





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

__lowerCamelCase	:					Any			  =					256



class  A__       (	__lowerCamelCase      ):
						_UpperCAmelCase     :Union[str, Any]						   =       ['melgan']





						def __init__(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							):
							'''simple docstring'''




							super().__init__()

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

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





						def 	__UpperCamelCase(			self	,							A_	,							A_=(-1.0, 1.0)	,							A_=False       ):
							'''simple docstring'''




							UpperCamelCase     ,   UpperCamelCase     : Dict	     =       output_range
							if clip:
								UpperCamelCase     : Tuple	     =       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 	__UpperCamelCase(			self	,							A_	,							A_=(-1.0, 1.0)	,							A_=False       ):
							'''simple docstring'''




							UpperCamelCase     ,   UpperCamelCase     : Dict	     =       input_range
							UpperCamelCase     : Optional[Any]	     =       torch.clip(UpperCamelCase_	,							UpperCamelCase_	,							UpperCamelCase_       ) if clip else outputs
							# Scale to [0, 1].
							UpperCamelCase     : Optional[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 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
							'''simple docstring'''




							UpperCamelCase     : Dict	     =       input_tokens > 0
							UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       self.notes_encoder(
							    encoder_input_tokens=UpperCamelCase_	,							encoder_inputs_mask=UpperCamelCase_       )

							UpperCamelCase     ,   UpperCamelCase     : Union[str, Any]	     =       self.continuous_encoder(
							    encoder_inputs=UpperCamelCase_	,							encoder_inputs_mask=UpperCamelCase_       )

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





						def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
							'''simple docstring'''




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

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

							UpperCamelCase     : Dict	     =       self.decoder(
							    encodings_and_masks=UpperCamelCase_	,							decoder_input_tokens=UpperCamelCase_	,							decoder_noise_time=UpperCamelCase_       )
							return logits





						@torch.no_grad()
						def __call__(			self	,							A_	,							A_ = None	,							A_ = 100	,							A_ = True	,							A_ = "numpy"	,							A_ = None	,							A_ = 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     : Tuple	     =       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     : str	     =       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     : Optional[int]	     =       ones

								UpperCamelCase     : int	     =       self.scale_features(
								    UpperCamelCase_	,							output_range=[-1.0, 1.0]	,							clip=UpperCamelCase_       )

								UpperCamelCase     : Union[str, Any]	     =       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     : Any	     =       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     : str	     =       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     : int	     =       self.scheduler.step(UpperCamelCase_	,							UpperCamelCase_	,							UpperCamelCase_	,							generator=UpperCamelCase_       ).prev_sample

								UpperCamelCase     : Any	     =       self.scale_to_features(UpperCamelCase_	,							input_range=[-1.0, 1.0]       )
								UpperCamelCase     : str	     =       mel[:1]
								UpperCamelCase     : List[str]	     =       mel.cpu().float().numpy()

								UpperCamelCase     : List[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     : Optional[Any]	     =       self.melgan(input_features=full_pred_mel.astype(np.floataa       )       )
							else:
								UpperCamelCase     : Tuple	     =       full_pred_mel

							if not return_dict:
								return (output,)

							return AudioPipelineOutput(audios=UpperCamelCase_       )







from __future__ import annotations



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			list[str]:
 if partitions <= 0:
  raise ValueError("partitions must be a positive number!"					)
 if partitions > number_of_bytes:
  raise ValueError("partitions can not > number_of_bytes!"					)
 UpperCamelCase     : str	     =       number_of_bytes // partitions
 UpperCamelCase     : List[Any]	     =       []
 for i in range(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       i * bytes_per_partition + 1
  UpperCamelCase     : Any	     =       (
      number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
  )
  allocation_list.append(F"""{start_bytes}-{end_bytes}"""					)
 return allocation_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()
