

import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
  import torch

  from transformers import EfficientNetForImageClassification, EfficientNetModel
  from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor







class    UpperCamelCase_			:


  """simple docstring"""



  def __init__(	self    :				Dict     ,  A_    :				List[str]     ,  A_    :				Dict=13     ,  A_    :				List[Any]=32     ,  A_    :				Optional[Any]=3     ,  A_    :				str=[3, 3, 5]     ,  A_    :				Optional[Any]=[32, 16, 24]     ,  A_    :				Union[str, Any]=[16, 24, 40]     ,  A_    :				Union[str, Any]=[1, 1, 2]     ,  A_    :				Tuple=[1, 1, 2]     ,  A_    :				Any=[1, 6, 6]     ,  A_    :				Optional[Any]=True     ,  A_    :				List[str]=True     ,  A_    :				Tuple=37     ,  A_    :				Tuple="gelu"     ,  A_    :				List[Any]=10     ,  )					-> List[Any]:
      SCREAMING_SNAKE_CASE__					     =						parent
      SCREAMING_SNAKE_CASE__					     =						batch_size
      SCREAMING_SNAKE_CASE__					     =						image_size
      SCREAMING_SNAKE_CASE__					     =						num_channels
      SCREAMING_SNAKE_CASE__					     =						kernel_sizes
      SCREAMING_SNAKE_CASE__					     =						in_channels
      SCREAMING_SNAKE_CASE__					     =						out_channels
      SCREAMING_SNAKE_CASE__					     =						strides
      SCREAMING_SNAKE_CASE__					     =						num_block_repeats
      SCREAMING_SNAKE_CASE__					     =						expand_ratios
      SCREAMING_SNAKE_CASE__					     =						is_training
      SCREAMING_SNAKE_CASE__					     =						hidden_act
      SCREAMING_SNAKE_CASE__					     =						num_labels
      SCREAMING_SNAKE_CASE__					     =						use_labels



  def      _UpperCamelCase    (	self    :				List[Any]	)					-> Tuple:
      SCREAMING_SNAKE_CASE__					     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

      SCREAMING_SNAKE_CASE__					     =						None
      if self.use_labels:
          SCREAMING_SNAKE_CASE__					     =						ids_tensor([self.batch_size]     ,  self.num_labels	)

      SCREAMING_SNAKE_CASE__					     =						self.get_config()
      return config, pixel_values, labels



  def      _UpperCamelCase    (	self    :				Tuple	)					-> Optional[Any]:
      return EfficientNetConfig(
          num_channels=self.num_channels     ,  kernel_sizes=self.kernel_sizes     ,  in_channels=self.in_channels     ,  out_channels=self.out_channels     ,  strides=self.strides     ,  num_block_repeats=self.num_block_repeats     ,  expand_ratios=self.expand_ratios     ,  hidden_act=self.hidden_act     ,  num_labels=self.num_labels     ,  )



  def      _UpperCamelCase    (	self    :				Union[str, Any]     ,  A_    :				Optional[Any]     ,  A_    :				Optional[int]     ,  A_    :				Optional[Any]	)					-> Tuple:
      SCREAMING_SNAKE_CASE__					     =						EfficientNetModel(config=A_	)
      model.to(A_	)
      model.eval()
      SCREAMING_SNAKE_CASE__					     =						model(A_	)
      # expected last hidden states: B, C, H // 4, W // 4
      self.parent.assertEqual(
          result.last_hidden_state.shape     ,  (self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)     ,  )



  def      _UpperCamelCase    (	self    :				int     ,  A_    :				str     ,  A_    :				List[str]     ,  A_    :				int	)					-> Tuple:
      SCREAMING_SNAKE_CASE__					     =						EfficientNetForImageClassification(A_	)
      model.to(A_	)
      model.eval()
      SCREAMING_SNAKE_CASE__					     =						model(A_     ,  labels=A_	)
      self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)	)







  def      _UpperCamelCase    (	self    :				Tuple	)					-> Union[str, Any]:
      SCREAMING_SNAKE_CASE__					     =						self.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						config_and_inputs
      SCREAMING_SNAKE_CASE__					     =						{"pixel_values": pixel_values}
      return config, inputs_dict







