





from typing import List, Optional, Tuple, Union

import torch

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


lowerCAmelCase							:       Optional[int]					 =	logging.get_logger(__name__)  # pylint: disable=invalid-name





class 				a			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




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






				@torch.no_grad()
				def __call__(  self			, _lowerCAmelCase = 1			, _lowerCAmelCase = 100			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = True			, ):

										"""simple docstring"""




										if audio_length_in_s is None:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   self.unet.config.sample_size / self.unet.config.sample_rate

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   audio_length_in_s * self.unet.config.sample_rate

										__SCREAMING_SNAKE_CASE:				Union[str, 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}."""					)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   int(_lowerCAmelCase					)
										if sample_size % down_scale_factor != 0:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   (
																    (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.'''					)
										__SCREAMING_SNAKE_CASE:				int           =   int(_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				str           =   next(iter(self.unet.parameters()					)					).dtype
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   (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."""					)

										__SCREAMING_SNAKE_CASE:				Dict           =   randn_tensor(_lowerCAmelCase			, generator=_lowerCAmelCase			, device=self.device			, dtype=_lowerCAmelCase					)

										# set step values
										self.scheduler.set_timesteps(_lowerCAmelCase			, device=audio.device					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.scheduler.timesteps.to(_lowerCAmelCase					)

										for t in self.progress_bar(self.scheduler.timesteps					):
																# 1. predict noise model_output
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.unet(_lowerCAmelCase			, _lowerCAmelCase					).sample

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

										__SCREAMING_SNAKE_CASE:				Tuple           =   audio.clamp(-1			, 1					).float().cpu().numpy()

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

										if not return_dict:
																return (audio,)

										return AudioPipelineOutput(audios=_lowerCAmelCase					)










from __future__ import annotations






def 		lowerCAmelCase  (	UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[int]       ,			UpperCamelCase__   :   list[list[str]]       ,			UpperCamelCase__   :   int       ,			)      ->    None:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   len(UpperCamelCase__					)

						# If row is equal to the size of the board it means there are a queen in each row in
						# the current board (possible_board)
						if row == n:
												# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
												# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
												boards.append(['''. ''' * i + '''Q ''' + '''. ''' * (n - 1 - i) for i in possible_board]					)
												return

						# We iterate each column in the row to find all possible results in each row
						for col in range(UpperCamelCase__					):
												# We apply that we learned previously. First we check that in the current board
												# (possible_board) there are not other same value because if there is it means
												# that there are a collision in vertical. Then we apply the two formulas we
												# learned before:
												#
												# 45º: y - x = b or 45: row - col = b
												# 135º: y + x = b or row + col = b.
												#
												# And we verify if the results of this two formulas not exist in their variables
												# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
												#
												# If any or these are True it means there is a collision so we continue to the
												# next value in the for loop.
												if (
												    col in possible_board
												    or row - col in diagonal_right_collisions
												    or row + col in diagonal_left_collisions
												):
																		continue

												# If it is False we call dfs function again and we update the inputs
												depth_first_search(
												    [*possible_board, col]       ,			[*diagonal_right_collisions, row - col]       ,			[*diagonal_left_collisions, row + col]       ,			UpperCamelCase__       ,			UpperCamelCase__       ,			)






def 		lowerCAmelCase  (	UpperCamelCase__   :   int					)      ->    None:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				list[list[str]]           =   []
						depth_first_search([]       ,			[]       ,			[]       ,			UpperCamelCase__       ,			UpperCamelCase__					)

						# Print all the boards
						for board in boards:
												for column in board:
																		print(UpperCamelCase__					)
												print(''''''					)

						print(len(UpperCamelCase__					)       ,			'''solutions were found.'''					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							n_queens_solution(4)



