
'''simple docstring'''






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate

@dataclass(frozen=A			)
class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		field(default='automatic-speech-recognition'    ,						metadata={'include_in_asdict_even_if_is_default': True}			)
    lowerCAmelCase_					     =		Features({'audio': Audio()}			)
    lowerCAmelCase_					     =		Features({'transcription': Value('string'			)}			)
    lowerCAmelCase_					     =		"audio"
    lowerCAmelCase_					     =		"transcription"



    def 	lowerCamelCase_	(						self   :							int,__A   :							Union[str, Any]  ):
          if self.audio_column not in features:
                raise ValueError(f'Column {self.audio_column} is not present in features.'  )
          if not isinstance(features[self.audio_column],__A  ):
                raise ValueError(f'Column {self.audio_column} is not an Audio type.'  )
          _lowerCamelCase					: Optional[int]		    = copy.deepcopy(self  )
          _lowerCamelCase					: int		    = self.input_schema.copy()
          _lowerCamelCase					: Any		    = features[self.audio_column]
          _lowerCamelCase					: Dict		    = input_schema
          return task_template



    @property
    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          return {self.audio_column: "audio", self.transcription_column: "transcription"}

'''simple docstring'''
lowerCAmelCase					:    Optional[Any]           = {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







def 		A_(     A						:		dict		,     A						:		str		,     A						:		Optional[Any]):
							UpperCamelCase   			=     set()
							# keep track of all the paths to be checked
							UpperCamelCase   			=     [[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   			=     queue.pop(0)
														# get the last node from the path
														UpperCamelCase   			=     path[-1]
														if node not in explored:
																					UpperCamelCase   			=     graph[node]
																					# go through all neighbour nodes, construct a new path and
																					# push it into the queue
																					for neighbour in neighbours:
																												UpperCamelCase   			=     list(A)
																												new_path.append(A)
																												queue.append(A)
																												# return path if neighbour is goal
																												if neighbour == goal:
																																			return new_path

            # mark node as explored
																					explored.add(A)

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







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




