





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


@skip_mps
class        __SCREAMING_SNAKE_CASE      (			a_					,a_					,unittest.TestCase ):
       _UpperCAmelCase				:  Dict			   =   StableDiffusionPanoramaPipeline
       _UpperCAmelCase				:  Any			   =   TEXT_TO_IMAGE_PARAMS
       _UpperCAmelCase				:  Optional[int]			   =   TEXT_TO_IMAGE_BATCH_PARAMS
       _UpperCAmelCase				:  Union[str, Any]			   =   TEXT_TO_IMAGE_IMAGE_PARAMS
       _UpperCAmelCase				:  List[Any]			   =   TEXT_TO_IMAGE_IMAGE_PARAMS







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Optional[int]:
         torch.manual_seed(0      )
         lowerCamelCase__       :						List[Any]        =							UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)		, layers_per_block=1		, 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		, )
         lowerCamelCase__       :						Tuple        =							DDIMScheduler()
         torch.manual_seed(0      )
         lowerCamelCase__       :						List[str]        =							AutoencoderKL(
             block_out_channels=[3_2, 6_4]		, in_channels=3		, out_channels=3		, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		, latent_channels=4		, )
         torch.manual_seed(0      )
         lowerCamelCase__       :						Optional[Any]        =							CLIPTextConfig(
             bos_token_id=0		, eos_token_id=2		, hidden_size=3_2		, intermediate_size=3_7		, layer_norm_eps=1e-05		, num_attention_heads=4		, num_hidden_layers=5		, pad_token_id=1		, vocab_size=1_0_0_0		, )
         lowerCamelCase__       :						Optional[int]        =							CLIPTextModel(lowercase_      )
         lowerCamelCase__       :						Optional[Any]        =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

         lowerCamelCase__       :						List[str]        =							{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     Dict		, A					:     Optional[Any]=0      )       ->Any:
         lowerCamelCase__       :						List[str]        =							torch.manual_seed(lowercase_      )
         lowerCamelCase__       :						str        =							{
             '''prompt''': '''a photo of the dolomites''',
             '''generator''': generator,
             # Setting height and width to None to prevent OOMs on CPU.
             '''height''': None,
             '''width''': None,
             '''num_inference_steps''': 1,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }
         return inputs







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Optional[int]:
         lowerCamelCase__       :						Tuple        =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         lowerCamelCase__       :						List[str]        =							self.get_dummy_components()
         lowerCamelCase__       :						int        =							StableDiffusionPanoramaPipeline(**lowercase_      )
         lowerCamelCase__       :						Any        =							sd_pipe.to(lowercase_      )
         sd_pipe.set_progress_bar_config(disable=lowercase_      )

         lowerCamelCase__       :						Optional[Any]        =							self.get_dummy_inputs(lowercase_      )
         lowerCamelCase__       :						int        =							sd_pipe(**lowercase_      ).images
         lowerCamelCase__       :						List[str]        =							image[0, -3:, -3:, -1]
         assert image.shape == (1, 6_4, 6_4, 3)

         lowerCamelCase__       :						Dict        =							np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Optional[Any]:
         super().test_inference_batch_consistent(batch_sizes=[1, 2]      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Union[str, Any]:
         super().test_inference_batch_single_identical(batch_size=2		, expected_max_diff=3.25e-3      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->str:
         lowerCamelCase__       :						Dict        =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         lowerCamelCase__       :						Any        =							self.get_dummy_components()
         lowerCamelCase__       :						Union[str, Any]        =							StableDiffusionPanoramaPipeline(**lowercase_      )
         lowerCamelCase__       :						str        =							sd_pipe.to(lowercase_      )
         sd_pipe.set_progress_bar_config(disable=lowercase_      )

         lowerCamelCase__       :						Optional[int]        =							self.get_dummy_inputs(lowercase_      )
         lowerCamelCase__       :						Any        =							'''french fries'''
         lowerCamelCase__       :						Optional[int]        =							sd_pipe(**lowercase_		, negative_prompt=lowercase_      )
         lowerCamelCase__       :						str        =							output.images
         lowerCamelCase__       :						str        =							image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         lowerCamelCase__       :						List[str]        =							np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2







       def 						__lowerCamelCase   (				self					:     int      )       ->str:
         lowerCamelCase__       :						int        =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         lowerCamelCase__       :						List[str]        =							self.get_dummy_components()
         lowerCamelCase__       :						int        =							StableDiffusionPanoramaPipeline(**lowercase_      )
         lowerCamelCase__       :						Optional[int]        =							sd_pipe.to(lowercase_      )
         sd_pipe.set_progress_bar_config(disable=lowercase_      )

