




'''simple docstring'''






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 					lowercase_			:





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





   def  _lowerCAmelCase			(    self    :				Dict ):
         snake_case__					:      Tuple  			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         snake_case__					:      Dict  			=						None
         if self.use_labels:
               snake_case__					:      List[str]  			=						ids_tensor([self.batch_size]    ,       self.num_labels )

         snake_case__					:      List[str]  			=						self.get_config()
         return config, pixel_values, labels





   def  _lowerCAmelCase			(    self    :				List[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  _lowerCAmelCase			(    self    :				Tuple    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				Dict    ,       __lowerCamelCase    :				Union[str, Any] ):
         snake_case__					:      int  			=						EfficientNetModel(config=__lowerCamelCase )
         model.to(__lowerCamelCase )
         model.eval()
         snake_case__					:      Tuple  			=						model(__lowerCamelCase )
         # 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  _lowerCAmelCase			(    self    :				Union[str, Any]    ,       __lowerCamelCase    :				Optional[int]    ,       __lowerCamelCase    :				List[str]    ,       __lowerCamelCase    :				Optional[Any] ):
         snake_case__					:      Union[str, Any]  			=						EfficientNetForImageClassification(__lowerCamelCase )
         model.to(__lowerCamelCase )
         model.eval()
         snake_case__					:      List[str]  			=						model(__lowerCamelCase    ,       labels=__lowerCamelCase )
         self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.num_labels) )





   def  _lowerCAmelCase			(    self    :				int ):
         snake_case__					:      str  			=						self.prepare_config_and_inputs()
         snake_case__					:      Any  			=						config_and_inputs
         snake_case__					:      Any  			=						{'pixel_values': pixel_values}
         return config, inputs_dict






@require_torch
class 					lowercase_			(	lowerCAmelCase_	,					lowerCAmelCase_	,					unittest.TestCase       ):
   A_       							= (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
   A_       							= (
       {"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
       if is_torch_available()
       else {}
   )

   A_       							= False
   A_       							= False
   A_       							= False
   A_       							= False
   A_       							= False





   def  _lowerCAmelCase			(    self    :				List[str] ):
         snake_case__					:      Union[str, Any]  			=						EfficientNetModelTester(self )
         snake_case__					:      List[Any]  			=						ConfigTester(
             self    ,       config_class=__lowerCamelCase    ,       has_text_modality=__lowerCamelCase    ,       hidden_size=37 )





   def  _lowerCAmelCase			(    self    :				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  _lowerCAmelCase			(    self    :				Dict ):
         return





   @unittest.skip(reason='EfficientNet does not use inputs_embeds' )
   def  _lowerCAmelCase			(    self    :				int ):
         pass





   @unittest.skip(reason='EfficientNet does not support input and output embeddings' )
   def  _lowerCAmelCase			(    self    :				Optional[int] ):
         pass





   @unittest.skip(reason='EfficientNet does not use feedforward chunking' )
   def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
         pass





   def  _lowerCAmelCase			(    self    :				List[str] ):
         snake_case__					:      Union[str, Any]  			=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               snake_case__					:      Dict  			=						model_class(__lowerCamelCase )
               snake_case__					:      int  			=						inspect.signature(model.forward )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               snake_case__					:      List[str]  			=						[*signature.parameters.keys()]

               snake_case__					:      Union[str, Any]  			=						['pixel_values']
               self.assertListEqual(arg_names[:1]    ,       __lowerCamelCase )





   def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
         snake_case__					:      Optional[Any]  			=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCamelCase )





   def  _lowerCAmelCase			(    self    :				int ):
         def check_hidden_states_output(__lowerCamelCase    :				Tuple    ,       __lowerCamelCase    :				Optional[Any]    ,       __lowerCamelCase    :				Optional[Any] ):
               snake_case__					:      Union[str, Any]  			=						model_class(__lowerCamelCase )
               model.to(__lowerCamelCase )
               model.eval()

               with torch.no_grad():
                     snake_case__					:      List[Any]  			=						model(**self._prepare_for_class(__lowerCamelCase    ,       __lowerCamelCase ) )

               snake_case__					:      int  			=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
               snake_case__					:      str  			=						sum(config.num_block_repeats ) * 4
               self.assertEqual(len(__lowerCamelCase )    ,       __lowerCamelCase )

