
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


snake_case__  							=							"\\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"

snake_case__  							=							"\\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"


snake_case__  							=							"\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_      (			UpperCAmelCase_							:				Optional[int]							,			UpperCAmelCase_							:				Optional[Any]							,			UpperCAmelCase_							:				Tuple							,			UpperCAmelCase_							:				List[Any]       ):
      lowercase       :    int											=  [rgram for rgrams in rgramslist for rgram in rgrams]
      lowercase       :    Any											=  Counter(snake_case_       )

      lowercase       :    Optional[Any]											=  Counter(snake_case_       )
      lowercase       :    List[str]											=  Counter()
      for sgram, scount in sgramcounter.items():
            lowercase       :    Any											=  scount * numref

      lowercase       :    int											=  Counter(snake_case_       )
      lowercase       :    str											=  Counter()
      for cgram, ccount in cgramcounter.items():
            lowercase       :    Union[str, Any]											=  ccount * numref

      # KEEP
      lowercase       :    Tuple											=  sgramcounter_rep & cgramcounter_rep
      lowercase       :    Tuple											=  keepgramcounter_rep & rgramcounter
      lowercase       :    Any											=  sgramcounter_rep & rgramcounter

      lowercase       :    Optional[int]											=  0
      lowercase       :    List[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.
      lowercase       :    Tuple											=  1
      lowercase       :    str											=  1
      if len(snake_case_       ) > 0:
            lowercase       :    Optional[Any]											=  keeptmpscorea / len(snake_case_       )
      if len(snake_case_       ) > 0:
            # Fix an alleged bug [2] in the keep score computation.
            # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
            lowercase       :    str											=  keeptmpscorea / sum(keepgramcounterall_rep.values()       )
      lowercase       :    List[Any]											=  0
      if keepscore_precision > 0 or keepscore_recall > 0:
            lowercase       :    List[Any]											=  2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

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

      # ADDITION
      lowercase       :    Dict											=  set(snake_case_       ) - set(snake_case_       )
      lowercase       :    Optional[Any]											=  set(snake_case_       ) & set(snake_case_       )
      lowercase       :    Any											=  set(snake_case_       ) - set(snake_case_       )

      lowercase       :    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.
      lowercase       :    List[str]											=  1
      lowercase       :    Tuple											=  1
      if len(snake_case_       ) > 0:
            lowercase       :    Dict											=  addtmpscore / len(snake_case_       )
      if len(snake_case_       ) > 0:
            lowercase       :    Tuple											=  addtmpscore / len(snake_case_       )
      lowercase       :    Union[str, Any]											=  0
      if addscore_precision > 0 or addscore_recall > 0:
            lowercase       :    List[Any]											=  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

      return (keepscore, delscore_precision, addscore)

def        lowerCamelCase_      (			UpperCAmelCase_							:				Optional[int]							,			UpperCAmelCase_							:				Tuple							,			UpperCAmelCase_							:				List[Any]       ):
      lowercase       :    Tuple											=  len(snake_case_       )

      lowercase       :    Optional[int]											=  ssent.split(''' '''       )
      lowercase       :    Dict											=  csent.split(''' '''       )
      lowercase       :    str											=  []
      lowercase       :    Dict											=  []
      lowercase       :    str											=  []
      lowercase       :    Optional[int]											=  []
      lowercase       :    Any											=  []
      lowercase       :    Union[str, Any]											=  []

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

      for i in range(0							,			len(snake_case_       ) - 1       ):
            if i < len(snake_case_       ) - 1:
                  lowercase       :    Dict											=  sagrams[i] + ''' ''' + sagrams[i + 1]
                  sagrams.append(snake_case_       )
            if i < len(snake_case_       ) - 2:
                  lowercase       :    Union[str, Any]											=  sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
                  sagrams.append(snake_case_       )
            if i < len(snake_case_       ) - 3:
                  lowercase       :    Optional[int]											=  sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
                  sagrams.append(snake_case_       )

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

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

def        lowerCamelCase_      (			UpperCAmelCase_							:				Dict							,			UpperCAmelCase_							:				List[Any] = True							,			UpperCAmelCase_							:				str = "13a"							,			UpperCAmelCase_							:				int = 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:
            lowercase       :    Any											=  sentence.lower()

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

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

      return normalized_sent







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

     def        _lowerCAmelCase	(			self      :					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      :					List[str] ,      lowerCAmelCase      :					int ,      lowerCAmelCase      :					Dict ,      lowerCAmelCase      :					int					):


           if not (len(__UpperCamelCase					) == len(__UpperCamelCase					) == len(__UpperCamelCase					)):
                 raise ValueError('''Sources length must match predictions and references lengths.'''					)
           lowercase       :    List[str]											=  0
           for src, pred, refs in zip(__UpperCamelCase ,      __UpperCamelCase ,      __UpperCamelCase					):
                 sari_score += SARIsent(normalize(__UpperCamelCase					) ,      normalize(__UpperCamelCase					) ,      [normalize(__UpperCamelCase					) for sent in refs]					)
           lowercase       :    Tuple											=  sari_score / len(__UpperCamelCase					)
           return {"sari": 100 * sari_score}




