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 						lowercase    (     _UpperCAmelCase     ):




							def __init__(						self   :					Any	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					List[Any]      )					->				Dict:



												'''simple docstring'''

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





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



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE    			=       audio_length_in_s * self.unet.config.sample_rate

												SCREAMING_SNAKE_CASE    			=       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    			=       int(lowerCamelCase_      )
												if sample_size % down_scale_factor != 0:
																	SCREAMING_SNAKE_CASE    			=       (
																	    (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(lowerCamelCase_      )

												SCREAMING_SNAKE_CASE    			=       next(iter(self.unet.parameters()      )      ).dtype
												SCREAMING_SNAKE_CASE    			=       (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    			=       randn_tensor(lowerCamelCase_	,							generator=lowerCamelCase_	,							device=self.device	,							dtype=lowerCamelCase_      )

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

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

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

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

												SCREAMING_SNAKE_CASE    			=       audio[:, :, :original_sample_size]

												if not return_dict:
																	return (audio,)

												return AudioPipelineOutput(audios=lowerCamelCase_      )





from __future__ import annotations

import math





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):

					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or number % 2 == 0 or number % 3 == 0:
										# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
										return False

					# All primes number are in format of 6k +/- 1
					for i in range(5      , int(math.sqrt(UpperCAmelCase__  ) + 1  )      , 6  ):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True


_lowerCamelCase					:   Tuple							=		[num for num in range(3, 10_00_01, 2) if not is_prime(num)]





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					if not isinstance(UpperCAmelCase__      , UpperCAmelCase__  ):
										raise ValueError("n must be an integer"  )
					if n <= 0:
										raise ValueError("n must be >= 0"  )

					SCREAMING_SNAKE_CASE    			=       []
					for num in range(len(UpperCAmelCase__  )  ):
										SCREAMING_SNAKE_CASE    			=       0
										while 2 * i * i <= odd_composites[num]:
															SCREAMING_SNAKE_CASE    			=       odd_composites[num] - 2 * i * i
															if is_prime(UpperCAmelCase__  ):
																				break
															i += 1
										else:
															list_nums.append(odd_composites[num]  )
															if len(UpperCAmelCase__  ) == n:
																				return list_nums

					return []





def 	__lowerCamelCase							():
					return compute_nums(1  )[0]


if __name__ == "__main__":
							print(f"""{solution() = }""")




