"""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

SCREAMING_SNAKE_CASE__      						=  random.Random()





def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				Tuple=1.0 ,			_UpperCamelCase       :				Optional[int]=None ,			_UpperCamelCase       :				List[Any]=None					)					->				Optional[Any]:





						"""simple docstring"""






						if rng is None:
												snake_case								=			global_rng

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

						return values



class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=7		,    lowerCAmelCase=4_00		,    lowerCAmelCase=20_00		,    lowerCAmelCase=20_48		,    lowerCAmelCase=1_28		,    lowerCAmelCase=1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=30		,    lowerCAmelCase=4_41_00		,    ):






										"""simple docstring"""

										snake_case								=			parent
										snake_case								=			batch_size
										snake_case								=			min_seq_length
										snake_case								=			max_seq_length
										snake_case								=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										snake_case								=			spectrogram_length
										snake_case								=			feature_size
										snake_case								=			num_audio_channels
										snake_case								=			hop_length
										snake_case								=			chunk_length
										snake_case								=			sampling_rate

				def 		snake_case (				self    ):






										"""simple docstring"""

										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 		snake_case (				self		,    lowerCAmelCase=False		,    lowerCAmelCase=False    ):






										"""simple docstring"""

										def _flatten(lowerCAmelCase    ):
																return list(itertools.chain(*lowerCAmelCase    )    )

										if equal_length:
																snake_case								=			[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								=			[
																    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								=			[np.asarray(lowerCAmelCase    ) for x in speech_inputs]
										return speech_inputs





@require_torch
@require_torchaudio
class   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						TvltFeatureExtractor

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			TvltFeatureExtractionTester(self    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.feature_extraction_class(**self.feat_extract_dict    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'spectrogram_length'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'feature_size'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'num_audio_channels'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'hop_length'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'chunk_length'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'sampling_rate'    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.feature_extraction_class(**self.feat_extract_dict    )

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

										snake_case								=			feat_extract_first.to_dict()
										snake_case								=			feat_extract_second.to_dict()
										snake_case								=			dict_first.pop('mel_filters'    )
										snake_case								=			dict_second.pop('mel_filters'    )
										self.assertTrue(np.allclose(lowerCAmelCase		,    lowerCAmelCase    )    )
										self.assertEqual(lowerCAmelCase		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.feature_extraction_class(**self.feat_extract_dict    )

										with tempfile.TemporaryDirectory() as tmpdirname:
																snake_case								=			os.path.join(lowerCAmelCase		,    'feat_extract.json'    )
																feat_extract_first.to_json_file(lowerCAmelCase    )
																snake_case								=			self.feature_extraction_class.from_json_file(lowerCAmelCase    )

										snake_case								=			feat_extract_first.to_dict()
										snake_case								=			feat_extract_second.to_dict()
										snake_case								=			dict_first.pop('mel_filters'    )
										snake_case								=			dict_second.pop('mel_filters'    )
										self.assertTrue(np.allclose(lowerCAmelCase		,    lowerCAmelCase    )    )
										self.assertEqual(lowerCAmelCase		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.feature_extraction_class(**self.feat_extract_dict    )

										# create three inputs of length 800, 1000, and 1200
										snake_case								=			[floats_list((1, x)    )[0] for x in range(8_00		,    14_00		,    2_00    )]
										snake_case								=			[np.asarray(lowerCAmelCase    ) for speech_input in speech_inputs]

										# Test not batched input
										snake_case								=			feature_extractor(np_speech_inputs[0]		,    return_tensors='np'		,    sampling_rate=4_41_00    ).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								=			feature_extractor(lowerCAmelCase		,    return_tensors='np'		,    sampling_rate=4_41_00    ).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								=			feature_extractor(
										    lowerCAmelCase		,    return_tensors='np'		,    sampling_rate=4_41_00		,    mask_audio=lowerCAmelCase    ).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								=			[floats_list((1, x)    )[0] for x in (8_00, 8_00, 8_00)]
										snake_case								=			np.asarray(lowerCAmelCase    )
										snake_case								=			feature_extractor(lowerCAmelCase		,    return_tensors='np'		,    sampling_rate=4_41_00    ).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 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			load_dataset('hf-internal-testing/librispeech_asr_dummy'		,    'clean'		,    split='validation'    )
										# automatic decoding with librispeech
										snake_case								=			ds.sort('id'    ).select(range(lowerCAmelCase    )    )[:num_samples]['audio']

