



'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class 						a_		(     unittest.TestCase    ):
							UpperCAmelCase			:		List[str]        =				StableDiffusionLDMaDPipeline
							UpperCAmelCase			:		Tuple        =				TEXT_TO_IMAGE_PARAMS
							UpperCAmelCase			:		Optional[int]        =				TEXT_TO_IMAGE_BATCH_PARAMS
							UpperCAmelCase			:		Dict        =				TEXT_TO_IMAGE_IMAGE_PARAMS


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					List[str]:
										torch.manual_seed(0			)
										snake_case:    int      							=UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)      ,						layers_per_block=2      ,						sample_size=3_2      ,						in_channels=4      ,						out_channels=4      ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,						cross_attention_dim=3_2      ,						)
										snake_case:    Tuple      							=DDIMScheduler(
										    beta_start=0.0_0_0_8_5      ,						beta_end=0.0_1_2      ,						beta_schedule='scaled_linear'      ,						clip_sample=a_      ,						set_alpha_to_one=a_      ,						)
										torch.manual_seed(0			)
										snake_case:    Optional[int]      							=AutoencoderKL(
										    block_out_channels=[3_2, 6_4]      ,						in_channels=6      ,						out_channels=6      ,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,						latent_channels=4      ,						)
										torch.manual_seed(0			)
										snake_case:    List[Any]      							=CLIPTextConfig(
										    bos_token_id=0      ,						eos_token_id=2      ,						hidden_size=3_2      ,						intermediate_size=3_7      ,						layer_norm_eps=1E-0_5      ,						num_attention_heads=4      ,						num_hidden_layers=5      ,						pad_token_id=1      ,						vocab_size=1_0_0_0      ,						)
										snake_case:    int      							=CLIPTextModel(a_			)
										snake_case:    Union[str, Any]      							=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

										snake_case:    int      							={
										    'unet': unet,
										    'scheduler': scheduler,
										    'vae': vae,
										    'text_encoder': text_encoder,
										    'tokenizer': tokenizer,
										    'safety_checker': None,
										    'feature_extractor': None,
										}
										return components


							def    UpperCamelCase     (				self   :						int      ,						a_   :						List[Any]      ,						a_   :						Dict=0			)			->					Tuple:
										if str(a_			).startswith('mps'			):
													snake_case:    str      							=torch.manual_seed(a_			)
										else:
													snake_case:    Tuple      							=torch.Generator(device=a_			).manual_seed(a_			)
										snake_case:    Dict      							={
										    'prompt': 'A painting of a squirrel eating a burger',
										    'generator': generator,
										    'num_inference_steps': 2,
										    'guidance_scale': 6.0,
										    'output_type': 'numpy',
										}
										return inputs


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Any:
										snake_case:    Tuple      							='cpu'  # ensure determinism for the device-dependent torch.Generator

										snake_case:    List[Any]      							=self.get_dummy_components()
										snake_case:    List[Any]      							=StableDiffusionLDMaDPipeline(**a_			)
										snake_case:    Optional[Any]      							=ldmad_pipe.to(a_			)
										ldmad_pipe.set_progress_bar_config(disable=a_			)

										snake_case:    int      							=self.get_dummy_inputs(a_			)
										snake_case:    Union[str, Any]      							=ldmad_pipe(**a_			)
										snake_case							,  snake_case:    List[str]      							=output.rgb, output.depth

										snake_case:    List[str]      							=rgb[0, -3:, -3:, -1]
										snake_case:    List[Any]      							=depth[0, -3:, -1]

										assert rgb.shape == (1, 6_4, 6_4, 3)
										assert depth.shape == (1, 6_4, 6_4)

										snake_case:    str      							=np.array(
										    [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2]			)
										snake_case:    Tuple      							=np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6]			)

										assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb			).max() < 1E-2
										assert np.abs(image_slice_depth.flatten() - expected_slice_depth			).max() < 1E-2


							def    UpperCamelCase     (				self   :						Dict			)			->					Tuple:
										snake_case:    List[str]      							=self.get_dummy_components()
										snake_case:    Tuple      							=StableDiffusionLDMaDPipeline(**a_			)
										snake_case:    Tuple      							=ldmad_pipe.to(a_			)
										ldmad_pipe.set_progress_bar_config(disable=a_			)

										snake_case:    Dict      							=self.get_dummy_inputs(a_			)
										snake_case:    Optional[int]      							=3 * [inputs['prompt']]

