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

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


if is_sentencepiece_available():
    from .tokenization_barthez import BarthezTokenizer
else:
    __lowerCAmelCase			:     Optional[int]           =   None

__lowerCAmelCase			:     Any           =   logging.get_logger(__name__)

__lowerCAmelCase			:     int           =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase			:     Dict           =   {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCAmelCase			:     Dict           =   {
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

__lowerCAmelCase			:     Optional[int]           =   '▁'




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		VOCAB_FILES_NAMES
  a__           =		PRETRAINED_VOCAB_FILES_MAP
  a__           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__           =		["""input_ids""", """attention_mask"""]
  a__           =		BarthezTokenizer
  def __init__( self			: Any			,							UpperCamelCase__			: List[str]=None			,							UpperCamelCase__			: Union[str, Any]=None			,							UpperCamelCase__			: Optional[int]="<s>"			,							UpperCamelCase__			: List[str]="</s>"			,							UpperCamelCase__			: str="</s>"			,							UpperCamelCase__			: Optional[int]="<s>"			,							UpperCamelCase__			: int="<unk>"			,							UpperCamelCase__			: Tuple="<pad>"			,							UpperCamelCase__			: Tuple="<mask>"			,							**UpperCamelCase__			: List[Any]			,							)			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       AddedToken(UpperCamelCase__			,							lstrip=UpperCamelCase__			,							rstrip=UpperCamelCase__    ) if isinstance(UpperCamelCase__			,							UpperCamelCase__    ) else mask_token

    super().__init__(
        UpperCamelCase__			,							tokenizer_file=UpperCamelCase__			,							bos_token=UpperCamelCase__			,							eos_token=UpperCamelCase__			,							unk_token=UpperCamelCase__			,							sep_token=UpperCamelCase__			,							cls_token=UpperCamelCase__			,							pad_token=UpperCamelCase__			,							mask_token=UpperCamelCase__			,							**UpperCamelCase__			,							)

    __magic_name__				     =       vocab_file
    __magic_name__				     =       False if not self.vocab_file else True
  def 							_lowercase							( self			: Optional[Any]			,							UpperCamelCase__			: List[int]			,							UpperCamelCase__			: Optional[List[int]] = None    )			->	List[int]:







    """simple docstring"""






    if token_ids_a is None:
      return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    __magic_name__				     =       [self.cls_token_id]
    __magic_name__				     =       [self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep
  def 							_lowercase							( self			: int			,							UpperCamelCase__			: List[int]			,							UpperCamelCase__			: Optional[List[int]] = None    )			->	List[int]:







    """simple docstring"""






    __magic_name__				     =       [self.sep_token_id]
    __magic_name__				     =       [self.cls_token_id]

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





  def 							_lowercase							( self			: int			,							UpperCamelCase__			: str			,							UpperCamelCase__			: Optional[str] = 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(UpperCamelCase__    ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
      return
    __magic_name__				     =       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,)






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
     import tensorflow as tf

     from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM






@require_tf
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""		)
        A :       Dict          =   AutoTokenizer.from_pretrained("""google/mt5-small"""		)

        A :       Tuple          =   tokenizer("""Hello there""",		return_tensors="""tf"""		).input_ids
        A :       Optional[int]          =   tokenizer("""Hi I am""",		return_tensors="""tf"""		).input_ids

        A :       List[Any]          =   model(lowerCamelCase__,		labels=lowerCamelCase__		).loss
        A :       str          =   -tf.math.reduce_mean(lowerCamelCase__		).numpy()

        A :       Tuple          =   -21.22_8168
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 2e-4		)


