


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						:					Tuple     ,			SCREAMING_SNAKE_CASE						:					List[str]     ,			SCREAMING_SNAKE_CASE						:					Dict  ):







 """simple docstring"""



 UpperCamelCase__       :     Optional[Any] 							= {'''small''': (512, 8, 24), '''medium''': (1024, 8, 36), '''large''': (1536, 16, 48)}
 UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       :     Optional[int] 							= MODELS[model_size]  # set model hyperparameters
 UpperCamelCase__       :     Any 							= 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__       :     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()     ,			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__":
       __UpperCamelCase							:							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."
       )
       __UpperCamelCase							:							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__      )


