



from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
    import tensorflow as tf

    from ..tf_utils import stable_softmax


if is_torch_available():
    import torch


UpperCAmelCase_									=							logging.get_logger(__name__)





@add_end_docstrings(
    snake_case_       ,R'''
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    '''       ,)
class 		UpperCAmelCase						(						snake_case_				):





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         if self.framework == "tf":
               _lowerCAmelCase 			=  tf.where(input_ids == self.tokenizer.mask_token_id			).numpy()
         elif self.framework == "pt":
               _lowerCAmelCase 			=  torch.nonzero(input_ids == self.tokenizer.mask_token_id       ,     as_tuple=_lowerCAmelCase			)
         else:
               raise ValueError('''Unsupported framework'''			)
         return masked_index





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self.get_masked_index(_lowerCAmelCase			)
         _lowerCAmelCase 			=  np.prod(masked_index.shape			)
         if numel < 1:
               raise PipelineException(
                   '''fill-mask'''       ,     self.model.base_model_prefix       ,     F'''No mask_token ({self.tokenizer.mask_token}) found on the input'''       ,     )





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
               for model_input in model_inputs:
                     self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0]			)
         else:
               for input_ids in model_inputs["input_ids"]:
                     self._ensure_exactly_one_mask_token(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=None       ,     **_lowerCAmelCase			):
         if return_tensors is None:
               _lowerCAmelCase 			=  self.framework
         _lowerCAmelCase 			=  self.tokenizer(_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase			)
         self.ensure_exactly_one_mask_token(_lowerCAmelCase			)
         return model_inputs





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self.model(**_lowerCAmelCase			)
         _lowerCAmelCase 			=  model_inputs['''input_ids''']
         return model_outputs





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=5       ,     _lowerCAmelCase=None			):
         # Cap top_k if there are targets
         if target_ids is not None and target_ids.shape[0] < top_k:
               _lowerCAmelCase 			=  target_ids.shape[0]
         _lowerCAmelCase 			=  model_outputs['''input_ids'''][0]
         _lowerCAmelCase 			=  model_outputs['''logits''']

         if self.framework == "tf":
               _lowerCAmelCase 			=  tf.where(input_ids == self.tokenizer.mask_token_id			).numpy()[:, 0]

               _lowerCAmelCase 			=  outputs.numpy()

               _lowerCAmelCase 			=  outputs[0, masked_index, :]
               _lowerCAmelCase 			=  stable_softmax(_lowerCAmelCase       ,     axis=-1			)
               if target_ids is not None:
                     _lowerCAmelCase 			=  tf.gather_nd(tf.squeeze(_lowerCAmelCase       ,     0			)       ,     target_ids.reshape(-1       ,     1			)			)
                     _lowerCAmelCase 			=  tf.expand_dims(_lowerCAmelCase       ,     0			)

               _lowerCAmelCase 			=  tf.math.top_k(_lowerCAmelCase       ,     k=_lowerCAmelCase			)
               _lowerCAmelCase			,   _lowerCAmelCase 			=  topk.values.numpy(), topk.indices.numpy()
         else:
               _lowerCAmelCase 			=  torch.nonzero(input_ids == self.tokenizer.mask_token_id       ,     as_tuple=_lowerCAmelCase			).squeeze(-1			)
               # Fill mask pipeline supports only one ${mask_token} per sample

               _lowerCAmelCase 			=  outputs[0, masked_index, :]
               _lowerCAmelCase 			=  logits.softmax(dim=-1			)
               if target_ids is not None:
                     _lowerCAmelCase 			=  probs[..., target_ids]

               _lowerCAmelCase			,   _lowerCAmelCase 			=  probs.topk(_lowerCAmelCase			)

         _lowerCAmelCase 			=  []
         _lowerCAmelCase 			=  values.shape[0] == 1
         for i, (_values, _predictions) in enumerate(zip(values.tolist()       ,     predictions.tolist()			)			):
               _lowerCAmelCase 			=  []
               for v, p in zip(_values       ,     _predictions			):
                     # Copy is important since we're going to modify this array in place
                     _lowerCAmelCase 			=  input_ids.numpy().copy()
                     if target_ids is not None:
                           _lowerCAmelCase 			=  target_ids[p].tolist()

                     _lowerCAmelCase 			=  p
                     # Filter padding out:
                     _lowerCAmelCase 			=  tokens[np.where(tokens != self.tokenizer.pad_token_id			)]
                     # Originally we skip special tokens to give readable output.
                     # For multi masks though, the other [MASK] would be removed otherwise
                     # making the output look odd, so we add them back
                     _lowerCAmelCase 			=  self.tokenizer.decode(_lowerCAmelCase       ,     skip_special_tokens=_lowerCAmelCase			)
                     _lowerCAmelCase 			=  {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p]			), '''sequence''': sequence}
                     row.append(_lowerCAmelCase			)
               result.append(_lowerCAmelCase			)
         if single_mask:
               return result[0]
         return result





