

import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowercase_									=   '\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

lowercase_									=   '\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


lowercase_									=   '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\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\n    sacrebleu: sacrebleu score\n    exact: exact score\n\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 ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'
def      a      (			A__	:   Tuple     )    ->      Tuple:





      """simple docstring"""



      def remove_articles(A__	:   List[str]     ):
            _lowercase	       =re.compile(r'\b(a|an|the)\b'      ,   re.UNICODE     )
            return re.sub(A__      ,   ' '      ,   A__     )

      def white_space_fix(A__	:   Optional[int]     ):
            return " ".join(text.split()     )

      def remove_punc(A__	:   Optional[Any]     ):
            _lowercase	       =set(string.punctuation     )
            return "".join(ch for ch in text if ch not in exclude     )

      def lower(A__	:   Any     ):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(A__     )     )     )     )
def      a      (			A__	:   Union[str, Any]      ,   A__	:   Dict     )    ->      Dict:





      """simple docstring"""



      return int(normalize_answer(A__     ) == normalize_answer(A__     )     )
def      a      (			A__	:   Optional[Any]      ,   A__	:   int     )    ->      List[str]:





      """simple docstring"""



      _lowercase	       =[any(compute_exact(A__      ,   A__     ) for ref in refs     ) for pred, refs in zip(A__      ,   A__     )]
      return (sum(A__     ) / len(A__     )) * 100
def      a      (			A__	:   Optional[Any]      ,   A__	:   Optional[Any]      ,   A__	:   Union[str, Any]      ,   A__	:   Optional[Any]     )    ->      Any:





      """simple docstring"""



      _lowercase	       =[rgram for rgrams in rgramslist for rgram in rgrams]
      _lowercase	       =Counter(A__     )

      _lowercase	       =Counter(A__     )
      _lowercase	       =Counter()
      for sgram, scount in sgramcounter.items():
            _lowercase	       =scount * numref

      _lowercase	       =Counter(A__     )
      _lowercase	       =Counter()
      for cgram, ccount in cgramcounter.items():
            _lowercase	       =ccount * numref

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

      _lowercase	       =0
      _lowercase	       =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.
      _lowercase	       =1
      _lowercase	       =1
      if len(A__     ) > 0:
            _lowercase	       =keeptmpscorea / len(A__     )
      if len(A__     ) > 0:
            # Fix an alleged bug [2] in the keep score computation.
            # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
            _lowercase	       =keeptmpscorea / sum(keepgramcounterall_rep.values()     )
      _lowercase	       =0
      if keepscore_precision > 0 or keepscore_recall > 0:
            _lowercase	       =2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

      # DELETION
      _lowercase	       =sgramcounter_rep - cgramcounter_rep
      _lowercase	       =delgramcounter_rep - rgramcounter
      _lowercase	       =sgramcounter_rep - rgramcounter
      _lowercase	       =0
      _lowercase	       =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.
      _lowercase	       =1
      if len(A__     ) > 0:
            _lowercase	       =deltmpscorea / len(A__     )

      # ADDITION
      _lowercase	       =set(A__     ) - set(A__     )
      _lowercase	       =set(A__     ) & set(A__     )
      _lowercase	       =set(A__     ) - set(A__     )

      _lowercase	       =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.
      _lowercase	       =1
      _lowercase	       =1
      if len(A__     ) > 0:
            _lowercase	       =addtmpscore / len(A__     )
      if len(A__     ) > 0:
            _lowercase	       =addtmpscore / len(A__     )
      _lowercase	       =0
      if addscore_precision > 0 or addscore_recall > 0:
            _lowercase	       =2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

      return (keepscore, delscore_precision, addscore)
def      a      (			A__	:   Any      ,   A__	:   List[str]      ,   A__	:   str     )    ->      List[Any]:





      """simple docstring"""



      _lowercase	       =len(A__     )