										# forward
										snake_case:    Dict      							=ldmad_pipe(**a_			)
										snake_case							,  snake_case:    Tuple      							=output.rgb, output.depth
										snake_case:    List[Any]      							=rgb_slice_a[0, -3:, -3:, -1]
										snake_case:    Tuple      							=depth_slice_a[0, -3:, -1]

										snake_case:    List[Any]      							=self.get_dummy_inputs(a_			)
										snake_case:    Any      							=3 * [inputs.pop('prompt'			)]

										snake_case:    List[Any]      							=ldmad_pipe.tokenizer(
										    a_      ,						padding='max_length'      ,						max_length=ldmad_pipe.tokenizer.model_max_length      ,						truncation=a_      ,						return_tensors='pt'      ,						)
										snake_case:    int      							=text_inputs['input_ids'].to(a_			)

										snake_case:    int      							=ldmad_pipe.text_encoder(a_			)[0]

										snake_case:    int      							=prompt_embeds

										# forward
										snake_case:    int      							=ldmad_pipe(**a_			)
										snake_case							,  snake_case:    int      							=output.rgb, output.depth
										snake_case:    Dict      							=rgb_slice_a[0, -3:, -3:, -1]
										snake_case:    List[Any]      							=depth_slice_a[0, -3:, -1]

										assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()			).max() < 1E-4
										assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()			).max() < 1E-4




							def    UpperCamelCase     (				self   :						Tuple			)			->					List[Any]:
										snake_case:    Optional[Any]      							='cpu'  # ensure determinism for the device-dependent torch.Generator
										snake_case:    Any      							=self.get_dummy_components()
										snake_case:    Dict      							=PNDMScheduler(skip_prk_steps=a_			)
										snake_case:    int      							=StableDiffusionLDMaDPipeline(**a_			)
										snake_case:    int      							=ldmad_pipe.to(a_			)
										ldmad_pipe.set_progress_bar_config(disable=a_			)

										snake_case:    Union[str, Any]      							=self.get_dummy_inputs(a_			)
										snake_case:    List[Any]      							='french fries'
										snake_case:    List[str]      							=ldmad_pipe(**a_      ,						negative_prompt=a_			)

										snake_case							,  snake_case:    Optional[int]      							=output.rgb, output.depth
										snake_case:    Optional[int]      							=rgb[0, -3:, -3:, -1]
										snake_case:    Optional[int]      							=depth[0, -3:, -1]

										assert rgb.shape == (1, 6_4, 6_4, 3)
										assert depth.shape == (1, 6_4, 6_4)

										snake_case:    str      							=np.array(
										    [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7]			)
										snake_case:    str      							=np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5]			)
										assert np.abs(rgb_slice.flatten() - expected_slice_rgb			).max() < 1E-2
										assert np.abs(depth_slice.flatten() - expected_slice_depth			).max() < 1E-2






@slow
@require_torch_gpu
class 						a_		(     unittest.TestCase    ):


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Union[str, Any]:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


							def    UpperCamelCase     (				self   :						Union[str, Any]      ,						a_   :						Optional[Any]      ,						a_   :						List[Any]="cpu"      ,						a_   :						Union[str, Any]=torch.floataa      ,						a_   :						List[str]=0			)			->					Optional[int]:
										snake_case:    List[Any]      							=torch.Generator(device=a_			).manual_seed(a_			)
										snake_case:    Optional[Any]      							=np.random.RandomState(a_			).standard_normal((1, 4, 6_4, 6_4)			)
										snake_case:    Any      							=torch.from_numpy(a_			).to(device=a_      ,						dtype=a_			)
										snake_case:    Dict      							={
										    'prompt': 'a photograph of an astronaut riding a horse',
										    'latents': latents,
										    'generator': generator,
										    'num_inference_steps': 3,
										    'guidance_scale': 7.5,
										    'output_type': 'numpy',
										}
										return inputs




							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Dict:
										snake_case:    List[Any]      							=StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'			)
										snake_case:    Tuple      							=ldmad_pipe.to(a_			)
										ldmad_pipe.set_progress_bar_config(disable=a_			)

										snake_case:    Tuple      							=self.get_inputs(a_			)
										snake_case:    Any      							=ldmad_pipe(**a_			)
										snake_case							,  snake_case:    List[Any]      							=output.rgb, output.depth
										snake_case:    Optional[int]      							=rgb[0, -3:, -3:, -1].flatten()
										snake_case:    List[str]      							=rgb[0, -3:, -1].flatten()

										assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
										assert depth.shape == (1, 5_1_2, 5_1_2)

										snake_case:    Tuple      							=np.array(
										    [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6]			)
										snake_case:    Tuple      							=np.array(
										    [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6]			)
										assert np.abs(rgb_slice - expected_slice_rgb			).max() < 3E-3
										assert np.abs(depth_slice - expected_slice_depth			).max() < 3E-3