               # 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]    ,       )

         snake_case__					:      Any  			=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               snake_case__					:      Optional[Any]  			=						True
               check_hidden_states_output(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )

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

               check_hidden_states_output(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )





   def  _lowerCAmelCase			(    self    :				Tuple ):
         snake_case__					:      Optional[int]  			=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )





   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
   def  _lowerCAmelCase			(    self    :				int ):
         pass





   @slow
   def  _lowerCAmelCase			(    self    :				Dict ):
         for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               snake_case__					:      List[Any]  			=						EfficientNetModel.from_pretrained(__lowerCamelCase )
               self.assertIsNotNone(__lowerCamelCase )





   @is_pipeline_test
   @require_vision
   @slow
   def  _lowerCAmelCase			(    self    :				List[Any] ):
         super().test_pipeline_image_classification()






def 		UpperCamelCase__			(		)						->      Dict:
      snake_case__					:      Tuple  			=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
      return image






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





   @cached_property
   def  _lowerCAmelCase			(    self    :				Tuple ):
         return AutoImageProcessor.from_pretrained('google/efficientnet-b7' ) if is_vision_available() else None





   @slow
   def  _lowerCAmelCase			(    self    :				Dict ):
         snake_case__					:      Union[str, Any]  			=						EfficientNetForImageClassification.from_pretrained('google/efficientnet-b7' ).to(__lowerCamelCase )

         snake_case__					:      str  			=						self.default_image_processor
         snake_case__					:      int  			=						prepare_img()
         snake_case__					:      Any  			=						image_processor(images=__lowerCamelCase    ,       return_tensors='pt' ).to(__lowerCamelCase )

         # forward pass
         with torch.no_grad():
               snake_case__					:      Tuple  			=						model(**__lowerCamelCase )

         # verify the logits
         snake_case__					:      List[str]  			=						torch.Size((1, 1000) )
         self.assertEqual(outputs.logits.shape    ,       __lowerCamelCase )

         snake_case__					:      Optional[int]  			=						torch.tensor([0.0_0_0_1, 0.0_0_0_2, 0.0_0_0_2] ).to(__lowerCamelCase )
         self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,       __lowerCamelCase    ,       atol=1E-4 ) )






'''simple docstring'''






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 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Dict:
      snake_case__					:      Dict  			=						[rgram for rgrams in rgramslist for rgram in rgrams]
      snake_case__					:      int  			=						Counter(__SCREAMING_SNAKE_CASE		)

      snake_case__					:      Tuple  			=						Counter(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Union[str, Any]  			=						Counter()
      for sgram, scount in sgramcounter.items():
            snake_case__					:      Any  			=						scount * numref

      snake_case__					:      Any  			=						Counter(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Dict  			=						Counter()
      for cgram, ccount in cgramcounter.items():
            snake_case__					:      Optional[int]  			=						ccount * numref

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

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

      # DELETION
      snake_case__					:      Optional[Any]  			=						sgramcounter_rep - cgramcounter_rep
      snake_case__					:      int  			=						delgramcounter_rep - rgramcounter
      snake_case__					:      Tuple  			=						sgramcounter_rep - rgramcounter
      snake_case__					:      List[str]  			=						0
      snake_case__					:      List[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.
      snake_case__					:      Optional[Any]  			=						1
      if len(__SCREAMING_SNAKE_CASE		) > 0:
            snake_case__					:      str  			=						deltmpscorea / len(__SCREAMING_SNAKE_CASE		)

