

# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/








"""simple docstring"""




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     (      __lowerCamelCase   ):
  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						:     List[Any]   ,A						:     Dict=None   ,A						:     List[str]=None   ,A						:     Union[str, Any]="</s>"   ,A						:     List[str]="<unk>"   ,A						:     Tuple=[]   ,**A						:     Optional[int]   ,):






   '''simple docstring'''
   super().__init__(
       A   ,tokenizer_file=A   ,eos_token=A   ,unk_token=A   ,additional_special_tokens=A   ,**A   ,)

   UpperCAmelCase__   :					Tuple   				=   vocab_file
   UpperCAmelCase__   :					Optional[Any]   				=   False if not self.vocab_file else True




  def 				__lowercase    (					self						:     Any   ,A						:     str   ,A						:     Optional[str] = None      ):






   '''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(A      ):
    logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
    return
   UpperCAmelCase__   :					Tuple   				=   os.path.join(
       A   ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

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

   return (out_vocab_file,)





