


from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
 import importlib_metadata
else:
 import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
__lowerCAmelCase									=				'''\
'''

__lowerCAmelCase									=				'''\
    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.
    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.
'''

__lowerCAmelCase									=				'''
Compute XTREME-S evaluation metric associated to each XTREME-S dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.
        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',
        \'tokenize\', \'use_effective_order\'.
    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.
        Keywords include \'concatenate_texts\'.
Returns: depending on the XTREME-S task, one or several of:
    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'
    "f1": F1 score - for \'minds14\'
    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'
    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'
    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'
Examples:

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'
    >>> references = ["it is sunny here", "paper and pen are essentials"]
    >>> predictions = ["it\'s sunny", "paper pen are essential"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'wer\': 0.56, \'cer\': 0.27}

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')
    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]
    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'bleu\': 31.65}

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'accuracy\': 0.6}

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'f1\': 0.58, \'accuracy\': 0.6}
'''

__lowerCAmelCase									=				['''fleurs-asr''', '''mls''', '''voxpopuli''', '''babel''', '''covost2''', '''fleurs-lang_id''', '''minds14''']
__lowerCAmelCase									=				''''''

try:
 from jiwer import transforms as tr

 __lowerCAmelCase									=				True
except ImportError:
 __lowerCAmelCase									=				False

if _jiwer_available and version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):





 class     __a     (       tr.AbstractTransform ):
  def __init__(  self							,					lowerCAmelCase__ = " "					)   ->							Dict:




         '''simple docstring'''
         lowercase__:  Any         =  sentence_delimiter
  def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__					)   ->							Any:




         '''simple docstring'''
         return list(lowerCAmelCase__					)
  def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__					)   ->							Dict:




         '''simple docstring'''
         lowercase__:  Optional[int]         =  []
         for sent_idx, sentence in enumerate(lowerCAmelCase__					):
                chars.extend(self.process_string(lowerCAmelCase__					)					)
                if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowerCAmelCase__					) - 1:
                       chars.append(self.sentence_delimiter					)
         return chars





 __lowerCAmelCase									=				tr.Compose(
     [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
 )
elif _jiwer_available:
 __lowerCAmelCase									=				tr.Compose(
     [
         tr.RemoveMultipleSpaces(),
         tr.Strip(),
         tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
         tr.ReduceToListOfListOfChars(),
     ]
 )
else:
 __lowerCAmelCase									=				None




def   snake_case_	(    snake_case     ,				snake_case	)    ->							int:
       return float((preds == labels).mean()	)




def   snake_case_	(    snake_case     ,				snake_case	)    ->							Optional[int]:
       return {
           "f1": float(fa_score(y_true=snake_case     ,				y_pred=snake_case     ,				average='macro'	)	),
           "accuracy": simple_accuracy(snake_case     ,				snake_case	),
       }




def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case="exp"     ,				snake_case=None     ,				snake_case=False     ,				snake_case=False     ,				snake_case=None     ,				snake_case=False     ,				)    ->							List[Any]:
       # xtreme-s can only have one label
       lowercase__:  Union[str, Any]         =  [[label] for label in labels]
       lowercase__:  Any         =  list(snake_case	)
       try:
              import sacrebleu as scb
       except ImportError:
              raise ValueError(
                  'sacrebleu has to be installed in order to apply the bleu metric for covost2.'
                  'You can install it via `pip install sacrebleu`.'	)

       if version.parse(scb.__version__	) < version.parse('1.4.12'	):
              raise ImportWarning(
                  'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
                  'You can install it with `pip install "sacrebleu>=1.4.12"`.'	)

       lowercase__:  List[Any]         =  len(labels[0]	)
       if any(len(snake_case	) != references_per_prediction for refs in labels	):
              raise ValueError('Sacrebleu requires the same number of references for each prediction'	)
       lowercase__:  List[str]         =  [[refs[i] for refs in labels] for i in range(snake_case	)]
       lowercase__:  Optional[int]         =  scb.corpus_bleu(
           snake_case     ,				snake_case     ,				smooth_method=snake_case     ,				smooth_value=snake_case     ,				force=snake_case     ,				lowercase=snake_case     ,				use_effective_order=snake_case     ,				**({'tokenize': tokenize} if tokenize else {})     ,				)
       return {"bleu": output.score}




def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case     ,				snake_case	)    ->							Optional[int]:
       try:
              from jiwer import compute_measures
       except ImportError:
              raise ValueError(
                  f'jiwer has to be installed in order to apply the wer metric for {config_name}.'
                  'You can install it via `pip install jiwer`.'	)

       if concatenate_texts:
              lowercase__:  List[Any]         =  compute_measures(snake_case     ,				snake_case	)['wer']

              lowercase__:  List[str]         =  compute_measures(snake_case     ,				snake_case     ,				truth_transform=snake_case     ,				hypothesis_transform=snake_case	)['wer']
              return {"wer": wer, "cer": cer}
       else:

