




'''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    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	'''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
            __snake_case            =	dict(zip(__SCREAMING_SNAKE_CASE     ,	range(len(__SCREAMING_SNAKE_CASE	)	)	)	)

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

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

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

            # load decoder from hub
            __snake_case            =	'''hf-internal-testing/ngram-beam-search-decoder'''


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	self.add_kwargs_tokens_map.copy()
            kwargs.update(__SCREAMING_SNAKE_CASE	)
            return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            shutil.rmtree(self.tmpdirname	)


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_feature_extractor()
            __snake_case            =	self.get_decoder()

            __snake_case            =	WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE     ,	feature_extractor=__SCREAMING_SNAKE_CASE     ,	decoder=__SCREAMING_SNAKE_CASE	)

            processor.save_pretrained(self.tmpdirname	)
            __snake_case            =	WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname	)

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

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

            # 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     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	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
            __snake_case            =	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	)   ->	List[Any]:



            '''simple docstring'''




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


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



            '''simple docstring'''




            __snake_case            =	self.get_feature_extractor()
            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_decoder()

            __snake_case            =	WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE     ,	feature_extractor=__SCREAMING_SNAKE_CASE     ,	decoder=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	floats_list((3, 1000)	)

            __snake_case            =	feature_extractor(__SCREAMING_SNAKE_CASE     ,	return_tensors='''np'''	)
            __snake_case            =	processor(__SCREAMING_SNAKE_CASE     ,	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	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	self.get_feature_extractor()
            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_decoder()

            __snake_case            =	WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE     ,	feature_extractor=__SCREAMING_SNAKE_CASE     ,	decoder=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	'''This is a test string'''

            __snake_case            =	processor(text=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	tokenizer(__SCREAMING_SNAKE_CASE	)

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


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE=(2, 10, 16)     ,	__SCREAMING_SNAKE_CASE=77	)   ->	Tuple:



            '''simple docstring'''




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


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



            '''simple docstring'''




            __snake_case            =	self.get_feature_extractor()
            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_decoder()

            __snake_case            =	WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE     ,	feature_extractor=__SCREAMING_SNAKE_CASE     ,	decoder=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self._get_dummy_logits(shape=(10, 16)     ,	seed=13	)

            __snake_case            =	processor.decode(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	decoder.decode_beams(__SCREAMING_SNAKE_CASE	)[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     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	self.get_feature_extractor()
            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_decoder()

            __snake_case            =	WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE     ,	feature_extractor=__SCREAMING_SNAKE_CASE     ,	decoder=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	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:
                 __snake_case            =	processor.batch_decode(__SCREAMING_SNAKE_CASE	)
            else:
                 with get_context(__SCREAMING_SNAKE_CASE	).Pool() as pool:
                      __snake_case            =	processor.batch_decode(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	list(__SCREAMING_SNAKE_CASE	)

            with get_context('''fork'''	).Pool() as p:
                 __snake_case            =	decoder.decode_beams_batch(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case       ,     __snake_case       ,     __snake_case            =	[], [], []
            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(__SCREAMING_SNAKE_CASE     ,	decoded_processor.text	)
            self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']     ,	decoded_processor.text	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	decoded_processor.logit_score	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	decoded_processor.lm_score	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	self.get_feature_extractor()
            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_decoder()

            __snake_case            =	WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE     ,	feature_extractor=__SCREAMING_SNAKE_CASE     ,	decoder=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self._get_dummy_logits()

            __snake_case            =	15
            __snake_case            =	-20.0
            __snake_case            =	-4.0

            __snake_case            =	processor.batch_decode(
                __SCREAMING_SNAKE_CASE     ,	beam_width=__SCREAMING_SNAKE_CASE     ,	beam_prune_logp=__SCREAMING_SNAKE_CASE     ,	token_min_logp=__SCREAMING_SNAKE_CASE     ,	)
            __snake_case            =	decoded_processor_out.text

            __snake_case            =	list(__SCREAMING_SNAKE_CASE	)

            with get_context('''fork'''	).Pool() as pool:
                 __snake_case            =	decoder.decode_beams_batch(
                     __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	beam_width=__SCREAMING_SNAKE_CASE     ,	beam_prune_logp=__SCREAMING_SNAKE_CASE     ,	token_min_logp=__SCREAMING_SNAKE_CASE     ,	)

