
lowercase__		:Tuple   =    {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						set()
					# keep track of all the paths to be checked
					lowercase							=						[[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
										lowercase							=						queue.pop(0		)
										# get the last node from the path
										lowercase							=						path[-1]
										if node not in explored:
															lowercase							=						graph[node]
															# go through all neighbour nodes, construct a new path and
															# push it into the queue
															for neighbour in neighbours:
																				lowercase							=						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        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					if not graph or start not in graph or target not in graph:
										return -1
					if start == target:
										return 0
					lowercase							=						[start]
					lowercase							=						set(lowerCAmelCase__		)
					# Keep tab on distances from `start` node.
					lowercase							=						{start: 0, target: -1}
					while queue:
										lowercase							=						queue.pop(0		)
										if node == target:
															lowercase							=						(
															    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__		)
																				lowercase							=						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






import os
import sys

lowercase__		:Tuple   =    os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowercase__		:List[Any]   =    [
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]



@add_start_docstrings(AutoConfig.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoConfig.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoTokenizer.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoTokenizer.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModel.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModel.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForCausalLM.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForMaskedLM.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForSequenceClassification.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForQuestionAnswering.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)




