



"""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 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       Any:

				"""simple docstring"""

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

				__lowerCAmelCase      :  List[str]												=       {
				    'unk_token': '<unk>',
				    'bos_token': '<s>',
				    'eos_token': '</s>',
				}
				__lowerCAmelCase      :  str												=       {
				    'feature_size': 1,
				    'padding_value': 0.0,
				    'sampling_rate': 1_6000,
				    'return_attention_mask': False,
				    'do_normalize': True,
				}

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

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

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.add_kwargs_tokens_map.copy()
				kwargs.update(_UpperCAmelCase)
				return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,			**_UpperCAmelCase)

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

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str]       ,			**_SCREAMING_SNAKE_CASE:		Optional[Any])				->       Dict:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Optional[Any]:

				"""simple docstring"""

				shutil.rmtree(self.tmpdirname)

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

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       self.get_tokenizer()
				__lowerCAmelCase      :  Any												=       self.get_feature_extractor()
				__lowerCAmelCase      :  int												=       self.get_decoder()

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

				processor.save_pretrained(self.tmpdirname)
				__lowerCAmelCase      :  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 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[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
				__lowerCAmelCase      :  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 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  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 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       Optional[int]:

				"""simple docstring"""

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

				__lowerCAmelCase      :  Tuple												=       WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase       ,			feature_extractor=_UpperCAmelCase       ,			decoder=_UpperCAmelCase)

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

				__lowerCAmelCase      :  int												=       feature_extractor(_UpperCAmelCase       ,			return_tensors="np")
				__lowerCAmelCase      :  Optional[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 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       int:

				"""simple docstring"""

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

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

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

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

				__lowerCAmelCase      :  Any												=       tokenizer(_UpperCAmelCase)

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		Dict=(2, 10, 16)       ,			_SCREAMING_SNAKE_CASE:		int=77)				->       Optional[Any]:

				"""simple docstring"""

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

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

				"""simple docstring"""

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

				__lowerCAmelCase      :  Optional[int]												=       WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase       ,			feature_extractor=_UpperCAmelCase       ,			decoder=_UpperCAmelCase)

				__lowerCAmelCase      :  Union[str, Any]												=       self._get_dummy_logits(shape=(10, 16)       ,			seed=13)

				__lowerCAmelCase      :  List[str]												=       processor.decode(_UpperCAmelCase)

				__lowerCAmelCase      :  Union[str, 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 							_SCREAMING_SNAKE_CASE   (     self:		List[str]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any])				->       str:

				"""simple docstring"""

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

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

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

				__lowerCAmelCase      :  List[str]												=       list(_UpperCAmelCase)

				with get_context("fork").Pool() as p:
							__lowerCAmelCase      :  List[Any]												=       decoder.decode_beams_batch(_UpperCAmelCase       ,			_UpperCAmelCase)

				__lowerCAmelCase      :  List[str]												=       [], [], []
				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 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Tuple:

				"""simple docstring"""

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

				__lowerCAmelCase      :  Optional[int]												=       WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase       ,			feature_extractor=_UpperCAmelCase       ,			decoder=_UpperCAmelCase)

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

				__lowerCAmelCase      :  Dict												=       15
				__lowerCAmelCase      :  str												=       -20.0
				__lowerCAmelCase      :  Optional[Any]												=       -4.0

				__lowerCAmelCase      :  int												=       processor.batch_decode(
				    _UpperCAmelCase       ,			beam_width=_UpperCAmelCase       ,			beam_prune_logp=_UpperCAmelCase       ,			token_min_logp=_UpperCAmelCase       ,			)
				__lowerCAmelCase      :  Any												=       decoded_processor_out.text

				__lowerCAmelCase      :  Any												=       list(_UpperCAmelCase)

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

				__lowerCAmelCase      :  List[str]												=       [d[0][0] for d in decoded_decoder_out]
				__lowerCAmelCase      :  List[str]												=       [d[0][2] for d in decoded_decoder_out]
				__lowerCAmelCase      :  Dict												=       [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 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Any:

				"""simple docstring"""

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

				__lowerCAmelCase      :  Optional[int]												=       WavaVecaProcessorWithLM(tokenizer=_UpperCAmelCase       ,			feature_extractor=_UpperCAmelCase       ,			decoder=_UpperCAmelCase)

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

				__lowerCAmelCase      :  List[str]												=       2.0
				__lowerCAmelCase      :  Union[str, Any]												=       5.0
				__lowerCAmelCase      :  Optional[Any]												=       -20.0
				__lowerCAmelCase      :  str												=       True

				__lowerCAmelCase      :  str												=       processor.batch_decode(
				    _UpperCAmelCase       ,			alpha=_UpperCAmelCase       ,			beta=_UpperCAmelCase       ,			unk_score_offset=_UpperCAmelCase       ,			lm_score_boundary=_UpperCAmelCase       ,			)
				__lowerCAmelCase      :  str												=       decoded_processor_out.text

