



'''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


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

_UpperCamelCase											=		256






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =["""melgan"""]




  def __init__(					self :      Dict     ,      _a :      SpectrogramNotesEncoder     ,      _a :      SpectrogramContEncoder     ,      _a :      TaFilmDecoder     ,      _a :      DDPMScheduler     ,      _a :      OnnxRuntimeModel if is_onnx_available() else Any     ,      )   ->   None:
    super().__init__()

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

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




  def 		_lowercase	(					self :      Tuple     ,      _a :      int     ,      _a :      List[Any]=(-1.0, 1.0)     ,      _a :      Any=False  )   ->   Dict:
    __lowerCamelCase			,__lowerCamelCase			:			Any       			=					output_range
    if clip:
      __lowerCamelCase			:			List[Any]       			=					torch.clip(_a     ,      self.min_value     ,      self.max_value  )
    # Scale to [0, 1].
    __lowerCamelCase			:			Union[str, 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 		_lowercase	(					self :      Dict     ,      _a :      List[str]     ,      _a :      int=(-1.0, 1.0)     ,      _a :      Dict=False  )   ->   List[str]:
    __lowerCamelCase			,__lowerCamelCase			:			List[Any]       			=					input_range
    __lowerCamelCase			:			Optional[Any]       			=					torch.clip(_a     ,      _a     ,      _a  ) if clip else outputs
    # Scale to [0, 1].
    __lowerCamelCase			:			str       			=					(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 		_lowercase	(					self :      int     ,      _a :      Dict     ,      _a :      List[str]     ,      _a :      Tuple  )   ->   Any:
    __lowerCamelCase			:			Tuple       			=					input_tokens > 0
    __lowerCamelCase			,__lowerCamelCase			:			int       			=					self.notes_encoder(
        encoder_input_tokens=_a     ,      encoder_inputs_mask=_a  )

    __lowerCamelCase			,__lowerCamelCase			:			Tuple       			=					self.continuous_encoder(
        encoder_inputs=_a     ,      encoder_inputs_mask=_a  )

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




  def 		_lowercase	(					self :      Tuple     ,      _a :      Tuple     ,      _a :      List[Any]     ,      _a :      int  )   ->   Dict:
    __lowerCamelCase			:			Any       			=					noise_time
    if not torch.is_tensor(_a  ):
      __lowerCamelCase			:			Union[str, Any]       			=					torch.tensor([timesteps]     ,      dtype=torch.long     ,      device=input_tokens.device  )
    elif torch.is_tensor(_a  ) and len(timesteps.shape  ) == 0:
      __lowerCamelCase			:			List[str]       			=					timesteps[None].to(input_tokens.device  )

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

    __lowerCamelCase			:			int       			=					self.decoder(
        encodings_and_masks=_a     ,      decoder_input_tokens=_a     ,      decoder_noise_time=_a  )
    return logits




  @torch.no_grad()
  def __call__(					self :      Optional[int]     ,      _a :      List[List[int]]     ,      _a :      Optional[torch.Generator] = None     ,      _a :      int = 100     ,      _a :      bool = True     ,      _a :      str = "numpy"     ,      _a :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,      _a :      int = 1     ,      )   ->   Union[AudioPipelineOutput, Tuple]:
    if (callback_steps is None) or (
        callback_steps is not None and (not isinstance(_a     ,      _a  ) or callback_steps <= 0)
    ):
      raise ValueError(
          f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
          f' {type(_a  )}.'  )

    __lowerCamelCase			:			Optional[int]       			=					np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]     ,      dtype=np.floataa  )
    __lowerCamelCase			:			Dict       			=					np.zeros([1, 0, self.n_dims]     ,      np.floataa  )
    __lowerCamelCase			:			List[Any]       			=					torch.ones((1, TARGET_FEATURE_LENGTH)     ,      dtype=_a     ,      device=self.device  )

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

      __lowerCamelCase			:			int       			=					self.scale_features(
          _a     ,      output_range=[-1.0, 1.0]     ,      clip=_a  )

      __lowerCamelCase			:			Tuple       			=					self.encode(
          input_tokens=torch.IntTensor([encoder_input_tokens]  ).to(device=self.device  )     ,      continuous_inputs=_a     ,      continuous_mask=_a     ,      )

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

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

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

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

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

      __lowerCamelCase			:			Dict       			=					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(_a     ,      _a  )

      logger.info('Generated segment'     ,      _a  )

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

    if not return_dict:
      return (output,)

    return AudioPipelineOutput(audios=_a  )











'''simple docstring'''





def 			a_	(				_lowerCAmelCase			) -> int:

  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    raise TypeError('only integers accepted as input'			)
  else:
    __lowerCamelCase			:			Any       			=					str(abs(_lowerCAmelCase			)			)
    __lowerCamelCase			:			Tuple       			=					[list(_lowerCAmelCase			) for char in range(len(_lowerCAmelCase			)			)]
    for index in range(len(_lowerCAmelCase			)			):
      num_transpositions[index].pop(_lowerCAmelCase			)
    return max(
        int(''.join(list(_lowerCAmelCase			)			)			) for transposition in num_transpositions			)


if __name__ == "__main__":
   __import__('doctest').testmod()






