





"""simple docstring"""




from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCamelCase_	:		int         =   '\\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'

lowerCamelCase_	:		List[Any]         =   '\\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'


lowerCamelCase_	:		str         =   '\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__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			List[Any]									=    [rgram for rgrams in rgramslist for rgram in rgrams]
  A_    :			Tuple									=    Counter(_UpperCAmelCase   )

  A_    :			str									=    Counter(_UpperCAmelCase   )
  A_    :			Optional[Any]									=    Counter()
  for sgram, scount in sgramcounter.items():
    A_    :			int									=    scount * numref

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

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

  A_    :			List[Any]									=    0
  A_    :			str									=    0
  for keepgram in keepgramcountergood_rep:
    keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
    # Fix an alleged bug [2] in the keep score computation.
    # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
    keeptmpscorea += keepgramcountergood_rep[keepgram]
  # Define 0/0=1 instead of 0 to give higher scores for predictions that match
  #      a target exactly.
  A_    :			List[Any]									=    1
  A_    :			Optional[Any]									=    1
  if len(_UpperCAmelCase   ) > 0:
    A_    :			str									=    keeptmpscorea / len(_UpperCAmelCase   )
  if len(_UpperCAmelCase   ) > 0:
    # Fix an alleged bug [2] in the keep score computation.
    # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
    A_    :			Union[str, Any]									=    keeptmpscorea / sum(keepgramcounterall_rep.values()   )
  A_    :			Optional[Any]									=    0
  if keepscore_precision > 0 or keepscore_recall > 0:
    A_    :			Dict									=    2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

  # DELETION
  A_    :			Dict									=    sgramcounter_rep - cgramcounter_rep
  A_    :			int									=    delgramcounter_rep - rgramcounter
  A_    :			str									=    sgramcounter_rep - rgramcounter
  A_    :			Dict									=    0
  A_    :			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.
  A_    :			str									=    1
  if len(_UpperCAmelCase   ) > 0:
    A_    :			List[str]									=    deltmpscorea / len(_UpperCAmelCase   )

  # ADDITION
  A_    :			Dict									=    set(_UpperCAmelCase   ) - set(_UpperCAmelCase   )
  A_    :			List[str]									=    set(_UpperCAmelCase   ) & set(_UpperCAmelCase   )
  A_    :			str									=    set(_UpperCAmelCase   ) - set(_UpperCAmelCase   )

  A_    :			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.
  A_    :			Union[str, Any]									=    1
  A_    :			Optional[Any]									=    1
  if len(_UpperCAmelCase   ) > 0:
    A_    :			str									=    addtmpscore / len(_UpperCAmelCase   )
  if len(_UpperCAmelCase   ) > 0:
    A_    :			Tuple									=    addtmpscore / len(_UpperCAmelCase   )
  A_    :			Optional[Any]									=    0
  if addscore_precision > 0 or addscore_recall > 0:
    A_    :			Tuple									=    2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

  return (keepscore, delscore_precision, addscore)





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Union[str, Any]									=    len(_UpperCAmelCase   )

  A_    :			str									=    ssent.split(' '   )
  A_    :			Any									=    csent.split(' '   )
  A_    :			Any									=    []
  A_    :			List[Any]									=    []
  A_    :			str									=    []
  A_    :			Optional[Any]									=    []
  A_    :			Dict									=    []
  A_    :			Optional[Any]									=    []

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

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

  for i in range(0					,   len(_UpperCAmelCase   ) - 1   ):
    if i < len(_UpperCAmelCase   ) - 1:
      A_    :			Dict									=    cagrams[i] + ' ' + cagrams[i + 1]
      cagrams.append(_UpperCAmelCase   )
    if i < len(_UpperCAmelCase   ) - 2:
      A_    :			Dict									=    cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
      cagrams.append(_UpperCAmelCase   )
    if i < len(_UpperCAmelCase   ) - 3:
      A_    :			str									=    cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
      cagrams.append(_UpperCAmelCase   )

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





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase = True					,   _UpperCAmelCase = "13a"					,   _UpperCAmelCase = True   ):






  """simple docstring"""