         lowerCamelCase__       :						str        =							self.get_dummy_inputs(lowercase_      )
         lowerCamelCase__       :						Any        =							sd_pipe(**lowercase_		, view_batch_size=2      )
         lowerCamelCase__       :						List[Any]        =							output.images
         lowerCamelCase__       :						Union[str, Any]        =							image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         lowerCamelCase__       :						Optional[int]        =							np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2







       def 						__lowerCamelCase   (				self					:     str      )       ->Optional[int]:
         lowerCamelCase__       :						List[Any]        =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         lowerCamelCase__       :						List[Any]        =							self.get_dummy_components()
         lowerCamelCase__       :						Union[str, Any]        =							EulerAncestralDiscreteScheduler(
             beta_start=0.0_00_85		, beta_end=0.0_12		, beta_schedule='''scaled_linear'''      )
         lowerCamelCase__       :						Dict        =							StableDiffusionPanoramaPipeline(**lowercase_      )
         lowerCamelCase__       :						List[str]        =							sd_pipe.to(lowercase_      )
         sd_pipe.set_progress_bar_config(disable=lowercase_      )

         lowerCamelCase__       :						Union[str, Any]        =							self.get_dummy_inputs(lowercase_      )
         lowerCamelCase__       :						List[Any]        =							sd_pipe(**lowercase_      ).images
         lowerCamelCase__       :						Optional[int]        =							image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         lowerCamelCase__       :						Any        =							np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[Any]:
         lowerCamelCase__       :						Optional[int]        =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         lowerCamelCase__       :						Optional[Any]        =							self.get_dummy_components()
         lowerCamelCase__       :						Optional[int]        =							PNDMScheduler(
             beta_start=0.0_00_85		, beta_end=0.0_12		, beta_schedule='''scaled_linear'''		, skip_prk_steps=lowercase_      )
         lowerCamelCase__       :						List[Any]        =							StableDiffusionPanoramaPipeline(**lowercase_      )
         lowerCamelCase__       :						List[str]        =							sd_pipe.to(lowercase_      )
         sd_pipe.set_progress_bar_config(disable=lowercase_      )

         lowerCamelCase__       :						Optional[Any]        =							self.get_dummy_inputs(lowercase_      )
         lowerCamelCase__       :						int        =							sd_pipe(**lowercase_      ).images
         lowerCamelCase__       :						List[str]        =							image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         lowerCamelCase__       :						List[Any]        =							np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2


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







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







       def 						__lowerCamelCase   (				self					:     int		, A					:     Any=0      )       ->List[str]:
         lowerCamelCase__       :						Optional[Any]        =							torch.manual_seed(lowercase_      )
         lowerCamelCase__       :						Tuple        =							{
             '''prompt''': '''a photo of the dolomites''',
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs







       def 						__lowerCamelCase   (				self					:     int      )       ->str:
         lowerCamelCase__       :						Any        =							'''stabilityai/stable-diffusion-2-base'''
         lowerCamelCase__       :						str        =							DDIMScheduler.from_pretrained(lowercase_		, subfolder='''scheduler'''      )
         lowerCamelCase__       :						Tuple        =							StableDiffusionPanoramaPipeline.from_pretrained(lowercase_		, scheduler=lowercase_		, safety_checker=lowercase_      )
         pipe.to(lowercase_      )
         pipe.set_progress_bar_config(disable=lowercase_      )
         pipe.enable_attention_slicing()

         lowerCamelCase__       :						List[Any]        =							self.get_inputs()
         lowerCamelCase__       :						Union[str, Any]        =							pipe(**lowercase_      ).images
         lowerCamelCase__       :						int        =							image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 2_0_4_8, 3)

         lowerCamelCase__       :						Optional[Any]        =							np.array(
             [
                 0.36_96_83_92,
                 0.27_02_53_72,
                 0.32_44_67_66,
                 0.28_37_93_87,
                 0.36_36_32_74,
                 0.30_73_33_47,
                 0.27_10_00_27,
                 0.27_05_41_25,
                 0.25_53_60_96,
             ]      )

         assert np.abs(expected_slice - image_slice      ).max() < 1e-2







       def 						__lowerCamelCase   (				self					:     Any      )       ->Optional[Any]:
         lowerCamelCase__       :						Optional[int]        =							StableDiffusionPanoramaPipeline.from_pretrained(
             '''stabilityai/stable-diffusion-2-base'''		, safety_checker=lowercase_      )
         lowerCamelCase__       :						List[str]        =							LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
         pipe.to(lowercase_      )
         pipe.set_progress_bar_config(disable=lowercase_      )
         pipe.enable_attention_slicing()

