




# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()




class    __lowerCAmelCase       (							lowerCAmelCase      , lowerCAmelCase      , lowerCAmelCase      , unittest.TestCase):
       _a 					=						StableDiffusionControlNetImgaImgPipeline
       _a 					=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
       _a 					=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       _a 					=						IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''})
       _a 					=						IMAGE_TO_IMAGE_IMAGE_PARAMS






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         torch.manual_seed(0				)
         lowercase    :Union[str, Any]      	=						UNetaDConditionModel(
             block_out_channels=(32, 64)       ,					layers_per_block=2       ,					sample_size=32       ,					in_channels=4       ,					out_channels=4       ,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,					cross_attention_dim=32       ,					)
         torch.manual_seed(0				)
         lowercase    :Optional[int]      	=						ControlNetModel(
             block_out_channels=(32, 64)       ,					layers_per_block=2       ,					in_channels=4       ,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,					cross_attention_dim=32       ,					conditioning_embedding_out_channels=(16, 32)       ,					)
         torch.manual_seed(0				)
         lowercase    :Tuple      	=						DDIMScheduler(
             beta_start=0.0_00_85       ,					beta_end=0.0_12       ,					beta_schedule="scaled_linear"       ,					clip_sample=_lowerCAmelCase       ,					set_alpha_to_one=_lowerCAmelCase       ,					)
         torch.manual_seed(0				)
         lowercase    :List[Any]      	=						AutoencoderKL(
             block_out_channels=[32, 64]       ,					in_channels=3       ,					out_channels=3       ,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       ,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       ,					latent_channels=4       ,					)
         torch.manual_seed(0				)
         lowercase    :Optional[int]      	=						CLIPTextConfig(
             bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=32       ,					intermediate_size=37       ,					layer_norm_eps=1e-0_5       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=10_00       ,					)
         lowercase    :str      	=						CLIPTextModel(_lowerCAmelCase				)
         lowercase    :Any      	=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

         lowercase    :Dict      	=						{
             "unet": unet,
             "controlnet": controlnet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "safety_checker": None,
             "feature_extractor": None,
         }
         return components






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					Dict       ,					_lowerCAmelCase:					Tuple=0				):
         if str(_lowerCAmelCase				).startswith("mps"				):
           lowercase    :int      	=						torch.manual_seed(_lowerCAmelCase				)
         else:
           lowercase    :Union[str, Any]      	=						torch.Generator(device=_lowerCAmelCase				).manual_seed(_lowerCAmelCase				)

         lowercase    :int      	=						2
         lowercase    :Optional[int]      	=						randn_tensor(
             (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,					generator=_lowerCAmelCase       ,					device=torch.device(_lowerCAmelCase				)       ,					)
         lowercase    :Optional[Any]      	=						floats_tensor(control_image.shape       ,					rng=random.Random(_lowerCAmelCase				)				).to(_lowerCAmelCase				)
         lowercase    :List[str]      	=						image.cpu().permute(0       ,					2       ,					3       ,					1				)[0]
         lowercase    :int      	=						Image.fromarray(np.uinta(_lowerCAmelCase				)				).convert("RGB"				).resize((64, 64)				)
         lowercase    :Optional[Any]      	=						{
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
             "output_type": "numpy",
             "image": image,
             "control_image": control_image,
         }

         return inputs






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3				)






       @unittest.skipIf(
           torch_device != "cuda" or not is_xformers_available()       ,					reason="XFormers attention is only available with CUDA and `xformers` installed"       ,					)
       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3				)







class    __lowerCAmelCase       (							lowerCAmelCase      , lowerCAmelCase      , unittest.TestCase):
       _a 					=						StableDiffusionControlNetImgaImgPipeline
       _a 					=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
       _a 					=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       _a 					=						frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         torch.manual_seed(0				)
         lowercase    :Optional[Any]      	=						UNetaDConditionModel(
             block_out_channels=(32, 64)       ,					layers_per_block=2       ,					sample_size=32       ,					in_channels=4       ,					out_channels=4       ,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,					cross_attention_dim=32       ,					)
         torch.manual_seed(0				)

