





import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

    from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
    from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor



class 					A__   :





      def __init__( self       ,							A_       ,							A_=13       ,							A_=30       ,							A_=2       ,							A_=3       ,							A_=True       ,							A_=True       ,							A_=32       ,							A_=5       ,							A_=4       ,							A_=37       ,							A_="gelu"       ,							A_=0.1       ,							A_=0.1       ,							A_=10       ,							A_=0.02       ,							A_=None       ,							A_=2       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   parent
       UpperCamelCase	:					Union[str, Any]       	=   batch_size
       UpperCamelCase	:					Optional[int]       	=   image_size
       UpperCamelCase	:					List[Any]       	=   patch_size
       UpperCamelCase	:					List[str]       	=   num_channels
       UpperCamelCase	:					Tuple       	=   is_training
       UpperCamelCase	:					Optional[int]       	=   use_labels
       UpperCamelCase	:					Dict       	=   hidden_size
       UpperCamelCase	:					Any       	=   num_hidden_layers
       UpperCamelCase	:					Dict       	=   num_attention_heads
       UpperCamelCase	:					Union[str, Any]       	=   intermediate_size
       UpperCamelCase	:					Optional[int]       	=   hidden_act
       UpperCamelCase	:					Dict       	=   hidden_dropout_prob
       UpperCamelCase	:					Dict       	=   attention_probs_dropout_prob
       UpperCamelCase	:					str       	=   type_sequence_label_size
       UpperCamelCase	:					Optional[int]       	=   initializer_range
       UpperCamelCase	:					Dict       	=   scope
       UpperCamelCase	:					str       	=   encoder_stride

       # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       UpperCamelCase	:					Any       	=   (image_size // patch_size) ** 2
       UpperCamelCase	:					List[Any]       	=   num_patches + 1





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

       UpperCamelCase	:					int       	=   None
       if self.use_labels:
        UpperCamelCase	:					List[Any]       	=   ids_tensor([self.batch_size]       ,							self.type_sequence_label_size   )

       UpperCamelCase	:					List[Any]       	=   self.get_config()

       return config, pixel_values, labels





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return ViTConfig(
           image_size=self.image_size       ,							patch_size=self.patch_size       ,							num_channels=self.num_channels       ,							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       ,							is_decoder=A_       ,							initializer_range=self.initializer_range       ,							encoder_stride=self.encoder_stride       ,							)





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   ViTModel(config=A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					Any       	=   model(A_   )
       self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   ViTForMaskedImageModeling(config=A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					Optional[int]       	=   model(A_   )
       self.parent.assertEqual(
           result.reconstruction.shape       ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)   )

       # test greyscale images
       UpperCamelCase	:					List[Any]       	=   1
       UpperCamelCase	:					Dict       	=   ViTForMaskedImageModeling(A_   )
       model.to(A_   )
       model.eval()

       UpperCamelCase	:					int       	=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
       UpperCamelCase	:					Optional[Any]       	=   model(A_   )
       self.parent.assertEqual(result.reconstruction.shape       ,							(self.batch_size, 1, self.image_size, self.image_size)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.type_sequence_label_size
       UpperCamelCase	:					Union[str, Any]       	=   ViTForImageClassification(A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					Any       	=   model(A_       ,							labels=A_   )
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)   )

       # test greyscale images
       UpperCamelCase	:					List[str]       	=   1
       UpperCamelCase	:					Optional[int]       	=   ViTForImageClassification(A_   )
       model.to(A_   )
       model.eval()

       UpperCamelCase	:					Union[str, Any]       	=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
       UpperCamelCase	:					Optional[int]       	=   model(A_   )
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    (
           UpperCamelCase
       )	,    
       )	:					str       	=   config_and_inputs
       UpperCamelCase	:					Optional[int]       	=   {"pixel_values": pixel_values}
       return config, inputs_dict



