



"""simple docstring"""







from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__snake_case	:				Optional[Any]  					=			'\\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'

__snake_case	:				str  					=			'\\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'


__snake_case	:				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   a_	(			__a		,							__a		,							__a		,							__a      ):
     A__				=   [rgram for rgrams in rgramslist for rgram in rgrams]
     A__				=   Counter(UpperCamelCase__      )

     A__				=   Counter(UpperCamelCase__      )
     A__				=   Counter()
     for sgram, scount in sgramcounter.items():
          A__				=   scount * numref

     A__				=   Counter(UpperCamelCase__      )
     A__				=   Counter()
     for cgram, ccount in cgramcounter.items():
          A__				=   ccount * numref

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

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

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

     # ADDITION
     A__				=   set(UpperCamelCase__      ) - set(UpperCamelCase__      )
     A__				=   set(UpperCamelCase__      ) & set(UpperCamelCase__      )
     A__				=   set(UpperCamelCase__      ) - set(UpperCamelCase__      )

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

     return (keepscore, delscore_precision, addscore)


def   a_	(			__a		,							__a		,							__a      ):
     A__				=   len(UpperCamelCase__      )

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

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

     for i in range(0		,							len(UpperCamelCase__      ) - 1      ):
          if i < len(UpperCamelCase__      ) - 1:
               A__				=   sagrams[i] + " " + sagrams[i + 1]
               sagrams.append(UpperCamelCase__      )
          if i < len(UpperCamelCase__      ) - 2:
               A__				=   sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
               sagrams.append(UpperCamelCase__      )
          if i < len(UpperCamelCase__      ) - 3:
               A__				=   sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
               sagrams.append(UpperCamelCase__      )

     for i in range(0		,							len(UpperCamelCase__      ) - 1      ):
          if i < len(UpperCamelCase__      ) - 1:
               A__				=   cagrams[i] + " " + cagrams[i + 1]
               cagrams.append(UpperCamelCase__      )
          if i < len(UpperCamelCase__      ) - 2:
               A__				=   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
               cagrams.append(UpperCamelCase__      )
          if i < len(UpperCamelCase__      ) - 3:
               A__				=   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
               cagrams.append(UpperCamelCase__      )

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


def   a_	(			__a		,							__a = True		,							__a = "13a"		,							__a = 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:
          A__				=   sentence.lower()

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

     if not return_str:
          A__				=   normalized_sent.split()

     return normalized_sent

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





     """simple docstring"""






     def 						A__  (					self :							Optional[Any] ):
          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 						A__  (					self :							Tuple	,      _lowerCamelCase :							int	,      _lowerCamelCase :							str	,      _lowerCamelCase :							Any ):
          if not (len(__lowerCamelCase ) == len(__lowerCamelCase ) == len(__lowerCamelCase )):
               raise ValueError('''Sources length must match predictions and references lengths.''' )
          A__				=   0
          for src, pred, refs in zip(__lowerCamelCase	,      __lowerCamelCase	,      __lowerCamelCase ):
               sari_score += SARIsent(normalize(__lowerCamelCase )	,      normalize(__lowerCamelCase )	,      [normalize(__lowerCamelCase ) for sent in refs] )
          A__				=   sari_score / len(__lowerCamelCase )
          return {"sari": 1_0_0 * sari_score}








"""simple docstring"""







from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

__snake_case	:				Union[str, Any]  					=			{
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

__snake_case	:				List[Any]  					=			datasets.load_dataset('xnli', split='test+validation')

__snake_case	:				int  					=			0
__snake_case	:				int  					=			0
__snake_case	:				int  					=			0
__snake_case	:				Optional[int]  					=			0



def   a_	(			__a		,							__a		,							__a		,							__a      ):
     if spm_diff == list(reversed(__a      )      ):
          # AAA -> AA+A vs A+AA case.
          return True
     elif len(__a      ) == len(__a      ) and fast.decode(__a      ) == fast.decode(__a      ):
          # Second order OK
          # Barrich -> Barr + ich vs Bar + rich
          return True
     A__				=   slow.encode(slow.decode(__a      )      )
     A__				=   fast.encode(fast.decode(__a      )      )
     if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
          # Type 3 error.
          # Snehagatha ->
          #       Sne, h, aga, th, a
          #       Sne, ha, gat, ha
          # Encoding the wrong with sp does not even recover what spm gave us
          # It fits tokenizer however...
          return True
     return False



