






'''simple docstring'''







import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class _lowercase     (    unittest.TestCase		):




       def 		lowerCamelCase_    (							self:    int					):
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()




       @property
       def 		lowerCamelCase_    (							self:    Any					):
              lowerCamelCase__ :     int	  =	1
              lowerCamelCase__ :     Union[str, Any]	  =	3
              lowerCamelCase__ :     Tuple	  =	(32, 32)

              lowerCamelCase__ :     List[str]	  =	floats_tensor((batch_size, num_channels) + sizes      ,							rng=random.Random(0					)					).to(UpperCamelCase__					)
              return image




       @property
       def 		lowerCamelCase_    (							self:    int					):
              torch.manual_seed(0					)
              lowerCamelCase__ :     Dict	  =	UNetaDConditionModel(
                  block_out_channels=(32, 32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=7      ,							out_channels=4      ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""")      ,							up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""")      ,							cross_attention_dim=32      ,							attention_head_dim=8      ,							use_linear_projection=UpperCamelCase__      ,							only_cross_attention=(True, True, False)      ,							num_class_embeds=100      ,							)
              return model




       @property
       def 		lowerCamelCase_    (							self:    Tuple					):
              torch.manual_seed(0					)
              lowerCamelCase__ :     Optional[Any]	  =	AutoencoderKL(
                  block_out_channels=[32, 32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,							latent_channels=4      ,							)
              return model




       @property
       def 		lowerCamelCase_    (							self:    List[Any]					):
              torch.manual_seed(0					)
              lowerCamelCase__ :     Union[str, Any]	  =	CLIPTextConfig(
                  bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=32      ,							intermediate_size=37      ,							layer_norm_eps=1e-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1_000      ,							hidden_act="""gelu"""      ,							projection_dim=512      ,							)
              return CLIPTextModel(UpperCamelCase__					)




       def 		lowerCamelCase_    (							self:    int					):
              lowerCamelCase__ :     Any	  =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
              lowerCamelCase__ :     Dict	  =	self.dummy_cond_unet_upscale
              lowerCamelCase__ :     Dict	  =	DDPMScheduler()
              lowerCamelCase__ :     Optional[int]	  =	DDIMScheduler(prediction_type="""v_prediction"""					)
              lowerCamelCase__ :     int	  =	self.dummy_vae
              lowerCamelCase__ :     int	  =	self.dummy_text_encoder
              lowerCamelCase__ :     Union[str, Any]	  =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

              lowerCamelCase__ :     Dict	  =	self.dummy_image.cpu().permute(0      ,							2      ,							3      ,							1					)[0]
              lowerCamelCase__ :     Optional[int]	  =	Image.fromarray(np.uinta(UpperCamelCase__					)					).convert("""RGB"""					).resize((64, 64)					)

              # make sure here that pndm scheduler skips prk
              lowerCamelCase__ :     Tuple	  =	StableDiffusionUpscalePipeline(
                  unet=UpperCamelCase__      ,							low_res_scheduler=UpperCamelCase__      ,							scheduler=UpperCamelCase__      ,							vae=UpperCamelCase__      ,							text_encoder=UpperCamelCase__      ,							tokenizer=UpperCamelCase__      ,							max_noise_level=350      ,							)
              lowerCamelCase__ :     Union[str, Any]	  =	sd_pipe.to(UpperCamelCase__					)
              sd_pipe.set_progress_bar_config(disable=UpperCamelCase__					)

              lowerCamelCase__ :     Any	  =	"""A painting of a squirrel eating a burger"""
              lowerCamelCase__ :     Any	  =	torch.Generator(device=UpperCamelCase__					).manual_seed(0					)
              lowerCamelCase__ :     Any	  =	sd_pipe(
                  [prompt]      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							guidance_scale=6.0      ,							noise_level=20      ,							num_inference_steps=2      ,							output_type="""np"""      ,							)

              lowerCamelCase__ :     Optional[int]	  =	output.images

              lowerCamelCase__ :     Tuple	  =	torch.Generator(device=UpperCamelCase__					).manual_seed(0					)
              lowerCamelCase__ :     Dict	  =	sd_pipe(
                  [prompt]      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							guidance_scale=6.0      ,							noise_level=20      ,							num_inference_steps=2      ,							output_type="""np"""      ,							return_dict=UpperCamelCase__      ,							)[0]

              lowerCamelCase__ :     List[Any]	  =	image[0, -3:, -3:, -1]
              lowerCamelCase__ :     str	  =	image_from_tuple[0, -3:, -3:, -1]

              lowerCamelCase__ :     Optional[int]	  =	low_res_image.size[0] * 4
              assert image.shape == (1, expected_height_width, expected_height_width, 3)
              lowerCamelCase__ :     str	  =	np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]					)

