






import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__lowerCamelCase	:		Dict										=   """\
@inproceedings{popovic-2015-chrf,
    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",
    month = sep,
    year = \"2015\",
    address = \"Lisbon, Portugal\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W15-3049\",
    doi = \"10.18653/v1/W15-3049\",
    pages = \"392--395\",
}
@inproceedings{popovic-2017-chrf,
    title = \"chr{F}++: words helping character n-grams\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Second Conference on Machine Translation\",
    month = sep,
    year = \"2017\",
    address = \"Copenhagen, Denmark\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W17-4770\",
    doi = \"10.18653/v1/W17-4770\",
    pages = \"612--618\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

__lowerCamelCase	:		Optional[int]										=   """\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
"""

__lowerCamelCase	:		List[str]										=   """
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    'score' (float): The chrF (chrF++) score,
    'char_order' (int): The character n-gram order,
    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    'beta' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION						)
class  _lowercase   (   datasets.Metric						):
      def 						lowercase__					(     self   ):
       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#chrf--chrf"""				,			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#chrf--chrf"""]				,			reference_urls=[
               """https://github.com/m-popovic/chrF""",
           ]				,			)



      def 						lowercase__					(     self				,			a				,			a				,			a = CHRF.CHAR_ORDER				,			a = CHRF.WORD_ORDER				,			a = CHRF.BETA				,			a = False				,			a = False				,			a = False				,			):
       snake_case__  :    str							       =len(references[0]   )
       if any(len(a   ) != references_per_prediction for refs in references   ):
        raise ValueError("""Sacrebleu requires the same number of references for each prediction"""   )
       snake_case__  :    List[Any]							       =[[refs[i] for refs in references] for i in range(a   )]

       snake_case__  :    int							       =CHRF(a				,			a				,			a				,			a				,			a				,			a   )
       snake_case__  :    int							       =sb_chrf.corpus_score(a				,			a   )

       return {
           "score": output.score,
           "char_order": output.char_order,
           "word_order": output.word_order,
           "beta": output.beta,
       }













import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)

__lowerCamelCase	:		Any										=   {"""vocab_file""": """spiece.model"""}

__lowerCamelCase	:		Optional[Any]										=   {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

__lowerCamelCase	:		List[str]										=   {
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}







class  _lowercase   (   _A						):
      _a							:						int										=							VOCAB_FILES_NAMES
      _a							:						List[str]										=							PRETRAINED_VOCAB_FILES_MAP
      _a							:						Any										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a							:						Tuple										=							['input_ids', 'attention_mask']
      _a							:						List[int]										=							[]
      def __init__(     self				,			a				,			a="<unk>"				,			a="<s>"				,			a="</s>"				,			a="<pad>"				,			a="[SEP]"				,			a="[MASK]"				,			a="[CLS]"				,			a = None				,			**a				,			):
       snake_case__  :    str							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else bos_token
       snake_case__  :    Optional[int]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else eos_token
       snake_case__  :    Any							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else unk_token
       snake_case__  :    List[str]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else pad_token
       snake_case__  :    int							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else cls_token
       snake_case__  :    Tuple							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else sep_token

       # Mask token behave like a normal word, i.e. include the space before it
       snake_case__  :    Union[str, Any]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else mask_token

       snake_case__  :    Union[str, Any]							       ={} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=a				,			eos_token=a				,			unk_token=a				,			pad_token=a				,			sep_token=a				,			mask_token=a				,			cls_token=a				,			sp_model_kwargs=self.sp_model_kwargs				,			**a				,			)

       snake_case__  :    Optional[int]							       =vocab_file

       snake_case__  :    List[str]							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(a   )
      @property
      def 						lowercase__					(     self   ):
       return self.sp_model.get_piece_size()
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       ={self.convert_ids_to_tokens(a   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab
      def __getstate__(     self   ):
       snake_case__  :    List[Any]							       =self.__dict__.copy()
       snake_case__  :    Optional[int]							       =None
       return state
      def __setstate__(     self				,			a   ):
       snake_case__  :    Optional[Any]							       =d

       # for backward compatibility
       if not hasattr(self				,			"""sp_model_kwargs"""   ):
        snake_case__  :    Optional[Any]							       ={}

       snake_case__  :    Tuple							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.vocab_file   )
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.encode(a				,			out_type=a   )
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.piece_to_id(a   )
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Dict							       =self.sp_model.IdToPiece(a   )
       return token
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Optional[int]							       =[]
       snake_case__  :    Dict							       =""""""
       snake_case__  :    Dict							       =False
       for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
         if not prev_is_special:
          out_string += " "
         out_string += self.sp_model.decode(a   ) + token
         snake_case__  :    Optional[Any]							       =True
         snake_case__  :    Dict							       =[]
        else:
         current_sub_tokens.append(a   )
         snake_case__  :    List[Any]							       =False
       out_string += self.sp_model.decode(a   )
       return out_string.strip()
      def 						lowercase__					(     self				,			a				,			a = False				,			a = None				,			a = True				,			**a				,			):
       snake_case__  :    List[str]							       =kwargs.pop("""use_source_tokenizer"""				,			a   )

       snake_case__  :    int							       =self.convert_ids_to_tokens(a				,			skip_special_tokens=a   )

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       snake_case__  :    List[Any]							       =[]
       snake_case__  :    int							       =[]
       for token in filtered_tokens:
        if skip_special_tokens and token in self.all_special_ids:
         continue
        if token in self.added_tokens_encoder:
         if current_sub_text:
          sub_texts.append(self.convert_tokens_to_string(a   )   )
          snake_case__  :    List[Any]							       =[]
         sub_texts.append(a   )
        else:
         current_sub_text.append(a   )
       if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(a   )   )

       # Mimic the behavior of the Rust tokenizer:
       # No space before [MASK] and [SEP]
       if spaces_between_special_tokens:
        snake_case__  :    Dict							       =re.sub(R""" (\[(MASK|SEP)\])"""				,			R"""\1"""				,			""" """.join(a   )   )
       else:
        snake_case__  :    Dict							       ="""""".join(a   )

       snake_case__  :    str							       =(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
        snake_case__  :    Any							       =self.clean_up_tokenization(a   )
        return clean_text
       else:
        return text
      def 						lowercase__					(     self				,			a				,			a = None   ):
       if not os.path.isdir(a   ):
        logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
        return
       snake_case__  :    Any							       =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   ) and os.path.isfile(self.vocab_file   ):
        copyfile(self.vocab_file				,			a   )
       elif not os.path.isfile(self.vocab_file   ):
        with open(a				,			"""wb"""   ) as fi:
         snake_case__  :    Any							       =self.sp_model.serialized_model_proto()
         fi.write(a   )

       return (out_vocab_file,)
      def 						lowercase__					(     self				,			a				,			a = None   ):
       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       snake_case__  :    Union[str, Any]							       =[self.cls_token_id]
       snake_case__  :    Dict							       =[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep
      def 						lowercase__					(     self				,			a				,			a = None				,			a = False   ):
       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=a				,			token_ids_a=a				,			already_has_special_tokens=a   )

       if token_ids_a is None:
        return [1] + ([0] * len(a   )) + [1]
       return [1] + ([0] * len(a   )) + [1] + ([0] * len(a   )) + [1]



      def 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    List[str]							       =[self.sep_token_id]
       snake_case__  :    List[Any]							       =[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   ) * [0] + len(token_ids_a + sep   ) * [1]





