






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__				:		Any    ,    lowerCAmelCase__				:		Union[str, Any]    ,    lowerCAmelCase__				:		Union[str, Any]    ,    lowerCAmelCase__				:		Union[str, Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Any													=   BigBirdConfig.from_json_file(__SCREAMING_SNAKE_CASE      )
				print(F'''Building PyTorch model from configuration: {config}'''      )

				if is_trivia_qa:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   BigBirdForQuestionAnswering(__SCREAMING_SNAKE_CASE      )
				else:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   BigBirdForPreTraining(__SCREAMING_SNAKE_CASE      )

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

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


if __name__ == "__main__":
				A        =			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        =			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 math



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				return math.pow(lowerCAmelCase__    ,    2      ) - a



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				return 2 * x



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   2.0

				while start <= a:
								SCREAMING_SNAKE_CASE_   :	str													=   math.pow(lowerCAmelCase__    ,    2      )

				return start



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		int = 9999    ,    lowerCAmelCase__				:		float = 0.00_000_000_000_001      ):





				'''simple docstring'''







				if a < 0:
								raise ValueError('math domain error'      )

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   get_initial_point(lowerCAmelCase__      )

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Tuple													=   value
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   value - fx(lowerCAmelCase__    ,    lowerCAmelCase__      ) / fx_derivative(lowerCAmelCase__      )
								if abs(prev_value - value      ) < tolerance:
												return value

				return value


if __name__ == "__main__":
				from doctest import testmod

				testmod()