
'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							["""image_processor""", """tokenizer"""]
				lowerCAmelCase_     			=							"""ChineseCLIPImageProcessor"""
				lowerCAmelCase_     			=							("""BertTokenizer""", """BertTokenizerFast""")







				def __init__(       self						,				A_=None						,				A_=None						,				**A_     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.'						,				A_						,				)
																		UpperCamelCase   			=     kwargs.pop('feature_extractor'     )

											UpperCamelCase   			=     image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError('You need to specify an `image_processor`.'     )
											if tokenizer is None:
																		raise ValueError('You need to specify a `tokenizer`.'     )

											super().__init__(A_						,				A_     )
											UpperCamelCase   			=     self.image_processor







				def __call__(       self						,				A_=None						,				A_=None						,				A_=None						,				**A_     )->		List[Any]:







											'''simple docstring'''






											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'     )

											if text is not None:
																		UpperCamelCase   			=     self.tokenizer(A_						,				return_tensors=A_						,				**A_     )

											if images is not None:
																		UpperCamelCase   			=     self.image_processor(A_						,				return_tensors=A_						,				**A_     )

											if text is not None and images is not None:
																		UpperCamelCase   			=     image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**A_     )						,				tensor_type=A_     )







				def 						UpperCAmelCase_    (       self						,				*A_						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






											return self.tokenizer.batch_decode(*A_						,				**A_     )







				def 						UpperCAmelCase_    (       self						,				*A_						,				**A_     )->		Tuple:







											'''simple docstring'''






											return self.tokenizer.decode(*A_						,				**A_     )







				@property
				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											UpperCamelCase   			=     self.tokenizer.model_input_names
											UpperCamelCase   			=     self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )







				@property
				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,				A_						,				)
											return self.image_processor_class






'''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




