


'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
		from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


__snake_case							   =  logging.get_logger(__name__)  # pylint: disable=invalid-name

__snake_case							   =  256
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       ['melgan']




		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     ):

							'''simple docstring'''


							super().__init__()

							# From MELGAN
							UpperCamelCase__     :str							      =				math.log(1e-5							)  # Matches MelGAN training.
							UpperCamelCase__     :Tuple							      =				4.0  # Largest value for most examples
							UpperCamelCase__     :List[str]							      =				128

							self.register_modules(
							    notes_encoder=UpperCamelCase_					,     continuous_encoder=UpperCamelCase_					,     decoder=UpperCamelCase_					,     scheduler=UpperCamelCase_					,     melgan=UpperCamelCase_					,     )




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=(-1.0, 1.0)					,     UpperCamelCase_=False							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Optional[Any]							      =				output_range
							if clip:
												UpperCamelCase__     :Optional[int]							      =				torch.clip(UpperCamelCase_					,     self.min_value					,     self.max_value							)
							# Scale to [0, 1].
							UpperCamelCase__     :Optional[Any]							      =				(features - self.min_value) / (self.max_value - self.min_value)
							# Scale to [min_out, max_out].
							return zero_one * (max_out - min_out) + min_out




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=(-1.0, 1.0)					,     UpperCamelCase_=False							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				input_range
							UpperCamelCase__     :List[str]							      =				torch.clip(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							) if clip else outputs
							# Scale to [0, 1].
							UpperCamelCase__     :Optional[Any]							      =				(outputs - min_out) / (max_out - min_out)
							# Scale to [self.min_value, self.max_value].
							return zero_one * (self.max_value - self.min_value) + self.min_value




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				input_tokens > 0
							UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.notes_encoder(
							    encoder_input_tokens=UpperCamelCase_					,     encoder_inputs_mask=UpperCamelCase_							)

							UpperCamelCase__      ,       UpperCamelCase__     :Optional[Any]							      =				self.continuous_encoder(
							    encoder_inputs=UpperCamelCase_					,     encoder_inputs_mask=UpperCamelCase_							)

							return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				noise_time
							if not torch.is_tensor(UpperCamelCase_							):
												UpperCamelCase__     :Any							      =				torch.tensor([timesteps]					,     dtype=torch.long					,     device=input_tokens.device							)
							elif torch.is_tensor(UpperCamelCase_							) and len(timesteps.shape							) == 0:
												UpperCamelCase__     :Optional[Any]							      =				timesteps[None].to(input_tokens.device							)

							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							UpperCamelCase__     :Optional[int]							      =				timesteps * torch.ones(input_tokens.shape[0]					,     dtype=timesteps.dtype					,     device=timesteps.device							)

							UpperCamelCase__     :int							      =				self.decoder(
							    encodings_and_masks=UpperCamelCase_					,     decoder_input_tokens=UpperCamelCase_					,     decoder_noise_time=UpperCamelCase_							)
							return logits




		@torch.no_grad()
		def __call__(	self					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     UpperCamelCase_ = 100					,     UpperCamelCase_ = True					,     UpperCamelCase_ = "numpy"					,     UpperCamelCase_ = None					,     UpperCamelCase_ = 1					,     ):

							'''simple docstring'''


							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(UpperCamelCase_					,     UpperCamelCase_							) or callback_steps <= 0)
							):
												raise ValueError(
												    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
												    F''' {type(UpperCamelCase_							)}.'''							)

							UpperCamelCase__     :Any							      =				np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]					,     dtype=np.floataa							)
							UpperCamelCase__     :Dict							      =				np.zeros([1, 0, self.n_dims]					,     np.floataa							)
							UpperCamelCase__     :str							      =				torch.ones((1, TARGET_FEATURE_LENGTH)					,     dtype=UpperCamelCase_					,     device=self.device							)

							for i, encoder_input_tokens in enumerate(UpperCamelCase_							):
												if i == 0:
																	UpperCamelCase__     :int							      =				torch.from_numpy(pred_mel[:1].copy()							).to(
																	    device=self.device					,     dtype=self.decoder.dtype							)
																	# The first chunk has no previous context.
																	UpperCamelCase__     :Dict							      =				torch.zeros((1, TARGET_FEATURE_LENGTH)					,     dtype=UpperCamelCase_					,     device=self.device							)
												else:
																	# The full song pipeline does not feed in a context feature, so the mask
																	# will be all 0s after the feature converter. Because we know we're
																	# feeding in a full context chunk from the previous prediction, set it
																	# to all 1s.
																	UpperCamelCase__     :List[str]							      =				ones

