



"""simple docstring"""






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			(DDPMParallelScheduler,)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    **_SCREAMING_SNAKE_CASE:	Any)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= {
				    "num_train_timesteps": 1000,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				    "variance_type": "fixed_small",
				    "clip_sample": True,
				}

				config.update(**_SCREAMING_SNAKE_CASE)
				return config

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

				"""simple docstring"""

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

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

				"""simple docstring"""

				for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,    [0.002, 0.02, 0.2, 2]):
							self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE ,    beta_end=_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Any:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Tuple:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Tuple:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Tuple:

				"""simple docstring"""

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

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

				"""simple docstring"""

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

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

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= self.scheduler_classes[0]
				__lowerCAmelCase   :						List[str]				= self.get_scheduler_config()
				__lowerCAmelCase   :						Optional[Any]				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_0979)) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.scheduler_classes[0]
				__lowerCAmelCase   :						Dict				= self.get_scheduler_config()
				__lowerCAmelCase   :						List[Any]				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Tuple				= len(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Optional[Any]				= self.dummy_model()
				__lowerCAmelCase   :						Union[str, Any]				= self.dummy_sample_deter
				__lowerCAmelCase   :						List[Any]				= self.dummy_sample_deter + 0.1
				__lowerCAmelCase   :						Dict				= self.dummy_sample_deter - 0.1

				__lowerCAmelCase   :						Tuple				= samplea.shape[0]
				__lowerCAmelCase   :						int				= torch.stack([samplea, samplea, samplea] ,    dim=0)
				__lowerCAmelCase   :						Tuple				= torch.arange(_SCREAMING_SNAKE_CASE)[0:3, None].repeat(1 ,    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Dict				= model(samples.flatten(0 ,    1) ,    timesteps.flatten(0 ,    1))
				__lowerCAmelCase   :						int				= scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE ,    timesteps.flatten(0 ,    1) ,    samples.flatten(0 ,    1))

				__lowerCAmelCase   :						Optional[Any]				= torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase   :						List[str]				= torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

				assert abs(result_sum.item() - 1153.1833) < 1e-2
				assert abs(result_mean.item() - 0.5005) < 1e-3

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= self.scheduler_classes[0]
				__lowerCAmelCase   :						Tuple				= self.get_scheduler_config()
				__lowerCAmelCase   :						List[Any]				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Tuple				= len(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Any				= self.dummy_model()
				__lowerCAmelCase   :						Dict				= self.dummy_sample_deter
				__lowerCAmelCase   :						Optional[Any]				= torch.manual_seed(0)

				for t in reversed(range(_SCREAMING_SNAKE_CASE)):
							# 1. predict noise residual
							__lowerCAmelCase   :						Optional[int]				= model(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

							# 2. predict previous mean of sample x_t-1
							__lowerCAmelCase   :						Optional[Any]				= scheduler.step(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE).prev_sample

							__lowerCAmelCase   :						str				= pred_prev_sample

				__lowerCAmelCase   :						List[str]				= torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase   :						List[Any]				= torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

				assert abs(result_sum.item() - 258.9606) < 1e-2
				assert abs(result_mean.item() - 0.3372) < 1e-3

	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.scheduler_classes[0]
				__lowerCAmelCase   :						List[Any]				= self.get_scheduler_config(prediction_type="v_prediction")
				__lowerCAmelCase   :						Optional[Any]				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						str				= len(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Optional[int]				= self.dummy_model()
				__lowerCAmelCase   :						Tuple				= self.dummy_sample_deter
				__lowerCAmelCase   :						Union[str, Any]				= torch.manual_seed(0)

				for t in reversed(range(_SCREAMING_SNAKE_CASE)):
							# 1. predict noise residual
							__lowerCAmelCase   :						Dict				= model(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

							# 2. predict previous mean of sample x_t-1
							__lowerCAmelCase   :						Union[str, Any]				= scheduler.step(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE).prev_sample

							__lowerCAmelCase   :						Dict				= pred_prev_sample

				__lowerCAmelCase   :						Optional[int]				= torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase   :						Dict				= torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

				assert abs(result_sum.item() - 202.0296) < 1e-2
				assert abs(result_mean.item() - 0.2631) < 1e-3

