




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	(UpperCamelCase_			:			Optional[int]   ,     UpperCamelCase_			:			str   ,     UpperCamelCase_			:			Dict							):
				'''simple docstring'''
				# Construct model
				if gpta_config_file == "":
								_lowerCAmelCase     :     Dict              =   GPTaConfig()
				else:
								_lowerCAmelCase     :     List[Any]              =   GPTaConfig.from_json_file(__lowerCamelCase							)
				_lowerCAmelCase     :     Any              =   GPTaModel(__lowerCamelCase							)

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

				# Save pytorch-model
				_lowerCAmelCase     :     str              =   pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
				_lowerCAmelCase     :     int              =   pytorch_dump_folder_path + """/""" + CONFIG_NAME
				print(F"Save PyTorch model to {pytorch_weights_dump_path}"							)
				torch.save(model.state_dict()   ,     __lowerCamelCase							)
				print(F"Save configuration file to {pytorch_config_dump_path}"							)
				with open(__lowerCamelCase   ,     """w"""   ,     encoding="""utf-8"""							) as f:
								f.write(config.to_json_string()							)


if __name__ == "__main__":
	_lowerCamelCase   :	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."
	    ),
	)
	_lowerCamelCase   :	Optional[Any]       =    parser.parse_args()
	convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)







import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


__UpperCamelCase   :			Optional[int]          = logging.get_logger(__name__)

class 						__lowerCAmelCase      (      __magic_name__					):
							def __init__(		self		:Union[str, Any]						, *__magic_name__		:int						, **__magic_name__		:str							):







								'''simple docstring'''







								warnings.warn(
								    """The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
								    """ Please use LayoutLMv2ImageProcessor instead."""						, __magic_name__						, )
								super().__init__(*__magic_name__						, **__magic_name__							)

