





"""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 copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType




@require_torch
class    lowerCamelCase__  :
   __UpperCAmelCase           =						()
   __UpperCAmelCase           =						True



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =self.config_class

        # test default config
        _UpperCamelCase  :List[Any]       =config_class()
        self.assertIsNotNone(lowerCAmelCase__     )
        _UpperCamelCase  :List[str]       =["""stem"""] + [f'''stage{idx}''' for idx in range(1					,      len(config.depths     ) + 1     )]
        self.assertEqual(config.stage_names					,      lowerCAmelCase__     )
        self.assertTrue(set(config.out_features     ).issubset(set(config.stage_names     )     )     )

        # Test out_features and out_indices are correctly set
        # out_features and out_indices both None
        _UpperCamelCase  :Any       =config_class(out_features=lowerCAmelCase__					,      out_indices=lowerCAmelCase__     )
        self.assertEqual(config.out_features					,      [config.stage_names[-1]]     )
        self.assertEqual(config.out_indices					,      [len(config.stage_names     ) - 1]     )

        # out_features and out_indices both set
        _UpperCamelCase  :int       =config_class(out_features=["""stem""", """stage1"""]					,      out_indices=[0, 1]     )
        self.assertEqual(config.out_features					,      ["""stem""", """stage1"""]     )
        self.assertEqual(config.out_indices					,      [0, 1]     )

        # Only out_features set
        _UpperCamelCase  :Dict       =config_class(out_features=["""stage1""", """stage3"""]     )
        self.assertEqual(config.out_features					,      ["""stage1""", """stage3"""]     )
        self.assertEqual(config.out_indices					,      [1, 3]     )

        # Only out_indices set
        _UpperCamelCase  :Tuple       =config_class(out_indices=[0, 2]     )
        self.assertEqual(config.out_features					,      [config.stage_names[0], config.stage_names[2]]     )
        self.assertEqual(config.out_indices					,      [0, 2]     )

        # Error raised when out_indices do not correspond to out_features
        with self.assertRaises(lowerCAmelCase__     ):
             _UpperCamelCase  :List[Any]       =config_class(out_features=["""stage1""", """stage2"""]					,      out_indices=[0, 2]     )



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





        _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _UpperCamelCase  :Union[str, Any]       =model_class(lowerCAmelCase__     )
             _UpperCamelCase  :Union[str, Any]       =inspect.signature(model.forward     )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             _UpperCamelCase  :Tuple       =[*signature.parameters.keys()]
             _UpperCamelCase  :Any       =["""pixel_values"""]
             self.assertListEqual(arg_names[:1]					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _UpperCamelCase  :int       =model_class(lowerCAmelCase__     )
             self.assertEqual(len(model.channels     )					,      len(config.out_features     )     )
             _UpperCamelCase  :List[str]       =model.num_features
             _UpperCamelCase  :int       =[config.stage_names.index(lowerCAmelCase__     ) for feat in config.out_features]
             _UpperCamelCase  :List[str]       =[num_features[idx] for idx in out_indices]
             self.assertListEqual(model.channels					,      lowerCAmelCase__     )

             _UpperCamelCase  :Any       =copy.deepcopy(lowerCAmelCase__     )
             _UpperCamelCase  :List[Any]       =None
             _UpperCamelCase  :Optional[Any]       =model_class(lowerCAmelCase__     )
             self.assertEqual(len(model.channels     )					,      1     )
             self.assertListEqual(model.channels					,      [num_features[-1]]     )

             _UpperCamelCase  :Tuple       =copy.deepcopy(lowerCAmelCase__     )
             _UpperCamelCase  :int       =None
             _UpperCamelCase  :int       =model_class(lowerCAmelCase__     )
             self.assertEqual(len(model.channels     )					,      1     )
             self.assertListEqual(model.channels					,      [num_features[-1]]     )



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :Any       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _UpperCamelCase  :List[Any]       =model_class(lowerCAmelCase__     )
             model.to(lowerCAmelCase__     )
             model.eval()
             _UpperCamelCase  :str       =model(**lowerCAmelCase__     )

             self.assertEqual(len(result.feature_maps     )					,      len(config.out_features     )     )
             self.assertEqual(len(model.channels     )					,      len(config.out_features     )     )
             self.assertEqual(len(result.feature_maps     )					,      len(config.out_indices     )     )
             self.assertEqual(len(model.channels     )					,      len(config.out_indices     )     )

