






"""simple docstring"""






import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCAmelCase_   : Optional[int]   			= """
@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\",
}
"""

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


UpperCAmelCase_   : Tuple   			= """
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 					_A			(__a							) ->       List[str]:
    """simple docstring"""






    def remove_articles(__a							):
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		re.compile(R'''\b(a|an|the)\b'''    ,				re.UNICODE							)
        return re.sub(__a    ,				''' '''    ,				__a							)

    def white_space_fix(__a							):
        return " ".join(text.split()							)

    def remove_punc(__a							):
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		set(string.punctuation							)
        return "".join(ch for ch in text if ch not in exclude							)

    def lower(__a							):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(__a							)							)							)							)



def 					_A			(__a    ,				__a							) ->       Optional[Any]:
    """simple docstring"""






    return int(normalize_answer(__a							) == normalize_answer(__a							)							)



def 					_A			(__a    ,				__a							) ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[any(compute_exact(__a    ,				__a							) for ref in refs							) for pred, refs in zip(__a    ,				__a							)]
    return (sum(__a							) / len(__a							)) * 1_00



def 					_A			(__a    ,				__a    ,				__a    ,				__a							) ->       str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[rgram for rgrams in rgramslist for rgram in rgrams]
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		Counter(__a							)

    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		Counter(__a							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		Counter()
    for sgram, scount in sgramcounter.items():
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scount * numref

    SCREAMING_SNAKE_CASE_      :  Any    						=		Counter(__a							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		Counter()
    for cgram, ccount in cgramcounter.items():
        SCREAMING_SNAKE_CASE_      :  int    						=		ccount * numref

    # KEEP
    SCREAMING_SNAKE_CASE_      :  Any    						=		sgramcounter_rep & cgramcounter_rep
    SCREAMING_SNAKE_CASE_      :  int    						=		keepgramcounter_rep & rgramcounter
    SCREAMING_SNAKE_CASE_      :  Dict    						=		sgramcounter_rep & rgramcounter

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

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

    # ADDITION
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		set(__a							) - set(__a							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		set(__a							) & set(__a							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		set(__a							) - set(__a							)

    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		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_      :  Dict    						=		1
    SCREAMING_SNAKE_CASE_      :  int    						=		1
    if len(__a							) > 0:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		addtmpscore / len(__a							)
    if len(__a							) > 0:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		addtmpscore / len(__a							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0
    if addscore_precision > 0 or addscore_recall > 0:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)



def 					_A			(__a    ,				__a    ,				__a							) ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		len(__a							)

    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		ssent.split(''' '''							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		csent.split(''' '''							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		[]
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[]
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		[]
    SCREAMING_SNAKE_CASE_      :  str    						=		[]
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[]
    SCREAMING_SNAKE_CASE_      :  int    						=		[]

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

    for i in range(0    ,				len(__a							) - 1							):
        if i < len(__a							) - 1:
            SCREAMING_SNAKE_CASE_      :  Dict    						=		sagrams[i] + ''' ''' + sagrams[i + 1]
            sagrams.append(__a							)
        if i < len(__a							) - 2:
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
            sagrams.append(__a							)
        if i < len(__a							) - 3:
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
            sagrams.append(__a							)

    for i in range(0    ,				len(__a							) - 1							):
        if i < len(__a							) - 1:
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		cagrams[i] + ''' ''' + cagrams[i + 1]
            cagrams.append(__a							)
        if i < len(__a							) - 2:
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
            cagrams.append(__a							)
        if i < len(__a							) - 3:
            SCREAMING_SNAKE_CASE_      :  int    						=		cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
            cagrams.append(__a							)

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



def 					_A			(__a    ,				__a = True    ,				__a = "13a"    ,				__a = True							) ->       Optional[int]:
    """simple docstring"""






    if lowercase:
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		sentence.lower()

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

    if not return_str:
        SCREAMING_SNAKE_CASE_      :  str    						=		normalized_sent.split()

    return normalized_sent



def 					_A			(__a    ,				__a    ,				__a							) ->       int:
    """simple docstring"""






    if not (len(__a							) == len(__a							) == len(__a							)):
        raise ValueError('''Sources length must match predictions and references lengths.'''							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		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_      :  Union[str, Any]    						=		sari_score / len(__a							)
    return 1_00 * sari_score



def 					_A			(__a    ,				__a    ,				__a="exp"    ,				__a=None    ,				__a=False    ,				__a=False    ,				__a=False    ,				) ->       Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		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_      :  Optional[int]    						=		[[refs[i] for refs in references] for i in range(__a							)]
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		sacrebleu.corpus_bleu(
        __a    ,				__a    ,				smooth_method=__a    ,				smooth_value=__a    ,				force=__a    ,				lowercase=__a    ,				use_effective_order=__a    ,				)
    return output.score





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






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  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     :  List[str]						,	lowercase_     :  Dict						,	lowercase_     :  List[Any]						,	lowercase_     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		{}
          result.update({'''sari''': compute_sari(sources=lowercase_						,	predictions=lowercase_						,	references=lowercase_)})
          result.update({'''sacrebleu''': compute_sacrebleu(predictions=lowercase_						,	references=lowercase_)})
          result.update({'''exact''': compute_em(predictions=lowercase_						,	references=lowercase_)})
          return result










"""simple docstring"""






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


UpperCAmelCase_   : str   			= {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Dict   			= ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : List[str]   			= [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_   : Tuple   			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


