






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def       snake_case_		(     lowerCAmelCase_       :						Tuple       ,       lowerCAmelCase_       :						Tuple       ,       lowerCAmelCase_       :						Tuple      ):
							# Initialise PyTorch model
							__lowercase      :							Union[str, Any]				=     BertConfig.from_json_file(lowerCAmelCase_      )
							print(F"Building PyTorch model from configuration: {config}"      )
							__lowercase      :							int				=     BertForPreTraining(lowerCAmelCase_      )

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_      )

							# Save pytorch-model
							print(F"Save PyTorch model to {pytorch_dump_path}"      )
							torch.save(model.state_dict()       ,       lowerCAmelCase_      )


if __name__ == "__main__":
	lowerCamelCase : Tuple						   =       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
	)
	parser.add_argument(
	    '''--bert_config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained BERT model. \n'''
	        '''This specifies the model architecture.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	lowerCamelCase : Union[str, Any]						   =       parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase : Union[str, Any]						   =       logging.get_logger(__name__)

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	int		,     *__a				:	Dict		,     **__a				:	Optional[Any]							)			->  None:


												"""simple docstring"""

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