






'''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


UpperCamelCase							=""


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




      class     A    (      tr.AbstractTransform						):






       """simple docstring"""





       def __init__(							self			,     __lowerCAmelCase = " " ):
             UpperCamelCase_				:   str					    =							sentence_delimiter
       def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
             return list(__lowerCAmelCase )





       def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
             UpperCamelCase_				:   List[Any]					    =							[]
             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






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


UpperCamelCase							="\\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"

UpperCamelCase							="\\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"

UpperCamelCase							="\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     A    (      datasets.Metric						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       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			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=False ):
       if concatenate_texts:
             return jiwer.compute_measures(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     truth_transform=__lowerCAmelCase			,     hypothesis_transform=__lowerCAmelCase			,     )["wer"]

       UpperCamelCase_				:   Optional[Any]					    =							0
       UpperCamelCase_				:   str					    =							0
       for prediction, reference in zip(__lowerCAmelCase			,     __lowerCAmelCase ):
             UpperCamelCase_				:   Optional[int]					    =							jiwer.compute_measures(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     truth_transform=__lowerCAmelCase			,     hypothesis_transform=__lowerCAmelCase			,     )
             incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
             total += measures["substitutions"] + measures["deletions"] + measures["hits"]

       return incorrect / total














'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
      from huggingface_hub import snapshot_download
      from pyctcdecode import BeamSearchDecoderCTC

      from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
      from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
      from transformers import WavaVecaForCTC




@require_pyctcdecode
class     A    (      unittest.TestCase						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							"""| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
       UpperCamelCase_				:   int					    =							dict(zip(__lowerCAmelCase			,     range(len(__lowerCAmelCase ) ) ) )

       UpperCamelCase_				:   Tuple					    =							{
           """unk_token""": """<unk>""",
           """bos_token""": """<s>""",
           """eos_token""": """</s>""",
       }
       UpperCamelCase_				:   Optional[Any]					    =							{
           """feature_size""": 1,
           """padding_value""": 0.0,
           """sampling_rate""": 1_60_00,
           """return_attention_mask""": False,
           """do_normalize""": True,
       }

       UpperCamelCase_				:   int					    =							tempfile.mkdtemp()
       UpperCamelCase_				:   Tuple					    =							os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["""vocab_file"""] )
       UpperCamelCase_				:   List[Any]					    =							os.path.join(self.tmpdirname			,     __lowerCAmelCase )
       with open(self.vocab_file			,     """w"""			,     encoding="""utf-8""" ) as fp:
             fp.write(json.dumps(__lowerCAmelCase ) + """\n""" )

       with open(self.feature_extraction_file			,     """w"""			,     encoding="""utf-8""" ) as fp:
             fp.write(json.dumps(__lowerCAmelCase ) + """\n""" )

       # load decoder from hub
       UpperCamelCase_				:   Union[str, Any]					    =							"""hf-internal-testing/ngram-beam-search-decoder"""
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   str					    =							self.add_kwargs_tokens_map.copy()
       kwargs.update(__lowerCAmelCase )
       return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,     **__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,     **__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name			,     **__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       shutil.rmtree(self.tmpdirname )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.get_tokenizer()
       UpperCamelCase_				:   str					    =							self.get_feature_extractor()
       UpperCamelCase_				:   Tuple					    =							self.get_decoder()

       UpperCamelCase_				:   int					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       processor.save_pretrained(self.tmpdirname )
       UpperCamelCase_				:   Dict					    =							WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )

       # tokenizer
       self.assertEqual(processor.tokenizer.get_vocab()			,     tokenizer.get_vocab() )
       self.assertIsInstance(processor.tokenizer			,     __lowerCAmelCase )

       # feature extractor
       self.assertEqual(processor.feature_extractor.to_json_string()			,     feature_extractor.to_json_string() )
       self.assertIsInstance(processor.feature_extractor			,     __lowerCAmelCase )

       # decoder
       self.assertEqual(processor.decoder._alphabet.labels			,     decoder._alphabet.labels )
       self.assertEqual(
           processor.decoder.model_container[decoder._model_key]._unigram_set			,     decoder.model_container[decoder._model_key]._unigram_set			,     )
       self.assertIsInstance(processor.decoder			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							WavaVecaProcessorWithLM(
           tokenizer=self.get_tokenizer()			,     feature_extractor=self.get_feature_extractor()			,     decoder=self.get_decoder() )
       processor.save_pretrained(self.tmpdirname )

