





"""simple docstring"""




import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Any         =DownBlockaD  # noqa F405
							lowerCamelCase     :      List[str]         ="""down"""






							def 					__a     (     self   )			->				List[str]:
								a   :	Optional[Any]			=       [-0.0_232, -0.9_869, 0.8_054, -0.0_637, -0.1_688, -1.4_264, 0.4_470, -1.3_394, 0.0_904]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Tuple         =ResnetDownsampleBlockaD  # noqa F405
							lowerCamelCase     :      Tuple         ="""down"""






							def 					__a     (     self   )			->				Dict:
								a   :	Any			=       [0.0_710, 0.2_410, -0.7_320, -1.0_757, -1.1_343, 0.3_540, -0.0_133, -0.2_576, 0.0_948]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Optional[Any]         =AttnDownBlockaD  # noqa F405
							lowerCamelCase     :      Optional[int]         ="""down"""






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	int			=       [0.0_636, 0.8_964, -0.6_234, -1.0_131, 0.0_844, 0.4_935, 0.3_437, 0.0_911, -0.2_957]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      List[str]         =CrossAttnDownBlockaD  # noqa F405
							lowerCamelCase     :      List[Any]         ="""down"""






							def 					__a     (     self   )			->				Dict:
								a, a   :	Union[str, Any]			=       super().prepare_init_args_and_inputs_for_common()
								a   :	int			=       32
								return init_dict, inputs_dict






							def 					__a     (     self   )			->				List[Any]:
								a   :	str			=       [0.2_238, -0.7_396, -0.2_255, -0.3_829, 0.1_925, 1.1_665, 0.0_603, -0.7_295, 0.1_983]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      List[str]         =SimpleCrossAttnDownBlockaD  # noqa F405
							lowerCamelCase     :      Union[str, Any]         ="""down"""






							@property
							def 					__a     (     self   )			->				Dict:
								return super().get_dummy_input(include_encoder_hidden_states=lowerCAmelCase__   )






							def 					__a     (     self   )			->				Optional[Any]:
								a, a   :	List[str]			=       super().prepare_init_args_and_inputs_for_common()
								a   :	Optional[Any]			=       32
								return init_dict, inputs_dict






							@unittest.skipIf(torch_device == "mps"   ,      "MPS result is not consistent"   )
							def 					__a     (     self   )			->				List[Any]:
								a   :	Optional[int]			=       [0.7_921, -0.0_992, -0.1_962, -0.7_695, -0.4_242, 0.7_804, 0.4_737, 0.2_765, 0.3_338]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Dict         =SkipDownBlockaD  # noqa F405
							lowerCamelCase     :      int         ="""down"""






							@property
							def 					__a     (     self   )			->				str:
								return super().get_dummy_input(include_skip_sample=lowerCAmelCase__   )






							def 					__a     (     self   )			->				Dict:
								a   :	int			=       [-0.0_845, -0.2_087, -0.2_465, 0.0_971, 0.1_900, -0.0_484, 0.2_664, 0.4_179, 0.5_069]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      List[str]         =AttnSkipDownBlockaD  # noqa F405
							lowerCamelCase     :      Optional[Any]         ="""down"""






							@property
							def 					__a     (     self   )			->				Optional[Any]:
								return super().get_dummy_input(include_skip_sample=lowerCAmelCase__   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	int			=       [0.5_539, 0.1_609, 0.4_924, 0.0_537, -0.1_995, 0.4_050, 0.0_979, -0.2_721, -0.0_642]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Optional[Any]         =DownEncoderBlockaD  # noqa F405
							lowerCamelCase     :      Optional[Any]         ="""down"""






							@property
							def 					__a     (     self   )			->				Optional[Any]:
								return super().get_dummy_input(include_temb=lowerCAmelCase__   )






							def 					__a     (     self   )			->				List[str]:
								a   :	List[Any]			=       {
								    "in_channels": 32,
								    "out_channels": 32,
								}
								a   :	int			=       self.dummy_input
								return init_dict, inputs_dict






							def 					__a     (     self   )			->				Tuple:
								a   :	Optional[Any]			=       [1.1_102, 0.5_302, 0.4_872, -0.0_023, -0.8_042, 0.0_483, -0.3_489, -0.5_632, 0.7_626]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      List[Any]         =AttnDownEncoderBlockaD  # noqa F405
							lowerCamelCase     :      str         ="""down"""






