

from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCAmelCase					:			Dict  				=			tuple[int, int]






class 	__magic_name__    :





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> None:




						'''simple docstring'''
						__a        =vertices
						__a        ={
						    (min(__snake_case					), max(__snake_case					)): weight for edge, weight in edges.items()
						}





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					)   -> None:




						'''simple docstring'''
						self.vertices.add(edge[0]					)
						self.vertices.add(edge[1]					)
						__a        =weight





			def 		__magic_name__    (		self					)   -> Graph:




						'''simple docstring'''
						__a        =Graph({min(self.vertices					)}					,  {}					)
						__a        =42
						__a        =42
						__a        =42
						__a        =42

						while len(subgraph.vertices					) < len(self.vertices					):
									__a        =max(self.edges.values()					) + 1
									for edge, weight in self.edges.items():
												if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
															if weight < min_weight:
																		__a        =edge
																		__a        =weight

									subgraph.add_edge(__snake_case					,  __snake_case					)

						return subgraph






def        UpperCamelCase_(							_snake_case   :  str = "p107_network.txt"	):




			"""simple docstring"""






			__a        =os.path.abspath(os.path.dirname(_snake_case	)	)
			__a        =os.path.join(_snake_case       ,     _snake_case	)
			__a        ={}
			__a        =42
			__a        =42
			__a        =42

			with open(_snake_case	) as f:
						__a        =f.read().strip().split('\n'	)

			__a        =[line.split(','	) for line in data]

			for edgea in range(1       ,     len(_snake_case	)	):
						for edgea in range(_snake_case	):
									if adjaceny_matrix[edgea][edgea] != "-":
												__a        =int(adjaceny_matrix[edgea][edgea]	)

			__a        =Graph(set(range(len(_snake_case	)	)	)       ,     _snake_case	)

			__a        =graph.prims_algorithm()

			__a        =sum(graph.edges.values()	)
			__a        =sum(subgraph.edges.values()	)

			return initial_total - optimal_total


if __name__ == "__main__":
						print(f'''{solution() = }''')









import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def        UpperCamelCase_(							_snake_case   :  Dict       ,     _snake_case   :  Optional[int]       ,     _snake_case   :  str	):




			"""simple docstring"""






			if openai_config_file == "":
						__a        =OpenAIGPTConfig()
			else:
						__a        =OpenAIGPTConfig.from_json_file(_snake_case	)
			__a        =OpenAIGPTModel(_snake_case	)

			# Load weights from numpy
			load_tf_weights_in_openai_gpt(_snake_case       ,     _snake_case       ,     _snake_case	)

			# Save pytorch-model
			__a        =pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			__a        =pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F'Save PyTorch model to {pytorch_weights_dump_path}'	)
			torch.save(model.state_dict()       ,     _snake_case	)
			print(F'Save configuration file to {pytorch_config_dump_path}'	)
			with open(_snake_case       ,     'w'       ,     encoding='utf-8'	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
						_lowerCAmelCase					:			Tuple  				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--openai_checkpoint_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the TensorFlow checkpoint path.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--openai_config_file",
						    default="",
						    type=str,
						    help=(
						        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
						        "This specifies the model architecture."
						    ),
						)
						_lowerCAmelCase					:			int  				=			parser.parse_args()
						convert_openai_checkpoint_to_pytorch(
						    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
						)






