code: 
  
  
  
  """simple docstring"""
  
  
  
  
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  _lowerCAmelCase       :      int							=							logging.get_logger(__name__)
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       snake_case__		):
  						__SCREAMING_SNAKE_CASE		:     List[Any]				  =				['input_ids', 'attention_mask']
  
  
  
  						def __init__(  self  :			Dict  ,			A  :			Dict="[PAD]"  ,			A  :			List[str]="[BOS]"  ,			A  :			str="[EOS]"  ,			A  :			Tuple="[MASK]"  ,			A  :			List[Any]="[CLS]"  ,			A  :			Optional[int]="[SEP]"  ,			A  :			List[str]=2_0_4_8  ,			**A  :			str  ,			):
  											_UpperCAmelCase    :			int              =					AddedToken(A  ,			lstrip=A  ,			rstrip=A		) if isinstance(A  ,			A		) else pad_token
  											_UpperCAmelCase    :			int              =					AddedToken(A  ,			lstrip=A  ,			rstrip=A		) if isinstance(A  ,			A		) else bos_token
  											_UpperCAmelCase    :			Tuple              =					AddedToken(A  ,			lstrip=A  ,			rstrip=A		) if isinstance(A  ,			A		) else eos_token
  											_UpperCAmelCase    :			List[Any]              =					AddedToken(A  ,			lstrip=A  ,			rstrip=A		) if isinstance(A  ,			A		) else mask_token
  											_UpperCAmelCase    :			Optional[Any]              =					AddedToken(A  ,			lstrip=A  ,			rstrip=A		) if isinstance(A  ,			A		) else cls_token
  											_UpperCAmelCase    :			List[Any]              =					AddedToken(A  ,			lstrip=A  ,			rstrip=A		) if isinstance(A  ,			A		) else sep_token
  
  											super().__init__(
  											    pad_token=A  ,			bos_token=A  ,			eos_token=A  ,			mask_token=A  ,			cls_token=A  ,			sep_token=A  ,			model_max_length=A  ,			**A  ,			)
  
  											_UpperCAmelCase    :			str              =					2**8  # utf is 8 bits
  
  											# define special tokens dict
  											_UpperCAmelCase    :			Dict[str, int]              =					{
  											    self.pad_token: 0,
  											    self.bos_token: 1,
  											    self.eos_token: 2,
  											    self.mask_token: 3,
  											    self.cls_token: 4,
  											    self.sep_token: 5,
  											}
  											_UpperCAmelCase    :			Optional[int]              =					len(self.special_tokens_encoder		)
  											_UpperCAmelCase    :			Dict[int, str]              =					{v: k for k, v in self.special_tokens_encoder.items()}
  
  
  
  						def 		snake_case_      (  self  :			List[str]		):
  											_UpperCAmelCase    :			Dict              =					self.special_tokens_encoder.copy()
  											vocab.update(self.added_tokens_encoder		)
  											for i in range(self._utf_vocab_size		):
  																_UpperCAmelCase    :			Tuple              =					chr(A		)
  																_UpperCAmelCase    :			List[str]              =					i + len(self.special_tokens_encoder		)
  											return vocab
  
  
  
  						@property
  						def 		snake_case_      (  self  :			Optional[Any]		):
  											return self._utf_vocab_size + self._num_special_tokens
  
  
  
  						def 		snake_case_      (  self  :			Tuple  ,			A  :			List[int]  ,			A  :			Optional[List[int]] = None  ,			A  :			bool = False		):
  											if already_has_special_tokens:
  																return super().get_special_tokens_mask(
  																    token_ids_a=A  ,			token_ids_a=A  ,			already_has_special_tokens=A		)
  
  											# normal case: some special tokens
  											if token_ids_a is None:
  																return [1] + [0] * len(A		) + [1]
  											return [1] + ([0] * len(A		)) + [1] + ([0] * len(A		)) + [1]
  
  
  
  						def 		snake_case_      (  self  :			Union[str, Any]  ,			A  :			List[int]  ,			A  :			Optional[List[int]] = None		):
  											if token_ids_a is None:
  																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  											else:
  																return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
  
  
  
  						def 		snake_case_      (  self  :			Optional[int]  ,			A  :			str		):
  											_UpperCAmelCase    :			Any              =					[chr(A		) for i in text.encode("utf-8"		)]
  											return tokens
  
  
  
  						def 		snake_case_      (  self  :			List[Any]  ,			A  :			Tuple		):
  											if token in self.special_tokens_encoder:
  																_UpperCAmelCase    :			List[str]              =					self.special_tokens_encoder[token]
  											elif token in self.added_tokens_encoder:
  																_UpperCAmelCase    :			Tuple              =					self.added_tokens_encoder[token]
  											elif len(A		) != 1:
  																_UpperCAmelCase    :			str              =					self.unk_token_id
  											else:
  																_UpperCAmelCase    :			Tuple              =					ord(A		) + self._num_special_tokens
  											return token_id
  
  
  
  						def 		snake_case_      (  self  :			Tuple  ,			A  :			Any		):
  											if index in self.special_tokens_decoder:
  																_UpperCAmelCase    :			Any              =					self.special_tokens_decoder[index]
  											elif index in self.added_tokens_decoder:
  																_UpperCAmelCase    :			List[Any]              =					self.added_tokens_decoder[index]
  											else:
  																_UpperCAmelCase    :			Union[str, Any]              =					chr(index - self._num_special_tokens		)
  											return token
  
  
  
  						def 		snake_case_      (  self  :			Optional[Any]  ,			A  :			Any		):
  											_UpperCAmelCase    :			Union[str, Any]              =					b''
  											for token in tokens:
  																if token in self.special_tokens_decoder:
  																					_UpperCAmelCase    :			Optional[int]              =					self.special_tokens_decoder[token].encode("utf-8"		)
  																elif token in self.added_tokens_decoder:
  																					_UpperCAmelCase    :			Any              =					self.special_tokens_decoder[token].encode("utf-8"		)
  																elif token in self.special_tokens_encoder:
  																					_UpperCAmelCase    :			Optional[Any]              =					token.encode("utf-8"		)
  																elif token in self.added_tokens_encoder:
  																					_UpperCAmelCase    :			str              =					token.encode("utf-8"		)
  																else:
  																					_UpperCAmelCase    :			Tuple              =					bytes([ord(A		)]		)
  																bstring += tok_string
  											_UpperCAmelCase    :			str              =					bstring.decode("utf-8"  ,			errors="replace"		)
  											return string
  
  
  
  						def 		snake_case_      (  self  :			int  ,			A  :			str  ,			A  :			Optional[str] = None		):
  											return ()
  
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
   )
  