

'''simple docstring'''




from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_a				: str 							=						'\\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				: 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'


_a				: Optional[Any] 							=						'\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       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   int       ,   SCREAMING_SNAKE_CASE      :   Any       ,   SCREAMING_SNAKE_CASE      :   List[str]       ,   SCREAMING_SNAKE_CASE      :   Union[str, Any]						):
  UpperCAmelCase													=	[rgram for rgrams in rgramslist for rgram in rgrams]
  UpperCAmelCase													=	Counter(SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	Counter(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	Counter()
  for sgram, scount in sgramcounter.items():
    UpperCAmelCase													=	scount * numref

  UpperCAmelCase													=	Counter(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	Counter()
  for cgram, ccount in cgramcounter.items():
    UpperCAmelCase													=	ccount * numref

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

  UpperCAmelCase													=	0
  UpperCAmelCase													=	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													=	1
  UpperCAmelCase													=	1
  if len(SCREAMING_SNAKE_CASE						) > 0:
    UpperCAmelCase													=	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)
    UpperCAmelCase													=	keeptmpscorea / sum(keepgramcounterall_rep.values()						)
  UpperCAmelCase													=	0
  if keepscore_precision > 0 or keepscore_recall > 0:
    UpperCAmelCase													=	2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

  # DELETION
  UpperCAmelCase													=	sgramcounter_rep - cgramcounter_rep
  UpperCAmelCase													=	delgramcounter_rep - rgramcounter
  UpperCAmelCase													=	sgramcounter_rep - rgramcounter
  UpperCAmelCase													=	0
  UpperCAmelCase													=	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													=	1
  if len(SCREAMING_SNAKE_CASE						) > 0:
    UpperCAmelCase													=	deltmpscorea / len(SCREAMING_SNAKE_CASE						)

  # ADDITION
  UpperCAmelCase													=	set(SCREAMING_SNAKE_CASE						) - set(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	set(SCREAMING_SNAKE_CASE						) & set(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	set(SCREAMING_SNAKE_CASE						) - set(SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	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													=	1
  UpperCAmelCase													=	1
  if len(SCREAMING_SNAKE_CASE						) > 0:
    UpperCAmelCase													=	addtmpscore / len(SCREAMING_SNAKE_CASE						)
  if len(SCREAMING_SNAKE_CASE						) > 0:
    UpperCAmelCase													=	addtmpscore / len(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	0
  if addscore_precision > 0 or addscore_recall > 0:
    UpperCAmelCase													=	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

  return (keepscore, delscore_precision, addscore)


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Optional[int]       ,   SCREAMING_SNAKE_CASE      :   List[Any]       ,   SCREAMING_SNAKE_CASE      :   Union[str, Any]						):
  UpperCAmelCase													=	len(SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	ssent.split(' '						)
  UpperCAmelCase													=	csent.split(' '						)
  UpperCAmelCase													=	[]
  UpperCAmelCase													=	[]
  UpperCAmelCase													=	[]
  UpperCAmelCase													=	[]
  UpperCAmelCase													=	[]
  UpperCAmelCase													=	[]

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

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


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

  if lowercase:
    UpperCAmelCase													=	sentence.lower()

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

  if not return_str:
    UpperCAmelCase													=	normalized_sent.split()

  return normalized_sent



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

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				List[str]:


    """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 						snake_case_			(     self   , a_   , a_   , a_     )							->				List[Any]:


    """simple docstring"""
    if not (len(a_     ) == len(a_     ) == len(a_     )):
      raise ValueError('Sources length must match predictions and references lengths.'     )
    UpperCAmelCase													=	0
    for src, pred, refs in zip(a_   , a_   , a_     ):
      sari_score += SARIsent(normalize(a_     )   , normalize(a_     )   , [normalize(a_     ) for sent in refs]     )
    UpperCAmelCase													=	sari_score / len(a_     )
    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 MobileNetVaImageProcessor



class        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  def __init__(     self   , a_   , a_=7   , a_=3   , a_=1_8   , a_=3_0   , a_=4_0_0   , a_=True   , a_=None   , a_=True   , a_=None   , )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	size if size is not None else {'shortest_edge': 2_0}
    UpperCAmelCase													=	crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
    UpperCAmelCase													=	parent
    UpperCAmelCase													=	batch_size
    UpperCAmelCase													=	num_channels
    UpperCAmelCase													=	image_size
    UpperCAmelCase													=	min_resolution
    UpperCAmelCase													=	max_resolution
    UpperCAmelCase													=	do_resize
    UpperCAmelCase													=	size
    UpperCAmelCase													=	do_center_crop
    UpperCAmelCase													=	crop_size





  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
    }




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

  '''simple docstring'''





  __lowerCAmelCase					: Union[str, Any]      =   MobileNetVaImageProcessor if is_vision_available() else None
  def 						snake_case_			(     self     )							->				Dict:


    """simple docstring"""
    UpperCAmelCase													=	MobileNetVaImageProcessingTester(self     )
  @property
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    return self.image_processor_tester.prepare_image_processor_dict()
  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    self.assertTrue(hasattr(a_   , 'do_resize'     )     )
    self.assertTrue(hasattr(a_   , 'size'     )     )
    self.assertTrue(hasattr(a_   , 'do_center_crop'     )     )
    self.assertTrue(hasattr(a_   , 'center_crop'     )     )
  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    UpperCAmelCase													=	self.image_processing_class.from_dict(self.image_processor_dict     )
    self.assertEqual(image_processor.size   , {'shortest_edge': 2_0}     )
    self.assertEqual(image_processor.crop_size   , {'height': 1_8, 'width': 1_8}     )

    UpperCAmelCase													=	self.image_processing_class.from_dict(self.image_processor_dict   , size=4_2   , crop_size=8_4     )
    self.assertEqual(image_processor.size   , {'shortest_edge': 4_2}     )
    self.assertEqual(image_processor.crop_size   , {'height': 8_4, 'width': 8_4}     )
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    pass
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random PIL images
    UpperCAmelCase													=	prepare_image_inputs(self.image_processor_tester   , equal_resolution=a_     )
    for image in image_inputs:
      self.assertIsInstance(a_   , Image.Image     )

    # Test not batched input
    UpperCAmelCase													=	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
    UpperCAmelCase													=	image_processing(a_   , 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 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random numpy tensors
    UpperCAmelCase													=	prepare_image_inputs(self.image_processor_tester   , equal_resolution=a_   , numpify=a_     )
    for image in image_inputs:
      self.assertIsInstance(a_   , np.ndarray     )

    # Test not batched input
    UpperCAmelCase													=	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
    UpperCAmelCase													=	image_processing(a_   , 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 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random PyTorch tensors
    UpperCAmelCase													=	prepare_image_inputs(self.image_processor_tester   , equal_resolution=a_   , torchify=a_     )
    for image in image_inputs:
      self.assertIsInstance(a_   , torch.Tensor     )

    # Test not batched input
    UpperCAmelCase													=	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
    UpperCAmelCase													=	image_processing(a_   , 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'],
        )   , )


