





'''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 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							Optional[Any]     =     {'''small''': (512, 8, 24), '''medium''': (1024, 8, 36), '''large''': (1536, 16, 48)}
		__UpperCAmelCase	:							Optional[int]     =     MODELS[model_size]  # set model hyperparameters
		__UpperCAmelCase	:							Any     =     ImageGPTConfig(n_embd=_UpperCAmelCase,       n_layer=_UpperCAmelCase,       n_head=_UpperCAmelCase  )
		__UpperCAmelCase	:							int     =     ImageGPTForCausalLM(_UpperCAmelCase  )

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

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


if __name__ == "__main__":
			lowerCAmelCase__      :   int      	=			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__      :   Union[str, Any]      	=			parser.parse_args()
			convert_imagegpt_checkpoint_to_pytorch(
			    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
			)








from hashlib import mda, shaaaa

__UpperCamelCase							:							Dict    							=					(shaaaa, mda)

class    __magic_name__						:


     def __init__(   self				:  Optional[Any]				,						lowerCamelCase__				:  int = 8      )	->   None:





      '''simple docstring'''






      UpperCamelCase__       :     int 							= 0b0
      UpperCamelCase__       :     Optional[int] 							= size


     def   UpperCAmelCase__						(   self				:  Tuple				,						lowerCamelCase__				:  str      )	->   None:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= self.hash_(lowerCamelCase__      )
      self.bitarray |= h


     def   UpperCAmelCase__						(   self				:  Any				,						lowerCamelCase__				:  str      )	->   bool:





      '''simple docstring'''






      UpperCamelCase__       :     Any 							= self.hash_(lowerCamelCase__      )
      return (h & self.bitarray) == h


     def __contains__(   self				:  Any				,						lowerCamelCase__				:  str      )	->   bool:





      '''simple docstring'''






      return self.exists(lowerCamelCase__      )


     def   UpperCAmelCase__						(   self				:  Union[str, Any]				,						lowerCamelCase__				:  int      )	->   str:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[int] 							= bin(lowerCamelCase__      )[2:]
      return res.zfill(self.size      )


     @property
     def   UpperCAmelCase__						(   self				:  Tuple      )	->   str:





      '''simple docstring'''






      return self.format_bin(self.bitarray      )


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  str      )	->   int:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= 0b0
      for func in HASH_FUNCTIONS:
       UpperCamelCase__       :     Optional[Any] 							= (
           int.from_bytes(func(value.encode()      ).digest()				,						'''little'''      ) % self.size
       )
       res |= 2**position
      return res


     def   UpperCAmelCase__						(   self				:  Union[str, Any]				,						lowerCamelCase__				:  str      )	->   str:





      '''simple docstring'''






      return self.format_bin(self.hash_(lowerCamelCase__      )      )


     @property
     def   UpperCAmelCase__						(   self				:  str      )	->   float:





      '''simple docstring'''






      UpperCamelCase__       :     Tuple 							= bin(self.bitarray      ).count('''1'''      )
      return (n_ones / self.size) ** len(lowerCamelCase__      )


