



"""simple docstring"""
import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCAmelCase						:		Dict			= "\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						:		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"


UpperCAmelCase						:		Tuple			= "\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_						(__lowerCAmelCase							) ->						Any:







  '''simple docstring'''


  def remove_articles(__lowerCAmelCase							):
    lowercase_								= re.compile(R"""\b(a|an|the)\b""" ,  re.UNICODE							)
    return re.sub(__lowerCAmelCase ,  """ """ ,  __lowerCAmelCase							)

  def white_space_fix(__lowerCAmelCase							):
    return " ".join(text.split()							)

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

  def lower(__lowerCAmelCase							):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase							)							)							)							)


def 			UpperCamelCase_						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						int:







  '''simple docstring'''


  return int(normalize_answer(__lowerCAmelCase							) == normalize_answer(__lowerCAmelCase							)							)


def 			UpperCamelCase_						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						Union[str, Any]:







  '''simple docstring'''


  lowercase_								= [any(compute_exact(__lowerCAmelCase ,  __lowerCAmelCase							) for ref in refs							) for pred, refs in zip(__lowerCAmelCase ,  __lowerCAmelCase							)]
  return (sum(__lowerCAmelCase							) / len(__lowerCAmelCase							)) * 1_00


def 			UpperCamelCase_						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						List[Any]:







  '''simple docstring'''


  lowercase_								= [rgram for rgrams in rgramslist for rgram in rgrams]
  lowercase_								= Counter(__lowerCAmelCase							)

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

  lowercase_								= Counter(__lowerCAmelCase							)
  lowercase_								= Counter()
  for cgram, ccount in cgramcounter.items():
    lowercase_								= ccount * numref

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

  lowercase_								= 0
  lowercase_								= 0
  for keepgram in keepgramcountergood_rep:
    keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
    # Fix an alleged bug [2] in the keep score computation.
    # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
    keeptmpscorea += keepgramcountergood_rep[keepgram]
  # Define 0/0=1 instead of 0 to give higher scores for predictions that match
  #      a target exactly.
  lowercase_								= 1
  lowercase_								= 1
  if len(__lowerCAmelCase							) > 0:
    lowercase_								= keeptmpscorea / len(__lowerCAmelCase							)
  if len(__lowerCAmelCase							) > 0:
    # Fix an alleged bug [2] in the keep score computation.
    # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
    lowercase_								= keeptmpscorea / sum(keepgramcounterall_rep.values()							)
  lowercase_								= 0
  if keepscore_precision > 0 or keepscore_recall > 0:
    lowercase_								= 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

  # DELETION
  lowercase_								= sgramcounter_rep - cgramcounter_rep
  lowercase_								= delgramcounter_rep - rgramcounter
  lowercase_								= sgramcounter_rep - rgramcounter
  lowercase_								= 0
  lowercase_								= 0
  for delgram in delgramcountergood_rep:
    deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
    deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
  # Define 0/0=1 instead of 0 to give higher scores for predictions that match
  # a target exactly.
  lowercase_								= 1
  if len(__lowerCAmelCase							) > 0:
    lowercase_								= deltmpscorea / len(__lowerCAmelCase							)

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

  lowercase_								= 0
  for addgram in addgramcountergood:
    addtmpscore += 1

  # Define 0/0=1 instead of 0 to give higher scores for predictions that match
  # a target exactly.
  lowercase_								= 1
  lowercase_								= 1
  if len(__lowerCAmelCase							) > 0:
    lowercase_								= addtmpscore / len(__lowerCAmelCase							)
  if len(__lowerCAmelCase							) > 0:
    lowercase_								= addtmpscore / len(__lowerCAmelCase							)
  lowercase_								= 0
  if addscore_precision > 0 or addscore_recall > 0:
    lowercase_								= 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

  return (keepscore, delscore_precision, addscore)


def 			UpperCamelCase_						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						List[str]:







  '''simple docstring'''


  lowercase_								= len(__lowerCAmelCase							)

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

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

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

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

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


def 			UpperCamelCase_						(__lowerCAmelCase ,  __lowerCAmelCase = True ,  __lowerCAmelCase = "13a" ,  __lowerCAmelCase = True							) ->						str:







  '''simple docstring'''


  if lowercase:
    lowercase_								= sentence.lower()

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

  if not return_str:
    lowercase_								= normalized_sent.split()

  return normalized_sent


def 			UpperCamelCase_						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Any:







  '''simple docstring'''


  if not (len(__lowerCAmelCase							) == len(__lowerCAmelCase							) == len(__lowerCAmelCase							)):
    raise ValueError("""Sources length must match predictions and references lengths."""							)
  lowercase_								= 0
  for src, pred, refs in zip(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							):
    sari_score += SARIsent(normalize(__lowerCAmelCase							) ,  normalize(__lowerCAmelCase							) ,  [normalize(__lowerCAmelCase							) for sent in refs]							)
  lowercase_								= sari_score / len(__lowerCAmelCase							)
  return 1_00 * sari_score


def 			UpperCamelCase_						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase="exp" ,  __lowerCAmelCase=None ,  __lowerCAmelCase=False ,  __lowerCAmelCase=False ,  __lowerCAmelCase=False ,  ) ->						int:







  '''simple docstring'''


  lowercase_								= len(references[0]							)
  if any(len(__lowerCAmelCase							) != references_per_prediction for refs in references							):
    raise ValueError("""Sacrebleu requires the same number of references for each prediction"""							)
  lowercase_								= [[refs[i] for refs in references] for i in range(__lowerCAmelCase							)]
  lowercase_								= sacrebleu.corpus_bleu(
      __lowerCAmelCase ,  __lowerCAmelCase ,  smooth_method=__lowerCAmelCase ,  smooth_value=__lowerCAmelCase ,  force=__lowerCAmelCase ,  lowercase=__lowerCAmelCase ,  use_effective_order=__lowerCAmelCase ,  )
  return output.score


