





'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				Optional[Any]											=       (DDPMScheduler,)


	def 						_UpperCAmelCase     (self     ,**_lowerCamelCase )							->							Any:







				'''simple docstring'''





				__lowercase										=							{
				    '''num_train_timesteps''': 1000,
				    '''beta_start''': 0.0_0_0_1,
				    '''beta_end''': 0.0_2,
				    '''beta_schedule''': '''linear''',
				    '''variance_type''': '''fixed_small''',
				    '''clip_sample''': True,
				}

				config.update(**_lowerCamelCase )
				return config


	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				for timesteps in [1, 5, 100, 1000]:
							self.check_over_configs(num_train_timesteps=_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





				for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]     ,[0.0_0_2, 0.0_2, 0.2, 2] ):
							self.check_over_configs(beta_start=_lowerCamelCase     ,beta_end=_lowerCamelCase )


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







				'''simple docstring'''





				for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				for variance in ["fixed_small", "fixed_large", "other"]:
							self.check_over_configs(variance_type=_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				self.check_over_configs(thresholding=_lowerCamelCase )
				for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(
										    thresholding=_lowerCamelCase     ,prediction_type=_lowerCamelCase     ,sample_max_value=_lowerCamelCase     ,)


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				for prediction_type in ["epsilon", "sample", "v_prediction"]:
							self.check_over_configs(prediction_type=_lowerCamelCase )


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







				'''simple docstring'''





				for t in [0, 500, 999]:
							self.check_over_forward(time_step=_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							self.scheduler_classes[0]
				__lowercase										=							self.get_scheduler_config()
				__lowercase										=							scheduler_class(**_lowerCamelCase )

				assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1E-5


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







				'''simple docstring'''





				__lowercase										=							self.scheduler_classes[0]
				__lowercase										=							self.get_scheduler_config()
				__lowercase										=							scheduler_class(**_lowerCamelCase )

				__lowercase										=							len(_lowerCamelCase )

				__lowercase										=							self.dummy_model()
				__lowercase										=							self.dummy_sample_deter
				__lowercase										=							torch.manual_seed(0 )

				for t in reversed(range(_lowerCamelCase ) ):
							# 1. predict noise residual
							__lowercase										=							model(_lowerCamelCase     ,_lowerCamelCase )

							# 2. predict previous mean of sample x_t-1
							__lowercase										=							scheduler.step(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,generator=_lowerCamelCase ).prev_sample

							# if t > 0:
							#     noise = self.dummy_sample_deter
							#     variance = scheduler.get_variance(t) ** (0.5) * noise
							#
							# sample = pred_prev_sample + variance
							__lowercase										=							pred_prev_sample

				__lowercase										=							torch.sum(torch.abs(_lowerCamelCase ) )
				__lowercase										=							torch.mean(torch.abs(_lowerCamelCase ) )

				assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1E-2
				assert abs(result_mean.item() - 0.3_3_7_2 ) < 1E-3


	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





				__lowercase										=							self.scheduler_classes[0]
				__lowercase										=							self.get_scheduler_config(prediction_type='''v_prediction''' )
				__lowercase										=							scheduler_class(**_lowerCamelCase )

				__lowercase										=							len(_lowerCamelCase )

				__lowercase										=							self.dummy_model()
				__lowercase										=							self.dummy_sample_deter
				__lowercase										=							torch.manual_seed(0 )

				for t in reversed(range(_lowerCamelCase ) ):
							# 1. predict noise residual
							__lowercase										=							model(_lowerCamelCase     ,_lowerCamelCase )

							# 2. predict previous mean of sample x_t-1
							__lowercase										=							scheduler.step(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,generator=_lowerCamelCase ).prev_sample

							# if t > 0:
							#     noise = self.dummy_sample_deter
							#     variance = scheduler.get_variance(t) ** (0.5) * noise
							#
							# sample = pred_prev_sample + variance
							__lowercase										=							pred_prev_sample