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

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.scheduler_classes[0]
				__lowerCAmelCase   :						List[str]				= self.get_scheduler_config()
				__lowerCAmelCase   :						Any				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						int				= [100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Optional[int]				= scheduler.timesteps

				for i, timestep in enumerate(_SCREAMING_SNAKE_CASE):
							if i == len(_SCREAMING_SNAKE_CASE) - 1:
										__lowerCAmelCase   :						List[str]				= -1
							else:
										__lowerCAmelCase   :						Tuple				= timesteps[i + 1]

							__lowerCAmelCase   :						Optional[int]				= scheduler.previous_timestep(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						Dict				= prev_t.item()

							self.assertEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				__lowerCAmelCase   :						str				= self.scheduler_classes[0]
				__lowerCAmelCase   :						Dict				= self.get_scheduler_config()
				__lowerCAmelCase   :						Dict				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Any				= [100, 87, 50, 51, 0]

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

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

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= self.scheduler_classes[0]
				__lowerCAmelCase   :						Any				= self.get_scheduler_config()
				__lowerCAmelCase   :						Dict				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[Any]				= [100, 87, 50, 1, 0]
				__lowerCAmelCase   :						Union[str, Any]				= len(_SCREAMING_SNAKE_CASE)

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







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

				"""simple docstring"""

				__lowerCAmelCase   :						str				= self.scheduler_classes[0]
				__lowerCAmelCase   :						str				= self.get_scheduler_config()
				__lowerCAmelCase   :						List[str]				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Union[str, Any]				= [scheduler.config.num_train_timesteps]

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




"""simple docstring"""






import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
	from transformers import SpeechaTextFeatureExtractor

__snake_case							:     Union[str, Any]   =					random.Random()


def     _lowercase       (						__snake_case      ,__snake_case=1.0      ,__snake_case=None      ,__snake_case=None			)		->     List[Any]:
			if rng is None:
						__lowerCAmelCase   :						Dict				= global_rng

			__lowerCAmelCase   :						Tuple				= []
			for batch_idx in range(shape[0]			):
						values.append([]			)
						for _ in range(shape[1]			):
									values[-1].append(rng.random() * scale			)

			return values



@require_torch
@require_torchaudio
class     A__     (						unittest.TestCase							):




	'''simple docstring'''





	def __init__(  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	List[Any]=7 ,    _SCREAMING_SNAKE_CASE:	int=400 ,    _SCREAMING_SNAKE_CASE:	List[str]=2000 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=24 ,    _SCREAMING_SNAKE_CASE:	Dict=24 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=0.0 ,    _SCREAMING_SNAKE_CASE:	Any=1_6000 ,    _SCREAMING_SNAKE_CASE:	int=True ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=True ,    )       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= parent
				__lowerCAmelCase   :						str				= batch_size
				__lowerCAmelCase   :						List[str]				= min_seq_length
				__lowerCAmelCase   :						Any				= max_seq_length
				__lowerCAmelCase   :						Dict				= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				__lowerCAmelCase   :						Union[str, Any]				= feature_size
				__lowerCAmelCase   :						int				= num_mel_bins
				__lowerCAmelCase   :						Optional[Any]				= padding_value
				__lowerCAmelCase   :						List[Any]				= sampling_rate
				__lowerCAmelCase   :						Dict				= return_attention_mask
				__lowerCAmelCase   :						int				= do_normalize

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

				"""simple docstring"""

				return {
				    "feature_size": self.feature_size,
				    "num_mel_bins": self.num_mel_bins,
				    "padding_value": self.padding_value,
				    "sampling_rate": self.sampling_rate,
				    "return_attention_mask": self.return_attention_mask,
				    "do_normalize": self.do_normalize,
				}







	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	Tuple=False ,    _SCREAMING_SNAKE_CASE:	int=False)       -> Optional[int]:

				"""simple docstring"""

				def _flatten(_SCREAMING_SNAKE_CASE:	Optional[Any]):
							return list(itertools.chain(*_SCREAMING_SNAKE_CASE))

				if equal_length:
							__lowerCAmelCase   :						Tuple				= [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
				else:
							# make sure that inputs increase in size
							__lowerCAmelCase   :						List[Any]				= [
							    floats_list((x, self.feature_size))
							    for x in range(self.min_seq_length ,    self.max_seq_length ,    self.seq_length_diff)
							]
				if numpify:
							__lowerCAmelCase   :						Any				= [np.asarray(_SCREAMING_SNAKE_CASE) for x in speech_inputs]
				return speech_inputs






