

import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    TaConfig.from_json_file(_a)
	print(f"Building PyTorch model from configuration: {config}")
	SCREAMING_SNAKE_CASE				: int			       =    TaForConditionalGeneration(_a)

	# Load weights from tf checkpoint
	load_tf_weights_in_ta(_a							,     _a							,     _a)

	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}")
	model.save_pretrained(_a)


if __name__ == "__main__":
			a_         =      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(
			    '--config_file',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
			    ),
			)
			parser.add_argument(
			    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			a_         =      parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


from __future__ import annotations






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	List[Any] , a							:	int		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    vertices
				SCREAMING_SNAKE_CASE				: Tuple			       =    [[0] * vertices for _ in range(a		)]
			def 							__UpperCamelCase	(     self							:	Dict , a							:	list[int]		)							->    None:

				"""simple docstring"""

				print("Vertex \t Distance from Source"		)
				for vertex in range(self.vertices		):
					print(a , "\t\t" , distances_from_source[vertex]		)
			def 							__UpperCamelCase	(     self							:	Tuple , a							:	list[int] , a							:	list[bool]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    1e7
				SCREAMING_SNAKE_CASE				: int			       =    0

				# Search not nearest vertex not in the shortest path tree
				for vertex in range(self.vertices		):
					if distances_from_source[vertex] < minimum and visited[vertex] is False:
						SCREAMING_SNAKE_CASE				: Optional[int]			       =    distances_from_source[vertex]
						SCREAMING_SNAKE_CASE				: Optional[Any]			       =    vertex
				return min_index






			def 							__UpperCamelCase	(     self							:	Any , a							:	int		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    [int(1e7		)] * self.vertices  # distances from the source
				SCREAMING_SNAKE_CASE				: Tuple			       =    0
				SCREAMING_SNAKE_CASE				: List[Any]			       =    [False] * self.vertices

				for _ in range(self.vertices		):
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.minimum_distance(a , a		)
					SCREAMING_SNAKE_CASE				: List[str]			       =    True

					# Update dist value of the adjacent vertices
					# of the picked vertex only if the current
					# distance is greater than new distance and
					# the vertex in not in the shortest path tree
					for v in range(self.vertices		):
						if (
						    self.graph[u][v] > 0
						    and visited[v] is False
						    and distances[v] > distances[u] + self.graph[u][v]
						):
							SCREAMING_SNAKE_CASE				: int			       =    distances[u] + self.graph[u][v]

				self.print_solution(a		)


if __name__ == "__main__":
			a_         =      Graph(9)
			a_         =      [
			    [0, 4, 0, 0, 0, 0, 0, 8, 0],
			    [4, 0, 8, 0, 0, 0, 0, 11, 0],
			    [0, 8, 0, 7, 0, 4, 0, 0, 2],
			    [0, 0, 7, 0, 9, 14, 0, 0, 0],
			    [0, 0, 0, 9, 0, 10, 0, 0, 0],
			    [0, 0, 4, 14, 10, 0, 2, 0, 0],
			    [0, 0, 0, 0, 0, 2, 0, 1, 6],
			    [8, 11, 0, 0, 0, 0, 1, 0, 7],
			    [0, 0, 2, 0, 0, 0, 6, 7, 0],
			]
			graph.dijkstra(0)