
import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCamelCase							=       """
@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
},
@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							=       """\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


__lowerCamelCase							=       """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
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
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):

       def remove_articles(__lowerCamelCase     : Dict    ):
              snake_case       :   Dict							=      re.compile(r"\b(a|an|the)\b"				,					re.UNICODE    )
              return re.sub(__lowerCamelCase				,					" "				,					__lowerCamelCase    )

       def white_space_fix(__lowerCamelCase     : Dict    ):
              return " ".join(text.split()    )

       def remove_punc(__lowerCamelCase     : Optional[int]    ):
              snake_case       :   List[Any]							=      set(string.punctuation    )
              return "".join(ch for ch in text if ch not in exclude    )

       def lower(__lowerCamelCase     : Optional[Any]    ):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase    )    )    )    )


def 			UpperCamelCase					(    __lowerCamelCase     : Any				,					__lowerCamelCase     : Dict    ):
       return int(normalize_answer(__lowerCamelCase    ) == normalize_answer(__lowerCamelCase    )    )


def 			UpperCamelCase					(    __lowerCamelCase     : List[Any]				,					__lowerCamelCase     : List[str]    ):
       snake_case       :   Optional[int]							=      [any(compute_exact(__lowerCamelCase				,					__lowerCamelCase    ) for ref in refs    ) for pred, refs in zip(__lowerCamelCase				,					__lowerCamelCase    )]
       return (sum(__lowerCamelCase    ) / len(__lowerCamelCase    )) * 100


def 			UpperCamelCase					(    __lowerCamelCase     : int				,					__lowerCamelCase     : List[str]				,					__lowerCamelCase     : Optional[Any]				,					__lowerCamelCase     : List[Any]    ):
       snake_case       :   Any							=      [rgram for rgrams in rgramslist for rgram in rgrams]
       snake_case       :   Optional[int]							=      Counter(__lowerCamelCase    )

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

       snake_case       :   Dict							=      Counter(__lowerCamelCase    )
       snake_case       :   Optional[int]							=      Counter()
       for cgram, ccount in cgramcounter.items():
              snake_case       :   int							=      ccount * numref

       # KEEP
       snake_case       :   List[Any]							=      sgramcounter_rep & cgramcounter_rep
       snake_case       :   Optional[Any]							=      keepgramcounter_rep & rgramcounter
       snake_case       :   List[Any]							=      sgramcounter_rep & rgramcounter

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

       # DELETION
       snake_case       :   Optional[Any]							=      sgramcounter_rep - cgramcounter_rep
       snake_case       :   Optional[int]							=      delgramcounter_rep - rgramcounter
       snake_case       :   Optional[int]							=      sgramcounter_rep - rgramcounter
       snake_case       :   int							=      0
       snake_case       :   Tuple							=      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.
       snake_case       :   Any							=      1
       if len(__lowerCamelCase    ) > 0:
              snake_case       :   Tuple							=      deltmpscorea / len(__lowerCamelCase    )

       # ADDITION
       snake_case       :   Any							=      set(__lowerCamelCase    ) - set(__lowerCamelCase    )
       snake_case       :   str							=      set(__lowerCamelCase    ) & set(__lowerCamelCase    )
       snake_case       :   str							=      set(__lowerCamelCase    ) - set(__lowerCamelCase    )

       snake_case       :   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.
       snake_case       :   List[str]							=      1
       snake_case       :   List[Any]							=      1
       if len(__lowerCamelCase    ) > 0:
              snake_case       :   Union[str, Any]							=      addtmpscore / len(__lowerCamelCase    )
       if len(__lowerCamelCase    ) > 0:
              snake_case       :   Tuple							=      addtmpscore / len(__lowerCamelCase    )
       snake_case       :   Optional[Any]							=      0
       if addscore_precision > 0 or addscore_recall > 0:
              snake_case       :   str							=      2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

       return (keepscore, delscore_precision, addscore)


def 			UpperCamelCase					(    __lowerCamelCase     : Dict				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : Optional[Any]    ):
       snake_case       :   List[str]							=      len(__lowerCamelCase    )