							@property
							def 					__a     (     self   )			->				Tuple:
								return super().get_dummy_input(include_temb=lowerCAmelCase__   )






							def 					__a     (     self   )			->				List[str]:
								a   :	int			=       {
								    "in_channels": 32,
								    "out_channels": 32,
								}
								a   :	Optional[int]			=       self.dummy_input
								return init_dict, inputs_dict






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Union[str, Any]			=       [0.8_966, -0.1_486, 0.8_568, 0.8_141, -0.9_046, -0.1_342, -0.0_972, -0.7_417, 0.1_538]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      str         =UNetMidBlockaD  # noqa F405
							lowerCamelCase     :      Optional[int]         ="""mid"""






							def 					__a     (     self   )			->				Dict:
								a   :	Optional[Any]			=       {
								    "in_channels": 32,
								    "temb_channels": 128,
								}
								a   :	Optional[int]			=       self.dummy_input
								return init_dict, inputs_dict






							def 					__a     (     self   )			->				Tuple:
								a   :	Tuple			=       [-0.1_062, 1.7_248, 0.3_494, 1.4_569, -0.0_910, -1.2_421, -0.9_984, 0.6_736, 1.0_028]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      List[str]         =UNetMidBlockaDCrossAttn  # noqa F405
							lowerCamelCase     :      Optional[Any]         ="""mid"""






							def 					__a     (     self   )			->				int:
								a, a   :	Any			=       super().prepare_init_args_and_inputs_for_common()
								a   :	List[str]			=       32
								return init_dict, inputs_dict






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Tuple			=       [0.0_187, 2.4_220, 0.4_484, 1.1_203, -0.6_121, -1.5_122, -0.8_270, 0.7_851, 1.8_335]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Union[str, Any]         =UNetMidBlockaDSimpleCrossAttn  # noqa F405
							lowerCamelCase     :      Dict         ="""mid"""






							@property
							def 					__a     (     self   )			->				Optional[int]:
								return super().get_dummy_input(include_encoder_hidden_states=lowerCAmelCase__   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a, a   :	str			=       super().prepare_init_args_and_inputs_for_common()
								a   :	str			=       32
								return init_dict, inputs_dict






							def 					__a     (     self   )			->				Dict:
								a   :	List[Any]			=       [0.7_143, 1.9_974, 0.5_448, 1.3_977, 0.1_282, -1.1_237, -1.4_238, 0.5_530, 0.8_880]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Optional[Any]         =UpBlockaD  # noqa F405
							lowerCamelCase     :      Dict         ="""up"""






							@property
							def 					__a     (     self   )			->				int:
								return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase__   )






							def 					__a     (     self   )			->				List[Any]:
								a   :	Tuple			=       [-0.2_041, -0.4_165, -0.3_022, 0.0_041, -0.6_628, -0.7_053, 0.1_928, -0.0_325, 0.0_523]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Optional[int]         =ResnetUpsampleBlockaD  # noqa F405
							lowerCamelCase     :      Dict         ="""up"""






							@property
							def 					__a     (     self   )			->				Any:
								return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase__   )






							def 					__a     (     self   )			->				int:
								a   :	Tuple			=       [0.2_287, 0.3_549, -0.1_346, 0.4_797, -0.1_715, -0.9_649, 0.7_305, -0.5_864, -0.6_244]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Dict         =CrossAttnUpBlockaD  # noqa F405
							lowerCamelCase     :      str         ="""up"""






							@property
							def 					__a     (     self   )			->				Tuple:
								return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase__   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a, a   :	List[str]			=       super().prepare_init_args_and_inputs_for_common()
								a   :	Optional[int]			=       32
								return init_dict, inputs_dict






