


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

import datasets


__lowerCAmelCase			=							'''\
@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			=							'''\
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			=							'''
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   __a      (   datasets.Metric				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = CHRF.CHAR_ORDER    ,			lowerCAmelCase__ = CHRF.WORD_ORDER    ,			lowerCAmelCase__ = CHRF.BETA    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       str        =       len(references[0]			)
        if any(len(lowerCAmelCase__			) != references_per_prediction for refs in references			):
               raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
        lowercase__:       List[str]        =       [[refs[i] for refs in references] for i in range(lowerCAmelCase__			)]

        lowercase__:       Union[str, Any]        =       CHRF(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)
        lowercase__:       str        =       sb_chrf.corpus_score(lowerCAmelCase__    ,			lowerCAmelCase__			)

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






import os
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			=							logging.get_logger(__name__)

__lowerCAmelCase			=							'''▁'''

__lowerCAmelCase			=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

__lowerCAmelCase			=							{
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

__lowerCAmelCase			=							{'''vinai/bartpho-syllable''': 10_24}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							int       					=			VOCAB_FILES_NAMES
 __lowercase			:							str       					=			PRETRAINED_VOCAB_FILES_MAP
 __lowercase			:							str       					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase			:							Optional[int]       					=			['input_ids', 'attention_mask']
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__="<s>"    ,			lowerCAmelCase__="</s>"    ,			lowerCAmelCase__="</s>"    ,			lowerCAmelCase__="<s>"    ,			lowerCAmelCase__="<unk>"    ,			lowerCAmelCase__="<pad>"    ,			lowerCAmelCase__="<mask>"    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    None:




        '''simple docstring'''
        # Mask token behave like a normal word, i.e. include the space before it
        lowercase__:       List[str]        =       AddedToken(lowerCAmelCase__    ,			lstrip=lowerCAmelCase__    ,			rstrip=lowerCAmelCase__			) if isinstance(lowerCAmelCase__    ,			lowerCAmelCase__			) else mask_token

        lowercase__:       Any        =       {} if sp_model_kwargs is None else sp_model_kwargs

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

        lowercase__:       Dict        =       vocab_file
        lowercase__:       str        =       monolingual_vocab_file
        lowercase__:       int        =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(str(lowerCAmelCase__			)			)

        # Load the reduced vocab

        # Keep order of special tokens for backward compatibility
        lowercase__:       List[Any]        =       {}
        lowercase__:       Optional[int]        =       0
        for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
               if str(lowerCAmelCase__			) not in self.fairseq_tokens_to_ids:
                      lowercase__:       str        =       cnt
                      cnt += 1
        with open(lowerCAmelCase__    ,			'r'    ,			encoding='utf-8'			) as f:
               for line in f.readlines():
                      lowercase__:       Optional[Any]        =       line.strip().split()[0]
                      lowercase__:       Optional[Any]        =       len(self.fairseq_tokens_to_ids			)
        if str(lowerCAmelCase__			) not in self.fairseq_tokens_to_ids:
               lowercase__:       Optional[int]        =       len(self.fairseq_tokens_to_ids			)

        lowercase__:       Optional[Any]        =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def __getstate__(       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.__dict__.copy()
        lowercase__:       Tuple        =       None
        lowercase__:       Optional[Any]        =       self.sp_model.serialized_model_proto()
        return state
 def __setstate__(       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       d

        # for backward compatibility
        if not hasattr(self    ,			'sp_model_kwargs'			):
               lowercase__:       Union[str, Any]        =       {}

        lowercase__:       List[Any]        =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    List[int]:




        '''simple docstring'''

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        lowercase__:       Optional[int]        =       [self.cls_token_id]
        lowercase__:       Union[str, Any]        =       [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = False			) ->    List[int]:




        '''simple docstring'''

        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCAmelCase__    ,			token_ids_a=lowerCAmelCase__    ,			already_has_special_tokens=lowerCAmelCase__			)

        if token_ids_a is None:
               return [1] + ([0] * len(lowerCAmelCase__			)) + [1]
        return [1] + ([0] * len(lowerCAmelCase__			)) + [1, 1] + ([0] * len(lowerCAmelCase__			)) + [1]
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    List[int]:




        '''simple docstring'''
        lowercase__:       Dict        =       [self.sep_token_id]
        lowercase__:       Dict        =       [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]
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return len(self.fairseq_ids_to_tokens			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       {self.convert_ids_to_tokens(lowerCAmelCase__			): i for i in range(self.vocab_size			)}
        vocab.update(self.added_tokens_encoder			)
        return vocab
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[str]:




        '''simple docstring'''
        return self.sp_model.encode(lowerCAmelCase__    ,			out_type=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
        else:
               return self.unk_token_id
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        return self.fairseq_ids_to_tokens[index]
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       ''.join(lowerCAmelCase__			).replace(lowerCAmelCase__    ,			' '			).strip()
        return out_string
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    Tuple[str]:




        '''simple docstring'''
        if not os.path.isdir(lowerCAmelCase__			):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
               return
        lowercase__:       int        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
        lowercase__:       List[str]        =       os.path.join(
            lowerCAmelCase__    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']    ,			)

        if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCAmelCase__			) and os.path.isfile(self.vocab_file			):
               copyfile(self.vocab_file    ,			lowerCAmelCase__			)
        elif not os.path.isfile(self.vocab_file			):
               with open(lowerCAmelCase__    ,			'wb'			) as fi:
                      lowercase__:       Optional[Any]        =       self.sp_model.serialized_model_proto()
                      fi.write(lowerCAmelCase__			)

        if os.path.abspath(self.monolingual_vocab_file			) != os.path.abspath(
            lowerCAmelCase__			) and os.path.isfile(self.monolingual_vocab_file			):
               copyfile(self.monolingual_vocab_file    ,			lowerCAmelCase__			)
        elif not os.path.isfile(self.monolingual_vocab_file			):
               with open(lowerCAmelCase__    ,			'w'    ,			encoding='utf-8'			) as fp:
                      for token in self.fairseq_tokens_to_ids:
                             if token not in self.all_special_tokens:
                                    fp.write(F'{str(lowerCAmelCase__			)} \n'			)

        return out_vocab_file, out_monolingual_vocab_file


