






import sacrebleu as scb
from packaging import version

import datasets


_UpperCAmelCase					:						List[str]         =     '\\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

_UpperCAmelCase					:						Union[str, Any]         =     '\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich\'s `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n'

_UpperCAmelCase					:						str         =     '\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `\'exp\'`. Possible values are:\n        - `\'none\'`: no smoothing\n        - `\'floor\'`: increment zero counts\n        - `\'add-k\'`: increment num/denom by k for n>1\n        - `\'exp\'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method=\'floor\'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method=\'add-k\'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `\'zh\'` for Chinese, `\'ja-mecab\'` for Japanese and `\'13a\'` (mteval) otherwise. Possible values are:\n        - `\'none\'`: No tokenization.\n        - `\'zh\'`: Chinese tokenization.\n        - `\'13a\'`: mimics the `mteval-v13a` script from Moses.\n        - `\'intl\'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `\'char\'`: Language-agnostic character-level tokenization.\n        - `\'ja-mecab\'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    \'score\': BLEU score,\n    \'counts\': Counts,\n    \'totals\': Totals,\n    \'precisions\': Precisions,\n    \'bp\': Brevity penalty,\n    \'sys_len\': predictions length,\n    \'ref_len\': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = ["hello there general kenobi", "foo bar foobar"]\n        >>> references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]\n        >>> sacrebleu = datasets.load_metric("sacrebleu")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results["score"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = ["hello there general kenobi",\n        ...                 "on our way to ankh morpork"]\n        >>> references = [["hello there general kenobi", "hello there !"],\n        ...                 ["goodbye ankh morpork", "ankh morpork"]]\n        >>> sacrebleu = datasets.load_metric("sacrebleu")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results["score"], 1))\n        39.8\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,     _KWARGS_DESCRIPTION )
class 		lowerCAmelCase_				(  datasets.Metric ):





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        if version.parse(scb.__version__  ) < version.parse("""1.4.12"""  ):
           raise ImportWarning(
               """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
               """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""  )
        return datasets.MetricInfo(
            description=_DESCRIPTION							, citation=_CITATION							, homepage="""https://github.com/mjpost/sacreBLEU"""							, inputs_description=_KWARGS_DESCRIPTION							, features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string"""							, id="""sequence"""  ),
                    """references""": datasets.Sequence(datasets.Value("""string"""							, id="""sequence"""  )							, id="""references"""  ),
                }  )							, codebase_urls=["""https://github.com/mjpost/sacreBLEU"""]							, reference_urls=[
                """https://github.com/mjpost/sacreBLEU""",
                """https://en.wikipedia.org/wiki/BLEU""",
                """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
            ]							, )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase="exp"							, _UpperCAmelCase=None							, _UpperCAmelCase=False							, _UpperCAmelCase=False							, _UpperCAmelCase=None							, _UpperCAmelCase=False							, )		->				List[Any]:
        lowerCamelCase									=len(references[0]  )
        if any(len(_UpperCAmelCase  ) != references_per_prediction for refs in references  ):
           raise ValueError("""Sacrebleu requires the same number of references for each prediction"""  )
        lowerCamelCase									=[[refs[i] for refs in references] for i in range(_UpperCAmelCase  )]
        lowerCamelCase									=scb.corpus_bleu(
            _UpperCAmelCase							, _UpperCAmelCase							, smooth_method=_UpperCAmelCase							, smooth_value=_UpperCAmelCase							, force=_UpperCAmelCase							, lowercase=_UpperCAmelCase							, use_effective_order=_UpperCAmelCase							, **({"""tokenize""": tokenize} if tokenize else {})							, )
        lowerCamelCase									={
            """score""": output.score,
            """counts""": output.counts,
            """totals""": output.totals,
            """precisions""": output.precisions,
            """bp""": output.bp,
            """sys_len""": output.sys_len,
            """ref_len""": output.ref_len,
        }
        return output_dict







import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase					:						Dict         =     logging.get_logger(__name__)

_UpperCAmelCase					:						int         =     {
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

_UpperCAmelCase					:						Optional[int]         =     {
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

