from __future__ import annotations

from pathlib import Path



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			str					,	__UpperCamelCase       :			List[Any]      )				->   Tuple:







  """simple docstring"""





  return pointa[0] * pointa[1] - pointa[1] * pointa[0]



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Optional[int]					,	__UpperCamelCase       :			List[Any]					,	__UpperCamelCase       :			Optional[Any]					,	__UpperCamelCase       :			Optional[int]					,	__UpperCamelCase       :			Optional[Any]					,	__UpperCamelCase       :			List[str]      )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= (xa, ya)
  SCREAMING_SNAKE_CASE__     				= (xa - xa, ya - ya)
  SCREAMING_SNAKE_CASE__     				= (xa - xa, ya - ya)
  SCREAMING_SNAKE_CASE__     				= -vector_product(__UpperCamelCase					,	__UpperCamelCase      ) / vector_product(
      __UpperCamelCase					,	__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__     				= +vector_product(__UpperCamelCase					,	__UpperCamelCase      ) / vector_product(
      __UpperCamelCase					,	__UpperCamelCase      )

  return a > 0 and b > 0 and a + b < 1



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			str = "p102_triangles.txt"      )				->   List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= Path(__UpperCamelCase      ).parent.joinpath(__UpperCamelCase      ).read_text(encoding="""utf-8"""      )

  SCREAMING_SNAKE_CASE__     				= []
  for line in data.strip().split("""\n"""      ):
    triangles.append([int(__UpperCamelCase      ) for number in line.split(""","""      )]      )

  SCREAMING_SNAKE_CASE__     				= 0
  SCREAMING_SNAKE_CASE__     				= 42

  for triangle in triangles:
    ret += contains_origin(*__UpperCamelCase      )

  return ret


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








"""simple docstring"""


import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					RoFormerConfig.from_json_file(__UpperCamelCase					)
  print(f'Building PyTorch model from configuration: {config}'					)
  __A		     =					RoFormerForMaskedLM(__UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_roformer(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)

  # Save pytorch-model
  print(f'Save PyTorch model to {pytorch_dump_path}'					)
  torch.save(model.state_dict()						,			__UpperCamelCase						,			_use_new_zipfile_serialization=__UpperCamelCase					)


if __name__ == "__main__":
       lowercase_								=	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.'
       )
       lowercase_								=	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