def   a_	(			__a		,							__a		,							__a      ):
     A__				=   fast.encode_plus(__a      )[0]
     A__				=   enc.offsets
     A__	,     A__				=   offsets[idx], offsets[idx - 1]
     if curr is not None and line[curr[0] : curr[1]] == "\u200f":
          return True
     if prev is not None and line[prev[0] : prev[1]] == "\u200f":
          return True



def   a_	(			__a		,							__a		,							__a		,							__a		,							__a      ):
     # Encoding can be the same with same result AAA -> A + AA vs AA + A
     # We can check that we use at least exactly the same number of tokens.
     for i, (spm_id, tok_id) in enumerate(zip(__a		,							__a      )      ):
          if spm_id != tok_id:
               break
     A__				=   i
     for i, (spm_id, tok_id) in enumerate(zip(reversed(__a      )		,							reversed(__a      )      )      ):
          if spm_id != tok_id:
               break
     A__				=   len(__a      ) - i

     A__				=   spm_ids[first:last]
     A__				=   tok_ids[first:last]

     if check_diff(__a		,							__a		,							__a		,							__a      ):
          return True

     if check_LTR_mark(__a		,							__a		,							__a      ):
          return True

     if last - first > 5:
          # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
          A__				=   Counter(spm_ids[first:last]      )
          A__				=   Counter(tok_ids[first:last]      )

          A__				=   {spm_ for (spm_, si) in spms.items() if toks.get(spm_		,							0      ) == si}
          A__				=   3
          for i in range(last - first - min_width      ):
               if all(spm_ids[first + i + j] in removable_tokens for j in range(__a      )      ):
                    A__				=   [
                        k
                        for k in range(last - first - min_width      )
                        if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
                    ]
                    for j in possible_matches:
                         if check_diff(spm_ids[first : first + i]		,							tok_ids[first : first + j]		,							__a		,							__a      ) and check_details(
                             __a		,							spm_ids[first + i : last]		,							tok_ids[first + j : last]		,							__a		,							__a		,							):
                              return True

     print(f'''Spm: {[fast.decode([spm_ids[i]]      ) for i in range(__a		,							__a      )]}'''      )
     try:
          print(f'''Tok: {[fast.decode([tok_ids[i]]      ) for i in range(__a		,							__a      )]}'''      )
     except Exception:
          pass

     A__				=   fast.decode(spm_ids[:first]      )
     A__				=   fast.decode(spm_ids[last:]      )
     A__				=   fast.decode(spm_ids[first:last]      )
     print()
     print(__a      )
     return False



def   a_	(			__a		,							__a		,							__a      ):
     global perfect
     global imperfect
     global wrong
     global total

     A__				=   slow.encode(__a      )
     A__				=   fast.encode(__a      )

     A__				=   False
     total += 1

     if slow_ids != fast_ids:
          if check_details(__a		,							__a		,							__a		,							__a		,							__a      ):
               A__				=   True
               imperfect += 1
          else:
               wrong += 1
     else:
          perfect += 1

     if total % 1_0000 == 0:
          print(f'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''      )

     if skip_assert:
          return

     assert (
         slow_ids == fast_ids
     ), f'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__a      )}\n{fast.tokenize(__a      )}'''



def   a_	(			__a		,							__a      ):
     global batch_total
     for i in range(len(__a      )      ):
          # premise, all languages
          for text in dataset[i]["premise"].values():
               test_string(__a		,							__a		,							__a      )

          # hypothesis, all languages
          for text in dataset[i]["hypothesis"]["translation"]:
               test_string(__a		,							__a		,							__a      )


if __name__ == "__main__":
 for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
  __snake_case	:				int  					=			list(slow_class.max_model_input_sizes.keys())
  for checkpoint in checkpoint_names:
   __snake_case	:				Union[str, Any]  					=			0
   __snake_case	:				Optional[Any]  					=			0
   __snake_case	:				List[Any]  					=			0
   __snake_case	:				List[str]  					=			0

   print(f'========================== Checking {name}: {checkpoint} ==========================')
   __snake_case	:				Union[str, Any]  					=			slow_class.from_pretrained(checkpoint, force_download=True)
   __snake_case	:				Optional[Any]  					=			fast_class.from_pretrained(checkpoint, force_download=True)
   test_tokenizer(slow, fast)
   print(f'Accuracy {perfect * 100 / total:.2f}')