         lowerCamelCase__       :						str        =							self.get_inputs()
         lowerCamelCase__       :						List[Any]        =							pipe(**lowercase_      ).images
         lowerCamelCase__       :						Tuple        =							image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 2_0_4_8, 3)

         lowerCamelCase__       :						int        =							np.array(
             [
                 [
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                     0.0,
                 ]
             ]      )

         assert np.abs(expected_slice - image_slice      ).max() < 1e-3







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->int:
         lowerCamelCase__       :						Optional[Any]        =							0

         def callback_fn(A					:     int		, A					:     int		, A					:     torch.FloatTensor      ) -> None:
           lowerCamelCase__       :						str        =							True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 1:
             lowerCamelCase__       :						Any        =							latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 6_4, 2_5_6)
             lowerCamelCase__       :						Optional[int]        =							latents[0, -3:, -3:, -1]

             lowerCamelCase__       :						List[str]        =							np.array(
                 [
                     0.18_68_18_69,
                     0.33_90_78_16,
                     0.5_36_12_76,
                     0.14_43_28_65,
                     -0.02_85_66_11,
                     -0.73_94_11_23,
                     0.23_39_79_87,
                     0.47_32_26_82,
                     -0.37_82_31_64,
                 ]      )
             assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2
           elif step == 2:
             lowerCamelCase__       :						Optional[Any]        =							latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 6_4, 2_5_6)
             lowerCamelCase__       :						Dict        =							latents[0, -3:, -3:, -1]

             lowerCamelCase__       :						Tuple        =							np.array(
                 [
                     0.18_53_96_45,
                     0.33_98_72_48,
                     0.5_37_85_59,
                     0.14_43_71_42,
                     -0.02_45_52_61,
                     -0.7_33_83_17,
                     0.23_99_07_55,
                     0.47_35_62_72,
                     -0.3_78_65_05,
                 ]      )

             assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2

         lowerCamelCase__       :						Union[str, Any]        =							False

         lowerCamelCase__       :						Tuple        =							'''stabilityai/stable-diffusion-2-base'''
         lowerCamelCase__       :						int        =							DDIMScheduler.from_pretrained(lowercase_		, subfolder='''scheduler'''      )
         lowerCamelCase__       :						Union[str, Any]        =							StableDiffusionPanoramaPipeline.from_pretrained(lowercase_		, scheduler=lowercase_		, safety_checker=lowercase_      )
         lowerCamelCase__       :						int        =							pipe.to(lowercase_      )
         pipe.set_progress_bar_config(disable=lowercase_      )
         pipe.enable_attention_slicing()

         lowerCamelCase__       :						List[Any]        =							self.get_inputs()
         pipe(**lowercase_		, callback=lowercase_		, callback_steps=1      )
         assert callback_fn.has_been_called
         assert number_of_steps == 3







       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[Any]:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         lowerCamelCase__       :						List[str]        =							'''stabilityai/stable-diffusion-2-base'''
         lowerCamelCase__       :						Optional[int]        =							DDIMScheduler.from_pretrained(lowercase_		, subfolder='''scheduler'''      )
         lowerCamelCase__       :						int        =							StableDiffusionPanoramaPipeline.from_pretrained(lowercase_		, scheduler=lowercase_		, safety_checker=lowercase_      )
         lowerCamelCase__       :						List[Any]        =							pipe.to(lowercase_      )
         pipe.set_progress_bar_config(disable=lowercase_      )
         pipe.enable_attention_slicing(1      )
         pipe.enable_sequential_cpu_offload()

         lowerCamelCase__       :						Tuple        =							self.get_inputs()
         lowerCamelCase__       :						Dict        =							pipe(**lowercase_      )

         lowerCamelCase__       :						Dict        =							torch.cuda.max_memory_allocated()
         # make sure that less than 5.2 GB is allocated
         assert mem_bytes < 5.5 * 1_0**9










from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


_A :   List[str]  			=  logging.get_logger(__name__)


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Any			   =   ["audio_values", "audio_mask"]







       def __init__(				self					:     Any		, A					:     Union[str, Any]=2_0_4_8		, A					:     Any=1		, A					:     int=[1_6, 1_6]		, A					:     Any=1_2_8		, A					:     List[Any]=4_4_1_0_0		, A					:     Dict=8_6		, A					:     Dict=2_0_4_8		, A					:     str=0.0		, **A					:     Union[str, Any]		, )       ->List[Any]:
          super().__init__(
              feature_size=A		, sampling_rate=A		, padding_value=A		, **A		, )

