


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

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


if is_sentencepiece_available():
     from .tokenization_reformer import ReformerTokenizer
else:
     __UpperCAmelCase      			=  None


__UpperCAmelCase      			=  logging.get_logger(__name__)


__UpperCAmelCase      			=  """▁"""

__UpperCAmelCase      			=  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__UpperCAmelCase      			=  {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    },
    """tokenizer_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"""
        )
    },
}

__UpperCAmelCase      			=  {
    """google/reformer-crime-and-punishment""": 52_4288,
}



class 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					VOCAB_FILES_NAMES
      snake_case__       =					PRETRAINED_VOCAB_FILES_MAP
      snake_case__       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__       =					['''input_ids''', '''attention_mask''']
      snake_case__       =					ReformerTokenizer




      def __init__(		self   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE="</s>"   ,			__SCREAMING_SNAKE_CASE="<unk>"   ,			__SCREAMING_SNAKE_CASE=[]   ,			**__SCREAMING_SNAKE_CASE   ,			)					->						Optional[int]:







         """simple docstring"""







         super().__init__(
             __SCREAMING_SNAKE_CASE   ,			tokenizer_file=__SCREAMING_SNAKE_CASE   ,			eos_token=__SCREAMING_SNAKE_CASE   ,			unk_token=__SCREAMING_SNAKE_CASE   ,			additional_special_tokens=__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE   ,			)

         UpperCamelCase__ :			Any 						=vocab_file
         UpperCamelCase__ :			str 						=False if not self.vocab_file else True




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = None)					->						Tuple[str]:







         """simple docstring"""







         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(__SCREAMING_SNAKE_CASE):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
            return
         UpperCamelCase__ :			Union[str, Any] 						=os.path.join(
             __SCREAMING_SNAKE_CASE   ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

         return (out_vocab_file,)





from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def 	_lowerCamelCase  (		A_  :	List[Any]=1_0      )			->			List[str]:

   '''simple docstring'''






   for _ in tqdm(range(0							,  args.n_examples							,  A_      )      ):
      yield [next(A_      )[args.text_column] for _ in range(A_      )]


# Configuration
__UpperCAmelCase      			=  HfArgumentParser(TokenizerTrainingArguments)
__UpperCAmelCase      			=  parser.parse_args()

# Base tokenizer
__UpperCAmelCase      			=  AutoTokenizer.from_pretrained(args.base_tokenizer)
__UpperCAmelCase      			=  list(bytes_to_unicode().values())

# Load dataset
__UpperCAmelCase      			=  load_dataset(args.dataset_name, split="""train""", streaming=True)
__UpperCAmelCase      			=  iter(dataset)


# Training and saving
__UpperCAmelCase      			=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)