         def init_weights(_lowerCAmelCase:					List[str]				):
           if isinstance(_lowerCAmelCase       ,					torch.nn.Convad				):
             torch.nn.init.normal(m.weight				)
             m.bias.data.fill_(1.0				)

         lowercase    :Tuple      	=						ControlNetModel(
             block_out_channels=(32, 64)       ,					layers_per_block=2       ,					in_channels=4       ,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,					cross_attention_dim=32       ,					conditioning_embedding_out_channels=(16, 32)       ,					)
         controlneta.controlnet_down_blocks.apply(_lowerCAmelCase				)

         torch.manual_seed(0				)
         lowercase    :Tuple      	=						ControlNetModel(
             block_out_channels=(32, 64)       ,					layers_per_block=2       ,					in_channels=4       ,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,					cross_attention_dim=32       ,					conditioning_embedding_out_channels=(16, 32)       ,					)
         controlneta.controlnet_down_blocks.apply(_lowerCAmelCase				)

         torch.manual_seed(0				)
         lowercase    :str      	=						DDIMScheduler(
             beta_start=0.0_00_85       ,					beta_end=0.0_12       ,					beta_schedule="scaled_linear"       ,					clip_sample=_lowerCAmelCase       ,					set_alpha_to_one=_lowerCAmelCase       ,					)
         torch.manual_seed(0				)
         lowercase    :int      	=						AutoencoderKL(
             block_out_channels=[32, 64]       ,					in_channels=3       ,					out_channels=3       ,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       ,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       ,					latent_channels=4       ,					)
         torch.manual_seed(0				)
         lowercase    :Optional[Any]      	=						CLIPTextConfig(
             bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=32       ,					intermediate_size=37       ,					layer_norm_eps=1e-0_5       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=10_00       ,					)
         lowercase    :Any      	=						CLIPTextModel(_lowerCAmelCase				)
         lowercase    :List[Any]      	=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)

         lowercase    :List[str]      	=						MultiControlNetModel([controlneta, controlneta]				)

         lowercase    :Dict      	=						{
             "unet": unet,
             "controlnet": controlnet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "safety_checker": None,
             "feature_extractor": None,
         }
         return components






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict       ,					_lowerCAmelCase:					str       ,					_lowerCAmelCase:					Union[str, Any]=0				):
         if str(_lowerCAmelCase				).startswith("mps"				):
           lowercase    :Optional[int]      	=						torch.manual_seed(_lowerCAmelCase				)
         else:
           lowercase    :List[Any]      	=						torch.Generator(device=_lowerCAmelCase				).manual_seed(_lowerCAmelCase				)

         lowercase    :Optional[int]      	=						2

         lowercase    :int      	=						[
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,					generator=_lowerCAmelCase       ,					device=torch.device(_lowerCAmelCase				)       ,					),
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,					generator=_lowerCAmelCase       ,					device=torch.device(_lowerCAmelCase				)       ,					),
         ]

         lowercase    :Tuple      	=						floats_tensor(control_image[0].shape       ,					rng=random.Random(_lowerCAmelCase				)				).to(_lowerCAmelCase				)
         lowercase    :Any      	=						image.cpu().permute(0       ,					2       ,					3       ,					1				)[0]
         lowercase    :Any      	=						Image.fromarray(np.uinta(_lowerCAmelCase				)				).convert("RGB"				).resize((64, 64)				)
         lowercase    :List[Any]      	=						{
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
             "output_type": "numpy",
             "image": image,
             "control_image": control_image,
         }

         return inputs






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         lowercase    :str      	=						self.get_dummy_components()
         lowercase    :str      	=						self.pipeline_class(**_lowerCAmelCase				)
         pipe.to(_lowerCAmelCase				)

         lowercase    :Optional[Any]      	=						10.0
         lowercase    :str      	=						4

         lowercase    :List[str]      	=						self.get_dummy_inputs(_lowerCAmelCase				)
         lowercase    :Optional[int]      	=						steps
         lowercase    :Any      	=						scale
         lowercase    :Dict      	=						pipe(**_lowerCAmelCase				)[0]

