


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      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     str    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Construct configuration depending on size
   __lowerCamelCase:					int	 =						{"""small""": (512, 8, 24), """medium""": (1024, 8, 36), """large""": (1536, 16, 48)}
   __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase:					Optional[int]	 =						MODELS[model_size]  # set model hyperparameters
   __lowerCamelCase:					str	 =						ImageGPTConfig(n_embd=snake_case    ,   n_layer=snake_case    ,   n_head=snake_case			)
   __lowerCamelCase:					Union[str, Any]	 =						ImageGPTForCausalLM(snake_case			)

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

   # Save pytorch-model
   __lowerCamelCase:					Union[str, Any]	 =						pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
   __lowerCamelCase:					Optional[Any]	 =						pytorch_dump_folder_path + """/""" + CONFIG_NAME
   print(f'Save PyTorch model to {pytorch_weights_dump_path}'			)
   torch.save(model.state_dict()    ,   snake_case			)
   print(f'Save configuration file to {pytorch_config_dump_path}'			)
   with open(snake_case    ,   """w"""    ,   encoding="""utf-8"""			) as f:
      f.write(config.to_json_string()			)


if __name__ == "__main__":
   _A		:					Tuple          =							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.'''
   )
   _A		:					Dict          =							parser.parse_args()
   convert_imagegpt_checkpoint_to_pytorch(
       args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
   )






_A		:					Optional[int]          =							{
    '''+''': lambda x, y: x + y,
    '''-''': lambda x, y: x - y,
    '''*''': lambda x, y: x * y,
    '''/''': lambda x, y: x / y,
}



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]			)							->				str:
   return c.isdigit()



def      __lowerCAmelCase    (				snake_case						:     Optional[Any]			)							->				str:
   __lowerCamelCase:					List[str]	 =						[]

   # iterate over the string in reverse order
   for c in expression.split()[::-1]:
      # push operand to stack
      if is_operand(snake_case			):
         stack.append(int(snake_case			)			)

      else:
         # pop values from stack can calculate the result
         # push the result onto the stack again
         __lowerCamelCase:					Any	 =						stack.pop()
         __lowerCamelCase:					int	 =						stack.pop()
         stack.append(calc[c](snake_case    ,   snake_case			)			)

   return stack.pop()


# Driver code
if __name__ == "__main__":
   _A		:					int          =							'''+ 9 * 2 6'''
   print(evaluate(test_expression))

   _A		:					str          =							'''/ * 10 2 + 4 1 '''
   print(evaluate(test_expression))


