




'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


_lowerCamelCase							:						str								=  logging.get_logger(__name__)

_lowerCamelCase							:						List[str]								=  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase							:						Any								=  {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase=None,  __UpperCamelCase=None,  __UpperCamelCase=False,  __UpperCamelCase=True,  __UpperCamelCase=False,  __UpperCamelCase="<s>",  __UpperCamelCase="</s>",  __UpperCamelCase="<unk>",  __UpperCamelCase="<sep>",  __UpperCamelCase="<pad>",  __UpperCamelCase="<cls>",  __UpperCamelCase="<mask>",  __UpperCamelCase=["<eop>", "<eod>"],  **__UpperCamelCase,  ):
							_snake_case	       =       AddedToken(__UpperCamelCase,  lstrip=__UpperCamelCase,  rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase,  __UpperCamelCase ) else mask_token

							super().__init__(
							    vocab_file=__UpperCamelCase,  tokenizer_file=__UpperCamelCase,  do_lower_case=__UpperCamelCase,  remove_space=__UpperCamelCase,  keep_accents=__UpperCamelCase,  bos_token=__UpperCamelCase,  eos_token=__UpperCamelCase,  unk_token=__UpperCamelCase,  sep_token=__UpperCamelCase,  pad_token=__UpperCamelCase,  cls_token=__UpperCamelCase,  mask_token=__UpperCamelCase,  additional_special_tokens=__UpperCamelCase,  **__UpperCamelCase,  )

							_snake_case	       =       3
							_snake_case	       =       do_lower_case
							_snake_case	       =       remove_space
							_snake_case	       =       keep_accents
							_snake_case	       =       vocab_file
							_snake_case	       =       False if not self.vocab_file else True

							try:
									import jieba
							except ModuleNotFoundError as error:
									raise error.__class__(
									    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
									    "See https://pypi.org/project/jieba/ for installation." )
							_snake_case	       =       jieba
							_snake_case	       =       str.maketrans(" \n",  "\u2582\u2583" )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):
							_snake_case	       =       [self.sep_token_id]
							_snake_case	       =       [self.cls_token_id]
							if token_ids_a is None:
									return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):
							_snake_case	       =       [self.sep_token_id]
							_snake_case	       =       [2]

							if token_ids_a is None:
									return len(token_ids_a + sep ) * [0] + cls_segment_id
							return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):
							if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer." )

							if not os.path.isdir(__UpperCamelCase ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
							_snake_case	       =       os.path.join(
							    __UpperCamelCase,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

							if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ):
									copyfile(self.vocab_file,  __UpperCamelCase )

							return (out_vocab_file,)






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  *__UpperCamelCase,  **__UpperCamelCase ):
							_snake_case	       =       [
							    " ".join([x.translate(self.translator ) for x in self.jieba.cut(__UpperCamelCase,  cut_all=__UpperCamelCase )] )
							    for text in batch_text_or_text_pairs
							]
							return super()._batch_encode_plus(__UpperCamelCase,  *__UpperCamelCase,  **__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  *__UpperCamelCase,  **__UpperCamelCase ):
							_snake_case	       =       super()._decode(*__UpperCamelCase,  **__UpperCamelCase )
							_snake_case	       =       text.replace(" ",  "" ).replace("\u2582",  " " ).replace("\u2583",  "\n" )
							return text












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_		(					lowerCAmelCase:		Optional[Any]			,							lowerCAmelCase:		Tuple			,							lowerCAmelCase:		List[Any] )->						Dict:
  # Construct configuration depending on size
  _snake_case      : Tuple														=		{'small': (5_12, 8, 24), 'medium': (10_24, 8, 36), 'large': (15_36, 16, 48)}
  _snake_case							,    _snake_case							,    _snake_case      : Optional[Any]														=		MODELS[model_size]  # set model hyperparameters
  _snake_case      : Union[str, Any]														=		ImageGPTConfig(n_embd=lowerCAmelCase			,							n_layer=lowerCAmelCase			,							n_head=lowerCAmelCase )
  _snake_case      : int														=		ImageGPTForCausalLM(lowerCAmelCase )

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

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


if __name__ == "__main__":
      lowerCAmelCase_        =			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_        =			parser.parse_args()
      convert_imagegpt_checkpoint_to_pytorch(
          args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
      )




