"""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 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Tuple:





  if openai_config_file == "":
    a__:					Dict      							=     OpenAIGPTConfig()
  else:
    a__:					Union[str, Any]      							=     OpenAIGPTConfig.from_json_file(_SCREAMING_SNAKE_CASE       )
  a__:					Tuple      							=     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__:					str      							=     pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  a__:					Optional[Any]      							=     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__":
 lowercase__			=     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__			=     parser.parse_args()
 convert_openai_checkpoint_to_pytorch(
     args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
 )



"""simple docstring"""
from __future__ import annotations




class        __snake_case	:



      def __init__(						self   ,      lowercase=None)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     data
        a__:					str      							=     None



      def __repr__(						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     []
        a__:					Union[str, Any]      							=     self
        while temp:
          string_rep.append(f'{temp.data}')
          a__:					Tuple      							=     temp.next
        return "->".join(lowercase)







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  if not elements_list:
    raise Exception('The Elements List is empty'       )

  a__:					Any      							=     Node(elements_list[0]       )
  for i in range(1		,					len(_SCREAMING_SNAKE_CASE       )       ):
    a__:					Optional[Any]      							=     Node(elements_list[i]       )
    a__:					Tuple      							=     current.next
  return head







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->None:
  if head_node is not None and isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
    print_reverse(head_node.next       )
    print(head_node.data       )







def 						__a       (     )     ->Optional[Any]:
  from doctest import testmod

  testmod()

  a__:					Tuple      							=     make_linked_list([14, 52, 14, 12, 43]       )
  print('Linked List:'       )
  print(_SCREAMING_SNAKE_CASE       )
  print('Elements in Reverse:'       )
  print_reverse(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
 main()


