





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       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				# Construct model
				if openai_config_file == "":
								_snake_case :			Optional[Any]  						=   OpenAIGPTConfig()
				else:
								_snake_case :			Union[str, Any]  						=   OpenAIGPTConfig.from_json_file(__lowerCAmelCase )
				_snake_case :			int  						=   OpenAIGPTModel(__lowerCAmelCase )

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

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


if __name__ == "__main__":
		lowercase_		:				Any				=						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.'''
		    ),
		)
		lowercase_		:				Dict				=						parser.parse_args()
		convert_openai_checkpoint_to_pytorch(
		    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
		)













from __future__ import annotations

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Dict  						=   list(range(len(__lowerCAmelCase ) ) )
				_snake_case :			Optional[int]  						=   [v / w for v, w in zip(__lowerCAmelCase	,			__lowerCAmelCase )]
				index.sort(key=lambda __lowerCAmelCase : ratio[i]	,			reverse=__lowerCAmelCase )

				_snake_case :			float  						=   0
				_snake_case :			list[float]  						=   [0] * len(__lowerCAmelCase )
				for i in index:
								if weight[i] <= capacity:
												_snake_case :			List[Any]  						=   1
												max_value += value[i]
												capacity -= weight[i]
								else:
												_snake_case :			Any  						=   capacity / weight[i]
												max_value += value[i] * capacity / weight[i]
												break

				return max_value, fractions


if __name__ == "__main__":
		import doctest

		doctest.testmod()






