






from datetime import datetime as dt
import os

from github import Github


lowerCamelCase : Dict						   =       [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]
def       snake_case_		(     ):
							__lowercase      :							str				=     Github(os.environ["""GITHUB_TOKEN"""]      )
							__lowercase      :							Optional[int]				=     g.get_repo("""huggingface/transformers"""      )
							__lowercase      :							Any				=     repo.get_issues(state="""open"""      )

							for issue in open_issues:
														__lowercase      :							int				=     sorted([comment for comment in issue.get_comments()]       ,						key=lambda lowerCAmelCase_      : i.created_at       ,						reverse=lowerCAmelCase_      )
														__lowercase      :							List[Any]				=     comments[0] if len(lowerCAmelCase_      ) > 0 else None
														if (
														    last_comment is not None
														    and last_comment.user.login == "github-actions[bot]"
														    and (dt.utcnow() - issue.updated_at).days > 7
														    and (dt.utcnow() - issue.created_at).days >= 30
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
														):
																					# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
																					issue.edit(state="""closed"""      )
														elif (
														    (dt.utcnow() - issue.updated_at).days > 23
														    and (dt.utcnow() - issue.created_at).days >= 30
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
														):
																					# print(f"Would add stale comment to {issue.number}")
																					issue.create_comment(
																					    """This issue has been automatically marked as stale because it has not had """
																					    """recent activity. If you think this still needs to be addressed """
																					    """please comment on this thread.\n\nPlease note that issues that do not follow the """
																					    """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
																					    """are likely to be ignored."""      )


if __name__ == "__main__":
	main()







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			):

					'''simple docstring'''

					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Any:


												"""simple docstring"""

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

												__lowercase      :							Dict				=     {
												    """unk_token""": """<unk>""",
												    """bos_token""": """<s>""",
												    """eos_token""": """</s>""",
												}
												__lowercase      :							List[str]				=     {
												    """feature_size""": 1,
												    """padding_value""": 0.0,
												    """sampling_rate""": 16000,
												    """return_attention_mask""": False,
												    """do_normalize""": True,
												}

												__lowercase      :							Tuple				=     tempfile.mkdtemp()
												__lowercase      :							List[Any]				=     os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["""vocab_file"""]							)
												__lowercase      :							str				=     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
												__lowercase      :							Optional[int]				=     """hf-internal-testing/ngram-beam-search-decoder"""



					def       lowerCAmelCase				(  self				:	Optional[Any]		,     **__a				:	Dict							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     self.add_kwargs_tokens_map.copy()
												kwargs.update(__a							)
												return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,     **__a							)



					def       lowerCAmelCase				(  self				:	str		,     **__a				:	int							)			->  Tuple:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     **__a				:	Union[str, Any]							)			->  List[Any]:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	int							)			->  Tuple:


												"""simple docstring"""

												shutil.rmtree(self.tmpdirname							)



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Any:


												"""simple docstring"""

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

												__lowercase      :							Tuple				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

												processor.save_pretrained(self.tmpdirname							)
												__lowercase      :							Tuple				=     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       lowerCAmelCase				(  self				:	Optional[int]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							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
												__lowercase      :							str				=     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       lowerCAmelCase				(  self				:	Any							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     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       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Any:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.get_feature_extractor()
												__lowercase      :							Union[str, Any]				=     self.get_tokenizer()
												__lowercase      :							int				=     self.get_decoder()

												__lowercase      :							int				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

												__lowercase      :							Optional[int]				=     floats_list((3, 1000)							)

												__lowercase      :							List[Any]				=     feature_extractor(__a		,     return_tensors="""np"""							)
												__lowercase      :							List[str]				=     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       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.get_feature_extractor()
												__lowercase      :							int				=     self.get_tokenizer()
												__lowercase      :							Dict				=     self.get_decoder()

												__lowercase      :							Union[str, Any]				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

												__lowercase      :							Dict				=     """This is a test string"""

												__lowercase      :							Any				=     processor(text=__a							)

												__lowercase      :							Dict				=     tokenizer(__a							)

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



					def       lowerCAmelCase				(  self				:	str		,     __a				:	Tuple=(2, 10, 16)		,     __a				:	int=77							)			->  Optional[Any]:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  List[Any]:


												"""simple docstring"""

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

												__lowercase      :							int				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

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

												__lowercase      :							Optional[Any]				=     processor.decode(__a							)

												__lowercase      :							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       lowerCAmelCase				(  self				:	List[str]		,     __a				:	Dict							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							str				=     self.get_feature_extractor()
												__lowercase      :							Dict				=     self.get_tokenizer()
												__lowercase      :							Optional[int]				=     self.get_decoder()

												__lowercase      :							Any				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

												__lowercase      :							Optional[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:
																			__lowercase      :							Union[str, Any]				=     processor.batch_decode(__a							)
												else:
																			with get_context(__a							).Pool() as pool:
																										__lowercase      :							Optional[Any]				=     processor.batch_decode(__a		,     __a							)

												__lowercase      :							Union[str, Any]				=     list(__a							)

												with get_context("""fork"""							).Pool() as p:
																			__lowercase      :							Optional[Any]				=     decoder.decode_beams_batch(__a		,     __a							)

												__lowercase			,			__lowercase			,			__lowercase      :							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       lowerCAmelCase				(  self				:	Any							)			->  Optional[int]:


