






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase_   : Any   			= {
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Optional[int]   			= [
      """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecAudioForAudioFrameClassification""",
      """Data2VecAudioForCTC""",
      """Data2VecAudioForSequenceClassification""",
      """Data2VecAudioForXVector""",
      """Data2VecAudioModel""",
      """Data2VecAudioPreTrainedModel""",
  ]
  UpperCAmelCase_   : Union[str, Any]   			= [
      """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecTextForCausalLM""",
      """Data2VecTextForMaskedLM""",
      """Data2VecTextForMultipleChoice""",
      """Data2VecTextForQuestionAnswering""",
      """Data2VecTextForSequenceClassification""",
      """Data2VecTextForTokenClassification""",
      """Data2VecTextModel""",
      """Data2VecTextPreTrainedModel""",
  ]
  UpperCAmelCase_   : Optional[Any]   			= [
      """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecVisionForImageClassification""",
      """Data2VecVisionForMaskedImageModeling""",
      """Data2VecVisionForSemanticSegmentation""",
      """Data2VecVisionModel""",
      """Data2VecVisionPreTrainedModel""",
  ]

if is_tf_available():
  UpperCAmelCase_   : Any   			= [
      """TFData2VecVisionForImageClassification""",
      """TFData2VecVisionForSemanticSegmentation""",
      """TFData2VecVisionModel""",
      """TFData2VecVisionPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
  from .configuration_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DataaVecTextConfig,
      DataaVecTextOnnxConfig,
  )
  from .configuration_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DataaVecVisionConfig,
      DataaVecVisionOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dataavec_audio import (
        DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecAudioForAudioFrameClassification,
        DataaVecAudioForCTC,
        DataaVecAudioForSequenceClassification,
        DataaVecAudioForXVector,
        DataaVecAudioModel,
        DataaVecAudioPreTrainedModel,
    )
    from .modeling_dataavec_text import (
        DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecTextForCausalLM,
        DataaVecTextForMaskedLM,
        DataaVecTextForMultipleChoice,
        DataaVecTextForQuestionAnswering,
        DataaVecTextForSequenceClassification,
        DataaVecTextForTokenClassification,
        DataaVecTextModel,
        DataaVecTextPreTrainedModel,
    )
    from .modeling_dataavec_vision import (
        DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecVisionForImageClassification,
        DataaVecVisionForMaskedImageModeling,
        DataaVecVisionForSemanticSegmentation,
        DataaVecVisionModel,
        DataaVecVisionPreTrainedModel,
    )
  if is_tf_available():
    from .modeling_tf_dataavec_vision import (
        TFDataaVecVisionForImageClassification,
        TFDataaVecVisionForSemanticSegmentation,
        TFDataaVecVisionModel,
        TFDataaVecVisionPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_   : List[Any]   			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""






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							) ->       Dict:
    """simple docstring"""






    if gpta_config_file == "":
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		GPTaConfig()
    else:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		GPTaConfig.from_json_file(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		GPTaModel(__a							)

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

    # Save pytorch-model
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    SCREAMING_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()    ,				__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__":
  UpperCAmelCase_   : str   			= 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_   : Union[str, Any]   			= parser.parse_args()
  convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


