





from __future__ import annotations

import math
from collections.abc import Callable


def       UpperCamelCase__(			UpperCamelCase__     :			Callable[[int | float], int | float]					,	UpperCamelCase__     :			int | float					,	UpperCamelCase__     :			int | float					,	UpperCamelCase__     :			int = 1_00					,	)->float:
       A__		   =		x_start
       A__		   =		fnc(UpperCamelCase__    )
       A__		   =		0.0

       for _ in range(UpperCamelCase__    ):
              # Approximates curve as a sequence of linear lines and sums their length
              A__		   =		(x_end - x_start) / steps + xa
              A__		   =		fnc(UpperCamelCase__    )
              length += math.hypot(xa - xa					,	fxa - fxa    )

              # Increment step
              A__		   =		xa
              A__		   =		fxa

       return length


if __name__ == "__main__":


       def       UpperCamelCase__(			UpperCamelCase__     :			Union[str, Any]    )->Dict:
              return math.sin(10 * x    )

       print('f(x) = sin(10 * x)')
       print('The length of the curve from x = -10 to x = 10 is:')
       a__: str							   =						10
       while i <= 100_000:
              print(F"With {i} steps: {line_length(f, -10, 10, i)}")
              i *= 10












import argparse

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


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			int    )->Union[str, Any]:
       # Initialise PyTorch model
       A__		   =		BigBirdConfig.from_json_file(UpperCamelCase__    )
       print(f"Building PyTorch model from configuration: {config}"    )

       if is_trivia_qa:
              A__		   =		BigBirdForQuestionAnswering(UpperCamelCase__    )
       else:
              A__		   =		BigBirdForPreTraining(UpperCamelCase__    )

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

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


if __name__ == "__main__":
       a__: Optional[int]							   =						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__: Tuple							   =						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
       )