       # make sure that error is thrown when decoder alphabet doesn't match
       UpperCamelCase_				:   Optional[Any]					    =							WavaVecaProcessorWithLM.from_pretrained(
           self.tmpdirname			,     alpha=5.0			,     beta=3.0			,     score_boundary=-7.0			,     unk_score_offset=3 )

       # decoder
       self.assertEqual(processor.language_model.alpha			,     5.0 )
       self.assertEqual(processor.language_model.beta			,     3.0 )
       self.assertEqual(processor.language_model.score_boundary			,     -7.0 )
       self.assertEqual(processor.language_model.unk_score_offset			,     3 )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							self.get_tokenizer()
       # add token to trigger raise
       tokenizer.add_tokens(["""xx"""] )
       with self.assertRaisesRegex(__lowerCAmelCase			,     """include""" ):
             WavaVecaProcessorWithLM(
                 tokenizer=__lowerCAmelCase			,     feature_extractor=self.get_feature_extractor()			,     decoder=self.get_decoder() )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							self.get_feature_extractor()
       UpperCamelCase_				:   Tuple					    =							self.get_tokenizer()
       UpperCamelCase_				:   Any					    =							self.get_decoder()

       UpperCamelCase_				:   List[Any]					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       UpperCamelCase_				:   List[Any]					    =							floats_list((3, 10_00) )

       UpperCamelCase_				:   Tuple					    =							feature_extractor(__lowerCAmelCase			,     return_tensors="""np""" )
       UpperCamelCase_				:   str					    =							processor(__lowerCAmelCase			,     return_tensors="""np""" )

       for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()			,     input_processor[key].sum()			,     delta=1E-2 )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							self.get_feature_extractor()
       UpperCamelCase_				:   List[Any]					    =							self.get_tokenizer()
       UpperCamelCase_				:   List[Any]					    =							self.get_decoder()

       UpperCamelCase_				:   Optional[Any]					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							"""This is a test string"""

       UpperCamelCase_				:   Optional[Any]					    =							processor(text=__lowerCAmelCase )

       UpperCamelCase_				:   int					    =							tokenizer(__lowerCAmelCase )

       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]			,     encoded_processor[key] )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=(2, 10, 16)			,     __lowerCAmelCase=77 ):
       np.random.seed(__lowerCAmelCase )
       return np.random.rand(*__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							self.get_feature_extractor()
       UpperCamelCase_				:   Tuple					    =							self.get_tokenizer()
       UpperCamelCase_				:   Optional[int]					    =							self.get_decoder()

       UpperCamelCase_				:   int					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							self._get_dummy_logits(shape=(10, 16)			,     seed=13 )

       UpperCamelCase_				:   Any					    =							processor.decode(__lowerCAmelCase )

       UpperCamelCase_				:   Any					    =							decoder.decode_beams(__lowerCAmelCase )[0]

       self.assertEqual(decoded_decoder[0]			,     decoded_processor.text )
       self.assertEqual("""</s> <s> </s>"""			,     decoded_processor.text )
       self.assertEqual(decoded_decoder[-2]			,     decoded_processor.logit_score )
       self.assertEqual(decoded_decoder[-1]			,     decoded_processor.lm_score )
 @parameterized.expand([[None], ["""fork"""], ["""spawn"""]] )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.get_feature_extractor()
       UpperCamelCase_				:   str					    =							self.get_tokenizer()
       UpperCamelCase_				:   List[Any]					    =							self.get_decoder()

       UpperCamelCase_				:   Optional[Any]					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       UpperCamelCase_				:   Union[str, Any]					    =							self._get_dummy_logits()

       # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
       #       otherwise, the LM won't be available to the pool's sub-processes.
       # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
       if pool_context is None:
             UpperCamelCase_				:   List[Any]					    =							processor.batch_decode(__lowerCAmelCase )
       else:
             with get_context(__lowerCAmelCase ).Pool() as pool:
                   UpperCamelCase_				:   Any					    =							processor.batch_decode(__lowerCAmelCase			,     __lowerCAmelCase )