@require_torch
@require_torchaudio
class     A__     (						__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			SpeechaTextFeatureExtractor if is_speech_available() else None

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

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= SpeechaTextFeatureExtractionTester(self)

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

				"""simple docstring"""

				self.assertTrue(np.all(np.mean(_SCREAMING_SNAKE_CASE ,    axis=0) < 1e-3))
				self.assertTrue(np.all(np.abs(np.var(_SCREAMING_SNAKE_CASE ,    axis=0) - 1) < 1e-3))

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

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				# create three inputs of length 800, 1000, and 1200
				__lowerCAmelCase   :						Tuple				= [floats_list((1, x))[0] for x in range(800 ,    1400 ,    200)]
				__lowerCAmelCase   :						Optional[int]				= [np.asarray(_SCREAMING_SNAKE_CASE) for speech_input in speech_inputs]

				# Test feature size
				__lowerCAmelCase   :						Optional[int]				= feature_extractor(_SCREAMING_SNAKE_CASE ,    padding=_SCREAMING_SNAKE_CASE ,    return_tensors="np").input_features
				self.assertTrue(input_features.ndim == 3)
				self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)

				# Test not batched input
				__lowerCAmelCase   :						Optional[Any]				= feature_extractor(speech_inputs[0] ,    return_tensors="np").input_features
				__lowerCAmelCase   :						Any				= feature_extractor(np_speech_inputs[0] ,    return_tensors="np").input_features
				self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    atol=1e-3))

				# Test batched
				__lowerCAmelCase   :						Tuple				= feature_extractor(_SCREAMING_SNAKE_CASE ,    return_tensors="np").input_features
				__lowerCAmelCase   :						Tuple				= feature_extractor(_SCREAMING_SNAKE_CASE ,    return_tensors="np").input_features
				for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    atol=1e-3))

				# Test 2-D numpy arrays are batched.
				__lowerCAmelCase   :						List[str]				= [floats_list((1, x))[0] for x in (800, 800, 800)]
				__lowerCAmelCase   :						Dict				= np.asarray(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Optional[int]				= feature_extractor(_SCREAMING_SNAKE_CASE ,    return_tensors="np").input_features
				__lowerCAmelCase   :						Optional[Any]				= feature_extractor(_SCREAMING_SNAKE_CASE ,    return_tensors="np").input_features
				for enc_seq_a, enc_seq_a in zip(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    atol=1e-3))

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

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				__lowerCAmelCase   :						Tuple				= [floats_list((1, x))[0] for x in range(800 ,    1400 ,    200)]

				__lowerCAmelCase   :						Optional[Any]				= ["longest", "max_length", "do_not_pad"]
				__lowerCAmelCase   :						str				= [None, 16, None]
				for max_length, padding in zip(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						str				= feature_extractor(
							    _SCREAMING_SNAKE_CASE ,    padding=_SCREAMING_SNAKE_CASE ,    max_length=_SCREAMING_SNAKE_CASE ,    return_attention_mask=_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						Optional[Any]				= inputs.input_features
							__lowerCAmelCase   :						Optional[Any]				= inputs.attention_mask
							__lowerCAmelCase   :						Tuple				= [np.sum(_SCREAMING_SNAKE_CASE) for x in attention_mask]

							self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
							self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
							self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])

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

				"""simple docstring"""

				__lowerCAmelCase   :						str				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				__lowerCAmelCase   :						Any				= [floats_list((1, x))[0] for x in range(800 ,    1400 ,    200)]

				__lowerCAmelCase   :						Dict				= ["longest", "max_length", "do_not_pad"]
				__lowerCAmelCase   :						List[Any]				= [None, 16, None]
				for max_length, padding in zip(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						List[str]				= feature_extractor(
							    _SCREAMING_SNAKE_CASE ,    max_length=_SCREAMING_SNAKE_CASE ,    padding=_SCREAMING_SNAKE_CASE ,    return_tensors="np" ,    return_attention_mask=_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						List[str]				= inputs.input_features
							__lowerCAmelCase   :						Dict				= inputs.attention_mask
							__lowerCAmelCase   :						Dict				= [np.sum(_SCREAMING_SNAKE_CASE) for x in attention_mask]

