



'''simple docstring'''






from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor

class 							a__:







    def __init__(				self			:  Optional[int]						,      __snake_case			:  List[Any]						,      __snake_case			:  List[Any]=3						,      __snake_case			:  str=32						,      __snake_case			:  int=3						,      __snake_case			:  Dict=10						,      __snake_case			:  Tuple=[10, 20, 30, 40]						,      __snake_case			:  Optional[int]=[1, 1, 2, 1]						,      __snake_case			:  str=True						,      __snake_case			:  Union[str, Any]=True						,      __snake_case			:  int="relu"						,      __snake_case			:  Any=3						,      __snake_case			:  int=None						,      ):
      a						: List[Any]					       =					parent
      a						: List[str]					       =					batch_size
      a						: List[str]					       =					image_size
      a						: Optional[Any]					       =					num_channels
      a						: int					       =					embeddings_size
      a						: Any					       =					hidden_sizes
      a						: str					       =					depths
      a						: Union[str, Any]					       =					is_training
      a						: Any					       =					use_labels
      a						: Tuple					       =					hidden_act
      a						: Optional[int]					       =					num_labels
      a						: Union[str, Any]					       =					scope
      a						: Dict					       =					len(__snake_case     )







    def       lowercase_	(				self			:  Dict     ):
      a						: int					       =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      a						: Optional[Any]					       =					None
      if self.use_labels:
        a						: Tuple					       =					ids_tensor([self.batch_size]						,      self.num_labels     )

      a						: Dict					       =					self.get_config()
      return config, pixel_values, labels







    def       lowercase_	(				self			:  Tuple     ):
      return RegNetConfig(
          num_channels=self.num_channels						,      embeddings_size=self.embeddings_size						,      hidden_sizes=self.hidden_sizes						,      depths=self.depths						,      hidden_act=self.hidden_act						,      num_labels=self.num_labels						,      )







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  Dict						,      __snake_case			:  Any						,      __snake_case			:  Tuple     ):
      a						: Dict					       =					TFRegNetModel(config=__snake_case     )
      a						: Optional[int]					       =					model(__snake_case						,      training=__snake_case     )
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape						,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,      )







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  int						,      __snake_case			:  Tuple     ):
      a						: Optional[Any]					       =					self.num_labels
      a						: List[Any]					       =					TFRegNetForImageClassification(__snake_case     )
      a						: int					       =					model(__snake_case						,      labels=__snake_case						,      training=__snake_case     )
      self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_labels)     )







    def       lowercase_	(				self			:  Union[str, Any]     ):
      a						: int					       =					self.prepare_config_and_inputs()
      a       ,     a       ,     a						: List[str]					       =					config_and_inputs
      a						: Tuple					       =					{'pixel_values': pixel_values}
      return config, inputs_dict

@require_tf
class 							a__( lowerCamelCase__		,			lowerCamelCase__		,			unittest.TestCase     ):
    lowercase__			=   (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
    lowercase__			=   (
        {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
        if is_tf_available()
        else {}
    )

    lowercase__			=   False
    lowercase__			=   False
    lowercase__			=   False
    lowercase__			=   False
    lowercase__			=   False







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Optional[int]					       =					TFRegNetModelTester(self     )
      a						: List[Any]					       =					ConfigTester(self						,      config_class=__snake_case						,      has_text_modality=__snake_case     )







    def       lowercase_	(				self			:  Tuple     ):
      return







    @unittest.skip(reason='RegNet does not use inputs_embeds'     )
    def       lowercase_	(				self			:  Dict     ):
      pass







    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices('GPU'     )     ) == 0						,      reason='TF does not support backprop for grouped convolutions on CPU.'						,      )
    @slow
    def       lowercase_	(				self			:  List[str]     ):
      super().test_keras_fit()







    @unittest.skip(reason='RegNet does not support input and output embeddings'     )
    def       lowercase_	(				self			:  Tuple     ):
      pass







    def       lowercase_	(				self			:  str     ):
      a       ,     a						: Optional[Any]					       =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a						: Tuple					       =					model_class(__snake_case     )
        a						: Dict					       =					inspect.signature(model.call     )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a						: Optional[Any]					       =					[*signature.parameters.keys()]

        a						: List[Any]					       =					['pixel_values']
        self.assertListEqual(arg_names[:1]						,      __snake_case     )







    def       lowercase_	(				self			:  str     ):
      a						: Optional[int]					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__snake_case     )







    def       lowercase_	(				self			:  Tuple     ):
      def check_hidden_states_output(__snake_case			:  Union[str, Any]						,      __snake_case			:  List[str]						,      __snake_case			:  Any     ):
        a						: Any					       =					model_class(__snake_case     )
        a						: Any					       =					model(**self._prepare_for_class(__snake_case						,      __snake_case     )						,      training=__snake_case     )

        a						: Dict					       =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        a						: Any					       =					self.model_tester.num_stages
        self.assertEqual(len(__snake_case     )						,      expected_num_stages + 1     )

        # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
        self.assertListEqual(
            list(hidden_states[0].shape[-2:]     )						,      [self.model_tester.image_size // 2, self.model_tester.image_size // 2]						,      )

      a       ,     a						: List[Any]					       =					self.model_tester.prepare_config_and_inputs_for_common()
      a						: Union[str, Any]					       =					['basic', 'bottleneck']
      for model_class in self.all_model_classes:
        for layer_type in layers_type:
          a						: Dict					       =					layer_type
          a						: Any					       =					True
          check_hidden_states_output(__snake_case						,      __snake_case						,      __snake_case     )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          a						: Any					       =					True

          check_hidden_states_output(__snake_case						,      __snake_case						,      __snake_case     )







    def       lowercase_	(				self			:  str     ):
      a       ,     a						: Any					       =					self.model_tester.prepare_config_and_inputs_for_common()

      def check_equivalence(__snake_case			:  Tuple						,      __snake_case			:  int						,      __snake_case			:  Dict						,      __snake_case			:  Tuple={}     ):
        a						: Optional[int]					       =					model(__snake_case						,      return_dict=__snake_case						,      **__snake_case     )
        a						: List[Any]					       =					model(__snake_case						,      return_dict=__snake_case						,      **__snake_case     ).to_tuple()

        def recursive_check(__snake_case			:  Dict						,      __snake_case			:  Tuple     ):
          if isinstance(__snake_case						,      (List, Tuple)     ):
            for tuple_iterable_value, dict_iterable_value in zip(__snake_case						,      __snake_case     ):
              recursive_check(__snake_case						,      __snake_case     )
          elif tuple_object is None:
            return
          else:
            self.assertTrue(
                all(tf.equal(__snake_case						,      __snake_case     )     )						,      msg=(
                    'Tuple and dict output are not equal. Difference:'
                    F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object     )     )}"""
                )						,      )

          recursive_check(__snake_case						,      __snake_case     )

      for model_class in self.all_model_classes:
        a						: str					       =					model_class(__snake_case     )

        a						: Any					       =					self._prepare_for_class(__snake_case						,      __snake_case     )
        a						: int					       =					self._prepare_for_class(__snake_case						,      __snake_case     )
        check_equivalence(__snake_case						,      __snake_case						,      __snake_case     )

        a						: Optional[Any]					       =					self._prepare_for_class(__snake_case						,      __snake_case						,      return_labels=__snake_case     )
        a						: Optional[int]					       =					self._prepare_for_class(__snake_case						,      __snake_case						,      return_labels=__snake_case     )
        check_equivalence(__snake_case						,      __snake_case						,      __snake_case     )

        a						: Dict					       =					self._prepare_for_class(__snake_case						,      __snake_case     )
        a						: int					       =					self._prepare_for_class(__snake_case						,      __snake_case     )
        check_equivalence(__snake_case						,      __snake_case						,      __snake_case						,      {'output_hidden_states': True}     )

        a						: List[Any]					       =					self._prepare_for_class(__snake_case						,      __snake_case						,      return_labels=__snake_case     )
        a						: List[Any]					       =					self._prepare_for_class(__snake_case						,      __snake_case						,      return_labels=__snake_case     )
        check_equivalence(__snake_case						,      __snake_case						,      __snake_case						,      {'output_hidden_states': True}     )







    def       lowercase_	(				self			:  Dict     ):
      a						: Optional[int]					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__snake_case     )







    @slow
    def       lowercase_	(				self			:  Any     ):
      for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a						: Any					       =					TFRegNetModel.from_pretrained(__snake_case     )
        self.assertIsNotNone(__snake_case     )
def 				lowerCamelCase__	(						):
  a						: List[Any]					       =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
  return image

@require_tf
@require_vision
class 							a__( unittest.TestCase     ):







    @cached_property
    def       lowercase_	(				self			:  Optional[Any]     ):
      return (
          AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
          if is_vision_available()
          else None
      )







