
from __future__ import annotations

lowercase						:      Optional[int]									= {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:List[Any]  ,     a	:dict[str, list[str]]  ,     a	:str		)	->				None:
								__UpperCamelCase			:     Optional[Any]         =      graph
								# mapping node to its parent in resulting breadth first tree
								__UpperCamelCase			:     dict[str, str | None]         =      {}
								__UpperCamelCase			:     List[Any]         =      source_vertex







	def      _lowerCamelCase       (      self	:int		)	->				None:
								__UpperCamelCase			:     Optional[Any]         =      {self.source_vertex}
								__UpperCamelCase			:     List[Any]         =      None
								__UpperCamelCase			:     str         =      [self.source_vertex]  # first in first out queue

								while queue:
															__UpperCamelCase			:     Optional[int]         =      queue.pop(0		)
															for adjacent_vertex in self.graph[vertex]:
																						if adjacent_vertex not in visited:
																													visited.add(a		)
																													__UpperCamelCase			:     int         =      vertex
																													queue.append(a		)







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:str		)	->				str:
								if target_vertex == self.source_vertex:
															return self.source_vertex

								__UpperCamelCase			:     Optional[int]         =      self.parent.get(a		)
								if target_vertex_parent is None:
															__UpperCamelCase			:     Union[str, Any]         =      (
															    f'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
															)
															raise ValueError(a		)

								return self.shortest_path(a		) + f'->{target_vertex}'


if __name__ == "__main__":
		lowercase						:      Tuple									= 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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any])      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase			:     List[Any]         =      BertForPreTraining(_lowerCamelCase)

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

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


if __name__ == "__main__":
		lowercase						:      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.'
		)
		lowercase						:      Tuple									= parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)