'''simple docstring'''





from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

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


_lowerCAmelCase  :Optional[int]  				=    """"""


if version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):






 class 				UpperCAmelCase (       tr.AbstractTransform ):




     '''simple docstring'''







     def __init__(       self    ,							lowercase__ = " " )							-> List[str]:
          SCREAMING_SNAKE_CASE					:			Tuple    	=       sentence_delimiter






     def       _UpperCamelCase    (       self    ,							lowercase__ )							-> str:
          return list(lowercase__ )






     def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Union[str, Any]:
          SCREAMING_SNAKE_CASE					:			int    	=       []
          for sent_idx, sentence in enumerate(lowercase__ ):
               chars.extend(self.process_string(lowercase__ ) )
               if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowercase__ ) - 1:
                    chars.append(self.sentence_delimiter )
          return chars






 _lowerCAmelCase  :Any  				=    tr.Compose(
     [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
 )
else:
 _lowerCAmelCase  :List[Any]  				=    tr.Compose(
     [
         tr.RemoveMultipleSpaces(),
         tr.Strip(),
         tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
         tr.ReduceToListOfListOfChars(),
     ]
 )


_lowerCAmelCase  :Optional[Any]  				=    """\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""

_lowerCAmelCase  :Optional[int]  				=    """\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
"""

_lowerCAmelCase  :Tuple  				=    """
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> cer = datasets.load_metric(\"cer\")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
"""






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




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         return datasets.MetricInfo(
             description=_DESCRIPTION    ,							citation=_CITATION    ,							inputs_description=_KWARGS_DESCRIPTION    ,							features=datasets.Features(
                 {
                     'predictions': datasets.Value('string'    ,							id='sequence' ),
                     'references': datasets.Value('string'    ,							id='sequence' ),
                 } )    ,							codebase_urls=['https://github.com/jitsi/jiwer/']    ,							reference_urls=[
                 'https://en.wikipedia.org/wiki/Word_error_rate',
                 'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
             ]    ,							)






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__=False )							-> int:
         if concatenate_texts:
              return jiwer.compute_measures(
                  lowercase__    ,							lowercase__    ,							truth_transform=lowercase__    ,							hypothesis_transform=lowercase__    ,							)["wer"]

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       0
         SCREAMING_SNAKE_CASE					:			Tuple    	=       0
         for prediction, reference in zip(lowercase__    ,							lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Any    	=       jiwer.compute_measures(
                  lowercase__    ,							lowercase__    ,							truth_transform=lowercase__    ,							hypothesis_transform=lowercase__    ,							)
              incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
              total += measures["substitutions"] + measures["deletions"] + measures["hits"]

         return incorrect / total




'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.activations import gelu_new, gelu_python, get_activation






@require_torch
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         self.assertTrue(torch.allclose(gelu_python(lowercase__ )    ,							torch_builtin(lowercase__ ) ) )
         self.assertFalse(torch.allclose(gelu_python(lowercase__ )    ,							gelu_new(lowercase__ ) ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			Dict    	=       get_activation('gelu_10' )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch_builtin(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       geluaa(lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.where(y_gelu_aa < 1_0.0    ,							1    ,							0 )

         self.assertTrue(torch.max(lowercase__ ).item() == 1_0.0 )
         self.assertTrue(torch.allclose(y_gelu * clipped_mask    ,							y_gelu_aa * clipped_mask ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         get_activation('gelu' )
         get_activation('gelu_10' )
         get_activation('gelu_fast' )
         get_activation('gelu_new' )
         get_activation('gelu_python' )
         get_activation('gelu_pytorch_tanh' )
         get_activation('linear' )
         get_activation('mish' )
         get_activation('quick_gelu' )
         get_activation('relu' )
         get_activation('sigmoid' )
         get_activation('silu' )
         get_activation('swish' )
         get_activation('tanh' )
         with self.assertRaises(lowercase__ ):
              get_activation('bogus' )
         with self.assertRaises(lowercase__ ):
              get_activation(lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			str    	=       1
         SCREAMING_SNAKE_CASE					:			List[str]    	=       get_activation('gelu' )
         self.assertEqual(acta.a    ,							1 )
         with self.assertRaises(lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Dict    	=       acta.a



