code: 
  
  """simple docstring"""
  from __future__ import annotations
  
  from pathlib import Path
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   int:
        return pointa[0] * pointa[1] - pointa[1] * pointa[0]
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   bool:
        UpperCAmelCase__ :       tuple[int, int]						=						(xa, ya)
        UpperCAmelCase__ :       tuple[int, int]						=						(xa - xa, ya - ya)
        UpperCAmelCase__ :       tuple[int, int]						=						(xa - xa, ya - ya)
        UpperCAmelCase__ :       float						=						-vector_product(lowerCAmelCase ,  lowerCAmelCase						) / vector_product(
            lowerCAmelCase ,  lowerCAmelCase						)
        UpperCAmelCase__ :       float						=						+vector_product(lowerCAmelCase ,  lowerCAmelCase						) / vector_product(
            lowerCAmelCase ,  lowerCAmelCase						)
  
        return a > 0 and b > 0 and a + b < 1
  
  
  
  
  def 				a__	(	lowerCAmelCase = "p102_triangles.txt"						)  ->   int:
        UpperCAmelCase__ :       str						=						Path(lowerCAmelCase						).parent.joinpath(lowerCAmelCase						).read_text(encoding="""utf-8"""						)
  
        UpperCAmelCase__ :       list[list[int]]						=						[]
        for line in data.strip().split("""\n"""						):
              triangles.append([int(lowerCAmelCase						) for number in line.split(""","""						)]						)
  
        UpperCAmelCase__ :       int						=						0
        UpperCAmelCase__ :       list[int]
  
        for triangle in triangles:
              ret += contains_origin(*lowerCAmelCase						)
  
        return ret
  
  
  if __name__ == "__main__":
      print(f'''{solution() = }''')
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
        # Initialise PyTorch model
        UpperCAmelCase__ :       Union[str, Any]						=						json.load(open(lowerCAmelCase ,  """r"""						)						)
        UpperCAmelCase__ :       Any						=						GPTNeoConfig(
            hidden_size=config_json["""n_embd"""] ,  num_layers=config_json["""n_layer"""] ,  num_heads=config_json["""n_head"""] ,  attention_types=config_json["""attention_types"""] ,  max_position_embeddings=config_json["""n_positions"""] ,  resid_dropout=config_json["""res_dropout"""] ,  embed_dropout=config_json["""embed_dropout"""] ,  attention_dropout=config_json["""attn_dropout"""] ,  )
        print(F"""Building PyTorch model from configuration: {config}"""						)
        UpperCAmelCase__ :       Dict						=						GPTNeoForCausalLM(lowerCAmelCase						)
  
        # Load weights from tf checkpoint
        load_tf_weights_in_gpt_neo(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
        # Save pytorch-model
        print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
        model.save_pretrained(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      _A						=     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(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained mesh-tf 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."""
      )
      _A						=     parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  