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

__lowerCamelCase      :			Any							       =		256







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["melgan"]




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



       """simple docstring"""






       super().__init__()

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

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




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



       """simple docstring"""






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



       """simple docstring"""






       SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				input_range
       SCREAMING_SNAKE_CASE__      							=				torch.clip(_lowercase	,     _lowercase	,     _lowercase				) if clip else outputs
       # Scale to [0, 1].
       SCREAMING_SNAKE_CASE__      							=				(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      __a						(   self      :    Dict	,     _lowercase      :    str	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Any				):



       """simple docstring"""






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

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

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




   def      __a						(   self      :    int	,     _lowercase      :    Optional[int]	,     _lowercase      :    int	,     _lowercase      :    Tuple				):



       """simple docstring"""






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

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

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




   @torch.no_grad()
   def __call__(   self      :    Optional[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				)}."""				)

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

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

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

           SCREAMING_SNAKE_CASE__      							=				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
           SCREAMING_SNAKE_CASE__      							=				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				)				):
               SCREAMING_SNAKE_CASE__      							=				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
               SCREAMING_SNAKE_CASE__      							=				self.scheduler.step(_lowercase	,     _lowercase	,     _lowercase	,     generator=_lowercase				).prev_sample

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

           SCREAMING_SNAKE_CASE__      							=				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":
           SCREAMING_SNAKE_CASE__      							=				self.melgan(input_features=full_pred_mel.astype(np.floataa				)				)
       else:
           SCREAMING_SNAKE_CASE__      							=				full_pred_mel

       if not return_dict:
           return (output,)

       return AudioPipelineOutput(audios=_lowercase				)

from __future__ import annotations

__lowerCamelCase      :			Dict							       =		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__lowerCamelCase      :			Union[str, Any]							       =		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )

  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for j in range(i + 1			,					__UpperCamelCase     ):
      if arr[i] < arr[j]:
        SCREAMING_SNAKE_CASE__      							=				arr[j]
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  for i, outer in enumerate(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for inner in arr[i + 1 :]:
      if outer < inner:
        SCREAMING_SNAKE_CASE__      							=				inner
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[-1] * arr_size

  for index in reversed(range(__UpperCamelCase     )     ):
    if stack:
      while stack[-1] <= arr[index]:
        stack.pop()
        if not stack:
          break
    if stack:
      SCREAMING_SNAKE_CASE__      							=				stack[-1]
    stack.append(arr[index]     )
  return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   __lowerCamelCase      :			List[Any]							       =		(
       '''from __main__ import arr, next_greatest_element_slow, '''
       '''next_greatest_element_fast, next_greatest_element'''
   )
   print(
       '''next_greatest_element_slow():''',
       timeit('''next_greatest_element_slow(arr)''', setup=setup),
   )
   print(
       '''next_greatest_element_fast():''',
       timeit('''next_greatest_element_fast(arr)''', setup=setup),
   )
   print(
       '''     next_greatest_element():''',
       timeit('''next_greatest_element(arr)''', setup=setup),
   )
