



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


a_      :      List[str]    =     ''


if version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):
     class    __UpperCamelCase			(       tr.AbstractTransform				):

      """simple docstring"""



      def __init__(	self		,				SCREAMING_SNAKE_CASE = " "      ) ->			str:
       a__												= sentence_delimiter

      def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			str:
       return list(SCREAMING_SNAKE_CASE      )



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





     a_      :      Union[str, Any]    =     tr.Compose(
         [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
     )
else:
     a_      :      Union[str, Any]    =     tr.Compose(
         [
             tr.RemoveMultipleSpaces(),
             tr.Strip(),
             tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
             tr.ReduceToListOfListOfChars(),
         ]
     )


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

a_      :      List[str]    =     '\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER\'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\nperformance of the ASR system with a CER of 0 being a perfect score.\n'

a_      :      Dict    =     '\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> cer = datasets.load_metric("cer")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,	_KWARGS_DESCRIPTION				)
class    __UpperCamelCase			(       datasets.Metric				):

 """simple docstring"""



 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  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		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=False      ) ->			Optional[Any]:
  if concatenate_texts:
   return jiwer.compute_measures(
       SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				truth_transform=SCREAMING_SNAKE_CASE		,				hypothesis_transform=SCREAMING_SNAKE_CASE		,				)["wer"]

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

  return incorrect / total










import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


a_      :      int    =     '\\n    Text data.\n    Second line of data.'

a_      :      Optional[Any]    =     'file'






@pytest.fixture(scope='''session'''       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / (FILE_PATH + '''.zstd''')
 a__												= bytes(__UpperCAmelCase							,  '''utf-8'''       )
 with zstd.open(__UpperCAmelCase							,  '''wb'''       ) as f:
  f.write(__UpperCAmelCase       )
 return path






@pytest.fixture
def     __a					(		__UpperCAmelCase       ):
 with open(os.path.join(tmpfs.local_root_dir							,  __UpperCAmelCase       )							,  '''w'''       ) as f:
  f.write(__UpperCAmelCase       )
 return FILE_PATH






@pytest.mark.parametrize('''compression_format'''							,  ['''gzip''', '''xz''', '''zstd''']       )
def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
 a__												= input_paths[compression_format]
 a__												= tmp_path / '''cache'''
 a__												= DownloadConfig(cache_dir=__UpperCAmelCase							,  extract_compressed_file=__UpperCAmelCase       )
 a__												= cached_path(__UpperCAmelCase							,  download_config=__UpperCAmelCase       )
 with open(__UpperCAmelCase       ) as f:
  a__												= f.read()
 with open(__UpperCAmelCase       ) as f:
  a__												= f.read()
 assert extracted_file_content == expected_file_content






@pytest.mark.parametrize('''default_extracted'''							,  [True, False]       )
@pytest.mark.parametrize('''default_cache_dir'''							,  [True, False]       )
def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= '''custom_cache'''
 a__												= '''custom_extracted_dir'''
 a__												= tmp_path / '''custom_extracted_path'''
 if default_extracted:
  a__												= ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
 else:
  monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''							,  __UpperCAmelCase       )
  monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''							,  str(__UpperCAmelCase       )       )
  a__												= custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

 a__												= xz_file
 a__												= (
     DownloadConfig(extract_compressed_file=__UpperCAmelCase       )
     if default_cache_dir
     else DownloadConfig(cache_dir=tmp_path / custom_cache_dir							,  extract_compressed_file=__UpperCAmelCase       )
 )
 a__												= cached_path(__UpperCAmelCase							,  download_config=__UpperCAmelCase       )
 assert Path(__UpperCAmelCase       ).parent.parts[-2:] == expected






def     __a					(		__UpperCAmelCase       ):
 # absolute path
 a__												= str(Path(__UpperCAmelCase       ).resolve()       )
 assert cached_path(__UpperCAmelCase       ) == text_file
 # relative path
 a__												= str(Path(__UpperCAmelCase       ).resolve().relative_to(Path(os.getcwd()       )       )       )
 assert cached_path(__UpperCAmelCase       ) == text_file






def     __a					(		__UpperCAmelCase       ):
 # absolute path
 a__												= str(tmp_path.resolve() / '''__missing_file__.txt'''       )
 with pytest.raises(__UpperCAmelCase       ):
  cached_path(__UpperCAmelCase       )
 # relative path
 a__												= '''./__missing_file__.txt'''
 with pytest.raises(__UpperCAmelCase       ):
  cached_path(__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase       ):
 a__												= get_from_cache(f"tmp://{tmpfs_file}"       )
 with open(__UpperCAmelCase       ) as f:
  a__												= f.read()
 assert output_file_content == FILE_CONTENT






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		):
 with pytest.raises(__UpperCAmelCase       ):
  cached_path('''https://huggingface.co'''       )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
 with pytest.raises(__UpperCAmelCase       ):
  http_get('''https://huggingface.co'''							,  temp_file=__UpperCAmelCase       )
 with pytest.raises(__UpperCAmelCase       ):
  http_head('''https://huggingface.co'''       )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
 with pytest.raises(__UpperCAmelCase       ):
  ftp_get('''ftp://huggingface.co'''							,  temp_file=__UpperCAmelCase       )
 with pytest.raises(__UpperCAmelCase       ):
  ftp_head('''ftp://huggingface.co'''       )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
 with pytest.raises(__UpperCAmelCase       ):
  fsspec_get('''s3://huggingface.co'''							,  temp_file=__UpperCAmelCase       )
 with pytest.raises(__UpperCAmelCase       ):
  fsspec_head('''s3://huggingface.co'''       )