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




       def 		lowerCamelCase_    (							self:    Dict					):
              lowerCamelCase__ :     Dict	  =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
              lowerCamelCase__ :     Union[str, Any]	  =	self.dummy_cond_unet_upscale
              lowerCamelCase__ :     str	  =	DDPMScheduler()
              lowerCamelCase__ :     List[str]	  =	DDIMScheduler(prediction_type="""v_prediction"""					)
              lowerCamelCase__ :     Optional[Any]	  =	self.dummy_vae
              lowerCamelCase__ :     Union[str, Any]	  =	self.dummy_text_encoder
              lowerCamelCase__ :     Any	  =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

              lowerCamelCase__ :     Union[str, Any]	  =	self.dummy_image.cpu().permute(0      ,							2      ,							3      ,							1					)[0]
              lowerCamelCase__ :     int	  =	Image.fromarray(np.uinta(UpperCamelCase__					)					).convert("""RGB"""					).resize((64, 64)					)

              # make sure here that pndm scheduler skips prk
              lowerCamelCase__ :     Union[str, Any]	  =	StableDiffusionUpscalePipeline(
                  unet=UpperCamelCase__      ,							low_res_scheduler=UpperCamelCase__      ,							scheduler=UpperCamelCase__      ,							vae=UpperCamelCase__      ,							text_encoder=UpperCamelCase__      ,							tokenizer=UpperCamelCase__      ,							max_noise_level=350      ,							)
              lowerCamelCase__ :     int	  =	sd_pipe.to(UpperCamelCase__					)
              sd_pipe.set_progress_bar_config(disable=UpperCamelCase__					)

              lowerCamelCase__ :     Any	  =	"""A painting of a squirrel eating a burger"""
              lowerCamelCase__ :     Optional[int]	  =	sd_pipe(
                  2 * [prompt]      ,							image=2 * [low_res_image]      ,							guidance_scale=6.0      ,							noise_level=20      ,							num_inference_steps=2      ,							output_type="""np"""      ,							)
              lowerCamelCase__ :     Optional[Any]	  =	output.images
              assert image.shape[0] == 2

              lowerCamelCase__ :     Tuple	  =	torch.Generator(device=UpperCamelCase__					).manual_seed(0					)
              lowerCamelCase__ :     Tuple	  =	sd_pipe(
                  [prompt]      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							num_images_per_prompt=2      ,							guidance_scale=6.0      ,							noise_level=20      ,							num_inference_steps=2      ,							output_type="""np"""      ,							)
              lowerCamelCase__ :     List[Any]	  =	output.images
              assert image.shape[0] == 2




       @unittest.skipIf(torch_device != """cuda"""      ,							"""This test requires a GPU"""					)
       def 		lowerCamelCase_    (							self:    Dict					):
              lowerCamelCase__ :     Any	  =	self.dummy_cond_unet_upscale
              lowerCamelCase__ :     int	  =	DDPMScheduler()
              lowerCamelCase__ :     str	  =	DDIMScheduler(prediction_type="""v_prediction"""					)
              lowerCamelCase__ :     List[Any]	  =	self.dummy_vae
              lowerCamelCase__ :     List[Any]	  =	self.dummy_text_encoder
              lowerCamelCase__ :     Dict	  =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

              lowerCamelCase__ :     Optional[Any]	  =	self.dummy_image.cpu().permute(0      ,							2      ,							3      ,							1					)[0]
              lowerCamelCase__ :     List[Any]	  =	Image.fromarray(np.uinta(UpperCamelCase__					)					).convert("""RGB"""					).resize((64, 64)					)

              # put models in fp16, except vae as it overflows in fp16
              lowerCamelCase__ :     Optional[int]	  =	unet.half()
              lowerCamelCase__ :     int	  =	text_encoder.half()

