



import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


a_								=       """\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"""

a_								=       """\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"""


a_								=       """\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\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\n    sacrebleu: sacrebleu score\n    exact: exact score\n\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 .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"""





def 		a__      (		_UpperCamelCase      :			List[Any]				):


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

   def white_space_fix(_UpperCamelCase      :			Dict				):
      return " ".join(text.split()				)

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

   def lower(_UpperCamelCase      :			Optional[Any]				):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_snake_case				)				)				)				)





def 		a__      (		_UpperCamelCase      :			Optional[Any]			,_UpperCamelCase      :			List[Any]				):
   return int(normalize_answer(_snake_case				) == normalize_answer(_snake_case				)				)





def 		a__      (		_UpperCamelCase      :			Optional[Any]			,_UpperCamelCase      :			Optional[int]				):
   __lowerCamelCase    				=     [any(compute_exact(_snake_case			,_snake_case				) for ref in refs				) for pred, refs in zip(_snake_case			,_snake_case				)]
   return (sum(_snake_case				) / len(_snake_case				)) * 1_00





def 		a__      (		_UpperCamelCase      :			Union[str, Any]			,_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Union[str, Any]			,_UpperCamelCase      :			str				):
   __lowerCamelCase    				=     [rgram for rgrams in rgramslist for rgram in rgrams]
   __lowerCamelCase    				=     Counter(_snake_case				)

   __lowerCamelCase    				=     Counter(_snake_case				)
   __lowerCamelCase    				=     Counter()
   for sgram, scount in sgramcounter.items():
      __lowerCamelCase    				=     scount * numref

   __lowerCamelCase    				=     Counter(_snake_case				)
   __lowerCamelCase    				=     Counter()
   for cgram, ccount in cgramcounter.items():
      __lowerCamelCase    				=     ccount * numref

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

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

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

   # ADDITION
   __lowerCamelCase    				=     set(_snake_case				) - set(_snake_case				)
   __lowerCamelCase    				=     set(_snake_case				) & set(_snake_case				)
   __lowerCamelCase    				=     set(_snake_case				) - set(_snake_case				)

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

   return (keepscore, delscore_precision, addscore)





def 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			Optional[Any]			,_UpperCamelCase      :			Dict				):
   __lowerCamelCase    				=     len(_snake_case				)

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

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

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

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

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





def 		a__      (		_UpperCamelCase      :			Union[str, Any]			,_UpperCamelCase      :			bool = True			,_UpperCamelCase      :			str = "13a"			,_UpperCamelCase      :			bool = True				):


   if lowercase:
      __lowerCamelCase    				=     sentence.lower()

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

   if not return_str:
      __lowerCamelCase    				=     normalized_sent.split()

   return normalized_sent





def 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			Optional[Any]			,_UpperCamelCase      :			List[Any]				):


   if not (len(_snake_case				) == len(_snake_case				) == len(_snake_case				)):
      raise ValueError('''Sources length must match predictions and references lengths.'''				)
   __lowerCamelCase    				=     0
   for src, pred, refs in zip(_snake_case			,_snake_case			,_snake_case				):
      sari_score += SARIsent(normalize(_snake_case				)			,normalize(_snake_case				)			,[normalize(_snake_case				) for sent in refs]				)
   __lowerCamelCase    				=     sari_score / len(_snake_case				)
   return 1_00 * sari_score





def 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			Union[str, Any]			,_UpperCamelCase      :			Optional[Any]="exp"			,_UpperCamelCase      :			int=None			,_UpperCamelCase      :			Any=False			,_UpperCamelCase      :			List[str]=False			,_UpperCamelCase      :			int=False			,):
   __lowerCamelCase    				=     len(references[0]				)
   if any(len(_snake_case				) != references_per_prediction for refs in references				):
      raise ValueError('''Sacrebleu requires the same number of references for each prediction'''				)
   __lowerCamelCase    				=     [[refs[i] for refs in references] for i in range(_snake_case				)]
   __lowerCamelCase    				=     sacrebleu.corpus_bleu(
       _snake_case			,_snake_case			,smooth_method=_snake_case			,smooth_value=_snake_case			,force=_snake_case			,lowercase=_snake_case			,use_effective_order=_snake_case			,)
   return output.score





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




 def 							lowerCamelCase   (    self					):



    '''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 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     {}
    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'''





def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    Union[str, Any]  ,_snake_case	:    Dict  ,_snake_case	:    List[str]  ,_snake_case	:    List[str]  ,_snake_case	:    List[str]							):





    '''simple docstring'''


    if index == r:
        for j in range(_snake_case							):
            print(data[j]  ,end=" "							)
        print(" "							)
        return
    #  When no more elements are there to put in data[]
    if i >= n:
        return
    # current is included, put next at next location
    lowercase__									=    arr[i]
    combination_util(_snake_case  ,_snake_case  ,_snake_case  ,index + 1  ,_snake_case  ,i + 1							)
    # current is excluded, replace it with
    # next (Note that i+1 is passed, but
    # index is not changed)
    combination_util(_snake_case  ,_snake_case  ,_snake_case  ,_snake_case  ,_snake_case  ,i + 1							)
    # The main function that prints all combinations
    # of size r in arr[] of size n. This function
    # mainly uses combinationUtil()




def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    Tuple  ,_snake_case	:    Union[str, Any]							):





    '''simple docstring'''


    lowercase__									=    [0] * r
    # Print all combination using temporary array 'data[]'
    combination_util(_snake_case  ,_snake_case  ,_snake_case  ,0  ,_snake_case  ,0							)


if __name__ == "__main__":
 # Driver code to check the function above
 SCREAMING_SNAKE_CASE__          =  [10, 20, 30, 40, 50]
 print_combination(arr, len(arr), 3)
 # This code is contributed by Ambuj sahu