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

    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """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			:				Dict ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= {}
      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






"""simple docstring"""
import unittest

from transformers import DebertaVaConfig, 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 (
           DebertaVaForMaskedLM,
           DebertaVaForMultipleChoice,
           DebertaVaForQuestionAnswering,
           DebertaVaForSequenceClassification,
           DebertaVaForTokenClassification,
           DebertaVaModel,
       )
       from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

    def __init__(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Optional[int]=1_3 ,  lowerCAmelCase_			:				Union[str, Any]=7 ,  lowerCAmelCase_			:				int=True ,  lowerCAmelCase_			:				Optional[int]=True ,  lowerCAmelCase_			:				List[Any]=True ,  lowerCAmelCase_			:				Tuple=True ,  lowerCAmelCase_			:				Dict=9_9 ,  lowerCAmelCase_			:				List[str]=3_2 ,  lowerCAmelCase_			:				Tuple=5 ,  lowerCAmelCase_			:				str=4 ,  lowerCAmelCase_			:				List[str]=3_7 ,  lowerCAmelCase_			:				List[str]="gelu" ,  lowerCAmelCase_			:				Optional[Any]=0.1 ,  lowerCAmelCase_			:				Union[str, Any]=0.1 ,  lowerCAmelCase_			:				List[str]=5_1_2 ,  lowerCAmelCase_			:				Union[str, Any]=1_6 ,  lowerCAmelCase_			:				List[Any]=2 ,  lowerCAmelCase_			:				Dict=0.02 ,  lowerCAmelCase_			:				int=False ,  lowerCAmelCase_			:				Any=True ,  lowerCAmelCase_			:				Tuple="None" ,  lowerCAmelCase_			:				int=3 ,  lowerCAmelCase_			:				Union[str, Any]=4 ,  lowerCAmelCase_			:				Dict=None ,  ):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







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

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

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

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

      lowercase_								= self.get_config()

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

    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      return DebertaVaConfig(
          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			:				Tuple ,  lowerCAmelCase_			:				Dict):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				List[Any] ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				List[str]):







      """simple docstring"""







      lowercase_								= DebertaVaModel(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      lowercase_								= model(lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_ ,  token_type_ids=lowerCAmelCase_)[0]
      lowercase_								= model(lowerCAmelCase_ ,  token_type_ids=lowerCAmelCase_)[0]
      lowercase_								= model(lowerCAmelCase_)[0]

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

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= DebertaVaForMaskedLM(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      lowercase_								= 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			:				Any ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				List[Any] ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Tuple):







      """simple docstring"""







      lowercase_								= self.num_labels
      lowercase_								= DebertaVaForSequenceClassification(lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      lowercase_								= 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			:				List[str] ,  lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Any):







      """simple docstring"""







      lowercase_								= self.num_labels
      lowercase_								= DebertaVaForTokenClassification(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      lowercase_								= 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			:				str ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				Dict):







      """simple docstring"""







      lowercase_								= DebertaVaForQuestionAnswering(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      lowercase_								= 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			:				Tuple ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= DebertaVaForMultipleChoice(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      lowercase_								= input_ids.unsqueeze(1).expand(-1 ,  self.num_choices ,  -1).contiguous()
      lowercase_								= token_type_ids.unsqueeze(1).expand(-1 ,  self.num_choices ,  -1).contiguous()
      lowercase_								= input_mask.unsqueeze(1).expand(-1 ,  self.num_choices ,  -1).contiguous()
      lowercase_								= model(
          lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_ ,  token_type_ids=lowerCAmelCase_ ,  labels=lowerCAmelCase_ ,  )
      self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_choices))




    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







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







@require_torch
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase      ,  unittest.TestCase   ):
    lowercase__      =   (
        (
            DebertaVaModel,
            DebertaVaForMaskedLM,
            DebertaVaForSequenceClassification,
            DebertaVaForTokenClassification,
            DebertaVaForQuestionAnswering,
            DebertaVaForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    lowercase__      =   (
        {
            "feature-extraction": DebertaVaModel,
            "fill-mask": DebertaVaForMaskedLM,
            "question-answering": DebertaVaForQuestionAnswering,
            "text-classification": DebertaVaForSequenceClassification,
            "token-classification": DebertaVaForTokenClassification,
            "zero-shot": DebertaVaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    lowercase__      =   True
    lowercase__      =   False
    lowercase__      =   False
    lowercase__      =   False
    lowercase__      =   False

    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= DebertaVaModelTester(self)
      lowercase_								= ConfigTester(self ,  config_class=lowerCAmelCase_ ,  hidden_size=3_7)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      self.config_tester.run_common_tests()

    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_model(*lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_question_answering(*lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_token_classification(*lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCAmelCase_)




    @slow
    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







      for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase_								= DebertaVaModel.from_pretrained(lowerCAmelCase_)
        self.assertIsNotNone(lowerCAmelCase_)







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

    @unittest.skip(reason="""Model not available yet""")
    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      pass




    @slow
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      lowercase_								= DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge""")

      lowercase_								= 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]])
      lowercase_								= torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
      with torch.no_grad():
        lowercase_								= model(lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_)[0]
      # compare the actual values for a slice.
      lowercase_								= torch.tensor(
          [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]])
      self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,  lowerCAmelCase_ ,  atol=1E-4) ,  F'''{output[:, 1:4, 1:4]}''')