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self._load_datasamples(1    )
										snake_case								=			TvltFeatureExtractor()
										snake_case								=			feature_extractor(lowerCAmelCase		,    return_tensors='pt'    ).audio_values

										self.assertEquals(audio_values.shape		,    (1, 1, 1_92, 1_28)    )

										snake_case								=			torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]    )
										self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]		,    lowerCAmelCase		,    atol=1E-4    )    )



"""simple docstring"""
SCREAMING_SNAKE_CASE__      						=  "0.18.2"

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
					if not is_onnx_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_onnx_objects import *  # noqa F403
else:
					from .pipelines import OnnxRuntimeModel

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_pt_objects import *  # noqa F403
else:
					from .models import (
					    AutoencoderKL,
					    ControlNetModel,
					    ModelMixin,
					    PriorTransformer,
					    TaFilmDecoder,
					    TransformeraDModel,
					    UNetaDModel,
					    UNetaDConditionModel,
					    UNetaDModel,
					    UNetaDConditionModel,
					    VQModel,
					)
					from .optimization import (
					    get_constant_schedule,
					    get_constant_schedule_with_warmup,
					    get_cosine_schedule_with_warmup,
					    get_cosine_with_hard_restarts_schedule_with_warmup,
					    get_linear_schedule_with_warmup,
					    get_polynomial_decay_schedule_with_warmup,
					    get_scheduler,
					)
					from .pipelines import (
					    AudioPipelineOutput,
					    ConsistencyModelPipeline,
					    DanceDiffusionPipeline,
					    DDIMPipeline,
					    DDPMPipeline,
					    DiffusionPipeline,
					    DiTPipeline,
					    ImagePipelineOutput,
					    KarrasVePipeline,
					    LDMPipeline,
					    LDMSuperResolutionPipeline,
					    PNDMPipeline,
					    RePaintPipeline,
					    ScoreSdeVePipeline,
					)
					from .schedulers import (
					    CMStochasticIterativeScheduler,
					    DDIMInverseScheduler,
					    DDIMParallelScheduler,
					    DDIMScheduler,
					    DDPMParallelScheduler,
					    DDPMScheduler,
					    DEISMultistepScheduler,
					    DPMSolverMultistepInverseScheduler,
					    DPMSolverMultistepScheduler,
					    DPMSolverSinglestepScheduler,
					    EulerAncestralDiscreteScheduler,
					    EulerDiscreteScheduler,
					    HeunDiscreteScheduler,
					    IPNDMScheduler,
					    KarrasVeScheduler,
					    KDPMaAncestralDiscreteScheduler,
					    KDPMaDiscreteScheduler,
					    PNDMScheduler,
					    RePaintScheduler,
					    SchedulerMixin,
					    ScoreSdeVeScheduler,
					    UnCLIPScheduler,
					    UniPCMultistepScheduler,
					    VQDiffusionScheduler,
					)
					from .training_utils import EMAModel

try:
					if not (is_torch_available() and is_scipy_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
					from .schedulers import LMSDiscreteScheduler

try:
					if not (is_torch_available() and is_torchsde_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
					from .schedulers import DPMSolverSDEScheduler