				__lowercase										=							torch.sum(torch.abs(_lowerCamelCase ) )
				__lowercase										=							torch.mean(torch.abs(_lowerCamelCase ) )

				assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1E-2
				assert abs(result_mean.item() - 0.2_6_3_1 ) < 1E-3


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							self.scheduler_classes[0]
				__lowercase										=							self.get_scheduler_config()
				__lowercase										=							scheduler_class(**_lowerCamelCase )

				__lowercase										=							[100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=_lowerCamelCase )

				__lowercase										=							scheduler.timesteps

				for i, timestep in enumerate(_lowerCamelCase ):
							if i == len(_lowerCamelCase ) - 1:
										__lowercase										=							-1
							else:
										__lowercase										=							timesteps[i + 1]

							__lowercase										=							scheduler.previous_timestep(_lowerCamelCase )
							__lowercase										=							prev_t.item()

							self.assertEqual(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							self.scheduler_classes[0]
				__lowercase										=							self.get_scheduler_config()
				__lowercase										=							scheduler_class(**_lowerCamelCase )

				__lowercase										=							[100, 87, 50, 51, 0]

				with self.assertRaises(_lowerCamelCase     ,msg='''`custom_timesteps` must be in descending order.''' ):
							scheduler.set_timesteps(timesteps=_lowerCamelCase )


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







				'''simple docstring'''





				__lowercase										=							self.scheduler_classes[0]
				__lowercase										=							self.get_scheduler_config()
				__lowercase										=							scheduler_class(**_lowerCamelCase )

				__lowercase										=							[100, 87, 50, 1, 0]
				__lowercase										=							len(_lowerCamelCase )

				with self.assertRaises(_lowerCamelCase     ,msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.''' ):
							scheduler.set_timesteps(num_inference_steps=_lowerCamelCase     ,timesteps=_lowerCamelCase )


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







				'''simple docstring'''





				__lowercase										=							self.scheduler_classes[0]
				__lowercase										=							self.get_scheduler_config()
				__lowercase										=							scheduler_class(**_lowerCamelCase )

				__lowercase										=							[scheduler.config.num_train_timesteps]

				with self.assertRaises(
				    _lowerCamelCase     ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''     ,):
							scheduler.set_timesteps(timesteps=_lowerCamelCase )












'''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						):







	'''simple docstring'''

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







				'''simple docstring'''





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

				__lowercase										=							{
				    '''unk_token''': '''<unk>''',
				    '''bos_token''': '''<s>''',
				    '''eos_token''': '''</s>''',
				}
				__lowercase										=							{
				    '''feature_size''': 1,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 16000,
				    '''return_attention_mask''': False,
				    '''do_normalize''': True,
				}

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

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

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


	def 						_UpperCAmelCase     (self     ,**_lowerCamelCase )							->							List[str]:







				'''simple docstring'''





				__lowercase										=							self.add_kwargs_tokens_map.copy()
				kwargs.update(_lowerCamelCase )
				return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,**_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,**_lowerCamelCase )							->							Dict:







				'''simple docstring'''





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


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







				'''simple docstring'''





				shutil.rmtree(self.tmpdirname )


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







				'''simple docstring'''





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

				__lowercase										=							WavaVecaProcessorWithLM(tokenizer=_lowerCamelCase     ,feature_extractor=_lowerCamelCase     ,decoder=_lowerCamelCase )

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

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

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

				# 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     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				__lowercase										=							WavaVecaProcessorWithLM(
				    tokenizer=self.get_tokenizer()     ,feature_extractor=self.get_feature_extractor()     ,decoder=self.get_decoder() )
				processor.save_pretrained(self.tmpdirname )

				# make sure that error is thrown when decoder alphabet doesn't match
				__lowercase										=							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 )							->							Dict:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





