






from typing import List, Optional, Tuple, Union

import torch

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


lowerCAmelCase_           = logging.get_logger(__name__)  # pylint: disable=invalid-name
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       int		)    ->   int:


						'''simple docstring'''
						super().__init__()
						self.register_modules(unet=_SCREAMING_SNAKE_CASE						,scheduler=_SCREAMING_SNAKE_CASE		)







		@torch.no_grad()
		def __call__(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       int = 1						,_SCREAMING_SNAKE_CASE		:       int = 1_0_0						,_SCREAMING_SNAKE_CASE		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[float] = None						,_SCREAMING_SNAKE_CASE		:       bool = True						,)    ->   Union[AudioPipelineOutput, Tuple]:


						'''simple docstring'''

						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(_SCREAMING_SNAKE_CASE		)
						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(_SCREAMING_SNAKE_CASE		)

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

						A	 =      randn_tensor(_SCREAMING_SNAKE_CASE						,generator=_SCREAMING_SNAKE_CASE						,device=self.device						,dtype=_SCREAMING_SNAKE_CASE		)

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

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

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

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

						A	 =      audio[:, :, :original_sample_size]

						if not return_dict:
										return (audio,)

						return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE		)













import math







def      snake_case		(							UpperCAmelCase	:       list,						UpperCAmelCase	:       int   ):
				A	 =      len(UpperCAmelCase   )
				A	 =      int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
				A	 =      0
				while arr[min(UpperCAmelCase,						UpperCAmelCase   ) - 1] < x:
								A	 =      step
								step += int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
								if prev >= n:
												return -1

				while arr[prev] < x:
								A	 =      prev + 1
								if prev == min(UpperCAmelCase,						UpperCAmelCase   ):
												return -1
				if arr[prev] == x:
								return prev
				return -1


if __name__ == "__main__":
					lowerCAmelCase_           = input('Enter numbers separated by a comma:\n').strip()
					lowerCAmelCase_           = [int(item) for item in user_input.split(',')]
					lowerCAmelCase_           = int(input('Enter the number to be searched:\n'))
					lowerCAmelCase_           = jump_search(arr, x)
					if res == -1:
										print('Number not found!')
					else:
										print(f'''Number {x} is at index {res}''')





