code: 
  
  """simple docstring"""
  from __future__ import annotations
  
  import math
  import random
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   list[int]:
        UpperCAmelCase__ :       Optional[int]						=						d * e - 1
        UpperCAmelCase__ :       int						=						0
        UpperCAmelCase__ :       List[Any]						=						0
        while p == 0:
              UpperCAmelCase__ :       int						=						random.randint(2 ,  n - 1						)
              UpperCAmelCase__ :       int						=						k
              while True:
                    if t % 2 == 0:
                          UpperCAmelCase__ :       Tuple						=						t // 2
                          UpperCAmelCase__ :       Tuple						=						(g**t) % n
                          UpperCAmelCase__ :       Optional[int]						=						math.gcd(x - 1 ,  lowerCAmelCase						)
                          if x > 1 and y > 1:
                                UpperCAmelCase__ :       Dict						=						y
                                UpperCAmelCase__ :       int						=						n // y
                                break  # find the correct factors
                    else:
                          break  # t is not divisible by 2, break and choose another g
        return sorted([p, q]						)
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()
  
  
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)
  
  