



'''simple docstring'''




from scipy.stats import spearmanr

import datasets


lowerCamelCase							:Dict				    =					'''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

lowerCamelCase							:Any				    =					'''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

lowerCamelCase							:List[Any]				    =					R'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''







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


	def     _a	(self							):
				return datasets.MetricInfo(
				    description=_DESCRIPTION       ,      citation=_CITATION       ,      inputs_description=_KWARGS_DESCRIPTION       ,      features=datasets.Features(
				        {
				            """predictions""": datasets.Value("""float"""							),
				            """references""": datasets.Value("""float"""							),
				        }							)       ,      reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""]       ,      )


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase=False							):
				A_	:						Tuple	=     spearmanr(_A       ,      _A							)
				if return_pvalue:
							return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
				else:
							return {"spearmanr": results[0]}



'''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 			lowerCamelCase_   (	unittest.TestCase      ):






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				'''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
          UpperCAmelCase__			:		str													=				dict(zip(_A						,						range(len(_A ) ) ) )

          UpperCAmelCase__			:		int													=				{
              '''unk_token''': '''<unk>''',
              '''bos_token''': '''<s>''',
              '''eos_token''': '''</s>''',
          }
          UpperCAmelCase__			:		List[Any]													=				{
              '''feature_size''': 1,
              '''padding_value''': 0.0,
              '''sampling_rate''': 16_000,
              '''return_attention_mask''': False,
              '''do_normalize''': True,
          }

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

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

          # load decoder from hub
          UpperCAmelCase__			:		Tuple													=				'''hf-internal-testing/ngram-beam-search-decoder'''






      def lowercase_							(					self				:    Union[str, Any]						,						**_A				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				self.add_kwargs_tokens_map.copy()
          kwargs.update(_A )
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname						,						**_A )






      def lowercase_							(					self				:    Dict						,						**_A				:    str ):


          '''simple docstring'''


          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname						,						**_A )






      def lowercase_							(					self				:    Tuple						,						**_A				:    int ):


          '''simple docstring'''


          return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name						,						**_A )






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          shutil.rmtree(self.tmpdirname )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				self.get_tokenizer()
          UpperCAmelCase__			:		Any													=				self.get_feature_extractor()
          UpperCAmelCase__			:		Dict													=				self.get_decoder()

          UpperCAmelCase__			:		Dict													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          processor.save_pretrained(self.tmpdirname )
          UpperCAmelCase__			:		Optional[Any]													=				WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )

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

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

          # 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						,						_A )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				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[int]													=				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 lowercase_							(					self				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				self.get_tokenizer()
          # add token to trigger raise
          tokenizer.add_tokens(['''xx'''] )
          with self.assertRaisesRegex(_A						,						'''include''' ):
              WavaVecaProcessorWithLM(
                  tokenizer=_A						,						feature_extractor=self.get_feature_extractor()						,						decoder=self.get_decoder() )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				self.get_feature_extractor()
          UpperCAmelCase__			:		List[Any]													=				self.get_tokenizer()
          UpperCAmelCase__			:		int													=				self.get_decoder()

          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          UpperCAmelCase__			:		str													=				floats_list((3, 1_000) )

          UpperCAmelCase__			:		str													=				feature_extractor(_A						,						return_tensors='''np''' )
          UpperCAmelCase__			:		Optional[Any]													=				processor(_A						,						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 lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				self.get_feature_extractor()
          UpperCAmelCase__			:		Any													=				self.get_tokenizer()
          UpperCAmelCase__			:		Any													=				self.get_decoder()

          UpperCAmelCase__			:		str													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          UpperCAmelCase__			:		Union[str, Any]													=				'''This is a test string'''

          UpperCAmelCase__			:		Union[str, Any]													=				processor(text=_A )

          UpperCAmelCase__			:		Dict													=				tokenizer(_A )

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]						,						encoded_processor[key] )






      def lowercase_							(					self				:    str						,						_A				:    int=(2, 10, 16)						,						_A				:    Optional[int]=77 ):


          '''simple docstring'''


          np.random.seed(_A )
          return np.random.rand(*_A )






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				self.get_feature_extractor()
          UpperCAmelCase__			:		Dict													=				self.get_tokenizer()
          UpperCAmelCase__			:		Tuple													=				self.get_decoder()

          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          UpperCAmelCase__			:		Tuple													=				self._get_dummy_logits(shape=(10, 16)						,						seed=13 )

          UpperCAmelCase__			:		Union[str, Any]													=				processor.decode(_A )

