

from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_UpperCamelCase			      =		"\\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			      =		"\\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			      =		"\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 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase = [rgram for rgrams in rgramslist for rgram in rgrams]
      __lowerCAmelCase = Counter(lowercase__						)

      __lowerCAmelCase = Counter(lowercase__						)
      __lowerCAmelCase = Counter()
      for sgram, scount in sgramcounter.items():
            __lowerCAmelCase = scount * numref

      __lowerCAmelCase = Counter(lowercase__						)
      __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(lowercase__						) > 0:
            __lowerCAmelCase = keeptmpscorea / len(lowercase__						)
      if len(lowercase__						) > 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(lowercase__						) > 0:
            __lowerCAmelCase = deltmpscorea / len(lowercase__						)

      # ADDITION
      __lowerCAmelCase = set(lowercase__						) - set(lowercase__						)
      __lowerCAmelCase = set(lowercase__						) & set(lowercase__						)
      __lowerCAmelCase = set(lowercase__						) - set(lowercase__						)

      __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(lowercase__						) > 0:
            __lowerCAmelCase = addtmpscore / len(lowercase__						)
      if len(lowercase__						) > 0:
            __lowerCAmelCase = addtmpscore / len(lowercase__						)
      __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 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase = len(lowercase__						)

      __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(lowercase__						)
            for i in range(0       ,     len(lowercase__						) - 1						):
                  if i < len(lowercase__						) - 1:
                        __lowerCAmelCase = ragrams[i] + ''' ''' + ragrams[i + 1]
                        ragrams.append(lowercase__						)
                  if i < len(lowercase__						) - 2:
                        __lowerCAmelCase = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
                        ragrams.append(lowercase__						)
                  if i < len(lowercase__						) - 3:
                        __lowerCAmelCase = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
                        ragrams.append(lowercase__						)
            ragramslist.append(lowercase__						)
            ragramslist.append(lowercase__						)
            ragramslist.append(lowercase__						)

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

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

      (__lowerCAmelCase) = SARIngram(lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						)
      (__lowerCAmelCase) = SARIngram(lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						)
      (__lowerCAmelCase) = SARIngram(lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						)
      (__lowerCAmelCase) = SARIngram(lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						)
      __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 		_lowercase			(					lowercase__       ,     lowercase__ = True       ,     lowercase__ = "13a"       ,     lowercase__ = 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:
            __lowerCAmelCase = sentence.lower()

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

      if not return_str:
            __lowerCAmelCase = normalized_sent.split()

      return normalized_sent







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,       _KWARGS_DESCRIPTION			)
class   __lowercase				(datasets.Metric			):
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''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      UpperCamelCase__  (							self				,							A_				,							A_				,							A_ ) ->Optional[Any]:



       '''simple docstring'''





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





from dataclasses import dataclass

_UpperCamelCase			      =		"0.1"
_UpperCamelCase			      =		"Lucia Harcekova"







@dataclass
class   __lowercase				:
 _UpperCamelCase        =  42
 _UpperCamelCase        =  42
 _UpperCamelCase        =  42
 def __repr__(							self ) ->str:



       '''simple docstring'''





       return f"""({self.offset}, {self.length}, {self.indicator})"""







class   __lowercase				:
 def __init__(							self				,							A_ = 13				,							A_ = 6 ) ->None:



       '''simple docstring'''





       __lowerCAmelCase :						List[str] = window_size
       __lowerCAmelCase :						str = lookahead_buffer_size
       __lowerCAmelCase :						Dict = self.window_size - self.lookahead_buffer_size
 def      UpperCamelCase__  (							self				,							A_ ) ->list[Token]:



       '''simple docstring'''





       __lowerCAmelCase :						int = []
       __lowerCAmelCase :						Any = ''''''

       # while there are still characters in text to compress
       while text:
             # find the next encoding phrase
             # - triplet with offset, length, indicator (the next encoding character)
             __lowerCAmelCase :						Optional[int] = self._find_encoding_token(A_				,							A_ )

             # update the search buffer:
             # - add new characters from text into it
             # - check if size exceed the max search buffer size, if so, drop the
             #   oldest elements
             search_buffer += text[: token.length + 1]
             if len(A_ ) > self.search_buffer_size:
                   __lowerCAmelCase :						Dict = search_buffer[-self.search_buffer_size :]

             # update the text
             __lowerCAmelCase :						List[Any] = text[token.length + 1 :]

             # append the token to output
             output.append(A_ )

       return output
 def      UpperCamelCase__  (							self				,							A_ ) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[int] = ''''''

       for token in tokens:
             for _ in range(token.length ):
                   output += output[-token.offset]
             output += token.indicator

       return output
 def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->Token:



       '''simple docstring'''





       if not text:
             raise ValueError('''We need some text to work with.''' )

       # Initialise result parameters to default values
       __lowerCAmelCase,							__lowerCAmelCase :						Union[str, Any] = 0, 0

       if not search_buffer:
             return Token(A_				,							A_				,							text[length] )

       for i, character in enumerate(A_ ):
             __lowerCAmelCase :						Dict = len(A_ ) - i
             if character == text[0]:
                   __lowerCAmelCase :						Dict = self._match_length_from_index(A_				,							A_				,							0				,							A_ )
                   # if the found length is bigger than the current or if it's equal,
                   # which means it's offset is smaller: update offset and length
                   if found_length >= length:
                         __lowerCAmelCase,							__lowerCAmelCase :						int = found_offset, found_length

       return Token(A_				,							A_				,							text[length] )





 def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_				,							A_ ) ->int:



       '''simple docstring'''





       if not text or text[text_index] != window[window_index]:
             return 0
       return 1 + self._match_length_from_index(
           A_				,							window + text[text_index]				,							text_index + 1				,							window_index + 1 )


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 # Initialize compressor class
 _UpperCamelCase			      =		LZaaCompressor(window_size=13, lookahead_buffer_size=6)

 # Example
 _UpperCamelCase			      =		"cabracadabrarrarrad"
 _UpperCamelCase			      =		lzaa_compressor.compress(TEXT)
 print(lzaa_compressor.compress("ababcbababaa"))
 _UpperCamelCase			      =		lzaa_compressor.decompress(compressed_text)
 assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."