    @slow
    def       lowercase_	(				self			:  List[str]     ):
      a						: Optional[Any]					       =					TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )

      a						: Dict					       =					self.default_image_processor
      a						: Tuple					       =					prepare_img()
      a						: Any					       =					image_processor(images=__snake_case						,      return_tensors='tf'     )

      # forward pass
      a						: Tuple					       =					model(**__snake_case						,      training=__snake_case     )

      # verify the logits
      a						: Union[str, Any]					       =					tf.TensorShape((1, 10_00)     )
      self.assertEqual(outputs.logits.shape						,      __snake_case     )

      a						: List[str]					       =					tf.constant([-0.4180, -1.5051, -3.4836]     )

      tf.debugging.assert_near(outputs.logits[0, :3]						,      __snake_case						,      atol=1e-4     )




'''simple docstring'''






import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase: 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'

lowerCAmelCase: Optional[Any]							=						'\\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'


lowerCAmelCase: List[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 				lowerCamelCase__	(						_A    ):

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

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

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

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

  return white_space_fix(remove_articles(remove_punc(lower(_A    )    )    )    )
def 				lowerCamelCase__	(						_A			,      _A    ):
  return int(normalize_answer(_A    ) == normalize_answer(_A    )    )
def 				lowerCamelCase__	(						_A			,      _A    ):
  a						: List[Any]					       =					[any(compute_exact(_A			,      _A    ) for ref in refs    ) for pred, refs in zip(_A			,      _A    )]
  return (sum(_A    ) / len(_A    )) * 100
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A    ):
  a						: List[Any]					       =					[rgram for rgrams in rgramslist for rgram in rgrams]
  a						: Any					       =					Counter(_A    )

  a						: Dict					       =					Counter(_A    )
  a						: Tuple					       =					Counter()
  for sgram, scount in sgramcounter.items():
    a						: List[str]					       =					scount * numref

  a						: Optional[int]					       =					Counter(_A    )
  a						: Optional[int]					       =					Counter()
  for cgram, ccount in cgramcounter.items():
    a						: List[str]					       =					ccount * numref

  # KEEP
  a						: Optional[Any]					       =					sgramcounter_rep & cgramcounter_rep
  a						: Union[str, Any]					       =					keepgramcounter_rep & rgramcounter
  a						: Any					       =					sgramcounter_rep & rgramcounter

  a						: str					       =					0
  a						: Optional[Any]					       =					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						: Tuple					       =					1
  a						: Any					       =					1
  if len(_A    ) > 0:
    a						: Optional[int]					       =					keeptmpscorea / len(_A    )
  if len(_A    ) > 0:
    # Fix an alleged bug [2] in the keep score computation.
    # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
    a						: Tuple					       =					keeptmpscorea / sum(keepgramcounterall_rep.values()    )
  a						: List[str]					       =					0
  if keepscore_precision > 0 or keepscore_recall > 0:
    a						: Dict					       =					2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

  # DELETION
  a						: List[Any]					       =					sgramcounter_rep - cgramcounter_rep
  a						: Any					       =					delgramcounter_rep - rgramcounter
  a						: Union[str, Any]					       =					sgramcounter_rep - rgramcounter
  a						: Tuple					       =					0
  a						: str					       =					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						: str					       =					1
  if len(_A    ) > 0:
    a						: Tuple					       =					deltmpscorea / len(_A    )

  # ADDITION
  a						: Any					       =					set(_A    ) - set(_A    )
  a						: Optional[int]					       =					set(_A    ) & set(_A    )
  a						: Union[str, Any]					       =					set(_A    ) - set(_A    )

  a						: List[str]					       =					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						: Tuple					       =					1
  a						: Optional[Any]					       =					1
  if len(_A    ) > 0:
    a						: str					       =					addtmpscore / len(_A    )
  if len(_A    ) > 0:
    a						: Optional[Any]					       =					addtmpscore / len(_A    )
  a						: str					       =					0
  if addscore_precision > 0 or addscore_recall > 0:
    a						: List[str]					       =					2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

  return (keepscore, delscore_precision, addscore)
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  a						: List[str]					       =					len(_A    )

  a						: List[str]					       =					ssent.split(' '    )
  a						: int					       =					csent.split(' '    )
  a						: Optional[Any]					       =					[]
  a						: Tuple					       =					[]
  a						: Optional[Any]					       =					[]
  a						: Optional[Any]					       =					[]
  a						: Union[str, Any]					       =					[]
  a						: str					       =					[]

