





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     lowerCamelCase_	(      lowerCAmelCase:	int				)->		Dict:

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

  def white_space_fix(lowerCAmelCase:	str				):
    return " ".join(text.split()				)

  def remove_punc(lowerCAmelCase:	str				):
    _snake_case   :				Union[str, Any]														=	set(string.punctuation				)
    return "".join(ch for ch in text if ch not in exclude				)

  def lower(lowerCAmelCase:	Dict				):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase				)				)				)				)


def     lowerCamelCase_	(      lowerCAmelCase:	str    ,							lowerCAmelCase:	int				)->		Union[str, Any]:
  return int(normalize_answer(lowerCAmelCase				) == normalize_answer(lowerCAmelCase				)				)


def     lowerCamelCase_	(      lowerCAmelCase:	Dict    ,							lowerCAmelCase:	List[Any]				)->		Tuple:
  _snake_case   :				List[Any]														=	[any(compute_exact(lowerCAmelCase    ,							lowerCAmelCase				) for ref in refs				) for pred, refs in zip(lowerCAmelCase    ,							lowerCAmelCase				)]
  return (sum(lowerCAmelCase				) / len(lowerCAmelCase				)) * 1_00


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]    ,							lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	Dict    ,							lowerCAmelCase:	Union[str, Any]				)->		Optional[int]:
  _snake_case   :				Dict														=	[rgram for rgrams in rgramslist for rgram in rgrams]
  _snake_case   :				str														=	Counter(lowerCAmelCase				)

  _snake_case   :				Union[str, Any]														=	Counter(lowerCAmelCase				)
  _snake_case   :				Dict														=	Counter()
  for sgram, scount in sgramcounter.items():
    _snake_case   :				Dict														=	scount * numref

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

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

  _snake_case   :				str														=	0
  _snake_case   :				Tuple														=	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   :				int														=	1
  _snake_case   :				Tuple														=	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   :				Optional[Any]														=	0
  if keepscore_precision > 0 or keepscore_recall > 0:
    _snake_case   :				Dict														=	2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

  # DELETION
  _snake_case   :				List[str]														=	sgramcounter_rep - cgramcounter_rep
  _snake_case   :				Tuple														=	delgramcounter_rep - rgramcounter
  _snake_case   :				Any														=	sgramcounter_rep - rgramcounter
  _snake_case   :				int														=	0
  _snake_case   :				List[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.
  _snake_case   :				List[str]														=	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   :				Optional[int]														=	set(lowerCAmelCase				) - set(lowerCAmelCase				)

  _snake_case   :				Dict														=	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   :				Optional[Any]														=	1
  if len(lowerCAmelCase				) > 0:
    _snake_case   :				Any														=	addtmpscore / len(lowerCAmelCase				)
  if len(lowerCAmelCase				) > 0:
    _snake_case   :				Optional[Any]														=	addtmpscore / len(lowerCAmelCase				)
  _snake_case   :				List[Any]														=	0
  if addscore_precision > 0 or addscore_recall > 0:
    _snake_case   :				Dict														=	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

  return (keepscore, delscore_precision, addscore)


def     lowerCamelCase_	(      lowerCAmelCase:	str    ,							lowerCAmelCase:	Any    ,							lowerCAmelCase:	Tuple				)->		int:
  _snake_case   :				Dict														=	len(lowerCAmelCase				)

  _snake_case   :				Optional[Any]														=	ssent.split(' '				)
  _snake_case   :				str														=	csent.split(' '				)
  _snake_case   :				List[Any]														=	[]
  _snake_case   :				Union[str, Any]														=	[]
  _snake_case   :				List[str]														=	[]
  _snake_case   :				Any														=	[]
  _snake_case   :				Union[str, Any]														=	[]
  _snake_case   :				List[str]														=	[]

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

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


def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	bool = True    ,							lowerCAmelCase:	str = "13a"    ,							lowerCAmelCase:	bool = True				)->		Union[str, Any]:
  # 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   :				Optional[int]														=	sacrebleu.metrics.bleu._get_tokenizer(lowerCAmelCase				)()(lowerCAmelCase				)
    else:
      _snake_case   :				Any														=	sacrebleu.TOKENIZERS[tokenizer]()(lowerCAmelCase				)
  elif tokenizer == "moses":
    _snake_case   :				Dict														=	sacremoses.MosesTokenizer().tokenize(lowerCAmelCase    ,							return_str=lowerCAmelCase    ,							escape=lowerCAmelCase				)
  elif tokenizer == "penn":
    _snake_case   :				int														=	sacremoses.MosesTokenizer().penn_tokenize(lowerCAmelCase    ,							return_str=lowerCAmelCase				)
  else:
    _snake_case   :				Optional[int]														=	sentence

  if not return_str:
    _snake_case   :				Tuple														=	normalized_sent.split()

