


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()






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)


