
'''simple docstring'''


import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       DebertaForMaskedLM,
       DebertaForQuestionAnswering,
       DebertaForSequenceClassification,
       DebertaForTokenClassification,
       DebertaModel,
   )
   from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=1_3 ,   lowerCamelCase__=7 ,   lowerCamelCase__=True ,   lowerCamelCase__=True ,   lowerCamelCase__=True ,   lowerCamelCase__=True ,   lowerCamelCase__=9_9 ,   lowerCamelCase__=3_2 ,   lowerCamelCase__=5 ,   lowerCamelCase__=4 ,   lowerCamelCase__=3_7 ,   lowerCamelCase__="gelu" ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=5_1_2 ,   lowerCamelCase__=1_6 ,   lowerCamelCase__=2 ,   lowerCamelCase__=0.02 ,   lowerCamelCase__=False ,   lowerCamelCase__=True ,   lowerCamelCase__="None" ,   lowerCamelCase__=3 ,   lowerCamelCase__=4 ,   lowerCamelCase__=None ,   ):

      '''simple docstring'''







      UpperCamelCase       =  parent
      UpperCamelCase       =  batch_size
      UpperCamelCase       =  seq_length
      UpperCamelCase       =  is_training
      UpperCamelCase       =  use_input_mask
      UpperCamelCase       =  use_token_type_ids
      UpperCamelCase       =  use_labels
      UpperCamelCase       =  vocab_size
      UpperCamelCase       =  hidden_size
      UpperCamelCase       =  num_hidden_layers
      UpperCamelCase       =  num_attention_heads
      UpperCamelCase       =  intermediate_size
      UpperCamelCase       =  hidden_act
      UpperCamelCase       =  hidden_dropout_prob
      UpperCamelCase       =  attention_probs_dropout_prob
      UpperCamelCase       =  max_position_embeddings
      UpperCamelCase       =  type_vocab_size
      UpperCamelCase       =  type_sequence_label_size
      UpperCamelCase       =  initializer_range
      UpperCamelCase       =  num_labels
      UpperCamelCase       =  num_choices
      UpperCamelCase       =  relative_attention
      UpperCamelCase       =  position_biased_input
      UpperCamelCase       =  pos_att_type
      UpperCamelCase       =  scope







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size   )

      UpperCamelCase       =  None
      if self.use_input_mask:
         UpperCamelCase       =  ids_tensor([self.batch_size, self.seq_length] ,   vocab_size=2   )

      UpperCamelCase       =  None
      if self.use_token_type_ids:
         UpperCamelCase       =  ids_tensor([self.batch_size, self.seq_length] ,   self.type_vocab_size   )

      UpperCamelCase       =  None
      UpperCamelCase       =  None
      UpperCamelCase       =  None
      if self.use_labels:
         UpperCamelCase       =  ids_tensor([self.batch_size] ,   self.type_sequence_label_size   )
         UpperCamelCase       =  ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels   )
         UpperCamelCase       =  ids_tensor([self.batch_size] ,   self.num_choices   )

      UpperCamelCase       =  self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return DebertaConfig(
          vocab_size=self.vocab_size ,   hidden_size=self.hidden_size ,   num_hidden_layers=self.num_hidden_layers ,   num_attention_heads=self.num_attention_heads ,   intermediate_size=self.intermediate_size ,   hidden_act=self.hidden_act ,   hidden_dropout_prob=self.hidden_dropout_prob ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob ,   max_position_embeddings=self.max_position_embeddings ,   type_vocab_size=self.type_vocab_size ,   initializer_range=self.initializer_range ,   relative_attention=self.relative_attention ,   position_biased_input=self.position_biased_input ,   pos_att_type=self.pos_att_type ,   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.get_config()
      UpperCamelCase       =  3_0_0
      return config