            __snake_case            =	[d[0][0] for d in decoded_decoder_out]
            __snake_case            =	[d[0][2] for d in decoded_decoder_out]
            __snake_case            =	[d[0][3] for d in decoded_decoder_out]

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

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

            self.assertTrue(np.array_equal(__SCREAMING_SNAKE_CASE     ,	decoded_processor_out.lm_score	)	)
            self.assertTrue(np.allclose([-15.554, -13.9_474]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-3	)	)


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



            '''simple docstring'''




            __snake_case            =	self.get_feature_extractor()
            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_decoder()

            __snake_case            =	WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE     ,	feature_extractor=__SCREAMING_SNAKE_CASE     ,	decoder=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self._get_dummy_logits()

            __snake_case            =	2.0
            __snake_case            =	5.0
            __snake_case            =	-20.0
            __snake_case            =	True

            __snake_case            =	processor.batch_decode(
                __SCREAMING_SNAKE_CASE     ,	alpha=__SCREAMING_SNAKE_CASE     ,	beta=__SCREAMING_SNAKE_CASE     ,	unk_score_offset=__SCREAMING_SNAKE_CASE     ,	lm_score_boundary=__SCREAMING_SNAKE_CASE     ,	)
            __snake_case            =	decoded_processor_out.text

            __snake_case            =	list(__SCREAMING_SNAKE_CASE	)
            decoder.reset_params(
                alpha=__SCREAMING_SNAKE_CASE     ,	beta=__SCREAMING_SNAKE_CASE     ,	unk_score_offset=__SCREAMING_SNAKE_CASE     ,	lm_score_boundary=__SCREAMING_SNAKE_CASE     ,	)

            with get_context('''fork'''	).Pool() as pool:
                 __snake_case            =	decoder.decode_beams_batch(
                     __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)

            __snake_case            =	[d[0][0] for d in decoded_decoder_out]

            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']     ,	__SCREAMING_SNAKE_CASE	)
            __snake_case            =	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     ,	__SCREAMING_SNAKE_CASE	)


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



            '''simple docstring'''




            __snake_case            =	WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''	)

            __snake_case            =	processor.decoder.model_container[processor.decoder._model_key]
            __snake_case            =	Path(language_model._kenlm_model.path.decode('''utf-8'''	)	).parent.parent.absolute()

            __snake_case            =	os.listdir(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	['''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(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	snapshot_download('''hf-internal-testing/processor_with_lm'''	)

            __snake_case            =	WavaVecaProcessorWithLM.from_pretrained(__SCREAMING_SNAKE_CASE	)

            __snake_case            =	processor.decoder.model_container[processor.decoder._model_key]
            __snake_case            =	Path(language_model._kenlm_model.path.decode('''utf-8'''	)	).parent.parent.absolute()

            __snake_case            =	os.listdir(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	os.listdir(__SCREAMING_SNAKE_CASE	)

            local_decoder_files.sort()
            expected_decoder_files.sort()

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


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



            '''simple docstring'''




            __snake_case            =	WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''	)
            __snake_case            =	AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''	)

            __snake_case            =	floats_list((3, 1000)	)

            __snake_case            =	processor_wavaveca(__SCREAMING_SNAKE_CASE     ,	return_tensors='''np'''	)
            __snake_case            =	processor_auto(__SCREAMING_SNAKE_CASE     ,	return_tensors='''np'''	)

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

            __snake_case            =	self._get_dummy_logits()

            __snake_case            =	processor_wavaveca.batch_decode(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	processor_auto.batch_decode(__SCREAMING_SNAKE_CASE	)

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


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



            '''simple docstring'''




            __snake_case            =	self.get_feature_extractor()
            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_decoder()

            __snake_case            =	WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE     ,	feature_extractor=__SCREAMING_SNAKE_CASE     ,	decoder=__SCREAMING_SNAKE_CASE	)

            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   ( __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	[d[key] for d in offsets]
            return retrieved_list


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''	)
            __snake_case            =	self._get_dummy_logits()[0]

