






'''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       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					str					=					'| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
			UpperCamelCase__				:					Dict					=					dict(zip(UpperCAmelCase_  ,					range(len(UpperCAmelCase_))))

			UpperCamelCase__				:					str					=					{
			    'unk_token': '<unk>',
			    'bos_token': '<s>',
			    'eos_token': '</s>',
			}
			UpperCamelCase__				:					str					=					{
			    'feature_size': 1,
			    'padding_value': 0.0,
			    'sampling_rate': 16_000,
			    'return_attention_mask': False,
			    'do_normalize': True,
			}

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

	def      __UpperCamelCase							(      self   :						Optional[Any]  ,					**UpperCAmelCase_   :						Optional[int]):
			UpperCamelCase__				:					Dict					=					self.add_kwargs_tokens_map.copy()
			kwargs.update(UpperCAmelCase_)
			return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Tuple  ,					**UpperCAmelCase_   :						Tuple):
			return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[Any]  ,					**UpperCAmelCase_   :						List[Any]):
			return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			shutil.rmtree(self.tmpdirname)

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					List[Any]					=					self.get_tokenizer()
			UpperCamelCase__				:					str					=					self.get_feature_extractor()
			UpperCamelCase__				:					Union[str, Any]					=					self.get_decoder()

			UpperCamelCase__				:					int					=					WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_  ,					feature_extractor=UpperCAmelCase_  ,					decoder=UpperCAmelCase_)

			processor.save_pretrained(self.tmpdirname)
			UpperCamelCase__				:					int					=					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      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__				:					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
			UpperCamelCase__				:					Tuple					=					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      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Union[str, 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      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Tuple					=					self.get_feature_extractor()
			UpperCamelCase__				:					Union[str, Any]					=					self.get_tokenizer()
			UpperCamelCase__				:					Tuple					=					self.get_decoder()

			UpperCamelCase__				:					Dict					=					WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_  ,					feature_extractor=UpperCAmelCase_  ,					decoder=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[int]					=					floats_list((3, 1_000))

			UpperCamelCase__				:					List[Any]					=					feature_extractor(UpperCAmelCase_  ,					return_tensors='np')
			UpperCamelCase__				:					str					=					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      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Optional[int]					=					self.get_feature_extractor()
			UpperCamelCase__				:					Union[str, Any]					=					self.get_tokenizer()
			UpperCamelCase__				:					str					=					self.get_decoder()

			UpperCamelCase__				:					Any					=					WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_  ,					feature_extractor=UpperCAmelCase_  ,					decoder=UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					'This is a test string'

			UpperCamelCase__				:					List[Any]					=					processor(text=UpperCAmelCase_)

			UpperCamelCase__				:					str					=					tokenizer(UpperCAmelCase_)

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

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						int=(2, 10, 16)  ,					UpperCAmelCase_   :						str=77):
			np.random.seed(UpperCAmelCase_)
			return np.random.rand(*UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					List[str]					=					self.get_feature_extractor()
			UpperCamelCase__				:					str					=					self.get_tokenizer()
			UpperCamelCase__				:					str					=					self.get_decoder()

			UpperCamelCase__				:					Any					=					WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_  ,					feature_extractor=UpperCAmelCase_  ,					decoder=UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					self._get_dummy_logits(shape=(10, 16)  ,					seed=13)

			UpperCamelCase__				:					List[str]					=					processor.decode(UpperCAmelCase_)

			UpperCamelCase__				:					str					=					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      __UpperCamelCase							(      self   :						Tuple  ,					UpperCAmelCase_   :						Tuple):
			UpperCamelCase__				:					Union[str, Any]					=					self.get_feature_extractor()
			UpperCamelCase__				:					List[Any]					=					self.get_tokenizer()
			UpperCamelCase__				:					str					=					self.get_decoder()

			UpperCamelCase__				:					List[str]					=					WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_  ,					feature_extractor=UpperCAmelCase_  ,					decoder=UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					self._get_dummy_logits()

			# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
			#       otherwise, the LM won't be available to the pool's sub-processes.
			# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
			if pool_context is None:
					UpperCamelCase__				:					Optional[Any]					=					processor.batch_decode(UpperCAmelCase_)
			else:
					with get_context(UpperCAmelCase_).Pool() as pool:
							UpperCamelCase__				:					Optional[int]					=					processor.batch_decode(UpperCAmelCase_  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					list(UpperCAmelCase_)

			with get_context('fork').Pool() as p:
					UpperCamelCase__				:					Optional[Any]					=					decoder.decode_beams_batch(UpperCAmelCase_  ,					UpperCAmelCase_)

			UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Optional[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      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					Dict					=					self.get_feature_extractor()
			UpperCamelCase__				:					List[Any]					=					self.get_tokenizer()
			UpperCamelCase__				:					str					=					self.get_decoder()

			UpperCamelCase__				:					Dict					=					WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_  ,					feature_extractor=UpperCAmelCase_  ,					decoder=UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					self._get_dummy_logits()

			UpperCamelCase__				:					Optional[Any]					=					15
			UpperCamelCase__				:					Optional[int]					=					-20.0
			UpperCamelCase__				:					int					=					-4.0

			UpperCamelCase__				:					List[str]					=					processor.batch_decode(
			    UpperCAmelCase_  ,					beam_width=UpperCAmelCase_  ,					beam_prune_logp=UpperCAmelCase_  ,					token_min_logp=UpperCAmelCase_  ,					)
			UpperCamelCase__				:					Any					=					decoded_processor_out.text

			UpperCamelCase__				:					List[Any]					=					list(UpperCAmelCase_)

			with get_context('fork').Pool() as pool:
					UpperCamelCase__				:					Any					=					decoder.decode_beams_batch(
					    UpperCAmelCase_  ,					UpperCAmelCase_  ,					beam_width=UpperCAmelCase_  ,					beam_prune_logp=UpperCAmelCase_  ,					token_min_logp=UpperCAmelCase_  ,					)

			UpperCamelCase__				:					List[str]					=					[d[0][0] for d in decoded_decoder_out]
			UpperCamelCase__				:					Union[str, Any]					=					[d[0][2] for d in decoded_decoder_out]
			UpperCamelCase__				:					Union[str, 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([-20.0_54, -18.4_47]  ,					UpperCAmelCase_  ,					atol=1e-3))

			self.assertTrue(np.array_equal(UpperCAmelCase_  ,					decoded_processor_out.lm_score))
			self.assertTrue(np.allclose([-15.5_54, -13.94_74]  ,					UpperCAmelCase_  ,					atol=1e-3))

	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Optional[Any]					=					self.get_feature_extractor()
			UpperCamelCase__				:					Optional[int]					=					self.get_tokenizer()
			UpperCamelCase__				:					List[Any]					=					self.get_decoder()

			UpperCamelCase__				:					List[str]					=					WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_  ,					feature_extractor=UpperCAmelCase_  ,					decoder=UpperCAmelCase_)

			UpperCamelCase__				:					int					=					self._get_dummy_logits()

			UpperCamelCase__				:					int					=					2.0
			UpperCamelCase__				:					List[Any]					=					5.0
			UpperCamelCase__				:					Union[str, Any]					=					-20.0
			UpperCamelCase__				:					Any					=					True

			UpperCamelCase__				:					Tuple					=					processor.batch_decode(
			    UpperCAmelCase_  ,					alpha=UpperCAmelCase_  ,					beta=UpperCAmelCase_  ,					unk_score_offset=UpperCAmelCase_  ,					lm_score_boundary=UpperCAmelCase_  ,					)
			UpperCamelCase__				:					List[str]					=					decoded_processor_out.text