@require_torch
class    UpperCamelCase_			(  _snake_case		,     _snake_case		,     unittest.TestCase			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: List[Any]										=      (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__				: List[Any]										=      (
      {"""feature-extraction""": EfficientNetModel, """image-classification""": EfficientNetForImageClassification}
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE__				: List[Any]										=      False
  SCREAMING_SNAKE_CASE__				: List[str]										=      False
  SCREAMING_SNAKE_CASE__				: int										=      False
  SCREAMING_SNAKE_CASE__				: Tuple										=      False
  SCREAMING_SNAKE_CASE__				: Any										=      False



  def      _UpperCamelCase    (	self    :				Dict	)					-> Dict:
      SCREAMING_SNAKE_CASE__					     =						EfficientNetModelTester(self	)
      SCREAMING_SNAKE_CASE__					     =						ConfigTester(
          self     ,  config_class=A_     ,  has_text_modality=A_     ,  hidden_size=37	)



  def      _UpperCamelCase    (	self    :				Optional[int]	)					-> Any:
      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()



  def      _UpperCamelCase    (	self    :				Optional[Any]	)					-> List[Any]:
      return



  @unittest.skip(reason="EfficientNet does not use inputs_embeds"	)
  def      _UpperCamelCase    (	self    :				Tuple	)					-> List[Any]:
      pass



  @unittest.skip(reason="EfficientNet does not support input and output embeddings"	)
  def      _UpperCamelCase    (	self    :				Optional[int]	)					-> Any:
      pass



  @unittest.skip(reason="EfficientNet does not use feedforward chunking"	)
  def      _UpperCamelCase    (	self    :				Union[str, Any]	)					-> Optional[int]:
      pass



  def      _UpperCamelCase    (	self    :				Union[str, Any]	)					-> List[str]:
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE__					     =						model_class(A_	)
          SCREAMING_SNAKE_CASE__					     =						inspect.signature(model.forward	)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          SCREAMING_SNAKE_CASE__					     =						[*signature.parameters.keys()]

          SCREAMING_SNAKE_CASE__					     =						["pixel_values"]
          self.assertListEqual(arg_names[:1]     ,  A_	)



  def      _UpperCamelCase    (	self    :				Optional[Any]	)					-> str:
      SCREAMING_SNAKE_CASE__					     =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A_	)



  def      _UpperCamelCase    (	self    :				int	)					-> List[Any]:
      def check_hidden_states_output(A_    :				List[str]     ,  A_    :				Dict     ,  A_    :				Union[str, Any]	):
          SCREAMING_SNAKE_CASE__					     =						model_class(A_	)
          model.to(A_	)
          model.eval()

          with torch.no_grad():
              SCREAMING_SNAKE_CASE__					     =						model(**self._prepare_for_class(A_     ,  A_	)	)

          SCREAMING_SNAKE_CASE__					     =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
          SCREAMING_SNAKE_CASE__					     =						sum(config.num_block_repeats	) * 4
          self.assertEqual(len(A_	)     ,  A_	)

          # EfficientNet'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]     ,  )

      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE__					     =						True
          check_hidden_states_output(A_     ,  A_     ,  A_	)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          SCREAMING_SNAKE_CASE__					     =						True

          check_hidden_states_output(A_     ,  A_     ,  A_	)



  def      _UpperCamelCase    (	self    :				List[str]	)					-> int:
      SCREAMING_SNAKE_CASE__					     =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A_	)



  @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."	)
  def      _UpperCamelCase    (	self    :				Union[str, Any]	)					-> int:
      pass



  @slow
  def      _UpperCamelCase    (	self    :				List[Any]	)					-> List[Any]:
      for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          SCREAMING_SNAKE_CASE__					     =						EfficientNetModel.from_pretrained(A_	)
          self.assertIsNotNone(A_	)







  @is_pipeline_test
  @require_vision
  @slow
  def      _UpperCamelCase    (	self    :				Optional[Any]	)					-> Union[str, Any]:
      super().test_pipeline_image_classification()





def 							__a       (					):
    SCREAMING_SNAKE_CASE__					     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
    return image







@require_torch
@require_vision
class    UpperCamelCase_			(  unittest.TestCase			):


  """simple docstring"""



  @cached_property
  def      _UpperCamelCase    (	self    :				int	)					-> List[str]:
      return AutoImageProcessor.from_pretrained("google/efficientnet-b7"	) if is_vision_available() else None