             # Check output of last stage is taken if out_features=None, out_indices=None
             _UpperCamelCase  :Any       =copy.deepcopy(lowerCAmelCase__     )
             _UpperCamelCase  :List[Any]       =None
             _UpperCamelCase  :Dict       =model_class(lowerCAmelCase__     )
             model.to(lowerCAmelCase__     )
             model.eval()
             _UpperCamelCase  :str       =model(**lowerCAmelCase__     )

             self.assertEqual(len(result.feature_maps     )					,      1     )
             self.assertEqual(len(model.channels     )					,      1     )

             _UpperCamelCase  :str       =copy.deepcopy(lowerCAmelCase__     )
             _UpperCamelCase  :Dict       =None
             _UpperCamelCase  :Tuple       =model_class(lowerCAmelCase__     )
             model.to(lowerCAmelCase__     )
             model.eval()
             _UpperCamelCase  :Dict       =model(**lowerCAmelCase__     )

             self.assertEqual(len(result.feature_maps     )					,      1     )
             self.assertEqual(len(model.channels     )					,      1     )

             # Check backbone can be initialized with fresh weights
             _UpperCamelCase  :Any       =copy.deepcopy(lowerCAmelCase__     )
             _UpperCamelCase  :Optional[int]       =False
             _UpperCamelCase  :Any       =model_class(lowerCAmelCase__     )
             model.to(lowerCAmelCase__     )
             model.eval()
             _UpperCamelCase  :Optional[int]       =model(**lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





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

        for backbone_class in self.all_model_classes:
             _UpperCamelCase  :Optional[int]       =backbone_class(lowerCAmelCase__     )

             self.assertTrue(hasattr(lowerCAmelCase__					,      """backbone_type"""     )     )
             self.assertTrue(hasattr(lowerCAmelCase__					,      """stage_names"""     )     )
             self.assertTrue(hasattr(lowerCAmelCase__					,      """num_features"""     )     )
             self.assertTrue(hasattr(lowerCAmelCase__					,      """out_indices"""     )     )
             self.assertTrue(hasattr(lowerCAmelCase__					,      """out_features"""     )     )
             self.assertTrue(hasattr(lowerCAmelCase__					,      """out_feature_channels"""     )     )
             self.assertTrue(hasattr(lowerCAmelCase__					,      """channels"""     )     )

             self.assertIsInstance(backbone.backbone_type					,      lowerCAmelCase__     )
             # Verify num_features has been initialized in the backbone init
             self.assertIsNotNone(backbone.num_features     )
             self.assertTrue(len(backbone.channels     ) == len(backbone.out_indices     )     )
             self.assertTrue(len(backbone.stage_names     ) == len(backbone.num_features     )     )
             self.assertTrue(len(backbone.channels     ) <= len(backbone.num_features     )     )
             self.assertTrue(len(backbone.out_feature_channels     ) == len(backbone.stage_names     )     )




   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :List[Any]       =self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCamelCase  :Union[str, Any]       =inputs_dict["""pixel_values"""].shape[0]

        for backbone_class in self.all_model_classes:
             _UpperCamelCase  :str       =backbone_class(lowerCAmelCase__     )
             backbone.to(lowerCAmelCase__     )
             backbone.eval()

             _UpperCamelCase  :Any       =backbone(**lowerCAmelCase__     )

             # Test default outputs and verify feature maps
             self.assertIsInstance(outputs.feature_maps					,      lowerCAmelCase__     )
             self.assertTrue(len(outputs.feature_maps     ) == len(backbone.channels     )     )
             for feature_map, n_channels in zip(outputs.feature_maps					,      backbone.channels     ):
                  self.assertTrue(feature_map.shape[:2]					,      (batch_size, n_channels)     )
             self.assertIsNone(outputs.hidden_states     )
             self.assertIsNone(outputs.attentions     )

             # Test output_hidden_states=True
             _UpperCamelCase  :Any       =backbone(**lowerCAmelCase__					,      output_hidden_states=lowerCAmelCase__     )
             self.assertIsNotNone(outputs.hidden_states     )
             self.assertTrue(len(outputs.hidden_states     )					,      len(backbone.stage_names     )     )
             for hidden_state, n_channels in zip(outputs.hidden_states					,      backbone.channels     ):
                  self.assertTrue(hidden_state.shape[:2]					,      (batch_size, n_channels)     )

             # Test output_attentions=True
             if self.has_attentions:
                  _UpperCamelCase  :int       =backbone(**lowerCAmelCase__					,      output_attentions=lowerCAmelCase__     )
                  self.assertIsNotNone(outputs.attentions     )