


"""simple docstring"""




from itertools import count
from math import asin, pi, sqrt
def     UpperCAmelCase_ (							__snake_case						)					->      float:







 """simple docstring"""


 return (
     (1 - 2 * point) * sqrt(point - point**2						) + 2 * point + asin(sqrt(1 - point						)						)
 ) / 4
def     UpperCAmelCase_ (							__snake_case						)					->      float:







 """simple docstring"""


 _lowercase											=(circles_number + 1 - sqrt(2 * circles_number						)) / (
     2 * (circles_number**2 + 1)
 )
 _lowercase											=circles_number * intersection_y

 _lowercase											=intersection_x * intersection_y / 2
 _lowercase											=circle_bottom_arc_integral(
     1 / 2						) - circle_bottom_arc_integral(SCREAMING_SNAKE_CASE_						)

 return triangle_area + concave_region_area
def     UpperCAmelCase_ (							__snake_case = 1 / 1000						)					->      int:







 """simple docstring"""


 _lowercase											=(1 - pi / 4) / 4

 for n in count(1						):
  if concave_triangle_area(SCREAMING_SNAKE_CASE_						) / l_section_area < fraction:
   return n

 return -1


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





import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      Tuple:







 """simple docstring"""


 _lowercase											=BertConfig.from_pretrained(
     '''bert-large-cased'''					,						vocab_size=__snake_case					,						max_position_embeddings=512					,						is_decoder=__snake_case					,						add_cross_attention=__snake_case					,						)
 _lowercase											=bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 _lowercase											=BertGenerationConfig(**__snake_case						)
 if is_encoder:
  _lowercase											=BertGenerationEncoder(__snake_case						)
 else:
  _lowercase											=BertGenerationDecoder(__snake_case						)
 print(F"Building PyTorch model from configuration: {config}"						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     __snake_case					,						__snake_case					,						model_class='''bert'''					,						is_encoder_named_decoder=__snake_case					,						is_encoder=__snake_case					,						)

 # Save pytorch-model
 print(F"Save PyTorch model and config to {pytorch_dump_path}"						)
 model.save_pretrained(__snake_case						)


if __name__ == "__main__":
  UpperCAmelCase__            =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  parser.add_argument(
      '''--is_encoder_named_decoder''',
      action='''store_true''',
      help='''If decoder has to be renamed to encoder in PyTorch model.''',
  )
  parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
  parser.add_argument('''--vocab_size''', default=5_0358, type=int, help='''Vocab size of model''')
  UpperCAmelCase__            =							parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_hub_path,
      args.pytorch_dump_path,
      args.is_encoder_named_decoder,
      args.vocab_size,
      is_encoder=args.is_encoder,
  )