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Tuple			=       [-0.1_403, -0.3_515, -0.0_420, -0.1_425, 0.3_167, 0.5_094, -0.2_181, 0.5_931, 0.5_582]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      int         =SimpleCrossAttnUpBlockaD  # noqa F405
							lowerCamelCase     :      Any         ="""up"""






							@property
							def 					__a     (     self   )			->				Tuple:
								return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase__   ,      include_encoder_hidden_states=lowerCAmelCase__   )






							def 					__a     (     self   )			->				Optional[int]:
								a, a   :	Any			=       super().prepare_init_args_and_inputs_for_common()
								a   :	Tuple			=       32
								return init_dict, inputs_dict






							def 					__a     (     self   )			->				Dict:
								a   :	List[Any]			=       [0.2_645, 0.1_480, 0.0_909, 0.8_044, -0.9_758, -0.9_083, 0.0_994, -1.1_453, -0.7_402]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      str         =AttnUpBlockaD  # noqa F405
							lowerCamelCase     :      Any         ="""up"""






							@property
							def 					__a     (     self   )			->				Any:
								return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase__   )






							@unittest.skipIf(torch_device == "mps"   ,      "MPS result is not consistent"   )
							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Union[str, Any]			=       [0.0_979, 0.1_326, 0.0_021, 0.0_659, 0.2_249, 0.0_059, 0.1_132, 0.5_952, 0.1_033]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      str         =SkipUpBlockaD  # noqa F405
							lowerCamelCase     :      Dict         ="""up"""






							@property
							def 					__a     (     self   )			->				Any:
								return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase__   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Dict			=       [-0.0_893, -0.1_234, -0.1_506, -0.0_332, 0.0_123, -0.0_211, 0.0_566, 0.0_143, 0.0_362]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      List[Any]         =AttnSkipUpBlockaD  # noqa F405
							lowerCamelCase     :      Tuple         ="""up"""






							@property
							def 					__a     (     self   )			->				List[Any]:
								return super().get_dummy_input(include_res_hidden_states_tuple=lowerCAmelCase__   )






							def 					__a     (     self   )			->				Tuple:
								a   :	Optional[int]			=       [0.0_361, 0.0_617, 0.2_787, -0.0_350, 0.0_342, 0.3_421, -0.0_843, 0.0_913, 0.3_015]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Optional[Any]         =UpDecoderBlockaD  # noqa F405
							lowerCamelCase     :      Optional[int]         ="""up"""






							@property
							def 					__a     (     self   )			->				Optional[Any]:
								return super().get_dummy_input(include_temb=lowerCAmelCase__   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Union[str, Any]			=       {"in_channels": 32, "out_channels": 32}

								a   :	Dict			=       self.dummy_input
								return init_dict, inputs_dict






							def 					__a     (     self   )			->				Dict:
								a   :	int			=       [0.4_404, 0.1_998, -0.9_886, -0.3_320, -0.3_128, -0.7_034, -0.6_955, -0.2_338, -0.3_137]
								super().test_output(lowerCAmelCase__   )


class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      int         =AttnUpDecoderBlockaD  # noqa F405
							lowerCamelCase     :      int         ="""up"""






							@property
							def 					__a     (     self   )			->				List[str]:
								return super().get_dummy_input(include_temb=lowerCAmelCase__   )






							def 					__a     (     self   )			->				Dict:
								a   :	Tuple			=       {"in_channels": 32, "out_channels": 32}

								a   :	List[str]			=       self.dummy_input
								return init_dict, inputs_dict






							def 					__a     (     self   )			->				Optional[int]:
								a   :	str			=       [0.6_738, 0.4_491, 0.1_055, 1.0_710, 0.7_316, 0.3_339, 0.3_352, 0.1_023, 0.3_568]
								super().test_output(lowerCAmelCase__   )









"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch


a		:				List[Any]  			=    random.Random()






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]					, _lowercase		:					int=1.0					, _lowercase		:					Optional[int]=None					, _lowercase		:					Union[str, Any]=None      )				->Optional[Any]:






	'''simple docstring'''






	if rng is None:
		a   :	Tuple			=       global_rng

	a   :	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
class  __UpperCamelCase       (		unittest.TestCase		):