              # make sure here that pndm scheduler skips prk
              lowerCamelCase__ :     List[str]	  =	StableDiffusionUpscalePipeline(
                  unet=UpperCamelCase__      ,							low_res_scheduler=UpperCamelCase__      ,							scheduler=UpperCamelCase__      ,							vae=UpperCamelCase__      ,							text_encoder=UpperCamelCase__      ,							tokenizer=UpperCamelCase__      ,							max_noise_level=350      ,							)
              lowerCamelCase__ :     Any	  =	sd_pipe.to(UpperCamelCase__					)
              sd_pipe.set_progress_bar_config(disable=UpperCamelCase__					)

              lowerCamelCase__ :     List[str]	  =	"""A painting of a squirrel eating a burger"""
              lowerCamelCase__ :     Dict	  =	torch.manual_seed(0					)
              lowerCamelCase__ :     Any	  =	sd_pipe(
                  [prompt]      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							num_inference_steps=2      ,							output_type="""np"""      ,							).images

              lowerCamelCase__ :     Dict	  =	low_res_image.size[0] * 4
              assert image.shape == (1, expected_height_width, expected_height_width, 3)



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




       def 		lowerCamelCase_    (							self:    Tuple					):
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()




       def 		lowerCamelCase_    (							self:    Any					):
              lowerCamelCase__ :     Union[str, Any]	  =	load_image(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                  """/sd2-upscale/low_res_cat.png"""					)
              lowerCamelCase__ :     Optional[int]	  =	load_numpy(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
                  """/upsampled_cat.npy"""					)

              lowerCamelCase__ :     List[Any]	  =	"""stabilityai/stable-diffusion-x4-upscaler"""
              lowerCamelCase__ :     Optional[int]	  =	StableDiffusionUpscalePipeline.from_pretrained(UpperCamelCase__					)
              pipe.to(UpperCamelCase__					)
              pipe.set_progress_bar_config(disable=UpperCamelCase__					)
              pipe.enable_attention_slicing()

              lowerCamelCase__ :     Union[str, Any]	  =	"""a cat sitting on a park bench"""

              lowerCamelCase__ :     Optional[int]	  =	torch.manual_seed(0					)
              lowerCamelCase__ :     Any	  =	pipe(
                  prompt=UpperCamelCase__      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							output_type="""np"""      ,							)
              lowerCamelCase__ :     str	  =	output.images[0]

              assert image.shape == (512, 512, 3)
              assert np.abs(expected_image - image					).max() < 1e-3




       def 		lowerCamelCase_    (							self:    List[Any]					):
              lowerCamelCase__ :     List[Any]	  =	load_image(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                  """/sd2-upscale/low_res_cat.png"""					)
              lowerCamelCase__ :     Any	  =	load_numpy(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
                  """/upsampled_cat_fp16.npy"""					)

              lowerCamelCase__ :     Union[str, Any]	  =	"""stabilityai/stable-diffusion-x4-upscaler"""
              lowerCamelCase__ :     List[Any]	  =	StableDiffusionUpscalePipeline.from_pretrained(
                  UpperCamelCase__      ,							torch_dtype=torch.floataa      ,							)
              pipe.to(UpperCamelCase__					)
              pipe.set_progress_bar_config(disable=UpperCamelCase__					)
              pipe.enable_attention_slicing()

              lowerCamelCase__ :     Optional[int]	  =	"""a cat sitting on a park bench"""

              lowerCamelCase__ :     Tuple	  =	torch.manual_seed(0					)
              lowerCamelCase__ :     Tuple	  =	pipe(
                  prompt=UpperCamelCase__      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							output_type="""np"""      ,							)
              lowerCamelCase__ :     int	  =	output.images[0]

              assert image.shape == (512, 512, 3)
              assert np.abs(expected_image - image					).max() < 5e-1




       def 		lowerCamelCase_    (							self:    int					):
              torch.cuda.empty_cache()
              torch.cuda.reset_max_memory_allocated()
              torch.cuda.reset_peak_memory_stats()

              lowerCamelCase__ :     Tuple	  =	load_image(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                  """/sd2-upscale/low_res_cat.png"""					)

              lowerCamelCase__ :     Optional[int]	  =	"""stabilityai/stable-diffusion-x4-upscaler"""
              lowerCamelCase__ :     List[Any]	  =	StableDiffusionUpscalePipeline.from_pretrained(
                  UpperCamelCase__      ,							torch_dtype=torch.floataa      ,							)
              pipe.to(UpperCamelCase__					)
              pipe.set_progress_bar_config(disable=UpperCamelCase__					)
              pipe.enable_attention_slicing(1					)
              pipe.enable_sequential_cpu_offload()

