





'''simple docstring'''

from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowercase_         =					"""\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
"""

lowercase_         =					"""\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


lowercase_         =					"""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""





def lowerCamelCase						(					__lowerCamelCase			:   Optional[Any] ,	__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   Union[str, Any]   )						->int:
    _SCREAMING_SNAKE_CASE     =      [rgram for rgrams in rgramslist for rgram in rgrams]
    _SCREAMING_SNAKE_CASE     =      Counter(__lowerCamelCase   )

    _SCREAMING_SNAKE_CASE     =      Counter(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      Counter()
    for sgram, scount in sgramcounter.items():
        _SCREAMING_SNAKE_CASE     =      scount * numref

    _SCREAMING_SNAKE_CASE     =      Counter(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      Counter()
    for cgram, ccount in cgramcounter.items():
        _SCREAMING_SNAKE_CASE     =      ccount * numref

    # KEEP
    _SCREAMING_SNAKE_CASE     =      sgramcounter_rep & cgramcounter_rep
    _SCREAMING_SNAKE_CASE     =      keepgramcounter_rep & rgramcounter
    _SCREAMING_SNAKE_CASE     =      sgramcounter_rep & rgramcounter

    _SCREAMING_SNAKE_CASE     =      0
    _SCREAMING_SNAKE_CASE     =      0
    for keepgram in keepgramcountergood_rep:
        keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
        # Fix an alleged bug [2] in the keep score computation.
        # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
        keeptmpscorea += keepgramcountergood_rep[keepgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    #      a target exactly.
    _SCREAMING_SNAKE_CASE     =      1
    _SCREAMING_SNAKE_CASE     =      1
    if len(__lowerCamelCase   ) > 0:
        _SCREAMING_SNAKE_CASE     =      keeptmpscorea / len(__lowerCamelCase   )
    if len(__lowerCamelCase   ) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        _SCREAMING_SNAKE_CASE     =      keeptmpscorea / sum(keepgramcounterall_rep.values()   )
    _SCREAMING_SNAKE_CASE     =      0
    if keepscore_precision > 0 or keepscore_recall > 0:
        _SCREAMING_SNAKE_CASE     =      2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    _SCREAMING_SNAKE_CASE     =      sgramcounter_rep - cgramcounter_rep
    _SCREAMING_SNAKE_CASE     =      delgramcounter_rep - rgramcounter
    _SCREAMING_SNAKE_CASE     =      sgramcounter_rep - rgramcounter
    _SCREAMING_SNAKE_CASE     =      0
    _SCREAMING_SNAKE_CASE     =      0
    for delgram in delgramcountergood_rep:
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
        deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    _SCREAMING_SNAKE_CASE     =      1
    if len(__lowerCamelCase   ) > 0:
        _SCREAMING_SNAKE_CASE     =      deltmpscorea / len(__lowerCamelCase   )

    # ADDITION
    _SCREAMING_SNAKE_CASE     =      set(__lowerCamelCase   ) - set(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      set(__lowerCamelCase   ) & set(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      set(__lowerCamelCase   ) - set(__lowerCamelCase   )

    _SCREAMING_SNAKE_CASE     =      0
    for addgram in addgramcountergood:
        addtmpscore += 1

    # Define 0/0=1 instead of 0 to give higher scores for predictions that match
    # a target exactly.
    _SCREAMING_SNAKE_CASE     =      1
    _SCREAMING_SNAKE_CASE     =      1
    if len(__lowerCamelCase   ) > 0:
        _SCREAMING_SNAKE_CASE     =      addtmpscore / len(__lowerCamelCase   )
    if len(__lowerCamelCase   ) > 0:
        _SCREAMING_SNAKE_CASE     =      addtmpscore / len(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      0
    if addscore_precision > 0 or addscore_recall > 0:
        _SCREAMING_SNAKE_CASE     =      2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)





def lowerCamelCase						(					__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   Union[str, Any]   )						->Dict:
    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )