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      self.parent.assertListEqual(list(result.loss.size()   ) ,   []   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  DebertaModel(config=lowerCamelCase__   )
      model.to(lowerCamelCase__   )
      model.eval()
      UpperCamelCase       =  model(lowerCamelCase__ ,   attention_mask=lowerCamelCase__ ,   token_type_ids=lowerCamelCase__   )[0]
      UpperCamelCase       =  model(lowerCamelCase__ ,   token_type_ids=lowerCamelCase__   )[0]
      UpperCamelCase       =  model(lowerCamelCase__   )[0]

      self.parent.assertListEqual(list(sequence_output.size()   ) ,   [self.batch_size, self.seq_length, self.hidden_size]   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  DebertaForMaskedLM(config=lowerCamelCase__   )
      model.to(lowerCamelCase__   )
      model.eval()
      UpperCamelCase       =  model(lowerCamelCase__ ,   attention_mask=lowerCamelCase__ ,   token_type_ids=lowerCamelCase__ ,   labels=lowerCamelCase__   )

      self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size)   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.num_labels
      UpperCamelCase       =  DebertaForSequenceClassification(lowerCamelCase__   )
      model.to(lowerCamelCase__   )
      model.eval()
      UpperCamelCase       =  model(lowerCamelCase__ ,   attention_mask=lowerCamelCase__ ,   token_type_ids=lowerCamelCase__ ,   labels=lowerCamelCase__   )
      self.parent.assertListEqual(list(result.logits.size()   ) ,   [self.batch_size, self.num_labels]   )
      self.check_loss_output(lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.num_labels
      UpperCamelCase       =  DebertaForTokenClassification(config=lowerCamelCase__   )
      model.to(lowerCamelCase__   )
      model.eval()
      UpperCamelCase       =  model(lowerCamelCase__ ,   attention_mask=lowerCamelCase__ ,   token_type_ids=lowerCamelCase__ ,   labels=lowerCamelCase__   )
      self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels)   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  DebertaForQuestionAnswering(config=lowerCamelCase__   )
      model.to(lowerCamelCase__   )
      model.eval()
      UpperCamelCase       =  model(
          lowerCamelCase__ ,   attention_mask=lowerCamelCase__ ,   token_type_ids=lowerCamelCase__ ,   start_positions=lowerCamelCase__ ,   end_positions=lowerCamelCase__ ,   )
      self.parent.assertEqual(result.start_logits.shape ,   (self.batch_size, self.seq_length)   )
      self.parent.assertEqual(result.end_logits.shape ,   (self.batch_size, self.seq_length)   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.prepare_config_and_inputs()
      (
          (
          UpperCamelCase
      )     , (
          UpperCamelCase
      )     , (
          UpperCamelCase
      )     , (
          UpperCamelCase
      )     , (
          UpperCamelCase
      )     , (
          UpperCamelCase
      )     , (
          UpperCamelCase
      )     , 
      )       =  config_and_inputs
      UpperCamelCase       =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
      return config, inputs_dict




@require_torch
class 						lowercase__	(  snake_case_,					snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							(
       (
           DebertaModel,
           DebertaForMaskedLM,
           DebertaForSequenceClassification,
           DebertaForTokenClassification,
           DebertaForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   _snake_case		   =							(
       {
           '''feature-extraction''': DebertaModel,
           '''fill-mask''': DebertaForMaskedLM,
           '''question-answering''': DebertaForQuestionAnswering,
           '''text-classification''': DebertaForSequenceClassification,
           '''token-classification''': DebertaForTokenClassification,
           '''zero-shot''': DebertaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )

   _snake_case		   =							True
   _snake_case		   =							False
   _snake_case		   =							False
   _snake_case		   =							False
   _snake_case		   =							False







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  DebertaModelTester(self   )
      UpperCamelCase       =  ConfigTester(self ,   config_class=lowerCamelCase__ ,   hidden_size=3_7   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      self.config_tester.run_common_tests()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_model(*lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase__   )







   @slow
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         UpperCamelCase       =  DebertaModel.from_pretrained(lowerCamelCase__   )
         self.assertIsNotNone(lowerCamelCase__   )




@require_torch
@require_sentencepiece
@require_tokenizers
class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   @unittest.skip(reason='''Model not available yet'''   )
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      pass







   @slow
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  DebertaModel.from_pretrained('''microsoft/deberta-base'''   )

      UpperCamelCase       =  torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   )
      UpperCamelCase       =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
      with torch.no_grad():
         UpperCamelCase       =  model(lowerCamelCase__ ,   attention_mask=lowerCamelCase__   )[0]
      # compare the actual values for a slice.
      UpperCamelCase       =  torch.tensor(
          [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]   )
      self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,   lowerCamelCase__ ,   atol=1e-4   ) ,   f'{output[:, 1:4, 1:4]}'   )







