code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import math
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int       )						->     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(SCREAMING_SNAKE_CASE__       ) + 1       )		,						6       ):
  										if number % i == 0 or number % (i + 2) == 0:
  															return False
  					return True
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int = 1_000		,						SCREAMING_SNAKE_CASE__			:		int = 1_000       )						->     int:
  
  
  
  
  
  					'''simple docstring'''
  
  					_UpperCAmelCase    :			List[Any]              =					[0, 0, 0]  # length, a, b
  					for a in range((a_limit * -1) + 1		,						SCREAMING_SNAKE_CASE__       ):
  										for b in range(2		,						SCREAMING_SNAKE_CASE__       ):
  															if is_prime(SCREAMING_SNAKE_CASE__       ):
  																				_UpperCAmelCase    :			int              =					0
  																				_UpperCAmelCase    :			Optional[Any]              =					0
  																				while is_prime((n**2) + (a * n) + b       ):
  																									count += 1
  																									n += 1
  																				if count > longest[0]:
  																									_UpperCAmelCase    :			Union[str, Any]              =					[count, a, b]
  					_UpperCAmelCase    :			Any              =					longest[1] * longest[2]
  					return ans
  
  
  if __name__ == "__main__":
  	print(solution(10_00, 10_00))
  
style_context: 
  
  
  
  """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 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Any              =					{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
       _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					MODELS[model_size]  # set model hyperparameters
       _UpperCAmelCase    :			Tuple              =					ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE__		,						n_layer=SCREAMING_SNAKE_CASE__		,						n_head=SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			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
       _UpperCAmelCase    :			Union[str, Any]              =					pytorch_dump_folder_path + "/" + WEIGHTS_NAME
       _UpperCAmelCase    :			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__":
   _lowerCAmelCase       :      List[str]							=							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."
   )
   _lowerCAmelCase       :      Optional[int]							=							parser.parse_args()
   convert_imagegpt_checkpoint_to_pytorch(
       args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
   )
  