






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


lowerCamelCase : List[Any]						   =       logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase : Union[str, Any]						   =       2_56

class 		lowerCAmelCase     ( __lowerCAmelCase			):

					'''simple docstring'''

					_A						:			List[Any]									=      ['''melgan''']



					def __init__(  self				:	Dict		,     __a				:	str		,     __a				:	Optional[int]		,     __a				:	str		,     __a				:	str		,     __a				:	int		,     )			->  None:


												"""simple docstring"""

												super().__init__()

												# From MELGAN
												__lowercase      :							Dict				=     math.log(1E-5							)  # Matches MelGAN training.
												__lowercase      :							Tuple				=     4.0  # Largest value for most examples
												__lowercase      :							Optional[int]				=     128

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



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	int		,     __a				:	List[str]=(-1.0, 1.0)		,     __a				:	int=False							)			->  str:


												"""simple docstring"""

												__lowercase			,			__lowercase      :							Dict				=     output_range
												if clip:
																			__lowercase      :							Tuple				=     torch.clip(lowerCAmelCase_		,     self.min_value		,     self.max_value							)
												# Scale to [0, 1].
												__lowercase      :							str				=     (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				:	List[Any]		,     __a				:	Optional[Any]		,     __a				:	Optional[Any]=(-1.0, 1.0)		,     __a				:	Optional[Any]=False							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase			,			__lowercase      :							Union[str, Any]				=     input_range
												__lowercase      :							Any				=     torch.clip(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_							) if clip else outputs
												# Scale to [0, 1].
												__lowercase      :							int				=     (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				:	Dict		,     __a				:	Tuple		,     __a				:	List[str]		,     __a				:	Any							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Any				=     input_tokens > 0
												__lowercase			,			__lowercase      :							Union[str, Any]				=     self.notes_encoder(
												    encoder_input_tokens=lowerCAmelCase_		,     encoder_inputs_mask=lowerCAmelCase_							)

												__lowercase			,			__lowercase      :							List[Any]				=     self.continuous_encoder(
												    encoder_inputs=lowerCAmelCase_		,     encoder_inputs_mask=lowerCAmelCase_							)

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



					def       lowerCAmelCase				(  self				:	str		,     __a				:	Optional[Any]		,     __a				:	List[Any]		,     __a				:	Tuple							)			->  int:


												"""simple docstring"""

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

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

												__lowercase      :							Any				=     self.decoder(
												    encodings_and_masks=lowerCAmelCase_		,     decoder_input_tokens=lowerCAmelCase_		,     decoder_noise_time=lowerCAmelCase_							)
												return logits



					@torch.no_grad()
					def __call__(  self				:	int		,     __a				:	Any		,     __a				:	Any = None		,     __a				:	List[Any] = 100		,     __a				:	Union[str, Any] = True		,     __a				:	str = "numpy"		,     __a				:	str = None		,     __a				:	Union[str, Any] = 1		,     )			->  Union[AudioPipelineOutput, Tuple]:


												"""simple docstring"""

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

												__lowercase      :							str				=     np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]		,     dtype=np.floataa							)
												__lowercase      :							List[Any]				=     np.zeros([1, 0, self.n_dims]		,     np.floataa							)
												__lowercase      :							Union[str, Any]				=     torch.ones((1, TARGET_FEATURE_LENGTH)		,     dtype=lowerCAmelCase_		,     device=self.device							)

												for i, encoder_input_tokens in enumerate(lowerCAmelCase_							):
																			if i == 0:
																										__lowercase      :							Union[str, Any]				=     torch.from_numpy(pred_mel[:1].copy()							).to(
																										    device=self.device		,     dtype=self.decoder.dtype							)
																										# The first chunk has no previous context.
																										__lowercase      :							List[str]				=     torch.zeros((1, TARGET_FEATURE_LENGTH)		,     dtype=lowerCAmelCase_		,     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.
																										__lowercase      :							Any				=     ones

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

																			__lowercase      :							Optional[Any]				=     self.encode(
																			    input_tokens=torch.IntTensor([encoder_input_tokens]							).to(device=self.device							)		,     continuous_inputs=lowerCAmelCase_		,     continuous_mask=lowerCAmelCase_		,     )

																			# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
																			__lowercase      :							Optional[Any]				=     randn_tensor(
																			    shape=encoder_continuous_inputs.shape		,     generator=lowerCAmelCase_		,     device=self.device		,     dtype=self.decoder.dtype		,     )

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

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

																										# Compute previous output: x_t -> x_t-1
																										__lowercase      :							Optional[int]				=     self.scheduler.step(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     generator=lowerCAmelCase_							).prev_sample

																			__lowercase      :							List[str]				=     self.scale_to_features(lowerCAmelCase_		,     input_range=[-1.0, 1.0]							)
																			__lowercase      :							Tuple				=     mel[:1]
																			__lowercase      :							Optional[Any]				=     mel.cpu().float().numpy()

																			__lowercase      :							List[str]				=     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(lowerCAmelCase_		,     lowerCAmelCase_							)

																			logger.info("""Generated segment"""		,     lowerCAmelCase_							)

												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":
																			__lowercase      :							Dict				=     self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
												else:
																			__lowercase      :							List[str]				=     full_pred_mel

												if not return_dict:
																			return (output,)

												return AudioPipelineOutput(audios=lowerCAmelCase_							)












from __future__ import annotations
def       snake_case_		(     lowerCAmelCase_       :						list[int]      ):
							if not nums:
														return 0
							__lowercase      :							Tuple				=     nums[0]
							__lowercase      :							Tuple				=     0
							for num in nums[1:]:
														__lowercase			,			__lowercase      :							List[str]				=     (
														    max_excluding + num,
														    max(lowerCAmelCase_       ,       lowerCAmelCase_      ),
														)
							return max(lowerCAmelCase_       ,       lowerCAmelCase_      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()