         lowercase    :Optional[int]      	=						self.get_dummy_inputs(_lowerCAmelCase				)
         lowercase    :Any      	=						steps
         lowercase    :Dict      	=						scale
         lowercase    :List[str]      	=						pipe(**_lowerCAmelCase       ,					control_guidance_start=0.1       ,					control_guidance_end=0.2				)[0]

         lowercase    :Dict      	=						self.get_dummy_inputs(_lowerCAmelCase				)
         lowercase    :Optional[int]      	=						steps
         lowercase    :Union[str, Any]      	=						scale
         lowercase    :Optional[Any]      	=						pipe(**_lowerCAmelCase       ,					control_guidance_start=[0.1, 0.3]       ,					control_guidance_end=[0.2, 0.7]				)[0]

         lowercase    :Optional[int]      	=						self.get_dummy_inputs(_lowerCAmelCase				)
         lowercase    :Tuple      	=						steps
         lowercase    :Any      	=						scale
         lowercase    :Dict      	=						pipe(**_lowerCAmelCase       ,					control_guidance_start=0.4       ,					control_guidance_end=[0.5, 0.8]				)[0]

         # make sure that all outputs are different
         assert np.sum(np.abs(output_a - output_a				)				) > 1e-3
         assert np.sum(np.abs(output_a - output_a				)				) > 1e-3
         assert np.sum(np.abs(output_a - output_a				)				) > 1e-3






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3				)






       @unittest.skipIf(
           torch_device != "cuda" or not is_xformers_available()       ,					reason="XFormers attention is only available with CUDA and `xformers` installed"       ,					)
       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :str      	=						self.get_dummy_components()
         lowercase    :str      	=						self.pipeline_class(**_lowerCAmelCase				)
         pipe.to(_lowerCAmelCase				)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase				)
         with tempfile.TemporaryDirectory() as tmpdir:
           try:
             # save_pretrained is not implemented for Multi-ControlNet
             pipe.save_pretrained(_lowerCAmelCase				)
           except NotImplementedError:
             pass







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






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Optional[Any]      	=						ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"				)

         lowercase    :List[str]      	=						StableDiffusionControlNetImgaImgPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"       ,					safety_checker=_lowerCAmelCase       ,					controlnet=_lowerCAmelCase				)
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=_lowerCAmelCase				)

         lowercase    :Tuple      	=						torch.Generator(device="cpu"				).manual_seed(0				)
         lowercase    :Optional[Any]      	=						"evil space-punk bird"
         lowercase    :str      	=						load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"				).resize((5_12, 5_12)				)
         lowercase    :str      	=						load_image(
             "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"				).resize((5_12, 5_12)				)

         lowercase    :Optional[int]      	=						pipe(
             _lowerCAmelCase       ,					_lowerCAmelCase       ,					control_image=_lowerCAmelCase       ,					generator=_lowerCAmelCase       ,					output_type="np"       ,					num_inference_steps=50       ,					strength=0.6       ,					)

         lowercase    :int      	=						output.images[0]

         assert image.shape == (5_12, 5_12, 3)

         lowercase    :Tuple      	=						load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"				)

         assert np.abs(expected_image - image				).max() < 9e-2









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

_UpperCAmelCase	:						List[str]			=     random.Random()



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=1.0,			lowerCamelCase=None,			lowerCamelCase=None   ):
  if rng is None:
    lowercase    :Union[str, Any]      	=						global_rng

  lowercase    :Any      	=						[]
  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):






       def __init__(   self:					Optional[Any]       ,					_lowerCAmelCase:					Tuple       ,					_lowerCAmelCase:					Union[str, Any]=7       ,					_lowerCAmelCase:					str=4_00       ,					_lowerCAmelCase:					List[str]=20_00       ,					_lowerCAmelCase:					Dict=20_48       ,					_lowerCAmelCase:					Any=1_28       ,					_lowerCAmelCase:					Any=1       ,					_lowerCAmelCase:					List[Any]=5_12       ,					_lowerCAmelCase:					Optional[int]=30       ,					_lowerCAmelCase:					List[Any]=4_41_00       ,					):
         lowercase    :Any      	=						parent
         lowercase    :Any      	=						batch_size
         lowercase    :Optional[int]      	=						min_seq_length
         lowercase    :Optional[Any]      	=						max_seq_length
         lowercase    :Dict      	=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         lowercase    :List[str]      	=						spectrogram_length
         lowercase    :int      	=						feature_size
         lowercase    :Union[str, Any]      	=						num_audio_channels
         lowercase    :Optional[int]      	=						hop_length
         lowercase    :str      	=						chunk_length
         lowercase    :List[str]      	=						sampling_rate