  @slow
  def      _UpperCamelCase    (	self    :				Optional[int]	)					-> Optional[Any]:
      SCREAMING_SNAKE_CASE__					     =						EfficientNetForImageClassification.from_pretrained("google/efficientnet-b7"	).to(A_	)

      SCREAMING_SNAKE_CASE__					     =						self.default_image_processor
      SCREAMING_SNAKE_CASE__					     =						prepare_img()
      SCREAMING_SNAKE_CASE__					     =						image_processor(images=A_     ,  return_tensors="pt"	).to(A_	)

      # forward pass
      with torch.no_grad():
          SCREAMING_SNAKE_CASE__					     =						model(**A_	)

      # verify the logits
      SCREAMING_SNAKE_CASE__					     =						torch.Size((1, 1000)	)
      self.assertEqual(outputs.logits.shape     ,  A_	)

      SCREAMING_SNAKE_CASE__					     =						torch.tensor([0.0_001, 0.0_002, 0.0_002]	).to(A_	)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,  A_     ,  atol=1E-4	)	)





from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A__        =			"\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n"

A__        =			"\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow's tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n"


A__        =			"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]\n    >>> sari = datasets.load_metric(\"sari\")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 26.953601953601954}\n"





def 							__a       (					__A ,       __A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						[rgram for rgrams in rgramslist for rgram in rgrams]
    SCREAMING_SNAKE_CASE__					     =						Counter(__A	)

    SCREAMING_SNAKE_CASE__					     =						Counter(__A	)
    SCREAMING_SNAKE_CASE__					     =						Counter()
    for sgram, scount in sgramcounter.items():
        SCREAMING_SNAKE_CASE__					     =						scount * numref

    SCREAMING_SNAKE_CASE__					     =						Counter(__A	)
    SCREAMING_SNAKE_CASE__					     =						Counter()
    for cgram, ccount in cgramcounter.items():
        SCREAMING_SNAKE_CASE__					     =						ccount * numref

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

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

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

    # ADDITION
    SCREAMING_SNAKE_CASE__					     =						set(__A	) - set(__A	)
    SCREAMING_SNAKE_CASE__					     =						set(__A	) & set(__A	)
    SCREAMING_SNAKE_CASE__					     =						set(__A	) - set(__A	)

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

    return (keepscore, delscore_precision, addscore)





def 							__a       (					__A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						len(__A	)

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

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

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





def 							__a       (					__A ,       __A = True ,       __A = "13a" ,       __A = True	):
    # Normalization is requried for the ASSET dataset (one of the primary
    # datasets in sentence simplification) to allow using space
    # to split the sentence. Even though Wiki-Auto and TURK datasets,
    # do not require normalization, we do it for consistency.
    # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
    # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

    if lowercase:
        SCREAMING_SNAKE_CASE__					     =						sentence.lower()

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

    if not return_str:
        SCREAMING_SNAKE_CASE__					     =						normalized_sent.split()

    return normalized_sent







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


  """simple docstring"""



  def      _UpperCamelCase    (	self    :				List[str]	)					-> int:
      return datasets.MetricInfo(
          description=_DESCRIPTION     ,  citation=_CITATION     ,  inputs_description=_KWARGS_DESCRIPTION     ,  features=datasets.Features(
              {
                  "sources": datasets.Value("string"     ,  id="sequence"	),
                  "predictions": datasets.Value("string"     ,  id="sequence"	),
                  "references": datasets.Sequence(datasets.Value("string"     ,  id="sequence"	)     ,  id="references"	),
              }	)     ,  codebase_urls=[
              "https://github.com/cocoxu/simplification/blob/master/SARI.py",
              "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
          ]     ,  reference_urls=["https://www.aclweb.org/anthology/Q16-1029.pdf"]     ,  )







  def      _UpperCamelCase    (	self    :				Optional[Any]     ,  A_    :				int     ,  A_    :				Tuple     ,  A_    :				int	)					-> Dict:
      if not (len(A_	) == len(A_	) == len(A_	)):
          raise ValueError("Sources length must match predictions and references lengths."	)
      SCREAMING_SNAKE_CASE__					     =						0
      for src, pred, refs in zip(A_     ,  A_     ,  A_	):
          sari_score += SARIsent(normalize(A_	)     ,  normalize(A_	)     ,  [normalize(A_	) for sent in refs]	)
      SCREAMING_SNAKE_CASE__					     =						sari_score / len(A_	)
      return {"sari": 100 * sari_score}


