





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


lowercase_          =		"""
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  (_SCREAMING_SNAKE_CASE		)    ->       str:







  def remove_articles(_SCREAMING_SNAKE_CASE		):
    lowercase__           =  re.compile(R'\b(a|an|the)\b'		,						re.UNICODE		)
    return re.sub(UpperCamelCase__		,						' '		,						UpperCamelCase__		)

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

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

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

  return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__		)		)		)		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       List[Any]:
  return int(normalize_answer(UpperCamelCase__		) == normalize_answer(UpperCamelCase__		)		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Optional[int]:
  lowercase__           =  [any(compute_exact(UpperCamelCase__		,						UpperCamelCase__		) for ref in refs		) for pred, refs in zip(UpperCamelCase__		,						UpperCamelCase__		)]
  return (sum(UpperCamelCase__		) / len(UpperCamelCase__		)) * 100




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Union[str, Any]:
  lowercase__           =  [rgram for rgrams in rgramslist for rgram in rgrams]
  lowercase__           =  Counter(UpperCamelCase__		)

  lowercase__           =  Counter(UpperCamelCase__		)
  lowercase__           =  Counter()
  for sgram, scount in sgramcounter.items():
    lowercase__           =  scount * numref

  lowercase__           =  Counter(UpperCamelCase__		)
  lowercase__           =  Counter()
  for cgram, ccount in cgramcounter.items():
    lowercase__           =  ccount * numref

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

  lowercase__           =  0
  lowercase__           =  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.
  lowercase__           =  1
  lowercase__           =  1
  if len(UpperCamelCase__		) > 0:
    lowercase__           =  keeptmpscorea / len(UpperCamelCase__		)
  if len(UpperCamelCase__		) > 0:
    # Fix an alleged bug [2] in the keep score computation.
    # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
    lowercase__           =  keeptmpscorea / sum(keepgramcounterall_rep.values()		)
  lowercase__           =  0
  if keepscore_precision > 0 or keepscore_recall > 0:
    lowercase__           =  2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

  # DELETION
  lowercase__           =  sgramcounter_rep - cgramcounter_rep
  lowercase__           =  delgramcounter_rep - rgramcounter
  lowercase__           =  sgramcounter_rep - rgramcounter
  lowercase__           =  0
  lowercase__           =  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.
  lowercase__           =  1
  if len(UpperCamelCase__		) > 0:
    lowercase__           =  deltmpscorea / len(UpperCamelCase__		)

  # ADDITION
  lowercase__           =  set(UpperCamelCase__		) - set(UpperCamelCase__		)
  lowercase__           =  set(UpperCamelCase__		) & set(UpperCamelCase__		)
  lowercase__           =  set(UpperCamelCase__		) - set(UpperCamelCase__		)

  lowercase__           =  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.
  lowercase__           =  1
  lowercase__           =  1
  if len(UpperCamelCase__		) > 0:
    lowercase__           =  addtmpscore / len(UpperCamelCase__		)
  if len(UpperCamelCase__		) > 0:
    lowercase__           =  addtmpscore / len(UpperCamelCase__		)
  lowercase__           =  0
  if addscore_precision > 0 or addscore_recall > 0:
    lowercase__           =  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

  return (keepscore, delscore_precision, addscore)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Union[str, Any]:
  lowercase__           =  len(UpperCamelCase__		)

  lowercase__           =  ssent.split(' '		)
  lowercase__           =  csent.split(' '		)
  lowercase__           =  []
  lowercase__           =  []
  lowercase__           =  []
  lowercase__           =  []
  lowercase__           =  []
  lowercase__           =  []

  lowercase__           =  []
  lowercase__           =  []
  lowercase__           =  []
  lowercase__           =  []
  for rsent in rsents:
    lowercase__           =  rsent.split(' '		)
    lowercase__           =  []
    lowercase__           =  []
    lowercase__           =  []
    ragramslist.append(UpperCamelCase__		)
    for i in range(0		,						len(UpperCamelCase__		) - 1		):
      if i < len(UpperCamelCase__		) - 1:
        lowercase__           =  ragrams[i] + ' ' + ragrams[i + 1]
        ragrams.append(UpperCamelCase__		)
      if i < len(UpperCamelCase__		) - 2:
        lowercase__           =  ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
        ragrams.append(UpperCamelCase__		)
      if i < len(UpperCamelCase__		) - 3:
        lowercase__           =  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:
      lowercase__           =  sagrams[i] + ' ' + sagrams[i + 1]
      sagrams.append(UpperCamelCase__		)
    if i < len(UpperCamelCase__		) - 2:
      lowercase__           =  sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
      sagrams.append(UpperCamelCase__		)
    if i < len(UpperCamelCase__		) - 3:
      lowercase__           =  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:
      lowercase__           =  cagrams[i] + ' ' + cagrams[i + 1]
      cagrams.append(UpperCamelCase__		)
    if i < len(UpperCamelCase__		) - 2:
      lowercase__           =  cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
      cagrams.append(UpperCamelCase__		)
    if i < len(UpperCamelCase__		) - 3:
      lowercase__           =  cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
      cagrams.append(UpperCamelCase__		)