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

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

      for i in range(0      ,   len(A__     ) - 1     ):
            if i < len(A__     ) - 1:
                  _lowercase	       =sagrams[i] + ' ' + sagrams[i + 1]
                  sagrams.append(A__     )
            if i < len(A__     ) - 2:
                  _lowercase	       =sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
                  sagrams.append(A__     )
            if i < len(A__     ) - 3:
                  _lowercase	       =sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
                  sagrams.append(A__     )

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

      ((_lowercase)       ,       (_lowercase)       ,       (_lowercase))	       =SARIngram(A__      ,   A__      ,   A__      ,   A__     )
      ((_lowercase)       ,       (_lowercase)       ,       (_lowercase))	       =SARIngram(A__      ,   A__      ,   A__      ,   A__     )
      ((_lowercase)       ,       (_lowercase)       ,       (_lowercase))	       =SARIngram(A__      ,   A__      ,   A__      ,   A__     )
      ((_lowercase)       ,       (_lowercase)       ,       (_lowercase))	       =SARIngram(A__      ,   A__      ,   A__      ,   A__     )
      _lowercase	       =sum([keepascore, keepascore, keepascore, keepascore]     ) / 4
      _lowercase	       =sum([delascore, delascore, delascore, delascore]     ) / 4
      _lowercase	       =sum([addascore, addascore, addascore, addascore]     ) / 4
      _lowercase	       =(avgkeepscore + avgdelscore + avgaddscore) / 3
      return finalscore
def      a      (			A__	:   Optional[int]      ,   A__	:   bool = True      ,   A__	:   str = "13a"      ,   A__	:   bool = True     )    ->      List[Any]:





      """simple docstring"""



      if lowercase:
            _lowercase	       =sentence.lower()

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

      if not return_str:
            _lowercase	       =normalized_sent.split()

      return normalized_sent
def      a      (			A__	:   List[Any]      ,   A__	:   Union[str, Any]      ,   A__	:   Optional[Any]     )    ->      List[Any]:





      """simple docstring"""



      if not (len(A__     ) == len(A__     ) == len(A__     )):
            raise ValueError('Sources length must match predictions and references lengths.'     )
      _lowercase	       =0
      for src, pred, refs in zip(A__      ,   A__      ,   A__     ):
            sari_score += SARIsent(normalize(A__     )      ,   normalize(A__     )      ,   [normalize(A__     ) for sent in refs]     )
      _lowercase	       =sari_score / len(A__     )
      return 100 * sari_score
def      a      (			A__	:   List[str]      ,   A__	:   List[str]      ,   A__	:   str="exp"      ,   A__	:   Optional[Any]=None      ,   A__	:   List[Any]=False      ,   A__	:   Union[str, Any]=False      ,   A__	:   List[Any]=False      ,   )    ->      Tuple:





      """simple docstring"""



      _lowercase	       =len(references[0]     )
      if any(len(A__     ) != references_per_prediction for refs in references     ):
            raise ValueError('Sacrebleu requires the same number of references for each prediction'     )
      _lowercase	       =[[refs[i] for refs in references] for i in range(A__     )]
      _lowercase	       =sacrebleu.corpus_bleu(
          A__      ,   A__      ,   smooth_method=A__      ,   smooth_value=A__      ,   force=A__      ,   lowercase=A__      ,   use_effective_order=A__      ,   )
      return output.score







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




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        return datasets.MetricInfo(
            description=_DESCRIPTION							,   citation=_CITATION							,   inputs_description=_KWARGS_DESCRIPTION							,   features=datasets.Features(
                {
                    'predictions': datasets.Value('string'							,   id='sequence'					),
                    'references': datasets.Sequence(datasets.Value('string'							,   id='sequence'					)							,   id='references'					),
                }					)							,   codebase_urls=[
                'https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py',
                'https://github.com/cocoxu/simplification/blob/master/SARI.py',
                'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
                'https://github.com/mjpost/sacreBLEU',
            ]							,   reference_urls=[
                'https://www.aclweb.org/anthology/Q16-1029.pdf',
                'https://github.com/mjpost/sacreBLEU',
                'https://en.wikipedia.org/wiki/BLEU',
                'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
            ]							,   )