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=None			):
         if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  [targets]
         try:
               _lowerCAmelCase 			=  self.tokenizer.get_vocab()
         except Exception:
               _lowerCAmelCase 			=  {}
         _lowerCAmelCase 			=  []
         for target in targets:
               _lowerCAmelCase 			=  vocab.get(_lowerCAmelCase       ,     _lowerCAmelCase			)
               if id_ is None:
                     _lowerCAmelCase 			=  self.tokenizer(
                         _lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase       ,     return_attention_mask=_lowerCAmelCase       ,     return_token_type_ids=_lowerCAmelCase       ,     max_length=1       ,     truncation=_lowerCAmelCase       ,     )['''input_ids''']
                     if len(_lowerCAmelCase			) == 0:
                           logger.warning(
                               F'''The specified target token `{target}` does not exist in the model vocabulary. '''
                               '''We cannot replace it with anything meaningful, ignoring it'''			)
                           continue
                     _lowerCAmelCase 			=  input_ids[0]
                     # XXX: If users encounter this pass
                     # it becomes pretty slow, so let's make sure
                     # The warning enables them to fix the input to
                     # get faster performance.
                     logger.warning(
                         F'''The specified target token `{target}` does not exist in the model vocabulary. '''
                         F'''Replacing with `{self.tokenizer.convert_ids_to_tokens(id_			)}`.'''			)
               target_ids.append(id_			)
         _lowerCAmelCase 			=  list(set(_lowerCAmelCase			)			)
         if len(_lowerCAmelCase			) == 0:
               raise ValueError('''At least one target must be provided when passed.'''			)
         _lowerCAmelCase 			=  np.array(_lowerCAmelCase			)
         return target_ids





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None			):
         _lowerCAmelCase 			=  {}

         if targets is not None:
               _lowerCAmelCase 			=  self.get_target_ids(_lowerCAmelCase       ,     _lowerCAmelCase			)
               _lowerCAmelCase 			=  target_ids

         if top_k is not None:
               _lowerCAmelCase 			=  top_k

         if self.tokenizer.mask_token_id is None:
               raise PipelineException(
                   '''fill-mask'''       ,     self.model.base_model_prefix       ,     '''The tokenizer does not define a `mask_token`.'''			)
         return {}, {}, postprocess_params





   def __call__(	self       ,     _lowerCAmelCase       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         _lowerCAmelCase 			=  super().__call__(_lowerCAmelCase       ,     **_lowerCAmelCase			)
         if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			) and len(_lowerCAmelCase			) == 1:
               return outputs[0]
         return outputs




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCAmelCase_									=							"\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"

UpperCAmelCase_									=							"\\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"


UpperCAmelCase_									=							"\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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[Any]				)->Optional[Any]:

      def remove_articles(_SCREAMING_SNAKE_CASE   :	List[str]				):
            _lowerCAmelCase 			=  re.compile(r'''\b(a|an|the)\b'''     ,				re.UNICODE				)
            return re.sub(_SCREAMING_SNAKE_CASE     ,				''' '''     ,				_SCREAMING_SNAKE_CASE				)

      def white_space_fix(_SCREAMING_SNAKE_CASE   :	List[Any]				):
            return " ".join(text.split()				)

      def remove_punc(_SCREAMING_SNAKE_CASE   :	Optional[Any]				):
            _lowerCAmelCase 			=  set(string.punctuation				)
            return "".join(ch for ch in text if ch not in exclude				)

      def lower(_SCREAMING_SNAKE_CASE   :	Optional[int]				):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE				)				)				)				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]				)->Any:
      return int(normalize_answer(_SCREAMING_SNAKE_CASE				) == normalize_answer(_SCREAMING_SNAKE_CASE				)				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	str				)->int:
      _lowerCAmelCase 			=  [any(compute_exact(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				) for ref in refs				) for pred, refs in zip(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)]
      return (sum(_SCREAMING_SNAKE_CASE				) / len(_SCREAMING_SNAKE_CASE				)) * 1_0_0
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	List[str]				)->Optional[int]:
      _lowerCAmelCase 			=  [rgram for rgrams in rgramslist for rgram in rgrams]
      _lowerCAmelCase 			=  Counter(_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase 			=  Counter(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  Counter()
      for sgram, scount in sgramcounter.items():
            _lowerCAmelCase 			=  scount * numref

      _lowerCAmelCase 			=  Counter(_SCREAMING_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(_SCREAMING_SNAKE_CASE				) > 0:
            _lowerCAmelCase 			=  keeptmpscorea / len(_SCREAMING_SNAKE_CASE				)
      if len(_SCREAMING_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(_SCREAMING_SNAKE_CASE				) > 0:
            _lowerCAmelCase 			=  deltmpscorea / len(_SCREAMING_SNAKE_CASE				)

