


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import BlipImageProcessor







class lowerCAmelCase_						( unittest.TestCase       ):

 '''simple docstring'''

 def __init__(	self		,      _UpperCAmelCase		,      _UpperCAmelCase=7		,      _UpperCAmelCase=3		,      _UpperCAmelCase=18		,      _UpperCAmelCase=30		,      _UpperCAmelCase=4_00		,      _UpperCAmelCase=True		,      _UpperCAmelCase=None		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=[0.48_145_466, 0.4_578_275, 0.40_821_073]		,      _UpperCAmelCase=[0.26_862_954, 0.26_130_258, 0.27_577_711]		,      _UpperCAmelCase=True		,      ):
       snake_case_       =      size if size is not None else {"height": 20, "width": 20}
       snake_case_       =      parent
       snake_case_       =      batch_size
       snake_case_       =      num_channels
       snake_case_       =      image_size
       snake_case_       =      min_resolution
       snake_case_       =      max_resolution
       snake_case_       =      do_resize
       snake_case_       =      size
       snake_case_       =      do_normalize
       snake_case_       =      image_mean
       snake_case_       =      image_std
       snake_case_       =      do_pad
       snake_case_       =      do_convert_rgb



 def 						UpperCamelCase__     (	self					):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
           "do_pad": self.do_pad,
       }





 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False					):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       if equal_resolution:
             snake_case_       =      []
             for i in range(self.batch_size					):
                   image_inputs.append(
                       np.random.randint(
                           2_55		,      size=(self.num_channels, self.max_resolution, self.max_resolution)		,      dtype=np.uinta					)					)
       else:
             snake_case_       =      []
             for i in range(self.batch_size					):
                   snake_case_       =      np.random.choice(np.arange(self.min_resolution		,      self.max_resolution					)		,      2					)
                   image_inputs.append(np.random.randint(2_55		,      size=(self.num_channels, width, height)		,      dtype=np.uinta					)					)

       if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             snake_case_       =      [Image.fromarray(np.moveaxis(UpperCAmelCase_		,      0		,      -1					)					) for x in image_inputs]

       if torchify:
             snake_case_       =      [torch.from_numpy(UpperCAmelCase_					) for x in image_inputs]

       return image_inputs







@require_torch
@require_vision
class lowerCAmelCase_						( lowerCamelCase__					,						unittest.TestCase       ):

 '''simple docstring'''

 __snake_case												=  BlipImageProcessor if is_vision_available() else None



 def 						UpperCamelCase__     (	self					):
       snake_case_       =      BlipImageProcessingTester(self					)



 @property
 def 						UpperCamelCase__     (	self					):
       return self.image_processor_tester.prepare_image_processor_dict()



 def 						UpperCamelCase__     (	self					):
       snake_case_       =      self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(UpperCAmelCase_		,      '''do_resize'''					)					)
       self.assertTrue(hasattr(UpperCAmelCase_		,      '''size'''					)					)
       self.assertTrue(hasattr(UpperCAmelCase_		,      '''do_normalize'''					)					)
       self.assertTrue(hasattr(UpperCAmelCase_		,      '''image_mean'''					)					)
       self.assertTrue(hasattr(UpperCAmelCase_		,      '''image_std'''					)					)
       self.assertTrue(hasattr(UpperCAmelCase_		,      '''do_convert_rgb'''					)					)



 def 						UpperCamelCase__     (	self					):
       pass



 def 						UpperCamelCase__     (	self					):
       # Initialize image_processor
       snake_case_       =      self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       snake_case_       =      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_					)
       for image in image_inputs:
             self.assertIsInstance(UpperCAmelCase_		,      Image.Image					)

       # Test not batched input
       snake_case_       =      image_processor(image_inputs[0]		,      return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape		,      (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		,      )

       # Test batched
       snake_case_       =      image_processor(UpperCAmelCase_		,      return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape		,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		,      )



 def 						UpperCamelCase__     (	self					):
       # Initialize image_processor
       snake_case_       =      self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       snake_case_       =      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_		,      numpify=UpperCAmelCase_					)
       for image in image_inputs:
             self.assertIsInstance(UpperCAmelCase_		,      np.ndarray					)

       # Test not batched input
       snake_case_       =      image_processor(image_inputs[0]		,      return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape		,      (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		,      )

       # Test batched
       snake_case_       =      image_processor(UpperCAmelCase_		,      return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape		,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		,      )





 def 						UpperCamelCase__     (	self					):
       # Initialize image_processor
       snake_case_       =      self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       snake_case_       =      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_		,      torchify=UpperCAmelCase_					)
       for image in image_inputs:
             self.assertIsInstance(UpperCAmelCase_		,      torch.Tensor					)

       # Test not batched input
       snake_case_       =      image_processor(image_inputs[0]		,      return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape		,      (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		,      )

       # Test batched
       snake_case_       =      image_processor(UpperCAmelCase_		,      return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape		,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		,      )







@require_torch
@require_vision
class lowerCAmelCase_						( lowerCamelCase__					,						unittest.TestCase       ):

 '''simple docstring'''

 __snake_case												=  BlipImageProcessor if is_vision_available() else None



 def 						UpperCamelCase__     (	self					):
       snake_case_       =      BlipImageProcessingTester(self		,      num_channels=4					)
       snake_case_       =      3



