





lowercase				:	int      						=  {
    """Pillow""": """Pillow<10.0.0""",
    """accelerate""": """accelerate>=0.20.3""",
    """av""": """av==9.2.0""",
    """beautifulsoup4""": """beautifulsoup4""",
    """black""": """black~=23.1""",
    """codecarbon""": """codecarbon==1.2.0""",
    """cookiecutter""": """cookiecutter==1.7.3""",
    """dataclasses""": """dataclasses""",
    """datasets""": """datasets!=2.5.0""",
    """decord""": """decord==0.6.0""",
    """deepspeed""": """deepspeed>=0.9.3""",
    """diffusers""": """diffusers""",
    """dill""": """dill<0.3.5""",
    """evaluate""": """evaluate>=0.2.0""",
    """fairscale""": """fairscale>0.3""",
    """faiss-cpu""": """faiss-cpu""",
    """fastapi""": """fastapi""",
    """filelock""": """filelock""",
    """flax""": """flax>=0.4.1,<=0.7.0""",
    """ftfy""": """ftfy""",
    """fugashi""": """fugashi>=1.0""",
    """GitPython""": """GitPython<3.1.19""",
    """hf-doc-builder""": """hf-doc-builder>=0.3.0""",
    """huggingface-hub""": """huggingface-hub>=0.14.1,<1.0""",
    """importlib_metadata""": """importlib_metadata""",
    """ipadic""": """ipadic>=1.0.0,<2.0""",
    """isort""": """isort>=5.5.4""",
    """jax""": """jax>=0.2.8,!=0.3.2,<=0.4.13""",
    """jaxlib""": """jaxlib>=0.1.65,<=0.4.13""",
    """jieba""": """jieba""",
    """kenlm""": """kenlm""",
    """keras-nlp""": """keras-nlp>=0.3.1""",
    """librosa""": """librosa""",
    """nltk""": """nltk""",
    """natten""": """natten>=0.14.6""",
    """numpy""": """numpy>=1.17""",
    """onnxconverter-common""": """onnxconverter-common""",
    """onnxruntime-tools""": """onnxruntime-tools>=1.4.2""",
    """onnxruntime""": """onnxruntime>=1.4.0""",
    """opencv-python""": """opencv-python""",
    """optuna""": """optuna""",
    """optax""": """optax>=0.0.8,<=0.1.4""",
    """packaging""": """packaging>=20.0""",
    """parameterized""": """parameterized""",
    """phonemizer""": """phonemizer""",
    """protobuf""": """protobuf""",
    """psutil""": """psutil""",
    """pyyaml""": """pyyaml>=5.1""",
    """pydantic""": """pydantic<2""",
    """pytest""": """pytest>=7.2.0""",
    """pytest-timeout""": """pytest-timeout""",
    """pytest-xdist""": """pytest-xdist""",
    """python""": """python>=3.8.0""",
    """ray[tune]""": """ray[tune]""",
    """regex""": """regex!=2019.12.17""",
    """requests""": """requests""",
    """rhoknp""": """rhoknp>=1.1.0,<1.3.1""",
    """rjieba""": """rjieba""",
    """rouge-score""": """rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1""",
    """ruff""": """ruff>=0.0.241,<=0.0.259""",
    """sacrebleu""": """sacrebleu>=1.4.12,<2.0.0""",
    """sacremoses""": """sacremoses""",
    """safetensors""": """safetensors>=0.3.1""",
    """sagemaker""": """sagemaker>=2.31.0""",
    """scikit-learn""": """scikit-learn""",
    """sentencepiece""": """sentencepiece>=0.1.91,!=0.1.92""",
    """sigopt""": """sigopt""",
    """starlette""": """starlette""",
    """sudachipy""": """sudachipy>=0.6.6""",
    """sudachidict_core""": """sudachidict_core>=20220729""",
    """tensorflow-cpu""": """tensorflow-cpu>=2.6,<2.14""",
    """tensorflow""": """tensorflow>=2.6,<2.14""",
    """tensorflow-text""": """tensorflow-text<2.14""",
    """tf2onnx""": """tf2onnx""",
    """timeout-decorator""": """timeout-decorator""",
    """timm""": """timm""",
    """tokenizers""": """tokenizers>=0.11.1,!=0.11.3,<0.14""",
    """torch""": """torch>=1.9,!=1.12.0""",
    """torchaudio""": """torchaudio""",
    """torchvision""": """torchvision""",
    """pyctcdecode""": """pyctcdecode>=0.4.0""",
    """tqdm""": """tqdm>=4.27""",
    """unidic""": """unidic>=1.0.2""",
    """unidic_lite""": """unidic_lite>=1.0.7""",
    """urllib3""": """urllib3<2.0.0""",
    """uvicorn""": """uvicorn""",
}









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_     (		A__		,				A__		,				A__    )  ->		Any:
       # Construct model
       if gpta_config_file == "":
              a__						:     Optional[int]			=    GPTaConfig()
       else:
              a__						:     List[str]			=    GPTaConfig.from_json_file(A__    )
       a__						:     List[str]			=    GPTaModel(A__    )

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

       # Save pytorch-model
       a__						:     Optional[Any]			=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
       a__						:     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__":
    lowercase				:	Dict      						=  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."""
        ),
    )
    lowercase				:	int      						=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


