





from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


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







class 	__lowerCAmelCase      (       __snake_case	):





   def __init__(			self       ,      snake_case       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





      super().__init__()
      self.register_modules(unet=__UpperCamelCase       ,      scheduler=__UpperCamelCase   )





   @torch.no_grad()
   def __call__(			self       ,      snake_case = 1       ,      snake_case = 100       ,      snake_case = None       ,      snake_case = None       ,      snake_case = True       ,      )					->	Tuple:
      """simple docstring"""





      if audio_length_in_s is None:
         a__						:    Optional[int]   		=	self.unet.config.sample_size / self.unet.config.sample_rate

      a__						:    Union[str, Any]   		=	audio_length_in_s * self.unet.config.sample_rate

      a__						:    Any   		=	2 ** len(self.unet.up_blocks   )
      if sample_size < 3 * down_scale_factor:
         raise ValueError(
             F"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
             F""" {3 * down_scale_factor / self.unet.config.sample_rate}."""   )

      a__						:    Tuple   		=	int(__UpperCamelCase   )
      if sample_size % down_scale_factor != 0:
         a__						:    Optional[int]   		=	(
             (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
         ) * down_scale_factor
         logger.info(
             F"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
             F""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
             " process."   )
      a__						:    List[Any]   		=	int(__UpperCamelCase   )

      a__						:    Dict   		=	next(iter(self.unet.parameters()   )   ).dtype
      a__						:    List[str]   		=	(batch_size, self.unet.config.in_channels, sample_size)
      if isinstance(__UpperCamelCase       ,      __UpperCamelCase   ) and len(__UpperCamelCase   ) != batch_size:
         raise ValueError(
             F"""You have passed a list of generators of length {len(__UpperCamelCase   )}, but requested an effective batch"""
             F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

      a__						:    Optional[int]   		=	randn_tensor(__UpperCamelCase       ,      generator=__UpperCamelCase       ,      device=self.device       ,      dtype=__UpperCamelCase   )

      # set step values
      self.scheduler.set_timesteps(__UpperCamelCase       ,      device=audio.device   )
      a__						:    Union[str, Any]   		=	self.scheduler.timesteps.to(__UpperCamelCase   )

      for t in self.progress_bar(self.scheduler.timesteps   ):
         # 1. predict noise model_output
         a__						:    List[Any]   		=	self.unet(__UpperCamelCase       ,      __UpperCamelCase   ).sample

         # 2. compute previous image: x_t -> t_t-1
         a__						:    Optional[Any]   		=	self.scheduler.step(__UpperCamelCase       ,      __UpperCamelCase       ,      __UpperCamelCase   ).prev_sample

      a__						:    Dict   		=	audio.clamp(-1       ,      1   ).float().cpu().numpy()

      a__						:    Any   		=	audio[:, :, :original_sample_size]

      if not return_dict:
         return (audio,)

      return AudioPipelineOutput(audios=__UpperCamelCase   )




def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			len(lowercase__						)
	for _ in range(lowercase__						):
		for i in range(_ % 2      ,			arr_size - 1      ,			2						):
			if arr[i + 1] < arr[i]:
				snake_case_		,							snake_case_   		=			arr[i + 1], arr[i]

	return arr


if __name__ == "__main__":
		A									=  list(range(10, 0, -1))
		print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")
