





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  A_     ( self     :					List[Any]						)				->			List[str]:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      List[Any]     =						"| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
									SCREAMING_SNAKE_CASE__       :      int     =						dict(zip(UpperCamelCase_,			range(len(UpperCamelCase_						)						)						)						)

									SCREAMING_SNAKE_CASE__       :      Tuple     =						{
									    "unk_token": "<unk>",
									    "bos_token": "<s>",
									    "eos_token": "</s>",
									}
									SCREAMING_SNAKE_CASE__       :      Tuple     =						{
									    "feature_size": 1,
									    "padding_value": 0.0,
									    "sampling_rate": 1_6_0_0_0,
									    "return_attention_mask": False,
									    "do_normalize": True,
									}

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

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

									# load decoder from hub
									SCREAMING_SNAKE_CASE__       :      List[Any]     =						"hf-internal-testing/ngram-beam-search-decoder"






		def  A_     ( self     :					str,			**_UpperCAmelCase     :					Optional[Any]						)				->			int:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.add_kwargs_tokens_map.copy()
									kwargs.update(UpperCamelCase_						)
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,			**UpperCamelCase_						)






		def  A_     ( self     :					Optional[Any],			**_UpperCAmelCase     :					Union[str, Any]						)				->			Union[str, Any]:





									"""simple docstring"""
									return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,			**UpperCamelCase_						)






		def  A_     ( self     :					Optional[Any],			**_UpperCAmelCase     :					List[str]						)				->			Tuple:





									"""simple docstring"""
									return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name,			**UpperCamelCase_						)






		def  A_     ( self     :					Dict						)				->			List[str]:





									"""simple docstring"""
									shutil.rmtree(self.tmpdirname						)






		def  A_     ( self     :					Dict						)				->			Union[str, Any]:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_tokenizer()
									SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.get_feature_extractor()
									SCREAMING_SNAKE_CASE__       :      str     =						self.get_decoder()

									SCREAMING_SNAKE_CASE__       :      Any     =						WavaVecaProcessorWithLM(tokenizer=UpperCamelCase_,			feature_extractor=UpperCamelCase_,			decoder=UpperCamelCase_						)

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

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

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

									# 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,			UpperCamelCase_						)






		def  A_     ( self     :					Any						)				->			Tuple:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      List[str]     =						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
									SCREAMING_SNAKE_CASE__       :      List[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  A_     ( self     :					Optional[Any]						)				->			str:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.get_tokenizer()
									# add token to trigger raise
									tokenizer.add_tokens(["xx"]						)
									with self.assertRaisesRegex(UpperCamelCase_,			"include"						):
																WavaVecaProcessorWithLM(
																    tokenizer=UpperCamelCase_,			feature_extractor=self.get_feature_extractor(),			decoder=self.get_decoder()						)






		def  A_     ( self     :					Union[str, Any]						)				->			int:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.get_feature_extractor()
									SCREAMING_SNAKE_CASE__       :      str     =						self.get_tokenizer()
									SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.get_decoder()

									SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						WavaVecaProcessorWithLM(tokenizer=UpperCamelCase_,			feature_extractor=UpperCamelCase_,			decoder=UpperCamelCase_						)

									SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						floats_list((3, 1_0_0_0)						)

									SCREAMING_SNAKE_CASE__       :      Any     =						feature_extractor(UpperCamelCase_,			return_tensors="np"						)
									SCREAMING_SNAKE_CASE__       :      Any     =						processor(UpperCamelCase_,			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  A_     ( self     :					Dict						)				->			Dict:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      List[str]     =						self.get_feature_extractor()
									SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_tokenizer()
									SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.get_decoder()

									SCREAMING_SNAKE_CASE__       :      Any     =						WavaVecaProcessorWithLM(tokenizer=UpperCamelCase_,			feature_extractor=UpperCamelCase_,			decoder=UpperCamelCase_						)

									SCREAMING_SNAKE_CASE__       :      List[Any]     =						"This is a test string"

									SCREAMING_SNAKE_CASE__       :      Any     =						processor(text=UpperCamelCase_						)

									SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer(UpperCamelCase_						)

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






		def  A_     ( self     :					Any,			_UpperCAmelCase     :					Union[str, Any]=(2, 1_0, 1_6),			_UpperCAmelCase     :					Optional[Any]=7_7						)				->			Dict:





									"""simple docstring"""
									np.random.seed(UpperCamelCase_						)
									return np.random.rand(*UpperCamelCase_						)






		def  A_     ( self     :					Tuple						)				->			Union[str, Any]:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_feature_extractor()
									SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_tokenizer()
									SCREAMING_SNAKE_CASE__       :      Dict     =						self.get_decoder()

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

									SCREAMING_SNAKE_CASE__       :      int     =						self._get_dummy_logits(shape=(1_0, 1_6),			seed=1_3						)

									SCREAMING_SNAKE_CASE__       :      str     =						processor.decode(UpperCamelCase_						)

