
"""simple docstring"""





import argparse

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


logging.set_verbosity_info()
def 							A_     (__a				,   __a				,   __a				,   __a				):
					A_				 =						BigBirdConfig.from_json_file(__a				)
					print(f'Building PyTorch model from configuration: {config}'				)

					if is_trivia_qa:
										A_				 =						BigBirdForQuestionAnswering(__a				)
					else:
										A_				 =						BigBirdForPreTraining(__a				)

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

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


if __name__ == "__main__":
		UpperCamelCase_					:						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.'''
		)
		UpperCamelCase_					:						Optional[Any]				= 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
		)






"""simple docstring"""





import baseaa
def 							A_     (__a				):

					'''simple docstring'''





					return baseaa.aaaencode(string.encode("utf-8"				)				)
def 							A_     (__a				):

					'''simple docstring'''





					return baseaa.aaadecode(__a				).decode("utf-8"				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