_UpperCAmelCase					:						Optional[int]         =     {'stas/tiny-wmt19-en-de': 1024}
_UpperCAmelCase					:						List[Any]         =     {
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 1024,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}






def  UpperCAmelCase__						(    lowerCamelCase	:			Dict    ):
   lowerCamelCase									=set()
   lowerCamelCase									=word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)    )
      lowerCamelCase									=char
   return pairs






def  UpperCAmelCase__						(    lowerCamelCase	:			Optional[Any]    ):
   lowerCamelCase									=text.replace("""，"""			,""","""    )
   lowerCamelCase									=re.sub(R"""。\s*"""			,""". """			,lowerCamelCase    )
   lowerCamelCase									=text.replace("""、"""			,""","""    )
   lowerCamelCase									=text.replace("""”"""			,"""\""""    )
   lowerCamelCase									=text.replace("""“"""			,"""\""""    )
   lowerCamelCase									=text.replace("""∶"""			,""":"""    )
   lowerCamelCase									=text.replace("""："""			,""":"""    )
   lowerCamelCase									=text.replace("""？"""			,"""?"""    )
   lowerCamelCase									=text.replace("""《"""			,"""\""""    )
   lowerCamelCase									=text.replace("""》"""			,"""\""""    )
   lowerCamelCase									=text.replace("""）"""			,""")"""    )
   lowerCamelCase									=text.replace("""！"""			,"""!"""    )
   lowerCamelCase									=text.replace("""（"""			,"""("""    )
   lowerCamelCase									=text.replace("""；"""			,""";"""    )
   lowerCamelCase									=text.replace("""１"""			,"""1"""    )
   lowerCamelCase									=text.replace("""」"""			,"""\""""    )
   lowerCamelCase									=text.replace("""「"""			,"""\""""    )
   lowerCamelCase									=text.replace("""０"""			,"""0"""    )
   lowerCamelCase									=text.replace("""３"""			,"""3"""    )
   lowerCamelCase									=text.replace("""２"""			,"""2"""    )
   lowerCamelCase									=text.replace("""５"""			,"""5"""    )
   lowerCamelCase									=text.replace("""６"""			,"""6"""    )
   lowerCamelCase									=text.replace("""９"""			,"""9"""    )
   lowerCamelCase									=text.replace("""７"""			,"""7"""    )
   lowerCamelCase									=text.replace("""８"""			,"""8"""    )
   lowerCamelCase									=text.replace("""４"""			,"""4"""    )
   lowerCamelCase									=re.sub(R"""．\s*"""			,""". """			,lowerCamelCase    )
   lowerCamelCase									=text.replace("""～"""			,"""~"""    )
   lowerCamelCase									=text.replace("""’"""			,"""'"""    )
   lowerCamelCase									=text.replace("""…"""			,"""..."""    )
   lowerCamelCase									=text.replace("""━"""			,"""-"""    )
   lowerCamelCase									=text.replace("""〈"""			,"""<"""    )
   lowerCamelCase									=text.replace("""〉"""			,""">"""    )
   lowerCamelCase									=text.replace("""【"""			,"""["""    )
   lowerCamelCase									=text.replace("""】"""			,"""]"""    )
   lowerCamelCase									=text.replace("""％"""			,"""%"""    )
   return text






def  UpperCAmelCase__						(    lowerCamelCase	:			Optional[int]    ):
   lowerCamelCase									=[]
   for char in text:
      lowerCamelCase									=unicodedata.category(lowerCamelCase    )
      if cat.startswith("""C"""    ):
         continue
      output.append(lowerCamelCase    )
   return "".join(lowerCamelCase    )







class 		lowerCAmelCase_				(  UpperCamelCase ):
     __lowerCamelCase       :    Optional[int] 	=     VOCAB_FILES_NAMES
     __lowerCamelCase       :    List[Any] 	=     PRETRAINED_VOCAB_FILES_MAP
     __lowerCamelCase       :    List[Any] 	=     PRETRAINED_INIT_CONFIGURATION
     __lowerCamelCase       :    Tuple 	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowerCamelCase       :    Optional[int] 	=     ['''input_ids''', '''attention_mask''']





     def __init__(	self							, _UpperCAmelCase=None							, _UpperCAmelCase=None							, _UpperCAmelCase=None							, _UpperCAmelCase=None							, _UpperCAmelCase=False							, _UpperCAmelCase="<unk>"							, _UpperCAmelCase="<s>"							, _UpperCAmelCase="</s>"							, _UpperCAmelCase="<pad>"							, **_UpperCAmelCase							, )		->				Tuple:
        super().__init__(
            langs=_UpperCAmelCase							, src_vocab_file=_UpperCAmelCase							, tgt_vocab_file=_UpperCAmelCase							, merges_file=_UpperCAmelCase							, do_lower_case=_UpperCAmelCase							, unk_token=_UpperCAmelCase							, bos_token=_UpperCAmelCase							, sep_token=_UpperCAmelCase							, pad_token=_UpperCAmelCase							, **_UpperCAmelCase							, )

        try:
           import sacremoses
        except ImportError:
           raise ImportError(
               """You need to install sacremoses to use XLMTokenizer. """
               """See https://pypi.org/project/sacremoses/ for installation."""  )

        lowerCamelCase									=sacremoses

        lowerCamelCase									=src_vocab_file
        lowerCamelCase									=tgt_vocab_file
        lowerCamelCase									=merges_file
        lowerCamelCase									=do_lower_case

