

'''simple docstring'''
import heapq


def       a__    (  _SCREAMING_SNAKE_CASE					:    Optional[int]     ,							_SCREAMING_SNAKE_CASE					:    Any     ,							_SCREAMING_SNAKE_CASE					:    List[str]							)			-> Union[str, Any]:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   [(0, start)]  # cost from start node,end node
	UpperCAmelCase_			:	Any      =   set()
	while heap:
		((UpperCAmelCase_) , (UpperCAmelCase_))			:	Optional[int]      =   heapq.heappop(_SCREAMING_SNAKE_CASE							)
		if u in visited:
			continue
		visited.add(_SCREAMING_SNAKE_CASE							)
		if u == end:
			return cost
		for v, c in graph[u]:
			if v in visited:
				continue
			UpperCAmelCase_			:	Optional[Any]      =   cost + c
			heapq.heappush(_SCREAMING_SNAKE_CASE     ,							(next_item, v)							)
	return -1


_lowerCamelCase	 =			{
    """A""": [["""B""", 2], ["""C""", 5]],
    """B""": [["""A""", 2], ["""D""", 3], ["""E""", 1], ["""F""", 1]],
    """C""": [["""A""", 5], ["""F""", 3]],
    """D""": [["""B""", 3]],
    """E""": [["""B""", 4], ["""F""", 3]],
    """F""": [["""C""", 3], ["""E""", 3]],
}
_lowerCamelCase	 =			{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""F""", 3]],
    """F""": [],
}
_lowerCamelCase	 =			{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}

_lowerCamelCase	 =			dijkstra(G, """E""", """C""")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

_lowerCamelCase	 =			dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 3 --> F == 3

_lowerCamelCase	 =			dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
					import doctest

					doctest.testmod()












"""simple docstring"""







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 						lowercase__(A					,		A					,		A						)				->int:




						"""simple docstring"""



						lowercase__				:      List[str]=       RoFormerConfig.from_json_file(A						)
						print(f'''Building PyTorch model from configuration: {config}'''						)
						lowercase__				:      Tuple=       RoFormerForMaskedLM(A						)

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(A					,		A					,		A						)

						# Save pytorch-model
						print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
						torch.save(model.state_dict()					,		A					,		_use_new_zipfile_serialization=A						)


if __name__ == "__main__":
			a :				List[str]      					=		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."""
			)
			a :				str      					=		parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






