





from typing import List, Optional, Tuple, Union

import torch

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


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






class        snake_case_    (		__lowercase						):






  def __init__(							self			:					List[Any]       ,  _snake_case			:					str       ,  _snake_case			:					Optional[Any]      )->List[str]:






   '''simple docstring'''

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






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






   '''simple docstring'''

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

   __lowerCAmelCase					:	int      =       audio_length_in_s * self.unet.config.sample_rate

   __lowerCAmelCase					:	List[str]      =       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}.'''      )

   __lowerCAmelCase					:	Union[str, Any]      =       int(_snake_case      )
   if sample_size % down_scale_factor != 0:
    __lowerCAmelCase					:	str      =       (
        (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."""      )
   __lowerCAmelCase					:	List[Any]      =       int(_snake_case      )

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

   __lowerCAmelCase					:	int      =       randn_tensor(_snake_case       ,  generator=_snake_case       ,  device=self.device       ,  dtype=_snake_case      )

   # set step values
   self.scheduler.set_timesteps(_snake_case       ,  device=audio.device      )
   __lowerCAmelCase					:	Dict      =       self.scheduler.timesteps.to(_snake_case      )

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

    # 2. compute previous image: x_t -> t_t-1
    __lowerCAmelCase					:	Union[str, Any]      =       self.scheduler.step(_snake_case       ,  _snake_case       ,  _snake_case      ).prev_sample

   __lowerCAmelCase					:	Optional[Any]      =       audio.clamp(-1       ,  1      ).float().cpu().numpy()

   __lowerCAmelCase					:	Dict      =       audio[:, :, :original_sample_size]

   if not return_dict:
    return (audio,)

   return AudioPipelineOutput(audios=_snake_case      )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   )      ->   Dict:
 _enforce_args(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 if n == 0:
  return 0
 __lowerCAmelCase					:	Union[str, Any]      =       float("""-inf"""   )
 for i in range(1   ,   n + 1   ):
  __lowerCAmelCase					:	Union[str, Any]      =       max(
      SCREAMING_SNAKE_CASE   ,   prices[i - 1] + naive_cut_rod_recursive(n - i   ,   SCREAMING_SNAKE_CASE   )   )

 return max_revue






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   )      ->   List[str]:
 _enforce_args(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       [float("""-inf"""   ) for _ in range(n + 1   )]
 return _top_down_cut_rod_recursive(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:list   )      ->   List[str]:
 if max_rev[n] >= 0:
  return max_rev[n]
 elif n == 0:
  return 0
 else:
  __lowerCAmelCase					:	str      =       float("""-inf"""   )
  for i in range(1   ,   n + 1   ):
   __lowerCAmelCase					:	List[Any]      =       max(
       SCREAMING_SNAKE_CASE   ,   prices[i - 1] + _top_down_cut_rod_recursive(n - i   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )   ,   )

  __lowerCAmelCase					:	List[str]      =       max_revenue

 return max_rev[n]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   )      ->   Union[str, Any]:
 _enforce_args(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
 # length 0.
 __lowerCAmelCase					:	Optional[int]      =       [float("""-inf"""   ) for _ in range(n + 1   )]
 __lowerCAmelCase					:	List[str]      =       0

 for i in range(1   ,   n + 1   ):
  __lowerCAmelCase					:	Optional[Any]      =       max_rev[i]
  for j in range(1   ,   i + 1   ):
   __lowerCAmelCase					:	Optional[int]      =       max(SCREAMING_SNAKE_CASE   ,   prices[j - 1] + max_rev[i - j]   )

  __lowerCAmelCase					:	Optional[int]      =       max_revenue_i

 return max_rev[n]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   )      ->   List[Any]:
 if n < 0:
  __lowerCAmelCase					:	Any      =       F'''n must be greater than or equal to 0. Got n = {n}'''
  raise ValueError(SCREAMING_SNAKE_CASE   )

 if n > len(SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase					:	Any      =       (
      """Each integral piece of rod must have a corresponding price. """
      F'''Got n = {n} but length of prices = {len(SCREAMING_SNAKE_CASE   )}'''
  )
  raise ValueError(SCREAMING_SNAKE_CASE   )






def 	_SCREAMING_SNAKE_CASE		(      )      ->   Optional[int]:
 __lowerCAmelCase					:	Tuple      =       [6, 10, 12, 15, 20, 23]
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

 # the best revenue comes from cutting the rod into 6 pieces, each
 # of length 1 resulting in a revenue of 6 * 6 = 36.
 __lowerCAmelCase					:	Union[str, Any]      =       36

 __lowerCAmelCase					:	Optional[int]      =       top_down_cut_rod(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       bottom_up_cut_rod(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	int      =       naive_cut_rod_recursive(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 assert expected_max_revenue == max_rev_top_down
 assert max_rev_top_down == max_rev_bottom_up
 assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
      main()