    _SCREAMING_SNAKE_CASE     =      ssent.split(""" """   )
    _SCREAMING_SNAKE_CASE     =      csent.split(""" """   )
    _SCREAMING_SNAKE_CASE     =      []
    _SCREAMING_SNAKE_CASE     =      []
    _SCREAMING_SNAKE_CASE     =      []
    _SCREAMING_SNAKE_CASE     =      []
    _SCREAMING_SNAKE_CASE     =      []
    _SCREAMING_SNAKE_CASE     =      []

    _SCREAMING_SNAKE_CASE     =      []
    _SCREAMING_SNAKE_CASE     =      []
    _SCREAMING_SNAKE_CASE     =      []
    _SCREAMING_SNAKE_CASE     =      []
    for rsent in rsents:
        _SCREAMING_SNAKE_CASE     =      rsent.split(""" """   )
        _SCREAMING_SNAKE_CASE     =      []
        _SCREAMING_SNAKE_CASE     =      []
        _SCREAMING_SNAKE_CASE     =      []
        ragramslist.append(__lowerCamelCase   )
        for i in range(0 ,	len(__lowerCamelCase   ) - 1   ):
            if i < len(__lowerCamelCase   ) - 1:
                _SCREAMING_SNAKE_CASE     =      ragrams[i] + """ """ + ragrams[i + 1]
                ragrams.append(__lowerCamelCase   )
            if i < len(__lowerCamelCase   ) - 2:
                _SCREAMING_SNAKE_CASE     =      ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
                ragrams.append(__lowerCamelCase   )
            if i < len(__lowerCamelCase   ) - 3:
                _SCREAMING_SNAKE_CASE     =      ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
                ragrams.append(__lowerCamelCase   )
        ragramslist.append(__lowerCamelCase   )
        ragramslist.append(__lowerCamelCase   )
        ragramslist.append(__lowerCamelCase   )

    for i in range(0 ,	len(__lowerCamelCase   ) - 1   ):
        if i < len(__lowerCamelCase   ) - 1:
            _SCREAMING_SNAKE_CASE     =      sagrams[i] + """ """ + sagrams[i + 1]
            sagrams.append(__lowerCamelCase   )
        if i < len(__lowerCamelCase   ) - 2:
            _SCREAMING_SNAKE_CASE     =      sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
            sagrams.append(__lowerCamelCase   )
        if i < len(__lowerCamelCase   ) - 3:
            _SCREAMING_SNAKE_CASE     =      sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
            sagrams.append(__lowerCamelCase   )

    for i in range(0 ,	len(__lowerCamelCase   ) - 1   ):
        if i < len(__lowerCamelCase   ) - 1:
            _SCREAMING_SNAKE_CASE     =      cagrams[i] + """ """ + cagrams[i + 1]
            cagrams.append(__lowerCamelCase   )
        if i < len(__lowerCamelCase   ) - 2:
            _SCREAMING_SNAKE_CASE     =      cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
            cagrams.append(__lowerCamelCase   )
        if i < len(__lowerCamelCase   ) - 3:
            _SCREAMING_SNAKE_CASE     =      cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
            cagrams.append(__lowerCamelCase   )

    ((_SCREAMING_SNAKE_CASE)    ,	(_SCREAMING_SNAKE_CASE)    ,	(_SCREAMING_SNAKE_CASE))     =      SARIngram(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )
    ((_SCREAMING_SNAKE_CASE)    ,	(_SCREAMING_SNAKE_CASE)    ,	(_SCREAMING_SNAKE_CASE))     =      SARIngram(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )
    ((_SCREAMING_SNAKE_CASE)    ,	(_SCREAMING_SNAKE_CASE)    ,	(_SCREAMING_SNAKE_CASE))     =      SARIngram(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )
    ((_SCREAMING_SNAKE_CASE)    ,	(_SCREAMING_SNAKE_CASE)    ,	(_SCREAMING_SNAKE_CASE))     =      SARIngram(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      sum([keepascore, keepascore, keepascore, keepascore]   ) / 4
    _SCREAMING_SNAKE_CASE     =      sum([delascore, delascore, delascore, delascore]   ) / 4
    _SCREAMING_SNAKE_CASE     =      sum([addascore, addascore, addascore, addascore]   ) / 4
    _SCREAMING_SNAKE_CASE     =      (avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore





