






from __future__ import annotations

__snake_case    :    Any         =     {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}


class     __SCREAMING_SNAKE_CASE					:






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						graph
												# mapping node to its parent in resulting breadth first tree
												lowerCAmelCase__    						=						{}
												lowerCAmelCase__    						=						source_vertex






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						{self.source_vertex}
												lowerCAmelCase__    						=						None
												lowerCAmelCase__    						=						[self.source_vertex]  # first in first out queue

												while queue:
																		lowerCAmelCase__    						=						queue.pop(0   )
																		for adjacent_vertex in self.graph[vertex]:
																								if adjacent_vertex not in visited:
																														visited.add(_UpperCamelCase   )
																														lowerCAmelCase__    						=						vertex
																														queue.append(_UpperCamelCase   )







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												if target_vertex == self.source_vertex:
																		return self.source_vertex

												lowerCAmelCase__    						=						self.parent.get(_UpperCamelCase   )
												if target_vertex_parent is None:
																		lowerCAmelCase__    						=						(
																		    F"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
																		)
																		raise ValueError(_UpperCamelCase   )

												return self.shortest_path(_UpperCamelCase   ) + F"->{target_vertex}"


if __name__ == "__main__":
					__snake_case    :    Optional[Any]         =     Graph(graph, """G""")
					g.breath_first_search()
					print(g.shortest_path("""D"""))
					print(g.shortest_path("""G"""))
					print(g.shortest_path("""Foo"""))













import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Optional[Any]		)  ->   Any:

						"""simple docstring"""



						lowerCAmelCase__    						=						BertConfig.from_json_file(UpperCamelCase_		)
						print(F"Building PyTorch model from configuration: {config}"		)
						lowerCAmelCase__    						=						BertForPreTraining(UpperCamelCase_		)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

						# Save pytorch-model
						print(F"Save PyTorch model to {pytorch_dump_path}"		)
						torch.save(model.state_dict()    ,       UpperCamelCase_		)


if __name__ == "__main__":
					__snake_case    :    Union[str, Any]         =     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--bert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					__snake_case    :    List[str]         =     parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