          UpperCAmelCase__			:		Union[str, Any]													=				decoder.decode_beams(_A )[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 lowercase_							(					self				:    Dict						,						_A				:    str ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				self.get_feature_extractor()
          UpperCAmelCase__			:		Union[str, Any]													=				self.get_tokenizer()
          UpperCAmelCase__			:		Union[str, Any]													=				self.get_decoder()

          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          UpperCAmelCase__			:		List[str]													=				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__			:		Union[str, Any]													=				processor.batch_decode(_A )
          else:
              with get_context(_A ).Pool() as pool:
                  UpperCAmelCase__			:		List[Any]													=				processor.batch_decode(_A						,						_A )

          UpperCAmelCase__			:		List[str]													=				list(_A )

          with get_context('''fork''' ).Pool() as p:
              UpperCAmelCase__			:		List[Any]													=				decoder.decode_beams_batch(_A						,						_A )

          UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		List[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(_A						,						decoded_processor.text )
          self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']						,						decoded_processor.text )
          self.assertListEqual(_A						,						decoded_processor.logit_score )
          self.assertListEqual(_A						,						decoded_processor.lm_score )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				self.get_feature_extractor()
          UpperCAmelCase__			:		str													=				self.get_tokenizer()
          UpperCAmelCase__			:		Optional[int]													=				self.get_decoder()

          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          UpperCAmelCase__			:		List[Any]													=				self._get_dummy_logits()

          UpperCAmelCase__			:		Optional[Any]													=				15
          UpperCAmelCase__			:		Union[str, Any]													=				-2_0.0
          UpperCAmelCase__			:		List[str]													=				-4.0

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

          UpperCAmelCase__			:		Tuple													=				list(_A )

          with get_context('''fork''' ).Pool() as pool:
              UpperCAmelCase__			:		Tuple													=				decoder.decode_beams_batch(
                  _A						,						_A						,						beam_width=_A						,						beam_prune_logp=_A						,						token_min_logp=_A						,						)

          UpperCAmelCase__			:		Optional[Any]													=				[d[0][0] for d in decoded_decoder_out]
          UpperCAmelCase__			:		Any													=				[d[0][2] for d in decoded_decoder_out]
          UpperCAmelCase__			:		Tuple													=				[d[0][3] for d in decoded_decoder_out]

          self.assertListEqual(_A						,						_A )
          self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>''']						,						_A )

          self.assertTrue(np.array_equal(_A						,						decoded_processor_out.logit_score ) )
          self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7]						,						_A						,						atol=1e-3 ) )

          self.assertTrue(np.array_equal(_A						,						decoded_processor_out.lm_score ) )
          self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4]						,						_A						,						atol=1e-3 ) )






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				self.get_feature_extractor()
          UpperCAmelCase__			:		str													=				self.get_tokenizer()
          UpperCAmelCase__			:		List[str]													=				self.get_decoder()

          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          UpperCAmelCase__			:		Optional[Any]													=				self._get_dummy_logits()

          UpperCAmelCase__			:		List[Any]													=				2.0
          UpperCAmelCase__			:		Union[str, Any]													=				5.0
          UpperCAmelCase__			:		Any													=				-2_0.0
          UpperCAmelCase__			:		List[Any]													=				True

          UpperCAmelCase__			:		Any													=				processor.batch_decode(
              _A						,						alpha=_A						,						beta=_A						,						unk_score_offset=_A						,						lm_score_boundary=_A						,						)
          UpperCAmelCase__			:		List[Any]													=				decoded_processor_out.text

          UpperCAmelCase__			:		List[Any]													=				list(_A )
          decoder.reset_params(
              alpha=_A						,						beta=_A						,						unk_score_offset=_A						,						lm_score_boundary=_A						,						)

          with get_context('''fork''' ).Pool() as pool:
              UpperCAmelCase__			:		Any													=				decoder.decode_beams_batch(
                  _A						,						_A						,						)

          UpperCAmelCase__			:		Union[str, Any]													=				[d[0][0] for d in decoded_decoder_out]

          self.assertListEqual(_A						,						_A )
          self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']						,						_A )
          UpperCAmelCase__			:		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						,						-2_0.0 )
          self.assertEqual(lm_model.score_boundary						,						_A )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )

          UpperCAmelCase__			:		List[Any]													=				processor.decoder.model_container[processor.decoder._model_key]
          UpperCAmelCase__			:		Optional[Any]													=				Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()

          UpperCAmelCase__			:		Dict													=				os.listdir(_A )
          UpperCAmelCase__			:		str													=				['''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(_A						,						_A )






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				snapshot_download('''hf-internal-testing/processor_with_lm''' )

          UpperCAmelCase__			:		Tuple													=				WavaVecaProcessorWithLM.from_pretrained(_A )