@require_torch
class 					A__   (       __snake_case       ,   __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Union[str, Any]						    =   (
          (
              ViTModel,
              ViTForImageClassification,
              ViTForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      _UpperCAmelCase	:List[str]						    =   (
          {'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
          if is_torch_available()
          else {}
      )
      _UpperCAmelCase	:str						    =   True

      _UpperCAmelCase	:Dict						    =   False
      _UpperCAmelCase	:Union[str, Any]						    =   False
      _UpperCAmelCase	:Tuple						    =   False





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   ViTModelTester(self   )
       UpperCamelCase	:					str       	=   ConfigTester(self       ,							config_class=A_       ,							has_text_modality=A_       ,							hidden_size=37   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      @unittest.skip(reason="ViT does not use inputs_embeds"   )
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       pass





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	,    UpperCamelCase	:					Dict       	=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCamelCase	:					Optional[int]       	=   model_class(A_   )
        self.assertIsInstance(model.get_input_embeddings()       ,							(nn.Module)   )
        UpperCamelCase	:					Any       	=   model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(A_       ,							nn.Linear   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	,    UpperCamelCase	:					List[Any]       	=   self.model_tester.prepare_config_and_inputs_for_common()

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

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_   )





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase	:					Tuple       	=   ViTModel.from_pretrained(A_   )
        self.assertIsNotNone(A_   )



def 		A_				(     )						-> Any:
 UpperCamelCase	:					Optional[Any]       	=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
 return image



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





      @cached_property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"   ) if is_vision_available() else None





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   ViTForImageClassification.from_pretrained("google/vit-base-patch16-224"   ).to(A_   )

       UpperCamelCase	:					str       	=   self.default_image_processor
       UpperCamelCase	:					Optional[Any]       	=   prepare_img()
       UpperCamelCase	:					Tuple       	=   image_processor(images=A_       ,							return_tensors="pt"   ).to(A_   )

       # forward pass
       with torch.no_grad():
        UpperCamelCase	:					Optional[int]       	=   model(**A_   )

       # verify the logits
       UpperCamelCase	:					Optional[Any]       	=   torch.Size((1, 1000)   )
       self.assertEqual(outputs.logits.shape       ,							A_   )

       UpperCamelCase	:					Any       	=   torch.tensor([-0.27_44, 0.82_15, -0.08_36]   ).to(A_   )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,							A_       ,							atol=1e-4   )   )





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   ViTModel.from_pretrained("facebook/dino-vits8"   ).to(A_   )

       UpperCamelCase	:					str       	=   ViTImageProcessor.from_pretrained("facebook/dino-vits8"       ,							size=480   )
       UpperCamelCase	:					str       	=   prepare_img()
       UpperCamelCase	:					Optional[Any]       	=   image_processor(images=A_       ,							return_tensors="pt"   )
       UpperCamelCase	:					str       	=   inputs.pixel_values.to(A_   )

       # forward pass
       with torch.no_grad():
        UpperCamelCase	:					Optional[Any]       	=   model(A_       ,							interpolate_pos_encoding=A_   )

       # verify the logits
       UpperCamelCase	:					Union[str, Any]       	=   torch.Size((1, 3601, 384)   )
       self.assertEqual(outputs.last_hidden_state.shape       ,							A_   )

       UpperCamelCase	:					str       	=   torch.tensor(
           [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]   ).to(A_   )

       self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]       ,							A_       ,							atol=1e-4   )   )





      @slow
      @require_accelerate
      @require_torch_gpu
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   ViTModel.from_pretrained("facebook/dino-vits8"       ,							torch_dtype=torch.floataa       ,							device_map="auto"   )
       UpperCamelCase	:					Tuple       	=   self.default_image_processor

       UpperCamelCase	:					Optional[int]       	=   prepare_img()
       UpperCamelCase	:					Tuple       	=   image_processor(images=A_       ,							return_tensors="pt"   )
       UpperCamelCase	:					Tuple       	=   inputs.pixel_values.to(A_   )

