code: 
  
  
  import argparse
  
  import torch
  
  from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def       a__						(		snake_case__				:       int	,      snake_case__				:       List[Any]	,      snake_case__				:       Optional[Any]    ):
  		_UpperCAmelCase		:       Optional[Any]       =    RoFormerConfig.from_json_file(lowerCamelCase__    )
  		print(f'''Building PyTorch model from configuration: {config}'''    )
  		_UpperCAmelCase		:       Optional[int]       =    RoFormerForMaskedLM(lowerCamelCase__    )
  
  		# Load weights from tf checkpoint
  		load_tf_weights_in_roformer(lowerCamelCase__	,      lowerCamelCase__	,      lowerCamelCase__    )
  
  		# Save pytorch-model
  		print(f'''Save PyTorch model to {pytorch_dump_path}'''    )
  		torch.save(model.state_dict()	,      lowerCamelCase__	,      _use_new_zipfile_serialization=lowerCamelCase__    )
  
  
  if __name__ == "__main__":
  	SCREAMING_SNAKE_CASE__				:    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(
  	    '--bert_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.'
  	)
  	SCREAMING_SNAKE_CASE__				:    Optional[Any]         =    parser.parse_args()
  	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
  
  
  
  
  
  
style_context: 
  
  
  from itertools import count
  from math import asin, pi, sqrt
  
  
  
  
  def       a__						(		snake_case__				:       float    ):
    return (
        (1 - 2 * point) * sqrt(point - point**2    ) + 2 * point + asin(sqrt(1 - point    )    )
    ) / 4
  
  
  
  
  def       a__						(		snake_case__				:       int    ):
    _UpperCAmelCase		:       int       =    (circles_number + 1 - sqrt(2 * circles_number    )) / (
        2 * (circles_number**2 + 1)
    )
    _UpperCAmelCase		:       Union[str, Any]       =    circles_number * intersection_y
  
    _UpperCAmelCase		:       Tuple       =    intersection_x * intersection_y / 2
    _UpperCAmelCase		:       str       =    circle_bottom_arc_integral(
        1 / 2    ) - circle_bottom_arc_integral(snake_case__    )
  
    return triangle_area + concave_region_area
  
  
  
  
  def       a__						(		snake_case__				:       float = 1 / 1000    ):
    _UpperCAmelCase		:       Dict       =    (1 - pi / 4) / 4
  
    for n in count(1    ):
      if concave_triangle_area(snake_case__    ) / l_section_area < fraction:
        return n
  
    return -1
  
  
  if __name__ == "__main__":
   print(F'{solution() = }')
  
  
  
  
  
  