train_and_eval, + nprocs=n_gpus, + args=( + n_gpus, + hps, + ), + ) + + +def train_and_eval(rank, n_gpus, hps): + global global_step + if rank == 0: + logger = utils.get_logger(hps.log_dir) + logger.info(hps) + utils.check_git_hash(hps.log_dir) + writer = SummaryWriter(log_dir=hps.log_dir) + writer_eval = SummaryWriter(log_dir=os.path.join(hps.log_dir, "eval")) + + dist.init_process_group( + backend="nccl", init_method="env://", world_size=n_gpus, rank=rank + ) + torch.manual_seed(hps.train.seed) + torch.cuda.set_device(rank) + + train_dataset = TextMelLoader(hps.data.training_files, hps.data) + train_sampler = torch.utils.data.distributed.DistributedSampler( + train_dataset, num_replicas=n_gpus, rank=rank, shuffle=True + ) + collate_fn = TextMelCollate(1) + train_loader = DataLoader( + train_dataset, + num_workers=8, + shuffle=False, + batch_size=hps.train.batch_size, + pin_memory=True, + drop_last=True, + collate_fn=collate_fn, + sampler=train_sampler, + ) + if rank == 0: + val_dataset = TextMelLoader(hps.data.validation_files, hps.data) + val_loader = DataLoader( + val_dataset, + num_workers=8, + shuffle=False, + batch_size=hps.train.batch_size, + pin_memory=True, + drop_last=True, + collate_fn=collate_fn, + ) + symbols = hps.data.punc + hps.data.chars + generator = models.FlowGenerator( + n_vocab=len(symbols) + getattr(hps.data, "add_blank", False), + out_channels=hps.data.n_mel_channels, + **hps.model + ).cuda(rank) + optimizer_g = commons.Adam( + generator.parameters(), + scheduler=hps.train.scheduler, + dim_model=hps.model.hidden_channels, + warmup_steps=hps.train.warmup_steps, + lr=hps.train.learning_rate, + betas=hps.train.betas, + eps=hps.train.eps, + ) + if hps.train.fp16_run: + generator, optimizer_g._optim = amp.initialize( + generator, optimizer_g._optim, opt_level="O1" + ) + generator = DDP(generator) + epoch_str = 1 + global_step = 0 + try: + _, _, _, epoch_str = utils.load_checkpoint( + utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), + generator, + optimizer_g, + ) + epoch_str += 1 + optimizer_g.step_num = (epoch_str - 1) * len(train_loader) + optimizer_g._update_learning_rate() + global_step = (epoch_str - 1) * len(train_loader) + except: + if hps.train.ddi and os.path.isfile(os.path.join(hps.model_dir, "ddi_G.pth")): + _ = utils.load_checkpoint( + os.path.join(hps.model_dir, "ddi_G.pth"), generator, optimizer_g + ) + + for epoch in range(epoch_str, hps.train.epochs + 1): + if rank == 0: + train( + rank, epoch, hps, generator, optimizer_g, train_loader, logger, writer + ) + evaluate( + rank, + epoch, + hps, + generator, + optimizer_g, + val_loader, + logger, + writer_eval, + ) + if epoch % hps.train.save_epoch == 0: + utils.save_checkpoint( + generator, + optimizer_g, + hps.train.learning_rate, + epoch, + os.path.join(hps.model_dir, "G_{}.pth".format(epoch)), + ) + else: + train(rank, epoch, hps, generator, optimizer_g, train_loader, None, None) + + +def train(rank, epoch, hps, generator, optimizer_g, train_loader, logger, writer): + train_loader.sampler.set_epoch(epoch) + global global_step + + generator.train() + for batch_idx, (x, x_lengths, y, y_lengths) in enumerate(train_loader): + x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda( + rank, non_blocking=True + ) + y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda( + rank, non_blocking=True + ) + + # Train Generator + optimizer_g.zero_grad() + + ( + (z, z_m, z_logs, logdet, z_mask), + (x_m, x_logs, x_mask), + (attn, logw, logw_), + ) = generator(x, x_lengths, y, y_lengths, gen=False) + l_mle = commons.mle_loss(z, z_m, z_logs, logdet, z_mask) + l_length = commons.duration_loss(logw, logw_, x_lengths) + + loss_gs = [l_mle, l_length] + loss_g = sum(loss_gs) + + if hps.train.fp16_run: + with amp.scale_loss(loss_g, optimizer_g._optim) as scaled_loss: + scaled_loss.backward() + grad_norm = commons.clip_grad_value_( + amp.master_params(optimizer_g._optim), 5 + ) + else: + loss_g.backward() + grad_norm = commons.clip_grad_value_(generator.parameters(), 5) + optimizer_g.step() + + if rank == 0: + if batch_idx % hps.train.log_interval == 0: + (y_gen, *_), *_ = generator.module(x[:1], x_lengths[:1], gen=True) + logger.info( + "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format( + epoch, + batch_idx * len(x), + len(train_loader.dataset), + 100.0 * batch_idx / len(train_loader), + loss_g.item(), + ) + ) + logger.info( + [x.item() for x in loss_gs] + [global_step, optimizer_g.get_lr()] + ) + + scalar_dict = { + "loss/g/total": loss_g, + "learning_rate": optimizer_g.get_lr(), + "grad_norm": grad_norm, + } + scalar_dict.update( + {"loss/g/{}".format(i): v for i, v in enumerate(loss_gs)} + ) + utils.summarize( + writer=writer, + global_step=global_step, + images={ + "y_org": utils.plot_spectrogram_to_numpy( + y[0].data.cpu().numpy() + ), + "y_gen": utils.plot_spectrogram_to_numpy( + y_gen[0].data.cpu().numpy() + ), + "attn": utils.plot_alignment_to_numpy( + attn[0, 0].data.cpu().numpy() + ), + }, + scalars=scalar_dict, + ) + global_step += 1 + + if rank == 0: + logger.info("====> Epoch: {}".format(epoch)) + + +def evaluate(rank, epoch, hps, generator, optimizer_g, val_loader, logger, writer_eval): + if rank == 0: + global global_step + generator.eval() + losses_tot = [] + with torch.no_grad(): + for batch_idx, (x, x_lengths, y, y_lengths) in enumerate(val_loader): + x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda( + rank, non_blocking=True + ) + y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda( + rank, non_blocking=True + ) + + ( + (z, z_m, z_logs, logdet, z_mask), + (x_m, x_logs, x_mask), + (attn, logw, logw_), + ) = generator(x, x_lengths, y, y_lengths, gen=False) + l_mle = commons.mle_loss(z, z_m, z_logs, logdet, z_mask) + l_length = commons.duration_loss(logw, logw_, x_lengths) + + loss_gs = [l_mle, l_length] + loss_g = sum(loss_gs) + + if batch_idx == 0: + losses_tot = loss_gs + else: + losses_tot = [x + y for (x, y) in zip(losses_tot, loss_gs)] + + if batch_idx % hps.train.log_interval == 0: + logger.info( + "Eval Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format( + epoch, + batch_idx * len(x), + len(val_loader.dataset), + 100.0 * batch_idx / len(val_loader), + loss_g.item(), + ) + ) + logger.info([x.item() for x in loss_gs]) + + losses_tot = [x / len(val_loader) for x in losses_tot] + loss_tot = sum(losses_tot) + scalar_dict = {"loss/g/total": loss_tot} + scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_tot)}) + utils.summarize( + writer=writer_eval, global_step=global_step, scalars=scalar_dict + ) + logger.info("====> Epoch: {}".format(epoch)) + + +if __name__ == "__main__": + main() diff --git a/src/glow_tts/utils.py b/src/glow_tts/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ee3243e0d3f87bf274b06cc44bde36da7a54f5be --- /dev/null +++ b/src/glow_tts/utils.py @@ -0,0 +1,284 @@ +import os +import glob +import sys +import argparse +import logging +import json +import subprocess +import numpy as np +from scipy.io.wavfile import read +import torch + +MATPLOTLIB_FLAG = False + +logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) +logger = logging + + +def load_checkpoint(checkpoint_path, model, optimizer=None): + assert os.path.isfile(checkpoint_path) + checkpoint_dict = torch.load(checkpoint_path, map_location="cpu") + iteration = 1 + if "iteration" in checkpoint_dict.keys(): + iteration = checkpoint_dict["iteration"] + if "learning_rate" in checkpoint_dict.keys(): + learning_rate = checkpoint_dict["learning_rate"] + if optimizer is not None and "optimizer" in checkpoint_dict.keys(): + optimizer.load_state_dict(checkpoint_dict["optimizer"]) + saved_state_dict = checkpoint_dict["model"] + if hasattr(model, "module"): + state_dict = model.module.state_dict() + else: + state_dict = model.state_dict() + new_state_dict = {} + for k, v in state_dict.items(): + try: + new_state_dict[k] = saved_state_dict[k] + except: + logger.info("%s is not in the checkpoint" % k) + new_state_dict[k] = v + if hasattr(model, "module"): + model.module.load_state_dict(new_state_dict) + else: + model.load_state_dict(new_state_dict) + logger.info( + "Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration) + ) + return model, optimizer, learning_rate, iteration + + +def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path): + logger.info( + "Saving model and optimizer state at iteration {} to {}".format( + iteration, checkpoint_path + ) + ) + if hasattr(model, "module"): + state_dict = model.module.state_dict() + else: + state_dict = model.state_dict() + torch.save( + { + "model": state_dict, + "iteration": iteration, + "optimizer": optimizer.state_dict(), + "learning_rate": learning_rate, + }, + checkpoint_path, + ) + + +def summarize(writer, global_step, scalars={}, histograms={}, images={}): + for k, v in scalars.items(): + writer.add_scalar(k, v, global_step) + for k, v in histograms.items(): + writer.add_histogram(k, v, global_step) + for k, v in images.items(): + writer.add_image(k, v, global_step, dataformats="HWC") + + +def latest_checkpoint_path(dir_path, regex="G_*.pth"): + f_list = glob.glob(os.path.join(dir_path, regex)) + f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f)))) + x = f_list[-1] + print(x) + return x + + +def plot_spectrogram_to_numpy(spectrogram): + global MATPLOTLIB_FLAG + if not MATPLOTLIB_FLAG: + import matplotlib + + matplotlib.use("Agg") + MATPLOTLIB_FLAG = True + mpl_logger = logging.getLogger("matplotlib") + mpl_logger.setLevel(logging.WARNING) + import matplotlib.pylab as plt + import numpy as np + + fig, ax = plt.subplots() + im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + plt.xlabel("Frames") + plt.ylabel("Channels") + plt.tight_layout() + + fig.canvas.draw() + data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") + data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) + plt.close() + return data + + +def plot_alignment_to_numpy(alignment, info=None): + global MATPLOTLIB_FLAG + if not MATPLOTLIB_FLAG: + import matplotlib + + matplotlib.use("Agg") + MATPLOTLIB_FLAG = True + mpl_logger = logging.getLogger("matplotlib") + mpl_logger.setLevel(logging.WARNING) + import matplotlib.pylab as plt + import numpy as np + + fig, ax = plt.subplots(figsize=(6, 4)) + im = ax.imshow(alignment, aspect="auto", origin="lower", interpolation="none") + fig.colorbar(im, ax=ax) + xlabel = "Decoder timestep" + if info is not None: + xlabel += "\n\n" + info + plt.xlabel(xlabel) + plt.ylabel("Encoder timestep") + plt.tight_layout() + + fig.canvas.draw() + data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") + data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) + plt.close() + return data + + +def load_wav_to_torch(full_path): + sampling_rate, data = read(full_path) + print(data) + print(sampling_rate) + return torch.FloatTensor(data.astype(np.float32)), sampling_rate + + +def load_filepaths_and_text(filename, split="|"): + with open(filename, encoding="utf-8") as f: + filepaths_and_text = [line.strip().split(split) for line in f] + return filepaths_and_text + + +def get_hparams(init=True): + parser = argparse.ArgumentParser() + parser.add_argument("-c", "--config", type=str, help="JSON file for configuration") + parser.add_argument("-m", "--model", type=str, help="Model name") + # parser.add_argument('-g', '--gan', type=str, + # help='Model name') + parser.add_argument("-l", "--logs", type=str, help="logs name") + # parser.add_argument('-s', '--mels', type=str, + # help='logs name') + + args = parser.parse_args() + # model_dir = os.path.join("./logs", args.model) + model_dir = args.model + if not os.path.exists(model_dir): + os.makedirs(model_dir) + + config_path = args.config + config_save_path = os.path.join(model_dir, "config.json") + + # if not config_path : config_path = config_save_path + + if init: + with open(config_path, "r") as f: + data = f.read() + with open(config_save_path, "w") as f: + f.write(data) + else: + with open(config_save_path, "r") as f: + data = f.read() + config = json.loads(data) + + hparams = HParams(**config) + hparams.model_dir = model_dir + hparams.log_dir = args.logs + # hparams.mels_dir = args.mels + # hparams.gan_dir = args.gan + return hparams + + +def get_hparams_from_dir(model_dir): + config_save_path = os.path.join(model_dir, "config.json") + with open(config_save_path, "r") as f: + data = f.read() + config = json.loads(data) + + hparams = HParams(**config) + hparams.model_dir = model_dir + return hparams + + +def get_hparams_from_file(config_path): + with open(config_path, "r") as f: + data = f.read() + config = json.loads(data) + + hparams = HParams(**config) + return hparams + + +def check_git_hash(model_dir): + source_dir = os.path.dirname(os.path.realpath(__file__)) + if not os.path.exists(os.path.join(source_dir, ".git")): + logger.warn( + "{} is not a git repository, therefore hash value comparison will be ignored.".format( + source_dir + ) + ) + return + + cur_hash = subprocess.getoutput("git rev-parse HEAD") + + path = os.path.join(model_dir, "githash") + if os.path.exists(path): + saved_hash = open(path).read() + if saved_hash != cur_hash: + logger.warn( + "git hash values are different. {}(saved) != {}(current)".format( + saved_hash[:8], cur_hash[:8] + ) + ) + else: + open(path, "w").write(cur_hash) + + +def get_logger(model_dir, filename="train.log"): + global logger + logger = logging.getLogger(os.path.basename(model_dir)) + logger.setLevel(logging.DEBUG) + + formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") + if not os.path.exists(model_dir): + os.makedirs(model_dir) + h = logging.FileHandler(os.path.join(model_dir, filename)) + h.setLevel(logging.DEBUG) + h.setFormatter(formatter) + logger.addHandler(h) + return logger + + +class HParams: + def __init__(self, **kwargs): + for k, v in kwargs.items(): + if type(v) == dict: + v = HParams(**v) + self[k] = v + + def keys(self): + return self.__dict__.keys() + + def items(self): + return self.__dict__.items() + + def values(self): + return self.__dict__.values() + + def __len__(self): + return len(self.__dict__) + + def __getitem__(self, key): + return getattr(self, key) + + def __setitem__(self, key, value): + return setattr(self, key, value) + + def __contains__(self, key): + return key in self.__dict__ + + def __repr__(self): + return self.__dict__.__repr__() diff --git a/src/hifi_gan/__pycache__/env.cpython-37.pyc b/src/hifi_gan/__pycache__/env.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..704b99aea58807fca2690fc69f32f2620eebc769 Binary files /dev/null and b/src/hifi_gan/__pycache__/env.cpython-37.pyc differ diff --git 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0000000000000000000000000000000000000000..2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056 --- /dev/null +++ b/src/hifi_gan/env.py @@ -0,0 +1,15 @@ +import os +import shutil + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super(AttrDict, self).__init__(*args, **kwargs) + self.__dict__ = self + + +def build_env(config, config_name, path): + t_path = os.path.join(path, config_name) + if config != t_path: + os.makedirs(path, exist_ok=True) + shutil.copyfile(config, os.path.join(path, config_name)) diff --git a/src/hifi_gan/inference.py b/src/hifi_gan/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..c70ee09b4110677b7cf9732d76a5e6ca93c8860c --- /dev/null +++ b/src/hifi_gan/inference.py @@ -0,0 +1,98 @@ +from __future__ import absolute_import, division, print_function, unicode_literals + +import glob +import os +import argparse +import json +import torch +from scipy.io.wavfile import write +from env import AttrDict +from meldataset import mel_spectrogram, MAX_WAV_VALUE, load_wav +from models import Generator + +h = None +device = None + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print("Loading '{}'".format(filepath)) + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def get_mel(x): + return mel_spectrogram( + x, h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax + ) + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + "*") + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return "" + return sorted(cp_list)[-1] + + +def inference(a): + generator = Generator(h).to(device) + + state_dict_g = load_checkpoint(a.checkpoint_file, device) + generator.load_state_dict(state_dict_g["generator"]) + + filelist = os.listdir(a.input_wavs_dir) + + os.makedirs(a.output_dir, exist_ok=True) + + generator.eval() + generator.remove_weight_norm() + with torch.no_grad(): + for i, filname in enumerate(filelist): + wav, sr = load_wav(os.path.join(a.input_wavs_dir, filname)) + wav = wav / MAX_WAV_VALUE + wav = torch.FloatTensor(wav).to(device) + x = get_mel(wav.unsqueeze(0)) + y_g_hat = generator(x) + audio = y_g_hat.squeeze() + audio = audio * MAX_WAV_VALUE + audio = audio.cpu().numpy().astype("int16") + + output_file = os.path.join( + a.output_dir, os.path.splitext(filname)[0] + "_generated.wav" + ) + write(output_file, h.sampling_rate, audio) + print(output_file) + + +def main(): + print("Initializing Inference Process..") + + parser = argparse.ArgumentParser() + parser.add_argument("--input_wavs_dir", default="test_files") + parser.add_argument("--output_dir", default="generated_files") + parser.add_argument("--checkpoint_file", required=True) + a = parser.parse_args() + + config_file = os.path.join(os.path.split(a.checkpoint_file)[0], "config.json") + with open(config_file) as f: + data = f.read() + + global h + json_config = json.loads(data) + h = AttrDict(json_config) + + torch.manual_seed(h.seed) + global device + if torch.cuda.is_available(): + torch.cuda.manual_seed(h.seed) + device = torch.device("cuda") + else: + device = torch.device("cpu") + + inference(a) + + +if __name__ == "__main__": + main() diff --git a/src/hifi_gan/inference_e2e.py b/src/hifi_gan/inference_e2e.py new file mode 100644 index 0000000000000000000000000000000000000000..062aecd4280925336ab1d36420d2cd47febf661c --- /dev/null +++ b/src/hifi_gan/inference_e2e.py @@ -0,0 +1,91 @@ +from __future__ import absolute_import, division, print_function, unicode_literals + +import glob +import os +import numpy as np +import argparse +import json +import torch +from scipy.io.wavfile import write +from env import AttrDict +from meldataset import MAX_WAV_VALUE +from models import Generator + +h = None +device = None + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print("Loading '{}'".format(filepath)) + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + "*") + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return "" + return sorted(cp_list)[-1] + + +def inference(a): + generator = Generator(h).to(device) + + state_dict_g = load_checkpoint(a.checkpoint_file, device) + generator.load_state_dict(state_dict_g["generator"]) + + filelist = os.listdir(a.input_mels_dir) + + os.makedirs(a.output_dir, exist_ok=True) + + generator.eval() + generator.remove_weight_norm() + with torch.no_grad(): + for i, filname in enumerate(filelist): + x = np.load(os.path.join(a.input_mels_dir, filname)) + x = torch.FloatTensor(x).to(device) + y_g_hat = generator(x) + audio = y_g_hat.squeeze() + audio = audio * MAX_WAV_VALUE + audio = audio.cpu().numpy().astype("int16") + + output_file = os.path.join( + a.output_dir, os.path.splitext(filname)[0] + "_generated_e2e.wav" + ) + write(output_file, h.sampling_rate, audio) + print(output_file) + + +def main(): + print("Initializing Inference Process..") + + parser = argparse.ArgumentParser() + parser.add_argument("--input_mels_dir", default="test_mel_files") + parser.add_argument("--output_dir", default="generated_files_from_mel") + parser.add_argument("--checkpoint_file", required=True) + a = parser.parse_args() + + config_file = os.path.join(os.path.split(a.checkpoint_file)[0], "config.json") + with open(config_file) as f: + data = f.read() + + global h + json_config = json.loads(data) + h = AttrDict(json_config) + + torch.manual_seed(h.seed) + global device + if torch.cuda.is_available(): + torch.cuda.manual_seed(h.seed) + device = torch.device("cuda") + else: + device = torch.device("cpu") + + inference(a) + + +if __name__ == "__main__": + main() diff --git a/src/hifi_gan/meldataset.py b/src/hifi_gan/meldataset.py new file mode 100644 index 0000000000000000000000000000000000000000..8c6ca9ec8a6cc6408a77492e795bffef7f86b611 --- /dev/null +++ b/src/hifi_gan/meldataset.py @@ -0,0 +1,233 @@ +import math +import os +import random +import torch +import torch.utils.data +import numpy as np +from librosa.util import normalize +from scipy.io.wavfile import read +from librosa.filters import mel as librosa_mel_fn + +MAX_WAV_VALUE = 32768.0 + + +def load_wav(full_path): + sampling_rate, data = read(full_path) + return data, sampling_rate + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} + + +def mel_spectrogram( + y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False +): + if torch.min(y) < -1.0: + print("min value is ", torch.min(y)) + if torch.max(y) > 1.0: + print("max value is ", torch.max(y)) + + global mel_basis, hann_window + if fmax not in mel_basis: + mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) + mel_basis[str(fmax) + "_" + str(y.device)] = ( + torch.from_numpy(mel).float().to(y.device) + ) + hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) + + y = torch.nn.functional.pad( + y.unsqueeze(1), + (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), + mode="reflect", + ) + y = y.squeeze(1) + + spec = torch.stft( + y, + n_fft, + hop_length=hop_size, + win_length=win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + ) + + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + + spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec + + +def get_dataset_filelist(a): + with open(a.input_training_file, "r", encoding="utf-8") as fi: + training_files = [x for x in fi.read().split("\n") if len(x) > 0] + + with open(a.input_validation_file, "r", encoding="utf-8") as fi: + validation_files = [x for x in fi.read().split("\n") if len(x) > 0] + return training_files, validation_files + + +class MelDataset(torch.utils.data.Dataset): + def __init__( + self, + training_files, + segment_size, + n_fft, + num_mels, + hop_size, + win_size, + sampling_rate, + fmin, + fmax, + split=True, + shuffle=True, + n_cache_reuse=1, + device=None, + fmax_loss=None, + fine_tuning=False, + base_mels_path=None, + ): + self.audio_files = training_files + random.seed(1234) + if shuffle: + random.shuffle(self.audio_files) + self.segment_size = segment_size + self.sampling_rate = sampling_rate + self.split = split + self.n_fft = n_fft + self.num_mels = num_mels + self.hop_size = hop_size + self.win_size = win_size + self.fmin = fmin + self.fmax = fmax + self.fmax_loss = fmax_loss + self.cached_wav = None + self.n_cache_reuse = n_cache_reuse + self._cache_ref_count = 0 + self.device = device + self.fine_tuning = fine_tuning + self.base_mels_path = base_mels_path + + def __getitem__(self, index): + filename = self.audio_files[index] + if self._cache_ref_count == 0: + audio, sampling_rate = load_wav(filename) + audio = audio / MAX_WAV_VALUE + if not self.fine_tuning: + audio = normalize(audio) * 0.95 + self.cached_wav = audio + if sampling_rate != self.sampling_rate: + raise ValueError( + "{} SR doesn't match target {} SR".format( + sampling_rate, self.sampling_rate + ) + ) + self._cache_ref_count = self.n_cache_reuse + else: + audio = self.cached_wav + self._cache_ref_count -= 1 + + audio = torch.FloatTensor(audio) + audio = audio.unsqueeze(0) + + if not self.fine_tuning: + if self.split: + if audio.size(1) >= self.segment_size: + max_audio_start = audio.size(1) - self.segment_size + audio_start = random.randint(0, max_audio_start) + audio = audio[:, audio_start : audio_start + self.segment_size] + else: + audio = torch.nn.functional.pad( + audio, (0, self.segment_size - audio.size(1)), "constant" + ) + + mel = mel_spectrogram( + audio, + self.n_fft, + self.num_mels, + self.sampling_rate, + self.hop_size, + self.win_size, + self.fmin, + self.fmax, + center=False, + ) + else: + mel = np.load( + os.path.join( + self.base_mels_path, + os.path.splitext(os.path.split(filename)[-1])[0] + ".npy", + ) + ) + mel = torch.from_numpy(mel) + + if len(mel.shape) < 3: + mel = mel.unsqueeze(0) + + if self.split: + frames_per_seg = math.ceil(self.segment_size / self.hop_size) + + if audio.size(1) >= self.segment_size: + mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1) + mel = mel[:, :, mel_start : mel_start + frames_per_seg] + audio = audio[ + :, + mel_start + * self.hop_size : (mel_start + frames_per_seg) + * self.hop_size, + ] + else: + mel = torch.nn.functional.pad( + mel, (0, frames_per_seg - mel.size(2)), "constant" + ) + audio = torch.nn.functional.pad( + audio, (0, self.segment_size - audio.size(1)), "constant" + ) + + mel_loss = mel_spectrogram( + audio, + self.n_fft, + self.num_mels, + self.sampling_rate, + self.hop_size, + self.win_size, + self.fmin, + self.fmax_loss, + center=False, + ) + + return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze()) + + def __len__(self): + return len(self.audio_files) diff --git a/src/hifi_gan/models.py b/src/hifi_gan/models.py new file mode 100644 index 0000000000000000000000000000000000000000..be51fa51407e6ce1daaee5e8d090f6acdbee0db9 --- /dev/null +++ b/src/hifi_gan/models.py @@ -0,0 +1,403 @@ +import torch +import torch.nn.functional as F +import torch.nn as nn +from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d +from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm +from utils import init_weights, get_padding + +LRELU_SLOPE = 0.1 + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super(ResBlock1, self).