



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

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

				# Save pytorch-model
				snake_case__      :   Tuple      =    pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				snake_case__      :   str      =    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__":
				_lowercase       :						Optional[Any]						=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."
				)
				_lowercase       :						Dict						=parser.parse_args()
				convert_imagegpt_checkpoint_to_pytorch(
				    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
				)






"""simple docstring"""




from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case					  =						logging.get_logger(__name__)




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				UpperCAmelCase_							: Dict		    =							["input_ids", "attention_mask"]


				def __init__(							self						,      lowercase__="[PAD]"						,      lowercase__="[BOS]"						,      lowercase__="[EOS]"						,      lowercase__="[MASK]"						,      lowercase__="[CLS]"						,      lowercase__="[SEP]"						,      lowercase__=2048						,      **lowercase__						,      )       ->       None:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else pad_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else bos_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else eos_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else mask_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else cls_token
									SCREAMING_SNAKE_CASE           =      AddedToken(lowercase__						,      lstrip=lowercase__						,      rstrip=lowercase__     ) if isinstance(lowercase__						,      lowercase__     ) else sep_token

									super().__init__(
									    pad_token=lowercase__						,      bos_token=lowercase__						,      eos_token=lowercase__						,      mask_token=lowercase__						,      cls_token=lowercase__						,      sep_token=lowercase__						,      model_max_length=lowercase__						,      **lowercase__						,      )

									SCREAMING_SNAKE_CASE           =      2**8  # utf is 8 bits

									# define special tokens dict
									SCREAMING_SNAKE_CASE           =      {
									    self.pad_token: 0,
									    self.bos_token: 1,
									    self.eos_token: 2,
									    self.mask_token: 3,
									    self.cls_token: 4,
									    self.sep_token: 5,
									}
									SCREAMING_SNAKE_CASE           =      len(self.special_tokens_encoder     )
									SCREAMING_SNAKE_CASE           =      {v: k for k, v in self.special_tokens_encoder.items()}


				def 				A   (							self     )       ->       Dict[str, int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.special_tokens_encoder.copy()
									vocab.update(self.added_tokens_encoder     )
									for i in range(self._utf_vocab_size     ):
														SCREAMING_SNAKE_CASE           =      chr(lowercase__     )
														SCREAMING_SNAKE_CASE           =      i + len(self.special_tokens_encoder     )
									return vocab


				@property
				def 				A   (							self     )       ->       List[Any]:
									"""simple docstring"""







									return self._utf_vocab_size + self._num_special_tokens


				def 				A   (							self						,      lowercase__						,      lowercase__ = None						,      lowercase__ = False     )       ->       List[int]:
									"""simple docstring"""







									if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=lowercase__						,      token_ids_a=lowercase__						,      already_has_special_tokens=lowercase__     )

									# normal case: some special tokens
									if token_ids_a is None:
														return [1] + [0] * len(lowercase__     ) + [1]
									return [1] + ([0] * len(lowercase__     )) + [1] + ([0] * len(lowercase__     )) + [1]


				def 				A   (							self						,      lowercase__						,      lowercase__ = None     )       ->       List[int]:
									"""simple docstring"""







									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 				A   (							self						,      lowercase__     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      [chr(lowercase__     ) for i in text.encode('utf-8'     )]
									return tokens


				def 				A   (							self						,      lowercase__     )       ->       str:
									"""simple docstring"""







									if token in self.special_tokens_encoder:
														SCREAMING_SNAKE_CASE           =      self.special_tokens_encoder[token]
									elif token in self.added_tokens_encoder:
														SCREAMING_SNAKE_CASE           =      self.added_tokens_encoder[token]
									elif len(lowercase__     ) != 1:
														SCREAMING_SNAKE_CASE           =      self.unk_token_id
									else:
														SCREAMING_SNAKE_CASE           =      ord(lowercase__     ) + self._num_special_tokens
									return token_id


				def 				A   (							self						,      lowercase__     )       ->       int:
									"""simple docstring"""







									if index in self.special_tokens_decoder:
														SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[index]
									elif index in self.added_tokens_decoder:
														SCREAMING_SNAKE_CASE           =      self.added_tokens_decoder[index]
									else:
														SCREAMING_SNAKE_CASE           =      chr(index - self._num_special_tokens     )
									return token


				def 				A   (							self						,      lowercase__     )       ->       Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      b''
									for token in tokens:
														if token in self.special_tokens_decoder:
																			SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[token].encode('utf-8'     )
														elif token in self.added_tokens_decoder:
																			SCREAMING_SNAKE_CASE           =      self.special_tokens_decoder[token].encode('utf-8'     )
														elif token in self.special_tokens_encoder:
																			SCREAMING_SNAKE_CASE           =      token.encode('utf-8'     )
														elif token in self.added_tokens_encoder:
																			SCREAMING_SNAKE_CASE           =      token.encode('utf-8'     )
														else:
																			SCREAMING_SNAKE_CASE           =      bytes([ord(lowercase__     )]     )
														bstring += tok_string
									SCREAMING_SNAKE_CASE           =      bstring.decode('utf-8'						,      errors='replace'     )
									return string




				def 				A   (							self						,      lowercase__						,      lowercase__ = None     )       ->       Tuple[str]:
									"""simple docstring"""







									return ()
