code: 
  
  
  import math
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list:
  
  
       """simple docstring"""
  
  
       if n <= 0:
            raise ValueError('Only positive integers have prime factors'   )
       __UpperCamelCase   = []
       while n % 2 == 0:
            pf.append(2   )
            __UpperCamelCase   = int(n / 2   )
       for i in range(3				,     int(math.sqrt(_lowercase   )   ) + 1				,     2   ):
            while n % i == 0:
                 pf.append(_lowercase   )
                 __UpperCamelCase   = int(n / i   )
       if n > 2:
            pf.append(_lowercase   )
       return pf
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   int:
  
  
       """simple docstring"""
  
  
       if n <= 0:
            raise ValueError('Only positive numbers are accepted'   )
       __UpperCamelCase   = 1
       __UpperCamelCase   = 1
       while n % 2 == 0:
            temp += 1
            __UpperCamelCase   = int(n / 2   )
       div *= temp
       for i in range(3				,     int(math.sqrt(_lowercase   )   ) + 1				,     2   ):
            __UpperCamelCase   = 1
            while n % i == 0:
                 temp += 1
                 __UpperCamelCase   = int(n / i   )
            div *= temp
       if n > 1:
            div *= 2
       return div
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   int:
  
  
       """simple docstring"""
  
  
       if n <= 0:
            raise ValueError('Only positive numbers are accepted'   )
       __UpperCamelCase   = 1
       __UpperCamelCase   = 1
       while n % 2 == 0:
            temp += 1
            __UpperCamelCase   = int(n / 2   )
       if temp > 1:
            s *= (2**temp - 1) / (2 - 1)
       for i in range(3				,     int(math.sqrt(_lowercase   )   ) + 1				,     2   ):
            __UpperCamelCase   = 1
            while n % i == 0:
                 temp += 1
                 __UpperCamelCase   = int(n / i   )
            if temp > 1:
                 s *= (i**temp - 1) / (i - 1)
       return int(_lowercase   )
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   int:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = n
       for x in set(prime_factors(_lowercase   )   ):
            s *= (x - 1) / x
       return int(_lowercase   )
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()
  
  
  
style_context: 
  
  
  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		(		_lowercase				,     _lowercase				,     _lowercase   )				->   List[str]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = json.load(open(_lowercase				,     'r'   )   )
       __UpperCamelCase   = 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   = GPTNeoForCausalLM(_lowercase   )
  
       # Load weights from tf checkpoint
       load_tf_weights_in_gpt_neo(_lowercase				,     _lowercase				,     _lowercase   )
  
       # Save pytorch-model
       print(f'''Save PyTorch model to {pytorch_dump_path}'''   )
       model.save_pretrained(_lowercase   )
  
  
  if __name__ == "__main__":
      __snake_case             =	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.'''
      )
      __snake_case             =	parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  