code: 
  
  
  from collections import Counter
  
  import sacrebleu
  import sacremoses
  from packaging import version
  
  import datasets
  
  
  lowerCAmelCase__   	=				'\\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__   	=				'\\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__   	=				'\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__  :						Optional[int]       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						int       ,							UpperCamelCase__  :						Dict		):
         _A   : Union[str, Any]       					=				[rgram for rgrams in rgramslist for rgram in rgrams]
         _A   : Any       					=				Counter(UpperCamelCase__		)
  
         _A   : List[Any]       					=				Counter(UpperCamelCase__		)
         _A   : Optional[Any]       					=				Counter()
         for sgram, scount in sgramcounter.items():
                _A   : Optional[Any]       					=				scount * numref
  
         _A   : Any       					=				Counter(UpperCamelCase__		)
         _A   : Optional[int]       					=				Counter()
         for cgram, ccount in cgramcounter.items():
                _A   : Optional[int]       					=				ccount * numref
  
         # KEEP
         _A   : int       					=				sgramcounter_rep & cgramcounter_rep
         _A   : Optional[Any]       					=				keepgramcounter_rep & rgramcounter
         _A   : Optional[int]       					=				sgramcounter_rep & rgramcounter
  
         _A   : List[str]       					=				0
         _A   : Optional[int]       					=				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   : str       					=				1
         _A   : str       					=				1
         if len(UpperCamelCase__		) > 0:
                _A   : Dict       					=				keeptmpscorea / len(UpperCamelCase__		)
         if len(UpperCamelCase__		) > 0:
                # Fix an alleged bug [2] in the keep score computation.
                # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
                _A   : Tuple       					=				keeptmpscorea / sum(keepgramcounterall_rep.values()		)
         _A   : Optional[int]       					=				0
         if keepscore_precision > 0 or keepscore_recall > 0:
                _A   : Optional[int]       					=				2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
  
         # DELETION
         _A   : Union[str, Any]       					=				sgramcounter_rep - cgramcounter_rep
         _A   : str       					=				delgramcounter_rep - rgramcounter
         _A   : int       					=				sgramcounter_rep - rgramcounter
         _A   : Tuple       					=				0
         _A   : Optional[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   : Optional[int]       					=				1
         if len(UpperCamelCase__		) > 0:
                _A   : Union[str, Any]       					=				deltmpscorea / len(UpperCamelCase__		)
  
         # ADDITION
         _A   : Optional[Any]       					=				set(UpperCamelCase__		) - set(UpperCamelCase__		)
         _A   : Any       					=				set(UpperCamelCase__		) & set(UpperCamelCase__		)
         _A   : Optional[Any]       					=				set(UpperCamelCase__		) - set(UpperCamelCase__		)
  
         _A   : Dict       					=				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   : int       					=				1
         _A   : str       					=				1
         if len(UpperCamelCase__		) > 0:
                _A   : Dict       					=				addtmpscore / len(UpperCamelCase__		)
         if len(UpperCamelCase__		) > 0:
                _A   : Dict       					=				addtmpscore / len(UpperCamelCase__		)
         _A   : str       					=				0
         if addscore_precision > 0 or addscore_recall > 0:
                _A   : Optional[int]       					=				2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
  
         return (keepscore, delscore_precision, addscore)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						Any		):
         _A   : Dict       					=				len(UpperCamelCase__		)
  
         _A   : Dict       					=				ssent.split(" "		)
         _A   : Union[str, Any]       					=				csent.split(" "		)
         _A   : List[str]       					=				[]
         _A   : Union[str, Any]       					=				[]
         _A   : List[str]       					=				[]
         _A   : Any       					=				[]
         _A   : Dict       					=				[]
         _A   : Dict       					=				[]
  
