'''simple docstring'''




from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__SCREAMING_SNAKE_CASE :  List[Any]  =						"""\
@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},
}
"""

__SCREAMING_SNAKE_CASE :  List[str]  =						"""\
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
"""


__SCREAMING_SNAKE_CASE :  Tuple  =						"""
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 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   str    ,			_UpperCAmelCase						:   Any					)					->					Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase  :						List[Any]							   =					[rgram for rgrams in rgramslist for rgram in rgrams]
       _UpperCAmelCase  :						str							   =					Counter(_UpperCAmelCase					)

       _UpperCAmelCase  :						List[Any]							   =					Counter(_UpperCAmelCase					)
       _UpperCAmelCase  :						Union[str, Any]							   =					Counter()
       for sgram, scount in sgramcounter.items():
              _UpperCAmelCase  :						Optional[int]							   =					scount * numref

       _UpperCAmelCase  :						int							   =					Counter(_UpperCAmelCase					)
       _UpperCAmelCase  :						int							   =					Counter()
       for cgram, ccount in cgramcounter.items():
              _UpperCAmelCase  :						Dict							   =					ccount * numref

       # KEEP
       _UpperCAmelCase  :						Union[str, Any]							   =					sgramcounter_rep & cgramcounter_rep
       _UpperCAmelCase  :						Optional[int]							   =					keepgramcounter_rep & rgramcounter
       _UpperCAmelCase  :						Optional[int]							   =					sgramcounter_rep & rgramcounter

       _UpperCAmelCase  :						Optional[int]							   =					0
       _UpperCAmelCase  :						List[str]							   =					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.
       _UpperCAmelCase  :						List[Any]							   =					1
       _UpperCAmelCase  :						Union[str, Any]							   =					1
       if len(_UpperCAmelCase					) > 0:
              _UpperCAmelCase  :						str							   =					keeptmpscorea / len(_UpperCAmelCase					)
       if len(_UpperCAmelCase					) > 0:
              # Fix an alleged bug [2] in the keep score computation.
              # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
              _UpperCAmelCase  :						Dict							   =					keeptmpscorea / sum(keepgramcounterall_rep.values()					)
       _UpperCAmelCase  :						str							   =					0
       if keepscore_precision > 0 or keepscore_recall > 0:
              _UpperCAmelCase  :						int							   =					2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

       # DELETION
       _UpperCAmelCase  :						List[str]							   =					sgramcounter_rep - cgramcounter_rep
       _UpperCAmelCase  :						Any							   =					delgramcounter_rep - rgramcounter
       _UpperCAmelCase  :						Dict							   =					sgramcounter_rep - rgramcounter
       _UpperCAmelCase  :						Optional[Any]							   =					0
       _UpperCAmelCase  :						Dict							   =					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.
       _UpperCAmelCase  :						Dict							   =					1
       if len(_UpperCAmelCase					) > 0:
              _UpperCAmelCase  :						List[Any]							   =					deltmpscorea / len(_UpperCAmelCase					)

       # ADDITION
       _UpperCAmelCase  :						List[Any]							   =					set(_UpperCAmelCase					) - set(_UpperCAmelCase					)
       _UpperCAmelCase  :						Union[str, Any]							   =					set(_UpperCAmelCase					) & set(_UpperCAmelCase					)
       _UpperCAmelCase  :						str							   =					set(_UpperCAmelCase					) - set(_UpperCAmelCase					)

       _UpperCAmelCase  :						int							   =					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.
       _UpperCAmelCase  :						Optional[Any]							   =					1
       _UpperCAmelCase  :						Optional[int]							   =					1
       if len(_UpperCAmelCase					) > 0:
              _UpperCAmelCase  :						int							   =					addtmpscore / len(_UpperCAmelCase					)
       if len(_UpperCAmelCase					) > 0:
              _UpperCAmelCase  :						Any							   =					addtmpscore / len(_UpperCAmelCase					)
       _UpperCAmelCase  :						Dict							   =					0
       if addscore_precision > 0 or addscore_recall > 0:
              _UpperCAmelCase  :						int							   =					2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

       return (keepscore, delscore_precision, addscore)

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[int]    ,			_UpperCAmelCase						:   Optional[int]    ,			_UpperCAmelCase						:   Dict					)					->					Union[str, Any]:


       """simple docstring"""







       _UpperCAmelCase  :						int							   =					len(_UpperCAmelCase					)