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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE = True		,						_SCREAMING_SNAKE_CASE = "13a"		,						_SCREAMING_SNAKE_CASE = True		)    ->       List[str]:







  if lowercase:
    lowercase__           =  sentence.lower()

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

  if not return_str:
    lowercase__           =  normalized_sent.split()

  return normalized_sent




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Tuple:







  if not (len(UpperCamelCase__		) == len(UpperCamelCase__		) == len(UpperCamelCase__		)):
    raise ValueError('Sources length must match predictions and references lengths.'		)
  lowercase__           =  0
  for src, pred, refs in zip(UpperCamelCase__		,						UpperCamelCase__		,						UpperCamelCase__		):
    sari_score += SARIsent(normalize(UpperCamelCase__		)		,						normalize(UpperCamelCase__		)		,						[normalize(UpperCamelCase__		) for sent in refs]		)
  lowercase__           =  sari_score / len(UpperCamelCase__		)
  return 100 * sari_score




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE="exp"		,						_SCREAMING_SNAKE_CASE=None		,						_SCREAMING_SNAKE_CASE=False		,						_SCREAMING_SNAKE_CASE=False		,						_SCREAMING_SNAKE_CASE=False		,						)    ->       Union[str, Any]:
  lowercase__           =  len(references[0]		)
  if any(len(UpperCamelCase__		) != references_per_prediction for refs in references		):
    raise ValueError('Sacrebleu requires the same number of references for each prediction'		)
  lowercase__           =  [[refs[i] for refs in references] for i in range(UpperCamelCase__		)]
  lowercase__           =  sacrebleu.corpus_bleu(
      UpperCamelCase__		,						UpperCamelCase__		,						smooth_method=UpperCamelCase__		,						smooth_value=UpperCamelCase__		,						force=UpperCamelCase__		,						lowercase=UpperCamelCase__		,						use_effective_order=UpperCamelCase__		,						)
  return output.score


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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Tuple:





     """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      :    str      ,					a      :    str      ,					a      :    Tuple      ,					a      :    Optional[Any]	)->       Union[str, Any]:





     """simple docstring"""







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









import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
      from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  if is_torch_version('<'		,						'2.0.0'		) or not hasattr(_SCREAMING_SNAKE_CASE		,						'_dynamo'		):
    return False
  return isinstance(_SCREAMING_SNAKE_CASE		,						torch._dynamo.eval_frame.OptimizedModule		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE = True		)    ->       Dict:
  lowercase__           =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

  lowercase__           =  is_compiled_module(_SCREAMING_SNAKE_CASE		)
  if is_compiled:
    lowercase__           =  model
    lowercase__           =  model._orig_mod

  if is_deepspeed_available():
    options += (DeepSpeedEngine,)

  while isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		):
    lowercase__           =  model.module

  if not keep_fpaa_wrapper:
    lowercase__           =  getattr(_SCREAMING_SNAKE_CASE		,						'forward'		)
    lowercase__           =  model.__dict__.pop('_original_forward'		,						_SCREAMING_SNAKE_CASE		)
    if original_forward is not None:
      while hasattr(_SCREAMING_SNAKE_CASE		,						'__wrapped__'		):
        lowercase__           =  forward.__wrapped__
        if forward == original_forward:
          break
      lowercase__           =  forward
    if getattr(_SCREAMING_SNAKE_CASE		,						'_converted_to_transformer_engine'		,						_SCREAMING_SNAKE_CASE		):
      convert_model(_SCREAMING_SNAKE_CASE		,						to_transformer_engine=_SCREAMING_SNAKE_CASE		)

  if is_compiled:
    lowercase__           =  model
    lowercase__           =  compiled_model

  return model




def       __UpperCamelCase  ()    ->       Tuple:
  PartialState().wait_for_everyone()




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       List[Any]:
  if PartialState().distributed_type == DistributedType.TPU:
    xm.save(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
  elif PartialState().local_process_index == 0:
    torch.save(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)




@contextmanager
def       __UpperCamelCase  (**_SCREAMING_SNAKE_CASE		)    ->       Optional[Any]:
  for key, value in kwargs.items():
    lowercase__           =  str(_SCREAMING_SNAKE_CASE		)

  yield

  for key in kwargs:
    if key.upper() in os.environ:
      del os.environ[key.upper()]




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  if not hasattr(_SCREAMING_SNAKE_CASE		,						'__qualname__'		) and not hasattr(_SCREAMING_SNAKE_CASE		,						'__name__'		):
    lowercase__           =  getattr(_SCREAMING_SNAKE_CASE		,						'__class__'		,						_SCREAMING_SNAKE_CASE		)
  if hasattr(_SCREAMING_SNAKE_CASE		,						'__qualname__'		):
    return obj.__qualname__
  if hasattr(_SCREAMING_SNAKE_CASE		,						'__name__'		):
    return obj.__name__
  return str(_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Dict:
  for key, value in source.items():
    if isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		):
      lowercase__           =  destination.setdefault(_SCREAMING_SNAKE_CASE		,						{}		)
      merge_dicts(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
    else:
      lowercase__           =  value

  return destination




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE = None		)    ->       bool:
  if port is None:
    lowercase__           =  29500
  with socket.socket(socket.AF_INET		,						socket.SOCK_STREAM		) as s:
    return s.connect_ex(('localhost', port)		) == 0


