





__lowerCamelCase       :     Dict   =     {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> list[str]:
 UpperCamelCase	:					Any       	=   set()
 # keep track of all the paths to be checked
 UpperCamelCase	:					int       	=   [[start]]

 # return path if start is goal
 if start == goal:
  return [start]

 # keeps looping until all possible paths have been checked
 while queue:
  # pop the first path from the queue
  UpperCamelCase	:					List[str]       	=   queue.pop(0     )
  # get the last node from the path
  UpperCamelCase	:					Tuple       	=   path[-1]
  if node not in explored:
   UpperCamelCase	:					List[str]       	=   graph[node]
   # go through all neighbour nodes, construct a new path and
   # push it into the queue
   for neighbour in neighbours:
    UpperCamelCase	:					str       	=   list(_lowerCAmelCase     )
    new_path.append(_lowerCAmelCase     )
    queue.append(_lowerCAmelCase     )
    # return path if neighbour is goal
    if neighbour == goal:
     return new_path

            # mark node as explored
   explored.add(_lowerCAmelCase     )

    # in case there's no path between the 2 nodes
 return []



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 if not graph or start not in graph or target not in graph:
  return -1
 if start == target:
  return 0
 UpperCamelCase	:					List[str]       	=   [start]
 UpperCamelCase	:					str       	=   set(_lowerCAmelCase     )
 # Keep tab on distances from `start` node.
 UpperCamelCase	:					Any       	=   {start: 0, target: -1}
 while queue:
  UpperCamelCase	:					Any       	=   queue.pop(0     )
  if node == target:
   UpperCamelCase	:					int       	=   (
       dist[node] if dist[target] == -1 else min(dist[target]							,   dist[node]     )
   )
  for adjacent in graph[node]:
   if adjacent not in visited:
    visited.add(_lowerCAmelCase     )
    queue.append(_lowerCAmelCase     )
    UpperCamelCase	:					str       	=   dist[node] + 1
 return dist[target]


if __name__ == "__main__":
    print(bfs_shortest_path(demo_graph, """G""", """D"""))  # returns ['G', 'C', 'A', 'B', 'D']
    print(bfs_shortest_path_distance(demo_graph, """G""", """D"""))  # returns 4







from typing import List, Optional, Tuple, Union

import torch

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


__lowerCamelCase       :     str   =     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




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





      @torch.no_grad()
      def __call__( self       ,							A_ = 1       ,							A_ = 100       ,							A_ = None       ,							A_ = None       ,							A_ = True       ,							):
       '''simple docstring'''




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

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

       UpperCamelCase	:					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}."""   )

       UpperCamelCase	:					Union[str, Any]       	=   int(A_   )
       if sample_size % down_scale_factor != 0:
        UpperCamelCase	:					List[str]       	=   (
            (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."   )
       UpperCamelCase	:					Any       	=   int(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   next(iter(self.unet.parameters()   )   ).dtype
       UpperCamelCase	:					Optional[int]       	=   (batch_size, self.unet.config.in_channels, sample_size)
       if isinstance(A_       ,							A_   ) and len(A_   ) != batch_size:
        raise ValueError(
            F"""You have passed a list of generators of length {len(A_   )}, but requested an effective batch"""
            F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

       UpperCamelCase	:					Optional[Any]       	=   randn_tensor(A_       ,							generator=A_       ,							device=self.device       ,							dtype=A_   )

       # set step values
       self.scheduler.set_timesteps(A_       ,							device=audio.device   )
       UpperCamelCase	:					Optional[int]       	=   self.scheduler.timesteps.to(A_   )

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

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

       UpperCamelCase	:					Optional[Any]       	=   audio.clamp(-1       ,							1   ).float().cpu().numpy()

       UpperCamelCase	:					Dict       	=   audio[:, :, :original_sample_size]

       if not return_dict:
        return (audio,)

       return AudioPipelineOutput(audios=A_   )
