





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  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      ['melgan']







  def __init__(      self  :Optional[Any]   ,							_lowercase  :SpectrogramNotesEncoder   ,							_lowercase  :SpectrogramContEncoder   ,							_lowercase  :TaFilmDecoder   ,							_lowercase  :DDPMScheduler   ,							_lowercase  :OnnxRuntimeModel if is_onnx_available() else Any   ,							):






     '''simple docstring'''





     super().__init__()

     # From MELGAN
     lowercase__			       =  math.log(1e-5 )  # Matches MelGAN training.
     lowercase__			       =  4.0  # Largest value for most examples
     lowercase__			       =  1_28

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







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :Dict   ,							_lowercase  :List[str]=(-1.0, 1.0)   ,							_lowercase  :List[str]=False ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  output_range
     if clip:
        lowercase__			       =  torch.clip(_lowercase   ,							self.min_value   ,							self.max_value )
     # Scale to [0, 1].
     lowercase__			       =  (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  :Optional[int]   ,							_lowercase  :List[str]   ,							_lowercase  :Any=(-1.0, 1.0)   ,							_lowercase  :Dict=False ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  input_range
     lowercase__			       =  torch.clip(_lowercase   ,							_lowercase   ,							_lowercase ) if clip else outputs
     # Scale to [0, 1].
     lowercase__			       =  (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  :Optional[int]   ,							_lowercase  :Any   ,							_lowercase  :Optional[int]   ,							_lowercase  :int ):






     '''simple docstring'''





     lowercase__			       =  input_tokens > 0
     lowercase__       ,							lowercase__			       =  self.notes_encoder(
         encoder_input_tokens=_lowercase   ,							encoder_inputs_mask=_lowercase )

     lowercase__       ,							lowercase__			       =  self.continuous_encoder(
         encoder_inputs=_lowercase   ,							encoder_inputs_mask=_lowercase )

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







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Dict   ,							_lowercase  :Optional[Any]   ,							_lowercase  :int ):






     '''simple docstring'''





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

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

     lowercase__			       =  self.decoder(
         encodings_and_masks=_lowercase   ,							decoder_input_tokens=_lowercase   ,							decoder_noise_time=_lowercase )
     return logits







  @torch.no_grad()
  def __call__(      self  :Union[str, Any]   ,							_lowercase  :List[List[int]]   ,							_lowercase  :Optional[torch.Generator] = None   ,							_lowercase  :int = 1_00   ,							_lowercase  :bool = True   ,							_lowercase  :str = "numpy"   ,							_lowercase  :Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,							_lowercase  :int = 1   ,							):






     '''simple docstring'''





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

     lowercase__			       =  np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]   ,							dtype=np.floataa )
     lowercase__			       =  np.zeros([1, 0, self.n_dims]   ,							np.floataa )
     lowercase__			       =  torch.ones((1, TARGET_FEATURE_LENGTH)   ,							dtype=_lowercase   ,							device=self.device )

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

        lowercase__			       =  self.scale_features(
            _lowercase   ,							output_range=[-1.0, 1.0]   ,							clip=_lowercase )

        lowercase__			       =  self.encode(
            input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device )   ,							continuous_inputs=_lowercase   ,							continuous_mask=_lowercase   ,							)

        # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
        lowercase__			       =  randn_tensor(
            shape=encoder_continuous_inputs.shape   ,							generator=_lowercase   ,							device=self.device   ,							dtype=self.decoder.dtype   ,							)

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

        # Denoising diffusion loop
        for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
           lowercase__			       =  self.decode(
               encodings_and_masks=_lowercase   ,							input_tokens=_lowercase   ,							noise_time=t / self.scheduler.config.num_train_timesteps   ,							)

           # Compute previous output: x_t -> x_t-1
           lowercase__			       =  self.scheduler.step(_lowercase   ,							_lowercase   ,							_lowercase   ,							generator=_lowercase ).prev_sample

        lowercase__			       =  self.scale_to_features(_lowercase   ,							input_range=[-1.0, 1.0] )
        lowercase__			       =  mel[:1]
        lowercase__			       =  mel.cpu().float().numpy()

        lowercase__			       =  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(_lowercase   ,							_lowercase )

        logger.info("Generated segment"   ,							_lowercase )

     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__			       =  self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
     else:
        lowercase__			       =  full_pred_mel

     if not return_dict:
        return (output,)

     return AudioPipelineOutput(audios=_lowercase )













def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__	):
   if height >= 1:
      move_tower(height - 1  , __magic_name__  , __magic_name__  , __magic_name__	)
      move_disk(__magic_name__  , __magic_name__	)
      move_tower(height - 1  , __magic_name__  , __magic_name__  , __magic_name__	)


def _A       (   __magic_name__  , __magic_name__	):
   print("moving disk from"  , __magic_name__  , "to"  , __magic_name__	)


def _A       (   ):
   lowercase__			       =  int(input("Height of hanoi: "	).strip()	)
   move_tower(__magic_name__  , "A"  , "B"  , "C"	)


if __name__ == "__main__":
  main()