												UpperCamelCase__     :Union[str, Any]							      =				self.scale_features(
												    UpperCamelCase_					,     output_range=[-1.0, 1.0]					,     clip=UpperCamelCase_							)

												UpperCamelCase__     :Dict							      =				self.encode(
												    input_tokens=torch.IntTensor([encoder_input_tokens]							).to(device=self.device							)					,     continuous_inputs=UpperCamelCase_					,     continuous_mask=UpperCamelCase_					,     )

												# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
												UpperCamelCase__     :Dict							      =				randn_tensor(
												    shape=encoder_continuous_inputs.shape					,     generator=UpperCamelCase_					,     device=self.device					,     dtype=self.decoder.dtype					,     )

												# set step values
												self.scheduler.set_timesteps(UpperCamelCase_							)

												# Denoising diffusion loop
												for j, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
																	UpperCamelCase__     :Optional[Any]							      =				self.decode(
																	    encodings_and_masks=UpperCamelCase_					,     input_tokens=UpperCamelCase_					,     noise_time=t / self.scheduler.config.num_train_timesteps					,     )

																	# Compute previous output: x_t -> x_t-1
																	UpperCamelCase__     :Optional[Any]							      =				self.scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     generator=UpperCamelCase_							).prev_sample

												UpperCamelCase__     :Optional[int]							      =				self.scale_to_features(UpperCamelCase_					,     input_range=[-1.0, 1.0]							)
												UpperCamelCase__     :Tuple							      =				mel[:1]
												UpperCamelCase__     :str							      =				mel.cpu().float().numpy()

												UpperCamelCase__     :Optional[Any]							      =				np.concatenate([full_pred_mel, pred_mel[:1]]					,     axis=1							)

												# call the callback, if provided
												if callback is not None and i % callback_steps == 0:
																	callback(UpperCamelCase_					,     UpperCamelCase_							)

												logger.info('''Generated segment'''					,     UpperCamelCase_							)

							if output_type == "numpy" and not is_onnx_available():
												raise ValueError(
												    '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.'''							)
							elif output_type == "numpy" and self.melgan is None:
												raise ValueError(
												    '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.'''							)

							if output_type == "numpy":
												UpperCamelCase__     :Tuple							      =				self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
							else:
												UpperCamelCase__     :str							      =				full_pred_mel

							if not return_dict:
												return (output,)

							return AudioPipelineOutput(audios=UpperCamelCase_							)



'''simple docstring'''
import torch
from torch import nn
class      lowercase			(							nn.Module      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=1					,     UpperCamelCase_=False							):

							'''simple docstring'''


							super().__init__()

							UpperCamelCase__     :Dict							      =				n_token
							UpperCamelCase__     :List[Any]							      =				d_embed
							UpperCamelCase__     :Dict							      =				d_proj

							UpperCamelCase__     :Dict							      =				cutoffs + [n_token]
							UpperCamelCase__     :Union[str, Any]							      =				[0] + self.cutoffs
							UpperCamelCase__     :Any							      =				div_val

							UpperCamelCase__     :int							      =				self.cutoffs[0]
							UpperCamelCase__     :List[Any]							      =				len(self.cutoffs							) - 1
							UpperCamelCase__     :List[Any]							      =				self.shortlist_size + self.n_clusters

							if self.n_clusters > 0:
												UpperCamelCase__     :Any							      =				nn.Parameter(torch.zeros(self.n_clusters					,     self.d_embed							)							)
												UpperCamelCase__     :Optional[Any]							      =				nn.Parameter(torch.zeros(self.n_clusters							)							)

