





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 		__lowercase					(       snake_case_  :  Tuple    ,snake_case_  :  Union[str, Any]    ,snake_case_  :  List[str]						)       ->Dict:



    '''simple docstring'''







    if openai_config_file == "":
        __A   :						Optional[int]									=					OpenAIGPTConfig()
    else:
        __A   :						List[Any]									=					OpenAIGPTConfig.from_json_file(a_						)
    __A   :						Dict									=					OpenAIGPTModel(a_						)

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

    # Save pytorch-model
    __A   :						Any									=					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()    ,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__":
       a_			=      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."""
           ),
       )
       a_			=      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 typing import TYPE_CHECKING

from ..utils import _LazyModule


a_			=      {
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