									SCREAMING_SNAKE_CASE__       :      List[Any]     =						decoder.decode_beams(UpperCamelCase_						)[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  A_     ( self     :					List[Any],			_UpperCAmelCase     :					Any						)				->			Tuple:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.get_feature_extractor()
									SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.get_tokenizer()
									SCREAMING_SNAKE_CASE__       :      str     =						self.get_decoder()

									SCREAMING_SNAKE_CASE__       :      Tuple     =						WavaVecaProcessorWithLM(tokenizer=UpperCamelCase_,			feature_extractor=UpperCamelCase_,			decoder=UpperCamelCase_						)

									SCREAMING_SNAKE_CASE__       :      Dict     =						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:
																SCREAMING_SNAKE_CASE__       :      List[Any]     =						processor.batch_decode(UpperCamelCase_						)
									else:
																with get_context(UpperCamelCase_						).Pool() as pool:
																							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						processor.batch_decode(UpperCamelCase_,			UpperCamelCase_						)

									SCREAMING_SNAKE_CASE__       :      str     =						list(UpperCamelCase_						)

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

									SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      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(UpperCamelCase_,			decoded_processor.text						)
									self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"],			decoded_processor.text						)
									self.assertListEqual(UpperCamelCase_,			decoded_processor.logit_score						)
									self.assertListEqual(UpperCamelCase_,			decoded_processor.lm_score						)






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





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      Dict     =						self.get_feature_extractor()
									SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_tokenizer()
									SCREAMING_SNAKE_CASE__       :      List[str]     =						self.get_decoder()

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

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

									SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						1_5
									SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						-20.0
									SCREAMING_SNAKE_CASE__       :      int     =						-4.0

									SCREAMING_SNAKE_CASE__       :      List[Any]     =						processor.batch_decode(
									    UpperCamelCase_,			beam_width=UpperCamelCase_,			beam_prune_logp=UpperCamelCase_,			token_min_logp=UpperCamelCase_,			)
									SCREAMING_SNAKE_CASE__       :      Optional[int]     =						decoded_processor_out.text

									SCREAMING_SNAKE_CASE__       :      Dict     =						list(UpperCamelCase_						)

									with get_context("fork"						).Pool() as pool:
																SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						decoder.decode_beams_batch(
																    UpperCamelCase_,			UpperCamelCase_,			beam_width=UpperCamelCase_,			beam_prune_logp=UpperCamelCase_,			token_min_logp=UpperCamelCase_,			)

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

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

									self.assertTrue(np.array_equal(UpperCamelCase_,			decoded_processor_out.logit_score						)						)
									self.assertTrue(np.allclose([-20.054, -18.447],			UpperCamelCase_,			atol=1E-3						)						)

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






		def  A_     ( self     :					Tuple						)				->			Any:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      Any     =						self.get_feature_extractor()
									SCREAMING_SNAKE_CASE__       :      str     =						self.get_tokenizer()
									SCREAMING_SNAKE_CASE__       :      int     =						self.get_decoder()

									SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						WavaVecaProcessorWithLM(tokenizer=UpperCamelCase_,			feature_extractor=UpperCamelCase_,			decoder=UpperCamelCase_						)

									SCREAMING_SNAKE_CASE__       :      Tuple     =						self._get_dummy_logits()

									SCREAMING_SNAKE_CASE__       :      Any     =						2.0
									SCREAMING_SNAKE_CASE__       :      List[str]     =						5.0
									SCREAMING_SNAKE_CASE__       :      str     =						-20.0
									SCREAMING_SNAKE_CASE__       :      List[Any]     =						True

									SCREAMING_SNAKE_CASE__       :      List[Any]     =						processor.batch_decode(
									    UpperCamelCase_,			alpha=UpperCamelCase_,			beta=UpperCamelCase_,			unk_score_offset=UpperCamelCase_,			lm_score_boundary=UpperCamelCase_,			)
									SCREAMING_SNAKE_CASE__       :      Dict     =						decoded_processor_out.text

									SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						list(UpperCamelCase_						)
									decoder.reset_params(
									    alpha=UpperCamelCase_,			beta=UpperCamelCase_,			unk_score_offset=UpperCamelCase_,			lm_score_boundary=UpperCamelCase_,			)

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

									SCREAMING_SNAKE_CASE__       :      List[str]     =						[d[0][0] for d in decoded_decoder_out]

									self.assertListEqual(UpperCamelCase_,			UpperCamelCase_						)
									self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"],			UpperCamelCase_						)
									SCREAMING_SNAKE_CASE__       :      List[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,			UpperCamelCase_						)






		def  A_     ( self     :					Tuple						)				->			List[Any]:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"						)

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

									SCREAMING_SNAKE_CASE__       :      List[str]     =						os.listdir(UpperCamelCase_						)
									SCREAMING_SNAKE_CASE__       :      List[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(UpperCamelCase_,			UpperCamelCase_						)






		def  A_     ( self     :					Dict						)				->			Any:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      Tuple     =						snapshot_download("hf-internal-testing/processor_with_lm"						)

									SCREAMING_SNAKE_CASE__       :      int     =						WavaVecaProcessorWithLM.from_pretrained(UpperCamelCase_						)

