





"""simple docstring"""
from typing import List, Optional, Tuple, Union

import torch

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


_snake_case     :		List[Any]       						= logging.get_logger(__name__)  # pylint: disable=invalid-name






class 					_UpperCAmelCase   (	lowercase_ ):


					def __init__(					self			:Any     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:str		):
												super().__init__()
												self.register_modules(unet=__UpperCamelCase     ,       scheduler=__UpperCamelCase		)


					@torch.no_grad()
					def __call__(					self			:Dict     ,       __UpperCamelCase			:int = 1     ,       __UpperCamelCase			:int = 1_00     ,       __UpperCamelCase			:Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,       __UpperCamelCase			:Optional[float] = None     ,       __UpperCamelCase			:bool = True     ,       ):

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

												A		=  audio_length_in_s * self.unet.config.sample_rate

												A		=  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		=  int(__UpperCamelCase		)
												if sample_size % down_scale_factor != 0:
																			A		=  (
																			    (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		=  int(__UpperCamelCase		)

												A		=  next(iter(self.unet.parameters()		)		).dtype
												A		=  (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		=  randn_tensor(__UpperCamelCase     ,       generator=__UpperCamelCase     ,       device=self.device     ,       dtype=__UpperCamelCase		)

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

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

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

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

												A		=  audio[:, :, :original_sample_size]

												if not return_dict:
																			return (audio,)

												return AudioPipelineOutput(audios=__UpperCamelCase		)









"""simple docstring"""
def 					A__				(			UpperCamelCase       ):
							A		=  generate_pascal_triangle(UpperCamelCase       )
							for row_idx in range(UpperCamelCase       ):
														# Print left spaces
														for _ in range(num_rows - row_idx - 1       ):
																					print(end=" "       )
														# Print row values
														for col_idx in range(row_idx + 1       ):
																					if col_idx != row_idx:
																												print(triangle[row_idx][col_idx]					,  end=" "       )
																					else:
																												print(triangle[row_idx][col_idx]					,  end=""       )
														print()






def 					A__				(			UpperCamelCase       ):

							if not isinstance(UpperCamelCase					,  UpperCamelCase       ):
														raise TypeError("The input value of 'num_rows' should be 'int'"       )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    "The input value of 'num_rows' should be greater than or equal to 0"       )

							A		=  []
							for current_row_idx in range(UpperCamelCase       ):
														A		=  populate_current_row(UpperCamelCase					,  UpperCamelCase       )
														triangle.append(UpperCamelCase       )
							return triangle






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  [-1] * (current_row_idx + 1)
							# first and last elements of current row are equal to 1
							A,	A		=  1, 1
							for current_col_idx in range(1					,  UpperCamelCase       ):
														calculate_current_element(
														    UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       )
							return current_row






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  ):
							A		=  triangle[current_row_idx - 1][current_col_idx - 1]
							A		=  triangle[current_row_idx - 1][current_col_idx]
							A		=  above_to_left_elt + above_to_right_elt






def 					A__				(			UpperCamelCase       ):

							if not isinstance(UpperCamelCase					,  UpperCamelCase       ):
														raise TypeError("The input value of 'num_rows' should be 'int'"       )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    "The input value of 'num_rows' should be greater than or equal to 0"       )

							A		=  [[1]]

							for row_index in range(1					,  UpperCamelCase       ):
														A		=  [0] + result[-1] + [0]
														A		=  row_index + 1
														# Calculate the number of distinct elements in a row
														A		=  sum(divmod(UpperCamelCase					,  2       )       )
														A		=  [
														    temp_row[i - 1] + temp_row[i] for i in range(1					,  distinct_elements + 1       )
														]
														A		=  row_first_half[: (row_index + 1) // 2]
														row_second_half.reverse()
														A		=  row_first_half + row_second_half
														result.append(UpperCamelCase       )

							return result






def 					A__				(			):
							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(UpperCamelCase					,  UpperCamelCase       ) -> None:
														A		=  F"{func.__name__}({value})"
														A		=  timeit(F"__main__.{call}"					,  setup="import __main__"       )
														# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
														print(F"{call:38} -- {timing:.4f} seconds"       )

							for value in range(15       ):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(UpperCamelCase					,  UpperCamelCase       )
														print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()