 @property
 def 						UpperCamelCase__     (	self					):
       return self.image_processor_tester.prepare_image_processor_dict()



 def 						UpperCamelCase__     (	self					):
       snake_case_       =      self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(UpperCAmelCase_		,      '''do_resize'''					)					)
       self.assertTrue(hasattr(UpperCAmelCase_		,      '''size'''					)					)
       self.assertTrue(hasattr(UpperCAmelCase_		,      '''do_normalize'''					)					)
       self.assertTrue(hasattr(UpperCAmelCase_		,      '''image_mean'''					)					)
       self.assertTrue(hasattr(UpperCAmelCase_		,      '''image_std'''					)					)
       self.assertTrue(hasattr(UpperCAmelCase_		,      '''do_convert_rgb'''					)					)



 def 						UpperCamelCase__     (	self					):
       pass





 def 						UpperCamelCase__     (	self					):
       # Initialize image_processor
       snake_case_       =      self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       snake_case_       =      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_					)
       for image in image_inputs:
             self.assertIsInstance(UpperCAmelCase_		,      Image.Image					)

       # Test not batched input
       snake_case_       =      image_processor(image_inputs[0]		,      return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape		,      (
               1,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		,      )

       # Test batched
       snake_case_       =      image_processor(UpperCAmelCase_		,      return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape		,      (
               self.image_processor_tester.batch_size,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )		,      )







from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case			 =						"""\
@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},
}
"""

snake_case			 =						"""\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


snake_case			 =						"""
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
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
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Dict		  =			[rgram for rgrams in rgramslist for rgram in rgrams]
  SCREAMING_SNAKE_CASE				:					Dict		  =			Counter(lowercase )

  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			Counter(lowercase )
  SCREAMING_SNAKE_CASE				:					Dict		  =			Counter()
  for sgram, scount in sgramcounter.items():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			scount * numref

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

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

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

  # DELETION
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			sgramcounter_rep - cgramcounter_rep
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			delgramcounter_rep - rgramcounter
  SCREAMING_SNAKE_CASE				:					Dict		  =			sgramcounter_rep - rgramcounter
  SCREAMING_SNAKE_CASE				:					List[str]		  =			0
  SCREAMING_SNAKE_CASE				:					int		  =			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				:					List[Any]		  =			1
  if len(lowercase ) > 0:
    SCREAMING_SNAKE_CASE				:					str		  =			deltmpscorea / len(lowercase )

  # ADDITION
  SCREAMING_SNAKE_CASE				:					Dict		  =			set(lowercase ) - set(lowercase )
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			set(lowercase ) & set(lowercase )
  SCREAMING_SNAKE_CASE				:					int		  =			set(lowercase ) - set(lowercase )

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

  return (keepscore, delscore_precision, addscore)


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[Any]		  =			len(lowercase )

  SCREAMING_SNAKE_CASE				:					Dict		  =			ssent.split(" " )
  SCREAMING_SNAKE_CASE				:					str		  =			csent.split(" " )
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			[]
  SCREAMING_SNAKE_CASE				:					Any		  =			[]
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[]
  SCREAMING_SNAKE_CASE				:					Any		  =			[]
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[]
  SCREAMING_SNAKE_CASE				:					Any		  =			[]

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

  for i in range(0  ,   len(lowercase ) - 1 ):
    if i < len(lowercase ) - 1:
      SCREAMING_SNAKE_CASE				:					List[Any]		  =			sagrams[i] + " " + sagrams[i + 1]
      sagrams.append(lowercase )
    if i < len(lowercase ) - 2:
      SCREAMING_SNAKE_CASE				:					int		  =			sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
      sagrams.append(lowercase )
    if i < len(lowercase ) - 3:
      SCREAMING_SNAKE_CASE				:					Dict		  =			sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
      sagrams.append(lowercase )

  for i in range(0  ,   len(lowercase ) - 1 ):
    if i < len(lowercase ) - 1:
      SCREAMING_SNAKE_CASE				:					Optional[int]		  =			cagrams[i] + " " + cagrams[i + 1]
      cagrams.append(lowercase )
    if i < len(lowercase ) - 2:
      SCREAMING_SNAKE_CASE				:					str		  =			cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
      cagrams.append(lowercase )
    if i < len(lowercase ) - 3:
      SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
      cagrams.append(lowercase )

  ((SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE))				:					Any		  =			SARIngram(lowercase  ,   lowercase  ,   lowercase  ,   lowercase )
  ((SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE))				:					List[Any]		  =			SARIngram(lowercase  ,   lowercase  ,   lowercase  ,   lowercase )
  ((SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE))				:					List[str]		  =			SARIngram(lowercase  ,   lowercase  ,   lowercase  ,   lowercase )
  ((SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE)  ,     (SCREAMING_SNAKE_CASE))				:					Optional[Any]		  =			SARIngram(lowercase  ,   lowercase  ,   lowercase  ,   lowercase )
  SCREAMING_SNAKE_CASE				:					int		  =			sum([keepascore, keepascore, keepascore, keepascore] ) / 4
  SCREAMING_SNAKE_CASE				:					List[str]		  =			sum([delascore, delascore, delascore, delascore] ) / 4
  SCREAMING_SNAKE_CASE				:					Any		  =			sum([addascore, addascore, addascore, addascore] ) / 4
  SCREAMING_SNAKE_CASE				:					int		  =			(avgkeepscore + avgdelscore + avgaddscore) / 3
  return finalscore


def      lowerCamelCase__	(   lowercase  ,   lowercase = True  ,   lowercase = "13a"  ,   lowercase = True ):

  """simple docstring"""






  if lowercase:
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			sentence.lower()

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

  if not return_str:
    SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			normalized_sent.split()

  return normalized_sent


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,						_KWARGS_DESCRIPTION    )
class        SCREAMING_SNAKE_CASE	(	datasets.Metric    ):
     '''simple docstring'''


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