  a						: Dict					       =					[]
  a						: Dict					       =					[]
  a						: str					       =					[]
  a						: Any					       =					[]
  for rsent in rsents:
    a						: List[Any]					       =					rsent.split(' '    )
    a						: Dict					       =					[]
    a						: Optional[Any]					       =					[]
    a						: Optional[Any]					       =					[]
    ragramslist.append(_A    )
    for i in range(0			,      len(_A    ) - 1    ):
      if i < len(_A    ) - 1:
        a						: List[str]					       =					ragrams[i] + ' ' + ragrams[i + 1]
        ragrams.append(_A    )
      if i < len(_A    ) - 2:
        a						: Union[str, Any]					       =					ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
        ragrams.append(_A    )
      if i < len(_A    ) - 3:
        a						: Tuple					       =					ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
        ragrams.append(_A    )
    ragramslist.append(_A    )
    ragramslist.append(_A    )
    ragramslist.append(_A    )

  for i in range(0			,      len(_A    ) - 1    ):
    if i < len(_A    ) - 1:
      a						: Tuple					       =					sagrams[i] + ' ' + sagrams[i + 1]
      sagrams.append(_A    )
    if i < len(_A    ) - 2:
      a						: Union[str, Any]					       =					sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
      sagrams.append(_A    )
    if i < len(_A    ) - 3:
      a						: List[str]					       =					sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
      sagrams.append(_A    )

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

  ((a)       ,     (a)       ,     (a))						: int					       =					SARIngram(_A			,      _A			,      _A			,      _A    )
  ((a)       ,     (a)       ,     (a))						: Optional[int]					       =					SARIngram(_A			,      _A			,      _A			,      _A    )
  ((a)       ,     (a)       ,     (a))						: Union[str, Any]					       =					SARIngram(_A			,      _A			,      _A			,      _A    )
  ((a)       ,     (a)       ,     (a))						: int					       =					SARIngram(_A			,      _A			,      _A			,      _A    )
  a						: Dict					       =					sum([keepascore, keepascore, keepascore, keepascore]    ) / 4
  a						: Any					       =					sum([delascore, delascore, delascore, delascore]    ) / 4
  a						: Union[str, Any]					       =					sum([addascore, addascore, addascore, addascore]    ) / 4
  a						: str					       =					(avgkeepscore + avgdelscore + avgaddscore) / 3
  return finalscore
def 				lowerCamelCase__	(						_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						: Dict					       =					sentence.lower()

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

  if not return_str:
    a						: Optional[Any]					       =					normalized_sent.split()

  return normalized_sent
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  if not (len(_A    ) == len(_A    ) == len(_A    )):
    raise ValueError('Sources length must match predictions and references lengths.'    )
  a						: Tuple					       =					0
  for src, pred, refs in zip(_A			,      _A			,      _A    ):
    sari_score += SARIsent(normalize(_A    )			,      normalize(_A    )			,      [normalize(_A    ) for sent in refs]    )
  a						: Tuple					       =					sari_score / len(_A    )
  return 100 * sari_score
def 				lowerCamelCase__	(						_A			,      _A			,      _A="exp"			,      _A=None			,      _A=False			,      _A=False			,      _A=False			,      ):
  a						: Optional[int]					       =					len(references[0]    )
  if any(len(_A    ) != references_per_prediction for refs in references    ):
    raise ValueError('Sacrebleu requires the same number of references for each prediction'    )
  a						: List[Any]					       =					[[refs[i] for refs in references] for i in range(_A    )]
  a						: Optional[Any]					       =					sacrebleu.corpus_bleu(
      _A			,      _A			,      smooth_method=_A			,      smooth_value=_A			,      force=_A			,      lowercase=_A			,      use_effective_order=_A			,      )
  return output.score

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







    def       lowercase_	(				self			:  Optional[int]     ):
      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       lowercase_	(				self			:  str						,      __snake_case			:  Optional[Any]						,      __snake_case			:  int						,      __snake_case			:  str     ):
      a						: int					       =					{}
      result.update({'sari': compute_sari(sources=__snake_case						,      predictions=__snake_case						,      references=__snake_case     )}     )
      result.update({'sacrebleu': compute_sacrebleu(predictions=__snake_case						,      references=__snake_case     )}     )
      result.update({'exact': compute_em(predictions=__snake_case						,      references=__snake_case     )}     )
      return result