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

				__lowercase										=							WavaVecaProcessorWithLM(tokenizer=_lowerCamelCase     ,feature_extractor=_lowerCamelCase     ,decoder=_lowerCamelCase )

				__lowercase										=							floats_list((3, 1000) )

				__lowercase										=							feature_extractor(_lowerCamelCase     ,return_tensors='''np''' )
				__lowercase										=							processor(_lowerCamelCase     ,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 )							->							List[Any]:







				'''simple docstring'''





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

				__lowercase										=							WavaVecaProcessorWithLM(tokenizer=_lowerCamelCase     ,feature_extractor=_lowerCamelCase     ,decoder=_lowerCamelCase )

				__lowercase										=							'''This is a test string'''

				__lowercase										=							processor(text=_lowerCamelCase )

				__lowercase										=							tokenizer(_lowerCamelCase )

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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase=(2, 10, 16)     ,_lowerCamelCase=77 )							->							Optional[int]:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





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

				__lowercase										=							WavaVecaProcessorWithLM(tokenizer=_lowerCamelCase     ,feature_extractor=_lowerCamelCase     ,decoder=_lowerCamelCase )

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

				__lowercase										=							processor.decode(_lowerCamelCase )

				__lowercase										=							decoder.decode_beams(_lowerCamelCase )[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     ,_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





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

				__lowercase										=							WavaVecaProcessorWithLM(tokenizer=_lowerCamelCase     ,feature_extractor=_lowerCamelCase     ,decoder=_lowerCamelCase )

				__lowercase										=							self._get_dummy_logits()

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

				__lowercase										=							list(_lowerCamelCase )

				with get_context('''fork''' ).Pool() as p:
							__lowercase										=							decoder.decode_beams_batch(_lowerCamelCase     ,_lowerCamelCase )

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


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





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

				__lowercase										=							WavaVecaProcessorWithLM(tokenizer=_lowerCamelCase     ,feature_extractor=_lowerCamelCase     ,decoder=_lowerCamelCase )

				__lowercase										=							self._get_dummy_logits()

				__lowercase										=							15
				__lowercase										=							-2_0.0
				__lowercase										=							-4.0

				__lowercase										=							processor.batch_decode(
				    _lowerCamelCase     ,beam_width=_lowerCamelCase     ,beam_prune_logp=_lowerCamelCase     ,token_min_logp=_lowerCamelCase     ,)
				__lowercase										=							decoded_processor_out.text

				__lowercase										=							list(_lowerCamelCase )

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

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

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

				self.assertTrue(np.array_equal(_lowerCamelCase     ,decoded_processor_out.logit_score ) )
				self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7]     ,_lowerCamelCase     ,atol=1E-3 ) )

				self.assertTrue(np.array_equal(_lowerCamelCase     ,decoded_processor_out.lm_score ) )
				self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4]     ,_lowerCamelCase     ,atol=1E-3 ) )


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







				'''simple docstring'''





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

				__lowercase										=							WavaVecaProcessorWithLM(tokenizer=_lowerCamelCase     ,feature_extractor=_lowerCamelCase     ,decoder=_lowerCamelCase )

				__lowercase										=							self._get_dummy_logits()

				__lowercase										=							2.0
				__lowercase										=							5.0
				__lowercase										=							-2_0.0
				__lowercase										=							True

				__lowercase										=							processor.batch_decode(
				    _lowerCamelCase     ,alpha=_lowerCamelCase     ,beta=_lowerCamelCase     ,unk_score_offset=_lowerCamelCase     ,lm_score_boundary=_lowerCamelCase     ,)
				__lowercase										=							decoded_processor_out.text

				__lowercase										=							list(_lowerCamelCase )
				decoder.reset_params(
				    alpha=_lowerCamelCase     ,beta=_lowerCamelCase     ,unk_score_offset=_lowerCamelCase     ,lm_score_boundary=_lowerCamelCase     ,)

				with get_context('''fork''' ).Pool() as pool:
							__lowercase										=							decoder.decode_beams_batch(
							    _lowerCamelCase     ,_lowerCamelCase     ,)