        # cache of sm.MosesPunctNormalizer instance
        lowerCamelCase									={}
        # cache of sm.MosesTokenizer instance
        lowerCamelCase									={}
        lowerCamelCase									={}

        if langs and len(_UpperCAmelCase  ) == 2:
           lowerCamelCase				,       lowerCamelCase									=langs
        else:
           raise ValueError(
               F'''arg `langs` needs to be a list of 2 langs, e.g. [\'en\', \'ru\'], but got {langs}. '''
               """Usually that means that tokenizer can't find a mapping for the given model path """
               """in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer."""  )

        with open(_UpperCAmelCase							, encoding="""utf-8"""  ) as src_vocab_handle:
           lowerCamelCase									=json.load(_UpperCAmelCase  )
        with open(_UpperCAmelCase							, encoding="""utf-8"""  ) as tgt_vocab_handle:
           lowerCamelCase									=json.load(_UpperCAmelCase  )
           lowerCamelCase									={v: k for k, v in tgt_vocab.items()}
        with open(_UpperCAmelCase							, encoding="""utf-8"""  ) as merges_handle:
           lowerCamelCase									=merges_handle.read().split("""\n"""  )[:-1]
        lowerCamelCase									=[tuple(merge.split()[:2]  ) for merge in merges]
        lowerCamelCase									=dict(zip(_UpperCAmelCase							, range(len(_UpperCAmelCase  )  )  )  )
        lowerCamelCase									={}





     def 						lowerCamelCase							(	self  )		->				Dict[str, int]:
        return self.get_src_vocab()





     @property
     def 						lowerCamelCase							(	self  )		->				int:
        return self.src_vocab_size





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				int:
        if lang not in self.cache_moses_punct_normalizer:
           lowerCamelCase									=self.sm.MosesPunctNormalizer(lang=_UpperCAmelCase  )
           lowerCamelCase									=punct_normalizer
        return self.cache_moses_punct_normalizer[lang].normalize(_UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				str:
        if lang not in self.cache_moses_tokenizer:
           lowerCamelCase									=self.sm.MosesTokenizer(lang=_UpperCAmelCase  )
           lowerCamelCase									=moses_tokenizer
        return self.cache_moses_tokenizer[lang].tokenize(
            _UpperCAmelCase							, aggressive_dash_splits=_UpperCAmelCase							, return_str=_UpperCAmelCase							, escape=_UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Dict:
        if lang not in self.cache_moses_detokenizer:
           lowerCamelCase									=self.sm.MosesDetokenizer(lang=_UpperCAmelCase  )
           lowerCamelCase									=moses_detokenizer
        return self.cache_moses_detokenizer[lang].detokenize(_UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase  )		->				List[str]:
        lowerCamelCase									=replace_unicode_punct(_UpperCAmelCase  )
        lowerCamelCase									=self.moses_punct_norm(_UpperCAmelCase							, _UpperCAmelCase  )
        lowerCamelCase									=remove_non_printing_char(_UpperCAmelCase  )
        return text





     @property
     def 						lowerCamelCase							(	self  )		->				Dict:
        return len(self.encoder  )





     @property
     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        return len(self.decoder  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        return dict(self.encoder							, **self.added_tokens_encoder  )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        return dict(self.decoder							, **self.added_tokens_decoder  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Union[str, Any]:
        lowerCamelCase									=tuple(token[:-1]  ) + (token[-1] + """</w>""",)
        if token in self.cache:
           return self.cache[token]
        lowerCamelCase									=get_pairs(_UpperCAmelCase  )

        if not pairs:
           return token + "</w>"

        while True:
           lowerCamelCase									=min(_UpperCAmelCase							, key=lambda _UpperCAmelCase  : self.bpe_ranks.get(_UpperCAmelCase							, float("""inf"""  )  )  )
           if bigram not in self.bpe_ranks:
              break
           lowerCamelCase				,       lowerCamelCase									=bigram
           lowerCamelCase									=[]
           lowerCamelCase									=0
           while i < len(_UpperCAmelCase  ):
              try:
                 lowerCamelCase									=word.index(_UpperCAmelCase							, _UpperCAmelCase  )
              except ValueError:
                 new_word.extend(word[i:]  )
                 break
              else:
                 new_word.extend(word[i:j]  )
                 lowerCamelCase									=j

              if word[i] == first and i < len(_UpperCAmelCase  ) - 1 and word[i + 1] == second:
                 new_word.append(first + second  )
                 i += 2
              else:
                 new_word.append(word[i]  )
                 i += 1
           lowerCamelCase									=tuple(_UpperCAmelCase  )
           lowerCamelCase									=new_word
           if len(_UpperCAmelCase  ) == 1:
              break
           else:
              lowerCamelCase									=get_pairs(_UpperCAmelCase  )
        lowerCamelCase									=""" """.join(_UpperCAmelCase  )
        if word == "\n  </w>":
           lowerCamelCase									="""\n</w>"""
        lowerCamelCase									=word
        return word





