


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   (      __magic_name__	:				Optional[int]       ,       __magic_name__	:				Any       ,       __magic_name__	:				int	)  ->    Optional[int]:
		"""simple docstring"""




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

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

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


if __name__ == "__main__":
	A			:						Optional[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.'
	    ),
	)
	A			:						Optional[int]        =  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__							)