@nightly
@require_torch_gpu
class 						a_		(     unittest.TestCase    ):


							def    UpperCamelCase     (				self   :						Any			)			->					Optional[int]:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


							def    UpperCamelCase     (				self   :						List[str]      ,						a_   :						int      ,						a_   :						Dict="cpu"      ,						a_   :						str=torch.floataa      ,						a_   :						Optional[int]=0			)			->					Dict:
										snake_case:    Tuple      							=torch.Generator(device=a_			).manual_seed(a_			)
										snake_case:    int      							=np.random.RandomState(a_			).standard_normal((1, 4, 6_4, 6_4)			)
										snake_case:    str      							=torch.from_numpy(a_			).to(device=a_      ,						dtype=a_			)
										snake_case:    Optional[Any]      							={
										    'prompt': 'a photograph of an astronaut riding a horse',
										    'latents': latents,
										    'generator': generator,
										    'num_inference_steps': 5_0,
										    'guidance_scale': 7.5,
										    'output_type': 'numpy',
										}
										return inputs


							def    UpperCamelCase     (				self   :						Any			)			->					Any:
										snake_case:    Optional[int]      							=StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'			).to(a_			)
										ldmad_pipe.set_progress_bar_config(disable=a_			)

										snake_case:    Dict      							=self.get_inputs(a_			)
										snake_case:    str      							=ldmad_pipe(**a_			)
										snake_case							,  snake_case:    Optional[int]      							=output.rgb, output.depth

										snake_case:    Optional[int]      							=0.4_9_5_5_8_6
										snake_case:    int      							=0.3_3_7_9_5_5_1_5
										snake_case:    List[Any]      							=1_1_2.4_8_5_1_8
										snake_case:    Optional[Any]      							=9_8.4_8_9_7_4_6
										assert np.abs(expected_rgb_mean - rgb.mean()			) < 1E-3
										assert np.abs(expected_rgb_std - rgb.std()			) < 1E-3
										assert np.abs(expected_depth_mean - depth.mean()			) < 1E-3
										assert np.abs(expected_depth_std - depth.std()			) < 1E-3




							def    UpperCamelCase     (				self   :						Optional[int]			)			->					List[str]:
										snake_case:    Tuple      							=StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c'			).to(a_			)
										ldmad_pipe.set_progress_bar_config(disable=a_			)

										snake_case:    int      							=self.get_inputs(a_			)
										snake_case:    Dict      							=ldmad_pipe(**a_			)
										snake_case							,  snake_case:    str      							=output.rgb, output.depth

										snake_case:    List[Any]      							=0.4_1_9_4_1_2_7
										snake_case:    Union[str, Any]      							=0.3_5_3_7_5_5_8_6
										snake_case:    int      							=0.5_6_3_8_5_0_2
										snake_case:    Optional[int]      							=0.3_4_6_8_6_1_0_3

										assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
										assert depth.shape == (1, 5_1_2, 5_1_2, 1)
										assert np.abs(expected_rgb_mean - rgb.mean()			) < 1E-3
										assert np.abs(expected_rgb_std - rgb.std()			) < 1E-3
										assert np.abs(expected_depth_mean - depth.mean()			) < 1E-3
										assert np.abs(expected_depth_std - depth.std()			) < 1E-3









'''simple docstring'''

import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch

if is_datasets_available():
	from datasets import load_dataset

a							   =				random.Random()







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=1.0				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None					)				->					List[str]:





			"""simple docstring"""







			if rng is None:
						snake_case:    Dict      							=global_rng

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

			return values






class 						a_		(     unittest.TestCase    ):


							def __init__(				self   :						int      ,						a_   :						Any      ,						a_   :						str=7      ,						a_   :						Tuple=4_0_0      ,						a_   :						List[Any]=2_0_0_0      ,						a_   :						str=2_0_4_8      ,						a_   :						List[str]=1_2_8      ,						a_   :						int=1      ,						a_   :						Tuple=5_1_2      ,						a_   :						Dict=3_0      ,						a_   :						Optional[int]=4_4_1_0_0      ,						)			->					Union[str, Any]:
										snake_case:    Union[str, Any]      							=parent
										snake_case:    Optional[Any]      							=batch_size
										snake_case:    Union[str, Any]      							=min_seq_length
										snake_case:    List[str]      							=max_seq_length
										snake_case:    str      							=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										snake_case:    int      							=spectrogram_length
										snake_case:    List[str]      							=feature_size
										snake_case:    Dict      							=num_audio_channels
										snake_case:    int      							=hop_length
										snake_case:    List[Any]      							=chunk_length
										snake_case:    Optional[Any]      							=sampling_rate


							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										return {
										    "spectrogram_length": self.spectrogram_length,
										    "feature_size": self.feature_size,
										    "num_audio_channels": self.num_audio_channels,
										    "hop_length": self.hop_length,
										    "chunk_length": self.chunk_length,
										    "sampling_rate": self.sampling_rate,
										}




							def    UpperCamelCase     (				self   :						Tuple      ,						a_   :						Optional[Any]=False      ,						a_   :						Tuple=False			)			->					Optional[int]:
										def _flatten(a_   :						Dict			):
													return list(itertools.chain(*a_			)			)

										if equal_length:
													snake_case:    Union[str, Any]      							=[floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
										else:
													# make sure that inputs increase in size
													snake_case:    Union[str, 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:
													snake_case:    Any      							=[np.asarray(a_			) for x in speech_inputs]
										return speech_inputs