       # forward pass to make sure inference works in fp16
       with torch.no_grad():
        UpperCamelCase	:					List[str]       	=   model(A_   )







import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCamelCase       :     List[Any]   =     """
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

__lowerCamelCase       :     Optional[int]   =     """\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


__lowerCamelCase       :     str   =     """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""



def 		A_				(     _lowerCAmelCase     )						-> str:

 def remove_articles(_lowerCAmelCase     ):
  UpperCamelCase	:					Tuple       	=   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     ):
  UpperCamelCase	:					int       	=   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 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Any:
 return int(normalize_answer(_lowerCAmelCase     ) == normalize_answer(_lowerCAmelCase     )     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Tuple       	=   [any(compute_exact(_lowerCAmelCase							,   _lowerCAmelCase     ) for ref in refs     ) for pred, refs in zip(_lowerCAmelCase							,   _lowerCAmelCase     )]
 return (sum(_lowerCAmelCase     ) / len(_lowerCAmelCase     )) * 100



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 UpperCamelCase	:					Union[str, Any]       	=   [rgram for rgrams in rgramslist for rgram in rgrams]
 UpperCamelCase	:					Union[str, Any]       	=   Counter(_lowerCAmelCase     )

 UpperCamelCase	:					Optional[int]       	=   Counter(_lowerCAmelCase     )
 UpperCamelCase	:					List[Any]       	=   Counter()
 for sgram, scount in sgramcounter.items():
  UpperCamelCase	:					Tuple       	=   scount * numref

 UpperCamelCase	:					Union[str, Any]       	=   Counter(_lowerCAmelCase     )
 UpperCamelCase	:					Tuple       	=   Counter()
 for cgram, ccount in cgramcounter.items():
  UpperCamelCase	:					Dict       	=   ccount * numref

 # KEEP
 UpperCamelCase	:					List[Any]       	=   sgramcounter_rep & cgramcounter_rep
 UpperCamelCase	:					Union[str, Any]       	=   keepgramcounter_rep & rgramcounter
 UpperCamelCase	:					Dict       	=   sgramcounter_rep & rgramcounter

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

 # DELETION
 UpperCamelCase	:					Any       	=   sgramcounter_rep - cgramcounter_rep
 UpperCamelCase	:					str       	=   delgramcounter_rep - rgramcounter
 UpperCamelCase	:					Any       	=   sgramcounter_rep - rgramcounter
 UpperCamelCase	:					Optional[int]       	=   0
 UpperCamelCase	:					Union[str, Any]       	=   0
 for delgram in delgramcountergood_rep:
  deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
  deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
 # Define 0/0=1 instead of 0 to give higher scores for predictions that match
 # a target exactly.
 UpperCamelCase	:					Dict       	=   1
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					str       	=   deltmpscorea / len(_lowerCAmelCase     )

 # ADDITION
 UpperCamelCase	:					List[str]       	=   set(_lowerCAmelCase     ) - set(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   set(_lowerCAmelCase     ) & set(_lowerCAmelCase     )
 UpperCamelCase	:					Dict       	=   set(_lowerCAmelCase     ) - set(_lowerCAmelCase     )

 UpperCamelCase	:					Optional[Any]       	=   0
 for addgram in addgramcountergood:
  addtmpscore += 1

 # Define 0/0=1 instead of 0 to give higher scores for predictions that match
 # a target exactly.
 UpperCamelCase	:					Tuple       	=   1
 UpperCamelCase	:					Tuple       	=   1
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					Dict       	=   addtmpscore / len(_lowerCAmelCase     )
 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					Tuple       	=   addtmpscore / len(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   0
 if addscore_precision > 0 or addscore_recall > 0:
  UpperCamelCase	:					List[str]       	=   2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

 return (keepscore, delscore_precision, addscore)



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[str]:
 UpperCamelCase	:					int       	=   len(_lowerCAmelCase     )