												"""simple docstring"""

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

												__lowercase      :							int				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

												__lowercase      :							Dict				=     self._get_dummy_logits()

												__lowercase      :							Tuple				=     15
												__lowercase      :							Tuple				=     -20.0
												__lowercase      :							Dict				=     -4.0

												__lowercase      :							Dict				=     processor.batch_decode(
												    __a		,     beam_width=__a		,     beam_prune_logp=__a		,     token_min_logp=__a		,     )
												__lowercase      :							Tuple				=     decoded_processor_out.text

												__lowercase      :							List[Any]				=     list(__a							)

												with get_context("""fork"""							).Pool() as pool:
																			__lowercase      :							Any				=     decoder.decode_beams_batch(
																			    __a		,     __a		,     beam_width=__a		,     beam_prune_logp=__a		,     token_min_logp=__a		,     )

												__lowercase      :							Optional[Any]				=     [d[0][0] for d in decoded_decoder_out]
												__lowercase      :							Optional[int]				=     [d[0][2] for d in decoded_decoder_out]
												__lowercase      :							Optional[int]				=     [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([-20.054, -18.447]		,     __a		,     atol=1E-3							)							)

												self.assertTrue(np.array_equal(__a		,     decoded_processor_out.lm_score							)							)
												self.assertTrue(np.allclose([-15.554, -13.9474]		,     __a		,     atol=1E-3							)							)



					def       lowerCAmelCase				(  self				:	List[str]							)			->  List[Any]:


												"""simple docstring"""

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

												__lowercase      :							Dict				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

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

												__lowercase      :							Optional[int]				=     2.0
												__lowercase      :							Tuple				=     5.0
												__lowercase      :							Optional[Any]				=     -20.0
												__lowercase      :							Tuple				=     True

												__lowercase      :							Union[str, Any]				=     processor.batch_decode(
												    __a		,     alpha=__a		,     beta=__a		,     unk_score_offset=__a		,     lm_score_boundary=__a		,     )
												__lowercase      :							Any				=     decoded_processor_out.text

												__lowercase      :							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:
																			__lowercase      :							Tuple				=     decoder.decode_beams_batch(
																			    __a		,     __a		,     )

												__lowercase      :							int				=     [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							)
												__lowercase      :							str				=     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		,     __a							)



					def       lowerCAmelCase				(  self				:	Any							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)

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

												__lowercase      :							int				=     os.listdir(__a							)
												__lowercase      :							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(__a		,     __a							)



					def       lowerCAmelCase				(  self				:	str							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     snapshot_download("""hf-internal-testing/processor_with_lm"""							)

												__lowercase      :							Union[str, Any]				=     WavaVecaProcessorWithLM.from_pretrained(__a							)

												__lowercase      :							Dict				=     processor.decoder.model_container[processor.decoder._model_key]
												__lowercase      :							List[Any]				=     Path(language_model._kenlm_model.path.decode("""utf-8"""							)							).parent.parent.absolute()

												__lowercase      :							Dict				=     os.listdir(__a							)
												__lowercase      :							List[Any]				=     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       lowerCAmelCase				(  self				:	Tuple							)			->  int:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)
												__lowercase      :							Dict				=     AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""							)

												__lowercase      :							Any				=     floats_list((3, 1000)							)

												__lowercase      :							List[str]				=     processor_wavaveca(__a		,     return_tensors="""np"""							)
												__lowercase      :							List[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							)

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

												__lowercase      :							List[str]				=     processor_wavaveca.batch_decode(__a							)
												__lowercase      :							Optional[int]				=     processor_auto.batch_decode(__a							)

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



					def       lowerCAmelCase				(  self				:	Any							)			->  Optional[Any]:


												"""simple docstring"""

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

												__lowercase      :							Optional[Any]				=     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       lowerCAmelCase				(  __a				:	Union[str, Any]		,     __a				:	List[Any]							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Any				=     [d[key] for d in offsets]
												return retrieved_list



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  str:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)
												__lowercase      :							Optional[Any]				=     self._get_dummy_logits()[0]

												__lowercase      :							Dict				=     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       lowerCAmelCase				(  self				:	List[str]							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							List[str]				=     WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)
												__lowercase      :							Any				=     self._get_dummy_logits()

												__lowercase      :							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       lowerCAmelCase				(  self				:	Dict							)			->  int:


												"""simple docstring"""

												import torch

												__lowercase      :							Any				=     load_dataset("""common_voice"""		,     """en"""		,     split="""train"""		,     streaming=__a							)
												__lowercase      :							str				=     ds.cast_column("""audio"""		,     datasets.Audio(sampling_rate=16000							)							)
												__lowercase      :							Tuple				=     iter(__a							)
												__lowercase      :							Union[str, Any]				=     next(__a							)

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

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

												__lowercase      :							Tuple				=     processor.decode(logits[0]		,     output_word_offsets=__a							)

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

												__lowercase      :							str				=     """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
												__lowercase      :							Tuple				=     torch.tensor(self.get_from_offsets(__a		,     """start_time"""							)							)
												__lowercase      :							Dict				=     torch.tensor(self.get_from_offsets(__a		,     """end_time"""							)							)

												# fmt: off
												__lowercase      :							List[Any]				=     torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599]							)
												__lowercase      :							Optional[int]				=     torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94]							)
												# fmt: on

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