         _A   : List[Any]       					=				[]
         _A   : Optional[int]       					=				[]
         _A   : List[Any]       					=				[]
         _A   : str       					=				[]
         for rsent in rsents:
                _A   : Any       					=				rsent.split(" "		)
                _A   : int       					=				[]
                _A   : Dict       					=				[]
                _A   : Any       					=				[]
                ragramslist.append(UpperCamelCase__		)
                for i in range(0       ,							len(UpperCamelCase__		) - 1		):
                       if i < len(UpperCamelCase__		) - 1:
                              _A   : Optional[int]       					=				ragrams[i] + " " + ragrams[i + 1]
                              ragrams.append(UpperCamelCase__		)
                       if i < len(UpperCamelCase__		) - 2:
                              _A   : str       					=				ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
                              ragrams.append(UpperCamelCase__		)
                       if i < len(UpperCamelCase__		) - 3:
                              _A   : List[str]       					=				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   : Union[str, Any]       					=				sagrams[i] + " " + sagrams[i + 1]
                       sagrams.append(UpperCamelCase__		)
                if i < len(UpperCamelCase__		) - 2:
                       _A   : List[str]       					=				sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
                       sagrams.append(UpperCamelCase__		)
                if i < len(UpperCamelCase__		) - 3:
                       _A   : List[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   : List[Any]       					=				cagrams[i] + " " + cagrams[i + 1]
                       cagrams.append(UpperCamelCase__		)
                if i < len(UpperCamelCase__		) - 2:
                       _A   : Any       					=				cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
                       cagrams.append(UpperCamelCase__		)
                if i < len(UpperCamelCase__		) - 3:
                       _A   : List[str]       					=				cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
                       cagrams.append(UpperCamelCase__		)
  
         (_A)   : Optional[int]       					=				SARIngram(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         (_A)   : int       					=				SARIngram(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         (_A)   : Optional[Any]       					=				SARIngram(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         (_A)   : Tuple       					=				SARIngram(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         _A   : Union[str, Any]       					=				sum([keepascore, keepascore, keepascore, keepascore]		) / 4
         _A   : Union[str, Any]       					=				sum([delascore, delascore, delascore, delascore]		) / 4
         _A   : Optional[int]       					=				sum([addascore, addascore, addascore, addascore]		) / 4
         _A   : Optional[int]       					=				(avgkeepscore + avgdelscore + avgaddscore) / 3
         return finalscore
  def 							_UpperCAmelCase       (UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						bool = True       ,							UpperCamelCase__  :						str = "13a"       ,							UpperCamelCase__  :						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:
                _A   : Optional[int]       					=				sentence.lower()
  
         if tokenizer in ["13a", "intl"]:
                if version.parse(sacrebleu.__version__		).major >= 2:
                       _A   : Dict       					=				sacrebleu.metrics.bleu._get_tokenizer(UpperCamelCase__		)()(UpperCamelCase__		)
                else:
                       _A   : Any       					=				sacrebleu.TOKENIZERS[tokenizer]()(UpperCamelCase__		)
         elif tokenizer == "moses":
                _A   : Dict       					=				sacremoses.MosesTokenizer().tokenize(UpperCamelCase__       ,							return_str=UpperCamelCase__       ,							escape=UpperCamelCase__		)
         elif tokenizer == "penn":
                _A   : Dict       					=				sacremoses.MosesTokenizer().penn_tokenize(UpperCamelCase__       ,							return_str=UpperCamelCase__		)
         else:
                _A   : int       					=				sentence
  
         if not return_str:
                _A   : Dict       					=				normalized_sent.split()
  
         return normalized_sent
  
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,					_KWARGS_DESCRIPTION)
  class   lowerCAmelCase__ (       datasets.Metric):
  
  
  
  
       '''simple docstring'''
  
  
       def 				_lowerCamelCase				(		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 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Optional[Any]:
              if not (len(__lowerCamelCase) == len(__lowerCamelCase) == len(__lowerCamelCase)):
                     raise ValueError("Sources length must match predictions and references lengths.")
              _A   : List[Any]       					=				0
              for src, pred, refs in zip(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase):
                     sari_score += SARIsent(normalize(__lowerCamelCase)				,		normalize(__lowerCamelCase)				,		[normalize(__lowerCamelCase) for sent in refs])
              _A   : Union[str, Any]       					=				sari_score / len(__lowerCamelCase)
              return {"sari": 1_0_0 * sari_score}
style_context: 
  
  
  import cva
  import numpy as np
  
  from digital_image_processing.filters.convolve import img_convolve
  from digital_image_processing.filters.sobel_filter import sobel_filter
  
