





'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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


lowercase__		:     str			=							logging.get_logger(__name__)  # pylint: disable=invalid-name
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       def __init__(				self  :						Any			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						Tuple	)       ->	Union[str, Any]:

            '''simple docstring'''




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







       @torch.no_grad()
       def __call__(				self  :						Optional[int]			,					lowerCAmelCase__  :						int = 1			,					lowerCAmelCase__  :						int = 100			,					lowerCAmelCase__  :						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,					lowerCAmelCase__  :						Optional[float] = None			,					lowerCAmelCase__  :						bool = True			,					)       ->	Union[AudioPipelineOutput, Tuple]:

            '''simple docstring'''




            if audio_length_in_s is None:
                 _UpperCamelCase							       =			self.unet.config.sample_size / self.unet.config.sample_rate

            _UpperCamelCase							       =			audio_length_in_s * self.unet.config.sample_rate

            _UpperCamelCase							       =			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}."""	)

            _UpperCamelCase							       =			int(lowerCAmelCase__	)
            if sample_size % down_scale_factor != 0:
                 _UpperCamelCase							       =			(
                     (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.'''	)
            _UpperCamelCase							       =			int(lowerCAmelCase__	)

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

            _UpperCamelCase							       =			randn_tensor(lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device=self.device			,					dtype=lowerCAmelCase__	)

            # set step values
            self.scheduler.set_timesteps(lowerCAmelCase__			,					device=audio.device	)
            _UpperCamelCase							       =			self.scheduler.timesteps.to(lowerCAmelCase__	)

            for t in self.progress_bar(self.scheduler.timesteps	):
                 # 1. predict noise model_output
                 _UpperCamelCase							       =			self.unet(lowerCAmelCase__			,					lowerCAmelCase__	).sample

                 # 2. compute previous image: x_t -> t_t-1
                 _UpperCamelCase							       =			self.scheduler.step(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	).prev_sample

            _UpperCamelCase							       =			audio.clamp(-1			,					1	).float().cpu().numpy()

            _UpperCamelCase							       =			audio[:, :, :original_sample_size]

            if not return_dict:
                 return (audio,)

            return AudioPipelineOutput(audios=lowerCAmelCase__	)










'''simple docstring'''

def    a__							(							lowercase   :	Dict,   lowercase   :	Optional[Any] )						->						int:





     """simple docstring"""

     _UpperCamelCase							       =			0
     _UpperCamelCase							       =			len(lowercase ) - 1

     while left <= right:
          # avoid divided by 0 during interpolation
          if sorted_collection[left] == sorted_collection[right]:
               if sorted_collection[left] == item:
                    return left
               else:
                    return None

          _UpperCamelCase							       =			left + ((item - sorted_collection[left]) * (right - left)) // (
              sorted_collection[right] - sorted_collection[left]
          )

          # out of range check
          if point < 0 or point >= len(lowercase ):
               return None

          _UpperCamelCase							       =			sorted_collection[point]
          if current_item == item:
               return point
          else:
               if point < left:
                    _UpperCamelCase							       =			left
                    _UpperCamelCase							       =			point
               elif point > right:
                    _UpperCamelCase							       =			right
                    _UpperCamelCase							       =			point
               else:
                    if item < current_item:
                         _UpperCamelCase							       =			point - 1
                    else:
                         _UpperCamelCase							       =			point + 1
     return None







def    a__							(							lowercase   :	int,   lowercase   :	Optional[int],   lowercase   :	List[Any],   lowercase   :	List[Any] )						->						Dict:





     """simple docstring"""

     if sorted_collection[left] == sorted_collection[right]:
          if sorted_collection[left] == item:
               return left
          else:
               return None

     _UpperCamelCase							       =			left + ((item - sorted_collection[left]) * (right - left)) // (
         sorted_collection[right] - sorted_collection[left]
     )

     # out of range check
     if point < 0 or point >= len(lowercase ):
          return None

     if sorted_collection[point] == item:
          return point
     elif point < left:
          return interpolation_search_by_recursion(lowercase,   lowercase,   lowercase,   lowercase )
     elif point > right:
          return interpolation_search_by_recursion(lowercase,   lowercase,   lowercase,   lowercase )
     else:
          if sorted_collection[point] > item:
               return interpolation_search_by_recursion(
                   lowercase,   lowercase,   lowercase,   point - 1 )
          else:
               return interpolation_search_by_recursion(
                   lowercase,   lowercase,   point + 1,   lowercase )







def    a__							(							lowercase   :	Any )						->						List[Any]:





     """simple docstring"""

     if collection != sorted(lowercase ):
          raise ValueError('''Collection must be ascending sorted''' )
     return True


if __name__ == "__main__":
   import sys
   lowercase__		:     int			=							0
   if debug == 1:
      lowercase__		:     Optional[Any]			=							[10, 30, 40, 45, 50, 66, 77, 93]
      try:
         __assert_sorted(collection)
      except ValueError:
         sys.exit('Sequence must be ascending sorted to apply interpolation search')
      lowercase__		:     List[str]			=							67

   lowercase__		:     Optional[int]			=							interpolation_search(collection, target)
   if result is not None:
      print(F"""{target} found at positions: {result}""")
   else:
      print('Not found')



