
'''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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if openai_config_file == "":
        snake_case_ :    Tuple     			=						OpenAIGPTConfig()
    else:
        snake_case_ :    str     			=						OpenAIGPTConfig.from_json_file(lowercase__       )
    snake_case_ :    int     			=						OpenAIGPTModel(lowercase__       )

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

    # Save pytorch-model
    snake_case_ :    int     			=						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
    snake_case_ :    List[Any]     			=						pytorch_dump_folder_path + "/" + CONFIG_NAME
    print(f'Save PyTorch model to {pytorch_weights_dump_path}'       )
    torch.save(model.state_dict(),		lowercase__       )
    print(f'Save configuration file to {pytorch_config_dump_path}'       )
    with open(lowercase__,		"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 OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_											=						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BioGptForCausalLM",
      "BioGptForTokenClassification",
      "BioGptForSequenceClassification",
      "BioGptModel",
      "BioGptPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

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




