"""simple docstring"""
import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  # Construct configuration depending on size
  a__:	Union[str, Any]     				=      {'small': (512, 8, 24), 'medium': (1024, 8, 36), 'large': (1536, 16, 48)}
  a__				,       a__				,       a__:	Optional[Any]     				=      MODELS[model_size]  # set model hyperparameters
  a__:	Dict     				=      ImageGPTConfig(n_embd=_SCREAMING_SNAKE_CASE						,	n_layer=_SCREAMING_SNAKE_CASE						,	n_head=_SCREAMING_SNAKE_CASE  )
  a__:	Optional[int]     				=      ImageGPTForCausalLM(_SCREAMING_SNAKE_CASE  )

  # Load weights from numpy
  load_tf_weights_in_imagegpt(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  # Save pytorch-model
  a__:	Any     				=      pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  a__:	int     				=      pytorch_dump_folder_path + '/' + CONFIG_NAME
  print(F'Save PyTorch model to {pytorch_weights_dump_path}'  )
  torch.save(model.state_dict()						,	_SCREAMING_SNAKE_CASE  )
  print(F'Save configuration file to {pytorch_config_dump_path}'  )
  with open(_SCREAMING_SNAKE_CASE						,	'w'						,	encoding='utf-8'  ) as f:
    f.write(config.to_json_string()  )


if __name__ == "__main__":
 lowercase__   			=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--imagegpt_checkpoint_path',
     default=None,
     type=str,
     required=True,
     help='Path to the TensorFlow checkpoint path.',
 )
 parser.add_argument(
     '--model_size',
     default=None,
     type=str,
     required=True,
     help='Size of the model (can be either \'small\', \'medium\' or \'large\').',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 lowercase__   			=      parser.parse_args()
 convert_imagegpt_checkpoint_to_pytorch(
     args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
 )





"""simple docstring"""




from __future__ import annotations

import math
from itertools import permutations



def 						UpperCAmelCase__ (					A__ )   ->   bool:




   """simple docstring"""

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5						,     int(math.sqrt(A__ ) + 1 )						,     6 ):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True



def 						UpperCAmelCase__ (					A__ = 7 )   ->   int:




   """simple docstring"""
   lowerCamelCase__						       =      "".join(str(A__ ) for i in range(1						,     n + 1 ) )
   lowerCamelCase__						       =      [int("".join(A__ ) ) for i in permutations(A__						,     A__ )]
   lowerCamelCase__						       =      [num for num in perm_list if is_prime(A__ )]
   return max(A__ ) if pandigitals else 0


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