       _UpperCAmelCase  :						str							   =					ssent.split(" "					)
       _UpperCAmelCase  :						Dict							   =					csent.split(" "					)
       _UpperCAmelCase  :						str							   =					[]
       _UpperCAmelCase  :						Union[str, Any]							   =					[]
       _UpperCAmelCase  :						Optional[int]							   =					[]
       _UpperCAmelCase  :						Optional[int]							   =					[]
       _UpperCAmelCase  :						Union[str, Any]							   =					[]
       _UpperCAmelCase  :						List[str]							   =					[]

       _UpperCAmelCase  :						int							   =					[]
       _UpperCAmelCase  :						Optional[Any]							   =					[]
       _UpperCAmelCase  :						Optional[Any]							   =					[]
       _UpperCAmelCase  :						Any							   =					[]
       for rsent in rsents:
              _UpperCAmelCase  :						Dict							   =					rsent.split(" "					)
              _UpperCAmelCase  :						int							   =					[]
              _UpperCAmelCase  :						Tuple							   =					[]
              _UpperCAmelCase  :						Union[str, Any]							   =					[]
              ragramslist.append(_UpperCAmelCase					)
              for i in range(0    ,			len(_UpperCAmelCase					) - 1					):
                     if i < len(_UpperCAmelCase					) - 1:
                            _UpperCAmelCase  :						Optional[Any]							   =					ragrams[i] + " " + ragrams[i + 1]
                            ragrams.append(_UpperCAmelCase					)
                     if i < len(_UpperCAmelCase					) - 2:
                            _UpperCAmelCase  :						int							   =					ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
                            ragrams.append(_UpperCAmelCase					)
                     if i < len(_UpperCAmelCase					) - 3:
                            _UpperCAmelCase  :						List[str]							   =					ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
                            ragrams.append(_UpperCAmelCase					)
              ragramslist.append(_UpperCAmelCase					)
              ragramslist.append(_UpperCAmelCase					)
              ragramslist.append(_UpperCAmelCase					)

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

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

       ((_UpperCAmelCase)   ,						(_UpperCAmelCase)   ,						(_UpperCAmelCase))  :						Optional[int]							   =					SARIngram(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)
       ((_UpperCAmelCase)   ,						(_UpperCAmelCase)   ,						(_UpperCAmelCase))  :						Optional[Any]							   =					SARIngram(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)
       ((_UpperCAmelCase)   ,						(_UpperCAmelCase)   ,						(_UpperCAmelCase))  :						Optional[Any]							   =					SARIngram(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)
       ((_UpperCAmelCase)   ,						(_UpperCAmelCase)   ,						(_UpperCAmelCase))  :						List[Any]							   =					SARIngram(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)
       _UpperCAmelCase  :						Dict							   =					sum([keepascore, keepascore, keepascore, keepascore]					) / 4
       _UpperCAmelCase  :						Dict							   =					sum([delascore, delascore, delascore, delascore]					) / 4
       _UpperCAmelCase  :						str							   =					sum([addascore, addascore, addascore, addascore]					) / 4
       _UpperCAmelCase  :						Optional[int]							   =					(avgkeepscore + avgdelscore + avgaddscore) / 3
       return finalscore

def 			UpperCamelCase_							(      _UpperCAmelCase						:   str    ,			_UpperCAmelCase						:   bool = True    ,			_UpperCAmelCase						:   str = "13a"    ,			_UpperCAmelCase						:   bool = True					)					->					Tuple:


       """simple docstring"""







       if lowercase:
              _UpperCAmelCase  :						Optional[int]							   =					sentence.lower()

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

       if not return_str:
              _UpperCAmelCase  :						Any							   =					normalized_sent.split()

       return normalized_sent


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,				_KWARGS_DESCRIPTION					)
class 			lowerCamelCase_			(datasets.Metric					):
      '''simple docstring'''

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


'''simple docstring'''




import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand

def 			UpperCamelCase_							(      _UpperCAmelCase						:   List[str]					)					->					Union[str, Any]:


       """simple docstring"""







       return EnvironmentCommand()


class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      @staticmethod
      def   _A			( A		:       ArgumentParser ):
             _UpperCAmelCase  :						List[str]							   =					parser.add_parser("env"		,      help="Print relevant system environment info." )
             download_parser.set_defaults(func=A )




      def   _A			( self		:       List[str] ):
             _UpperCAmelCase  :						str							   =					{
                 "`datasets` version": version,
                 "Platform": platform.platform(),
                 "Python version": platform.python_version(),
                 "Huggingface_hub version": huggingface_hub.__version__,
                 "PyArrow version": pyarrow.__version__,
                 "Pandas version": pandas.__version__,
             }

             print("\nCopy-and-paste the text below in your GitHub issue.\n" )
             print(self.format_dict(A ) )

             return info





      @staticmethod
      def   _A			( A		:       Tuple ):
             return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"