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=7   ,      lowerCAmelCase__=400   ,      lowerCAmelCase__=2000   ,      lowerCAmelCase__=1   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=1_6000   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=80   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__=64   ,      lowerCAmelCase__="hann_window"   ,      lowerCAmelCase__=80   ,      lowerCAmelCase__=7600   ,      lowerCAmelCase__=1E-10   ,      lowerCAmelCase__=True   ,      )			->				Optional[Any]:
								a   :	int			=       parent
								a   :	Tuple			=       batch_size
								a   :	Dict			=       min_seq_length
								a   :	Any			=       max_seq_length
								a   :	Optional[int]			=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								a   :	Union[str, Any]			=       feature_size
								a   :	Tuple			=       padding_value
								a   :	str			=       sampling_rate
								a   :	Dict			=       do_normalize
								a   :	str			=       num_mel_bins
								a   :	List[str]			=       hop_length
								a   :	str			=       win_length
								a   :	Optional[Any]			=       win_function
								a   :	List[str]			=       fmin
								a   :	Any			=       fmax
								a   :	Optional[int]			=       mel_floor
								a   :	Tuple			=       return_attention_mask






							def 					__a     (     self   )			->				Optional[Any]:
								return {
								    "feature_size": self.feature_size,
								    "padding_value": self.padding_value,
								    "sampling_rate": self.sampling_rate,
								    "do_normalize": self.do_normalize,
								    "num_mel_bins": self.num_mel_bins,
								    "hop_length": self.hop_length,
								    "win_length": self.win_length,
								    "win_function": self.win_function,
								    "fmin": self.fmin,
								    "fmax": self.fmax,
								    "mel_floor": self.mel_floor,
								    "return_attention_mask": self.return_attention_mask,
								}






							def 					__a     (     self   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   )			->				Tuple:
								def _flatten(lowerCAmelCase__   ):
									return list(itertools.chain(*lowerCAmelCase__   )   )

								if equal_length:
									a   :	Union[str, Any]			=       floats_list((self.batch_size, self.max_seq_length)   )
								else:
									# make sure that inputs increase in size
									a   :	str			=       [
									    _flatten(floats_list((x, self.feature_size)   )   )
									    for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff   )
									]

								if numpify:
									a   :	Any			=       [np.asarray(lowerCAmelCase__   ) for x in speech_inputs]

								return speech_inputs






							def 					__a     (     self   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   )			->				Dict:
								if equal_length:
									a   :	str			=       [floats_list((self.max_seq_length, self.num_mel_bins)   ) for _ in range(self.batch_size   )]
								else:
									# make sure that inputs increase in size
									a   :	Any			=       [
									    floats_list((x, self.num_mel_bins)   )
									    for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff   )
									]

								if numpify:
									a   :	Optional[int]			=       [np.asarray(lowerCAmelCase__   ) for x in speech_inputs]

								return speech_inputs


@require_torch
class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Tuple         =SpeechTaFeatureExtractor






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Tuple			=       SpeechTaFeatureExtractionTester(self   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								self.assertTrue(np.all(np.mean(lowerCAmelCase__   ,      axis=0   ) < 1E-3   )   )
								self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase__   ,      axis=0   ) - 1   ) < 1E-3   )   )






							def 					__a     (     self   )			->				Union[str, Any]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								# create three inputs of length 800, 1000, and 1200
								a   :	Any			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Any			=       [np.asarray(lowerCAmelCase__   ) for speech_input in speech_inputs]