              def compute_score(snake_case     ,				snake_case     ,				snake_case="wer"	):
                     lowercase__:  Union[str, Any]         =  0
                     lowercase__:  Any         =  0
                     for prediction, reference in zip(snake_case     ,				snake_case	):
                            if score_type == "wer":
                                   lowercase__:  str         =  compute_measures(snake_case     ,				snake_case	)
                            elif score_type == "cer":
                                   lowercase__:  Dict         =  compute_measures(
                                       snake_case     ,				snake_case     ,				truth_transform=snake_case     ,				hypothesis_transform=snake_case	)
                            incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                            total += measures["substitutions"] + measures["deletions"] + measures["hits"]
                     return incorrect / total

              return {"wer": compute_score(snake_case     ,				snake_case     ,				'wer'	), "cer": compute_score(snake_case     ,				snake_case     ,				'cer'	)}





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,			_KWARGS_DESCRIPTION )
class     __a     (       datasets.Metric ):
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Union[str, Any]:




        '''simple docstring'''
        if self.config_name not in _CONFIG_NAMES:
               raise KeyError(F'You should supply a configuration name selected in {_CONFIG_NAMES}'					)

        lowercase__:  List[str]         =  'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

        return datasets.MetricInfo(
            description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
                {'predictions': datasets.Value(lowerCAmelCase__					), 'references': datasets.Value(lowerCAmelCase__					)}					)							,					codebase_urls=[]							,					reference_urls=[]							,					format='numpy'							,					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__=None							,					lowerCAmelCase__=None					)   ->							str:




        '''simple docstring'''
        lowercase__:  Any         =  bleu_kwargs if bleu_kwargs is not None else {}
        lowercase__:  Union[str, Any]         =  wer_kwargs if wer_kwargs is not None else {}

        if self.config_name == "fleurs-lang_id":
               return {"accuracy": simple_accuracy(lowerCAmelCase__							,					lowerCAmelCase__					)}
        elif self.config_name == "minds14":
               return fa_and_simple_accuracy(lowerCAmelCase__							,					lowerCAmelCase__					)
        elif self.config_name == "covost2":
               lowercase__:  int         =  bleu_kwargs.pop('smooth_method'							,					'exp'					)
               lowercase__:  Optional[int]         =  bleu_kwargs.pop('smooth_value'							,					lowerCAmelCase__					)
               lowercase__:  Optional[Any]         =  bleu_kwargs.pop('force'							,					lowerCAmelCase__					)
               lowercase__:  Optional[Any]         =  bleu_kwargs.pop('lowercase'							,					lowerCAmelCase__					)
               lowercase__:  Any         =  bleu_kwargs.pop('tokenize'							,					lowerCAmelCase__					)
               lowercase__:  Tuple         =  bleu_kwargs.pop('use_effective_order'							,					lowerCAmelCase__					)
               return bleu(
                   preds=lowerCAmelCase__							,					labels=lowerCAmelCase__							,					smooth_method=lowerCAmelCase__							,					smooth_value=lowerCAmelCase__							,					force=lowerCAmelCase__							,					lowercase=lowerCAmelCase__							,					tokenize=lowerCAmelCase__							,					use_effective_order=lowerCAmelCase__							,					)
        elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
               lowercase__:  Optional[int]         =  wer_kwargs.pop('concatenate_texts'							,					lowerCAmelCase__					)
               return wer_and_cer(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					self.config_name					)
        else:
               raise KeyError(F'You should supply a configuration name selected in {_CONFIG_NAMES}'					)






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def   snake_case_	(    snake_case	)    ->							List[str]:
       lowercase__:  Any         =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]	)
       lowercase__:  Optional[Any]         =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]	)

       lowercase__:  Optional[int]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       lowercase__:  List[Any]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       # modify the pix within [0, 255]
       lowercase__:  Optional[Any]         =  dst_x * 2_55 / np.max(snake_case	)
       lowercase__:  Dict         =  dst_y * 2_55 / np.max(snake_case	)

       lowercase__:  Tuple         =  np.sqrt((np.square(snake_case	)) + (np.square(snake_case	))	)
       lowercase__:  str         =  dst_xy * 2_55 / np.max(snake_case	)
       lowercase__:  Tuple         =  dst_xy.astype(np.uinta	)

       lowercase__:  List[Any]         =  np.arctana(snake_case     ,				snake_case	)
       return dst, theta


if __name__ == "__main__":
 # read original image
 __lowerCAmelCase									=				imread('''../image_data/lena.jpg''')
 # turn image in gray scale value
 __lowerCAmelCase									=				cvtColor(img, COLOR_BGR2GRAY)

 __lowerCAmelCase			,__lowerCAmelCase									=				sobel_filter(gray)

 # show result images
 imshow('''sobel filter''', sobel_grad)
 imshow('''sobel theta''', sobel_theta)
 waitKey(0)


