

'''simple docstring'''




from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_a				: str 							=						'\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n'

_a				: List[Any] 							=						'\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n'


_a				: Optional[Any] 							=						'\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]\n    >>> sari = datasets.load_metric("sari")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 26.953601953601954}\n'


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   int       ,   SCREAMING_SNAKE_CASE      :   Any       ,   SCREAMING_SNAKE_CASE      :   List[str]       ,   SCREAMING_SNAKE_CASE      :   Union[str, Any]						):
  UpperCAmelCase													=	[rgram for rgrams in rgramslist for rgram in rgrams]
  UpperCAmelCase													=	Counter(SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	Counter(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	Counter()
  for sgram, scount in sgramcounter.items():
    UpperCAmelCase													=	scount * numref

  UpperCAmelCase													=	Counter(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	Counter()
  for cgram, ccount in cgramcounter.items():
    UpperCAmelCase													=	ccount * numref

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

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

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

  # ADDITION
  UpperCAmelCase													=	set(SCREAMING_SNAKE_CASE						) - set(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	set(SCREAMING_SNAKE_CASE						) & set(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	set(SCREAMING_SNAKE_CASE						) - set(SCREAMING_SNAKE_CASE						)

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

  return (keepscore, delscore_precision, addscore)


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Optional[int]       ,   SCREAMING_SNAKE_CASE      :   List[Any]       ,   SCREAMING_SNAKE_CASE      :   Union[str, Any]						):
  UpperCAmelCase													=	len(SCREAMING_SNAKE_CASE						)

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

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

  for i in range(0       ,   len(SCREAMING_SNAKE_CASE						) - 1						):
    if i < len(SCREAMING_SNAKE_CASE						) - 1:
      UpperCAmelCase													=	sagrams[i] + ' ' + sagrams[i + 1]
      sagrams.append(SCREAMING_SNAKE_CASE						)
    if i < len(SCREAMING_SNAKE_CASE						) - 2:
      UpperCAmelCase													=	sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
      sagrams.append(SCREAMING_SNAKE_CASE						)
    if i < len(SCREAMING_SNAKE_CASE						) - 3:
      UpperCAmelCase													=	sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
      sagrams.append(SCREAMING_SNAKE_CASE						)

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

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


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   List[str]       ,   SCREAMING_SNAKE_CASE      :   bool = True       ,   SCREAMING_SNAKE_CASE      :   str = "13a"       ,   SCREAMING_SNAKE_CASE      :   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:
    UpperCAmelCase													=	sentence.lower()

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

  if not return_str:
    UpperCAmelCase													=	normalized_sent.split()

  return normalized_sent



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

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    return datasets.MetricInfo(
        description=_DESCRIPTION   , citation=_CITATION   , inputs_description=_KWARGS_DESCRIPTION   , features=datasets.Features(
            {
                'sources': datasets.Value('string'   , id='sequence'     ),
                'predictions': datasets.Value('string'   , id='sequence'     ),
                'references': datasets.Sequence(datasets.Value('string'   , id='sequence'     )   , id='references'     ),
            }     )   , codebase_urls=[
            'https://github.com/cocoxu/simplification/blob/master/SARI.py',
            'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
        ]   , reference_urls=['https://www.aclweb.org/anthology/Q16-1029.pdf']   , )





  def 						snake_case_			(     self   , a_   , a_   , a_     )							->				List[Any]:


    """simple docstring"""
    if not (len(a_     ) == len(a_     ) == len(a_     )):
      raise ValueError('Sources length must match predictions and references lengths.'     )
    UpperCAmelCase													=	0
    for src, pred, refs in zip(a_   , a_   , a_     ):
      sari_score += SARIsent(normalize(a_     )   , normalize(a_     )   , [normalize(a_     ) for sent in refs]     )
    UpperCAmelCase													=	sari_score / len(a_     )
    return {"sari": 1_0_0 * sari_score}





'''simple docstring'''




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   list[list[int]]						):
  if len(SCREAMING_SNAKE_CASE						) == 0 or not all(len(SCREAMING_SNAKE_CASE						) == 2 for pair in coordinates						):
    raise ValueError('The program cannot work out a fitting polynomial.'						)

  if len({tuple(SCREAMING_SNAKE_CASE						) for pair in coordinates}						) != len(SCREAMING_SNAKE_CASE						):
    raise ValueError('The program cannot work out a fitting polynomial.'						)

  UpperCAmelCase													=	{x for x, _ in coordinates}
  if len(SCREAMING_SNAKE_CASE						) == 1:
    return f'''x={coordinates[0][0]}'''

  if len(SCREAMING_SNAKE_CASE						) != len(SCREAMING_SNAKE_CASE						):
    raise ValueError('The program cannot work out a fitting polynomial.'						)

  UpperCAmelCase													=	len(SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	0
  UpperCAmelCase													=	[]
  # put the x and x to the power values in a matrix
  while count_of_line < x:
    UpperCAmelCase													=	0
    UpperCAmelCase													=	coordinates[count_of_line][0]
    UpperCAmelCase													=	[]
    while count_in_line < x:
      count_line.append(a ** (x - (count_in_line + 1))						)
      count_in_line += 1
    matrix.append(SCREAMING_SNAKE_CASE						)
    count_of_line += 1

  UpperCAmelCase													=	0
  # put the y values into a vector
  UpperCAmelCase													=	[]
  while count_of_line < x:
    vector.append(coordinates[count_of_line][1]						)
    count_of_line += 1

  UpperCAmelCase													=	0

  while count < x:
    UpperCAmelCase													=	0
    while zahlen < x:
      if count == zahlen:
        zahlen += 1
      if zahlen == x:
        break
      UpperCAmelCase													=	matrix[zahlen][count] / matrix[count][count]
      for counting_columns, item in enumerate(matrix[count]						):
        # manipulating all the values in the matrix
        matrix[zahlen][counting_columns] -= item * bruch
      # manipulating the values in the vector
      vector[zahlen] -= vector[count] * bruch
      zahlen += 1
    count += 1

  UpperCAmelCase													=	0
  # make solutions
  UpperCAmelCase													=	[]
  while count < x:
    solution.append(str(vector[count] / matrix[count][count]						)						)
    count += 1

  UpperCAmelCase													=	0
  UpperCAmelCase													=	'f(x)='

  while count < x:
    UpperCAmelCase													=	solution[count].split('E'						)
    if len(SCREAMING_SNAKE_CASE						) > 1:
      UpperCAmelCase													=	f'''{remove_e[0]}*10^{remove_e[1]}'''
    solved += f'''x^{x - (count + 1)}*{solution[count]}'''
    if count + 1 != x:
      solved += "+"
    count += 1

  return solved


if __name__ == "__main__":
  print(points_to_polynomial([]))
  print(points_to_polynomial([[]]))
  print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
  print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
  print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
  print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
  print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
  print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
  print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
  print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))