def lowerCamelCase						(					__lowerCamelCase			:   Union[str, Any] ,	__lowerCamelCase			:   bool = True ,	__lowerCamelCase			:   str = "13a" ,	__lowerCamelCase			:   bool = True   )						->str:
    # Normalization is requried for the ASSET dataset (one of the primary
    # datasets in sentence simplification) to allow using space
    # to split the sentence. Even though Wiki-Auto and TURK datasets,
    # do not require normalization, we do it for consistency.
    # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
    # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

    if lowercase:
        _SCREAMING_SNAKE_CASE     =      sentence.lower()

    if tokenizer in ["13a", "intl"]:
        if version.parse(sacrebleu.__version__   ).major >= 2:
            _SCREAMING_SNAKE_CASE     =      sacrebleu.metrics.bleu._get_tokenizer(__lowerCamelCase   )()(__lowerCamelCase   )
        else:
            _SCREAMING_SNAKE_CASE     =      sacrebleu.TOKENIZERS[tokenizer]()(__lowerCamelCase   )
    elif tokenizer == "moses":
        _SCREAMING_SNAKE_CASE     =      sacremoses.MosesTokenizer().tokenize(__lowerCamelCase ,	return_str=__lowerCamelCase ,	escape=__lowerCamelCase   )
    elif tokenizer == "penn":
        _SCREAMING_SNAKE_CASE     =      sacremoses.MosesTokenizer().penn_tokenize(__lowerCamelCase ,	return_str=__lowerCamelCase   )
    else:
        _SCREAMING_SNAKE_CASE     =      sentence

    if not return_str:
        _SCREAMING_SNAKE_CASE     =      normalized_sent.split()

    return normalized_sent






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

    '''simple docstring'''


    def       snake_case_(		self	)		->       List[str]:
        return datasets.MetricInfo(
            description=_DESCRIPTION			,		citation=_CITATION			,		inputs_description=_KWARGS_DESCRIPTION			,		features=datasets.Features(
                {
                    """sources""": datasets.Value("""string"""			,		id="""sequence"""	),
                    """predictions""": datasets.Value("""string"""			,		id="""sequence"""	),
                    """references""": datasets.Sequence(datasets.Value("""string"""			,		id="""sequence"""	)			,		id="""references"""	),
                }	)			,		codebase_urls=[
                """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
                """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
            ]			,		reference_urls=["""https://www.aclweb.org/anthology/Q16-1029.pdf"""]			,		)





    def       snake_case_(		self			,		A			,		A			,		A	)		->       int:
        if not (len(A	) == len(A	) == len(A	)):
            raise ValueError("""Sources length must match predictions and references lengths."""	)
        _SCREAMING_SNAKE_CASE     =      0
        for src, pred, refs in zip(A			,		A			,		A	):
            sari_score += SARIsent(normalize(A	)			,		normalize(A	)			,		[normalize(A	) for sent in refs]	)
        _SCREAMING_SNAKE_CASE     =      sari_score / len(A	)
        return {"sari": 100 * sari_score}










'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


lowercase_         =			"""\
@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\",
}
"""

lowercase_         =			"""\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

lowercase_         =			"""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    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).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    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`).
    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:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    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`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""


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

    '''simple docstring'''


    def       snake_case_(		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       snake_case_(		self			,		A			,		A			,		A="exp"			,		A=None			,		A=False			,		A=False			,		A=None			,		A=False			,		)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      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"""	)
        _SCREAMING_SNAKE_CASE     =      [[refs[i] for refs in references] for i in range(A	)]
        _SCREAMING_SNAKE_CASE     =      scb.corpus_bleu(
            A			,		A			,		smooth_method=A			,		smooth_value=A			,		force=A			,		lowercase=A			,		use_effective_order=A			,		**({"""tokenize""": tokenize} if tokenize else {})			,		)
        _SCREAMING_SNAKE_CASE     =      {
            """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



