



"""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    (		__UpperCamelCase   :      Union[str, Any]       ,			__UpperCamelCase   :      Optional[int]       ,			__UpperCamelCase   :      Optional[Any]     ):


						'''simple docstring'''






						if openai_config_file == "":
												snake_case_ :			List[Any]            =      OpenAIGPTConfig()
						else:
												snake_case_ :			Optional[int]            =      OpenAIGPTConfig.from_json_file(__UpperCamelCase     )
						snake_case_ :			List[Any]            =      OpenAIGPTModel(__UpperCamelCase     )

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

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


if __name__ == "__main__":
					__lowerCAmelCase							:       Dict          =       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							:       Union[str, Any]          =       parser.parse_args()
					convert_openai_checkpoint_to_pytorch(
					    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
					)




"""simple docstring"""


import re

from filelock import FileLock


try:
  import nltk

  UpperCAmelCase													=True
except (ImportError, ModuleNotFoundError):
  UpperCAmelCase													=False

if NLTK_AVAILABLE:
  with FileLock(".lock") as lock:
    nltk.download("punkt", quiet=True)



def   _A					(	_a	:						str				):



      """simple docstring"""






      re.sub("""<n>"""     ,   """"""     ,   _a				)  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(_a				)				)