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					)  -> List[str]:
        '''simple docstring'''






        _lowercase	       ={}
        result.update({'sari': compute_sari(sources=lowerCAmelCase							,   predictions=lowerCAmelCase							,   references=lowerCAmelCase					)}					)
        result.update({'sacrebleu': compute_sacrebleu(predictions=lowerCAmelCase							,   references=lowerCAmelCase					)}					)
        result.update({'exact': compute_em(predictions=lowerCAmelCase							,   references=lowerCAmelCase					)}					)
        return result





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowercase_									=   logging.get_logger(__name__)





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   **lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =feature_size
        _lowercase	       =sampling_rate
        _lowercase	       =padding_value

        _lowercase	       =kwargs.pop('padding_side'							,   'right'					)
        _lowercase	       =kwargs.pop('return_attention_mask'							,   lowerCAmelCase					)

        super().__init__(**lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = True							,   lowerCAmelCase = None							,   lowerCAmelCase = False							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   )  -> BatchFeature:
        '''simple docstring'''






        if isinstance(lowerCAmelCase							,   (list, tuple)					) and isinstance(processed_features[0]							,   (dict, BatchFeature)					):
              _lowercase	       ={
                  key: [example[key] for example in processed_features] for key in processed_features[0].keys()
              }

        # The model's main input name, usually `input_values`, has be passed for padding
        if self.model_input_names[0] not in processed_features:
              raise ValueError(
                  'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
                  F''' to this method that includes {self.model_input_names[0]}, but you provided'''
                  F''' {list(processed_features.keys()					)}'''					)

        _lowercase	       =processed_features[self.model_input_names[0]]
        _lowercase	       =(
            return_attention_mask if return_attention_mask is not None else self.return_attention_mask
        )

        if len(lowerCAmelCase					) == 0:
              if return_attention_mask:
                    _lowercase	       =[]
              return processed_features

        # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
        # and rebuild them afterwards if no return_tensors is specified
        # Note that we lose the specific device the tensor may be on for PyTorch

        _lowercase	       =required_input[0]
        if isinstance(lowerCAmelCase							,   (list, tuple)					):
              # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
              _lowercase	       =0
              while len(required_input[index]					) == 0:
                    index += 1
              if index < len(lowerCAmelCase					):
                    _lowercase	       =required_input[index][0]

        if return_tensors is None:
              if is_tf_tensor(lowerCAmelCase					):
                    _lowercase	       ='tf'
              elif is_torch_tensor(lowerCAmelCase					):
                    _lowercase	       ='pt'
              elif isinstance(lowerCAmelCase							,   (int, float, list, tuple, np.ndarray)					):
                    _lowercase	       ='np'
              else:
                    raise ValueError(
                        F'''type of {first_element} unknown: {type(lowerCAmelCase					)}. '''
                        'Should be one of a python, numpy, pytorch or tensorflow object.'					)

        for key, value in processed_features.items():
              if isinstance(value[0]							,   (int, float)					):
                    _lowercase	       =to_numpy(lowerCAmelCase					)
              else:
                    _lowercase	       =[to_numpy(lowerCAmelCase					) for v in value]

        # Convert padding_strategy in PaddingStrategy
        _lowercase	       =self._get_padding_strategies(padding=lowerCAmelCase							,   max_length=lowerCAmelCase					)

        _lowercase	       =processed_features[self.model_input_names[0]]

        _lowercase	       =len(lowerCAmelCase					)
        if not all(len(lowerCAmelCase					) == batch_size for v in processed_features.values()					):
              raise ValueError('Some items in the output dictionary have a different batch size than others.'					)

