






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 	lowerCAmelCase_		(lowercase__						:       Tuple     ,					lowercase__						:       Any     ,					lowercase__						:       Optional[Any]       )		->    Dict:


				'''simple docstring'''




				if gpta_config_file == "":
								lowerCAmelCase__      			=						GPTaConfig()
				else:
								lowerCAmelCase__      			=						GPTaConfig.from_json_file(lowercase__       )
				lowerCAmelCase__      			=						GPTaModel(lowercase__       )

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

				# Save pytorch-model
				lowerCAmelCase__      			=						pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				lowerCAmelCase__      			=						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__":
			_UpperCAmelCase			:		int   				=    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			:		Dict   				=    parser.parse_args()
			convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)













from typing import TYPE_CHECKING

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


_UpperCAmelCase			:		Optional[Any]   				=    {
    "configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "ErnieForCausalLM",
			    "ErnieForMaskedLM",
			    "ErnieForMultipleChoice",
			    "ErnieForNextSentencePrediction",
			    "ErnieForPreTraining",
			    "ErnieForQuestionAnswering",
			    "ErnieForSequenceClassification",
			    "ErnieForTokenClassification",
			    "ErnieModel",
			    "ErniePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ernie import (
						    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ErnieForCausalLM,
						    ErnieForMaskedLM,
						    ErnieForMultipleChoice,
						    ErnieForNextSentencePrediction,
						    ErnieForPreTraining,
						    ErnieForQuestionAnswering,
						    ErnieForSequenceClassification,
						    ErnieForTokenClassification,
						    ErnieModel,
						    ErniePreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