			UpperCamelCase__				:					Optional[int]					=					list(UpperCAmelCase_)
			decoder.reset_params(
			    alpha=UpperCAmelCase_  ,					beta=UpperCAmelCase_  ,					unk_score_offset=UpperCAmelCase_  ,					lm_score_boundary=UpperCAmelCase_  ,					)

			with get_context('fork').Pool() as pool:
					UpperCamelCase__				:					List[str]					=					decoder.decode_beams_batch(
					    UpperCAmelCase_  ,					UpperCAmelCase_  ,					)

			UpperCamelCase__				:					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_)
			UpperCamelCase__				:					Any					=					processor.decoder.model_container[processor.decoder._model_key]
			self.assertEqual(lm_model.alpha  ,					2.0)
			self.assertEqual(lm_model.beta  ,					5.0)
			self.assertEqual(lm_model.unk_score_offset  ,					-20.0)
			self.assertEqual(lm_model.score_boundary  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Optional[Any]					=					WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')

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

			UpperCamelCase__				:					List[str]					=					os.listdir(UpperCAmelCase_)
			UpperCamelCase__				:					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      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__				:					List[str]					=					snapshot_download('hf-internal-testing/processor_with_lm')

			UpperCamelCase__				:					str					=					WavaVecaProcessorWithLM.from_pretrained(UpperCAmelCase_)

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

			UpperCamelCase__				:					str					=					os.listdir(UpperCAmelCase_)
			UpperCamelCase__				:					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      __UpperCamelCase							(      self   :						Tuple):
			UpperCamelCase__				:					int					=					WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
			UpperCamelCase__				:					Optional[int]					=					AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm')

			UpperCamelCase__				:					List[str]					=					floats_list((3, 1_000))

			UpperCamelCase__				:					Union[str, Any]					=					processor_wavaveca(UpperCAmelCase_  ,					return_tensors='np')
			UpperCamelCase__				:					Dict					=					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)

			UpperCamelCase__				:					Any					=					self._get_dummy_logits()

			UpperCamelCase__				:					int					=					processor_wavaveca.batch_decode(UpperCAmelCase_)
			UpperCamelCase__				:					Union[str, Any]					=					processor_auto.batch_decode(UpperCAmelCase_)

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

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Dict					=					self.get_feature_extractor()
			UpperCamelCase__				:					Union[str, Any]					=					self.get_tokenizer()
			UpperCamelCase__				:					Optional[Any]					=					self.get_decoder()

			UpperCamelCase__				:					List[str]					=					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      __UpperCamelCase							(      UpperCAmelCase_   :						Optional[Any]  ,					UpperCAmelCase_   :						List[Any]):
			UpperCamelCase__				:					Tuple					=					[d[key] for d in offsets]
			return retrieved_list

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					List[Any]					=					WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
			UpperCamelCase__				:					Dict					=					self._get_dummy_logits()[0]

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

			UpperCamelCase__				:					Optional[int]					=					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      __UpperCamelCase							(      self   :						Optional[int]):
			import torch

			UpperCamelCase__				:					List[str]					=					load_dataset('common_voice'  ,					'en'  ,					split='train'  ,					streaming=UpperCAmelCase_)
			UpperCamelCase__				:					int					=					ds.cast_column('audio'  ,					datasets.Audio(sampling_rate=16_000))
			UpperCamelCase__				:					Any					=					iter(UpperCAmelCase_)
			UpperCamelCase__				:					int					=					next(UpperCAmelCase_)

			UpperCamelCase__				:					List[Any]					=					AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm')
			UpperCamelCase__				:					List[str]					=					WavaVecaForCTC.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm')

			# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
			UpperCamelCase__				:					int					=					processor(sample['audio']['array']  ,					return_tensors='pt').input_values

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

			UpperCamelCase__				:					Optional[Any]					=					processor.decode(logits[0]  ,					output_word_offsets=UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
			UpperCamelCase__				:					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']
			]

			UpperCamelCase__				:					Union[str, Any]					=					'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
			UpperCamelCase__				:					Tuple					=					torch.tensor(self.get_from_offsets(UpperCAmelCase_  ,					'start_time'))
			UpperCamelCase__				:					Tuple					=					torch.tensor(self.get_from_offsets(UpperCAmelCase_  ,					'end_time'))