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

          UpperCAmelCase__			:		Optional[Any]													=				os.listdir(_A )
          UpperCAmelCase__			:		int													=				os.listdir(_A )

          local_decoder_files.sort()
          expected_decoder_files.sort()

          # test that both decoder form hub and local files in cache are the same
          self.assertListEqual(_A						,						_A )






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
          UpperCAmelCase__			:		Any													=				AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )

          UpperCAmelCase__			:		Tuple													=				floats_list((3, 1_000) )

          UpperCAmelCase__			:		Tuple													=				processor_wavaveca(_A						,						return_tensors='''np''' )
          UpperCAmelCase__			:		Union[str, Any]													=				processor_auto(_A						,						return_tensors='''np''' )

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

          UpperCAmelCase__			:		str													=				self._get_dummy_logits()

          UpperCAmelCase__			:		List[Any]													=				processor_wavaveca.batch_decode(_A )
          UpperCAmelCase__			:		List[Any]													=				processor_auto.batch_decode(_A )

          self.assertListEqual(decoded_wavaveca.text						,						decoded_auto.text )






      def lowercase_							(					self				:    Union[str, Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				self.get_feature_extractor()
          UpperCAmelCase__			:		Any													=				self.get_tokenizer()
          UpperCAmelCase__			:		List[str]													=				self.get_decoder()

          UpperCAmelCase__			:		Dict													=				WavaVecaProcessorWithLM(tokenizer=_A						,						feature_extractor=_A						,						decoder=_A )

          self.assertListEqual(
              processor.model_input_names						,						feature_extractor.model_input_names						,						msg='''`processor` and `feature_extractor` model input names do not match'''						,						)






      @staticmethod
      def lowercase_							(					_A				:    Tuple						,						_A				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				[d[key] for d in offsets]
          return retrieved_list






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
          UpperCAmelCase__			:		Tuple													=				self._get_dummy_logits()[0]

          UpperCAmelCase__			:		int													=				processor.decode(_A						,						output_word_offsets=_A )
          # 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(_A						,						_A ) )

          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 lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
          UpperCAmelCase__			:		List[str]													=				self._get_dummy_logits()

          UpperCAmelCase__			:		Dict													=				processor.batch_decode(_A						,						output_word_offsets=_A )

          # 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(_A						,						_A ) )

          self.assertListEqual(
              [''' '''.join(self.get_from_offsets(_A						,						'''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 lowercase_							(					self				:    str ):


          '''simple docstring'''


          import torch

          UpperCAmelCase__			:		Dict													=				load_dataset('''common_voice'''						,						'''en'''						,						split='''train'''						,						streaming=_A )
          UpperCAmelCase__			:		Optional[Any]													=				ds.cast_column('''audio'''						,						datasets.Audio(sampling_rate=16_000 ) )
          UpperCAmelCase__			:		Any													=				iter(_A )
          UpperCAmelCase__			:		Dict													=				next(_A )

          UpperCAmelCase__			:		Optional[int]													=				AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
          UpperCAmelCase__			:		List[Any]													=				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__			:		Union[str, Any]													=				processor(sample['''audio''']['''array''']						,						return_tensors='''pt''' ).input_values

          with torch.no_grad():
              UpperCAmelCase__			:		Dict													=				model(_A ).logits.cpu().numpy()

          UpperCAmelCase__			:		List[str]													=				processor.decode(logits[0]						,						output_word_offsets=_A )

          UpperCAmelCase__			:		Tuple													=				model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
          UpperCAmelCase__			:		List[str]													=				[
              {
                  '''start_time''': d['''start_offset'''] * time_offset,
                  '''end_time''': d['''end_offset'''] * time_offset,
                  '''word''': d['''word'''],
              }
              for d in output['''word_offsets''']
          ]

          UpperCAmelCase__			:		Tuple													=				'''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(_A						,						'''word''' ) )						,						_A )
          self.assertEqual(''' '''.join(self.get_from_offsets(_A						,						'''word''' ) )						,						output.text )

          # output times
          UpperCAmelCase__			:		List[Any]													=				torch.tensor(self.get_from_offsets(_A						,						'''start_time''' ) )
          UpperCAmelCase__			:		Dict													=				torch.tensor(self.get_from_offsets(_A						,						'''end_time''' ) )

          # fmt: off
          UpperCAmelCase__			:		Any													=				torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9] )
          UpperCAmelCase__			:		Any													=				torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4] )
          # fmt: on

          self.assertTrue(torch.allclose(_A						,						_A						,						atol=0.0_1 ) )
          self.assertTrue(torch.allclose(_A						,						_A						,						atol=0.0_1 ) )