__init__() + self.h = h + 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): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + 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, h, channels, kernel_size=3, dilation=(1, 3)): + super(ResBlock2, self).__init__() + self.h = h + 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): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class Generator(torch.nn.Module): + def __init__(self, h): + super(Generator, self).__init__() + self.h = h + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + self.conv_pre = weight_norm( + Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3) + ) + resblock = ResBlock1 if h.resblock == "1" else ResBlock2 + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): + self.ups.append( + weight_norm( + ConvTranspose1d( + h.upsample_initial_channel // (2 ** i), + h.upsample_initial_channel // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = h.upsample_initial_channel // (2 ** (i + 1)) + for j, (k, d) in enumerate( + zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes) + ): + self.resblocks.append(resblock(h, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x): + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print("Removing weight norm...") + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super(DiscriminatorP, self).__init__() + self.period = period + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f( + Conv2d( + 1, + 32, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f( + Conv2d( + 32, + 128, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f( + Conv2d( + 128, + 512, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f( + Conv2d( + 512, + 1024, + (kernel_size, 1), + (stride, 1), + padding=(get_padding(5, 1), 0), + ) + ), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), + ] + ) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self): + super(MultiPeriodDiscriminator, self).__init__() + self.discriminators = nn.ModuleList( + [ + DiscriminatorP(2), + DiscriminatorP(3), + DiscriminatorP(5), + DiscriminatorP(7), + DiscriminatorP(11), + ] + ) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super(DiscriminatorS, self).__init__() + norm_f = weight_norm if use_spectral_norm == False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f(Conv1d(1, 128, 15, 1, padding=7)), + norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ] + ) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiScaleDiscriminator(torch.nn.Module): + def __init__(self): + super(MultiScaleDiscriminator, self).__init__() + self.discriminators = nn.ModuleList( + [ + DiscriminatorS(use_spectral_norm=True), + DiscriminatorS(), + DiscriminatorS(), + ] + ) + self.meanpools = nn.ModuleList( + [AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)] + ) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + if i != 0: + y = self.meanpools[i - 1](y) + y_hat = self.meanpools[i - 1](y_hat) + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1 - dr) ** 2) + g_loss = torch.mean(dg ** 2) + loss += r_loss + g_loss + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + l = torch.mean((1 - dg) ** 2) + gen_losses.append(l) + loss += l + + return loss, gen_losses diff --git a/src/hifi_gan/train.py b/src/hifi_gan/train.py new file mode 100644 index 0000000000000000000000000000000000000000..709e085d019eb98006b26555f7fe2582d759efa6 --- /dev/null +++ b/src/hifi_gan/train.py @@ -0,0 +1,400 @@ +import warnings + +warnings.simplefilter(action="ignore", category=FutureWarning) +import itertools +import os +import time +import argparse +import json +import torch +import torch.nn.functional as F +from torch.utils.tensorboard import SummaryWriter +from torch.utils.data import DistributedSampler, DataLoader +import torch.multiprocessing as mp +from torch.distributed import init_process_group +from torch.nn.parallel import DistributedDataParallel +from env import AttrDict, build_env +from meldataset import MelDataset, mel_spectrogram, get_dataset_filelist +from models import ( + Generator, + MultiPeriodDiscriminator, + MultiScaleDiscriminator, + feature_loss, + generator_loss, + discriminator_loss, +) +from utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint + +torch.backends.cudnn.benchmark = True + + +def train(rank, a, h): + if h.num_gpus > 1: + init_process_group( + backend=h.dist_config["dist_backend"], + init_method=h.dist_config["dist_url"], + world_size=h.dist_config["world_size"] * h.num_gpus, + rank=rank, + ) + + torch.cuda.manual_seed(h.seed) + device = torch.device("cuda:{:d}".format(rank)) + + generator = Generator(h).to(device) + mpd = MultiPeriodDiscriminator().to(device) + msd = MultiScaleDiscriminator().to(device) + + if rank == 0: + print(generator) + os.makedirs(a.checkpoint_path, exist_ok=True) + print("checkpoints directory : ", a.checkpoint_path) + + if os.path.isdir(a.checkpoint_path): + cp_g = scan_checkpoint(a.checkpoint_path, "g_") + cp_do = scan_checkpoint(a.checkpoint_path, "do_") + + steps = 0 + if cp_g is None or cp_do is None: + state_dict_do = None + last_epoch = -1 + else: + state_dict_g = load_checkpoint(cp_g, device) + state_dict_do = load_checkpoint(cp_do, device) + generator.load_state_dict(state_dict_g["generator"]) + mpd.load_state_dict(state_dict_do["mpd"]) + msd.load_state_dict(state_dict_do["msd"]) + steps = state_dict_do["steps"] + 1 + last_epoch = state_dict_do["epoch"] + + if h.num_gpus > 1: + generator = DistributedDataParallel(generator, device_ids=[rank]).to(device) + mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device) + msd = DistributedDataParallel(msd, device_ids=[rank]).to(device) + + optim_g = torch.optim.AdamW( + generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2] + ) + optim_d = torch.optim.AdamW( + itertools.chain(msd.parameters(), mpd.parameters()), + h.learning_rate, + betas=[h.adam_b1, h.adam_b2], + ) + + if state_dict_do is not None: + optim_g.load_state_dict(state_dict_do["optim_g"]) + optim_d.load_state_dict(state_dict_do["optim_d"]) + + scheduler_g = torch.optim.lr_scheduler.ExponentialLR( + optim_g, gamma=h.lr_decay, last_epoch=last_epoch + ) + scheduler_d = torch.optim.lr_scheduler.ExponentialLR( + optim_d, gamma=h.lr_decay, last_epoch=last_epoch + ) + + training_filelist, validation_filelist = get_dataset_filelist(a) + + trainset = MelDataset( + training_filelist, + h.segment_size, + h.n_fft, + h.num_mels, + h.hop_size, + h.win_size, + h.sampling_rate, + h.fmin, + h.fmax, + n_cache_reuse=0, + shuffle=False if h.num_gpus > 1 else True, + fmax_loss=h.fmax_for_loss, + device=device, + fine_tuning=a.fine_tuning, + base_mels_path=a.input_mels_dir, + ) + + train_sampler = DistributedSampler(trainset) if h.num_gpus > 1 else None + + train_loader = DataLoader( + trainset, + num_workers=h.num_workers, + shuffle=False, + sampler=train_sampler, + batch_size=h.batch_size, + pin_memory=True, + drop_last=True, + ) + + if rank == 0: + validset = MelDataset( + validation_filelist, + h.segment_size, + h.n_fft, + h.num_mels, + h.hop_size, + h.win_size, + h.sampling_rate, + h.fmin, + h.fmax, + False, + False, + n_cache_reuse=0, + fmax_loss=h.fmax_for_loss, + device=device, + fine_tuning=a.fine_tuning, + base_mels_path=a.input_mels_dir, + ) + validation_loader = DataLoader( + validset, + num_workers=1, + shuffle=False, + sampler=None, + batch_size=1, + pin_memory=True, + drop_last=True, + ) + + sw = SummaryWriter(os.path.join(a.logs_path)) + + generator.train() + mpd.train() + msd.train() + for epoch in range(max(0, last_epoch), a.training_epochs): + if rank == 0: + start = time.time() + print("Epoch: {}".format(epoch + 1)) + + if h.num_gpus > 1: + train_sampler.set_epoch(epoch) + + for i, batch in enumerate(train_loader): + if rank == 0: + start_b = time.time() + x, y, _, y_mel = batch + x = torch.autograd.Variable(x.to(device, non_blocking=True)) + y = torch.autograd.Variable(y.to(device, non_blocking=True)) + y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True)) + y = y.unsqueeze(1) + + y_g_hat = generator(x) + y_g_hat_mel = mel_spectrogram( + y_g_hat.squeeze(1), + h.n_fft, + h.num_mels, + h.sampling_rate, + h.hop_size, + h.win_size, + h.fmin, + h.fmax_for_loss, + ) + + optim_d.zero_grad() + + # MPD + y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach()) + loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss( + y_df_hat_r, y_df_hat_g + ) + + # MSD + y_ds_hat_r, y_ds_hat_g, _, _ = msd(y, y_g_hat.detach()) + loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss( + y_ds_hat_r, y_ds_hat_g + ) + + loss_disc_all = loss_disc_s + loss_disc_f + + loss_disc_all.backward() + optim_d.step() + + # Generator + optim_g.zero_grad() + + # L1 Mel-Spectrogram Loss + loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45 + + y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat) + y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat) + loss_fm_f = feature_loss(fmap_f_r, fmap_f_g) + loss_fm_s = feature_loss(fmap_s_r, fmap_s_g) + loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g) + loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g) + loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel + + loss_gen_all.backward() + optim_g.step() + + if rank == 0: + # STDOUT logging + if steps % a.stdout_interval == 0: + with torch.no_grad(): + mel_error = F.l1_loss(y_mel, y_g_hat_mel).item() + + print( + "Steps : {:d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, s/b : {:4.3f}".format( + steps, loss_gen_all, mel_error, time.time() - start_b + ) + ) + + # checkpointing + if steps % a.checkpoint_interval == 0 and steps != 0: + checkpoint_path = "{}/g_{:08d}".format(a.checkpoint_path, steps) + save_checkpoint( + checkpoint_path, + { + "generator": ( + generator.module if h.num_gpus > 1 else generator + ).state_dict() + }, + ) + checkpoint_path = "{}/do_{:08d}".format(a.checkpoint_path, steps) + save_checkpoint( + checkpoint_path, + { + "mpd": (mpd.module if h.num_gpus > 1 else mpd).state_dict(), + "msd": (msd.module if h.num_gpus > 1 else msd).state_dict(), + "optim_g": optim_g.state_dict(), + "optim_d": optim_d.state_dict(), + "steps": steps, + "epoch": epoch, + }, + ) + + # Tensorboard summary logging + if steps % a.summary_interval == 0: + sw.add_scalar("training/gen_loss_total", loss_gen_all, steps) + sw.add_scalar("training/mel_spec_error", mel_error, steps) + + # Validation + if steps % a.validation_interval == 0: # and steps != 0: + generator.eval() + torch.cuda.empty_cache() + val_err_tot = 0 + with torch.no_grad(): + for j, batch in enumerate(validation_loader): + x, y, _, y_mel = batch + y_g_hat = generator(x.to(device)) + y_mel = torch.autograd.Variable( + y_mel.to(device, non_blocking=True) + ) + y_g_hat_mel = mel_spectrogram( + y_g_hat.squeeze(1), + h.n_fft, + h.num_mels, + h.sampling_rate, + h.hop_size, + h.win_size, + h.fmin, + h.fmax_for_loss, + ) + val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item() + + if j <= 4: + if steps == 0: + sw.add_audio( + "gt/y_{}".format(j), + y[0], + steps, + h.sampling_rate, + ) + sw.add_figure( + "gt/y_spec_{}".format(j), + plot_spectrogram(x[0]), + steps, + ) + + sw.add_audio( + "generated/y_hat_{}".format(j), + y_g_hat[0], + steps, + h.sampling_rate, + ) + y_hat_spec = mel_spectrogram( + y_g_hat.squeeze(1), + h.n_fft, + h.num_mels, + h.sampling_rate, + h.hop_size, + h.win_size, + h.fmin, + h.fmax, + ) + sw.add_figure( + "generated/y_hat_spec_{}".format(j), + plot_spectrogram( + y_hat_spec.squeeze(0).cpu().numpy() + ), + steps, + ) + + val_err = val_err_tot / (j + 1) + sw.add_scalar("validation/mel_spec_error", val_err, steps) + + generator.train() + + steps += 1 + + scheduler_g.step() + scheduler_d.step() + + if rank == 0: + print( + "Time taken for epoch {} is {} sec\n".format( + epoch + 1, int(time.time() - start) + ) + ) + + +def main(): + print("Initializing Training Process..") + + parser = argparse.ArgumentParser() + + parser.add_argument("--group_name", default=None) + parser.add_argument("--input_wavs_dir", default="LJSpeech-1.1/wavs") + parser.add_argument("--input_mels_dir", default="ft_dataset") + parser.add_argument("--input_training_file", default="LJSpeech-1.1/training.txt") + parser.add_argument( + "--input_validation_file", default="LJSpeech-1.1/validation.txt" + ) + parser.add_argument("--checkpoint_path", default="cp_hifigan") + parser.add_argument("--logs_path", default="") + parser.add_argument("--config", default="") + parser.add_argument("--training_epochs", default=3100, type=int) + parser.add_argument("--stdout_interval", default=5, type=int) + parser.add_argument("--checkpoint_interval", default=5000, type=int) + parser.add_argument("--summary_interval", default=100, type=int) + parser.add_argument("--validation_interval", default=1000, type=int) + parser.add_argument("--fine_tuning", default=False, type=bool) + + a = parser.parse_args() + + with open(a.config) as f: + data = f.read() + + json_config = json.loads(data) + h = AttrDict(json_config) + build_env(a.config, "config.json", a.checkpoint_path) + + torch.manual_seed(h.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(h.seed) + h.num_gpus = torch.cuda.device_count() + h.batch_size = int(h.batch_size / h.num_gpus) + print("Batch size per GPU :", h.batch_size) + else: + pass + + if h.num_gpus > 1: + mp.spawn( + train, + nprocs=h.num_gpus, + args=( + a, + h, + ), + ) + else: + train(0, a, h) + + +if __name__ == "__main__": + main() diff --git a/src/hifi_gan/utils.py b/src/hifi_gan/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..71e9b2c99e053e2d4239074a67d64b834898c348 --- /dev/null +++ b/src/hifi_gan/utils.py @@ -0,0 +1,57 @@ +import glob +import os +import matplotlib +import torch +from torch.nn.utils import weight_norm + +matplotlib.use("Agg") +import matplotlib.pylab as plt + + +def plot_spectrogram(spectrogram): + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print("Loading '{}'".format(filepath)) + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def save_checkpoint(filepath, obj): + print("Saving checkpoint to {}".format(filepath)) + torch.save(obj, filepath) + print("Complete.") + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + "????????") + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return None + return sorted(cp_list)[-1] diff --git a/tts_infer/__init__.py b/tts_infer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tts_infer/__pycache__/__init__.cpython-37.pyc b/tts_infer/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8fb895113b897cad7fd2f4c5d04ae4bcbbc24ae7 Binary files /dev/null and b/tts_infer/__pycache__/__init__.cpython-37.pyc differ diff --git a/tts_infer/__pycache__/num_to_word_on_sent.cpython-37.pyc b/tts_infer/__pycache__/num_to_word_on_sent.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0faca0a26b0d9c187d0717376d9dd29d518556ca Binary files /dev/null and b/tts_infer/__pycache__/num_to_word_on_sent.cpython-37.pyc differ diff --git a/tts_infer/__pycache__/transliterate.cpython-37.pyc b/tts_infer/__pycache__/transliterate.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..032f629b027b80ca35f6e5cc2be16c086e66e650 Binary files /dev/null and b/tts_infer/__pycache__/transliterate.cpython-37.pyc differ diff --git a/tts_infer/__pycache__/tts.cpython-37.pyc b/tts_infer/__pycache__/tts.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..70806ca7bdd96e36114d338da1c3dbcba9fb835a Binary files /dev/null and b/tts_infer/__pycache__/tts.cpython-37.pyc differ diff --git a/tts_infer/example_inference.py b/tts_infer/example_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..676718fff3c6a7120cea91b0cfc95f8872929da7 --- /dev/null +++ b/tts_infer/example_inference.py @@ -0,0 +1,79 @@ +''' Example file to test tts_infer after installing it. Refer to section 1.1 in README.md for steps of installation. ''' + +from tts_infer.tts import TextToMel, MelToWav +from tts_infer.transliterate import XlitEngine +from tts_infer.num_to_word_on_sent import normalize_nums + +import re +import numpy as np +from scipy.io.wavfile import write + +from mosestokenizer import * +from indicnlp.tokenize import sentence_tokenize + +INDIC = ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"] + +def split_sentences(paragraph, language): + if language == "en": + with MosesSentenceSplitter(language) as splitter: + return splitter([paragraph]) + elif language in INDIC: + return sentence_tokenize.sentence_split(paragraph, lang=language) + + +device='cpu' +text_to_mel = TextToMel(glow_model_dir='/path/to/glow_ckp', device=device) +mel_to_wav = MelToWav(hifi_model_dir='/path/to/hifi_ckp', device=device) + +lang='hi' # transliteration from En to Hi +engine = XlitEngine(lang) # loading translit model globally + +def translit(text, lang): + reg = re.compile(r'[a-zA-Z]') + words = [engine.translit_word(word, topk=1)[lang][0] if reg.match(word) else word for word in text.split()] + updated_sent = ' '.join(words) + return updated_sent + +def run_tts(text, lang): + text = text.replace('।', '.') # only for hindi models + text_num_to_word = normalize_nums(text, lang) # converting numbers to words in lang + text_num_to_word_and_transliterated = translit(text_num_to_word, lang) # transliterating english words to lang + final_text = ' ' + text_num_to_word_and_transliterated + + mel = text_to_mel.generate_mel(final_text) + audio, sr = mel_to_wav.generate_wav(mel) + write(filename='temp.wav', rate=sr, data=audio) # for saving wav file, if needed + return (sr, audio) + +def run_tts_paragraph(text, lang): + audio_list = [] + split_sentences_list = split_sentences(text, language='hi') + + for sent in split_sentences_list: + sr, audio = run_tts(sent, lang) + audio_list.append(audio) + + concatenated_audio = np.concatenate([i for i in audio_list]) + write(filename='temp_long.wav', rate=sr, data=concatenated_audio) + return (sr, concatenated_audio) + +if __name__ == "__main__": + _, audio = run_tts('mera naam neeraj hai', 'hi') + + para = ''' + भारत मेरा देश है और मुझे भारतीय होने पर गर्व है। ये विश्व का सातवाँ सबसे बड़ा और विश्व में दूसरा सबसे अधिक जनसंख्या वाला देश है। + इसे भारत, हिन्दुस्तान और आर्यव्रत के नाम से भी जाना जाता है। ये एक प्रायद्वीप है जो पूरब में बंगाल की खाड़ी, + पश्चिम में अरेबियन सागर और दक्षिण में भारतीय महासागर जैसे तीन महासगरों से घिरा हुआ है। + भारत का राष्ट्रीय पशु चीता, राष्ट्रीय पक्षी मोर, राष्ट्रीय फूल कमल, और राष्ट्रीय फल आम है। + भारत मेरा देश है और मुझे भारतीय होने पर गर्व है। ये विश्व का सातवाँ सबसे बड़ा और विश्व में दूसरा सबसे अधिक जनसंख्या वाला देश है। + इसे भारत, हिन्दुस्तान और आर्यव्रत के नाम से भी जाना जाता है। ये एक प्रायद्वीप है जो पूरब में बंगाल की खाड़ी, + पश्चिम में अरेबियन सागर और दक्षिण में भारतीय महासागर जैसे तीन महासगरों से घिरा हुआ है। + भारत का राष्ट्रीय पशु चीता, राष्ट्रीय पक्षी मोर, राष्ट्रीय फूल कमल, और राष्ट्रीय फल आम है। + भारत मेरा देश है और मुझे भारतीय होने पर गर्व है। ये विश्व का सातवाँ सबसे बड़ा और विश्व में दूसरा सबसे अधिक जनसंख्या वाला देश है। + इसे भारत, हिन्दुस्तान और आर्यव्रत के नाम से भी जाना जाता है। ये एक प्रायद्वीप है जो पूरब में बंगाल की खाड़ी, + पश्चिम में अरेबियन सागर और दक्षिण में भारतीय महासागर जैसे तीन महासगरों से घिरा हुआ है। + भारत का राष्ट्रीय पशु चीता, राष्ट्रीय पक्षी मोर, राष्ट्रीय फूल कमल, और राष्ट्रीय फल आम है। + ''' + + print('Num chars in paragraph: ', len(para)) + _, audio_long = run_tts_paragraph(para, 'hi') diff --git a/tts_infer/num_to_word_on_sent.py b/tts_infer/num_to_word_on_sent.py new file mode 100644 index 0000000000000000000000000000000000000000..de571c2be63fa467491d01daf0e2f38dada67de9 --- /dev/null +++ b/tts_infer/num_to_word_on_sent.py @@ -0,0 +1,1319 @@ +import re +import string + +# ----------------------------- indic_num.py ----------------------------- +supported_lang = {"en", "hi", "gu", "mr", "bn", "te", "ta", "kn", "or", "pa"} +# supported_lang = {'eng', 'hin', 'guj', 'mar', 'ben', 'tel', 'tam', 'kan', 'ori', 'pan'} # Three alphabet lang code + +all_num = { + "en": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], + "hi": ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"], + "gu": ["૦", "૧", "૨", "૩", "૪", "૫", "૬", "૭", "૮", "૯"], + "mr": ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"], + "bn": ["০", "১", "২", "৩", "৪", "৫", "৬", "৭", "৮", "৯"], + "te": ["౦", "౧", "౨", "౩", "౪", "౫", "౬", "౭", "౮", "౯"], + "ta": ["0", "௧", "௨", "௩", "௪", "௫", "௬", "௭", "௮", "௯", "௰"], + "kn": ["೦", "೧", "೨", "೩", "೪", "೫", "೬", "೭", "೮", "೯"], + "or": ["୦", "୧", "୨", "୩", "୪", "୫", "୬", "୭", "୮", "୯"], + "pa": ["੦", "੧", "੨", "੩", "੪", "੫", "੬", "੭", "੮", "੯"], +} + +num_dict = dict() +num_dict["en"] = { + "0": "zero", + "1": "one", + "2": "two", + "3": "three", + "4": "four", + "5": "five", + "6": "six", + "7": "seven", + "8": "eight", + "9": "nine", + "10": "ten", + "11": "eleven", + "12": "twelve", + "13": "thirteen", + "14": "fourteen", + "15": "fifteen", + "16": "sixteen", + "17": "seventeen", + "18": "eighteen", + "19": "nineteen", + "20": "twenty", + "21": "twenty-one", + "22": "twenty-two", + "23": "twenty-three", + "24": "twenty-four", + "25": "twenty-five", + "26": "twenty-six", + "27": "twenty-seven", + "28": "twenty-eight", + "29": "twenty-nine", + "30": "thirty", + "31": "thirty-one", + "32": "thirty-two", + "33": "thirty-three", + "34": "thirty-four", + "35": "thirty-five", + "36": "thirty-six", + "37": "thirty-seven", + "38": "thirty-eight", + "39": "thirty-nine", + "40": "forty", + "41": "forty-one", + "42": "forty-two", + "43": "forty-three", + "44": "forty-four", + "45": "forty-five", + "46": "forty-six", + "47": "forty-seven", + "48": "forty-eight", + "49": "forty-nine", + "50": "fifty", + "51": "fifty-one", + "52": "fifty-two", + "53": "fifty-three", + "54": "fifty-four", + "55": "fifty-five", + "56": "fifty-six", + "57": "fifty-seven", + "58": "fifty-eight", + "59": "fifty-nine", + "60": "sixty", + "61": "sixty-one", + "62": "sixty-two", + "63": "sixty-three", + "64": "sixty-four", + "65": "sixty-five", + "66": "sixty-six", + "67": "sixty-seven", + "68": "sixty-eight", + "69": "sixty-nine", + "70": "seventy", + "71": "seventy-one", + "72": "seventy-two", + "73": "seventy-three", + "74": "seventy-four", + "75": "seventy-five", + "76": "seventy-six", + "77": "seventy-seven", + "78": "seventy-eight", + "79": "seventy-nine", + "80": "eighty", + "81": "eighty-one", + "82": "eighty-two", + "83": "eighty-three", + "84": "eighty-four", + "85": "eighty-five", + "86": "eighty-six", + "87": "eighty-seven", + "88": "eighty-eight", + "89": "eighty-nine", + "90": "ninety", + "91": "ninety-one", + "92": "ninety-two", + "93": "ninety-three", + "94": "ninety-four", + "95": "ninety-five", + "96": "ninety-six", + "97": "ninety-seven", + "98": "ninety-eight", + "99": "ninety-nine", + "100": "hundred", + "1000": "thousand", + "100000": "lac", + "10000000": "crore", + "1000000000": "arab", +} # English-India +num_dict["hi"] = { + "0": "शून्य", + "1": "एक", + "2": "दो", + "3": "तीन", + "4": "चार", + "5": "पाँच", + "6": "छः", + "7": "सात", + "8": "आठ", + "9": "नौ", + "10": "दस", + "11": "ग्यारह", + "12": "बारह", + "13": "तेरह", + "14": "चौदह", + "15": "पंद्रह", + "16": "सोलह", + "17": "सत्रह", + "18": "अट्ठारह", + "19": "उन्नीस", + "20": "बीस", + "21": "इक्कीस", + "22": "बाईस", + "23": "तेईस", + "24": "चौबिस", + "25": "पच्चीस", + "26": "छब्बीस", + "27": "सत्ताईस", + "28": "अट्ठाईस", + "29": "उनतीस", + "30": "तीस", + "31": "इकतीस", + "32": "बत्तीस", + "33": "तैंतीस", + "34": "चौंतीस", + "35": "पैंतीस", + "36": "छत्तीस", + "37": "सैंतीस", + "38": "अड़तीस", + "39": "उनतालीस", + "40": "चालीस", + "41": "इकतालीस", + "42": "बयालीस", + "43": "तैंतालीस", + "44": "चौंतालीस", + "45": "पैंतालीस", + "46": "छियालीस", + "47": "सैंतालीस", + "48": "अड़तालीस", + "49": "उनचास", + "50": "पचास", + "51": "इक्यावन​", + "52": "बावन", + "53": "तिरेपन", + "54": "चौवन", + "55": "पचपन", + "56": "छप्पन", + "57": "सत्तावन", + "58": "अट्ठावन", + "59": "उनसठ", + "60": "साठ", + "61": "इकसठ", + "62": "बासठ", + "63": "तिरेसठ", + "64": "चौंसठ", + "65": "पैंसठ", + "66": "छयासठ", + "67": "सरसठ​", + "68": "अड़सठ", + "69": "उनहत्तर", + "70": "सत्तर", + "71": "इकहत्तर", + "72": "बहत्तर", + "73": "तिहत्तर", + "74": "चौहत्तर", + "75": "पचहत्तर", + "76": "छिहत्तर", + "77": "सतहत्तर", + "78": "अठहत्तर", + "79": "उन्यासी", + "80": "अस्सी", + "81": "इक्यासी", + "82": "बयासी", + "83": "तिरासी", + "84": "चौरासी", + "85": "पचासी", + "86": "छियासी", + "87": "सत्तासी", + "88": "अठासी", + "89": "नवासी", + "90": "नब्बे", + "91": "इक्यानवे", + "92": "बानवे", + "93": "तिरानवे", + "94": "चौरानवे", + "95": "पचानवे", + "96": "छियानवे", + "97": "सत्तानवे", + "98": "अट्ठानवे", + "99": "निन्यानवे", + "100": "सौ", + "1000": "हज़ार", + "100000": "लाख", + "10000000": "करोड़", + "1000000000": "अरब", +} # Hindi +num_dict["gu"] = { + "0": "શૂન્ય", + "1": "એક", + "2": "બે", + "3": "ત્રણ", + "4": "ચાર", + "5": "પાંચ", + "6": "છ", + "7": "સાત", + "8": "આઠ", + "9": "નવ", + "10": "દસ", + "11": "અગિયાર", + "12": "બાર", + "13": "તેર", + "14": "ચૌદ", + "15": "પંદર", + "16": "સોળ", + "17": "સત્તર", + "18": "અઢાર", + "19": "ઓગણિસ", + "20": "વીસ", + "21": "એકવીસ", + "22": "બાવીસ", + "23": "તેવીસ", + "24": "ચોવીસ", + "25": "પચ્ચીસ", + "26": "છવીસ", + "27": "સત્તાવીસ", + "28": "અઠ્ઠાવીસ", + "29": "ઓગણત્રીસ", + "30": "ત્રીસ", + "31": "એકત્રીસ", + "32": "બત્રીસ", + "33": "તેત્રીસ", + "34": "ચોત્રીસ", + "35": "પાંત્રીસ", + "36": "છત્રીસ", + "37": "સડત્રીસ", + "38": "અડત્રીસ", + "39": "ઓગણચાલીસ", + "40": "ચાલીસ", + "41": "એકતાલીસ", + "42": "બેતાલીસ", + "43": "ત્રેતાલીસ", + "44": "ચુંમાલીસ", + "45": "પિસ્તાલીસ", + "46": "છેતાલીસ", + "47": "સુડતાલીસ", + "48": "અડતાલીસ", + "49": "ઓગણપચાસ", + "50": "પચાસ", + "51": "એકાવન", + "52": "બાવન", + "53": "ત્રેપન", + "54": "ચોપન", + "55": "પંચાવન", + "56": "છપ્પન", + "57": "સત્તાવન", + "58": "અઠ્ઠાવન", + "59": "ઓગણસાઠ", + "60": "સાઈઠ", + "61": "એકસઠ", + "62": "બાસઠ", + "63": "ત્રેસઠ", + "64": "ચોસઠ", + "65": "પાંસઠ", + "66": "છાસઠ", + "67": "સડસઠ", + "68": "અડસઠ", + "69": "અગણોસિત્તેર", + "70": "સિત્તેર", + "71": "એકોતેર", + "72": "બોતેર", + "73": "તોતેર", + "74": "ચુમોતેર", + "75": "પંચોતેર", + "76": "છોતેર", + "77": "સિત્યોતેર", + "78": "ઇઠ્યોતેર", + "79": "ઓગણાએંસી", + "80": "એંસી", + "81": "એક્યાસી", + "82": "બ્યાસી", + "83": "ત્યાસી", + "84": "ચોર્યાસી", + "85": "પંચાસી", + "86": "છ્યાસી", + "87": "સિત્યાસી", + "88": "ઈઠ્યાસી", + "89": "નેવ્યાસી", + "90": "નેવું", + "91": "એકાણું", + "92": "બાણું", + "93": "ત્રાણું", + "94": "ચોરાણું", + "95": "પંચાણું", + "96": "છન્નું", + "97": "સત્તાણું", + "98": "અઠ્ઠાણું", + "99": "નવ્વાણું", + "100": "સો", + "1000": "હજાર", + "100000": "લાખ", + "1000000": "દસ લાખ", + "10000000": "કરોડ઼", +} # Gujarati +num_dict["mr"] = { + "0": "शून्य", + "1": "एक", + "2": "दोन", + "3": "तीन", + "4": "चार", + "5": "पाच", + "6": "सहा", + "7": "सात", + "8": "आठ", + "9": "नऊ", + "10": "दहा", + "11": "अकरा", + "12": "बारा", + "13": "तेरा", + "14": "चौदा", + "15": "पंधरा", + "16": "सोळा", + "17": "सतरा", + "18": "अठरा", + "19": "एकोणीस", + "20": "वीस", + "21": "एकवीस", + "22": "बावीस", + "23": "तेवीस", + "24": "चोवीस", + "25": "पंचवीस", + "26": "सव्वीस", + "27": "सत्तावीस", + "28": "अठ्ठावीस", + "29": "एकोणतीस", + "30": "तीस", + "31": "एकतीस", + "32": "बत्तीस", + "33": "तेहेतीस", + "34": "चौतीस", + "35": "पस्तीस", + "36": "छत्तीस", + "37": "सदतीस", + "38": "अडतीस", + "39": "एकोणचाळीस", + "40": "चाळीस", + "41": "एक्केचाळीस", + "42": "बेचाळीस", + "43": "त्रेचाळीस", + "44": "चव्वेचाळीस", + "45": "पंचेचाळीस", + "46": "सेहेचाळीस", + "47": "सत्तेचाळीस", + "48": "अठ्ठेचाळीस", + "49": "एकोणपन्नास", + "50": "पन्नास", + "51": "एक्कावन्न", + "52": "बावन्न", + "53": "त्रेपन्न", + "54": "चोपन्न", + "55": "पंचावन्न", + "56": "छप्पन्न", + "57": "सत्तावन्न", + "58": "अठ्ठावन्न", + "59": "एकोणसाठ", + "60": "साठ", + "61": "एकसष्ठ", + "62": "बासष्ठ", + "63": "त्रेसष्ठ", + "64": "चौसष्ठ", + "65": "पासष्ठ", + "66": "सहासष्ठ", + "67": "सदुसष्ठ", + "68": "अडुसष्ठ", + "69": "एकोणसत्तर", + "70": "सत्तर", + "71": "एक्काहत्तर", + "72": "बाहत्तर", + "73": "त्र्याहत्तर", + "74": "चौर्‍याहत्तर", + "75": "पंच्याहत्तर", + "76": "शहात्तर", + "77": "सत्याहत्तर", + "78": "अठ्ठ्याहत्तर", + "79": "एकोण ऐंशी", + "80": "ऐंशी", + "81": "एक्क्याऐंशी", + "82": "ब्याऐंशी", + "83": "त्र्याऐंशी", + "84": "चौऱ्याऐंशी", + "85": "पंच्याऐंशी", + "86": "शहाऐंशी", + "87": "सत्त्याऐंशी", + "88": "अठ्ठ्याऐंशी", + "89": "एकोणनव्वद", + "90": "नव्वद", + "91": "एक्क्याण्णव", + "92": "ब्याण्णव", + "93": "त्र्याण्णव", + "94": "चौऱ्याण्णव", + "95": "पंच्याण्णव", + "96": "शहाण्णव", + "97": "सत्त्याण्णव", + "98": "अठ्ठ्याण्णव", + "99": "नव्व्याण्णव", + "100": "शे", + "1000": "हजार", + "100000": "लाख", + "10000000": "कोटी", + "1000000000": "अब्ज", +} # Marathi +num_dict["bn"] = { + "0": "শূন্য", + "1": "এক", + "2": "দুই", + "3": "তিন", + "4": "চার", + "5": "পাঁচ", + "6": "ছয়", + "7": "সাত", + "8": "আট", + "9": "নয়", + "10": "দশ", + "11": "এগার", + "12": "বার", + "13": "তের", + "14": "চৌদ্দ", + "15": "পনের", + "16": "ষোল", + "17": "সতের", + "18": "আঠার", + "19": "ঊনিশ", + "20": "বিশ", + "21": "একুশ", + "22": "বাইশ", + "23": "তেইশ", + "24": "চব্বিশ", + "25": "পঁচিশ", + "26": "ছাব্বিশ", + "27": "সাতাশ", + "28": "আঠাশ", + "29": "ঊনত্রিশ", + "30": "ত্রিশ", + "31": "একত্রিশ", + "32": "বত্রিশ", + "33": "তেত্রিশ", + "34": "চৌত্রিশ", + "35": "পঁয়ত্রিশ", + "36": "ছত্রিশ", + "37": "সাঁইত্রিশ", + "38": "আটত্রিশ", + "39": "ঊনচল্লিশ", + "40": "চল্লিশ", + "41": "একচল্লিশ", + "42": "বিয়াল্লিশ", + "43": "তেতাল্লিশ", + "44": "চুয়াল্লিশ", + "45": "পঁয়তাল্লিশ", + "46": "ছেচল্লিশ", + "47": "সাতচল্লিশ", + "48": "আটচল্লিশ", + "49": "ঊনপঞ্চাশ", + "50": "পঞ্চাশ", + "51": "একান্ন", + "52": "বায়ান্ন", + "53": "তিপ্পান্ন", + "54": "চুয়ান্ন", + "55": "পঞ্চান্ন", + "56": "ছাপ্পান্ন", + "57": "সাতান্ন", + "58": "আটান্ন", + "59": "ঊনষাট", + "60": "ষাট", + "61": "একষট্টি", + "62": "বাষট্টি", + "63": "তেষট্টি", + "64": "চৌষট্টি", + "65": "পঁয়ষট্টি", + "66": "ছেষট্টি", + "67": "সাতষট্টি", + "68": "আটষট্টি", + "69": "ঊনসত্তর", + "70": "সত্তর", + "71": "একাত্তর", + "72": "বাহাত্তর", + "73": "তিয়াত্তর", + "74": "চুয়াত্তর", + "75": "পঁচাত্তর", + "76": "ছিয়াত্তর", + "77": "সাতাত্তর", + "78": "আটাত্তর", + "79": "ঊনআশি", + "80": "আশি", + "81": "একাশি", + "82": "বিরাশি", + "83": "তিরাশি", + "84": "চুরাশি", + "85": "পঁচাশি", + "86": "ছিয়াশি", + "87": "সাতাশি", + "88": "আটাশি", + "89": "ঊননব্বই", + "90": "নব্বই", + "91": "একানব্বই", + "92": "বিরানব্বই", + "93": "তিরানব্বই", + "94": "চুরানব্বই", + "95": "পঁচানব্বই", + "96": "ছিয়ানব্বই", + "97": "সাতানব্বই", + "98": "আটানব্বই", + "99": "নিরানব্বই", + "100": "শো", + "1000": "হাজার", + "100000": "লাখ", + "10000000": "কোটি", + "1000000000": "একশ’ কোটি", +} # Bengali +num_dict["te"] = { + "0": "సున్నా", + "1": "ఒకటి", + "2": "రెండు", + "3": "మూడు", + "4": "నాలుగు", + "5": "ఐదు", + "6": "ఆరు", + "7": "ఏడు", + "8": "ఎనిమిది", + "9": "తొమ్మిది", + "10": "పది", + "11": "పదకొండు", + "12": "పన్నెండు", + "13": "పదమూడు", + "14": "పద్నాలుగు", + "15": "పదిహేను", + "16": "పదహారు", + "17": "పదిహేడు", + "18": "పద్దెనిమిది", + "19": "పందొమ్మిది", + "20": "ఇరవై", + "21": "ఇరవై ఒకటి", + "22": "ఇరవై రెండు", + "23": "ఇరవై మూడు", + "24": "ఇరవై నాలుగు", + "25": "ఇరవై ఐదు", + "26": "ఇరవై ఆరు", + "27": "ఇరవై ఏడు", + "28": "ఇరవై ఎనిమిది", + "29": "ఇరవై తొమ్మిది", + "30": "ముప్పై", + "31": "ముప్పై ఒకటి", + "32": "ముప్పై రెండు", + "33": "ముప్పై మూడు", + "34": "ముప్పై నాలుగు", + "35": "ముప్పై ఐదు", + "36": "ముప్పై ఆరు", + "37": "ముప్పై ఏడు", + "38": "ముప్పై ఎనిమిది", + "39": "ముప్పై తొమ్మిది", + "40": "నలభై", + "41": "నలభై ఒకటి", + "42": "నలభై రెండు", + "43": "నలభై మూడు", + "44": "నలభై నాలుగు", + "45": "నలభై ఐదు", + "46": "నలభై ఆరు", + "47": "నలభై ఏడు", + "48": "నలభై ఎనిమిది", + "49": "నలభై తొమ్మిది", + "50": "యాభై", + "51": "యాభై ఒకటి", + "52": "యాభై రెండు", + "53": "యాభై మూడు", + "54": "యాభై నాలుగు", + "55": "యాభై ఐదు", + "56": "యాభై ఆరు", + "57": "యాభై ఏడు", + "58": "యాభై ఎనిమిది", + "59": "యాభై తొమ్మిది", + "60": "అరవై", + "61": "అరవై ఒకటి", + "62": "అరవై రెండు", + "63": "అరవై మూడు", + "64": "అరవై నాలుగు", + "65": "అరవై ఐదు", + "66": "అరవై ఆరు", + "67": "అరవై ఏడు", + "68": "అరవై ఎనిమిది", + "69": "అరవై తొమ్మిది", + "70": "డెబ్బై", + "71": "డెబ్బై ఒకటి", + "72": "డెబ్బై రెండు", + "73": "డెబ్బై మూడు", + "74": "డెబ్బై నాలుగు", + "75": "డెబ్బై ఐదు", + "76": "డెబ్బై ఆరు", + "77": "డెబ్బై ఏడు", + "78": "డెబ్బై ఎనిమిది", + "79": "డెబ్బై తొమ్మిది", + "80": "ఎనభై", + "81": "ఎనభై ఒకటి", + "82": "ఎనభై రెండు", + "83": "ఎనభై మూడు", + "84": "ఎనభై నాలుగు", + "85": "ఎనభై ఐదు", + "86": "ఎనభై ఆరు", + "87": "ఎనభై ఏడు", + "88": "ఎనభై ఎనిమిది", + "89": "ఎనభై తొమ్మిది", + "90": "తొంభై", + "91": "తొంభై ఒకటి", + "92": "తొంభై రెండు", + "93": "తొంభై మూడు", + "94": "తొంభై నాలుగు", + "95": "తొంభై ఐదు", + "96": "తొంభై ఆరు", + "97": "తొంభై ఏడు", + "98": "తొంభై ఎనిమిది", + "99": "తొంభై తొమ్మిది", + "100": "వందల", + "1000": "వేల", + "100000": "లక్షల", + "10000000": "కోట్ల", + "1000000000": "బిలియన్", +} # Telugu +num_dict["ta"] = { + "0": "பூஜ்ஜியம்", + "1": "ஒன்று", + "2": "இரண்டு", + "3": "மூன்று", + "4": "நான்கு", + "5": "ஐந்து", + "6": "ஆறு", + "7": "ஏழு", + "8": "எட்டு", + "9": "ஒன்பது", + "10": "பத்து", + "11": "பதினொன்று", + "12": "பன்னிரண்டு", + "13": "பதிமூன்று", + "14": "பதினான்கு", + "15": "பதினைந்து", + "16": "பதினாறு", + "17": "பதினேழு", + "18": "பதினெட்டு", + "19": "பத்தொன்பது", + "20": "இருபது", + "21": "இருபது ஒன்று", + "22": "இருபத்து இரண்டு", + "23": "இருபத்து மூன்று", + "24": "இருபத்து நான்கு", + "25": "இருபத்து ஐந்து", + "26": "இருபத்து ஆறு", + "27": "இருபத்து ஏழு", + "28": "இருபத்து எட்டு", + "29": "இருபத்து ஒன்பது", + "30": "முப்பது", + "31": "முப்பத்து ஒன்று", + "32": "முப்பத்து இரண்டு", + "33": "முப்பத்து மூன்று", + "34": "முப்பத்து நான்கு", + "35": "முப்பத்து ஐந்து", + "36": "முப்பத்து ஆறு", + "37": "முப்பத்து ஏழு", + "38": "முப்பத்து எட்டு", + "39": "முப்பத்து ஒன்பது", + "40": "நாற்பது", + "41": "நாற்பத்து ஒன்று", + "42": "நாற்பத்து இரண்டு", + "43": "நாற்பத்து மூன்று", + "44": "நாற்பத்து நான்கு", + "45": "நாற்பத்து ஐந்து", + "46": "நாற்பத்து ஆறு", + "47": " நாற்பத்து ஏழு", + "48": "நாற்பத்து எட்டு", + "49": "நாற்பத்து ஒன்பது", + "50": "ஐம்பது", + "51": "ஐம்பத்து ஒன்று", + "52": "ஐம்பத்து இரண்டு", + "53": "ஐம்பத்து மூன்று", + "54": "ஐம்பத்து நான்கு", + "55": "ஐம்பத்து ஐந்து", + "56": "ஐம்பத்து ஆறு", + "57": "ஐம்பத்து ஏழு", + "58": "ஐம்பத்து எட்டு", + "59": "ஐம்பத்து ஒன்பது", + "60": "அறுபது", + "61": "அறுபத்து ஒன்று", + "62": "அறுபத்து இரண்டு", + "63": "அறுபத்து மூன்று", + "64": "அறுபத்து நான்கு", + "65": "அறுபத்து ஐந்து", + "66": "அறுபத்து ஆறு", + "67": "அறுபத்து ஏழு", + "68": "அறுபத்து எட்டு", + "69": "அறுபத்து ஒன்பது", + "70": "எழுபது", + "71": "எழுபத்தி ஒன்று", + "72": "எழுபத்தி இரண்டு", + "73": "எழுபத்தி முச்சக்கர", + "74": "எழுபத்தி நான்கு", + "75": "எழுபத்தி ஐந்து", + "76": "எழுபத்தி ஆறு", + "77": "எழுபத்தி ஏழு", + "78": "எழுபத்தி எட்டு", + "79": "எழுபத்தி ஒன்பது", + "80": "எண்பது", + "81": "எண்பத்தியொன்று", + "82": "எண்பத்திரண்டு", + "83": "எண்பத்திமூன்று", + "84": "என்பதினான்கு", + "85": "என்பதினைந்து", + "86": "எண்பத்திஆறு", + "87": "எண்பத்திஏழு", + "88": "எண்பத்தியெட்டு", + "89": "எண்பத்தியொன்பது", + "90": "தொன்னூறு", + "91": "தொண்ணூற்றியொன்று", + "92": "தொண்ணூற்றிரண்டு", + "93": "தொண்ணூற்றிமூன்று", + "94": "தொண்ணூற்றிநான்கு", + "95": "தொண்ணூற்றிஐந்து", + "96": "தொண்ணூற்றியாறு", + "97": "தொண்ணூற்றியேழு", + "98": "தொண்ணூற்றியெட்டு", + "99": "தொண்ணூற்றிஒன்பது", + "100": "நூறு", + "1000": "ஆயிரம்", + "100000": "இலட்சம்", + "10000000": "கோடி", + "1000000000": "பில்லியன்", +} # Tamil +num_dict["kn"] = { + "0": "ಸೊನ್ನೆ", + "1": "ಒಂದು", + "2": "ಎರಡು", + "3": "ಮೂರು", + "4": "ನಾಲ್ಕು", + "5": "ಅಯ್ದು", + "6": "ಆರು", + "7": "ಏಳು", + "8": "ಎಂಟು", + "9": "ಒಂಬತ್ತು", + "10": "ಹತ್ತು", + "11": "ಹನ್ನೊಂದು", + "12": "ಹನ್ನೆರಡು", + "13": "ಹದಿಮೂರು", + "14": "ಹದಿನಾಲ್ಕು", + "15": "ಹದಿನೈದು", + "16": "ಹದಿನಾರು", + "17": "ಹದಿನೇಳು", + "18": "ಹದಿನೆಂಟು", + "19": "ಹತ್ತೊಂಬತ್ತು", + "20": "ಇಪ್ಪತ್ತು", + "21": "ಇಪ್ಪತ್ತ್’ಒಂದು", + "22": "ಇಪ್ಪತ್ತ್’ಎರಡು", + "23": "ಇಪ್ಪತ್ತ್’ಮೂರು", + "24": "ಇಪ್ಪತ್ತ್’ನಾಲ್ಕು", + "25": "ಇಪ್ಪತ್ತ್’ಐದು", + "26": "ಇಪ್ಪತ್ತ್’ಆರು", + "27": "ಇಪ್ಪತ್ತ್’ಏಳು", + "28": "ಇಪ್ಪತ್ತ್’ಎಂಟು", + "29": "ಇಪ್ಪತ್ತ್’ಒಂಬತ್ತು", + "30": "ಮೂವತ್ತು", + "31": "ಮುವತ್ತ್’ಒಂದು", + "32": "ಮುವತ್ತ್’ಎರಡು", + "33": "ಮುವತ್ತ್’ಮೂರು", + "34": "ಮೂವತ್ತ್’ನಾಲ್ಕು", + "35": "ಮೂವತ್ತ್’ಐದು", + "36": "ಮೂವತ್ತ್’ಆರು", + "37": "ಮೂವತ್ತ್’ಏಳು", + "38": "ಮೂವತ್ತ್’ಎಂಟು", + "39": "ಮೂವತ್ತ್’ಒಂಬತ್ತು", + "40": "ನಲವತ್ತು", + "41": "ನಲವತ್ತೊಂದು", + "42": "ನಲವತ್ತ್ ಎರಡು", + "43": "ನಲವತ್ತ್ ಮೂರು", + "44": "ನಲವತ್ತ್ ನಾಲ್ಕು", + "45": "ನಲವತ್ತೈದು", + "46": "ನಲವತ್ತಾರು", + "47": "ನಲವತ್ತೇಳು", + "48": "ನಲವತ್ತೆಂಟು", + "49": "ನಲವತ್ತೊಂಬತ್ತು", + "50": "ಐವತ್ತು", + "51": "ಐವತ್ತೊಂದು", + "52": "ಐವತ್ತೆರಡು", + "53": "ಐವತ್ತಮೂರು", + "54": "ಐವತ್ತ್ನಾಲ್ಕು", + "55": "ಐವತ್ತೈದು", + "56": "ಐವತ್ತಾರು", + "57": "ಐವತ್ತೇಳು", + "58": "ಐವತ್ತೆಂಟು", + "59": "ಐವತ್ತೊಂಬತ್ತು", + "60": "ಅರವತ್ತು", + "61": "ಅರವತ್ತೊಂದು", + "62": "ಅರವತ್ತೆರಡು", + "63": "ಅರವತ್ತ್ ಮೂರು", + "64": "ಅರವತ್ತ್ ನಾಲ್ಕು", + "65": "ಅರವತ್ತೈದು", + "66": "ಅರವತ್ತಾರು", + "67": "ಅರವತ್ತೇಳು", + "68": "ಅರವತ್ತೆಂಟು", + "69": "ಅರವತ್ತೊಂಬತ್ತು", + "70": "ಎಪ್ಪತ್ತು", + "71": "ಎಪ್ಪತ್ತೊಂದು", + "72": "ಎಪ್ಪತ್ತೆರಡು", + "73": "ಎಪ್ಪತ್ತ್ ಮೂರು", + "74": "ಎಪ್ಪತ್ತ್ ನಾಲ್ಕು", + "75": "ಎಪ್ಪತ್ತೈದು", + "76": "ಎಪ್ಪತ್ತಾರು", + "77": "ಎಪ್ಪತ್ತೇಳು", + "78": "ಎಪ್ಪತ್ತೆಂಟು", + "79": "ಎಪ್ಪತ್ತೊಂಬತ್ತು", + "80": "ಎಂಬತ್ತು", + "81": "ಎಂಬತ್ತೊಂದು", + "82": "ಎಂಬತ್ತೆರಡು", + "83": "ಎಂಬತ್ತ್ ಮೂರು", + "84": "ಎಂಬತ್ತ್ ನಾಲ್ಕು", + "85": "ಎಂಬತ್ತೈದು", + "86": "ಎಂಬತ್ತಾರು", + "87": "ಎಂಬತ್ತೇಳು", + "88": "ಎಂಬತ್ತೆಂಟು", + "89": "ಎಂಬತ್ತೊಂಬತ್ತು", + "90": "ತೊಂಬತ್ತು", + "91": "ತೊಂಬತ್ತೊಂದು", + "92": "ತೊಂಬತ್ತೆರಡು", + "93": "ತೊಂಬತ್ತ ಮೂರು", + "94": "ತೊಂಬತ್ತ ನಾಲ್ಕು", + "95": "ತೊಂಬತ್ತೈದು", + "96": "ತೊಂಬತ್ತಾರು", + "97": "ತೊಂಬತ್ತೇಳು", + "98": "ತೊಂಬತ್ತೆಂಟು", + "99": "ತೊಂಬತ್ತೊಂಬತ್ತು", + "100": "ನೂರ", + "1000": "ಸಾವಿರದ", + "100000": "ಲಕ್ಷದ", + "10000000": "ಕೋಟಿ", + "1000000000": "ಶತಕೋಟಿ", +} # Kannada +num_dict["or"] = { + "0": "ଶୁନ୍ୟ", + "1": "ଏକ", + "2": "ଦୁଇ", + "3": "ତିନି", + "4": "ଚାରି", + "5": "ପାଞ୍ଚ", + "6": "ଛଅ", + "7": "ସାତ", + "8": "ଆଠ", + "9": "ନଅ", + "10": "ନଅ", + "11": "ଏଗାର", + "12": "ବାର", + "13": "ତେର", + "14": "ଚଉଦ", + "15": "ପନ୍ଦର", + "16": "ଷୋହଳ", + "17": "ସତର", + "18": "ଅଠର", + "19": "ଊଣାଇଶ", + "20": "କୋଡିଏ", + "21": "ଏକୋଇଶି", + "22": "ବାଇଶି", + "23": "ତେଇଶି", + "24": "ଚବିଶି", + "25": "ପଚିଶି", + "26": "ଛବିଶି", + "27": "ସତାଇଶି", + "28": "ଅଠାଇଶି", + "29": "ଅଣତିରିଶି", + "30": "ତିରିଶି", + "31": "ଏକତିରିଶି", + "32": "ବତିଶି", + "33": "ତେତିଶି", + "34": "ଚଉତିରିଶି", + "35": "ପଞ୍ଚତିରିଶି", + "36": "ଛତିଶି", + "37": "ସଂଇତିରିଶି", + "38": "ଅଠତିରିଶି", + "39": "ଅଣଚାଳିଶି", + "40": "ଚାଳିଶି", + "41": "ଏକଚାଳିଶି", + "42": "ବୟାଳିଶି", + "43": "ତେୟାଳିଶି", + "44": "ଚଉରାଳିଶି", + "45": "ପଞ୍ଚଚାଳିଶି", + "46": "ଛୟାଳିଶି", + "47": "ସତଚାଳିଶି", + "48": "ଅଠଚାଳିଶି", + "49": "ଅଣଚାଶ", + "50": "ପଚାଶ", + "51": "ଏକାବନ", + "52": "ବାଉନ", + "53": "ତେପନ", + "54": "ଚଉବନ", + "55": "ପଞ୍ଚାବନ", + "56": "ଛପନ", + "57": "ସତାବନ", + "58": "ଅଠାବନ", + "59": "ଅଣଷଠି", + "60": "ଷାଠିଏ", + "61": "ଏକଷଠି", + "62": "ବାଷଠି", + "63": "ତେଷଠି", + "64": "ଚଉଷଠି", + "65": "ପଞ୍ଚଷଠି", + "66": "ଛଅଷଠି", + "67": "ସତଷଠି", + "68": "ଅଠଷଠି", + "69": "ଅଣସ୍ତରୀ", + "70": "ସତୂରୀ", + "71": "ଏକସ୍ତରୀ", + "72": "ବାସ୍ତରୀ", + "73": "ତେସ୍ତରୀ", + "74": "ଚଉସ୍ତରୀ", + "75": "ପଞ୍ଚସ୍ତରୀ", + "76": "ଛଅସ୍ତରୀ", + "77": "ସତସ୍ତରୀ", + "78": "ଅଠସ୍ତରୀ", + "79": "ଅଣାଅଶୀ", + "80": "ଅଶୀ", + "81": "ଏକାଅଶୀ", + "82": "ବୟାଅଶୀ", + "83": "ତେୟାଅଶୀ", + "84": "ଚଉରାଅଶୀ", + "85": "ପଞ୍ଚାଅଶୀ", + "86": "ଛୟାଅଶୀ", + "87": "ସତାଅଶୀ", + "88": "ଅଠାଅଶୀ", + "89": "ଅଣାନବେ", + "90": "ନବେ", + "91": "ଏକାନବେ", + "92": "ବୟାନବେ", + "93": "ତେୟାନବେ", + "94": "ଚଉରାନବେ", + "95": "ପଞ୍ଚାନବେ", + "96": "ଛୟାନବେ", + "97": "ସତାନବେ", + "98": "ଅଠାନବେ", + "99": "ଅନେଶତ", + "100": "ଶହେ", + "1000": "ହଜାର", + "100000": "ଲକ୍ଷ", + "10000000": "କୋଟି", + "1000000000": "କୋଟି", +} # Oriya +num_dict["pa"] = { + "0": "ਸਿਫਰ ", + "1": "ਇੱਕ", + "2": "ਦੋ", + "3": "ਤਿੰਨ", + "4": "ਚਾਰ", + "5": "ਪੰਜ", + "6": "ਛੇ", + "7": "ਸੱਤ", + "8": "ਅੱਠ", + "9": "ਨੌਂ", + "10": "ਦੱਸ", + "11": "ਗਿਆਰਾਂ", + "12": "ਬਾਰਾਂ", + "13": "ਤੇਰਾਂ", + "14": "ਚੌਦਾਂ", + "15": "ਪੰਦਰਾਂ", + "16": "ਸੋਲ਼ਾਂ", + "17": "ਸਤਾਰਾਂ", + "18": "ਅਠਾਰਾਂ", + "19": "ਉਨੀ", + "20": "ਵੀਹ", + "21": "ਇੱਕੀ", + "22": "ਬਾਈ", + "23": "ਤੇਈ", + "24": "ਚੌਵੀ", + "25": "ਪੰਝੀ", + "26": "ਛੱਬੀ", + "27": "ਸਤਾਈ", + "28": "ਅਠਾਈ", + "29": "ਉਨੱਤੀ", + "30": "ਤੀਹ", + "31": "ਇਕੱਤੀ", + "32": "ਬੱਤੀ", + "33": "ਤੇਤੀ", + "34": "ਚੌਂਤੀ", + "35": "ਪੈਂਤੀ", + "36": "ਛੱਤੀ", + "37": "ਸੈਂਤੀ", + "38": "ਅਠੱਤੀ", + "39": "ਉਨਤਾਲੀ", + "40": "ਚਾਲੀ", + "41": "ਇਕਤਾਲੀ", + "42": "ਬਤਾਲੀ", + "43": "ਤਰਤਾਲੀ", + "44": "ਚੌਤਾਲੀ", + "45": "ਪੰਜਤਾਲੀ", + "46": "ਛਿਆਲੀ", + "47": "ਸੰਤਾਲੀ", + "48": "ਅੱਠਤਾਲੀ", + "49": "ਉਣਿੰਜਾ", + "50": "ਪੰਜਾਹ", + "51": "ਇਕਵਿੰਜਾ", + "52": "ਬਵਿੰਜਾ", + "53": "ਤਰਵਿੰਜਾ", + "54": "ਚਰਿੰਜਾ", + "55": "ਪਚਵਿੰਜਾ", + "56": "ਛਪਿੰਜਾ", + "57": "ਸਤਵਿੰਜਾ", + "58": "ਅੱਠਵਿੰਜਾ", + "59": "ਉਣਾਠ", + "60": "ਸੱਠ", + "61": "ਇਕਾਠ", + "62": "ਬਾਠ੍ਹ", + "63": "ਤਰੇਠ੍ਹ", + "64": "ਚੌਠ੍ਹ", + "65": "ਪੈਂਠ", + "66": "ਛਿਆਠ", + "67": "ਸਤਾਹਠ", + "68": "ਅੱਠਾਠ", + "69": "ਉਣੱਤਰ", + "70": "ਸੱਤਰ", + "71": "ਇਕ੍ਹੱਤਰ", + "72": "ਬਹੱਤਰ", + "73": "ਤਹੱਤਰ", + "74": "ਚੌਹੱਤਰ", + "75": "ਪੰਜੱਤਰ", + "76": "ਛਿਹੱਤਰ", + "77": "ਸਤੱਤਰ", + "78": "ਅਠੱਤਰ", + "79": "ਉਣਾਸੀ", + "80": "ਅੱਸੀ", + "81": "ਇਕਾਸੀ", + "82": "ਬਿਆਸੀ", + "83": "ਤਰਾਸੀ", + "84": "ਚਰਾਸੀ", + "85": "ਪੰਜਾਸੀ", + "86": "ਛਿਆਸੀ", + "87": "ਸਤਾਸੀ", + "88": "ਅਠਾਸੀ", + "89": "ਉਣਾਨਵੇਂ", + "90": "ਨੱਬੇ", + "91": "ਇਕਾਨਵੇਂ", + "92": "ਬਿਆਨਵੇਂ", + "93": "ਤਰਾਨਵੇਂ", + "94": "ਚਰਾਨਵੇਂ", + "95": "ਪਚਾਨਵੇਂ", + "96": "ਛਿਆਨਵੇਂ", + "97": "ਸਤਾਨਵੇਂ", + "98": "ਅਠਾਨਵੇਂ", + "99": "ਨਿੜਾਨਵੇਂ", + "100": "ਸੌ", + "1000": "ਹਜਾਰ", + "100000": "ਲੱਖ", + "10000000": "ਕਰੋੜ", + "1000000000": "ਅਰਬ", +} # Punjabi + +# --------------------------- num_to_word.py ------------------------------ +""" +Method to convert Numbers to Words +for indian languages + +Use cases:- +1) Speech recognition pre-processing +2) Language modeling Data pre-processing + +------------------------- +check indic_numbers.py to add support +for any indian language +""" + + +def language_specific_exception(words, lang, combiner): + """ + Language Specific Exception will come here + """ + + def occurs_at_end(piece): + return words[-len(piece) :] == piece + + if lang == "mr": + words = words.replace("एक" + combiner + "शे", "शंभर") + elif lang == "gu": + words = words.replace("બે" + combiner + "સો", "બસ્સો") + elif lang == "te": + exception_dict = { + "1": "ఒక", + "100": "వంద", + "100+": "వందలు", + "1000": "వెయ్యి", + "1000+": "వేలు", + "100000": "లక్ష", + "100000+": "లక్షలు", + "10000000": "కోటి", + "10000000+": "కోట్లు", + } + + test_case = ["100", "1000", "100000", "10000000"] + for test in test_case: + test_word = num_dict["te"][test] + match = num_dict["te"]["1"] + combiner + test_word + # for numbers like : 100, 1000, 100000 + if words == match: + return exception_dict[test] + # for numbers like : 200, 4000, 800000 + elif occurs_at_end(test_word): + words = words.replace(test_word, exception_dict[test + "+"]) + # for numbers like : 105, 1076, 123993 + elif not occurs_at_end(match): + replacement = exception_dict["1"] + combiner + exception_dict[test] + words = words.replace(match, replacement) + + # Exception case for 101...199 + special_case = "ఒక" + combiner + "వంద" + words = words.replace(special_case, "నూట") + elif lang == "kn": + # special case for 100 + if words == ("ಒಂದು" + combiner + "ನೂರ"): + return "ನೂರು" + exception_dict = { + "ನೂರ": "ನೂರು", + "ಸಾವಿರದ": "ಸಾವಿರ", + "ಲಕ್ಷದ": "ಲಕ್ಷ", + "ಕೋಟಿಯ": "ಕೋಟಿ", + } + for expt in exception_dict: + if occurs_at_end(expt): + words = words.replace(expt, exception_dict[expt]) + return words + + +def num_to_word(num, lang, separator=", ", combiner=" "): + """ + Main Method + :param num: Number digits from any indian language + :param lang: Language Code from supported Language + :param separator: Separator character i.e. separator = '-' --> 'two hundred-sixty' + :param combiner: combine number with position i.e. combiner = '-' --> 'two-hundred sixty' + :return: UTF-8 String of numbers in words + """ + lang = lang.lower() + num = str(num) + + # Load dictionary according to language code + assert lang in supported_lang, "Language not supported" + num_dic = num_dict[lang] + + # dash default combiner for english-india + if (lang == "en") & (combiner == " "): + combiner = "-" + + # Remove punctuations from numbers + num = str(num).replace(",", "").replace(" ", "") + + # return word as it is if not number + if not num.isdecimal(): + return num + + # Replace native language numbers with english digits + for language in supported_lang: + for num_index in range(10): + num = num.replace(all_num[language][num_index], all_num["en"][num_index]) + + # Assert that input contains only integer number + for digit in num: + assert digit in all_num["en"], "Give proper input" + + # Process + # For Number longer than 9 digits + def all_two_digit(digits_2): + if len(digits_2) <= 1: # Provided only one/zero digit + return num_dic.get(digits_2, "") + elif digits_2 == "00": # Two Zero provided + return num_dic["0"] + separator + num_dic["0"] + elif digits_2[0] == "0": # First digit is zero + return num_dic["0"] + separator + num_dic[digits_2[1]] + else: # Both digit provided + return num_dic[digits_2] + + # For Number less than 9 digits + def two_digit(digits_2): + digits_2 = digits_2.lstrip("0") + if len(digits_2) != 0: + return num_dic[digits_2] + else: + return "" + + def all_digit(digits): + digits = digits.lstrip("0") + digit_len = len(digits) + if digit_len > 3: + num_of_digits_to_process = (digit_len % 2) + 1 + process_digits = digits[:num_of_digits_to_process] + base = str(10 ** (int(digit_len / 2) * 2 - 1)) + remain_digits = digits[num_of_digits_to_process:] + return ( + num_dic[process_digits] + + combiner + + num_dic[base] + + separator + + all_digit(remain_digits) + ) + elif len(digits) == 3: + return ( + num_dic[digits[:1]] + + combiner + + num_dic["100"] + + separator + + two_digit(digits[1:]) + ) + else: + return two_digit(digits) + + num = num.lstrip("0") + full_digit_len = len(num) + + if full_digit_len == 0: + output = num_dic["0"] + elif full_digit_len <= 9: + output = all_digit(num) + else: + iteration = round(full_digit_len / 2) + output = all_two_digit(num[:2]) # First to digit + for i in range(1, iteration): + output = ( + output + separator + all_two_digit(num[i * 2 : (i + 1) * 2]) + ) # Next two digit pairs + remaining_digits = num[iteration * 2 :] + if not all_two_digit(remaining_digits) == "": + output = ( + output + separator + all_two_digit(remaining_digits) + ) # remaining Last one/two digits + + output = output.strip(separator) + + output = language_specific_exception(output, lang, combiner) + + return output + + +# --------------------------------- num_to_word_on_a_sent --------------------------------- + + +def is_digit(word, digit_pattern): + return re.search(digit_pattern, word) + + +def remove_punct(sent): + clean = re.sub("[%s]" % re.escape(string.punctuation), " ", sent) + return " ".join([word for word in clean.split() if word]) + + +def normalize_nums(text, lang): + """ + text: str (eg) + lang: lang code ['en', 'hi'] + + returns: str + (eg) + """ + + if lang in supported_lang: + text = text.replace('-',' - ') # space separate hyphen + words = text.split() + lang_digits = [str(i) for i in range(0, 10)] + + digit_pattern = "[" + "".join(lang_digits) + "]" + num_indices = [ + ind for ind, word in enumerate(words) if is_digit(word, digit_pattern) + ] + + words_up = [ + num_to_word(word, lang, separator=" ", combiner=" ") + if ind in num_indices + else word + for ind, word in enumerate(words) + ] + return " ".join(words_up) + else: + return text + + +if __name__ == "__main__": + print(normalize_nums("रीटा के पास 16 बिल्लियाँ हैं।", "hi")) diff --git a/tts_infer/requirements.txt b/tts_infer/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..cbbb8fff6386b8b986fd69a4328cbd93fdc4ce6a --- /dev/null +++ b/tts_infer/requirements.txt @@ -0,0 +1,6 @@ +# will be installed with main setup.py, no need to reinstall + +ai4bharat-transliteration==0.5.0.3 +numpy==1.19.5 +pandas +pydload \ No newline at end of file diff --git a/tts_infer/transliterate.py b/tts_infer/transliterate.py new file mode 100644 index 0000000000000000000000000000000000000000..575430562683434cd44fd8d2e77d26dab9ced73b --- /dev/null +++ b/tts_infer/transliterate.py @@ -0,0 +1,919 @@ +import torch +import torch.nn as nn +import numpy as np +import pandas as pd +import random +import sys +import os +import json +import enum +import traceback +import re + +F_DIR = os.path.dirname(os.environ.get('translit_model_base_path', os.path.realpath(__file__))) + + +class XlitError(enum.Enum): + lang_err = "Unsupported langauge ID requested ;( Please check available languages." + string_err = "String passed is incompatable ;(" + internal_err = "Internal crash ;(" + unknown_err = "Unknown Failure" + loading_err = "Loading failed ;( Check if metadata/paths are correctly configured." + + +##=================== Network ================================================== + + +class Encoder(nn.Module): + def __init__( + self, + input_dim, + embed_dim, + hidden_dim, + rnn_type="gru", + layers=1, + bidirectional=False, + dropout=0, + device="cpu", + ): + super(Encoder, self).__init__() + + self.input_dim = input_dim # src_vocab_sz + self.enc_embed_dim = embed_dim + self.enc_hidden_dim = hidden_dim + self.enc_rnn_type = rnn_type + self.enc_layers = layers + self.enc_directions = 2 if bidirectional else 1 + self.device = device + + self.embedding = nn.Embedding(self.input_dim, self.enc_embed_dim) + + if self.enc_rnn_type == "gru": + self.enc_rnn = nn.GRU( + input_size=self.enc_embed_dim, + hidden_size=self.enc_hidden_dim, + num_layers=self.enc_layers, + bidirectional=bidirectional, + ) + elif self.enc_rnn_type == "lstm": + self.enc_rnn = nn.LSTM( + input_size=self.enc_embed_dim, + hidden_size=self.enc_hidden_dim, + num_layers=self.enc_layers, + bidirectional=bidirectional, + ) + else: + raise Exception("XlitError: unknown RNN type mentioned") + + def forward(self, x, x_sz, hidden=None): + """ + x_sz: (batch_size, 1) - Unpadded sequence lengths used for pack_pad + """ + batch_sz = x.shape[0] + # x: batch_size, max_length, enc_embed_dim + x = self.embedding(x) + + ## pack the padded data + # x: max_length, batch_size, enc_embed_dim -> for pack_pad + x = x.permute(1, 0, 2) + x = nn.utils.rnn.pack_padded_sequence(x, x_sz, enforce_sorted=False) # unpad + + # output: packed_size, batch_size, enc_embed_dim + # hidden: n_layer**num_directions, batch_size, hidden_dim | if LSTM (h_n, c_n) + output, hidden = self.enc_rnn( + x + ) # gru returns hidden state of all timesteps as well as hidden state at last timestep + + ## pad the sequence to the max length in the batch + # output: max_length, batch_size, enc_emb_dim*directions) + output, _ = nn.utils.rnn.pad_packed_sequence(output) + + # output: batch_size, max_length, hidden_dim + output = output.permute(1, 0, 2) + + return output, hidden + + def get_word_embedding(self, x): + """ """ + x_sz = torch.tensor([len(x)]) + x_ = torch.tensor(x).unsqueeze(0).to(dtype=torch.long) + # x: 1, max_length, enc_embed_dim + x = self.embedding(x_) + + ## pack the padded data + # x: max_length, 1, enc_embed_dim -> for pack_pad + x = x.permute(1, 0, 2) + x = nn.utils.rnn.pack_padded_sequence(x, x_sz, enforce_sorted=False) # unpad + + # output: packed_size, 1, enc_embed_dim + # hidden: n_layer**num_directions, 1, hidden_dim | if LSTM (h_n, c_n) + output, hidden = self.enc_rnn( + x + ) # gru returns hidden state of all timesteps as well as hidden state at last timestep + + out_embed = hidden[0].squeeze() + + return out_embed + + +class Decoder(nn.Module): + def __init__( + self, + output_dim, + embed_dim, + hidden_dim, + rnn_type="gru", + layers=1, + use_attention=True, + enc_outstate_dim=None, # enc_directions * enc_hidden_dim + dropout=0, + device="cpu", + ): + super(Decoder, self).__init__() + + self.output_dim = output_dim # tgt_vocab_sz + self.dec_hidden_dim = hidden_dim + self.dec_embed_dim = embed_dim + self.dec_rnn_type = rnn_type + self.dec_layers = layers + self.use_attention = use_attention + self.device = device + if self.use_attention: + self.enc_outstate_dim = enc_outstate_dim if enc_outstate_dim else hidden_dim + else: + self.enc_outstate_dim = 0 + + self.embedding = nn.Embedding(self.output_dim, self.dec_embed_dim) + + if self.dec_rnn_type == "gru": + self.dec_rnn = nn.GRU( + input_size=self.dec_embed_dim + + self.enc_outstate_dim, # to concat attention_output + hidden_size=self.dec_hidden_dim, # previous Hidden + num_layers=self.dec_layers, + batch_first=True, + ) + elif self.dec_rnn_type == "lstm": + self.dec_rnn = nn.LSTM( + input_size=self.dec_embed_dim + + self.enc_outstate_dim, # to concat attention_output + hidden_size=self.dec_hidden_dim, # previous Hidden + num_layers=self.dec_layers, + batch_first=True, + ) + else: + raise Exception("XlitError: unknown RNN type mentioned") + + self.fc = nn.Sequential( + nn.Linear(self.dec_hidden_dim, self.dec_embed_dim), + nn.LeakyReLU(), + # nn.Linear(self.dec_embed_dim, self.dec_embed_dim), nn.LeakyReLU(), # removing to reduce size + nn.Linear(self.dec_embed_dim, self.output_dim), + ) + + ##----- Attention ---------- + if self.use_attention: + self.W1 = nn.Linear(self.enc_outstate_dim, self.dec_hidden_dim) + self.W2 = nn.Linear(self.dec_hidden_dim, self.dec_hidden_dim) + self.V = nn.Linear(self.dec_hidden_dim, 1) + + def attention(self, x, hidden, enc_output): + """ + x: (batch_size, 1, dec_embed_dim) -> after Embedding + enc_output: batch_size, max_length, enc_hidden_dim *num_directions + hidden: n_layers, batch_size, hidden_size | if LSTM (h_n, c_n) + """ + + ## perform addition to calculate the score + + # hidden_with_time_axis: batch_size, 1, hidden_dim + ## hidden_with_time_axis = hidden.permute(1, 0, 2) ## replaced with below 2lines + hidden_with_time_axis = ( + torch.sum(hidden, axis=0) + if self.dec_rnn_type != "lstm" + else torch.sum(hidden[0], axis=0) + ) # h_n + + hidden_with_time_axis = hidden_with_time_axis.unsqueeze(1) + + # score: batch_size, max_length, hidden_dim + score = torch.tanh(self.W1(enc_output) + self.W2(hidden_with_time_axis)) + + # attention_weights: batch_size, max_length, 1 + # we get 1 at the last axis because we are applying score to self.V + attention_weights = torch.softmax(self.V(score), dim=1) + + # context_vector shape after sum == (batch_size, hidden_dim) + context_vector = attention_weights * enc_output + context_vector = torch.sum(context_vector, dim=1) + # context_vector: batch_size, 1, hidden_dim + context_vector = context_vector.unsqueeze(1) + + # attend_out (batch_size, 1, dec_embed_dim + hidden_size) + attend_out = torch.cat((context_vector, x), -1) + + return attend_out, attention_weights + + def forward(self, x, hidden, enc_output): + """ + x: (batch_size, 1) + enc_output: batch_size, max_length, dec_embed_dim + hidden: n_layer, batch_size, hidden_size | lstm: (h_n, c_n) + """ + if (hidden is None) and (self.use_attention is False): + raise Exception( + "XlitError: No use of a decoder with No attention and No Hidden" + ) + + batch_sz = x.shape[0] + + if hidden is None: + # hidden: n_layers, batch_size, hidden_dim + hid_for_att = torch.zeros( + (self.dec_layers, batch_sz, self.dec_hidden_dim) + ).to(self.device) + elif self.dec_rnn_type == "lstm": + hid_for_att = hidden[1] # c_n + + # x (batch_size, 1, dec_embed_dim) -> after embedding + x = self.embedding(x) + + if self.use_attention: + # x (batch_size, 1, dec_embed_dim + hidden_size) -> after attention + # aw: (batch_size, max_length, 1) + x, aw = self.attention(x, hidden, enc_output) + else: + x, aw = x, 0 + + # passing the concatenated vector to the GRU + # output: (batch_size, n_layers, hidden_size) + # hidden: n_layers, batch_size, hidden_size | if LSTM (h_n, c_n) + output, hidden = ( + self.dec_rnn(x, hidden) if hidden is not None else self.dec_rnn(x) + ) + + # output :shp: (batch_size * 1, hidden_size) + output = output.view(-1, output.size(2)) + + # output :shp: (batch_size * 1, output_dim) + output = self.fc(output) + + return output, hidden, aw + + +class Seq2Seq(nn.Module): + """ + Class dependency: Encoder, Decoder + """ + + def __init__( + self, encoder, decoder, pass_enc2dec_hid=False, dropout=0, device="cpu" + ): + super(Seq2Seq, self).__init__() + + self.encoder = encoder + self.decoder = decoder + self.device = device + self.pass_enc2dec_hid = pass_enc2dec_hid + _force_en2dec_hid_conv = False + + if self.pass_enc2dec_hid: + assert ( + decoder.dec_hidden_dim == encoder.enc_hidden_dim + ), "Hidden Dimension of encoder and decoder must be same, or unset `pass_enc2dec_hid`" + if decoder.use_attention: + assert ( + decoder.enc_outstate_dim + == encoder.enc_directions * encoder.enc_hidden_dim + ), "Set `enc_out_dim` correctly in decoder" + assert ( + self.pass_enc2dec_hid or decoder.use_attention + ), "No use of a decoder with No attention and No Hidden from Encoder" + + self.use_conv_4_enc2dec_hid = False + if ( + self.pass_enc2dec_hid + and (encoder.enc_directions * encoder.enc_layers != decoder.dec_layers) + ) or _force_en2dec_hid_conv: + if encoder.enc_rnn_type == "lstm" or encoder.enc_rnn_type == "lstm": + raise Exception( + "XlitError: conv for enc2dec_hid not implemented; Change the layer numbers appropriately" + ) + + self.use_conv_4_enc2dec_hid = True + self.enc_hid_1ax = encoder.enc_directions * encoder.enc_layers + self.dec_hid_1ax = decoder.dec_layers + self.e2d_hidden_conv = nn.Conv1d(self.enc_hid_1ax, self.dec_hid_1ax, 1) + + def enc2dec_hidden(self, enc_hidden): + """ + enc_hidden: n_layer, batch_size, hidden_dim*num_directions + TODO: Implement the logic for LSTm bsed model + """ + # hidden: batch_size, enc_layer*num_directions, enc_hidden_dim + hidden = enc_hidden.permute(1, 0, 2).contiguous() + # hidden: batch_size, dec_layers, dec_hidden_dim -> [N,C,Tstep] + hidden = self.e2d_hidden_conv(hidden) + + # hidden: dec_layers, batch_size , dec_hidden_dim + hidden_for_dec = hidden.permute(1, 0, 2).contiguous() + + return hidden_for_dec + + def active_beam_inference(self, src, beam_width=3, max_tgt_sz=50): + """Search based decoding + src: (sequence_len) + """ + + def _avg_score(p_tup): + """Used for Sorting + TODO: Dividing by length of sequence power alpha as hyperparam + """ + return p_tup[0] + + import sys + + batch_size = 1 + start_tok = src[0] + end_tok = src[-1] + src_sz = torch.tensor([len(src)]) + src_ = src.unsqueeze(0) + + # enc_output: (batch_size, padded_seq_length, enc_hidden_dim*num_direction) + # enc_hidden: (enc_layers*num_direction, batch_size, hidden_dim) + enc_output, enc_hidden = self.encoder(src_, src_sz) + + if self.pass_enc2dec_hid: + # dec_hidden: dec_layers, batch_size , dec_hidden_dim + if self.use_conv_4_enc2dec_hid: + init_dec_hidden = self.enc2dec_hidden(enc_hidden) + else: + init_dec_hidden = enc_hidden + else: + # dec_hidden -> Will be initialized to zeros internally + init_dec_hidden = None + + # top_pred[][0] = Σ-log_softmax + # top_pred[][1] = sequence torch.tensor shape: (1) + # top_pred[][2] = dec_hidden + top_pred_list = [(0, start_tok.unsqueeze(0), init_dec_hidden)] + + for t in range(max_tgt_sz): + cur_pred_list = [] + + for p_tup in top_pred_list: + if p_tup[1][-1] == end_tok: + cur_pred_list.append(p_tup) + continue + + # dec_hidden: dec_layers, 1, hidden_dim + # dec_output: 1, output_dim + dec_output, dec_hidden, _ = self.decoder( + x=p_tup[1][-1].view(1, 1), # dec_input: (1,1) + hidden=p_tup[2], + enc_output=enc_output, + ) + + ## π{prob} = Σ{log(prob)} -> to prevent diminishing + # dec_output: (1, output_dim) + dec_output = nn.functional.log_softmax(dec_output, dim=1) + # pred_topk.values & pred_topk.indices: (1, beam_width) + pred_topk = torch.topk(dec_output, k=beam_width, dim=1) + + for i in range(beam_width): + sig_logsmx_ = p_tup[0] + pred_topk.values[0][i] + # seq_tensor_ : (seq_len) + seq_tensor_ = torch.cat((p_tup[1], pred_topk.indices[0][i].view(1))) + + cur_pred_list.append((sig_logsmx_, seq_tensor_, dec_hidden)) + + cur_pred_list.sort(key=_avg_score, reverse=True) # Maximized order + top_pred_list = cur_pred_list[:beam_width] + + # check if end_tok of all topk + end_flags_ = [1 if t[1][-1] == end_tok else 0 for t in top_pred_list] + if beam_width == sum(end_flags_): + break + + pred_tnsr_list = [t[1] for t in top_pred_list] + + return pred_tnsr_list + + +##===================== Glyph handlers ======================================= + + +class GlyphStrawboss: + def __init__(self, glyphs="en"): + """list of letters in a language in unicode + lang: ISO Language code + glyphs: json file with script information + """ + if glyphs == "en": + # Smallcase alone + self.glyphs = [chr(alpha) for alpha in range(97, 122 + 1)] + else: + self.dossier = json.load(open(glyphs, encoding="utf-8")) + self.glyphs = self.dossier["glyphs"] + self.numsym_map = self.dossier["numsym_map"] + + self.char2idx = {} + self.idx2char = {} + self._create_index() + + def _create_index(self): + + self.char2idx["_"] = 0 # pad + self.char2idx["$"] = 1 # start + self.char2idx["#"] = 2 # end + self.char2idx["*"] = 3 # Mask + self.char2idx["'"] = 4 # apostrophe U+0027 + self.char2idx["%"] = 5 # unused + self.char2idx["!"] = 6 # unused + + # letter to index mapping + for idx, char in enumerate(self.glyphs): + self.char2idx[char] = idx + 7 # +7 token initially + + # index to letter mapping + for char, idx in self.char2idx.items(): + self.idx2char[idx] = char + + def size(self): + return len(self.char2idx) + + def word2xlitvec(self, word): + """Converts given string of gyphs(word) to vector(numpy) + Also adds tokens for start and end + """ + try: + vec = [self.char2idx["$"]] # start token + for i in list(word): + vec.append(self.char2idx[i]) + vec.append(self.char2idx["#"]) # end token + + vec = np.asarray(vec, dtype=np.int64) + return vec + + except Exception as error: + print("XlitError: In word:", word, "Error Char not in Token:", error) + sys.exit() + + def xlitvec2word(self, vector): + """Converts vector(numpy) to string of glyphs(word)""" + char_list = [] + for i in vector: + char_list.append(self.idx2char[i]) + + word = "".join(char_list).replace("$", "").replace("#", "") # remove tokens + word = word.replace("_", "").replace("*", "") # remove tokens + return word + + +class VocabSanitizer: + def __init__(self, data_file): + """ + data_file: path to file conatining vocabulary list + """ + extension = os.path.splitext(data_file)[-1] + if extension == ".json": + self.vocab_set = set(json.load(open(data_file, encoding="utf-8"))) + elif extension == ".csv": + self.vocab_df = pd.read_csv(data_file).set_index("WORD") + self.vocab_set = set(self.vocab_df.index) + else: + print("XlitError: Only Json/CSV file extension supported") + + def reposition(self, word_list): + """Reorder Words in list""" + new_list = [] + temp_ = word_list.copy() + for v in word_list: + if v in self.vocab_set: + new_list.append(v) + temp_.remove(v) + new_list.extend(temp_) + + return new_list + + +##=============== INSTANTIATION ================================================ + + +class XlitPiston: + """ + For handling prediction & post-processing of transliteration for a single language + Class dependency: Seq2Seq, GlyphStrawboss, VocabSanitizer + Global Variables: F_DIR + """ + + def __init__( + self, + weight_path, + vocab_file, + tglyph_cfg_file, + iglyph_cfg_file="en", + device="cpu", + ): + + self.device = device + self.in_glyph_obj = GlyphStrawboss(iglyph_cfg_file) + self.tgt_glyph_obj = GlyphStrawboss(glyphs=tglyph_cfg_file) + self.voc_sanity = VocabSanitizer(vocab_file) + + self._numsym_set = set( + json.load(open(tglyph_cfg_file, encoding="utf-8"))["numsym_map"].keys() + ) + self._inchar_set = set("abcdefghijklmnopqrstuvwxyz") + self._natscr_set = set().union( + self.tgt_glyph_obj.glyphs, sum(self.tgt_glyph_obj.numsym_map.values(), []) + ) + + ## Model Config Static TODO: add defining in json support + input_dim = self.in_glyph_obj.size() + output_dim = self.tgt_glyph_obj.size() + enc_emb_dim = 300 + dec_emb_dim = 300 + enc_hidden_dim = 512 + dec_hidden_dim = 512 + rnn_type = "lstm" + enc2dec_hid = True + attention = True + enc_layers = 1 + dec_layers = 2 + m_dropout = 0 + enc_bidirect = True + enc_outstate_dim = enc_hidden_dim * (2 if enc_bidirect else 1) + + enc = Encoder( + input_dim=input_dim, + embed_dim=enc_emb_dim, + hidden_dim=enc_hidden_dim, + rnn_type=rnn_type, + layers=enc_layers, + dropout=m_dropout, + device=self.device, + bidirectional=enc_bidirect, + ) + dec = Decoder( + output_dim=output_dim, + embed_dim=dec_emb_dim, + hidden_dim=dec_hidden_dim, + rnn_type=rnn_type, + layers=dec_layers, + dropout=m_dropout, + use_attention=attention, + enc_outstate_dim=enc_outstate_dim, + device=self.device, + ) + self.model = Seq2Seq(enc, dec, pass_enc2dec_hid=enc2dec_hid, device=self.device) + self.model = self.model.to(self.device) + weights = torch.load(weight_path, map_location=torch.device(self.device)) + + self.model.load_state_dict(weights) + self.model.eval() + + def character_model(self, word, beam_width=1): + in_vec = torch.from_numpy(self.in_glyph_obj.word2xlitvec(word)).to(self.device) + ## change to active or passive beam + p_out_list = self.model.active_beam_inference(in_vec, beam_width=beam_width) + p_result = [ + self.tgt_glyph_obj.xlitvec2word(out.cpu().numpy()) for out in p_out_list + ] + + result = self.voc_sanity.reposition(p_result) + + # List type + return result + + def numsym_model(self, seg): + """tgt_glyph_obj.numsym_map[x] returns a list object""" + if len(seg) == 1: + return [seg] + self.tgt_glyph_obj.numsym_map[seg] + + a = [self.tgt_glyph_obj.numsym_map[n][0] for n in seg] + return [seg] + ["".join(a)] + + def _word_segementer(self, sequence): + + sequence = sequence.lower() + accepted = set().union(self._numsym_set, self._inchar_set, self._natscr_set) + # sequence = ''.join([i for i in sequence if i in accepted]) + + segment = [] + idx = 0 + seq_ = list(sequence) + while len(seq_): + # for Number-Symbol + temp = "" + while len(seq_) and seq_[0] in self._numsym_set: + temp += seq_[0] + seq_.pop(0) + if temp != "": + segment.append(temp) + + # for Target Chars + temp = "" + while len(seq_) and seq_[0] in self._natscr_set: + temp += seq_[0] + seq_.pop(0) + if temp != "": + segment.append(temp) + + # for Input-Roman Chars + temp = "" + while len(seq_) and seq_[0] in self._inchar_set: + temp += seq_[0] + seq_.pop(0) + if temp != "": + segment.append(temp) + + temp = "" + while len(seq_) and seq_[0] not in accepted: + temp += seq_[0] + seq_.pop(0) + if temp != "": + segment.append(temp) + + return segment + + def inferencer(self, sequence, beam_width=10): + + seg = self._word_segementer(sequence[:120]) + lit_seg = [] + + p = 0 + while p < len(seg): + if seg[p][0] in self._natscr_set: + lit_seg.append([seg[p]]) + p += 1 + + elif seg[p][0] in self._inchar_set: + lit_seg.append(self.character_model(seg[p], beam_width=beam_width)) + p += 1 + + elif seg[p][0] in self._numsym_set: # num & punc + lit_seg.append(self.numsym_model(seg[p])) + p += 1 + else: + lit_seg.append([seg[p]]) + p += 1 + + ## IF segment less/equal to 2 then return combinotorial, + ## ELSE only return top1 of each result concatenated + if len(lit_seg) == 1: + final_result = lit_seg[0] + + elif len(lit_seg) == 2: + final_result = [""] + for seg in lit_seg: + new_result = [] + for s in seg: + for f in final_result: + new_result.append(f + s) + final_result = new_result + + else: + new_result = [] + for seg in lit_seg: + new_result.append(seg[0]) + final_result = ["".join(new_result)] + + return final_result + + +from collections.abc import Iterable +from pydload import dload +import zipfile + +MODEL_DOWNLOAD_URL_PREFIX = "https://github.com/AI4Bharat/IndianNLP-Transliteration/releases/download/xlit_v0.5.0/" + + +def is_folder_writable(folder): + try: + os.makedirs(folder, exist_ok=True) + tmp_file = os.path.join(folder, ".write_test") + with open(tmp_file, "w") as f: + f.write("Permission Check") + os.remove(tmp_file) + return True + except: + return False + + +def is_directory_writable(path): + if os.name == "nt": + return is_folder_writable(path) + return os.access(path, os.W_OK | os.X_OK) + + +class XlitEngine: + """ + For Managing the top level tasks and applications of transliteration + Global Variables: F_DIR + """ + + def __init__( + self, lang2use="all", config_path="translit_models/default_lineup.json" + ): + + lineup = json.load(open(os.path.join(F_DIR, config_path), encoding="utf-8")) + self.lang_config = {} + if isinstance(lang2use, str): + if lang2use == "all": + self.lang_config = lineup + elif lang2use in lineup: + self.lang_config[lang2use] = lineup[lang2use] + else: + raise Exception( + "XlitError: The entered Langauge code not found. Available are {}".format( + lineup.keys() + ) + ) + + elif isinstance(lang2use, Iterable): + for l in lang2use: + try: + self.lang_config[l] = lineup[l] + except: + print( + "XlitError: Language code {} not found, Skipping...".format(l) + ) + else: + raise Exception( + "XlitError: lang2use must be a list of language codes (or) string of single language code" + ) + + if is_directory_writable(F_DIR): + models_path = os.path.join(F_DIR, "translit_models") + else: + user_home = os.path.expanduser("~") + models_path = os.path.join(user_home, ".AI4Bharat_Xlit_Models") + os.makedirs(models_path, exist_ok=True) + self.download_models(models_path) + + self.langs = {} + self.lang_model = {} + for la in self.lang_config: + try: + print("Loading {}...".format(la)) + self.lang_model[la] = XlitPiston( + weight_path=os.path.join( + models_path, self.lang_config[la]["weight"] + ), + vocab_file=os.path.join(models_path, self.lang_config[la]["vocab"]), + tglyph_cfg_file=os.path.join( + models_path, self.lang_config[la]["script"] + ), + iglyph_cfg_file="en", + ) + self.langs[la] = self.lang_config[la]["name"] + except Exception as error: + print("XlitError: Failure in loading {} \n".format(la), error) + print(XlitError.loading_err.value) + + def download_models(self, models_path): + """ + Download models from GitHub Releases if not exists + """ + for l in self.lang_config: + lang_name = self.lang_config[l]["eng_name"] + lang_model_path = os.path.join(models_path, lang_name) + if not os.path.isdir(lang_model_path): + print("Downloading model for language: %s" % lang_name) + remote_url = MODEL_DOWNLOAD_URL_PREFIX + lang_name + ".zip" + downloaded_zip_path = os.path.join(models_path, lang_name + ".zip") + dload(url=remote_url, save_to_path=downloaded_zip_path, max_time=None) + + if not os.path.isfile(downloaded_zip_path): + exit( + f"ERROR: Unable to download model from {remote_url} into {models_path}" + ) + + with zipfile.ZipFile(downloaded_zip_path, "r") as zip_ref: + zip_ref.extractall(models_path) + + if os.path.isdir(lang_model_path): + os.remove(downloaded_zip_path) + else: + exit( + f"ERROR: Unable to find models in {lang_model_path} after download" + ) + return + + def translit_word(self, eng_word, lang_code="default", topk=7, beam_width=10): + if eng_word == "": + return [] + + if lang_code in self.langs: + try: + res_list = self.lang_model[lang_code].inferencer( + eng_word, beam_width=beam_width + ) + return res_list[:topk] + + except Exception as error: + print("XlitError:", traceback.format_exc()) + print(XlitError.internal_err.value) + return XlitError.internal_err + + elif lang_code == "default": + try: + res_dict = {} + for la in self.lang_model: + res = self.lang_model[la].inferencer( + eng_word, beam_width=beam_width + ) + res_dict[la] = res[:topk] + return res_dict + + except Exception as error: + print("XlitError:", traceback.format_exc()) + print(XlitError.internal_err.value) + return XlitError.internal_err + + else: + print("XlitError: Unknown Langauge requested", lang_code) + print(XlitError.lang_err.value) + return XlitError.lang_err + + def translit_sentence(self, eng_sentence, lang_code="default", beam_width=10): + if eng_sentence == "": + return [] + + if lang_code in self.langs: + try: + out_str = "" + for word in eng_sentence.split(): + res_ = self.lang_model[lang_code].inferencer( + word, beam_width=beam_width + ) + out_str = out_str + res_[0] + " " + return out_str[:-1] + + except Exception as error: + print("XlitError:", traceback.format_exc()) + print(XlitError.internal_err.value) + return XlitError.internal_err + + elif lang_code == "default": + try: + res_dict = {} + for la in self.lang_model: + out_str = "" + for word in eng_sentence.split(): + res_ = self.lang_model[la].inferencer( + word, beam_width=beam_width + ) + out_str = out_str + res_[0] + " " + res_dict[la] = out_str[:-1] + return res_dict + + except Exception as error: + print("XlitError:", traceback.format_exc()) + print(XlitError.internal_err.value) + return XlitError.internal_err + + else: + print("XlitError: Unknown Langauge requested", lang_code) + print(XlitError.lang_err.value) + return XlitError.lang_err + + +if __name__ == "__main__": + + available_lang = [ + "bn", + "gu", + "hi", + "kn", + "gom", + "mai", + "ml", + "mr", + "pa", + "sd", + "si", + "ta", + "te", + "ur", + ] + + reg = re.compile(r"[a-zA-Z]") + lang = "hi" + engine = XlitEngine( + lang + ) # if you don't specify lang code here, this will give results in all langs available + sent = "Hello World! ABCD क्या हाल है आपका?" + words = [ + engine.translit_word(word, topk=1)[lang][0] if reg.match(word) else word + for word in sent.split() + ] # only transliterated en words, leaves rest as it is + updated_sent = " ".join(words) + + print(updated_sent) + + # output : हेलो वर्ल्ड! क्या हाल है आपका? + + # y = engine.translit_sentence("Hello World !")['hi'] + # print(y) diff --git a/tts_infer/tts.py b/tts_infer/tts.py new file mode 100644 index 0000000000000000000000000000000000000000..b373de8d62ce4aeb6ba5db5a07e8b018c347217b --- /dev/null +++ b/tts_infer/tts.py @@ -0,0 +1,158 @@ +from __future__ import absolute_import, division, print_function, unicode_literals +from typing import Tuple +import sys +from argparse import ArgumentParser + +import torch +import numpy as np +import os +import json +import torch + +sys.path.append(os.path.join(os.path.dirname(__file__), "../src/glow_tts")) + +from scipy.io.wavfile import write +from hifi.env import AttrDict +from hifi.models import Generator + + +from text import text_to_sequence +import commons +import models +import utils + + +def check_directory(dir): + if not os.path.exists(dir): + sys.exit("Error: {} directory does not exist".format(dir)) + + +class TextToMel: + def __init__(self, glow_model_dir, device="cuda"): + self.glow_model_dir = glow_model_dir + check_directory(self.glow_model_dir) + self.device = device + self.hps, self.glow_tts_model = self.load_glow_tts() + pass + + def load_glow_tts(self): + hps = utils.get_hparams_from_dir(self.glow_model_dir) + checkpoint_path = utils.latest_checkpoint_path(self.glow_model_dir) + symbols = list(hps.data.punc) + list(hps.data.chars) + glow_tts_model = models.FlowGenerator( + len(symbols) + getattr(hps.data, "add_blank", False), + out_channels=hps.data.n_mel_channels, + **hps.model + ) # .to(self.device) + + if self.device == "cuda": + glow_tts_model.to("cuda") + + utils.load_checkpoint(checkpoint_path, glow_tts_model) + glow_tts_model.decoder.store_inverse() + _ = glow_tts_model.eval() + + return hps, glow_tts_model + + def generate_mel(self, text, noise_scale=0.667, length_scale=1.0): + symbols = list(self.hps.data.punc) + list(self.hps.data.chars) + cleaner = self.hps.data.text_cleaners + if getattr(self.hps.data, "add_blank", False): + text_norm = text_to_sequence(text, symbols, cleaner) + text_norm = commons.intersperse(text_norm, len(symbols)) + else: # If not using "add_blank" option during training, adding spaces at the beginning and the end of utterance improves quality + text = " " + text.strip() + " " + text_norm = text_to_sequence(text, symbols, cleaner) + + sequence = np.array(text_norm)[None, :] + + del symbols + del cleaner + del text + del text_norm + + if self.device == "cuda": + x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long() + x_tst_lengths = torch.tensor([x_tst.shape[1]]).cuda() + else: + x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).long() + x_tst_lengths = torch.tensor([x_tst.shape[1]]) + + with torch.no_grad(): + (y_gen_tst, *_), *_, (attn_gen, *_) = self.glow_tts_model( + x_tst, + x_tst_lengths, + gen=True, + noise_scale=noise_scale, + length_scale=length_scale, + ) + del x_tst + del x_tst_lengths + torch.cuda.empty_cache() + return y_gen_tst + #return y_gen_tst.cpu().detach().numpy() + + +class MelToWav: + def __init__(self, hifi_model_dir, device="cuda"): + self.hifi_model_dir = hifi_model_dir + check_directory(self.hifi_model_dir) + self.device = device + self.h, self.hifi_gan_generator = self.load_hifi_gan() + pass + + def load_hifi_gan(self): + checkpoint_path = utils.latest_checkpoint_path(self.hifi_model_dir, regex="g_*") + config_file = os.path.join(self.hifi_model_dir, "config.json") + data = open(config_file).read() + json_config = json.loads(data) + h = AttrDict(json_config) + torch.manual_seed(h.seed) + + generator = Generator(h).to(self.device) + + assert os.path.isfile(checkpoint_path) + print("Loading '{}'".format(checkpoint_path)) + state_dict_g = torch.load(checkpoint_path, map_location=self.device) + print("Complete.") + + generator.load_state_dict(state_dict_g["generator"]) + + generator.eval() + generator.remove_weight_norm() + + return h, generator + + def generate_wav(self, mel): + #mel = torch.FloatTensor(mel).to(self.device) + + y_g_hat = self.hifi_gan_generator(mel.to(self.device)) # passing through vocoder + audio = y_g_hat.squeeze() + audio = audio * 32768.0 + audio = audio.cpu().detach().numpy().astype("int16") + + del y_g_hat + del mel + torch.cuda.empty_cache() + return audio, self.h.sampling_rate + + +if __name__ == "__main__": + + parser = ArgumentParser() + parser.add_argument("-m", "--model", required=True, type=str) + parser.add_argument("-g", "--gan", required=True, type=str) + parser.add_argument("-d", "--device", type=str, default="cpu") + parser.add_argument("-t", "--text", type=str, required=True) + parser.add_argument("-w", "--wav", type=str, required=True) + args = parser.parse_args() + + text_to_mel = TextToMel(glow_model_dir=args.model, device=args.device) + mel_to_wav = MelToWav(hifi_model_dir=args.gan, device=args.device) + + mel = text_to_mel.generate_mel(args.text) + audio, sr = mel_to_wav.generate_wav(mel) + + write(filename=args.wav, rate=sr, data=audio) + + pass diff --git a/utils/data/duration.py b/utils/data/duration.py new file mode 100644 index 0000000000000000000000000000000000000000..c3b5e112b72dd5a07ea2463f604d98bb8d961496 --- /dev/null +++ b/utils/data/duration.py @@ -0,0 +1,33 @@ +# Usage -> python duration.py /src/folder/path + +import soundfile as sf +import sys +import os +from glob import glob +from joblib import Parallel, delayed +from tqdm import tqdm + + +def get_duration(fpath): + w = sf.SoundFile(fpath) + sr = w.samplerate + assert 22050 == sr, "Sample rate is not 22050" + return len(w) / sr + + +def main(folder, ext="wav"): + file_list = glob(folder + "/**/*." + ext, recursive=True) + print(f"\n\tTotal number of wav files {len(file_list)}") + duration_list = Parallel(n_jobs=1)( + delayed(get_duration)(i) for i in tqdm(file_list) + ) + print( + f"\n\tMin Duration {min(duration_list):.2f} Max Duration {max(duration_list):.2f} in secs" + ) + print(f"\n\tTotal Duration {sum(duration_list)/3600:.2f} in hours") + + +if __name__ == "__main__": + folder = sys.argv[1] + folder = os.path.abspath(folder) + main(folder) diff --git a/utils/data/resample.py b/utils/data/resample.py new file mode 100644 index 0000000000000000000000000000000000000000..c77109ef4d5142cd9094f46dd186a17571071ab8 --- /dev/null +++ b/utils/data/resample.py @@ -0,0 +1,59 @@ +import argparse +import librosa +import numpy as np +import os +import scipy +import scipy.io.wavfile +import sys + +from glob import glob +from tqdm import tqdm +from joblib import Parallel, delayed + + +def check_directories(dir_input, dir_output): + if not os.path.exists(dir_input): + sys.exit("Error: Input directory does not exist: {}".format(dir_input)) + if not os.path.exists(dir_output): + sys.exit("Error: Output directory does not exist: {}".format(dir_output)) + abs_a = os.path.abspath(dir_input) + abs_b = os.path.abspath(dir_output) + if abs_a == abs_b: + sys.exit("Error: Paths are the same: {}".format(abs_a)) + + +def resample_file(input_filename, output_filename, sample_rate): + mono = ( + True # librosa converts signal to mono by default, so I'm just surfacing this + ) + audio, existing_rate = librosa.load(input_filename, sr=sample_rate, mono=mono) + audio /= 1.414 # Scale to [-1.0, 1.0] + audio *= 32767 # Scale to int16 + audio = audio.astype(np.int16) + scipy.io.wavfile.write(output_filename, sample_rate, audio) + + +def downsample_wav_files(input_dir, output_dir, output_sample_rate): + check_directories(input_dir, output_dir) + inp_wav_paths = glob(input_dir + "/*.wav") + out_wav_paths = [ + os.path.join(output_dir, os.path.basename(p)) for p in inp_wav_paths + ] + _ = Parallel(n_jobs=-1)( + delayed(resample_file)(i, o, output_sample_rate) + for i, o in tqdm(zip(inp_wav_paths, out_wav_paths)) + ) + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--input_dir", "-i", type=str, required=True) + parser.add_argument("--output_dir", "-o", type=str, required=True) + parser.add_argument("--output_sample_rate", "-s", type=int, required=True) + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_args() + downsample_wav_files(args.input_dir, args.output_dir, args.output_sample_rate) + print(f"\n\tCompleted") diff --git a/utils/glow/prepare_iitm_data_glow.py b/utils/glow/prepare_iitm_data_glow.py new file mode 100644 index 0000000000000000000000000000000000000000..9e1e5cb8cd85c88892371851917ec721c2c4b08e --- /dev/null +++ b/utils/glow/prepare_iitm_data_glow.py @@ -0,0 +1,134 @@ +import os +from glob import glob +import re +import string +import argparse + +import random +random.seed(42) + +def replace_extra_chars(line): + line = line.replace("(", "").replace( + ")", "" + ) # .replace('\u200d', ' ').replace('\ufeff', ' ').replace('\u200c', ' ').replace('\u200e', ' ') + # line = line.replace('“', ' ').replace('”', ' ').replace(':', ' ') + + return line.strip() + + +def write_txt(content, filename): + with open(filename, "w+", encoding="utf-8") as f: + f.write(content) + + +def save_train_test_valid_split(annotations_txt, num_samples_valid, num_samples_test): + with open(annotations_txt, encoding="utf-8") as f: + all_lines = [line.strip() for line in f.readlines()] + test_val_indices = random.sample( + range(len(all_lines)), num_samples_valid + num_samples_test + ) + valid_ix = test_val_indices[:num_samples_valid] + test_ix = test_val_indices[num_samples_valid:] + train = [line for i, line in enumerate(all_lines) if i not in test_val_indices] + valid = [line for i, line in enumerate(all_lines) if i in valid_ix] + test = [line for i, line in enumerate(all_lines) if i in test_ix] + + print(f"Num samples in train: {len(train)}") + print(f"Num samples in valid: {len(valid)}") + print(f"Num samples in test: {len(test)}") + + out_dir_path = "/".join(annotations_txt.split("/")[:-1]) + with open(os.path.join(out_dir_path, "train.txt"), "w+", encoding="utf-8") as f: + for line in train: + print(line, file=f) + with open(os.path.join(out_dir_path, "valid.txt"), "w+", encoding="utf-8") as f: + for line in valid: + print(line, file=f) + with open(os.path.join(out_dir_path, "test.txt"), "w+", encoding="utf-8") as f: + for line in test: + print(line, file=f) + print(f"train, test and valid txts saved in {out_dir_path}") + + +def save_txts_from_txt_done_data( + text_path, + wav_path_for_annotations_txt, + out_path_for_txts, + num_samples_valid, + num_samples_test, +): + outfile = os.path.join(out_path_for_txts, "annotations.txt") + with open(text_path) as file: + file_lines = file.readlines() + + # print(file_lines[0]) + + file_lines = [replace_extra_chars(line) for line in file_lines] + # print(file_lines[0]) + + fnames, ftexts = [], [] + for line in file_lines: + elems = line.split('"') + fnames.append(elems[0].strip()) + ftexts.append(elems[1].strip()) + + all_chars = list(set("".join(ftexts))) + punct_with_space = [i for i in all_chars if i in list(string.punctuation)] + [" "] + chars = [i for i in all_chars if i not in punct_with_space if i.strip()] + chars = "".join(chars) + punct_with_space = "".join(punct_with_space) + + with open('../../config/glow/base_blank.json', 'r') as jfile: + json_config = json.load(jfile) + + json_config["data"]["chars"] = chars + json_config["data"]["punc"] = punct_with_space + json_config["data"]["training_files"]=out_path_for_txts + '/train.txt' + json_config["data"]["validation_files"] = out_path_for_txts + '/valid.txt' + new_config_name = out_path_for_txts.split('/')[-1] + with open(f'../../config/glow/{new_config_name}.json','w+') as jfile: + json.dump(json_config, jfile) + + print(f"Characters: {chars}") + print(f"Punctuation: {punct_with_space}") + print(f"Config file is stored at ../../config/glow/{new_config_name}.json") + + outfile_f = open(outfile, "w+", encoding="utf-8") + for f, t in zip(fnames, ftexts): + print( + os.path.join(wav_path_for_annotations_txt, f) + ".wav", + t, + sep="|", + file=outfile_f, + ) + outfile_f.close() + write_txt(punct_with_space, os.path.join(out_path_for_txts, "punc.txt")) + write_txt(chars, os.path.join(out_path_for_txts, "chars.txt")) + + save_train_test_valid_split( + annotations_txt=outfile, + num_samples_valid=num_samples_valid, + num_samples_test=num_samples_test, + ) + + + + +if __name__ == "__main__": + + + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--text-path", type=str, required=True) + parser.add_argument("-o", "--output-path", type=str, required=True) + parser.add_argument("-w", "--wav-path", type=str, required=True) + parser.add_argument("-v", "--valid-samples", type=int, default = 100) + parser.add_argument("-t", "--test-samples", type=int, default = 10) + args = parser.parse_args() + + save_txts_from_txt_done_data( + args.text_path, + args.wav_path, + args.output_path, + args.valid_samples, + args.test_samples, + ) diff --git a/utils/glow/prepare_iitm_data_glow_en.py b/utils/glow/prepare_iitm_data_glow_en.py new file mode 100644 index 0000000000000000000000000000000000000000..2771328d726775f85e5a9bb5f02f6f5927fa868e --- /dev/null +++ b/utils/glow/prepare_iitm_data_glow_en.py @@ -0,0 +1,135 @@ +import os +from glob import glob +import re +import string +import argparse +import json +import random +random.seed(42) + +def replace_extra_chars(line): + line = line.replace("(", "").replace( + ")", "" + ) # .replace('\u200d', ' ').replace('\ufeff', ' ').replace('\u200c', ' ').replace('\u200e', ' ') + # line = line.replace('“', ' ').replace('”', ' ').replace(':', ' ') + + return line.strip() + + +def write_txt(content, filename): + with open(filename, "w+", encoding="utf-8") as f: + f.write(content) + + +def save_train_test_valid_split(annotations_txt, num_samples_valid, num_samples_test): + with open(annotations_txt, encoding="utf-8") as f: + all_lines = [line.strip() for line in f.readlines()] + test_val_indices = random.sample( + range(len(all_lines)), num_samples_valid + num_samples_test + ) + valid_ix = test_val_indices[:num_samples_valid] + test_ix = test_val_indices[num_samples_valid:] + train = [line for i, line in enumerate(all_lines) if i not in test_val_indices] + valid = [line for i, line in enumerate(all_lines) if i in valid_ix] + test = [line for i, line in enumerate(all_lines) if i in test_ix] + + print(f"Num samples in train: {len(train)}") + print(f"Num samples in valid: {len(valid)}") + print(f"Num samples in test: {len(test)}") + + out_dir_path = "/".join(annotations_txt.split("/")[:-1]) + with open(os.path.join(out_dir_path, "train.txt"), "w+", encoding="utf-8") as f: + for line in train: + print(line, file=f) + with open(os.path.join(out_dir_path, "valid.txt"), "w+", encoding="utf-8") as f: + for line in valid: + print(line, file=f) + with open(os.path.join(out_dir_path, "test.txt"), "w+", encoding="utf-8") as f: + for line in test: + print(line, file=f) + print(f"train, test and valid txts saved in {out_dir_path}") + + +def save_txts_from_txt_done_data( + text_path, + wav_path_for_annotations_txt, + out_path_for_txts, + num_samples_valid, + num_samples_test, +): + outfile = os.path.join(out_path_for_txts, "annotations.txt") + with open(text_path) as file: + file_lines = file.readlines() + + # print(file_lines[0]) + + file_lines = [replace_extra_chars(line) for line in file_lines] + # print(file_lines[0]) + + fnames, ftexts = [], [] + for line in file_lines: + elems = line.split('"') + fnames.append(elems[0].strip()) + ftexts.append(elems[1].strip().lower().replace('‘','\'').replace('’','\'')) + + all_chars = list(set("".join(ftexts))) + punct_with_space = [i for i in all_chars if i in list(string.punctuation)] + [" "] + chars = [i for i in all_chars if i not in punct_with_space if i.strip()] + chars = "".join(chars) + punct_with_space = "".join(punct_with_space)#.replace("'",r"\'") + + with open('../../config/glow/base_blank.json', 'r') as jfile: + json_config = json.load(jfile) + + json_config["data"]["chars"] = chars + json_config["data"]["punc"] = punct_with_space + json_config["data"]["training_files"]=out_path_for_txts + '/train.txt' + json_config["data"]["validation_files"] = out_path_for_txts + '/valid.txt' + new_config_name = out_path_for_txts.split('/')[-1] + with open(f'../../config/glow/{new_config_name}.json','w+') as jfile: + json.dump(json_config, jfile) + + print(f"Characters: {chars}") + print(f"Len of vocab: {len(chars)}") + print(f"Punctuation: {punct_with_space}") + print(f"Config file is stored at ../../config/glow/{new_config_name}.json") + + outfile_f = open(outfile, "w+", encoding="utf-8") + for f, t in zip(fnames, ftexts): + print( + os.path.join(wav_path_for_annotations_txt, f), + t, + sep="|", + file=outfile_f, + ) + outfile_f.close() + write_txt(punct_with_space, os.path.join(out_path_for_txts, "punc.txt")) + write_txt(chars, os.path.join(out_path_for_txts, "chars.txt")) + + save_train_test_valid_split( + annotations_txt=outfile, + num_samples_valid=num_samples_valid, + num_samples_test=num_samples_test, + ) + + + + +if __name__ == "__main__": + + + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--text-path", type=str, required=True) + parser.add_argument("-o", "--output-path", type=str, required=True) + parser.add_argument("-w", "--wav-path", type=str, required=True) + parser.add_argument("-v", "--valid-samples", type=int, default = 100) + parser.add_argument("-t", "--test-samples", type=int, default = 10) + args = parser.parse_args() + + save_txts_from_txt_done_data( + args.text_path, + args.wav_path, + args.output_path, + args.valid_samples, + args.test_samples, + ) diff --git a/utils/hifi/prepare_iitm_data_hifi.py b/utils/hifi/prepare_iitm_data_hifi.py new file mode 100644 index 0000000000000000000000000000000000000000..1e1de2e28735143aeef8ddb10bc5a4672c02564b --- /dev/null +++ b/utils/hifi/prepare_iitm_data_hifi.py @@ -0,0 +1,64 @@ + +import glob +import random +import sys +import os +import argparse + + + + +def process_data(args): + + path = args.input_path + valid_files = args.valid_files + test_files = args.test_files + dest_path = args.dest_path + + list_paths = path.split(',') + + valid_set = [] + training_set = [] + test_set = [] + + for local_path in list_paths: + files = glob.glob(local_path+'/*.wav') + print(f"Total files: {len(files)}") + + valid_set_local = random.sample(files, valid_files) + + test_set_local = random.sample(valid_set_local, test_files) + valid_set.extend(list(set(valid_set_local) - set(test_set_local))) + test_set.extend(test_set_local) + + print(len(valid_set_local)) + + training_set_local = set(files) - set(valid_set_local) + print(len(training_set_local)) + training_set.extend(training_set_local) + + + valid_set = random.sample(valid_set, len(valid_set)) + test_set = random.sample(test_set, len(test_set)) + training_set = random.sample(training_set, len(training_set)) + + with open(os.path.join(dest_path , 'valid.txt'), mode = 'w+') as file: + file.write("\n".join(list(valid_set))) + + with open(os.path.join(dest_path , 'train.txt'), mode = 'w+') as file: + file.write("\n".join(list(training_set))) + + with open(os.path.join(dest_path , 'test.txt'), mode = 'w+') as file: + file.write("\n".join(list(test_set))) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('-i','--input-path',type=str,help='path to input wav files') + parser.add_argument('-v','--valid-files',type=int,help='number of valid files') + parser.add_argument('-t','--test-files',type=int,help='number of test files') + parser.add_argument('-d','--dest-path',type=str,help='destination path to output filelists') + + args = parser.parse_args() + + process_data(args) \ No newline at end of file diff --git a/utils/inference/__pycache__/advanced_tts.cpython-37.pyc b/utils/inference/__pycache__/advanced_tts.