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

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

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


def 			UpperCamelCase					(    __lowerCamelCase     : Dict				,					__lowerCamelCase     : bool = True				,					__lowerCamelCase     : str = "13a"				,					__lowerCamelCase     : bool = True    ):
       # 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:
              snake_case       :   str							=      sentence.lower()

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

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

       return normalized_sent


def 			UpperCamelCase					(    __lowerCamelCase     : Dict				,					__lowerCamelCase     : Optional[Any]				,					__lowerCamelCase     : Tuple    ):
       if not (len(__lowerCamelCase    ) == len(__lowerCamelCase    ) == len(__lowerCamelCase    )):
              raise ValueError("Sources length must match predictions and references lengths."    )
       snake_case       :   int							=      0
       for src, pred, refs in zip(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    ):
              sari_score += SARIsent(normalize(__lowerCamelCase    )				,					normalize(__lowerCamelCase    )				,					[normalize(__lowerCamelCase    ) for sent in refs]    )
       snake_case       :   Optional[int]							=      sari_score / len(__lowerCamelCase    )
       return 100 * sari_score


def 			UpperCamelCase					(    __lowerCamelCase     : Dict				,					__lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : List[Any]="exp"				,					__lowerCamelCase     : List[str]=None				,					__lowerCamelCase     : Optional[Any]=False				,					__lowerCamelCase     : Union[str, Any]=False				,					__lowerCamelCase     : Optional[int]=False				,					):
       snake_case       :   Dict							=      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"    )
       snake_case       :   Union[str, Any]							=      [[refs[i] for refs in references] for i in range(__lowerCamelCase    )]
       snake_case       :   Optional[int]							=      sacrebleu.corpus_bleu(
           __lowerCamelCase				,					__lowerCamelCase				,					smooth_method=__lowerCamelCase				,					smooth_value=__lowerCamelCase				,					force=__lowerCamelCase				,					lowercase=__lowerCamelCase				,					use_effective_order=__lowerCamelCase				,					)
       return output.score






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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	Optional[int]:


          '''simple docstring'''


          return datasets.MetricInfo(
              description=_DESCRIPTION    ,						citation=_CITATION    ,						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/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
                  "https://github.com/cocoxu/simplification/blob/master/SARI.py",
                  "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
                  "https://github.com/mjpost/sacreBLEU",
              ]    ,						reference_urls=[
                  "https://www.aclweb.org/anthology/Q16-1029.pdf",
                  "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 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					List[Any] )		->	int:


          '''simple docstring'''


          snake_case       :   str							=      {}
          result.update({"sari": compute_sari(sources=snake_case__    ,						predictions=snake_case__    ,						references=snake_case__ )} )
          result.update({"sacrebleu": compute_sacrebleu(predictions=snake_case__    ,						references=snake_case__ )} )
          result.update({"exact": compute_em(predictions=snake_case__    ,						references=snake_case__ )} )
          return result







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
   from ..ta.tokenization_ta import TaTokenizer
else:
   from ...utils.dummy_sentencepiece_objects import TaTokenizer

__lowerCamelCase							=       TaTokenizer

if is_tokenizers_available():
   from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
   from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__lowerCamelCase							=       TaTokenizerFast

__lowerCamelCase							=       {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """MT5EncoderModel""",
       """MT5ForConditionalGeneration""",
       """MT5ForQuestionAnswering""",
       """MT5Model""",
       """MT5PreTrainedModel""",
       """MT5Stack""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
   from .configuration_mta import MTaConfig, MTaOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mta import (
          MTaEncoderModel,
          MTaForConditionalGeneration,
          MTaForQuestionAnswering,
          MTaModel,
          MTaPreTrainedModel,
          MTaStack,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
   import sys

   __lowerCamelCase							=       _LazyModule(
       __name__,
       globals()["""__file__"""],
       _import_structure,
       extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
       module_spec=__spec__,
   )