       UpperCamelCase_				:   Tuple					    =							list(__lowerCAmelCase )

       with get_context("""fork""" ).Pool() as p:
             UpperCamelCase_				:   Optional[int]					    =							decoder.decode_beams_batch(__lowerCAmelCase			,     __lowerCAmelCase )

       UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Union[str, Any]					    =							[], [], []
       for beams in decoded_beams:
             texts_decoder.append(beams[0][0] )
             logit_scores_decoder.append(beams[0][-2] )
             lm_scores_decoder.append(beams[0][-1] )

       self.assertListEqual(__lowerCAmelCase			,     decoded_processor.text )
       self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""]			,     decoded_processor.text )
       self.assertListEqual(__lowerCAmelCase			,     decoded_processor.logit_score )
       self.assertListEqual(__lowerCAmelCase			,     decoded_processor.lm_score )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							self.get_feature_extractor()
       UpperCamelCase_				:   Tuple					    =							self.get_tokenizer()
       UpperCamelCase_				:   Tuple					    =							self.get_decoder()

       UpperCamelCase_				:   Optional[Any]					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							self._get_dummy_logits()

       UpperCamelCase_				:   Dict					    =							15
       UpperCamelCase_				:   str					    =							-20.0
       UpperCamelCase_				:   Dict					    =							-4.0

       UpperCamelCase_				:   Union[str, Any]					    =							processor.batch_decode(
           __lowerCAmelCase			,     beam_width=__lowerCAmelCase			,     beam_prune_logp=__lowerCAmelCase			,     token_min_logp=__lowerCAmelCase			,     )
       UpperCamelCase_				:   Any					    =							decoded_processor_out.text

       UpperCamelCase_				:   Tuple					    =							list(__lowerCAmelCase )

       with get_context("""fork""" ).Pool() as pool:
             UpperCamelCase_				:   str					    =							decoder.decode_beams_batch(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     beam_width=__lowerCAmelCase			,     beam_prune_logp=__lowerCAmelCase			,     token_min_logp=__lowerCAmelCase			,     )

       UpperCamelCase_				:   str					    =							[d[0][0] for d in decoded_decoder_out]
       UpperCamelCase_				:   List[str]					    =							[d[0][2] for d in decoded_decoder_out]
       UpperCamelCase_				:   Union[str, Any]					    =							[d[0][3] for d in decoded_decoder_out]

       self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )
       self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""]			,     __lowerCAmelCase )

       self.assertTrue(np.array_equal(__lowerCAmelCase			,     decoded_processor_out.logit_score ) )
       self.assertTrue(np.allclose([-20.0_54, -18.4_47]			,     __lowerCAmelCase			,     atol=1E-3 ) )

       self.assertTrue(np.array_equal(__lowerCAmelCase			,     decoded_processor_out.lm_score ) )
       self.assertTrue(np.allclose([-15.5_54, -13.94_74]			,     __lowerCAmelCase			,     atol=1E-3 ) )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							self.get_feature_extractor()
       UpperCamelCase_				:   Union[str, Any]					    =							self.get_tokenizer()
       UpperCamelCase_				:   int					    =							self.get_decoder()

       UpperCamelCase_				:   Dict					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       UpperCamelCase_				:   str					    =							self._get_dummy_logits()

       UpperCamelCase_				:   Optional[int]					    =							2.0
       UpperCamelCase_				:   List[str]					    =							5.0
       UpperCamelCase_				:   Optional[Any]					    =							-20.0
       UpperCamelCase_				:   Optional[Any]					    =							True

       UpperCamelCase_				:   Union[str, Any]					    =							processor.batch_decode(
           __lowerCAmelCase			,     alpha=__lowerCAmelCase			,     beta=__lowerCAmelCase			,     unk_score_offset=__lowerCAmelCase			,     lm_score_boundary=__lowerCAmelCase			,     )
       UpperCamelCase_				:   List[str]					    =							decoded_processor_out.text

