


import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	List[Any]:


						'''simple docstring'''






						__snake_case				=		BigBirdConfig.from_json_file(_lowerCAmelCase )
						print(F'''Building PyTorch model from configuration: {config}''' )

						if is_trivia_qa:
												__snake_case				=		BigBirdForQuestionAnswering(_lowerCAmelCase )
						else:
												__snake_case				=		BigBirdForPreTraining(_lowerCAmelCase )

						# Load weights from tf checkpoint
						load_tf_weights_in_big_bird(_lowerCAmelCase      ,	_lowerCAmelCase      ,	is_trivia_qa=_lowerCAmelCase )

						# Save pytorch-model
						print(F'''Save PyTorch model to {pytorch_dump_path}''' )
						model.save_pretrained(_lowerCAmelCase )


if __name__ == "__main__":
	A							:					List[Any]     						=     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(
	    '--big_bird_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.'
	)
	parser.add_argument(
	    '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
	)
	A							:					Dict     						=     parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
	)









import warnings

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


A							:					Any     						=     logging.get_logger(__name__)






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[int]     ,    *SCREAMING_SNAKE_CASE	:		Tuple     ,    **SCREAMING_SNAKE_CASE	:		List[Any]						)			->	None:




											'''simple docstring'''

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