  lowerCAmelCase__   	=				1_80
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]		):
         _A   : int       					=				k_size // 2
         _A					,    _A   : Tuple       					=				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
         _A   : Union[str, Any]       					=				(
             1
             / (2 * np.pi * sigma)
             * np.exp(-(np.square(UpperCamelCase__		) + np.square(UpperCamelCase__		)) / (2 * np.square(UpperCamelCase__		))		)
         )
         return g
  def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Tuple		):
         _A   : Optional[int]       					=				np.zeros(UpperCamelCase__		)
  
         for row in range(1       ,							image_shape[0] - 1		):
                for col in range(1       ,							image_shape[1] - 1		):
                       _A   : Dict       					=				gradient_direction[row, col]
  
                       if (
                           0 <= direction < PI / 8
                           or 15 * PI / 8 <= direction <= 2 * PI
                           or 7 * PI / 8 <= direction <= 9 * PI / 8
                       ):
                              _A   : Dict       					=				sobel_grad[row, col - 1]
                              _A   : Any       					=				sobel_grad[row, col + 1]
                              if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                     _A   : List[Any]       					=				sobel_grad[row, col]
  
                       elif (
                           PI / 8 <= direction < 3 * PI / 8
                           or 9 * PI / 8 <= direction < 11 * PI / 8
                       ):
                              _A   : List[str]       					=				sobel_grad[row + 1, col - 1]
                              _A   : str       					=				sobel_grad[row - 1, col + 1]
                              if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                     _A   : List[Any]       					=				sobel_grad[row, col]
  
                       elif (
                           3 * PI / 8 <= direction < 5 * PI / 8
                           or 11 * PI / 8 <= direction < 13 * PI / 8
                       ):
                              _A   : Any       					=				sobel_grad[row - 1, col]
                              _A   : int       					=				sobel_grad[row + 1, col]
                              if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                     _A   : Any       					=				sobel_grad[row, col]
  
                       elif (
                           5 * PI / 8 <= direction < 7 * PI / 8
                           or 13 * PI / 8 <= direction < 15 * PI / 8
                       ):
                              _A   : List[str]       					=				sobel_grad[row - 1, col - 1]
                              _A   : Optional[Any]       					=				sobel_grad[row + 1, col + 1]
                              if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                     _A   : List[Any]       					=				sobel_grad[row, col]
  
         return destination
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Any		):
         for row in range(1       ,							image_shape[0] - 1		):
                for col in range(1       ,							image_shape[1] - 1		):
                       if destination[row, col] >= threshold_high:
                              _A   : Optional[int]       					=				strong
                       elif destination[row, col] <= threshold_low:
                              _A   : Optional[int]       					=				0
                       else:
                              _A   : Union[str, Any]       					=				weak
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Any		):
         for row in range(1       ,							image_shape[0]		):
                for col in range(1       ,							image_shape[1]		):
                       if destination[row, col] == weak:
                              if 255 in (
                                  destination[row, col + 1],
                                  destination[row, col - 1],
                                  destination[row - 1, col],
                                  destination[row + 1, col],
                                  destination[row - 1, col - 1],
                                  destination[row + 1, col - 1],
                                  destination[row - 1, col + 1],
                                  destination[row + 1, col + 1],
                              ):
                                     _A   : str       					=				strong
                              else:
                                     _A   : Any       					=				0
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						int=15       ,							UpperCamelCase__  :						Dict=30       ,							UpperCamelCase__  :						int=128       ,							UpperCamelCase__  :						Union[str, Any]=255		):
         # gaussian_filter
         _A   : Union[str, Any]       					=				img_convolve(UpperCamelCase__       ,							gen_gaussian_kernel(9       ,							sigma=1.4		)		)
         # get the gradient and degree by sobel_filter
         _A					,    _A   : Dict       					=				sobel_filter(UpperCamelCase__		)
         _A   : Tuple       					=				PI + np.radadeg(UpperCamelCase__		)
  
         _A   : Optional[int]       					=				suppress_non_maximum(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         detect_high_low_threshold(
             image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         track_edge(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         return destination
  
  
  if __name__ == "__main__":
      # read original image in gray mode
      lowerCAmelCase__   	=				cva.imread(R'../image_data/lena.jpg', 0)
      # canny edge detection
      lowerCAmelCase__   	=				canny(lena)
      cva.imshow('canny', canny_destination)
      cva.waitKey(0)
  