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

									SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						os.listdir(UpperCamelCase_						)
									SCREAMING_SNAKE_CASE__       :      int     =						os.listdir(UpperCamelCase_						)

									local_decoder_files.sort()
									expected_decoder_files.sort()

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






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





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      List[str]     =						WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"						)
									SCREAMING_SNAKE_CASE__       :      Optional[int]     =						AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"						)

									SCREAMING_SNAKE_CASE__       :      List[str]     =						floats_list((3, 1_0_0_0)						)

									SCREAMING_SNAKE_CASE__       :      Optional[int]     =						processor_wavaveca(UpperCamelCase_,			return_tensors="np"						)
									SCREAMING_SNAKE_CASE__       :      List[str]     =						processor_auto(UpperCamelCase_,			return_tensors="np"						)

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

									SCREAMING_SNAKE_CASE__       :      Dict     =						self._get_dummy_logits()

									SCREAMING_SNAKE_CASE__       :      Optional[int]     =						processor_wavaveca.batch_decode(UpperCamelCase_						)
									SCREAMING_SNAKE_CASE__       :      Optional[int]     =						processor_auto.batch_decode(UpperCamelCase_						)

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






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





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.get_feature_extractor()
									SCREAMING_SNAKE_CASE__       :      int     =						self.get_tokenizer()
									SCREAMING_SNAKE_CASE__       :      Dict     =						self.get_decoder()

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

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






		@staticmethod
		def  A_     ( _UpperCAmelCase     :					Any,			_UpperCAmelCase     :					Union[str, Any]						)				->			Optional[int]:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[d[key] for d in offsets]
									return retrieved_list






		def  A_     ( self     :					str						)				->			str:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"						)
									SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self._get_dummy_logits()[0]

									SCREAMING_SNAKE_CASE__       :      List[Any]     =						processor.decode(UpperCamelCase_,			output_word_offsets=UpperCamelCase_						)
									# 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(UpperCamelCase_,			UpperCamelCase_						)						)

									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  A_     ( self     :					Optional[int]						)				->			Tuple:





									"""simple docstring"""
									SCREAMING_SNAKE_CASE__       :      List[Any]     =						WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"						)
									SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self._get_dummy_logits()

									SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						processor.batch_decode(UpperCamelCase_,			output_word_offsets=UpperCamelCase_						)

									# 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(UpperCamelCase_,			UpperCamelCase_						)						)

									self.assertListEqual(
									    [" ".join(self.get_from_offsets(UpperCamelCase_,			"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  A_     ( self     :					Dict						)				->			str:





									"""simple docstring"""
									import torch

									SCREAMING_SNAKE_CASE__       :      int     =						load_dataset("common_voice",			"en",			split="train",			streaming=UpperCamelCase_						)
									SCREAMING_SNAKE_CASE__       :      Tuple     =						ds.cast_column("audio",			datasets.Audio(sampling_rate=1_6_0_0_0						)						)
									SCREAMING_SNAKE_CASE__       :      Any     =						iter(UpperCamelCase_						)
									SCREAMING_SNAKE_CASE__       :      List[str]     =						next(UpperCamelCase_						)

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

									with torch.no_grad():
																SCREAMING_SNAKE_CASE__       :      Tuple     =						model(UpperCamelCase_						).logits.cpu().numpy()

									SCREAMING_SNAKE_CASE__       :      List[Any]     =						processor.decode(logits[0],			output_word_offsets=UpperCamelCase_						)

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

									SCREAMING_SNAKE_CASE__       :      Dict     =						"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(UpperCamelCase_,			"word"						)						),			UpperCamelCase_						)
									self.assertEqual(" ".join(self.get_from_offsets(UpperCamelCase_,			"word"						)						),			output.text						)

									# output times
									SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.tensor(self.get_from_offsets(UpperCamelCase_,			"start_time"						)						)
									SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.tensor(self.get_from_offsets(UpperCamelCase_,			"end_time"						)						)

									# fmt: off
									SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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]						)
									SCREAMING_SNAKE_CASE__       :      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(UpperCamelCase_,			UpperCamelCase_,			atol=0.01						)						)
									self.assertTrue(torch.allclose(UpperCamelCase_,			UpperCamelCase_,			atol=0.01						)						)













def _a (  SCREAMING_SNAKE_CASE__      :  int       )	->    str:

							'''simple docstring'''






							if isinstance(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														raise TypeError("'float' object cannot be interpreted as an integer"       )
							if isinstance(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														raise TypeError("'str' object cannot be interpreted as an integer"       )

							if num == 0:
														return "0b0"

							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						False

							if num < 0:
														SCREAMING_SNAKE_CASE__       :      Any     =						True
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						-num

							SCREAMING_SNAKE_CASE__       :      list[int]     =						[]
							while num > 0:
														binary.insert(0			,				num % 2       )
														num >>= 1

							if negative:
														return "-0b" + "".join(str(SCREAMING_SNAKE_CASE__       ) for e in binary       )

							return "0b" + "".join(str(SCREAMING_SNAKE_CASE__       ) for e in binary       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






