"""simple docstring"""


from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__UpperCamelCase       :    List[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"

__UpperCamelCase       :    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"


__UpperCamelCase       :    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   __UpperCAmelCase     (	_snake_case		:       List[Any],				_snake_case		:       List[str],				_snake_case		:       Union[str, Any],				_snake_case		:       Union[str, Any]       ):
   _lowercase							     =       [rgram for rgrams in rgramslist for rgram in rgrams]
   _lowercase							     =       Counter(_snake_case       )

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

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

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

   _lowercase							     =       0
   _lowercase							     =       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.
   _lowercase							     =       1
   _lowercase							     =       1
   if len(_snake_case       ) > 0:
      _lowercase							     =       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)
      _lowercase							     =       keeptmpscorea / sum(keepgramcounterall_rep.values()       )
   _lowercase							     =       0
   if keepscore_precision > 0 or keepscore_recall > 0:
      _lowercase							     =       2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

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

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

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

   return (keepscore, delscore_precision, addscore)


def   __UpperCAmelCase     (	_snake_case		:       List[Any],				_snake_case		:       Optional[int],				_snake_case		:       int       ):
   _lowercase							     =       len(_snake_case       )

   _lowercase							     =       ssent.split(" "       )
   _lowercase							     =       csent.split(" "       )
   _lowercase							     =       []
   _lowercase							     =       []
   _lowercase							     =       []
   _lowercase							     =       []
   _lowercase							     =       []
   _lowercase							     =       []

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

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


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       bool = True,				_snake_case		:       str = "13a",				_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:
      _lowercase							     =       sentence.lower()

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

   if not return_str:
      _lowercase							     =       normalized_sent.split()

   return normalized_sent
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,    _KWARGS_DESCRIPTION   )
class   UpperCAmelCase_	(				datasets.Metric   ):
    def 		_lowerCamelCase      (	self			:       int		)						->    Optional[int]:
       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 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       int   ,						_lowercase			:       Tuple   ,						_lowercase			:       Any		)						->    Optional[Any]:
       if not (len(_lowercase		) == len(_lowercase		) == len(_lowercase		)):
          raise ValueError("Sources length must match predictions and references lengths."		)
       _lowercase							     =       0
       for src, pred, refs in zip(_lowercase   ,						_lowercase   ,						_lowercase		):
          sari_score += SARIsent(normalize(_lowercase		)   ,						normalize(_lowercase		)   ,						[normalize(_lowercase		) for sent in refs]		)
       _lowercase							     =       sari_score / len(_lowercase		)
       return {"sari": 1_0_0 * sari_score}
"""simple docstring"""


from .number_theory.prime_numbers import next_prime
class   UpperCAmelCase_	:
    def __init__(	self			:       str   ,						_lowercase			:       int   ,						_lowercase			:       int | None = None   ,						_lowercase			:       float | None = None   ,						)						->    None:
       _lowercase							     =       size_table
       _lowercase							     =       [None] * self.size_table
       _lowercase							     =       0.75 if lim_charge is None else lim_charge
       _lowercase							     =       1 if charge_factor is None else charge_factor
       _lowercase							     =       []
       _lowercase							     =       {}
    def 		_lowerCamelCase      (	self			:       Any		)						->    Optional[Any]:
       return self._keys
    def 		_lowerCamelCase      (	self			:       Dict		)						->    Dict:
       return sum(1 for slot in self.values if slot is not None		) / (
           self.size_table * self.charge_factor
       )
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       List[str]		)						->    str:
       return key % self.size_table
    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       Union[str, Any]		)						->    Optional[Any]:
       print(f"""step {step_ord}"""		)
       print(list(range(len(self.values		)		)		)		)
       print(self.values		)
    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       List[Any]		)						->    List[Any]:
       _lowercase							     =       1
       _lowercase							     =       values
       for value in values:
          self.insert_data(_lowercase		)
          self._step_by_step(_lowercase		)
          i += 1
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       Union[str, Any]		)						->    List[Any]:
       _lowercase							     =       data
       _lowercase							     =       data
    def 		_lowerCamelCase      (	self			:       int   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Tuple=None		)						->    Any:
       _lowercase							     =       self.hash_function(key + 1		)

       while self.values[new_key] is not None and self.values[new_key] != key:
          if self.values.count(_lowercase		) > 0:
             _lowercase							     =       self.hash_function(new_key + 1		)
          else:
             _lowercase							     =       None
             break

       return new_key
    def 		_lowerCamelCase      (	self			:       str		)						->    Any:
       _lowercase							     =       [value for value in self.values if value is not None]
       _lowercase							     =       next_prime(self.size_table   ,						factor=2		)
       self._keys.clear()
       _lowercase							     =       [None] * self.size_table  # hell's pointers D: don't DRY ;/
       for value in survivor_values:
          self.insert_data(_lowercase		)




    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Tuple		)						->    Optional[int]:
       _lowercase							     =       self.hash_function(_lowercase		)

       if self.values[key] is None:
          self._set_value(_lowercase   ,						_lowercase		)

       elif self.values[key] == data:
          pass

       else:
          _lowercase							     =       self._collision_resolution(_lowercase   ,						_lowercase		)
          if collision_resolution is not None:
             self._set_value(_lowercase   ,						_lowercase		)
          else:
             self.rehashing()
             self.insert_data(_lowercase		)