							UpperCamelCase__     :Union[str, Any]							      =				nn.ModuleList()
							UpperCamelCase__     :str							      =				nn.ParameterList()

							if div_val == 1:
												for i in range(len(self.cutoffs							)							):
																	if d_proj != d_embed:
																						self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCamelCase_					,     UpperCamelCase_							)							)							)
																	else:
																						self.out_projs.append(UpperCamelCase_							)

												self.out_layers.append(nn.Linear(UpperCamelCase_					,     UpperCamelCase_							)							)
							else:
												for i in range(len(self.cutoffs							)							):
																	UpperCamelCase__      ,       UpperCamelCase__     :List[str]							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																	UpperCamelCase__     :Dict							      =				d_embed // (div_val**i)

																	self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCamelCase_					,     UpperCamelCase_							)							)							)

																	self.out_layers.append(nn.Linear(UpperCamelCase_					,     r_idx - l_idx							)							)

							UpperCamelCase__     :Tuple							      =				keep_order




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							if proj is None:
												UpperCamelCase__     :List[str]							      =				nn.functional.linear(UpperCamelCase_					,     UpperCamelCase_					,     bias=UpperCamelCase_							)
							else:
												# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
												UpperCamelCase__     :Any							      =				nn.functional.linear(UpperCamelCase_					,     proj.t().contiguous()							)
												UpperCamelCase__     :Union[str, Any]							      =				nn.functional.linear(UpperCamelCase_					,     UpperCamelCase_					,     bias=UpperCamelCase_							)
												# else:
												#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
												#     if bias is not None:
												#         logit = logit + bias

							return logit




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=None					,     UpperCamelCase_=False							):

							'''simple docstring'''


							if labels is not None:
												# Shift so that tokens < n predict n
												UpperCamelCase__     :Optional[Any]							      =				hidden[..., :-1, :].contiguous()
												UpperCamelCase__     :Optional[Any]							      =				labels[..., 1:].contiguous()
												UpperCamelCase__     :Optional[Any]							      =				hidden.view(-1					,     hidden.size(-1							)							)
												UpperCamelCase__     :str							      =				labels.view(-1							)
												if hidden.size(0							) != labels.size(0							):
																	raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''							)
							else:
												UpperCamelCase__     :int							      =				hidden.view(-1					,     hidden.size(-1							)							)

							if self.n_clusters == 0:
												UpperCamelCase__     :Optional[int]							      =				self._compute_logit(UpperCamelCase_					,     self.out_layers[0].weight					,     self.out_layers[0].bias					,     self.out_projs[0]							)
												if labels is not None:
																	UpperCamelCase__     :int							      =				labels != -100
																	UpperCamelCase__     :List[Any]							      =				torch.zeros_like(UpperCamelCase_					,     dtype=hidden.dtype					,     device=hidden.device							)
																	UpperCamelCase__     :str							      =				(
																	    -nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)[mask].gather(1					,     labels[mask].unsqueeze(1							)							).squeeze(1							)
																	)
												else:
																	UpperCamelCase__     :Dict							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)
							else:
												# construct weights and biases
												UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				[], []
												for i in range(len(self.cutoffs							)							):
																	if self.div_val == 1:
																						UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						UpperCamelCase__     :str							      =				self.out_layers[0].weight[l_idx:r_idx]
																						UpperCamelCase__     :Optional[int]							      =				self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						UpperCamelCase__     :Optional[Any]							      =				self.out_layers[i].weight
																						UpperCamelCase__     :Optional[int]							      =				self.out_layers[i].bias

																	if i == 0:
																						UpperCamelCase__     :str							      =				torch.cat([weight_i, self.cluster_weight]					,     dim=0							)
																						UpperCamelCase__     :List[str]							      =				torch.cat([bias_i, self.cluster_bias]					,     dim=0							)

																	weights.append(UpperCamelCase_							)
																	biases.append(UpperCamelCase_							)

												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Optional[Any]							      =				weights[0], biases[0], self.out_projs[0]

												UpperCamelCase__     :str							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
												UpperCamelCase__     :List[Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

												if labels is None:
																	UpperCamelCase__     :Union[str, Any]							      =				hidden.new_empty((head_logit.size(0							), self.n_token)							)
												else:
																	UpperCamelCase__     :Any							      =				torch.zeros_like(UpperCamelCase_					,     dtype=hidden.dtype					,     device=hidden.device							)