      # ADDITION
      snake_case__					:      Optional[int]  			=						set(__SCREAMING_SNAKE_CASE		) - set(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      str  			=						set(__SCREAMING_SNAKE_CASE		) & set(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Any  			=						set(__SCREAMING_SNAKE_CASE		) - set(__SCREAMING_SNAKE_CASE		)

      snake_case__					:      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.
      snake_case__					:      Optional[Any]  			=						1
      snake_case__					:      Union[str, Any]  			=						1
      if len(__SCREAMING_SNAKE_CASE		) > 0:
            snake_case__					:      str  			=						addtmpscore / len(__SCREAMING_SNAKE_CASE		)
      if len(__SCREAMING_SNAKE_CASE		) > 0:
            snake_case__					:      List[Any]  			=						addtmpscore / len(__SCREAMING_SNAKE_CASE		)
      snake_case__					:      str  			=						0
      if addscore_precision > 0 or addscore_recall > 0:
            snake_case__					:      int  			=						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

      return (keepscore, delscore_precision, addscore)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Tuple:
      snake_case__					:      Any  			=						len(__SCREAMING_SNAKE_CASE		)

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

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

      for i in range(0							,			len(__SCREAMING_SNAKE_CASE		) - 1		):
            if i < len(__SCREAMING_SNAKE_CASE		) - 1:
                  snake_case__					:      Optional[int]  			=						sagrams[i] + ' ' + sagrams[i + 1]
                  sagrams.append(__SCREAMING_SNAKE_CASE		)
            if i < len(__SCREAMING_SNAKE_CASE		) - 2:
                  snake_case__					:      Dict  			=						sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
                  sagrams.append(__SCREAMING_SNAKE_CASE		)
            if i < len(__SCREAMING_SNAKE_CASE		) - 3:
                  snake_case__					:      List[Any]  			=						sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
                  sagrams.append(__SCREAMING_SNAKE_CASE		)

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

      ((snake_case__)			,       (snake_case__)			,       (snake_case__))					:      List[str]  			=						SARIngram(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      ((snake_case__)			,       (snake_case__)			,       (snake_case__))					:      str  			=						SARIngram(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      ((snake_case__)			,       (snake_case__)			,       (snake_case__))					:      Dict  			=						SARIngram(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      ((snake_case__)			,       (snake_case__)			,       (snake_case__))					:      Tuple  			=						SARIngram(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      snake_case__					:      Tuple  			=						sum([keepascore, keepascore, keepascore, keepascore]		) / 4
      snake_case__					:      Optional[int]  			=						sum([delascore, delascore, delascore, delascore]		) / 4
      snake_case__					:      Optional[int]  			=						sum([addascore, addascore, addascore, addascore]		) / 4
      snake_case__					:      Union[str, Any]  			=						(avgkeepscore + avgdelscore + avgaddscore) / 3
      return finalscore






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

      if lowercase:
            snake_case__					:      Dict  			=						sentence.lower()

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

      if not return_str:
            snake_case__					:      List[Any]  			=						normalized_sent.split()

      return normalized_sent






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





     def  _lowerCAmelCase			(    self    :				Tuple ):
           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  _lowerCAmelCase			(    self    :				List[Any]    ,       __lowerCamelCase    :				List[Any]    ,       __lowerCamelCase    :				Optional[int]    ,       __lowerCamelCase    :				List[str] ):
           if not (len(__lowerCamelCase ) == len(__lowerCamelCase ) == len(__lowerCamelCase )):
                 raise ValueError('Sources length must match predictions and references lengths.' )
           snake_case__					:      List[Any]  			=						0
           for src, pred, refs in zip(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase ):
                 sari_score += SARIsent(normalize(__lowerCamelCase )    ,       normalize(__lowerCamelCase )    ,       [normalize(__lowerCamelCase ) for sent in refs] )
           snake_case__					:      int  			=						sari_score / len(__lowerCamelCase )
           return {"sari": 100 * sari_score}