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         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 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					int=False       ,					_lowerCAmelCase:					int=False				):
         def _flatten(_lowerCAmelCase:					Optional[Any]				):
           return list(itertools.chain(*_lowerCAmelCase				)				)

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







@require_torch
@require_torchaudio
class    __lowerCAmelCase       (							lowerCAmelCase      , unittest.TestCase):
       _a 					=						TvltFeatureExtractor






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Dict      	=						TvltFeatureExtractionTester(self				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :List[Any]      	=						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 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :Any      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase    :Tuple      	=						feat_extract_first.save_pretrained(_lowerCAmelCase				)[0]
           check_json_file_has_correct_format(_lowerCAmelCase				)
           lowercase    :Tuple      	=						self.feature_extraction_class.from_pretrained(_lowerCAmelCase				)

         lowercase    :Optional[Any]      	=						feat_extract_first.to_dict()
         lowercase    :List[str]      	=						feat_extract_second.to_dict()
         lowercase    :Any      	=						dict_first.pop("mel_filters"				)
         lowercase    :Any      	=						dict_second.pop("mel_filters"				)
         self.assertTrue(np.allclose(_lowerCAmelCase       ,					_lowerCAmelCase				)				)
         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :Union[str, Any]      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase    :Any      	=						os.path.join(_lowerCAmelCase       ,					"feat_extract.json"				)
           feat_extract_first.to_json_file(_lowerCAmelCase				)
           lowercase    :List[Any]      	=						self.feature_extraction_class.from_json_file(_lowerCAmelCase				)

         lowercase    :Tuple      	=						feat_extract_first.to_dict()
         lowercase    :int      	=						feat_extract_second.to_dict()
         lowercase    :Tuple      	=						dict_first.pop("mel_filters"				)
         lowercase    :Tuple      	=						dict_second.pop("mel_filters"				)
         self.assertTrue(np.allclose(_lowerCAmelCase       ,					_lowerCAmelCase				)				)
         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         # Initialize feature_extractor
         lowercase    :int      	=						self.feature_extraction_class(**self.feat_extract_dict				)

         # create three inputs of length 800, 1000, and 1200
         lowercase    :List[Any]      	=						[floats_list((1, x)				)[0] for x in range(8_00       ,					14_00       ,					2_00				)]
         lowercase    :Union[str, Any]      	=						[np.asarray(_lowerCAmelCase				) for speech_input in speech_inputs]

         # Test not batched input
         lowercase    :Any      	=						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
         lowercase    :Optional[int]      	=						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
         lowercase    :Dict      	=						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.
         lowercase    :Optional[int]      	=						[floats_list((1, x)				)[0] for x in (8_00, 8_00, 8_00)]
         lowercase    :Union[str, Any]      	=						np.asarray(_lowerCAmelCase				)
         lowercase    :Dict      	=						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 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]       ,					_lowerCAmelCase:					Optional[Any]				):
         lowercase    :str      	=						load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,					"clean"       ,					split="validation"				)
         # automatic decoding with librispeech
         lowercase    :Any      	=						ds.sort("id"				).select(range(_lowerCAmelCase				)				)[:num_samples]["audio"]

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         lowercase    :Optional[int]      	=						self._load_datasamples(1				)
         lowercase    :Union[str, Any]      	=						TvltFeatureExtractor()
         lowercase    :str      	=						feature_extractor(_lowerCAmelCase       ,					return_tensors="pt"				).audio_values

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

         lowercase    :Optional[int]      	=						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				)				)