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..27ebc6ca029a34bdf6f672b57f21932acb7e79ac Binary files /dev/null and b/utils/inference/__pycache__/advanced_tts.cpython-37.pyc differ diff --git a/utils/inference/__pycache__/api.cpython-37.pyc b/utils/inference/__pycache__/api.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1dc6747510d35b9fe19b212850e31a84f11a31ce Binary files /dev/null and b/utils/inference/__pycache__/api.cpython-37.pyc differ diff --git a/utils/inference/__pycache__/num_to_word_on_sent.cpython-37.pyc b/utils/inference/__pycache__/num_to_word_on_sent.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d5cfdafb8f75bb2f6e552808c53116d692512398 Binary files /dev/null and b/utils/inference/__pycache__/num_to_word_on_sent.cpython-37.pyc differ diff --git a/utils/inference/__pycache__/transliterate.cpython-37.pyc b/utils/inference/__pycache__/transliterate.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..69a2cf15fd5d171f77361b7f585450c103f415d1 Binary files /dev/null and b/utils/inference/__pycache__/transliterate.cpython-37.pyc differ diff --git a/utils/inference/__pycache__/tts.cpython-37.pyc b/utils/inference/__pycache__/tts.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f6b69ef7435b90519d48dcef6332ea66a1f1b76a Binary files /dev/null and b/utils/inference/__pycache__/tts.cpython-37.pyc differ diff --git a/utils/inference/advanced_tts.py b/utils/inference/advanced_tts.py new file mode 100644 index 0000000000000000000000000000000000000000..ccf42704b83aee57487359f447a0966c05de704e --- /dev/null +++ b/utils/inference/advanced_tts.py @@ -0,0 +1,135 @@ + +from tts import TextToMel, MelToWav +from transliterate import XlitEngine +from num_to_word_on_sent import normalize_nums + +import re +import numpy as np +from scipy.io.wavfile import write + +from mosestokenizer import * +from indicnlp.tokenize import sentence_tokenize +import argparse + +_INDIC = ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"] +_PURAM_VIRAM_LANGUAGES = ["hi", "or", "bn", "as"] +_TRANSLITERATION_NOT_AVAILABLE_IN = ["en","or"] +#_NUM2WORDS_NOT_AVAILABLE_IN = [] + +def normalize_text(text, lang): + if lang in _PURAM_VIRAM_LANGUAGES: + text = text.replace('|', '।') + text = text.replace('.', '।') + return text + +def split_sentences(paragraph, language): + if language == "en": + with MosesSentenceSplitter(language) as splitter: + return splitter([paragraph]) + elif language in _INDIC: + return sentence_tokenize.sentence_split(paragraph, lang=language) + + + +def load_models(acoustic, vocoder, device): + text_to_mel = TextToMel(glow_model_dir=acoustic, device=device) + mel_to_wav = MelToWav(hifi_model_dir=vocoder, device=device) + return text_to_mel, mel_to_wav + + +def translit(text, lang): + reg = re.compile(r'[a-zA-Z]') + words = [engine.translit_word(word, topk=1)[lang][0] if reg.match(word) else word for word in text.split()] + updated_sent = ' '.join(words) + return updated_sent + + + +def run_tts(text, lang, args): + if lang == 'hi': + text = text.replace('।', '.') # only for hindi models + + if lang == 'en' and text[-1] != '.': + text = text + '. ' + + if args.number_conversion == 1 and lang!='en': + print("Doing number conversion") + text_num_to_word = normalize_nums(text, lang) # converting numbers to words in lang + else: + text_num_to_word = text + + + if args.transliteration == 1 and lang not in _TRANSLITERATION_NOT_AVAILABLE_IN: + print("Doing transliteration") + text_num_to_word_and_transliterated = translit(text_num_to_word, lang) # transliterating english words to lang + else: + text_num_to_word_and_transliterated = text_num_to_word + + final_text = ' ' + text_num_to_word_and_transliterated + + mel = text_to_mel.generate_mel(final_text, args.noise_scale, args.length_scale) + audio, sr = mel_to_wav.generate_wav(mel) + return sr, audio + +def run_tts_paragraph(args): + audio_list = [] + if args.split_sentences == 1: + text = normalize_text(args.text, args.lang) + split_sentences_list = split_sentences(text, args.lang) + + for sent in split_sentences_list: + sr, audio = run_tts(sent, args.lang, args) + audio_list.append(audio) + + concatenated_audio = np.concatenate([i for i in audio_list]) + if args.wav: + write(filename=args.wav, rate=sr, data=concatenated_audio) + return (sr, concatenated_audio) + else: + sr, audio = run_tts(args.text, args.lang, args) + if args.wav: + write(filename=args.wav, rate=sr, data=audio) + return (sr, audio) + + +def load_all_models(args): + global engine + if args.lang not in _TRANSLITERATION_NOT_AVAILABLE_IN: + engine = XlitEngine(args.lang) # loading translit model globally + + global text_to_mel + global mel_to_wav + + text_to_mel, mel_to_wav = load_models(args.acoustic, args.vocoder, args.device) + + try: + args.noise_scale = float(args.noise_scale) + args.length_scale = float(args.length_scale) + except: + pass + + print(args) + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-a", "--acoustic", required=True, type=str) + parser.add_argument("-v", "--vocoder", required=True, type=str) + parser.add_argument("-d", "--device", type=str, default="cpu") + parser.add_argument("-t", "--text", type=str, required=True) + parser.add_argument("-w", "--wav", type=str, required=True) + parser.add_argument("-n", "--noise-scale", default='0.667', type=str ) + parser.add_argument("-l", "--length-scale", default='1.0', type=str) + + parser.add_argument("-T", "--transliteration", default=1, type=int) + parser.add_argument("-N", "--number-conversion", default=1, type=int) + parser.add_argument("-S", "--split-sentences", default=1, type=int) + parser.add_argument("-L", "--lang", type=str, required=True) + + args = parser.parse_args() + + load_all_models(args) + run_tts_paragraph(args) + + diff --git a/utils/inference/api.py b/utils/inference/api.py new file mode 100644 index 0000000000000000000000000000000000000000..d6bcabd194a4531801941d5e1d248dc134ce255f --- /dev/null +++ b/utils/inference/api.py @@ -0,0 +1,66 @@ +from starlette.responses import StreamingResponse +from tts import MelToWav, TextToMel +from advanced_tts import load_all_models, run_tts_paragraph +from typing import Optional +from pydantic import BaseModel +from fastapi import FastAPI, HTTPException +import uvicorn +import base64 +import argparse +import json +import time +from argparse import Namespace + +app = FastAPI() + + +class TextJson(BaseModel): + text: str + lang: Optional[str] = "hi" + noise_scale: Optional[float]=0.667 + length_scale: Optional[float]=1.0 + transliteration: Optional[int]=1 + number_conversion: Optional[int]=1 + split_sentences: Optional[int]=1 + + + + +@app.post("/TTS/") +async def tts(input: TextJson): + text = input.text + lang = input.lang + + args = Namespace(**input.dict()) + + args.wav = '../../results/api/'+str(int(time.time())) + '.wav' + + if text: + sr, audio = run_tts_paragraph(args) + else: + raise HTTPException(status_code=400, detail={"error": "No text"}) + + ## to return outpur as a file + audio = open(args.wav, mode='rb') + return StreamingResponse(audio, media_type="audio/wav") + + # with open(args.wav, "rb") as audio_file: + # encoded_bytes = base64.b64encode(audio_file.read()) + # encoded_string = encoded_bytes.decode() + # return {"encoding": "base64", "data": encoded_string, "sr": sr} + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-a", "--acoustic", required=True, type=str) + parser.add_argument("-v", "--vocoder", required=True, type=str) + parser.add_argument("-d", "--device", type=str, default="cpu") + parser.add_argument("-L", "--lang", type=str, required=True) + + args = parser.parse_args() + + load_all_models(args) + + uvicorn.run( + "api:app", host="0.0.0.0", port=6006, log_level="debug" + ) diff --git a/utils/inference/num_to_word_on_sent.py b/utils/inference/num_to_word_on_sent.py new file mode 100644 index 0000000000000000000000000000000000000000..ce878a8c3ee6f5ef629abeaee418d5959f7179ed --- /dev/null +++ b/utils/inference/num_to_word_on_sent.py @@ -0,0 +1,1314 @@ +import re +import string + +# ----------------------------- indic_num.py ----------------------------- +supported_lang = {"en", "hi", "gu", "mr", "bn", "te", "ta", "kn", "or", "pa"} +# supported_lang = {'eng', 'hin', 'guj', 'mar', 'ben', 'tel', 'tam', 'kan', 'ori', 'pan'} # Three alphabet lang code + +all_num = { + "en": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], + "hi": ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"], + "gu": ["૦", "૧", "૨", "૩", "૪", "૫", "૬", "૭", "૮", "૯"], + "mr": ["०", "१", "२", "३", "४", "५", "६", "७", "८", "९"], + "bn": ["০", "১", "২", "৩", "৪", "৫", "৬", "৭", "৮", "৯"], + "te": ["౦", "౧", "౨", "౩", "౪", "౫", "౬", "౭", "౮", "౯"], + "ta": ["0", "௧", "௨", "௩", "௪", "௫", "௬", "௭", "௮", "௯", "௰"], + "kn": ["೦", "೧", "೨", "೩", "೪", "೫", "೬", "೭", "೮", "೯"], + "or": ["୦", "୧", "୨", "୩", "୪", "୫", "୬", "୭", "୮", "୯"], + "pa": ["੦", "੧", "੨", "੩", "੪", "੫", "੬", "੭", "੮", "੯"], +} + +num_dict = dict() +num_dict["en"] = { + "0": "zero", + "1": "one", + "2": "two", + "3": "three", + "4": "four", + "5": "five", + "6": "six", + "7": "seven", + "8": "eight", + "9": "nine", + "10": "ten", + "11": "eleven", + "12": "twelve", + "13": "thirteen", + "14": "fourteen", + "15": "fifteen", + "16": "sixteen", + "17": "seventeen", + "18": "eighteen", + "19": "nineteen", + "20": "twenty", + "21": "twenty-one", + "22": "twenty-two", + "23": "twenty-three", + "24": "twenty-four", + "25": "twenty-five", + "26": "twenty-six", + "27": "twenty-seven", + "28": "twenty-eight", + "29": "twenty-nine", + "30": "thirty", + "31": "thirty-one", + "32": "thirty-two", + "33": "thirty-three", + "34": "thirty-four", + "35": "thirty-five", + "36": "thirty-six", + "37": "thirty-seven", + "38": "thirty-eight", + "39": "thirty-nine", + "40": "forty", + "41": "forty-one", + "42": "forty-two", + "43": "forty-three", + "44": "forty-four", + "45": "forty-five", + "46": "forty-six", + "47": "forty-seven", + "48": "forty-eight", + "49": "forty-nine", + "50": "fifty", + "51": "fifty-one", + "52": "fifty-two", + "53": "fifty-three", + "54": "fifty-four", + "55": "fifty-five", + "56": "fifty-six", + "57": "fifty-seven", + "58": "fifty-eight", + "59": "fifty-nine", + "60": "sixty", + "61": "sixty-one", + "62": "sixty-two", + "63": "sixty-three", + "64": "sixty-four", + "65": "sixty-five", + "66": "sixty-six", + "67": "sixty-seven", + "68": "sixty-eight", + "69": "sixty-nine", + "70": "seventy", + "71": "seventy-one", + "72": "seventy-two", + "73": "seventy-three", + "74": "seventy-four", + "75": "seventy-five", + "76": "seventy-six", + "77": "seventy-seven", + "78": "seventy-eight", + "79": "seventy-nine", + "80": "eighty", + "81": "eighty-one", + "82": "eighty-two", + "83": "eighty-three", + "84": "eighty-four", + "85": "eighty-five", + "86": "eighty-six", + "87": "eighty-seven", + "88": "eighty-eight", + "89": "eighty-nine", + "90": "ninety", + "91": "ninety-one", + "92": "ninety-two", + "93": "ninety-three", + "94": "ninety-four", + "95": "ninety-five", + "96": "ninety-six", + "97": "ninety-seven", + "98": "ninety-eight", + "99": "ninety-nine", + "100": "hundred", + "1000": "thousand", + "100000": "lac", + "10000000": "crore", + "1000000000": "arab", +} # English-India +num_dict["hi"] = { + "0": "शून्य", + "1": "एक", + "2": "दो", + "3": "तीन", + "4": "चार", + "5": "पाँच", + "6": "छः", + "7": "सात", + "8": "आठ", + "9": "नौ", + "10": "दस", + "11": "ग्यारह", + "12": "बारह", + "13": "तेरह", + "14": "चौदह", + "15": "पंद्रह", + "16": "सोलह", + "17": "सत्रह", + "18": "अट्ठारह", + "19": "उन्नीस", + "20": "बीस", + "21": "इक्कीस", + "22": "बाईस", + "23": "तेईस", + "24": "चौबिस", + "25": "पच्चीस", + "26": "छब्बीस", + "27": "सत्ताईस", + "28": "अट्ठाईस", + "29": "उनतीस", + "30": "तीस", + "31": "इकतीस", + "32": "बत्तीस", + "33": "तैंतीस", + "34": "चौंतीस", + "35": "पैंतीस", + "36": "छत्तीस", + "37": "सैंतीस", + "38": "अड़तीस", + "39": "उनतालीस", + "40": "चालीस", + "41": "इकतालीस", + "42": "बयालीस", + "43": "तैंतालीस", + "44": "चौंतालीस", + "45": "पैंतालीस", + "46": "छियालीस", + "47": "सैंतालीस", + "48": "अड़तालीस", + "49": "उनचास", + "50": "पचास", + "51": "इक्यावन​", + "52": "बावन", + "53": "तिरेपन", + "54": "चौवन", + "55": "पचपन", + "56": "छप्पन", + "57": "सत्तावन", + "58": "अट्ठावन", + "59": "उनसठ", + "60": "साठ", + "61": "इकसठ", + "62": "बासठ", + "63": "तिरेसठ", + "64": "चौंसठ", + "65": "पैंसठ", + "66": "छयासठ", + "67": "सरसठ​", + "68": "अड़सठ", + "69": "उनहत्तर", + "70": "सत्तर", + "71": "इकहत्तर", + "72": "बहत्तर", + "73": "तिहत्तर", + "74": "चौहत्तर", + "75": "पचहत्तर", + "76": "छिहत्तर", + "77": "सतहत्तर", + "78": "अठहत्तर", + "79": "उन्यासी", + "80": "अस्सी", + "81": "इक्यासी", + "82": "बयासी", + "83": "तिरासी", + "84": "चौरासी", + "85": "पचासी", + "86": "छियासी", + "87": "सत्तासी", + "88": "अठासी", + "89": "नवासी", + "90": "नब्बे", + "91": "इक्यानवे", + "92": "बानवे", + "93": "तिरानवे", + "94": "चौरानवे", + "95": "पचानवे", + "96": "छियानवे", + "97": "सत्तानवे", + "98": "अट्ठानवे", + "99": "निन्यानवे", + "100": "सौ", + "1000": "हज़ार", + "100000": "लाख", + "10000000": "करोड़", + "1000000000": "अरब", +} # Hindi +num_dict["gu"] = { + "0": "શૂન્ય", + "1": "એક", + "2": "બે", + "3": "ત્રણ", + "4": "ચાર", + "5": "પાંચ", + "6": "છ", + "7": "સાત", + "8": "આઠ", + "9": "નવ", + "10": "દસ", + "11": "અગિયાર", + "12": "બાર", + "13": "તેર", + "14": "ચૌદ", + "15": "પંદર", + "16": "સોળ", + "17": "સત્તર", + "18": "અઢાર", + "19": "ઓગણિસ", + "20": "વીસ", + "21": "એકવીસ", + "22": "બાવીસ", + "23": "તેવીસ", + "24": "ચોવીસ", + "25": "પચ્ચીસ", + "26": "છવીસ", + "27": "સત્તાવીસ", + "28": "અઠ્ઠાવીસ", + "29": "ઓગણત્રીસ", + "30": "ત્રીસ", + "31": "એકત્રીસ", + "32": "બત્રીસ", + "33": "તેત્રીસ", + "34": "ચોત્રીસ", + "35": "પાંત્રીસ", + "36": "છત્રીસ", + "37": "સડત્રીસ", + "38": "અડત્રીસ", + "39": "ઓગણચાલીસ", + "40": "ચાલીસ", + "41": "એકતાલીસ", + "42": "બેતાલીસ", + "43": "ત્રેતાલીસ", + "44": "ચુંમાલીસ", + "45": "પિસ્તાલીસ", + "46": "છેતાલીસ", + "47": "સુડતાલીસ", + "48": "અડતાલીસ", + "49": "ઓગણપચાસ", + "50": "પચાસ", + "51": "એકાવન", + "52": "બાવન", + "53": "ત્રેપન", + "54": "ચોપન", + "55": "પંચાવન", + "56": "છપ્પન", + "57": "સત્તાવન", + "58": "અઠ્ઠાવન", + "59": "ઓગણસાઠ", + "60": "સાઈઠ", + "61": "એકસઠ", + "62": "બાસઠ", + "63": "ત્રેસઠ", + "64": "ચોસઠ", + "65": "પાંસઠ", + "66": "છાસઠ", + "67": "સડસઠ", + "68": "અડસઠ", + "69": "અગણોસિત્તેર", + "70": "સિત્તેર", + "71": "એકોતેર", + "72": "બોતેર", + "73": "તોતેર", + "74": "ચુમોતેર", + "75": "પંચોતેર", + "76": "છોતેર", + "77": "સિત્યોતેર", + "78": "ઇઠ્યોતેર", + "79": "ઓગણાએંસી", + "80": "એંસી", + "81": "એક્યાસી", + "82": "બ્યાસી", + "83": "ત્યાસી", + "84": "ચોર્યાસી", + "85": "પંચાસી", + "86": "છ્યાસી", + "87": "સિત્યાસી", + "88": "ઈઠ્યાસી", + "89": "નેવ્યાસી", + "90": "નેવું", + "91": "એકાણું", + "92": "બાણું", + "93": "ત્રાણું", + "94": "ચોરાણું", + "95": "પંચાણું", + "96": "છન્નું", + "97": "સત્તાણું", + "98": "અઠ્ઠાણું", + "99": "નવ્વાણું", + "100": "સો", + "1000": "હજાર", + "100000": "લાખ", + "1000000": "દસ લાખ", + "10000000": "કરોડ઼", +} # Gujarati +num_dict["mr"] = { + "0": "शून्य", + "1": "एक", + "2": "दोन", + "3": "तीन", + "4": "चार", + "5": "पाच", + "6": "सहा", + "7": "सात", + "8": "आठ", + "9": "नऊ", + "10": "दहा", + "11": "अकरा", + "12": "बारा", + "13": "तेरा", + "14": "चौदा", + "15": "पंधरा", + "16": "सोळा", + "17": "सतरा", + "18": "अठरा", + "19": "एकोणीस", + "20": "वीस", + "21": "एकवीस", + "22": "बावीस", + "23": "तेवीस", + "24": "चोवीस", + "25": "पंचवीस", + "26": "सव्वीस", + "27": "सत्तावीस", + "28": "अठ्ठावीस", + "29": "एकोणतीस", + "30": "तीस", + "31": "एकतीस", + "32": "बत्तीस", + "33": "तेहेतीस", + "34": "चौतीस", + "35": "पस्तीस", + "36": "छत्तीस", + "37": "सदतीस", + "38": "अडतीस", + "39": "एकोणचाळीस", + "40": "चाळीस", + "41": "एक्केचाळीस", + "42": "बेचाळीस", + "43": "त्रेचाळीस", + "44": "चव्वेचाळीस", + "45": "पंचेचाळीस", + "46": "सेहेचाळीस", + "47": "सत्तेचाळीस", + "48": "अठ्ठेचाळीस", + "49": "एकोणपन्नास", + "50": "पन्नास", + "51": "एक्कावन्न", + "52": "बावन्न", + "53": "त्रेपन्न", + "54": "चोपन्न", + "55": "पंचावन्न", + "56": "छप्पन्न", + "57": "सत्तावन्न", + "58": "अठ्ठावन्न", + "59": "एकोणसाठ", + "60": "साठ", + "61": "एकसष्ठ", + "62": "बासष्ठ", + "63": "त्रेसष्ठ", + "64": "चौसष्ठ", + "65": "पासष्ठ", + "66": "सहासष्ठ", + "67": "सदुसष्ठ", + "68": "अडुसष्ठ", + "69": "एकोणसत्तर", + "70": "सत्तर", + "71": "एक्काहत्तर", + "72": "बाहत्तर", + "73": "त्र्याहत्तर", + "74": "चौर्‍याहत्तर", + "75": "पंच्याहत्तर", + "76": "शहात्तर", + "77": "सत्याहत्तर", + "78": "अठ्ठ्याहत्तर", + "79": "एकोण ऐंशी", + "80": "ऐंशी", + "81": "एक्क्याऐंशी", + "82": "ब्याऐंशी", + "83": "त्र्याऐंशी", + "84": "चौऱ्याऐंशी", + "85": "पंच्याऐंशी", + "86": "शहाऐंशी", + "87": "सत्त्याऐंशी", + "88": "अठ्ठ्याऐंशी", + "89": "एकोणनव्वद", + "90": "नव्वद", + "91": "एक्क्याण्णव", + "92": "ब्याण्णव", + "93": "त्र्याण्णव", + "94": "चौऱ्याण्णव", + "95": "पंच्याण्णव", + "96": "शहाण्णव", + "97": "सत्त्याण्णव", + "98": "अठ्ठ्याण्णव", + "99": "नव्व्याण्णव", + "100": "शे", + "1000": "हजार", + "100000": "लाख", + "10000000": "कोटी", + "1000000000": "अब्ज", +} # Marathi +num_dict["bn"] = { + "0": "শূন্য", + "1": "এক", + "2": "দুই", + "3": "তিন", + "4": "চার", + "5": "পাঁচ", + "6": "ছয়", + "7": "সাত", + "8": "আট", + "9": "নয়", + "10": "দশ", + "11": "এগার", + "12": "বার", + "13": "তের", + "14": "চৌদ্দ", + "15": "পনের", + "16": "ষোল", + "17": "সতের", + "18": "আঠার", + "19": "ঊনিশ", + "20": "বিশ", + "21": "একুশ", + "22": "বাইশ", + "23": "তেইশ", + "24": "চব্বিশ", + "25": "পঁচিশ", + "26": "ছাব্বিশ", + "27": "সাতাশ", + "28": "আঠাশ", + "29": "ঊনত্রিশ", + "30": "ত্রিশ", + "31": "একত্রিশ", + "32": "বত্রিশ", + "33": "তেত্রিশ", + "34": "চৌত্রিশ", + "35": "পঁয়ত্রিশ", + "36": "ছত্রিশ", + "37": "সাঁইত্রিশ", + "38": "আটত্রিশ", + "39": "ঊনচল্লিশ", + "40": "চল্লিশ", + "41": "একচল্লিশ", + "42": "বিয়াল্লিশ", + "43": "তেতাল্লিশ", + "44": "চুয়াল্লিশ", + "45": "পঁয়তাল্লিশ", + "46": "ছেচল্লিশ", + "47": "সাতচল্লিশ", + "48": "আটচল্লিশ", + "49": "ঊনপঞ্চাশ", + "50": "পঞ্চাশ", + "51": "একান্ন", + "52": "বায়ান্ন", + "53": "তিপ্পান্ন", + "54": "চুয়ান্ন", + "55": "পঞ্চান্ন", + "56": "ছাপ্পান্ন", + "57": "সাতান্ন", + "58": "আটান্ন", + "59": "ঊনষাট", + "60": "ষাট", + "61": "একষট্টি", + "62": "বাষট্টি", + "63": "তেষট্টি", + "64": "চৌষট্টি", + "65": "পঁয়ষট্টি", + "66": "ছেষট্টি", + "67": "সাতষট্টি", + "68": "আটষট্টি", + "69": "ঊনসত্তর", + "70": "সত্তর", + "71": "একাত্তর", + "72": "বাহাত্তর", + "73": "তিয়াত্তর", + "74": "চুয়াত্তর", + "75": "পঁচাত্তর", + "76": "ছিয়াত্তর", + "77": "সাতাত্তর", + "78": "আটাত্তর", + "79": "ঊনআশি", + "80": "আশি", + "81": "একাশি", + "82": "বিরাশি", + "83": "তিরাশি", + "84": "চুরাশি", + "85": "পঁচাশি", + "86": "ছিয়াশি", + "87": "সাতাশি", + "88": "আটাশি", + "89": "ঊননব্বই", + "90": "নব্বই", + "91": "একানব্বই", + "92": "বিরানব্বই", + "93": "তিরানব্বই", + "94": "চুরানব্বই", + "95": "পঁচানব্বই", + "96": "ছিয়ানব্বই", + "97": "সাতানব্বই", + "98": "আটানব্বই", + "99": "নিরানব্বই", + "100": "শো", + "1000": "হাজার", + "100000": "লাখ", + "10000000": "কোটি", + "1000000000": "একশ’ কোটি", +} # Bengali +num_dict["te"] = { + "0": "సున్నా", + "1": "ఒకటి", + "2": "రెండు", + "3": "మూడు", + "4": "నాలుగు", + "5": "ఐదు", + "6": "ఆరు", + "7": "ఏడు", + "8": "ఎనిమిది", + "9": "తొమ్మిది", + "10": "పది", + "11": "పదకొండు", + "12": "పన్నెండు", + "13": "పదమూడు", + "14": "పద్నాలుగు", + "15": "పదిహేను", + "16": "పదహారు", + "17": "పదిహేడు", + "18": "పద్దెనిమిది", + "19": "పందొమ్మిది", + "20": "ఇరవై", + "21": "ఇరవై ఒకటి", + "22": "ఇరవై రెండు", + "23": "ఇరవై మూడు", + "24": "ఇరవై నాలుగు", + "25": "ఇరవై ఐదు", + "26": "ఇరవై ఆరు", + "27": "ఇరవై ఏడు", + "28": "ఇరవై ఎనిమిది", + "29": "ఇరవై తొమ్మిది", + "30": "ముప్పై", + "31": "ముప్పై ఒకటి", + "32": "ముప్పై రెండు", + "33": "ముప్పై మూడు", + "34": "ముప్పై నాలుగు", + "35": "ముప్పై ఐదు", + "36": "ముప్పై ఆరు", + "37": "ముప్పై ఏడు", + "38": "ముప్పై ఎనిమిది", + "39": "ముప్పై తొమ్మిది", + "40": "నలభై", + "41": "నలభై ఒకటి", + "42": "నలభై రెండు", + "43": "నలభై మూడు", + "44": "నలభై నాలుగు", + "45": "నలభై ఐదు", + "46": "నలభై ఆరు", + "47": "నలభై ఏడు", + "48": "నలభై ఎనిమిది", + "49": "నలభై తొమ్మిది", + "50": "యాభై", + "51": "యాభై ఒకటి", + "52": "యాభై రెండు", + "53": "యాభై మూడు", + "54": "యాభై నాలుగు", + "55": "యాభై ఐదు", + "56": "యాభై ఆరు", + "57": "యాభై ఏడు", + "58": "యాభై ఎనిమిది", + "59": "యాభై తొమ్మిది", + "60": "అరవై", + "61": "అరవై ఒకటి", + "62": "అరవై రెండు", + "63": "అరవై మూడు", + "64": "అరవై నాలుగు", + "65": "అరవై ఐదు", + "66": "అరవై ఆరు", + "67": "అరవై ఏడు", + "68": "అరవై ఎనిమిది", + "69": "అరవై తొమ్మిది", + "70": "డెబ్బై", + "71": "డెబ్బై ఒకటి", + "72": "డెబ్బై రెండు", + "73": "డెబ్బై మూడు", + "74": "డెబ్బై నాలుగు", + "75": "డెబ్బై ఐదు", + "76": "డెబ్బై ఆరు", + "77": "డెబ్బై ఏడు", + "78": "డెబ్బై ఎనిమిది", + "79": "డెబ్బై తొమ్మిది", + "80": "ఎనభై", + "81": "ఎనభై ఒకటి", + "82": "ఎనభై రెండు", + "83": "ఎనభై మూడు", + "84": "ఎనభై నాలుగు", + "85": "ఎనభై ఐదు", + "86": "ఎనభై ఆరు", + "87": "ఎనభై ఏడు", + "88": "ఎనభై ఎనిమిది", + "89": "ఎనభై తొమ్మిది", + "90": "తొంభై", + "91": "తొంభై ఒకటి", + "92": "తొంభై రెండు", + "93": "తొంభై మూడు", + "94": "తొంభై నాలుగు", + "95": "తొంభై ఐదు", + "96": "తొంభై ఆరు", + "97": "తొంభై ఏడు", + "98": "తొంభై ఎనిమిది", + "99": "తొంభై తొమ్మిది", + "100": "వందల", + "1000": "వేల", + "100000": "లక్షల", + "10000000": "కోట్ల", + "1000000000": "బిలియన్", +} # Telugu +num_dict["ta"] = { + "0": "பூஜ்ஜியம்", + "1": "ஒன்று", + "2": "இரண்டு", + "3": "மூன்று", + "4": "நான்கு", + "5": "ஐந்து", + "6": "ஆறு", + "7": "ஏழு", + "8": "எட்டு", + "9": "ஒன்பது", + "10": "பத்து", + "11": "பதினொன்று", + "12": "பன்னிரண்டு", + "13": "பதிமூன்று", + "14": "பதினான்கு", + "15": "பதினைந்து", + "16": "பதினாறு", + "17": "பதினேழு", + "18": "பதினெட்டு", + "19": "பத்தொன்பது", + "20": "இருபது", + "21": "இருபது ஒன்று", + "22": "இருபத்து இரண்டு", + "23": "இருபத்து மூன்று", + "24": "இருபத்து நான்கு", + "25": "இருபத்து ஐந்து", + "26": "இருபத்து ஆறு", + "27": "இருபத்து ஏழு", + "28": "இருபத்து எட்டு", + "29": "இருபத்து ஒன்பது", + "30": "முப்பது", + "31": "முப்பத்து ஒன்று", + "32": "முப்பத்து இரண்டு", + "33": "முப்பத்து மூன்று", + "34": "முப்பத்து நான்கு", + "35": "முப்பத்து ஐந்து", + "36": "முப்பத்து ஆறு", + "37": "முப்பத்து ஏழு", + "38": "முப்பத்து எட்டு", + "39": "முப்பத்து ஒன்பது", + "40": "நாற்பது", + "41": "நாற்பத்து