			# fmt: off
			UpperCamelCase__				:					Any					=					torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99])
			UpperCamelCase__				:					Union[str, Any]					=					torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 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 numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize







def       __UpperCAmelCase						(  lowerCamelCase_)							->     float:
		return np.dot(lowerCamelCase_     ,      lowerCamelCase_)







class       __lowercase					:

	def __init__(      self   :						Tuple  ,					*,
	    UpperCAmelCase_   :						float = np.inf  ,					UpperCAmelCase_   :						str = "linear"  ,					UpperCAmelCase_   :						float = 0.0  ,					):
			UpperCamelCase__				:					Union[str, Any]					=					regularization
			UpperCamelCase__				:					Optional[int]					=					gamma
			if kernel == "linear":
					UpperCamelCase__				:					List[str]					=					self.__linear
			elif kernel == "rbf":
					if self.gamma == 0:
							raise ValueError('rbf kernel requires gamma')
					if not isinstance(self.gamma  ,					(float, int)):
							raise ValueError('gamma must be float or int')
					if not self.gamma > 0:
							raise ValueError('gamma must be > 0')
					UpperCamelCase__				:					Union[str, Any]					=					self.__rbf
					# in the future, there could be a default value like in sklearn
					# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
					# previously it was 1/(n_features)
			else:
					UpperCamelCase__				:					Optional[int]					=					F'Unknown kernel: {kernel}'
					raise ValueError(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.dot(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.exp(-(self.gamma * norm_squared(vectora - vectora)))

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						list[ndarray]  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Any					=					observations
			UpperCamelCase__				:					Tuple					=					classes

			# using Wolfe's Dual to calculate w.
			# Primal problem: minimize 1/2*norm_squared(w)
			#   constraint: yn(w . xn + b) >= 1
			#
			# With l a vector
			# Dual problem: maximize sum_n(ln) -
			#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
			#   constraint: self.C >= ln >= 0
			#           and sum_n(ln*yn) = 0
			# Then we get w using w = sum_n(ln*yn*xn)
			# At the end we can get b ~= mean(yn - w . xn)
			#
			# Since we use kernels, we only need l_star to calculate b
			# and to classify observations

			((UpperCamelCase__),	)				:					Optional[Any]					=					np.shape(UpperCAmelCase_)

			def to_minimize(UpperCAmelCase_   :						ndarray) -> float:
					UpperCamelCase__				:					Union[str, Any]					=					0
					((UpperCamelCase__),	)				:					int					=					np.shape(UpperCAmelCase_)
					for i in range(UpperCAmelCase_):
							for j in range(UpperCAmelCase_):
									s += (
									    candidate[i]
									    * candidate[j]
									    * classes[i]
									    * classes[j]
									    * self.kernel(observations[i]  ,					observations[j])
									)
					return 1 / 2 * s - sum(UpperCAmelCase_)

			UpperCamelCase__				:					List[str]					=					LinearConstraint(UpperCAmelCase_  ,					0  ,					0)
			UpperCamelCase__				:					Dict					=					Bounds(0  ,					self.regularization)

			UpperCamelCase__				:					Any					=					minimize(
			    UpperCAmelCase_  ,					np.ones(UpperCAmelCase_)  ,					bounds=UpperCAmelCase_  ,					constraints=[ly_contraint]).x
			UpperCamelCase__				:					str					=					l_star

			# calculating mean offset of separation plane to points
			UpperCamelCase__				:					Any					=					0
			for i in range(UpperCAmelCase_):
					for j in range(UpperCAmelCase_):
							s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
							    observations[i]  ,					observations[j])
			UpperCamelCase__				:					List[str]					=					s / n





	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Optional[int]					=					sum(
			    self.optimum[n]
			    * self.classes[n]
			    * self.kernel(self.observations[n]  ,					UpperCAmelCase_)
			    for n in range(len(self.classes)))
			return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()




