

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_       (						_A				,		_A				,		_A    ):





     '''simple docstring'''




     if openai_config_file == "":
          SCREAMING_SNAKE_CASE__   =   OpenAIGPTConfig()
     else:
          SCREAMING_SNAKE_CASE__   =   OpenAIGPTConfig.from_json_file(_A    )
     SCREAMING_SNAKE_CASE__   =   OpenAIGPTModel(_A    )

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

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


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Union[str, 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.'''
           ),
       )
       _SCREAMING_SNAKE_CASE					:  List[str]             =      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     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   len(_A    ) // 2

     # choose the middle 3 elements
     SCREAMING_SNAKE_CASE__   =   lst[m - 1 : m + 2]

     # if middle element is peak
     if three[1] > three[0] and three[1] > three[2]:
          return three[1]

     # if increasing, recurse on right
     elif three[0] < three[2]:
          if len(lst[:m]    ) == 2:
               m -= 1
          return peak(lst[m:]    )

     # decreasing
     else:
          if len(lst[:m]    ) == 2:
               m += 1
          return peak(lst[:m]    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