              lowerCamelCase__ :     str	  =	"""a cat sitting on a park bench"""

              lowerCamelCase__ :     str	  =	torch.manual_seed(0					)
              lowerCamelCase__ :     Union[str, Any]	  =	pipe(
                  prompt=UpperCamelCase__      ,							image=UpperCamelCase__      ,							generator=UpperCamelCase__      ,							num_inference_steps=5      ,							output_type="""np"""      ,							)

              lowerCamelCase__ :     int	  =	torch.cuda.max_memory_allocated()
              # make sure that less than 2.9 GB is allocated
              assert mem_bytes < 2.9 * 10**9









'''simple docstring'''







from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


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

class _lowercase     (    _lowercase		):
							a    				=	["""input_features""", """attention_mask"""]




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    str=80      ,							UpperCamelCase__:    Union[str, Any]=16_000      ,							UpperCamelCase__:    str=0.0      ,							UpperCamelCase__:    Tuple=10      ,							UpperCamelCase__:    Dict=25      ,							UpperCamelCase__:    List[Any]="hamming_window"      ,							UpperCamelCase__:    Tuple=32_768.0      ,							UpperCamelCase__:    List[Any]=0.97      ,							UpperCamelCase__:    Optional[int]=1.0      ,							UpperCamelCase__:    str=True      ,							UpperCamelCase__:    Optional[Any]=True      ,							UpperCamelCase__:    Optional[int]=False      ,							**UpperCamelCase__:    Optional[int]      ,							):
														super().__init__(feature_size=UpperCamelCase__      ,							sampling_rate=UpperCamelCase__      ,							padding_value=UpperCamelCase__      ,							**UpperCamelCase__					)

														lowerCamelCase__ :     Dict	  =	feature_size
														lowerCamelCase__ :     Tuple	  =	sampling_rate
														lowerCamelCase__ :     Union[str, Any]	  =	padding_value
														lowerCamelCase__ :     Union[str, Any]	  =	hop_length
														lowerCamelCase__ :     Any	  =	win_length
														lowerCamelCase__ :     Tuple	  =	frame_signal_scale
														lowerCamelCase__ :     Dict	  =	preemphasis_coeff
														lowerCamelCase__ :     Optional[Any]	  =	mel_floor
														lowerCamelCase__ :     Any	  =	normalize_means
														lowerCamelCase__ :     Optional[int]	  =	normalize_vars
														lowerCamelCase__ :     Dict	  =	win_function
														lowerCamelCase__ :     Dict	  =	return_attention_mask

														lowerCamelCase__ :     Optional[Any]	  =	win_length * sampling_rate // 1_000
														lowerCamelCase__ :     Tuple	  =	hop_length * sampling_rate // 1_000

														lowerCamelCase__ :     List[Any]	  =	optimal_fft_length(self.sample_size					)
														lowerCamelCase__ :     List[str]	  =	(self.n_fft // 2) + 1




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    np.array					):
														if self.win_function == "hamming_window":
																					lowerCamelCase__ :     Dict	  =	window_function(window_length=self.sample_size      ,							name=self.win_function      ,							periodic=UpperCamelCase__					)
														else:
																					lowerCamelCase__ :     Union[str, Any]	  =	window_function(window_length=self.sample_size      ,							name=self.win_function					)

														lowerCamelCase__ :     List[Any]	  =	mel_filter_bank(
														    num_frequency_bins=self.n_freqs      ,							num_mel_filters=self.feature_size      ,							min_frequency=0.0      ,							max_frequency=self.sampling_rate / 2.0      ,							sampling_rate=self.sampling_rate      ,							)

														lowerCamelCase__ :     Tuple	  =	spectrogram(
														    one_waveform * self.frame_signal_scale      ,							window=UpperCamelCase__      ,							frame_length=self.sample_size      ,							hop_length=self.sample_stride      ,							fft_length=self.n_fft      ,							center=UpperCamelCase__      ,							preemphasis=self.preemphasis_coeff      ,							mel_filters=UpperCamelCase__      ,							mel_floor=self.mel_floor      ,							log_mel="""log"""      ,							)
														return msfc_features.T