ஒன்று", + "42": "நாற்பத்து இரண்டு", + "43": "நாற்பத்து மூன்று", + "44": "நாற்பத்து நான்கு", + "45": "நாற்பத்து ஐந்து", + "46": "நாற்பத்து ஆறு", + "47": " நாற்பத்து ஏழு", + "48": "நாற்பத்து எட்டு", + "49": "நாற்பத்து ஒன்பது", + "50": "ஐம்பது", + "51": "ஐம்பத்து ஒன்று", + "52": "ஐம்பத்து இரண்டு", + "53": "ஐம்பத்து மூன்று", + "54": "ஐம்பத்து நான்கு", + "55": "ஐம்பத்து ஐந்து", + "56": "ஐம்பத்து ஆறு", + "57": "ஐம்பத்து ஏழு", + "58": "ஐம்பத்து எட்டு", + "59": "ஐம்பத்து ஒன்பது", + "60": "அறுபது", + "61": "அறுபத்து ஒன்று", + "62": "அறுபத்து இரண்டு", + "63": "அறுபத்து மூன்று", + "64": "அறுபத்து நான்கு", + "65": "அறுபத்து ஐந்து", + "66": "அறுபத்து ஆறு", + "67": "அறுபத்து ஏழு", + "68": "அறுபத்து எட்டு", + "69": "அறுபத்து ஒன்பது", + "70": "எழுபது", + "71": "எழுபத்தி ஒன்று", + "72": "எழுபத்தி இரண்டு", + "73": "எழுபத்தி முச்சக்கர", + "74": "எழுபத்தி நான்கு", + "75": "எழுபத்தி ஐந்து", + "76": "எழுபத்தி ஆறு", + "77": "எழுபத்தி ஏழு", + "78": "எழுபத்தி எட்டு", + "79": "எழுபத்தி ஒன்பது", + "80": "எண்பது", + "81": "எண்பத்தியொன்று", + "82": "எண்பத்திரண்டு", + "83": "எண்பத்திமூன்று", + "84": "என்பதினான்கு", + "85": "என்பதினைந்து", + "86": "எண்பத்திஆறு", + "87": "எண்பத்திஏழு", + "88": "எண்பத்தியெட்டு", + "89": "எண்பத்தியொன்பது", + "90": "தொன்னூறு", + "91": "தொண்ணூற்றியொன்று", + "92": "தொண்ணூற்றிரண்டு", + "93": "தொண்ணூற்றிமூன்று", + "94": "தொண்ணூற்றிநான்கு", + "95": "தொண்ணூற்றிஐந்து", + "96": "தொண்ணூற்றியாறு", + "97": "தொண்ணூற்றியேழு", + "98": "தொண்ணூற்றியெட்டு", + "99": "தொண்ணூற்றிஒன்பது", + "100": "நூறு", + "1000": "ஆயிரம்", + "100000": "இலட்சம்", + "10000000": "கோடி", + "1000000000": "பில்லியன்", +} # Tamil +num_dict["kn"] = { + "0": "ಸೊನ್ನೆ", + "1": "ಒಂದು", + "2": "ಎರಡು", + "3": "ಮೂರು", + "4": "ನಾಲ್ಕು", + "5": "ಅಯ್ದು", + "6": "ಆರು", + "7": "ಏಳು", + "8": "ಎಂಟು", + "9": "ಒಂಬತ್ತು", + "10": "ಹತ್ತು", + "11": "ಹನ್ನೊಂದು", + "12": "ಹನ್ನೆರಡು", + "13": "ಹದಿಮೂರು", + "14": "ಹದಿನಾಲ್ಕು", + "15": "ಹದಿನೈದು", + "16": "ಹದಿನಾರು", + "17": "ಹದಿನೇಳು", + "18": "ಹದಿನೆಂಟು", + "19": "ಹತ್ತೊಂಬತ್ತು", + "20": "ಇಪ್ಪತ್ತು", + "21": "ಇಪ್ಪತ್ತ್’ಒಂದು", + "22": "ಇಪ್ಪತ್ತ್’ಎರಡು", + "23": "ಇಪ್ಪತ್ತ್’ಮೂರು", + "24": "ಇಪ್ಪತ್ತ್’ನಾಲ್ಕು", + "25": "ಇಪ್ಪತ್ತ್’ಐದು", + "26": "ಇಪ್ಪತ್ತ್’ಆರು", + "27": "ಇಪ್ಪತ್ತ್’ಏಳು", + "28": "ಇಪ್ಪತ್ತ್’ಎಂಟು", + "29": "ಇಪ್ಪತ್ತ್’ಒಂಬತ್ತು", + "30": "ಮೂವತ್ತು", + "31": "ಮುವತ್ತ್’ಒಂದು", + "32": "ಮುವತ್ತ್’ಎರಡು", + "33": "ಮುವತ್ತ್’ಮೂರು", + "34": "ಮೂವತ್ತ್’ನಾಲ್ಕು", + "35": "ಮೂವತ್ತ್’ಐದು", + "36": "ಮೂವತ್ತ್’ಆರು", + "37": "ಮೂವತ್ತ್’ಏಳು", + "38": "ಮೂವತ್ತ್’ಎಂಟು", + "39": "ಮೂವತ್ತ್’ಒಂಬತ್ತು", + "40": "ನಲವತ್ತು", + "41": "ನಲವತ್ತೊಂದು", + "42": "ನಲವತ್ತ್ ಎರಡು", + "43": "ನಲವತ್ತ್ ಮೂರು", + "44": "ನಲವತ್ತ್ ನಾಲ್ಕು", + "45": "ನಲವತ್ತೈದು", + "46": "ನಲವತ್ತಾರು", + "47": "ನಲವತ್ತೇಳು", + "48": "ನಲವತ್ತೆಂಟು", + "49": "ನಲವತ್ತೊಂಬತ್ತು", + "50": "ಐವತ್ತು", + "51": "ಐವತ್ತೊಂದು", + "52": "ಐವತ್ತೆರಡು", + "53": "ಐವತ್ತಮೂರು", + "54": "ಐವತ್ತ್ನಾಲ್ಕು", + "55": "ಐವತ್ತೈದು", + "56": "ಐವತ್ತಾರು", + "57": "ಐವತ್ತೇಳು", + "58": "ಐವತ್ತೆಂಟು", + "59": "ಐವತ್ತೊಂಬತ್ತು", + "60": "ಅರವತ್ತು", + "61": "ಅರವತ್ತೊಂದು", + "62": "ಅರವತ್ತೆರಡು", + "63": "ಅರವತ್ತ್ ಮೂರು", + "64": "ಅರವತ್ತ್ ನಾಲ್ಕು", + "65": "ಅರವತ್ತೈದು", + "66": "ಅರವತ್ತಾರು", + "67": "ಅರವತ್ತೇಳು", + "68": "ಅರವತ್ತೆಂಟು", + "69": "ಅರವತ್ತೊಂಬತ್ತು", + "70": "ಎಪ್ಪತ್ತು", + "71": "ಎಪ್ಪತ್ತೊಂದು", + "72": "ಎಪ್ಪತ್ತೆರಡು", + "73": "ಎಪ್ಪತ್ತ್ ಮೂರು", + "74": "ಎಪ್ಪತ್ತ್ ನಾಲ್ಕು", + "75": "ಎಪ್ಪತ್ತೈದು", + "76": "ಎಪ್ಪತ್ತಾರು", + "77": "ಎಪ್ಪತ್ತೇಳು", + "78": "ಎಪ್ಪತ್ತೆಂಟು", + "79": "ಎಪ್ಪತ್ತೊಂಬತ್ತು", + "80": "ಎಂಬತ್ತು", + "81": "ಎಂಬತ್ತೊಂದು", + "82": "ಎಂಬತ್ತೆರಡು", + "83": "ಎಂಬತ್ತ್ ಮೂರು", + "84": "ಎಂಬತ್ತ್ ನಾಲ್ಕು", + "85": "ಎಂಬತ್ತೈದು", + "86": "ಎಂಬತ್ತಾರು", + "87": "ಎಂಬತ್ತೇಳು", + "88": "ಎಂಬತ್ತೆಂಟು", + "89": "ಎಂಬತ್ತೊಂಬತ್ತು", + "90": "ತೊಂಬತ್ತು", + "91": "ತೊಂಬತ್ತೊಂದು", + "92": "ತೊಂಬತ್ತೆರಡು", + "93": "ತೊಂಬತ್ತ ಮೂರು", + "94": "ತೊಂಬತ್ತ ನಾಲ್ಕು", + "95": "ತೊಂಬತ್ತೈದು", + "96": "ತೊಂಬತ್ತಾರು", + "97": "ತೊಂಬತ್ತೇಳು", + "98": "ತೊಂಬತ್ತೆಂಟು", + "99": "ತೊಂಬತ್ತೊಂಬತ್ತು", + "100": "ನೂರ", + "1000": "ಸಾವಿರದ", + "100000": "ಲಕ್ಷದ", + "10000000": "ಕೋಟಿ", + "1000000000": "ಶತಕೋಟಿ", +} # Kannada +num_dict["or"] = { + "0": "ଶୁନ୍ୟ", + "1": "ଏକ", + "2": "ଦୁଇ", + "3": "ତିନି", + "4": "ଚାରି", + "5": "ପାଞ୍ଚ", + "6": "ଛଅ", + "7": "ସାତ", + "8": "ଆଠ", + "9": "ନଅ", + "10": "ନଅ", + "11": "ଏଗାର", + "12": "ବାର", + "13": "ତେର", + "14": "ଚଉଦ", + "15": "ପନ୍ଦର", + "16": "ଷୋହଳ", + "17": "ସତର", + "18": "ଅଠର", + "19": "ଊଣାଇଶ", + "20": "କୋଡିଏ", + "21": "ଏକୋଇଶି", + "22": "ବାଇଶି", + "23": "ତେଇଶି", + "24": "ଚବିଶି", + "25": "ପଚିଶି", + "26": "ଛବିଶି", + "27": "ସତାଇଶି", + "28": "ଅଠାଇଶି", + "29": "ଅଣତିରିଶି", + "30": "ତିରିଶି", + "31": "ଏକତିରିଶି", + "32": "ବତିଶି", + "33": "ତେତିଶି", + "34": "ଚଉତିରିଶି", + "35": "ପଞ୍ଚତିରିଶି", + "36": "ଛତିଶି", + "37": "ସଂଇତିରିଶି", + "38": "ଅଠତିରିଶି", + "39": "ଅଣଚାଳିଶି", + "40": "ଚାଳିଶି", + "41": "ଏକଚାଳିଶି", + "42": "ବୟାଳିଶି", + "43": "ତେୟାଳିଶି", + "44": "ଚଉରାଳିଶି", + "45": "ପଞ୍ଚଚାଳିଶି", + "46": "ଛୟାଳିଶି", + "47": "ସତଚାଳିଶି", + "48": "ଅଠଚାଳିଶି", + "49": "ଅଣଚାଶ", + "50": "ପଚାଶ", + "51": "ଏକାବନ", + "52": "ବାଉନ", + "53": "ତେପନ", + "54": "ଚଉବନ", + "55": "ପଞ୍ଚାବନ", + "56": "ଛପନ", + "57": "ସତାବନ", + "58": "ଅଠାବନ", + "59": "ଅଣଷଠି", + "60": "ଷାଠିଏ", + "61": "ଏକଷଠି", + "62": "ବାଷଠି", + "63": "ତେଷଠି", + "64": "ଚଉଷଠି", + "65": "ପଞ୍ଚଷଠି", + "66": "ଛଅଷଠି", + "67": "ସତଷଠି", + "68": "ଅଠଷଠି", + "69": "ଅଣସ୍ତରୀ", + "70": "ସତୂରୀ", + "71": "ଏକସ୍ତରୀ", + "72": "ବାସ୍ତରୀ", + "73": "ତେସ୍ତରୀ", + "74": "ଚଉସ୍ତରୀ", + "75": "ପଞ୍ଚସ୍ତରୀ", + "76": "ଛଅସ୍ତରୀ", + "77": "ସତସ୍ତରୀ", + "78": "ଅଠସ୍ତରୀ", + "79": "ଅଣାଅଶୀ", + "80": "ଅଶୀ", + "81": "ଏକାଅଶୀ", + "82": "ବୟାଅଶୀ", + "83": "ତେୟାଅଶୀ", + "84": "ଚଉରାଅଶୀ", + "85": "ପଞ୍ଚାଅଶୀ", + "86": "ଛୟାଅଶୀ", + "87": "ସତାଅଶୀ", + "88": "ଅଠାଅଶୀ", + "89": "ଅଣାନବେ", + "90": "ନବେ", + "91": "ଏକାନବେ", + "92": "ବୟାନବେ", + "93": "ତେୟାନବେ", + "94": "ଚଉରାନବେ", + "95": "ପଞ୍ଚାନବେ", + "96": "ଛୟାନବେ", + "97": "ସତାନବେ", + "98": "ଅଠାନବେ", + "99": "ଅନେଶତ", + "100": "ଶହେ", + "1000": "ହଜାର", + "100000": "ଲକ୍ଷ", + "10000000": "କୋଟି", + "1000000000": "କୋଟି", +} # Oriya +num_dict["pa"] = { + "0": "ਸਿਫਰ ", + "1": "ਇੱਕ", + "2": "ਦੋ", + "3": "ਤਿੰਨ", + "4": "ਚਾਰ", + "5": "ਪੰਜ", + "6": "ਛੇ", + "7": "ਸੱਤ", + "8": "ਅੱਠ", + "9": "ਨੌਂ", + "10": "ਦੱਸ", + "11": "ਗਿਆਰਾਂ", + "12": "ਬਾਰਾਂ", + "13": "ਤੇਰਾਂ", + "14": "ਚੌਦਾਂ", + "15": "ਪੰਦਰਾਂ", + "16": "ਸੋਲ਼ਾਂ", + "17": "ਸਤਾਰਾਂ", + "18": "ਅਠਾਰਾਂ", + "19": "ਉਨੀ", + "20": "ਵੀਹ", + "21": "ਇੱਕੀ", + "22": "ਬਾਈ", + "23": "ਤੇਈ", + "24": "ਚੌਵੀ", + "25": "ਪੰਝੀ", + "26": "ਛੱਬੀ", + "27": "ਸਤਾਈ", + "28": "ਅਠਾਈ", + "29": "ਉਨੱਤੀ", + "30": "ਤੀਹ", + "31": "ਇਕੱਤੀ", + "32": "ਬੱਤੀ", + "33": "ਤੇਤੀ", + "34": "ਚੌਂਤੀ", + "35": "ਪੈਂਤੀ", + "36": "ਛੱਤੀ", + "37": "ਸੈਂਤੀ", + "38": "ਅਠੱਤੀ", + "39": "ਉਨਤਾਲੀ", + "40": "ਚਾਲੀ", + "41": "ਇਕਤਾਲੀ", + "42": "ਬਤਾਲੀ", + "43": "ਤਰਤਾਲੀ", + "44": "ਚੌਤਾਲੀ", + "45": "ਪੰਜਤਾਲੀ", + "46": "ਛਿਆਲੀ", + "47": "ਸੰਤਾਲੀ", + "48": "ਅੱਠਤਾਲੀ", + "49": "ਉਣਿੰਜਾ", + "50": "ਪੰਜਾਹ", + "51": "ਇਕਵਿੰਜਾ", + "52": "ਬਵਿੰਜਾ", + "53": "ਤਰਵਿੰਜਾ", + "54": "ਚਰਿੰਜਾ", + "55": "ਪਚਵਿੰਜਾ", + "56": "ਛਪਿੰਜਾ", + "57": "ਸਤਵਿੰਜਾ", + "58": "ਅੱਠਵਿੰਜਾ", + "59": "ਉਣਾਠ", + "60": "ਸੱਠ", + "61": "ਇਕਾਠ", + "62": "ਬਾਠ੍ਹ", + "63": "ਤਰੇਠ੍ਹ", + "64": "ਚੌਠ੍ਹ", + "65": "ਪੈਂਠ", + "66": "ਛਿਆਠ", + "67": "ਸਤਾਹਠ", + "68": "ਅੱਠਾਠ", + "69": "ਉਣੱਤਰ", + "70": "ਸੱਤਰ", + "71": "ਇਕ੍ਹੱਤਰ", + "72": "ਬਹੱਤਰ", + "73": "ਤਹੱਤਰ", + "74": "ਚੌਹੱਤਰ", + "75": "ਪੰਜੱਤਰ", + "76": "ਛਿਹੱਤਰ", + "77": "ਸਤੱਤਰ", + "78": "ਅਠੱਤਰ", + "79": "ਉਣਾਸੀ", + "80": "ਅੱਸੀ", + "81": "ਇਕਾਸੀ", + "82": "ਬਿਆਸੀ", + "83": "ਤਰਾਸੀ", + "84": "ਚਰਾਸੀ", + "85": "ਪੰਜਾਸੀ", + "86": "ਛਿਆਸੀ", + "87": "ਸਤਾਸੀ", + "88": "ਅਠਾਸੀ", + "89": "ਉਣਾਨਵੇਂ", + "90": "ਨੱਬੇ", + "91": "ਇਕਾਨਵੇਂ", + "92": "ਬਿਆਨਵੇਂ", + "93": "ਤਰਾਨਵੇਂ", + "94": "ਚਰਾਨਵੇਂ", + "95": "ਪਚਾਨਵੇਂ", + "96": "ਛਿਆਨਵੇਂ", + "97": "ਸਤਾਨਵੇਂ", + "98": "ਅਠਾਨਵੇਂ", + "99": "ਨਿੜਾਨਵੇਂ", + "100": "ਸੌ", + "1000": "ਹਜਾਰ", + "100000": "ਲੱਖ", + "10000000": "ਕਰੋੜ", + "1000000000": "ਅਰਬ", +} # Punjabi + +# --------------------------- num_to_word.py ------------------------------ +""" +Method to convert Numbers to Words +for indian languages + +Use cases:- +1) Speech recognition pre-processing +2) Language modeling Data pre-processing + +------------------------- +check indic_numbers.py to add support +for any indian language +""" + + +def language_specific_exception(words, lang, combiner): + """ + Language Specific Exception will come here + """ + + def occurs_at_end(piece): + return words[-len(piece) :] == piece + + if lang == "mr": + words = words.replace("एक" + combiner + "शे", "शंभर") + elif lang == "gu": + words = words.replace("બે" + combiner + "સો", "બસ્સો") + elif lang == "te": + exception_dict = { + "1": "ఒక", + "100": "వంద", + "100+": "వందలు", + "1000": "వెయ్యి", + "1000+": "వేలు", + "100000": "లక్ష", + "100000+": "లక్షలు", + "10000000": "కోటి", + "10000000+": "కోట్లు", + } + + test_case = ["100", "1000", "100000", "10000000"] + for test in test_case: + test_word = num_dict["te"][test] + match = num_dict["te"]["1"] + combiner + test_word + # for numbers like : 100, 1000, 100000 + if words == match: + return exception_dict[test] + # for numbers like : 200, 4000, 800000 + elif occurs_at_end(test_word): + words = words.replace(test_word, exception_dict[test + "+"]) + # for numbers like : 105, 1076, 123993 + elif not occurs_at_end(match): + replacement = exception_dict["1"] + combiner + exception_dict[test] + words = words.replace(match, replacement) + + # Exception case for 101...199 + special_case = "ఒక" + combiner + "వంద" + words = words.replace(special_case, "నూట") + elif lang == "kn": + # special case for 100 + if words == ("ಒಂದು" + combiner + "ನೂರ"): + return "ನೂರು" + exception_dict = { + "ನೂರ": "ನೂರು", + "ಸಾವಿರದ": "ಸಾವಿರ", + "ಲಕ್ಷದ": "ಲಕ್ಷ", + "ಕೋಟಿಯ": "ಕೋಟಿ", + } + for expt in exception_dict: + if occurs_at_end(expt): + words = words.replace(expt, exception_dict[expt]) + return words + + +def num_to_word(num, lang, separator=", ", combiner=" "): + """ + Main Method + :param num: Number digits from any indian language + :param lang: Language Code from supported Language + :param separator: Separator character i.e. separator = '-' --> 'two hundred-sixty' + :param combiner: combine number with position i.e. combiner = '-' --> 'two-hundred sixty' + :return: UTF-8 String of numbers in words + """ + lang = lang.lower() + num = str(num) + + # Load dictionary according to language code + assert lang in supported_lang, "Language not supported" + num_dic = num_dict[lang] + + # dash default combiner for english-india + if (lang == "en") & (combiner == " "): + combiner = "-" + + # Remove punctuations from numbers + num = str(num).replace(",", "").replace(" ", "") + + # Replace native language numbers with english digits + for language in supported_lang: + for num_index in range(10): + num = num.replace(all_num[language][num_index], all_num["en"][num_index]) + + # Assert that input contains only integer number + for digit in num: + assert digit in all_num["en"], "Give proper input" + + # Process + # For Number longer than 9 digits + def all_two_digit(digits_2): + if len(digits_2) <= 1: # Provided only one/zero digit + return num_dic.get(digits_2, "") + elif digits_2 == "00": # Two Zero provided + return num_dic["0"] + separator + num_dic["0"] + elif digits_2[0] == "0": # First digit is zero + return num_dic["0"] + separator + num_dic[digits_2[1]] + else: # Both digit provided + return num_dic[digits_2] + + # For Number less than 9 digits + def two_digit(digits_2): + digits_2 = digits_2.lstrip("0") + if len(digits_2) != 0: + return num_dic[digits_2] + else: + return "" + + def all_digit(digits): + digits = digits.lstrip("0") + digit_len = len(digits) + if digit_len > 3: + num_of_digits_to_process = (digit_len % 2) + 1 + process_digits = digits[:num_of_digits_to_process] + base = str(10 ** (int(digit_len / 2) * 2 - 1)) + remain_digits = digits[num_of_digits_to_process:] + return ( + num_dic[process_digits] + + combiner + + num_dic[base] + + separator + + all_digit(remain_digits) + ) + elif len(digits) == 3: + return ( + num_dic[digits[:1]] + + combiner + + num_dic["100"] + + separator + + two_digit(digits[1:]) + ) + else: + return two_digit(digits) + + num = num.lstrip("0") + full_digit_len = len(num) + + if full_digit_len == 0: + output = num_dic["0"] + elif full_digit_len <= 9: + output = all_digit(num) + else: + iteration = round(full_digit_len / 2) + output = all_two_digit(num[:2]) # First to digit + for i in range(1, iteration): + output = ( + output + separator + all_two_digit(num[i * 2 : (i + 1) * 2]) + ) # Next two digit pairs + remaining_digits = num[iteration * 2 :] + if not all_two_digit(remaining_digits) == "": + output = ( + output + separator + all_two_digit(remaining_digits) + ) # remaining Last one/two digits + + output = output.strip(separator) + + output = language_specific_exception(output, lang, combiner) + + return output + + +# --------------------------------- num_to_word_on_a_sent --------------------------------- + + +def is_digit(word, digit_pattern): + return re.search(digit_pattern, word) + + +def remove_punct(sent): + clean = re.sub("[%s]" % re.escape(string.punctuation), " ", sent) + return " ".join([word for word in clean.split() if word]) + + +def normalize_nums(text, lang): + """ + text: str (eg) + lang: lang code ['en', 'hi'] + + returns: str + (eg) + """ + + if lang in supported_lang: + words = text.split() + lang_digits = [str(i) for i in range(0, 10)] + + digit_pattern = "[" + "".join(lang_digits) + "]" + num_indices = [ + ind for ind, word in enumerate(words) if is_digit(word, digit_pattern) + ] + + words_up = [ + num_to_word(word, lang, separator=" ", combiner=" ") + if ind in num_indices + else word + for ind, word in enumerate(words) + ] + return " ".join(words_up) + else: + return text + + +if __name__ == "__main__": + print(normalize_nums("रीटा के पास 16 बिल्लियाँ हैं।", "hi")) diff --git a/utils/inference/run_gradio.py b/utils/inference/run_gradio.py new file mode 100644 index 0000000000000000000000000000000000000000..5b4c46a79fc0b7e1760d1d321a2902e14e3020e3 --- /dev/null +++ b/utils/inference/run_gradio.py @@ -0,0 +1,53 @@ +import gradio as gr +import argparse +import numpy as np +from argparse import Namespace +from advanced_tts import load_all_models, run_tts_paragraph + + +def hit_tts(textbox, slider_noise_scale, slider_length_sclae, choice_transliteration, choice_number_conversion, choice_split_sentences): + inputs_to_gradio = {'text' : textbox, + 'noise_scale': slider_noise_scale, + 'length_scale': slider_length_sclae, + 'transliteration' : 1 if choice_transliteration else 0, + 'number_conversion' : 1 if choice_number_conversion else 0, + 'split_sentences' : 1 if choice_split_sentences else 0 + } + + args = Namespace(**inputs_to_gradio) + args.wav = None + args.lang = lang + + if args.text: + sr, audio = run_tts_paragraph(args) + return (sr, audio) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("-a", "--acoustic", required=True, type=str) + parser.add_argument("-v", "--vocoder", required=True, type=str) + parser.add_argument("-d", "--device", type=str, default="cpu") + parser.add_argument("-L", "--lang", type=str, required=True) + + global lang + + args = parser.parse_args() + lang = args.lang + load_all_models(args) + + textbox = gr.inputs.Textbox(placeholder="Enter Text to run", default="", label="TTS") + slider_noise_scale = gr.inputs.Slider(minimum=0, maximum=1.0, step=0.001, default=0.667, label='Enter Noise Scale') + slider_length_sclae = gr.inputs.Slider(minimum=0, maximum=2.0, step=0.1, default=1.0, label='Enter Slider Scale') + + choice_transliteration = gr.inputs.Checkbox(default=True, label="Transliteration") + choice_number_conversion = gr.inputs.Checkbox(default=True, label="Number Conversion") + choice_split_sentences = gr.inputs.Checkbox(default=True, label="Split Sentences") + + + + op = gr.outputs.Audio(type="numpy", label=None) + + inputs_to_gradio = [textbox, slider_noise_scale, slider_length_sclae, choice_transliteration, choice_number_conversion, choice_split_sentences] + iface = gr.Interface(fn=hit_tts, inputs=inputs_to_gradio, outputs=op, theme='huggingface', title='Run TTS example') + iface.launch(share=True, enable_queue=True) \ No newline at end of file diff --git a/utils/inference/transliterate.py b/utils/inference/transliterate.py new file mode 100644 index 0000000000000000000000000000000000000000..5ffe9f423d95c5476f97285419dab6d409b173ba --- /dev/null +++ b/utils/inference/transliterate.py @@ -0,0 +1,919 @@ +import torch +import torch.nn as nn +import numpy as np +import pandas as pd +import random +import sys +import os +import json +import enum +import traceback +import re + +#F_DIR = os.path.dirname(os.path.realpath(__file__)) +F_DIR = '../../checkpoints/' + +class XlitError(enum.Enum): + lang_err = "Unsupported langauge ID requested ;( Please check available languages." + string_err = "String passed is incompatable ;(" + internal_err = "Internal crash ;(" + unknown_err = "Unknown Failure" + loading_err = "Loading failed ;( Check if metadata/paths are correctly configured." + + +##=================== Network ================================================== + + +class Encoder(nn.Module): + def __init__( + self, + input_dim, + embed_dim, + hidden_dim, + rnn_type="gru", + layers=1, + bidirectional=False, + dropout=0, + device="cpu", + ): + super(Encoder, self).__init__() + + self.input_dim = input_dim # src_vocab_sz + self.enc_embed_dim = embed_dim + self.enc_hidden_dim = hidden_dim + self.enc_rnn_type = rnn_type + self.enc_layers = layers + self.enc_directions = 2 if bidirectional else 1 + self.device = device + + self.embedding = nn.Embedding(self.input_dim, self.enc_embed_dim) + + if self.enc_rnn_type == "gru": + self.enc_rnn = nn.GRU( + input_size=self.enc_embed_dim, + hidden_size=self.enc_hidden_dim, + num_layers=self.enc_layers, + bidirectional=bidirectional, + ) + elif self.enc_rnn_type == "lstm": + self.enc_rnn = nn.LSTM( + input_size=self.enc_embed_dim, + hidden_size=self.enc_hidden_dim, + num_layers=self.enc_layers, + bidirectional=bidirectional, + ) + else: + raise Exception("XlitError: unknown RNN type mentioned") + + def forward(self, x, x_sz, hidden=None): + """ + x_sz: (batch_size, 1) - Unpadded sequence lengths used for pack_pad + """ + batch_sz = x.shape[0] + # x: batch_size, max_length, enc_embed_dim + x = self.embedding(x) + + ## pack the padded data + # x: max_length, batch_size, enc_embed_dim -> for pack_pad + x = x.permute(1, 0, 2) + x = nn.utils.rnn.pack_padded_sequence(x, x_sz, enforce_sorted=False) # unpad + + # output: packed_size, batch_size, enc_embed_dim + # hidden: n_layer**num_directions, batch_size, hidden_dim | if LSTM (h_n, c_n) + output, hidden = self.enc_rnn( + x + ) # gru returns hidden state of all timesteps as well as hidden state at last timestep + + ## pad the sequence to the max length in the batch + # output: max_length, batch_size, enc_emb_dim*directions) + output, _ = nn.utils.rnn.pad_packed_sequence(output) + + # output: batch_size, max_length, hidden_dim + output = output.permute(1, 0, 2) + + return output, hidden + + def get_word_embedding(self, x): + """ """ + x_sz = torch.tensor([len(x)]) + x_ = torch.tensor(x).unsqueeze(0).to(dtype=torch.long) + # x: 1, max_length, enc_embed_dim + x = self.embedding(x_) + + ## pack the padded data + # x: max_length, 1, enc_embed_dim -> for pack_pad + x = x.permute(1, 0, 2) + x = nn.utils.rnn.pack_padded_sequence(x, x_sz, enforce_sorted=False) # unpad + + # output: packed_size, 1, enc_embed_dim + # hidden: n_layer**num_directions, 1, hidden_dim | if LSTM (h_n, c_n) + output, hidden = self.enc_rnn( + x + ) # gru returns hidden state of all timesteps as well as hidden state at last timestep + + out_embed = hidden[0].squeeze() + + return out_embed + + +class Decoder(nn.Module): + def __init__( + self, + output_dim, + embed_dim, + hidden_dim, + rnn_type="gru", + layers=1, + use_attention=True, + enc_outstate_dim=None, # enc_directions * enc_hidden_dim + dropout=0, + device="cpu", + ): + super(Decoder, self).__init__() + + self.output_dim = output_dim # tgt_vocab_sz + self.dec_hidden_dim = hidden_dim + self.dec_embed_dim = embed_dim + self.dec_rnn_type = rnn_type + self.dec_layers = layers + self.use_attention = use_attention + self.device = device + if self.use_attention: + self.enc_outstate_dim = enc_outstate_dim if enc_outstate_dim else hidden_dim + else: + self.enc_outstate_dim = 0 + + self.embedding = nn.Embedding(self.output_dim, self.dec_embed_dim) + + if self.dec_rnn_type == "gru": + self.dec_rnn = nn.GRU( + input_size=self.dec_embed_dim + + self.enc_outstate_dim, # to concat attention_output + hidden_size=self.dec_hidden_dim, # previous Hidden + num_layers=self.dec_layers, + batch_first=True, + ) + elif self.dec_rnn_type == "lstm": + self.dec_rnn = nn.LSTM( + input_size=self.dec_embed_dim + + self.enc_outstate_dim, # to concat attention_output + hidden_size=self.dec_hidden_dim, # previous Hidden + num_layers=self.dec_layers, + batch_first=True, + ) + else: + raise Exception("XlitError: unknown RNN type mentioned") + + self.fc = nn.Sequential( + nn.Linear(self.dec_hidden_dim, self.dec_embed_dim), + nn.LeakyReLU(), + # nn.Linear(self.dec_embed_dim, self.dec_embed_dim), nn.LeakyReLU(), # removing to reduce size + nn.Linear(self.dec_embed_dim, self.output_dim), + ) + + ##----- Attention ---------- + if self.use_attention: + self.W1 = nn.Linear(self.enc_outstate_dim, self.dec_hidden_dim) + self.W2 = nn.Linear(self.dec_hidden_dim, self.dec_hidden_dim) + self.V = nn.Linear(self.dec_hidden_dim, 1) + + def attention(self, x, hidden, enc_output): + """ + x: (batch_size, 1, dec_embed_dim) -> after Embedding + enc_output: batch_size, max_length, enc_hidden_dim *num_directions + hidden: n_layers, batch_size, hidden_size | if LSTM (h_n, c_n) + """ + + ## perform addition to calculate the score + + # hidden_with_time_axis: batch_size, 1, hidden_dim + ## hidden_with_time_axis = hidden.permute(1, 0, 2) ## replaced with below 2lines + hidden_with_time_axis = ( + torch.sum(hidden, axis=0) + if self.dec_rnn_type != "lstm" + else torch.sum(hidden[0], axis=0) + ) # h_n + + hidden_with_time_axis = hidden_with_time_axis.unsqueeze(1) + + # score: batch_size, max_length, hidden_dim + score = torch.tanh(self.W1(enc_output) + self.W2(hidden_with_time_axis)) + + # attention_weights: batch_size, max_length, 1 + # we get 1 at the last axis because we are applying score to self.V + attention_weights = torch.softmax(self.V(score), dim=1) + + # context_vector shape after sum == (batch_size, hidden_dim) + context_vector = attention_weights * enc_output + context_vector = torch.sum(context_vector, dim=1) + # context_vector: batch_size, 1, hidden_dim + context_vector = context_vector.unsqueeze(1) + + # attend_out (batch_size, 1, dec_embed_dim + hidden_size) + attend_out = torch.cat((context_vector, x), -1) + + return attend_out, attention_weights + + def forward(self, x, hidden, enc_output): + """ + x: (batch_size, 1) + enc_output: batch_size, max_length, dec_embed_dim + hidden: n_layer, batch_size, hidden_size | lstm: (h_n, c_n) + """ + if (hidden is None) and (self.use_attention is False): + raise Exception( + "XlitError: No use of a decoder with No attention and No Hidden" + ) + + batch_sz = x.shape[0] + + if hidden is None: + # hidden: n_layers, batch_size, hidden_dim + hid_for_att = torch.zeros( + (self.dec_layers, batch_sz, self.dec_hidden_dim) + ).to(self.device) + elif self.dec_rnn_type == "lstm": + hid_for_att = hidden[1] # c_n + + # x (batch_size, 1, dec_embed_dim) -> after embedding + x = self.embedding(x) + + if self.use_attention: + # x (batch_size, 1, dec_embed_dim + hidden_size) -> after attention + # aw: (batch_size, max_length, 1) + x, aw = self.attention(x, hidden, enc_output) + else: + x, aw = x, 0 + + # passing the concatenated vector to the GRU + # output: (batch_size, n_layers, hidden_size) + # hidden: n_layers, batch_size, hidden_size | if LSTM (h_n, c_n) + output, hidden = ( + self.dec_rnn(x, hidden) if hidden is not None else self.dec_rnn(x) + ) + + # output :shp: (batch_size * 1, hidden_size) + output = output.view(-1, output.size(2)) + + # output :shp: (batch_size * 1, output_dim) + output = self.fc(output) + + return output, hidden, aw + + +class Seq2Seq(nn.Module): + """ + Class dependency: Encoder, Decoder + """ + + def __init__( + self, encoder, decoder, pass_enc2dec_hid=False, dropout=0, device="cpu" + ): + super(Seq2Seq, self).