code: 
  
  
  
  
  
  '''simple docstring'''
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from tokenizers import processors
  
  from ...tokenization_utils import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
    from .tokenization_mbartaa import MBartaaTokenizer
  else:
    __lowerCAmelCase							:			List[str]				=None
  
  
  __lowerCAmelCase							:			Optional[Any]				=logging.get_logger(__name__)
  
  __lowerCAmelCase							:			Union[str, Any]				={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
  
  __lowerCAmelCase							:			int				={
      "vocab_file": {
          "facebook/mbart-large-50-one-to-many-mmt": (
              "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
          ),
      },
      "tokenizer_file": {
          "facebook/mbart-large-50-one-to-many-mmt": (
              "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"
          ),
      },
  }
  
  __lowerCAmelCase							:			Optional[Any]				={
      "facebook/mbart-large-50-one-to-many-mmt": 1024,
  }
  
  # fmt: off
  __lowerCAmelCase							:			Tuple				=["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   __lowercase     	=       VOCAB_FILES_NAMES
   __lowercase     	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowercase     	=       PRETRAINED_VOCAB_FILES_MAP
   __lowercase     	=       ["""input_ids""", """attention_mask"""]
   __lowercase     	=       MBartaaTokenizer
  
   __lowercase     	=       []
   __lowercase     	=       []
   def __init__(       self   :int		, lowercase_   :List[Any]=None		, lowercase_   :int=None		, lowercase_   :int=None		, lowercase_   :Optional[Any]=None		, lowercase_   :Optional[Any]="</s>"		, lowercase_   :Optional[Any]="</s>"		, lowercase_   :Union[str, Any]="<s>"		, lowercase_   :List[str]="<unk>"		, lowercase_   :Dict="<pad>"		, lowercase_   :Optional[Any]="<mask>"		, **lowercase_   :Dict		, )->							Dict:
       # Mask token behave like a normal word, i.e. include the space before it
       A__       =    AddedToken(lowercase_		, lstrip=lowercase_		, rstrip=lowercase_							) if isinstance(lowercase_		, lowercase_							) else mask_token
  
       A__       =    kwargs.get("additional_special_tokens"		, []							)
       kwargs["additional_special_tokens"] += [
           code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
       ]
  
       super().__init__(
           lowercase_		, src_lang=lowercase_		, tgt_lang=lowercase_		, tokenizer_file=lowercase_		, eos_token=lowercase_		, sep_token=lowercase_		, cls_token=lowercase_		, unk_token=lowercase_		, pad_token=lowercase_		, mask_token=lowercase_		, **lowercase_		, )
  
       A__       =    vocab_file
       A__       =    False if not self.vocab_file else True
  
       A__       =    {
           lang_code: self.convert_tokens_to_ids(lowercase_							) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }
  
       A__       =    src_lang if src_lang is not None else "en_XX"
       A__       =    tgt_lang
       A__       =    self.lang_code_to_id[self._src_lang]
       self.set_src_lang_special_tokens(self._src_lang							)
   @property
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							str:
       return self._src_lang
   @src_lang.setter
   def   UpperCAmelCase_							(       self   :str		, lowercase_   :str							)->							None:
       A__       =    new_src_lang
       self.set_src_lang_special_tokens(self._src_lang							)
   def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :List[int]		, lowercase_   :Optional[List[int]] = None							)->							List[int]:
       if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
   def   UpperCAmelCase_							(       self   :Dict		, lowercase_   :List[str]		, lowercase_   :str = "en_XX"		, lowercase_   :Optional[List[str]] = None		, lowercase_   :str = "ro_RO"		, **lowercase_   :Optional[Any]		, )->							BatchEncoding:
       A__       =    src_lang
       A__       =    tgt_lang
       return super().prepare_seqaseq_batch(lowercase_		, lowercase_		, **lowercase_							)
   def   UpperCAmelCase_							(       self   :int							)->							Tuple:
       return self.set_src_lang_special_tokens(self.src_lang							)
   def   UpperCAmelCase_							(       self   :Any							)->							List[Any]:
       return self.set_tgt_lang_special_tokens(self.tgt_lang							)
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :str							)->							None:
       A__       =    self.convert_tokens_to_ids(lowercase_							)
       A__       =    [self.cur_lang_code_id]
       A__       =    [self.eos_token_id]
  
       A__       =    self.convert_ids_to_tokens(self.prefix_tokens							)
       A__       =    self.convert_ids_to_tokens(self.suffix_tokens							)
  
       A__       =    processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str		, pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str		, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		, self.prefix_tokens + self.suffix_tokens							)							)		, )
   def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :str							)->							None:
       A__       =    self.convert_tokens_to_ids(lowercase_							)
       A__       =    [self.cur_lang_code_id]
       A__       =    [self.eos_token_id]
  
       A__       =    self.convert_ids_to_tokens(self.prefix_tokens							)
       A__       =    self.convert_ids_to_tokens(self.suffix_tokens							)
  
       A__       =    processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str		, pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str		, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		, self.prefix_tokens + self.suffix_tokens							)							)		, )
   def   UpperCAmelCase_							(       self   :int		, lowercase_   :Dict		, lowercase_   :str		, lowercase_   :Optional[str]		, lowercase_   :Optional[str]		, **lowercase_   :Dict							)->							List[Any]:
       if src_lang is None or tgt_lang is None:
           raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"							)
       A__       =    src_lang
       A__       =    self(lowercase_		, add_special_tokens=lowercase_		, return_tensors=lowercase_		, **lowercase_							)
       A__       =    self.convert_tokens_to_ids(lowercase_							)
       A__       =    tgt_lang_id
       return inputs
  
  
  
   def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :str		, lowercase_   :Optional[str] = None							)->							Tuple[str]:
       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(lowercase_							):
           logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
           return
       A__       =    os.path.join(
           lowercase_		, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
  
       if os.path.abspath(self.vocab_file							) != os.path.abspath(lowercase_							):
           copyfile(self.vocab_file		, lowercase_							)
  
       return (out_vocab_file,)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import sacrebleu as scb
  from packaging import version
  
  import datasets
  
  
  __lowerCAmelCase							:			Tuple				="\\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"
  
  __lowerCAmelCase							:			Dict				="\\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"
  
  __lowerCAmelCase							:			Dict				="\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      UpperCAmelCase			( datasets.Metric       ):
   def   UpperCAmelCase_							(       self   :int							)->							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   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Optional[int]		, lowercase_   :Optional[Any]		, lowercase_   :List[str]="exp"		, lowercase_   :str=None		, lowercase_   :Tuple=False		, lowercase_   :Any=False		, lowercase_   :Optional[Any]=None		, lowercase_   :Any=False		, )->							Tuple:
       A__       =    len(references[0]							)
       if any(len(lowercase_							) != references_per_prediction for refs in references							):
           raise ValueError("Sacrebleu requires the same number of references for each prediction"							)
       A__       =    [[refs[i] for refs in references] for i in range(lowercase_							)]
       A__       =    scb.corpus_bleu(
           lowercase_		, lowercase_		, smooth_method=lowercase_		, smooth_value=lowercase_		, force=lowercase_		, lowercase=lowercase_		, use_effective_order=lowercase_		, **({"tokenize": tokenize} if tokenize else {})		, )
       A__       =    {
           "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
  
  
  
  