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Union[str, Any]					):
														# make sure we normalize float32 arrays
														if self.normalize_means:
																					lowerCamelCase__ :     List[str]	  =	x[:input_length].mean(axis=0					)
																					lowerCamelCase__ :     List[str]	  =	np.subtract(UpperCamelCase__      ,							UpperCamelCase__					)
														if self.normalize_vars:
																					lowerCamelCase__ :     Union[str, Any]	  =	x[:input_length].std(axis=0					)
																					lowerCamelCase__ :     str	  =	np.divide(UpperCamelCase__      ,							UpperCamelCase__					)

														if input_length < x.shape[0]:
																					lowerCamelCase__ :     List[Any]	  =	padding_value

														# make sure array is in float32
														lowerCamelCase__ :     str	  =	x.astype(np.floataa					)

														return x




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    List[np.ndarray]      ,							UpperCamelCase__:    Optional[np.ndarray] = None					):
														lowerCamelCase__ :     Optional[int]	  =	attention_mask.sum(-1					) if attention_mask is not None else [x.shape[0] for x in input_features]
														return [self._normalize_one(UpperCamelCase__      ,							UpperCamelCase__      ,							self.padding_value					) for x, n in zip(UpperCamelCase__      ,							UpperCamelCase__					)]




							def __call__(							self:    List[str]      ,							UpperCamelCase__:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,							UpperCamelCase__:    Union[bool, str, PaddingStrategy] = False      ,							UpperCamelCase__:    Optional[int] = None      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    Optional[int] = None      ,							UpperCamelCase__:    Optional[bool] = None      ,							UpperCamelCase__:    Optional[Union[str, TensorType]] = None      ,							UpperCamelCase__:    Optional[int] = None      ,							**UpperCamelCase__:    Tuple      ,							):

														if sampling_rate is not None:
																					if sampling_rate != self.sampling_rate:
																												raise ValueError(
																												    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																												    F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
																												    F''' {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(UpperCamelCase__      ,							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__ :     List[str]	  =	is_batched_numpy or (
														    isinstance(UpperCamelCase__      ,							(list, tuple)					) and (isinstance(raw_speech[0]      ,							(np.ndarray, tuple, list)					))
														)

														if is_batched:
																					lowerCamelCase__ :     str	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.floataa					) for speech in raw_speech]
														elif not is_batched and not isinstance(UpperCamelCase__      ,							np.ndarray					):
																					lowerCamelCase__ :     List[Any]	  =	np.asarray(UpperCamelCase__      ,							dtype=np.floataa					)
														elif isinstance(UpperCamelCase__      ,							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__ :     Optional[Any]	  =	[raw_speech]

														# extract fbank features
														lowerCamelCase__ :     str	  =	[self._extract_mfsc_features(UpperCamelCase__					) for one_waveform in raw_speech]

														# convert into correct format for padding
														lowerCamelCase__ :     str	  =	BatchFeature({"""input_features""": features}					)

														lowerCamelCase__ :     str	  =	self.pad(
														    UpperCamelCase__      ,							padding=UpperCamelCase__      ,							max_length=UpperCamelCase__      ,							truncation=UpperCamelCase__      ,							pad_to_multiple_of=UpperCamelCase__      ,							return_attention_mask=UpperCamelCase__      ,							**UpperCamelCase__      ,							)
														# make sure list is in array format
														lowerCamelCase__ :     str	  =	padded_inputs.get("""input_features"""					)
														if isinstance(input_features[0]      ,							UpperCamelCase__					):
																					lowerCamelCase__ :     Dict	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.floataa					) for feature in input_features]

														lowerCamelCase__ :     List[str]	  =	padded_inputs.get("""attention_mask"""					)
														if attention_mask is not None:
																					lowerCamelCase__ :     Any	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.intaa					) for array in attention_mask]

														if self.normalize_means or self.normalize_vars:
																					lowerCamelCase__ :     str	  =	(
																					    np.array(UpperCamelCase__      ,							dtype=np.intaa					)
																					    if self._get_padding_strategies(UpperCamelCase__      ,							max_length=UpperCamelCase__					) is not PaddingStrategy.DO_NOT_PAD
																					    and padding
																					    else None
																					)
																					lowerCamelCase__ :     Union[str, Any]	  =	self.normalize(
																					    padded_inputs["""input_features"""]      ,							attention_mask=UpperCamelCase__					)

														if return_tensors is not None:
																					lowerCamelCase__ :     Dict	  =	padded_inputs.convert_to_tensors(UpperCamelCase__					)

														return padded_inputs