'''simple docstring'''


import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case_ :  int     =					'\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'

snake_case_ :  Optional[int]     =					'\\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'


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. 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  __snake_case     (    _UpperCAmelCase					:       Optional[Any]):

   def remove_articles(_UpperCAmelCase					:       str):
      UpperCamelCase       =  re.compile(R'''\b(a|an|the)\b''',      re.UNICODE)
      return re.sub(_UpperCAmelCase,      ''' ''',      _UpperCAmelCase)

   def white_space_fix(_UpperCAmelCase					:       Union[str, Any]):
      return " ".join(text.split())

   def remove_punc(_UpperCAmelCase					:       Dict):
      UpperCamelCase       =  set(string.punctuation)
      return "".join(ch for ch in text if ch not in exclude)

   def lower(_UpperCAmelCase					:       List[str]):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase))))



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       List[str]):
   return int(normalize_answer(_UpperCAmelCase) == normalize_answer(_UpperCAmelCase))



def  __snake_case     (    _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Optional[int]):
   UpperCamelCase       =  [any(compute_exact(_UpperCAmelCase,      _UpperCAmelCase) for ref in refs) for pred, refs in zip(_UpperCAmelCase,      _UpperCAmelCase)]
   return (sum(_UpperCAmelCase) / len(_UpperCAmelCase)) * 100



def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       List[str]):
   UpperCamelCase       =  [rgram for rgrams in rgramslist for rgram in rgrams]
   UpperCamelCase       =  Counter(_UpperCAmelCase)

   UpperCamelCase       =  Counter(_UpperCAmelCase)
   UpperCamelCase       =  Counter()
   for sgram, scount in sgramcounter.items():
      UpperCamelCase       =  scount * numref

   UpperCamelCase       =  Counter(_UpperCAmelCase)
   UpperCamelCase       =  Counter()
   for cgram, ccount in cgramcounter.items():
      UpperCamelCase       =  ccount * numref

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

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

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

   # ADDITION
   UpperCamelCase       =  set(_UpperCAmelCase) - set(_UpperCAmelCase)
   UpperCamelCase       =  set(_UpperCAmelCase) & set(_UpperCAmelCase)
   UpperCamelCase       =  set(_UpperCAmelCase) - set(_UpperCAmelCase)

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

   return (keepscore, delscore_precision, addscore)



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       str):
   UpperCamelCase       =  len(_UpperCAmelCase)

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

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

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



def  __snake_case     (    _UpperCAmelCase					:       List[str],      _UpperCAmelCase					:       bool = True,      _UpperCAmelCase					:       str = "13a",      _UpperCAmelCase					:       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:
      UpperCamelCase       =  sentence.lower()

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

   if not return_str:
      UpperCamelCase       =  normalized_sent.split()

   return normalized_sent



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       Optional[Any]):
   if not (len(_UpperCAmelCase) == len(_UpperCAmelCase) == len(_UpperCAmelCase)):
      raise ValueError('''Sources length must match predictions and references lengths.''')
   UpperCamelCase       =  0
   for src, pred, refs in zip(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase):
      sari_score += SARIsent(normalize(_UpperCAmelCase),      normalize(_UpperCAmelCase),      [normalize(_UpperCAmelCase) for sent in refs])
   UpperCamelCase       =  sari_score / len(_UpperCAmelCase)
   return 100 * sari_score



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Any="exp",      _UpperCAmelCase					:       str=None,      _UpperCAmelCase					:       Optional[Any]=False,      _UpperCAmelCase					:       Union[str, Any]=False,      _UpperCAmelCase					:       List[Any]=False,      ):
   UpperCamelCase       =  len(references[0])
   if any(len(_UpperCAmelCase) != references_per_prediction for refs in references):
      raise ValueError('''Sacrebleu requires the same number of references for each prediction''')
   UpperCamelCase       =  [[refs[i] for refs in references] for i in range(_UpperCAmelCase)]
   UpperCamelCase       =  sacrebleu.corpus_bleu(
       _UpperCAmelCase,      _UpperCAmelCase,      smooth_method=_UpperCAmelCase,      smooth_value=_UpperCAmelCase,      force=_UpperCAmelCase,      lowercase=_UpperCAmelCase,      use_effective_order=_UpperCAmelCase,      )
   return output.score


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






   '''simple docstring'''







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  {}
      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