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

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

  if not return_str:
    A_    :			Any									=    normalized_sent.split()

  return normalized_sent





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



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        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   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        if not (len(snake_case_    ) == len(snake_case_    ) == len(snake_case_    )):
          raise ValueError('Sources length must match predictions and references lengths.'    )
        A_    :			Any									=    0
        for src, pred, refs in zip(snake_case_   , snake_case_   , snake_case_    ):
          sari_score += SARIsent(normalize(snake_case_    )   , normalize(snake_case_    )   , [normalize(snake_case_    ) for sent in refs]    )
        A_    :			List[str]									=    sari_score / len(snake_case_    )
        return {"sari": 1_0_0 * sari_score}


"""simple docstring"""





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, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import OwlViTImageProcessor




class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 def __init__(     self				:     Union[str, Any],   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     Optional[int]=7,   UpperCAmelCase__				:     List[str]=3,   UpperCAmelCase__				:     List[Any]=1_8,   UpperCAmelCase__				:     Any=3_0,   UpperCAmelCase__				:     str=4_0_0,   UpperCAmelCase__				:     Union[str, Any]=True,   UpperCAmelCase__				:     Dict=None,   UpperCAmelCase__				:     int=True,   UpperCAmelCase__				:     str=None,   UpperCAmelCase__				:     Dict=True,   UpperCAmelCase__				:     str=[0.48_145_466, 0.4_578_275, 0.40_821_073],   UpperCAmelCase__				:     Dict=[0.26_862_954, 0.26_130_258, 0.27_577_711],   UpperCAmelCase__				:     Any=True,   ):
    __lowercase								=		parent
    __lowercase								=		batch_size
    __lowercase								=		num_channels
    __lowercase								=		image_size
    __lowercase								=		min_resolution
    __lowercase								=		max_resolution
    __lowercase								=		do_resize
    __lowercase								=		size if size is not None else {"height": 1_8, "width": 1_8}
    __lowercase								=		do_center_crop
    __lowercase								=		crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
    __lowercase								=		do_normalize
    __lowercase								=		image_mean
    __lowercase								=		image_std
    __lowercase								=		do_convert_rgb



 def    _lowercase					(     self				:     List[str]      ):
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_convert_rgb": self.do_convert_rgb,
    }







@require_torch
@require_vision
class    _lowerCAmelCase    (      lowercase	,unittest.TestCase  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[str]  						=      OwlViTImageProcessor if is_vision_available() else None


 def    _lowercase					(     self				:     Dict      ):
    __lowercase								=		OwlViTImageProcessingTester(self      )


 @property
 def    _lowercase					(     self				:     int      ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def    _lowercase					(     self				:     Tuple      ):
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "do_resize"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "size"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "do_center_crop"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "center_crop"      )      )
    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    _lowercase					(     self				:     Dict      ):
    __lowercase								=		self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size,   {"height": 1_8, "width": 1_8}      )
    self.assertEqual(image_processor.crop_size,   {"height": 1_8, "width": 1_8}      )

    __lowercase								=		self.image_processing_class.from_dict(self.image_processor_dict,   size=4_2,   crop_size=8_4      )
    self.assertEqual(image_processor.size,   {"height": 4_2, "width": 4_2}      )
    self.assertEqual(image_processor.crop_size,   {"height": 8_4, "width": 8_4}      )


 def    _lowercase					(     self				:     Any      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random PIL images
    __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__      )

    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,   Image.Image      )

    # Test not batched input
    __lowercase								=		image_processing(image_inputs[0],   return_tensors="pt"      ).pixel_values
    self.assertEqual(
        encoded_images.shape,   (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),   )

    # Test batched
    __lowercase								=		image_processing(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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),   )


 def    _lowercase					(     self				:     Optional[Any]      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__,   numpify=UpperCAmelCase__      )
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,   np.ndarray      )

    # Test not batched input
    __lowercase								=		image_processing(image_inputs[0],   return_tensors="pt"      ).pixel_values
    self.assertEqual(
        encoded_images.shape,   (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),   )

    # Test batched
    __lowercase								=		image_processing(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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),   )



 def    _lowercase					(     self				:     List[str]      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__,   torchify=UpperCAmelCase__      )
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,   torch.Tensor      )

    # Test not batched input
    __lowercase								=		image_processing(image_inputs[0],   return_tensors="pt"      ).pixel_values
    self.assertEqual(
        encoded_images.shape,   (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),   )

    # Test batched
    __lowercase								=		image_processing(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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),   )