				__lowerCAmelCase      :  str												=       list(_UpperCAmelCase)
				decoder.reset_params(
				    alpha=_UpperCAmelCase       ,			beta=_UpperCAmelCase       ,			unk_score_offset=_UpperCAmelCase       ,			lm_score_boundary=_UpperCAmelCase       ,			)

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

				__lowerCAmelCase      :  int												=       [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)
				__lowerCAmelCase      :  Tuple												=       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 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")

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

				__lowerCAmelCase      :  Any												=       os.listdir(_UpperCAmelCase)
				__lowerCAmelCase      :  List[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(_UpperCAmelCase       ,			_UpperCAmelCase)

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

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       snapshot_download("hf-internal-testing/processor_with_lm")

				__lowerCAmelCase      :  Optional[int]												=       WavaVecaProcessorWithLM.from_pretrained(_UpperCAmelCase)

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

				__lowerCAmelCase      :  Tuple												=       os.listdir(_UpperCAmelCase)
				__lowerCAmelCase      :  str												=       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 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")
				__lowerCAmelCase      :  Optional[Any]												=       AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm")

				__lowerCAmelCase      :  str												=       floats_list((3, 1000))

				__lowerCAmelCase      :  Dict												=       processor_wavaveca(_UpperCAmelCase       ,			return_tensors="np")
				__lowerCAmelCase      :  Optional[Any]												=       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)

				__lowerCAmelCase      :  str												=       self._get_dummy_logits()

				__lowerCAmelCase      :  Optional[int]												=       processor_wavaveca.batch_decode(_UpperCAmelCase)
				__lowerCAmelCase      :  int												=       processor_auto.batch_decode(_UpperCAmelCase)

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       self.get_feature_extractor()
				__lowerCAmelCase      :  Tuple												=       self.get_tokenizer()
				__lowerCAmelCase      :  Tuple												=       self.get_decoder()

				__lowerCAmelCase      :  Optional[int]												=       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 							_SCREAMING_SNAKE_CASE   (     _SCREAMING_SNAKE_CASE:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       [d[key] for d in offsets]
				return retrieved_list

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

				"""simple docstring"""

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

				__lowerCAmelCase      :  Optional[int]												=       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 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       Tuple:

				"""simple docstring"""

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

				__lowerCAmelCase      :  Tuple												=       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 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Dict:

				"""simple docstring"""

				import torch

				__lowerCAmelCase      :  Dict												=       load_dataset("common_voice"       ,			"en"       ,			split="train"       ,			streaming=_UpperCAmelCase)
				__lowerCAmelCase      :  Any												=       ds.cast_column("audio"       ,			datasets.Audio(sampling_rate=1_6000))
				__lowerCAmelCase      :  List[str]												=       iter(_UpperCAmelCase)
				__lowerCAmelCase      :  List[str]												=       next(_UpperCAmelCase)

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

				with torch.no_grad():
							__lowerCAmelCase      :  Union[str, Any]												=       model(_UpperCAmelCase).logits.cpu().numpy()

				__lowerCAmelCase      :  List[str]												=       processor.decode(logits[0]       ,			output_word_offsets=_UpperCAmelCase)

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

				__lowerCAmelCase      :  List[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(_UpperCAmelCase       ,			"word"))       ,			_UpperCAmelCase)
				self.assertEqual(" ".join(self.get_from_offsets(_UpperCAmelCase       ,			"word"))       ,			output.text)

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

				# fmt: off
				__lowerCAmelCase      :  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])
				__lowerCAmelCase      :  str												=       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))






'''simple docstring'''




import random






def        _lowerCAmelCase			(  __snake_case  : int ,							__snake_case  : float ,							__snake_case  : bool = False			)     ->     dict:
     __A						: dict      =       {i: [] for i in range(__snake_case			)}

     # if probability is greater or equal than 1, then generate a complete graph
     if probability >= 1:
          return complete_graph(__snake_case			)
     # if probability is lower or equal than 0, then return a graph without edges
     if probability <= 0:
          return graph

     # for each couple of nodes, add an edge from u to v
     # if the number randomly generated is greater than probability probability
     for i in range(__snake_case			):
          for j in range(i + 1 ,							__snake_case			):
               if random.random() < probability:
                    graph[i].append(__snake_case			)
                    if not directed:
                         # if the graph is undirected, add an edge in from j to i, either
                         graph[j].append(__snake_case			)
     return graph






def        _lowerCAmelCase			(  __snake_case  : int			)     ->     dict:
     return {
         i: [j for j in range(__snake_case			) if i != j] for i in range(__snake_case			)
     }


if __name__ == "__main__":
  import doctest

  doctest.testmod()