       UpperCamelCase_				:   List[str]					    =							list(__lowerCAmelCase )
       decoder.reset_params(
           alpha=__lowerCAmelCase			,     beta=__lowerCAmelCase			,     unk_score_offset=__lowerCAmelCase			,     lm_score_boundary=__lowerCAmelCase			,     )

       with get_context("""fork""" ).Pool() as pool:
             UpperCamelCase_				:   int					    =							decoder.decode_beams_batch(
                 __lowerCAmelCase			,     __lowerCAmelCase			,     )

       UpperCamelCase_				:   Any					    =							[d[0][0] for d in decoded_decoder_out]

       self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )
       self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""]			,     __lowerCAmelCase )
       UpperCamelCase_				:   Union[str, Any]					    =							processor.decoder.model_container[processor.decoder._model_key]
       self.assertEqual(lm_model.alpha			,     2.0 )
       self.assertEqual(lm_model.beta			,     5.0 )
       self.assertEqual(lm_model.unk_score_offset			,     -20.0 )
       self.assertEqual(lm_model.score_boundary			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )

       UpperCamelCase_				:   Optional[int]					    =							processor.decoder.model_container[processor.decoder._model_key]
       UpperCamelCase_				:   int					    =							Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()

       UpperCamelCase_				:   Any					    =							os.listdir(__lowerCAmelCase )
       UpperCamelCase_				:   Optional[Any]					    =							["""alphabet.json""", """language_model"""]

       downloaded_decoder_files.sort()
       expected_decoder_files.sort()

       # test that only decoder relevant files from
       # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
       # are downloaded and none of the rest (e.g. README.md, ...)
       self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							snapshot_download("""hf-internal-testing/processor_with_lm""" )

       UpperCamelCase_				:   Union[str, Any]					    =							WavaVecaProcessorWithLM.from_pretrained(__lowerCAmelCase )

       UpperCamelCase_				:   Union[str, Any]					    =							processor.decoder.model_container[processor.decoder._model_key]
       UpperCamelCase_				:   Tuple					    =							Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()

       UpperCamelCase_				:   Union[str, Any]					    =							os.listdir(__lowerCAmelCase )
       UpperCamelCase_				:   str					    =							os.listdir(__lowerCAmelCase )

       local_decoder_files.sort()
       expected_decoder_files.sort()

       # test that both decoder form hub and local files in cache are the same
       self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Any					    =							WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
       UpperCamelCase_				:   Tuple					    =							AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )

       UpperCamelCase_				:   Dict					    =							floats_list((3, 10_00) )

       UpperCamelCase_				:   List[Any]					    =							processor_wavaveca(__lowerCAmelCase			,     return_tensors="""np""" )
       UpperCamelCase_				:   Tuple					    =							processor_auto(__lowerCAmelCase			,     return_tensors="""np""" )

       for key in input_wavaveca.keys():
             self.assertAlmostEqual(input_wavaveca[key].sum()			,     input_auto[key].sum()			,     delta=1E-2 )

       UpperCamelCase_				:   Optional[int]					    =							self._get_dummy_logits()

       UpperCamelCase_				:   Dict					    =							processor_wavaveca.batch_decode(__lowerCAmelCase )
       UpperCamelCase_				:   Any					    =							processor_auto.batch_decode(__lowerCAmelCase )

       self.assertListEqual(decoded_wavaveca.text			,     decoded_auto.text )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							self.get_feature_extractor()
       UpperCamelCase_				:   int					    =							self.get_tokenizer()
       UpperCamelCase_				:   List[Any]					    =							self.get_decoder()

       UpperCamelCase_				:   List[str]					    =							WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase			,     feature_extractor=__lowerCAmelCase			,     decoder=__lowerCAmelCase )

       self.assertListEqual(
           processor.model_input_names			,     feature_extractor.model_input_names			,     msg="""`processor` and `feature_extractor` model input names do not match"""			,     )
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							[d[key] for d in offsets]
       return retrieved_list
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
       UpperCamelCase_				:   int					    =							self._get_dummy_logits()[0]