  return normalized_sent


def     lowerCamelCase_	(      lowerCAmelCase:	str    ,							lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	str				)->		int:
  if not (len(lowerCAmelCase				) == len(lowerCAmelCase				) == len(lowerCAmelCase				)):
    raise ValueError('Sources length must match predictions and references lengths.'				)
  _snake_case   :				Dict														=	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   :				Any														=	sari_score / len(lowerCAmelCase				)
  return 1_00 * sari_score


def     lowerCamelCase_	(      lowerCAmelCase:	Union[str, Any]    ,							lowerCAmelCase:	List[str]    ,							lowerCAmelCase:	Optional[Any]="exp"    ,							lowerCAmelCase:	List[Any]=None    ,							lowerCAmelCase:	List[str]=False    ,							lowerCAmelCase:	int=False    ,							lowerCAmelCase:	List[str]=False    ,							)->		Union[str, Any]:
  _snake_case   :				Optional[Any]														=	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   :				Optional[Any]														=	[[refs[i] for refs in references] for i in range(lowerCAmelCase				)]
  _snake_case   :				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        _lowerCAmelCase    (			datasets.Metric							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Dict							):


    '''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 UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					Optional[Any]      ,				UpperCamelCase      :					Union[str, Any]      ,				UpperCamelCase      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				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











from __future__ import annotations

from random import random



class        _lowerCAmelCase    :


  '''simple docstring'''







  def __init__(      self      :					Dict      ,				UpperCamelCase      :					int | None = None							):


    '''simple docstring'''





    _snake_case   :				str														=	value
    _snake_case   :				List[Any]														=	random()
    _snake_case   :				Node | None														=	None
    _snake_case   :				Node | None														=	None







  def __repr__(      self      :					Optional[Any]							):


    '''simple docstring'''





    from pprint import pformat

    if self.left is None and self.right is None:
      return f"""'{self.value}: {self.prior:.5}'"""
    else:
      return pformat(
          {f"""{self.value}: {self.prior:.5}""": (self.left, self.right)}      ,				indent=1							)







  def __str__(      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	str(self.value							) + ' '
    _snake_case   :				List[Any]														=	str(self.left or ''							)
    _snake_case   :				int														=	str(self.right or ''							)
    return value + left + right




def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		tuple[Node | None, Node | None]:
  if root is None:  # None tree is split into 2 Nones
    return None, None
  elif root.value is None:
    return None, None
  else:
    if value < root.value:
      _snake_case							,					_snake_case   :				Optional[Any]														=	split(root.left    ,							lowerCAmelCase				)
      return left, root
    else:
      _snake_case							,					_snake_case   :				List[str]														=	split(root.right    ,							lowerCAmelCase				)
      return root, right


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	Node | None				)->		Node | None:
  if (not left) or (not right):  # If one node is None, return the other
    return left or right
  elif left.prior < right.prior:
    _snake_case   :				str														=	merge(left.right    ,							lowerCAmelCase				)
    return left
  else:
    _snake_case   :				Union[str, Any]														=	merge(lowerCAmelCase    ,							right.left				)
    return right


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		Node | None:
  _snake_case   :				Tuple														=	Node(lowerCAmelCase				)
  _snake_case							,					_snake_case   :				Optional[int]														=	split(lowerCAmelCase    ,							lowerCAmelCase				)
  return merge(merge(lowerCAmelCase    ,							lowerCAmelCase				)    ,							lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		Node | None:
  _snake_case							,					_snake_case   :				Optional[int]														=	split(lowerCAmelCase    ,							value - 1				)
  _snake_case							,					_snake_case   :				List[str]														=	split(lowerCAmelCase    ,							lowerCAmelCase				)
  return merge(lowerCAmelCase    ,							lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None				)->		None:
  if not root:  # None
    return
  else:
    inorder(root.left				)
    print(root.value    ,							end=','				)
    inorder(root.right				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	str				)->		Node | None:
  for arg in args.split():
    if arg[0] == "+":
      _snake_case   :				List[str]														=	insert(lowerCAmelCase    ,							int(arg[1:]				)				)

    elif arg[0] == "-":
      _snake_case   :				Any														=	erase(lowerCAmelCase    ,							int(arg[1:]				)				)

    else:
      print('Unknown command'				)

  return root


def     lowerCamelCase_	(      )->		None:
  _snake_case   :				Tuple														=	None
  print(
      'enter numbers to create a tree, + value to add value into treap, '
      '- value to erase all nodes with value. \'q\' to quit. '				)

  _snake_case   :				List[Any]														=	input()
  while args != "q":
    _snake_case   :				int														=	interact_treap(lowerCAmelCase    ,							lowerCAmelCase				)
    print(lowerCAmelCase				)
    _snake_case   :				Tuple														=	input()

  print('good by!'				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()




