


from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_A		:					Dict          =							'''\\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		:					Optional[int]          =							'''\\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		:					str          =							'''\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    (				snake_case						:     Any    ,   snake_case						:     str    ,   snake_case						:     Optional[Any]    ,   snake_case						:     Any			)							->				List[str]:
   __lowerCamelCase:					List[Any]	 =						[rgram for rgrams in rgramslist for rgram in rgrams]
   __lowerCamelCase:					Tuple	 =						Counter(a_			)

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

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

   # KEEP
   __lowerCamelCase:					Optional[Any]	 =						sgramcounter_rep & cgramcounter_rep
   __lowerCamelCase:					str	 =						keepgramcounter_rep & rgramcounter
   __lowerCamelCase:					Optional[int]	 =						sgramcounter_rep & rgramcounter

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

   # DELETION
   __lowerCamelCase:					Union[str, Any]	 =						sgramcounter_rep - cgramcounter_rep
   __lowerCamelCase:					Optional[int]	 =						delgramcounter_rep - rgramcounter
   __lowerCamelCase:					List[str]	 =						sgramcounter_rep - rgramcounter
   __lowerCamelCase:					str	 =						0
   __lowerCamelCase:					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.
   __lowerCamelCase:					List[str]	 =						1
   if len(a_			) > 0:
      __lowerCamelCase:					int	 =						deltmpscorea / len(a_			)

   # ADDITION
   __lowerCamelCase:					Optional[Any]	 =						set(a_			) - set(a_			)
   __lowerCamelCase:					Any	 =						set(a_			) & set(a_			)
   __lowerCamelCase:					Any	 =						set(a_			) - set(a_			)

   __lowerCamelCase:					List[Any]	 =						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:					Optional[Any]	 =						1
   __lowerCamelCase:					List[Any]	 =						1
   if len(a_			) > 0:
      __lowerCamelCase:					Any	 =						addtmpscore / len(a_			)
   if len(a_			) > 0:
      __lowerCamelCase:					Dict	 =						addtmpscore / len(a_			)
   __lowerCamelCase:					Optional[Any]	 =						0
   if addscore_precision > 0 or addscore_recall > 0:
      __lowerCamelCase:					Union[str, Any]	 =						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

   return (keepscore, delscore_precision, addscore)



def      __lowerCAmelCase    (				snake_case						:     Optional[Any]    ,   snake_case						:     List[str]    ,   snake_case						:     Union[str, Any]			)							->				Optional[int]:
   __lowerCamelCase:					List[Any]	 =						len(a_			)

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

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

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



def      __lowerCAmelCase    (				snake_case						:     Optional[int]    ,   snake_case						:     bool = True    ,   snake_case						:     str = "13a"    ,   snake_case						:     bool = True			)							->				str:







   if lowercase:
      __lowerCamelCase:					Dict	 =						sentence.lower()

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

   if not return_str:
      __lowerCamelCase:					List[str]	 =						normalized_sent.split()

   return normalized_sent







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





     def 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        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 			SCREAMING_SNAKE_CASE__							(     self       :					Tuple     ,						SCREAMING_SNAKE_CASE_       :					Dict     ,						SCREAMING_SNAKE_CASE_       :					List[Any]     ,						SCREAMING_SNAKE_CASE_       :					str				):





        if not (len(__UpperCamelCase				) == len(__UpperCamelCase				) == len(__UpperCamelCase				)):
           raise ValueError("""Sources length must match predictions and references lengths."""				)
        __lowerCamelCase:					Optional[int]	 =						0
        for src, pred, refs in zip(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase				):
           sari_score += SARIsent(normalize(__UpperCamelCase				)     ,						normalize(__UpperCamelCase				)     ,						[normalize(__UpperCamelCase				) for sent in refs]				)
        __lowerCamelCase:					Tuple	 =						sari_score / len(__UpperCamelCase				)
        return {"sari": 100 * sari_score}





from hashlib import mda, shaaaa

A__							=				(shaaaa, mda)

class 					__UpperCamelCase					:







	def __init__( self:			Optional[int]	,			__UpperCamelCase:			int = 8   ):

						'''simple docstring'''





						__magic_name__				=     0b0
						__magic_name__				=     size







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.hash_(__UpperCamelCase   )
						self.bitarray |= h







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.hash_(__UpperCamelCase   )
						return (h & self.bitarray) == h







	def __contains__( self:			Optional[int]	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						return self.exists(__UpperCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						__magic_name__				=     bin(__UpperCamelCase   )[2:]
						return res.zfill(self.size   )







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						return self.format_bin(self.bitarray   )







	def 						_SCREAMING_SNAKE_CASE				( self:			str	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     0b0
						for func in HASH_FUNCTIONS:
											__magic_name__				=     (
											    int.from_bytes(func(value.encode()   ).digest()	,			'little'   ) % self.size
											)
											res |= 2**position
						return res







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						return self.format_bin(self.hash_(__UpperCamelCase   )   )







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     bin(self.bitarray   ).count('1'   )
						return (n_ones / self.size) ** len(__UpperCamelCase   )