          lowerCamelCase__       :						Dict        =							spectrogram_length
          lowerCamelCase__       :						Optional[int]        =							num_channels
          lowerCamelCase__       :						str        =							patch_size
          lowerCamelCase__       :						Any        =							feature_size // self.patch_size[1]
          lowerCamelCase__       :						Union[str, Any]        =							n_fft
          lowerCamelCase__       :						Union[str, Any]        =							sampling_rate // hop_length_to_sampling_rate
          lowerCamelCase__       :						Optional[Any]        =							sampling_rate
          lowerCamelCase__       :						Optional[Any]        =							padding_value
          lowerCamelCase__       :						Union[str, Any]        =							mel_filter_bank(
              num_frequency_bins=1 + n_fft // 2		, num_mel_filters=A		, min_frequency=0.0		, max_frequency=2_20_50.0		, sampling_rate=A		, norm='''slaney'''		, mel_scale='''slaney'''		, ).T







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     np.array      )       ->np.ndarray:
          lowerCamelCase__       :						Any        =							spectrogram(
              A		, window_function(self.n_fft		, '''hann'''      )		, frame_length=self.n_fft		, hop_length=self.hop_length		, power=2.0		, mel_filters=self.mel_filters.T		, log_mel='''dB'''		, db_range=80.0		, )
          lowerCamelCase__       :						Any        =							log_spec[:, :-1]
          lowerCamelCase__       :						int        =							log_spec - 20.0
          lowerCamelCase__       :						int        =							np.clip(log_spec / 40.0		, -2.0		, 0.0      ) + 1.0
          return log_spec







       def __call__(				self					:     Union[str, Any]		, A					:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		, A					:     Optional[Union[str, TensorType]] = None		, A					:     Optional[bool] = True		, A					:     Optional[int] = None		, A					:     bool = False		, A					:     bool = False		, **A					:     str		, )       ->BatchFeature:

          if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                raise ValueError(
                    '''This feature extractor is set to support sampling rate'''
                    F" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
                    F" with {self.sampling_rate} and not {sampling_rate}."      )
          else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''      )

          lowerCamelCase__       :						List[str]        =							isinstance(A		, np.ndarray      ) and len(raw_speech.shape      ) > 1
          if is_batched_numpy and len(raw_speech.shape      ) > 2:
             raise ValueError(F"Only mono-channel audio is supported for input to {self}"      )
          lowerCamelCase__       :						Any        =							is_batched_numpy or (
              isinstance(A		, (list, tuple)      ) and (isinstance(raw_speech[0]		, (np.ndarray, tuple, list)      ))
          )
          if is_batched:
             lowerCamelCase__       :						Optional[int]        =							[np.asarray([speech]		, dtype=np.floataa      ).T for speech in raw_speech]
          elif not is_batched and not isinstance(A		, np.ndarray      ):
             lowerCamelCase__       :						Optional[int]        =							np.asarray(A		, dtype=np.floataa      )
          elif isinstance(A		, np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
             lowerCamelCase__       :						Optional[Any]        =							raw_speech.astype(np.floataa      )
          # always return batch
          if not is_batched:
             lowerCamelCase__       :						int        =							[np.asarray([raw_speech]      ).T]

          # Convert audio signals to log mel spectrograms, truncate by time axis
          lowerCamelCase__       :						List[str]        =							[
              self._np_extract_fbank_features(waveform.squeeze()      ).T[: self.spectrogram_length] for waveform in raw_speech
          ]
          if isinstance(audio_features[0]		, A      ):
             lowerCamelCase__       :						Dict        =							[np.asarray(A		, dtype=np.floataa      ) for feature in audio_features]

          # Create audio attention mask
          lowerCamelCase__       :						Optional[Any]        =							max(
              [ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len for feature in audio_features]      )  # The maximum number of audio patches in a batch
          if return_attention_mask:
             lowerCamelCase__       :						List[Any]        =							[
                 (ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             lowerCamelCase__       :						List[str]        =							np.array(A      ).astype(np.floataa      )

          # convert into correct format for padding
          lowerCamelCase__       :						str        =							max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
          lowerCamelCase__       :						Optional[Any]        =							np.ones([len(A      ), 1, max_time_len, self.feature_size]      ).astype(np.floataa      )
          lowerCamelCase__       :						Tuple        =							padded_audio_features * self.padding_value
          for i in range(len(A      )      ):
             lowerCamelCase__       :						int        =							audio_features[i]
             lowerCamelCase__       :						Optional[int]        =							feature

          # return as BatchFeature
          if return_attention_mask:
             lowerCamelCase__       :						Optional[int]        =							{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
          else:
             lowerCamelCase__       :						Tuple        =							{'''audio_values''': padded_audio_features}

          lowerCamelCase__       :						Union[str, Any]        =							BatchFeature(data=A		, tensor_type=A      )
          return encoded_inputs