							self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
							self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6)
							self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
							self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6)
							self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				__lowerCAmelCase   :						str				= [floats_list((1, x))[0] for x in range(800 ,    1400 ,    200)]
				__lowerCAmelCase   :						str				= feature_extractor(
				    _SCREAMING_SNAKE_CASE ,    padding="max_length" ,    max_length=4 ,    truncation=_SCREAMING_SNAKE_CASE ,    return_tensors="np" ,    return_attention_mask=_SCREAMING_SNAKE_CASE ,    )
				__lowerCAmelCase   :						List[str]				= inputs.input_features
				__lowerCAmelCase   :						Dict				= inputs.attention_mask
				__lowerCAmelCase   :						Any				= np.sum(attention_mask == 1 ,    axis=1)

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
				self._check_zero_mean_unit_variance(input_features[1])
				self._check_zero_mean_unit_variance(input_features[2])

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

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				__lowerCAmelCase   :						List[Any]				= [floats_list((1, x))[0] for x in range(800 ,    1400 ,    200)]
				__lowerCAmelCase   :						Any				= feature_extractor(
				    _SCREAMING_SNAKE_CASE ,    padding="longest" ,    max_length=4 ,    truncation=_SCREAMING_SNAKE_CASE ,    return_tensors="np" ,    return_attention_mask=_SCREAMING_SNAKE_CASE ,    )
				__lowerCAmelCase   :						List[str]				= inputs.input_features
				__lowerCAmelCase   :						Optional[Any]				= inputs.attention_mask
				__lowerCAmelCase   :						Optional[Any]				= np.sum(attention_mask == 1 ,    axis=1)

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
				self._check_zero_mean_unit_variance(input_features[2])

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape ,    (3, 4, 24))

				__lowerCAmelCase   :						List[str]				= [floats_list((1, x))[0] for x in range(800 ,    1400 ,    200)]
				__lowerCAmelCase   :						List[str]				= feature_extractor(
				    _SCREAMING_SNAKE_CASE ,    padding="longest" ,    max_length=16 ,    truncation=_SCREAMING_SNAKE_CASE ,    return_tensors="np" ,    return_attention_mask=_SCREAMING_SNAKE_CASE ,    )
				__lowerCAmelCase   :						Optional[Any]				= inputs.input_features
				__lowerCAmelCase   :						Optional[int]				= inputs.attention_mask
				__lowerCAmelCase   :						str				= np.sum(attention_mask == 1 ,    axis=1)

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
				self._check_zero_mean_unit_variance(input_features[2])

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape ,    (3, 6, 24))

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Dict:

				"""simple docstring"""

				import torch

				__lowerCAmelCase   :						Dict				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				__lowerCAmelCase   :						List[str]				= np.random.rand(100 ,    32).astype(np.floataa)
				__lowerCAmelCase   :						List[Any]				= np_speech_inputs.tolist()

				for inputs in [py_speech_inputs, np_speech_inputs]:
							__lowerCAmelCase   :						Any				= feature_extractor.pad([{"input_features": inputs}] ,    return_tensors="np")
							self.assertTrue(np_processed.input_features.dtype == np.floataa)
							__lowerCAmelCase   :						List[Any]				= feature_extractor.pad([{"input_features": inputs}] ,    return_tensors="pt")
							self.assertTrue(pt_processed.input_features.dtype == torch.floataa)

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

				"""simple docstring"""

				from datasets import load_dataset

				__lowerCAmelCase   :						Any				= load_dataset("hf-internal-testing/librispeech_asr_dummy" ,    "clean" ,    split="validation")
				# automatic decoding with librispeech
				__lowerCAmelCase   :						List[Any]				= ds.sort("id").select(range(_SCREAMING_SNAKE_CASE))[:num_samples]["audio"]

				return [x["array"] for x in speech_samples]







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

				"""simple docstring"""

				__lowerCAmelCase   :						str				= np.array([
				    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
				    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
				    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
				])
				# fmt: on

				__lowerCAmelCase   :						str				= self._load_datasamples(1)
				__lowerCAmelCase   :						Any				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				__lowerCAmelCase   :						List[str]				= feature_extractor(_SCREAMING_SNAKE_CASE ,    return_tensors="pt").input_features
				self.assertEquals(input_features.shape ,    (1, 584, 24))
				self.assertTrue(np.allclose(input_features[0, 0, :30] ,    _SCREAMING_SNAKE_CASE ,    atol=1e-4))