


"""simple docstring"""







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 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  def remove_articles(A_	):
    a						:		Optional[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_	):
    a						:		str											=      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 					lowercase      (    A_  ,       A_	)->				Optional[int]:
  '''simple docstring'''







  return int(normalize_answer(A_	) == normalize_answer(A_	)	)


def 					lowercase      (    A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[Any]											=      [any(compute_exact(A_  ,       A_	) for ref in refs	) for pred, refs in zip(A_  ,       A_	)]
  return (sum(A_	) / len(A_	)) * 100


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_	)->				int:
  '''simple docstring'''







  a						:		List[Any]											=      [rgram for rgrams in rgramslist for rgram in rgrams]
  a						:		Dict											=      Counter(A_	)

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

  a						:		Dict											=      Counter(A_	)
  a						:		List[str]											=      Counter()
  for cgram, ccount in cgramcounter.items():
    a						:		Union[str, Any]											=      ccount * numref

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

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

  # DELETION
  a						:		Dict											=      sgramcounter_rep - cgramcounter_rep
  a						:		Tuple											=      delgramcounter_rep - rgramcounter
  a						:		Union[str, Any]											=      sgramcounter_rep - rgramcounter
  a						:		Tuple											=      0
  a						:		str											=      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.
  a						:		Any											=      1
  if len(A_	) > 0:
    a						:		Optional[Any]											=      deltmpscorea / len(A_	)

  # ADDITION
  a						:		Dict											=      set(A_	) - set(A_	)
  a						:		Tuple											=      set(A_	) & set(A_	)
  a						:		Any											=      set(A_	) - set(A_	)

  a						:		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.
  a						:		str											=      1
  a						:		int											=      1
  if len(A_	) > 0:
    a						:		int											=      addtmpscore / len(A_	)
  if len(A_	) > 0:
    a						:		Optional[int]											=      addtmpscore / len(A_	)
  a						:		Any											=      0
  if addscore_precision > 0 or addscore_recall > 0:
    a						:		Union[str, Any]											=      2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

  return (keepscore, delscore_precision, addscore)


def 					lowercase      (    A_  ,       A_  ,       A_	)->				Optional[int]:
  '''simple docstring'''







  a						:		List[str]											=      len(A_	)

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

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

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


def 					lowercase      (    A_  ,       A_ = True  ,       A_ = "13a"  ,       A_ = True	)->				Union[str, Any]:
  '''simple docstring'''







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

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

  if not return_str:
    a						:		Optional[Any]											=      normalized_sent.split()

  return normalized_sent


def 					lowercase      (    A_  ,       A_  ,       A_	)->				str:
  '''simple docstring'''







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


def 					lowercase      (    A_  ,       A_  ,       A_="exp"  ,       A_=None  ,       A_=False  ,       A_=False  ,       A_=False  ,       )->				List[Any]:
  '''simple docstring'''







  a						:		Any											=      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"	)
  a						:		Tuple											=      [[refs[i] for refs in references] for i in range(A_	)]
  a						:		int											=      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 						_A  (  datasets.Metric  ):







    """simple docstring"""




    def      __snake_case							( self    :   str):
      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      __snake_case							( self    :   Optional[Any]    ,			__UpperCAmelCase    :   List[Any]    ,			__UpperCAmelCase    :   List[Any]    ,			__UpperCAmelCase    :   List[str]):
      a						:		str											=      {}
      result.update({"sari": compute_sari(sources=__UpperCAmelCase    ,			predictions=__UpperCAmelCase    ,			references=__UpperCAmelCase)})
      result.update({"sacrebleu": compute_sacrebleu(predictions=__UpperCAmelCase    ,			references=__UpperCAmelCase)})
      result.update({"exact": compute_em(predictions=__UpperCAmelCase    ,			references=__UpperCAmelCase)})
      return result










"""simple docstring"""







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=5	)->				Union[str, Any]:
  '''simple docstring'''







  assert masked_input.count("<mask>"	) == 1
  a						:		List[str]											=      torch.tensor(tokenizer.encode(A_  ,       add_special_tokens=A_	)	).unsqueeze(0	)  # Batch size 1
  a						:		Dict											=      model(A_	)[0]  # The last hidden-state is the first element of the output tuple
  a						:		int											=      (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  a						:		Optional[Any]											=      logits[0, masked_index, :]
  a						:		Dict											=      logits.softmax(dim=0	)
  a							,		a						:		Any											=      prob.topk(k=A_  ,       dim=0	)
  a						:		Optional[Any]											=      " ".join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A_	)	)]	)
  a						:		str											=      tokenizer.mask_token
  a						:		Any											=      []
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "	)	):
    a						:		Dict											=      predicted_token_bpe.replace("\u2581"  ,       " "	)
    if " {0}".format(A_	) in masked_input:
      topk_filled_outputs.append(
          (
              masked_input.replace(" {0}".format(A_	)  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
    else:
      topk_filled_outputs.append(
          (
              masked_input.replace(A_  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
  return topk_filled_outputs


__lowercase    =				CamembertTokenizer.from_pretrained("""camembert-base""")
__lowercase    =				CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

__lowercase    =				"""Le camembert est <mask> :)"""







print(fill_mask(masked_input, model, tokenizer, topk=3))






