

from math import isclose, sqrt





def 	__a     (      A__			:  float			,  A__			:  float			,  A__			:  float       ):
    SCREAMING_SNAKE_CASE										=   point_y / 4 / point_x
    SCREAMING_SNAKE_CASE										=   2 * normal_gradient / (1 + normal_gradient * normal_gradient)
    SCREAMING_SNAKE_CASE										=   (1 - normal_gradient * normal_gradient) / (
        1 + normal_gradient * normal_gradient
    )
    SCREAMING_SNAKE_CASE										=   (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

    # to find the next point, solve the simultaeneous equations:
    # y^2 + 4x^2 = 100
    # y - b = m * (x - a)
    # ==> A x^2 + B x + C = 0
    SCREAMING_SNAKE_CASE										=   outgoing_gradient**2 + 4
    SCREAMING_SNAKE_CASE										=   2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
    SCREAMING_SNAKE_CASE										=   (point_y - outgoing_gradient * point_x) ** 2 - 100

    SCREAMING_SNAKE_CASE										=   (
        -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
    ) / (2 * quadratic_term)
    SCREAMING_SNAKE_CASE										=   (
        -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
    ) / (2 * quadratic_term)

    # two solutions, one of which is our input point
    SCREAMING_SNAKE_CASE										=   x_minus if isclose(A__			,  A__       ) else x_plus
    SCREAMING_SNAKE_CASE										=   point_y + outgoing_gradient * (next_x - point_x)

    return next_x, next_y, outgoing_gradient





def 	__a     (      A__			:  float = 1.4			,  A__			:  float = -9.6       ):
    SCREAMING_SNAKE_CASE										=   0
    SCREAMING_SNAKE_CASE										=   first_x_coord
    SCREAMING_SNAKE_CASE										=   first_y_coord
    SCREAMING_SNAKE_CASE										=   (1_0.1 - point_y) / (0.0 - point_x)

    while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
        SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   next_point(A__			,  A__			,  A__       )
        num_reflections += 1

    return num_reflections


if __name__ == "__main__":
    print(f'{solution() = }')

"""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 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  BigBirdConfig.from_json_file(_UpperCamelCase    )
  print(f'''Building PyTorch model from configuration: {config}'''    )

  if is_trivia_qa:
    _lowercase:      Union[str, Any]   			=  BigBirdForQuestionAnswering(_UpperCamelCase    )
  else:
    _lowercase:      Dict   			=  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__ :       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__ :       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
  )
