




'''simple docstring'''
import os





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_ = "input.txt"     )		->		int:

			with open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE_     )       ,SCREAMING_SNAKE_CASE_     )     ) as input_file:
						A_									= [
						    [int(SCREAMING_SNAKE_CASE_     ) for element in line.split(','     )]
						    for line in input_file.readlines()
						]

			A_									= len(SCREAMING_SNAKE_CASE_     )
			A_									= len(matrix[0]     )

			A_									= [[-1 for _ in range(SCREAMING_SNAKE_CASE_     )] for _ in range(SCREAMING_SNAKE_CASE_     )]
			for i in range(SCREAMING_SNAKE_CASE_     ):
						A_									= matrix[i][0]

			for j in range(1       ,SCREAMING_SNAKE_CASE_     ):
						for i in range(SCREAMING_SNAKE_CASE_     ):
									A_									= minimal_path_sums[i][j - 1] + matrix[i][j]

						for i in range(1       ,SCREAMING_SNAKE_CASE_     ):
									A_									= min(
									    minimal_path_sums[i][j]       ,minimal_path_sums[i - 1][j] + matrix[i][j]     )

						for i in range(rows - 2       ,-1       ,-1     ):
									A_									= min(
									    minimal_path_sums[i][j]       ,minimal_path_sums[i + 1][j] + matrix[i][j]     )

			return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums     )


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










'''simple docstring'''
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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		str:
			# Construct model
			if openai_config_file == "":
						A_									= OpenAIGPTConfig()
			else:
						A_									= OpenAIGPTConfig.from_json_file(SCREAMING_SNAKE_CASE_     )
			A_									= OpenAIGPTModel(SCREAMING_SNAKE_CASE_     )

			# Load weights from numpy
			load_tf_weights_in_openai_gpt(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_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()       ,SCREAMING_SNAKE_CASE_     )
			print(F'Save configuration file to {pytorch_config_dump_path}'     )
			with open(SCREAMING_SNAKE_CASE_       ,'w'       ,encoding='utf-8'     ) as f:
						f.write(config.to_json_string()     )


if __name__ == "__main__":
							_SCREAMING_SNAKE_CASE			=						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."
							    ),
							)
							_SCREAMING_SNAKE_CASE			=						parser.parse_args()
							convert_openai_checkpoint_to_pytorch(
							    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
							)




