"""simple docstring"""




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__(	lowercase__       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Dict:
													super().__init__()
													self.register_modules(unet=lowercase_						,scheduler=lowercase_   )





						@torch.no_grad()
						def __call__(				self						,__UpperCAmelCase = 1						,__UpperCAmelCase = 1_00						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,)    -> Dict:

													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(lowercase_   )
													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(lowercase_   )

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

													A__		   =							randn_tensor(lowercase_						,generator=lowercase_						,device=self.device						,dtype=lowercase_   )

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

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

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

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

													A__		   =							audio[:, :, :original_sample_size]

													if not return_dict:
																				return (audio,)

													return AudioPipelineOutput(audios=lowercase_   )







"""simple docstring"""





from __future__ import annotations







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    set(__lowerCamelCase  ), [start]

   while stack:
      UpperCAmelCase_					:					Any			     =    stack.pop()
      explored.add(__lowerCamelCase  )
      # Differences from BFS:
      # 1) pop last element instead of first one
      # 2) add adjacent elements to stack without exploring them
      for adj in reversed(graph[v]  ):
         if adj not in explored:
            stack.append(__lowerCamelCase  )
   return explored


_a		       =							{
    'A': ['B', 'C', 'D'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B', 'D'],
    'E': ['B', 'F'],
    'F': ['C', 'E', 'G'],
    'G': ['F'],
}

if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(depth_first_search(G, 'A'))


