






'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

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


__A							:							Optional[Any]  							=		logging.get_logger(__name__)  # pylint: disable=invalid-name
class 				lowercase       (							_lowerCamelCase      ):


				'''simple docstring'''

				def __init__(					self     :     Optional[int]				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     str     )							->							Optional[Any]:

					'''simple docstring'''




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




				@torch.no_grad()
				def __call__(					self     :     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:
						lowerCamelCase__  =     self.unet.config.sample_size / self.unet.config.sample_rate

					lowerCamelCase__  =     audio_length_in_s * self.unet.config.sample_rate

					lowerCamelCase__  =     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__  =     int(__lowerCamelCase     )
					if sample_size % down_scale_factor != 0:
						lowerCamelCase__  =     (
						    (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__  =     int(__lowerCamelCase     )

					lowerCamelCase__  =     next(iter(self.unet.parameters()     )     ).dtype
					lowerCamelCase__  =     (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.'''     )

					lowerCamelCase__  =     randn_tensor(__lowerCamelCase				,     generator=__lowerCamelCase				,     device=self.device				,     dtype=__lowerCamelCase     )

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

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

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

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

					lowerCamelCase__  =     audio[:, :, :original_sample_size]

					if not return_dict:
						return (audio,)

					return AudioPipelineOutput(audios=__lowerCamelCase     )









'''simple docstring'''





def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):

 def update_area_of_max_square(lowercase__				,     lowercase__) -> int:
  # BASE CASE
  if row >= rows or col >= cols:
   return 0

  lowerCamelCase__  =     update_area_of_max_square(lowercase__				,     col + 1)
  lowerCamelCase__  =     update_area_of_max_square(row + 1				,     col + 1)
  lowerCamelCase__  =     update_area_of_max_square(row + 1				,     lowercase__)

  if mat[row][col]:
   lowerCamelCase__  =     1 + min([right, diagonal, down])
   lowerCamelCase__  =     max(largest_square_area[0]				,     lowercase__)
   return sub_problem_sol
  else:
   return 0

 lowerCamelCase__  =     [0]
 update_area_of_max_square(0				,     0)
 return largest_square_area[0]






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):

 def update_area_of_max_square_using_dp_array(
     lowercase__				,     lowercase__				,     lowercase__) -> int:
  if row >= rows or col >= cols:
   return 0
  if dp_array[row][col] != -1:
   return dp_array[row][col]

  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(lowercase__				,     col + 1				,     lowercase__)
  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(row + 1				,     col + 1				,     lowercase__)
  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(row + 1				,     lowercase__				,     lowercase__)

  if mat[row][col]:
   lowerCamelCase__  =     1 + min([right, diagonal, down])
   lowerCamelCase__  =     max(largest_square_area[0]				,     lowercase__)
   lowerCamelCase__  =     sub_problem_sol
   return sub_problem_sol
  else:
   return 0

 lowerCamelCase__  =     [0]
 lowerCamelCase__  =     [[-1] * cols for _ in range(lowercase__)]
 update_area_of_max_square_using_dp_array(0				,     0				,     lowercase__)

 return largest_square_area[0]






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     [[0] * (cols + 1) for _ in range(rows + 1)]
 lowerCamelCase__  =     0
 for row in range(rows - 1				,     -1				,     -1):
  for col in range(cols - 1				,     -1				,     -1):
   lowerCamelCase__  =     dp_array[row][col + 1]
   lowerCamelCase__  =     dp_array[row + 1][col + 1]
   lowerCamelCase__  =     dp_array[row + 1][col]

   if mat[row][col] == 1:
    lowerCamelCase__  =     1 + min(lowercase__				,     lowercase__				,     lowercase__)
    lowerCamelCase__  =     max(dp_array[row][col]				,     lowercase__)
   else:
    lowerCamelCase__  =     0

 return largest_square_area






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     [0] * (cols + 1)
 lowerCamelCase__  =     [0] * (cols + 1)
 lowerCamelCase__  =     0
 for row in range(rows - 1				,     -1				,     -1):
  for col in range(cols - 1				,     -1				,     -1):
   lowerCamelCase__  =     current_row[col + 1]
   lowerCamelCase__  =     next_row[col + 1]
   lowerCamelCase__  =     next_row[col]

   if mat[row][col] == 1:
    lowerCamelCase__  =     1 + min(lowercase__				,     lowercase__				,     lowercase__)
    lowerCamelCase__  =     max(current_row[col]				,     lowercase__)
   else:
    lowerCamelCase__  =     0
  lowerCamelCase__  =     current_row

 return largest_square_area


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