								# Test not batched input
								a   :	Optional[int]			=       feat_extract(speech_inputs[0]   ,      return_tensors="np"   ).input_values
								a   :	Optional[Any]			=       feat_extract(np_speech_inputs[0]   ,      return_tensors="np"   ).input_values
								self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test batched
								a   :	int			=       feat_extract(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	int			=       feat_extract(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	List[Any]			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Dict			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]

								a   :	int			=       ["longest", "max_length", "do_not_pad"]
								a   :	Tuple			=       [None, 1600, None]
								for max_length, padding in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	Dict			=       feat_extract(lowerCAmelCase__   ,      padding=lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      return_tensors="np"   )
									a   :	List[Any]			=       processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:800]   )
									self.assertTrue(input_values[0][800:].sum() < 1E-6   )
									self._check_zero_mean_unit_variance(input_values[1][:1000]   )
									self.assertTrue(input_values[0][1000:].sum() < 1E-6   )
									self._check_zero_mean_unit_variance(input_values[2][:1200]   )






							def 					__a     (     self   )			->				str:
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	List[str]			=       range(800   ,      1400   ,      200   )
								a   :	List[str]			=       [floats_list((1, x)   )[0] for x in lengths]

								a   :	Any			=       ["longest", "max_length", "do_not_pad"]
								a   :	Any			=       [None, 1600, None]

								for max_length, padding in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	List[Any]			=       feat_extract(lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      padding=lowerCAmelCase__   )
									a   :	Dict			=       processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:800]   )
									self._check_zero_mean_unit_variance(input_values[1][:1000]   )
									self._check_zero_mean_unit_variance(input_values[2][:1200]   )






							def 					__a     (     self   )			->				Dict:
								a   :	Any			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	int			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Union[str, Any]			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=1000   ,      padding="max_length"   ,      return_tensors="np"   )
								a   :	List[Any]			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1]   )
								self._check_zero_mean_unit_variance(input_values[2]   )






							def 					__a     (     self   )			->				Dict:
								a   :	Dict			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Tuple			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	List[Any]			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=1000   ,      padding="longest"   ,      return_tensors="np"   )
								a   :	Union[str, Any]			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1, :1000]   )
								self._check_zero_mean_unit_variance(input_values[2]   )

								# make sure that if max_length < longest -> then pad to max_length
								self.assertTrue(input_values.shape == (3, 1000)   )

								a   :	List[Any]			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	int			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=2000   ,      padding="longest"   ,      return_tensors="np"   )
								a   :	Dict			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1, :1000]   )
								self._check_zero_mean_unit_variance(input_values[2]   )

								# make sure that if max_length > longest -> then pad to longest
								self.assertTrue(input_values.shape == (3, 1200)   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Any			=       np.random.rand(100   ).astype(np.floataa   )
								a   :	Optional[Any]			=       np_speech_inputs.tolist()

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






							def 					__a     (     self   )			->				Tuple:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								a   :	Union[str, Any]			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								# create three inputs of length 800, 1000, and 1200
								a   :	Optional[Any]			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Tuple			=       [np.asarray(lowerCAmelCase__   ) for speech_input in speech_inputs]

								# Test feature size
								a   :	Union[str, Any]			=       feature_extractor(audio_target=lowerCAmelCase__   ,      padding=lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								self.assertTrue(input_values.ndim == 3   )
								self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins   )

								# Test not batched input
								a   :	Dict			=       feature_extractor(speech_inputs[0]   ,      return_tensors="np"   ).input_values
								a   :	List[Any]			=       feature_extractor(np_speech_inputs[0]   ,      return_tensors="np"   ).input_values
								self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test batched
								a   :	Optional[int]			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	Any			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

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






							def 					__a     (     self   )			->				str:
								a   :	Optional[Any]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Any			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Union[str, Any]			=       feat_extract.model_input_names[0]

								a   :	List[str]			=       BatchFeature({input_name: speech_inputs}   )

								self.assertTrue(all(len(lowerCAmelCase__   ) == len(lowerCAmelCase__   ) for x, y in zip(lowerCAmelCase__   ,      processed_features[input_name]   )   )   )

								a   :	Tuple			=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__   )
								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   ,      tensor_type="np"   )

								a   :	Tuple			=       processed_features[input_name]

								if len(batch_features_input.shape   ) < 3:
									a   :	Dict			=       batch_features_input[:, :, None]

								self.assertTrue(
								    batch_features_input.shape
								    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.num_mel_bins)   )






							@require_torch
							def 					__a     (     self   )			->				Tuple:
								a   :	Tuple			=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__   )
								a   :	Optional[Any]			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Optional[int]			=       feat_extract.model_input_names[0]