 UpperCamelCase	:					Optional[Any]       	=   ssent.split(" "     )
 UpperCamelCase	:					Dict       	=   csent.split(" "     )
 UpperCamelCase	:					str       	=   []
 UpperCamelCase	:					Any       	=   []
 UpperCamelCase	:					Any       	=   []
 UpperCamelCase	:					Union[str, Any]       	=   []
 UpperCamelCase	:					str       	=   []
 UpperCamelCase	:					str       	=   []

 UpperCamelCase	:					Dict       	=   []
 UpperCamelCase	:					int       	=   []
 UpperCamelCase	:					Optional[Any]       	=   []
 UpperCamelCase	:					Tuple       	=   []
 for rsent in rsents:
  UpperCamelCase	:					List[Any]       	=   rsent.split(" "     )
  UpperCamelCase	:					List[str]       	=   []
  UpperCamelCase	:					int       	=   []
  UpperCamelCase	:					Tuple       	=   []
  ragramslist.append(_lowerCAmelCase     )
  for i in range(0							,   len(_lowerCAmelCase     ) - 1     ):
   if i < len(_lowerCAmelCase     ) - 1:
    UpperCamelCase	:					Dict       	=   ragrams[i] + " " + ragrams[i + 1]
    ragrams.append(_lowerCAmelCase     )
   if i < len(_lowerCAmelCase     ) - 2:
    UpperCamelCase	:					Dict       	=   ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
    ragrams.append(_lowerCAmelCase     )
   if i < len(_lowerCAmelCase     ) - 3:
    UpperCamelCase	:					List[Any]       	=   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:
   UpperCamelCase	:					Union[str, Any]       	=   sagrams[i] + " " + sagrams[i + 1]
   sagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 2:
   UpperCamelCase	:					List[str]       	=   sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
   sagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 3:
   UpperCamelCase	:					Optional[int]       	=   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:
   UpperCamelCase	:					Union[str, Any]       	=   cagrams[i] + " " + cagrams[i + 1]
   cagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 2:
   UpperCamelCase	:					Optional[Any]       	=   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
   cagrams.append(_lowerCAmelCase     )
  if i < len(_lowerCAmelCase     ) - 3:
   UpperCamelCase	:					Union[str, Any]       	=   cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
   cagrams.append(_lowerCAmelCase     )

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



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

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

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

 if not return_str:
  UpperCamelCase	:					Tuple       	=   normalized_sent.split()

 return normalized_sent



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



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase="exp"							,   _lowerCAmelCase=None							,   _lowerCAmelCase=False							,   _lowerCAmelCase=False							,   _lowerCAmelCase=False							,   )						-> List[str]:
 UpperCamelCase	:					Optional[Any]       	=   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"     )
 UpperCamelCase	:					Optional[int]       	=   [[refs[i] for refs in references] for i in range(_lowerCAmelCase     )]
 UpperCamelCase	:					Tuple       	=   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 					A__   (       datasets.Metric			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return datasets.MetricInfo(
           description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
               {
                   "predictions": datasets.Value("string"       ,							id="sequence"   ),
                   "references": datasets.Sequence(datasets.Value("string"       ,							id="sequence"   )       ,							id="references"   ),
               }   )       ,							codebase_urls=[
               "https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
               "https://github.com/cocoxu/simplification/blob/master/SARI.py",
               "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
               "https://github.com/mjpost/sacreBLEU",
           ]       ,							reference_urls=[
               "https://www.aclweb.org/anthology/Q16-1029.pdf",
               "https://github.com/mjpost/sacreBLEU",
               "https://en.wikipedia.org/wiki/BLEU",
               "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
           ]       ,							)





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   {}
       result.update({"sari": compute_sari(sources=A_       ,							predictions=A_       ,							references=A_   )}   )
       result.update({"sacrebleu": compute_sacrebleu(predictions=A_       ,							references=A_   )}   )
       result.update({"exact": compute_em(predictions=A_       ,							references=A_   )}   )
       return result
