





"""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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						# Construct model
						if gpta_config_file == "":
												UpperCamelCase				:		Union[str, Any]				   =			GPTaConfig()
						else:
												UpperCamelCase				:		Dict				   =			GPTaConfig.from_json_file(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Optional[Any]				   =			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				:		str				   =			pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
						UpperCamelCase				:		Dict				   =			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__":
		__magic_name__       :					Tuple						=			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."""
		    ),
		)
		__magic_name__       :					int						=			parser.parse_args()
		convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)










"""simple docstring"""


from typing import TYPE_CHECKING

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


__magic_name__       :					int						=			{
    """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:
		__magic_name__       :					List[Any]						=			[
		    """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecAudioForAudioFrameClassification""",
		    """Data2VecAudioForCTC""",
		    """Data2VecAudioForSequenceClassification""",
		    """Data2VecAudioForXVector""",
		    """Data2VecAudioModel""",
		    """Data2VecAudioPreTrainedModel""",
		]
		__magic_name__       :					Optional[int]						=			[
		    """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecTextForCausalLM""",
		    """Data2VecTextForMaskedLM""",
		    """Data2VecTextForMultipleChoice""",
		    """Data2VecTextForQuestionAnswering""",
		    """Data2VecTextForSequenceClassification""",
		    """Data2VecTextForTokenClassification""",
		    """Data2VecTextModel""",
		    """Data2VecTextPreTrainedModel""",
		]
		__magic_name__       :					str						=			[
		    """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecVisionForImageClassification""",
		    """Data2VecVisionForMaskedImageModeling""",
		    """Data2VecVisionForSemanticSegmentation""",
		    """Data2VecVisionModel""",
		    """Data2VecVisionPreTrainedModel""",
		]

if is_tf_available():
		__magic_name__       :					Optional[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

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