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase="en"							, _UpperCAmelCase=False  )		->				str:
        lowerCamelCase									=self.src_lang

        if self.do_lower_case:
           lowerCamelCase									=text.lower()

        if bypass_tokenizer:
           lowerCamelCase									=text.split()
        else:
           lowerCamelCase									=self.moses_pipeline(_UpperCAmelCase							, lang=_UpperCAmelCase  )
           lowerCamelCase									=self.moses_tokenize(_UpperCAmelCase							, lang=_UpperCAmelCase  )

        lowerCamelCase									=[]
        for token in text:
           if token:
              split_tokens.extend(list(self.bpe(_UpperCAmelCase  ).split(""" """  )  )  )

        return split_tokens





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				List[str]:
        return self.encoder.get(_UpperCAmelCase							, self.encoder.get(self.unk_token  )  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				Tuple:
        return self.decoder.get(_UpperCAmelCase							, self.unk_token  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				List[Any]:
        lowerCamelCase									=[t.replace(""" """							, """"""  ).replace("""</w>"""							, """ """  ) for t in tokens]
        lowerCamelCase									="""""".join(_UpperCAmelCase  ).split()
        # detokenize
        lowerCamelCase									=self.moses_detokenize(_UpperCAmelCase							, self.tgt_lang  )
        return text





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase = None  )		->				List[int]:
        lowerCamelCase									=[self.sep_token_id]

        # no bos used in fairseq
        if token_ids_a is None:
           return token_ids_a + sep
        return token_ids_a + sep + token_ids_a + sep





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase = None							, _UpperCAmelCase = False  )		->				List[int]:

        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_UpperCAmelCase							, token_ids_a=_UpperCAmelCase							, already_has_special_tokens=_UpperCAmelCase  )
        # no bos used in fairseq
        if token_ids_a is not None:
           return ([0] * len(_UpperCAmelCase  )) + [1] + ([0] * len(_UpperCAmelCase  )) + [1]
        return ([0] * len(_UpperCAmelCase  )) + [1]





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase = None  )		->				List[int]:
        lowerCamelCase									=[self.sep_token_id]

        # no bos used in fairseq
        if token_ids_a is None:
           return len(token_ids_a + sep  ) * [0]
        return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase = None  )		->				Tuple[str]:
        if not os.path.isdir(_UpperCAmelCase  ):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
           return

        lowerCamelCase									=os.path.join(
            _UpperCAmelCase							, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""src_vocab_file"""]  )
        lowerCamelCase									=os.path.join(
            _UpperCAmelCase							, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""tgt_vocab_file"""]  )
        lowerCamelCase									=os.path.join(
            _UpperCAmelCase							, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]  )

        with open(_UpperCAmelCase							, """w"""							, encoding="""utf-8"""  ) as f:
           f.write(json.dumps(self.encoder							, indent=2							, sort_keys=_UpperCAmelCase							, ensure_ascii=_UpperCAmelCase  ) + """\n"""  )

        with open(_UpperCAmelCase							, """w"""							, encoding="""utf-8"""  ) as f:
           lowerCamelCase									={v: k for k, v in self.decoder.items()}
           f.write(json.dumps(_UpperCAmelCase							, indent=2							, sort_keys=_UpperCAmelCase							, ensure_ascii=_UpperCAmelCase  ) + """\n"""  )

        lowerCamelCase									=0
        with open(_UpperCAmelCase							, """w"""							, encoding="""utf-8"""  ) as writer:
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							, key=lambda _UpperCAmelCase  : kv[1]  ):
              if index != token_index:
                 logger.warning(
                     F'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
                     """ Please check that the tokenizer is not corrupted!"""  )
                 lowerCamelCase									=token_index
              writer.write(""" """.join(_UpperCAmelCase  ) + """\n"""  )
              index += 1

        return src_vocab_file, tgt_vocab_file, merges_file





     def __getstate__(	self  )		->				List[Any]:
        lowerCamelCase									=self.__dict__.copy()
        lowerCamelCase									=None
        return state





     def __setstate__(	self							, _UpperCAmelCase  )		->				Union[str, Any]:
        lowerCamelCase									=d

        try:
           import sacremoses
        except ImportError:
           raise ImportError(
               """You need to install sacremoses to use XLMTokenizer. """
               """See https://pypi.org/project/sacremoses/ for installation."""  )

        lowerCamelCase									=sacremoses