      # ADDITION
      _lowerCAmelCase 			=  set(_SCREAMING_SNAKE_CASE				) - set(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  set(_SCREAMING_SNAKE_CASE				) & set(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  set(_SCREAMING_SNAKE_CASE				) - set(_SCREAMING_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(_SCREAMING_SNAKE_CASE				) > 0:
            _lowerCAmelCase 			=  addtmpscore / len(_SCREAMING_SNAKE_CASE				)
      if len(_SCREAMING_SNAKE_CASE				) > 0:
            _lowerCAmelCase 			=  addtmpscore / len(_SCREAMING_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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Any     ,				_SCREAMING_SNAKE_CASE   :	int     ,				_SCREAMING_SNAKE_CASE   :	str				)->List[Any]:
      _lowerCAmelCase 			=  len(_SCREAMING_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(_SCREAMING_SNAKE_CASE				)
            for i in range(0     ,				len(_SCREAMING_SNAKE_CASE				) - 1				):
                  if i < len(_SCREAMING_SNAKE_CASE				) - 1:
                        _lowerCAmelCase 			=  ragrams[i] + ''' ''' + ragrams[i + 1]
                        ragrams.append(_SCREAMING_SNAKE_CASE				)
                  if i < len(_SCREAMING_SNAKE_CASE				) - 2:
                        _lowerCAmelCase 			=  ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
                        ragrams.append(_SCREAMING_SNAKE_CASE				)
                  if i < len(_SCREAMING_SNAKE_CASE				) - 3:
                        _lowerCAmelCase 			=  ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
                        ragrams.append(_SCREAMING_SNAKE_CASE				)
            ragramslist.append(_SCREAMING_SNAKE_CASE				)
            ragramslist.append(_SCREAMING_SNAKE_CASE				)
            ragramslist.append(_SCREAMING_SNAKE_CASE				)

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

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

      ((_lowerCAmelCase)			,   (_lowerCAmelCase)			,   (_lowerCAmelCase)) 			=  SARIngram(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
      ((_lowerCAmelCase)			,   (_lowerCAmelCase)			,   (_lowerCAmelCase)) 			=  SARIngram(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
      ((_lowerCAmelCase)			,   (_lowerCAmelCase)			,   (_lowerCAmelCase)) 			=  SARIngram(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
      ((_lowerCAmelCase)			,   (_lowerCAmelCase)			,   (_lowerCAmelCase)) 			=  SARIngram(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	int     ,				_SCREAMING_SNAKE_CASE   :	bool = True     ,				_SCREAMING_SNAKE_CASE   :	str = "13a"     ,				_SCREAMING_SNAKE_CASE   :	bool = True				)->int:
      # 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(_SCREAMING_SNAKE_CASE				)()(_SCREAMING_SNAKE_CASE				)
            else:
                  _lowerCAmelCase 			=  sacrebleu.TOKENIZERS[tokenizer]()(_SCREAMING_SNAKE_CASE				)
      elif tokenizer == "moses":
            _lowerCAmelCase 			=  sacremoses.MosesTokenizer().tokenize(_SCREAMING_SNAKE_CASE     ,				return_str=_SCREAMING_SNAKE_CASE     ,				escape=_SCREAMING_SNAKE_CASE				)
      elif tokenizer == "penn":
            _lowerCAmelCase 			=  sacremoses.MosesTokenizer().penn_tokenize(_SCREAMING_SNAKE_CASE     ,				return_str=_SCREAMING_SNAKE_CASE				)
      else:
            _lowerCAmelCase 			=  sentence

      if not return_str:
            _lowerCAmelCase 			=  normalized_sent.split()

      return normalized_sent
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	int     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[str]				)->str:
      if not (len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				) == len(_SCREAMING_SNAKE_CASE				)):
            raise ValueError('''Sources length must match predictions and references lengths.'''				)
      _lowerCAmelCase 			=  0
      for src, pred, refs in zip(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				):
            sari_score += SARIsent(normalize(_SCREAMING_SNAKE_CASE				)     ,				normalize(_SCREAMING_SNAKE_CASE				)     ,				[normalize(_SCREAMING_SNAKE_CASE				) for sent in refs]				)
      _lowerCAmelCase 			=  sari_score / len(_SCREAMING_SNAKE_CASE				)
      return 1_0_0 * sari_score
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	Optional[Any]="exp"     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]=None     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]=False     ,				_SCREAMING_SNAKE_CASE   :	str=False     ,				_SCREAMING_SNAKE_CASE   :	int=False     ,				)->str:
      _lowerCAmelCase 			=  len(references[0]				)
      if any(len(_SCREAMING_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(_SCREAMING_SNAKE_CASE				)]
      _lowerCAmelCase 			=  sacrebleu.corpus_bleu(
          _SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				smooth_method=_SCREAMING_SNAKE_CASE     ,				smooth_value=_SCREAMING_SNAKE_CASE     ,				force=_SCREAMING_SNAKE_CASE     ,				lowercase=_SCREAMING_SNAKE_CASE     ,				use_effective_order=_SCREAMING_SNAKE_CASE     ,				)
      return output.score





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





   def 	__lowerCAmelCase							(	self			):
         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       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  {}
         result.update({'''sari''': compute_sari(sources=_lowerCAmelCase       ,     predictions=_lowerCAmelCase       ,     references=_lowerCAmelCase			)}			)
         result.update({'''sacrebleu''': compute_sacrebleu(predictions=_lowerCAmelCase       ,     references=_lowerCAmelCase			)}			)
         result.update({'''exact''': compute_em(predictions=_lowerCAmelCase       ,     references=_lowerCAmelCase			)}			)
         return result