__init__() + + self.encoder = encoder + self.decoder = decoder + self.device = device + self.pass_enc2dec_hid = pass_enc2dec_hid + _force_en2dec_hid_conv = False + + if self.pass_enc2dec_hid: + assert ( + decoder.dec_hidden_dim == encoder.enc_hidden_dim + ), "Hidden Dimension of encoder and decoder must be same, or unset `pass_enc2dec_hid`" + if decoder.use_attention: + assert ( + decoder.enc_outstate_dim + == encoder.enc_directions * encoder.enc_hidden_dim + ), "Set `enc_out_dim` correctly in decoder" + assert ( + self.pass_enc2dec_hid or decoder.use_attention + ), "No use of a decoder with No attention and No Hidden from Encoder" + + self.use_conv_4_enc2dec_hid = False + if ( + self.pass_enc2dec_hid + and (encoder.enc_directions * encoder.enc_layers != decoder.dec_layers) + ) or _force_en2dec_hid_conv: + if encoder.enc_rnn_type == "lstm" or encoder.enc_rnn_type == "lstm": + raise Exception( + "XlitError: conv for enc2dec_hid not implemented; Change the layer numbers appropriately" + ) + + self.use_conv_4_enc2dec_hid = True + self.enc_hid_1ax = encoder.enc_directions * encoder.enc_layers + self.dec_hid_1ax = decoder.dec_layers + self.e2d_hidden_conv = nn.Conv1d(self.enc_hid_1ax, self.dec_hid_1ax, 1) + + def enc2dec_hidden(self, enc_hidden): + """ + enc_hidden: n_layer, batch_size, hidden_dim*num_directions + TODO: Implement the logic for LSTm bsed model + """ + # hidden: batch_size, enc_layer*num_directions, enc_hidden_dim + hidden = enc_hidden.permute(1, 0, 2).contiguous() + # hidden: batch_size, dec_layers, dec_hidden_dim -> [N,C,Tstep] + hidden = self.e2d_hidden_conv(hidden) + + # hidden: dec_layers, batch_size , dec_hidden_dim + hidden_for_dec = hidden.permute(1, 0, 2).contiguous() + + return hidden_for_dec + + def active_beam_inference(self, src, beam_width=3, max_tgt_sz=50): + """Search based decoding + src: (sequence_len) + """ + + def _avg_score(p_tup): + """Used for Sorting + TODO: Dividing by length of sequence power alpha as hyperparam + """ + return p_tup[0] + + import sys + + batch_size = 1 + start_tok = src[0] + end_tok = src[-1] + src_sz = torch.tensor([len(src)]) + src_ = src.unsqueeze(0) + + # enc_output: (batch_size, padded_seq_length, enc_hidden_dim*num_direction) + # enc_hidden: (enc_layers*num_direction, batch_size, hidden_dim) + enc_output, enc_hidden = self.encoder(src_, src_sz) + + if self.pass_enc2dec_hid: + # dec_hidden: dec_layers, batch_size , dec_hidden_dim + if self.use_conv_4_enc2dec_hid: + init_dec_hidden = self.enc2dec_hidden(enc_hidden) + else: + init_dec_hidden = enc_hidden + else: + # dec_hidden -> Will be initialized to zeros internally + init_dec_hidden = None + + # top_pred[][0] = Σ-log_softmax + # top_pred[][1] = sequence torch.tensor shape: (1) + # top_pred[][2] = dec_hidden + top_pred_list = [(0, start_tok.unsqueeze(0), init_dec_hidden)] + + for t in range(max_tgt_sz): + cur_pred_list = [] + + for p_tup in top_pred_list: + if p_tup[1][-1] == end_tok: + cur_pred_list.append(p_tup) + continue + + # dec_hidden: dec_layers, 1, hidden_dim + # dec_output: 1, output_dim + dec_output, dec_hidden, _ = self.decoder( + x=p_tup[1][-1].view(1, 1), # dec_input: (1,1) + hidden=p_tup[2], + enc_output=enc_output, + ) + + ## π{prob} = Σ{log(prob)} -> to prevent diminishing + # dec_output: (1, output_dim) + dec_output = nn.functional.log_softmax(dec_output, dim=1) + # pred_topk.values & pred_topk.indices: (1, beam_width) + pred_topk = torch.topk(dec_output, k=beam_width, dim=1) + + for i in range(beam_width): + sig_logsmx_ = p_tup[0] + pred_topk.values[0][i] + # seq_tensor_ : (seq_len) + seq_tensor_ = torch.cat((p_tup[1], pred_topk.indices[0][i].view(1))) + + cur_pred_list.append((sig_logsmx_, seq_tensor_, dec_hidden)) + + cur_pred_list.sort(key=_avg_score, reverse=True) # Maximized order + top_pred_list = cur_pred_list[:beam_width] + + # check if end_tok of all topk + end_flags_ = [1 if t[1][-1] == end_tok else 0 for t in top_pred_list] + if beam_width == sum(end_flags_): + break + + pred_tnsr_list = [t[1] for t in top_pred_list] + + return pred_tnsr_list + + +##===================== Glyph handlers ======================================= + + +class GlyphStrawboss: + def __init__(self, glyphs="en"): + """list of letters in a language in unicode + lang: ISO Language code + glyphs: json file with script information + """ + if glyphs == "en": + # Smallcase alone + self.glyphs = [chr(alpha) for alpha in range(97, 122 + 1)] + else: + self.dossier = json.load(open(glyphs, encoding="utf-8")) + self.glyphs = self.dossier["glyphs"] + self.numsym_map = self.dossier["numsym_map"] + + self.char2idx = {} + self.idx2char = {} + self._create_index() + + def _create_index(self): + + self.char2idx["_"] = 0 # pad + self.char2idx["$"] = 1 # start + self.char2idx["#"] = 2 # end + self.char2idx["*"] = 3 # Mask + self.char2idx["'"] = 4 # apostrophe U+0027 + self.char2idx["%"] = 5 # unused + self.char2idx["!"] = 6 # unused + + # letter to index mapping + for idx, char in enumerate(self.glyphs): + self.char2idx[char] = idx + 7 # +7 token initially + + # index to letter mapping + for char, idx in self.char2idx.items(): + self.idx2char[idx] = char + + def size(self): + return len(self.char2idx) + + def word2xlitvec(self, word): + """Converts given string of gyphs(word) to vector(numpy) + Also adds tokens for start and end + """ + try: + vec = [self.char2idx["$"]] # start token + for i in list(word): + vec.append(self.char2idx[i]) + vec.append(self.char2idx["#"]) # end token + + vec = np.asarray(vec, dtype=np.int64) + return vec + + except Exception as error: + print("XlitError: In word:", word, "Error Char not in Token:", error) + sys.exit() + + def xlitvec2word(self, vector): + """Converts vector(numpy) to string of glyphs(word)""" + char_list = [] + for i in vector: + char_list.append(self.idx2char[i]) + + word = "".join(char_list).replace("$", "").replace("#", "") # remove tokens + word = word.replace("_", "").replace("*", "") # remove tokens + return word + + +class VocabSanitizer: + def __init__(self, data_file): + """ + data_file: path to file conatining vocabulary list + """ + extension = os.path.splitext(data_file)[-1] + if extension == ".json": + self.vocab_set = set(json.load(open(data_file, encoding="utf-8"))) + elif extension == ".csv": + self.vocab_df = pd.read_csv(data_file).set_index("WORD") + self.vocab_set = set(self.vocab_df.index) + else: + print("XlitError: Only Json/CSV file extension supported") + + def reposition(self, word_list): + """Reorder Words in list""" + new_list = [] + temp_ = word_list.copy() + for v in word_list: + if v in self.vocab_set: + new_list.append(v) + temp_.remove(v) + new_list.extend(temp_) + + return new_list + + +##=============== INSTANTIATION ================================================ + + +class XlitPiston: + """ + For handling prediction & post-processing of transliteration for a single language + Class dependency: Seq2Seq, GlyphStrawboss, VocabSanitizer + Global Variables: F_DIR + """ + + def __init__( + self, + weight_path, + vocab_file, + tglyph_cfg_file, + iglyph_cfg_file="en", + device="cpu", + ): + + self.device = device + self.in_glyph_obj = GlyphStrawboss(iglyph_cfg_file) + self.tgt_glyph_obj = GlyphStrawboss(glyphs=tglyph_cfg_file) + self.voc_sanity = VocabSanitizer(vocab_file) + + self._numsym_set = set( + json.load(open(tglyph_cfg_file, encoding="utf-8"))["numsym_map"].keys() + ) + self._inchar_set = set("abcdefghijklmnopqrstuvwxyz") + self._natscr_set = set().union( + self.tgt_glyph_obj.glyphs, sum(self.tgt_glyph_obj.numsym_map.values(), []) + ) + + ## Model Config Static TODO: add defining in json support + input_dim = self.in_glyph_obj.size() + output_dim = self.tgt_glyph_obj.size() + enc_emb_dim = 300 + dec_emb_dim = 300 + enc_hidden_dim = 512 + dec_hidden_dim = 512 + rnn_type = "lstm" + enc2dec_hid = True + attention = True + enc_layers = 1 + dec_layers = 2 + m_dropout = 0 + enc_bidirect = True + enc_outstate_dim = enc_hidden_dim * (2 if enc_bidirect else 1) + + enc = Encoder( + input_dim=input_dim, + embed_dim=enc_emb_dim, + hidden_dim=enc_hidden_dim, + rnn_type=rnn_type, + layers=enc_layers, + dropout=m_dropout, + device=self.device, + bidirectional=enc_bidirect, + ) + dec = Decoder( + output_dim=output_dim, + embed_dim=dec_emb_dim, + hidden_dim=dec_hidden_dim, + rnn_type=rnn_type, + layers=dec_layers, + dropout=m_dropout, + use_attention=attention, + enc_outstate_dim=enc_outstate_dim, + device=self.device, + ) + self.model = Seq2Seq(enc, dec, pass_enc2dec_hid=enc2dec_hid, device=self.device) + self.model = self.model.to(self.device) + weights = torch.load(weight_path, map_location=torch.device(self.device)) + + self.model.load_state_dict(weights) + self.model.eval() + + def character_model(self, word, beam_width=1): + in_vec = torch.from_numpy(self.in_glyph_obj.word2xlitvec(word)).to(self.device) + ## change to active or passive beam + p_out_list = self.model.active_beam_inference(in_vec, beam_width=beam_width) + p_result = [ + self.tgt_glyph_obj.xlitvec2word(out.cpu().numpy()) for out in p_out_list + ] + + result = self.voc_sanity.reposition(p_result) + + # List type + return result + + def numsym_model(self, seg): + """tgt_glyph_obj.numsym_map[x] returns a list object""" + if len(seg) == 1: + return [seg] + self.tgt_glyph_obj.numsym_map[seg] + + a = [self.tgt_glyph_obj.numsym_map[n][0] for n in seg] + return [seg] + ["".join(a)] + + def _word_segementer(self, sequence): + + sequence = sequence.lower() + accepted = set().union(self._numsym_set, self._inchar_set, self._natscr_set) + # sequence = ''.join([i for i in sequence if i in accepted]) + + segment = [] + idx = 0 + seq_ = list(sequence) + while len(seq_): + # for Number-Symbol + temp = "" + while len(seq_) and seq_[0] in self._numsym_set: + temp += seq_[0] + seq_.pop(0) + if temp != "": + segment.append(temp) + + # for Target Chars + temp = "" + while len(seq_) and seq_[0] in self._natscr_set: + temp += seq_[0] + seq_.pop(0) + if temp != "": + segment.append(temp) + + # for Input-Roman Chars + temp = "" + while len(seq_) and seq_[0] in self._inchar_set: + temp += seq_[0] + seq_.pop(0) + if temp != "": + segment.append(temp) + + temp = "" + while len(seq_) and seq_[0] not in accepted: + temp += seq_[0] + seq_.pop(0) + if temp != "": + segment.append(temp) + + return segment + + def inferencer(self, sequence, beam_width=10): + + seg = self._word_segementer(sequence[:120]) + lit_seg = [] + + p = 0 + while p < len(seg): + if seg[p][0] in self._natscr_set: + lit_seg.append([seg[p]]) + p += 1 + + elif seg[p][0] in self._inchar_set: + lit_seg.append(self.character_model(seg[p], beam_width=beam_width)) + p += 1 + + elif seg[p][0] in self._numsym_set: # num & punc + lit_seg.append(self.numsym_model(seg[p])) + p += 1 + else: + lit_seg.append([seg[p]]) + p += 1 + + ## IF segment less/equal to 2 then return combinotorial, + ## ELSE only return top1 of each result concatenated + if len(lit_seg) == 1: + final_result = lit_seg[0] + + elif len(lit_seg) == 2: + final_result = [""] + for seg in lit_seg: + new_result = [] + for s in seg: + for f in final_result: + new_result.append(f + s) + final_result = new_result + + else: + new_result = [] + for seg in lit_seg: + new_result.append(seg[0]) + final_result = ["".join(new_result)] + + return final_result + + +from collections.abc import Iterable +from pydload import dload +import zipfile + +MODEL_DOWNLOAD_URL_PREFIX = "https://github.com/AI4Bharat/IndianNLP-Transliteration/releases/download/xlit_v0.5.0/" + + +def is_folder_writable(folder): + try: + os.makedirs(folder, exist_ok=True) + tmp_file = os.path.join(folder, ".write_test") + with open(tmp_file, "w") as f: + f.write("Permission Check") + os.remove(tmp_file) + return True + except: + return False + + +def is_directory_writable(path): + if os.name == "nt": + return is_folder_writable(path) + return os.access(path, os.W_OK | os.X_OK) + + +class XlitEngine: + """ + For Managing the top level tasks and applications of transliteration + Global Variables: F_DIR + """ + + def __init__( + self, lang2use="all", config_path="translit_models/default_lineup.json" + ): + + lineup = json.load(open(os.path.join(F_DIR, config_path), encoding="utf-8")) + self.lang_config = {} + if isinstance(lang2use, str): + if lang2use == "all": + self.lang_config = lineup + elif lang2use in lineup: + self.lang_config[lang2use] = lineup[lang2use] + else: + raise Exception( + "XlitError: The entered Langauge code not found. Available are {}".format( + lineup.keys() + ) + ) + + elif isinstance(lang2use, Iterable): + for l in lang2use: + try: + self.lang_config[l] = lineup[l] + except: + print( + "XlitError: Language code {} not found, Skipping...".format(l) + ) + else: + raise Exception( + "XlitError: lang2use must be a list of language codes (or) string of single language code" + ) + + if is_directory_writable(F_DIR): + models_path = os.path.join(F_DIR, "translit_models") + else: + user_home = os.path.expanduser("~") + models_path = os.path.join(user_home, ".AI4Bharat_Xlit_Models") + os.makedirs(models_path, exist_ok=True) + self.download_models(models_path) + + self.langs = {} + self.lang_model = {} + for la in self.lang_config: + try: + print("Loading {}...".format(la)) + self.lang_model[la] = XlitPiston( + weight_path=os.path.join( + models_path, self.lang_config[la]["weight"] + ), + vocab_file=os.path.join(models_path, self.lang_config[la]["vocab"]), + tglyph_cfg_file=os.path.join( + models_path, self.lang_config[la]["script"] + ), + iglyph_cfg_file="en", + ) + self.langs[la] = self.lang_config[la]["name"] + except Exception as error: + print("XlitError: Failure in loading {} \n".format(la), error) + print(XlitError.loading_err.value) + + def download_models(self, models_path): + """ + Download models from GitHub Releases if not exists + """ + for l in self.lang_config: + lang_name = self.lang_config[l]["eng_name"] + lang_model_path = os.path.join(models_path, lang_name) + if not os.path.isdir(lang_model_path): + print("Downloading model for language: %s" % lang_name) + remote_url = MODEL_DOWNLOAD_URL_PREFIX + lang_name + ".zip" + downloaded_zip_path = os.path.join(models_path, lang_name + ".zip") + dload(url=remote_url, save_to_path=downloaded_zip_path, max_time=None) + + if not os.path.isfile(downloaded_zip_path): + exit( + f"ERROR: Unable to download model from {remote_url} into {models_path}" + ) + + with zipfile.ZipFile(downloaded_zip_path, "r") as zip_ref: + zip_ref.extractall(models_path) + + if os.path.isdir(lang_model_path): + os.remove(downloaded_zip_path) + else: + exit( + f"ERROR: Unable to find models in {lang_model_path} after download" + ) + return + + def translit_word(self, eng_word, lang_code="default", topk=7, beam_width=10): + if eng_word == "": + return [] + + if lang_code in self.langs: + try: + res_list = self.lang_model[lang_code].inferencer( + eng_word, beam_width=beam_width + ) + return res_list[:topk] + + except Exception as error: + print("XlitError:", traceback.format_exc()) + print(XlitError.internal_err.value) + return XlitError.internal_err + + elif lang_code == "default": + try: + res_dict = {} + for la in self.lang_model: + res = self.lang_model[la].inferencer( + eng_word, beam_width=beam_width + ) + res_dict[la] = res[:topk] + return res_dict + + except Exception as error: + print("XlitError:", traceback.format_exc()) + print(XlitError.internal_err.value) + return XlitError.internal_err + + else: + print("XlitError: Unknown Langauge requested", lang_code) + print(XlitError.lang_err.value) + return XlitError.lang_err + + def translit_sentence(self, eng_sentence, lang_code="default", beam_width=10): + if eng_sentence == "": + return [] + + if lang_code in self.langs: + try: + out_str = "" + for word in eng_sentence.split(): + res_ = self.lang_model[lang_code].inferencer( + word, beam_width=beam_width + ) + out_str = out_str + res_[0] + " " + return out_str[:-1] + + except Exception as error: + print("XlitError:", traceback.format_exc()) + print(XlitError.internal_err.value) + return XlitError.internal_err + + elif lang_code == "default": + try: + res_dict = {} + for la in self.lang_model: + out_str = "" + for word in eng_sentence.split(): + res_ = self.lang_model[la].inferencer( + word, beam_width=beam_width + ) + out_str = out_str + res_[0] + " " + res_dict[la] = out_str[:-1] + return res_dict + + except Exception as error: + print("XlitError:", traceback.format_exc()) + print(XlitError.internal_err.value) + return XlitError.internal_err + + else: + print("XlitError: Unknown Langauge requested", lang_code) + print(XlitError.lang_err.value) + return XlitError.lang_err + + +if __name__ == "__main__": + + available_lang = [ + "bn", + "gu", + "hi", + "kn", + "gom", + "mai", + "ml", + "mr", + "pa", + "sd", + "si", + "ta", + "te", + "ur", + ] + + reg = re.compile(r"[a-zA-Z]") + lang = "hi" + engine = XlitEngine( + lang + ) # if you don't specify lang code here, this will give results in all langs available + sent = "Hello World! ABCD क्या हाल है आपका?" + words = [ + engine.translit_word(word, topk=1)[lang][0] if reg.match(word) else word + for word in sent.split() + ] # only transliterated en words, leaves rest as it is + updated_sent = " ".join(words) + + print(updated_sent) + + # output : हेलो वर्ल्ड! क्या हाल है आपका? + + # y = engine.translit_sentence("Hello World !")['hi'] + # print(y) diff --git a/utils/inference/tts.py b/utils/inference/tts.py new file mode 100644 index 0000000000000000000000000000000000000000..dc485ec44dbf34ddbb69c15ad524c0fab189c3c5 --- /dev/null +++ b/utils/inference/tts.py @@ -0,0 +1,167 @@ +from __future__ import absolute_import, division, print_function, unicode_literals +from typing import Tuple +import sys +from argparse import ArgumentParser + +import torch +import numpy as np +import os +import json +import torch + +sys.path.append(os.path.join(os.path.dirname(__file__), "../../src/glow_tts")) + +from scipy.io.wavfile import write +from hifi.env import AttrDict +from hifi.models import Generator + + +from text import text_to_sequence +import commons +import models +import utils + + +def check_directory(dir): + if not os.path.exists(dir): + sys.exit("Error: {} directory does not exist".format(dir)) + + +class TextToMel: + def __init__(self, glow_model_dir, device="cuda"): + self.glow_model_dir = glow_model_dir + check_directory(self.glow_model_dir) + self.device = device + self.hps, self.glow_tts_model = self.load_glow_tts() + + def load_glow_tts(self): + hps = utils.get_hparams_from_dir(self.glow_model_dir) + checkpoint_path = utils.latest_checkpoint_path(self.glow_model_dir) + symbols = list(hps.data.punc) + list(hps.data.chars) + glow_tts_model = models.FlowGenerator( + len(symbols) + getattr(hps.data, "add_blank", False), + out_channels=hps.data.n_mel_channels, + **hps.model + ) # .to(self.device) + + if self.device == "cuda": + glow_tts_model.to("cuda") + + utils.load_checkpoint(checkpoint_path, glow_tts_model) + glow_tts_model.decoder.store_inverse() + _ = glow_tts_model.eval() + + return hps, glow_tts_model + + def generate_mel(self, text, noise_scale=0.667, length_scale=1.0): + print(f"Noise scale: {noise_scale} and Length scale: {length_scale}") + symbols = list(self.hps.data.punc) + list(self.hps.data.chars) + cleaner = self.hps.data.text_cleaners + if getattr(self.hps.data, "add_blank", False): + text_norm = text_to_sequence(text, symbols, cleaner) + text_norm = commons.intersperse(text_norm, len(symbols)) + else: # If not using "add_blank" option during training, adding spaces at the beginning and the end of utterance improves quality + text = " " + text.strip() + " " + text_norm = text_to_sequence(text, symbols, cleaner) + + sequence = np.array(text_norm)[None, :] + + del symbols + del cleaner + del text + del text_norm + + if self.device == "cuda": + x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long() + x_tst_lengths = torch.tensor([x_tst.shape[1]]).cuda() + else: + x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).long() + x_tst_lengths = torch.tensor([x_tst.shape[1]]) + + with torch.no_grad(): + (y_gen_tst, *_), *_, (attn_gen, *_) = self.glow_tts_model( + x_tst, + x_tst_lengths, + gen=True, + noise_scale=noise_scale, + length_scale=length_scale, + ) + del x_tst + del x_tst_lengths + torch.cuda.empty_cache() + return y_gen_tst.cpu().detach().numpy() + + +class MelToWav: + def __init__(self, hifi_model_dir, device="cuda"): + self.hifi_model_dir = hifi_model_dir + check_directory(self.hifi_model_dir) + self.device = device + self.h, self.hifi_gan_generator = self.load_hifi_gan() + + def load_hifi_gan(self): + checkpoint_path = utils.latest_checkpoint_path(self.hifi_model_dir, regex="g_*") + config_file = os.path.join(self.hifi_model_dir, "config.json") + data = open(config_file).read() + json_config = json.loads(data) + h = AttrDict(json_config) + torch.manual_seed(h.seed) + + generator = Generator(h).to(self.device) + + assert os.path.isfile(checkpoint_path) + print("Loading '{}'".format(checkpoint_path)) + state_dict_g = torch.load(checkpoint_path, map_location=self.device) + print("Complete.") + + generator.load_state_dict(state_dict_g["generator"]) + + generator.eval() + generator.remove_weight_norm() + + return h, generator + + def generate_wav(self, mel): + mel = torch.FloatTensor(mel).to(self.device) + + y_g_hat = self.hifi_gan_generator(mel) # passing through vocoder + audio = y_g_hat.squeeze() + audio = audio * 32768.0 + audio = audio.cpu().detach().numpy().astype("int16") + + del y_g_hat + del mel + torch.cuda.empty_cache() + return audio, self.h.sampling_rate + +def restricted_float(x): + try: + x = float(x) + except ValueError: + raise argparse.ArgumentTypeError("%r not a floating-point literal" % (x,)) + + if x < 0.0 or x > 1.0: + raise argparse.ArgumentTypeError("%r not in range [0.0, 1.0]"%(x,)) + return x + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("-a", "--acoustic", required=True, type=str) + parser.add_argument("-v", "--vocoder", required=True, type=str) + parser.add_argument("-d", "--device", type=str, default="cpu") + parser.add_argument("-t", "--text", type=str, required=True) + parser.add_argument("-w", "--wav", type=str, required=True) + parser.add_argument("-n", "--noise-scale", default=0.667, type=restricted_float ) + parser.add_argument("-l", "--length-scale", default=1.0, type=float) + + args = parser.parse_args() + + text_to_mel = TextToMel(glow_model_dir=args.acoustic, device=args.device) + mel_to_wav = MelToWav(hifi_model_dir=args.vocoder, device=args.device) + + mel = text_to_mel.generate_mel(args.text, args.noise_scale, args.length_scale) + audio, sr = mel_to_wav.generate_wav(mel) + + write(filename=args.wav, rate=sr, data=audio) +