try:
					if not (is_torch_available() and is_transformers_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipelines import (
					    AltDiffusionImgaImgPipeline,
					    AltDiffusionPipeline,
					    AudioLDMPipeline,
					    CycleDiffusionPipeline,
					    IFImgaImgPipeline,
					    IFImgaImgSuperResolutionPipeline,
					    IFInpaintingPipeline,
					    IFInpaintingSuperResolutionPipeline,
					    IFPipeline,
					    IFSuperResolutionPipeline,
					    ImageTextPipelineOutput,
					    KandinskyImgaImgPipeline,
					    KandinskyInpaintPipeline,
					    KandinskyPipeline,
					    KandinskyPriorPipeline,
					    KandinskyVaaControlnetImgaImgPipeline,
					    KandinskyVaaControlnetPipeline,
					    KandinskyVaaImgaImgPipeline,
					    KandinskyVaaInpaintPipeline,
					    KandinskyVaaPipeline,
					    KandinskyVaaPriorEmbaEmbPipeline,
					    KandinskyVaaPriorPipeline,
					    LDMTextToImagePipeline,
					    PaintByExamplePipeline,
					    SemanticStableDiffusionPipeline,
					    ShapEImgaImgPipeline,
					    ShapEPipeline,
					    StableDiffusionAttendAndExcitePipeline,
					    StableDiffusionControlNetImgaImgPipeline,
					    StableDiffusionControlNetInpaintPipeline,
					    StableDiffusionControlNetPipeline,
					    StableDiffusionDepthaImgPipeline,
					    StableDiffusionDiffEditPipeline,
					    StableDiffusionImageVariationPipeline,
					    StableDiffusionImgaImgPipeline,
					    StableDiffusionInpaintPipeline,
					    StableDiffusionInpaintPipelineLegacy,
					    StableDiffusionInstructPixaPixPipeline,
					    StableDiffusionLatentUpscalePipeline,
					    StableDiffusionLDMaDPipeline,
					    StableDiffusionModelEditingPipeline,
					    StableDiffusionPanoramaPipeline,
					    StableDiffusionParadigmsPipeline,
					    StableDiffusionPipeline,
					    StableDiffusionPipelineSafe,
					    StableDiffusionPixaPixZeroPipeline,
					    StableDiffusionSAGPipeline,
					    StableDiffusionUpscalePipeline,
					    StableUnCLIPImgaImgPipeline,
					    StableUnCLIPPipeline,
					    TextToVideoSDPipeline,
					    TextToVideoZeroPipeline,
					    UnCLIPImageVariationPipeline,
					    UnCLIPPipeline,
					    UniDiffuserModel,
					    UniDiffuserPipeline,
					    UniDiffuserTextDecoder,
					    VersatileDiffusionDualGuidedPipeline,
					    VersatileDiffusionImageVariationPipeline,
					    VersatileDiffusionPipeline,
					    VersatileDiffusionTextToImagePipeline,
					    VideoToVideoSDPipeline,
					    VQDiffusionPipeline,
					)

try:
					if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
					from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
					if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
					from .pipelines import StableDiffusionKDiffusionPipeline

try:
					if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
					from .pipelines import (
					    OnnxStableDiffusionImgaImgPipeline,
					    OnnxStableDiffusionInpaintPipeline,
					    OnnxStableDiffusionInpaintPipelineLegacy,
					    OnnxStableDiffusionPipeline,
					    OnnxStableDiffusionUpscalePipeline,
					    StableDiffusionOnnxPipeline,
					)

try:
					if not (is_torch_available() and is_librosa_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
					from .pipelines import AudioDiffusionPipeline, Mel

try:
					if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
					from .pipelines import SpectrogramDiffusionPipeline

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_flax_objects import *  # noqa F403
else:
					from .models.controlnet_flax import FlaxControlNetModel
					from .models.modeling_flax_utils import FlaxModelMixin
					from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
					from .models.vae_flax import FlaxAutoencoderKL
					from .pipelines import FlaxDiffusionPipeline
					from .schedulers import (
					    FlaxDDIMScheduler,
					    FlaxDDPMScheduler,
					    FlaxDPMSolverMultistepScheduler,
					    FlaxKarrasVeScheduler,
					    FlaxLMSDiscreteScheduler,
					    FlaxPNDMScheduler,
					    FlaxSchedulerMixin,
					    FlaxScoreSdeVeScheduler,
					)


try:
					if not (is_flax_available() and is_transformers_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
					from .pipelines import (
					    FlaxStableDiffusionControlNetPipeline,
					    FlaxStableDiffusionImgaImgPipeline,
					    FlaxStableDiffusionInpaintPipeline,
					    FlaxStableDiffusionPipeline,
					)

try:
					if not (is_note_seq_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_note_seq_objects import *  # noqa F403
else:
					from .pipelines import MidiProcessor