@require_torch
@require_torchaudio
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		Optional[Any]        =				TvltFeatureExtractor


							def    UpperCamelCase     (				self   :						int			)			->					int:
										snake_case:    Union[str, Any]      							=TvltFeatureExtractionTester(self			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					str:
										snake_case:    int      							=self.feature_extraction_class(**self.feat_extract_dict			)
										self.assertTrue(hasattr(a_      ,						'spectrogram_length'			)			)
										self.assertTrue(hasattr(a_      ,						'feature_size'			)			)
										self.assertTrue(hasattr(a_      ,						'num_audio_channels'			)			)
										self.assertTrue(hasattr(a_      ,						'hop_length'			)			)
										self.assertTrue(hasattr(a_      ,						'chunk_length'			)			)
										self.assertTrue(hasattr(a_      ,						'sampling_rate'			)			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					Optional[int]:
										snake_case:    Tuple      							=self.feature_extraction_class(**self.feat_extract_dict			)

										with tempfile.TemporaryDirectory() as tmpdirname:
													snake_case:    Optional[Any]      							=feat_extract_first.save_pretrained(a_			)[0]
													check_json_file_has_correct_format(a_			)
													snake_case:    str      							=self.feature_extraction_class.from_pretrained(a_			)

										snake_case:    Optional[int]      							=feat_extract_first.to_dict()
										snake_case:    List[str]      							=feat_extract_second.to_dict()
										snake_case:    Optional[int]      							=dict_first.pop('mel_filters'			)
										snake_case:    List[Any]      							=dict_second.pop('mel_filters'			)
										self.assertTrue(np.allclose(a_      ,						a_			)			)
										self.assertEqual(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						str			)			->					str:
										snake_case:    Any      							=self.feature_extraction_class(**self.feat_extract_dict			)

										with tempfile.TemporaryDirectory() as tmpdirname:
													snake_case:    int      							=os.path.join(a_      ,						'feat_extract.json'			)
													feat_extract_first.to_json_file(a_			)
													snake_case:    Union[str, Any]      							=self.feature_extraction_class.from_json_file(a_			)

										snake_case:    Optional[Any]      							=feat_extract_first.to_dict()
										snake_case:    Optional[Any]      							=feat_extract_second.to_dict()
										snake_case:    Union[str, Any]      							=dict_first.pop('mel_filters'			)
										snake_case:    Union[str, Any]      							=dict_second.pop('mel_filters'			)
										self.assertTrue(np.allclose(a_      ,						a_			)			)
										self.assertEqual(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					List[str]:
										# Initialize feature_extractor
										snake_case:    Tuple      							=self.feature_extraction_class(**self.feat_extract_dict			)

										# create three inputs of length 800, 1000, and 1200
										snake_case:    Any      							=[floats_list((1, x)			)[0] for x in range(8_0_0      ,						1_4_0_0      ,						2_0_0			)]
										snake_case:    Optional[Any]      							=[np.asarray(a_			) for speech_input in speech_inputs]

										# Test not batched input
										snake_case:    List[Any]      							=feature_extractor(np_speech_inputs[0]      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0			).audio_values

										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

										# Test batched
										snake_case:    Any      							=feature_extractor(a_      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0			).audio_values

										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

										# Test audio masking
										snake_case:    Optional[Any]      							=feature_extractor(
										    a_      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0      ,						mask_audio=a_			).audio_values

										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

										# Test 2-D numpy arrays are batched.
										snake_case:    List[Any]      							=[floats_list((1, x)			)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
										snake_case:    List[str]      							=np.asarray(a_			)
										snake_case:    str      							=feature_extractor(a_      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0			).audio_values
										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						str			)			->					Union[str, Any]:
										snake_case:    Dict      							=load_dataset('hf-internal-testing/librispeech_asr_dummy'      ,						'clean'      ,						split='validation'			)
										# automatic decoding with librispeech
										snake_case:    Optional[int]      							=ds.sort('id'			).select(range(a_			)			)[:num_samples]['audio']

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




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[int]:
										snake_case:    Tuple      							=self._load_datasamples(1			)
										snake_case:    Any      							=TvltFeatureExtractor()
										snake_case:    Any      							=feature_extractor(a_      ,						return_tensors='pt'			).audio_values

										self.assertEquals(audio_values.shape      ,						(1, 1, 1_9_2, 1_2_8)			)

										snake_case:    int      							=torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]			)
										self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      ,						a_      ,						atol=1E-4			)			)