            __snake_case            =	processor.decode(__SCREAMING_SNAKE_CASE     ,	output_word_offsets=__SCREAMING_SNAKE_CASE	)
            # 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(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)	)

            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	)   ->	str:



            '''simple docstring'''




            __snake_case            =	WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''	)
            __snake_case            =	self._get_dummy_logits()

            __snake_case            =	processor.batch_decode(__SCREAMING_SNAKE_CASE     ,	output_word_offsets=__SCREAMING_SNAKE_CASE	)

            # 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(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)	)

            self.assertListEqual(
                [''' '''.join(self.get_from_offsets(__SCREAMING_SNAKE_CASE     ,	'''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	)   ->	Union[str, Any]:



            '''simple docstring'''




            import torch

            __snake_case            =	load_dataset('''common_voice'''     ,	'''en'''     ,	split='''train'''     ,	streaming=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	ds.cast_column('''audio'''     ,	datasets.Audio(sampling_rate=1_6000	)	)
            __snake_case            =	iter(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	next(__SCREAMING_SNAKE_CASE	)

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

            with torch.no_grad():
                 __snake_case            =	model(__SCREAMING_SNAKE_CASE	).logits.cpu().numpy()

            __snake_case            =	processor.decode(logits[0]     ,	output_word_offsets=__SCREAMING_SNAKE_CASE	)

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

            __snake_case            =	'''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(__SCREAMING_SNAKE_CASE     ,	'''word'''	)	)     ,	__SCREAMING_SNAKE_CASE	)
            self.assertEqual(''' '''.join(self.get_from_offsets(__SCREAMING_SNAKE_CASE     ,	'''word'''	)	)     ,	output.text	)

            # output times
            __snake_case            =	torch.tensor(self.get_from_offsets(__SCREAMING_SNAKE_CASE     ,	'''start_time'''	)	)
            __snake_case            =	torch.tensor(self.get_from_offsets(__SCREAMING_SNAKE_CASE     ,	'''end_time'''	)	)

            # fmt: off
            __snake_case            =	torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]	)
            __snake_case            =	torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]	)
            # fmt: on

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













"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

__UpperCamelCase     :						Optional[Any]						       =  tuple[int, int]







class       a							:



 def __init__(     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= vertices
  lowerCAmelCase    				= {
      (min(_snake_case     ), max(_snake_case     )): weight for edge, weight in edges.items()
  }



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  self.vertices.add(edge[0]     )
  self.vertices.add(edge[1]     )
  lowerCAmelCase    				= weight



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Graph({min(self.vertices     )}				,       {}     )
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42
  lowerCAmelCase    				= 42

  while len(subgraph.vertices     ) < len(self.vertices     ):
   lowerCAmelCase    				= max(self.edges.values()     ) + 1
   for edge, weight in self.edges.items():
    if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
     if weight < min_weight:
      lowerCAmelCase    				= edge
      lowerCAmelCase    				= weight

   subgraph.add_edge(_snake_case				,       _snake_case     )

  return subgraph


def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str = "p107_network.txt"			):
 lowerCAmelCase    				= os.path.abspath(os.path.dirname(_UpperCAmelCase			)			)
 lowerCAmelCase    				= os.path.join(_UpperCAmelCase  ,     _UpperCAmelCase			)
 lowerCAmelCase    				= {}
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42
 lowerCAmelCase    				= 42

 with open(_UpperCAmelCase			) as f:
  lowerCAmelCase    				= f.read().strip().split('\n'			)

 lowerCAmelCase    				= [line.split(','			) for line in data]

 for edgea in range(1  ,     len(_UpperCAmelCase			)			):
  for edgea in range(_UpperCAmelCase			):
   if adjaceny_matrix[edgea][edgea] != "-":
    lowerCAmelCase    				= int(adjaceny_matrix[edgea][edgea]			)

 lowerCAmelCase    				= Graph(set(range(len(_UpperCAmelCase			)			)			)  ,     _UpperCAmelCase			)

 lowerCAmelCase    				= graph.prims_algorithm()

 lowerCAmelCase    				= sum(graph.edges.values()			)
 lowerCAmelCase    				= sum(subgraph.edges.values()			)

 return initial_total - optimal_total


if __name__ == "__main__":
       print(f'''{solution() = }''')