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


snake_case__  							=							logging.get_logger(__name__)







class 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: str			  =				["""input_values""", """attention_mask"""]

					def __init__(			self      :					Union[str, Any] ,      lowerCAmelCase      :					Tuple=1 ,      lowerCAmelCase      :					Dict=1_6000 ,      lowerCAmelCase      :					Optional[Any]=128 ,      lowerCAmelCase      :					Dict=1024 ,      lowerCAmelCase      :					List[Any]=0.0 ,      lowerCAmelCase      :					Any=True ,      lowerCAmelCase      :					Tuple=-4.2_67_73_93 ,      lowerCAmelCase      :					Dict=4.5_68_99_74 ,      lowerCAmelCase      :					int=False ,      **lowerCAmelCase      :					Optional[Any] ,      ):
											super().__init__(feature_size=lowerCAmelCase ,      sampling_rate=lowerCAmelCase ,      padding_value=lowerCAmelCase ,      **lowerCAmelCase					)
											lowercase       :    Optional[int]											=  num_mel_bins
											lowercase       :    List[Any]											=  max_length
											lowercase       :    Any											=  do_normalize
											lowercase       :    Any											=  mean
											lowercase       :    Union[str, Any]											=  std
											lowercase       :    int											=  return_attention_mask

					def        _lowerCAmelCase	(			self      :					int ,      lowerCAmelCase      :					np.ndarray ,      lowerCAmelCase      :					int ,      ):
											lowercase       :    List[Any]											=  torch.from_numpy(lowerCAmelCase					).unsqueeze(0					)
											lowercase       :    List[str]											=  ta_kaldi.fbank(
											    lowerCAmelCase ,      htk_compat=lowerCAmelCase ,      sample_frequency=self.sampling_rate ,      use_energy=lowerCAmelCase ,      window_type='''hanning''' ,      num_mel_bins=self.num_mel_bins ,      dither=0.0 ,      frame_shift=10 ,      )

											lowercase       :    Tuple											=  fbank.shape[0]
											lowercase       :    Optional[Any]											=  max_length - n_frames

											# pad or truncate, depending on difference
											if difference > 0:
																	lowercase       :    int											=  torch.nn.ZeroPadad((0, 0, 0, difference)					)
																	lowercase       :    str											=  pad_module(lowerCAmelCase					)
											elif difference < 0:
																	lowercase       :    Optional[int]											=  fbank[0:max_length, :]

											lowercase       :    List[str]											=  fbank.numpy()

											return fbank

					def        _lowerCAmelCase	(			self      :					Tuple ,      lowerCAmelCase      :					np.ndarray					):
											return (input_values - (self.mean)) / (self.std * 2)




					def __call__(			self      :					Any ,      lowerCAmelCase      :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,      lowerCAmelCase      :					Optional[int] = None ,      lowerCAmelCase      :					Optional[Union[str, TensorType]] = None ,      **lowerCAmelCase      :					Tuple ,      ):

											if sampling_rate is not None:
																	if sampling_rate != self.sampling_rate:
																							raise ValueError(
																							    f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																							    f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
																							    f''' {self.sampling_rate} and not {sampling_rate}.'''					)
											else:
																	logger.warning(
																	    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
																	    '''Failing to do so can result in silent errors that might be hard to debug.'''					)

											lowercase       :    Optional[Any]											=  isinstance(lowerCAmelCase ,      np.ndarray					) and len(raw_speech.shape					) > 1
											if is_batched_numpy and len(raw_speech.shape					) > 2:
																	raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''					)
											lowercase       :    Tuple											=  is_batched_numpy or (
											    isinstance(lowerCAmelCase ,      (list, tuple)					) and (isinstance(raw_speech[0] ,      (np.ndarray, tuple, list)					))
											)

											if is_batched:
																	lowercase       :    Optional[int]											=  [np.asarray(lowerCAmelCase ,      dtype=np.floataa					) for speech in raw_speech]
											elif not is_batched and not isinstance(lowerCAmelCase ,      np.ndarray					):
																	lowercase       :    Dict											=  np.asarray(lowerCAmelCase ,      dtype=np.floataa					)
											elif isinstance(lowerCAmelCase ,      np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
																	lowercase       :    int											=  raw_speech.astype(np.floataa					)

											# always return batch
											if not is_batched:
																	lowercase       :    Dict											=  [raw_speech]

											# extract fbank features and pad/truncate to max_length
											lowercase       :    Dict											=  [self._extract_fbank_features(lowerCAmelCase ,      max_length=self.max_length					) for waveform in raw_speech]

											# convert into BatchFeature
											lowercase       :    Any											=  BatchFeature({'''input_values''': features}					)

											# make sure list is in array format
											lowercase       :    str											=  padded_inputs.get('''input_values'''					)
											if isinstance(input_values[0] ,      lowerCAmelCase					):
																	lowercase       :    Any											=  [np.asarray(lowerCAmelCase ,      dtype=np.floataa					) for feature in input_values]

											# normalization
											if self.do_normalize:
																	lowercase       :    List[str]											=  [self.normalize(lowerCAmelCase					) for feature in input_values]

											if return_tensors is not None:
																	lowercase       :    str											=  padded_inputs.convert_to_tensors(lowerCAmelCase					)

											return padded_inputs