       UpperCamelCase_				:   List[Any]					    =							processor.decode(__lowerCAmelCase			,     output_word_offsets=__lowerCAmelCase )
       # check Wav2Vec2CTCTokenizerOutput keys for word
       self.assertEqual(len(outputs.keys() )			,     4 )
       self.assertTrue("""text""" in outputs )
       self.assertTrue("""word_offsets""" in outputs )
       self.assertTrue(isinstance(__lowerCAmelCase			,     __lowerCAmelCase ) )

       self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""]			,     """word""" ) )			,     outputs.text )
       self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]			,     """word""" )			,     ["""<s>""", """<s>""", """</s>"""] )
       self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]			,     """start_offset""" )			,     [0, 2, 4] )
       self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]			,     """end_offset""" )			,     [1, 3, 5] )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Any					    =							WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
       UpperCamelCase_				:   Union[str, Any]					    =							self._get_dummy_logits()

       UpperCamelCase_				:   Dict					    =							processor.batch_decode(__lowerCAmelCase			,     output_word_offsets=__lowerCAmelCase )

       # check Wav2Vec2CTCTokenizerOutput keys for word
       self.assertEqual(len(outputs.keys() )			,     4 )
       self.assertTrue("""text""" in outputs )
       self.assertTrue("""word_offsets""" in outputs )
       self.assertTrue(isinstance(__lowerCAmelCase			,     __lowerCAmelCase ) )

       self.assertListEqual(
           [""" """.join(self.get_from_offsets(__lowerCAmelCase			,     """word""" ) ) for o in outputs["""word_offsets"""]]			,     outputs.text )
       self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]			,     """word""" )			,     ["""<s>""", """<s>""", """</s>"""] )
       self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]			,     """start_offset""" )			,     [0, 2, 4] )
       self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]			,     """end_offset""" )			,     [1, 3, 5] )





 @slow
 @require_torch
 @require_torchaudio
 def   _UpperCAmelCase       (							self ):
       import torch

       UpperCamelCase_				:   str					    =							load_dataset("""common_voice"""			,     """en"""			,     split="""train"""			,     streaming=__lowerCAmelCase )
       UpperCamelCase_				:   Union[str, Any]					    =							ds.cast_column("""audio"""			,     datasets.Audio(sampling_rate=1_60_00 ) )
       UpperCamelCase_				:   Union[str, Any]					    =							iter(__lowerCAmelCase )
       UpperCamelCase_				:   int					    =							next(__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
       UpperCamelCase_				:   Tuple					    =							WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )

       # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
       UpperCamelCase_				:   Dict					    =							processor(sample["""audio"""]["""array"""]			,     return_tensors="""pt""" ).input_values

       with torch.no_grad():
             UpperCamelCase_				:   List[Any]					    =							model(__lowerCAmelCase ).logits.cpu().numpy()

       UpperCamelCase_				:   Tuple					    =							processor.decode(logits[0]			,     output_word_offsets=__lowerCAmelCase )

       UpperCamelCase_				:   List[Any]					    =							model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
       UpperCamelCase_				:   Dict					    =							[
           {
               """start_time""": d["""start_offset"""] * time_offset,
               """end_time""": d["""end_offset"""] * time_offset,
               """word""": d["""word"""],
           }
           for d in output["""word_offsets"""]
       ]

       UpperCamelCase_				:   Optional[Any]					    =							"""WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""

       # output words
       self.assertEqual(""" """.join(self.get_from_offsets(__lowerCAmelCase			,     """word""" ) )			,     __lowerCAmelCase )
       self.assertEqual(""" """.join(self.get_from_offsets(__lowerCAmelCase			,     """word""" ) )			,     output.text )

       # output times
       UpperCamelCase_				:   str					    =							torch.tensor(self.get_from_offsets(__lowerCAmelCase			,     """start_time""" ) )
       UpperCamelCase_				:   Union[str, Any]					    =							torch.tensor(self.get_from_offsets(__lowerCAmelCase			,     """end_time""" ) )

       # fmt: off
       UpperCamelCase_				:   Union[str, Any]					    =							torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99] )
       UpperCamelCase_				:   Union[str, Any]					    =							torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94] )
       # fmt: on

       self.assertTrue(torch.allclose(__lowerCAmelCase			,     __lowerCAmelCase			,     atol=0.01 ) )
       self.assertTrue(torch.allclose(__lowerCAmelCase			,     __lowerCAmelCase			,     atol=0.01 ) )