												UpperCamelCase__     :Any							      =				0
												UpperCamelCase__     :str							      =				[0] + self.cutoffs
												for i in range(len(UpperCamelCase_							) - 1							):
																	UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				cutoff_values[i], cutoff_values[i + 1]

																	if labels is not None:
																						UpperCamelCase__     :Any							      =				(labels >= l_idx) & (labels < r_idx)
																						UpperCamelCase__     :int							      =				mask_i.nonzero().squeeze()

																						if indices_i.numel() == 0:
																											continue

																						UpperCamelCase__     :Tuple							      =				labels.index_select(0					,     UpperCamelCase_							) - l_idx
																						UpperCamelCase__     :str							      =				head_logprob.index_select(0					,     UpperCamelCase_							)
																						UpperCamelCase__     :int							      =				hidden.index_select(0					,     UpperCamelCase_							)
																	else:
																						UpperCamelCase__     :Dict							      =				hidden

																	if i == 0:
																						if labels is not None:
																											UpperCamelCase__     :Any							      =				head_logprob_i.gather(1					,     target_i[:, None]							).squeeze(1							)
																						else:
																											UpperCamelCase__     :List[str]							      =				head_logprob[:, : self.cutoffs[0]]
																	else:
																						UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				weights[i], biases[i], self.out_projs[i]

																						UpperCamelCase__     :str							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
																						UpperCamelCase__     :Union[str, Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)
																						UpperCamelCase__     :Any							      =				self.cutoffs[0] + i - 1  # No probability for the head cluster
																						if labels is not None:
																											UpperCamelCase__     :List[Any]							      =				head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																											    1					,     target_i[:, None]							).squeeze(1							)
																						else:
																											UpperCamelCase__     :List[str]							      =				head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																											UpperCamelCase__     :Dict							      =				logprob_i

																	if labels is not None:
																						if (hasattr(self					,     '''keep_order'''							) and self.keep_order) or keep_order:
																											out.index_copy_(0					,     UpperCamelCase_					,     -logprob_i							)
																						else:
																											out[offset : offset + logprob_i.size(0							)].copy_(-logprob_i							)
																						offset += logprob_i.size(0							)

							return out




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.n_clusters == 0:
												UpperCamelCase__     :Optional[Any]							      =				self._compute_logit(UpperCamelCase_					,     self.out_layers[0].weight					,     self.out_layers[0].bias					,     self.out_projs[0]							)
												return nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)
							else:
												# construct weights and biases
												UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				[], []
												for i in range(len(self.cutoffs							)							):
																	if self.div_val == 1:
																						UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						UpperCamelCase__     :Union[str, Any]							      =				self.out_layers[0].weight[l_idx:r_idx]
																						UpperCamelCase__     :str							      =				self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						UpperCamelCase__     :List[Any]							      =				self.out_layers[i].weight
																						UpperCamelCase__     :List[Any]							      =				self.out_layers[i].bias

																	if i == 0:
																						UpperCamelCase__     :Dict							      =				torch.cat([weight_i, self.cluster_weight]					,     dim=0							)
																						UpperCamelCase__     :Optional[Any]							      =				torch.cat([bias_i, self.cluster_bias]					,     dim=0							)

																	weights.append(UpperCamelCase_							)
																	biases.append(UpperCamelCase_							)

												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :str							      =				weights[0], biases[0], self.out_projs[0]
												UpperCamelCase__     :Optional[int]							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)

												UpperCamelCase__     :int							      =				hidden.new_empty((head_logit.size(0							), self.n_token)							)
												UpperCamelCase__     :Union[str, Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

												UpperCamelCase__     :int							      =				[0] + self.cutoffs
												for i in range(len(UpperCamelCase_							) - 1							):
																	UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				cutoff_values[i], cutoff_values[i + 1]

																	if i == 0:
																						UpperCamelCase__     :str							      =				head_logprob[:, : self.cutoffs[0]]
																	else:
																						UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				weights[i], biases[i], self.out_projs[i]

																						UpperCamelCase__     :List[str]							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
																						UpperCamelCase__     :Tuple							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

																						UpperCamelCase__     :Optional[Any]							      =				head_logprob[:, -i] + tail_logprob_i
																						UpperCamelCase__     :Union[str, Any]							      =				logprob_i

												return out