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

				self.assertListEqual(_lowerCamelCase     ,_lowerCamelCase )
				self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']     ,_lowerCamelCase )
				__lowercase										=							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     ,-2_0.0 )
				self.assertEqual(lm_model.score_boundary     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





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

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

				__lowercase										=							os.listdir(_lowerCamelCase )
				__lowercase										=							['''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(_lowerCamelCase     ,_lowerCamelCase )


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







				'''simple docstring'''





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

				__lowercase										=							WavaVecaProcessorWithLM.from_pretrained(_lowerCamelCase )

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

				__lowercase										=							os.listdir(_lowerCamelCase )
				__lowercase										=							os.listdir(_lowerCamelCase )

				local_decoder_files.sort()
				expected_decoder_files.sort()

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


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





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

				__lowercase										=							floats_list((3, 1000) )

				__lowercase										=							processor_wavaveca(_lowerCamelCase     ,return_tensors='''np''' )
				__lowercase										=							processor_auto(_lowerCamelCase     ,return_tensors='''np''' )

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

				__lowercase										=							self._get_dummy_logits()

				__lowercase										=							processor_wavaveca.batch_decode(_lowerCamelCase )
				__lowercase										=							processor_auto.batch_decode(_lowerCamelCase )

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


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





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

				__lowercase										=							WavaVecaProcessorWithLM(tokenizer=_lowerCamelCase     ,feature_extractor=_lowerCamelCase     ,decoder=_lowerCamelCase )

				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     (_lowerCamelCase     ,_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





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

				__lowercase										=							processor.decode(_lowerCamelCase     ,output_word_offsets=_lowerCamelCase )
				# 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(_lowerCamelCase     ,_lowerCamelCase ) )

				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 )							->							Optional[Any]:







				'''simple docstring'''





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

				__lowercase										=							processor.batch_decode(_lowerCamelCase     ,output_word_offsets=_lowerCamelCase )

				# 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(_lowerCamelCase     ,_lowerCamelCase ) )

				self.assertListEqual(
				    [''' '''.join(self.get_from_offsets(_lowerCamelCase     ,'''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 )							->							List[Any]:







				'''simple docstring'''





				import torch

				__lowercase										=							load_dataset('''common_voice'''     ,'''en'''     ,split='''train'''     ,streaming=_lowerCamelCase )
				__lowercase										=							ds.cast_column('''audio'''     ,datasets.Audio(sampling_rate=16000 ) )
				__lowercase										=							iter(_lowerCamelCase )
				__lowercase										=							next(_lowerCamelCase )

				__lowercase										=							AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
				__lowercase										=							WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )

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

				with torch.no_grad():
							__lowercase										=							model(_lowerCamelCase ).logits.cpu().numpy()

				__lowercase										=							processor.decode(logits[0]     ,output_word_offsets=_lowerCamelCase )

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

				__lowercase										=							'''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(_lowerCamelCase     ,'''word''' ) )     ,_lowerCamelCase )
				self.assertEqual(''' '''.join(self.get_from_offsets(_lowerCamelCase     ,'''word''' ) )     ,output.text )

				# output times
				__lowercase										=							torch.tensor(self.get_from_offsets(_lowerCamelCase     ,'''start_time''' ) )
				__lowercase										=							torch.tensor(self.get_from_offsets(_lowerCamelCase     ,'''end_time''' ) )

				# fmt: off
				__lowercase										=							torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9] )
				__lowercase										=							torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4] )
				# fmt: on

				self.assertTrue(torch.allclose(_lowerCamelCase     ,_lowerCamelCase     ,atol=0.0_1 ) )
				self.assertTrue(torch.allclose(_lowerCamelCase     ,_lowerCamelCase     ,atol=0.0_1 ) )