        _lowercase	       =[]
        for i in range(lowerCAmelCase					):
              _lowercase	       ={k: v[i] for k, v in processed_features.items()}
              # truncation
              _lowercase	       =self._truncate(
                  lowerCAmelCase							,   max_length=lowerCAmelCase							,   pad_to_multiple_of=lowerCAmelCase							,   truncation=lowerCAmelCase							,   )
              truncated_inputs.append(lowerCAmelCase					)

        if padding_strategy == PaddingStrategy.LONGEST:
              # make sure that `max_length` cannot be longer than the longest truncated length
              _lowercase	       =max(len(input_slice[self.model_input_names[0]]					) for input_slice in truncated_inputs					)
              _lowercase	       =PaddingStrategy.MAX_LENGTH

        _lowercase	       ={}
        for i in range(lowerCAmelCase					):
              # padding
              _lowercase	       =self._pad(
                  truncated_inputs[i]							,   max_length=lowerCAmelCase							,   padding_strategy=lowerCAmelCase							,   pad_to_multiple_of=lowerCAmelCase							,   return_attention_mask=lowerCAmelCase							,   )

              for key, value in outputs.items():
                    if key not in batch_outputs:
                          _lowercase	       =[]
                    if value.dtype is np.dtype(np.floataa					):
                          _lowercase	       =value.astype(np.floataa					)
                    batch_outputs[key].append(lowerCAmelCase					)

        return BatchFeature(lowerCAmelCase							,   tensor_type=lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = PaddingStrategy.DO_NOT_PAD							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   )  -> dict:
        '''simple docstring'''






        _lowercase	       =processed_features[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
              _lowercase	       =len(lowerCAmelCase					)

        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
              _lowercase	       =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        _lowercase	       =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCAmelCase					) < max_length

        if return_attention_mask and "attention_mask" not in processed_features:
              _lowercase	       =np.ones(len(lowerCAmelCase					)							,   dtype=np.intaa					)

        if needs_to_be_padded:
              _lowercase	       =max_length - len(lowerCAmelCase					)
              if self.padding_side == "right":
                    if return_attention_mask:
                          _lowercase	       =np.pad(
                              processed_features['attention_mask']							,   (0, difference)					)
                    _lowercase	       =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                    _lowercase	       =np.pad(
                        lowerCAmelCase							,   lowerCAmelCase							,   'constant'							,   constant_values=self.padding_value					)
              elif self.padding_side == "left":
                    if return_attention_mask:
                          _lowercase	       =np.pad(
                              processed_features['attention_mask']							,   (difference, 0)					)
                    _lowercase	       =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                    _lowercase	       =np.pad(
                        lowerCAmelCase							,   lowerCAmelCase							,   'constant'							,   constant_values=self.padding_value					)
              else:
                    raise ValueError('Invalid padding strategy:' + str(self.padding_side					)					)

        return processed_features




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   )  -> Any:
        '''simple docstring'''






        if not truncation:
              return processed_features
        elif truncation and max_length is None:
              raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'					)

        _lowercase	       =processed_features[self.model_input_names[0]]

        # find `max_length` that fits `pad_to_multiple_of`
        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
              _lowercase	       =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        _lowercase	       =len(lowerCAmelCase					) > max_length

        if needs_to_be_truncated:
              _lowercase	       =processed_features[self.model_input_names[0]][:max_length]
              if "attention_mask" in processed_features:
                    _lowercase	       =processed_features['attention_mask'][:max_length]

        return processed_features




  def   A__     (				self							,   lowerCAmelCase=False							,   lowerCAmelCase=None					)  -> Optional[int]:
        '''simple docstring'''






        if padding is not False:
              if padding is True:
                    _lowercase	       =PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
              elif not isinstance(lowerCAmelCase							,   lowerCAmelCase					):
                    _lowercase	       =PaddingStrategy(lowerCAmelCase					)
              elif isinstance(lowerCAmelCase							,   lowerCAmelCase					):
                    _lowercase	       =padding
        else:
              _lowercase	       =PaddingStrategy.DO_NOT_PAD

        # Set max length if needed
        if max_length is None:
              if padding_strategy == PaddingStrategy.MAX_LENGTH:
                    raise ValueError(
                        F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''					)

        # Test if we have a padding value
        if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
              raise ValueError(
                  'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
                  ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'					)

        return padding_strategy


