





import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[str]				,	SCREAMING_SNAKE_CASE_   :      Any				,	SCREAMING_SNAKE_CASE_   :      Tuple						)  ->  int:






   """simple docstring"""







   if gpta_config_file == "":
      _UpperCAmelCase           =   GPTaConfig()
   else:
      _UpperCAmelCase           =   GPTaConfig.from_json_file(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   GPTaModel(SCREAMING_SNAKE_CASE_						)

   # Load weights from numpy
   load_tf_weights_in_gpta(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)

   # Save pytorch-model
   _UpperCAmelCase           =   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
   _UpperCAmelCase           =   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__":
   UpperCAmelCase_           =	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--gpt2_checkpoint_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(
       "--gpt2_config_file",
       default="",
       type=str,
       help=(
           "An optional config json file corresponding to the pre-trained OpenAI model. \n"
           "This specifies the model architecture."
       ),
   )
   UpperCAmelCase_           =	parser.parse_args()
   convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=A__    )
class 			__UpperCamelCase    (							A__    ):
     __A	:  str							  =   field(default="""language-modeling"""	, metadata={"""include_in_asdict_even_if_is_default""": True}    )

     __A	:  ClassVar[Features]							  =   Features({"""text""": Value("""string"""    )}    )
     __A	:  ClassVar[Features]							  =   Features({}    )
     __A	:  str							  =   "text"





     @property
     def 		UpperCamelCase(     self					):
        return {self.text_column: "text"}