
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 argparse

import torch

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


logging.set_verbosity_info()

def 			lowercase_				(			_lowerCamelCase							:       str		,	_lowerCamelCase							:       List[Any]		,	_lowerCamelCase							:       Dict):
    # Initialise PyTorch model
    lowercase__ :   List[str]    					=				BertConfig.from_json_file(_lowerCamelCase)
    print(f'''Building PyTorch model from configuration: {config}''')
    lowercase__ :   Optional[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__":
      UpperCamelCase  							=						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.'''
      )
      UpperCamelCase  							=						parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