								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   ,      tensor_type="pt"   )

								a   :	Tuple			=       processed_features[input_name]

								if len(batch_features_input.shape   ) < 3:
									a   :	List[str]			=       batch_features_input[:, :, None]

								self.assertTrue(
								    batch_features_input.shape
								    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.num_mel_bins)   )






							@require_torch
							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Dict			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Optional[int]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Optional[Any]			=       feat_extract.model_input_names[0]

								a   :	List[str]			=       BatchFeature({input_name: speech_inputs}   )

								a   :	Tuple			=       feat_extract.num_mel_bins  # hack!

								a   :	List[Any]			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="np"   )[input_name]
								a   :	Any			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="pt"   )[input_name]

								self.assertTrue(abs(input_np.astype(np.floataa   ).sum() - input_pt.numpy().astype(np.floataa   ).sum()   ) < 1E-2   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Any			=       self.feat_extract_dict
								a   :	Optional[Any]			=       True
								a   :	Union[str, Any]			=       self.feature_extraction_class(**lowerCAmelCase__   )
								a   :	Any			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Dict			=       [len(lowerCAmelCase__   ) for x in speech_inputs]
								a   :	int			=       feat_extract.model_input_names[0]

								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   )

								a   :	Union[str, Any]			=       feat_extract.num_mel_bins  # hack!

								a   :	Dict			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="np"   )
								self.assertIn("attention_mask"   ,      lowerCAmelCase__   )
								self.assertListEqual(list(processed.attention_mask.shape   )   ,      list(processed[input_name].shape[:2]   )   )
								self.assertListEqual(processed.attention_mask.sum(-1   ).tolist()   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Tuple			=       self.feat_extract_dict
								a   :	str			=       True
								a   :	Optional[Any]			=       self.feature_extraction_class(**lowerCAmelCase__   )
								a   :	List[Any]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Dict			=       [len(lowerCAmelCase__   ) for x in speech_inputs]
								a   :	Optional[Any]			=       feat_extract.model_input_names[0]

								a   :	str			=       BatchFeature({input_name: speech_inputs}   )
								a   :	Optional[Any]			=       min(lowerCAmelCase__   )

								a   :	List[Any]			=       feat_extract.num_mel_bins  # hack!

								a   :	Any			=       feat_extract.pad(
								    lowerCAmelCase__   ,      padding="max_length"   ,      max_length=lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      return_tensors="np"   )
								self.assertIn("attention_mask"   ,      lowerCAmelCase__   )
								self.assertListEqual(
								    list(processed_pad.attention_mask.shape   )   ,      [processed_pad[input_name].shape[0], max_length]   )
								self.assertListEqual(
								    processed_pad.attention_mask[:, :max_length].sum(-1   ).tolist()   ,      [max_length for x in speech_inputs]   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								from datasets import load_dataset

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

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






							def 					__a     (     self   )			->				Union[str, Any]:
								# fmt: off
								a   :	List[Any]			=       torch.tensor(
								    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
								     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
								     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
								     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
								     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
								     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]   )
								# fmt: on

								a   :	List[str]			=       self._load_datasamples(1   )
								a   :	Union[str, Any]			=       SpeechTaFeatureExtractor()
								a   :	str			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="pt"   ).input_values
								self.assertEquals(input_values.shape   ,      (1, 9_3680)   )
								self.assertTrue(torch.allclose(input_values[0, :30]   ,      lowerCAmelCase__   ,      atol=1E-6   )   )






							def 					__a     (     self   )			->				Union[str, Any]:
								# fmt: off
								a   :	Tuple			=       torch.tensor(
								    [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
								     -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
								     -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
								     -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]   )
								# fmt: on

								a   :	Dict			=       self._load_datasamples(1   )
								a   :	Tuple			=       SpeechTaFeatureExtractor()
								a   :	Optional[int]			=       feature_extractor(audio_target=lowerCAmelCase__   ,      return_tensors="pt"   ).input_values
								self.assertEquals(input_values.shape   ,      (1, 366, 80)   )
								self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,      lowerCAmelCase__   ,      atol=1E-4   )   )


